bayespy-0.2.3/ 0000775 5104112 5104112 00000000000 12437607672 014730 5 ustar jluttine jluttine 0000000 0000000 bayespy-0.2.3/bayespy/ 0000775 5104112 5104112 00000000000 12437607670 016402 5 ustar jluttine jluttine 0000000 0000000 bayespy-0.2.3/bayespy/demos/ 0000775 5104112 5104112 00000000000 12437607670 017511 5 ustar jluttine jluttine 0000000 0000000 bayespy-0.2.3/bayespy/demos/__init__.py 0000664 5104112 5104112 00000002007 12374370350 021611 0 ustar jluttine jluttine 0000000 0000000 ######################################################################
# Copyright (C) 2013 Jaakko Luttinen
#
# This file is licensed under Version 3.0 of the GNU General Public
# License. See LICENSE for a text of the license.
######################################################################
######################################################################
# This file is part of BayesPy.
#
# BayesPy is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# BayesPy is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with BayesPy. If not, see .
######################################################################
bayespy-0.2.3/bayespy/demos/categorical.py 0000664 5104112 5104112 00000003231 12374370350 022327 0 ustar jluttine jluttine 0000000 0000000 ######################################################################
# Copyright (C) 2011-2013 Jaakko Luttinen
#
# This file is licensed under Version 3.0 of the GNU General Public
# License. See LICENSE for a text of the license.
######################################################################
######################################################################
# This file is part of BayesPy.
#
# BayesPy is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# BayesPy is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with BayesPy. If not, see .
######################################################################
import numpy as np
from bayespy.utils import random
from bayespy import nodes
from bayespy.inference.vmp.vmp import VB
def run(M=30, D=5):
# Generate data
y = np.random.randint(D, size=(M,))
# Construct model
p = nodes.Dirichlet(1*np.ones(D),
name='p')
z = nodes.Categorical(p,
plates=(M,),
name='z')
# Observe the data with randomly missing values
mask = random.mask(M, p=0.5)
z.observe(y, mask=mask)
# Run VB-EM
Q = VB(p, z)
Q.update()
# Show results
z.show()
p.show()
if __name__ == '__main__':
run()
bayespy-0.2.3/bayespy/demos/hmm.py 0000664 5104112 5104112 00000012765 12430651206 020642 0 ustar jluttine jluttine 0000000 0000000 ######################################################################
# Copyright (C) 2014 Jaakko Luttinen
#
# This file is licensed under Version 3.0 of the GNU General Public
# License. See LICENSE for a text of the license.
######################################################################
######################################################################
# This file is part of BayesPy.
#
# BayesPy is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# BayesPy is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with BayesPy. If not, see .
######################################################################
"""
Demonstrate categorical Markov chain with hidden Markov model (HMM)
"""
import numpy as np
import matplotlib.pyplot as plt
from bayespy.nodes import Gaussian, \
CategoricalMarkovChain, \
Dirichlet, \
Mixture, \
Categorical
from bayespy.inference.vmp.vmp import VB
import bayespy.plot as bpplt
def hidden_markov_model(distribution, *args, K=3, N=100):
# Prior for initial state probabilities
alpha = Dirichlet(1e-3*np.ones(K),
name='alpha')
# Prior for state transition probabilities
A = Dirichlet(1e-3*np.ones(K),
plates=(K,),
name='A')
# Hidden states (with unknown initial state probabilities and state
# transition probabilities)
Z = CategoricalMarkovChain(alpha, A,
states=N,
name='Z')
# Emission/observation distribution
Y = Mixture(Z, distribution, *args,
name='Y')
Q = VB(Y, Z, alpha, A)
return Q
def mixture_model(distribution, *args, K=3, N=100):
# Prior for state probabilities
alpha = Dirichlet(1e-3*np.ones(K),
name='alpha')
# Cluster assignments
Z = Categorical(alpha,
plates=(N,),
name='Z')
# Observation distribution
Y = Mixture(Z, distribution, *args,
name='Y')
Q = VB(Y, Z, alpha)
return Q
@bpplt.interactive
def run(N=200, maxiter=10, seed=42, std=2.0, plot=True):
# Use deterministic random numbers
if seed is not None:
np.random.seed(seed)
#
# Generate data
#
mu = np.array([ [0,0], [3,4], [6,0] ])
K = 3
p0 = np.ones(K) / K
q = 0.9 # probability to stay in the same state
r = (1-q)/(K-1)
P = q*np.identity(K) + r*(np.ones((3,3))-np.identity(3))
y = np.zeros((N,2))
z = np.zeros(N)
state = np.random.choice(K, p=p0)
for n in range(N):
z[n] = state
y[n,:] = std*np.random.randn(2) + mu[state]
state = np.random.choice(K, p=P[state])
plt.figure()
# Plot data
plt.subplot(1,3,1)
plt.axis('equal')
plt.title('True classification')
colors = [ [[1,0,0], [0,1,0], [0,0,1]][int(state)] for state in z ]
plt.plot(y[:,0], y[:,1], 'k-', zorder=-10)
plt.scatter(y[:,0], y[:,1], c=colors, s=40)
#
# Use HMM
#
# Run VB inference for HMM
Q_hmm = hidden_markov_model(Gaussian, mu, K*[std**(-2)*np.identity(2)],
K=K, N=N)
Q_hmm['Y'].observe(y)
Q_hmm.update(repeat=maxiter)
# Plot results
plt.subplot(1,3,2)
plt.axis('equal')
plt.title('Classification with HMM')
colors = Q_hmm['Y'].parents[0]._message_to_child()[0]
plt.plot(y[:,0], y[:,1], 'k-', zorder=-10)
plt.scatter(y[:,0], y[:,1], c=colors, s=40)
#
# Use mixture model
#
# For comparison, run VB for Gaussian mixture
Q_mix = mixture_model(Gaussian, mu, K*[std**(-2)*np.identity(2)],
K=K, N=N)
Q_mix['Y'].observe(y)
Q_mix.update(repeat=maxiter)
# Plot results
plt.subplot(1,3,3)
plt.axis('equal')
plt.title('Classification with mixture')
colors = Q_mix['Y'].parents[0]._message_to_child()[0]
plt.plot(y[:,0], y[:,1], 'k-', zorder=-10)
plt.scatter(y[:,0], y[:,1], c=colors, s=40)
if __name__ == '__main__':
import sys, getopt, os
try:
opts, args = getopt.getopt(sys.argv[1:],
"",
["n=",
"seed=",
"std=",
"maxiter="])
except getopt.GetoptError:
print('python demo_lssm.py ')
print('--n= Number of data vectors')
print('--std= Standard deviation of the Gaussians')
print('--maxiter= Maximum number of VB iterations')
print('--seed= Seed (integer) for the random number generator')
sys.exit(2)
kwargs = {}
for opt, arg in opts:
if opt == "--maxiter":
kwargs["maxiter"] = int(arg)
elif opt == "--std":
kwargs["std"] = float(arg)
elif opt == "--seed":
kwargs["seed"] = int(arg)
elif opt in ("--n",):
kwargs["N"] = int(arg)
else:
raise ValueError("Unhandled option given")
run(**kwargs)
plt.show()
bayespy-0.2.3/bayespy/demos/lssm.py 0000664 5104112 5104112 00000026320 12430651206 021027 0 ustar jluttine jluttine 0000000 0000000 ######################################################################
# Copyright (C) 2013-2014 Jaakko Luttinen
#
# This file is licensed under Version 3.0 of the GNU General Public
# License. See LICENSE for a text of the license.
######################################################################
######################################################################
# This file is part of BayesPy.
#
# BayesPy is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# BayesPy is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with BayesPy. If not, see .
######################################################################
"""
Demonstrate linear Gaussian state-space model.
Some of the functions in this module are re-usable:
* ``model`` can be used to construct the classical linear state-space model.
* ``infer`` can be used to apply linear state-space model to given data.
"""
import numpy as np
import scipy
import matplotlib.pyplot as plt
from bayespy.nodes import GaussianMarkovChain
from bayespy.nodes import Gaussian, GaussianARD
from bayespy.nodes import Gamma
from bayespy.nodes import SumMultiply
from bayespy.inference.vmp.nodes.gamma import diagonal
from bayespy.utils import random
from bayespy.inference.vmp.vmp import VB
from bayespy.inference.vmp import transformations
import bayespy.plot as bpplt
def model(M=10, N=100, D=3):
"""
Construct linear state-space model.
See, for instance, the following publication:
"Fast variational Bayesian linear state-space model"
Luttinen (ECML 2013)
"""
# Dynamics matrix with ARD
alpha = Gamma(1e-5,
1e-5,
plates=(D,),
name='alpha')
A = GaussianARD(0,
alpha,
shape=(D,),
plates=(D,),
plotter=bpplt.GaussianHintonPlotter(rows=0,
cols=1,
scale=0),
name='A')
A.initialize_from_value(np.identity(D))
# Latent states with dynamics
X = GaussianMarkovChain(np.zeros(D), # mean of x0
1e-3*np.identity(D), # prec of x0
A, # dynamics
np.ones(D), # innovation
n=N, # time instances
plotter=bpplt.GaussianMarkovChainPlotter(scale=2),
name='X')
X.initialize_from_value(np.random.randn(N,D))
# Mixing matrix from latent space to observation space using ARD
gamma = Gamma(1e-5,
1e-5,
plates=(D,),
name='gamma')
gamma.initialize_from_value(1e-2*np.ones(D))
C = GaussianARD(0,
gamma,
shape=(D,),
plates=(M,1),
plotter=bpplt.GaussianHintonPlotter(rows=0,
cols=2,
scale=0),
name='C')
C.initialize_from_value(np.random.randn(M,1,D))
# Observation noise
tau = Gamma(1e-5,
1e-5,
name='tau')
tau.initialize_from_value(1e2)
# Underlying noiseless function
F = SumMultiply('i,i',
C,
X,
name='F')
# Noisy observations
Y = GaussianARD(F,
tau,
name='Y')
Q = VB(Y, F, C, gamma, X, A, alpha, tau, C)
return Q
def infer(y, D,
mask=True,
maxiter=100,
rotate=True,
debug=False,
precompute=False,
update_hyper=0,
start_rotating=0,
plot_C=True,
monitor=True,
autosave=None):
"""
Apply linear state-space model for the given data.
"""
(M, N) = np.shape(y)
# Construct the model
Q = model(M, N, D)
if not plot_C:
Q['C'].set_plotter(None)
if autosave is not None:
Q.set_autosave(autosave, iterations=10)
# Observe data
Q['Y'].observe(y, mask=mask)
# Set up rotation speed-up
if rotate:
# Initial rotate the D-dimensional state space (X, A, C)
# Does not update hyperparameters
rotA_init = transformations.RotateGaussianARD(Q['A'],
axis=0,
precompute=precompute)
rotX_init = transformations.RotateGaussianMarkovChain(Q['X'],
rotA_init)
rotC_init = transformations.RotateGaussianARD(Q['C'],
axis=0,
precompute=precompute)
R_X_init = transformations.RotationOptimizer(rotX_init, rotC_init, D)
# Rotate the D-dimensional state space (X, A, C)
rotA = transformations.RotateGaussianARD(Q['A'],
Q['alpha'],
axis=0,
precompute=precompute)
rotX = transformations.RotateGaussianMarkovChain(Q['X'],
rotA)
rotC = transformations.RotateGaussianARD(Q['C'],
Q['gamma'],
axis=0,
precompute=precompute)
R_X = transformations.RotationOptimizer(rotX, rotC, D)
# Keyword arguments for the rotation
if debug:
rotate_kwargs = {'maxiter': 10,
'check_bound': True,
'check_gradient': True}
else:
rotate_kwargs = {'maxiter': 10}
# Plot initial distributions
if monitor:
Q.plot()
# Run inference using rotations
for ind in range(maxiter):
if ind < update_hyper:
# It might be a good idea to learn the lower level nodes a bit
# before starting to learn the upper level nodes.
Q.update('X', 'C', 'A', 'tau', plot=monitor)
if rotate and ind >= start_rotating:
# Use the rotation which does not update alpha nor beta
R_X_init.rotate(**rotate_kwargs)
else:
Q.update(plot=monitor)
if rotate and ind >= start_rotating:
# It might be a good idea to not rotate immediately because it
# might lead to pruning out components too efficiently before
# even estimating them roughly
R_X.rotate(**rotate_kwargs)
# Return the posterior approximation
return Q
def simulate_data(M, N):
"""
Generate a dataset using linear state-space model.
The process has two latent oscillation components and one random walk
component.
"""
# Simulate some data
D = 3
c = np.random.randn(M, D)
w = 0.3
a = np.array([[np.cos(w), -np.sin(w), 0],
[np.sin(w), np.cos(w), 0],
[0, 0, 1]])
x = np.empty((N,D))
f = np.empty((M,N))
y = np.empty((M,N))
x[0] = 10*np.random.randn(D)
f[:,0] = np.dot(c,x[0])
y[:,0] = f[:,0] + 3*np.random.randn(M)
for n in range(N-1):
x[n+1] = np.dot(a,x[n]) + np.random.randn(D)
f[:,n+1] = np.dot(c,x[n+1])
y[:,n+1] = f[:,n+1] + 3*np.random.randn(M)
return (y, f)
@bpplt.interactive
def demo(M=6, N=200, D=3, maxiter=100, debug=False, seed=42, rotate=True,
precompute=False, plot=True, monitor=True):
"""
Run the demo for linear state-space model.
"""
# Use deterministic random numbers
if seed is not None:
np.random.seed(seed)
# Get data
(y, f) = simulate_data(M, N)
# Add missing values randomly
mask = random.mask(M, N, p=0.3)
# Add missing values to a period of time
mask[:,30:80] = False
y[~mask] = np.nan # BayesPy doesn't require this. Just for plotting.
# Run inference
Q = infer(y, D,
mask=mask,
rotate=rotate,
debug=debug,
monitor=monitor,
maxiter=maxiter)
if plot:
# Show results
plt.figure()
bpplt.timeseries_normal(Q['F'], scale=2)
bpplt.timeseries(f, linestyle='-', color='b')
bpplt.timeseries(y, linestyle='None', color='r', marker='.')
if __name__ == '__main__':
import sys, getopt, os
try:
opts, args = getopt.getopt(sys.argv[1:],
"",
["m=",
"n=",
"d=",
"seed=",
"maxiter=",
"debug",
"precompute",
"no-plot",
"no-monitor",
"no-rotation"])
except getopt.GetoptError:
print('python lssm.py ')
print('--m= Dimensionality of data vectors')
print('--n= Number of data vectors')
print('--d= Dimensionality of the latent vectors in the model')
print('--no-rotation Do not apply speed-up rotations')
print('--maxiter= Maximum number of VB iterations')
print('--seed= Seed (integer) for the random number generator')
print('--debug Check that the rotations are implemented correctly')
print('--no-plot Do not plot the results')
print('--no-monitor Do not plot distributions during learning')
print('--precompute Precompute some moments when rotating. May '
'speed up or slow down.')
sys.exit(2)
kwargs = {}
for opt, arg in opts:
if opt == "--no-rotation":
kwargs["rotate"] = False
elif opt == "--maxiter":
kwargs["maxiter"] = int(arg)
elif opt == "--debug":
kwargs["debug"] = True
elif opt == "--precompute":
kwargs["precompute"] = True
elif opt == "--seed":
kwargs["seed"] = int(arg)
elif opt in ("--m",):
kwargs["M"] = int(arg)
elif opt in ("--n",):
kwargs["N"] = int(arg)
elif opt in ("--d",):
kwargs["D"] = int(arg)
elif opt in ("--no-plot"):
kwargs["plot"] = False
elif opt in ("--no-monitor"):
kwargs["monitor"] = False
else:
raise ValueError("Unhandled option given")
demo(**kwargs)
plt.show()
bayespy-0.2.3/bayespy/demos/lssm_sd.py 0000664 5104112 5104112 00000026334 12430651206 021522 0 ustar jluttine jluttine 0000000 0000000 ######################################################################
# Copyright (C) 2014 Jaakko Luttinen
#
# This file is licensed under Version 3.0 of the GNU General Public
# License. See LICENSE for a text of the license.
######################################################################
######################################################################
# This file is part of BayesPy.
#
# BayesPy is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# BayesPy is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with BayesPy. If not, see .
######################################################################
"""
Demonstrate the linear state-space model with switching dynamics.
The model differs from the classical linear state-space model in that it has a
set of state dynamics matrices of which one is used at each time instance. A
hidden Markov model is used to select the dynamics matrix.
Some functions in this module are re-usable:
* ``model`` can be used to construct the LSSM with switching dynamics.
* ``infer`` can be used to apply the model to given data.
"""
import numpy as np
import matplotlib.pyplot as plt
from bayespy.nodes import (GaussianARD,
SwitchingGaussianMarkovChain,
CategoricalMarkovChain,
Dirichlet,
Mixture,
Gamma,
SumMultiply)
from bayespy.inference.vmp.vmp import VB
from bayespy.inference.vmp import transformations
import bayespy.plot as bpplt
def model(M=20, N=100, D=10, K=3):
"""
Construct the linear state-space model with switching dynamics.
"""
#
# Switching dynamics (HMM)
#
# Prior for initial state probabilities
rho = Dirichlet(1e-3*np.ones(K),
name='rho')
# Prior for state transition probabilities
V = Dirichlet(1e-3*np.ones(K),
plates=(K,),
name='V')
v = 10*np.identity(K) + 1*np.ones((K,K))
v /= np.sum(v, axis=-1, keepdims=True)
V.initialize_from_value(v)
# Hidden states (with unknown initial state probabilities and state
# transition probabilities)
Z = CategoricalMarkovChain(rho, V,
states=N-1,
name='Z',
plotter=bpplt.CategoricalMarkovChainPlotter(),
initialize=False)
Z.u[0] = np.random.dirichlet(np.ones(K))
Z.u[1] = np.reshape(np.random.dirichlet(0.5*np.ones(K*K), size=(N-2)),
(N-2, K, K))
#
# Linear state-space models
#
# Dynamics matrix with ARD
# (K,D) x ()
alpha = Gamma(1e-5,
1e-5,
plates=(K,1,D),
name='alpha')
# (K,1,1,D) x (D)
A = GaussianARD(0,
alpha,
shape=(D,),
plates=(K,D),
name='A',
plotter=bpplt.GaussianHintonPlotter())
A.initialize_from_value(np.identity(D)*np.ones((K,D,D))
+ 0.1*np.random.randn(K,D,D))
# Latent states with dynamics
# (K,1) x (N,D)
X = SwitchingGaussianMarkovChain(np.zeros(D), # mean of x0
1e-3*np.identity(D), # prec of x0
A, # dynamics
Z, # dynamics selection
np.ones(D), # innovation
n=N, # time instances
name='X',
plotter=bpplt.GaussianMarkovChainPlotter())
X.initialize_from_value(10*np.random.randn(N,D))
# Mixing matrix from latent space to observation space using ARD
# (K,1,1,D) x ()
gamma = Gamma(1e-5,
1e-5,
plates=(D,),
name='gamma')
# (K,M,1) x (D)
C = GaussianARD(0,
gamma,
shape=(D,),
plates=(M,1),
name='C',
plotter=bpplt.GaussianHintonPlotter(rows=-3,cols=-1))
C.initialize_from_value(np.random.randn(M,1,D))
# Underlying noiseless function
# (K,M,N) x ()
F = SumMultiply('i,i',
C,
X,
name='F')
#
# Mixing the models
#
# Observation noise
tau = Gamma(1e-5,
1e-5,
name='tau')
tau.initialize_from_value(1e2)
# Emission/observation distribution
Y = GaussianARD(F, tau,
name='Y')
Q = VB(Y, F,
Z, rho, V,
C, gamma, X, A, alpha,
tau)
return Q
def infer(y, D, K, rotate=True, debug=False, maxiter=100, mask=True,
plot_C=True, monitor=False, update_hyper=0, autosave=None):
"""
Apply LSSM with switching dynamics to the given data.
"""
(M, N) = np.shape(y)
# Construct model
Q = model(M=M, K=K, N=N, D=D)
if not plot_C:
Q['C'].set_plotter(None)
if autosave is not None:
Q.set_autosave(autosave, iterations=10)
Q['Y'].observe(y, mask=mask)
# Set up rotation speed-up
if rotate:
# Initial rotate the D-dimensional state space (X, A, C)
# Do not update hyperparameters
rotA_init = transformations.RotateGaussianARD(Q['A'])
rotX_init = transformations.RotateSwitchingMarkovChain(Q['X'],
Q['A'],
Q['Z'],
rotA_init)
rotC_init = transformations.RotateGaussianARD(Q['C'])
R_init = transformations.RotationOptimizer(rotX_init, rotC_init, D)
# Rotate the D-dimensional state space (X, A, C)
rotA = transformations.RotateGaussianARD(Q['A'],
Q['alpha'])
rotX = transformations.RotateSwitchingMarkovChain(Q['X'],
Q['A'],
Q['Z'],
rotA)
rotC = transformations.RotateGaussianARD(Q['C'],
Q['gamma'])
R = transformations.RotationOptimizer(rotX, rotC, D)
if debug:
rotate_kwargs = {'maxiter': 10,
'check_bound': True,
'check_gradient': True}
else:
rotate_kwargs = {'maxiter': 10}
# Run inference
if monitor:
Q.plot()
for n in range(maxiter):
if n < update_hyper:
Q.update('X', 'C', 'A', 'tau', plot=monitor)
if rotate:
R_init.rotate(**rotate_kwargs)
else:
Q.update(plot=monitor)
if rotate:
R.rotate(**rotate_kwargs)
return Q
def simulate_data(N):
"""
Generate time-series data with switching dynamics.
"""
# Two states: 1) oscillation, 2) random walk
w1 = 0.02 * 2*np.pi
A = [ [[np.cos(w1), -np.sin(w1)],
[np.sin(w1), np.cos(w1)]],
[[ 1.0, 0.0],
[ 0.0, 0.0]] ]
C = [[1.0, 0.0]]
# State switching probabilities
q = 0.993 # probability to stay in the same state
r = (1-q)/(2-1) # probability to switch
P = q*np.identity(2) + r*(np.ones((2,2))-np.identity(2))
X = np.zeros((N, 2))
Z = np.zeros(N)
Y = np.zeros(N)
F = np.zeros(N)
z = np.random.randint(2)
x = np.random.randn(2)
Z[0] = z
X[0,:] = x
for n in range(1,N):
x = np.dot(A[z], x) + np.random.randn(2)
f = np.dot(C, x)
y = f + 5*np.random.randn()
z = np.random.choice(2, p=P[z])
Z[n] = z
X[n,:] = x
Y[n] = y
F[n] = f
Y = Y[None,:]
return (Y, F)
@bpplt.interactive
def demo(N=1000, maxiter=100, D=3, K=2, seed=42, plot=True, debug=False,
rotate=True, monitor=True):
"""
Run the demo for linear state-space model with switching dynamics.
"""
# Use deterministic random numbers
if seed is not None:
np.random.seed(seed)
# Generate data
(Y, F) = simulate_data(N)
# Plot observations
if plot:
plt.figure()
bpplt.timeseries(F, linestyle='-', color='b')
bpplt.timeseries(Y, linestyle='None', color='r', marker='x')
# Apply the linear state-space model with switching dynamics
Q = infer(Y, D, K,
debug=debug,
maxiter=maxiter,
monitor=monitor,
rotate=rotate,
update_hyper=5)
# Show results
if plot:
Q.plot()
return
if __name__ == '__main__':
import sys, getopt, os
try:
opts, args = getopt.getopt(sys.argv[1:],
"",
["n=",
"d=",
"k=",
"seed=",
"debug",
"no-rotation",
"no-monitor",
"no-plot",
"maxiter="])
except getopt.GetoptError:
print('python lssm_sd.py ')
print('--n= Number of data vectors')
print('--d= Latent space dimensionality')
print('--k= Number of mixed models')
print('--maxiter= Maximum number of VB iterations')
print('--seed= Seed (integer) for the random number generator')
print('--no-rotation Do not peform rotation speed ups')
print('--no-plot Do not plot results')
print('--no-monitor Do not plot distributions during VB learning')
print('--debug Check that the rotations are implemented correctly')
sys.exit(2)
kwargs = {}
for opt, arg in opts:
if opt == "--maxiter":
kwargs["maxiter"] = int(arg)
elif opt == "--d":
kwargs["D"] = int(arg)
elif opt == "--k":
kwargs["K"] = int(arg)
elif opt == "--seed":
kwargs["seed"] = int(arg)
elif opt == "--no-rotation":
kwargs["rotate"] = False
elif opt == "--no-monitor":
kwargs["monitor"] = False
elif opt == "--no-plot":
kwargs["plot"] = False
elif opt == "--debug":
kwargs["debug"] = True
elif opt in ("--n",):
kwargs["N"] = int(arg)
else:
raise ValueError("Unhandled option given")
demo(**kwargs)
plt.show()
bayespy-0.2.3/bayespy/demos/lssm_tvd.py 0000664 5104112 5104112 00000036224 12430651206 021710 0 ustar jluttine jluttine 0000000 0000000 ######################################################################
# Copyright (C) 2013-2014 Jaakko Luttinen
#
# This file is licensed under Version 3.0 of the GNU General Public
# License. See LICENSE for a text of the license.
######################################################################
######################################################################
# This file is part of BayesPy.
#
# BayesPy is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# BayesPy is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with BayesPy. If not, see .
######################################################################
"""
Demonstrate the linear state-space model with time-varying dynamics.
The observation is 1-D signal with changing frequency. The frequency oscillates
so it can be learnt too. Missing values are used to create a few gaps in the
data so the task is to reconstruct the gaps.
For reference, see the following publication:
(TODO)
Some functions in this module are re-usable:
* ``model`` can be used to construct the LSSM with switching dynamics.
* ``infer`` can be used to apply the model to given data.
"""
import numpy as np
import scipy
import matplotlib.pyplot as plt
from bayespy.nodes import (GaussianMarkovChain,
VaryingGaussianMarkovChain,
GaussianARD,
Gamma,
SumMultiply)
from bayespy.utils import misc
from bayespy.utils import random
from bayespy.inference.vmp.vmp import VB
from bayespy.inference.vmp import transformations
from bayespy.inference.vmp.nodes.gaussian import GaussianMoments
import bayespy.plot as bpplt
def model(M, N, D, K):
"""
Construct the linear state-space model with time-varying dynamics
For reference, see the following publication:
(TODO)
"""
#
# The model block for the latent mixing weight process
#
# Dynamics matrix with ARD
# beta : (K) x ()
beta = Gamma(1e-5,
1e-5,
plates=(K,),
name='beta')
# B : (K) x (K)
B = GaussianARD(np.identity(K),
beta,
shape=(K,),
plates=(K,),
name='B',
plotter=bpplt.GaussianHintonPlotter(rows=0,
cols=1,
scale=0),
initialize=False)
B.initialize_from_value(np.identity(K))
# Mixing weight process, that is, the weights in the linear combination of
# state dynamics matrices
# S : () x (N,K)
S = GaussianMarkovChain(np.ones(K),
1e-6*np.identity(K),
B,
np.ones(K),
n=N,
name='S',
plotter=bpplt.GaussianMarkovChainPlotter(scale=2),
initialize=False)
s = 10*np.random.randn(N,K)
s[:,0] = 10
S.initialize_from_value(s)
#
# The model block for the latent states
#
# Projection matrix of the dynamics matrix
# alpha : (K) x ()
alpha = Gamma(1e-5,
1e-5,
plates=(D,K),
name='alpha')
alpha.initialize_from_value(1*np.ones((D,K)))
# A : (D) x (D,K)
A = GaussianARD(0,
alpha,
shape=(D,K),
plates=(D,),
name='A',
plotter=bpplt.GaussianHintonPlotter(rows=0,
cols=1,
scale=0),
initialize=False)
# Initialize S and A such that A*S is almost an identity matrix
a = np.zeros((D,D,K))
a[np.arange(D),np.arange(D),np.zeros(D,dtype=int)] = 1
a[:,:,0] = np.identity(D) / s[0,0]
a[:,:,1:] = 0.1/s[0,0]*np.random.randn(D,D,K-1)
A.initialize_from_value(a)
# Latent states with dynamics
# X : () x (N,D)
X = VaryingGaussianMarkovChain(np.zeros(D), # mean of x0
1e-3*np.identity(D), # prec of x0
A, # dynamics matrices
S._convert(GaussianMoments)[1:], # temporal weights
np.ones(D), # innovation
n=N, # time instances
name='X',
plotter=bpplt.GaussianMarkovChainPlotter(scale=2),
initialize=False)
X.initialize_from_value(np.random.randn(N,D))
#
# The model block for observations
#
# Mixing matrix from latent space to observation space using ARD
# gamma : (D) x ()
gamma = Gamma(1e-5,
1e-5,
plates=(D,),
name='gamma')
gamma.initialize_from_value(1e-2*np.ones(D))
# C : (M,1) x (D)
C = GaussianARD(0,
gamma,
shape=(D,),
plates=(M,1),
name='C',
plotter=bpplt.GaussianHintonPlotter(rows=0,
cols=2,
scale=0))
C.initialize_from_value(np.random.randn(M,1,D))
# Noiseless process
# F : (M,N) x ()
F = SumMultiply('d,d',
C,
X,
name='F')
# Observation noise
# tau : () x ()
tau = Gamma(1e-5,
1e-5,
name='tau')
tau.initialize_from_value(1e2)
# Observations
# Y: (M,N) x ()
Y = GaussianARD(F,
tau,
name='Y')
# Construct inference machine
Q = VB(Y, F, C, gamma, X, A, alpha, tau, S, B, beta)
return Q
def infer(y, D, K,
mask=True,
maxiter=100,
rotate=False,
debug=False,
precompute=False,
update_hyper=0,
start_rotating=0,
start_rotating_weights=0,
plot_C=True,
monitor=True,
autosave=None):
"""
Run VB inference for linear state-space model with time-varying dynamics.
"""
y = misc.atleast_nd(y, 2)
(M, N) = np.shape(y)
# Construct the model
Q = model(M, N, D, K)
if not plot_C:
Q['C'].set_plotter(None)
if autosave is not None:
Q.set_autosave(autosave, iterations=10)
# Observe data
Q['Y'].observe(y, mask=mask)
# Set up rotation speed-up
if rotate:
# Initial rotate the D-dimensional state space (X, A, C)
# Does not update hyperparameters
rotA_init = transformations.RotateGaussianARD(Q['A'],
axis=0,
precompute=precompute)
rotX_init = transformations.RotateVaryingMarkovChain(Q['X'],
Q['A'],
Q['S']._convert(GaussianMoments)[...,1:,None],
rotA_init)
rotC_init = transformations.RotateGaussianARD(Q['C'],
axis=0,
precompute=precompute)
R_X_init = transformations.RotationOptimizer(rotX_init, rotC_init, D)
# Rotate the D-dimensional state space (X, A, C)
rotA = transformations.RotateGaussianARD(Q['A'],
Q['alpha'],
axis=0,
precompute=precompute)
rotX = transformations.RotateVaryingMarkovChain(Q['X'],
Q['A'],
Q['S']._convert(GaussianMoments)[...,1:,None],
rotA)
rotC = transformations.RotateGaussianARD(Q['C'],
Q['gamma'],
axis=0,
precompute=precompute)
R_X = transformations.RotationOptimizer(rotX, rotC, D)
# Rotate the K-dimensional latent dynamics space (S, A, C)
rotB = transformations.RotateGaussianARD(Q['B'],
Q['beta'],
precompute=precompute)
rotS = transformations.RotateGaussianMarkovChain(Q['S'], rotB)
rotA = transformations.RotateGaussianARD(Q['A'],
Q['alpha'],
axis=-1,
precompute=precompute)
R_S = transformations.RotationOptimizer(rotS, rotA, K)
if debug:
rotate_kwargs = {'maxiter': 10,
'check_bound': True,
'check_gradient': True}
else:
rotate_kwargs = {'maxiter': 10}
# Plot initial distributions
if monitor:
Q.plot()
# Run inference using rotations
for ind in range(maxiter):
if ind < update_hyper:
# It might be a good idea to learn the lower level nodes a bit
# before starting to learn the upper level nodes.
Q.update('X', 'C', 'A', 'tau', plot=monitor)
if rotate and ind >= start_rotating:
# Use the rotation which does not update alpha nor beta
R_X_init.rotate(**rotate_kwargs)
else:
Q.update(plot=monitor)
if rotate and ind >= start_rotating:
# It might be a good idea to not rotate immediately because it
# might lead to pruning out components too efficiently before
# even estimating them roughly
R_X.rotate(**rotate_kwargs)
if ind >= start_rotating_weights:
R_S.rotate(**rotate_kwargs)
# Return the posterior approximation
return Q
def simulate_data(N):
"""
Generate a signal with changing frequency
"""
t = np.arange(N)
a = 0.1 * 2*np.pi # base frequency
b = 0.01 * 2*np.pi # frequency of the frequency change
c = 8 # magnitude of the frequency change
f = np.sin( a * (t + c*np.sin(b*t)) )
y = f + 0.1*np.random.randn(N)
return (y, f)
@bpplt.interactive
def demo(N=1000, D=5, K=4, seed=42, maxiter=200, rotate=True, debug=False,
precompute=False, plot=True):
# Seed for random number generator
if seed is not None:
np.random.seed(seed)
# Create data
(y, f) = simulate_data(N)
# Create some gaps
mask_gaps = misc.trues(N)
for m in range(100, N, 140):
start = m
end = min(m+15, N-1)
mask_gaps[start:end] = False
# Randomly missing values
mask_random = np.logical_or(random.mask(N, p=0.8),
np.logical_not(mask_gaps))
# Remove the observations
mask = np.logical_and(mask_gaps, mask_random)
y[~mask] = np.nan # BayesPy doesn't require NaNs, they're just for plotting.
# Add row axes
y = y[None,...]
f = f[None,...]
mask = mask[None,...]
mask_gaps = mask_gaps[None,...]
mask_random = mask_random[None,...]
# Run the method
Q = infer(y, D, K,
mask=mask,
maxiter=maxiter,
rotate=rotate,
debug=debug,
precompute=precompute,
update_hyper=10,
start_rotating_weights=20,
monitor=True)
if plot:
# Plot observations
plt.figure()
bpplt.timeseries_normal(Q['F'], scale=2)
bpplt.timeseries(f, linestyle='-', color='b')
bpplt.timeseries(y, linestyle='None', color='r', marker='.')
plt.ylim([-2, 2])
# Plot latent space
Q.plot('X')
# Plot mixing weight space
Q.plot('S')
# Compute RMSE
rmse_random = misc.rmse(Q['Y'].get_moments()[0][~mask_random],
f[~mask_random])
rmse_gaps = misc.rmse(Q['Y'].get_moments()[0][~mask_gaps],
f[~mask_gaps])
print("RMSE for randomly missing values: %f" % rmse_random)
print("RMSE for gap values: %f" % rmse_gaps)
if __name__ == '__main__':
import sys, getopt, os
try:
opts, args = getopt.getopt(sys.argv[1:],
"",
[
"n=",
"d=",
"k=",
"seed=",
"maxiter=",
"debug",
"precompute",
"no-plot",
"no-rotation"])
except getopt.GetoptError:
print('python lssm_tvd.py ')
print('--n= Number of data vectors')
print('--d= Dimensionality of the latent vectors in the model')
print('--k= Dimensionality of the latent mixing weights')
print('--no-rotation Do not apply speed-up rotations')
print('--maxiter= Maximum number of VB iterations')
print('--seed= Seed (integer) for the random number generator')
print('--debug Check that the rotations are implemented correctly')
print('--no-plot Do not plot results')
print('--precompute Precompute some moments when rotating. May '
'speed up or slow down.')
sys.exit(2)
kwargs = {}
for opt, arg in opts:
if opt == "--no-rotation":
kwargs["rotate"] = False
elif opt == "--maxiter":
kwargs["maxiter"] = int(arg)
elif opt == "--debug":
kwargs["debug"] = True
elif opt == "--precompute":
kwargs["precompute"] = True
elif opt == "--seed":
kwargs["seed"] = int(arg)
elif opt == "--n":
kwargs["N"] = int(arg)
elif opt == "--d":
kwargs["D"] = int(arg)
elif opt == "--k":
if int(arg) == 0:
kwargs["K"] = None
else:
kwargs["K"] = int(arg)
elif opt == "--no-plot":
kwargs["plot"] = False
else:
raise ValueError("Unhandled argument given")
demo(**kwargs)
plt.show()
bayespy-0.2.3/bayespy/demos/mog.py 0000664 5104112 5104112 00000006463 12430651206 020641 0 ustar jluttine jluttine 0000000 0000000 ######################################################################
# Copyright (C) 2011,2012 Jaakko Luttinen
#
# This file is licensed under Version 3.0 of the GNU General Public
# License. See LICENSE for a text of the license.
######################################################################
######################################################################
# This file is part of BayesPy.
#
# BayesPy is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# BayesPy is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with BayesPy. If not, see .
######################################################################
import numpy as np
import matplotlib.pyplot as plt
import time
from bayespy.utils import misc
import bayespy.plot as bpplt
from bayespy.inference.vmp import nodes
from bayespy.inference.vmp.vmp import VB
def gaussianmix_model(N, K, D):
# N = number of data vectors
# K = number of clusters
# D = dimensionality
# Construct the Gaussian mixture model
# K prior weights (for components)
alpha = nodes.Dirichlet(1e-3*np.ones(K),
name='alpha')
# N K-dimensional cluster assignments (for data)
z = nodes.Categorical(alpha,
plates=(N,),
name='z')
# K D-dimensional component means
X = nodes.Gaussian(np.zeros(D), 0.01*np.identity(D),
plates=(K,),
name='X')
# K D-dimensional component covariances
Lambda = nodes.Wishart(D, 0.01*np.identity(D),
plates=(K,),
name='Lambda')
# N D-dimensional observation vectors
Y = nodes.Mixture(z, nodes.Gaussian, X, Lambda, plates=(N,), name='Y')
# TODO: Plates should be learned automatically if not given (it
# would be the smallest shape broadcasted from the shapes of the
# parents)
z.initialize_from_random()
return VB(Y, X, Lambda, z, alpha)
@bpplt.interactive
def run(N=50, K=5, D=2):
# Generate data
N1 = np.floor(0.5*N)
N2 = N - N1
y = np.vstack([np.random.normal(0, 0.5, size=(N1,D)),
np.random.normal(10, 0.5, size=(N2,D))])
# Construct model
Q = gaussianmix_model(N,K,D)
# Observe data
Q['Y'].observe(y)
# Run inference
Q.update(repeat=30)
# Run predictive model
zh = nodes.Categorical(Q['alpha'], name='zh')
Yh = nodes.Mixture(zh, nodes.Gaussian, Q['X'], Q['Lambda'], name='Yh')
zh.update()
# Plot predictive pdf
N1 = 400
N2 = 400
x1 = np.linspace(-3, 15, N1)
x2 = np.linspace(-3, 15, N2)
xh = misc.grid(x1, x2)
lpdf = Yh.integrated_logpdf_from_parents(xh, 0)
pdf = np.reshape(np.exp(lpdf), (N2,N1))
plt.clf()
plt.contourf(x1, x2, pdf, 100)
plt.scatter(y[:,0], y[:,1])
print('integrated pdf:', np.sum(pdf)*(18*18)/(N1*N2))
Q['X'].show()
Q['alpha'].show()
if __name__ == '__main__':
run()
plt.show()
bayespy-0.2.3/bayespy/demos/pca.py 0000664 5104112 5104112 00000012663 12430651206 020621 0 ustar jluttine jluttine 0000000 0000000 ######################################################################
# Copyright (C) 2011-2014 Jaakko Luttinen
#
# This file is licensed under Version 3.0 of the GNU General Public
# License. See LICENSE for a text of the license.
######################################################################
######################################################################
# This file is part of BayesPy.
#
# BayesPy is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# BayesPy is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with BayesPy. If not, see .
######################################################################
import numpy as np
import matplotlib.pyplot as plt
import bayespy.plot as myplt
from bayespy.utils import misc
from bayespy.utils import random
from bayespy import nodes
from bayespy.inference.vmp.vmp import VB
from bayespy.inference.vmp import transformations
import bayespy.plot as bpplt
def model(M, N, D):
# Construct the PCA model with ARD
# ARD
alpha = nodes.Gamma(1e-2,
1e-2,
plates=(D,),
name='alpha')
# Loadings
W = nodes.GaussianARD(0,
alpha,
shape=(D,),
plates=(M,1),
name='W')
# States
X = nodes.GaussianARD(0,
1,
shape=(D,),
plates=(1,N),
name='X')
# PCA
F = nodes.SumMultiply('i,i', W, X,
name='F')
# Noise
tau = nodes.Gamma(1e-2, 1e-2,
name='tau')
# Noisy observations
Y = nodes.GaussianARD(F, tau,
name='Y')
return (Y, F, W, X, tau, alpha)
@bpplt.interactive
def run(M=10, N=100, D_y=3, D=5, seed=42, rotate=False, maxiter=100, debug=False, plot=True):
if seed is not None:
np.random.seed(seed)
# Generate data
w = np.random.normal(0, 1, size=(M,1,D_y))
x = np.random.normal(0, 1, size=(1,N,D_y))
f = misc.sum_product(w, x, axes_to_sum=[-1])
y = f + np.random.normal(0, 0.2, size=(M,N))
# Construct model
(Y, F, W, X, tau, alpha) = model(M, N, D)
# Data with missing values
mask = random.mask(M, N, p=0.5) # randomly missing
y[~mask] = np.nan
Y.observe(y, mask=mask)
# Construct inference machine
Q = VB(Y, W, X, tau, alpha)
# Initialize some nodes randomly
X.initialize_from_random()
W.initialize_from_random()
# Run inference algorithm
if rotate:
# Use rotations to speed up learning
rotW = transformations.RotateGaussianARD(W, alpha)
rotX = transformations.RotateGaussianARD(X)
R = transformations.RotationOptimizer(rotW, rotX, D)
for ind in range(maxiter):
Q.update()
if debug:
R.rotate(check_bound=True,
check_gradient=True)
else:
R.rotate()
else:
# Use standard VB-EM alone
Q.update(repeat=maxiter)
# Plot results
if plot:
plt.figure()
bpplt.timeseries_normal(F, scale=2)
bpplt.timeseries(f, color='g', linestyle='-')
bpplt.timeseries(y, color='r', linestyle='None', marker='+')
if __name__ == '__main__':
import sys, getopt, os
try:
opts, args = getopt.getopt(sys.argv[1:],
"",
["m=",
"n=",
"d=",
"k=",
"seed=",
"maxiter=",
"debug",
"rotate"])
except getopt.GetoptError:
print('python demo_pca.py ')
print('--m= Dimensionality of data vectors')
print('--n= Number of data vectors')
print('--d= Dimensionality of the latent vectors in the model')
print('--k= Dimensionality of the true latent vectors')
print('--rotate Apply speed-up rotations')
print('--maxiter= Maximum number of VB iterations')
print('--seed= Seed (integer) for the random number generator')
print('--debug Check that the rotations are implemented correctly')
sys.exit(2)
kwargs = {}
for opt, arg in opts:
if opt == "--rotate":
kwargs["rotate"] = True
elif opt == "--maxiter":
kwargs["maxiter"] = int(arg)
elif opt == "--debug":
kwargs["debug"] = True
elif opt == "--seed":
kwargs["seed"] = int(arg)
elif opt in ("--m",):
kwargs["M"] = int(arg)
elif opt in ("--n",):
kwargs["N"] = int(arg)
elif opt in ("--d",):
kwargs["D"] = int(arg)
elif opt in ("--k",):
kwargs["D_y"] = int(arg)
run(**kwargs)
plt.show()
bayespy-0.2.3/bayespy/demos/saving.py 0000664 5104112 5104112 00000011302 12430651206 021332 0 ustar jluttine jluttine 0000000 0000000 ######################################################################
# Copyright (C) 2011-2013 Jaakko Luttinen
#
# This file is licensed under Version 3.0 of the GNU General Public
# License. See LICENSE for a text of the license.
######################################################################
######################################################################
# This file is part of BayesPy.
#
# BayesPy is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# BayesPy is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with BayesPy. If not, see .
######################################################################
import numpy as np
import matplotlib.pyplot as plt
import h5py
import tempfile
import bayespy.plot as bpplt
from bayespy.utils import misc
from bayespy.utils import random
from bayespy.inference.vmp import nodes
from bayespy.inference.vmp.vmp import VB
def pca_model(M, N, D):
# Construct the PCA model with ARD
# ARD
alpha = nodes.Gamma(1e-2,
1e-2,
plates=(D,),
name='alpha')
# Loadings
W = nodes.Gaussian(np.zeros(D),
alpha.as_diagonal_wishart(),
name="W",
plates=(M,1))
# States
X = nodes.Gaussian(np.zeros(D),
np.identity(D),
name="X",
plates=(1,N))
# PCA
WX = nodes.Dot(W, X, name="WX")
# Noise
tau = nodes.Gamma(1e-2, 1e-2, name="tau", plates=())
# Noisy observations
Y = nodes.GaussianARD(WX, tau, name="Y", plates=(M,N))
return (Y, WX, W, X, tau, alpha)
@bpplt.interactive
def run(M=10, N=100, D_y=3, D=5):
seed = 45
print('seed =', seed)
np.random.seed(seed)
# Check HDF5 version.
if h5py.version.hdf5_version_tuple < (1,8,7):
print("WARNING! Your HDF5 version is %s. HDF5 versions <1.8.7 are not "
"able to save empty arrays, thus you may experience problems if "
"you for instance try to save before running any iteration steps."
% str(h5py.version.hdf5_version_tuple))
# Generate data
w = np.random.normal(0, 1, size=(M,1,D_y))
x = np.random.normal(0, 1, size=(1,N,D_y))
f = misc.sum_product(w, x, axes_to_sum=[-1])
y = f + np.random.normal(0, 0.5, size=(M,N))
# Construct model
(Y, WX, W, X, tau, alpha) = pca_model(M, N, D)
# Data with missing values
mask = random.mask(M, N, p=0.9) # randomly missing
mask[:,20:40] = False # gap missing
y[~mask] = np.nan
Y.observe(y, mask=mask)
# Construct inference machine
Q = VB(Y, W, X, tau, alpha, autosave_iterations=5)
# Initialize some nodes randomly
X.initialize_from_value(X.random())
W.initialize_from_value(W.random())
# Save the state into a HDF5 file
filename = tempfile.NamedTemporaryFile(suffix='hdf5').name
Q.update(X, W, alpha, tau, repeat=1)
Q.save(filename)
# Inference loop.
Q.update(X, W, alpha, tau, repeat=10)
# Reload the state from the HDF5 file
Q.load(filename=filename)
# Inference loop again.
Q.update(X, W, alpha, tau, repeat=10)
# NOTE: Saving and loading requires that you have the model
# constructed. "Save" does not store the model structure nor does "load"
# read it. They are just used for reading and writing the contents of the
# nodes. Thus, if you want to load, you first need to construct the same
# model that was used for saving and then use load to set the states of the
# nodes.
plt.clf()
WX_params = WX.get_parameters()
fh = WX_params[0] * np.ones(y.shape)
err_fh = 2*np.sqrt(WX_params[1] + 1/tau.get_moments()[0]) * np.ones(y.shape)
for m in range(M):
plt.subplot(M,1,m+1)
#errorplot(y, error=None, x=None, lower=None, upper=None):
bpplt.errorplot(fh[m], x=np.arange(N), error=err_fh[m])
plt.plot(np.arange(N), f[m], 'g')
plt.plot(np.arange(N), y[m], 'r+')
plt.figure()
Q.plot_iteration_by_nodes()
plt.figure()
plt.subplot(2,2,1)
bpplt.binary_matrix(W.mask)
plt.subplot(2,2,2)
bpplt.binary_matrix(X.mask)
plt.subplot(2,2,3)
#bpplt.binary_matrix(WX.get_mask())
plt.subplot(2,2,4)
bpplt.binary_matrix(Y.mask)
if __name__ == '__main__':
run()
plt.show()
bayespy-0.2.3/bayespy/inference/ 0000775 5104112 5104112 00000000000 12437607670 020340 5 ustar jluttine jluttine 0000000 0000000 bayespy-0.2.3/bayespy/inference/vmp/ 0000775 5104112 5104112 00000000000 12437607670 021142 5 ustar jluttine jluttine 0000000 0000000 bayespy-0.2.3/bayespy/inference/vmp/nodes/ 0000775 5104112 5104112 00000000000 12437607670 022252 5 ustar jluttine jluttine 0000000 0000000 bayespy-0.2.3/bayespy/inference/vmp/nodes/tests/ 0000775 5104112 5104112 00000000000 12437607670 023414 5 ustar jluttine jluttine 0000000 0000000 bayespy-0.2.3/bayespy/inference/vmp/nodes/tests/__init__.py 0000664 5104112 5104112 00000000000 12231156672 025504 0 ustar jluttine jluttine 0000000 0000000 bayespy-0.2.3/bayespy/inference/vmp/nodes/tests/test_bernoulli.py 0000664 5104112 5104112 00000010354 12431131341 027000 0 ustar jluttine jluttine 0000000 0000000 ######################################################################
# Copyright (C) 2014 Jaakko Luttinen
#
# This file is licensed under Version 3.0 of the GNU General Public
# License. See LICENSE for a text of the license.
######################################################################
######################################################################
# This file is part of BayesPy.
#
# BayesPy is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# BayesPy is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with BayesPy. If not, see .
######################################################################
"""
Unit tests for `bernoulli` module.
"""
import numpy as np
import scipy
from bayespy.nodes import (Bernoulli,
Beta,
Mixture)
from bayespy.utils import random
from bayespy.utils.misc import TestCase
class TestBernoulli(TestCase):
"""
Unit tests for Bernoulli node
"""
def test_init(self):
"""
Test the creation of Bernoulli nodes.
"""
# Some simple initializations
X = Bernoulli(0.5)
X = Bernoulli(Beta([2,3]))
# Check that plates are correct
X = Bernoulli(0.7, plates=(4,3))
self.assertEqual(X.plates,
(4,3))
X = Bernoulli(0.7*np.ones((4,3)))
self.assertEqual(X.plates,
(4,3))
X = Bernoulli(Beta([4,3], plates=(4,3)))
self.assertEqual(X.plates,
(4,3))
# Invalid probability
self.assertRaises(ValueError,
Bernoulli,
-0.5)
self.assertRaises(ValueError,
Bernoulli,
1.5)
# Inconsistent plates
self.assertRaises(ValueError,
Bernoulli,
0.5*np.ones(4),
plates=(3,))
# Explicit plates too small
self.assertRaises(ValueError,
Bernoulli,
0.5*np.ones(4),
plates=(1,))
pass
def test_moments(self):
"""
Test the moments of Bernoulli nodes.
"""
# Simple test
X = Bernoulli(0.7)
u = X._message_to_child()
self.assertEqual(len(u), 1)
self.assertAllClose(u[0],
0.7)
# Test plates in p
p = np.random.rand(3)
X = Bernoulli(p)
u = X._message_to_child()
self.assertAllClose(u[0],
p)
# Test with beta prior
P = Beta([7, 3])
logp = P._message_to_child()[0]
p0 = np.exp(logp[0]) / (np.exp(logp[0]) + np.exp(logp[1]))
X = Bernoulli(P)
u = X._message_to_child()
self.assertAllClose(u[0],
p0)
# Test with broadcasted plates
P = Beta([7, 3], plates=(10,))
X = Bernoulli(P)
u = X._message_to_child()
self.assertAllClose(u[0] * np.ones(X.get_shape(0)),
p0*np.ones(10))
pass
def test_mixture(self):
"""
Test mixture of Bernoulli
"""
P = Mixture([2,0,0], Bernoulli, [0.1, 0.2, 0.3])
u = P._message_to_child()
self.assertEqual(len(u), 1)
self.assertAllClose(u[0], [0.3, 0.1, 0.1])
pass
def test_observed(self):
"""
Test observation of Bernoulli node
"""
Z = Bernoulli(0.3)
Z.observe(2 < 3)
pass
def test_random(self):
"""
Test random sampling in Bernoulli node
"""
p = [1.0, 0.0]
with np.errstate(divide='ignore'):
Z = Bernoulli(p, plates=(3,2)).random()
self.assertArrayEqual(Z, np.ones((3,2))*p)
bayespy-0.2.3/bayespy/inference/vmp/nodes/tests/test_beta.py 0000664 5104112 5104112 00000006571 12374370351 025742 0 ustar jluttine jluttine 0000000 0000000 ######################################################################
# Copyright (C) 2014 Jaakko Luttinen
#
# This file is licensed under Version 3.0 of the GNU General Public
# License. See LICENSE for a text of the license.
######################################################################
######################################################################
# This file is part of BayesPy.
#
# BayesPy is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# BayesPy is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with BayesPy. If not, see .
######################################################################
"""
Unit tests for `beta` module.
"""
import numpy as np
from scipy import special
from bayespy.nodes import Beta
from bayespy.utils import random
from bayespy.utils.misc import TestCase
class TestBeta(TestCase):
"""
Unit tests for Beta node
"""
def test_init(self):
"""
Test the creation of beta nodes.
"""
# Some simple initializations
p = Beta([1.5, 4.2])
# Check that plates are correct
p = Beta([2, 3], plates=(4,3))
self.assertEqual(p.plates,
(4,3))
p = Beta(np.ones((4,3,2)))
self.assertEqual(p.plates,
(4,3))
# Parent not a vector
self.assertRaises(ValueError,
Beta,
4)
# Parent vector has wrong shape
self.assertRaises(ValueError,
Beta,
[4])
self.assertRaises(ValueError,
Beta,
[4,4,4])
# Parent vector has invalid values
self.assertRaises(ValueError,
Beta,
[-2,3])
# Plates inconsistent
self.assertRaises(ValueError,
Beta,
np.ones((4,2)),
plates=(3,))
# Explicit plates too small
self.assertRaises(ValueError,
Beta,
np.ones((4,2)),
plates=(1,))
pass
def test_moments(self):
"""
Test the moments of beta nodes.
"""
p = Beta([2, 3])
u = p._message_to_child()
self.assertAllClose(u[0],
special.psi([2,3]) - special.psi(2+3))
pass
def test_random(self):
"""
Test random sampling of beta nodes.
"""
p = Beta([1e20, 3e20])
x = p.random()
self.assertAllClose(x,
0.25)
p = Beta([[1e20, 3e20],
[1e20, 1e20]])
x = p.random()
self.assertAllClose(x,
[0.25, 0.5])
p = Beta([1e20, 3e20], plates=(3,))
x = p.random()
self.assertAllClose(x,
[0.25, 0.25, 0.25])
pass
bayespy-0.2.3/bayespy/inference/vmp/nodes/tests/test_binomial.py 0000664 5104112 5104112 00000013333 12431131067 026604 0 ustar jluttine jluttine 0000000 0000000 ######################################################################
# Copyright (C) 2014 Jaakko Luttinen
#
# This file is licensed under Version 3.0 of the GNU General Public
# License. See LICENSE for a text of the license.
######################################################################
######################################################################
# This file is part of BayesPy.
#
# BayesPy is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# BayesPy is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with BayesPy. If not, see .
######################################################################
"""
Unit tests for `binomial` module.
"""
import numpy as np
import scipy
from bayespy.nodes import (Binomial,
Beta,
Mixture)
from bayespy.utils import random
from bayespy.utils.misc import TestCase
class TestBinomial(TestCase):
"""
Unit tests for Binomial node
"""
def test_init(self):
"""
Test the creation of binomial nodes.
"""
# Some simple initializations
X = Binomial(10, 0.5)
X = Binomial(10, Beta([2,3]))
# Check that plates are correct
X = Binomial(10, 0.7, plates=(4,3))
self.assertEqual(X.plates,
(4,3))
X = Binomial(10, 0.7*np.ones((4,3)))
self.assertEqual(X.plates,
(4,3))
n = np.ones((4,3), dtype=np.int)
X = Binomial(n, 0.7)
self.assertEqual(X.plates,
(4,3))
X = Binomial(10, Beta([4,3], plates=(4,3)))
self.assertEqual(X.plates,
(4,3))
# Invalid probability
self.assertRaises(ValueError,
Binomial,
10,
-0.5)
self.assertRaises(ValueError,
Binomial,
10,
1.5)
# Invalid number of trials
self.assertRaises(ValueError,
Binomial,
-1,
0.5)
self.assertRaises(ValueError,
Binomial,
8.5,
0.5)
# Inconsistent plates
self.assertRaises(ValueError,
Binomial,
10,
0.5*np.ones(4),
plates=(3,))
# Explicit plates too small
self.assertRaises(ValueError,
Binomial,
10,
0.5*np.ones(4),
plates=(1,))
pass
def test_moments(self):
"""
Test the moments of binomial nodes.
"""
# Simple test
X = Binomial(1, 0.7)
u = X._message_to_child()
self.assertEqual(len(u), 1)
self.assertAllClose(u[0],
0.7)
# Test n
X = Binomial(10, 0.7)
u = X._message_to_child()
self.assertAllClose(u[0],
10*0.7)
# Test plates in p
n = np.random.randint(1, 10)
p = np.random.rand(3)
X = Binomial(n, p)
u = X._message_to_child()
self.assertAllClose(u[0],
p*n)
# Test plates in n
n = np.random.randint(1, 10, size=(3,))
p = np.random.rand()
X = Binomial(n, p)
u = X._message_to_child()
self.assertAllClose(u[0],
p*n)
# Test plates in p and n
n = np.random.randint(1, 10, size=(4,1))
p = np.random.rand(3)
X = Binomial(n, p)
u = X._message_to_child()
self.assertAllClose(u[0],
p*n)
# Test with beta prior
P = Beta([7, 3])
logp = P._message_to_child()[0]
p0 = np.exp(logp[0]) / (np.exp(logp[0]) + np.exp(logp[1]))
X = Binomial(1, P)
u = X._message_to_child()
self.assertAllClose(u[0],
p0)
# Test with broadcasted plates
P = Beta([7, 3], plates=(10,))
X = Binomial(5, P)
u = X._message_to_child()
self.assertAllClose(u[0] * np.ones(X.get_shape(0)),
5*p0*np.ones(10))
pass
def test_mixture(self):
"""
Test binomial mixture
"""
X = Mixture(2, Binomial, 10, [0.1, 0.2, 0.3, 0.4])
u = X._message_to_child()
self.assertAllClose(u[0],
3.0)
pass
def test_observed(self):
"""
Test observation of Bernoulli node
"""
Z = Binomial(10, 0.3)
Z.observe(10)
u = Z._message_to_child()
self.assertAllClose(u[0],
10)
Z = Binomial(10, 0.9)
Z.observe(2)
u = Z._message_to_child()
self.assertAllClose(u[0],
2)
pass
def test_random(self):
"""
Test random sampling in Binomial node
"""
N = [ [5], [50] ]
p = [1.0, 0.0]
with np.errstate(divide='ignore'):
Z = Binomial(N, p, plates=(3,2,2)).random()
self.assertArrayEqual(Z, np.ones((3,2,2))*N*p)
bayespy-0.2.3/bayespy/inference/vmp/nodes/tests/test_categorical.py 0000664 5104112 5104112 00000015576 12374370351 027311 0 ustar jluttine jluttine 0000000 0000000 ######################################################################
# Copyright (C) 2014 Jaakko Luttinen
#
# This file is licensed under Version 3.0 of the GNU General Public
# License. See LICENSE for a text of the license.
######################################################################
######################################################################
# This file is part of BayesPy.
#
# BayesPy is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# BayesPy is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with BayesPy. If not, see .
######################################################################
"""
Unit tests for `categorical` module.
"""
import warnings
import numpy as np
import scipy
from bayespy.nodes import (Categorical,
Dirichlet,
Mixture,
Gamma)
from bayespy.utils import random
from bayespy.utils.misc import TestCase
class TestCategorical(TestCase):
"""
Unit tests for Categorical node
"""
def test_init(self):
"""
Test the creation of categorical nodes.
"""
# Some simple initializations
X = Categorical([0.1, 0.3, 0.6])
X = Categorical(Dirichlet([5,4,3]))
# Check that plates are correct
X = Categorical([0.1, 0.3, 0.6], plates=(3,4))
self.assertEqual(X.plates,
(3,4))
X = Categorical(0.25*np.ones((2,3,4)))
self.assertEqual(X.plates,
(2,3))
X = Categorical(Dirichlet([2,1,9], plates=(3,4)))
self.assertEqual(X.plates,
(3,4))
# Probabilities not a vector
self.assertRaises(ValueError,
Categorical,
0.5)
# Invalid probability
self.assertRaises(ValueError,
Categorical,
[-0.5, 1.5],
n=10)
self.assertRaises(ValueError,
Categorical,
[0.5, 1.5],
n=10)
# Inconsistent plates
self.assertRaises(ValueError,
Categorical,
0.25*np.ones((2,4)),
plates=(3,),
n=10)
# Explicit plates too small
self.assertRaises(ValueError,
Categorical,
0.25*np.ones((2,4)),
plates=(1,),
n=10)
pass
def test_moments(self):
"""
Test the moments of categorical nodes.
"""
# Simple test
X = Categorical([0.7,0.2,0.1])
u = X._message_to_child()
self.assertEqual(len(u), 1)
self.assertAllClose(u[0],
[0.7,0.2,0.1])
# Test plates in p
p = np.random.dirichlet([1,1], size=3)
X = Categorical(p)
u = X._message_to_child()
self.assertAllClose(u[0],
p)
# Test with Dirichlet prior
P = Dirichlet([7, 3])
logp = P._message_to_child()[0]
p0 = np.exp(logp[0]) / (np.exp(logp[0]) + np.exp(logp[1]))
p1 = np.exp(logp[1]) / (np.exp(logp[0]) + np.exp(logp[1]))
X = Categorical(P)
u = X._message_to_child()
p = np.array([p0, p1])
self.assertAllClose(u[0],
p)
# Test with broadcasted plates
P = Dirichlet([7, 3], plates=(10,))
X = Categorical(P)
u = X._message_to_child()
self.assertAllClose(u[0] * np.ones(X.get_shape(0)),
p*np.ones((10,1)))
pass
def test_observed(self):
"""
Test observed categorical nodes
"""
# Single observation
X = Categorical([0.7,0.2,0.1])
X.observe(2)
u = X._message_to_child()
self.assertAllClose(u[0],
[0,0,1])
# One plate axis
X = Categorical([0.7,0.2,0.1], plates=(2,))
X.observe([2,1])
u = X._message_to_child()
self.assertAllClose(u[0],
[[0,0,1],
[0,1,0]])
# Several plate axes
X = Categorical([0.7,0.1,0.1,0.1], plates=(2,3,))
X.observe([[2,1,1],
[0,2,3]])
u = X._message_to_child()
self.assertAllClose(u[0],
[ [[0,0,1,0],
[0,1,0,0],
[0,1,0,0]],
[[1,0,0,0],
[0,0,1,0],
[0,0,0,1]] ])
# Check invalid observations
X = Categorical([0.7,0.2,0.1])
self.assertRaises(ValueError,
X.observe,
-1)
self.assertRaises(ValueError,
X.observe,
3)
self.assertRaises(ValueError,
X.observe,
1.5)
pass
def test_constant(self):
"""
Test constant categorical nodes
"""
# Basic test
Y = Mixture(2, Gamma, [1, 2, 3], [1, 1, 1])
u = Y._message_to_child()
self.assertAllClose(u[0],
3/1)
# Test with one plate axis
alpha = [[1, 2, 3],
[4, 5, 6]]
Y = Mixture([2, 1], Gamma, alpha, 1)
u = Y._message_to_child()
self.assertAllClose(u[0],
[3, 5])
# Test with two plate axes
alpha = [ [[1, 2, 3],
[4, 5, 6]],
[[7, 8, 9],
[10, 11, 12]] ]
Y = Mixture([[2, 1], [0, 2]], Gamma, alpha, 1)
u = Y._message_to_child()
self.assertAllClose(u[0],
[[3, 5],
[7, 12]])
pass
def test_initialization(self):
"""
Test initialization of categorical nodes
"""
# Test initialization from random
with warnings.catch_warnings(record=True) as w:
Z = Categorical([[0.0, 1.0, 0.0],
[0.0, 0.0, 1.0]])
Z.initialize_from_random()
u = Z._message_to_child()
self.assertAllClose(u[0],
[[0, 1, 0],
[0, 0, 1]])
pass
bayespy-0.2.3/bayespy/inference/vmp/nodes/tests/test_categorical_markov_chain.py 0000664 5104112 5104112 00000016660 12374370351 032025 0 ustar jluttine jluttine 0000000 0000000 ######################################################################
# Copyright (C) 2014 Jaakko Luttinen
#
# This file is licensed under Version 3.0 of the GNU General Public
# License. See LICENSE for a text of the license.
######################################################################
######################################################################
# This file is part of BayesPy.
#
# BayesPy is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# BayesPy is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with BayesPy. If not, see .
######################################################################
"""
Unit tests for bayespy.inference.vmp.nodes.categorical_markov_chain module.
"""
import warnings
import numpy as np
from bayespy.utils import misc
from bayespy.inference.vmp.nodes import CategoricalMarkovChain, \
Dirichlet
class TestCategoricalMarkovChain(misc.TestCase):
def test_init(self):
"""
Test the creation of CategoricalMarkovChain object
"""
#
# Plates
#
p0 = np.random.dirichlet([1, 1])
P = np.random.dirichlet([1, 1], size=(3,2))
Z = CategoricalMarkovChain(p0, P)
self.assertEqual((), Z.plates, msg="Incorrect plates")
self.assertEqual(((2,),(3,2,2)), Z.dims, msg="Incorrect dimensions")
p0 = np.random.dirichlet([1, 1], size=(4,))
P = np.random.dirichlet([1, 1], size=(3,2))
Z = CategoricalMarkovChain(p0, P)
self.assertEqual((4,), Z.plates, msg="Incorrect plates")
self.assertEqual(((2,),(3,2,2)), Z.dims, msg="Incorrect dimensions")
p0 = np.random.dirichlet([1, 1])
P = np.random.dirichlet([1, 1], size=(4,3,2))
Z = CategoricalMarkovChain(p0, P)
self.assertEqual((4,), Z.plates, msg="Incorrect plates")
self.assertEqual(((2,),(3,2,2)), Z.dims, msg="Incorrect dimensions")
# Test some overflow bugs
p0 = np.array([0.5, 0.5])
P = Dirichlet(1e-3*np.ones(2),
plates=(2,))
Z = CategoricalMarkovChain(p0, P,
states=2000)
u = Z._message_to_child()
self.assertTrue(np.all(~np.isnan(u[0])),
msg="Nans in moments")
self.assertTrue(np.all(~np.isnan(u[1])),
msg="Nans in moments")
pass
def test_message_to_child(self):
"""
Test the message of CategoricalMarkovChain to child
"""
with warnings.catch_warnings(record=True) as w:
# Deterministic oscillator
p0 = np.array([1.0, 0.0])
P = np.array(3*[[[0.0, 1.0],
[1.0, 0.0]]])
Z = CategoricalMarkovChain(p0, P)
u = Z._message_to_child()
self.assertAllClose(u[0],
[1.0, 0])
self.assertAllClose(u[1],
[ [[0.0, 1.0],
[0.0, 0.0]],
[[0.0, 0.0],
[1.0, 0.0]],
[[0.0, 1.0],
[0.0, 0.0]] ])
# Maximum randomness
p0 = np.array([0.5, 0.5])
P = np.array(3*[[[0.5, 0.5],
[0.5, 0.5]]])
Z = CategoricalMarkovChain(p0, P)
u = Z._message_to_child()
self.assertAllClose(u[0],
[0.5, 0.5])
self.assertAllClose(u[1],
[ [[0.25, 0.25],
[0.25, 0.25]],
[[0.25, 0.25],
[0.25, 0.25]],
[[0.25, 0.25],
[0.25, 0.25]] ])
# Random init, deterministic dynamics
p0 = np.array([0.5, 0.5])
P = np.array(3*[[[0, 1],
[1, 0]]])
Z = CategoricalMarkovChain(p0, P)
u = Z._message_to_child()
self.assertAllClose(u[0],
[0.5, 0.5])
self.assertAllClose(u[1],
[ [[0.0, 0.5],
[0.5, 0.0]],
[[0.0, 0.5],
[0.5, 0.0]],
[[0.0, 0.5],
[0.5, 0.0]] ])
# Test plates
p0 = np.array([ [1.0, 0.0],
[0.5, 0.5] ])
P = np.array([ [ [[0.0, 1.0],
[1.0, 0.0]] ],
[ [[0.5, 0.5],
[0.5, 0.5]] ] ])
Z = CategoricalMarkovChain(p0, P)
u = Z._message_to_child()
self.assertAllClose(u[0],
[[1.0, 0.0],
[0.5, 0.5]])
self.assertAllClose(u[1],
[ [ [[0.0, 1.0],
[0.0, 0.0]] ],
[ [[0.25, 0.25],
[0.25, 0.25]] ] ])
# Test broadcasted state transition probabilities
p0 = np.array([1.0, 0.0])
P = Dirichlet([1e-10, 1e10],
plates=(3,2))
Z = CategoricalMarkovChain(p0, P)
u = Z._message_to_child()
self.assertAllClose(u[0],
[1.0, 0])
self.assertAllClose(u[1],
[ [[0.0, 1.0],
[0.0, 0.0]],
[[0.0, 0.0],
[0.0, 1.0]],
[[0.0, 0.0],
[0.0, 1.0]] ])
pass
def test_random(self):
"""
Test random sampling of categorical Markov chain
"""
# Simple random sample
Z = CategoricalMarkovChain([1, 0], [[0, 1],
[1, 0]],
states=3)
z = Z.random()
self.assertAllClose(z, [0, 1, 0])
# Draw sample with plates
p0 = [ [1,0], [0,1] ]
P = [ [ [[0,1],
[1,0]] ],
[ [[1,0],
[0,1]] ] ]
Z = CategoricalMarkovChain(p0, P,
states=3)
z = Z.random()
self.assertAllClose(z, [[0, 1, 0], [1, 1, 1]])
# Draw sample with plates, parameters broadcasted
Z = CategoricalMarkovChain([1, 0], [[0, 1],
[1, 0]],
states=3,
plates=(3,4))
z = Z.random()
self.assertAllClose(z, np.ones((3,4,1))*[0, 1, 0])
# Draw sample with time-varying transition matrix
p0 = [1, 0]
P = [ [[0,1],
[1,0]],
[[1,0],
[0,1]],
[[1, 0],
[1, 0]] ]
Z = CategoricalMarkovChain(p0, P,
states=4)
z = Z.random()
self.assertAllClose(z, [0, 1, 1, 0])
pass
bayespy-0.2.3/bayespy/inference/vmp/nodes/tests/test_deterministic.py 0000664 5104112 5104112 00000027622 12374370351 027672 0 ustar jluttine jluttine 0000000 0000000 ######################################################################
# Copyright (C) 2013 Jaakko Luttinen
#
# This file is licensed under Version 3.0 of the GNU General Public
# License. See LICENSE for a text of the license.
######################################################################
######################################################################
# This file is part of BayesPy.
#
# BayesPy is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# BayesPy is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with BayesPy. If not, see .
######################################################################
"""
Unit tests for `deterministic` module.
"""
import unittest
import numpy as np
import scipy
from numpy import testing
from ..node import Node, Moments
from ..deterministic import tile
from ..stochastic import Stochastic
class TestTile(unittest.TestCase):
def check_message_to_children(self, tiles, u_parent, u_tiled,
dims=None, plates=None):
# Set up the dummy model
class Dummy(Stochastic):
_moments = Moments()
def __init__(self, u, dims=dims, plates=plates):
super().__init__(dims=dims, plates=plates, initialize=False)
self.u = u
X = Dummy(u_parent, dims=dims, plates=plates)
Y = tile(X, tiles)
u_Y = Y._compute_moments(u_parent)
for (x,y) in zip(u_Y, u_tiled):
self.assertEqual(np.shape(x), np.shape(y),
msg="Incorrect shape.")
testing.assert_allclose(x, y,
err_msg="Incorrect moments.")
def test_message_to_children(self):
"""
Test the moments of Tile node.
"""
# Define th check function
check_message_to_children = self.check_message_to_children
# Check scalar (and broadcasting)
check_message_to_children(2,
(5,),
(5,),
dims=[()],
plates=()),
# Check 1-D
check_message_to_children(2,
([1,2],),
([1,2,1,2],),
dims=[()],
plates=(2,))
# Check N-D
check_message_to_children(2,
([[1,2],
[3,4],
[5,6]],),
([[1,2,1,2],
[3,4,3,4],
[5,6,5,6]],),
dims=[()],
plates=(3,2))
# Check not-last plate
check_message_to_children([2,1],
([[1,2],
[3,4]],),
([[1,2],
[3,4],
[1,2],
[3,4]],),
dims=[()],
plates=(2,2))
# Check several plates
check_message_to_children([2,3],
([[1,2],
[3,4]],),
([[1,2,1,2,1,2],
[3,4,3,4,3,4],
[1,2,1,2,1,2],
[3,4,3,4,3,4]],),
dims=[()],
plates=(2,2))
# Check non-zero dimensional variables
check_message_to_children(2,
([[1,2],
[3,4]],),
([[1,2],
[3,4],
[1,2],
[3,4]],),
dims=[(2,)],
plates=(2,))
# Check several moments
check_message_to_children(2,
([[1,2],
[3,4]],
[1,2]),
([[1,2],
[3,4],
[1,2],
[3,4]],
[1,2,1,2]),
dims=[(2,),()],
plates=(2,))
# Check broadcasting of tiled plate
check_message_to_children(2,
([[1,],
[2,]],),
([[1,],
[2,]],),
dims=[()],
plates=(2,2))
# Check broadcasting of non-tiled plate
check_message_to_children(2,
([[1,2]],),
([[1,2,1,2]],),
dims=[()],
plates=(2,2))
# Check broadcasting of leading plates that are not in parent
check_message_to_children([2,1],
([1,2],),
([1,2],),
dims=[()],
plates=(2,))
def check_message_to_parent(self, tiles, m_children, m_true,
dims=None, plates_parent=None,
plates_children=None):
# Set up the dummy model
class Dummy(Stochastic):
_moments = Moments()
X = Dummy(dims=dims, plates=plates_parent, initialize=False)
Y = tile(X, tiles)
m = Y._compute_message_to_parent(0, m_children, None)
for (x,y) in zip(m, m_true):
self.assertEqual(np.shape(x), np.shape(y),
msg="Incorrect shape.")
testing.assert_allclose(x, y,
err_msg="Incorrect message.")
def test_message_to_parent(self):
"""
Test the parent message of Tile node.
"""
# Define th check function
check = self.check_message_to_parent
# Check scalar
check(2,
([5,5],),
(10,),
dims=[()],
plates_parent=(),
plates_children=(2,)),
# Check 1-D
check(2,
([1,2,3,4],),
([4,6],),
dims=[()],
plates_parent=(2,),
plates_children=(4,))
# Check N-D
check(2,
([[1,2,7,8],
[3,4,9,0],
[5,6,1,2]],),
([[8,10],
[12,4],
[6,8]],),
dims=[()],
plates_parent=(3,2),
plates_children=(3,4))
# Check not-last plate
check([2,1],
([[1,2],
[3,4],
[5,6],
[7,8]],),
([[6,8],
[10,12]],),
dims=[()],
plates_parent=(2,2),
plates_children=(4,2))
# Check several plates
check([2,3],
([[1,2,1,2,1,2],
[3,4,3,4,3,4],
[1,2,1,2,1,2],
[3,4,3,4,3,4]],),
([[6,12],
[18,24]],),
dims=[()],
plates_parent=(2,2),
plates_children=(4,6))
# Check broadcasting if message has unit axis for tiled plate
check(2,
([[1,],
[2,],
[3,]],),
([[2,],
[4,],
[6,]],),
dims=[()],
plates_parent=(3,2),
plates_children=(3,4))
# Check broadcasting if message has unit axis for non-tiled plate
check(2,
([[1,2,3,4]],),
([[4,6]],),
dims=[()],
plates_parent=(3,2),
plates_children=(3,4))
# Check non-zero dimensional variables
check(2,
([[1,2],
[3,4],
[5,6],
[7,8]],),
([[6,8],
[10,12]],),
dims=[(2,)],
plates_parent=(2,),
plates_children=(4,))
# Check several moments
check(2,
([[1,2],
[3,4],
[5,6],
[7,8]],
[1,2,3,4]),
([[6,8],
[10,12]],
[4,6]),
dims=[(2,),()],
plates_parent=(2,),
plates_children=(4,))
def check_mask_to_parent(self, tiles, mask_child, mask_true,
plates_parent=None,
plates_children=None):
# Set up the dummy model
class Dummy(Stochastic):
_moments = Moments()
X = Dummy(dims=[()], plates=plates_parent, initialize=False)
Y = tile(X, tiles)
mask = Y._compute_mask_to_parent(0, mask_child)
self.assertEqual(np.shape(mask), np.shape(mask_true),
msg="Incorrect shape.")
testing.assert_equal(mask, mask_true,
err_msg="Incorrect mask.")
def test_mask_to_parent(self):
"""
Test the mask message to parent of Tile node.
"""
# Define th check function
check = self.check_mask_to_parent
# Check scalar parent
check(2,
[True,False],
True,
plates_parent=(),
plates_children=(2,))
check(2,
[False,False],
False,
plates_parent=(),
plates_children=(2,))
# Check 1-D
check(2,
[True,False,False,False],
[True,False],
plates_parent=(2,),
plates_children=(4,))
# Check N-D
check(2,
[[True,False,True,False],
[False,True,False,False]],
[[True,False],
[False,True]],
plates_parent=(2,2),
plates_children=(2,4))
# Check not-last plate
check([2,1],
[[True,False],
[False,True],
[True,False],
[False,False]],
[[True,False],
[False,True]],
plates_parent=(2,2),
plates_children=(4,2))
# Check several plates
check([2,3],
[[False,False,True,False,False,False],
[False,False,False,False,False,False],
[False,False,False,False,False,False],
[False,True,False,False,False,False]],
[[True,False],
[False,True]],
plates_parent=(2,2),
plates_children=(4,6))
# Check broadcasting if message has unit axis for tiled plate
check(2,
[[True,],
[False,],
[True,]],
[[True,],
[False,],
[True,]],
plates_parent=(3,2),
plates_children=(3,4))
# Check broadcasting if message has unit axis for non-tiled plate
check(2,
[[False,False,False,True]],
[[False,True]],
plates_parent=(3,2),
plates_children=(3,4))
bayespy-0.2.3/bayespy/inference/vmp/nodes/tests/test_dirichlet.py 0000664 5104112 5104112 00000006001 12374370351 026762 0 ustar jluttine jluttine 0000000 0000000 ######################################################################
# Copyright (C) 2014 Jaakko Luttinen
#
# This file is licensed under Version 3.0 of the GNU General Public
# License. See LICENSE for a text of the license.
######################################################################
######################################################################
# This file is part of BayesPy.
#
# BayesPy is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# BayesPy is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with BayesPy. If not, see .
######################################################################
"""
Unit tests for `dirichlet` module.
"""
import numpy as np
from scipy import special
from bayespy.nodes import Dirichlet
from bayespy.utils import random
from bayespy.utils.misc import TestCase
class TestDirichlet(TestCase):
"""
Unit tests for Dirichlet node
"""
def test_init(self):
"""
Test the creation of Dirichlet nodes.
"""
# Some simple initializations
p = Dirichlet([1.5, 4.2, 3.5])
# Check that plates are correct
p = Dirichlet([2, 3, 4], plates=(4,3))
self.assertEqual(p.plates,
(4,3))
p = Dirichlet(np.ones((4,3,5)))
self.assertEqual(p.plates,
(4,3))
# Parent not a vector
self.assertRaises(ValueError,
Dirichlet,
4)
# Parent vector has invalid values
self.assertRaises(ValueError,
Dirichlet,
[-2,3,1])
# Plates inconsistent
self.assertRaises(ValueError,
Dirichlet,
np.ones((4,3)),
plates=(3,))
# Explicit plates too small
self.assertRaises(ValueError,
Dirichlet,
np.ones((4,3)),
plates=(1,))
pass
def test_moments(self):
"""
Test the moments of Dirichlet nodes.
"""
p = Dirichlet([2, 3, 4])
u = p._message_to_child()
self.assertAllClose(u[0],
special.psi([2,3,4]) - special.psi(2+3+4))
pass
def test_constant(self):
"""
Test the constant moments of Dirichlet nodes.
"""
p = Dirichlet([1, 1, 1])
p.initialize_from_value([0.5, 0.4, 0.1])
u = p._message_to_child()
self.assertAllClose(u[0],
np.log([0.5, 0.4, 0.1]))
pass
bayespy-0.2.3/bayespy/inference/vmp/nodes/tests/test_dot.py 0000664 5104112 5104112 00000077307 12374370351 025622 0 ustar jluttine jluttine 0000000 0000000 ######################################################################
# Copyright (C) 2013,2014 Jaakko Luttinen
#
# This file is licensed under Version 3.0 of the GNU General Public
# License. See LICENSE for a text of the license.
######################################################################
######################################################################
# This file is part of BayesPy.
#
# BayesPy is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# BayesPy is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with BayesPy. If not, see .
######################################################################
"""
Unit tests for `dot` module.
"""
import unittest
import numpy as np
import scipy
from numpy import testing
from ..dot import Dot, SumMultiply
from ..gaussian import Gaussian, GaussianARD
from bayespy.nodes import GaussianGammaISO
from ...vmp import VB
from bayespy.utils import misc
from bayespy.utils import linalg
from bayespy.utils import random
from bayespy.utils.misc import TestCase
class TestSumMultiply(TestCase):
def test_parent_validity(self):
"""
Test that the parent nodes are validated properly in SumMultiply
"""
V = GaussianARD(1, 1)
X = Gaussian(np.ones(1), np.identity(1))
Y = Gaussian(np.ones(3), np.identity(3))
Z = Gaussian(np.ones(5), np.identity(5))
A = SumMultiply(X, ['i'])
self.assertEqual(A.dims, ((), ()))
A = SumMultiply('i', X)
self.assertEqual(A.dims, ((), ()))
A = SumMultiply(X, ['i'], ['i'])
self.assertEqual(A.dims, ((1,), (1,1)))
A = SumMultiply('i->i', X)
self.assertEqual(A.dims, ((1,), (1,1)))
A = SumMultiply(X, ['i'], Y, ['j'], ['i','j'])
self.assertEqual(A.dims, ((1,3), (1,3,1,3)))
A = SumMultiply('i,j->ij', X, Y)
self.assertEqual(A.dims, ((1,3), (1,3,1,3)))
A = SumMultiply(V, [], X, ['i'], Y, ['i'], [])
self.assertEqual(A.dims, ((), ()))
A = SumMultiply(',i,i->', V, X, Y)
self.assertEqual(A.dims, ((), ()))
# Gaussian-gamma parents
C = GaussianGammaISO(np.ones(3), np.identity(3), 1, 1)
A = SumMultiply(Y, ['i'], C, ['i'], ['i'])
self.assertEqual(A.dims, ((3,), (3,3), (), ()))
A = SumMultiply('i,i->i', Y, C)
self.assertEqual(A.dims, ((3,), (3,3), (), ()))
C = GaussianGammaISO(np.ones(3), np.identity(3), 1, 1)
A = SumMultiply(Y, ['i'], C, ['i'], [])
self.assertEqual(A.dims, ((), (), (), ()))
A = SumMultiply('i,i->', Y, C)
self.assertEqual(A.dims, ((), (), (), ()))
# Error: not enough inputs
self.assertRaises(ValueError,
SumMultiply)
self.assertRaises(ValueError,
SumMultiply,
X)
# Error: too many keys
self.assertRaises(ValueError,
SumMultiply,
Y,
['i', 'j'])
self.assertRaises(ValueError,
SumMultiply,
'ij',
Y)
# Error: not broadcastable
self.assertRaises(ValueError,
SumMultiply,
Y,
['i'],
Z,
['i'])
self.assertRaises(ValueError,
SumMultiply,
'i,i',
Y,
Z)
# Error: output key not in inputs
self.assertRaises(ValueError,
SumMultiply,
X,
['i'],
['j'])
self.assertRaises(ValueError,
SumMultiply,
'i->j',
X)
# Error: non-unique input keys
self.assertRaises(ValueError,
SumMultiply,
X,
['i','i'])
self.assertRaises(ValueError,
SumMultiply,
'ii',
X)
# Error: non-unique output keys
self.assertRaises(ValueError,
SumMultiply,
X,
['i'],
['i','i'])
self.assertRaises(ValueError,
SumMultiply,
'i->ii',
X)
# String has too many '->'
self.assertRaises(ValueError,
SumMultiply,
'i->i->i',
X)
# String has too many input nodes
self.assertRaises(ValueError,
SumMultiply,
'i,i->i',
X)
# Same parent several times
self.assertRaises(ValueError,
SumMultiply,
'i,i->i',
X,
X)
# Same parent several times via deterministic node
Xh = SumMultiply('i->i', X)
self.assertRaises(ValueError,
SumMultiply,
'i,i->i',
X,
Xh)
def test_message_to_child(self):
"""
Test the message from SumMultiply to its children.
"""
def compare_moments(u0, u1, *args):
Y = SumMultiply(*args)
u_Y = Y.get_moments()
self.assertAllClose(u_Y[0], u0)
self.assertAllClose(u_Y[1], u1)
# Test constant parent
y = np.random.randn(2,3,4)
compare_moments(y,
linalg.outer(y, y, ndim=2),
'ij->ij',
y)
# Do nothing for 2-D array
Y = GaussianARD(np.random.randn(5,2,3),
np.random.rand(5,2,3),
plates=(5,),
shape=(2,3))
y = Y.get_moments()
compare_moments(y[0],
y[1],
'ij->ij',
Y)
compare_moments(y[0],
y[1],
Y,
[0,1],
[0,1])
# Sum over the rows of a matrix
Y = GaussianARD(np.random.randn(5,2,3),
np.random.rand(5,2,3),
plates=(5,),
shape=(2,3))
y = Y.get_moments()
mu = np.einsum('...ij->...j', y[0])
cov = np.einsum('...ijkl->...jl', y[1])
compare_moments(mu,
cov,
'ij->j',
Y)
compare_moments(mu,
cov,
Y,
[0,1],
[1])
# Inner product of three vectors
X1 = GaussianARD(np.random.randn(2),
np.random.rand(2),
plates=(),
shape=(2,))
x1 = X1.get_moments()
X2 = GaussianARD(np.random.randn(6,1,2),
np.random.rand(6,1,2),
plates=(6,1),
shape=(2,))
x2 = X2.get_moments()
X3 = GaussianARD(np.random.randn(7,6,5,2),
np.random.rand(7,6,5,2),
plates=(7,6,5),
shape=(2,))
x3 = X3.get_moments()
mu = np.einsum('...i,...i,...i->...', x1[0], x2[0], x3[0])
cov = np.einsum('...ij,...ij,...ij->...', x1[1], x2[1], x3[1])
compare_moments(mu,
cov,
'i,i,i',
X1,
X2,
X3)
compare_moments(mu,
cov,
'i,i,i->',
X1,
X2,
X3)
compare_moments(mu,
cov,
X1,
[9],
X2,
[9],
X3,
[9])
compare_moments(mu,
cov,
X1,
[9],
X2,
[9],
X3,
[9],
[])
# Outer product of two vectors
X1 = GaussianARD(np.random.randn(2),
np.random.rand(2),
plates=(5,),
shape=(2,))
x1 = X1.get_moments()
X2 = GaussianARD(np.random.randn(6,1,2),
np.random.rand(6,1,2),
plates=(6,1),
shape=(2,))
x2 = X2.get_moments()
mu = np.einsum('...i,...j->...ij', x1[0], x2[0])
cov = np.einsum('...ik,...jl->...ijkl', x1[1], x2[1])
compare_moments(mu,
cov,
'i,j->ij',
X1,
X2)
compare_moments(mu,
cov,
X1,
[9],
X2,
[7],
[9,7])
# Matrix product
Y1 = GaussianARD(np.random.randn(3,2),
np.random.rand(3,2),
plates=(),
shape=(3,2))
y1 = Y1.get_moments()
Y2 = GaussianARD(np.random.randn(5,2,3),
np.random.rand(5,2,3),
plates=(5,),
shape=(2,3))
y2 = Y2.get_moments()
mu = np.einsum('...ik,...kj->...ij', y1[0], y2[0])
cov = np.einsum('...ikjl,...kmln->...imjn', y1[1], y2[1])
compare_moments(mu,
cov,
'ik,kj->ij',
Y1,
Y2)
compare_moments(mu,
cov,
Y1,
['i','k'],
Y2,
['k','j'],
['i','j'])
# Trace of a matrix product
Y1 = GaussianARD(np.random.randn(3,2),
np.random.rand(3,2),
plates=(),
shape=(3,2))
y1 = Y1.get_moments()
Y2 = GaussianARD(np.random.randn(5,2,3),
np.random.rand(5,2,3),
plates=(5,),
shape=(2,3))
y2 = Y2.get_moments()
mu = np.einsum('...ij,...ji->...', y1[0], y2[0])
cov = np.einsum('...ikjl,...kilj->...', y1[1], y2[1])
compare_moments(mu,
cov,
'ij,ji',
Y1,
Y2)
compare_moments(mu,
cov,
'ij,ji->',
Y1,
Y2)
compare_moments(mu,
cov,
Y1,
['i','j'],
Y2,
['j','i'])
compare_moments(mu,
cov,
Y1,
['i','j'],
Y2,
['j','i'],
[])
# Vector-matrix-vector product
X1 = GaussianARD(np.random.randn(3),
np.random.rand(3),
plates=(),
shape=(3,))
x1 = X1.get_moments()
X2 = GaussianARD(np.random.randn(6,1,2),
np.random.rand(6,1,2),
plates=(6,1),
shape=(2,))
x2 = X2.get_moments()
Y = GaussianARD(np.random.randn(3,2),
np.random.rand(3,2),
plates=(),
shape=(3,2))
y = Y.get_moments()
mu = np.einsum('...i,...ij,...j->...', x1[0], y[0], x2[0])
cov = np.einsum('...ia,...ijab,...jb->...', x1[1], y[1], x2[1])
compare_moments(mu,
cov,
'i,ij,j',
X1,
Y,
X2)
compare_moments(mu,
cov,
X1,
[1],
Y,
[1,2],
X2,
[2])
# Complex sum-product of 0-D, 1-D, 2-D and 3-D arrays
V = GaussianARD(np.random.randn(7,6,5),
np.random.rand(7,6,5),
plates=(7,6,5),
shape=())
v = V.get_moments()
X = GaussianARD(np.random.randn(6,1,2),
np.random.rand(6,1,2),
plates=(6,1),
shape=(2,))
x = X.get_moments()
Y = GaussianARD(np.random.randn(3,4),
np.random.rand(3,4),
plates=(5,),
shape=(3,4))
y = Y.get_moments()
Z = GaussianARD(np.random.randn(4,2,3),
np.random.rand(4,2,3),
plates=(6,5),
shape=(4,2,3))
z = Z.get_moments()
mu = np.einsum('...,...i,...kj,...jik->...k', v[0], x[0], y[0], z[0])
cov = np.einsum('...,...ia,...kjcb,...jikbac->...kc', v[1], x[1], y[1], z[1])
compare_moments(mu,
cov,
',i,kj,jik->k',
V,
X,
Y,
Z)
compare_moments(mu,
cov,
V,
[],
X,
['i'],
Y,
['k','j'],
Z,
['j','i','k'],
['k'])
#
# Gaussian-gamma parents
#
# Outer product of vectors
X1 = GaussianARD(np.random.randn(2),
np.random.rand(2),
shape=(2,))
x1 = X1.get_moments()
X2 = GaussianGammaISO(np.random.randn(6,1,2),
random.covariance(2),
np.random.rand(6,1),
np.random.rand(6,1),
plates=(6,1))
x2 = X2.get_moments()
Y = SumMultiply('i,j->ij', X1, X2)
u = Y._message_to_child()
y = np.einsum('...i,...j->...ij', x1[0], x2[0])
yy = np.einsum('...ik,...jl->...ijkl', x1[1], x2[1])
self.assertAllClose(u[0], y)
self.assertAllClose(u[1], yy)
self.assertAllClose(u[2], x2[2])
self.assertAllClose(u[3], x2[3])
pass
def test_message_to_parent(self):
"""
Test the message from SumMultiply node to its parents.
"""
data = 2
tau = 3
def check_message(true_m0, true_m1, parent, *args, F=None):
if F is None:
A = SumMultiply(*args)
B = GaussianARD(A, tau)
B.observe(data*np.ones(A.plates + A.dims[0]))
else:
A = F
(A_m0, A_m1) = A._message_to_parent(parent)
self.assertAllClose(true_m0, A_m0)
self.assertAllClose(true_m1, A_m1)
pass
# Check: different message to each of multiple parents
X1 = GaussianARD(np.random.randn(2),
np.random.rand(2),
ndim=1)
x1 = X1.get_moments()
X2 = GaussianARD(np.random.randn(2),
np.random.rand(2),
ndim=1)
x2 = X2.get_moments()
m0 = tau * data * x2[0]
m1 = -0.5 * tau * x2[1] * np.identity(2)
check_message(m0, m1, 0,
'i,i->i',
X1,
X2)
check_message(m0, m1, 0,
X1,
[9],
X2,
[9],
[9])
m0 = tau * data * x1[0]
m1 = -0.5 * tau * x1[1] * np.identity(2)
check_message(m0, m1, 1,
'i,i->i',
X1,
X2)
check_message(m0, m1, 1,
X1,
[9],
X2,
[9],
[9])
# Check: key not in output
X1 = GaussianARD(np.random.randn(2),
np.random.rand(2),
ndim=1)
x1 = X1.get_moments()
m0 = tau * data * np.ones(2)
m1 = -0.5 * tau * np.ones((2,2))
check_message(m0, m1, 0,
'i',
X1)
check_message(m0, m1, 0,
'i->',
X1)
check_message(m0, m1, 0,
X1,
[9])
check_message(m0, m1, 0,
X1,
[9],
[])
# Check: key not in some input
X1 = GaussianARD(np.random.randn(),
np.random.rand())
x1 = X1.get_moments()
X2 = GaussianARD(np.random.randn(2),
np.random.rand(2),
ndim=1)
x2 = X2.get_moments()
m0 = tau * data * np.sum(x2[0], axis=-1)
m1 = -0.5 * tau * np.sum(x2[1] * np.identity(2),
axis=(-1,-2))
check_message(m0, m1, 0,
',i->i',
X1,
X2)
check_message(m0, m1, 0,
X1,
[],
X2,
[9],
[9])
m0 = tau * data * x1[0] * np.ones(2)
m1 = -0.5 * tau * x1[1] * np.identity(2)
check_message(m0, m1, 1,
',i->i',
X1,
X2)
check_message(m0, m1, 1,
X1,
[],
X2,
[9],
[9])
# Check: keys in different order
Y1 = GaussianARD(np.random.randn(3,2),
np.random.rand(3,2),
ndim=2)
y1 = Y1.get_moments()
Y2 = GaussianARD(np.random.randn(2,3),
np.random.rand(2,3),
ndim=2)
y2 = Y2.get_moments()
m0 = tau * data * y2[0].T
m1 = -0.5 * tau * np.einsum('ijlk->jikl', y2[1] * misc.identity(2,3))
check_message(m0, m1, 0,
'ij,ji->ij',
Y1,
Y2)
check_message(m0, m1, 0,
Y1,
['i','j'],
Y2,
['j','i'],
['i','j'])
m0 = tau * data * y1[0].T
m1 = -0.5 * tau * np.einsum('ijlk->jikl', y1[1] * misc.identity(3,2))
check_message(m0, m1, 1,
'ij,ji->ij',
Y1,
Y2)
check_message(m0, m1, 1,
Y1,
['i','j'],
Y2,
['j','i'],
['i','j'])
# Check: plates when different dimensionality
X1 = GaussianARD(np.random.randn(5),
np.random.rand(5),
shape=(),
plates=(5,))
x1 = X1.get_moments()
X2 = GaussianARD(np.random.randn(5,3),
np.random.rand(5,3),
shape=(3,),
plates=(5,))
x2 = X2.get_moments()
m0 = tau * data * np.sum(np.ones((5,3)) * x2[0], axis=-1)
m1 = -0.5 * tau * np.sum(x2[1] * misc.identity(3), axis=(-1,-2))
check_message(m0, m1, 0,
',i->i',
X1,
X2)
check_message(m0, m1, 0,
X1,
[],
X2,
['i'],
['i'])
m0 = tau * data * x1[0][:,np.newaxis] * np.ones((5,3))
m1 = -0.5 * tau * x1[1][:,np.newaxis,np.newaxis] * misc.identity(3)
check_message(m0, m1, 1,
',i->i',
X1,
X2)
check_message(m0, m1, 1,
X1,
[],
X2,
['i'],
['i'])
# Check: other parent's moments broadcasts over plates when node has the
# same plates
X1 = GaussianARD(np.random.randn(5,4,3),
np.random.rand(5,4,3),
shape=(3,),
plates=(5,4))
x1 = X1.get_moments()
X2 = GaussianARD(np.random.randn(3),
np.random.rand(3),
shape=(3,),
plates=(5,4))
x2 = X2.get_moments()
m0 = tau * data * np.ones((5,4,3)) * x2[0]
m1 = -0.5 * tau * x2[1] * misc.identity(3)
check_message(m0, m1, 0,
'i,i->i',
X1,
X2)
check_message(m0, m1, 0,
X1,
['i'],
X2,
['i'],
['i'])
# Check: other parent's moments broadcasts over plates when node does
# not have that plate
X1 = GaussianARD(np.random.randn(3),
np.random.rand(3),
shape=(3,),
plates=())
x1 = X1.get_moments()
X2 = GaussianARD(np.random.randn(3),
np.random.rand(3),
shape=(3,),
plates=(5,4))
x2 = X2.get_moments()
m0 = tau * data * np.sum(np.ones((5,4,3)) * x2[0], axis=(0,1))
m1 = -0.5 * tau * np.sum(np.ones((5,4,1,1))
* misc.identity(3)
* x2[1],
axis=(0,1))
check_message(m0, m1, 0,
'i,i->i',
X1,
X2)
check_message(m0, m1, 0,
X1,
['i'],
X2,
['i'],
['i'])
# Check: other parent's moments broadcasts over plates when the node
# only broadcasts that plate
X1 = GaussianARD(np.random.randn(3),
np.random.rand(3),
shape=(3,),
plates=(1,1))
x1 = X1.get_moments()
X2 = GaussianARD(np.random.randn(3),
np.random.rand(3),
shape=(3,),
plates=(5,4))
x2 = X2.get_moments()
m0 = tau * data * np.sum(np.ones((5,4,3)) * x2[0], axis=(0,1), keepdims=True)
m1 = -0.5 * tau * np.sum(np.ones((5,4,1,1))
* misc.identity(3)
* x2[1],
axis=(0,1),
keepdims=True)
check_message(m0, m1, 0,
'i,i->i',
X1,
X2)
check_message(m0, m1, 0,
X1,
['i'],
X2,
['i'],
['i'])
# Check: broadcasted dimensions
X1 = GaussianARD(np.random.randn(1,1),
np.random.rand(1,1),
ndim=2)
x1 = X1.get_moments()
X2 = GaussianARD(np.random.randn(3,2),
np.random.rand(3,2),
ndim=2)
x2 = X2.get_moments()
m0 = tau * data * np.sum(np.ones((3,2)) * x2[0],
keepdims=True)
m1 = -0.5 * tau * np.sum(misc.identity(3,2) * x2[1],
keepdims=True)
check_message(m0, m1, 0,
'ij,ij->ij',
X1,
X2)
check_message(m0, m1, 0,
X1,
[0,1],
X2,
[0,1],
[0,1])
m0 = tau * data * np.ones((3,2)) * x1[0]
m1 = -0.5 * tau * misc.identity(3,2) * x1[1]
check_message(m0, m1, 1,
'ij,ij->ij',
X1,
X2)
check_message(m0, m1, 1,
X1,
[0,1],
X2,
[0,1],
[0,1])
# Check: non-ARD observations
X1 = GaussianARD(np.random.randn(2),
np.random.rand(2),
ndim=1)
x1 = X1.get_moments()
Lambda = np.array([[2, 1.5], [1.5, 2]])
F = SumMultiply('i->i', X1)
Y = Gaussian(F, Lambda)
y = np.random.randn(2)
Y.observe(y)
m0 = np.dot(Lambda, y)
m1 = -0.5 * Lambda
check_message(m0, m1, 0,
'i->i',
X1,
F=F)
check_message(m0, m1, 0,
X1,
['i'],
['i'],
F=F)
# Check: mask with same shape
X1 = GaussianARD(np.random.randn(3,2),
np.random.rand(3,2),
shape=(2,),
plates=(3,))
x1 = X1.get_moments()
mask = np.array([True, False, True])
F = SumMultiply('i->i', X1)
Y = GaussianARD(F, tau)
Y.observe(data*np.ones((3,2)), mask=mask)
m0 = tau * data * mask[:,np.newaxis] * np.ones(2)
m1 = -0.5 * tau * mask[:,np.newaxis,np.newaxis] * np.identity(2)
check_message(m0, m1, 0,
'i->i',
X1,
F=F)
check_message(m0, m1, 0,
X1,
['i'],
['i'],
F=F)
# Check: mask larger
X1 = GaussianARD(np.random.randn(2),
np.random.rand(2),
shape=(2,),
plates=())
x1 = X1.get_moments()
X2 = GaussianARD(np.random.randn(3,2),
np.random.rand(3,2),
shape=(2,),
plates=(3,))
x2 = X2.get_moments()
mask = np.array([True, False, True])
F = SumMultiply('i,i->i', X1, X2)
Y = GaussianARD(F, tau,
plates=(3,))
Y.observe(data*np.ones((3,2)), mask=mask)
m0 = tau * data * np.sum(mask[:,np.newaxis] * x2[0], axis=0)
m1 = -0.5 * tau * np.sum(mask[:,np.newaxis,np.newaxis]
* x2[1]
* np.identity(2),
axis=0)
check_message(m0, m1, 0,
'i,i->i',
X1,
X2,
F=F)
check_message(m0, m1, 0,
X1,
['i'],
X2,
['i'],
['i'],
F=F)
# Check: mask for broadcasted plate
X1 = GaussianARD(np.random.randn(2),
np.random.rand(2),
ndim=1,
plates=(1,))
x1 = X1.get_moments()
X2 = GaussianARD(np.random.randn(2),
np.random.rand(2),
ndim=1,
plates=(3,))
x2 = X2.get_moments()
mask = np.array([True, False, True])
F = SumMultiply('i,i->i', X1, X2)
Y = GaussianARD(F, tau,
plates=(3,))
Y.observe(data*np.ones((3,2)), mask=mask)
m0 = tau * data * np.sum(mask[:,np.newaxis] * x2[0],
axis=0,
keepdims=True)
m1 = -0.5 * tau * np.sum(mask[:,np.newaxis,np.newaxis]
* x2[1]
* np.identity(2),
axis=0,
keepdims=True)
check_message(m0, m1, 0,
'i->i',
X1,
F=F)
check_message(m0, m1, 0,
X1,
['i'],
['i'],
F=F)
# Check: Gaussian-gamma parents
X1 = GaussianGammaISO(np.random.randn(2),
random.covariance(2),
np.random.rand(),
np.random.rand())
x1 = X1.get_moments()
X2 = GaussianGammaISO(np.random.randn(2),
random.covariance(2),
np.random.rand(),
np.random.rand())
x2 = X2.get_moments()
F = SumMultiply('i,i->i', X1, X2)
V = random.covariance(2)
y = np.random.randn(2)
Y = Gaussian(F, V)
Y.observe(y)
m0 = np.dot(V, y) * x2[0]
m1 = -0.5 * V * x2[1]
m2 = -0.5 * np.einsum('i,ij,j', y, V, y) * x2[2]#linalg.inner(V, x2[2], ndim=2)
m3 = 0.5 * 2 #linalg.chol_logdet(linalg.chol(V)) + 2*x2[3]
m = F._message_to_parent(0)
self.assertAllClose(m[0], m0)
self.assertAllClose(m[1], m1)
self.assertAllClose(m[2], m2)
self.assertAllClose(m[3], m3)
pass
def check_performance(scale=1e2):
"""
Tests that the implementation of SumMultiply is efficient.
This is not a unit test (not run automatically), but rather a
performance test, which you may run to test the performance of the
node. A naive implementation of SumMultiply will run out of memory in
some cases and this method checks that the implementation is not naive
but good.
"""
# Check: Broadcasted plates are computed efficiently
# (bad implementation will take a long time to run)
s = scale
X1 = GaussianARD(np.random.randn(s,s),
np.random.rand(s,s),
shape=(s,),
plates=(s,))
X2 = GaussianARD(np.random.randn(s,1,s),
np.random.rand(s,1,s),
shape=(s,),
plates=(s,1))
F = SumMultiply('i,i', X1, X2)
Y = GaussianARD(F, 1)
Y.observe(np.ones((s,s)))
try:
F._message_to_parent(1)
except e:
print(e)
print('SOMETHING BAD HAPPENED')
# Check: Broadcasted dimensions are computed efficiently
# (bad implementation will run out of memory)
pass
bayespy-0.2.3/bayespy/inference/vmp/nodes/tests/test_gate.py 0000664 5104112 5104112 00000026454 12374370351 025751 0 ustar jluttine jluttine 0000000 0000000 ######################################################################
# Copyright (C) 2014 Jaakko Luttinen
#
# This file is licensed under Version 3.0 of the GNU General Public
# License. See LICENSE for a text of the license.
######################################################################
######################################################################
# This file is part of BayesPy.
#
# BayesPy is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# BayesPy is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with BayesPy. If not, see .
######################################################################
"""
Unit tests for `gate` module.
"""
import warnings
import numpy as np
from bayespy.nodes import (Gate,
GaussianARD,
Gamma,
Categorical,
Bernoulli,
Multinomial)
from bayespy.inference.vmp.nodes.gaussian import GaussianMoments
from bayespy.utils import random
from bayespy.utils import linalg
from bayespy.utils.misc import TestCase
class TestGate(TestCase):
"""
Unit tests for Gate node.
"""
def test_init(self):
"""
Test the creation of Gate node
"""
# Gating scalar node
Z = Categorical(np.ones(3)/3)
X = GaussianARD(0, 1, shape=(), plates=(3,))
Y = Gate(Z, X)
self.assertEqual(Y.plates, ())
self.assertEqual(Y.dims, ( (), () ))
# Gating non-scalar node
Z = Categorical(np.ones(3)/3)
X = GaussianARD(0, 1, shape=(2,), plates=(3,))
Y = Gate(Z, X)
self.assertEqual(Y.plates, ())
self.assertEqual(Y.dims, ( (2,), (2,2) ))
# Plates from Z
Z = Categorical(np.ones(3)/3, plates=(4,))
X = GaussianARD(0, 1, shape=(2,), plates=(3,))
Y = Gate(Z, X)
self.assertEqual(Y.plates, (4,))
self.assertEqual(Y.dims, ( (2,), (2,2) ))
# Plates from X
Z = Categorical(np.ones(3)/3)
X = GaussianARD(0, 1, shape=(2,), plates=(4,3))
Y = Gate(Z, X)
self.assertEqual(Y.plates, (4,))
self.assertEqual(Y.dims, ( (2,), (2,2) ))
# Plates from Z and X
Z = Categorical(np.ones(3)/3, plates=(5,))
X = GaussianARD(0, 1, shape=(2,), plates=(4,1,3))
Y = Gate(Z, X)
self.assertEqual(Y.plates, (4,5))
self.assertEqual(Y.dims, ( (2,), (2,2) ))
# Gating non-default plate
Z = Categorical(np.ones(3)/3)
X = GaussianARD(0, 1, shape=(), plates=(3,4))
Y = Gate(Z, X, gated_plate=-2)
self.assertEqual(Y.plates, (4,))
self.assertEqual(Y.dims, ( (), () ))
# Fixed gating
Z = 2
X = GaussianARD(0, 1, shape=(2,), plates=(3,))
Y = Gate(Z, X)
self.assertEqual(Y.plates, ())
self.assertEqual(Y.dims, ( (2,), (2,2) ))
# Fixed X
Z = Categorical(np.ones(3)/3)
X = [1, 2, 3]
Y = Gate(Z, X, moments=GaussianMoments(0))
self.assertEqual(Y.plates, ())
self.assertEqual(Y.dims, ( (), () ))
# Do not accept non-negative cluster plates
Z = Categorical(np.ones(3)/3)
X = GaussianARD(0, 1, plates=(3,))
self.assertRaises(ValueError,
Gate,
Z,
X,
gated_plate=0)
# None of the parents have the cluster plate axis
Z = Categorical(np.ones(3)/3)
X = GaussianARD(0, 1)
self.assertRaises(ValueError,
Gate,
Z,
X)
# Inconsistent cluster plate
Z = Categorical(np.ones(3)/3)
X = GaussianARD(0, 1, plates=(2,))
self.assertRaises(ValueError,
Gate,
Z,
X)
pass
def test_message_to_child(self):
"""
Test the message to child of Gate node.
"""
# Gating scalar node
Z = 2
X = GaussianARD([1,2,3], 1, shape=(), plates=(3,))
Y = Gate(Z, X)
u = Y._message_to_child()
self.assertEqual(len(u), 2)
self.assertAllClose(u[0], 3)
self.assertAllClose(u[1], 3**2+1)
# Fixed X
Z = 2
X = [1, 2, 3]
Y = Gate(Z, X, moments=GaussianMoments(0))
u = Y._message_to_child()
self.assertEqual(len(u), 2)
self.assertAllClose(u[0], 3)
self.assertAllClose(u[1], 3**2)
# Uncertain gating
Z = Categorical([0.2,0.3,0.5])
X = GaussianARD([1,2,3], 1, shape=(), plates=(3,))
Y = Gate(Z, X)
u = Y._message_to_child()
self.assertAllClose(u[0], 0.2*1 + 0.3*2 + 0.5*3)
self.assertAllClose(u[1], 0.2*2 + 0.3*5 + 0.5*10)
# Plates in Z
Z = [2, 0]
X = GaussianARD([1,2,3], 1, shape=(), plates=(3,))
Y = Gate(Z, X)
u = Y._message_to_child()
self.assertAllClose(u[0], [3, 1])
self.assertAllClose(u[1], [10, 2])
# Plates in X
Z = 2
X = GaussianARD([1,2,3], 1, shape=(), plates=(4,3,))
Y = Gate(Z, X)
u = Y._message_to_child()
self.assertAllClose(np.ones(4)*u[0], np.ones(4)*3)
self.assertAllClose(np.ones(4)*u[1], np.ones(4)*10)
# Gating non-default plate
Z = 2
X = GaussianARD([[1],[2],[3]], 1, shape=(), plates=(3,4))
Y = Gate(Z, X, gated_plate=-2)
u = Y._message_to_child()
self.assertAllClose(np.ones(4)*u[0], np.ones(4)*3)
self.assertAllClose(np.ones(4)*u[1], np.ones(4)*10)
# Gating non-scalar node
Z = 2
X = GaussianARD([1*np.ones(4),
2*np.ones(4),
3*np.ones(4)],
1,
shape=(4,), plates=(3,))
Y = Gate(Z, X)
u = Y._message_to_child()
self.assertAllClose(u[0], 3*np.ones(4))
self.assertAllClose(u[1], 9*np.ones((4,4)) + 1*np.identity(4))
# Broadcasting the moments on the cluster axis
Z = 2
X = GaussianARD(1, 1, shape=(), plates=(3,))
Y = Gate(Z, X)
u = Y._message_to_child()
self.assertEqual(len(u), 2)
self.assertAllClose(u[0], 1)
self.assertAllClose(u[1], 1**2+1)
pass
def test_message_to_parent(self):
"""
Test the message to parents of Gate node.
"""
# Unobserved and broadcasting
Z = 2
X = GaussianARD(0, 1, shape=(), plates=(3,))
F = Gate(Z, X)
Y = GaussianARD(F, 1)
m = F._message_to_parent(0)
self.assertEqual(len(m), 1)
self.assertAllClose(m[0], 0*np.ones(3))
m = F._message_to_parent(1)
self.assertEqual(len(m), 2)
self.assertAllClose(m[0]*np.ones(3), [0, 0, 0])
self.assertAllClose(m[1]*np.ones(3), [0, 0, 0])
# Gating scalar node
Z = 2
X = GaussianARD([1,2,3], 1, shape=(), plates=(3,))
F = Gate(Z, X)
Y = GaussianARD(F, 1)
Y.observe(10)
m = F._message_to_parent(0)
self.assertAllClose(m[0], [10*1-0.5*2, 10*2-0.5*5, 10*3-0.5*10])
m = F._message_to_parent(1)
self.assertAllClose(m[0], [0, 0, 10])
self.assertAllClose(m[1], [0, 0, -0.5])
# Fixed X
Z = 2
X = [1,2,3]
F = Gate(Z, X, moments=GaussianMoments(0))
Y = GaussianARD(F, 1)
Y.observe(10)
m = F._message_to_parent(0)
self.assertAllClose(m[0], [10*1-0.5*1, 10*2-0.5*4, 10*3-0.5*9])
m = F._message_to_parent(1)
self.assertAllClose(m[0], [0, 0, 10])
self.assertAllClose(m[1], [0, 0, -0.5])
# Uncertain gating
Z = Categorical([0.2, 0.3, 0.5])
X = GaussianARD([1,2,3], 1, shape=(), plates=(3,))
F = Gate(Z, X)
Y = GaussianARD(F, 1)
Y.observe(10)
m = F._message_to_parent(0)
self.assertAllClose(m[0], [10*1-0.5*2, 10*2-0.5*5, 10*3-0.5*10])
m = F._message_to_parent(1)
self.assertAllClose(m[0], [0.2*10, 0.3*10, 0.5*10])
self.assertAllClose(m[1], [-0.5*0.2, -0.5*0.3, -0.5*0.5])
# Plates in Z
Z = [2, 0]
X = GaussianARD([1,2,3], 1, shape=(), plates=(3,))
F = Gate(Z, X)
Y = GaussianARD(F, 1)
Y.observe([10, 20])
m = F._message_to_parent(0)
self.assertAllClose(m[0], [[10*1-0.5*2, 10*2-0.5*5, 10*3-0.5*10],
[20*1-0.5*2, 20*2-0.5*5, 20*3-0.5*10]])
m = F._message_to_parent(1)
self.assertAllClose(m[0], [20, 0, 10])
self.assertAllClose(m[1], [-0.5, 0, -0.5])
# Plates in X
Z = 2
X = GaussianARD([[1,2,3], [4,5,6]], 1, shape=(), plates=(2,3,))
F = Gate(Z, X)
Y = GaussianARD(F, 1)
Y.observe([10, 20])
m = F._message_to_parent(0)
self.assertAllClose(m[0], [10*1-0.5*2 + 20*4-0.5*17,
10*2-0.5*5 + 20*5-0.5*26,
10*3-0.5*10 + 20*6-0.5*37])
m = F._message_to_parent(1)
self.assertAllClose(m[0], [[0, 0, 10],
[0, 0, 20]])
self.assertAllClose(m[1]*np.ones((2,3)), [[0, 0, -0.5],
[0, 0, -0.5]])
# Gating non-default plate
Z = 2
X = GaussianARD([[1],[2],[3]], 1, shape=(), plates=(3,1))
F = Gate(Z, X, gated_plate=-2)
Y = GaussianARD(F, 1)
Y.observe([10])
m = F._message_to_parent(0)
self.assertAllClose(m[0], [10*1-0.5*2, 10*2-0.5*5, 10*3-0.5*10])
m = F._message_to_parent(1)
self.assertAllClose(m[0], [[0], [0], [10]])
self.assertAllClose(m[1], [[0], [0], [-0.5]])
# Gating non-scalar node
Z = 2
X = GaussianARD([[1,4],[2,5],[3,6]], 1, shape=(2,), plates=(3,))
F = Gate(Z, X)
Y = GaussianARD(F, 1)
Y.observe([10,20])
m = F._message_to_parent(0)
self.assertAllClose(m[0], [10*1-0.5*2 + 20*4-0.5*17,
10*2-0.5*5 + 20*5-0.5*26,
10*3-0.5*10 + 20*6-0.5*37])
m = F._message_to_parent(1)
I = np.identity(2)
self.assertAllClose(m[0], [[0,0], [0,0], [10,20]])
self.assertAllClose(m[1], [0*I, 0*I, -0.5*I])
# Broadcasting the moments on the cluster axis
Z = 2
X = GaussianARD(2, 1, shape=(), plates=(3,))
F = Gate(Z, X)
Y = GaussianARD(F, 1)
Y.observe(10)
m = F._message_to_parent(0)
self.assertAllClose(m[0], [10*2-0.5*5, 10*2-0.5*5, 10*2-0.5*5])
m = F._message_to_parent(1)
self.assertAllClose(m[0], [0, 0, 10])
self.assertAllClose(m[1], [0, 0, -0.5])
pass
def test_mask_to_parent(self):
"""
Test the mask handling in Gate node
"""
pass
bayespy-0.2.3/bayespy/inference/vmp/nodes/tests/test_gaussian.py 0000664 5104112 5104112 00000115605 12374370351 026640 0 ustar jluttine jluttine 0000000 0000000 ######################################################################
# Copyright (C) 2013 Jaakko Luttinen
#
# This file is licensed under Version 3.0 of the GNU General Public
# License. See LICENSE for a text of the license.
######################################################################
######################################################################
# This file is part of BayesPy.
#
# BayesPy is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# BayesPy is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with BayesPy. If not, see .
######################################################################
"""
Unit tests for `gaussian` module.
"""
import numpy as np
from scipy import special
from numpy import testing
from .. import gaussian
from bayespy.nodes import (Gaussian,
GaussianARD,
GaussianGammaISO,
Gamma,
Wishart)
from ...vmp import VB
from bayespy.utils import misc
from bayespy.utils import linalg
from bayespy.utils import random
from bayespy.utils.misc import TestCase
class TestGaussianFunctions(TestCase):
def test_rotate_covariance(self):
"""
Test the Gaussian array covariance rotation.
"""
# Check matrix
R = np.random.randn(2,2)
Cov = np.random.randn(2,2)
self.assertAllClose(gaussian.rotate_covariance(Cov, R),
np.einsum('ik,kl,lj', R, Cov, R.T))
# Check matrix with plates
R = np.random.randn(2,2)
Cov = np.random.randn(4,3,2,2)
self.assertAllClose(gaussian.rotate_covariance(Cov, R),
np.einsum('...ik,...kl,...lj', R, Cov, R.T))
# Check array, first axis
R = np.random.randn(2,2)
Cov = np.random.randn(2,3,3,2,3,3)
self.assertAllClose(gaussian.rotate_covariance(Cov, R,
ndim=3,
axis=-3),
np.einsum('...ik,...kablcd,...lj->...iabjcd',
R,
Cov,
R.T))
self.assertAllClose(gaussian.rotate_covariance(Cov, R,
ndim=3,
axis=0),
np.einsum('...ik,...kablcd,...lj->...iabjcd',
R,
Cov,
R.T))
# Check array, middle axis
R = np.random.randn(2,2)
Cov = np.random.randn(3,2,3,3,2,3)
self.assertAllClose(gaussian.rotate_covariance(Cov, R,
ndim=3,
axis=-2),
np.einsum('...ik,...akbcld,...lj->...aibcjd',
R,
Cov,
R.T))
self.assertAllClose(gaussian.rotate_covariance(Cov, R,
ndim=3,
axis=1),
np.einsum('...ik,...akbcld,...lj->...aibcjd',
R,
Cov,
R.T))
# Check array, last axis
R = np.random.randn(2,2)
Cov = np.random.randn(3,3,2,3,3,2)
self.assertAllClose(gaussian.rotate_covariance(Cov, R,
ndim=3,
axis=-1),
np.einsum('...ik,...abkcdl,...lj->...abicdj',
R,
Cov,
R.T))
self.assertAllClose(gaussian.rotate_covariance(Cov, R,
ndim=3,
axis=2),
np.einsum('...ik,...abkcdl,...lj->...abicdj',
R,
Cov,
R.T))
# Check array, middle axis with plates
R = np.random.randn(2,2)
Cov = np.random.randn(4,4,3,2,3,3,2,3)
self.assertAllClose(gaussian.rotate_covariance(Cov, R,
ndim=3,
axis=-2),
np.einsum('...ik,...akbcld,...lj->...aibcjd',
R,
Cov,
R.T))
self.assertAllClose(gaussian.rotate_covariance(Cov, R,
ndim=3,
axis=1),
np.einsum('...ik,...akbcld,...lj->...aibcjd',
R,
Cov,
R.T))
pass
class TestGaussianARD(TestCase):
def test_init(self):
"""
Test the constructor of GaussianARD
"""
def check_init(true_plates, true_shape, mu, alpha, **kwargs):
X = GaussianARD(mu, alpha, **kwargs)
self.assertEqual(X.dims, (true_shape, true_shape+true_shape),
msg="Constructed incorrect dimensionality")
self.assertEqual(X.plates, true_plates,
msg="Constructed incorrect plates")
#
# Create from constant parents
#
# Use ndim=0 for constant mu
check_init((),
(),
0,
1)
check_init((3,2),
(),
np.zeros((3,2,)),
np.ones((2,)))
check_init((4,2,2,3),
(),
np.zeros((2,1,3,)),
np.ones((4,1,2,3)))
# Use ndim
check_init((4,2),
(2,3),
np.zeros((2,1,3,)),
np.ones((4,1,2,3)),
ndim=2)
# Use shape
check_init((4,2),
(2,3),
np.zeros((2,1,3,)),
np.ones((4,1,2,3)),
shape=(2,3))
# Use ndim and shape
check_init((4,2),
(2,3),
np.zeros((2,1,3,)),
np.ones((4,1,2,3)),
ndim=2,
shape=(2,3))
#
# Create from node parents
#
# Infer ndim from parent mu
check_init((3,),
(),
GaussianARD(0, 1,
plates=(3,)),
Gamma(1, 1,
plates=(3,)))
# Infer ndim from mu, take broadcasted shape
check_init((4,),
(2,2,3),
GaussianARD(np.zeros((2,1,3)),
np.ones((2,1,3)),
ndim=3),
Gamma(np.ones((4,1,2,3)),
np.ones((4,1,2,3))))
# Use ndim
check_init((4,),
(2,2,3),
GaussianARD(np.zeros((4,1,2,3)),
np.ones((4,1,2,3)),
ndim=2),
Gamma(np.ones((4,2,1,3)),
np.ones((4,2,1,3))),
ndim=3)
# Use shape
check_init((4,),
(2,2,3),
GaussianARD(np.zeros((4,1,2,3)),
np.ones((4,1,2,3)),
ndim=2),
Gamma(np.ones((4,2,1,3)),
np.ones((4,2,1,3))),
shape=(2,2,3))
# Use ndim and shape
check_init((4,2),
(2,3),
GaussianARD(np.zeros((2,1,3)),
np.ones((2,1,3)),
ndim=2),
Gamma(np.ones((4,1,2,3)),
np.ones((4,1,2,3))),
ndim=2,
shape=(2,3))
# Test for a found bug
check_init((),
(3,),
np.ones(3),
1,
ndim=1)
# Add axes if necessary
check_init((),
(1,2,3),
GaussianARD(np.zeros((2,3)),
np.ones((2,3)),
ndim=2),
1,
ndim=3)
#
# Errors
#
# Inconsistent shapes
self.assertRaises(ValueError,
GaussianARD,
GaussianARD(np.zeros((2,3)),
np.ones((2,3)),
ndim=1),
np.ones((4,3)),
ndim=2)
# Inconsistent dims of mu and alpha
self.assertRaises(ValueError,
GaussianARD,
np.zeros((2,3)),
np.ones((2,)))
# Inconsistent plates of mu and alpha
self.assertRaises(ValueError,
GaussianARD,
GaussianARD(np.zeros((3,2,3)),
np.ones((3,2,3)),
ndim=2),
np.ones((3,4,2,3)),
ndim=3)
# Inconsistent ndim and shape
self.assertRaises(ValueError,
GaussianARD,
np.zeros((2,3)),
np.ones((2,)),
shape=(2,3),
ndim=1)
# Parent mu has more axes
self.assertRaises(ValueError,
GaussianARD,
GaussianARD(np.zeros((2,3)),
np.ones((2,3)),
ndim=2),
np.ones((2,3)),
ndim=1)
# Incorrect shape
self.assertRaises(ValueError,
GaussianARD,
GaussianARD(np.zeros((2,3)),
np.ones((2,3)),
ndim=2),
np.ones((2,3)),
shape=(2,2))
pass
def test_message_to_child(self):
"""
Test moments of GaussianARD.
"""
# Check that moments have full shape when broadcasting
X = GaussianARD(np.zeros((2,)),
np.ones((3,2)),
shape=(4,3,2))
(u0, u1) = X._message_to_child()
self.assertEqual(np.shape(u0),
(4,3,2))
self.assertEqual(np.shape(u1),
(4,3,2,4,3,2))
# Check the formula
X = GaussianARD(2, 3)
(u0, u1) = X._message_to_child()
self.assertAllClose(u0, 2)
self.assertAllClose(u1, 2**2 + 1/3)
# Check the formula for multidimensional arrays
X = GaussianARD(2*np.ones((2,1,4)),
3*np.ones((2,3,1)),
ndim=3)
(u0, u1) = X._message_to_child()
self.assertAllClose(u0, 2*np.ones((2,3,4)))
self.assertAllClose(u1,
2**2 * np.ones((2,3,4,2,3,4))
+ 1/3 * misc.identity(2,3,4))
# Check the formula for dim-broadcasted mu
X = GaussianARD(2*np.ones((3,1)),
3*np.ones((2,3,4)),
ndim=3)
(u0, u1) = X._message_to_child()
self.assertAllClose(u0, 2*np.ones((2,3,4)))
self.assertAllClose(u1,
2**2 * np.ones((2,3,4,2,3,4))
+ 1/3 * misc.identity(2,3,4))
# Check the formula for dim-broadcasted alpha
X = GaussianARD(2*np.ones((2,3,4)),
3*np.ones((3,1)),
ndim=3)
(u0, u1) = X._message_to_child()
self.assertAllClose(u0, 2*np.ones((2,3,4)))
self.assertAllClose(u1,
2**2 * np.ones((2,3,4,2,3,4))
+ 1/3 * misc.identity(2,3,4))
# Check the formula for dim-broadcasted mu and alpha
X = GaussianARD(2*np.ones((3,1)),
3*np.ones((3,1)),
shape=(2,3,4))
(u0, u1) = X._message_to_child()
self.assertAllClose(u0, 2*np.ones((2,3,4)))
self.assertAllClose(u1,
2**2 * np.ones((2,3,4,2,3,4))
+ 1/3 * misc.identity(2,3,4))
# Check the formula for dim-broadcasted mu with plates
mu = GaussianARD(2*np.ones((5,1,3,4)),
np.ones((5,1,3,4)),
shape=(3,4),
plates=(5,1))
X = GaussianARD(mu,
3*np.ones((5,2,3,4)),
shape=(2,3,4),
plates=(5,))
(u0, u1) = X._message_to_child()
self.assertAllClose(u0, 2*np.ones((5,2,3,4)))
self.assertAllClose(u1,
2**2 * np.ones((5,2,3,4,2,3,4))
+ 1/3 * misc.identity(2,3,4))
# Check posterior
X = GaussianARD(2, 3)
Y = GaussianARD(X, 1)
Y.observe(10)
X.update()
(u0, u1) = X._message_to_child()
self.assertAllClose(u0,
1/(3+1) * (3*2 + 1*10))
self.assertAllClose(u1,
(1/(3+1) * (3*2 + 1*10))**2 + 1/(3+1))
pass
def test_message_to_parent_mu(self):
"""
Test that GaussianARD computes the message to the 1st parent correctly.
"""
# Check formula with uncertain parent alpha
mu = GaussianARD(0, 1)
alpha = Gamma(2,1)
X = GaussianARD(mu,
alpha)
X.observe(3)
(m0, m1) = mu._message_from_children()
#(m0, m1) = X._message_to_parent(0)
self.assertAllClose(m0,
2*3)
self.assertAllClose(m1,
-0.5*2)
# Check formula with uncertain node
mu = GaussianARD(1, 1e10)
X = GaussianARD(mu, 2)
Y = GaussianARD(X, 1)
Y.observe(5)
X.update()
(m0, m1) = mu._message_from_children()
self.assertAllClose(m0,
2 * 1/(2+1)*(2*1+1*5))
self.assertAllClose(m1,
-0.5*2)
# Check alpha larger than mu
mu = GaussianARD(np.zeros((2,3)), 1e10, shape=(2,3))
X = GaussianARD(mu,
2*np.ones((3,2,3)))
X.observe(3*np.ones((3,2,3)))
(m0, m1) = mu._message_from_children()
self.assertAllClose(m0,
2*3 * 3 * np.ones((2,3)))
self.assertAllClose(m1,
-0.5 * 3 * 2*misc.identity(2,3))
# Check mu larger than alpha
mu = GaussianARD(np.zeros((3,2,3)), 1e10, shape=(3,2,3))
X = GaussianARD(mu,
2*np.ones((2,3)))
X.observe(3*np.ones((3,2,3)))
(m0, m1) = mu._message_from_children()
self.assertAllClose(m0,
2 * 3 * np.ones((3,2,3)))
self.assertAllClose(m1,
-0.5 * 2*misc.identity(3,2,3))
# Check node larger than mu and alpha
mu = GaussianARD(np.zeros((2,3)), 1e10, shape=(2,3))
X = GaussianARD(mu,
2*np.ones((3,)),
shape=(3,2,3))
X.observe(3*np.ones((3,2,3)))
(m0, m1) = mu._message_from_children()
self.assertAllClose(m0,
2*3 * 3*np.ones((2,3)))
self.assertAllClose(m1,
-0.5 * 2 * 3*misc.identity(2,3))
# Check broadcasting of dimensions
mu = GaussianARD(np.zeros((2,1)), 1e10, shape=(2,1))
X = GaussianARD(mu,
2*np.ones((2,3)),
shape=(2,3))
X.observe(3*np.ones((2,3)))
(m0, m1) = mu._message_from_children()
self.assertAllClose(m0,
2*3 * 3*np.ones((2,1)))
self.assertAllClose(m1,
-0.5 * 2 * 3*misc.identity(2,1))
# Check plates for smaller mu than node
mu = GaussianARD(0,1,
shape=(3,),
plates=(4,1,1))
X = GaussianARD(mu,
2*np.ones((3,)),
shape=(2,3),
plates=(4,5))
X.observe(3*np.ones((4,5,2,3)))
(m0, m1) = mu._message_from_children()
self.assertAllClose(m0 * np.ones((4,1,1,3)),
2*3 * 5*2*np.ones((4,1,1,3)))
self.assertAllClose(m1 * np.ones((4,1,1,3,3)),
-0.5*2 * 5*2*misc.identity(3) * np.ones((4,1,1,3,3)))
# Check mask
mu = GaussianARD(np.zeros((2,1,3)), 1e10, shape=(3,))
X = GaussianARD(mu,
2*np.ones((2,4,3)),
shape=(3,),
plates=(2,4,))
X.observe(3*np.ones((2,4,3)), mask=[[True, True, True, False],
[False, True, False, True]])
(m0, m1) = mu._message_from_children()
self.assertAllClose(m0,
(2*3 * np.ones((2,1,3))
* np.array([[[3]], [[2]]])))
self.assertAllClose(m1,
(-0.5*2 * misc.identity(3)
* np.ones((2,1,1,1))
* np.array([[[[3]]], [[[2]]]])))
# Check mask with different shapes
mu = GaussianARD(np.zeros((2,1,3)), 1e10, shape=())
X = GaussianARD(mu,
2*np.ones((2,4,3)),
shape=(3,),
plates=(2,4,))
mask = np.array([[True, True, True, False],
[False, True, False, True]])
X.observe(3*np.ones((2,4,3)), mask=mask)
(m0, m1) = mu._message_from_children()
self.assertAllClose(m0,
2*3 * np.sum(np.ones((2,4,3))*mask[...,None],
axis=-2,
keepdims=True))
self.assertAllClose(m1,
(-0.5*2 * np.sum(np.ones((2,4,3))*mask[...,None],
axis=-2,
keepdims=True)))
# Check non-ARD Gaussian child
mu = np.array([1,2])
Mu = GaussianARD(mu, 1e10, shape=(2,))
alpha = np.array([3,4])
Lambda = np.array([[1, 0.5],
[0.5, 1]])
X = GaussianARD(Mu, alpha)
Y = Gaussian(X, Lambda)
y = np.array([5,6])
Y.observe(y)
X.update()
(m0, m1) = Mu._message_from_children()
mean = np.dot(np.linalg.inv(np.diag(alpha)+Lambda),
np.dot(np.diag(alpha), mu)
+ np.dot(Lambda, y))
self.assertAllClose(m0,
np.dot(np.diag(alpha), mean))
self.assertAllClose(m1,
-0.5*np.diag(alpha))
# Check broadcasted variable axes
mu = GaussianARD(np.zeros(1), 1e10, shape=(1,))
X = GaussianARD(mu,
2,
shape=(3,))
X.observe(3*np.ones(3))
(m0, m1) = mu._message_from_children()
self.assertAllClose(m0,
2*3 * np.sum(np.ones(3), axis=-1, keepdims=True))
self.assertAllClose(m1,
-0.5*2 * np.sum(np.identity(3),
axis=(-1,-2),
keepdims=True))
pass
def test_message_to_parent_alpha(self):
"""
Test the message from GaussianARD the 2nd parent (alpha).
"""
# Check formula with uncertain parent mu
mu = GaussianARD(1,1)
tau = Gamma(0.5*1e10, 1e10)
X = GaussianARD(mu,
tau)
X.observe(3)
(m0, m1) = tau._message_from_children()
self.assertAllClose(m0,
-0.5*(3**2 - 2*3*1 + 1**2+1))
self.assertAllClose(m1,
0.5)
# Check formula with uncertain node
tau = Gamma(1e10, 1e10)
X = GaussianARD(2, tau)
Y = GaussianARD(X, 1)
Y.observe(5)
X.update()
(m0, m1) = tau._message_from_children()
self.assertAllClose(m0,
-0.5*(1/(1+1)+3.5**2 - 2*3.5*2 + 2**2))
self.assertAllClose(m1,
0.5)
# Check alpha larger than mu
alpha = Gamma(np.ones((3,2,3))*1e10, 1e10)
X = GaussianARD(np.ones((2,3)),
alpha,
ndim=3)
X.observe(2*np.ones((3,2,3)))
(m0, m1) = alpha._message_from_children()
self.assertAllClose(m0 * np.ones((3,2,3)),
-0.5*(2**2 - 2*2*1 + 1**2) * np.ones((3,2,3)))
self.assertAllClose(m1*np.ones((3,2,3)),
0.5*np.ones((3,2,3)))
# Check mu larger than alpha
tau = Gamma(np.ones((2,3))*1e10, 1e10)
X = GaussianARD(np.ones((3,2,3)),
tau,
ndim=3)
X.observe(2*np.ones((3,2,3)))
(m0, m1) = tau._message_from_children()
self.assertAllClose(m0,
-0.5*(2**2 - 2*2*1 + 1**2) * 3 * np.ones((2,3)))
self.assertAllClose(m1 * np.ones((2,3)),
0.5 * 3 * np.ones((2,3)))
# Check node larger than mu and alpha
tau = Gamma(np.ones((3,))*1e10, 1e10)
X = GaussianARD(np.ones((2,3)),
tau,
shape=(3,2,3))
X.observe(2*np.ones((3,2,3)))
(m0, m1) = tau._message_from_children()
self.assertAllClose(m0 * np.ones(3),
-0.5*(2**2 - 2*2*1 + 1**2) * 6 * np.ones((3,)))
self.assertAllClose(m1 * np.ones(3),
0.5 * 6 * np.ones(3))
# Check plates for smaller mu than node
tau = Gamma(np.ones((4,1,2,3))*1e10, 1e10)
X = GaussianARD(GaussianARD(1, 1,
shape=(3,),
plates=(4,1,1)),
tau,
shape=(2,3),
plates=(4,5))
X.observe(2*np.ones((4,5,2,3)))
(m0, m1) = tau._message_from_children()
self.assertAllClose(m0 * np.ones((4,1,2,3)),
(-0.5 * (2**2 - 2*2*1 + 1**2+1)
* 5*np.ones((4,1,2,3))))
self.assertAllClose(m1 * np.ones((4,1,2,3)),
5*0.5 * np.ones((4,1,2,3)))
# Check mask
tau = Gamma(np.ones((4,3))*1e10, 1e10)
X = GaussianARD(np.ones(3),
tau,
shape=(3,),
plates=(2,4,))
X.observe(2*np.ones((2,4,3)), mask=[[True, False, True, False],
[False, True, True, False]])
(m0, m1) = tau._message_from_children()
self.assertAllClose(m0 * np.ones((4,3)),
(-0.5 * (2**2 - 2*2*1 + 1**2)
* np.ones((4,3))
* np.array([[1], [1], [2], [0]])))
self.assertAllClose(m1 * np.ones((4,3)),
0.5 * np.array([[1], [1], [2], [0]]) * np.ones((4,3)))
# Check non-ARD Gaussian child
mu = np.array([1,2])
alpha = np.array([3,4])
Alpha = Gamma(alpha*1e10, 1e10)
Lambda = np.array([[1, 0.5],
[0.5, 1]])
X = GaussianARD(mu, Alpha, ndim=1)
Y = Gaussian(X, Lambda)
y = np.array([5,6])
Y.observe(y)
X.update()
(m0, m1) = Alpha._message_from_children()
Cov = np.linalg.inv(np.diag(alpha)+Lambda)
mean = np.dot(Cov, np.dot(np.diag(alpha), mu)
+ np.dot(Lambda, y))
self.assertAllClose(m0 * np.ones(2),
-0.5 * np.diag(
np.outer(mean, mean) + Cov
- np.outer(mean, mu)
- np.outer(mu, mean)
+ np.outer(mu, mu)))
self.assertAllClose(m1 * np.ones(2),
0.5 * np.ones(2))
pass
def test_lowerbound(self):
"""
Test the variational Bayesian lower bound term for GaussianARD.
"""
# Test vector formula with full noise covariance
m = np.random.randn(2)
alpha = np.random.rand(2)
y = np.random.randn(2)
X = GaussianARD(m, alpha, ndim=1)
V = np.array([[3,1],[1,3]])
Y = Gaussian(X, V)
Y.observe(y)
X.update()
Cov = np.linalg.inv(np.diag(alpha) + V)
mu = np.dot(Cov, np.dot(V, y) + alpha*m)
x2 = np.outer(mu, mu) + Cov
logH_X = (+ 2*0.5*(1+np.log(2*np.pi))
+ 0.5*np.log(np.linalg.det(Cov)))
logp_X = (- 2*0.5*np.log(2*np.pi)
+ 0.5*np.log(np.linalg.det(np.diag(alpha)))
- 0.5*np.sum(np.diag(alpha)
* (x2
- np.outer(mu,m)
- np.outer(m,mu)
+ np.outer(m,m))))
self.assertAllClose(logp_X + logH_X,
X.lower_bound_contribution())
def check_lower_bound(shape_mu, shape_alpha, plates_mu=(), **kwargs):
M = GaussianARD(np.ones(plates_mu + shape_mu),
np.ones(plates_mu + shape_mu),
shape=shape_mu,
plates=plates_mu)
X = GaussianARD(M,
2*np.ones(shape_alpha),
**kwargs)
Y = GaussianARD(X,
3*np.ones(X.get_shape(0)),
**kwargs)
Y.observe(4*np.ones(Y.get_shape(0)))
X.update()
Cov = 1/(2+3)
mu = Cov * (2*1 + 3*4)
x2 = mu**2 + Cov
logH_X = (+ 0.5*(1+np.log(2*np.pi))
+ 0.5*np.log(Cov))
logp_X = (- 0.5*np.log(2*np.pi)
+ 0.5*np.log(2)
- 0.5*2*(x2 - 2*mu*1 + 1**2+1))
r = np.prod(X.get_shape(0))
self.assertAllClose(r * (logp_X + logH_X),
X.lower_bound_contribution())
# Test scalar formula
check_lower_bound((), ())
# Test array formula
check_lower_bound((2,3), (2,3))
# Test dim-broadcasting of mu
check_lower_bound((3,1), (2,3,4))
# Test dim-broadcasting of alpha
check_lower_bound((2,3,4), (3,1))
# Test dim-broadcasting of mu and alpha
check_lower_bound((3,1), (3,1),
shape=(2,3,4))
# Test dim-broadcasting of mu with plates
check_lower_bound((), (),
plates_mu=(),
shape=(),
plates=(5,))
# BUG: Scalar parents for array variable caused einsum error
check_lower_bound((), (),
shape=(3,))
# BUG: Log-det was summed over plates
check_lower_bound((), (),
shape=(3,),
plates=(4,))
pass
def test_rotate(self):
"""
Test the rotation of Gaussian ARD arrays.
"""
def check(shape, plates, einsum_x, einsum_xx, axis=-1):
# TODO/FIXME: Improve by having non-diagonal precision/covariance
# parameter for the Gaussian X
D = shape[axis]
X = GaussianARD(np.random.randn(*(plates+shape)),
np.random.rand(*(plates+shape)),
shape=shape,
plates=plates)
(x, xx) = X.get_moments()
R = np.random.randn(D,D)
X.rotate(R, axis=axis)
(rx, rxxr) = X.get_moments()
self.assertAllClose(rx,
np.einsum(einsum_x, R, x))
self.assertAllClose(rxxr,
np.einsum(einsum_xx, R, xx, R))
pass
# Rotate vector
check((3,), (),
'...jk,...k->...j',
'...mk,...kl,...nl->...mn')
check((3,), (2,4),
'...jk,...k->...j',
'...mk,...kl,...nl->...mn')
# Rotate array
check((2,3,4), (),
'...jc,...abc->...abj',
'...mc,...abcdef,...nf->...abmden',
axis=-1)
check((2,3,4), (5,6),
'...jc,...abc->...abj',
'...mc,...abcdef,...nf->...abmden',
axis=-1)
check((2,3,4), (),
'...jb,...abc->...ajc',
'...mb,...abcdef,...ne->...amcdnf',
axis=-2)
check((2,3,4), (5,6),
'...jb,...abc->...ajc',
'...mb,...abcdef,...ne->...amcdnf',
axis=-2)
check((2,3,4), (),
'...ja,...abc->...jbc',
'...ma,...abcdef,...nd->...mbcnef',
axis=-3)
check((2,3,4), (5,6),
'...ja,...abc->...jbc',
'...ma,...abcdef,...nd->...mbcnef',
axis=-3)
pass
def test_rotate_plates(self):
# Basic test for Gaussian vectors
X = GaussianARD(np.random.randn(3,2),
np.random.rand(3,2),
shape=(2,),
plates=(3,))
(u0, u1) = X.get_moments()
Cov = u1 - linalg.outer(u0, u0, ndim=1)
Q = np.random.randn(3,3)
Qu0 = np.einsum('ik,kj->ij', Q, u0)
QCov = np.einsum('k,kij->kij', np.sum(Q, axis=0)**2, Cov)
Qu1 = QCov + linalg.outer(Qu0, Qu0, ndim=1)
X.rotate_plates(Q, plate_axis=-1)
(u0, u1) = X.get_moments()
self.assertAllClose(u0, Qu0)
self.assertAllClose(u1, Qu1)
# Test full covariance, that is, with observations
X = GaussianARD(np.random.randn(3,2),
np.random.rand(3,2),
shape=(2,),
plates=(3,))
Y = Gaussian(X, [[2.0, 1.5], [1.5, 3.0]],
plates=(3,))
Y.observe(np.random.randn(3,2))
X.update()
(u0, u1) = X.get_moments()
Cov = u1 - linalg.outer(u0, u0, ndim=1)
Q = np.random.randn(3,3)
Qu0 = np.einsum('ik,kj->ij', Q, u0)
QCov = np.einsum('k,kij->kij', np.sum(Q, axis=0)**2, Cov)
Qu1 = QCov + linalg.outer(Qu0, Qu0, ndim=1)
X.rotate_plates(Q, plate_axis=-1)
(u0, u1) = X.get_moments()
self.assertAllClose(u0, Qu0)
self.assertAllClose(u1, Qu1)
pass
def test_initialization(self):
"""
Test initialization methods of GaussianARD
"""
X = GaussianARD(1, 2, shape=(2,), plates=(3,))
# Prior initialization
mu = 1 * np.ones((3, 2))
alpha = 2 * np.ones((3, 2))
X.initialize_from_prior()
u = X._message_to_child()
self.assertAllClose(u[0]*np.ones((3,2)),
mu)
self.assertAllClose(u[1]*np.ones((3,2,2)),
linalg.outer(mu, mu, ndim=1) +
misc.diag(1/alpha, ndim=1))
# Parameter initialization
mu = np.random.randn(3, 2)
alpha = np.random.rand(3, 2)
X.initialize_from_parameters(mu, alpha)
u = X._message_to_child()
self.assertAllClose(u[0], mu)
self.assertAllClose(u[1], linalg.outer(mu, mu, ndim=1) +
misc.diag(1/alpha, ndim=1))
# Value initialization
x = np.random.randn(3, 2)
X.initialize_from_value(x)
u = X._message_to_child()
self.assertAllClose(u[0], x)
self.assertAllClose(u[1], linalg.outer(x, x, ndim=1))
# Random initialization
X.initialize_from_random()
pass
class TestGaussianGammaISO(TestCase):
"""
Unit tests for GaussianGammaISO node.
"""
def test_init(self):
"""
Test the creation of GaussianGammaISO node
"""
# Simple construction
X_alpha = GaussianGammaISO([1,2,3], np.identity(3), 2, 10)
self.assertEqual(X_alpha.plates, ())
self.assertEqual(X_alpha.dims, ( (3,), (3,3), (), () ))
# Plates
X_alpha = GaussianGammaISO([1,2,3], np.identity(3), 2, 10, plates=(4,))
self.assertEqual(X_alpha.plates, (4,))
self.assertEqual(X_alpha.dims, ( (3,), (3,3), (), () ))
# Plates in mu
X_alpha = GaussianGammaISO(np.ones((4,3)), np.identity(3), 2, 10)
self.assertEqual(X_alpha.plates, (4,))
self.assertEqual(X_alpha.dims, ( (3,), (3,3), (), () ))
# Plates in Lambda
X_alpha = GaussianGammaISO(np.ones(3), np.ones((4,3,3))*np.identity(3), 2, 10)
self.assertEqual(X_alpha.plates, (4,))
self.assertEqual(X_alpha.dims, ( (3,), (3,3), (), () ))
# Plates in a
X_alpha = GaussianGammaISO(np.ones(3), np.identity(3), np.ones(4), 10)
self.assertEqual(X_alpha.plates, (4,))
self.assertEqual(X_alpha.dims, ( (3,), (3,3), (), () ))
# Plates in Lambda
X_alpha = GaussianGammaISO(np.ones(3), np.identity(3), 2, np.ones(4))
self.assertEqual(X_alpha.plates, (4,))
self.assertEqual(X_alpha.dims, ( (3,), (3,3), (), () ))
# Inconsistent plates
self.assertRaises(ValueError,
GaussianGammaISO,
np.ones((4,3)),
np.identity(3),
2,
10,
plates=())
# Inconsistent plates
self.assertRaises(ValueError,
GaussianGammaISO,
np.ones((4,3)),
np.identity(3),
2,
10,
plates=(5,))
# Unknown parameters
mu = Gaussian(np.zeros(3), np.identity(3))
Lambda = Wishart(10, np.identity(3))
b = Gamma(1, 1)
X_alpha = GaussianGammaISO(mu, Lambda, 2, b)
self.assertEqual(X_alpha.plates, ())
self.assertEqual(X_alpha.dims, ( (3,), (3,3), (), () ))
# mu is Gaussian-gamma
mu_tau = GaussianGammaISO(np.ones(3), np.identity(3), 5, 5)
X_alpha = GaussianGammaISO(mu_tau, np.identity(3), 5, 5)
self.assertEqual(X_alpha.plates, ())
self.assertEqual(X_alpha.dims, ( (3,), (3,3), (), () ))
pass
def test_message_to_child(self):
"""
Test the message to child of GaussianGammaISO node.
"""
# Simple test
mu = np.array([1,2,3])
Lambda = np.identity(3)
a = 2
b = 10
X_alpha = GaussianGammaISO(mu, Lambda, a, b)
u = X_alpha._message_to_child()
self.assertEqual(len(u), 4)
tau = np.array(a/b)
self.assertAllClose(u[0],
tau[...,None] * mu)
self.assertAllClose(u[1],
(linalg.inv(Lambda)
+ tau[...,None,None] * linalg.outer(mu, mu)))
self.assertAllClose(u[2],
tau)
self.assertAllClose(u[3],
-np.log(b) + special.psi(a))
# Test with unknown parents
mu = Gaussian(np.arange(3), 10*np.identity(3))
Lambda = Wishart(10, np.identity(3))
a = 2
b = Gamma(3, 15)
X_alpha = GaussianGammaISO(mu, Lambda, a, b)
u = X_alpha._message_to_child()
(mu, mumu) = mu._message_to_child()
Cov_mu = mumu - linalg.outer(mu, mu)
(Lambda, _) = Lambda._message_to_child()
(b, _) = b._message_to_child()
(tau, logtau) = Gamma(a, b + 0.5*np.sum(Lambda*Cov_mu))._message_to_child()
self.assertAllClose(u[0],
tau[...,None] * mu)
self.assertAllClose(u[1],
(linalg.inv(Lambda)
+ tau[...,None,None] * linalg.outer(mu, mu)))
self.assertAllClose(u[2],
tau)
self.assertAllClose(u[3],
logtau)
# Test with plates
mu = Gaussian(np.reshape(np.arange(3*4), (4,3)),
10*np.identity(3),
plates=(4,))
Lambda = Wishart(10, np.identity(3))
a = 2
b = Gamma(3, 15)
X_alpha = GaussianGammaISO(mu, Lambda, a, b, plates=(4,))
u = X_alpha._message_to_child()
(mu, mumu) = mu._message_to_child()
Cov_mu = mumu - linalg.outer(mu, mu)
(Lambda, _) = Lambda._message_to_child()
(b, _) = b._message_to_child()
(tau, logtau) = Gamma(a,
b + 0.5*np.sum(Lambda*Cov_mu,
axis=(-1,-2)))._message_to_child()
self.assertAllClose(u[0] * np.ones((4,1)),
np.ones((4,1)) * tau[...,None] * mu)
self.assertAllClose(u[1] * np.ones((4,1,1)),
np.ones((4,1,1)) * (linalg.inv(Lambda)
+ tau[...,None,None] * linalg.outer(mu, mu)))
self.assertAllClose(u[2] * np.ones(4),
np.ones(4) * tau)
self.assertAllClose(u[3] * np.ones(4),
np.ones(4) * logtau)
pass
def test_mask_to_parent(self):
"""
Test the mask handling in GaussianGammaISO node
"""
pass
bayespy-0.2.3/bayespy/inference/vmp/nodes/tests/test_gaussian_markov_chain.py 0000664 5104112 5104112 00000073466 12374370351 031371 0 ustar jluttine jluttine 0000000 0000000 ######################################################################
# Copyright (C) 2013-2014 Jaakko Luttinen
#
# This file is licensed under Version 3.0 of the GNU General Public
# License. See LICENSE for a text of the license.
######################################################################
######################################################################
# This file is part of BayesPy.
#
# BayesPy is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# BayesPy is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with BayesPy. If not, see .
######################################################################
"""
Unit tests for gaussian_markov_chain module.
"""
import numpy as np
from ..gaussian_markov_chain import GaussianMarkovChain
from ..gaussian_markov_chain import VaryingGaussianMarkovChain
from ..gaussian import Gaussian
from ..gaussian import GaussianARD
from ..wishart import Wishart
from ..gamma import Gamma
from bayespy.utils import random
from bayespy.utils import linalg
from bayespy.utils import misc
from bayespy.utils.misc import TestCase
class TestGaussianMarkovChain(TestCase):
def create_model(self, N, D):
# Construct the model
Mu = Gaussian(np.random.randn(D),
np.identity(D))
Lambda = Wishart(D,
random.covariance(D))
A = Gaussian(np.random.randn(D,D),
np.identity(D))
V = Gamma(D,
np.random.rand(D))
X = GaussianMarkovChain(Mu, Lambda, A, V, n=N)
Y = Gaussian(X, np.identity(D))
return (Y, X, Mu, Lambda, A, V)
def test_plates(self):
"""
Test that plates are handled correctly.
"""
def test_message_to_mu0(self):
pass
def test_message_to_Lambda0(self):
pass
def test_message_to_A(self):
pass
def test_message_to_v(self):
pass
def test_message_to_child(self):
"""
Test the updating of GaussianMarkovChain.
Check that the moments and the lower bound contribution are computed
correctly.
"""
# TODO: Add plates and missing values!
# Dimensionalities
D = 3
N = 5
(Y, X, Mu, Lambda, A, V) = self.create_model(N, D)
# Inference with arbitrary observations
y = np.random.randn(N,D)
Y.observe(y)
X.update()
(x_vb, xnxn_vb, xpxn_vb) = X.get_moments()
# Get parameter moments
(mu0, mumu0) = Mu.get_moments()
(icov0, logdet0) = Lambda.get_moments()
(a, aa) = A.get_moments()
(icov_x, logdetx) = V.get_moments()
icov_x = np.diag(icov_x)
# Prior precision
Z = np.einsum('...kij,...kk->...ij', aa, icov_x)
U_diag = [icov0+Z] + (N-2)*[icov_x+Z] + [icov_x]
U_super = (N-1) * [-np.dot(a.T, icov_x)]
U = misc.block_banded(U_diag, U_super)
# Prior mean
mu_prior = np.zeros(D*N)
mu_prior[:D] = np.dot(icov0,mu0)
# Data
Cov = np.linalg.inv(U + np.identity(D*N))
mu = np.dot(Cov, mu_prior + y.flatten())
# Moments
xx = mu[:,np.newaxis]*mu[np.newaxis,:] + Cov
mu = np.reshape(mu, (N,D))
xx = np.reshape(xx, (N,D,N,D))
# Check results
self.assertAllClose(x_vb, mu,
msg="Incorrect mean")
for n in range(N):
self.assertAllClose(xnxn_vb[n,:,:], xx[n,:,n,:],
msg="Incorrect second moment")
for n in range(N-1):
self.assertAllClose(xpxn_vb[n,:,:], xx[n,:,n+1,:],
msg="Incorrect lagged second moment")
# Compute the entropy H(X)
ldet = linalg.logdet_cov(Cov)
H = random.gaussian_entropy(-ldet, N*D)
# Compute
xx = np.reshape(xx, (N*D, N*D))
mu = np.reshape(mu, (N*D,))
ldet = -logdet0 - np.sum(np.ones((N-1,D))*logdetx)
P = random.gaussian_logpdf(np.einsum('...ij,...ij',
xx,
U),
np.einsum('...i,...i',
mu,
mu_prior),
np.einsum('...ij,...ij',
mumu0,
icov0),
-ldet,
N*D)
# The VB bound from the net
l = X.lower_bound_contribution()
self.assertAllClose(l, H+P)
# Compute the true bound + H(X)
#
# Simple tests
#
def check(N, D, plates=None, mu=None, Lambda=None, A=None, V=None):
if mu is None:
mu = np.random.randn(D)
if Lambda is None:
Lambda = random.covariance(D)
if A is None:
A = np.random.randn(D,D)
if V is None:
V = np.random.rand(D)
X = GaussianMarkovChain(mu,
Lambda,
A,
V,
plates=plates,
n=N)
(u0, u1, u2) = X._message_to_child()
(mu, mumu) = X.parents[0].get_moments()
(Lambda, _) = X.parents[1].get_moments()
(a, aa) = X.parents[2].get_moments()
a = a * np.ones((N-1,D,D)) # explicit broadcasting for simplicity
aa = aa * np.ones((N-1,D,D,D)) # explicit broadcasting for simplicity
(v, _) = X.parents[3].get_moments()
v = v * np.ones((N-1,D))
plates_C = X.plates
plates_mu = X.plates
C = np.zeros(plates_C + (N,D,N,D))
plates_mu = np.shape(mu)[:-1]
m = np.zeros(plates_mu + (N,D))
m[...,0,:] = np.einsum('...ij,...j->...i', Lambda, mu)
C[...,0,:,0,:] = Lambda + np.einsum('...dij,...d->...ij',
aa[...,0,:,:,:],
v[...,0,:])
for n in range(1,N-1):
C[...,n,:,n,:] = (np.einsum('...dij,...d->...ij',
aa[...,n,:,:,:],
v[...,n,:])
+ v[...,n,:,None] * np.identity(D))
for n in range(N-1):
C[...,n,:,n+1,:] = -np.einsum('...di,...d->...id',
a[...,n,:,:],
v[...,n,:])
C[...,n+1,:,n,:] = -np.einsum('...di,...d->...di',
a[...,n,:,:],
v[...,n,:])
C[...,-1,:,-1,:] = v[...,-1,:,None]*np.identity(D)
C = np.reshape(C, plates_C+(N*D,N*D))
Cov = np.linalg.inv(C)
Cov = np.reshape(Cov, plates_C+(N,D,N,D))
m0 = np.einsum('...minj,...nj->...mi', Cov, m)
m1 = np.zeros(plates_C+(N,D,D))
m2 = np.zeros(plates_C+(N-1,D,D))
for n in range(N):
m1[...,n,:,:] = Cov[...,n,:,n,:] + np.einsum('...i,...j->...ij',
m0[...,n,:],
m0[...,n,:])
for n in range(N-1):
m2[...,n,:,:] = Cov[...,n,:,n+1,:] + np.einsum('...i,...j->...ij',
m0[...,n,:],
m0[...,n+1,:])
self.assertAllClose(m0, u0*np.ones(np.shape(m0)))
self.assertAllClose(m1, u1*np.ones(np.shape(m1)))
self.assertAllClose(m2, u2*np.ones(np.shape(m2)))
pass
check(4,1)
check(4,3)
#
# Test mu
#
# Simple
check(4,3,
mu=Gaussian(np.random.randn(3),
random.covariance(3)))
# Plates
check(4,3,
mu=Gaussian(np.random.randn(5,6,3),
random.covariance(3),
plates=(5,6)))
# Plates with moments broadcasted over plates
check(4,3,
mu=Gaussian(np.random.randn(3),
random.covariance(3),
plates=(5,)))
check(4,3,
mu=Gaussian(np.random.randn(1,3),
random.covariance(3),
plates=(5,)))
# Plates broadcasting
check(4,3,
plates=(5,),
mu=Gaussian(np.random.randn(3),
random.covariance(3),
plates=()))
check(4,3,
plates=(5,),
mu=Gaussian(np.random.randn(1,3),
random.covariance(3),
plates=(1,)))
#
# Test Lambda
#
# Simple
check(4,3,
Lambda=Wishart(10+np.random.rand(),
random.covariance(3)))
# Plates
check(4,3,
Lambda=Wishart(10+np.random.rand(),
random.covariance(3),
plates=(5,6)))
# Plates with moments broadcasted over plates
check(4,3,
Lambda=Wishart(10+np.random.rand(),
random.covariance(3),
plates=(5,)))
check(4,3,
Lambda=Wishart(10+np.random.rand(1),
random.covariance(3),
plates=(5,)))
# Plates broadcasting
check(4,3,
plates=(5,),
Lambda=Wishart(10+np.random.rand(),
random.covariance(3),
plates=()))
check(4,3,
plates=(5,),
Lambda=Wishart(10+np.random.rand(),
random.covariance(3),
plates=(1,)))
#
# Test A
#
# Simple
check(4,3,
A=GaussianARD(np.random.randn(3,3),
np.random.rand(3,3),
shape=(3,),
plates=(3,)))
# Plates on time axis
check(5,3,
A=GaussianARD(np.random.randn(4,3,3),
np.random.rand(4,3,3),
shape=(3,),
plates=(4,3)))
# Plates on time axis with broadcasted moments
check(5,3,
A=GaussianARD(np.random.randn(1,3,3),
np.random.rand(1,3,3),
shape=(3,),
plates=(4,3)))
check(5,3,
A=GaussianARD(np.random.randn(3,3),
np.random.rand(3,3),
shape=(3,),
plates=(4,3)))
# Plates
check(4,3,
A=GaussianARD(np.random.randn(5,6,1,3,3),
np.random.rand(5,6,1,3,3),
shape=(3,),
plates=(5,6,1,3)))
# Plates with moments broadcasted over plates
check(4,3,
A=GaussianARD(np.random.randn(3,3),
np.random.rand(3,3),
shape=(3,),
plates=(5,1,3)))
check(4,3,
A=GaussianARD(np.random.randn(1,1,3,3),
np.random.rand(1,1,3,3),
shape=(3,),
plates=(5,1,3)))
# Plates broadcasting
check(4,3,
plates=(5,),
A=GaussianARD(np.random.randn(3,3),
np.random.rand(3,3),
shape=(3,),
plates=(3,)))
check(4,3,
plates=(5,),
A=GaussianARD(np.random.randn(3,3),
np.random.rand(3,3),
shape=(3,),
plates=(1,1,3)))
#
# Test v
#
# Simple
check(4,3,
V=Gamma(np.random.rand(1,3),
np.random.rand(1,3),
plates=(1,3)))
check(4,3,
V=Gamma(np.random.rand(3),
np.random.rand(3),
plates=(3,)))
# Plates
check(4,3,
V=Gamma(np.random.rand(5,6,1,3),
np.random.rand(5,6,1,3),
plates=(5,6,1,3)))
# Plates with moments broadcasted over plates
check(4,3,
V=Gamma(np.random.rand(1,3),
np.random.rand(1,3),
plates=(5,1,3)))
check(4,3,
V=Gamma(np.random.rand(1,1,3),
np.random.rand(1,1,3),
plates=(5,1,3)))
# Plates broadcasting
check(4,3,
plates=(5,),
V=Gamma(np.random.rand(1,3),
np.random.rand(1,3),
plates=(1,3)))
check(4,3,
plates=(5,),
V=Gamma(np.random.rand(1,1,3),
np.random.rand(1,1,3),
plates=(1,1,3)))
pass
def test_smoothing(self):
"""
Test the posterior estimation of GaussianMarkovChain.
Create time-variant dynamics and compare the results of BayesPy VB
inference and standard Kalman filtering & smoothing.
This is not that useful anymore, because the moments are checked much
better in another test method.
"""
#
# Set up an artificial system
#
# Dimensions
N = 500
D = 2
# Dynamics (time varying)
A0 = np.array([[.9, -.4], [.4, .9]])
A1 = np.array([[.98, -.1], [.1, .98]])
l = np.linspace(0, 1, N-1).reshape((-1,1,1))
A = (1-l)*A0 + l*A1
# Innovation covariance matrix (time varying)
v = np.random.rand(D)
V = np.diag(v)
# Observation noise covariance matrix
C = np.identity(D)
#
# Simulate data
#
X = np.empty((N,D))
Y = np.empty((N,D))
x = np.array([0.5, -0.5])
X[0,:] = x
Y[0,:] = x + np.random.multivariate_normal(np.zeros(D), C)
for n in range(N-1):
x = np.dot(A[n,:,:],x) + np.random.multivariate_normal(np.zeros(D), V)
X[n+1,:] = x
Y[n+1,:] = x + np.random.multivariate_normal(np.zeros(D), C)
#
# BayesPy inference
#
# Construct VB model
Xh = GaussianMarkovChain(np.zeros(D), np.identity(D), A, 1/v, n=N)
Yh = Gaussian(Xh, np.identity(D), plates=(N,))
# Put data
Yh.observe(Y)
# Run inference
Xh.update()
# Store results
Xh_vb = Xh.u[0]
CovXh_vb = Xh.u[1] - Xh_vb[...,np.newaxis,:] * Xh_vb[...,:,np.newaxis]
#
# "The ground truth" using standard Kalman filter and RTS smoother
#
V = N*(V,)
UY = Y
U = N*(C,)
(Xh, CovXh) = misc.kalman_filter(UY, U, A, V, np.zeros(D), np.identity(D))
(Xh, CovXh) = misc.rts_smoother(Xh, CovXh, A, V)
#
# Check results
#
self.assertTrue(np.allclose(Xh_vb, Xh))
self.assertTrue(np.allclose(CovXh_vb, CovXh))
class TestVaryingGaussianMarkovChain(TestCase):
def test_plates_from_parents(self):
"""
Test that VaryingGaussianMarkovChain deduces plates correctly
"""
def check(plates_X,
plates_mu=(),
plates_Lambda=(),
plates_B=(),
plates_S=(),
plates_v=()):
D = 3
K = 2
N = 4
np.random.seed(42)
mu = Gaussian(np.random.randn(*(plates_mu+(D,))),
random.covariance(D))
Lambda = Wishart(D+np.ones(plates_Lambda),
random.covariance(D))
B = GaussianARD(np.random.randn(*(plates_B+(D,D,K))),
1+np.random.rand(*(plates_B+(D,D,K))),
shape=(D,K),
plates=plates_B+(D,))
S = GaussianARD(np.random.randn(*(plates_S+(N,K))),
1+np.random.rand(*(plates_S+(N,K))),
shape=(K,),
plates=plates_S+(N,))
v = Gamma(1+np.random.rand(*(plates_v+(1,D))),
1+np.random.rand(*(plates_v+(1,D))))
X = VaryingGaussianMarkovChain(mu,
Lambda,
B,
S,
v,
name="X")
self.assertEqual(plates_X, X.plates,
msg="Incorrect plates deduced")
pass
check(())
check((2,3),
plates_mu=(2,3))
check((2,3),
plates_Lambda=(2,3))
check((2,3),
plates_B=(2,3))
check((2,3),
plates_S=(2,3))
check((2,3),
plates_v=(2,3))
pass
def test_message_to_child(self):
# A very simple check before the more complex ones:
# 1-D process, k=1, fixed constant parameters
m = 1.0
l = 4.0
b = 2.0
s = [3.0, 8.0]
v = 5.0
X = VaryingGaussianMarkovChain([m],
[[l]],
[[[b]]],
[[s[0]],[s[1]]],
[v])
(u0, u1, u2) = X._message_to_child()
C = np.array([[l+b**2*s[0]**2*v, -b*s[0]*v, 0],
[ -b*s[0]*v, v+b**2*s[1]**2*v, -b*s[1]*v],
[ 0, -b*s[1]*v, v]])
Cov = np.linalg.inv(C)
m0 = np.dot(Cov, [[l*m], [0], [0]])
m1 = np.diag(Cov)[:,None,None] + m0[:,:,None]**2
m2 = np.diag(Cov, k=1)[:,None,None] + m0[1:,:,None]*m0[:-1,:,None]
self.assertAllClose(m0, u0)
self.assertAllClose(m1, u1)
self.assertAllClose(m2, u2)
def check(N, D, K, plates=None, mu=None, Lambda=None, B=None, S=None, V=None):
if mu is None:
mu = np.random.randn(D)
if Lambda is None:
Lambda = random.covariance(D)
if B is None:
B = np.random.randn(D,D,K)
if S is None:
S = np.random.randn(N-1,K)
if V is None:
V = np.random.rand(D)
X = VaryingGaussianMarkovChain(mu,
Lambda,
B,
S,
V,
plates=plates,
n=N)
(u0, u1, u2) = X._message_to_child()
(mu, mumu) = X.parents[0].get_moments()
(Lambda, _) = X.parents[1].get_moments()
(b, bb) = X.parents[2].get_moments()
(s, ss) = X.parents[3].get_moments()
(v, _) = X.parents[4].get_moments()
v = v * np.ones((N-1,D))
#V = np.atleast_3d(v)[...,-1,:,None]*np.identity(D)
plates_C = X.plates
plates_mu = X.plates
C = np.zeros(plates_C + (N,D,N,D))
plates_mu = np.shape(mu)[:-1]
m = np.zeros(plates_mu + (N,D))
m[...,0,:] = np.einsum('...ij,...j->...i', Lambda, mu)
#m = np.reshape(m, plates_mu + (N*D,))
A = np.einsum('...dik,...nk->...ndi', b, s)
AA = np.einsum('...dikjl,...nkl->...ndij', bb, ss)
C[...,0,:,0,:] = Lambda + np.einsum('...dij,...d->...ij',
AA[...,0,:,:,:],
v[...,0,:])
for n in range(1,N-1):
C[...,n,:,n,:] = (np.einsum('...dij,...d->...ij',
AA[...,n,:,:,:],
v[...,n,:])
+ v[...,n,:,None] * np.identity(D))
for n in range(N-1):
C[...,n,:,n+1,:] = -np.einsum('...di,...d->...id',
A[...,n,:,:],
v[...,n,:])
C[...,n+1,:,n,:] = -np.einsum('...di,...d->...di',
A[...,n,:,:],
v[...,n,:])
C[...,-1,:,-1,:] = v[...,-1,:,None]*np.identity(D)
C = np.reshape(C, plates_C+(N*D,N*D))
Cov = np.linalg.inv(C)
Cov = np.reshape(Cov, plates_C+(N,D,N,D))
m0 = np.einsum('...minj,...nj->...mi', Cov, m)
m1 = np.zeros(plates_C+(N,D,D))
m2 = np.zeros(plates_C+(N-1,D,D))
for n in range(N):
m1[...,n,:,:] = Cov[...,n,:,n,:] + np.einsum('...i,...j->...ij',
m0[...,n,:],
m0[...,n,:])
for n in range(N-1):
m2[...,n,:,:] = Cov[...,n,:,n+1,:] + np.einsum('...i,...j->...ij',
m0[...,n,:],
m0[...,n+1,:])
self.assertAllClose(m0, u0*np.ones(np.shape(m0)))
self.assertAllClose(m1, u1*np.ones(np.shape(m1)))
self.assertAllClose(m2, u2*np.ones(np.shape(m2)))
pass
check(2,1,1)
check(2,3,1)
check(2,1,3)
check(4,3,2)
#
# Test mu
#
# Simple
check(4,3,2,
mu=Gaussian(np.random.randn(3),
random.covariance(3)))
# Plates
check(4,3,2,
mu=Gaussian(np.random.randn(5,6,3),
random.covariance(3),
plates=(5,6)))
# Plates with moments broadcasted over plates
check(4,3,2,
mu=Gaussian(np.random.randn(3),
random.covariance(3),
plates=(5,)))
check(4,3,2,
mu=Gaussian(np.random.randn(1,3),
random.covariance(3),
plates=(5,)))
# Plates broadcasting
check(4,3,2,
plates=(5,),
mu=Gaussian(np.random.randn(3),
random.covariance(3),
plates=()))
check(4,3,2,
plates=(5,),
mu=Gaussian(np.random.randn(1,3),
random.covariance(3),
plates=(1,)))
#
# Test Lambda
#
# Simple
check(4,3,2,
Lambda=Wishart(10+np.random.rand(),
random.covariance(3)))
# Plates
check(4,3,2,
Lambda=Wishart(10+np.random.rand(),
random.covariance(3),
plates=(5,6)))
# Plates with moments broadcasted over plates
check(4,3,2,
Lambda=Wishart(10+np.random.rand(),
random.covariance(3),
plates=(5,)))
check(4,3,2,
Lambda=Wishart(10+np.random.rand(1),
random.covariance(3),
plates=(5,)))
# Plates broadcasting
check(4,3,2,
plates=(5,),
Lambda=Wishart(10+np.random.rand(),
random.covariance(3),
plates=()))
check(4,3,2,
plates=(5,),
Lambda=Wishart(10+np.random.rand(),
random.covariance(3),
plates=(1,)))
#
# Test B
#
# Simple
check(4,3,2,
B=GaussianARD(np.random.randn(3,3,2),
np.random.rand(3,3,2),
shape=(3,2),
plates=(3,)))
# Plates
check(4,3,2,
B=GaussianARD(np.random.randn(5,6,3,3,2),
np.random.rand(5,6,3,3,2),
shape=(3,2),
plates=(5,6,3)))
# Plates with moments broadcasted over plates
check(4,3,2,
B=GaussianARD(np.random.randn(3,3,2),
np.random.rand(3,3,2),
shape=(3,2),
plates=(5,3)))
check(4,3,2,
B=GaussianARD(np.random.randn(1,3,3,2),
np.random.rand(1,3,3,2),
shape=(3,2),
plates=(5,3)))
# Plates broadcasting
check(4,3,2,
plates=(5,),
B=GaussianARD(np.random.randn(3,3,2),
np.random.rand(3,3,2),
shape=(3,2),
plates=(3,)))
check(4,3,2,
plates=(5,),
B=GaussianARD(np.random.randn(3,3,2),
np.random.rand(3,3,2),
shape=(3,2),
plates=(1,3)))
#
# Test S
#
# Simple
check(4,3,2,
S=GaussianARD(np.random.randn(4-1,2),
np.random.rand(4-1,2),
shape=(2,),
plates=(4-1,)))
# Plates
check(4,3,2,
S=GaussianARD(np.random.randn(5,6,4-1,2),
np.random.rand(5,6,4-1,2),
shape=(2,),
plates=(5,6,4-1,)))
# Plates with moments broadcasted over plates
check(4,3,2,
S=GaussianARD(np.random.randn(4-1,2),
np.random.rand(4-1,2),
shape=(2,),
plates=(5,4-1,)))
check(4,3,2,
S=GaussianARD(np.random.randn(1,4-1,2),
np.random.rand(1,4-1,2),
shape=(2,),
plates=(5,4-1,)))
# Plates broadcasting
check(4,3,2,
plates=(5,),
S=GaussianARD(np.random.randn(4-1,2),
np.random.rand(4-1,2),
shape=(2,),
plates=(4-1,)))
check(4,3,2,
plates=(5,),
S=GaussianARD(np.random.randn(4-1,2),
np.random.rand(4-1,2),
shape=(2,),
plates=(1,4-1,)))
#
# Test v
#
# Simple
check(4,3,2,
V=Gamma(np.random.rand(1,3),
np.random.rand(1,3),
plates=(1,3)))
check(4,3,2,
V=Gamma(np.random.rand(3),
np.random.rand(3),
plates=(3,)))
# Plates
check(4,3,2,
V=Gamma(np.random.rand(5,6,1,3),
np.random.rand(5,6,1,3),
plates=(5,6,1,3)))
# Plates with moments broadcasted over plates
check(4,3,2,
V=Gamma(np.random.rand(1,3),
np.random.rand(1,3),
plates=(5,1,3)))
check(4,3,2,
V=Gamma(np.random.rand(1,1,3),
np.random.rand(1,1,3),
plates=(5,1,3)))
# Plates broadcasting
check(4,3,2,
plates=(5,),
V=Gamma(np.random.rand(1,3),
np.random.rand(1,3),
plates=(1,3)))
check(4,3,2,
plates=(5,),
V=Gamma(np.random.rand(1,1,3),
np.random.rand(1,1,3),
plates=(1,1,3)))
#
# Uncertainty in both B and S
#
check(4,3,2,
B=GaussianARD(np.random.randn(3,3,2),
np.random.rand(3,3,2),
shape=(3,2),
plates=(3,)),
S=GaussianARD(np.random.randn(4-1,2),
np.random.rand(4-1,2),
shape=(2,),
plates=(4-1,)))
pass
def test_message_to_mu(self):
# TODO
pass
def test_message_to_Lambda(self):
# TODO
pass
def test_message_to_B(self):
# TODO
pass
def test_message_to_S(self):
# TODO
pass
def test_message_to_v(self):
# TODO
pass
bayespy-0.2.3/bayespy/inference/vmp/nodes/tests/test_mixture.py 0000664 5104112 5104112 00000024673 12374370351 026527 0 ustar jluttine jluttine 0000000 0000000 ######################################################################
# Copyright (C) 2014 Jaakko Luttinen
#
# This file is licensed under Version 3.0 of the GNU General Public
# License. See LICENSE for a text of the license.
######################################################################
######################################################################
# This file is part of BayesPy.
#
# BayesPy is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# BayesPy is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with BayesPy. If not, see .
######################################################################
"""
Unit tests for mixture module.
"""
import warnings
import numpy as np
from bayespy.nodes import (GaussianARD,
Gamma,
Mixture,
Categorical,
Bernoulli,
Multinomial)
from bayespy.utils import random
from bayespy.utils import linalg
from bayespy.utils.misc import TestCase
class TestMixture(TestCase):
def test_init(self):
"""
Test the creation of Mixture node
"""
# Do not accept non-negative cluster plates
z = Categorical(np.random.dirichlet([1,1]))
self.assertRaises(ValueError,
Mixture,
z,
GaussianARD,
GaussianARD(0, 1, plates=(2,)),
Gamma(1, 1, plates=(2,)),
cluster_plate=0)
# Try constructing a mixture without any of the parents having the
# cluster plate axis
z = Categorical(np.random.dirichlet([1,1]))
self.assertRaises(ValueError,
Mixture,
z,
GaussianARD,
GaussianARD(0, 1, plates=()),
Gamma(1, 1, plates=()))
def test_message_to_child(self):
"""
Test the message to child of Mixture node.
"""
K = 3
#
# Estimate moments from parents only
#
# Simple case
mu = GaussianARD([0,2,4], 1,
ndim=0,
plates=(K,))
alpha = Gamma(1, 1,
plates=(K,))
z = Categorical(np.ones(K)/K)
X = Mixture(z, GaussianARD, mu, alpha)
self.assertEqual(X.plates, ())
self.assertEqual(X.dims, ( (), () ))
u = X._message_to_child()
self.assertAllClose(u[0],
2)
self.assertAllClose(u[1],
2**2+1)
# Broadcasting the moments on the cluster axis
mu = GaussianARD(2, 1,
ndim=0,
plates=(K,))
alpha = Gamma(1, 1,
plates=(K,))
z = Categorical(np.ones(K)/K)
X = Mixture(z, GaussianARD, mu, alpha)
self.assertEqual(X.plates, ())
self.assertEqual(X.dims, ( (), () ))
u = X._message_to_child()
self.assertAllClose(u[0],
2)
self.assertAllClose(u[1],
2**2+1)
#
# Estimate moments with observed children
#
pass
def test_message_to_parent(self):
"""
Test the message to parents of Mixture node.
"""
K = 3
# Broadcasting the moments on the cluster axis
Mu = GaussianARD(2, 1,
ndim=0,
plates=(K,))
(mu, mumu) = Mu._message_to_child()
Alpha = Gamma(3, 1,
plates=(K,))
(alpha, logalpha) = Alpha._message_to_child()
z = Categorical(np.ones(K)/K)
X = Mixture(z, GaussianARD, Mu, Alpha)
tau = 4
Y = GaussianARD(X, tau)
y = 5
Y.observe(y)
(x, xx) = X._message_to_child()
m = z._message_from_children()
self.assertAllClose(m[0] * np.ones(K),
random.gaussian_logpdf(xx*alpha,
x*alpha*mu,
mumu*alpha,
logalpha,
0)
* np.ones(K))
m = Mu._message_from_children()
self.assertAllClose(m[0],
1/K * (alpha*x) * np.ones(3))
self.assertAllClose(m[1],
-0.5 * 1/K * alpha * np.ones(3))
# Some parameters do not have cluster plate axis
Mu = GaussianARD(2, 1,
ndim=0,
plates=(K,))
(mu, mumu) = Mu._message_to_child()
Alpha = Gamma(3, 1) # Note: no cluster plate axis!
(alpha, logalpha) = Alpha._message_to_child()
z = Categorical(np.ones(K)/K)
X = Mixture(z, GaussianARD, Mu, Alpha)
tau = 4
Y = GaussianARD(X, tau)
y = 5
Y.observe(y)
(x, xx) = X._message_to_child()
m = z._message_from_children()
self.assertAllClose(m[0] * np.ones(K),
random.gaussian_logpdf(xx*alpha,
x*alpha*mu,
mumu*alpha,
logalpha,
0)
* np.ones(K))
m = Mu._message_from_children()
self.assertAllClose(m[0],
1/K * (alpha*x) * np.ones(3))
self.assertAllClose(m[1],
-0.5 * 1/K * alpha * np.ones(3))
# Cluster assignments do not have as many plate axes as parameters.
M = 2
Mu = GaussianARD(2, 1,
ndim=0,
plates=(K,M))
(mu, mumu) = Mu._message_to_child()
Alpha = Gamma(3, 1,
plates=(K,M))
(alpha, logalpha) = Alpha._message_to_child()
z = Categorical(np.ones(K)/K)
X = Mixture(z, GaussianARD, Mu, Alpha, cluster_plate=-2)
tau = 4
Y = GaussianARD(X, tau)
y = 5 * np.ones(M)
Y.observe(y)
(x, xx) = X._message_to_child()
m = z._message_from_children()
self.assertAllClose(m[0]*np.ones(K),
np.sum(random.gaussian_logpdf(xx*alpha,
x*alpha*mu,
mumu*alpha,
logalpha,
0) *
np.ones((K,M)),
axis=-1))
m = Mu._message_from_children()
self.assertAllClose(m[0] * np.ones((K,M)),
1/K * (alpha*x) * np.ones((K,M)))
self.assertAllClose(m[1] * np.ones((K,M)),
-0.5 * 1/K * alpha * np.ones((K,M)))
# Mixed distribution broadcasts g
# This tests for a found bug. The bug caused an error.
Z = Categorical([0.3, 0.5, 0.2])
X = Mixture(Z, Categorical, [[0.2,0.8], [0.1,0.9], [0.3,0.7]])
m = Z._message_from_children()
pass
def test_lowerbound(self):
"""
Test log likelihood lower bound for Mixture node
"""
# Mixed distribution broadcasts g
# This tests for a found bug. The bug caused an error.
Z = Categorical([0.3, 0.5, 0.2])
X = Mixture(Z, Categorical, [[0.2,0.8], [0.1,0.9], [0.3,0.7]])
X.lower_bound_contribution()
pass
def test_mask_to_parent(self):
"""
Test the mask handling in Mixture node
"""
K = 3
Z = Categorical(np.ones(K)/K,
plates=(4,5))
Mu = GaussianARD(0, 1,
shape=(2,),
plates=(4,K,5))
Alpha = Gamma(1, 1,
plates=(4,K,5,2))
X = Mixture(Z, GaussianARD, Mu, Alpha, cluster_plate=-2)
Y = GaussianARD(X, 1)
mask = np.reshape((np.mod(np.arange(4*5), 2) == 0),
(4,5))
Y.observe(np.ones((4,5,2)),
mask=mask)
self.assertArrayEqual(Z.mask,
mask)
self.assertArrayEqual(Mu.mask,
mask[:,None,:])
self.assertArrayEqual(Alpha.mask,
mask[:,None,:,None])
pass
def test_nans(self):
"""
Test multinomial mixture
"""
# The probabilities p1 cause problems
p0 = [0.1, 0.9]
p1 = [1.0-1e-50, 1e-50]
Z = Categorical([1-1e-10, 1e-10])
X = Mixture(Z, Multinomial, 10, [p0, p1])
u = X._message_to_child()
self.assertAllClose(u[0],
[1, 9])
p0 = [0.1, 0.9]
p1 = [1.0-1e-10, 1e-10]
Z = Categorical([1-1e-50, 1e-50])
X = Mixture(Z, Multinomial, 10, [p0, p1])
u = X._message_to_child()
self.assertAllClose(u[0],
[1, 9])
with warnings.catch_warnings(record=True) as w:
p0 = [0.1, 0.9]
p1 = [1.0, 0.0]
X = Mixture(0, Multinomial, 10, [p0, p1])
u = X._message_to_child()
self.assertAllClose(u[0],
np.nan*np.ones(2))
pass
def test_random(self):
"""
Test random sampling of mixture node
"""
o = 1e-20
X = Mixture([1, 0, 2], Categorical, [ [o, o, o, 1],
[o, o, 1, o],
[1, o, o, o] ])
x = X.random()
self.assertAllClose(x, [2, 3, 0])
pass
bayespy-0.2.3/bayespy/inference/vmp/nodes/tests/test_multinomial.py 0000664 5104112 5104112 00000013725 12374370351 027360 0 ustar jluttine jluttine 0000000 0000000 ######################################################################
# Copyright (C) 2014 Jaakko Luttinen
#
# This file is licensed under Version 3.0 of the GNU General Public
# License. See LICENSE for a text of the license.
######################################################################
######################################################################
# This file is part of BayesPy.
#
# BayesPy is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# BayesPy is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with BayesPy. If not, see .
######################################################################
"""
Unit tests for `multinomial` module.
"""
import numpy as np
import scipy
from bayespy.nodes import (Multinomial,
Dirichlet,
Mixture)
from bayespy.utils import random
from bayespy.utils.misc import TestCase
class TestMultinomial(TestCase):
"""
Unit tests for Multinomial node
"""
def test_init(self):
"""
Test the creation of multinomial nodes.
"""
# Some simple initializations
X = Multinomial(10, [0.1, 0.3, 0.6])
X = Multinomial(10, Dirichlet([5,4,3]))
# Check that plates are correct
X = Multinomial(10, [0.1, 0.3, 0.6], plates=(3,4))
self.assertEqual(X.plates,
(3,4))
X = Multinomial(10, 0.25*np.ones((2,3,4)))
self.assertEqual(X.plates,
(2,3))
n = 10 * np.ones((3,4), dtype=np.int)
X = Multinomial(n, [0.1, 0.3, 0.6])
self.assertEqual(X.plates,
(3,4))
X = Multinomial(n, Dirichlet([2,1,9], plates=(3,4)))
self.assertEqual(X.plates,
(3,4))
# Probabilities not a vector
self.assertRaises(ValueError,
Multinomial,
10,
0.5)
# Invalid probability
self.assertRaises(ValueError,
Multinomial,
10,
[-0.5, 1.5])
self.assertRaises(ValueError,
Multinomial,
10,
[0.5, 1.5])
# Invalid number of trials
self.assertRaises(ValueError,
Multinomial,
-1,
[0.5, 0.5])
self.assertRaises(ValueError,
Multinomial,
8.5,
[0.5, 0.5])
# Inconsistent plates
self.assertRaises(ValueError,
Multinomial,
10,
0.25*np.ones((2,4)),
plates=(3,))
# Explicit plates too small
self.assertRaises(ValueError,
Multinomial,
10,
0.25*np.ones((2,4)),
plates=(1,))
pass
def test_moments(self):
"""
Test the moments of multinomial nodes.
"""
# Simple test
X = Multinomial(1, [0.7,0.2,0.1])
u = X._message_to_child()
self.assertEqual(len(u), 1)
self.assertAllClose(u[0],
[0.7,0.2,0.1])
# Test n
X = Multinomial(10, [0.7,0.2,0.1])
u = X._message_to_child()
self.assertAllClose(u[0],
[7,2,1])
# Test plates in p
n = np.random.randint(1, 10)
p = np.random.dirichlet([1,1], size=3)
X = Multinomial(n, p)
u = X._message_to_child()
self.assertAllClose(u[0],
p*n)
# Test plates in n
n = np.random.randint(1, 10, size=(3,))
p = np.random.dirichlet([1,1,1,1])
X = Multinomial(n, p)
u = X._message_to_child()
self.assertAllClose(u[0],
p*n[:,None])
# Test plates in p and n
n = np.random.randint(1, 10, size=(4,1))
p = np.random.dirichlet([1,1], size=3)
X = Multinomial(n, p)
u = X._message_to_child()
self.assertAllClose(u[0],
p*n[...,None])
# Test with Dirichlet prior
P = Dirichlet([7, 3])
logp = P._message_to_child()[0]
p0 = np.exp(logp[0]) / (np.exp(logp[0]) + np.exp(logp[1]))
p1 = np.exp(logp[1]) / (np.exp(logp[0]) + np.exp(logp[1]))
X = Multinomial(1, P)
u = X._message_to_child()
p = np.array([p0, p1])
self.assertAllClose(u[0],
p)
# Test with broadcasted plates
P = Dirichlet([7, 3], plates=(10,))
X = Multinomial(5, P)
u = X._message_to_child()
self.assertAllClose(u[0] * np.ones(X.get_shape(0)),
5*p*np.ones((10,1)))
pass
def test_lower_bound(self):
"""
Test lower bound for multinomial node.
"""
# Test for a bug found in multinomial
X = Multinomial(10, [0.3, 0.5, 0.2])
l = X.lower_bound_contribution()
self.assertAllClose(l, 0.0)
pass
def test_mixture(self):
"""
Test multinomial mixture
"""
p0 = [0.1, 0.5, 0.2, 0.2]
p1 = [0.5, 0.1, 0.1, 0.3]
p2 = [0.3, 0.2, 0.1, 0.4]
X = Mixture(2, Multinomial, 10, [p0, p1, p2])
u = X._message_to_child()
self.assertAllClose(u[0],
10*np.array(p2))
pass
bayespy-0.2.3/bayespy/inference/vmp/nodes/tests/test_node.py 0000664 5104112 5104112 00000102125 12374370351 025744 0 ustar jluttine jluttine 0000000 0000000 ######################################################################
# Copyright (C) 2013,2014 Jaakko Luttinen
#
# This file is licensed under Version 3.0 of the GNU General Public
# License. See LICENSE for a text of the license.
######################################################################
######################################################################
# This file is part of BayesPy.
#
# BayesPy is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# BayesPy is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with BayesPy. If not, see .
######################################################################
"""
Unit tests for `dot` module.
"""
import unittest
import numpy as np
import scipy
from numpy import testing
from ..node import Node, Moments
from ...vmp import VB
from bayespy.utils import misc
class TestMoments(unittest.TestCase):
def test_converter(self):
"""
Tests complex conversions for moment classes
"""
# Simple one step conversions
class A(Moments):
pass
class B(Moments):
_converters = {A: lambda x: x+1}
f = B().get_converter(B)
self.assertEqual(f(3), 3)
f = B().get_converter(Moments)
self.assertEqual(f(3), 3)
f = B().get_converter(A)
self.assertEqual(f(3), 4)
f = A().get_converter(A)
self.assertEqual(f(3), 3)
f = A().get_converter(Moments)
self.assertEqual(f(3), 3)
self.assertRaises(Moments.NoConverterError,
A().get_converter,
B)
# Convert via parent
class C(B):
pass
f = C().get_converter(A)
self.assertEqual(f(3), 4)
# Convert via grand parent
class D(C):
pass
class E(D):
pass
f = E().get_converter(A)
self.assertEqual(f(3), 4)
# Can't convert to child
self.assertRaises(Moments.NoConverterError,
Moments().get_converter,
A)
# Convert to grand child
class F(Moments):
_converters = {E: lambda x: 2*x}
f = F().get_converter(B)
self.assertEqual(f(3), 6)
# Use two conversions
f = F().get_converter(A)
self.assertEqual(f(3), 2*3+1)
# Can't use child's converter
class H(Moments):
pass
class I(Moments):
_converters = {A: lambda x: x+1}
self.assertRaises(Moments.NoConverterError,
H().get_converter,
A)
# Conversion to parent is not success
class J(A):
pass
self.assertRaises(Moments.NoConverterError,
I().get_converter,
J)
# Infinite loop
class X(Moments):
pass
class Y(Moments):
pass
X.add_converter(Y, lambda x: x+1)
Y.add_converter(X, lambda x: x+1)
self.assertRaises(Moments.NoConverterError,
X().get_converter,
A)
# Test that add_converter function does not change the converters of
# parent classes
class Z(Moments):
pass
class W(Z):
pass
W.add_converter(Y, lambda x: x)
self.assertRaises(Moments.NoConverterError,
Z().get_converter,
Y)
# Test that after using add_converter for a child class and then for the
# parent class, the child class is still able to use the parent's
# converters
class X(Moments):
pass
class Y(Moments):
pass
class A(Moments):
pass
class B(A):
pass
B.add_converter(Y, lambda x: x+1)
A.add_converter(X, lambda x: 2*x)
f = B().get_converter(X)
self.assertEqual(f(3), 6)
pass
class TestNode(misc.TestCase):
def check_message_to_parent(self, plates_child, plates_message,
plates_mask, plates_parent, dims=(2,)):
# Dummy message
msg = np.random.randn(*(plates_message+dims))
# Mask with every other True and every other False
mask = np.mod(np.arange(np.prod(plates_mask)).reshape(plates_mask),
2) == 0
# Set up the dummy model
class Dummy(Node):
_moments = Moments()
def __init__(self, *args, **kwargs):
self._parent_moments = len(args)*(Moments(),)
super().__init__(*args, **kwargs)
def _get_message_and_mask_to_parent(self, index):
return ([msg], mask)
def _get_id_list(self):
return []
parent = Dummy(dims=[dims], plates=plates_parent)
child = Dummy(parent, dims=[dims], plates=plates_child)
m = child._message_to_parent(0)[0] * np.ones(plates_parent+dims)
# Brute-force computation of the message without too much checking
m_true = msg * misc.squeeze(mask[...,np.newaxis]) * np.ones(plates_child+dims)
for ind in range(len(plates_child)):
axis = -ind - 2
if ind >= len(plates_parent):
m_true = np.sum(m_true, axis=axis, keepdims=False)
elif plates_parent[-ind-1] == 1:
m_true = np.sum(m_true, axis=axis, keepdims=True)
testing.assert_allclose(m, m_true,
err_msg="Incorrect message.")
def test_message_to_parent(self):
"""
Test plate handling in _message_to_parent.
"""
# Test empty plates with scalar messages
self.check_message_to_parent((),
(),
(),
(),
dims=())
# Test singular plates
self.check_message_to_parent((2,3,4),
(2,3,4),
(2,3,4),
(2,3,4))
self.check_message_to_parent((2,3,4),
(2,1,4),
(2,3,4),
(2,3,4))
self.check_message_to_parent((2,3,4),
(2,3,4),
(2,1,4),
(2,3,4))
self.check_message_to_parent((2,3,4),
(2,3,4),
(2,3,4),
(2,1,4))
self.check_message_to_parent((2,3,4),
(2,1,4),
(2,1,4),
(2,3,4))
self.check_message_to_parent((2,3,4),
(2,3,4),
(2,1,4),
(2,1,4))
self.check_message_to_parent((2,3,4),
(2,1,4),
(2,3,4),
(2,1,4))
self.check_message_to_parent((2,3,4),
(2,1,4),
(2,1,4),
(2,1,4))
# Test missing plates
self.check_message_to_parent((4,3),
(4,3),
(4,3),
(4,3))
self.check_message_to_parent((4,3),
( 3,),
(4,3),
(4,3))
self.check_message_to_parent((4,3),
(4,3),
( 3,),
(4,3))
self.check_message_to_parent((4,3),
(4,3),
(4,3),
( 3,))
self.check_message_to_parent((4,3),
( 3,),
( 3,),
(4,3))
self.check_message_to_parent((4,3),
( 3,),
(4,3),
( 3,))
self.check_message_to_parent((4,3),
(4,3),
( 3,),
( 3,))
self.check_message_to_parent((4,3),
( 3,),
( 3,),
( 3,))
# A complex test
self.check_message_to_parent((7,6,5,4,3),
( 6,1,4,3),
(1,1,5,4,1),
( 6,5,1,3))
# Test errors for inconsistent shapes
self.assertRaises(ValueError,
self.check_message_to_parent,
(3,),
(1,3,),
(3,),
(3,))
## self.assertRaises(ValueError,
## self.check_message_to_parent,
## (3,),
## (3,),
## (1,3,),
## (3,))
self.assertRaises(ValueError,
self.check_message_to_parent,
(3,),
(1,3,),
(1,3,),
(3,))
self.assertRaises(ValueError,
self.check_message_to_parent,
(3,),
(4,),
(3,),
(3,))
self.assertRaises(ValueError,
self.check_message_to_parent,
(3,),
(3,),
(4,),
(3,))
self.assertRaises(ValueError,
self.check_message_to_parent,
(3,),
(4,),
(4,),
(3,))
self.assertRaises(ValueError,
self.check_message_to_parent,
(3,),
(4,),
(3,),
(1,))
self.assertRaises(ValueError,
self.check_message_to_parent,
(3,),
(3,),
(4,),
(1,))
self.assertRaises(ValueError,
self.check_message_to_parent,
(3,),
(4,),
(4,),
(1,))
self.assertRaises(ValueError,
self.check_message_to_parent,
(1,),
(4,),
(3,),
(1,))
self.assertRaises(ValueError,
self.check_message_to_parent,
(1,),
(3,),
(4,),
(1,))
self.assertRaises(ValueError,
self.check_message_to_parent,
(1,),
(4,),
(4,),
(1,))
def test_compute_message(self):
"""
Test the general sum-multiply function for message computations
"""
self.assertAllClose(Node._compute_message(3,
plates_from=(),
plates_to=(),
ndim=0),
3)
# Sum over one array
self.assertAllClose(Node._compute_message([1, 2, 3],
plates_from=(3,),
plates_to=(),
ndim=0),
6)
# Sum plates
self.assertAllClose(Node._compute_message([1, 2, 3],
[4, 4, 4],
[5, 5, 5],
plates_from=(3,),
plates_to=(),
ndim=0),
20+40+60)
# Do not sum plates
self.assertAllClose(Node._compute_message([1, 2, 3],
[4, 4, 4],
[5, 5, 5],
plates_from=(3,),
plates_to=(3,),
ndim=0),
[20, 40, 60])
# Give ndim
self.assertAllClose(Node._compute_message([1, 2, 3],
[4, 4, 4],
[5, 5, 5],
plates_from=(),
plates_to=(),
ndim=1),
[20, 40, 60])
# Broadcast plates_from
self.assertAllClose(Node._compute_message(3,
4,
5,
plates_from=(3,),
plates_to=(),
ndim=0),
3 * (3*4*5))
# Broadcast plates_to
self.assertAllClose(Node._compute_message(3,
4,
5,
plates_from=(3,),
plates_to=(3,),
ndim=0),
3*4*5)
# Different ndims
self.assertAllClose(Node._compute_message([1, 2, 3],
[4],
5,
plates_from=(3,),
plates_to=(3,),
ndim=0),
[1*4*5, 2*4*5, 3*4*5])
# Broadcasting dims for some arrays
self.assertAllClose(Node._compute_message([1, 2, 3],
[4],
5,
plates_from=(),
plates_to=(),
ndim=1),
[1*4*5, 2*4*5, 3*4*5])
# Complex example
x1 = np.random.randn(5,4,1,2,1)
x2 = np.random.randn( 1,2,1)
x3 = np.random.randn(5,1,1,1,1)
self.assertAllClose(Node._compute_message(x1, x2, x3,
plates_from=(6,5,4,3),
plates_to=(5,1,3),
ndim=2),
6*np.sum(x1*x2*x3, axis=(-4,), keepdims=True))
pass
class TestSlice(misc.TestCase):
def test_init(self):
"""
Test the constructor of the X[..] node operator.
"""
class MyNode(Node):
_moments = Moments()
def _get_id_list(self):
return []
# Integer index
X = MyNode(plates=(3,4), dims=((),))
Y = X[2,-4]
self.assertEqual(Y.plates, ())
X = MyNode(plates=(3,4,5), dims=((),))
Y = X[2,1]
self.assertEqual(Y.plates, (5,))
# Full slices
X = MyNode(plates=(3,4,5), dims=((),))
Y = X[:,1,:]
self.assertEqual(Y.plates, (3,5,))
X = MyNode(plates=(3,4,5), dims=((),))
Y = X[1,:,:]
self.assertEqual(Y.plates, (4,5,))
X = MyNode(plates=(3,4,5), dims=((),))
Y = X[:,:,1]
self.assertEqual(Y.plates, (3,4,))
# Slice with step
X = MyNode(plates=(9,), dims=((),))
Y = X[::3]
self.assertEqual(Y.plates, (3,))
X = MyNode(plates=(10,), dims=((),))
Y = X[::3]
self.assertEqual(Y.plates, (4,))
X = MyNode(plates=(11,), dims=((),))
Y = X[::3]
self.assertEqual(Y.plates, (4,))
# Slice with a start value
X = MyNode(plates=(10,), dims=((),))
Y = X[3:]
self.assertEqual(Y.plates, (7,))
# Slice with an end value
X = MyNode(plates=(10,), dims=((),))
Y = X[:7]
self.assertEqual(Y.plates, (7,))
# Slice with only one element
X = MyNode(plates=(10,), dims=((),))
Y = X[6:7]
self.assertEqual(Y.plates, (1,))
# Slice starts out of range
X = MyNode(plates=(10,), dims=((),))
Y = X[-20:]
self.assertEqual(Y.plates, (10,))
# Slice ends out of range
X = MyNode(plates=(10,), dims=((),))
Y = X[:20]
self.assertEqual(Y.plates, (10,))
# Counter-intuitive: This slice is not empty
X = MyNode(plates=(3,), dims=((),))
Y = X[-4::4]
self.assertEqual(Y.plates, (1,))
# One ellipsis
X = MyNode(plates=(3,4,5,6), dims=((),))
Y = X[...,2,1]
self.assertEqual(Y.plates, (3,4))
X = MyNode(plates=(3,4,5,6), dims=((),))
Y = X[2,...,1]
self.assertEqual(Y.plates, (4,5))
X = MyNode(plates=(3,4,5,6), dims=((),))
Y = X[2,1,...]
self.assertEqual(Y.plates, (5,6))
# Multiple ellipsis
X = MyNode(plates=(3,4,5), dims=((),))
Y = X[...,2,...]
self.assertEqual(Y.plates, (3,5))
X = MyNode(plates=(3,4,5), dims=((),))
Y = X[...,2,...,...]
self.assertEqual(Y.plates, (4,5))
X = MyNode(plates=(3,4,5), dims=((),))
Y = X[...,...,...,...]
self.assertEqual(Y.plates, (3,4,5))
# New axis
X = MyNode(plates=(3,), dims=((),))
Y = X[None]
self.assertEqual(Y.plates, (1,3))
X = MyNode(plates=(3,), dims=((),))
Y = X[:,None]
self.assertEqual(Y.plates, (3,1))
X = MyNode(plates=(3,4), dims=((),))
Y = X[None,:,None,:]
self.assertEqual(Y.plates, (1,3,1,4))
#
# Test errors
#
class Z:
def __getitem__(self, obj):
return obj
# Invalid argument
self.assertRaises(TypeError,
MyNode(plates=(3,),
dims=((),)).__getitem__,
Z()['a'])
self.assertRaises(TypeError,
MyNode(plates=(3,),
dims=((),)).__getitem__,
Z()[[2,1]])
# Too many indices
self.assertRaises(IndexError,
MyNode(plates=(3,),
dims=((),)).__getitem__,
Z()[:,:])
self.assertRaises(IndexError,
MyNode(plates=(3,),
dims=((),)).__getitem__,
Z()[...,...,...])
# Index out of range
self.assertRaises(IndexError,
MyNode(plates=(3,),
dims=((),)).__getitem__,
Z()[3])
self.assertRaises(IndexError,
MyNode(plates=(3,),
dims=((),)).__getitem__,
Z()[-4])
# Empty slice
self.assertRaises(IndexError,
MyNode(plates=(3,),
dims=((),)).__getitem__,
Z()[3:])
self.assertRaises(IndexError,
MyNode(plates=(3,),
dims=((),)).__getitem__,
Z()[:-3])
pass
def test_message_to_child(self):
"""
Test message to child of X[..] node operator.
"""
class DummyNode(Node):
_moments = Moments()
_parent_moments = (Moments(),)
def __init__(self, u, **kwargs):
self.u = u
super().__init__(**kwargs)
def _message_to_child(self):
return self.u
def _get_id_list(self):
return []
# Message not a reference to X.u but a copy of it
X = DummyNode([np.random.randn(3)],
plates=(3,),
dims=((),))
Y = X[2]
self.assertTrue(Y._message_to_child() is not X.u,
msg="Slice node operator sends a reference to the "
"node's moment list as a message instead of a copy "
"of the list.")
# Integer indices
X = DummyNode([np.random.randn(3,4)],
plates=(3,4),
dims=((),))
Y = X[2,1]
self.assertMessageToChild(Y, [X.u[0][2,1]])
# Too few integer indices
X = DummyNode([np.random.randn(3,4)],
plates=(3,4),
dims=((),))
Y = X[2]
self.assertMessageToChild(Y, [X.u[0][2]])
# Integer for broadcasted moment
X = DummyNode([np.random.randn(4)],
plates=(3,4),
dims=((),))
Y = X[2,1]
self.assertMessageToChild(Y, [X.u[0][1]])
X = DummyNode([np.random.randn(4,1)],
plates=(3,4),
dims=((),))
Y = X[2,1]
self.assertMessageToChild(Y, [X.u[0][2,0]])
# Ignore leading new axes
X = DummyNode([np.random.randn(3)],
plates=(3,),
dims=((),))
Y = X[None,None,2]
self.assertMessageToChild(Y, [X.u[0][2]])
# Ignore new axes before missing+broadcasted plate axes
X = DummyNode([np.random.randn(3)],
plates=(4,3,),
dims=((),))
Y = X[1,None,None,2]
self.assertMessageToChild(Y, [X.u[0][2]])
# New axes
X = DummyNode([np.random.randn(3,4)],
plates=(3,4),
dims=((),))
Y = X[2,None,None,1]
self.assertMessageToChild(Y, [X.u[0][2,None,None,1]])
# New axes for broadcasted axes
X = DummyNode([np.random.randn(4)],
plates=(3,4),
dims=((),))
Y = X[2,1,None,None]
self.assertMessageToChild(Y, [X.u[0][1,None,None]])
# Full slice
X = DummyNode([np.random.randn(3,4)],
plates=(3,4),
dims=((),))
Y = X[:,2]
self.assertMessageToChild(Y, [X.u[0][:,2]])
# Slice with start
X = DummyNode([np.random.randn(3,4)],
plates=(3,4),
dims=((),))
Y = X[1:,2]
self.assertMessageToChild(Y, [X.u[0][1:,2]])
# Slice with end
X = DummyNode([np.random.randn(3,4)],
plates=(3,4),
dims=((),))
Y = X[:2,2]
self.assertMessageToChild(Y, [X.u[0][:2,2]])
# Slice with step
X = DummyNode([np.random.randn(3,4)],
plates=(3,4),
dims=((),))
Y = X[::2,2]
self.assertMessageToChild(Y, [X.u[0][::2,2]])
# Slice for broadcasted axes
X = DummyNode([np.random.randn(4)],
plates=(3,4),
dims=((),))
Y = X[0:2:2,2]
self.assertMessageToChild(Y, [X.u[0][2]])
X = DummyNode([np.random.randn(1,4)],
plates=(3,4),
dims=((),))
Y = X[0:2:2,2]
self.assertMessageToChild(Y, [X.u[0][0:1,2]])
# One ellipsis
X = DummyNode([np.random.randn(3,4)],
plates=(3,4),
dims=((),))
Y = X[...,2]
self.assertMessageToChild(Y, [X.u[0][...,2]])
# Ellipsis over broadcasted axes
X = DummyNode([np.random.randn(5,6)],
plates=(3,4,5,6),
dims=((),))
Y = X[1,...,2]
self.assertMessageToChild(Y, [X.u[0][:,2]])
X = DummyNode([np.random.randn(3,1,5,6)],
plates=(3,4,5,6),
dims=((),))
Y = X[1,...,2]
self.assertMessageToChild(Y, [X.u[0][1,:,:,2]])
# Multiple ellipsis
X = DummyNode([np.random.randn(2,3,4,5)],
plates=(2,3,4,5),
dims=((),))
Y = X[...,2,...]
self.assertMessageToChild(Y, [X.u[0][:,:,2,:]])
# Ellipsis when dimensions
X = DummyNode([np.random.randn(2,3,4)],
plates=(2,3),
dims=((4,),))
Y = X[...,2]
self.assertMessageToChild(Y, [X.u[0][:,2,:]])
# Indexing for multiple moments
X = DummyNode([np.random.randn(2,3,4),
np.random.randn(2,3)],
plates=(2,3),
dims=((4,),()))
Y = X[1,1]
self.assertMessageToChild(Y, [X.u[0][1,1],
X.u[1][1,1]])
pass
def test_message_to_parent(self):
"""
Test message to parent of X[..] node operator.
"""
class ParentNode(Node):
_moments = Moments()
def _get_id_list(self):
return []
class ChildNode(Node):
_moments = Moments()
_parent_moments = (Moments(),)
def __init__(self, X, m, mask, **kwargs):
super().__init__(X, **kwargs)
self.m = m
self.mask2 = mask
def _message_to_parent(self, index):
return self.m
def _mask_to_parent(self, index):
return self.mask2
def _get_id_list(self):
return []
# General broadcasting
V = ParentNode(plates=(3,3,3),
dims=((),))
X = V[...]
m = [ np.random.randn(3,1) ]
msg = [ np.zeros((1,3,1)) ]
msg[0][:,:,:] = m[0]
Y = ChildNode(X, m, True, dims=((),))
X._update_mask()
self.assertMessage(X._message_to_parent(0),
msg)
# Integer indices
V = ParentNode(plates=(3,4),
dims=((),))
X = V[2,1]
m = [np.random.randn()]
msg = [ np.zeros((3,4)) ]
msg[0][2,1] = m[0]
Y = ChildNode(X, m, True, dims=((),))
X._update_mask()
self.assertMessage(X._message_to_parent(0),
msg)
# Integer indices with broadcasting
V = ParentNode(plates=(3,3),
dims=((),))
X = V[2,2]
m = [ np.random.randn(1) ]
msg = [ np.zeros((3,3)) ]
msg[0][2,2] = m[0]
Y = ChildNode(X, m, True, dims=((),))
X._update_mask()
self.assertMessage(X._message_to_parent(0),
msg)
# Slice indices
V = ParentNode(plates=(2,3,4,5),
dims=((),))
X = V[:,:2,1:,::2]
m = [np.random.randn(2,2,3,3)]
msg = [ np.zeros((2,3,4,5)) ]
msg[0][:,:2,1:,::2] = m[0]
Y = ChildNode(X, m, True, dims=((),))
X._update_mask()
self.assertMessage(X._message_to_parent(0),
msg)
# Full slice with broadcasting
V = ParentNode(plates=(2,3),
dims=((),))
X = V[:,:]
m = [np.random.randn(1)]
msg = [ np.zeros((1,1)) ]
msg[0][:] = m[0]
Y = ChildNode(X, m, True, dims=((),))
X._update_mask()
self.assertMessage(X._message_to_parent(0),
msg)
# Start slice with broadcasting
V = ParentNode(plates=(3,3,3,3),
dims=((),))
X = V[0:,1:,-2:,-3:]
m = [np.random.randn(1,1)]
msg = [ np.zeros((1,3,3,1)) ]
msg[0][:,1:,-2:,:] = m[0]
Y = ChildNode(X, m, True, dims=((),))
X._update_mask()
self.assertMessage(X._message_to_parent(0),
msg)
# End slice with broadcasting
V = ParentNode(plates=(3,3,3,3),
dims=((),))
X = V[:2,:3,:4,:-1]
m = [np.random.randn(1,1)]
msg = [ np.zeros((3,1,1,3)) ]
msg[0][:2,:,:,:-1] = m[0]
Y = ChildNode(X, m, True, dims=((),))
X._update_mask()
self.assertMessage(X._message_to_parent(0),
msg)
# Step slice with broadcasting
V = ParentNode(plates=(3,3,1),
dims=((),))
X = V[::1,::2,::2]
m = [np.random.randn(1)]
msg = [ np.zeros((1,3,1)) ]
msg[0][:,::2,:] = m[0]
Y = ChildNode(X, m, True, dims=((),))
X._update_mask()
self.assertMessage(X._message_to_parent(0),
msg)
# Ellipsis
V = ParentNode(plates=(3,3,3),
dims=((),))
X = V[...,0]
m = [np.random.randn(3,3)]
msg = [ np.zeros((3,3,3)) ]
msg[0][:,:,0] = m[0]
Y = ChildNode(X, m, True, dims=((),))
X._update_mask()
self.assertMessage(X._message_to_parent(0),
msg)
# New axes
V = ParentNode(plates=(3,3),
dims=((),))
X = V[None,:,None,None,:]
m = [np.random.randn(1,3,1,1,3)]
msg = [ np.zeros((3,3)) ]
msg[0][:,:] = m[0][0,:,0,0,:]
Y = ChildNode(X, m, True, dims=((),))
X._update_mask()
self.assertMessage(X._message_to_parent(0),
msg)
# New axes with broadcasting
V = ParentNode(plates=(3,3),
dims=((),))
X = V[None,:,None,:,None]
m = [np.random.randn(1,3,1)]
msg = [ np.zeros((1,3)) ]
msg[0][:,:] = m[0][0,:,0]
Y = ChildNode(X, m, True, dims=((),))
X._update_mask()
self.assertMessage(X._message_to_parent(0),
msg)
# Multiple messages
V = ParentNode(plates=(3,),
dims=((),()))
X = V[:]
m = [np.random.randn(3),
np.random.randn(3)]
msg = [ np.zeros((3)), np.zeros((3)) ]
msg[0][:] = m[0][:]
msg[1][:] = m[1][:]
Y = ChildNode(X, m, True, dims=((),()))
X._update_mask()
self.assertMessage(X._message_to_parent(0),
msg)
# Non-scalar variables
V = ParentNode(plates=(2,3),
dims=((4,),))
X = V[...]
m = [np.random.randn(2,3,4)]
msg = [ np.zeros((2,3,4)) ]
msg[0][:,:,:] = m[0][:,:,:]
Y = ChildNode(X, m, True, dims=((4,),))
X._update_mask()
self.assertMessage(X._message_to_parent(0),
msg)
# Missing values
V = ParentNode(plates=(3,3,3),
dims=((3,),))
X = V[:,0,::2,None]
m = [np.random.randn(3,2,1,3)]
# mask shape: (3, 2, 1)
mask = np.array([ [[True], [False]],
[[False], [False]],
[[False], [True]] ])
msg = [ np.zeros((3,3,3,3)) ]
msg[0][:,0,::2,:] = (m[0] * mask[...,None])[:,:,0,:]
Y = ChildNode(X, m, mask, dims=((3,),))
X._update_mask()
self.assertMessage(X._message_to_parent(0),
msg)
# Found bug: int index after slice
V = ParentNode(plates=(3,3),
dims=((),))
X = V[:,0]
m = [np.random.randn(3)]
msg = [ np.zeros((3,3)) ]
msg[0][:,0] = m[0]
Y = ChildNode(X, m, True, dims=((),))
X._update_mask()
self.assertMessage(X._message_to_parent(0),
msg)
# Found bug: message requires reshaping after reverse indexing
pass
bayespy-0.2.3/bayespy/inference/vmp/nodes/tests/test_poisson.py 0000664 5104112 5104112 00000006322 12374370351 026513 0 ustar jluttine jluttine 0000000 0000000 ######################################################################
# Copyright (C) 2014 Jaakko Luttinen
#
# This file is licensed under Version 3.0 of the GNU General Public
# License. See LICENSE for a text of the license.
######################################################################
######################################################################
# This file is part of BayesPy.
#
# BayesPy is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# BayesPy is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with BayesPy. If not, see .
######################################################################
"""
Unit tests for `poisson` module.
"""
import numpy as np
import scipy
from bayespy.nodes import Poisson
from bayespy.nodes import Gamma
from bayespy.utils import random
from bayespy.utils.misc import TestCase
class TestPoisson(TestCase):
"""
Unit tests for Poisson node
"""
def test_init(self):
"""
Test the creation of Poisson nodes.
"""
# Some simple initializations
X = Poisson(12.8)
X = Poisson(Gamma(43, 24))
# Check that plates are correct
X = Poisson(np.ones((2,3)))
self.assertEqual(X.plates,
(2,3))
X = Poisson(Gamma(1, 1, plates=(2,3)))
self.assertEqual(X.plates,
(2,3))
# Invalid rate
self.assertRaises(ValueError,
Poisson,
-0.1)
# Inconsistent plates
self.assertRaises(ValueError,
Poisson,
np.ones(3),
plates=(2,))
# Explicit plates too small
self.assertRaises(ValueError,
Poisson,
np.ones(3),
plates=(1,))
pass
def test_moments(self):
"""
Test the moments of Poisson nodes.
"""
# Simple test
X = Poisson(12.8)
u = X._message_to_child()
self.assertEqual(len(u),
1)
self.assertAllClose(u[0],
12.8)
# Test plates in rate
X = Poisson(12.8*np.ones((2,3)))
u = X._message_to_child()
self.assertAllClose(u[0],
12.8*np.ones((2,3)))
# Test with gamma prior
alpha = Gamma(5, 2)
r = np.exp(alpha._message_to_child()[1])
X = Poisson(alpha)
u = X._message_to_child()
self.assertAllClose(u[0],
r)
# Test with broadcasted plates in parents
X = Poisson(Gamma(5, 2, plates=(2,3)))
u = X._message_to_child()
self.assertAllClose(u[0]*np.ones((2,3)),
r*np.ones((2,3)))
pass
bayespy-0.2.3/bayespy/inference/vmp/nodes/CovarianceFunctions.py 0000664 5104112 5104112 00000071363 12374370350 026571 0 ustar jluttine jluttine 0000000 0000000 ######################################################################
# Copyright (C) 2011,2012 Jaakko Luttinen
#
# This file is licensed under Version 3.0 of the GNU General Public
# License. See LICENSE for a text of the license.
######################################################################
######################################################################
# This file is part of BayesPy.
#
# BayesPy is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# BayesPy is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with BayesPy. If not, see .
######################################################################
import itertools
import numpy as np
#import scipy as sp
import scipy.sparse as sp # prefer CSC format
#import scipy.linalg.decomp_cholesky as decomp
#import scipy.linalg as linalg
#import scipy.special as special
#import matplotlib.pyplot as plt
#import time
#import profile
import scipy.spatial.distance as dist
#import scikits.sparse.distance as spdist
from . import node as ef
from bayespy.utils import misc as utils
# Covariance matrices can be either arrays or matrices so be careful
# with products and powers! Use explicit multiply or dot instead of
# *-operator.
def gp_cov_se(D2, overwrite=False):
if overwrite:
K = D2
K *= -0.5
np.exp(K, out=K)
else:
K = np.exp(-0.5*D2)
return K
def gp_cov_pp2_new(r, d, derivative=False):
# Dimension dependent parameter
q = 2
j = np.floor(d/2) + q + 1
# Polynomial coefficients
a2 = j**2 + 4*j + 3
a1 = 3*j + 6
a0 = 3
# Two parts of the covariance function
k1 = (1-r) ** (j+2)
k2 = (a2*r**2 + a1*r + 3)
# TODO: Check that derivative is 0, 1 or 2!
if derivative == 0:
# Return covariance
return k1 * k2 / 3
dk1 = - (j+2) * (1-r)**(j+1)
dk2 = 2*a2*r + a1
if derivative == 1:
# Return first derivative of the covariance
return (k1 * dk2 + dk1 * k2) / 3
ddk1 = (j+2) * (j+1) * (1-r)**j
ddk2 = 2*a2
if derivative == 2:
# Return second derivative of the covariance
return (ddk1*k2 + 2*dk1*dk2 + k1*ddk2) / 3
def gp_cov_pp2(r, d, gradient=False):
# Dimension dependent parameter
j = np.floor(d/2) + 2 + 1
# Polynomial coefficients
a2 = j**2 + 4*j + 3
a1 = 3*j + 6
a0 = 3
# Two parts of the covariance function
k1 = (1-r) ** (j+2)
k2 = (a2*r**2 + a1*r + 3)
# The covariance function
k = k1 * k2 / 3
if gradient:
# The gradient w.r.t. r
dk = k * (j+2) / (r-1) + k1 * (2*a2*r + a1) / 3
return (k, dk)
else:
return k
def gp_cov_delta(N):
# TODO: Use sparse matrices here!
if N > 0:
#print('in gpcovdelta', N, sp.identity(N).shape)
return sp.identity(N)
else:
# Sparse matrices do not allow zero-length dimensions
return np.identity(N)
#return np.identity(N)
#return np.asmatrix(np.identity(N))
def squared_distance(x1, x2):
## # Reshape arrays to 2-D arrays
## sh1 = np.shape(x1)[:-1]
## sh2 = np.shape(x2)[:-1]
## d = np.shape(x1)[-1]
## x1 = np.reshape(x1, (-1,d))
## x2 = np.reshape(x2, (-1,d))
(m1,n1) = x1.shape
(m2,n2) = x2.shape
if m1 == 0 or m2 == 0:
D2 = np.empty((m1,m2))
else:
# Compute squared Euclidean distance
D2 = dist.cdist(x1, x2, metric='sqeuclidean')
#D2 = np.asmatrix(D2)
# Reshape the result
#D2 = np.reshape(D2, sh1 + sh2)
return D2
# General rule for the parameters for covariance functions:
#
# (value, [ [dvalue1, ...], [dvalue2, ...], [dvalue3, ...], ...])
#
# For instance,
#
# k = covfunc_se((1.0, []), (15, [ [1,update_grad] ]))
# K = k((x1, [ [dx1,update_grad] ]), (x2, []))
#
# Plain values are converted as:
# value -> (value, [])
def gp_standardize_input(x):
if np.size(x) == 0:
x = np.reshape(x, (0,0))
elif np.ndim(x) == 0:
x = np.reshape(x, (1,1))
elif np.ndim(x) == 1:
x = np.reshape(x, (-1,1))
elif np.ndim(x) == 2:
x = np.atleast_2d(x)
else:
raise Exception("Standard GP inputs must be 2-dimensional")
return x
def gp_preprocess_inputs(x1,x2=None):
#args = list(args)
#if len(args) < 1 or len(args) > 2:
#raise Exception("Number of inputs must be one or two")
if x2 is None:
x1 = gp_standardize_input(x1)
return x1
else:
if x1 is x2:
x1 = gp_standardize_input(x1)
x2 = x1
else:
x1 = gp_standardize_input(x1)
x2 = gp_standardize_input(x2)
return (x1, x2)
#return args
## def gp_preprocess_inputs(x1,x2=None):
## #args = list(args)
## #if len(args) < 1 or len(args) > 2:
## #raise Exception("Number of inputs must be one or two")
## if x2 is not None: len(args) == 2:
## if args[0] is args[1]:
## args[0] = gp_standardize_input(args[0])
## args[1] = args[0]
## else:
## args[1] = gp_standardize_input(args[1])
## args[0] = gp_standardize_input(args[0])
## else:
## args[0] = gp_standardize_input(args[0])
## return args
# TODO:
# General syntax for these covariance functions:
# covfunc(hyper1,
# hyper2,
# ...
# hyperN,
# x1,
# x2=None,
# gradient=list_of_booleans_for_each_hyperparameter)
def covfunc_zeros(x1, x2=None, gradient=False):
inputs = gp_preprocess_inputs(*inputs)
# Compute distance and covariance matrix
if x2 is None:
x1 = gp_preprocess_inputs(x1)
# Only variance vector asked
N = np.shape(x1)[0]
# TODO: Use sparse matrices!
K = np.zeros(N)
#K = np.asmatrix(np.zeros((N,1)))
else:
(x1,x2) = gp_preprocess_inputs(x1,x2)
# Full covariance matrix asked
#x1 = inputs[0]
#x2 = inputs[1]
# Number of inputs x1
N1 = np.shape(x1)[0]
N2 = np.shape(x2)[0]
# TODO: Use sparse matrices!
K = np.zeros((N1,N2))
#K = np.asmatrix(np.zeros((N1,N2)))
if gradient is not False:
return (K, [])
else:
return K
def covfunc_delta(amplitude, x1, x2=None, gradient=False):
# Make sure that amplitude is a scalar, not an array object
amplitude = utils.array_to_scalar(amplitude)
## if gradient:
## gradient_amplitude = gradient[0]
## else:
## gradient_amplitude = []
## inputs = gp_preprocess_inputs(*inputs)
# Compute distance and covariance matrix
if x2 is None:
x1 = gp_preprocess_inputs(x1)
# Only variance vector asked
#x = inputs[0]
N = np.shape(x1)[0]
K = np.ones(N) * amplitude**2
else:
(x1,x2) = gp_preprocess_inputs(x1,x2)
# Full covariance matrix asked
#x1 = inputs[0]
#x2 = inputs[1]
# Number of inputs x1
N1 = np.shape(x1)[0]
# x1 == x2?
if x1 is x2:
delta = True
# Delta covariance
#
# FIXME: Broadcasting doesn't work with sparse matrices,
# so must use scalar multiplication
K = gp_cov_delta(N1) * amplitude**2
#K = gp_cov_delta(N1).multiply(amplitude**2)
else:
delta = False
# Number of inputs x2
N2 = np.shape(x2)[0]
# Zero covariance
if N1 > 0 and N2 > 0:
K = sp.csc_matrix((N1,N2))
else:
K = np.zeros((N1,N2))
# Gradient w.r.t. amplitude
if gradient:
# FIXME: Broadcasting doesn't work with sparse matrices,
# so must use scalar multiplication
gradient_amplitude = K*(2/amplitude)
print("noise grad", gradient_amplitude)
return (K, (gradient_amplitude,))
else:
return K
def covfunc_pp2(amplitude, lengthscale, x1, x2, gradient=False):
# Make sure that hyperparameters are scalars, not an array objects
amplitude = utils.array_to_scalar(amplitude)
lengthscale = utils.array_to_scalar(lengthscale)
#amplitude = theta[0]
#lengthscale = theta[1]
## if gradient:
## gradient_amplitude = gradient[0]
## gradient_lengthscale = gradient[1]
## else:
## gradient_amplitude = []
## gradient_lengthscale = []
## inputs = gp_preprocess_inputs(*inputs)
# Compute covariance matrix
if x2 is None:
x1 = gp_preprocess_inputs(x1)
# Compute variance vector
K = np.ones(np.shape(x)[:-1])
K *= amplitude**2
# Compute gradient w.r.t. lengthscale
if gradient:
gradient_lengthscale = np.zeros(np.shape(x1)[:-1])
else:
(x1,x2) = gp_preprocess_inputs(x1,x2)
# Compute (sparse) distance matrix
if x1 is x2:
x1 = inputs[0] / (lengthscale)
x2 = x1
D2 = spdist.pdist(x1, 1.0, form="full", format="csc")
else:
x1 = inputs[0] / (lengthscale)
x2 = inputs[1] / (lengthscale)
D2 = spdist.cdist(x1, x2, 1.0, format="csc")
r = np.sqrt(D2.data)
N1 = np.shape(x1)[0]
N2 = np.shape(x2)[0]
# Compute the covariances
if gradient:
(k, dk) = gp_cov_pp2(r, np.shape(x1)[-1], gradient=True)
else:
k = gp_cov_pp2(r, np.shape(x1)[-1])
k *= amplitude**2
# Compute gradient w.r.t. lengthscale
if gradient:
if N1 >= 1 and N2 >= 1:
dk *= r * (-amplitude**2 / lengthscale)
gradient_lengthscale = sp.csc_matrix((dk, D2.indices, D2.indptr),
shape=(N1,N2))
else:
gradient_lengthscale = np.empty((N1,N2))
# Form sparse covariance matrix
if N1 >= 1 and N2 >= 1:
## K = sp.csc_matrix((k, ij), shape=(N1,N2))
K = sp.csc_matrix((k, D2.indices, D2.indptr), shape=(N1,N2))
else:
K = np.empty((N1, N2))
#print(K.__class__)
# Gradient w.r.t. amplitude
if gradient:
gradient_amplitude = K * (2 / amplitude)
# Return values
if gradient:
print("pp2 grad", gradient_lengthscale)
return (K, (gradient_amplitude, gradient_lengthscale))
else:
return K
def covfunc_se(amplitude, lengthscale, x1, x2=None, gradient=False):
# Make sure that hyperparameters are scalars, not an array objects
amplitude = utils.array_to_scalar(amplitude)
lengthscale = utils.array_to_scalar(lengthscale)
# Compute covariance matrix
if x2 is None:
x1 = gp_preprocess_inputs(x1)
#x = inputs[0]
# Compute variance vector
N = np.shape(x1)[0]
K = np.ones(N)
np.multiply(K, amplitude**2, out=K)
# Compute gradient w.r.t. lengthscale
if gradient:
# TODO: Use sparse matrices?
gradient_lengthscale = np.zeros(N)
else:
(x1,x2) = gp_preprocess_inputs(x1,x2)
x1 = x1 / (lengthscale)
x2 = x2 / (lengthscale)
# Compute distance matrix
K = squared_distance(x1, x2)
# Compute gradient partly
if gradient:
gradient_lengthscale = np.divide(K, lengthscale)
# Compute covariance matrix
gp_cov_se(K, overwrite=True)
np.multiply(K, amplitude**2, out=K)
# Compute gradient w.r.t. lengthscale
if gradient:
gradient_lengthscale *= K
# Gradient w.r.t. amplitude
if gradient:
gradient_amplitude = K * (2 / amplitude)
# Return values
if gradient:
print("se grad", gradient_amplitude, gradient_lengthscale)
return (K, (gradient_amplitude, gradient_lengthscale))
else:
return K
class CovarianceFunctionWrapper():
def __init__(self, covfunc, *params):
# Parse parameter values and their gradients to separate lists
self.covfunc = covfunc
self.params = list(params)
self.gradient_params = list()
## print(params)
for ind in range(len(params)):
if isinstance(params[ind], tuple):
# Parse the value and the list of gradients from the
# form:
# ([value, ...], [ [grad1, ...], [grad2, ...], ... ])
self.gradient_params.append(params[ind][1])
self.params[ind] = params[ind][0][0]
else:
# No gradients, parse from the form:
# [value, ...]
self.gradient_params.append([])
self.params[ind] = params[ind][0]
def fixed_covariance_function(self, *inputs, gradient=False):
# What if this is called several times??
if gradient:
## grads = [[grad[0] for grad in self.gradient_params[ind]]
## for ind in range(len(self.gradient_params))]
## (K, dK) = self.covfunc(self.params,
## *inputs,
## gradient=self.gradient_params)
arguments = tuple(self.params) + tuple(inputs)
(K, dK) = self.covfunc(*arguments,
gradient=True)
## (K, dK) = self.covfunc(self.params,
## *inputs,
## gradient=grads)
DK = []
for ind in range(len(dK)):
# Gradient w.r.t. covariance function's ind-th
# hyperparameter
dk = dK[ind]
# Chain rule: Multiply by the gradient of the
# hyperparameter w.r.t. parent node and append the
# list DK:
# DK = [ (dx1_1, callback), ..., (dx1_n, callback) ]
for grad in self.gradient_params[ind]:
#print(grad[0])
#print(grad[1:])
#print(dk)
if sp.issparse(dk):
print(dk.shape)
print(grad[0].shape)
DK += [ [dk.multiply(grad[0])] + grad[1:] ]
else:
DK += [ [np.multiply(dk,grad[0])] + grad[1:] ]
#DK += [ [np.multiply(grad[0], dk)] + grad[1:] ]
## DK += [ (np.multiply(grad, dk),) + grad[1:]
## for grad in self.gradient_params[ind] ]
## for grad in self.gradient_params[ind]:
## DK += ( (np.multiply(grad, dk),) + grad[1:] )
## DK = []
## for ind in range(len(dK)):
## for (grad, dk) in zip(self.gradient_params[ind], dK[ind]):
## DK += [ [dk] + grad[1:] ]
K = [K]
return (K, DK)
else:
arguments = tuple(self.params) + tuple(inputs)
#print(arguments)
K = self.covfunc(*arguments,
gradient=False)
return [K]
class CovarianceFunction(ef.Node):
def __init__(self, covfunc, *args, **kwargs):
self.covfunc = covfunc
params = list(args)
for i in range(len(args)):
# Check constant parameters
if utils.is_numeric(args[i]):
params[i] = ef.NodeConstant([np.asanyarray(args[i])],
dims=[np.shape(args[i])])
# TODO: Parameters could be constant functions? :)
ef.Node.__init__(self, *params, dims=[(np.inf, np.inf)], **kwargs)
def __call__(self, x1, x2):
""" Compute covariance matrix for inputs x1 and x2. """
covfunc = self.message_to_child()
return covfunc(x1, x2)[0]
def message_to_child(self, gradient=False):
params = [parent.message_to_child(gradient=gradient) for parent in self.parents]
covfunc = self.get_fixed_covariance_function(*params)
return covfunc
def get_fixed_covariance_function(self, *params):
get_cov_func = CovarianceFunctionWrapper(self.covfunc, *params)
return get_cov_func.fixed_covariance_function
## def covariance_function(self, *params):
## # Parse parameter values and their gradients to separate lists
## params = list(params)
## gradient_params = list()
## print(params)
## for ind in range(len(params)):
## if isinstance(params[ind], tuple):
## # Parse the value and the list of gradients from the
## # form:
## # ([value, ...], [ [grad1, ...], [grad2, ...], ... ])
## gradient_params.append(params[ind][1])
## params[ind] = params[ind][0][0]
## else:
## # No gradients, parse from the form:
## # [value, ...]
## gradient_params.append([])
## params[ind] = params[ind][0]
## # This gradient_params changes mysteriously..
## print('grad_params before')
## if isinstance(self, SquaredExponential):
## print(gradient_params)
## def cov(*inputs, gradient=False):
## if gradient:
## print('grad_params after')
## print(gradient_params)
## grads = [[grad[0] for grad in gradient_params[ind]]
## for ind in range(len(gradient_params))]
## print('CovarianceFunction.cov')
## #if isinstance(self, SquaredExponential):
## #print(self.__class__)
## #print(grads)
## (K, dK) = self.covfunc(params,
## *inputs,
## gradient=grads)
## for ind in range(len(dK)):
## for (grad, dk) in zip(gradient_params[ind], dK[ind]):
## grad[0] = dk
## K = [K]
## dK = []
## for grad in gradient_params:
## dK += grad
## return (K, dK)
## else:
## K = self.covfunc(params,
## *inputs,
## gradient=False)
## return [K]
## return cov
class Sum(CovarianceFunction):
def __init__(self, *args, **kwargs):
CovarianceFunction.__init__(self,
None,
*args,
**kwargs)
def get_fixed_covariance_function(self, *covfunc_parents):
def covfunc(*inputs, gradient=False):
K_sum = None
if gradient:
dK_sum = list()
for k in covfunc_parents:
if gradient:
(K, dK) = k(*inputs, gradient=gradient)
print("dK in sum", dK)
dK_sum += dK
#print("dK_sum in sum", dK_sum)
else:
K = k(*inputs, gradient=gradient)
if K_sum is None:
K_sum = K[0]
else:
try:
K_sum += K[0]
except:
# You have to do this way, for instance, if
# K_sum is sparse and K[0] is dense.
K_sum = K_sum + K[0]
if gradient:
#print("dK_sum on: ", dK_sum)
#print('covsum', dK_sum)
return ([K_sum], dK_sum)
else:
return [K_sum]
return covfunc
class Delta(CovarianceFunction):
def __init__(self, amplitude, **kwargs):
CovarianceFunction.__init__(self,
covfunc_delta,
amplitude,
**kwargs)
class Zeros(CovarianceFunction):
def __init__(self, **kwargs):
CovarianceFunction.__init__(self,
covfunc_zeros,
**kwargs)
class SquaredExponential(CovarianceFunction):
def __init__(self, amplitude, lengthscale, **kwargs):
CovarianceFunction.__init__(self,
covfunc_se,
amplitude,
lengthscale,
**kwargs)
class PiecewisePolynomial2(CovarianceFunction):
def __init__(self, amplitude, lengthscale, **kwargs):
CovarianceFunction.__init__(self,
covfunc_pp2,
amplitude,
lengthscale,
**kwargs)
# TODO: Rename to Blocks or Joint ?
class Multiple(CovarianceFunction):
def __init__(self, covfuncs, **kwargs):
self.d = len(covfuncs)
#self.sparse = sparse
parents = [covfunc for row in covfuncs for covfunc in row]
CovarianceFunction.__init__(self,
None,
*parents,
**kwargs)
def get_fixed_covariance_function(self, *covfuncs):
def cov(*inputs, gradient=False):
# Computes the covariance matrix from blocks which all
# have their corresponding covariance functions
if len(inputs) < 2:
# For one input, return the variance vector instead of
# the covariance matrix
x1 = inputs[0]
# Collect variance vectors from the covariance
# functions corresponding to the diagonal blocks
K = [covfuncs[i*self.d+i](x1[i], gradient=gradient)[0]
for i in range(self.d)]
# Form the variance vector from the collected vectors
if gradient:
raise Exception('Gradient not yet implemented.')
else:
## print("in cov multiple")
## for (k,kf) in zip(K,covfuncs):
## print(np.shape(k), k.__class__, kf)
#K = np.vstack(K)
K = np.concatenate(K)
else:
x1 = inputs[0]
x2 = inputs[1]
# Collect the covariance matrix (and possibly
# gradients) from each block.
#print('cov mat collection begins')
K = [[covfuncs[i*self.d+j](x1[i], x2[j], gradient=gradient)
for j in range(self.d)]
for i in range(self.d)]
#print('cov mat collection ends')
# Remove matrices that have zero length dimensions?
if gradient:
K = [[K[i][j]
for j in range(self.d)
if np.shape(K[i][j][0][0])[1] != 0]
for i in range(self.d)
if np.shape(K[i][0][0][0])[0] != 0]
else:
K = [[K[i][j]
for j in range(self.d)
if np.shape(K[i][j][0])[1] != 0]
for i in range(self.d)
if np.shape(K[i][0][0])[0] != 0]
n_blocks = len(K)
#print("nblocks", n_blocks)
#print("K", K)
# Check whether all blocks are sparse
is_sparse = True
for i in range(n_blocks):
for j in range(n_blocks):
if gradient:
A = K[i][j][0][0]
else:
A = K[i][j][0]
if not sp.issparse(A):
is_sparse = False
if gradient:
## Compute the covariance matrix and the gradients
# Create block matrices of zeros. This helps in
# computing the gradient.
if is_sparse:
# Empty sparse matrices. Some weird stuff here
# because sparse matrices can't have zero
# length dimensions.
Z = [[sp.csc_matrix(np.shape(K[i][j][0][0]))
for j in range(n_blocks)]
for i in range(n_blocks)]
else:
# Empty dense matrices
Z = [[np.zeros(np.shape(K[i][j][0][0]))
for j in range(n_blocks)]
for i in range(n_blocks)]
## for j in range(self.d)]
## for i in range(self.d)]
# Compute gradients block by block
dK = list()
for i in range(n_blocks):
for j in range(n_blocks):
# Store the zero block
z_old = Z[i][j]
# Go through the gradients for the (i,j)
# block
for dk in K[i][j][1]:
# Keep other blocks at zero and set
# the gradient to (i,j) block. Form
# the matrix from blocks
if is_sparse:
Z[i][j] = dk[0]
dk[0] = sp.bmat(Z).tocsc()
else:
if sp.issparse(dk[0]):
Z[i][j] = dk[0].toarray()
else:
Z[i][j] = dk[0]
#print("Z on:", Z)
dk[0] = np.asarray(np.bmat(Z))
# Append the computed gradient matrix
# to the list of gradients
dK.append(dk)
# Restore the zero block
Z[i][j] = z_old
## Compute the covariance matrix but not the
## gradients
if is_sparse:
# Form the full sparse covariance matrix from
# blocks. Ignore blocks having a zero-length
# axis because sparse matrices consider zero
# length as an invalid shape (BUG IN SCIPY?).
K = [[K[i][j][0][0]
for j in range(n_blocks)]
for i in range(n_blocks)]
K = sp.bmat(K).tocsc()
else:
# Form the full dense covariance matrix from
# blocks. Transform sparse blocks to dense
# blocks.
K = [[K[i][j][0][0]
if not sp.issparse(K[i][j][0][0]) else
K[i][j][0][0].toarray()
for j in range(n_blocks)]
for i in range(n_blocks)]
K = np.asarray(np.bmat(K))
else:
## Compute the covariance matrix but not the
## gradients
if is_sparse:
# Form the full sparse covariance matrix from
# blocks. Ignore blocks having a zero-length
# axis because sparse matrices consider zero
# length as an invalid shape (BUG IN SCIPY?).
K = [[K[i][j][0]
for j in range(n_blocks)]
for i in range(n_blocks)]
K = sp.bmat(K).tocsc()
else:
# Form the full dense covariance matrix from
# blocks. Transform sparse blocks to dense
# blocks.
K = [[K[i][j][0]
if not sp.issparse(K[i][j][0]) else
K[i][j][0].toarray()
for j in range(n_blocks)]
for i in range(n_blocks)]
K = np.asarray(np.bmat(K))
if gradient:
return ([K], dK)
else:
return [K]
return cov
bayespy-0.2.3/bayespy/inference/vmp/nodes/GaussianProcesses.py 0000664 5104112 5104112 00000064157 12374370350 026272 0 ustar jluttine jluttine 0000000 0000000 ######################################################################
# Copyright (C) 2011,2012 Jaakko Luttinen
#
# This file is licensed under Version 3.0 of the GNU General Public
# License. See LICENSE for a text of the license.
######################################################################
######################################################################
# This file is part of BayesPy.
#
# BayesPy is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# BayesPy is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with BayesPy. If not, see .
######################################################################
import itertools
import numpy as np
#import scipy as sp
#import scipy.linalg.decomp_cholesky as decomp
import scipy.linalg as linalg
#import scipy.special as special
#import matplotlib.pyplot as plt
#import time
#import profile
#import scipy.spatial.distance as distance
import scipy.sparse as sp
from bayespy.utils import misc as utils
from . import node as EF
from . import CovarianceFunctions as CF
class CovarianceMatrix:
def cholesky(self):
pass
def multiply(A, B):
return np.multiply(A,B)
# m prior mean function
# k prior covariance function
# x data inputs
# z processed data outputs (z = inv(Cov) * (y-m(x)))
# U data covariance Cholesky factor
def gp_posterior_moment_function(m, k, x, y, k_sparse=None, pseudoinputs=None, noise=None):
# Prior
# FIXME: We are ignoring the covariance of mu now..
mu = m(x)[0]
## if np.ndim(mu) == 1:
## mu = np.asmatrix(mu).T
## else:
## mu = np.asmatrix(mu)
K_noise = None
if noise != None:
if K_noise is None:
K_noise = noise
else:
K_noise += noise
if k_sparse != None:
if K_noise is None:
K_noise = k_sparse(x,x)[0]
else:
K_noise += k_sparse(x,x)[0]
if pseudoinputs != None:
p = pseudoinputs
#print('in pseudostuff')
#print(K_noise)
#print(np.shape(K_noise))
K_pp = k(p,p)[0]
K_xp = k(x,p)[0]
U = utils.chol(K_noise)
# Compute Lambda
Lambda = K_pp + np.dot(K_xp.T, utils.chol_solve(U, K_xp))
U_lambda = utils.chol(Lambda)
# Compute statistics for posterior predictions
#print(np.shape(U_lambda))
#print(np.shape(y))
z = utils.chol_solve(U_lambda,
np.dot(K_xp.T,
utils.chol_solve(U,
y - mu)))
U = utils.chol(K_pp)
# Now we can forget the location of the observations and
# consider only the pseudoinputs when predicting.
x = p
else:
K = K_noise
if K is None:
K = k(x,x)[0]
else:
try:
K += k(x,x)[0]
except:
K = K + k(x,x)[0]
# Compute posterior GP
N = len(y)
U = None
z = None
if N > 0:
U = utils.chol(K)
z = utils.chol_solve(U, y-mu)
def get_moments(h, covariance=1, mean=True):
K_xh = k(x, h)[0]
if k_sparse != None:
try:
# This may not work, for instance, if either one is a
# sparse matrix.
K_xh += k_sparse(x, h)[0]
except:
K_xh = K_xh + k_sparse(x, h)[0]
# NumPy has problems when mixing matrices and arrays.
# Matrices may appear, for instance, when you sum an array and
# a sparse matrix. Make sure the result is either an array or
# a sparse matrix (not dense matrix!), because matrix objects
# cause lots of problems:
#
# array.dot(array) = array
# matrix.dot(array) = matrix
# sparse.dot(array) = array
if not sp.issparse(K_xh):
K_xh = np.asarray(K_xh)
# Function for computing posterior moments
if mean:
# Mean vector
# FIXME: Ignoring the covariance of prior mu
m_h = m(h)[0]
if z != None:
m_h += K_xh.T.dot(z)
else:
m_h = None
# Compute (co)variance matrix/vector
if covariance:
if covariance == 1:
## Compute variance vector
k_h = k(h)[0]
if k_sparse != None:
k_h += k_sparse(h)[0]
if U != None:
if isinstance(K_xh, np.ndarray):
k_h -= np.einsum('i...,i...',
K_xh,
utils.chol_solve(U, K_xh))
else:
# TODO: This isn't very efficient way, but
# einsum doesn't work for sparse matrices..
# This may consume A LOT of memory for sparse
# matrices.
k_h -= np.asarray(K_xh.multiply(utils.chol_solve(U, K_xh))).sum(axis=0)
if pseudoinputs != None:
if isinstance(K_xh, np.ndarray):
k_h += np.einsum('i...,i...',
K_xh,
utils.chol_solve(U_lambda, K_xh))
else:
# TODO: This isn't very efficient way, but
# einsum doesn't work for sparse matrices..
# This may consume A LOT of memory for sparse
# matrices.
k_h += np.asarray(K_xh.multiply(utils.chol_solve(U_lambda, K_xh))).sum(axis=0)
# Ensure non-negative variances
k_h[k_h<0] = 0
return (m_h, k_h)
elif covariance == 2:
## Compute full covariance matrix
K_hh = k(h,h)[0]
if k_sparse != None:
K_hh += k_sparse(h)[0]
if U != None:
K_hh -= K_xh.T.dot(utils.chol_solve(U,K_xh))
#K_hh -= np.dot(K_xh.T, utils.chol_solve(U,K_xh))
if pseudoinputs != None:
K_hh += K_xh.T.dot(utils.chol_solve(U_lambda, K_xh))
#K_hh += np.dot(K_xh.T, utils.chol_solve(U_lambda, K_xh))
return (m_h, K_hh)
else:
return (m_h, None)
return get_moments
# Constant function using GP mean protocol
class Constant(EF.Node):
def __init__(self, f, **kwargs):
self.f = f
EF.Node.__init__(self, dims=[(np.inf,)], **kwargs)
def message_to_child(self, gradient=False):
# Wrapper
def func(x, gradient=False):
if gradient:
return ([self.f(x), None], [])
else:
return [self.f(x), None]
return func
#class MultiDimensional(EF.NodeVariable):
# """ A multi-dimensional Gaussian process f(x). """
## class ToGaussian(EF.NodeVariable):
## """ Deterministic node which transform a Gaussian process into
## finite-dimensional Gaussian variable. """
## def __init__(self, f, x, **kwargs):
## EF.NodeVariable.__init__(self,
## f,
## x,
## plates=
## dims=
# Deterministic node for creating a set of GPs which can be used as a
# mean function to a general GP node.
class Multiple(EF.Node):
def __init__(self, GPs, **kwargs):
# Ignore plates
EF.NodeVariable.__init__(self,
*GPs,
plates=(),
dims=[(np.inf,), (np.inf,np.inf)],
**kwargs)
def message_to_parent(self, index):
raise Exception("not implemented yet")
def message_to_child(self, gradient=False):
u = [parent.message_to_child() for parent in self.parents]
def get_moments(xh, **kwargs):
mh_all = []
khh_all = []
for i in range(len(self.parents)):
xi = np.array(xh[i])
#print(xi)
#print(np.shape(xi))
#print(xi)
# FIXME: We are ignoring the covariance of mu now..
if gradient:
((mh, khh), dm) = u[i](xi, **kwargs)
else:
(mh, khh) = u[i](xi, **kwargs)
#mh = u[i](xi, **kwargs)[0]
#print(mh)
#print(mh_all)
## print(mh)
## print(khh)
## print(np.shape(mh))
mh_all = np.concatenate([mh_all, mh])
#print(np.shape(mh_all))
if khh != None:
print(khh)
raise Exception('Not implemented yet for covariances')
#khh_all = np.concatenate([khh_all, khh])
# FIXME: Compute gradients!
if gradient:
return ([mh_all, khh_all], [])
else:
return [mh_all, khh_all]
#return [mh_all, khh_all]
return get_moments
# Gaussian process distribution
class GaussianProcess(EF.Node):
def __init__(self, m, k, k_sparse=None, pseudoinputs=None, **kwargs):
self.x = np.array([])
self.f = np.array([])
## self.x_obs = np.zeros((0,1))
## self.f_obs = np.zeros((0,))
if pseudoinputs != None:
pseudoinputs = EF.NodeConstant([pseudoinputs],
dims=[np.shape(pseudoinputs)])
# By default, posterior == prior
self.m = None #m
self.k = None #k
if isinstance(k, list) and isinstance(m, list):
if len(k) != len(m):
raise Exception('The number of mean and covariance functions must be equal.')
k = CF.Multiple(k)
m = Multiple(m)
elif isinstance(k, list):
D = len(k)
k = CF.Multiple(k)
m = Multiple(D*[m])
elif isinstance(m, list):
D = len(m)
k = CF.Multiple(D*[k])
m = Multiple(m)
# Ignore plates
EF.NodeVariable.__init__(self,
m,
k,
k_sparse,
pseudoinputs,
plates=(),
dims=[(np.inf,), (np.inf,np.inf)],
**kwargs)
def __call__(self, x, covariance=None):
if not covariance:
return self.u(x, covariance=False)[0]
elif covariance.lower() == 'vector':
return self.u(x, covariance=1)
elif covariance.lower() == 'matrix':
return self.u(x, covariance=2)
else:
raise Exception("Unknown covariance type requested")
def message_to_parent(self, index):
if index == 0:
k = self.parents[1].message_to_child()[0]
K = k(self.x, self.x)
return [self.x,
self.mu,
K]
if index == 1:
raise Exception("not implemented yet")
def message_to_child(self):
if self.observed:
raise Exception("Observable GP should not have children.")
return self.u
def get_parameters(self):
return self.u
def observe(self, x, f):
self.observed = True
self.x = x
self.f = f
## if np.ndim(f) == 1:
## self.f = np.asmatrix(f).T
## else:
## self.f = np.asmatrix(f)
# You might want:
# - mean for x
# - covariance (and mean) for x
# - variance (and mean) for x
# - i.e., mean and/or (co)variance for x
# - covariance for x1 and x2
def lower_bound_contribution(self, gradient=False):
# Get moment functions from parents
m = self.parents[0].message_to_child(gradient=gradient)
k = self.parents[1].message_to_child(gradient=gradient)
if self.parents[2]:
k_sparse = self.parents[2].message_to_child(gradient=gradient)
else:
k_sparse = None
if self.parents[3]:
pseudoinputs = self.parents[3].message_to_child(gradient=gradient)
#pseudoinputs = self.parents[3].message_to_child(gradient=gradient)[0]
else:
pseudoinputs = None
## m = self.parents[0].message_to_child(gradient=gradient)[0]
## k = self.parents[1].message_to_child(gradient=gradient)[0]
# Compute the parameters (covariance matrices etc) using
# parents' moment functions
DKs_xx = []
DKd_xx = []
DKd_xp = []
DKd_pp = []
Dxp = []
Dmu = []
if gradient:
# FIXME: We are ignoring the covariance of mu now..
((mu, _), Dmu) = m(self.x, gradient=True)
## if k_sparse:
## ((Ks_xx,), DKs_xx) = k_sparse(self.x, self.x, gradient=True)
if pseudoinputs:
((Ks_xx,), DKs_xx) = k_sparse(self.x, self.x, gradient=True)
((xp,), Dxp) = pseudoinputs
((Kd_pp,), DKd_pp) = k(xp,xp, gradient=True)
((Kd_xp,), DKd_xp) = k(self.x, xp, gradient=True)
else:
((K_xx,), DKd_xx) = k(self.x, self.x, gradient=True)
if k_sparse:
((Ks_xx,), DKs_xx) = k_sparse(self.x, self.x, gradient=True)
try:
K_xx += Ks_xx
except:
K_xx = K_xx + Ks_xx
else:
# FIXME: We are ignoring the covariance of mu now..
(mu, _) = m(self.x)
## if k_sparse:
## (Ks_xx,) = k_sparse(self.x, self.x)
if pseudoinputs:
(Ks_xx,) = k_sparse(self.x, self.x)
(xp,) = pseudoinputs
(Kd_pp,) = k(xp, xp)
(Kd_xp,) = k(self.x, xp)
else:
(K_xx,) = k(self.x, self.x)
if k_sparse:
(Ks_xx,) = k_sparse(self.x, self.x)
try:
K_xx += Ks_xx
except:
K_xx = K_xx + Ks_xx
mu = mu[0]
#K = K[0]
# Log pdf
if self.observed:
## Log pdf for directly observed GP
f0 = self.f - mu
#print('hereiam')
#print(K)
if pseudoinputs:
## Pseudo-input approximation
# Decompose the full-rank sparse/noise covariance matrix
try:
Us_xx = utils.cholesky(Ks_xx)
except linalg.LinAlgError:
print('Noise/sparse covariance not positive definite')
return -np.inf
# Use Woodbury-Sherman-Morrison formula with the
# following notation:
#
# y2 = f0' * inv(Kd_xp*inv(Kd_pp)*Kd_xp' + Ks_xx) * f0
#
# z = Ks_xx \ f0
# Lambda = Kd_pp + Kd_xp'*inv(Ks_xx)*Kd_xp
# nu = inv(Lambda) * (Kd_xp' * (Ks_xx \ f0))
# rho = Kd_xp * inv(Lambda) * (Kd_xp' * (Ks_xx \ f0))
#
# y2 = f0' * z - z' * rho
z = Us_xx.solve(f0)
Lambda = Kd_pp + np.dot(Kd_xp.T,
Us_xx.solve(Kd_xp))
## z = utils.chol_solve(Us_xx, f0)
## Lambda = Kd_pp + np.dot(Kd_xp.T,
## utils.chol_solve(Us_xx, Kd_xp))
try:
U_Lambda = utils.cholesky(Lambda)
#U_Lambda = utils.chol(Lambda)
except linalg.LinAlgError:
print('Lambda not positive definite')
return -np.inf
nu = U_Lambda.solve(np.dot(Kd_xp.T, z))
#nu = utils.chol_solve(U_Lambda, np.dot(Kd_xp.T, z))
rho = np.dot(Kd_xp, nu)
y2 = np.dot(f0, z) - np.dot(z, rho)
# Use matrix determinant lemma
#
# det(Kd_xp*inv(Kd_pp)*Kd_xp' + Ks_xx)
# = det(Kd_pp + Kd_xp'*inv(Ks_xx)*Kd_xp)
# * det(inv(Kd_pp)) * det(Ks_xx)
# = det(Lambda) * det(Ks_xx) / det(Kd_pp)
try:
Ud_pp = utils.cholesky(Kd_pp)
#Ud_pp = utils.chol(Kd_pp)
except linalg.LinAlgError:
print('Covariance of pseudo inputs not positive definite')
return -np.inf
logdet = (U_Lambda.logdet()
+ Us_xx.logdet()
- Ud_pp.logdet())
## logdet = (utils.logdet_chol(U_Lambda)
## + utils.logdet_chol(Us_xx)
## - utils.logdet_chol(Ud_pp))
# Compute the log pdf
L = gaussian_logpdf(y2,
0,
0,
logdet,
np.size(self.f))
# Add the variational cost of the pseudo-input
# approximation
# Compute gradients
for (dmu, func) in Dmu:
# Derivative w.r.t. mean vector
d = np.nan
# Send the derivative message
func(d)
for (dKs_xx, func) in DKs_xx:
# Compute derivative w.r.t. covariance matrix
d = np.nan
# Send the derivative message
func(d)
for (dKd_xp, func) in DKd_xp:
# Compute derivative w.r.t. covariance matrix
d = np.nan
# Send the derivative message
func(d)
V = Ud_pp.solve(Kd_xp.T)
Z = Us_xx.solve(V.T)
## V = utils.chol_solve(Ud_pp, Kd_xp.T)
## Z = utils.chol_solve(Us_xx, V.T)
for (dKd_pp, func) in DKd_pp:
# Compute derivative w.r.t. covariance matrix
d = (0.5 * np.trace(Ud_pp.solve(dKd_pp))
- 0.5 * np.trace(U_Lambda.solve(dKd_pp))
+ np.dot(nu, np.dot(dKd_pp, nu))
+ np.trace(np.dot(dKd_pp,
np.dot(V,Z))))
## d = (0.5 * np.trace(utils.chol_solve(Ud_pp, dKd_pp))
## - 0.5 * np.trace(utils.chol_solve(U_Lambda, dKd_pp))
## + np.dot(nu, np.dot(dKd_pp, nu))
## + np.trace(np.dot(dKd_pp,
## np.dot(V,Z))))
# Send the derivative message
func(d)
for (dxp, func) in Dxp:
# Compute derivative w.r.t. covariance matrix
d = np.nan
# Send the derivative message
func(d)
else:
## Full exact (no pseudo approximations)
try:
U = utils.cholesky(K_xx)
#U = utils.chol(K_xx)
except linalg.LinAlgError:
print('non positive definite, return -inf')
return -np.inf
z = U.solve(f0)
#z = utils.chol_solve(U, f0)
#print(K)
L = utils.gaussian_logpdf(np.dot(f0, z),
0,
0,
U.logdet(),
## utils.logdet_chol(U),
np.size(self.f))
for (dmu, func) in Dmu:
# Derivative w.r.t. mean vector
d = -np.sum(z)
# Send the derivative message
func(d)
for (dK, func) in DKd_xx:
# Compute derivative w.r.t. covariance matrix
#
# TODO: trace+chol_solve should be handled better
# for sparse matrices. Use sparse-inverse!
d = 0.5 * (dK.dot(z).dot(z)
- U.trace_solve_gradient(dK))
## - np.trace(U.solve(dK)))
## d = 0.5 * (dK.dot(z).dot(z)
## - np.trace(utils.chol_solve(U, dK)))
#print('derivate', d, dK)
## d = 0.5 * (np.dot(z, np.dot(dK, z))
## - np.trace(utils.chol_solve(U, dK)))
#
# Send the derivative message
func(d)
for (dK, func) in DKs_xx:
# Compute derivative w.r.t. covariance matrix
d = 0.5 * (dK.dot(z).dot(z)
- U.trace_solve_gradient(dK))
## - np.trace(U.solve(dK)))
## d = 0.5 * (dK.dot(z).dot(z)
## - np.trace(utils.chol_solve(U, dK)))
## d = 0.5 * (np.dot(z, np.dot(dK, z))
## - np.trace(utils.chol_solve(U, dK)))
# Send the derivative message
func(d)
else:
## Log pdf for latent GP
raise Exception('Not implemented yet')
return L
## Let f1 be observed and f2 latent function values.
# Compute
#L = gaussian_logpdf(sum_product(np.outer(self.f,self.f) + self.Cov,
# Compute
def update(self):
# Messages from parents
m = self.parents[0].message_to_child()
k = self.parents[1].message_to_child()
if self.parents[2]:
k_sparse = self.parents[2].message_to_child()
else:
k_sparse = None
if self.parents[3]:
pseudoinputs = self.parents[3].message_to_child()[0]
else:
pseudoinputs = None
## m = self.parents[0].message_to_child()[0]
## k = self.parents[1].message_to_child()[0]
if self.observed:
# Observations of this node
self.u = gp_posterior_moment_function(m,
k,
self.x,
self.f,
k_sparse=k_sparse,
pseudoinputs=pseudoinputs)
else:
x = np.array([])
y = np.array([])
# Messages from children
for (child,index) in self.children:
(msg, mask) = child.message_to_parent(index)
# Ignoring masks and plates..
# m[0] is the inputs
x = np.concatenate((x, msg[0]), axis=-2)
# m[1] is the observations
y = np.concatenate((y, msg[1]))
# m[2] is the covariance matrix
V = linalg.block_diag(V, msg[2])
self.u = gp_posterior_moment_function(m, k, x, y, covariance=V)
self.x = x
self.f = y
# At least for now, simplify this GP node such that a GP is either
# observed or latent. If it is observed, it doesn't take messages from
# children, actually, it should not even have children!
## # Pseudo for GPFA:
## k1 = gp_cov_se(magnitude=theta1, lengthscale=theta2)
## k2 = gp_cov_periodic(magnitude=.., lengthscale=.., period=..)
## k3 = gp_cov_rq(magnitude=.., lengthscale=.., alpha=..)
## f = NodeGPSet(0, [k1,k2,k3]) # assumes block diagonality
## # f = NodeGPSet(0, [[k11,k12,k13],[k21,k22,k23],[k31,k32,k33]])
## X = GaussianFromGP(f, [ [[t0,0],[t0,1],[t0,2]], [t1,0],[t1,1],[t1,2], ..])
## ...
## # Construct a sum of GPs if interested only in the sum term
## k1 = gp_cov_se(magnitude=theta1, lengthscale=theta2)
## k2 = gp_cov_periodic(magnitude=.., lengthscale=.., period=..)
## k = gp_cov_sum(k1, k2)
## f = NodeGP(0, k)
## f.observe(x, y)
## f.update()
## (mp, kp) = f.get_parameters()
## # Construct a sum of GPs when interested also in the individual
## # GPs:
## k1 = gp_cov_se(magnitude=theta1, lengthscale=theta2)
## k2 = gp_cov_periodic(magnitude=.., lengthscale=.., period=..)
## k3 = gp_cov_delta(magnitude=theta3)
## f = NodeGPSum(0, [k1,k2,k3])
## x = np.array([1,2,3,4,5,6,7,8,9,10])
## y = np.sin(x[0]) + np.random.normal(0, 0.1, (10,))
## # Observe the sum (index 0)
## f.observe((0,x), y)
## # Inference
## f.update()
## (mp, kp) = f.get_parameters()
## # Mean of the sum
## mp[0](...)
## # Mean of the individual terms
## mp[1](...)
## mp[2](...)
## mp[3](...)
## # Covariance of the sum
## kp[0][0](..., ...)
## # Other covariances
## kp[1][1](..., ...)
## kp[2][2](..., ...)
## kp[3][3](..., ...)
## kp[1][2](..., ...)
## kp[1][3](..., ...)
## kp[2][3](..., ...)
bayespy-0.2.3/bayespy/inference/vmp/nodes/__init__.py 0000664 5104112 5104112 00000003610 12374370350 024353 0 ustar jluttine jluttine 0000000 0000000 ######################################################################
# Copyright (C) 2011,2012 Jaakko Luttinen
#
# This file is licensed under Version 3.0 of the GNU General Public
# License. See LICENSE for a text of the license.
######################################################################
######################################################################
# This file is part of BayesPy.
#
# BayesPy is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# BayesPy is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with BayesPy. If not, see .
######################################################################
# Import some most commonly used nodes
from . import *
from .bernoulli import Bernoulli
from .binomial import Binomial
from .categorical import Categorical
from .multinomial import Multinomial
from .poisson import Poisson
from .beta import Beta
from .dirichlet import Dirichlet
from .exponential import Exponential
from .gaussian import Gaussian, GaussianARD
from .wishart import Wishart
from .gamma import Gamma
from .gaussian import (GaussianGammaISO,
GaussianGammaARD,
GaussianWishart)
from .gaussian_markov_chain import GaussianMarkovChain
from .gaussian_markov_chain import VaryingGaussianMarkovChain
from .gaussian_markov_chain import SwitchingGaussianMarkovChain
from .categorical_markov_chain import CategoricalMarkovChain
from .mixture import Mixture
from .gate import Gate
from .dot import Dot
from .dot import SumMultiply
bayespy-0.2.3/bayespy/inference/vmp/nodes/bernoulli.py 0000664 5104112 5104112 00000011515 12374370350 024612 0 ustar jluttine jluttine 0000000 0000000 ######################################################################
# Copyright (C) 2014 Jaakko Luttinen
#
# This file is licensed under Version 3.0 of the GNU General Public
# License. See LICENSE for a text of the license.
######################################################################
######################################################################
# This file is part of BayesPy.
#
# BayesPy is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# BayesPy is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with BayesPy. If not, see .
######################################################################
"""
A module for the Bernoulli distribution node
"""
import numpy as np
from .binomial import (BinomialMoments,
BinomialDistribution)
from .expfamily import ExponentialFamily
from .beta import BetaMoments
class BernoulliMoments(BinomialMoments):
"""
Class for the moments of Bernoulli variables.
"""
def __init__(self):
super().__init__(1)
class BernoulliDistribution(BinomialDistribution):
"""
Class for the VMP formulas of Bernoulli variables.
"""
def __init__(self):
super().__init__(1)
class Bernoulli(ExponentialFamily):
r"""
Node for Bernoulli random variables.
The node models a binary random variable :math:`z \in \{0,1\}` with prior
probability :math:`p \in [0,1]` for value one:
.. math::
z \sim \mathrm{Bernoulli}(p).
Parameters
----------
p : beta-like node
Probability of a successful trial
Examples
--------
.. code-block:: python
from bayespy.nodes import Bernoulli, Beta
p = Beta([1e-3, 1e-3])
z = Bernoulli(p, plates=(10,))
z.observe([0, 1, 1, 1, 0, 1, 1, 1, 0, 1])
p.update()
import bayespy.plot as bpplt
import numpy as np
bpplt.pdf(p, np.linspace(0, 1, num=100))
"""
_moments = BernoulliMoments()
_parent_moments = (BetaMoments(),)
_distribution = BernoulliDistribution()
def __init__(self, p, **kwargs):
"""
Create Bernoulli node.
"""
super().__init__(p, **kwargs)
@classmethod
def _constructor(cls, p, **kwargs):
"""
Constructs distribution and moments objects.
"""
p = cls._ensure_moments(p, cls._parent_moments[0])
parents = [p]
return ( parents,
kwargs,
( (), ),
cls._total_plates(kwargs.get('plates'),
cls._distribution.plates_from_parent(0, p.plates)),
cls._distribution,
cls._moments,
cls._parent_moments)
def show(self):
"""
Print the distribution using standard parameterization.
"""
p = 1 / (1 + np.exp(-self.phi[0]))
print("%s ~ Bernoulli(p)" % self.name)
print(" p = ")
print(p)
from .deterministic import Deterministic
from .categorical import Categorical, CategoricalMoments
class CategoricalToBernoulli(Deterministic):
"""
A node for converting 2-class categorical moments to Bernoulli moments.
"""
def __init__(self, Z, **kwargs):
"""
Create a categorical MC moments to categorical moments conversion node.
"""
# Convert parent to proper type. Z must be a node.
Z = Z._convert(CategoricalMoments)
K = Z.dims[0][-1]
if K != 2:
raise ValueError("Only 2-class categorical can be converted to "
"Bernoulli")
dims = ( (), )
self._moments = BernoulliMoments()
self._parent_moments = (CategoricalMoments(2),)
super().__init__(Z, dims=dims, **kwargs)
def _compute_moments(self, u_Z):
"""
Compute the moments given the moments of the parents.
"""
u0 = u_Z[0][...,0]
u = [u0]
return u
def _compute_message_to_parent(self, index, m, u_Z):
"""
Compute the message to a parent.
"""
if index == 0:
m0 = np.concatenate([m[0][...,None],
np.zeros(np.shape(m[0]))[...,None]],
axis=-1)
return [m0]
else:
raise ValueError("Incorrect parent index")
# Make use of the conversion node
CategoricalMoments.add_converter(BernoulliMoments,
CategoricalToBernoulli)
bayespy-0.2.3/bayespy/inference/vmp/nodes/beta.py 0000664 5104112 5104112 00000011602 12374370350 023527 0 ustar jluttine jluttine 0000000 0000000 ######################################################################
# Copyright (C) 2014 Jaakko Luttinen
#
# This file is licensed under Version 3.0 of the GNU General Public
# License. See LICENSE for a text of the license.
######################################################################
######################################################################
# This file is part of BayesPy.
#
# BayesPy is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# BayesPy is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with BayesPy. If not, see .
######################################################################
"""
A module for the beta distribution node
"""
import numpy as np
import scipy.special as special
from .dirichlet import (DirichletMoments,
DirichletDistribution,
Dirichlet)
from .node import Moments, ensureparents
class BetaMoments(DirichletMoments):
"""
Class for the moments of beta variables.
"""
def compute_fixed_moments(self, p):
"""
Compute the moments for a fixed value
"""
p = np.asanyarray(p)[...,None] * [1,-1] + [0,1]
return super().compute_fixed_moments(p)
def compute_dims_from_values(self, p):
"""
Return the shape of the moments for a fixed value.
"""
return ( (2,), )
class BetaDistribution(DirichletDistribution):
"""
Class for the VMP formulas of beta variables.
Although the realizations are scalars (probability p), the moments is a
two-dimensional vector: [log(p), log(1-p)].
"""
def compute_message_to_parent(self, parent, index, u_self, u_alpha):
"""
Compute the message to a parent node.
"""
return super().compute_message_to_parent(parent, index, u_self, u_alpha)
def compute_phi_from_parents(self, u_alpha, mask=True):
"""
Compute the natural parameter vector given parent moments.
"""
return super().compute_phi_from_parents(u_alpha, mask=mask)
def compute_moments_and_cgf(self, phi, mask=True):
"""
Compute the moments and :math:`g(\phi)`.
"""
return super().compute_moments_and_cgf(phi, mask)
def compute_cgf_from_parents(self, u_alpha):
"""
Compute :math:`\mathrm{E}_{q(p)}[g(p)]`
"""
return super().compute_cgf_from_parents(u_alpha)
def compute_fixed_moments_and_f(self, p, mask=True):
"""
Compute the moments and :math:`f(x)` for a fixed value.
"""
p = np.asanyarray(p)[...,None] * [1,-1] + [0,1]
return super().compute_fixed_moments_and_f(p, mask=mask)
def random(self, *phi, plates=None):
"""
Draw a random sample from the distribution.
"""
p = super().random(*phi, plates=plates)
return p[...,0]
class Beta(Dirichlet):
r"""
Node for beta random variables.
The node models a probability variable :math:`p \in [0,1]` as
.. math::
p \sim \mathrm{Beta}(a, b)
where :math:`a` and :math:`b` are prior counts for success and failure,
respectively.
Parameters
----------
alpha : (...,2)-shaped array
Two-element vector containing :math:`a` and :math:`b`
Examples
--------
.. code-block:: python
from bayespy.nodes import Bernoulli, Beta
p = Beta([1e-3, 1e-3])
z = Bernoulli(p, plates=(10,))
z.observe([0, 1, 1, 1, 0, 1, 1, 1, 0, 1])
p.update()
import bayespy.plot as bpplt
import numpy as np
bpplt.pdf(p, np.linspace(0, 1, num=100))
"""
_moments = BetaMoments()
_distribution = BetaDistribution()
def __init__(self, alpha, **kwargs):
"""
Create beta node
"""
super().__init__(alpha, **kwargs)
@classmethod
@ensureparents
def _constructor(cls, alpha, **kwargs):
"""
Constructs distribution and moments objects.
"""
D = alpha.dims[0][0]
if D != 2:
raise ValueError("Parent has wrong dimensionality. Must be a "
"two-dimensional vector.")
return super()._constructor(alpha, **kwargs)
def show(self):
"""
Print the distribution using standard parameterization.
"""
a = self.phi[0][...,0]
b = self.phi[0][...,1]
print("%s ~ Beta(a, b)" % self.name)
print(" a = ")
print(a)
print(" b = ")
print(b)
bayespy-0.2.3/bayespy/inference/vmp/nodes/binomial.py 0000664 5104112 5104112 00000015005 12431130115 024372 0 ustar jluttine jluttine 0000000 0000000 ######################################################################
# Copyright (C) 2014 Jaakko Luttinen
#
# This file is licensed under Version 3.0 of the GNU General Public
# License. See LICENSE for a text of the license.
######################################################################
######################################################################
# This file is part of BayesPy.
#
# BayesPy is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# BayesPy is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with BayesPy. If not, see .
######################################################################
"""
A module for the binomial distribution node
"""
import numpy as np
import scipy.special as special
from .expfamily import (ExponentialFamily,
ExponentialFamilyDistribution,
useconstructor)
from .beta import BetaMoments
from .poisson import PoissonMoments
from .node import (Moments,
ensureparents)
from bayespy.utils import misc, random
class BinomialMoments(PoissonMoments):
"""
Class for the moments of binomial variables
"""
def __init__(self, N):
self.N = N
super().__init__()
def compute_fixed_moments(self, x):
"""
Compute the moments for a fixed value
"""
# Make sure the values are integers in valid range
x = np.asanyarray(x)
if np.any(x > self.N):
raise ValueError("Invalid count")
return super().compute_fixed_moments()
def compute_dims_from_values(self, x):
"""
Return the shape of the moments for a fixed value.
The realizations are scalars, thus the shape of the moment is ().
"""
return super().compute_dims_from_values()
class BinomialDistribution(ExponentialFamilyDistribution):
"""
Class for the VMP formulas of binomial variables.
"""
def __init__(self, N):
N = np.asanyarray(N)
if not misc.isinteger(N):
raise ValueError("Number of trials must be integer")
if np.any(N < 0):
raise ValueError("Number of trials must be non-negative")
self.N = np.asanyarray(N)
super().__init__()
def compute_message_to_parent(self, parent, index, u_self, u_p):
"""
Compute the message to a parent node.
"""
if index == 0:
x = u_self[0][...,None]
n = self.N[...,None]
m0 = x*[1, -1] + n*[0, 1]
m = [m0]
return m
else:
raise ValueError("Incorrect parent index")
def compute_phi_from_parents(self, u_p, mask=True):
"""
Compute the natural parameter vector given parent moments.
"""
logp0 = u_p[0][...,0]
logp1 = u_p[0][...,1]
phi0 = logp0 - logp1
return [phi0]
def compute_moments_and_cgf(self, phi, mask=True):
"""
Compute the moments and :math:`g(\phi)`.
"""
u0 = self.N / (1 + np.exp(-phi[0]))
g = -self.N * np.log1p(np.exp(phi[0]))
return ( [u0], g )
def compute_cgf_from_parents(self, u_p):
"""
Compute :math:`\mathrm{E}_{q(p)}[g(p)]`
"""
logp0 = u_p[0][...,0]
logp1 = u_p[0][...,1]
return self.N * logp1
def compute_fixed_moments_and_f(self, x, mask=True):
"""
Compute the moments and :math:`f(x)` for a fixed value.
"""
# Make sure the values are integers in valid range
x = np.asanyarray(x)
if not misc.isinteger(x):
raise ValueError("Counts must be integer")
if np.any(x < 0) or np.any(x > self.N):
raise ValueError("Invalid count")
# Now, the moments are just the counts
u = [x]
f = (special.gammaln(self.N+1) -
special.gammaln(x+1) -
special.gammaln(self.N-x+1))
return (u, f)
def random(self, *phi, plates=None):
"""
Draw a random sample from the distribution.
"""
p = random.logodds_to_probability(phi[0])
return np.random.binomial(self.N, p, size=plates)
class Binomial(ExponentialFamily):
r"""
Node for binomial random variables.
The node models the number of successes :math:`x \in \{0, \ldots, n\}` in
:math:`n` trials with probability :math:`p` for success:
.. math::
x \sim \mathrm{Binomial}(n, p).
Parameters
----------
n : scalar or array
Number of trials
p : beta-like node or scalar or array
Probability of a success in a trial
Examples
--------
.. code-block:: python
from bayespy.nodes import Binomial, Beta
p = Beta([1e-3, 1e-3])
x = Binomial(10, p)
x.observe(7)
p.update()
import bayespy.plot as bpplt
import numpy as np
bpplt.pdf(p, np.linspace(0, 1, num=100))
See also
--------
Bernoulli, Multinomial, Beta
"""
_parent_moments = (BetaMoments(),)
def __init__(self, n, p, **kwargs):
"""
Create binomial node
"""
super().__init__(n, p, **kwargs)
@classmethod
def _constructor(cls, n, p, **kwargs):
"""
Constructs distribution and moments objects.
"""
p = cls._ensure_moments(p, cls._parent_moments[0])
parents = [p]
moments = BinomialMoments(n)
distribution = BinomialDistribution(n)
return ( parents,
kwargs,
( (), ),
cls._total_plates(kwargs.get('plates'),
distribution.plates_from_parent(0, p.plates),
np.shape(n)),
distribution,
moments,
cls._parent_moments)
def show(self):
"""
Print the distribution using standard parameterization.
"""
p = 1 / (1 + np.exp(-self.phi[0]))
n = self._distribution.N
print("%s ~ Binomial(n, p)" % self.name)
print(" n = ")
print(n)
print(" p = ")
print(p)
bayespy-0.2.3/bayespy/inference/vmp/nodes/categorical.py 0000664 5104112 5104112 00000015152 12374370350 025075 0 ustar jluttine jluttine 0000000 0000000 ######################################################################
# Copyright (C) 2011,2012,2014 Jaakko Luttinen
#
# This file is licensed under Version 3.0 of the GNU General Public
# License. See LICENSE for a text of the license.
######################################################################
######################################################################
# This file is part of BayesPy.
#
# BayesPy is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# BayesPy is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with BayesPy. If not, see .
######################################################################
"""
Module for the categorical distribution node.
"""
import numpy as np
from .node import ensureparents
from .expfamily import (ExponentialFamily,
useconstructor)
from .multinomial import (MultinomialMoments,
MultinomialDistribution,
Multinomial)
from .dirichlet import DirichletMoments
from bayespy.utils import random
from bayespy.utils import misc
class CategoricalMoments(MultinomialMoments):
"""
Class for the moments of categorical variables.
"""
def __init__(self, categories):
"""
Create moments object for categorical variables
"""
self.D = categories
super().__init__()
def compute_fixed_moments(self, x):
"""
Compute the moments for a fixed value
"""
# Check that x is valid
x = np.asanyarray(x)
if not misc.isinteger(x):
raise ValueError("Values must be integers")
if np.any(x < 0) or np.any(x >= self.D):
raise ValueError("Invalid category index")
u0 = np.zeros((np.size(x), self.D))
u0[[np.arange(np.size(x)), np.ravel(x)]] = 1
u0 = np.reshape(u0, np.shape(x) + (self.D,))
return [u0]
def compute_dims_from_values(self, x):
"""
Return the shape of the moments for a fixed value.
The observations are scalar.
"""
return ( (self.D,), )
class CategoricalDistribution(MultinomialDistribution):
"""
Class for the VMP formulas of categorical variables.
"""
def __init__(self, categories):
"""
Create VMP formula node for a categorical variable
`categories` is the total number of categories.
"""
if not isinstance(categories, int):
raise ValueError("Number of categories must be integer")
if categories < 0:
raise ValueError("Number of categoriess must be non-negative")
self.D = categories
super().__init__(1)
def compute_message_to_parent(self, parent, index, u, u_p):
"""
Compute the message to a parent node.
"""
return super().compute_message_to_parent(parent, index, u, u_p)
def compute_phi_from_parents(self, u_p, mask=True):
"""
Compute the natural parameter vector given parent moments.
"""
return super().compute_phi_from_parents(u_p, mask=mask)
def compute_moments_and_cgf(self, phi, mask=True):
"""
Compute the moments and :math:`g(\phi)`.
"""
return super().compute_moments_and_cgf(phi, mask=mask)
def compute_cgf_from_parents(self, u_p):
"""
Compute :math:`\mathrm{E}_{q(p)}[g(p)]`
"""
return super().compute_cgf_from_parents(u_p)
def compute_fixed_moments_and_f(self, x, mask=True):
"""
Compute the moments and :math:`f(x)` for a fixed value.
"""
# Check the validity of x
x = np.asanyarray(x)
if not misc.isinteger(x):
raise ValueError("Values must be integers")
if np.any(x < 0) or np.any(x >= self.D):
raise ValueError("Invalid category index")
# Form a binary matrix with only one non-zero (1) in the last axis
u0 = np.zeros((np.size(x), self.D))
u0[[np.arange(np.size(x)), np.ravel(x)]] = 1
u0 = np.reshape(u0, np.shape(x) + (self.D,))
u = [u0]
# f(x) is zero
f = 0
return (u, f)
def random(self, *phi, plates=None):
"""
Draw a random sample from the distribution.
"""
logp = phi[0]
logp -= np.amax(logp, axis=-1, keepdims=True)
p = np.exp(logp)
return random.categorical(p, size=plates)
class Categorical(ExponentialFamily):
r"""
Node for categorical random variables.
The node models a categorical random variable :math:`x \in \{0,\ldots,K-1\}`
with prior probabilities :math:`\{p_0, \ldots, p_{K-1}\}` for each category:
.. math::
p(x=k) = p_k \quad \text{for } k\in \{0,\ldots,K-1\}.
Parameters
----------
p : Dirichlet-like node or (...,K)-array
Probabilities for each category
See also
--------
Bernoulli, Multinomial, Dirichlet
"""
_parent_moments = [DirichletMoments()]
def __init__(self, p, **kwargs):
"""
Create Categorical node.
"""
super().__init__(p, **kwargs)
@classmethod
@ensureparents
def _constructor(cls, p, **kwargs):
"""
Constructs distribution and moments objects.
This method is called if useconstructor decorator is used for __init__.
Becase the distribution and moments object depend on the number of
categories, that is, they depend on the parent node, this method can be
used to construct those objects.
"""
# Get the number of categories
D = p.dims[0][0]
parents = [p]
moments = CategoricalMoments(D)
distribution = CategoricalDistribution(D)
return (parents,
kwargs,
( (D,), ),
cls._total_plates(kwargs.get('plates'),
distribution.plates_from_parent(0, p.plates)),
distribution,
moments,
cls._parent_moments)
def show(self):
"""
Print the distribution using standard parameterization.
"""
p = self.u[0]
print("%s ~ Categorical(p)" % self.name)
print(" p = ")
print(p)
bayespy-0.2.3/bayespy/inference/vmp/nodes/categorical_markov_chain.py 0000664 5104112 5104112 00000034004 12374370350 027613 0 ustar jluttine jluttine 0000000 0000000 ######################################################################
# Copyright (C) 2014 Jaakko Luttinen
#
# This file is licensed under Version 3.0 of the GNU General Public
# License. See LICENSE for a text of the license.
######################################################################
######################################################################
# This file is part of BayesPy.
#
# BayesPy is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# BayesPy is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with BayesPy. If not, see .
######################################################################
"""
Module for the categorical Markov chain node.
"""
import numpy as np
from .deterministic import Deterministic
from .expfamily import (ExponentialFamily,
ExponentialFamilyDistribution,
useconstructor)
from .node import (Moments,
ensureparents)
from .categorical import CategoricalMoments
from .dirichlet import (Dirichlet,
DirichletMoments)
from bayespy.utils import misc, random
class CategoricalMarkovChainMoments(Moments):
"""
Class for the moments of categorical Markov chain variables.
"""
def __init__(self, categories):
"""
Create moments object for categorical Markov chain variables.
"""
self.D = categories
def compute_fixed_moments(self, x):
"""
Compute the moments for a fixed value
"""
raise NotImplementedError("compute_fixed_moments not implemented for "
"%s"
% (self.__class__.__name__))
def compute_dims_from_values(self, x):
"""
Return the shape of the moments for a fixed value.
"""
raise NotImplementedError("compute_dims_from_values not implemented "
"for %s"
% (self.__class__.__name__))
class CategoricalMarkovChainDistribution(ExponentialFamilyDistribution):
"""
Class for the VMP formulas of categorical Markov chain variables.
"""
def __init__(self, categories, states):
"""
Create VMP formula node for a categorical variable
`categories` is the total number of categories.
`states` is the length of the chain.
"""
self.K = categories
self.N = states
def compute_message_to_parent(self, parent, index, u, u_p0, u_P):
"""
Compute the message to a parent node.
"""
if index == 0:
return [ u[0] ]
elif index == 1:
return [ u[1] ]
else:
raise ValueError("Parent index out of bounds")
def compute_mask_to_parent(self, index, mask):
"""
Maps the mask to the plates of a parent.
"""
if index == 0:
return mask
elif index == 1:
# Add plate axis for the time axis and row axis of the transition
# matrix
return np.asanyarray(mask)[...,None,None]
else:
raise ValueError("Parent index out of bounds")
def compute_phi_from_parents(self, u_p0, u_P, mask=True):
"""
Compute the natural parameter vector given parent moments.
"""
phi0 = u_p0[0]
phi1 = u_P[0] * np.ones((self.N-1,self.K,self.K))
return [phi0, phi1]
def compute_moments_and_cgf(self, phi, mask=True):
"""
Compute the moments and :math:`g(\phi)`.
"""
logp0 = phi[0]
logP = phi[1]
(z0, zz, cgf) = random.alpha_beta_recursion(logp0, logP)
u = [z0, zz]
return (u, cgf)
def compute_cgf_from_parents(self, u_p0, u_P):
"""
Compute :math:`\mathrm{E}_{q(p)}[g(p)]`
"""
return 0
def compute_fixed_moments_and_f(self, x, mask=True):
"""
Compute the moments and :math:`f(x)` for a fixed value.
"""
raise NotImplementedError()
def plates_to_parent(self, index, plates):
"""
Resolves the plate mapping to a parent.
Given the plates of the node's moments, this method returns the plates
that the message to a parent has for the parent's distribution.
"""
if index == 0:
return plates
elif index == 1:
return plates + (self.N-1, self.K)
else:
raise ValueError("Parent index out of bounds")
def plates_from_parent(self, index, plates):
"""
Resolve the plate mapping from a parent.
Given the plates of a parent's moments, this method returns the plates
that the moments has for this distribution.
"""
if index == 0:
return plates
elif index == 1:
return plates[:-2]
else:
raise ValueError("Parent index out of bounds")
def random(self, *phi, plates=None):
"""
Draw a random sample from the distribution.
"""
# Convert natural parameters to transition probabilities
p0 = np.exp(phi[0] - misc.logsumexp(phi[0],
axis=-1,
keepdims=True))
P = np.exp(phi[1] - misc.logsumexp(phi[1],
axis=-1,
keepdims=True))
# Explicit broadcasting
P = P * np.ones(plates)[...,None,None,None]
# Allocate memory
Z = np.zeros(plates + (self.N,), dtype=np.int)
# Draw initial state
Z[...,0] = random.categorical(p0, size=plates)
# Create [0,1,2,...,len(plate_axis)] indices for each plate axis and
# make them broadcast properly
nplates = len(plates)
plates_ind = [np.arange(plate)[(Ellipsis,)+(nplates-i-1)*(None,)]
for (i, plate) in enumerate(plates)]
plates_ind = tuple(plates_ind)
# Draw next states iteratively
for n in range(self.N-1):
# Select the transition probabilities for the current state but take
# into account the plates. This leads to complex NumPy
# indexing.. :)
time_ind = min(n, np.shape(P)[-3]-1)
ind = plates_ind + (time_ind, Z[...,n], Ellipsis)
p = P[ind]
# Draw next state
z = random.categorical(P[ind])
Z[...,n+1] = z
return Z
class CategoricalMarkovChain(ExponentialFamily):
r"""
Node for categorical Markov chain random variables.
The node models a Markov chain which has a discrete set of K possible states
and the next state depends only on the previous state and the state
transition probabilities. The graphical model is shown below:
.. bayesnet::
\tikzstyle{latent} += [minimum size=30pt];
\node[latent] (x0) {$x_0$};
\node[latent, right=of x0] (x1) {$x_1$};
\node[right=of x1] (dots) {$\cdots$};
\node[latent, right=of dots] (xn) {$x_{N-1}$};
\edge {x0}{x1};
\edge {x1}{dots};
\edge {dots}{xn};
\node[latent, above=of x0] (pi) {$\boldsymbol{\pi}$};
\node[latent, above=of dots] (A) {$\mathbf{A}$};
\edge {pi} {x0};
\edge {A} {x1,dots,xn};
where :math:`\boldsymbol{\pi}` contains the probabilities for the initial
state and :math:`\mathbf{A}` is the state transition probability matrix. It
is possible to have :math:`\mathbf{A}` varying in time.
.. math::
p(x_0, \ldots, x_{N-1}) &= p(x_0) \prod^{N-1}_{n=1} p(x_n|x_{n-1}),
where
.. math::
p(x_0=k) &= \pi_k, \quad \text{for } k \in \{0,\ldots,K-1\},
\\
p(x_n=j|x_{n-1}=i) &= a_{ij}^{(n-1)} \quad \text{for } n=1,\ldots,N-1,\,
i\in\{1,\ldots,K-1\},\, j\in\{1,\ldots,K-1\}
\\
a_{ij}^{(n)} &= [\mathbf{A}_n]_{ij}
This node can be used to construct hidden Markov models by using
:class:`Mixture` for the emission distribution.
Parameters
----------
pi : Dirichlet-like node or (...,K)-array
:math:`\boldsymbol{\pi}`, probabilities for the first
state. :math:`K`-dimensional Dirichlet.
A : Dirichlet-like node or (K,K)-array or (...,1,K,K)-array or (...,N-1,K,K)-array
:math:`\mathbf{A}`, probabilities for state
transitions. :math:`K`-dimensional Dirichlet with plates (K,) or
(...,1,K) or (...,N-1,K).
states : int, optional
:math:`N`, the length of the chain.
See also
--------
Categorical, Dirichlet, GaussianMarkovChain, Mixture,
SwitchingGaussianMarkovChain
"""
_parent_moments = (DirichletMoments(),
DirichletMoments())
def __init__(self, pi, A, states=None, **kwargs):
"""
Create categorical Markov chain
"""
super().__init__(pi, A, states=states, **kwargs)
@classmethod
@ensureparents
def _constructor(cls, p0, P, states=None, **kwargs):
"""
Constructs distribution and moments objects.
This method is called if useconstructor decorator is used for __init__.
Becase the distribution and moments object depend on the number of
categories, that is, they depend on the parent node, this method can be
used to construct those objects.
"""
# Number of categories
D = p0.dims[0][0]
# Number of states
if len(P.plates) < 2:
if states is None:
raise ValueError("Could not infer the length of the Markov "
"chain")
N = int(states)
else:
if P.plates[-2] == 1:
if states is None:
N = 2
else:
N = int(states)
else:
if states is not None and P.plates[-2]+1 != states:
raise ValueError("Given length of the Markov chain is "
"inconsistent with the transition "
"probability matrix")
N = P.plates[-2] + 1
if p0.dims != P.dims:
raise ValueError("Initial state probability vector and state "
"transition probability matrix have different "
"size")
if len(P.plates) < 1 or P.plates[-1] != D:
raise ValueError("Transition probability matrix is not square")
dims = ( (D,), (N-1,D,D) )
parents = [p0, P]
distribution = CategoricalMarkovChainDistribution(D, N)
moments = CategoricalMarkovChainMoments(D)
parent_moments = cls._parent_moments
return (parents,
kwargs,
dims,
cls._total_plates(kwargs.get('plates'),
distribution.plates_from_parent(0, p0.plates),
distribution.plates_from_parent(1, P.plates)),
distribution,
moments,
parent_moments)
def show(self):
"""
Print the distribution using standard parameterization.
"""
raise NotImplementedError()
class CategoricalMarkovChainToCategorical(Deterministic):
"""
A node for converting categorical MC moments to categorical moments.
"""
def __init__(self, Z, **kwargs):
"""
Create a categorical MC moments to categorical moments conversion node.
"""
# Convert parent to proper type. Z must be a node.
Z = Z._convert(CategoricalMarkovChainMoments)
K = Z.dims[0][-1]
dims = ( (K,), )
self._moments = CategoricalMoments(K)
self._parent_moments = (CategoricalMarkovChainMoments(K),)
super().__init__(Z, dims=dims, **kwargs)
def _compute_moments(self, u_Z):
"""
Compute the moments given the moments of the parents.
"""
# Add time axis to p0
p0 = u_Z[0][...,None,:]
# Sum joint probability arrays to marginal probability vectors
zz = u_Z[1]
p = np.sum(zz, axis=-2)
# Broadcast p0 and p to same shape, except the time axis
plates_p0 = np.shape(p0)[:-2]
plates_p = np.shape(p)[:-2]
shape = misc.broadcasted_shape(plates_p0, plates_p) + (1,1)
p0 = p0 * np.ones(shape)
p = p * np.ones(shape)
# Concatenate
P = np.concatenate((p0,p), axis=-2)
return [P]
def _compute_message_to_parent(self, index, m, u_Z):
"""
Compute the message to a parent.
"""
m0 = m[0][...,0,:]
m1 = m[0][...,1:,None,:]
return [m0, m1]
def _compute_mask_to_parent(self, index, mask):
"""
Compute the mask used for messages sent to a parent.
"""
if index == 0:
# "Sum" over the last axis
# TODO/FIXME: Check this. BUG I THINK.
return np.any(mask, axis=-1)
else:
raise ValueError("Parent index out of bounds")
def _plates_to_parent(self, index):
if index == 0:
return self.plates[:-1]
else:
raise ValueError("Parent index out of bounds")
def _plates_from_parent(self, index):
if index == 0:
N = self.parents[0].dims[1][0]
return self.parents[0].plates + (N+1,)
else:
raise ValueError("Parent index out of bounds")
# Make use of the conversion node
CategoricalMarkovChainMoments.add_converter(CategoricalMoments,
CategoricalMarkovChainToCategorical)
bayespy-0.2.3/bayespy/inference/vmp/nodes/constant.py 0000664 5104112 5104112 00000004736 12374370350 024457 0 ustar jluttine jluttine 0000000 0000000 ######################################################################
# Copyright (C) 2011,2012,2014 Jaakko Luttinen
#
# This file is licensed under Version 3.0 of the GNU General Public
# License. See LICENSE for a text of the license.
######################################################################
######################################################################
# This file is part of BayesPy.
#
# BayesPy is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# BayesPy is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with BayesPy. If not, see .
######################################################################
import numpy as np
from .node import Node
class Constant(Node):
r"""
Node for presenting constant values.
The node wraps arrays into proper node type.
"""
def __init__(self, moments, x, **kwargs):
self._moments = moments
x = np.asanyarray(x)
# Compute moments
self.u = self._moments.compute_fixed_moments(x)
# Dimensions of the moments
dims = self._moments.compute_dims_from_values(x)
# Resolve plates
D = len(dims[0])
if D > 0:
plates = np.shape(self.u[0])[:-D]
else:
plates = np.shape(self.u[0])
# Parent constructor
super().__init__(dims=dims, plates=plates, **kwargs)
def _get_id_list(self):
"""
Returns the stochastic ID list.
This method is used to check that same stochastic nodes are not direct
parents of a node several times. It is only valid if there are
intermediate stochastic nodes.
To put it another way: each ID corresponds to one factor q(..) in the
posterior approximation. Different IDs mean different factors, thus they
mean independence. The parents must have independent factors.
Stochastic nodes should return their unique ID. Deterministic nodes
should return the IDs of their parents. Constant nodes should return
empty list of IDs.
"""
return []
def get_moments(self):
return self.u
bayespy-0.2.3/bayespy/inference/vmp/nodes/deterministic.py 0000664 5104112 5104112 00000030354 12374370350 025464 0 ustar jluttine jluttine 0000000 0000000 ######################################################################
# Copyright (C) 2013,2014 Jaakko Luttinen
#
# This file is licensed under Version 3.0 of the GNU General Public
# License. See LICENSE for a text of the license.
######################################################################
######################################################################
# This file is part of BayesPy.
#
# BayesPy is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# BayesPy is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with BayesPy. If not, see .
######################################################################
import functools
import numpy as np
from bayespy.utils import misc
from .node import Node, Moments
class Deterministic(Node):
"""
Base class for deterministic nodes.
Sub-classes must implement:
1. For implementing the deterministic function:
_compute_moments(self, *u)
2. One of the following options:
a) Simple methods:
_compute_message_to_parent(self, index, m, *u)
not? _compute_mask_to_parent(self, index, mask)
b) More control with:
_compute_message_and_mask_to_parent(self, index, m, *u)
Sub-classes may need to re-implement:
1. If they manipulate plates:
_compute_mask_to_parent(index, mask)
_plates_to_parent(self, index)
_plates_from_parent(self, index)
"""
def __init__(self, *args, **kwargs):
super().__init__(*args, plates=None, notify_parents=False, **kwargs)
def _get_id_list(self):
"""
Returns the stochastic ID list.
This method is used to check that same stochastic nodes are not direct
parents of a node several times. It is only valid if there are
intermediate stochastic nodes.
To put it another way: each ID corresponds to one factor q(..) in the
posterior approximation. Different IDs mean different factors, thus they
mean independence. The parents must have independent factors.
Stochastic nodes should return their unique ID. Deterministic nodes
should return the IDs of their parents. Constant nodes should return
empty list of IDs.
"""
id_list = []
for parent in self.parents:
id_list = id_list + parent._get_id_list()
return id_list
def get_moments(self):
u_parents = self._message_from_parents()
return self._compute_moments(*u_parents)
def _compute_message_and_mask_to_parent(self, index, m_children, *u_parents):
# The following methods should be implemented by sub-classes.
m = self._compute_message_to_parent(index, m_children, *u_parents)
mask = self._compute_mask_to_parent(index, self.mask)
return (m, mask)
def _get_message_and_mask_to_parent(self, index):
u_parents = self._message_from_parents(exclude=index)
m_children = self._message_from_children()
return self._compute_message_and_mask_to_parent(index,
m_children,
*u_parents)
def _compute_moments(self, *u_parents):
"""
Compute the moments given the moments of the parents.
"""
raise NotImplementedError()
def _compute_message_to_parent(self, index, m_children, *u_parents):
"""
Compute the message to a parent.
"""
raise NotImplementedError()
def _add_child(self, child, index):
"""
Add a child node.
Only child nodes that are stochastic (or have stochastic children
recursively) are counted as children because deterministic nodes without
stochastic children do not have any messages to send so the parents do
not need to know about the deterministic node.
A deterministic node does not notify its parents when created, but if it
gets a stochastic child node, then notify parents. This method is called
only if a stochastic child (recursively) node is added, thus there is at
least one stochastic node below this deterministic node.
Parameters
----------
child : node
index : int
The parent index of this node for the child node.
The child node recognizes its parents by their index
number.
"""
super()._add_child(child, index)
# Now that this deterministic node has non-deterministic children,
# notify parents
for (ind,parent) in enumerate(self.parents):
parent._add_child(self, ind)
def _remove_child(self, child, index):
"""
Remove a child node.
Only child nodes that are stochastic (or have stochastic children
recursively) are counted as children because deterministic nodes without
stochastic children do not have any messages to send so the parents do
not need to know about the deterministic node.
So, if the deterministic node does not have any stochastic children left
after removal, remove it from its parents.
"""
super()._remove_child(child, index)
# Check whether there are any children left. If not, remove from parents
if len(self.children) == 0:
for (ind, parent) in enumerate(self.parents):
parent._remove_child(self, ind)
def lower_bound_contribution(self, gradient=False):
# Deterministic functions are delta distributions so the lower bound
# contribuion is zero.
return 0
def tile(X, tiles):
"""
Tile the plates of the input node.
x = [a,b,c]
y = tile(x, 2) = [a,b,c,a,b,c]
There should be no need to tile plates that have unit length because they
are handled properly by the broadcasting rules already.
Parameters:
-----------
X : Node
Input node to be tiled.
tiles : int, tuple
Tiling of the plates (broadcasting rules for plates apply).
See also:
---------
numpy.tile
"""
# Make sure `tiles` is tuple (even if an integer is given)
tiles = tuple(np.ravel(tiles))
class _Tile(Deterministic):
_parent_moments = (Moments(),)
def __init__(self, X, **kwargs):
self._moments = X._moments
super().__init__(X, dims=X.dims, **kwargs)
def _plates_to_parent(self, index):
plates = list(self.plates)
for i in range(-len(tiles), 0):
plates[i] = plates[i] // tiles[i]
return tuple(plates)
def _plates_from_parent(self, index):
return tuple(misc.multiply_shapes(self.parents[index].plates,
tiles))
def _compute_mask_to_parent(self, index, mask):
# Idea: Reshape the message array such that every other axis
# will be summed and every other kept.
# Make plates equal length
plates = self._plates_to_parent(index)
shape_m = np.shape(mask)
(plates, tiles_m, shape_m) = misc.make_equal_length(plates,
tiles,
shape_m)
# Handle broadcasting rules for axes that have unit length in
# the message (although the plate may be non-unit length). Also,
# compute the corresponding plate_multiplier.
plates = list(plates)
tiles_m = list(tiles_m)
for j in range(len(plates)):
if shape_m[j] == 1:
plates[j] = 1
tiles_m[j] = 1
# Combine the tuples by picking every other from tiles_ind and
# every other from shape
shape = functools.reduce(lambda x,y: x+y,
zip(tiles_m, plates))
# ..and reshape the array, that is, every other axis corresponds
# to tiles and every other to plates/dimensions in parents
mask = np.reshape(mask, shape)
# Sum over every other axis
axes = tuple(range(0,len(shape),2))
mask = np.any(mask, axis=axes)
# Remove extra leading axes
ndim_parent = len(self.parents[index].plates)
mask = misc.squeeze_to_dim(mask, ndim_parent)
return mask
def _compute_message_to_parent(self, index, m, u_X):
m = list(m)
for ind in range(len(m)):
# Idea: Reshape the message array such that every other axis
# will be summed and every other kept.
shape_ind = self._plates_to_parent(index) + self.dims[ind]
# Add variable dimensions to tiles
tiles_ind = tiles + (1,)*len(self.dims[ind])
# Make shape tuples equal length
shape_m = np.shape(m[ind])
(tiles_ind, shape, shape_m) = misc.make_equal_length(tiles_ind,
shape_ind,
shape_m)
# Handle broadcasting rules for axes that have unit length in
# the message (although the plate may be non-unit length). Also,
# compute the corresponding plate_multiplier.
r = 1
shape = list(shape)
tiles_ind = list(tiles_ind)
for j in range(len(shape)):
if shape_m[j] == 1:
r *= tiles_ind[j]
shape[j] = 1
tiles_ind[j] = 1
# Combine the tuples by picking every other from tiles_ind and
# every other from shape
shape = functools.reduce(lambda x,y: x+y,
zip(tiles_ind, shape))
# ..and reshape the array, that is, every other axis corresponds
# to tiles and every other to plates/dimensions in parents
m[ind] = np.reshape(m[ind], shape)
# Sum over every other axis
axes = tuple(range(0,len(shape),2))
m[ind] = r * np.sum(m[ind], axis=axes)
# Remove extra leading axes
ndim_parent = len(self.parents[index].get_shape(ind))
m[ind] = misc.squeeze_to_dim(m[ind], ndim_parent)
return m
def _compute_moments(self, u_X):
"""
Tile the plates of the parent's moments.
"""
# Utilize broadcasting: If a tiled axis is unit length in u_X, there
# is no need to tile it.
u = list()
for ind in range(len(u_X)):
ui = u_X[ind]
shape_u = np.shape(ui)
if np.ndim(ui) > 0:
# Add variable dimensions
tiles_ind = tiles + (1,)*len(self.dims[ind])
# Utilize broadcasting: Do not tile leading empty axes
nd = min(len(tiles_ind), np.ndim(ui))
tiles_ind = tiles_ind[(-nd):]
# For simplicity, make tiles and shape equal length
(tiles_ind, shape_u) = misc.make_equal_length(tiles_ind,
shape_u)
# Utilize broadcasting: Use tiling only if the parent's
# moment has non-unit axis length.
tiles_ind = [tile if sh > 1 else 1
for (tile, sh) in zip(tiles_ind, shape_u)]
# Tile
ui = np.tile(ui, tiles_ind)
u.append(ui)
return u
return _Tile(X, name="tile(%s, %s)" % (X.name, tiles))
bayespy-0.2.3/bayespy/inference/vmp/nodes/dirichlet.py 0000664 5104112 5104112 00000016124 12425127216 024565 0 ustar jluttine jluttine 0000000 0000000 ######################################################################
# Copyright (C) 2011,2012,2014 Jaakko Luttinen
#
# This file is licensed under Version 3.0 of the GNU General Public
# License. See LICENSE for a text of the license.
######################################################################
######################################################################
# This file is part of BayesPy.
#
# BayesPy is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# BayesPy is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with BayesPy. If not, see .
######################################################################
"""
Module for the Dirichlet distribution node.
"""
import numpy as np
import scipy.special as special
from bayespy.utils import random
from .expfamily import ExponentialFamily, ExponentialFamilyDistribution
from .constant import Constant
from .node import Node, Moments, ensureparents
class DirichletPriorMoments(Moments):
"""
Class for the moments of Dirichlet conjugate-prior variables.
"""
def compute_fixed_moments(self, alpha):
"""
Compute the moments for a fixed value
"""
alpha = np.asanyarray(alpha)
if np.ndim(alpha) < 1:
raise ValueError("The prior sample sizes must be a vector")
if np.any(alpha < 0):
raise ValueError("The prior sample sizes must be non-negative")
gammaln_sum = special.gammaln(np.sum(alpha, axis=-1))
sum_gammaln = np.sum(special.gammaln(alpha), axis=-1)
z = gammaln_sum - sum_gammaln
return [alpha, z]
def compute_dims_from_values(self, alpha):
"""
Return the shape of the moments for a fixed value.
"""
if np.ndim(alpha) < 1:
raise ValueError("The array must be at least 1-dimensional array.")
d = np.shape(alpha)[-1]
return [(d,), ()]
class DirichletMoments(Moments):
"""
Class for the moments of Dirichlet variables.
"""
def compute_fixed_moments(self, p):
"""
Compute the moments for a fixed value
"""
# Check that probabilities are non-negative
p = np.asanyarray(p)
if np.ndim(p) < 1:
raise ValueError("Probabilities must be given as a vector")
if np.any(p < 0) or np.any(p > 1):
raise ValueError("Probabilities must be in range [0,1]")
if not np.allclose(np.sum(p, axis=-1), 1.0):
raise ValueError("Probabilities must sum to one")
# Normalize probabilities
p = p / np.sum(p, axis=-1, keepdims=True)
# Message is log-probabilities
logp = np.log(p)
u = [logp]
return u
def compute_dims_from_values(self, x):
"""
Return the shape of the moments for a fixed value.
"""
if np.ndim(x) < 1:
raise ValueError("Probabilities must be given as a vector")
D = np.shape(x)[-1]
return ( (D,), )
class DirichletDistribution(ExponentialFamilyDistribution):
"""
Class for the VMP formulas of Dirichlet variables.
"""
def compute_message_to_parent(self, parent, index, u_self, u_alpha):
"""
Compute the message to a parent node.
"""
raise NotImplementedError()
def compute_phi_from_parents(self, u_alpha, mask=True):
"""
Compute the natural parameter vector given parent moments.
"""
return [u_alpha[0]]
def compute_moments_and_cgf(self, phi, mask=True):
"""
Compute the moments and :math:`g(\phi)`.
"""
sum_gammaln = np.sum(special.gammaln(phi[0]), axis=-1)
gammaln_sum = special.gammaln(np.sum(phi[0], axis=-1))
psi_sum = special.psi(np.sum(phi[0], axis=-1, keepdims=True))
# Moments
u0 = special.psi(phi[0]) - psi_sum
u = [u0]
# G
g = gammaln_sum - sum_gammaln
return (u, g)
def compute_cgf_from_parents(self, u_alpha):
"""
Compute :math:`\mathrm{E}_{q(p)}[g(p)]`
"""
return u_alpha[1]
def compute_fixed_moments_and_f(self, x, mask=True):
"""
Compute the moments and :math:`f(x)` for a fixed value.
"""
# Check that probabilities are non-negative
p = np.asanyarray(p)
if np.ndim(p) < 1:
raise ValueError("Probabilities must be given as a vector")
if np.any(p < 0) or np.any(p > 1):
raise ValueError("Probabilities must be in range [0,1]")
if not np.allclose(np.sum(p, axis=-1), 1.0):
raise ValueError("Probabilities must sum to one")
# Normalize probabilities
p = p / np.sum(p, axis=-1, keepdims=True)
# Message is log-probabilities
logp = np.log(p)
u = [logp]
f = np.nan
raise NotImplementedError("Check formula for f")
return (u, f)
def random(self, *phi, plates=None):
"""
Draw a random sample from the distribution.
"""
return random.dirichlet(phi[0], size=plates)
class Dirichlet(ExponentialFamily):
r"""
Node for Dirichlet random variables.
The node models a set of probabilities :math:`\{\pi_0, \ldots, \pi_{K-1}\}`
which satisfy :math:`\sum_{k=0}^{K-1} \pi_k = 1` and :math:`\pi_k \in [0,1]
\ \forall k=0,\ldots,K-1`.
.. math::
p(\pi_0, \ldots, \pi_{K-1}) = \mathrm{Dirichlet}(\alpha_0, \ldots,
\alpha_{K-1})
where :math:`\alpha_k` are concentration parameters.
The posterior approximation has the same functional form but with different
concentration parameters.
Parameters
----------
alpha : (...,K)-shaped array
Prior counts :math:`\alpha_k`
See also
--------
Beta, Categorical, Multinomial, CategoricalMarkovChain
"""
_moments = DirichletMoments()
_parent_moments = (DirichletPriorMoments(),)
_distribution = DirichletDistribution()
@classmethod
@ensureparents
def _constructor(cls, alpha, **kwargs):
"""
Constructs distribution and moments objects.
"""
# Number of categories
D = alpha.dims[0][0]
parents = [alpha]
return ( parents,
kwargs,
( (D,), ),
cls._total_plates(kwargs.get('plates'), alpha.plates),
cls._distribution,
cls._moments,
cls._parent_moments)
def show(self):
"""
Print the distribution using standard parameterization.
"""
alpha = self.phi[0]
print("%s ~ Dirichlet(alpha)" % self.name)
print(" alpha = ")
print(alpha)
bayespy-0.2.3/bayespy/inference/vmp/nodes/dot.py 0000664 5104112 5104112 00000050767 12374370351 023422 0 ustar jluttine jluttine 0000000 0000000 ######################################################################
# Copyright (C) 2011-2014 Jaakko Luttinen
#
# This file is licensed under Version 3.0 of the GNU General Public
# License. See LICENSE for a text of the license.
######################################################################
######################################################################
# This file is part of BayesPy.
#
# BayesPy is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# BayesPy is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with BayesPy. If not, see .
######################################################################
import numpy as np
from bayespy.utils import misc
from .node import Node
from .deterministic import Deterministic
from .gaussian import Gaussian, GaussianMoments
from .gaussian import GaussianGammaISOMoments
class SumMultiply(Deterministic):
r"""
Node for computing general products and sums of Gaussian nodes.
The node is similar to `numpy.einsum`, which is a very general
function for computing dot products, sums, products and other sums
of products of arrays.
For instance, the equivalent of
.. code-block:: python
np.einsum('abc,bd,ca->da', X, Y, Z)
would be given as
.. code-block:: python
SumMultiply('abc,bd,ca->da', X, Y, Z)
or
.. code-block:: python
SumMultiply(X, [0,1,2], Y, [1,3], Z, [2,0], [3,0])
which is similar to the other syntax of numpy.einsum.
This node operates similarly as numpy.einsum. However, you must use all the
elements of each node, that is, an operation like np.einsum('ii->i',X) is
not allowed. Thus, for each node, each axis must be given unique id. The id
identifies which axes correspond to which axes between the different
nodes. Also, Ellipsis ('...') is not yet supported for simplicity. It would
also have some problems with constant inputs (because how to determine
ndim), so let us just forget it for now.
Each output axis must appear in the input mappings.
The keys must refer to variable dimension axes only, not plate axes.
The input nodes may be Gaussian-gamma (isotropic) nodes.
The output message is Gaussian-gamma (isotropic) if any of the input nodes
is Gaussian-gamma.
Examples
--------
Sum over the rows:
'ij->j'
Inner product of three vectors:
'i,i,i'
Matrix-vector product:
'ij,j->i'
Matrix-matrix product:
'ik,kj->ij'
Outer product:
'i,j->ij'
Vector-matrix-vector product:
'i,ij,j'
Notes
-----
This operation can be extremely slow if not used wisely. For large and
complex operations, it is sometimes more efficient to split the operation
into multiple nodes. For instance, the example above could probably be
computed faster by
.. code-block:: python
XZ = SumMultiply(X, [0,1,2], Z, [2,0], [0,1])
F = SumMultiply(XZ, [0,1], Y, [1,2], [2,0])
because the third axis ('c') could be summed out already in the first
operation. This same effect applies also to numpy.einsum in general.
"""
def __init__(self, *args, iterator_axis=None, **kwargs):
"""
SumMultiply(Node1, map1, Node2, map2, ..., NodeN, mapN [, map_out])
"""
args = list(args)
if len(args) < 2:
raise ValueError("Not enough inputs")
if iterator_axis is not None:
raise NotImplementedError("Iterator axis not implemented yet")
if iterator_axis is not None and not isinstance(iterator_axis, int):
raise ValueError("Iterator axis must be integer")
# Two different parsing methods, depends on how the arguments are given
if misc.is_string(args[0]):
# This is the format:
# SumMultiply('ik,k,kj->ij', X, Y, Z)
strings = args[0]
nodes = args[1:]
# Remove whitespace
strings = misc.remove_whitespace(strings)
# Split on '->' (should contain only one '->' or none)
strings = strings.split('->')
if len(strings) > 2:
raise ValueError('The string contains too many ->')
strings_in = strings[0]
if len(strings) == 2:
string_out = strings[1]
else:
string_out = ''
# Split former part on ',' (the number of parts should be equal to
# nodes)
strings_in = strings_in.split(',')
if len(strings_in) != len(nodes):
raise ValueError('Number of given input nodes is different '
'from the input keys in the string')
# Split strings into key lists using single character keys
keysets = [list(string_in) for string_in in strings_in]
keys_out = list(string_out)
else:
# This is the format:
# SumMultiply(X, [0,2], Y, [2], Z, [2,1], [0,1])
# If given, the output mapping is the last argument
if len(args) % 2 == 0:
keys_out = []
else:
keys_out = args.pop(-1)
# Node and axis mapping are given in turns
nodes = args[::2]
keysets = args[1::2]
# Find all the keys (store only once each)
full_keyset = []
for keyset in keysets:
full_keyset += keyset
#full_keyset += list(keyset.keys())
full_keyset = list(set(full_keyset))
# Input and output messages are Gaussian unless there is at least one
# Gaussian-gamma message from the parents
self._moments = GaussianMoments(len(keys_out))
self._parent_moments = [GaussianMoments(len(keyset))
for keyset in keysets]
self.gaussian_gamma = False
for n in range(len(nodes)):
try:
# Try to convert into Gaussian moments
nodes[n] = self._ensure_moments(nodes[n],
self._parent_moments[n])
except GaussianMoments.NoConverterError:
# Failure. It is probably a Gaussian-gamma node
self.gaussian_gamma = True
if self.gaussian_gamma:
self._moments = GaussianGammaISOMoments(len(keys_out))
self._parent_moments = [GaussianGammaISOMoments(len(keyset))
for keyset in keysets]
for n in range(len(nodes)):
# Convert into Gaussian-gamma nodes
nodes[n] = self._ensure_moments(nodes[n],
self._parent_moments[n])
#
# Check the validity of each node
#
for n in range(len(nodes)):
# Check that the maps and the size of the variable are consistent
if len(nodes[n].dims[0]) != len(keysets[n]):
raise ValueError("Wrong number of keys (%d) for the node "
"number %d with %d dimensions"
% (len(keysets[n]),
n,
len(nodes[n].dims[0])))
# Check that the keys are unique
if len(set(keysets[n])) != len(keysets[n]):
raise ValueError("Axis keys for node number %d are not unique"
% n)
# Check the validity of output keys: each output key must be included in
# the input keys
if len(keys_out) != len(set(keys_out)):
raise ValueError("Output keys are not unique")
for key in keys_out:
if key not in full_keyset:
raise ValueError("Output key %s does not appear in any input"
% key)
# Check the validity of the nodes with respect to the key mapping.
# Check that the node dimensions map and broadcast properly, that is,
# all the nodes using the same key for axes must have equal size for
# those axes (or size 1).
broadcasted_size = {}
for key in full_keyset:
broadcasted_size[key] = 1
for (node, keyset) in zip(nodes, keysets):
try:
# Find the axis for the key
index = keyset.index(key)
except ValueError:
# OK, this node doesn't use this key for any axis
pass
else:
# Length of the axis for that key
node_size = node.dims[0][index]
if node_size != broadcasted_size[key]:
if broadcasted_size[key] == 1:
# Apply broadcasting
broadcasted_size[key] = node_size
elif node_size != 1:
# Different sizes and neither has size 1
raise ValueError("Axes using key %s do not "
"broadcast properly"
% key)
# Compute the shape of the output
dim0 = [broadcasted_size[key] for key in keys_out]
dim1 = dim0 + dim0
dims = ( tuple(dim0), tuple(dim1) )
if self.gaussian_gamma:
dims = dims + ( (), () )
# Rename the keys to [0,1,...,N-1] where N is the total number of keys
self.N_keys = len(full_keyset)
self.out_keys = [full_keyset.index(key) for key in keys_out]
self.in_keys = [ [full_keyset.index(key) for key in keyset]
for keyset in keysets ]
super().__init__(*nodes,
dims=dims,
**kwargs)
def _compute_moments(self, *u_parents):
# Compute the number of plate axes for each node
plate_counts0 = [(np.ndim(u_parent[0]) - len(keys))
for (keys,u_parent) in zip(self.in_keys, u_parents)]
plate_counts1 = [(np.ndim(u_parent[1]) - 2*len(keys))
for (keys,u_parent) in zip(self.in_keys, u_parents)]
# The number of plate axes for the output
N0 = max(plate_counts0)
N1 = max(plate_counts1)
# The total number of unique keys used (keys are 0,1,...,N_keys-1)
D = self.N_keys
#
# Compute the mean
#
out_all_keys = list(range(D+N0-1, D-1, -1)) + self.out_keys
#nodes_dim_keys = self.nodes_dim_keys
in_all_keys = [list(range(D+plate_count-1, D-1, -1)) + keys
for (plate_count, keys) in zip(plate_counts0,
self.in_keys)]
u0 = [u[0] for u in u_parents]
args = misc.zipper_merge(u0, in_all_keys) + [out_all_keys]
x0 = np.einsum(*args)
#
# Compute the covariance
#
out_all_keys = (list(range(2*D+N1-1, 2*D-1, -1))
+ [D+key for key in self.out_keys]
+ self.out_keys)
in_all_keys = [list(range(2*D+plate_count-1, 2*D-1, -1))
+ [D+key for key in node_keys]
+ node_keys
for (plate_count, node_keys) in zip(plate_counts1,
self.in_keys)]
u1 = [u[1] for u in u_parents]
args = misc.zipper_merge(u1, in_all_keys) + [out_all_keys]
x1 = np.einsum(*args)
if not self.gaussian_gamma:
return [x0, x1]
# Compute Gaussian-gamma specific moments
x2 = 1
x3 = 0
for i in range(len(u_parents)):
x2 = x2 * u_parents[i][2]
x3 = x3 + u_parents[i][3]
return [x0, x1, x2, x3]
def get_parameters(self):
# Compute mean and variance
u = self.get_moments()
u[1] -= u[0]**2
return u
def _message_to_parent(self, index):
"""
Compute the message and mask to a parent node.
"""
# Check index
if index >= len(self.parents):
raise ValueError("Parent index larger than the number of parents")
# Get messages from other parents and children
u_parents = self._message_from_parents(exclude=index)
m = self._message_from_children()
mask = self.mask
# Normally we don't need to care about masks when computing the
# message. However, in this node we want to avoid computing huge message
# arrays so we sum some axes already here. Thus, we need to apply the
# mask.
parent = self.parents[index]
#
# Compute the first message
#
msg = [None, None]
# Compute the two messages
for ind in range(2):
# The total number of keys for the non-plate dimensions
N = (ind+1) * self.N_keys
# Add an array of ones to ensure proper shape and number of
# plates. Note that this adds an axis for each plate. At the end, we
# want to remove axes that were created only because of this
parent_num_dims = len(parent.dims[ind])
parent_num_plates = len(parent.plates)
parent_plate_keys = list(range(N + parent_num_plates,
N,
-1))
parent_dim_keys = self.in_keys[index]
if ind == 1:
parent_dim_keys = ([key + self.N_keys
for key in self.in_keys[index]]
+ parent_dim_keys)
args = []
args.append(np.ones((1,)*parent_num_plates + parent.dims[ind]))
args.append(parent_plate_keys + parent_dim_keys)
# This variable counts the maximum number of plates of the
# arguments, thus it will tell the number of plates in the result
# (if the artificially added plates above were ignored).
result_num_plates = 0
result_plates = ()
# Mask and its keysr
mask_num_plates = np.ndim(mask)
mask_plates = np.shape(mask)
mask_plate_keys = list(range(N + mask_num_plates,
N,
-1))
result_num_plates = max(result_num_plates,
mask_num_plates)
result_plates = misc.broadcasted_shape(result_plates,
mask_plates)
args.append(mask)
args.append(mask_plate_keys)
# Moments and keys of other parents
for (k, u) in enumerate(u_parents):
if k != index:
num_dims = (ind+1) * len(self.in_keys[k])
num_plates = np.ndim(u[ind]) - num_dims
plates = np.shape(u[ind])[:num_plates]
plate_keys = list(range(N + num_plates,
N,
-1))
dim_keys = self.in_keys[k]
if ind == 1:
dim_keys = ([key + self.N_keys
for key in self.in_keys[k]]
+ dim_keys)
args.append(u[ind])
args.append(plate_keys + dim_keys)
result_num_plates = max(result_num_plates, num_plates)
result_plates = misc.broadcasted_shape(result_plates,
plates)
# Message and keys from children
child_num_dims = (ind+1) * len(self.out_keys)
child_num_plates = np.ndim(m[ind]) - child_num_dims
child_plates = np.shape(m[ind])[:child_num_plates]
child_plate_keys = list(range(N + child_num_plates,
N,
-1))
child_dim_keys = self.out_keys
if ind == 1:
child_dim_keys = ([key + self.N_keys
for key in self.out_keys]
+ child_dim_keys)
args.append(m[ind])
args.append(child_plate_keys + child_dim_keys)
result_num_plates = max(result_num_plates, child_num_plates)
result_plates = misc.broadcasted_shape(result_plates,
child_plates)
# Output keys, that is, the keys of the parent[index]
parent_keys = parent_plate_keys + parent_dim_keys
# Performance trick: Check which axes can be summed because they
# have length 1 or are non-existing in parent[index]. Thus, remove
# keys corresponding to unit length axes in parent[index] so that
# einsum sums over those axes. After computations, these axes must
# be added back in order to get the correct shape for the message.
parent_shape = parent.get_shape(ind)
removed_axes = []
for j in range(len(parent_keys)):
if parent_shape[j] == 1:
# Remove the key (take into account the number of keys that
# have already been removed)
del parent_keys[j-len(removed_axes)]
removed_axes.append(j)
args.append(parent_keys)
# THE BEEF: Compute the message
msg[ind] = np.einsum(*args)
# Find the correct shape for the message array
message_shape = list(np.shape(msg[ind]))
# First, add back the axes with length 1
for ax in removed_axes:
message_shape.insert(ax, 1)
# Second, remove leading axes for plates that were not present in
# the child nor other parents' messages. This is not really
# necessary, but it is just elegant to remove the leading unit
# length axes that we added artificially at the beginning just
# because we wanted the key mapping to be simple.
if parent_num_plates > result_num_plates:
del message_shape[:(parent_num_plates-result_num_plates)]
# Then, the actual reshaping
msg[ind] = np.reshape(msg[ind], message_shape)
# Apply plate multiplier: If this node has non-unit plates that are
# unit plates in the parent, those plates are summed. However, if
# the message has unit axis for that plate, it should be first
# broadcasted to the plates of this node and then summed to the
# plates of the parent. In order to avoid this broadcasting and
# summing, it is more efficient to just multiply by the correct
# factor.
r = self._plate_multiplier(self.plates,
result_plates,
parent.plates)
if r != 1:
msg[ind] *= r
if self.gaussian_gamma:
alphas = [u_parents[i][2]
for i in range(len(u_parents)) if i != index]
## logalphas = [u_parents[i][3]
## for i in range(len(u_parents)) if i != index]
m2 = self._compute_message(mask, m[2], *alphas,
ndim=0,
plates_from=self.plates,
plates_to=parent.plates)
m3 = self._compute_message(mask, m[3],#*logalphas,
ndim=0,
plates_from=self.plates,
plates_to=parent.plates)
msg = msg + [m2, m3]
return msg
def Dot(*args, **kwargs):
"""
Node for computing inner product of several Gaussian vectors.
This is a simple wrapper of the much more general SumMultiply. For now, it
is here for backward compatibility.
"""
einsum = 'i' + ',i'*(len(args)-1)
return SumMultiply(einsum, *args, **kwargs)
bayespy-0.2.3/bayespy/inference/vmp/nodes/expfamily.py 0000664 5104112 5104112 00000034707 12425127216 024623 0 ustar jluttine jluttine 0000000 0000000 ######################################################################
# Copyright (C) 2013-2014 Jaakko Luttinen
#
# This file is licensed under Version 3.0 of the GNU General Public
# License. See LICENSE for a text of the license.
######################################################################
######################################################################
# This file is part of BayesPy.
#
# BayesPy is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# BayesPy is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with BayesPy. If not, see .
######################################################################
import numpy as np
from bayespy.utils import misc
from .node import ensureparents
from .stochastic import Stochastic, Distribution
class ExponentialFamilyDistribution(Distribution):
"""
Sub-classes implement distribution specific computations.
"""
#
# The following methods are for ExponentialFamily distributions
#
def compute_message_to_parent(self, parent, index, u_self, *u_parents):
raise NotImplementedError()
def compute_phi_from_parents(self, *u_parents, mask=True):
raise NotImplementedError()
def compute_moments_and_cgf(self, phi, mask=True):
raise NotImplementedError()
#
# The following methods are for Mixture class
#
def compute_cgf_from_parents(self, *u_parents):
raise NotImplementedError()
def compute_fixed_moments_and_f(self, x, mask=True):
raise NotImplementedError()
def compute_logpdf(self, u, phi, g, f, ndims):
""" Compute E[log p(X)] given E[u], E[phi], E[g] and
E[f]. Does not sum over plates."""
# TODO/FIXME: Should I take into account what is latent or
# observed, or what is even totally ignored (by the mask).
L = g + f
for (phi_i, u_i, ndims_i) in zip(phi, u, ndims):
# Axes to sum (dimensions of the variable, not the plates)
axis_sum = tuple(range(-ndims_i,0))
# Compute the term
# TODO/FIXME: Use einsum!
L = L + np.sum(phi_i * u_i, axis=axis_sum)
return L
def useconstructor(__init__):
def constructor_decorator(self, *args, **kwargs):
if (self.dims is None or
self._distribution is None or
self._moments is None or
self._parent_moments is None):
(args, kwargs, dims, plates, dist, stats, pstats) = \
self._constructor(*args, **kwargs)
self.dims = dims
self._distribution = dist
self._moments = stats
self._parent_moments = pstats
self.plates = plates
__init__(self, *args, **kwargs)
return constructor_decorator
class ExponentialFamily(Stochastic):
"""
A base class for nodes using natural parameterization `phi`.
phi
Sub-classes must implement the following static methods:
_compute_message_to_parent(index, u_self, *u_parents)
_compute_phi_from_parents(*u_parents, mask)
_compute_moments_and_cgf(phi, mask)
_compute_fixed_moments_and_f(x, mask=True)
Sub-classes may need to re-implement:
1. If they manipulate plates:
_compute_mask_to_parent(index, mask)
_plates_to_parent(self, index)
_plates_from_parent(self, index)
"""
# Sub-classes should overwrite this (possibly using _constructor)
dims = None
# Sub-classes should overwrite this
_distribution = None
@useconstructor
def __init__(self, *parents, initialize=True, **kwargs):
# Terms for the lower bound (G for latent and F for observed)
self.g = np.array(np.nan)
self.f = np.array(np.nan)
super().__init__(*parents,
initialize=initialize,
dims=self.dims,
**kwargs)
if not initialize:
axes = len(self.plates)*(1,)
self.phi = [misc.nans(axes+dim) for dim in self.dims]
@classmethod
@ensureparents
def _constructor(cls, *parents, **kwargs):
"""
Constructs distribution and moments objects.
If __init__ uses useconstructor decorator, this method is called to
construct distribution and moments objects.
The method is given the same inputs as __init__. For some nodes, some of
these can't be "static" class attributes, then the node class must
overwrite this method to construct the objects manually.
The point of distribution class is to move general distribution but
not-node specific code. The point of moments class is to define the
messaging protocols.
"""
parent_plates = [cls._distribution.plates_from_parent(ind, parent.plates)
for (ind, parent) in enumerate(parents)]
return (parents,
kwargs,
cls.dims,
cls._total_plates(kwargs.get('plates'), *parent_plates),
cls._distribution,
cls._moments,
cls._parent_moments)
def _initialize_from_parent_moments(self, *u_parents):
if not np.all(self.observed):
# Update natural parameters using parents
self._update_phi_from_parents(*u_parents)
# Update moments
mask = np.logical_not(self.observed)
(u, g) = self._distribution.compute_moments_and_cgf(self.phi,
mask=mask)
# TODO/FIXME/BUG: You should use observation mask in order to not
# overwrite them!
self._set_moments_and_cgf(u, g, mask=mask)
def initialize_from_prior(self):
u_parents = self._message_from_parents()
self._initialize_from_parent_moments(*u_parents)
def initialize_from_parameters(self, *args):
u_parents = [p_mom.compute_fixed_moments(x)
for (p_mom, x) in zip(self._parent_moments, args)]
self._initialize_from_parent_moments(*u_parents)
def initialize_from_value(self, x, *args):
# Update moments from value
mask = np.logical_not(self.observed)
u = self._moments.compute_fixed_moments(x, *args)
# Check that the shape is correct
for i in range(len(u)):
ndim = len(self.dims[i])
if ndim > 0:
if np.shape(u[i])[-ndim:] != self.dims[i]:
raise ValueError("The initial value for node %s has invalid shape %s."
% (np.shape(x)))
self._set_moments_and_cgf(u, np.inf, mask=mask)
def initialize_from_random(self):
"""
Set the variable to a random sample from the current distribution.
"""
#self.initialize_from_prior()
X = self.random()
self.initialize_from_value(X)
def _update_phi_from_parents(self, *u_parents):
# TODO/FIXME: Could this be combined to the function
# _update_distribution_and_lowerbound ?
# No, because some initialization methods may want to use this.
# This makes correct broadcasting
self.phi = self._distribution.compute_phi_from_parents(*u_parents)
#self.phi = self._compute_phi_from_parents(*u_parents)
self.phi = list(self.phi)
# Make sure phi has the correct number of axes. It makes life
# a bit easier elsewhere.
for i in range(len(self.phi)):
axes = len(self.plates) + self.ndims[i] - np.ndim(self.phi[i])
if axes > 0:
# Add axes
self.phi[i] = misc.add_leading_axes(self.phi[i], axes)
elif axes < 0:
# Remove extra leading axes
first = -(len(self.plates)+self.ndims[i])
sh = np.shape(self.phi[i])[first:]
self.phi[i] = np.reshape(self.phi[i], sh)
# Check that the shape is correct
if not misc.is_shape_subset(np.shape(self.phi[i]),
self.get_shape(i)):
raise ValueError("Incorrect shape of phi[%d] in node class %s. "
"Shape is %s but it should be broadcastable "
"to shape %s."
% (i,
self.__class__.__name__,
np.shape(self.phi[i]),
self.get_shape(i)))
def _set_moments_and_cgf(self, u, g, mask=True):
self._set_moments(u, mask=mask)
# TODO/FIXME: Apply mask to g too!!
self.g = g
def _update_distribution_and_lowerbound(self, m_children, *u_parents):
# Update phi first from parents..
self._update_phi_from_parents(*u_parents)
# .. then just add children's message
for i in range(len(self.phi)):
self.phi[i] = self.phi[i] + m_children[i]
# Update u and g
self._update_moments_and_cgf()
def _update_moments_and_cgf(self):
"""
Update moments and cgf based on current phi.
"""
# Mask for plates to update (i.e., unobserved plates)
update_mask = np.logical_not(self.observed)
# Compute the moments (u) and CGF (g)...
(u, g) = self._distribution.compute_moments_and_cgf(self.phi,
mask=update_mask)
# ... and store them
self._set_moments_and_cgf(u, g, mask=update_mask)
def observe(self, x, *args, mask=True):
"""
Fix moments, compute f and propagate mask.
"""
# Compute fixed moments
(u, f) = self._distribution.compute_fixed_moments_and_f(x, *args,
mask=mask)
# Check the dimensionality of the observations
for (i,v) in enumerate(u):
# This is what the dimensionality "should" be
s = self.plates + self.dims[i]
t = np.shape(v)
if s != t:
msg = "Dimensionality of the observations incorrect."
msg += "\nShape of input: " + str(t)
msg += "\nExpected shape: " + str(s)
msg += "\nCheck plates."
raise Exception(msg)
# Set the moments
self._set_moments(u, mask=mask)
# TODO/FIXME: Use the mask?
self.f = f
# Observed nodes should not be ignored
self.observed = mask
self._update_mask()
def lower_bound_contribution(self, gradient=False):
# Compute E[ log p(X|parents) - log q(X) ] over q(X)q(parents)
# Messages from parents
#u_parents = [parent.message_to_child() for parent in self.parents]
u_parents = self._message_from_parents()
phi = self._distribution.compute_phi_from_parents(*u_parents)
# G from parents
L = self._distribution.compute_cgf_from_parents(*u_parents)
# L = g
# G for unobserved variables (ignored variables are handled
# properly automatically)
latent_mask = np.logical_not(self.observed)
#latent_mask = np.logical_and(self.mask, np.logical_not(self.observed))
# F for observed, G for latent
L = L + np.where(self.observed, self.f, -self.g)
for (phi_p, phi_q, u_q, dims) in zip(phi, self.phi, self.u, self.dims):
# Form a mask which puts observed variables to zero and
# broadcasts properly
latent_mask_i = misc.add_trailing_axes(
misc.add_leading_axes(
latent_mask,
len(self.plates) - np.ndim(latent_mask)),
len(dims))
axis_sum = tuple(range(-len(dims),0))
# Compute the term
phi_q = np.where(latent_mask_i, phi_q, 0)
# TODO/FIXME: Use einsum here?
Z = np.sum((phi_p-phi_q) * u_q, axis=axis_sum)
L = L + Z
return (np.sum(np.where(self.mask, L, 0))
* self._plate_multiplier(self.plates,
np.shape(L),
np.shape(self.mask)))
#return L
def logpdf(self, X, mask=True):
"""
Compute the log probability density function Q(X) of this node.
"""
if mask is not True:
raise NotImplementedError('Mask not yet implemented')
(u, f) = self._distribution.compute_fixed_moments_and_f(X, mask=mask)
Z = 0
for (phi_d, u_d, dims) in zip(self.phi, u, self.dims):
axis_sum = tuple(range(-len(dims),0))
# TODO/FIXME: Use einsum here?
Z = Z + np.sum(phi_d * u_d, axis=axis_sum)
#Z = Z + misc.sum_multiply(phi_d, u_d, axis=axis_sum)
## print('Z', Z)
## print('f', f)
## print('g', self.g)
return (self.g + f + Z)
def pdf(self, X, mask=True):
"""
Compute the probability density function of this node.
"""
return np.exp(self.logpdf(X, mask=mask))
def save(self, group):
"""
Save the state of the node into a HDF5 file.
group can be the root
"""
## if name is None:
## name = self.name
## subgroup = group.create_group(name)
for i in range(len(self.phi)):
misc.write_to_hdf5(group, self.phi[i], 'phi%d' % i)
misc.write_to_hdf5(group, self.f, 'f')
misc.write_to_hdf5(group, self.g, 'g')
super().save(group)
def load(self, group):
"""
Load the state of the node from a HDF5 file.
"""
# TODO/FIXME: Check that the shapes are correct!
for i in range(len(self.phi)):
phii = group['phi%d' % i][...]
self.phi[i] = phii
self.f = group['f'][...]
self.g = group['g'][...]
super().load(group)
def random(self):
"""
Draw a random sample from the distribution.
"""
return self._distribution.random(*(self.phi), plates=self.plates)
bayespy-0.2.3/bayespy/inference/vmp/nodes/exponential.py 0000664 5104112 5104112 00000005550 12374370351 025150 0 ustar jluttine jluttine 0000000 0000000 ######################################################################
# Copyright (C) 2014 Jaakko Luttinen
#
# This file is licensed under Version 3.0 of the GNU General Public
# License. See LICENSE for a text of the license.
######################################################################
######################################################################
# This file is part of BayesPy.
#
# BayesPy is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# BayesPy is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with BayesPy. If not, see .
######################################################################
"""
Module for the exponential distribution node.
"""
from .gamma import (GammaMoments,
Gamma)
from .expfamily import ExponentialFamily
ExponentialMoments = GammaMoments
class Exponential(Gamma):
r"""
Node for exponential random variables.
.. warning::
Use :class:`Gamma` instead of this. `Exponential(l)` is equivalent to
`Gamma(1, l)`.
Parameters
----------
l : gamma-like node or scalar or array
Rate parameter
See also
--------
Gamma, Poisson
Notes
-----
For simplicity, this is just a gamma node with the first parent fixed to
one. Note that this is a bit inconsistent with the BayesPy philosophy which
states that the node does not only define the form of the prior distribution
but more importantly the form of the posterior approximation. Thus, one
might expect that this node would have exponential posterior distribution
approximation. However, it has a gamma distribution. Also, the moments are
gamma moments although only E[x] would be the moment of a exponential random
variable. All this was done because: a) gamma was already implemented, so
there was no need to implement anything, and b) people might easily use
Exponential node as a prior definition and expect to get gamma posterior
(which is what happens now). Maybe some day a pure Exponential node is
implemented and the users are advised to use Gamma(1,b) if they want to use
an exponential prior distribution but gamma posterior approximation.
"""
def __init__(self, l, **kwargs):
raise NotImplementedError("Not yet implemented. Use Gamma(1, lambda)")
super().__init__(1, l, **kwargs)
@classmethod
def _constructor(cls, l, **kwargs):
raise NotImplementedError("Not yet implemented. Use Gamma(1, lambda)")
bayespy-0.2.3/bayespy/inference/vmp/nodes/gamma.py 0000664 5104112 5104112 00000017377 12430651206 023710 0 ustar jluttine jluttine 0000000 0000000 ######################################################################
# Copyright (C) 2011,2012,2014 Jaakko Luttinen
#
# This file is licensed under Version 3.0 of the GNU General Public
# License. See LICENSE for a text of the license.
######################################################################
######################################################################
# This file is part of BayesPy.
#
# BayesPy is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# BayesPy is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with BayesPy. If not, see .
######################################################################
"""
Module for the gamma distribution node.
"""
import numpy as np
import scipy.special as special
from .node import Node
from .deterministic import Deterministic
from .expfamily import ExponentialFamily, ExponentialFamilyDistribution
from .constant import Constant
from .node import Moments
from .wishart import WishartMoments
from bayespy.utils import misc
def diagonal(alpha):
"""
Create a diagonal Wishart node from a Gamma node.
"""
return _GammaToDiagonalWishart(alpha,
name=alpha.name + " as Wishart")
class GammaPriorMoments(Moments):
"""
Class for the moments of the shape parameter in gamma distributions.
"""
def compute_fixed_moments(self, a):
"""
Compute the moments for a fixed value
"""
u0 = a
u1 = special.gammaln(a)
return [u0, u1]
def compute_dims_from_values(self, a):
"""
Return the shape of the moments for a fixed value.
"""
return ( (), () )
class GammaMoments(Moments):
"""
Class for the moments of gamma variables.
"""
def compute_fixed_moments(self, x):
"""
Compute the moments for a fixed value
"""
if np.any(x < 0):
raise ValueError("Values must be positive")
u0 = x
u1 = np.log(x)
return [u0, u1]
def compute_dims_from_values(self, x):
"""
Return the shape of the moments for a fixed value.
"""
return ( (), () )
class GammaDistribution(ExponentialFamilyDistribution):
"""
Class for the VMP formulas of gamma variables.
"""
def compute_message_to_parent(self, parent, index, u_self, *u_parents):
"""
Compute the message to a parent node.
"""
if index == 0:
raise Exception("No analytic solution exists")
elif index == 1:
return [-u[0],
u_parents[0][0]]
else:
raise ValueError("Index out of bounds")
def compute_phi_from_parents(self, *u_parents, mask=True):
"""
Compute the natural parameter vector given parent moments.
"""
return [-u_parents[1][0],
1*u_parents[0][0]]
def compute_moments_and_cgf(self, phi, mask=True):
"""
Compute the moments and :math:`g(\phi)`.
"""
log_b = np.log(-phi[0])
u0 = phi[1] / (-phi[0])
u1 = special.digamma(phi[1]) - log_b
u = [u0, u1]
g = phi[1] * log_b - special.gammaln(phi[1])
return (u, g)
def compute_cgf_from_parents(self, *u_parents):
"""
Compute :math:`\mathrm{E}_{q(p)}[g(p)]`
"""
a = u_parents[0][0]
gammaln_a = u_parents[0][1] #special.gammaln(a)
b = u_parents[1][0]
log_b = u_parents[1][1]
g = a * log_b - gammaln_a
return g
def compute_fixed_moments_and_f(self, x, mask=True):
"""
Compute the moments and :math:`f(x)` for a fixed value.
"""
if np.any(x < 0):
raise ValueError("Values must be positive")
logx = np.log(x)
u = [x, logx]
f = -logx
return (u, f)
def random(self, *phi, plates=None):
"""
Draw a random sample from the distribution.
"""
return np.random.gamma(phi[1],
-1/phi[0],
size=plates)
class Gamma(ExponentialFamily):
"""
Node for gamma random variables.
Parameters
----------
a : scalar or array
Shape parameter
b : gamma-like node or scalar or array
Rate parameter
"""
dims = ( (), () )
_distribution = GammaDistribution()
_moments = GammaMoments()
_parent_moments = (GammaPriorMoments(),
GammaMoments())
def __init__(self, a, b, **kwargs):
"""
Create gamma random variable node
"""
super().__init__(a, b, **kwargs)
def show(self):
"""
Print the distribution using standard parameterization.
"""
a = self.phi[1]
b = -self.phi[0]
print("%s ~ Gamma(a, b)" % self.name)
print(" a =", a)
print(" b =", b)
def as_diagonal_wishart(self):
return _GammaToDiagonalWishart(self,
name=self.name + " as Wishart")
class _GammaToDiagonalWishart(Deterministic):
"""
Transform a set of gamma scalars into a diagonal Wishart matrix.
The last plate is used as the diagonal dimension.
"""
_moments = WishartMoments()
_parent_moments = [GammaMoments()]
def __init__(self, alpha, **kwargs):
# Check for constant
if misc.is_numeric(alpha):
alpha = Constant(Gamma)(alpha)
# Remove the last plate...
#plates = alpha.plates[:-1]
# ... and use it as the dimensionality of the Wishart
# distribution
if len(alpha.plates) == 0:
raise Exception("Gamma variable needs to have plates in "
"order to be used as a diagonal Wishart.")
D = alpha.plates[-1]
dims = ( (D,D), () )
# Construct the node
super().__init__(alpha,
#plates=plates,
dims=dims,
**kwargs)
def _plates_to_parent(self, index):
D = self.dims[0][0]
return self.plates + (D,)
def _plates_from_parent(self, index):
return self.parents[index].plates[:-1]
@staticmethod
def _compute_mask_to_parent(index, mask):
return mask[..., np.newaxis]
def get_moments(self):
u = self.parents[0].get_moments()
# Form a diagonal matrix from the gamma variables
return [np.identity(self.dims[0][0]) * u[0][...,np.newaxis],
np.sum(u[1], axis=(-1))]
@staticmethod
def _compute_message_to_parent(index, m_children, *u_parents):
# Take the diagonal
m0 = np.einsum('...ii->...i', m_children[0])
m1 = np.reshape(m_children[1], np.shape(m_children[1]) + (1,))
return [m0, m1]
## def _compute_message_and_mask_to_parent(self, index, m_children, *u_parents):
## m = self._message_from_children()
## #(m, mask) = self.message_from_children()
## # Take the diagonal
## m[0] = np.einsum('...ii->...i', m[0])
## # TODO/FIXME: I think m[1] is wrong..
## m[1] = np.reshape(m[1], np.shape(m[1]) + (1,))
## # m[1] is ok
## mask = self._compute_mask_to_parent(index, self.mask)
## #mask = mask[...,np.newaxis]
## return (m, mask)
## #return m
bayespy-0.2.3/bayespy/inference/vmp/nodes/gate.py 0000664 5104112 5104112 00000016357 12374370351 023551 0 ustar jluttine jluttine 0000000 0000000 ######################################################################
# Copyright (C) 2014 Jaakko Luttinen
#
# This file is licensed under Version 3.0 of the GNU General Public
# License. See LICENSE for a text of the license.
######################################################################
######################################################################
# This file is part of BayesPy.
#
# BayesPy is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# BayesPy is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with BayesPy. If not, see .
######################################################################
"""
"""
import numpy as np
from bayespy.utils import misc
from .node import Node, Moments
from .deterministic import Deterministic
from .categorical import CategoricalMoments
class Gate(Deterministic):
"""
Deterministic gating of one node.
Gating is performed over one plate axis.
Note: You should not use gating for several variables which parents of a
same node if the gates use the same gate assignments. In such case, the
results will be wrong. The reason is a general one: A stochastic node may
not be a parent of another node via several paths unless at most one path
has no other stochastic nodes between them.
"""
def __init__(self, Z, X, gated_plate=-1, moments=None, **kwargs):
"""
"""
if gated_plate >= 0:
raise ValueError("Cluster plate must be negative integer")
self.gated_plate = gated_plate
if moments is not None:
X = self._ensure_moments(X, moments)
if not isinstance(X, Node):
raise ValueError("X must be a node or moments should be provided")
X_moments = X._moments
self._moments = X_moments
dims = X.dims
if len(X.plates) < abs(gated_plate):
raise ValueError("The gated node does not have a plate axis is "
"gated")
K = X.plates[gated_plate]
self._parent_moments = [CategoricalMoments(K),
X_moments]
Z = self._ensure_moments(Z, self._parent_moments[0])
if Z.dims != ( (K,), ):
raise ValueError("Inconsistent number of clusters")
self.K = K
super().__init__(Z, X, dims=dims, **kwargs)
def _compute_moments(self, u_Z, u_X):
"""
"""
u = []
for i in range(len(u_X)):
# Make the moments of Z and X broadcastable and move the gated plate
# to be the last axis in the moments, then sum-product over that
# axis
ndim = len(self.dims[i])
z = misc.add_trailing_axes(u_Z[0], ndim)
z = misc.moveaxis(z, -ndim-1, -1)
gated_axis = self.gated_plate - ndim
if np.ndim(u_X[i]) < abs(gated_axis):
x = misc.add_trailing_axes(u_X[i], 1)
else:
x = misc.moveaxis(u_X[i], gated_axis, -1)
ui = misc.sum_product(z, x, axes_to_sum=-1)
u.append(ui)
return u
def _compute_message_to_parent(self, index, m_child, u_Z, u_X):
"""
"""
if index == 0:
m0 = 0
# Compute Child * X, sum over variable axes and move the gated axis
# to be the last. Need to do some shape changing in order to make
# Child and X to broadcast properly.
for i in range(len(m_child)):
ndim = len(self.dims[i])
c = m_child[i][...,None]
c = misc.moveaxis(c, -1, -ndim-1)
gated_axis = self.gated_plate - ndim
x = u_X[i]
if np.ndim(x) < abs(gated_axis):
x = np.expand_dims(x, -ndim-1)
else:
x = misc.moveaxis(x, gated_axis, -ndim-1)
axes = tuple(range(-ndim, 0))
m0 = m0 + misc.sum_product(c, x, axes_to_sum=axes)
# Make sure the variable axis does not use broadcasting
m0 = m0 * np.ones(self.K)
# Send the message
m = [m0]
return m
elif index == 1:
m = []
for i in range(len(m_child)):
# Make the moments of Z and the message from children
# broadcastable. The gated plate is handled as the last axis in
# the arrays and moved to the correct position at the end.
# Add variable axes to Z moments
ndim = len(self.dims[i])
z = misc.add_trailing_axes(u_Z[0], ndim)
z = misc.moveaxis(z, -ndim-1, -1)
# Axis index of the gated plate
gated_axis = self.gated_plate - ndim
# Add the gate axis to the message from the children
c = misc.add_trailing_axes(m_child[i], 1)
# Compute the message to parent
mi = z * c
# Add extra axes if necessary
if np.ndim(mi) < abs(gated_axis):
mi = misc.add_leading_axes(mi,
abs(gated_axis) - np.ndim(mi))
# Move the axis to the correct position
mi = misc.moveaxis(mi, -1, gated_axis)
m.append(mi)
return m
else:
raise ValueError("Invalid parent index")
def _compute_mask_to_parent(self, index, mask):
"""
"""
if index == 0:
return mask
elif index == 1:
if self.gated_plate >= 0:
raise ValueError("Gated plate axis must be negative")
if np.ndim(mask) >= abs(self.gated_plate):
mask = np.expand_dims(mask, axis=self.gated_plate)
return mask
else:
raise ValueError("Invalid parent index")
def _plates_to_parent(self, index):
"""
"""
if index == 0:
return self.plates
elif index == 1:
# Add the cluster plate axis
plates = list(self.plates)
if self.gated_plate < 0:
knd = len(plates) + self.gated_plate + 1
else:
raise RuntimeError("Cluster plate axis must be negative")
plates.insert(knd, self.K)
return tuple(plates)
else:
raise ValueError("Invalid parent index")
def _plates_from_parent(self, index):
"""
"""
if index == 0:
return self.parents[index].plates
elif index == 1:
# Remove the cluster plate, if the parent has it
plates = list(self.parents[index].plates)
if len(plates) >= abs(self.gated_plate):
plates.pop(self.gated_plate)
return tuple(plates)
else:
raise ValueError("Invalid parent index")
bayespy-0.2.3/bayespy/inference/vmp/nodes/gaussian.py 0000664 5104112 5104112 00000227235 12374370351 024442 0 ustar jluttine jluttine 0000000 0000000 ######################################################################
# Copyright (C) 2011-2014 Jaakko Luttinen
#
# This file is licensed under Version 3.0 of the GNU General Public
# License. See LICENSE for a text of the license.
######################################################################
######################################################################
# This file is part of BayesPy.
#
# BayesPy is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# BayesPy is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with BayesPy. If not, see .
######################################################################
"""
Module for the Gaussian distribution and similar distributions.
"""
import numpy as np
from scipy import special
from bayespy.utils import (random,
misc,
linalg)
from bayespy.utils.linalg import dot, mvdot
from .expfamily import (ExponentialFamily,
ExponentialFamilyDistribution,
useconstructor)
from .wishart import (WishartMoments,
WishartPriorMoments)
from .gamma import (GammaMoments,
GammaPriorMoments)
from .deterministic import Deterministic
from .node import (Moments,
ensureparents)
#
# MOMENTS
#
## class GaussianISOMoments(Moments):
## """
## Class for the moments of Gaussian ISO variables.
## """
## def __init__(self, ndim):
## self.ndim = ndim
## super().__init__()
## def compute_fixed_moments(self, x):
## """
## Compute the moments for a fixed value
## """
## if np.ndim(x) < self.ndim:
## raise ValueError("Not enough dimensions in x")
## u0 = x
## u1 = linalg.sum_multiply(x, x, axis=tuple(range(-self.ndim,0)))
## u = [u0, u1]
## return u
## def compute_dims_from_values(self, x):
## """
## Return the shape of the moments for a fixed value.
## """
## x = misc.atleast_nd(x, self.ndim)
## if self.ndim == 0:
## shape = ()
## else:
## shape = np.shape(x)[-self.ndim:]
## return (shape, ())
## class GaussianARDMoments(Moments):
## """
## Class for the moments of Gaussian ARD variables.
## """
## def __init__(self):
## super().__init__()
## def compute_fixed_moments(self, x):
## """
## Compute the moments for a fixed value
## """
## u0 = x
## u1 = x**2
## u = [u0, u1]
## return u
## def compute_dims_from_values(self, x):
## """
## Return the shape of the moments for a fixed value.
## """
## return ((), ())
class GaussianMoments(Moments):
r"""
Class for the moments of Gaussian variables.
"""
def __init__(self, ndim):
self.ndim = ndim
super().__init__()
def compute_fixed_moments(self, x):
r"""
Compute the moments for a fixed value
"""
x = misc.atleast_nd(x, self.ndim)
return [x, linalg.outer(x, x, ndim=self.ndim)]
def compute_dims_from_values(self, x):
r"""
Return the shape of the moments for a fixed value.
"""
x = misc.atleast_nd(x, self.ndim)
if self.ndim == 0:
shape = ()
else:
shape = np.shape(x)[-self.ndim:]
return (shape, shape+shape)
class GaussianGammaISOMoments(Moments):
r"""
Class for the moments of Gaussian-gamma-ISO variables.
"""
def __init__(self, ndim):
r"""
Create moments object for Gaussian-gamma isotropic variables
ndim=0: scalar
ndim=1: vector
ndim=2: matrix
...
"""
self.ndim = ndim
super().__init__()
def compute_fixed_moments(self, x, alpha):
r"""
Compute the moments for a fixed value
`x` is a mean vector.
`alpha` is a precision scale
"""
x = np.asanyarray(x)
alpha = np.asanyarray(alpha)
u0 = x * misc.add_trailing_axes(alpha, self.ndim)
u1 = (linalg.outer(x, x, ndim=self.ndim)
* misc.addtrailing_axes(alpha, 2*self.ndim))
u2 = np.copy(alpha)
u3 = np.log(alpha)
u = [u0, u1, u2, u3]
return u
def compute_dims_from_values(self, x, alpha):
r"""
Return the shape of the moments for a fixed value.
"""
if np.ndim(x) < 1:
raise ValueError("Mean must be a vector")
if ndim == 0:
return ( (), (), (), () )
dims = np.shape(x)[-ndim:]
return ( dims, 2*dims, (), () )
class GaussianGammaARDMoments(Moments):
r"""
Class for the moments of Gaussian-gamma-ARD variables.
"""
def __init__(self, ndim):
r"""
Create moments object for Gaussian-gamma isotropic variables
ndim=0: scalar
ndim=1: vector
ndim=2: matrix
...
"""
self.ndim = ndim
super().__init__()
def compute_fixed_moments(self, x, alpha):
r"""
Compute the moments for a fixed value
`x` is a mean vector.
`alpha` is a precision scale
"""
x = np.asanyarray(x)
alpha = np.asanyarray(alpha)
if np.ndim(x) < self.ndim:
raise ValueError("Not enough dimensions in x")
if np.ndim(alpha) < self.ndim:
raise ValueError("Not enough dimensions in alpha")
if np.shape(x) != np.shape(alpha):
raise ValueError("Mean and ARD scales have inconsistent shapes")
u0 = alpha * x
u1 = u0 * x
u2 = np.copy(alpha)
u3 = np.log(alpha)
u = [u0, u1, u2, u3]
return u
def compute_dims_from_values(self, x, alpha):
r"""
Return the shape of the moments for a fixed value.
"""
if np.ndim(x) < self.ndim:
raise ValueError("Not enough dimensions in x")
if np.ndim(alpha) < self.ndim:
raise ValueError("Not enough dimensions in alpha")
if np.shape(x) != np.shape(alpha):
raise ValueError("Mean and ARD scales have inconsistent shapes")
if ndim > 0:
shape = np.shape(x)[-self.ndim:]
else:
shape = ()
return ( shape, shape, shape, shape )
class GaussianWishartMoments(Moments):
r"""
Class for the moments of Gaussian-Wishart variables.
"""
def compute_fixed_moments(self, x, Lambda):
r"""
Compute the moments for a fixed value
`x` is a vector.
`Lambda` is a precision matrix
"""
x = np.asanyarray(x)
Lambda = np.asanyarray(Lambda)
u0 = np.einsum('...ik,...k->...i', Lambda, x)
u1 = np.einsum('...i,...ij,...j->...', x, Lambda, x)
u2 = np.copy(Lambda)
u3 = linalg.logdet_cov(Lambda)
return [u0, u1, u2, u3]
def compute_dims_from_values(self, x, Lambda):
r"""
Return the shape of the moments for a fixed value.
"""
if np.ndim(x) < 1:
raise ValueError("Mean must be a vector")
if np.ndim(Lambda) < 2:
raise ValueError("Precision must be a matrix")
D = np.shape(x)[-1]
if np.shape(Lambda)[-2:] != (D,D):
raise ValueError("Mean vector and precision matrix have "
"inconsistent shapes")
return ( (D,), (), (D,D), () )
#
# DISTRIBUTIONS
#
class GaussianDistribution(ExponentialFamilyDistribution):
r"""
Class for the VMP formulas of Gaussian variables.
Currently, supports only vector variables.
Notes
-----
Message passing equations:
.. math::
\mathbf{x} &\sim \mathcal{N}(\boldsymbol{\mu}, \mathbf{\Lambda}),
.. math::
\mathbf{x},\boldsymbol{\mu} \in \mathbb{R}^{D},
\quad \mathbf{\Lambda} \in \mathbb{R}^{D \times D},
\quad \mathbf{\Lambda} \text{ symmetric positive definite}
.. math::
\log\mathcal{N}( \mathbf{x} | \boldsymbol{\mu}, \mathbf{\Lambda} )
&=
- \frac{1}{2} \mathbf{x}^{\mathrm{T}} \mathbf{\Lambda} \mathbf{x}
+ \mathbf{x}^{\mathrm{T}} \mathbf{\Lambda} \boldsymbol{\mu}
- \frac{1}{2} \boldsymbol{\mu}^{\mathrm{T}} \mathbf{\Lambda}
\boldsymbol{\mu}
+ \frac{1}{2} \log |\mathbf{\Lambda}|
- \frac{D}{2} \log (2\pi)
.. math::
\mathbf{u} (\mathbf{x})
&=
\left[ \begin{matrix}
\mathbf{x}
\\
\mathbf{xx}^{\mathrm{T}}
\end{matrix} \right]
\\
\boldsymbol{\phi} (\boldsymbol{\mu}, \mathbf{\Lambda})
&=
\left[ \begin{matrix}
\mathbf{\Lambda} \boldsymbol{\mu}
\\
- \frac{1}{2} \mathbf{\Lambda}
\end{matrix} \right]
\\
\boldsymbol{\phi}_{\boldsymbol{\mu}} (\mathbf{x}, \mathbf{\Lambda})
&=
\left[ \begin{matrix}
\mathbf{\Lambda} \mathbf{x}
\\
- \frac{1}{2} \mathbf{\Lambda}
\end{matrix} \right]
\\
\boldsymbol{\phi}_{\mathbf{\Lambda}} (\mathbf{x}, \boldsymbol{\mu})
&=
\left[ \begin{matrix}
- \frac{1}{2} \mathbf{xx}^{\mathrm{T}}
+ \frac{1}{2} \mathbf{x}\boldsymbol{\mu}^{\mathrm{T}}
+ \frac{1}{2} \boldsymbol{\mu}\mathbf{x}^{\mathrm{T}}
- \frac{1}{2} \boldsymbol{\mu\mu}^{\mathrm{T}}
\\
\frac{1}{2}
\end{matrix} \right]
\\
g (\boldsymbol{\mu}, \mathbf{\Lambda})
&=
- \frac{1}{2} \operatorname{tr}(\boldsymbol{\mu\mu}^{\mathrm{T}}
\mathbf{\Lambda} )
+ \frac{1}{2} \log |\mathbf{\Lambda}|
\\
g_{\boldsymbol{\phi}} (\boldsymbol{\phi})
&=
\frac{1}{4} \boldsymbol{\phi}^{\mathrm{T}}_1 \boldsymbol{\phi}^{-1}_2
\boldsymbol{\phi}_1
+ \frac{1}{2} \log | -2 \boldsymbol{\phi}_2 |
\\
f(\mathbf{x})
&= - \frac{D}{2} \log(2\pi)
\\
\overline{\mathbf{u}} (\boldsymbol{\phi})
&=
\left[ \begin{matrix}
- \frac{1}{2} \boldsymbol{\phi}^{-1}_2 \boldsymbol{\phi}_1
\\
\frac{1}{4} \boldsymbol{\phi}^{-1}_2 \boldsymbol{\phi}_1
\boldsymbol{\phi}^{\mathrm{T}}_1 \boldsymbol{\phi}^{-1}_2
- \frac{1}{2} \boldsymbol{\phi}^{-1}_2
\end{matrix} \right]
"""
def compute_message_to_parent(self, parent, index, u, u_mu_Lambda):
r"""
Compute the message to a parent node.
"""
if index == 0:
x = u[0]
xx = u[1]
m0 = x
m1 = -0.5
m2 = -0.5*xx
m3 = 0.5
return [m0, m1, m2, m3]
else:
raise ValueError("Index out of bounds")
def compute_phi_from_parents(self, u_mu_Lambda, mask=True):
r"""
Compute the natural parameter vector given parent moments.
"""
Lambda_mu = u_mu_Lambda[0]
Lambda = u_mu_Lambda[2]
return [Lambda_mu,
-0.5 * Lambda]
def compute_moments_and_cgf(self, phi, mask=True):
r"""
Compute the moments and :math:`g(\phi)`.
"""
# TODO: Compute -2*phi[1] and simplify the formulas
L = misc.m_chol(-2*phi[1])
k = np.shape(phi[0])[-1]
# Moments
u0 = misc.m_chol_solve(L, phi[0])
u1 = misc.m_outer(u0, u0) + misc.m_chol_inv(L)
u = [u0, u1]
# G
g = (-0.5 * np.einsum('...i,...i', u[0], phi[0])
+ 0.5 * misc.m_chol_logdet(L))
#+ 0.5 * np.log(2) * self.dims[0][0])
return (u, g)
def compute_cgf_from_parents(self, u_mu_Lambda):
r"""
Compute :math:`\mathrm{E}_{q(p)}[g(p)]`
"""
mu_Lambda_mu = u_mu_Lambda[1]
logdet_Lambda = u_mu_Lambda[3]
g = -0.5*mu_Lambda_mu + 0.5*logdet_Lambda
return g
def compute_fixed_moments_and_f(self, x, mask=True):
r"""
Compute the moments and :math:`f(x)` for a fixed value.
"""
k = np.shape(x)[-1]
u = [x, misc.m_outer(x,x)]
f = -k/2*np.log(2*np.pi)
return (u, f)
def random(self, *phi, plates=None):
r"""
Draw a random sample from the distribution.
"""
# TODO/FIXME: You shouldn't draw random values for
# observed/fixed elements!
# Note that phi[1] is -0.5*inv(Cov)
U = misc.m_chol(-2*phi[1])
mu = misc.m_chol_solve(U, phi[0])
z = np.random.normal(0, 1, plates + np.shape(mu)[-1:])
# Compute mu + U'*z
z = misc.m_solve_triangular(U, z, trans='T', lower=False)
return mu + z
class GaussianARDDistribution(ExponentialFamilyDistribution):
r"""
...
Log probability density function:
.. math::
\log p(x|\mu, \alpha) = -\frac{1}{2} x^T \mathrm{diag}(\alpha) x + x^T
\mathrm{diag}(\alpha) \mu - \frac{1}{2} \mu^T \mathrm{diag}(\alpha) \mu
+ \frac{1}{2} \sum_i \log \alpha_i - \frac{D}{2} \log(2\pi)
Parent has moments:
.. math::
\begin{bmatrix}
\alpha \circ \mu
\\
\alpha \circ \mu \circ \mu
\\
\alpha
\\
\log(\alpha)
\end{bmatrix}
"""
def __init__(self, shape, ndim_mu):
self.shape = shape
self.ndim_mu = ndim_mu
self.ndim = len(shape)
super().__init__()
def compute_message_to_parent(self, parent, index, u, u_mu_alpha):
r"""
...
.. math::
m =
\begin{bmatrix}
x
\\
[-\frac{1}{2}, \ldots, -\frac{1}{2}]
\\
-\frac{1}{2} \mathrm{diag}(xx^T)
\\
[\frac{1}{2}, \ldots, \frac{1}{2}]
\end{bmatrix}
"""
if index == 0:
x = u[0]
x2 = misc.get_diag(u[1], ndim=self.ndim)
m0 = x
m1 = -0.5 * np.ones(self.shape)
m2 = -0.5 * x2
m3 = 0.5 * np.ones(self.shape)
m = [m0, m1, m2, m3]
return m
## if index == 0:
## x = u[0]
## alpha = u_alpha[0]
## axes0 = list(range(-self.ndim, -self.ndim_mu))
## m0 = misc.sum_multiply(alpha, x, axis=axes0)
## Alpha = misc.diag(alpha, ndim=self.ndim)
## axes1 = [axis+self.ndim for axis in axes0] + axes0
## m1 = -0.5 * misc.sum_multiply(Alpha,
## misc.identity(*self.shape),
## axis=axes1)
## return [m0, m1]
## elif index == 1:
## x = u[0]
## x2 = misc.get_diag(u[1], ndim=self.ndim)
## mu = u_mu[0]
## mu2 = misc.get_diag(u_mu[1], ndim=self.ndim_mu)
## if self.ndim_mu == 0:
## mu_shape = np.shape(mu) + (1,)*self.ndim
## else:
## mu_shape = (np.shape(mu)[:-self.ndim_mu]
## + (1,)*(self.ndim-self.ndim_mu)
## + np.shape(mu)[-self.ndim_mu:])
## mu = np.reshape(mu, mu_shape)
## mu2 = np.reshape(mu2, mu_shape)
## m0 = -0.5*x2 + x*mu - 0.5*mu2
## m1 = 0.5
## return [m0, m1]
else:
raise ValueError("Invalid parent index")
def compute_mask_to_parent(self, index, mask):
r"""
Maps the mask to the plates of a parent.
"""
if index == 0:
if self.ndim_mu == self.ndim:
return mask
elif self.ndim_mu < self.ndim:
diff = self.ndim - self.ndim_mu
return misc.add_trailing_axes(mask, diff)
else:
raise RuntimeError("Parent's ndim is larger")
else:
raise ValueError("Invalid parent index")
def compute_phi_from_parents(self, u_mu_alpha, mask=True):
alpha_mu = u_mu_alpha[0]
alpha = u_mu_alpha[2]
#mu = u_mu[0]
#alpha = u_alpha[0]
## if np.ndim(mu) < self.ndim_mu:
## raise ValueError("Moment of mu does not have enough dimensions")
## mu = misc.add_axes(mu,
## axis=np.ndim(mu)-self.ndim_mu,
## num=self.ndim-self.ndim_mu)
phi0 = alpha_mu
phi1 = -0.5 * alpha
if self.ndim > 0:
# Ensure that phi is not using broadcasting for variable
# dimension axes
ones = np.ones(self.shape)
phi0 = ones * phi0
phi1 = ones * phi1
# Make a diagonal matrix
phi1 = misc.diag(phi1, ndim=self.ndim)
return [phi0, phi1]
def compute_moments_and_cgf(self, phi, mask=True):
if self.ndim == 0:
# Use scalar equations
u0 = -phi[0] / (2*phi[1])
u1 = u0**2 - 1 / (2*phi[1])
u = [u0, u1]
g = (-0.5 * u[0] * phi[0] + 0.5 * np.log(-2*phi[1]))
# TODO/FIXME: You could use these equations if phi is a scalar
# in practice although ndim>0 (because the shape can be, e.g.,
# (1,1,1,1) for ndim=4).
else:
# Reshape to standard vector and matrix
D = np.prod(self.shape)
phi0 = np.reshape(phi[0], phi[0].shape[:-self.ndim] + (D,))
phi1 = np.reshape(phi[1], phi[1].shape[:-2*self.ndim] + (D,D))
# Compute the moments
L = linalg.chol(-2*phi1)
Cov = linalg.chol_inv(L)
u0 = linalg.chol_solve(L, phi0)
u1 = linalg.outer(u0, u0) + Cov
# Compute CGF
g = (- 0.5 * np.einsum('...i,...i', u0, phi0)
+ 0.5 * linalg.chol_logdet(L))
# Reshape to arrays
u0 = np.reshape(u0, u0.shape[:-1] + self.shape)
u1 = np.reshape(u1, u1.shape[:-2] + self.shape + self.shape)
u = [u0, u1]
return (u, g)
def compute_cgf_from_parents(self, u_mu_alpha):
r"""
Compute the value of the cumulant generating function.
"""
# Compute sum(mu^2 * alpha) correctly for broadcasted shapes
alpha_mu2 = u_mu_alpha[1]
logdet_alpha = u_mu_alpha[3]
axes = tuple(range(-self.ndim, 0))
# TODO/FIXME: You could use plate multiplier type of correction instead
# of explicitly broadcasting with ones.
if self.ndim > 0:
alpha_mu2 = misc.sum_multiply(alpha_mu2, np.ones(self.shape),
axis=axes)
if self.ndim > 0:
logdet_alpha = misc.sum_multiply(logdet_alpha, np.ones(self.shape),
axis=axes)
# Compute g
g = -0.5*alpha_mu2 + 0.5*logdet_alpha
return g
def compute_fixed_moments_and_f(self, x, mask=True):
r""" Compute u(x) and f(x) for given x. """
if self.ndim > 0 and np.shape(x)[-self.ndim:] != self.shape:
raise ValueError("Invalid shape")
k = np.prod(self.shape)
u = [x, linalg.outer(x, x, ndim=self.ndim)]
f = -k/2*np.log(2*np.pi)
return (u, f)
def plates_to_parent(self, index, plates):
r"""
Resolves the plate mapping to a parent.
Given the plates of the node's moments, this method returns the plates
that the message to a parent has for the parent's distribution.
"""
if index == 0:
if self.ndim_mu == self.ndim:
return plates
elif self.ndim_mu < self.ndim:
diff = self.ndim - self.ndim_mu
return plates + self.shape[:diff]
else:
raise RuntimeError("Parent's ndim is larger")
else:
raise ValueError("Invalid parent index")
def plates_from_parent(self, index, plates):
r"""
Resolve the plate mapping from a parent.
Given the plates of a parent's moments, this method returns the plates
that the moments has for this distribution.
"""
if index == 0:
if self.ndim_mu == self.ndim:
return plates
elif self.ndim_mu < self.ndim:
diff = self.ndim - self.ndim_mu
return plates[:-diff]
else:
raise RuntimeError("Parent's ndim is larger")
else:
raise ValueError("Invalid parent index")
def random(self, *phi, plates=None):
r"""
Draw a random sample from the Gaussian distribution.
"""
# TODO/FIXME: You shouldn't draw random values for
# observed/fixed elements!
D = self.ndim
if D == 0:
dims = ()
else:
dims = np.shape(phi[0])[-D:]
if np.prod(dims) == 1.0:
# Scalar Gaussian
phi1 = phi[1]
if D > 0:
# Because the covariance matrix has shape (1,1,...,1,1),
# that is 2*D number of ones, remove the extra half of the
# shape
phi1 = np.reshape(phi1, np.shape(phi1)[:-2*D] + D*(1,))
var = -0.5 / phi1
std = np.sqrt(var)
mu = var * phi[0]
z = np.random.normal(0, 1, plates + dims)
x = mu + std * z
else:
N = np.prod(dims)
dims_cov = dims + dims
# Reshape precision matrix
plates_cov = np.shape(phi[1])[:-2*D]
V = -2 * np.reshape(phi[1], plates_cov + (N,N))
# Compute Cholesky
U = linalg.chol(V)
# Reshape mean vector
plates_phi0 = np.shape(phi[0])[:-D]
phi0 = np.reshape(phi[0], plates_phi0 + (N,))
mu = linalg.chol_solve(U, phi0)
# Compute mu + U'*z
z = np.random.normal(0, 1, plates + (N,))
x = mu + linalg.solve_triangular(U, z,
trans='T',
lower=False)
x = np.reshape(x, plates + dims)
return x
class GaussianGammaISODistribution(ExponentialFamilyDistribution):
r"""
Class for the VMP formulas of Gaussian-Gamma-ISO variables.
Currently, supports only vector variables.
"""
def compute_message_to_parent(self, parent, index, u, u_mu_Lambda, u_a, u_b):
r"""
Compute the message to a parent node.
"""
if index == 0:
raise NotImplementedError()
elif index == 1:
raise NotImplementedError()
elif index == 2:
raise NotImplementedError()
else:
raise ValueError("Index out of bounds")
def compute_phi_from_parents(self, u_mu_Lambda, u_a, u_b, mask=True):
r"""
Compute the natural parameter vector given parent moments.
"""
Lambda_mu = u_mu_Lambda[0]
mu_Lambda_mu = u_mu_Lambda[1]
Lambda = u_mu_Lambda[2]
a = u_a[0]
b = u_b[0]
phi = [Lambda_mu,
-0.5*Lambda,
-0.5*mu_Lambda_mu - b,
a]
return phi
def compute_moments_and_cgf(self, phi, mask=True):
r"""
Compute the moments and :math:`g(\phi)`.
"""
# Compute helpful variables
V = -2*phi[1]
L_V = linalg.chol(V)
logdet_V = linalg.chol_logdet(L_V)
mu = linalg.chol_solve(L_V, phi[0])
Cov = linalg.chol_inv(L_V)
a = phi[3]
b = -phi[2] - 0.5 * linalg.inner(mu, phi[0])
log_b = np.log(b)
# Compute moments
u2 = a / b
u3 = -log_b + special.psi(a)
u0 = u2[...,None] * mu
u1 = Cov + u2[...,None,None] * linalg.outer(mu, mu)
u = [u0, u1, u2, u3]
# Compute g
g = 0.5*logdet_V + a*log_b - special.gammaln(a)
return (u, g)
def compute_cgf_from_parents(self, u_mu_Lambda, u_a, u_b):
r"""
Compute :math:`\mathrm{E}_{q(p)}[g(p)]`
"""
logdet_Lambda = u_mu_Lambda[3]
a = u_a[0]
gammaln_a = u_a[1]
log_b = u_b[1]
g = 0.5*logdet_Lambda + a*log_b - gammaln_a
return g
def compute_fixed_moments_and_f(self, x, alpha, mask=True):
r"""
Compute the moments and :math:`f(x)` for a fixed value.
"""
logalpha = np.log(alpha)
u0 = x * misc.add_trailing_axes(alpha, 1)
u1 = linalg.outer(x, x, ndim=1) * misc.add_trailing_axes(alpha, 2)
u2 = alpha
u3 = logalpha
u = [u0, u1, u2, u3]
D = np.shape(x)[-1]
f = (D/2 - 1) * logalpha - D/2 * np.log(2*np.pi)
return (u, f)
def random(self, *params, plates=None):
r"""
Draw a random sample from the distribution.
"""
raise NotImplementedError()
class GaussianGammaARDDistribution(ExponentialFamilyDistribution):
r"""
"""
def __init__(self):
raise NotImplementedError()
class GaussianWishartDistribution(ExponentialFamilyDistribution):
r"""
Class for the VMP formulas of Gaussian-Wishart variables.
Currently, supports only vector variables.
"""
def compute_message_to_parent(self, parent, index, u, u_mu_alpha, u_V, u_n):
r"""
Compute the message to a parent node.
"""
if index == 0:
raise NotImplementedError()
elif index == 1:
raise NotImplementedError()
elif index == 2:
raise NotImplementedError()
else:
raise ValueError("Index out of bounds")
def compute_phi_from_parents(self, u_mu_alpha, u_V, u_n, mask=True):
r"""
Compute the natural parameter vector given parent moments.
"""
raise NotImplementedError()
def compute_moments_and_cgf(self, phi, mask=True):
r"""
Compute the moments and :math:`g(\phi)`.
"""
raise NotImplementedError()
return (u, g)
def compute_cgf_from_parents(self, u_mu_alpha, u_V, u_n):
r"""
Compute :math:`\mathrm{E}_{q(p)}[g(p)]`
"""
raise NotImplementedError()
return g
def compute_fixed_moments_and_f(self, x, Lambda, mask=True):
r"""
Compute the moments and :math:`f(x)` for a fixed value.
"""
raise NotImplementedError()
return (u, f)
def random(self, *params, plates=None):
r"""
Draw a random sample from the distribution.
"""
raise NotImplementedError()
#
# NODES
#
class Gaussian(ExponentialFamily):
r"""
Node for Gaussian variables.
The node represents a :math:`D`-dimensional vector from the Gaussian
distribution:
.. math::
\mathbf{x} &\sim \mathcal{N}(\boldsymbol{\mu}, \mathbf{\Lambda}),
where :math:`\boldsymbol{\mu}` is the mean vector and
:math:`\mathbf{\Lambda}` is the precision matrix (i.e., inverse of the
covariance matrix).
.. math::
\mathbf{x},\boldsymbol{\mu} \in \mathbb{R}^{D},
\quad \mathbf{\Lambda} \in \mathbb{R}^{D \times D},
\quad \mathbf{\Lambda} \text{ symmetric positive definite}
Parameters
----------
mu : Gaussian-like node or GaussianGammaISO-like node or GaussianWishart-like node or array
Mean vector
Lambda : Wishart-like node or array
Precision matrix
See also
--------
Wishart, GaussianARD, GaussianWishart, GaussianGammaARD, GaussianGammaISO
"""
_distribution = GaussianDistribution()
_moments = GaussianMoments(1)
_parent_moments = [GaussianWishartMoments()]
def __init__(self, mu, Lambda, **kwargs):
r"""
Create Gaussian node
"""
super().__init__(mu, Lambda, **kwargs)
@classmethod
def _constructor(cls, mu, Lambda, **kwargs):
r"""
Constructs distribution and moments objects.
"""
mu_Lambda = WrapToGaussianWishart(mu, Lambda)
D = mu_Lambda.dims[0][0]
if mu_Lambda.dims != ( (D,), (), (D,D), () ):
raise Exception("Parents have wrong dimensionality")
parents = [mu_Lambda]
dims = ( (D,), (D,D) )
return (parents,
kwargs,
dims,
cls._total_plates(kwargs.get('plates'),
cls._distribution.plates_from_parent(0, mu_Lambda.plates)),
cls._distribution,
cls._moments,
cls._parent_moments)
def initialize_from_parameters(self, mu, Lambda):
u = self._parent_moments[0].compute_fixed_moments(mu, Lambda)
self._initialize_from_parent_moments(u)
def show(self):
mu = self.u[0]
Cov = self.u[1] - misc.m_outer(mu, mu)
print("%s ~ Gaussian(mu, Cov)" % self.name)
print(" mu = ")
print(mu)
print(" Cov = ")
print(str(Cov))
def rotate(self, R, inv=None, logdet=None, Q=None):
if inv is not None:
invR = inv
else:
invR = np.linalg.inv(R)
if logdet is not None:
logdetR = logdet
else:
logdetR = np.linalg.slogdet(R)[1]
# It would be more efficient and simpler, if you just rotated the
# moments and didn't touch phi. However, then you would need to call
# update() before lower_bound_contribution. This is more error-safe.
# Rotate plates, if plate rotation matrix is given. Assume that there's
# only one plate-axis
if Q is not None:
# Rotate moments using Q
self.u[0] = np.einsum('ik,kj->ij', Q, self.u[0])
sumQ = np.sum(Q, axis=0)
# Rotate natural parameters using Q
self.phi[1] = np.einsum('d,dij->dij', sumQ**(-2), self.phi[1])
self.phi[0] = np.einsum('dij,dj->di', -2*self.phi[1], self.u[0])
# Transform parameters using R
self.phi[0] = mvdot(invR.T, self.phi[0])
self.phi[1] = dot(invR.T, self.phi[1], invR)
if Q is not None:
self._update_moments_and_cgf()
else:
# Transform moments and g using R
self.u[0] = mvdot(R, self.u[0])
self.u[1] = dot(R, self.u[1], R.T)
self.g -= logdetR
def rotate_matrix(self, R1, R2, inv1=None, logdet1=None, inv2=None, logdet2=None, Q=None):
r"""
The vector is reshaped into a matrix by stacking the row vectors.
Computes R1*X*R2', which is identical to kron(R1,R2)*x (??)
Note that this is slightly different from the standard Kronecker product
definition because Numpy stacks row vectors instead of column vectors.
Parameters
----------
R1 : ndarray
A matrix from the left
R2 : ndarray
A matrix from the right
"""
if Q is not None:
# Rotate moments using Q
self.u[0] = np.einsum('ik,kj->ij', Q, self.u[0])
sumQ = np.sum(Q, axis=0)
# Rotate natural parameters using Q
self.phi[1] = np.einsum('d,dij->dij', sumQ**(-2), self.phi[1])
self.phi[0] = np.einsum('dij,dj->di', -2*self.phi[1], self.u[0])
if inv1 is None:
inv1 = np.linalg.inv(R1)
if logdet1 is None:
logdet1 = np.linalg.slogdet(R1)[1]
if inv2 is None:
inv2 = np.linalg.inv(R2)
if logdet2 is None:
logdet2 = np.linalg.slogdet(R2)[1]
D1 = np.shape(R1)[0]
D2 = np.shape(R2)[0]
# Reshape into matrices
sh0 = np.shape(self.phi[0])[:-1] + (D1,D2)
sh1 = np.shape(self.phi[1])[:-2] + (D1,D2,D1,D2)
phi0 = np.reshape(self.phi[0], sh0)
phi1 = np.reshape(self.phi[1], sh1)
# Apply rotations to phi
#phi0 = dot(inv1, phi0, inv2.T)
phi0 = dot(inv1.T, phi0, inv2)
phi1 = np.einsum('...ia,...abcd->...ibcd', inv1.T, phi1)
phi1 = np.einsum('...ic,...abcd->...abid', inv1.T, phi1)
phi1 = np.einsum('...ib,...abcd->...aicd', inv2.T, phi1)
phi1 = np.einsum('...id,...abcd->...abci', inv2.T, phi1)
# Reshape back into vectors
self.phi[0] = np.reshape(phi0, self.phi[0].shape)
self.phi[1] = np.reshape(phi1, self.phi[1].shape)
# It'd be better to rotate the moments too..
self._update_moments_and_cgf()
class GaussianARD(ExponentialFamily):
r"""
Node for Gaussian variables with ARD prior.
The node represents a :math:`D`-dimensional vector from the Gaussian
distribution:
.. math::
\mathbf{x} &\sim \mathcal{N}(\boldsymbol{\mu}, \mathrm{diag}(\boldsymbol{\alpha})),
where :math:`\boldsymbol{\mu}` is the mean vector and
:math:`\mathrm{diag}(\boldsymbol{\alpha})` is the diagonal precision matrix
(i.e., inverse of the covariance matrix).
.. math::
\mathbf{x},\boldsymbol{\mu} \in \mathbb{R}^{D}, \quad \alpha_d > 0 \text{
for } d=0,\ldots,D-1
*Note:* The form of the posterior approximation is a Gaussian distribution with full
covariance matrix instead of a diagonal matrix.
Parameters
----------
mu : Gaussian-like node or GaussianGammaISO-like node or GaussianGammaARD-like node or array
Mean vector
alpha : gamma-like node or array
Diagonal elements of the precision matrix
See also
--------
Gamma, Gaussian, GaussianGammaARD, GaussianGammaISO, GaussianWishart
"""
def __init__(self, mu, alpha, ndim=None, shape=None, **kwargs):
r"""
Create GaussianARD node.
"""
super().__init__(mu, alpha, ndim=ndim, shape=shape, **kwargs)
@classmethod
def _constructor(cls, mu, alpha, ndim=None, shape=None, **kwargs):
r"""
Constructs distribution and moments objects.
If __init__ uses useconstructor decorator, this method is called to
construct distribution and moments objects.
The method is given the same inputs as __init__. For some nodes, some of
these can't be "static" class attributes, then the node class must
overwrite this method to construct the objects manually.
The point of distribution class is to move general distribution but
not-node specific code. The point of moments class is to define the
messaging protocols.
"""
# Check consistency
if ndim is not None and shape is not None and ndim != len(shape):
raise ValueError("Given shape and ndim inconsistent")
if ndim is None and shape is not None:
ndim = len(shape)
# Infer shape of mu
try:
# Case: mu is a node
mu = mu._convert(GaussianGammaARDMoments)
except AttributeError:
# Case: mu is constant, we can use it as a scalar
shape_mu = ()
mu = cls._ensure_moments(mu, GaussianMoments(0))
mu = mu._convert(GaussianGammaARDMoments)
else:
shape_mu = mu.dims[0]
ndim_mu = len(shape_mu)
# Infer dimensionality
if ndim is None:
ndim = ndim_mu #max(ndim_mu, ndim_alpha)
elif ndim < ndim_mu: # or ndim < ndim_alpha:
raise ValueError("Parent mu has more axes")
# Infer shape of alpha
alpha = cls._ensure_moments(alpha, GammaMoments())
if ndim == 0:
shape_alpha = ()
else:
shape_alpha = alpha.plates[-ndim:]
# Infer shape of the node
#shape_bc = misc.broadcasted_shape(shape_mu, shape_alpha)
try:
shape_bc = misc.broadcasted_shape(mu.plates+shape_mu, alpha.plates)
except ValueError:
raise ValueError("Parent nodes have incompatible shapes")
if ndim == 0:
shape_bc = ()
elif ndim > len(shape_bc):
shape_bc = (ndim-len(shape_bc))*(1,) + shape_bc
else:
shape_bc = shape_bc[-ndim:]
# By default, use the broadcasted shape
if shape is None:
shape = shape_bc
if not misc.is_shape_subset(shape_bc, shape):
raise ValueError("Broadcasted shape of the parents %s does not "
"broadcast to the given shape %s"
% (shape_bc, shape))
mu_alpha = WrapToGaussianGammaARD(mu, alpha)
# Check shape consistency
shape_cov = shape[-ndim_mu:] + shape[-ndim_mu:]
moments = GaussianMoments(ndim)
parent_moments = [mu_alpha._moments]
distribution = GaussianARDDistribution(shape, ndim_mu)
dims = (shape, shape+shape)
plates = cls._total_plates(kwargs.get('plates'),
distribution.plates_from_parent(0, mu_alpha.plates))
parents = [mu_alpha]
return (parents,
kwargs,
dims,
plates,
distribution,
moments,
parent_moments)
def initialize_from_parameters(self, mu, alpha):
# Explicit broadcasting so the shapes match
mu = mu * np.ones(np.shape(alpha))
alpha = alpha * np.ones(np.shape(mu))
# Compute parent moments
u = self._parent_moments[0].compute_fixed_moments(mu, alpha)
# Initialize distribution
self._initialize_from_parent_moments(u)
def initialize_from_mean_and_covariance(self, mu, Cov):
ndim = len(self._distribution.shape)
u = [mu, Cov + linalg.outer(mu, mu, ndim=ndim)]
mask = np.logical_not(self.observed)
# TODO: You could compute the CGF but it requires Cholesky of
# Cov. Do it later.
self._set_moments_and_cgf(u, np.nan, mask=mask)
return
def show(self):
raise NotImplementedError()
mu = self.u[0]
Cov = self.u[1] - misc.m_outer(mu, mu)
print("%s ~ Gaussian(mu, Cov)" % self.name)
print(" mu = ")
print(mu)
print(" Cov = ")
print(str(Cov))
def rotate(self, R, inv=None, logdet=None, axis=-1, Q=None):
ndim = len(self._distribution.shape)
if inv is not None:
invR = inv
else:
invR = np.linalg.inv(R)
if logdet is not None:
logdetR = logdet
else:
logdetR = np.linalg.slogdet(R)[1]
self.phi[0] = rotate_mean(self.phi[0], invR.T,
axis=axis,
ndim=ndim)
self.phi[1] = rotate_covariance(self.phi[1], invR.T,
axis=axis,
ndim=ndim)
self.u[0] = rotate_mean(self.u[0], R,
axis=axis,
ndim=ndim)
self.u[1] = rotate_covariance(self.u[1], R,
axis=axis,
ndim=ndim)
s = list(self.dims[0])
s.pop(axis)
self.g -= logdetR * np.prod(s)
return
def rotate_plates(self, Q, plate_axis=-1):
r"""
Approximate rotation of a plate axis.
Mean is rotated exactly but covariance/precision matrix is rotated
approximately.
"""
ndim = len(self._distribution.shape)
# Rotate moments using Q
if not isinstance(plate_axis, int):
raise ValueError("Plate axis must be integer")
if plate_axis >= 0:
plate_axis -= len(self.plates)
if plate_axis < -len(self.plates) or plate_axis >= 0:
raise ValueError("Axis out of bounds")
u0 = rotate_mean(self.u[0], Q,
ndim=ndim+(-plate_axis),
axis=0)
sumQ = misc.add_trailing_axes(np.sum(Q, axis=0),
2*ndim-plate_axis-1)
phi1 = sumQ**(-2) * self.phi[1]
phi0 = -2 * matrix_dot_vector(phi1, u0, ndim=ndim)
self.phi[0] = phi0
self.phi[1] = phi1
self._update_moments_and_cgf()
return
class GaussianGammaISO(ExponentialFamily):
r"""
Node for Gaussian-gamma (isotropic) random variables.
The prior:
.. math::
p(x, \alpha| \mu, \Lambda, a, b)
p(x|\alpha, \mu, \Lambda) = \mathcal{N}(x | \mu, \alpha Lambda)
p(\alpha|a, b) = \mathcal{G}(\alpha | a, b)
The posterior approximation :math:`q(x, \alpha)` has the same Gaussian-gamma
form.
Currently, supports only vector variables.
"""
_moments = GaussianGammaISOMoments(1)
_parent_moments = (GaussianWishartMoments(),
GammaPriorMoments(),
GammaMoments())
_distribution = GaussianGammaISODistribution()
@classmethod
def _constructor(cls, mu, Lambda, a, b, **kwargs):
r"""
Constructs distribution and moments objects.
This method is called if useconstructor decorator is used for __init__.
`mu` is the mean/location vector
`alpha` is the scale
`V` is the scale matrix
`n` is the degrees of freedom
"""
# Convert parent nodes
mu_Lambda = WrapToGaussianWishart(mu, Lambda)
a = cls._ensure_moments(a, cls._parent_moments[1])
b = cls._ensure_moments(b, cls._parent_moments[2])
D = mu_Lambda.dims[0][0]
# Check shapes
if mu_Lambda.dims != ( (D,), (), (D,D), () ):
raise ValueError("mu and Lambda have wrong shape")
if a.dims != ( (), () ):
raise ValueError("a has wrong shape")
if b.dims != ( (), () ):
raise ValueError("b has wrong shape")
# Shapes of the moments / natural parameters
dims = ( (D,), (D,D), (), () )
# List of parent nodes
parents = [mu_Lambda, a, b]
return (parents,
kwargs,
dims,
cls._total_plates(kwargs.get('plates'),
cls._distribution.plates_from_parent(0, mu_Lambda.plates),
cls._distribution.plates_from_parent(1, a.plates),
cls._distribution.plates_from_parent(2, b.plates)),
cls._distribution,
cls._moments,
cls._parent_moments)
def show(self):
r"""
Print the distribution using standard parameterization.
"""
raise NotImplementedError()
def plotmatrix(self):
r"""
Creates a matrix of marginal plots.
On diagonal, are marginal plots of each variable. Off-diagonal plot
(i,j) shows the joint marginal density of x_i and x_j.
"""
import bayespy.plot as bpplt
if np.prod(self.plates) != 1:
raise ValueError("Currently, does not support plates in the node.")
if len(self.dims[0]) != 1:
raise ValueError("Currently, supports only vector variables")
# Dimensionality of the Gaussian
D = self.dims[0][0]
# Compute standard parameters
tau = self.u[2]
mu = self.u[0]
mu = mu / misc.add_trailing_axes(tau, 1)
Cov = self.u[1] - linalg.outer(self.u[0], mu, ndim=1)
Cov = Cov / misc.add_trailing_axes(tau, 2)
a = self.phi[3]
b = -self.phi[2] - 0.5*linalg.inner(self.phi[0], mu, ndim=1)
# Create subplots
(fig, axes) = bpplt.pyplot.subplots(D+1, D+1)
# Plot marginal Student t distributions
for i in range(D):
for j in range(i+1):
if i == j:
bpplt._pdf_t(*(random.gaussian_gamma_to_t(mu[i],
Cov[i,i],
a,
b,
ndim=0)),
axes=axes[i,i])
else:
S = Cov[np.ix_([i,j],[i,j])]
(m, S, nu) = random.gaussian_gamma_to_t(mu[[i,j]],
S,
a,
b)
bpplt._contour_t(m, S, nu, axes=axes[i,j])
bpplt._contour_t(m, S, nu, axes=axes[j,i], transpose=True)
# Plot Gaussian-gamma marginal distributions
for k in range(D):
bpplt._contour_gaussian_gamma(mu[k], Cov[k,k], a, b,
axes=axes[D,k])
bpplt._contour_gaussian_gamma(mu[k], Cov[k,k], a, b,
axes=axes[k,D],
transpose=True)
# Plot gamma marginal distribution
bpplt._pdf_gamma(a, b, axes=axes[D,D])
return axes
def get_gaussian_mean_and_variance(self):
r"""
Return the mean and variance of the distribution
"""
a = self.phi[3]
nu = 2*a
if nu <= 1:
raise ValueError("Mean not defined for degrees of freedom <= 1")
if nu <= 2:
raise ValueError("Variance not defined if degrees of freedom <= 2")
tau = self.u[2]
tau_mu = self.u[0]
mu = tau_mu / misc.add_trailing_axes(tau, 1)
var = misc.get_diag(self.u[1], ndim=1) - tau_mu*mu
var = var / misc.add_trailing_axes(tau, 1)
var = nu / (nu-2) * var
return (mu, var)
def get_marginal_logpdf(self, gaussian=None, gamma=None):
r"""
Get the (marginal) log pdf of a subset of the variables
Parameters
----------
gaussian : list or None
Indices of the Gaussian variables to keep or None
gamma : bool or None
True if keep the gamma variable, otherwise False or None
Returns
-------
function
A function which computes log-pdf
"""
if gaussian is None and not gamma:
raise ValueError("Must give some variables")
# Compute standard parameters
tau = self.u[2]
mu = self.u[0]
mu = mu / misc.add_trailing_axes(tau, 1)
Cov = np.linalg.inv(-2*self.phi[1])
if not np.allclose(Cov,
self.u[1] - linalg.outer(self.u[0], mu, ndim=1)):
raise Exception("WAAAT")
#Cov = Cov / misc.add_trailing_axes(tau, 2)
a = self.phi[3]
b = -self.phi[2] - 0.5*linalg.inner(self.phi[0], mu, ndim=1)
if not gamma:
# Student t distributions
inds = list(gaussian)
mu = mu[inds]
Cov = Cov[np.ix_(inds, inds)]
(mu, Cov, nu) = random.gaussian_gamma_to_t(mu,
Cov,
a,
b,
ndim=1)
L = linalg.chol(Cov)
logdet_Cov = linalg.chol_logdet(L)
D = len(inds)
def logpdf(x):
y = x - mu
v = linalg.chol_solve(L, y)
z2 = linalg.inner(y, v, ndim=1)
return random.t_logpdf(z2, logdet_Cov, nu, D)
return logpdf
elif gaussian is None:
# Gamma distribution
def logpdf(x):
logx = np.log(x)
return random.gamma_logpdf(b*x,
logx,
a*logx,
a*np.log(b),
special.gammaln(a))
return logpdf
else:
# Gaussian-gamma distribution
inds = list(gaussian)
mu = mu[inds]
Cov = Cov[np.ix_(inds, inds)]
D = len(inds)
L = linalg.chol(Cov)
logdet_Cov = linalg.chol_logdet(L)
def logpdf(x):
tau = x[...,-1]
logtau = np.log(tau)
x = x[...,:-1]
y = x - mu
v = linalg.chol_solve(L, y) * tau[...,None]
z2 = linalg.inner(y, v, ndim=1)
return (random.gaussian_logpdf(z2,
0,
0,
logdet_Cov + D*logtau,
D) +
random.gamma_logpdf(b*tau,
logtau,
a*logtau,
a*np.log(b),
special.gammaln(a)))
return logpdf
class GaussianGammaARD(ExponentialFamily):
r"""
Node for Gaussian and gamma random variables with ARD form.
The prior:
.. math::
p(x, \tau| \mu, \alpha, a, b) = p(x|\tau, \mu, \alpha) p(\tau|a, b)
p(x|\alpha, \mu, \alpha) = \mathcal{N}(x | \mu, \mathrm{diag}(
\boldsymbol{\alpha} \boldsymbol{\tau} ))
p(\tau|a, b) = \mathcal{G}(\tau | a, b)
The posterior approximation :math:`q(x, \tau)` has the same Gaussian-gamma
form.
.. warning:: Not yet implemented.
See also
--------
Gaussian, GaussianARD, Gamma, GaussianGammaISO, GaussianWishart
"""
def __init__(self, mu, alpha, a, b, **kwargs):
r"""
"""
raise NotImplementedError()
class GaussianWishart(ExponentialFamily):
r"""
Node for Gaussian-Wishart random variables.
The prior:
.. math::
p(x, \Lambda| \mu, \alpha, V, n)
p(x|\Lambda, \mu, \alpha) = \mathcal(N)(x | \mu, \alpha^{-1} Lambda^{-1})
p(\Lambda|V, n) = \mathcal(W)(\Lambda | n, V)
The posterior approximation :math:`q(x, \Lambda)` has the same Gaussian-Wishart form.
Currently, supports only vector variables.
"""
_moments = GaussianWishartMoments()
_distribution = GaussianWishartDistribution()
@classmethod
def _constructor(cls, mu, alpha, n, V, **kwargs):
r"""
Constructs distribution and moments objects.
This method is called if useconstructor decorator is used for __init__.
`mu` is the mean/location vector
`alpha` is the scale
`n` is the degrees of freedom
`V` is the scale matrix
"""
# Convert parent nodes
mu_alpha = WrapToGaussianGammaISO(mu, alpha)
D = mu_alpha.dims[0][0]
parent_moments = (GaussianGammaISOMoments(1),
WishartMoments(),
WishartPriorMoments(D))
n = cls._ensure_moments(n, parent_moments[1])
V = cls._ensure_moments(V, parent_moments[2])
# Check shapes
if mu_alpha.dims != ( (D,), (D,D), (), () ):
raise ValueError("mu and alpha have wrong shape")
if V.dims != ( (D,D), () ):
raise ValueError("Precision matrix has wrong shape")
if n.dims != ( (), () ):
raise ValueError("Degrees of freedom has wrong shape")
dims = ( (D,), (), (D,D), () )
parents = [mu_alpha, n, V]
return (parents,
kwargs,
dims,
cls._total_plates(kwargs.get('plates'),
cls._distribution.plates_from_parent(0, mu_alpha.plates),
cls._distribution.plates_from_parent(1, n.plates),
cls._distribution.plates_from_parent(2, V.plates)),
cls._distribution,
cls._moments,
parent_moments)
def show(self):
r"""
Print the distribution using standard parameterization.
"""
raise NotImplementedError()
#
# CONVERTERS
#
## class GaussianToGaussianARD(Deterministic):
## """
## Converter for Gaussian moments to Gaussian ARD moments
## """
## def __init__(self, X, **kwargs):
## """
## """
## self.ndim = X._moments.ndim
## self._moments = GaussianARDMoments()
## self._parent_moments = [GaussianMoments(self.ndim)]
## dims = ( (), () )
## super().__init__(X, dims=dims, **kwargs)
## def _compute_moments(self, u_X):
## """
## """
## x = u_X[0]
## xx = u_X[1]
## u0 = x
## u1 = misc.get_diag(xx, ndim=self.ndim)
## u = [u0, u1]
## return u
## def _compute_message_to_parent(self, index, m_child, u_X):
## """
## """
## if index == 0:
## m0 = m_child[0]
## m1 = misc.diag(m_child[1], ndim=self.ndim)
## m = [m0, m1]
## return m
## else:
## raise ValueError("Invalid parent index")
## def _compute_mask_to_parent(self, index, mask):
## """
## """
## if index == 0:
## if ndim == 0:
## return mask
## else:
## axes = tuple(range(-self.ndim, 0))
## return np.any(mask, axis=axes)
## else:
## raise ValueError("Invalid parent index")
## def _plates_to_parent(self, index):
## """
## """
## if index == 0:
## if ndim == 0:
## return self.plates
## else:
## return self.plates[:-self.ndim]
## else:
## raise ValueError("Invalid parent index")
## def _plates_from_parent(self, index):
## """
## """
## if index == 0:
## shape = self.parents[0].dims[0]
## return self.parents[0].plates + shape
## else:
## raise ValueError("Invalid parent index")
## GaussianMoments.add_converter(GaussianARDMoments,
## GaussianToGaussianARD)
## class GaussianARDToGaussianISO(Deterministic):
## """
## Converter for Gaussian moments to Gaussian-gamma isotropic moments
## Combines the Gaussian moments with gamma moments for a fixed value 1.
## """
## def __init__(self, X, **kwargs):
## """
## """
## self.ndim = X._moments.ndim
## self._moments = GaussianISOMoments(self.ndim)
## self._parent_moments = [GaussianARDMoments()]
## shape = X.dims[0]
## dims = ( shape, 2*shape, (), () )
## super().__init__(X, dims=dims, **kwargs)
## def _compute_moments(self, u_X):
## """
## """
## x = u_X[0]
## xx = u_X[1]
## u = [x, xx, 1, 0]
## return u
## def _compute_message_to_parent(self, index, m_child, u_X):
## """
## """
## if index == 0:
## m = m_child[:2]
## return m
## else:
## raise ValueError("Invalid parent index")
## GaussianARDMoments.add_converter(GaussianISOMoments,
## GaussianARDToGaussianISO)
class GaussianToGaussianGammaISO(Deterministic):
r"""
Converter for Gaussian moments to Gaussian-gamma isotropic moments
Combines the Gaussian moments with gamma moments for a fixed value 1.
"""
def __init__(self, X, **kwargs):
r"""
"""
self.ndim = X._moments.ndim
self._moments = GaussianGammaISOMoments(self.ndim)
self._parent_moments = [GaussianMoments(self.ndim)]
shape = X.dims[0]
dims = ( shape, 2*shape, (), () )
super().__init__(X, dims=dims, **kwargs)
def _compute_moments(self, u_X):
r"""
"""
x = u_X[0]
xx = u_X[1]
u = [x, xx, 1, 0]
return u
def _compute_message_to_parent(self, index, m_child, u_X):
r"""
"""
if index == 0:
m = m_child[:2]
return m
else:
raise ValueError("Invalid parent index")
GaussianMoments.add_converter(GaussianGammaISOMoments,
GaussianToGaussianGammaISO)
class GaussianGammaISOToGaussianGammaARD(Deterministic):
r"""
Converter for Gaussian-gamma ISO moments to Gaussian-gamma ARD moments
"""
def __init__(self, X, **kwargs):
r"""
"""
self.ndim = X._moments.ndim
self._moments = GaussianGammaARDMoments(self.ndim)
self._parent_moments = [GaussianGammaISOMoments(self.ndim)]
shape = X.dims[0]
dims = ( shape, shape, shape, shape )
super().__init__(X, dims=dims, **kwargs)
def _compute_moments(self, u_X_alpha):
r"""
...
.. math::
u_0 &= \alpha x
\\
u_1 &= \alpha \mathrm{diag}(xx^T)
\\
u_2 &= [\alpha, \ldots, \alpha]
\\
u_3 &= [\log(\alpha), \ldots, \log(\alpha)]
"""
shape = self.dims[0]
alpha_x = u_X_alpha[0]
alpha_xx = misc.get_diag(u_X_alpha[1], ndim=self.ndim)
alpha = misc.add_trailing_axes(u_X_alpha[2], self.ndim) * np.ones(shape)
logalpha = misc.add_trailing_axes(u_X_alpha[3], self.ndim) * np.ones(shape)
u = [alpha_x, alpha_xx, alpha, logalpha]
return u
def _compute_message_to_parent(self, index, m_child, u_X_alpha):
r"""
...
Message from the child is :math:`[m_0, m_1, m_2, m_3]`:
.. math::
\alpha m_0^T x + m_1 \alpha m_1^T \mathrm{diag}(xx^T) +
\alpha\mathrm{sum}(m_2) + \mathrm{sum}(m_3) \log|\alpha|
Thus, message to the first parent is (in case of Gaussian-gamma and
Wishart parents):
.. math::
\tilde{m_0} &= m_0
\\
\tilde{m_1} &= \mathrm{diag}(m_1)
\\
\tilde{m_2} &= \mathrm{sum}(m_2)
\\
\tilde{m_3} &= \mathrm{sum}(m_3)
"""
if index == 0:
m0 = m_child[0]
m1 = misc.diag(m_child[1], ndim=self.ndim)
m2 = np.sum(m_child[2], axis=tuple(range(-self.ndim,0)))
m3 = np.sum(m_child[3], axis=tuple(range(-self.ndim,0)))
m = [m0, m1, m2, m3]
return m
else:
raise ValueError("Invalid parent index")
GaussianGammaISOMoments.add_converter(GaussianGammaARDMoments,
GaussianGammaISOToGaussianGammaARD)
class GaussianGammaARDToGaussianWishart(Deterministic):
r"""
"""
def __init__(self, X_alpha, **kwargs):
raise NotImplementedError()
GaussianGammaARDMoments.add_converter(GaussianWishartMoments,
GaussianGammaARDToGaussianWishart)
## class GaussianGammaISOToGamma(Deterministic):
## """
## """
## def __init__(self):
## raise NotImplementedError()
## class GaussianGammaARDToGamma(Deterministic):
## """
## """
## def __init__(self):
## raise NotImplementedError()
## class GaussianWishartToWishart(Deterministic):
## """
## """
## def __init__(self):
## raise NotImplementedError()
#
# WRAPPERS
#
# These wrappers form a single node from two nodes for messaging purposes.
#
class WrapToGaussianGammaISO(Deterministic):
r"""
"""
_moments = GaussianGammaISOMoments(1)
_parent_moments = [GaussianGammaISOMoments(1),
GammaMoments()]
@ensureparents
def __init__(self, X, alpha, **kwargs):
r"""
"""
D = X.dims[0][0]
dims = ( (D,), (D,D), (), () )
super().__init__(X, alpha, dims=dims, **kwargs)
def _compute_moments(self, u_X, u_alpha):
r"""
"""
raise NotImplementedError()
def _compute_message_to_parent(self, index, m_child, u_X, u_alpha):
r"""
"""
if index == 0:
raise NotImplementedError()
elif index == 1:
raise NotImplementedError()
else:
raise ValueError("Invalid parent index")
def _compute_mask_to_parent(self, index, mask):
r"""
"""
if index == 0:
raise NotImplementedError()
elif index == 1:
raise NotImplementedError()
else:
raise ValueError("Invalid parent index")
def _plates_to_parent(self, index):
r"""
"""
if index == 0:
raise NotImplementedError()
elif index == 1:
raise NotImplementedError()
else:
raise ValueError("Invalid parent index")
def _plates_from_parent(self, index):
r"""
"""
if index == 0:
raise NotImplementedError()
elif index == 1:
raise NotImplementedError()
else:
raise ValueError("Invalid parent index")
class WrapToGaussianGammaARD(Deterministic):
r"""
"""
def __init__(self, mu_alpha, tau, **kwargs):
r"""
"""
# First, just in case mu_alpha is a numeric array, convert mu_alpha to
# (constant) Gaussian.
## if not isinstance(mu_alpha, Node):
## raise ValueError("Mu must be a node")
## if ndim is None:
## raise ValueError("For non-node mu, provide ndim")
## try:
## mu_alpha = self._ensure_moments(mu_alpha, GaussianMoments(ndim))
## except Moments.NoConverterError:
## pass
# Ensure proper moments from parents
try:
mu_alpha = mu_alpha._convert(GaussianGammaARDMoments)
except AttributeError:
raise ValueError("Mu must be a node")
# Parent moments
self._parent_moments = [mu_alpha._moments,
GammaMoments()]
tau = self._ensure_moments(tau, self._parent_moments[1])
ndim = len(mu_alpha.dims[0])
if ndim == 0:
shape = ()
else:
shape_mu = mu_alpha.dims[0]
shape_tau = tau.plates[-ndim:]
shape = misc.broadcasted_shape(shape_mu, shape_tau)
self.ndim = len(shape)
dims = ( shape, shape, shape, shape )
self._moments = GaussianGammaARDMoments(self.ndim)
super().__init__(mu_alpha, tau, dims=dims, **kwargs)
def _compute_moments(self, u_mu_alpha, u_tau):
r"""
"""
mu_alpha = u_mu_alpha[0]
mu2_alpha = u_mu_alpha[1]
alpha = u_mu_alpha[2]
logalpha = u_mu_alpha[3]
tau = u_tau[0]
logtau = u_tau[1]
u0 = mu_alpha * tau
u1 = mu2_alpha * tau
u2 = alpha * tau
u3 = logalpha + logtau
u = [u0, u1, u2, u3]
return u
def _compute_message_to_parent(self, index, m_child, u_mu_alpha, u_tau):
r"""
...
Message from the child is :math:`[m_0, m_1, m_2, m_3]`:
.. math::
m_0^T \mathrm{diag}(\alpha \circ \tau) \mu +
m_1^T \mathrm{diag}(\alpha \circ \tau) (\mu \circ \mu) +
m_2^T (\alpha \circ \tau) +
m_3^T \circ (\log \alpha + \log \tau)
Thus, message to the first parent is:
.. math::
\tilde{m_0} &= m_0 \circ \tau
\\
\tilde{m_1} &= m_1 \circ \tau
\\
\tilde{m_2} &= m_2 \circ \tau
\\
\tilde{m_3} &= m_3
Sum those to proper shape of mu-alpha.
The message to the second parent is:
.. math::
\tilde{m_0} &= \alpha \circ (m_0 \circ \mu + m_1 \circ \mu \circ \mu + m_2)
\\
\tilde{m_1} &= m_3
"""
if index == 0:
tau = u_tau[0]
m0 = m_child[0] * tau
m1 = m_child[1] * tau
m2 = m_child[2] * tau
m3 = m_child[3]
# Sum the broadcasted variable axes. Plate axes are handled by
# default.
shape_mu = self.parents[0].dims[0]
ndim_mu = len(shape_mu)
if ndim_mu > 0 and shape_mu != self.dims[0]:
plates_m0 = np.shape(m0)[:-ndim_mu]
plates_m1 = np.shape(m1)[:-ndim_mu]
plates_m2 = np.shape(m2)[:-ndim_mu]
plates_m3 = np.shape(m3)[:-ndim_mu]
m0 = misc.sum_to_shape(m0, plates_m0 + shape_mu)
m1 = misc.sum_to_shape(m1, plates_m1 + shape_mu)
m2 = misc.sum_to_shape(m2, plates_m2 + shape_mu)
m3 = misc.sum_to_shape(m3, plates_m3 + shape_mu)
m = [m0, m1, m2, m3]
return m
elif index == 1:
ndim_mu = len(self.parents[0].dims[0])
alpha_mu = u_mu_alpha[0]
alpha_mu2 = u_mu_alpha[1]
alpha = u_mu_alpha[2]
m0 = m_child[0]*alpha_mu + m_child[1]*alpha_mu2 + m_child[2]*alpha
m1 = m_child[3]
m = [m0, m1]
return m
else:
raise ValueError("Invalid parent index")
def _compute_mask_to_parent(self, index, mask):
r"""
"""
if index == 0:
return mask
elif index == 1:
return misc.add_trailing_axes(mask, self.ndim)
else:
raise ValueError("Invalid parent index")
def _plates_to_parent(self, index):
r"""
"""
if index == 0:
return self.plates
elif index == 1:
shape = self.dims[0]
return self.plates + shape
else:
raise ValueError("Invalid parent index")
def _plates_from_parent(self, index):
r"""
"""
if index == 0:
return self.parents[0].plates
elif index == 1:
if self.ndim == 0:
return self.parents[1].plates
else:
return self.parents[1].plates[:-self.ndim]
else:
raise ValueError("Invalid parent index")
class WrapToGaussianWishart(Deterministic):
r"""
Wraps Gaussian and Wishart nodes into a Gaussian-Wishart node.
The following node combinations can be wrapped:
* Gaussian and Wishart
* Gaussian-gamma and Wishart
* Gaussian-Wishart and gamma
"""
_moments = GaussianWishartMoments()
def __init__(self, X, Lambda, **kwargs):
r"""
"""
# Just in case X is an array, convert it to a Gaussian node first.
try:
X = self._ensure_moments(X, GaussianMoments(1))
except Moments.NoConverterError:
pass
try:
# Try combo Gaussian-Gamma and Wishart
X = self._ensure_moments(X, GaussianGammaISOMoments(1))
except ValueError:
# Have to use Gaussian-Wishart and Gamma
self._parent_moments = [GaussianWishartMoments(),
GammaMoments()]
X = self._ensure_moments(X, GaussianWishartMoments())
self.wishart = False
else:
self._parent_moments = [GaussianGammaISOMoments(1),
WishartMoments()]
self.wishart = True
D = X.dims[0][0]
dims = ( (D,), (), (D,D), () )
super().__init__(X, Lambda, dims=dims, **kwargs)
def _compute_moments(self, u_X_alpha, u_Lambda):
r"""
"""
if self.wishart:
alpha_x = u_X_alpha[0]
alpha_xx = u_X_alpha[1]
alpha = u_X_alpha[2]
log_alpha = u_X_alpha[3]
Lambda = u_Lambda[0]
logdet_Lambda = u_Lambda[1]
D = np.prod(self.dims[0])
u0 = linalg.mvdot(Lambda, alpha_x)
u1 = linalg.inner(Lambda, alpha_xx, ndim=2)
u2 = Lambda * misc.add_trailing_axes(alpha, 2)
u3 = logdet_Lambda + D * log_alpha
u = [u0, u1, u2, u3]
return u
else:
raise NotImplementedError()
def _compute_message_to_parent(self, index, m_child, u_X_alpha, u_Lambda):
r"""
...
Message from the child is :math:`[m_0, m_1, m_2, m_3]`:
.. math::
\alpha m_0^T \Lambda x + m_1 \alpha x^T \Lambda x
+ \mathrm{tr}(\alpha m_2 \Lambda) + m_3 (\log | \alpha \Lambda |)
In case of Gaussian-gamma and Wishart parents:
Message to the first parent (x, alpha):
.. math::
\tilde{m_0} &= \Lambda m_0
\\
\tilde{m_1} &= m_1 \Lambda
\\
\tilde{m_2} &= \mathrm{tr}(m_2 \Lambda)
\\
\tilde{m_3} &= m_3 \cdot D
Message to the second parent (Lambda):
.. math::
\tilde{m_0} &= \alpha (\frac{1}{2} m_0 x^T + \frac{1}{2} x m_0^T +
m_1 xx^T + m_2)
\\
\tilde{m_1} &= m_3
"""
if index == 0:
if self.wishart:
# Message to Gaussian-gamma (isotropic)
Lambda = u_Lambda[0]
D = np.prod(self.dims[0])
m0 = linalg.mvdot(Lambda, m_child[0])
m1 = Lambda * misc.add_trailing_axes(m_child[1], 2)
m2 = linalg.inner(Lambda, m_child[2], ndim=2)
m3 = D * m_child[3]
m = [m0, m1, m2, m3]
return m
else:
# Message to Gaussian-Wishart
raise NotImplementedError()
elif index == 1:
if self.wishart:
# Message to Wishart
alpha_x = u_X_alpha[0]
alpha_xx = u_X_alpha[1]
alpha = u_X_alpha[2]
m0 = (0.5*linalg.outer(alpha_x, m_child[0], ndim=1) +
0.5*linalg.outer(m_child[0], alpha_x, ndim=1) +
alpha_xx * misc.add_trailing_axes(m_child[1], 2) +
misc.add_trailing_axes(alpha, 2) * m_child[2])
m1 = m_child[3]
m = [m0, m1]
return m
else:
# Message to gamma (isotropic)
raise NotImplementedError()
else:
raise ValueError("Invalid parent index")
def reshape_gaussian_array(dims_from, dims_to, x0, x1):
r"""
Reshape the moments Gaussian array variable.
The plates remain unaffected.
"""
num_dims_from = len(dims_from)
num_dims_to = len(dims_to)
# Reshape the first moment / mean
num_plates_from = np.ndim(x0) - num_dims_from
plates_from = np.shape(x0)[:num_plates_from]
shape = (
plates_from
+ (1,)*(num_dims_to-num_dims_from) + dims_from
)
x0 = np.ones(dims_to) * np.reshape(x0, shape)
# Reshape the second moment / covariance / precision
num_plates_from = np.ndim(x1) - 2*num_dims_from
plates_from = np.shape(x1)[:num_plates_from]
shape = (
plates_from
+ (1,)*(num_dims_to-num_dims_from) + dims_from
+ (1,)*(num_dims_to-num_dims_from) + dims_from
)
x1 = np.ones(dims_to+dims_to) * np.reshape(x1, shape)
return (x0, x1)
def transpose_covariance(Cov, ndim=1):
r"""
Transpose the covariance array of Gaussian array variable.
That is, swap the last ndim axes with the ndim axes before them. This makes
transposing easy for array variables when the covariance is not a matrix but
a multidimensional array.
"""
axes_in = [Ellipsis] + list(range(2*ndim,0,-1))
axes_out = [Ellipsis] + list(range(ndim,0,-1)) + list(range(2*ndim,ndim,-1))
return np.einsum(Cov, axes_in, axes_out)
def left_rotate_covariance(Cov, R, axis=-1, ndim=1):
r"""
Rotate the covariance array of Gaussian array variable.
ndim is the number of axes for the Gaussian variable.
For vector variable, ndim=1 and covariance is a matrix.
"""
if not isinstance(axis, int):
raise ValueError("Axis must be an integer")
if axis < -ndim or axis >= ndim:
raise ValueError("Axis out of range")
# Force negative axis
if axis >= 0:
axis -= ndim
# Rotation from left
axes_R = [Ellipsis, ndim+abs(axis)+1, ndim+abs(axis)]
axes_Cov = [Ellipsis] + list(range(ndim+abs(axis),
0,
-1))
axes_out = [Ellipsis, ndim+abs(axis)+1] + list(range(ndim+abs(axis)-1,
0,
-1))
Cov = np.einsum(R, axes_R, Cov, axes_Cov, axes_out)
return Cov
def right_rotate_covariance(Cov, R, axis=-1, ndim=1):
r"""
Rotate the covariance array of Gaussian array variable.
ndim is the number of axes for the Gaussian variable.
For vector variable, ndim=1 and covariance is a matrix.
"""
if not isinstance(axis, int):
raise ValueError("Axis must be an integer")
if axis < -ndim or axis >= ndim:
raise ValueError("Axis out of range")
# Force negative axis
if axis >= 0:
axis -= ndim
# Rotation from right
axes_R = [Ellipsis, abs(axis)+1, abs(axis)]
axes_Cov = [Ellipsis] + list(range(abs(axis),
0,
-1))
axes_out = [Ellipsis, abs(axis)+1] + list(range(abs(axis)-1,
0,
-1))
Cov = np.einsum(R, axes_R, Cov, axes_Cov, axes_out)
return Cov
def rotate_covariance(Cov, R, axis=-1, ndim=1):
r"""
Rotate the covariance array of Gaussian array variable.
ndim is the number of axes for the Gaussian variable.
For vector variable, ndim=1 and covariance is a matrix.
"""
# Rotate from left and right
Cov = left_rotate_covariance(Cov, R, ndim=ndim, axis=axis)
Cov = right_rotate_covariance(Cov, R, ndim=ndim, axis=axis)
return Cov
def rotate_mean(mu, R, axis=-1, ndim=1):
r"""
Rotate the mean array of Gaussian array variable.
ndim is the number of axes for the Gaussian variable.
For vector variable, ndim=1 and mu is a vector.
"""
if not isinstance(axis, int):
raise ValueError("Axis must be an integer")
if axis < -ndim or axis >= ndim:
raise ValueError("Axis out of range")
# Force negative axis
if axis >= 0:
axis -= ndim
# Rotation from right
axes_R = [Ellipsis, abs(axis)+1, abs(axis)]
axes_mu = [Ellipsis] + list(range(abs(axis),
0,
-1))
axes_out = [Ellipsis, abs(axis)+1] + list(range(abs(axis)-1,
0,
-1))
mu = np.einsum(R, axes_R, mu, axes_mu, axes_out)
return mu
def array_to_vector(x, ndim=1):
if ndim == 0:
return x
shape_x = np.shape(x)
D = np.prod(shape_x[-ndim:])
return np.reshape(x, shape_x[:-ndim] + (D,))
def array_to_matrix(A, ndim=1):
if ndim == 0:
return A
shape_A = np.shape(A)
D = np.prod(shape_A[-ndim:])
return np.reshape(A, shape_A[:-2*ndim] + (D,D))
def vector_to_array(x, shape):
shape_x = np.shape(x)
return np.reshape(x, np.shape(x)[:-1] + tuple(shape))
def matrix_dot_vector(A, x, ndim=1):
if ndim < 0:
raise ValueError("ndim must be non-negative integer")
if ndim == 0:
return A*x
dims_x = np.shape(x)[-ndim:]
A = array_to_matrix(A, ndim=ndim)
x = array_to_vector(x, ndim=ndim)
y = np.einsum('...ik,...k->...i', A, x)
return vector_to_array(y, dims_x)
bayespy-0.2.3/bayespy/inference/vmp/nodes/gaussian_markov_chain.py 0000664 5104112 5104112 00000175153 12374370351 027164 0 ustar jluttine jluttine 0000000 0000000 ######################################################################
# Copyright (C) 2012-2014 Jaakko Luttinen
#
# This file is licensed under Version 3.0 of the GNU General Public
# License. See LICENSE for a text of the license.
######################################################################
######################################################################
# This file is part of BayesPy.
#
# BayesPy is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# BayesPy is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with BayesPy. If not, see .
######################################################################
"""
This module contains VMP nodes for Gaussian Markov chains.
"""
import numpy as np
import scipy
from bayespy.utils import misc
from bayespy.utils import linalg
from .node import Node, message_sum_multiply
from .deterministic import Deterministic
from .expfamily import ExponentialFamily
from .expfamily import ExponentialFamilyDistribution
from .expfamily import useconstructor
from .gaussian import Gaussian, GaussianMoments
from .wishart import Wishart, WishartMoments
from .gamma import Gamma, GammaMoments
from .categorical import CategoricalMoments
from .node import Moments, ensureparents
class GaussianMarkovChainMoments(Moments):
def compute_fixed_moments(self, x):
u0 = x
u1 = x[...,:,np.newaxis] * x[...,np.newaxis,:]
u2 = x[...,:-1,:,np.newaxis] * x[...,1:,np.newaxis,:]
return [u0, u1, u2]
class TemplateGaussianMarkovChainDistribution(ExponentialFamilyDistribution):
"""
Sub-classes implement distribution specific computations.
"""
def __init__(self, N, D):
self.N = N
self.D = D
super().__init__()
def compute_message_to_parent(self, parent, index, u_self, *u_parents):
raise NotImplementedError()
def compute_mask_to_parent(self, index, mask):
raise NotImplementedError()
def compute_phi_from_parents(self, *u_parents, mask=True):
raise NotImplementedError()
def compute_moments_and_cgf(self, phi, mask=True):
"""
Compute the moments and the cumulant-generating function.
This basically performs the filtering and smoothing for the variable.
Parameters
----------
phi
Returns
-------
u
g
"""
# Solve the Kalman filtering and smoothing problem
y = phi[0]
A = -2*phi[1]
# Don't multiply phi[2] by two because it is a sum of the super- and
# sub-diagonal blocks so we would need to divide by two anyway.
B = -phi[2]
(CovXnXn, CovXpXn, Xn, ldet) = linalg.block_banded_solve(A, B, y)
# Compute moments
u0 = Xn
u1 = CovXnXn + Xn[...,:,np.newaxis] * Xn[...,np.newaxis,:]
u2 = CovXpXn + Xn[...,:-1,:,np.newaxis] * Xn[...,1:,np.newaxis,:]
u = [u0, u1, u2]
# Compute cumulant-generating function
g = -0.5 * np.einsum('...ij,...ij', u[0], phi[0]) + 0.5*ldet
return (u, g)
def compute_cgf_from_parents(self, *u_parents):
raise NotImplementedError()
def compute_fixed_moments_and_f(self, x, mask=True):
"""
Compute u(x) and f(x) for given x.
"""
u0 = x
u1 = x[...,:,np.newaxis] * x[...,np.newaxis,:]
u2 = x[...,:-1,:,np.newaxis] * x[...,1:,np.newaxis,:]
u = [u0, u1, u2]
f = -0.5 * np.shape(x)[-2] * np.shape(x)[-1] * np.log(2*np.pi)
return (u, f)
def plates_to_parent(self, index, plates):
"""
Computes the plates of this node with respect to a parent.
Child classes must implement this.
Parameters:
-----------
index : int
The index of the parent node to use.
"""
raise NotImplementedError()
def plates_from_parent(self, index, plates):
"""
Compute the plates using information of a parent node.
Child classes must implement this.
Parameters
----------
index : int
Index of the parent to use.
"""
raise NotImplementedError()
class _TemplateGaussianMarkovChain(ExponentialFamily):
r"""
VMP abstract node for Gaussian Markov chain.
This is a general base class for different Gaussian Markov chain nodes.
Output is Gaussian variables with mean, covariance and one-step
cross-covariance.
self.phi and self.u are defined in a particular way but otherwise the parent
nodes may vary.
Child classes must implement the following methods:
_plates_to_parent
_plates_from_parent
See also
--------
bayespy.inference.vmp.nodes.gaussian.Gaussian
bayespy.inference.vmp.nodes.wishart.Wishart
"""
_moments = GaussianMarkovChainMoments()
def show(self):
raise NotImplementedError()
def rotate(self, R, inv=None, logdet=None):
if inv is not None:
invR = inv
else:
invR = np.linalg.inv(R)
if logdet is not None:
logdetR = logdet
else:
logdetR = np.linalg.slogdet(R)[1]
# It would be more efficient and simpler, if you just rotated the
# moments and didn't touch phi. However, then you would need to call
# update() before lower_bound_contribution. This is more error-safe.
# Transform parameters
self.phi[0] = linalg.mvdot(invR.T, self.phi[0])
self.phi[1] = linalg.dot(invR.T, self.phi[1], invR)
self.phi[2] = linalg.dot(invR.T, self.phi[2], invR)
N = self.dims[0][0]
if False:
self._update_moments_and_cgf()
else:
# Transform moments and g
u0 = linalg.mvdot(R, self.u[0])
u1 = linalg.dot(R, self.u[1], R.T)
u2 = linalg.dot(R, self.u[2], R.T)
self.u = [u0, u1, u2]
self.g -= N*logdetR
def _compute_cgf_for_gaussian_markov_chain(mumu, Lambda, logdet_Lambda,
logdet_v, N):
"""
Compute CGF using the moments of the parents.
"""
g0 = -0.5 * np.einsum('...ij,...ij->...', mumu, Lambda)
g1 = 0.5 * logdet_Lambda
if np.ndim(logdet_v) == 1:
g1 = g1 + 0.5 * (N-1) * np.sum(logdet_v, axis=-1)
elif np.shape(logdet_v)[-2] == 1:
g1 = g1 + 0.5 * (N-1) * np.sum(logdet_v, axis=(-1,-2))
else:
g1 = g1 + 0.5 * np.sum(logdet_v, axis=(-1,-2))
return g0 + g1
class GaussianMarkovChainDistribution(TemplateGaussianMarkovChainDistribution):
"""
Sub-classes implement distribution specific computations.
"""
def compute_message_to_parent(self, parent, index, u, u_mu, u_Lambda, u_A, u_v):
"""
Compute a message to a parent.
Parameters:
-----------
index : int
Index of the parent requesting the message.
u : list of ndarrays
Moments of this node.
u_mu : list of ndarrays
Moments of parent `mu`.
u_Lambda : list of ndarrays
Moments of parent `Lambda`.
u_A : list of ndarrays
Moments of parent `A`.
u_v : list of ndarrays
Moments of parent `v`.
u_N : list of ndarrays
Moments of parent `N`.
"""
if index == 0: # mu
raise NotImplementedError()
elif index == 1: # Lambda
raise NotImplementedError()
elif index == 2: # A
XnXn = u[1]
XpXn = u[2]
v = u_v[0]
m0 = v[...,np.newaxis] * XpXn.swapaxes(-1,-2)
# The following message matrix could be huge, so let's use a help
# function which computes sum(v*XnXn) without computing the huge
# v*XnXn explicitly.
m1 = -0.5 * message_sum_multiply(parent.plates,
parent.dims[1],
v[...,np.newaxis,np.newaxis],
XnXn[..., :-1, np.newaxis, :, :])
#m1 = -0.5 * v[...,np.newaxis,np.newaxis] * XnXn[..., :-1, np.newaxis, :, :]
elif index == 3: # v
XnXn = u[1] # (...,N,D,D)
XpXn = u[2] # (...,N-1,D,D)
A = u_A[0] # (...,N-1,D,D)
AA = u_A[1] # (...,N-1,D,D,D)
m0 = (- 0.5*np.einsum('...ii->...i', XnXn[...,1:,:,:])
+ np.einsum('...ik,...ki->...i', A, XpXn)
- 0.5*np.einsum('...ikl,...kl->...i', AA, XnXn[...,:-1,:,:]))
m1 = 0.5
elif index == 4: # N
raise NotImplementedError()
return [m0, m1]
def compute_mask_to_parent(self, index, mask):
if index == 0: # mu
return mask
elif index == 1: # Lambda
return mask
elif index == 2: # A
return mask[...,np.newaxis,np.newaxis]
elif index == 3: # v
return mask[...,np.newaxis,np.newaxis]
else:
raise ValueError("Index out of bounds")
def compute_phi_from_parents(self, u_mu, u_Lambda, u_A, u_v, mask=True):
"""
Compute the natural parameters using parents' moments.
Parameters
----------
u_parents : list of list of arrays
List of parents' lists of moments.
Returns
-------
phi : list of arrays
Natural parameters.
dims : tuple
Shape of the variable part of phi.
"""
# Dimensionality of the Gaussian states
D = np.shape(u_mu[0])[-1]
# Number of time instances in the process
N = self.N
# Helpful variables (show shapes in comments)
mu = u_mu[0] # (..., D)
Lambda = u_Lambda[0] # (..., D, D)
A = u_A[0] # (..., N-1, D, D)
AA = u_A[1] # (..., N-1, D, D, D)
v = u_v[0] # (..., N-1, D)
# Allocate memory (take into account effective plates)
plates_phi0 = misc.broadcasted_shape(np.shape(mu)[:-1],
np.shape(Lambda)[:-2])
plates_phi1 = misc.broadcasted_shape(np.shape(Lambda)[:-2],
np.shape(v)[:-2],
np.shape(AA)[:-4])
plates_phi2 = misc.broadcasted_shape(np.shape(v)[:-2],
np.shape(A)[:-3])
phi0 = np.zeros(plates_phi0+(N,D))
phi1 = np.zeros(plates_phi1+(N,D,D))
phi2 = np.zeros(plates_phi2+(N-1,D,D))
# Parameters for x0
phi0[...,0,:] = np.einsum('...ik,...k->...i', Lambda, mu)
phi1[...,0,:,:] = Lambda
# Diagonal blocks: -0.5 * (V_i + A_{i+1}' * V_{i+1} * A_{i+1})
phi1[..., 1:, :, :] = v[...,np.newaxis]*np.identity(D)
phi1[..., :-1, :, :] += np.einsum('...kij,...k->...ij', AA, v)
phi1 *= -0.5
# Super-diagonal blocks: 0.5 * A.T * V
# However, don't multiply by 0.5 because there are both super- and
# sub-diagonal blocks (sum them together)
phi2[..., :, :, :] = np.einsum('...ji,...j->...ij', A, v)
return (phi0, phi1, phi2)
def compute_cgf_from_parents(self, u_mu, u_Lambda, u_A, u_v):
"""
Compute CGF using the moments of the parents.
"""
return _compute_cgf_for_gaussian_markov_chain(u_mu[1],
u_Lambda[0],
u_Lambda[1],
u_v[1],
self.N)
def plates_to_parent(self, index, plates):
"""
Computes the plates of this node with respect to a parent.
If this node has plates (...), the latent dimensionality is D
and the number of time instances is N, the plates with respect
to the parents are:
mu: (...)
Lambda: (...)
A: (...,N-1,D)
v: (...,N-1,D)
Parameters:
-----------
index : int
The index of the parent node to use.
"""
if index == 0: # mu
return plates
elif index == 1: # Lambda
return plates
elif index == 2: # A
return plates + (self.N-1, self.D)
elif index == 3: # v
return plates + (self.N-1, self.D)
else:
raise ValueError("Invalid parent index.")
def plates_from_parent(self, index, plates):
"""
Compute the plates using information of a parent node.
If the plates of the parents are:
mu: (...)
Lambda: (...)
A: (...,N-1,D)
v: (...,N-1,D)
N: ()
the resulting plates of this node are (...)
Parameters
----------
index : int
Index of the parent to use.
"""
if index == 0: # mu
return plates
elif index == 1: # Lambda
return plates
elif index == 2: # A
return plates[:-2]
elif index == 3: # v
return plates[:-2]
else:
raise ValueError("Invalid parent index.")
class GaussianMarkovChain(_TemplateGaussianMarkovChain):
r"""
Node for Gaussian Markov chain random variables.
In a simple case, the graphical model can be presented as:
.. bayesnet::
\tikzstyle{latent} += [minimum size=30pt];
\node[latent] (x0) {$\mathbf{x}_0$};
\node[latent, right=of x0] (x1) {$\mathbf{x}_1$};
\node[right=of x1] (dots) {$\cdots$};
\node[latent, right=of dots] (xn) {$\mathbf{x}_{N-1}$};
\edge {x0}{x1};
\edge {x1}{dots};
\edge {dots}{xn};
\node[latent, above left=1 and 0.1 of x0] (mu) {$\boldsymbol{\mu}$};
\node[latent, above right=1 and 0.1 of x0] (Lambda) {$\mathbf{\Lambda}$};
\node[latent, above left=1 and 0.1 of dots] (A) {$\mathbf{A}$};
\node[latent, above right=1 and 0.1 of dots] (nu) {$\boldsymbol{\nu}$};
\edge {mu,Lambda} {x0};
\edge {A,nu} {x1,dots,xn};
where :math:`\boldsymbol{\mu}` and :math:`\mathbf{\Lambda}` are the mean and
the precision matrix of the initial state, :math:`\mathbf{A}` is the state
dynamics matrix and :math:`\boldsymbol{\nu}` is the precision of the
innovation noise. It is possible that :math:`\mathbf{A}` and/or
:math:`\boldsymbol{\nu}` are different for each transition instead of being
constant.
The probability distribution is
.. math::
p(\mathbf{x}_0, \ldots, \mathbf{x}_{N-1}) = p(\mathbf{x}_0)
\prod^{N-1}_{n=1} p(\mathbf{x}_n | \mathbf{x}_{n-1})
where
.. math::
p(\mathbf{x}_0) &= \mathcal{N}(\mathbf{x}_0 | \boldsymbol{\mu}, \mathbf{\Lambda})
\\
p(\mathbf{x}_n|\mathbf{x}_{n-1}) &= \mathcal{N}(\mathbf{x}_n |
\mathbf{A}_{n-1}\mathbf{x}_{n-1}, \mathrm{diag}(\boldsymbol{\nu}_{n-1})).
Parameters
----------
mu : Gaussian-like node or (...,D)-array
:math:`\boldsymbol{\mu}`, mean of :math:`x_0`, :math:`D`-dimensional
with plates (...)
Lambda : Wishart-like node or (...,D,D)-array
:math:`\mathbf{\Lambda}`, precision matrix of :math:`x_0`,
:math:`D\times D` -dimensional with plates (...)
A : Gaussian-like node or (D,D)-array or (...,1,D,D)-array or (...,N-1,D,D)-array
:math:`\mathbf{A}`, state dynamics matrix, :math:`D`-dimensional with
plates (D,) or (...,1,D) or (...,N-1,D)
nu : gamma-like node or (D,)-array or (...,1,D)-array or (...,N-1,D)-array
:math:`\boldsymbol{\nu}`, diagonal elements of the precision of the
innovation process, plates (D,) or (...,1,D) or (...,N-1,D)
n : int, optional
:math:`N`, the length of the chain. Must be given if :math:`\mathbf{A}`
and :math:`\boldsymbol{\nu}` are constant over time.
See also
--------
Gaussian, GaussianARD, Wishart, Gamma, SwitchingGaussianMarkovChain,
VaryingGaussianMarkovChain, CategoricalMarkovChain
"""
_parent_moments = (GaussianMoments(1),
WishartMoments(),
GaussianMoments(1),
GammaMoments())
def __init__(self, mu, Lambda, A, nu, n=None, **kwargs):
"""
Create GaussianMarkovChain node.
"""
super().__init__(mu, Lambda, A, nu, n=n, **kwargs)
@classmethod
@ensureparents
def _constructor(cls, mu, Lambda, A, v, n=None, **kwargs):
"""
Constructs distribution and moments objects.
Compute the dimensions of phi and u.
The plates and dimensions of the parents should be:
mu: (...) and D-dimensional
Lambda: (...) and D-dimensional
A: (...,1,D) or (...,N-1,D) and D-dimensional
v: (...,1,D) or (...,N-1,D) and 0-dimensional
N: () and 0-dimensional (dummy parent)
Check that the dimensionalities of the parents are proper.
For instance, A should be a collection of DxD matrices, thus
the dimensionality and the last plate should both equal D.
Similarly, `v` should be a collection of diagonal innovation
matrix elements, thus the last plate should equal D.
"""
n_A = 1
if len(A.plates) >= 2:
n_A = A.plates[-2]
n_v = 1
if len(v.plates) >= 2:
n_v = v.plates[-2]
if n_v != n_A and n_v != 1 and n_A != 1:
raise Exception("Plates of A and v are giving different number of time instances")
n_A = max(n_v, n_A)
if n is None:
if n_A == 1:
raise Exception("The number of time instances could not be "
"determined automatically. Give the number of "
"time instances.")
n = n_A + 1
elif n_A != 1 and n_A+1 != n:
raise Exception("The number of time instances must match "
"the number of last plates of parents: "
"%d != %d+1" % (n, n_A))
D = mu.dims[0][0]
#M = N.get_moments()[0]
M = n
# Check mu
if mu.dims != ( (D,), (D,D) ):
raise Exception("First parent has wrong dimensionality")
# Check Lambda
if Lambda.dims != ( (D,D), () ):
raise Exception("Second parent has wrong dimensionality")
# Check A
if A.dims != ( (D,), (D,D) ):
raise Exception("Third parent has wrong dimensionality")
if len(A.plates) == 0 or A.plates[-1] != D:
raise Exception("Third parent should have a last plate "
"equal to the dimensionality of the "
"system.")
if (len(A.plates) >= 2
and A.plates[-2] != 1
and A.plates[-2] != M-1):
raise ValueError("The second last plate of the third "
"parent should have length equal to one or "
"N-1, where N is the number of time "
"instances.")
# Check v
if v.dims != ( (), () ):
raise Exception("Fourth parent has wrong dimensionality")
if len(v.plates) == 0 or v.plates[-1] != D:
raise Exception("Fourth parent should have a last plate "
"equal to the dimensionality of the "
"system.")
if (len(v.plates) >= 2
and v.plates[-2] != 1
and v.plates[-2] != M-1):
raise ValueError("The second last plate of the fourth "
"parent should have length equal to one or "
"N-1 where N is the number of time "
"instances.")
dims = ( (M,D), (M,D,D), (M-1,D,D) )
distribution = GaussianMarkovChainDistribution(M, D)
parents = [mu, Lambda, A, v]
return ( parents,
kwargs,
dims,
cls._total_plates(kwargs.get('plates'),
distribution.plates_from_parent(0, mu.plates),
distribution.plates_from_parent(1, Lambda.plates),
distribution.plates_from_parent(2, A.plates),
distribution.plates_from_parent(3, v.plates)),
distribution,
cls._moments,
cls._parent_moments)
class VaryingGaussianMarkovChainDistribution(TemplateGaussianMarkovChainDistribution):
"""
Sub-classes implement distribution specific computations.
"""
def compute_message_to_parent(self, parent, index, u, u_mu, u_Lambda, u_B,
u_S, u_v):
"""
Compute a message to a parent.
Parameters:
-----------
index : int
Index of the parent requesting the message.
u : list of ndarrays
Moments of this node.
u_mu : list of ndarrays
Moments of parent `mu`.
u_Lambda : list of ndarrays
Moments of parent `Lambda`.
u_B : list of ndarrays
Moments of parent `B`.
u_S : list of ndarrays
Moments of parent `S`.
u_v : list of ndarrays
Moments of parent `v`.
"""
if index == 0: # mu
raise NotImplementedError()
elif index == 1: # Lambda
raise NotImplementedError()
elif index == 2: # B, (...,D)x(D,K)
XnXn = u[1] # (...,N,D,D)
XpXn = u[2] # (...,N,D,D)
S = misc.atleast_nd(u_S[0], 2) # (...,N,K)
SS = misc.atleast_nd(u_S[1], 3) # (...,N,K,K)
v = misc.atleast_nd(u_v[0], 2) # (...,N,D)
# m0: (...,D,D,K)
m0 = np.einsum('...nji,...nk,...ni->...ijk',
XpXn,
S,
v)
# m1: (...,D,D,K,D,K)
if np.ndim(v) >= 2 and np.shape(v)[-2] > 1:
raise ValueError("Innovation noise is time dependent")
m1 = np.einsum('...nij,...nkl->...ikjl',
XnXn[...,:-1,:,:],
SS)
m1 = -0.5 * np.einsum('...ikjl,...d->...dikjl',
m1,
v[...,0,:])
elif index == 3: # S, (...,N-1)x(K)
XnXn = u[1] # (...,N,D,D)
XpXn = u[2] # (...,N,D,D)
B = u_B[0] # (...,D,D,K)
BB = u_B[1] # (...,D,D,K,D,K)
v = u_v[0] # (...,N,D)
# m0: (...,N,K)
m0 = np.einsum('...nji,...ijk,...ni->...nk',
XpXn,
B,
np.atleast_2d(v))
# m1: (...,N,K,K)
if np.ndim(v) >= 2 and np.shape(v)[-2] > 1:
raise ValueError("Innovation noise is time dependent")
m1 = np.einsum('...dikjl,...d->...ikjl',
BB,
np.atleast_2d(v)[...,0,:])
m1 = -0.5 * np.einsum('...nij,...ikjl->...nkl',
XnXn[...,:-1,:,:],
m1)
elif index == 4: # v
raise NotImplementedError()
elif index == 5: # N
raise NotImplementedError()
return [m0, m1]
def compute_mask_to_parent(self, index, mask):
if index == 0: # mu
return mask
elif index == 1: # Lambda
return mask
elif index == 2: # B
return mask[...,np.newaxis] # new plate axis for D
elif index == 3: # S
return mask[...,np.newaxis] # new plate axis for N
elif index == 4: # v
return mask[...,np.newaxis,np.newaxis] # new plate axis for N and D
elif index == 5: # N
return mask
else:
raise ValueError("Invalid index")
def compute_phi_from_parents(self, u_mu, u_Lambda, u_B, u_S, u_v,
mask=True):
"""
Compute the natural parameters using parents' moments.
Parameters
----------
u_parents : list of list of arrays
List of parents' lists of moments.
Returns
-------
phi : list of arrays
Natural parameters.
dims : tuple
Shape of the variable part of phi.
"""
# Dimensionality of the Gaussian states
D = np.shape(u_mu[0])[-1]
# Number of time instances in the process
N = self.N
# Helpful variables (show shapes in comments)
mu = u_mu[0] # (..., D)
Lambda = u_Lambda[0] # (..., D, D)
B = u_B[0] # (..., D, D, K)
BB = u_B[1] # (..., D, D, K, D, K)
S = u_S[0] # (..., N-1, K) or (..., 1, K)
SS = u_S[1] # (..., N-1, K, K)
v = u_v[0] # (..., N-1, D) or (..., 1, D)
# TODO/FIXME: Take into account plates!
plates_phi0 = misc.broadcasted_shape(np.shape(mu)[:-1],
np.shape(Lambda)[:-2])
plates_phi1 = misc.broadcasted_shape(np.shape(Lambda)[:-2],
np.shape(v)[:-2],
np.shape(BB)[:-5],
np.shape(SS)[:-3])
plates_phi2 = misc.broadcasted_shape(np.shape(B)[:-3],
np.shape(S)[:-2],
np.shape(v)[:-2])
phi0 = np.zeros(plates_phi0 + (N,D))
phi1 = np.zeros(plates_phi1 + (N,D,D))
phi2 = np.zeros(plates_phi2 + (N-1,D,D))
# Parameters for x0
phi0[...,0,:] = np.einsum('...ik,...k->...i', Lambda, mu)
phi1[...,0,:,:] = Lambda
# Diagonal blocks: -0.5 * (V_i + A_{i+1}' * V_{i+1} * A_{i+1})
phi1[..., 1:, :, :] = v[...,np.newaxis]*np.identity(D)
if np.ndim(v) >= 2 and np.shape(v)[-2] > 1:
raise Exception("This implementation is not efficient if "
"innovation noise is time-dependent.")
phi1[..., :-1, :, :] += np.einsum('...dikjl,...kl,...d->...ij',
BB[...,None,:,:,:,:,:],
SS,
v)
else:
# We know that S does not have the D plate so we can sum that plate
# axis out
v_BB = np.einsum('...dikjl,...d->...ikjl',
BB[...,None,:,:,:,:,:],
v)
phi1[..., :-1, :, :] += np.einsum('...ikjl,...kl->...ij',
v_BB,
SS)
#phi1[..., :-1, :, :] += np.einsum('...kij,...k->...ij', AA, v)
phi1 *= -0.5
# Super-diagonal blocks: 0.5 * A.T * V
# However, don't multiply by 0.5 because there are both super- and
# sub-diagonal blocks (sum them together)
phi2[..., :, :, :] = np.einsum('...jik,...k,...j->...ij',
B[...,None,:,:,:],
S,
v)
#phi2[..., :, :, :] = np.einsum('...ji,...j->...ij', A, v)
return (phi0, phi1, phi2)
def compute_cgf_from_parents(self, u_mu, u_Lambda, u_B, u_S, u_v):
"""
Compute CGF using the moments of the parents.
"""
return _compute_cgf_for_gaussian_markov_chain(u_mu[1],
u_Lambda[0],
u_Lambda[1],
u_v[1],
self.N)
def plates_to_parent(self, index, plates):
"""
Computes the plates of this node with respect to a parent.
If this node has plates (...), the latent dimensionality is D
and the number of time instances is N, the plates with respect
to the parents are:
mu: (...)
Lambda: (...)
A: (...,N-1,D)
v: (...,N-1,D)
Parameters:
-----------
index : int
The index of the parent node to use.
"""
if index == 0: # mu
return plates
elif index == 1: # Lambda
return plates
elif index == 2: # B
return plates + (self.D,)
elif index == 3: # S
return plates + (self.N-1,)
elif index == 4: # v
return plates + (self.N-1,self.D)
else:
raise ValueError("Invalid parent index.")
def plates_from_parent(self, index, plates):
"""
Compute the plates using information of a parent node.
If the plates of the parents are:
mu: (...)
Lambda: (...)
B: (...,D)
S: (...,N-1)
v: (...,N-1,D)
N: ()
the resulting plates of this node are (...)
Parameters
----------
index : int
Index of the parent to use.
"""
if index == 0: # mu
return plates
elif index == 1: # Lambda
return plates
elif index == 2: # B, remove last plate D
return plates[:-1]
elif index == 3: # S, remove last plate N-1
return plates[:-1]
elif index == 4: # v, remove last plates N-1,D
return plates[:-2]
else:
raise ValueError("Invalid parent index.")
class VaryingGaussianMarkovChain(_TemplateGaussianMarkovChain):
r"""
Node for Gaussian Markov chain random variables with time-varying dynamics.
The node models a sequence of Gaussian variables
:math:`\mathbf{x}_0,\ldots,\mathbf{x}_{N-1}` with linear Markovian dynamics.
The time variability of the dynamics is obtained by modelling the state
dynamics matrix as a linear combination of a set of matrices with
time-varying linear combination weights. The
graphical model can be presented as:
.. bayesnet::
\tikzstyle{latent} += [minimum size=40pt];
\node[latent] (x0) {$\mathbf{x}_0$};
\node[latent, right=of x0] (x1) {$\mathbf{x}_1$};
\node[right=of x1] (dots) {$\cdots$};
\node[latent, right=of dots] (xn) {$\mathbf{x}_{N-1}$};
\edge {x0}{x1};
\edge {x1}{dots};
\edge {dots}{xn};
\node[latent, above left=1 and 0.1 of x0] (mu) {$\boldsymbol{\mu}$};
\node[latent, above right=1 and 0.1 of x0] (Lambda) {$\mathbf{\Lambda}$};
\node[det, below=of x1] (A0) {$\mathbf{A}_0$};
\node[right=of A0] (Adots) {$\cdots$};
\node[det, right=of Adots] (An) {$\mathbf{A}_{N-2}$};
\node[latent, above=of dots] (nu) {$\boldsymbol{\nu}$};
\edge {mu,Lambda} {x0};
\edge {nu} {x1,dots,xn};
\edge {A0} {x1};
\edge {Adots} {dots};
\edge {An} {xn};
\node[latent, below=of A0] (s0) {$s_{0,k}$};
\node[right=of s0] (sdots) {$\cdots$};
\node[latent, right=of sdots] (sn) {$\mathbf{s}_{N-2,k}$};
\node[latent, left=of s0] (B) {$\mathbf{B}_k$};
\edge {B} {A0, Adots, An};
\edge {s0} {A0};
\edge {sdots} {Adots};
\edge {sn} {An};
\plate {K} {(B)(s0)(sdots)(sn)} {$k=0,\ldots,K-1$};
where :math:`\boldsymbol{\mu}` and :math:`\mathbf{\Lambda}` are the mean and
the precision matrix of the initial state, :math:`\boldsymbol{\nu}` is the
precision of the innovation noise, and :math:`\mathbf{A}_n` are the state
dynamics matrix obtained by mixing matrices :math:`\mathbf{B}_k` with
weights :math:`s_{n,k}`.
The probability distribution is
.. math::
p(\mathbf{x}_0, \ldots, \mathbf{x}_{N-1}) = p(\mathbf{x}_0)
\prod^{N-1}_{n=1} p(\mathbf{x}_n | \mathbf{x}_{n-1})
where
.. math::
p(\mathbf{x}_0) &= \mathcal{N}(\mathbf{x}_0 | \boldsymbol{\mu}, \mathbf{\Lambda})
\\
p(\mathbf{x}_n|\mathbf{x}_{n-1}) &= \mathcal{N}(\mathbf{x}_n |
\mathbf{A}_{n-1}\mathbf{x}_{n-1}, \mathrm{diag}(\boldsymbol{\nu})),
\quad \text{for } n=1,\ldots,N-1,
\\
\mathbf{A}_n & = \sum^{K-1}_{k=0} s_{n,k} \mathbf{B}_k, \quad \text{for }
n=0,\ldots,N-2.
Parameters
----------
mu : Gaussian-like node or (...,D)-array
:math:`\boldsymbol{\mu}`, mean of :math:`x_0`, :math:`D`-dimensional
with plates (...)
Lambda : Wishart-like node or (...,D,D)-array
:math:`\mathbf{\Lambda}`, precision matrix of :math:`x_0`,
:math:`D\times D` -dimensional with plates (...)
B : Gaussian-like node or (...,D,D,K)-array
:math:`\{\mathbf{B}_k\}_{k=0}^{K-1}`, a set of state dynamics matrix,
:math:`D \times K`-dimensional with plates (...,D)
S : Gaussian-like node or (...,N-1,K)-array
:math:`\{\mathbf{s}_0,\ldots,\mathbf{s}_{N-2}\}`, time-varying weights
of the linear combination, :math:`K`-dimensional with plates (...,N-1)
nu : gamma-like node or (...,D)-array
:math:`\boldsymbol{\nu}`, diagonal elements of the precision of the
innovation process, plates (...,D)
n : int, optional
:math:`N`, the length of the chain. Must be given if :math:`\mathbf{S}`
does not have plates over the time domain (which would not make sense).
See also
--------
Gaussian, GaussianARD, Wishart, Gamma, GaussianMarkovChain,
SwitchingGaussianMarkovChain
Notes
-----
Equivalent model block can be constructed with :class:`GaussianMarkovChain`
by explicitly using :class:`SumMultiply` to compute the linear combination.
However, that approach is not very efficient for large datasets because it
does not utilize the structure of :math:`\mathbf{A}_n`, thus it explicitly
computes huge moment arrays.
References
----------
:cite:`Luttinen:2014`
"""
_parent_moments = (GaussianMoments(1),
WishartMoments(),
GaussianMoments(2),
GaussianMoments(1),
GammaMoments())
def __init__(self, mu, Lambda, B, S, nu, n=None, **kwargs):
"""
Create VaryingGaussianMarkovChain node.
"""
super().__init__(mu, Lambda, B, S, nu, n=n, **kwargs)
@classmethod
@ensureparents
def _constructor(cls, mu, Lambda, B, S, v, n=None, **kwargs):
"""
Constructs distribution and moments objects.
Compute the dimensions of phi and u.
The plates and dimensions of the parents should be:
mu: (...) and D-dimensional
Lambda: (...) and D-dimensional
B: (...,D) and (D,K)-dimensional
S: (...,N-1) and K-dimensional
v: (...,1,D) or (...,N-1,D) and 0-dimensional
N: () and 0-dimensional (dummy parent)
Check that the dimensionalities of the parents are proper.
"""
# A dummy wrapper for the number of time instances.
n_S = 1
if len(S.plates) >= 1:
n_S = S.plates[-1]
n_v = 1
if len(v.plates) >= 2:
n_v = v.plates[-2]
if n_v != n_S and n_v != 1 and n_S != 1:
raise Exception(
"Plates of A and v are giving different number of time "
"instances")
n_S = max(n_v, n_S)
if n is None:
if n_S == 1:
raise Exception(
"The number of time instances could not be determined "
"automatically. Give the number of time instances.")
n = n_S + 1
elif n_S != 1 and n_S+1 != n:
raise Exception(
"The number of time instances must match the number of last "
"plates of parents:" "%d != %d+1"
% (n, n_S))
D = mu.dims[0][0]
K = B.dims[0][-1]
M = n #N.get_moments()[0]
# Check mu
if mu.dims != ( (D,), (D,D) ):
raise ValueError("First parent has wrong dimensionality")
# Check Lambda
if Lambda.dims != ( (D,D), () ):
raise ValueError("Second parent has wrong dimensionality")
# Check B
if B.dims != ( (D,K), (D,K,D,K) ):
raise ValueError("Third parent has wrong dimensionality")
if len(B.plates) == 0 or B.plates[-1] != D:
raise ValueError("Third parent should have a last plate "
"equal to the dimensionality of the "
"system.")
if S.dims != ( (K,), (K,K) ):
raise ValueError("Fourth parent has wrong dimensionality %s, "
"should be %s"
% (S.dims, ( (K,), (K,K) )))
if (len(S.plates) >= 1
and S.plates[-1] != 1
and S.plates[-1] != M-1):
raise ValueError("The last plate of the fourth "
"parent should have length equal to one or "
"N-1, where N is the number of time "
"instances.")
# Check v
if v.dims != ( (), () ):
raise Exception("Fifth parent has wrong dimensionality")
if len(v.plates) == 0 or v.plates[-1] != D:
raise Exception("Fifth parent should have a last plate "
"equal to the dimensionality of the "
"system.")
if (len(v.plates) >= 2
and v.plates[-2] != 1
and v.plates[-2] != M-1):
raise ValueError("The second last plate of the fifth "
"parent should have length equal to one or "
"N-1 where N is the number of time "
"instances.")
dims = ( (M,D), (M,D,D), (M-1,D,D) )
distribution = VaryingGaussianMarkovChainDistribution(M, D)
parents = [mu, Lambda, B, S, v]
return (parents,
kwargs,
dims,
cls._total_plates(kwargs.get('plates'),
distribution.plates_from_parent(0, mu.plates),
distribution.plates_from_parent(1, Lambda.plates),
distribution.plates_from_parent(2, B.plates),
distribution.plates_from_parent(3, S.plates),
distribution.plates_from_parent(4, v.plates)),
distribution,
cls._moments,
cls._parent_moments)
class SwitchingGaussianMarkovChainDistribution(TemplateGaussianMarkovChainDistribution):
"""
Sub-classes implement distribution specific computations.
"""
def __init__(self, N, D, K):
self.K = K
super().__init__(N, D)
def compute_message_to_parent(self, parent, index, u, u_mu, u_Lambda, u_B,
u_Z, u_v):
"""
Compute a message to a parent.
Parameters:
-----------
index : int
Index of the parent requesting the message.
u : list of ndarrays
Moments of this node.
u_mu : list of ndarrays
Moments of parent `mu`.
u_Lambda : list of ndarrays
Moments of parent `Lambda`.
u_B : list of ndarrays
Moments of parent `B`.
u_Z : list of ndarrays
Moments of parent `Z`.
u_v : list of ndarrays
Moments of parent `v`.
"""
if index == 0: # mu
raise NotImplementedError()
elif index == 1: # Lambda
raise NotImplementedError()
elif index == 2: # B, (...,K,D)x(D)
XnXn = u[1] # (...,N,D,D)
XpXn = u[2] # (...,N-1,D,D)
Z = u_Z[0] # (...,N-1,K)
v = misc.atleast_nd(u_v[0], 2) # (...,N-1,D)
# Check that there is no time-dependency in v and remove the axis
if np.ndim(v) >= 2 and np.shape(v)[-2] > 1:
raise ValueError("Innovation noise is time dependent")
v = np.squeeze(v, axis=-2)
# m0: (...,K,D,D)
m0 = np.einsum('...nji,...nk,...i->...kij',
XpXn,
Z,
v)
# m1: (...,K,D,D,D)
m1 = np.einsum('...nij,...nk->...kij',
XnXn[...,:-1,:,:],
Z)
m1 = -0.5 * np.einsum('...kij,...d->...kdij',
m1,
v)
return [m0, m1]
elif index == 3: # Z, (...,N-1)x(K)
XnXn = u[1] # (...,N,D,D)
XpXn = u[2] # (...,N-1,D,D)
B = u_B[0] # (...,K,D,D)
BB = u_B[1] # (...,K,D,D,D)
v = misc.atleast_nd(u_v[0], 2) # (...,N-1,D)
logv = misc.atleast_nd(u_v[1], 2) # (...,N-1,D)
# Check that there is no time-dependency in v and remove the axis
if np.ndim(v) >= 2 and np.shape(v)[-2] > 1:
raise ValueError("Innovation noise is time dependent")
v = np.squeeze(v, axis=-2)
if np.ndim(logv) >= 2 and np.shape(logv)[-2] > 1:
raise ValueError("Innovation noise is time dependent")
logv = np.squeeze(logv, axis=-2)
XnXn_v = np.einsum('...nii,...i->...n',
XnXn[...,1:,:,:],
v)
XpXn_v_B = np.einsum('...nil,...l,...kli->...nk',
XpXn,
v,
B)
BvB = np.einsum('...kdij,...d->...kij',
BB,
v)
XpXp_BvB = np.einsum('...nij,...kij->...nk',
XnXn[...,:-1,:,:],
BvB)
m0 = ( -0.5 * XnXn_v[...,None]
+ XpXn_v_B
-0.5 * XpXp_BvB
+0.5 * np.sum(logv, axis=-1)[...,None,None]
-0.5 * self.D * np.log(2*np.pi) )
return [m0]
elif index == 4: # v
raise NotImplementedError()
elif index == 5: # N
raise NotImplementedError()
def compute_mask_to_parent(self, index, mask):
if index == 0: # mu: (...)x(N,D) -> (...)x(D)
return mask
elif index == 1: # Lambda: (...)x(N,D) -> (...)x(D,D)
return mask
elif index == 2: # B: (...)x(N,D) -> (...,K,D)x(D)
return mask[...,None,None]
elif index == 3: # Z: (...)x(N,D) -> (...,N-1)x(K)
return mask[...,None]
elif index == 4: # v: (...)x(N,D) -> (...,N-1,D)x()
return mask[...,None,None]
else:
raise ValueError("Invalid index")
def compute_phi_from_parents(self, u_mu, u_Lambda, u_B, u_Z, u_v,
mask=True):
"""
Compute the natural parameters using parents' moments.
Parameters
----------
u_parents : list of list of arrays
List of parents' lists of moments.
Returns
-------
phi : list of arrays
Natural parameters.
dims : tuple
Shape of the variable part of phi.
"""
# Dimensionality of the Gaussian states
D = np.shape(u_mu[0])[-1]
# Number of time instances in the process
N = self.N
# Helpful variables (show shapes in comments)
mu = u_mu[0] # (..., D)
Lambda = u_Lambda[0] # (..., D, D)
B = u_B[0] # (..., K, D, D)
BB = u_B[1] # (..., K, D, D, D)
Z = u_Z[0] # (..., N-1, K)
v = misc.atleast_nd(u_v[0], 2) # (..., N-1, D) or (..., 1, D)
# TODO/FIXME: Take into account plates!
plates_phi0 = misc.broadcasted_shape(np.shape(mu)[:-1],
np.shape(Lambda)[:-2])
plates_phi1 = misc.broadcasted_shape(np.shape(Lambda)[:-2],
np.shape(v)[:-2],
np.shape(BB)[:-4],
np.shape(Z)[:-2])
plates_phi2 = misc.broadcasted_shape(np.shape(B)[:-3],
np.shape(Z)[:-2],
np.shape(v)[:-2])
phi0 = np.zeros(plates_phi0 + (N,D))
phi1 = np.zeros(plates_phi1 + (N,D,D))
phi2 = np.zeros(plates_phi2 + (N-1,D,D))
# Parameters for x0
phi0[...,0,:] = np.einsum('...ik,...k->...i', Lambda, mu)
phi1[...,0,:,:] = Lambda
# Diagonal blocks: -0.5 * (V_i + A_{i+1}' * V_{i+1} * A_{i+1})
phi1[..., 1:, :, :] = v[...,None]*np.identity(D)
if np.shape(v)[-2] > 1:
raise Exception("This implementation is not efficient if "
"innovation noise is time-dependent.")
phi1[..., :-1, :, :] += np.einsum('...kdij,...nk,...nd->...nij',
BB[...,:,:,:,:],
Z,
v)
else:
# We know that S does not have the D plate so we can sum that plate
# axis out
v_BB = np.einsum('...kdij,...nd->...nkij',
BB[...,:,:,:,:],
v)
phi1[..., :-1, :, :] += np.einsum('...nkij,...nk->...nij',
v_BB,
Z)
phi1 *= -0.5
# Super-diagonal blocks: 0.5 * A.T * V
# However, don't multiply by 0.5 because there are both super- and
# sub-diagonal blocks (sum them together)
phi2[..., :, :, :] = np.einsum('...kji,...nk,...nj->...nij',
B[...,:,:,:],
Z,
v)
return (phi0, phi1, phi2)
def compute_cgf_from_parents(self, u_mu, u_Lambda, u_B, u_Z, u_v):
"""
Compute CGF using the moments of the parents.
"""
return _compute_cgf_for_gaussian_markov_chain(u_mu[1],
u_Lambda[0],
u_Lambda[1],
u_v[1],
self.N)
def plates_to_parent(self, index, plates):
"""
Computes the plates of this node with respect to a parent.
If this node has plates (...), the latent dimensionality is D
and the number of time instances is N, the plates with respect
to the parents are:
mu: (...)
Lambda: (...)
A: (...,N-1,D)
v: (...,N-1,D)
Parameters:
-----------
index : int
The index of the parent node to use.
"""
if index == 0: # mu: (...)x(N,D) -> (...)x(D)
return plates
elif index == 1: # Lambda: (...)x(N,D) -> (...)x(D,D)
return plates
elif index == 2: # B: (...)x(N,D) -> (...,K,D)x(D)
return plates + (self.K,self.D)
elif index == 3: # Z: (...)x(N,D) -> (...,N-1)x(K)
return plates + (self.N-1,)
elif index == 4: # v: (...)x(N,D) -> (...,N-1,D)x()
return plates + (self.N-1,self.D)
else:
raise ValueError("Invalid parent index.")
def plates_from_parent(self, index, plates):
"""
Compute the plates using information of a parent node.
If the plates of the parents are:
mu: (...)
Lambda: (...)
B: (...,D)
S: (...,N-1)
v: (...,N-1,D)
N: ()
the resulting plates of this node are (...)
Parameters
----------
index : int
Index of the parent to use.
"""
if index == 0: # mu: (...)x(D) -> (...)x(N,D)
return plates
elif index == 1: # Lambda: (...)x(D,D) -> (...)x(N,D)
return plates
elif index == 2: # B: (...,K,D)x(D) -> (...)x(N,D)
return plates[:-2]
elif index == 3: # Z: (...,N-1)x(K) -> (...)x(N,D)
return plates[:-1]
elif index == 4: # v: (...,N-1,D)x() -> (...)x(N,D)
return plates[:-2]
else:
raise ValueError("Invalid parent index.")
class SwitchingGaussianMarkovChain(_TemplateGaussianMarkovChain):
r"""
Node for Gaussian Markov chain random variables with switching dynamics.
The node models a sequence of Gaussian variables
:math:`\mathbf{x}_0,\ldots,\mathbf{x}_{N-1}$ with linear Markovian dynamics.
The dynamics may change in time, which is obtained by having a set of
matrices and at each time selecting one of them as the state dynamics
matrix. The graphical model can be presented as:
.. bayesnet::
\tikzstyle{latent} += [minimum size=40pt];
\node[latent] (x0) {$\mathbf{x}_0$};
\node[latent, right=of x0] (x1) {$\mathbf{x}_1$};
\node[right=of x1] (dots) {$\cdots$};
\node[latent, right=of dots] (xn) {$\mathbf{x}_{N-1}$};
\edge {x0}{x1};
\edge {x1}{dots};
\edge {dots}{xn};
\node[latent, above left=1 and 0.1 of x0] (mu) {$\boldsymbol{\mu}$};
\node[latent, above right=1 and 0.1 of x0] (Lambda) {$\mathbf{\Lambda}$};
\node[det, below=of x1] (A0) {$\mathbf{A}_0$};
\node[right=of A0] (Adots) {$\cdots$};
\node[det, right=of Adots] (An) {$\mathbf{A}_{N-2}$};
\node[latent, above=of dots] (nu) {$\boldsymbol{\nu}$};
\edge {mu,Lambda} {x0};
\edge {nu} {x1,dots,xn};
\edge {A0} {x1};
\edge {Adots} {dots};
\edge {An} {xn};
\node[latent, below=of A0] (z0) {$z_0$};
\node[right=of z0] (zdots) {$\cdots$};
\node[latent, right=of zdots] (zn) {$z_{N-2}$};
\node[latent, left=of z0] (B) {$\mathbf{B}_k$};
\edge {B} {A0, Adots, An};
\edge {z0} {A0};
\edge {zdots} {Adots};
\edge {zn} {An};
\plate {K} {(B)} {$k=0,\ldots,K-1$};
where :math:`\boldsymbol{\mu}` and :math:`\mathbf{\Lambda}` are the mean and
the precision matrix of the initial state, :math:`\boldsymbol{\nu}` is the
precision of the innovation noise, and :math:`\mathbf{A}_n` are the state
dynamics matrix obtained by selecting one of the matrices
:math:`\{\mathbf{B}_k\}^{K-1}_{k=0}` at each time. The selections are
provided by :math:`z_n\in\{0,\ldots,K-1\}`. The probability distribution is
.. math::
p(\mathbf{x}_0, \ldots, \mathbf{x}_{N-1}) = p(\mathbf{x}_0)
\prod^{N-1}_{n=1} p(\mathbf{x}_n | \mathbf{x}_{n-1})
where
.. math::
p(\mathbf{x}_0) &= \mathcal{N}(\mathbf{x}_0 | \boldsymbol{\mu}, \mathbf{\Lambda})
\\
p(\mathbf{x}_n|\mathbf{x}_{n-1}) &= \mathcal{N}(\mathbf{x}_n |
\mathbf{A}_{n-1}\mathbf{x}_{n-1}, \mathrm{diag}(\boldsymbol{\nu})),
\quad \text{for } n=1,\ldots,N-1,
\\
\mathbf{A}_n &= \mathbf{B}_{z_n}, \quad \text{for }
n=0,\ldots,N-2.
Parameters
----------
mu : Gaussian-like node or (...,D)-array
:math:`\boldsymbol{\mu}`, mean of :math:`x_0`, :math:`D`-dimensional
with plates (...)
Lambda : Wishart-like node or (...,D,D)-array
:math:`\mathbf{\Lambda}`, precision matrix of :math:`x_0`,
:math:`D\times D` -dimensional with plates (...)
B : Gaussian-like node or (...,D,D,K)-array
:math:`\{\mathbf{B}_k\}_{k=0}^{K-1}`, a set of state dynamics matrix,
:math:`D \times K`-dimensional with plates (...,D)
Z : categorical-like node or (...,N-1)-array
:math:`\{z_0,\ldots,z_{N-2}\}`, time-dependent selection,
:math:`K`-categorical with plates (...,N-1)
nu : gamma-like node or (...,D)-array
:math:`\boldsymbol{\nu}`, diagonal elements of the precision of the
innovation process, plates (...,D)
n : int, optional
:math:`N`, the length of the chain. Must be given if :math:`\mathbf{Z}`
does not have plates over the time domain (which would not make sense).
See also
--------
Gaussian, GaussianARD, Wishart, Gamma, GaussianMarkovChain,
VaryingGaussianMarkovChain, Categorical, CategoricalMarkovChain
Notes
-----
Equivalent model block can be constructed with :class:`GaussianMarkovChain`
by explicitly using :class:`Gate` to select the state dynamics matrix.
However, that approach is not very efficient for large datasets because it
does not utilize the structure of :math:`\mathbf{A}_n`, thus it explicitly
computes huge moment arrays.
"""
def __init__(self, mu, Lambda, B, Z, nu, n=None, **kwargs):
"""
Create SwitchingGaussianMarkovChain node.
"""
super().__init__(mu, Lambda, B, Z, nu, n=n, **kwargs)
@classmethod
def _constructor(cls, mu, Lambda, B, Z, v, n=None, **kwargs):
"""
Constructs distribution and moments objects.
Compute the dimensions of phi and u.
The plates and dimensions of the parents should be:
mu: (...) and D-dimensional
Lambda: (...) and D-dimensional
B: (...,K,D) and D-dimensional
Z: (...,N-1) and K-dimensional
v: (...,1,D) or (...,N-1,D) and 0-dimensional
Check that the dimensionalities of the parents are proper.
"""
# Infer the number of dynamic matrices
B = cls._ensure_moments(B, GaussianMoments(2))
K = B.plates[-2]
parent_moments = (GaussianMoments(1),
WishartMoments(),
GaussianMoments(1),
CategoricalMoments(K),
GammaMoments())
# Infer the length of the chain
Z = cls._ensure_moments(Z, parent_moments[3])
v = cls._ensure_moments(v, parent_moments[4])
n_Z = 1
if len(Z.plates) == 0:
raise ValueError("Z must have temporal axis on plates")
n_Z = Z.plates[-1]
n_v = 1
if len(v.plates) >= 2:
n_v = v.plates[-2]
if n_v != n_Z and n_v != 1 and n_Z != 1:
raise Exception(
"Plates of Z and v are giving different number of time "
"instances")
n_Z = max(n_v, n_Z)
if n is None:
if n_Z == 1:
raise Exception(
"The number of time instances could not be determined "
"automatically. Give the number of time instances.")
n = n_Z + 1
elif n_Z != 1 and n_Z+1 != n:
raise Exception(
"The number of time instances must match the number of last "
"plates of parents:" "%d != %d+1"
% (n, n_Z))
mu = cls._ensure_moments(mu, parent_moments[0])
D = mu.dims[0][0]
K = Z.dims[0][0]
M = n #N.get_moments()[0]
# Check mu
if mu.dims != ( (D,), (D,D) ):
raise ValueError("First parent has wrong dimensionality")
# Check Lambda
Lambda = cls._ensure_moments(Lambda, parent_moments[1])
if Lambda.dims != ( (D,D), () ):
raise ValueError("Second parent has wrong dimensionality")
# Check B
if B.dims != ( (D,), (D,D) ):
raise ValueError("Third parent has wrong dimensionality")
if len(B.plates) < 2 or B.plates[-2:] != (K,D):
raise ValueError("Third parent should have a last plate "
"equal to the dimensionality of the "
"system.")
if Z.dims != ( (K,), ):
raise ValueError("Fourth parent has wrong dimensionality %s, "
"should be %s"
% (Z.dims, ( (K,), )))
if Z.plates[-1] != M-1:
raise ValueError("The last plate of the fourth "
"parent should have length equal to one or "
"N-1, where N is the number of time "
"instances.")
# Check v
if v.dims != ( (), () ):
raise Exception("Fifth parent has wrong dimensionality")
if len(v.plates) == 0 or v.plates[-1] != D:
raise Exception("Fifth parent should have a last plate "
"equal to the dimensionality of the "
"system.")
if (len(v.plates) >= 2
and v.plates[-2] != 1
and v.plates[-2] != M-1):
raise ValueError("The second last plate of the fifth "
"parent should have length equal to one or "
"N-1 where N is the number of time "
"instances.")
dims = ( (M,D), (M,D,D), (M-1,D,D) )
distribution = SwitchingGaussianMarkovChainDistribution(M, D, K)
parents = [mu, Lambda, B, Z, v]
return (parents,
kwargs,
dims,
cls._total_plates(kwargs.get('plates'),
distribution.plates_from_parent(0, mu.plates),
distribution.plates_from_parent(1, Lambda.plates),
distribution.plates_from_parent(2, B.plates),
distribution.plates_from_parent(3, Z.plates),
distribution.plates_from_parent(4, v.plates)),
distribution,
cls._moments,
parent_moments)
class _MarkovChainToGaussian(Deterministic):
"""
Transform a Gaussian Markov chain node into a Gaussian node.
This node is deterministic.
"""
_moments = GaussianMoments(1)
_parent_moments = (GaussianMarkovChainMoments(),)
def __init__(self, X, **kwargs):
# Check for constant n
if misc.is_numeric(X):
X = Constant(GaussianMarkovChain)(X)
# Make the time dimension a plate dimension...
#plates = X.plates + (X.dims[0][0],)
# ... and remove it from the variable dimensions
dims = ( X.dims[0][-1:], X.dims[1][-2:] )
super().__init__(X,
#plates=plates,
dims=dims,
**kwargs)
def _plates_to_parent(self, index):
"""
Return the number of plates to the parent node.
Normally, the parent sees the same number of plates as the
node itself. However, now that one of the variable dimensions
of the parents corresponds to a plate in this node, it is
necessary to fix it here: the last plate is ignored when
calculating plates with respect to the parent.
Parent:
Plates = (...)
Dims = (N, ...)
This node:
Plates = (..., N)
Dims = (...)
"""
return self.plates[:-1]
def _plates_from_parent(self, index):
# Sub-classes may want to overwrite this if they manipulate plates
if index != 0:
raise ValueError("Invalid parent index.")
parent = self.parents[0]
plates = parent.plates + (parent.dims[0][0],)
return plates
def _compute_moments(self, u):
"""
Transform the moments of a GMC to moments of a Gaussian.
There is no need to worry about the plates and variable
dimensions because the child node is free to interpret the
axes as it pleases. However, the Gaussian moments contain
only and but not , thus the
last moment is discarded.
"""
# Get the moments from the parent Gaussian Markov Chain
#u = self.parents[0].get_moments() #message_to_child()
# Send only moments and but not
return u[:2]
def _compute_mask_to_parent(self, index, mask):
# Remove the last axis of the mask
if np.ndim(mask) >= 1:
mask = np.any(mask, axis=-1)
return mask
@staticmethod
def _compute_message_to_parent(index, m_children, *u_parents):
"""
Transform a message to a Gaussian into a message to a GMC.
The messages to a Gaussian are almost correct, there are only
two minor things to be done:
1) The last plate is changed into a variable/time dimension.
Because a message mask is applied for plates only, the last
axis of the mask must be applied to the message because the
last plate is changed to a variable/time dimension.
2) Because the message does not contain part,
we'll put the last/third message to None meaning that it is
empty.
Parameters:
-----------
index : int
Index of the parent requesting the message.
u_parents : list of list of ndarrays
List of parents' moments.
Returns:
--------
m : list of ndarrays
Message as a list of arrays.
mask : boolean ndarray
Mask telling which plates should be taken into account.
"""
# Add the third empty message
return [m_children[0], m_children[1], None]
# Make use of the converter
GaussianMarkovChainMoments.add_converter(GaussianMoments,
_MarkovChainToGaussian)
bayespy-0.2.3/bayespy/inference/vmp/nodes/gp.py 0000664 5104112 5104112 00000057225 12374370351 023236 0 ustar jluttine jluttine 0000000 0000000 ######################################################################
# Copyright (C) 2011,2012 Jaakko Luttinen
#
# This file is licensed under Version 3.0 of the GNU General Public
# License. See LICENSE for a text of the license.
######################################################################
######################################################################
# This file is part of BayesPy.
#
# BayesPy is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# BayesPy is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with BayesPy. If not, see .
######################################################################
import itertools
import numpy as np
import scipy as sp
import scipy.linalg.decomp_cholesky as decomp
import scipy.linalg as linalg
import scipy.special as special
import matplotlib.pyplot as plt
import time
import profile
import scipy.spatial.distance as distance
from .node import Node
from .stochastic import Stochastic
from bayespy.utils.misc import *
# Computes log probability density function of the Gaussian
# distribution
def gaussian_logpdf(y_invcov_y,
y_invcov_mu,
mu_invcov_mu,
logdetcov,
D):
return (-0.5*D*np.log(2*np.pi)
-0.5*logdetcov
-0.5*y_invcov_y
+y_invcov_mu
-0.5*mu_invcov_mu)
# m prior mean function
# k prior covariance function
# x data inputs
# z processed data outputs (z = inv(Cov) * (y-m(x)))
# U data covariance Cholesky factor
def gp_posterior_moment_function(m, k, x, y, noise=None):
# Prior
mu = m(x)[0]
K = k(x,x)[0]
if noise != None:
K += noise
#print('hereiamagain')
#print(K)
# Compute posterior GP
N = len(y)
if N == 0:
U = None
z = None
else:
U = chol(K)
z = chol_solve(U, y-mu)
def get_moments(xh, covariance=1, mean=True):
(kh,) = k(x, xh)
# Function for computing posterior moments
if mean:
# Mean vector
mh = m(xh)
if z != None:
mh += np.dot(kh.T, z)
else:
mh = None
if covariance:
if covariance == 1:
# Variance vector
khh = k(xh)
if U != None:
khh -= np.einsum('i...,i...', kh, chol_solve(U, kh))
elif covariance == 2:
# Full covariance matrix
khh = k(xh,xh)
if U != None:
khh -= np.dot(kh.T, chol_solve(U,kh))
else:
khh = None
return [mh, khh]
return get_moments
# m prior mean function
# k prior covariance function
# x data inputs
# z processed data outputs (z = inv(Cov) * (y-m(x)))
# U data covariance Cholesky factor
## def gp_multi_posterior_moment_function(m, k, x, y, noise=None):
## # Prior
## mu = m(x)[0]
## K = k(x,x)[0]
## if noise != None:
## K += noise
## #print('hereiamagain')
## #print(K)
## # Compute posterior GP
## N = len(y)
## if N == 0:
## U = None
## z = None
## else:
## U = chol(K)
## z = chol_solve(U, y-mu)
## def get_moments(xh, covariance=1, mean=True):
## (kh,) = k(x, xh)
## # Function for computing posterior moments
## if mean:
## # Mean vector
## mh = m(xh)
## if z != None:
## mh += np.dot(kh.T, z)
## else:
## mh = None
## if covariance:
## if covariance == 1:
## # Variance vector
## khh = k(xh)
## if U != None:
## khh -= np.einsum('i...,i...', kh, chol_solve(U, kh))
## elif covariance == 2:
## # Full covariance matrix
## khh = k(xh,xh)
## if U != None:
## khh -= np.dot(kh.T, chol_solve(U,kh))
## else:
## khh = None
## return [mh, khh]
## return get_moments
def gp_cov_se(D2, overwrite=False):
if overwrite:
K = D2
K *= -0.5
np.exp(K, out=K)
else:
K = np.exp(-0.5*D2)
return K
def gp_cov_delta(N):
return np.identity(N)
def squared_distance(x1, x2):
# Reshape arrays to 2-D arrays
sh1 = np.shape(x1)[:-1]
sh2 = np.shape(x2)[:-1]
d = np.shape(x1)[-1]
x1 = np.reshape(x1, (-1,d))
x2 = np.reshape(x2, (-1,d))
# Compute squared Euclidean distance
D2 = distance.cdist(x1, x2, metric='sqeuclidean')
# Reshape the result
D2 = np.reshape(D2, sh1 + sh2)
return D2
# General rule for the parameters for covariance functions:
#
# (value, [ [dvalue1, ...], [dvalue2, ...], [dvalue3, ...], ...])
#
# For instance,
#
# k = covfunc_se((1.0, []), (15, [ [1,update_grad] ]))
# K = k((x1, [ [dx1,update_grad] ]), (x2, []))
#
# Plain values are converted as:
# value -> (value, [])
def gp_standardize_input(x):
if np.ndim(x) == 0:
x = add_trailing_axes(x, 2)
elif np.ndim(x) == 1:
x = add_trailing_axes(x, 1)
return x
def gp_preprocess_inputs(*args):
args = list(args)
if len(args) < 1 or len(args) > 2:
raise Exception("Number of inputs must be one or two")
if len(args) == 2:
if args[0] is args[1]:
args[0] = gp_standardize_input(args[0])
args[1] = args[0]
else:
args[1] = gp_standardize_input(args[1])
args[0] = gp_standardize_input(args[0])
else:
args[0] = gp_standardize_input(args[0])
return args
def covfunc_delta(theta, *inputs, gradient=False):
amplitude = theta[0]
if gradient:
gradient_amplitude = gradient[0]
else:
gradient_amplitude = []
inputs = gp_preprocess_inputs(*inputs)
# Compute distance and covariance matrix
if len(inputs) == 1:
# Only variance vector asked
x = inputs[0]
K = np.ones(np.shape(x)[:-1]) * amplitude**2
else:
# Full covariance matrix asked
x1 = inputs[0]
x2 = inputs[1]
# Number of inputs x1
N1 = np.shape(x1)[-2]
# x1 == x2?
if x1 is x2:
delta = True
# Delta covariance
K = gp_cov_delta(N1) * amplitude**2
else:
delta = False
# Number of inputs x2
N2 = np.shape(x2)[-2]
# Zero covariance
K = np.zeros((N1,N2))
# Gradient w.r.t. amplitude
if gradient:
for ind in range(len(gradient_amplitude)):
gradient_amplitude[ind] = K * (2 * gradient_amplitude[ind] / amplitude)
if gradient:
return (K, gradient)
else:
return K
def covfunc_se(theta, *inputs, gradient=False):
amplitude = theta[0]
lengthscale = theta[1]
## print('in se')
## print(amplitude)
## print(lengthscale)
if gradient:
gradient_amplitude = gradient[0]
gradient_lengthscale = gradient[1]
else:
gradient_amplitude = []
gradient_lengthscale = []
inputs = gp_preprocess_inputs(*inputs)
# Compute covariance matrix
if len(inputs) == 1:
x = inputs[0]
# Compute variance vector
K = np.ones(np.shape(x)[:-1])
K *= amplitude**2
# Compute gradient w.r.t. lengthscale
for ind in range(len(gradient_lengthscale)):
gradient_lengthscale[ind] = np.zeros(np.shape(x)[:-1])
else:
x1 = inputs[0] / (lengthscale)
x2 = inputs[1] / (lengthscale)
# Compute distance matrix
K = squared_distance(x1, x2)
# Compute gradient partly
if gradient:
for ind in range(len(gradient_lengthscale)):
gradient_lengthscale[ind] = K * ((lengthscale**-1) * gradient_lengthscale[ind])
# Compute covariance matrix
gp_cov_se(K, overwrite=True)
K *= amplitude**2
# Compute gradient w.r.t. lengthscale
if gradient:
for ind in range(len(gradient_lengthscale)):
gradient_lengthscale[ind] *= K
# Gradient w.r.t. amplitude
if gradient:
for ind in range(len(gradient_amplitude)):
gradient_amplitude[ind] = K * (2 * gradient_amplitude[ind] / amplitude)
# Return values
if gradient:
return (K, gradient)
else:
return K
class NodeCovarianceFunction(Node):
def __init__(self, covfunc, *args, **kwargs):
self.covfunc = covfunc
params = list(args)
for i in range(len(args)):
# Check constant parameters
if is_numeric(args[i]):
params[i] = NodeConstant([np.asanyarray(args[i])],
dims=[np.shape(args[i])])
# TODO: Parameters could be constant functions? :)
Node.__init__(self, *params, dims=[(np.inf, np.inf)], **kwargs)
def message_to_child(self, gradient=False):
params = [parent.message_to_child(gradient=gradient) for parent in self.parents]
return self.covariance_function(*params)
def covariance_function(self, *params):
params = list(params)
gradient_params = list()
for ind in range(len(params)):
if isinstance(params[ind], tuple):
gradient_params.append(params[ind][1])
params[ind] = params[ind][0][0]
else:
gradient_params.append([])
params[ind] = params[ind][0]
def cov(*inputs, gradient=False):
if gradient:
grads = [[grad[0] for grad in gradient_params[ind]]
for ind in range(len(gradient_params))]
(K, dK) = self.covfunc(params,
*inputs,
gradient=grads)
for ind in range(len(dK)):
for (grad, dk) in zip(gradient_params[ind], dK[ind]):
grad[0] = dk
K = [K]
dK = []
for grad in gradient_params:
dK += grad
return (K, dK)
else:
K = self.covfunc(params,
*inputs,
gradient=False)
return [K]
return cov
class NodeCovarianceFunctionSum(NodeCovarianceFunction):
def __init__(self, *args, **kwargs):
NodeCovarianceFunction.__init__(self,
None,
*args,
**kwargs)
def covariance_function(self, *covfuncs):
def cov(*inputs, gradient=False):
K_sum = 0
if gradient:
dK_sum = list()
for k in covfuncs:
if gradient:
(K, dK) = k(*inputs, gradient=gradient)
dK_sum += dK
else:
K = k(*inputs, gradient=gradient)
K_sum += K[0]
if gradient:
return ([K_sum], dK_sum)
else:
return [K_sum]
return cov
class NodeCovarianceFunctionDelta(NodeCovarianceFunction):
def __init__(self, amplitude, **kwargs):
NodeCovarianceFunction.__init__(self,
covfunc_delta,
amplitude,
**kwargs)
class NodeCovarianceFunctionSquaredExponential(NodeCovarianceFunction):
def __init__(self, amplitude, lengthscale, **kwargs):
NodeCovarianceFunction.__init__(self,
covfunc_se,
amplitude,
lengthscale,
**kwargs)
class NodeMultiCovarianceFunction(NodeCovarianceFunction):
def __init__(self, *args, **kwargs):
NodeCovarianceFunction.__init__(self,
None,
*args,
**kwargs)
def covfunc(self, *covfuncs):
def cov(*inputs, gradient=False):
K_sum = 0
if gradient:
dK_sum = list()
for k in covfuncs:
if gradient:
(K, dK) = k(*inputs, gradient=gradient)
dK_sum += dK
else:
K = k(*inputs, gradient=gradient)
K_sum += K[0]
if gradient:
return ([K_sum], dK_sum)
else:
return [K_sum]
return cov
class NodeConstantGaussianProcess(Node):
def __init__(self, f, **kwargs):
self.f = f
Node.__init__(self, dims=[(np.inf,)], **kwargs)
def message_to_child(self, gradient=False):
# Wrapper
def func(x, gradient=False):
if gradient:
return ([self.f(x)], [])
else:
return [self.f(x)]
return func
# At least for now, simplify this GP node such that a GP is either
# observed or latent. If it is observed, it doesn't take messages from
# children, actually, it should not even have children!
#class NodeMultiGaussianProcess(NodeVariable):
class NodeMultiGaussianProcess(Stochastic):
def __init__(self, m, k, **kwargs):
self.x = []
self.f = []
# By default, posterior == prior
self.m = m
self.k = k
# Ignore plates
NodeVariable.__init__(self,
m,
k,
plates=(),
dims=[(np.inf,), (np.inf,np.inf)],
**kwargs)
def message_to_parent(self, index):
if index == 0:
k = self.parents[1].message_to_child()[0]
K = k(self.x, self.x)
return [self.x,
self.mu,
K]
if index == 1:
raise Exception("not implemented yet")
def message_to_child(self):
if self.observed:
raise Exception("Observable GP should not have children.")
return self.u
def get_parameters(self):
return self.u
def observe(self, x, f):
if np.ndim(x) == 1:
if np.shape(f) != np.shape(x):
print(np.shape(f))
print(np.shape(x))
raise Exception("Number of inputs and function values do not match")
elif np.shape(f) != np.shape(x)[:-1]:
print(np.shape(f))
print(np.shape(x))
raise Exception("Number of inputs and function values do not match")
self.observed = True
self.x = x
self.f = f
## self.x_obs = x
## self.f_obs = f
# You might want:
# - mean for x
# - covariance (and mean) for x
# - variance (and mean) for x
# - i.e., mean and/or (co)variance for x
# - covariance for x1 and x2
def lower_bound_contribution(self, gradient=False):
m = self.parents[0].message_to_child(gradient=gradient)
k = self.parents[1].message_to_child(gradient=gradient)
## m = self.parents[0].message_to_child(gradient=gradient)[0]
## k = self.parents[1].message_to_child(gradient=gradient)[0]
# Prior
if gradient:
(mu, dmus) = m(self.x, gradient=True)
(K, dKs) = k(self.x, self.x, gradient=True)
else:
mu = m(self.x)
K = k(self.x, self.x)
dmus = []
dKs = []
mu = mu[0]
K = K[0]
# Log pdf
if self.observed:
# Vector of f-mu
f0 = np.vstack([(f-m) for (f,m) in zip(self.f,mu)])
# Full covariance matrix
K_full = np.bmat(K)
try:
U = chol(K_full)
except linalg.LinAlgError:
print('non positive definite, return -inf')
return -np.inf
z = chol_solve(U, f0)
#print(K)
L = gaussian_logpdf(np.dot(f0, z),
0,
0,
logdet_chol(U),
np.size(self.f))
for (dmu, func) in dmus:
# Derivative w.r.t. mean vector
d = -np.sum(z)
# Send the derivative message
func += d
#func(d)
for (dK, func) in dKs:
# Compute derivative w.r.t. covariance matrix
d = 0.5 * (np.dot(z, np.dot(dK, z))
- np.trace(chol_solve(U, dK)))
# Send the derivative message
#print('add gradient')
#func += d
func(d)
else:
raise Exception('Not implemented yet')
return L
## Let f1 be observed and f2 latent function values.
# Compute
#L = gaussian_logpdf(sum_product(np.outer(self.f,self.f) + self.Cov,
# Compute
def update(self):
# Messages from parents
m = self.parents[0].message_to_child()
k = self.parents[1].message_to_child()
## m = self.parents[0].message_to_child()[0]
## k = self.parents[1].message_to_child()[0]
if self.observed:
# Observations of this node
self.u = gp_posterior_moment_function(m, k, self.x, self.f)
else:
x = np.array([])
y = np.array([])
# Messages from children
for (child,index) in self.children:
(msg, mask) = child.message_to_parent(index)
# Ignoring masks and plates..
# m[0] is the inputs
x = np.concatenate((x, msg[0]), axis=-2)
# m[1] is the observations
y = np.concatenate((y, msg[1]))
# m[2] is the covariance matrix
V = linalg.block_diag(V, msg[2])
self.u = gp_posterior_moment_function(m, k, x, y, covariance=V)
self.x = x
self.f = y
class NodeGaussianProcess(Stochastic):
#class NodeGaussianProcess(NodeVariable):
def __init__(self, m, k, **kwargs):
self.x = np.array([])
self.f = np.array([])
## self.x_obs = np.zeros((0,1))
## self.f_obs = np.zeros((0,))
# By default, posterior == prior
self.m = m
self.k = k
# Ignore plates
NodeVariable.__init__(self,
m,
k,
plates=(),
dims=[(np.inf,), (np.inf,np.inf)],
**kwargs)
def message_to_parent(self, index):
if index == 0:
k = self.parents[1].message_to_child()[0]
K = k(self.x, self.x)
return [self.x,
self.mu,
K]
if index == 1:
raise Exception("not implemented yet")
def message_to_child(self):
if self.observed:
raise Exception("Observable GP should not have children.")
return self.u
def get_parameters(self):
return self.u
def observe(self, x, f):
if np.ndim(x) == 1:
if np.shape(f) != np.shape(x):
print(np.shape(f))
print(np.shape(x))
raise Exception("Number of inputs and function values do not match")
elif np.shape(f) != np.shape(x)[:-1]:
print(np.shape(f))
print(np.shape(x))
raise Exception("Number of inputs and function values do not match")
self.observed = True
self.x = x
self.f = f
## self.x_obs = x
## self.f_obs = f
# You might want:
# - mean for x
# - covariance (and mean) for x
# - variance (and mean) for x
# - i.e., mean and/or (co)variance for x
# - covariance for x1 and x2
def lower_bound_contribution(self, gradient=False):
m = self.parents[0].message_to_child(gradient=gradient)
k = self.parents[1].message_to_child(gradient=gradient)
## m = self.parents[0].message_to_child(gradient=gradient)[0]
## k = self.parents[1].message_to_child(gradient=gradient)[0]
# Prior
if gradient:
(mu, dmus) = m(self.x, gradient=True)
(K, dKs) = k(self.x, self.x, gradient=True)
else:
mu = m(self.x)
K = k(self.x, self.x)
dmus = []
dKs = []
mu = mu[0]
K = K[0]
# Log pdf
if self.observed:
f0 = self.f - mu
#print('hereiam')
#print(K)
try:
U = chol(K)
except linalg.LinAlgError:
print('non positive definite, return -inf')
return -np.inf
z = chol_solve(U, f0)
#print(K)
L = gaussian_logpdf(np.dot(f0, z),
0,
0,
logdet_chol(U),
np.size(self.f))
for (dmu, func) in dmus:
# Derivative w.r.t. mean vector
d = -np.sum(z)
# Send the derivative message
func += d
#func(d)
for (dK, func) in dKs:
# Compute derivative w.r.t. covariance matrix
d = 0.5 * (np.dot(z, np.dot(dK, z))
- np.trace(chol_solve(U, dK)))
# Send the derivative message
#print('add gradient')
#func += d
func(d)
else:
raise Exception('Not implemented yet')
return L
## Let f1 be observed and f2 latent function values.
# Compute
#L = gaussian_logpdf(sum_product(np.outer(self.f,self.f) + self.Cov,
# Compute
def update(self):
# Messages from parents
m = self.parents[0].message_to_child()
k = self.parents[1].message_to_child()
## m = self.parents[0].message_to_child()[0]
## k = self.parents[1].message_to_child()[0]
if self.observed:
# Observations of this node
self.u = gp_posterior_moment_function(m, k, self.x, self.f)
else:
x = np.array([])
y = np.array([])
# Messages from children
for (child,index) in self.children:
(msg, mask) = child.message_to_parent(index)
# Ignoring masks and plates..
# m[0] is the inputs
x = np.concatenate((x, msg[0]), axis=-2)
# m[1] is the observations
y = np.concatenate((y, msg[1]))
# m[2] is the covariance matrix
V = linalg.block_diag(V, msg[2])
self.u = gp_posterior_moment_function(m, k, x, y, covariance=V)
self.x = x
self.f = y
bayespy-0.2.3/bayespy/inference/vmp/nodes/mixture.py 0000664 5104112 5104112 00000050671 12374370351 024323 0 ustar jluttine jluttine 0000000 0000000 ######################################################################
# Copyright (C) 2011,2012,2014 Jaakko Luttinen
#
# This file is licensed under Version 3.0 of the GNU General Public
# License. See LICENSE for a text of the license.
######################################################################
######################################################################
# This file is part of BayesPy.
#
# BayesPy is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# BayesPy is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with BayesPy. If not, see .
######################################################################
"""
Module for the mixture distribution node.
"""
import warnings
import numpy as np
from bayespy.utils import misc
from .expfamily import ExponentialFamily, \
ExponentialFamilyDistribution, \
useconstructor
from .categorical import Categorical, \
CategoricalMoments
class MixtureDistribution(ExponentialFamilyDistribution):
"""
Class for the VMP formulas of mixture variables.
"""
def __init__(self, distribution, cluster_plate, n_clusters, ndims,
ndims_parents):
"""
Create VMP formula node for a mixture variable
"""
self.distribution = distribution
self.cluster_plate = cluster_plate
self.ndims = ndims
self.ndims_parents = ndims_parents
self.K = n_clusters
def compute_message_to_parent(self, parent, index, u, *u_parents):
"""
Compute the message to a parent node.
"""
if index == 0:
# Shape(phi) = [Nn,..,K,..,N0,Dd,..,D0]
# Shape(L) = [Nn,..,K,..,N0]
# Shape(u) = [Nn,..,N0,Dd,..,D0]
# Shape(result) = [Nn,..,N0,K]
# Compute g:
# Shape(g) = [Nn,..,K,..,N0]
g = self.distribution.compute_cgf_from_parents(*(u_parents[1:]))
# Reshape(g):
# Shape(g) = [Nn,..,N0,K]
if np.ndim(g) < abs(self.cluster_plate):
# Not enough axes, just add the cluster plate axis
g = np.expand_dims(g, -1)
else:
# Move the cluster plate axis
g = misc.moveaxis(g, self.cluster_plate, -1)
# Compute phi:
# Shape(phi) = [Nn,..,K,..,N0,Dd,..,D0]
phi = self.distribution.compute_phi_from_parents(*(u_parents[1:]))
# Move phi axis:
# Shape(phi) = [Nn,..,N0,K,Dd,..,D0]
for ind in range(len(phi)):
if self.cluster_plate < 0:
axis_from = self.cluster_plate-self.ndims[ind]
else:
raise RuntimeError("Cluster plate axis must be negative")
axis_to = -1-self.ndims[ind]
if np.ndim(phi[ind]) >= abs(axis_from):
# Cluster plate axis exists, move it to the correct position
phi[ind] = misc.moveaxis(phi[ind], axis_from, axis_to)
else:
# No cluster plate axis, just add a new axis to the correct
# position, if phi has something on that axis
if np.ndim(phi[ind]) >= abs(axis_to):
phi[ind] = np.expand_dims(phi[ind], axis=axis_to)
# Reshape u:
# Shape(u) = [Nn,..,N0,1,Dd,..,D0]
u_self = list()
for ind in range(len(u)):
u_self.append(np.expand_dims(u[ind],
axis=(-1-self.ndims[ind])))
# Compute logpdf:
# Shape(L) = [Nn,..,N0,K]
L = self.distribution.compute_logpdf(u_self, phi, g, 0, self.ndims)
# Sum over other than the cluster dimensions? No!
# Hmm.. I think the message passing method will do
# that automatically
m = [L]
return m
elif index >= 1:
# Parent index for the distribution used for the
# mixture.
index = index - 1
# Reshape u:
# Shape(u) = [Nn,..1,..,N0,Dd,..,D0]
u_self = list()
for ind in range(len(u)):
if self.cluster_plate < 0:
cluster_axis = self.cluster_plate - self.ndims[ind]
else:
cluster_axis = self.cluster_plate
u_self.append(np.expand_dims(u[ind], axis=cluster_axis))
# Message from the mixed distribution
m = self.distribution.compute_message_to_parent(parent,
index,
u_self,
*(u_parents[1:]))
# Weigh the messages with the responsibilities
for i in range(len(m)):
# Shape(m) = [Nn,..,K,..,N0,Dd,..,D0]
# Shape(p) = [Nn,..,N0,K]
# Shape(result) = [Nn,..,K,..,N0,Dd,..,D0]
# Number of axes for the variable dimensions for
# the parent message.
D = self.ndims_parents[index][i]
# Responsibilities for clusters are the first
# parent's first moment:
# Shape(p) = [Nn,..,N0,K]
p = u_parents[0][0]
# Move the cluster axis to the proper place:
# Shape(p) = [Nn,..,K,..,N0]
p = misc.atleast_nd(p, abs(self.cluster_plate))
p = misc.moveaxis(p, -1, self.cluster_plate)
# Add axes for variable dimensions to the contributions
# Shape(p) = [Nn,..,K,..,N0,1,..,1]
p = misc.add_trailing_axes(p, D)
if self.cluster_plate < 0:
# Add the variable dimensions
cluster_axis = self.cluster_plate - D
# Add axis for clusters:
# Shape(m) = [Nn,..,1,..,N0,Dd,..,D0]
#m[i] = np.expand_dims(m[i], axis=cluster_axis)
#
# TODO: You could do summing here already so that
# you wouldn't compute huge matrices as
# intermediate result. Use einsum.
# Compute the message contributions for each
# cluster:
# Shape(result) = [Nn,..,K,..,N0,Dd,..,D0]
m[i] = m[i] * p
return m
def compute_mask_to_parent(self, index, mask):
"""
Maps the mask to the plates of a parent.
"""
if index == 0:
return mask
else:
if self.cluster_plate >= 0:
raise ValueError("Cluster plate axis must be negative")
if np.ndim(mask) >= abs(self.cluster_plate):
mask = np.expand_dims(mask, axis=self.cluster_plate)
return self.distribution.compute_mask_to_parent(index-1, mask)
def compute_phi_from_parents(self, *u_parents, mask=True):
"""
Compute the natural parameter vector given parent moments.
"""
# Compute weighted average of the parameters
# Cluster parameters
Phi = self.distribution.compute_phi_from_parents(*(u_parents[1:]))
# Contributions/weights/probabilities
P = u_parents[0][0]
phi = list()
nans = False
for ind in range(len(Phi)):
# Compute element-wise product and then sum over K clusters.
# Note that the dimensions aren't perfectly aligned because
# the cluster dimension (K) may be arbitrary for phi, and phi
# also has dimensions (Dd,..,D0) of the parameters.
# Shape(phi) = [Nn,..,K,..,N0,Dd,..,D0]
# Shape(p) = [Nn,..,N0,K]
# Shape(result) = [Nn,..,N0,Dd,..,D0]
# General broadcasting rules apply for Nn,..,N0, that is,
# preceding dimensions may be missing or dimension may be
# equal to one. Probably, shape(phi) has lots of missing
# dimensions and/or dimensions that are one.
if self.cluster_plate < 0:
cluster_axis = self.cluster_plate - self.ndims[ind]
else:
raise RuntimeError("Cluster plate should be negative")
# Move cluster axis to the last:
# Shape(phi) = [Nn,..,N0,Dd,..,D0,K]
if np.ndim(Phi[ind]) >= abs(cluster_axis):
phi.append(misc.moveaxis(Phi[ind], cluster_axis, -1))
else:
phi.append(Phi[ind][...,None])
# Add axes to p:
# Shape(p) = [Nn,..,N0,K,1,..,1]
p = misc.add_trailing_axes(P, self.ndims[ind])
# Move cluster axis to the last:
# Shape(p) = [Nn,..,N0,1,..,1,K]
p = misc.moveaxis(p, -(self.ndims[ind]+1), -1)
# Now the shapes broadcast perfectly and we can sum
# p*phi over the last axis:
# Shape(result) = [Nn,..,N0,Dd,..,D0]
phi[ind] = misc.sum_product(p, phi[ind], axes_to_sum=-1)
if np.any(np.isnan(phi[ind])):
nans = True
if nans:
warnings.warn("The natural parameters of mixture distribution "
"contain nans. This may happen if you use fixed "
"parameters in your model. Technically, one possible "
"reason is that the cluster assignment probability "
"for some element is zero (p=0) and the natural "
"parameter of that cluster is -inf, thus "
"0*(-inf)=nan. Solution: Use parameters that assign "
"non-zero probabilities for the whole domain.")
return phi
def compute_moments_and_cgf(self, phi, mask=True):
"""
Compute the moments and :math:`g(\phi)`.
"""
return self.distribution.compute_moments_and_cgf(phi, mask=mask)
def compute_cgf_from_parents(self, *u_parents):
"""
Compute :math:`\mathrm{E}_{q(p)}[g(p)]`
"""
# Compute weighted average of g over the clusters.
# Shape(g) = [Nn,..,K,..,N0]
# Shape(p) = [Nn,..,N0,K]
# Shape(result) = [Nn,..,N0]
# Compute g for clusters:
# Shape(g) = [Nn,..,K,..,N0]
g = self.distribution.compute_cgf_from_parents(*(u_parents[1:]))
# Move cluster axis to last:
# Shape(g) = [Nn,..,N0,K]
if np.ndim(g) < abs(self.cluster_plate):
# Not enough axes, just add the cluster plate axis
g = np.expand_dims(g, -1)
else:
# Move the cluster plate axis
g = misc.moveaxis(g, self.cluster_plate, -1)
# Cluster assignments/contributions/probabilities/weights:
# Shape(p) = [Nn,..,N0,K]
p = u_parents[0][0]
# Weighted average of g over the clusters. As p and g are
# properly aligned, you can just sum p*g over the last
# axis and utilize broadcasting:
# Shape(result) = [Nn,..,N0]
g = misc.sum_product(p, g, axes_to_sum=-1)
return g
def compute_fixed_moments_and_f(self, x, mask=True):
"""
Compute the moments and :math:`f(x)` for a fixed value.
"""
return self.distribution.compute_fixed_moments_and_f(x, mask=True)
def plates_to_parent(self, index, plates):
"""
Resolves the plate mapping to a parent.
Given the plates of the node's moments, this method returns the plates
that the message to a parent has for the parent's distribution.
"""
if index == 0:
return plates
else:
# Add the cluster plate axis
plates = list(plates)
if self.cluster_plate < 0:
knd = len(plates) + self.cluster_plate + 1
else:
raise RuntimeError("Cluster plate axis must be negative")
plates.insert(knd, self.K)
plates = tuple(plates)
return self.distribution.plates_to_parent(index-1, plates)
def plates_from_parent(self, index, plates):
"""
Resolve the plate mapping from a parent.
Given the plates of a parent's moments, this method returns the plates
that the moments has for this distribution.
"""
if index == 0:
return plates
else:
plates = self.distribution.plates_from_parent(index-1, plates)
# Remove the cluster plate, if the parent has it
plates = list(plates)
if len(plates) >= abs(self.cluster_plate):
plates.pop(self.cluster_plate)
return tuple(plates)
def random(self, *phi, plates=None):
"""
Draw a random sample from the distribution.
"""
return self.distribution.random(*phi, plates=plates)
class Mixture(ExponentialFamily):
r"""
Node for exponential family mixture variables.
The node represents a random variable which is sampled from a
mixture distribution. It is possible to mix any exponential family
distribution. The probability density function is
.. math::
p(x|z=k,\boldsymbol{\theta}_0,\ldots,\boldsymbol{\theta}_{K-1})
= \phi(x|\boldsymbol{\theta}_k),
where :math:`\phi` is the probability density function of the mixed
exponential family distribution and :math:`\boldsymbol{\theta}_0,
\ldots, \boldsymbol{\theta}_{K-1}` are the parameters of each
cluster. For instance, :math:`\phi` could be the Gaussian
probability density function :math:`\mathcal{N}` and
:math:`\boldsymbol{\theta}_k = \{\boldsymbol{\mu}_k,
\mathbf{\Lambda}_k\}` where :math:`\boldsymbol{\mu}_k` and
:math:`\mathbf{\Lambda}_k` are the mean vector and precision matrix
for cluster :math:`k`.
Parameters
----------
z : categorical-like node or array
:math:`z`, cluster assignment
node_class : stochastic exponential family node class
Mixed distribution
params : types specified by the mixed distribution
Parameters of the mixed distribution. If some parameters should
vary between clusters, those parameters' plate axis
`cluster_plate` should have a size which equals the number of
clusters. For parameters with shared values, that plate axis
should have length 1. At least one parameter should vary between
clusters.
cluster_plate : int, optional
Negative integer defining which plate axis is used for the
clusters in the parameters. That plate axis is ignored from the
parameters when considering the plates for this node. By
default, mix over the last plate axis.
See also
--------
Categorical, CategoricalMarkovChain
Examples
--------
A simple 2-dimensional Gaussian mixture model with three clusters
for 100 samples can be constructed, for instance, as:
.. code-block:: python
from bayespy.nodes import (Dirichlet, Categorical, Mixture,
Gaussian, Wishart)
alpha = Dirichlet([1e-3, 1e-3, 1e-3])
Z = Categorical(alpha, plates=(100,))
mu = Gaussian(np.zeros(2), 1e-6*np.identity(2), plates=(3,))
Lambda = Wishart(2, 1e-6*np.identity(2), plates=(3,))
X = Mixture(Z, Gaussian, mu, Lambda)
"""
def __init__(self, z, node_class, *params, cluster_plate=-1, **kwargs):
self.cluster_plate = cluster_plate
super().__init__(z, node_class, *params, cluster_plate=cluster_plate,
**kwargs)
@classmethod
def _constructor(cls, z, node_class, *args, cluster_plate=-1, **kwargs):
"""
Constructs distribution and moments objects.
"""
if cluster_plate >= 0:
raise ValueError("Cluster plate axis must be negative")
# Get the stuff for the mixed distribution
(parents, _, dims, mixture_plates, distribution, moments, parent_moments) = \
node_class._constructor(*args)
# Check that at least one of the parents has the cluster plate axis
if len(mixture_plates) < abs(cluster_plate):
raise ValueError("The mixed distribution does not have a plates "
"axis for the cluster plate axis")
# Resolve the number of clusters
mixture_plates = list(mixture_plates)
K = mixture_plates.pop(cluster_plate)
# Convert a node to get the number of clusters
z = cls._ensure_moments(z, CategoricalMoments(K))
if z.dims[0][0] != K:
raise ValueError("Inconsistent number of clusters")
plates = cls._total_plates(kwargs.get('plates'), mixture_plates, z.plates)
ndims = [len(dim) for dim in dims]
parents = [cls._ensure_moments(p_i, m_i)
for (p_i, m_i) in zip(parents, parent_moments)]
ndims_parents = [[len(dims_i) for dims_i in parent.dims]
for parent in parents]
# Convert the distribution to a mixture
distribution = MixtureDistribution(distribution,
cluster_plate,
K,
ndims,
ndims_parents)
# Add cluster assignments to parents
parent_moments = [CategoricalMoments(K)] + list(parent_moments)
parents = [z] + list(parents)
return (parents,
kwargs,
dims,
plates,
distribution,
moments,
parent_moments)
def integrated_logpdf_from_parents(self, x, index):
""" Approximates the posterior predictive pdf \int p(x|parents)
q(parents) dparents in log-scale as \int q(parents_i) exp( \int
q(parents_\i) \log p(x|parents) dparents_\i ) dparents_i."""
if index == 0:
# Integrate out the cluster assignments
# First, integrate the cluster parameters in log-scale
# compute_logpdf(cls, u, phi, g, f):
# Shape(x) = [M1,..,Mm,N1,..,Nn,D1,..,Dd]
u_parents = self._message_from_parents()
# Shape(u) = [M1,..,Mm,N1,..,1,..,Nn,D1,..,Dd]
# Shape(f) = [M1,..,Mm,N1,..,1,..,Nn]
(u, f) = self._distribution.distribution.compute_fixed_moments_and_f(x)
f = np.expand_dims(f, axis=self.cluster_plate)
for i in range(len(u)):
ndim_i = len(self.dims[i])
cluster_axis = self.cluster_plate - ndim_i
u[i] = np.expand_dims(u[i], axis=cluster_axis)
# Shape(phi) = [N1,..,K,..,Nn,D1,..,Dd]
phi = self._distribution.distribution.compute_phi_from_parents(*(u_parents[1:]))
# Shape(g) = [N1,..,K,..,Nn]
g = self._distribution.distribution.compute_cgf_from_parents(*(u_parents[1:]))
# Shape(lpdf) = [M1,..,Mm,N1,..,K,..,Nn]
lpdf = self._distribution.distribution.compute_logpdf(u, phi, g, f, self.ndims)
# From logpdf to pdf, but avoid over/underflow
lpdf_max = np.max(lpdf, axis=self.cluster_plate, keepdims=True)
pdf = np.exp(lpdf-lpdf_max)
# Move cluster axis to be the last:
# Shape(pdf) = [M1,..,Mm,N1,..,Nn,K]
pdf = misc.moveaxis(pdf, self.cluster_plate, -1)
# Cluster assignments/probabilities/weights
# Shape(p) = [N1,..,Nn,K]
p = u_parents[0][0]
# Weighted average. TODO/FIXME: Use einsum!
# Shape(pdf) = [M1,..,Mm,N1,..,Nn]
pdf = np.sum(pdf * p, axis=self.cluster_plate)
# Back to log-scale (add the overflow fix!)
lpdf_max = np.squeeze(lpdf_max, axis=self.cluster_plate)
lpdf = np.log(pdf) + lpdf_max
return lpdf
raise NotImplementedError()
bayespy-0.2.3/bayespy/inference/vmp/nodes/multinomial.py 0000664 5104112 5104112 00000016454 12374370351 025161 0 ustar jluttine jluttine 0000000 0000000 ######################################################################
# Copyright (C) 2014 Jaakko Luttinen
#
# This file is licensed under Version 3.0 of the GNU General Public
# License. See LICENSE for a text of the license.
######################################################################
######################################################################
# This file is part of BayesPy.
#
# BayesPy is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# BayesPy is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with BayesPy. If not, see .
######################################################################
"""
Module for the multinomial distribution node.
"""
import numpy as np
from scipy import special
from .expfamily import ExponentialFamily
from .expfamily import ExponentialFamilyDistribution
from .expfamily import useconstructor
from .dirichlet import Dirichlet, DirichletMoments
from .node import Moments, ensureparents
from bayespy.utils import random
from bayespy.utils import misc
class MultinomialMoments(Moments):
"""
Class for the moments of multinomial variables.
"""
def compute_fixed_moments(self, x):
"""
Compute the moments for a fixed value
`x` must be a vector of counts.
"""
# Check that counts are valid
x = np.asanyarray(x)
if not misc.isinteger(x):
raise ValueError("Counts must be integer")
if np.any(x < 0):
raise ValueError("Counts must be non-negative")
# Moments is just the counts vector
u0 = x.copy()
return [u0]
def compute_dims_from_values(self, x):
"""
Return the shape of the moments for a fixed value.
"""
D = np.shape(x)[-1]
return ( (D,), )
class MultinomialDistribution(ExponentialFamilyDistribution):
"""
Class for the VMP formulas of multinomial variables.
"""
def __init__(self, trials):
"""
Create VMP formula node for a multinomial variable
`trials` is the total number of trials.
"""
if not misc.isinteger(trials):
raise ValueError("Number of trials must be integer")
if np.any(trials < 0):
raise ValueError("Number of trials must be non-negative")
self.N = trials
super().__init__()
def compute_message_to_parent(self, parent, index, u, u_p):
"""
Compute the message to a parent node.
"""
if index == 0:
return [ u[0].copy() ]
else:
raise ValueError("Index out of bounds")
def compute_phi_from_parents(self, u_p, mask=True):
"""
Compute the natural parameter vector given parent moments.
"""
logp = u_p[0]
return [logp]
def compute_moments_and_cgf(self, phi, mask=True):
"""
Compute the moments and :math:`g(\phi)`.
"""
# Compute the normalized probabilities in a numerically stable way
logsum_p = misc.logsumexp(phi[0], axis=-1, keepdims=True)
logp = phi[0] - logsum_p
p = np.exp(logp)
# Because of small numerical inaccuracy, normalize the probabilities
# again for more accurate results
N = np.expand_dims(self.N, -1)
u0 = N * p / np.sum(p, axis=-1, keepdims=True)
u = [u0]
g = -np.squeeze(N * logsum_p, axis=-1)
return (u, g)
def compute_cgf_from_parents(self, u_p):
"""
Compute :math:`\mathrm{E}_{q(p)}[g(p)]`
"""
return 0
def compute_fixed_moments_and_f(self, x, mask=True):
"""
Compute the moments and :math:`f(x)` for a fixed value.
"""
# Check that counts are valid
x = np.asanyarray(x)
if not misc.isinteger(x):
raise ValueError("Counts must be integers")
if np.any(x < 0):
raise ValueError("Counts must be non-negative")
if np.any(np.sum(x, axis=-1) != self.N):
raise ValueError("Counts must sum to the number of trials")
# Moments is just the counts vector
u0 = x.copy()
u = [u0]
f = special.gammaln(self.N+1) - np.sum(special.gammaln(x+1), axis=-1)
return (u, f)
def random(self, *phi):
"""
Draw a random sample from the distribution.
"""
raise NotImplementedError()
class Multinomial(ExponentialFamily):
r"""
Node for multinomial random variables.
Assume there are :math:`K` categories and :math:`N` trials each of which
leads a success for exactly one of the categories. Given the probabilities
:math:`p_0,\ldots,p_{K-1}` for the categories, multinomial distribution is
gives the probability of any combination of numbers of successes for the
categories.
The node models the number of successes :math:`x_k \in \{0, \ldots, n\}` in
:math:`n` trials with probability :math:`p_k` for success in :math:`K`
categories.
.. math::
\mathrm{Multinomial}(\mathbf{x}| N, \mathbf{p}) = \frac{N!}{x_0!\cdots
x_{K-1}!} p_0^{x_0} \cdots p_{K-1}^{x_{K-1}}
Parameters
----------
n : scalar or array
:math:`N`, number of trials
p : Dirichlet-like node or (...,K)-array
:math:`\mathbf{p}`, probabilities of successes for the categories
See also
--------
Dirichlet, Binomial, Categorical
"""
_moments = MultinomialMoments()
_parent_moments = (DirichletMoments(),)
def __init__(self, n, p, **kwargs):
"""
Create Multinomial node.
"""
super().__init__(n, p, **kwargs)
@classmethod
def _constructor(cls, n, p, **kwargs):
"""
Constructs distribution and moments objects.
This method is called if useconstructor decorator is used for __init__.
Becase the distribution and moments object depend on the number of
categories, that is, they depend on the parent node, this method can be
used to construct those objects.
"""
# Get the number of categories
p = cls._ensure_moments(p, cls._parent_moments[0])
D = p.dims[0][0]
parents = [p]
distribution = MultinomialDistribution(n)
return (parents,
kwargs,
( (D,), ),
cls._total_plates(kwargs.get('plates'),
distribution.plates_from_parent(0, p.plates),
np.shape(n)),
distribution,
cls._moments,
cls._parent_moments)
def show(self):
"""
Print the distribution using standard parameterization.
"""
logsum_p = misc.logsumexp(self.phi[0], axis=-1, keepdims=True)
p = np.exp(self.phi[0] - logsum_p)
p /= np.sum(p, axis=-1, keepdims=True)
print("%s ~ Multinomial(p)" % self.name)
print(" p = ")
print(p)
return
bayespy-0.2.3/bayespy/inference/vmp/nodes/node.py 0000664 5104112 5104112 00000127031 12430651206 023540 0 ustar jluttine jluttine 0000000 0000000 ######################################################################
# Copyright (C) 2013-2014 Jaakko Luttinen
#
# This file is licensed under Version 3.0 of the GNU General Public
# License. See LICENSE for a text of the license.
######################################################################
######################################################################
# This file is part of BayesPy.
#
# BayesPy is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# BayesPy is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with BayesPy. If not, see .
######################################################################
import numpy as np
import matplotlib.pyplot as plt
from bayespy.utils import misc
"""
This module contains a sketch of a new implementation of the framework.
"""
def message_sum_multiply(plates_parent, dims_parent, *arrays):
"""
Compute message to parent and sum over plates.
Divide by the plate multiplier.
"""
# The shape of the full message
shapes = [np.shape(array) for array in arrays]
shape_full = misc.broadcasted_shape(*shapes)
# Find axes that should be summed
shape_parent = plates_parent + dims_parent
sum_axes = misc.axes_to_collapse(shape_full, shape_parent)
# Compute the multiplier for cancelling the
# plate-multiplier. Because we are summing over the
# dimensions already in this function (for efficiency), we
# need to cancel the effect of the plate-multiplier
# applied in the message_to_parent function.
r = 1
for j in sum_axes:
if j >= 0 and j < len(plates_parent):
r *= shape_full[j]
elif j < 0 and j < -len(dims_parent):
r *= shape_full[j]
# Compute the sum-product
m = misc.sum_multiply(*arrays,
axis=sum_axes,
sumaxis=True,
keepdims=True) / r
# Remove extra axes
m = misc.squeeze_to_dim(m, len(shape_parent))
return m
class Moments():
"""
Base class for defining the expectation of the sufficient statistics.
The benefits:
* Write statistic-specific features in one place only. For instance,
covariance from Gaussian message.
* Different nodes may have identically defined statistic so you need to
implement related features only once. For instance, Gaussian and
GaussianARD differ on the prior but the moments are the same.
* General processing nodes which do not change the type of the moments may
"inherit" the features from the parent node. For instance, slicing
operator.
* Conversions can be done easily in both of the above cases if the message
conversion is defined in the moments class. For instance,
GaussianMarkovChain to Gaussian and VaryingGaussianMarkovChain to
Gaussian.
"""
_converters = {}
class NoConverterError(Exception):
pass
@classmethod
def add_converter(cls, moments_to, converter):
cls._converters = cls._converters.copy()
cls._converters[moments_to] = converter
return
def get_converter(self, moments_to):
"""
Finds conversion to another moments type if possible.
Note that a conversion from moments A to moments B may require
intermediate conversions. For instance: A->C->D->B. This method finds
the path which uses the least amount of conversions and returns that
path as a single conversion. If no conversion path is available, an
error is raised.
The search algorithm starts from the original moments class and applies
all possible converters to get a new list of moments classes. This list
is extended by adding recursively all parent classes because their
converters are applicable. Then, all possible converters are applied to
this list to get a new list of current moments classes. This is iterated
until the algorithm hits the target moments class or its subclass.
"""
# Check if there is no need for a conversion
if isinstance(self, moments_to):
return lambda X: X
# Initialize variables
visited = set()
visited.add(self.__class__)
converted_list = [(self.__class__, [])]
# Each iteration step consists of two parts:
# 1) form a set of the current classes and all their parent classes
# recursively
# 2) from the current set, apply possible conversions to get a new set
# of classes
# Repeat these two steps until in step (1) you hit the target class.
while len(converted_list) > 0:
# Go through all parents recursively so we can then use all
# converters that are available
current_list = []
for (moments_class, converter_path) in converted_list:
if issubclass(moments_class, moments_to):
# Shortest conversion path found, return the resulting total
# conversion function
return misc.composite_function(converter_path)
current_list.append((moments_class, converter_path))
parents = list(moments_class.__bases__)
for parent in parents:
# Recursively add parents
for p in parent.__bases__:
if isinstance(p, Moments):
parents.append(p)
# Add un-visited parents
if issubclass(parent, Moments) and parent not in visited:
visited.add(parent)
current_list.append((parent, converter_path))
# Find all converters and extend the converter paths
converted_list = []
for (moments_class, converter_path) in current_list:
for (conv_mom_cls, conv) in moments_class._converters.items():
if conv_mom_cls not in visited:
visited.add(conv_mom_cls)
converted_list.append((conv_mom_cls,
converter_path + [conv]))
raise self.NoConverterError("No conversion defined from %s to %s"
% (self.__class__.__name__,
moments_to.__name__))
def compute_fixed_moments(self, x):
# This method can't be static because the computation of the moments may
# depend on, for instance, ndim in Gaussian arrays.
raise NotImplementedError("compute_fixed_moments not implemented for "
"%s"
% (self.__class__.__name__))
def compute_dims_from_values(self, x):
# This method can't be static because the computation of the moments may
# depend on, for instance, ndim in Gaussian arrays.
raise NotImplementedError("compute_dims_from_values not implemented "
"for %s"
% (self.__class__.__name__))
def ensureparents(func):
def new_func(self, *parents, **kwargs):
# Convert parents to proper nodes
parents = list(parents)
for (ind, parent) in enumerate(parents):
parents[ind] = self._ensure_moments(parent,
self._parent_moments[ind])
# Run the function
return func(self, *parents, **kwargs)
return new_func
class Node():
"""
Base class for all nodes.
mask
dims
plates
parents
children
name
Sub-classes must implement:
1. For computing the message to children:
get_moments(self):
2. For computing the message to parents:
_get_message_and_mask_to_parent(self, index)
Sub-classes may need to re-implement:
1. If they manipulate plates:
_compute_mask_to_parent(index, mask)
_plates_to_parent(self, index)
_plates_from_parent(self, index)
"""
# These are objects of the _parent_moments_class. If the default way of
# creating them is not correct, write your own creation code.
_moments = None
_parent_moments = None
plates = None
_id_counter = 0
@ensureparents
def __init__(self, *parents, dims=None, plates=None, name="",
notify_parents=True, plotter=None):
self.parents = parents
self.dims = dims
self.name = name
self._plotter = plotter
parent_id_list = []
for parent in parents:
parent_id_list = parent_id_list + list(parent._get_id_list())
if len(parent_id_list) != len(set(parent_id_list)):
raise ValueError("Parent nodes are not independent")
# Inform parent nodes
if notify_parents:
for (index,parent) in enumerate(self.parents):
parent._add_child(self, index)
# Check plates
parent_plates = [self._plates_from_parent(index)
for index in range(len(self.parents))]
if any(p is None for p in parent_plates):
raise ValueError("Method _plates_from_parent returned None")
# Get and validate the plates for this node
plates = self._total_plates(plates, *parent_plates)
if self.plates is None:
self.plates = plates
# By default, ignore all plates
self.mask = np.array(False)
# Children
self.children = set()
def _get_id_list(self):
"""
Returns the stochastic ID list.
This method is used to check that same stochastic nodes are not direct
parents of a node several times. It is only valid if there are
intermediate stochastic nodes.
To put it another way: each ID corresponds to one factor q(..) in the
posterior approximation. Different IDs mean different factors, thus they
mean independence. The parents must have independent factors.
Stochastic nodes should return their unique ID. Deterministic nodes
should return the IDs of their parents. Constant nodes should return
empty list of IDs.
"""
raise NotImplementedError()
@classmethod
def _total_plates(cls, plates, *parent_plates):
if plates is None:
# By default, use the minimum number of plates determined
# from the parent nodes
try:
return misc.broadcasted_shape(*parent_plates)
except ValueError:
raise ValueError("The plates of the parents do not broadcast.")
else:
# Check that the parent_plates are a subset of plates.
for (ind, p) in enumerate(parent_plates):
if not misc.is_shape_subset(p, plates):
raise ValueError("The plates %s of the parents "
"are not broadcastable to the given "
"plates %s."
% (p,
plates))
return plates
@staticmethod
def _ensure_moments(node, moments):
if isinstance(node, Node):
# Convert to correct type
return node._convert(moments.__class__)
# Convert to constant node
from .constant import Constant
return Constant(moments, node)
def _plates_to_parent(self, index):
# Sub-classes may want to overwrite this if they manipulate plates
return self.plates
def _plates_from_parent(self, index):
# Sub-classes may want to overwrite this if they manipulate plates
return self.parents[index].plates
def get_shape(self, ind):
return self.plates + self.dims[ind]
def _add_child(self, child, index):
"""
Add a child node.
Parameters
----------
child : node
index : int
The parent index of this node for the child node.
The child node recognizes its parents by their index
number.
"""
self.children.add((child, index))
def _remove_child(self, child, index):
"""
Remove a child node.
"""
self.children.remove((child, index))
def get_mask(self):
return self.mask
## def _get_message_mask(self):
## return self.mask
def _set_mask(self, mask):
# Sub-classes may overwrite this method if they have some other masks to
# be combined (for instance, observation mask)
self.mask = mask
def _update_mask(self):
# Combine masks from children
mask = np.array(False)
for (child, index) in self.children:
mask = np.logical_or(mask, child._mask_to_parent(index))
# Set the mask of this node
self._set_mask(mask)
if not misc.is_shape_subset(np.shape(self.mask), self.plates):
raise ValueError("The mask of the node %s has updated "
"incorrectly. The plates in the mask %s are not a "
"subset of the plates of the node %s."
% (self.name,
np.shape(self.mask),
self.plates))
# Tell parents to update their masks
for parent in self.parents:
parent._update_mask()
## @staticmethod
## def _compute_mask_to_parent(index, mask):
## # Sub-classes may want to overwrite this if they do something to plates.
## return mask
# TODO: Rename to _compute_message_mask_to_parent(index, mask)
def _compute_mask_to_parent(self, index, mask):
"""
Compute the mask used for messages sent to parent[index].
The mask tells which plates in the messages are active. This method is
used for obtaining the mask which is used to set plates in the messages
to parent to zero.
Sub-classes may want to overwrite this method if they do something to
plates so that the mask is somehow altered.
"""
return mask
def _mask_to_parent(self, index):
"""
Get the mask with respect to parent[index].
The mask tells which plate connections are active. The mask is "summed"
(logical or) and reshaped into the plate shape of the parent. Thus, it
can't be used for masking messages, because some plates have been summed
already. This method is used for propagating the mask to parents.
"""
mask = self._compute_mask_to_parent(index, self.mask)
# Check the shape of the mask
plates_to_parent = self._plates_to_parent(index)
if not misc.is_shape_subset(np.shape(mask), plates_to_parent):
raise ValueError("In node %s, the mask being sent to "
"parent[%d] (%s) has invalid shape: The shape of "
"the mask %s is not a sub-shape of the plates of "
"the node with respect to the parent %s. It could "
"be that this node (%s) is manipulating plates "
"but has not overwritten the method "
"_compute_mask_to_parent."
% (self.name,
index,
self.parents[index].name,
np.shape(mask),
plates_to_parent,
self.__class__.__name__))
# "Sum" (i.e., logical or) over the plates that have unit length in
# the parent node.
parent_plates = self.parents[index].plates
s = misc.axes_to_collapse(np.shape(mask), parent_plates)
mask = np.any(mask, axis=s, keepdims=True)
mask = misc.squeeze_to_dim(mask, len(parent_plates))
return mask
#return self._compute_mask_to_parent(index, self.get_mask())
def _message_to_child(self):
u = self.get_moments()
# Debug: Check that the message has appropriate shape
for (ui, dim) in zip(u, self.dims):
ndim = len(dim)
if ndim > 0:
if np.shape(ui)[-ndim:] != dim:
raise RuntimeError(
"A bug found by _message_to_child for %s: "
"The variable axes of the moments %s are not equal to "
"the axes %s defined by the node %s. A possible reason "
"is that the plates of the node are inferred "
"incorrectly from the parents, and the method "
"_plates_from_parents should be implemented."
% (self.__class__.__name__,
np.shape(ui)[-ndim:],
dim,
self.name))
if not misc.is_shape_subset(np.shape(ui)[:-ndim],
self.plates):
raise RuntimeError(
"A bug found by _message_to_child for %s: "
"The plate axes of the moments %s are not a subset of "
"the plate axes %s defined by the node %s."
% (self.__class__.__name__,
np.shape(ui)[:-ndim],
self.plates,
self.name))
else:
if not misc.is_shape_subset(np.shape(ui), self.plates):
raise RuntimeError(
"A bug found by _message_to_child for %s: "
"The plate axes of the moments %s are not a subset of "
"the plate axes %s defined by the node %s."
% (self.__class__.__name__,
np.shape(ui),
self.plates,
self.name))
return u
def _message_to_parent(self, index):
# Compute the message, check plates, apply mask and sum over some plates
if index >= len(self.parents):
raise ValueError("Parent index larger than the number of parents")
# Compute the message and mask
(m, mask) = self._get_message_and_mask_to_parent(index)
mask = misc.squeeze(mask)
# Plates in the mask
plates_mask = np.shape(mask)
# The parent we're sending the message to
parent = self.parents[index]
# Compact the message to a proper shape
for i in range(len(m)):
# Empty messages are given as None. We can ignore those.
if m[i] is not None:
# Plates in the message
shape_m = np.shape(m[i])
dim_parent = len(parent.dims[i])
if dim_parent > 0:
plates_m = shape_m[:-dim_parent]
else:
plates_m = shape_m
# Compute the multiplier (multiply by the number of plates for
# which the message, the mask and the parent have single
# plates). Such a plate is meant to be broadcasted but because
# the parent has singular plate axis, it won't broadcast (and
# sum over it), so we need to multiply it.
plates_self = self._plates_to_parent(index)
try:
r = self._plate_multiplier(plates_self,
plates_m,
plates_mask,
parent.plates)
except ValueError:
raise ValueError("The plates of the message, the mask and "
"parent[%d] node (%s) are not a "
"broadcastable subset of the plates of "
"this node (%s). The message has shape "
"%s, meaning plates %s. The mask has "
"plates %s. This node has plates %s with "
"respect to the parent[%d], which has "
"plates %s."
% (index,
parent.name,
self.name,
np.shape(m[i]),
plates_m,
plates_mask,
plates_self,
index,
parent.plates))
# Add variable axes to the mask
shape_mask = np.shape(mask) + (1,) * len(parent.dims[i])
mask_i = np.reshape(mask, shape_mask)
# Sum over plates that are not in the message nor in the parent
shape_parent = parent.get_shape(i)
shape_msg = misc.broadcasted_shape(shape_m, shape_parent)
axes_mask = misc.axes_to_collapse(shape_mask, shape_msg)
mask_i = np.sum(mask_i, axis=axes_mask, keepdims=True)
# Compute the masked message and sum over the plates that the
# parent does not have.
axes_msg = misc.axes_to_collapse(shape_msg, shape_parent)
m[i] = misc.sum_multiply(mask_i, m[i], r,
axis=axes_msg,
keepdims=True)
# Remove leading singular plates if the parent does not have
# those plate axes.
m[i] = misc.squeeze_to_dim(m[i], len(shape_parent))
return m
def _message_from_children(self):
msg = [np.zeros(shape) for shape in self.dims]
#msg = [np.array(0.0) for i in range(len(self.dims))]
for (child,index) in self.children:
m = child._message_to_parent(index)
for i in range(len(self.dims)):
if m[i] is not None:
# Check broadcasting shapes
sh = misc.broadcasted_shape(self.get_shape(i), np.shape(m[i]))
try:
# Try exploiting broadcasting rules
msg[i] += m[i]
except ValueError:
msg[i] = msg[i] + m[i]
return msg
def _message_from_parents(self, exclude=None):
return [list(parent._message_to_child())
if ind != exclude else
None
for (ind,parent) in enumerate(self.parents)]
def get_moments(self):
raise NotImplementedError()
def delete(self):
"""
Delete this node and the children
"""
for child in self.children:
child.delete()
for (ind, parent) in enumerate(self.parents):
parent._remove_child(self, ind)
@staticmethod
def _plate_multiplier(plates, *args):
"""
Compute the plate multiplier for given shapes.
The first shape is compared to all other shapes (using NumPy
broadcasting rules). All the elements which are non-unit in the first
shape but 1 in all other shapes are multiplied together.
This method is used, for instance, for computing a correction factor for
messages to parents: If this node has non-unit plates that are unit
plates in the parent, those plates are summed. However, if the message
has unit axis for that plate, it should be first broadcasted to the
plates of this node and then summed to the plates of the parent. In
order to avoid this broadcasting and summing, it is more efficient to
just multiply by the correct factor. This method computes that
factor. The first argument is the full plate shape of this node (with
respect to the parent). The other arguments are the shape of the message
array and the plates of the parent (with respect to this node).
"""
# Check broadcasting of the shapes
for arg in args:
misc.broadcasted_shape(plates, arg)
# Check that each arg-plates are a subset of plates?
for arg in args:
if not misc.is_shape_subset(arg, plates):
raise ValueError("The shapes in args are not a sub-shape of "
"plates.")
r = 1
for j in range(-len(plates),0):
mult = True
for arg in args:
# if -j <= len(arg) and arg[j] != 1:
if not (-j > len(arg) or arg[j] == 1):
mult = False
if mult:
r *= plates[j]
return r
def move_plates(self, from_plate, to_plate):
return _MovePlate(self,
from_plate,
to_plate,
name=self.name + ".move_plates")
def add_plate_axis(self, to_plate):
return AddPlateAxis(to_plate)(self,
name=self.name+".add_plate_axis")
def __getitem__(self, index):
return Slice(self, index,
name=(self.name+".__getitem__"))
def has_plotter(self):
"""
Return True if the node has a plotter
"""
return callable(self._plotter)
def set_plotter(self, plotter):
self._plotter = plotter
def plot(self, fig=None, **kwargs):
"""
Plot the node distribution using the plotter of the node
Because the distributions are in general very difficult to plot, the
user must specify some functions which performs the plotting as
wanted. See, for instance, bayespy.plot.plotting for available plotters,
that is, functions that perform plotting for a node.
"""
if fig is None:
fig = plt.gcf()
if callable(self._plotter):
ax = self._plotter(self, fig=fig, **kwargs)
fig.suptitle('q(%s)' % self.name)
return ax
else:
raise Exception("No plotter defined, can not plot")
def _convert(self, moments_class):
converter = self._moments.get_converter(moments_class)
return converter(self)
## def convert(self, node_class):
## return self._convert(node_class._moments_class)
@staticmethod
def _compute_message(*arrays, plates_from=(), plates_to=(), ndim=0):
"""
A general function for computing messages by sum-multiply
The function computes the product of the input arrays and then sums to
the requested plates.
"""
# Check that the plates broadcast properly
if not misc.is_shape_subset(plates_to, plates_from):
raise ValueError("plates_to must be broadcastable to plates_from")
# Compute the explicit shape of the product
shapes = [np.shape(array) for array in arrays]
arrays_shape = misc.broadcasted_shape(*shapes)
# Compute plates and dims that are present
if ndim == 0:
arrays_plates = arrays_shape
dims = ()
else:
arrays_plates = arrays_shape[:-ndim]
dims = arrays_shape[-ndim:]
# Compute the correction term. If some of the plates that should be
# summed are actually broadcasted, one must multiply by the size of the
# corresponding plate
r = Node._plate_multiplier(plates_from, arrays_plates, plates_to)
# For simplicity, make the arrays equal ndim
arrays = misc.make_equal_ndim(*arrays)
# Keys for the input plates: (N-1, N-2, ..., 0)
nplates = len(arrays_plates)
in_plate_keys = list(range(nplates))
# Keys for the output plates
out_plate_keys = [key
for key in in_plate_keys
if key < len(plates_to) and plates_to[-key-1] != 1]
# Keys for the dims
dim_keys = list(range(nplates, nplates+ndim))
# Total input and output keys
in_keys = len(arrays) * [in_plate_keys + dim_keys]
out_keys = out_plate_keys + dim_keys
# Compute the sum-product with correction
einsum_args = misc.zipper_merge(arrays, in_keys) + [out_keys]
y = r * np.einsum(*einsum_args)
# Reshape the result and apply correction
nplates_result = min(len(plates_to), len(arrays_plates))
if nplates_result == 0:
plates_result = []
else:
plates_result = [min(plates_to[ind], arrays_plates[ind])
for ind in range(-nplates_result, 0)]
y = np.reshape(y, plates_result + list(dims))
return y
from .deterministic import Deterministic
def slicelen(s, length=None):
if length is not None:
s = slice(*(s.indices(length)))
return max(0, misc.ceildiv(s.stop - s.start, s.step))
class Slice(Deterministic):
"""
Basic slicing for plates.
Slicing occurs when index is a slice object (constructed by start:stop:step
notation inside of brackets), an integer, or a tuple of slice objects and
integers.
Currently, accept slices, newaxis, ellipsis and integers. For instance, does
not accept lists/tuples to pick multiple indices of the same axis.
Ellipsis expand to the number of : objects needed to make a selection tuple
of the same length as x.ndim. Only the first ellipsis is expanded, any
others are interpreted as :.
Similar to:
http://docs.scipy.org/doc/numpy/reference/arrays.indexing.html#basic-slicing
"""
_parent_moments = (Moments(),)
def __init__(self, X, slices, **kwargs):
self._moments = X._moments
# Force a list
if not isinstance(slices, tuple):
slices = [slices]
else:
slices = list(slices)
#
# Expand Ellipsis
#
# Compute the number of required axes and how Ellipsis is expanded
num_axis = 0
ellipsis_index = None
for (k, s) in enumerate(slices):
if isinstance(s, int) or isinstance(s, slice):
num_axis += 1
elif s is None:
pass
elif s is Ellipsis:
# Index is an ellipsis, e.g., [...]
if ellipsis_index is None:
# Expand ...
ellipsis_index = k
else:
# Interpret ... as :
num_axis += 1
slices[k] = slice(None)
else:
raise TypeError("Invalid argument type.")
if num_axis > len(X.plates):
raise IndexError("Too many indices")
# The number of plates that were not given explicit slicing (either
# Ellipsis was used or the number of slices was smaller than the number
# of plate axes)
expand_len = len(X.plates) - num_axis
if ellipsis_index is not None:
# Replace Ellipsis with correct number of :
k = ellipsis_index
del slices[k]
slices = slices[:k] + [slice(None)] * expand_len + slices[k:]
else:
# Add trailing : so that each plate has explicit slicing
slices = slices + [slice(None)] * expand_len
#
# Preprocess indexing:
# - integer indices to non-negative values
# - slice start/stop values to non-negative
# - slice start/stop values based on the size of the plate
#
# Index for parent plates
j = 0
for (k, s) in enumerate(slices):
if isinstance(s, int):
# Index is an integer, e.g., [3]
if s < 0:
# Handle negative index
s += X.plates[j]
if s < 0 or s >= X.plates[j]:
raise IndexError("Index out of range")
# Store the preprocessed integer index
slices[k] = s
j += 1
elif isinstance(s, slice):
# Index is a slice, e.g., [2:6]
# Normalize the slice
s = slice(*(s.indices(X.plates[j])))
if slicelen(s) <= 0:
raise IndexError("Slicing leads to empty plates")
slices[k] = s
j += 1
self.slices = slices
super().__init__(X,
dims=X.dims,
**kwargs)
def _plates_to_parent(self, index):
return self.parents[index].plates
def _plates_from_parent(self, index):
plates = list(self.parents[index].plates)
# Compute the plates. Note that Ellipsis has already been preprocessed
# to a proper number of :
k = 0
for s in self.slices:
# Then, each case separately: slice, newaxis, integer
if isinstance(s, slice):
# Slice, e.g., [2:5]
N = slicelen(s)
if N <= 0:
raise IndexError("Slicing leads to empty plates")
plates[k] = N
k += 1
elif s is None:
# [np.newaxis]
plates = plates[:k] + [1] + plates[k:]
k += 1
elif isinstance(s, int):
# Integer, e.g., [3]
del plates[k]
return tuple(plates)
@staticmethod
def __reverse_indexing(slices, m_child, plates, dims):
"""
A helpful function for performing reverse indexing/slicing
"""
j = -1 # plate index for parent
i = -1 # plate index for child
child_slices = ()
parent_slices = ()
msg_plates = ()
# Compute plate axes in the message from children
ndim = len(dims)
if ndim > 0:
m_plates = np.shape(m_child)[:-ndim]
else:
m_plates = np.shape(m_child)
for s in reversed(slices):
if isinstance(s, int):
# Case: integer
parent_slices = (s,) + parent_slices
msg_plates = (plates[j],) + msg_plates
j -= 1
elif s is None:
# Case: newaxis
if -i <= len(m_plates):
child_slices = (0,) + child_slices
i -= 1
else:
# Case: slice
if -i <= len(m_plates):
child_slices = (slice(None),) + child_slices
parent_slices = (s,) + parent_slices
if ((-i > len(m_plates) or m_plates[i] == 1)
and slicelen(s) == plates[j]):
# Broadcasting can be applied. The message does not need
# to be explicitly shaped to the full size
msg_plates = (1,) + msg_plates
else:
# No broadcasting. Must explicitly form the full size
# axis
msg_plates = (plates[j],) + msg_plates
j -= 1
i -= 1
# Set the elements of the message
m_parent = np.zeros(msg_plates + dims)
if np.ndim(m_parent) == 0 and np.ndim(m_child) == 0:
m_parent = m_child
elif np.ndim(m_parent) == 0:
m_parent = m_child[child_slices]
elif np.ndim(m_child) == 0:
m_parent[parent_slices] = m_child
else:
m_parent[parent_slices] = m_child[child_slices]
return m_parent
def _compute_mask_to_parent(self, index, mask):
"""
Compute the mask to the parent node.
"""
if index != 0:
raise ValueError("Invalid index")
parent = self.parents[0]
return self.__reverse_indexing(self.slices,
self.mask,
parent.plates,
())
def _compute_message_and_mask_to_parent(self, index, m, u):
"""
Compute the message to a parent node.
"""
if index != 0:
raise ValueError("Invalid index")
parent = self.parents[0]
# Apply reverse indexing for the message arrays
msg = [self.__reverse_indexing(self.slices,
m_child,
parent.plates,
dims)
for (m_child, dims) in zip(m, parent.dims)]
# Apply reverse indexing for the mask
mask = self._compute_mask_to_parent(0, self.mask)
return (msg, mask)
def _compute_moments(self, u):
"""
Get the moments with an added plate axis.
"""
# Process each moment
for n in range(len(u)):
# Compute the effective plates in the message/moment
ndim = len(self.dims[n])
if ndim > 0:
shape = np.shape(u[n])[:-ndim]
else:
shape = np.shape(u[n])
# Construct a list of slice objects
u_slices = []
# Index for the shape
j = -len(self.parents[0].plates)
for (k, s) in enumerate(self.slices):
if s is None:
# [np.newaxis]
if -j < len(shape):
# Only add newaxis if there are some axes before
# this. It does not make any difference if you added
# leading unit axes
u_slices.append(s)
else:
# slice or integer index
if -j <= len(shape):
# The moment has this axis, so it is not broadcasting it
if shape[j] != 1:
# Use the slice as it is
u_slices.append(s)
elif isinstance(s, slice):
# Slice.
# The moment is using broadcasting, just pick the
# first element but use slice in order to keep the
# axis
u_slices.append(slice(0,1,1))
else:
# Integer.
# The moment is using broadcasting, just pick the
# first element
u_slices.append(0)
j += 1
# Slice the message/moment
u[n] = u[n][tuple(u_slices)]
return u
def AddPlateAxis(to_plate):
if to_plate >= 0:
raise Exception("Give negative value for axis index to_plate.")
class _AddPlateAxis(Deterministic):
def __init__(self, X, **kwargs):
nonlocal to_plate
N = len(X.plates) + 1
# Check the parameters
if to_plate >= 0 or to_plate < -N:
raise ValueError("Invalid plate position to add.")
# Use positive indexing only
## if to_plate < 0:
## to_plate += N
# Use negative indexing only
if to_plate >= 0:
to_plate -= N
#self.to_plate = to_plate
super().__init__(X,
dims=X.dims,
**kwargs)
def _plates_to_parent(self, index):
plates = list(self.plates)
plates.pop(to_plate)
return tuple(plates)
#return self.plates[:to_plate] + self.plates[(to_plate+1):]
def _plates_from_parent(self, index):
plates = list(self.parents[index].plates)
plates.insert(len(plates)-to_plate+1, 1)
return tuple(plates)
def _compute_mask_to_parent(self, index, mask):
# Remove the added mask plate
if abs(to_plate) <= np.ndim(mask):
sh_mask = list(np.shape(mask))
sh_mask.pop(to_plate)
mask = np.reshape(mask, sh_mask)
return mask
def _compute_message_and_mask_to_parent(self, index, m, *u_parents):
"""
Compute the message to a parent node.
"""
# Get the message from children
#(m, mask) = self.message_from_children()
# Remove the added message plate
for i in range(len(m)):
# Make sure the message has all the axes
#diff = len(self.plates) + len(self.dims[i]) - np.ndim(m[i])
#m[i] = misc.add_leading_axes(m[i], diff)
# Remove the axis
if np.ndim(m[i]) >= abs(to_plate) + len(self.dims[i]):
axis = to_plate - len(self.dims[i])
sh_m = list(np.shape(m[i]))
sh_m.pop(axis)
m[i] = np.reshape(m[i], sh_m)
mask = self._compute_mask_to_parent(index, self.mask)
return (m, mask)
def _compute_moments(self, u):
"""
Get the moments with an added plate axis.
"""
# Get parents' moments
#u = self.parents[0].message_to_child()
# Move a plate axis
u = list(u)
for i in range(len(u)):
# Make sure the moments have all the axes
#diff = len(self.plates) + len(self.dims[i]) - np.ndim(u[i]) - 1
#u[i] = misc.add_leading_axes(u[i], diff)
# The location of the new axis/plate:
axis = np.ndim(u[i]) - abs(to_plate) - len(self.dims[i]) + 1
if axis > 0:
# Add one axes to the correct position
sh_u = list(np.shape(u[i]))
sh_u.insert(axis, 1)
u[i] = np.reshape(u[i], sh_u)
return u
return _AddPlateAxis
class NodeConstantScalar(Node):
@staticmethod
def compute_fixed_u_and_f(x):
""" Compute u(x) and f(x) for given x. """
return ([x], 0)
def __init__(self, a, **kwargs):
self.u = [a]
super().__init__(self,
plates=np.shape(a),
dims=[()],
**kwargs)
def start_optimization(self):
# FIXME: Set the plate sizes appropriately!!
x0 = self.u[0]
#self.gradient = np.zeros(np.shape(x0))
def transform(x):
# E.g., for positive scalars you could have exp here.
self.gradient = np.zeros(np.shape(x0))
self.u[0] = x
def gradient():
# This would need to apply the gradient of the
# transformation to the computed gradient
return self.gradient
return (x0, transform, gradient)
def add_to_gradient(self, d):
self.gradient += d
def message_to_child(self, gradient=False):
if gradient:
return (self.u, [ [np.ones(np.shape(self.u[0])),
#self.gradient] ])
self.add_to_gradient] ])
else:
return self.u
def stop_optimization(self):
#raise Exception("Not implemented for " + str(self.__class__))
pass
bayespy-0.2.3/bayespy/inference/vmp/nodes/poisson.py 0000664 5104112 5104112 00000011124 12374370351 024306 0 ustar jluttine jluttine 0000000 0000000 ######################################################################
# Copyright (C) 2014 Jaakko Luttinen
#
# This file is licensed under Version 3.0 of the GNU General Public
# License. See LICENSE for a text of the license.
######################################################################
######################################################################
# This file is part of BayesPy.
#
# BayesPy is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# BayesPy is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with BayesPy. If not, see .
######################################################################
"""
Module for the Poisson distribution node.
"""
import numpy as np
from scipy import special
from .expfamily import ExponentialFamily
from .expfamily import ExponentialFamilyDistribution
from .node import Moments
from .gamma import GammaMoments
from bayespy.utils import misc
class PoissonMoments(Moments):
"""
Class for the moments of Poisson variables
"""
def compute_fixed_moments(self, x):
"""
Compute the moments for a fixed value
"""
# Make sure the values are integers in valid range
x = np.asanyarray(x)
if not misc.isinteger(x):
raise ValueError("Count not integer")
# Now, the moments are just the counts
return [x]
def compute_dims_from_values(self, x):
"""
Return the shape of the moments for a fixed value.
The realizations are scalars, thus the shape of the moment is ().
"""
return ( (), )
class PoissonDistribution(ExponentialFamilyDistribution):
"""
Class for the VMP formulas of Poisson variables.
"""
def compute_message_to_parent(self, parent, index, u, u_lambda):
"""
Compute the message to a parent node.
"""
if index == 0:
m0 = -1
m1 = np.copy(u[0])
return [m0, m1]
else:
raise ValueError("Index out of bounds")
def compute_phi_from_parents(self, u_lambda, mask=True):
"""
Compute the natural parameter vector given parent moments.
"""
l = u_lambda[0]
logl = u_lambda[1]
phi0 = logl
return [phi0]
def compute_moments_and_cgf(self, phi, mask=True):
"""
Compute the moments and :math:`g(\phi)`.
"""
u0 = np.exp(phi[0])
u = [u0]
g = -u0
return (u, g)
def compute_cgf_from_parents(self, u_lambda):
"""
Compute :math:`\mathrm{E}_{q(p)}[g(p)]`
"""
l = u_lambda[0]
g = -l
return g
def compute_fixed_moments_and_f(self, x, mask=True):
"""
Compute the moments and :math:`f(x)` for a fixed value.
"""
# Check the validity of x
x = np.asanyarray(x)
if not misc.isinteger(x):
raise ValueError("Values must be integers")
if np.any(x < 0):
raise ValueError("Values must be positive")
# Compute moments
u0 = np.copy(x)
u = [u0]
# Compute f(x)
f = -special.gammaln(x+1)
return (u, f)
def random(self, *phi):
"""
Draw a random sample from the distribution.
"""
raise NotImplementedError()
class Poisson(ExponentialFamily):
"""
Node for Poisson random variables.
The node uses Poisson distribution:
.. math::
p(x) = \mathrm{Poisson}(x|\lambda)
where :math:`\lambda` is the rate parameter.
Parameters
----------
l : gamma-like node or scalar or array
:math:`\lambda`, rate parameter
See also
--------
Gamma, Exponential
"""
dims = ( (), )
_moments = PoissonMoments()
_parent_moments = [GammaMoments()]
_distribution = PoissonDistribution()
def __init__(self, l, **kwargs):
"""
Create Poisson random variable node
"""
super().__init__(l, **kwargs)
def show(self):
"""
Print the distribution using standard parameterization.
"""
l = self.u[0]
print("%s ~ Categorical(lambda)" % self.name)
print(" lambda = ")
print(l)
bayespy-0.2.3/bayespy/inference/vmp/nodes/stochastic.py 0000664 5104112 5104112 00000023272 12374370351 024767 0 ustar jluttine jluttine 0000000 0000000 ######################################################################
# Copyright (C) 2013-2014 Jaakko Luttinen
#
# This file is licensed under Version 3.0 of the GNU General Public
# License. See LICENSE for a text of the license.
######################################################################
######################################################################
# This file is part of BayesPy.
#
# BayesPy is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# BayesPy is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with BayesPy. If not, see .
######################################################################
import numpy as np
from bayespy.utils import misc
from .node import Node
class Distribution():
"""
A base class for the VMP formulas of variables.
Sub-classes implement distribution specific computations.
If a sub-class maps the plates differently, it needs to overload the
following methods:
* compute_mask_to_parent
* plates_to_parent
* plates_from_parent
"""
def compute_message_to_parent(self, parent, index, u_self, *u_parents):
"""
Compute the message to a parent node.
"""
raise NotImplementedError()
def compute_mask_to_parent(self, index, mask):
"""
Maps the mask to the plates of a parent.
"""
# Sub-classes may need to overwrite this method
return mask
def plates_to_parent(self, index, plates):
"""
Resolves the plate mapping to a parent.
Given the plates of the node's moments, this method returns the plates
that the message to a parent has for the parent's distribution.
"""
return plates
def plates_from_parent(self, index, plates):
"""
Resolve the plate mapping from a parent.
Given the plates of a parent's moments, this method returns the plates
that the moments has for this distribution.
"""
return plates
def random(self, *params, plates=None):
"""
Draw a random sample from the distribution.
"""
raise NotImplementedError()
class Stochastic(Node):
"""
Base class for nodes that are stochastic.
u
observed
Sub-classes must implement:
_compute_message_to_parent(parent, index, u_self, *u_parents)
_update_distribution_and_lowerbound(self, m, *u)
lowerbound(self)
_compute_dims
initialize_from_prior()
If you want to be able to observe the variable:
_compute_fixed_moments_and_f
Sub-classes may need to re-implement:
1. If they manipulate plates:
_compute_mask_to_parent(index, mask)
_plates_to_parent(self, index)
_plates_from_parent(self, index)
"""
# Sub-classes must over-write this
_distribution = None
def __init__(self, *args, initialize=True, dims=None, **kwargs):
self._id = Node._id_counter
Node._id_counter += 1
super().__init__(*args,
dims=dims,
**kwargs)
# Initialize moment array
axes = len(self.plates)*(1,)
self.u = [misc.nans(axes+dim) for dim in dims]
# Not observed
self.observed = False
self.ndims = [len(dim) for dim in self.dims]
if initialize:
self.initialize_from_prior()
def _get_id_list(self):
"""
Returns the stochastic ID list.
This method is used to check that same stochastic nodes are not direct
parents of a node several times. It is only valid if there are
intermediate stochastic nodes.
To put it another way: each ID corresponds to one factor q(..) in the
posterior approximation. Different IDs mean different factors, thus they
mean independence. The parents must have independent factors.
Stochastic nodes should return their unique ID. Deterministic nodes
should return the IDs of their parents. Constant nodes should return
empty list of IDs.
"""
return [self._id]
def _plates_to_parent(self, index):
return self._distribution.plates_to_parent(index, self.plates)
def _plates_from_parent(self, index):
return self._distribution.plates_from_parent(index,
self.parents[index].plates)
def _compute_mask_to_parent(self, index, mask):
return self._distribution.compute_mask_to_parent(index, mask)
def get_moments(self):
# Just for safety, do not return a reference to the moment list of this
# node but instead create a copy of the list.
return [ui for ui in self.u]
def _get_message_and_mask_to_parent(self, index):
u_parents = self._message_from_parents(exclude=index)
m = self._distribution.compute_message_to_parent(self.parents[index],
index,
self.u,
*u_parents)
mask = self._distribution.compute_mask_to_parent(index, self.mask)
## m = self._compute_message_to_parent(self.parents[index], index, self.u, *u_parents)
## mask = self._compute_mask_to_parent(index, self.mask)
return (m, mask)
def _set_mask(self, mask):
self.mask = np.logical_or(mask, self.observed)
def _set_moments(self, u, mask=True):
# Store the computed moments u but do not change moments for
# observations, i.e., utilize the mask.
for ind in range(len(u)):
# Add axes to the mask for the variable dimensions (mask
# contains only axes for the plates).
u_mask = misc.add_trailing_axes(mask, self.ndims[ind])
# Enlarge self.u[ind] as necessary so that it can store the
# broadcasted result.
sh = misc.broadcasted_shape_from_arrays(self.u[ind], u[ind], u_mask)
self.u[ind] = misc.repeat_to_shape(self.u[ind], sh)
# TODO/FIXME/BUG: The mask of observations is not used, observations
# may be overwritten!!! ???
# Hah, this function is used to set the observations! The caller
# should be careful what mask he uses! If you want to set only
# latent variables, then use such a mask.
# Use mask to update only unobserved plates and keep the
# observed as before
np.copyto(self.u[ind],
u[ind],
where=u_mask)
# Make sure u has the correct number of dimensions:
# TODO/FIXME: Maybe it would be good to also check that u has a
# shape that is a sub-shape of get_shape.
shape = self.get_shape(ind)
ndim = len(shape)
ndim_u = np.ndim(self.u[ind])
if ndim > ndim_u:
self.u[ind] = misc.add_leading_axes(u[ind], ndim - ndim_u)
elif ndim < ndim_u:
raise RuntimeError(
"The size of the variable %s's %s-th moment "
"array is %s which is larger than it should "
"be, that is, %s, based on the plates %s and "
"dimension %s. Check that you have provided "
"plates properly."
% (self.name,
ind,
np.shape(self.u[ind]),
shape,
self.plates,
self.dims[ind]))
def update(self):
if not np.all(self.observed):
u_parents = self._message_from_parents()
m_children = self._message_from_children()
self._update_distribution_and_lowerbound(m_children, *u_parents)
def observe(self, x, mask=True):
"""
Fix moments, compute f and propagate mask.
"""
raise NotImplementedError()
def unobserve(self):
# Update mask
self.observed = False
self._update_mask()
def lowerbound(self):
# Sub-class should implement this
raise NotImplementedError()
def _update_distribution_and_lowerbound(self, m_children, *u_parents):
# Sub-classes should implement this
raise NotImplementedError()
def save(self, group):
"""
Save the state of the node into a HDF5 file.
group can be the root
"""
## if name is None:
## name = self.name
## subgroup = group.create_group(name)
for i in range(len(self.u)):
misc.write_to_hdf5(group, self.u[i], 'u%d' % i)
misc.write_to_hdf5(group, self.observed, 'observed')
def load(self, group):
"""
Load the state of the node from a HDF5 file.
"""
# TODO/FIXME: Check that the shapes are correct!
for i in range(len(self.u)):
ui = group['u%d' % i][...]
self.u[i] = ui
old_observed = self.observed
self.observed = group['observed'][...]
# Update masks if necessary
if np.any(old_observed != self.observed):
self._update_mask()
def random(self):
"""
Draw a random sample from the distribution.
"""
raise NotImplementedError()
bayespy-0.2.3/bayespy/inference/vmp/nodes/wishart.py 0000664 5104112 5104112 00000013620 12374370351 024300 0 ustar jluttine jluttine 0000000 0000000 ######################################################################
# Copyright (C) 2011,2012,2014 Jaakko Luttinen
#
# This file is licensed under Version 3.0 of the GNU General Public
# License. See LICENSE for a text of the license.
######################################################################
######################################################################
# This file is part of BayesPy.
#
# BayesPy is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# BayesPy is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with BayesPy. If not, see .
######################################################################
import numpy as np
import scipy.special as special
from bayespy.utils import misc
from .expfamily import ExponentialFamily
from .expfamily import ExponentialFamilyDistribution
from .expfamily import useconstructor
from .constant import Constant
from .node import Moments, Node
class WishartPriorMoments(Moments):
def __init__(self, k):
self.k = k
return
def compute_fixed_moments(self, n):
""" Compute moments for fixed x. """
u0 = np.asanyarray(n)
u1 = special.multigammaln(0.5*u0, self.k)
return [u0, u1]
def compute_dims_from_values(self, n):
""" Compute the dimensions of phi or u. """
return ( (), () )
class WishartMoments(Moments):
def compute_fixed_moments(self, Lambda):
""" Compute moments for fixed x. """
ldet = misc.m_chol_logdet(misc.m_chol(Lambda))
u = [Lambda,
ldet]
return u
def compute_dims_from_values(self, x):
""" Compute the dimensions of phi and u. """
if np.ndim(x) < 2:
raise ValueError("Values for Wishart distribution must be at least "
"2-D arrays.")
if np.shape(x)[-1] != np.shape(x)[-2]:
raise ValueError("Values for Wishart distribution must be square "
"matrices, thus the two last axes must have equal "
"length.")
d = np.shape(x)[-1]
return ( (d,d), () )
class WishartDistribution(ExponentialFamilyDistribution):
"""
Sub-classes implement distribution specific computations.
"""
def compute_message_to_parent(self, parent, index, u_self, *u_parents):
raise NotImplementedError()
def compute_phi_from_parents(self, *u_parents, mask=True):
return [-0.5 * u_parents[1][0],
0.5 * u_parents[0][0]]
def compute_moments_and_cgf(self, phi, mask=True):
U = misc.m_chol(-phi[0])
k = np.shape(phi[0])[-1]
#k = self.dims[0][0]
logdet_phi0 = misc.m_chol_logdet(U)
u0 = phi[1][...,np.newaxis,np.newaxis] * misc.m_chol_inv(U)
u1 = -logdet_phi0 + misc.m_digamma(phi[1], k)
u = [u0, u1]
g = phi[1] * logdet_phi0 - special.multigammaln(phi[1], k)
return (u, g)
def compute_cgf_from_parents(self, *u_parents):
n = u_parents[0][0]
gammaln_n = u_parents[0][1]
V = u_parents[1][0]
logdet_V = u_parents[1][1]
k = np.shape(V)[-1]
#k = self.dims[0][0]
# TODO: Check whether this is correct:
#g = 0.5*n*logdet_V - special.multigammaln(n/2, k)
g = 0.5*n*logdet_V - 0.5*k*n*np.log(2) - gammaln_n #special.multigammaln(n/2, k)
return g
def compute_fixed_moments_and_f(self, Lambda, mask=True):
""" Compute u(x) and f(x) for given x. """
k = np.shape(Lambda)[-1]
ldet = misc.m_chol_logdet(misc.m_chol(Lambda))
u = [Lambda,
ldet]
f = -(k+1)/2 * ldet
return (u, f)
class Wishart(ExponentialFamily):
r"""
Node for Wishart random variables.
The random variable :math:`\mathbf{\Lambda}` is a :math:`D\times{}D`
positive-definite symmetric matrix.
.. math::
p(\mathbf{\Lambda}) = \mathrm{Wishart}(\mathbf{\Lambda} | N,
\mathbf{V})
Parameters
----------
n : scalar or array
:math:`N`, degrees of freedom, :math:`N>D-1`.
V : Wishart-like node or (...,D,D)-array
:math:`\mathbf{V}`, scale matrix.
"""
_distribution = WishartDistribution()
_moments = WishartMoments()
def __init__(self, n, V, **kwargs):
"""
Create Wishart node.
"""
super().__init__(n, V, **kwargs)
@classmethod
def _constructor(cls, n, V, **kwargs):
"""
Constructs distribution and moments objects.
"""
# Make V a proper parent node and get the dimensionality of the matrix
V = cls._ensure_moments(V, WishartMoments())
k = V.dims[0][-1]
# Parent node message types
parent_moments = (WishartPriorMoments(k),
WishartMoments())
# Dimensionality of the natural parameters
dims = ( (k,k), () )
n = cls._ensure_moments(n, parent_moments[0])
parents = [n, V]
return (parents,
kwargs,
dims,
cls._total_plates(kwargs.get('plates'),
cls._distribution.plates_from_parent(0, n.plates),
cls._distribution.plates_from_parent(1, V.plates)),
cls._distribution,
cls._moments,
parent_moments)
def show(self):
print("%s ~ Wishart(n, A)" % self.name)
print(" n =")
print(2*self.phi[1])
print(" A =")
print(0.5 * self.u[0] / self.phi[1][...,np.newaxis,np.newaxis])
bayespy-0.2.3/bayespy/inference/vmp/tests/ 0000775 5104112 5104112 00000000000 12437607670 022304 5 ustar jluttine jluttine 0000000 0000000 bayespy-0.2.3/bayespy/inference/vmp/tests/__init__.py 0000664 5104112 5104112 00000000000 12121570321 024361 0 ustar jluttine jluttine 0000000 0000000 bayespy-0.2.3/bayespy/inference/vmp/tests/test_transformations.py 0000664 5104112 5104112 00000123654 12374370351 027152 0 ustar jluttine jluttine 0000000 0000000 ######################################################################
# Copyright (C) 2014 Jaakko Luttinen
#
# This file is licensed under Version 3.0 of the GNU General Public
# License. See LICENSE for a text of the license.
######################################################################
######################################################################
# This file is part of BayesPy.
#
# BayesPy is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# BayesPy is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with BayesPy. If not, see .
######################################################################
"""
Unit tests for `transformations` module.
"""
import numpy as np
from bayespy.inference.vmp.nodes.gaussian import GaussianARD
from bayespy.inference.vmp.nodes.gaussian import Gaussian
from bayespy.inference.vmp.nodes.gamma import Gamma
from bayespy.inference.vmp.nodes.wishart import Wishart
from bayespy.inference.vmp.nodes.dot import SumMultiply
from bayespy.inference.vmp.nodes.gaussian_markov_chain import GaussianMarkovChain
from bayespy.utils import linalg
from bayespy.utils import random
from bayespy.utils import optimize
from ..transformations import RotateGaussianARD
from ..transformations import RotateGaussianMarkovChain
from ..transformations import RotateVaryingMarkovChain
from bayespy.utils.misc import TestCase
class TestRotateGaussianARD(TestCase):
def test_cost_function(self):
"""
Test the speed-up rotation of Gaussian ARD arrays.
"""
# Use seed for deterministic testing
np.random.seed(42)
def test(shape, plates,
axis=-1,
alpha_plates=None,
plate_axis=None,
mu=3):
if plate_axis is not None:
precomputes = [False, True]
else:
precomputes = [False]
for precompute in precomputes:
# Construct the model
D = shape[axis]
if alpha_plates is not None:
alpha = Gamma(2, 2,
plates=alpha_plates)
alpha.initialize_from_random()
else:
alpha = 2
X = GaussianARD(mu, alpha,
shape=shape,
plates=plates)
# Some initial learning and rotator constructing
X.initialize_from_random()
Y = GaussianARD(X, 1)
Y.observe(np.random.randn(*(Y.get_shape(0))))
X.update()
if alpha_plates is not None:
alpha.update()
true_cost0_alpha = alpha.lower_bound_contribution()
rotX = RotateGaussianARD(X, alpha,
axis=axis,
precompute=precompute)
else:
rotX = RotateGaussianARD(X,
axis=axis,
precompute=precompute)
true_cost0_X = X.lower_bound_contribution()
# Rotation matrices
I = np.identity(D)
R = np.random.randn(D, D)
if plate_axis is not None:
C = plates[plate_axis]
Q = np.random.randn(C, C)
Ic = np.identity(C)
else:
Q = None
Ic = None
# Compute bound terms
rotX.setup(plate_axis=plate_axis)
rot_cost0 = rotX.get_bound_terms(I, Q=Ic)
rot_cost1 = rotX.get_bound_terms(R, Q=Q)
self.assertAllClose(sum(rot_cost0.values()),
rotX.bound(I, Q=Ic)[0],
msg="Bound terms and total bound differ")
self.assertAllClose(sum(rot_cost1.values()),
rotX.bound(R, Q=Q)[0],
msg="Bound terms and total bound differ")
# Perform rotation
rotX.rotate(R, Q=Q)
# Check bound terms
true_cost1_X = X.lower_bound_contribution()
self.assertAllClose(true_cost1_X - true_cost0_X,
rot_cost1[X] - rot_cost0[X],
msg="Incorrect rotation cost for X")
if alpha_plates is not None:
true_cost1_alpha = alpha.lower_bound_contribution()
self.assertAllClose(true_cost1_alpha - true_cost0_alpha,
rot_cost1[alpha] - rot_cost0[alpha],
msg="Incorrect rotation cost for alpha")
return
# Rotating a vector (zero mu)
test( (3,), (), axis=-1, mu=0)
test( (3,), (), axis=-1, alpha_plates=(1,), mu=0)
test( (3,), (), axis=-1, alpha_plates=(3,), mu=0)
test( (3,), (2,4), axis=-1, mu=0)
test( (3,), (2,4), axis=-1, alpha_plates=(1,), mu=0)
test( (3,), (2,4), axis=-1, alpha_plates=(3,), mu=0)
test( (3,), (2,4), axis=-1, alpha_plates=(2,4,3), mu=0)
test( (3,), (2,4), axis=-1, alpha_plates=(1,4,3), mu=0)
# Rotating a vector (full mu)
test( (3,), (), axis=-1, mu=3*np.ones((3,)))
test( (3,), (), axis=-1, alpha_plates=(), mu=3*np.ones((3,)))
test( (3,), (), axis=-1, alpha_plates=(1,), mu=3*np.ones((3,)))
test( (3,), (), axis=-1, alpha_plates=(3,), mu=3*np.ones((3,)))
test( (3,), (2,4), axis=-1, mu=3*np.ones((2,4,3)))
test( (3,), (2,4), axis=-1, alpha_plates=(1,), mu=3*np.ones((2,4,3)))
test( (3,), (2,4), axis=-1, alpha_plates=(3,), mu=3*np.ones((2,4,3)))
test( (3,), (2,4), axis=-1, alpha_plates=(2,4,3), mu=3*np.ones((2,4,3)))
test( (3,), (2,4), axis=-1, alpha_plates=(1,4,3), mu=3*np.ones((2,4,3)))
# Rotating a vector (broadcast mu)
test( (3,), (), axis=-1, mu=3*np.ones((1,)))
test( (3,), (), axis=-1, alpha_plates=(1,), mu=3*np.ones((1,)))
test( (3,), (), axis=-1, alpha_plates=(3,), mu=3*np.ones((1,)))
test( (3,), (2,4,5), axis=-1, mu=3*np.ones((4,1,1)))
test( (3,), (2,4,5), axis=-1, alpha_plates=(1,), mu=3*np.ones((4,1,1)))
test( (3,), (2,4,5), axis=-1, alpha_plates=(3,), mu=3*np.ones((4,1,1)))
test( (3,), (2,4,5), axis=-1, alpha_plates=(2,4,5,3), mu=3*np.ones((4,1,1))) #!!
test( (3,), (2,4,5), axis=-1, alpha_plates=(1,4,5,3), mu=3*np.ones((4,1,1)))
# Rotating an array
test( (2,3,4), (), axis=-1)
test( (2,3,4), (), axis=-2)
test( (2,3,4), (), axis=-3)
test( (2,3,4), (5,6), axis=-1)
test( (2,3,4), (5,6), axis=-2)
test( (2,3,4), (5,6), axis=-3)
test( (2,3,4), (5,6), axis=-1, alpha_plates=(3,1))
test( (2,3,4), (5,6), axis=-2, alpha_plates=(3,1))
test( (2,3,4), (5,6), axis=-3, alpha_plates=(3,1))
test( (2,3), (4,5,6), axis=-1, alpha_plates=(5,1,2,1))
test( (2,3), (4,5,6), axis=-2, alpha_plates=(5,1,2,1))
# Test mu array broadcasting
test( (2,3,4), (5,6,7), axis=-2,
mu=GaussianARD(3, 1,
shape=(3,1),
plates=(6,1,1)))
test( (2,3,4), (5,6,7), axis=-3,
mu=GaussianARD(3, 1,
shape=(3,1),
plates=(6,1,1)))
test( (2,3,4), (5,6,7), axis=-2, alpha_plates=(5,1,7,2,1,1),
mu=GaussianARD(3, 1,
shape=(3,1),
plates=(6,1,1)))
# Plate rotation
test( (3,), (5,), axis=-1, plate_axis=-1)
test( (3,), (4,5,6), axis=-1, plate_axis=-2)
test( (2,3), (4,5,6), axis=-2, plate_axis=-2)
test( (2,3,4), (5,6,7), axis=-2, plate_axis=-3)
# Plate rotation with alpha
test( (2,3,4), (5,6,7), axis=-2, alpha_plates=(3,1), plate_axis=-2)
test( (2,3,4), (5,6,7), axis=-2, alpha_plates=(6,1,2,1,4), plate_axis=-3)
# Plate rotation with mu
test( (2,3,4), (5,6,7), axis=-2, plate_axis=-2,
mu=GaussianARD(3, 1,
shape=(3,1),
plates=(6,1,1)))
test( (2,3,4), (5,6,7), axis=-3, plate_axis=-2,
mu=GaussianARD(3, 1,
shape=(3,1),
plates=(6,1,1)))
test( (2,3,4), (5,6,7), axis=-2, alpha_plates=(5,1,7,2,1,1), plate_axis=-2,
mu=GaussianARD(3, 1,
shape=(3,1),
plates=(6,1,1)))
#
# Plate rotation with mu and alpha
#
# Basic, matching sizes
test( (3,), (4,), axis=-1, plate_axis=-1,
alpha_plates=(4,3),
mu=GaussianARD(3, 1,
shape=(3,),
plates=(4,)))
# Broadcast for mu
test( (3,), (4,), axis=-1, plate_axis=-1,
alpha_plates=(4,3),
mu=GaussianARD(3, 1,
shape=(1,),
plates=(4,)))
test( (3,), (4,), axis=-1, plate_axis=-1,
alpha_plates=(4,3),
mu=GaussianARD(3, 1,
shape=(),
plates=(1,)))
test( (3,), (4,), axis=-1, plate_axis=-1,
alpha_plates=(4,3),
mu=GaussianARD(3, 1,
shape=(3,),
plates=(1,)))
# Broadcast for alpha
test( (3,), (4,), axis=-1, plate_axis=-1,
alpha_plates=(4,1),
mu=GaussianARD(3, 1,
shape=(3,),
plates=(4,)))
test( (3,), (4,), axis=-1, plate_axis=-1,
alpha_plates=(3,),
mu=GaussianARD(3, 1,
shape=(3,),
plates=(4,)))
# Several variable dimensions
test( (3,4,5), (2,), axis=-2, plate_axis=-1,
alpha_plates=(2,3,4,5),
mu=GaussianARD(3, 1,
shape=(3,4,5),
plates=(2,)))
test( (3,4,5), (2,), axis=-2, plate_axis=-1,
alpha_plates=(2,3,1,5),
mu=GaussianARD(3, 1,
shape=(4,1),
plates=(2,1)))
# Several plate dimensions
test( (5,), (2,3,4), axis=-1, plate_axis=-2,
alpha_plates=(2,3,4,5),
mu=GaussianARD(3, 1,
shape=(5,),
plates=(2,3,4)))
# Several plate dimensions, rotated plate broadcasted in alpha
test( (5,), (2,3,4), axis=-1, plate_axis=-2,
alpha_plates=(2,1,4,5),
mu=GaussianARD(3, 1,
shape=(5,),
plates=(2,3,4)))
test( (5,), (2,3,4), axis=-1, plate_axis=-2,
alpha_plates=(4,5),
mu=GaussianARD(3, 1,
shape=(5,),
plates=(2,3,4)))
# Several plate dimensions, rotated plate broadcasted in mu
test( (5,), (2,3,4), axis=-1, plate_axis=-2,
alpha_plates=(2,3,4,5),
mu=GaussianARD(3, 1,
shape=(5,),
plates=(2,1,4)))
test( (5,), (2,3,4), axis=-1, plate_axis=-2,
alpha_plates=(2,3,4,5),
mu=GaussianARD(3, 1,
shape=(5,),
plates=(4,)))
# Several plate dimensions, rotated plate broadcasted in mu and alpha
test( (5,), (2,3,4), axis=-1, plate_axis=-2,
alpha_plates=(2,1,4,5),
mu=GaussianARD(3, 1,
shape=(5,),
plates=(2,1,4)))
test( (5,), (2,3,4), axis=-1, plate_axis=-2,
alpha_plates=(4,5),
mu=GaussianARD(3, 1,
shape=(5,),
plates=(4,)))
# TODO: Missing values
pass
def test_cost_gradient(self):
"""
Test gradient of the rotation cost function for Gaussian ARD arrays.
"""
# Use seed for deterministic testing
np.random.seed(42)
def test(shape, plates,
axis=-1,
alpha_plates=None,
plate_axis=None,
mu=3):
if plate_axis is not None:
precomputes = [False, True]
else:
precomputes = [False]
for precompute in precomputes:
# Construct the model
D = shape[axis]
if alpha_plates is not None:
alpha = Gamma(3, 5,
plates=alpha_plates)
alpha.initialize_from_random()
else:
alpha = 2
X = GaussianARD(mu, alpha,
shape=shape,
plates=plates)
# Some initial learning and rotator constructing
X.initialize_from_random()
Y = GaussianARD(X, 1)
Y.observe(np.random.randn(*(Y.get_shape(0))))
X.update()
if alpha_plates is not None:
alpha.update()
rotX = RotateGaussianARD(X, alpha,
axis=axis,
precompute=precompute)
else:
rotX = RotateGaussianARD(X,
axis=axis,
precompute=precompute)
try:
mu.update()
except:
pass
# Rotation matrices
R = np.random.randn(D, D)
if plate_axis is not None:
C = plates[plate_axis]
Q = np.random.randn(C, C)
else:
Q = None
# Compute bound terms
rotX.setup(plate_axis=plate_axis)
if plate_axis is None:
def f_r(r):
(b, dr) = rotX.bound(np.reshape(r, np.shape(R)))
return (b, np.ravel(dr))
else:
def f_r(r):
(b, dr, dq) = rotX.bound(np.reshape(r, np.shape(R)),
Q=Q)
return (b, np.ravel(dr))
def f_q(q):
(b, dr, dq) = rotX.bound(R,
Q=np.reshape(q, np.shape(Q)))
return (b, np.ravel(dq))
# Check gradient with respect to R
err = optimize.check_gradient(f_r,
np.ravel(R),
verbose=False)
self.assertAllClose(err, 0,
atol=1e-4,
msg="Gradient incorrect for R")
# Check gradient with respect to Q
if plate_axis is not None:
err = optimize.check_gradient(f_q,
np.ravel(Q),
verbose=False)
self.assertAllClose(err, 0,
atol=1e-4,
msg="Gradient incorrect for Q")
return
#
# Basic rotation
#
test((3,), (), axis=-1)
test((2,3,4), (), axis=-1)
test((2,3,4), (), axis=-2)
test((2,3,4), (), axis=-3)
test((2,3,4), (5,6), axis=-2)
#
# Rotation with mu
#
# Simple
test((1,), (), axis=-1,
mu=GaussianARD(2, 4,
shape=(1,),
plates=()))
test((3,), (), axis=-1,
mu=GaussianARD(2, 4,
shape=(3,),
plates=()))
# Broadcast mu over rotated dim
test((3,), (), axis=-1,
mu=GaussianARD(2, 4,
shape=(1,),
plates=()))
test((3,), (), axis=-1,
mu=GaussianARD(2, 4,
shape=(),
plates=()))
# Broadcast mu over dim when multiple dims
test((2,3), (), axis=-1,
mu=GaussianARD(2, 4,
shape=(1,3),
plates=()))
test((2,3), (), axis=-1,
mu=GaussianARD(2, 4,
shape=(3,),
plates=()))
# Broadcast mu over rotated dim when multiple dims
test((2,3), (), axis=-2,
mu=GaussianARD(2, 4,
shape=(1,3),
plates=()))
test((2,3), (), axis=-2,
mu=GaussianARD(2, 4,
shape=(3,),
plates=()))
# Broadcast mu over plates
test((3,), (4,5), axis=-1,
mu=GaussianARD(2, 4,
shape=(3,),
plates=(4,1)))
test((3,), (4,5), axis=-1,
mu=GaussianARD(2, 4,
shape=(3,),
plates=(5,)))
#
# Rotation with alpha
#
# Simple
test((1,), (), axis=-1,
alpha_plates=())
test((3,), (), axis=-1,
alpha_plates=(3,))
# Broadcast alpha over rotated dim
test((3,), (), axis=-1,
alpha_plates=())
test((3,), (), axis=-1,
alpha_plates=(1,))
# Broadcast alpha over dim when multiple dims
test((2,3), (), axis=-1,
alpha_plates=(1,3))
test((2,3), (), axis=-1,
alpha_plates=(3,))
# Broadcast alpha over rotated dim when multiple dims
test((2,3), (), axis=-2,
alpha_plates=(1,3))
test((2,3), (), axis=-2,
alpha_plates=(3,))
# Broadcast alpha over plates
test((3,), (4,5), axis=-1,
alpha_plates=(4,1,3))
test((3,), (4,5), axis=-1,
alpha_plates=(5,3))
#
# Rotation with alpha and mu
#
# Simple
test((1,), (), axis=-1,
alpha_plates=(1,),
mu=GaussianARD(2, 4,
shape=(1,),
plates=()))
test((3,), (), axis=-1,
alpha_plates=(3,),
mu=GaussianARD(2, 4,
shape=(3,),
plates=()))
# Broadcast mu over rotated dim
test((3,), (), axis=-1,
alpha_plates=(3,),
mu=GaussianARD(2, 4,
shape=(1,),
plates=()))
test((3,), (), axis=-1,
alpha_plates=(3,),
mu=GaussianARD(2, 4,
shape=(),
plates=()))
# Broadcast alpha over rotated dim
test((3,), (), axis=-1,
alpha_plates=(1,),
mu=GaussianARD(2, 4,
shape=(3,),
plates=()))
test((3,), (), axis=-1,
alpha_plates=(),
mu=GaussianARD(2, 4,
shape=(3,),
plates=()))
# Broadcast both mu and alpha over rotated dim
test((3,), (), axis=-1,
alpha_plates=(1,),
mu=GaussianARD(2, 4,
shape=(1,),
plates=()))
test((3,), (), axis=-1,
alpha_plates=(),
mu=GaussianARD(2, 4,
shape=(),
plates=()))
# Broadcast mu over plates
test((3,), (4,5), axis=-1,
alpha_plates=(4,5,3),
mu=GaussianARD(2, 4,
shape=(3,),
plates=(4,1)))
test((3,), (4,5), axis=-1,
alpha_plates=(4,5,3),
mu=GaussianARD(2, 4,
shape=(3,),
plates=(5,)))
# Broadcast alpha over plates
test((3,), (4,5), axis=-1,
alpha_plates=(4,1,3),
mu=GaussianARD(2, 4,
shape=(3,),
plates=(4,5)))
test((3,), (4,5), axis=-1,
alpha_plates=(5,3),
mu=GaussianARD(2, 4,
shape=(3,),
plates=(4,5)))
# Broadcast both mu and alpha over plates
test((3,), (4,5), axis=-1,
alpha_plates=(4,1,3),
mu=GaussianARD(2, 4,
shape=(3,),
plates=(4,1)))
test((3,), (4,5), axis=-1,
alpha_plates=(5,3),
mu=GaussianARD(2, 4,
shape=(3,),
plates=(5,)))
# Broadcast both mu and alpha over plates but different plates
test((3,), (4,5), axis=-1,
alpha_plates=(4,1,3),
mu=GaussianARD(2, 4,
shape=(3,),
plates=(5,)))
test((3,), (4,5), axis=-1,
alpha_plates=(5,3),
mu=GaussianARD(2, 4,
shape=(3,),
plates=(4,1)))
#
# Rotation with missing values
#
# TODO
#
# Plate rotation
#
# Simple
test((2,), (3,), axis=-1, plate_axis=-1)
test((2,), (3,4,5), axis=-1, plate_axis=-1)
test((2,), (3,4,5), axis=-1, plate_axis=-2)
test((2,), (3,4,5), axis=-1, plate_axis=-3)
test((2,3), (4,5), axis=-2, plate_axis=-2)
# With mu
test((2,), (3,), axis=-1, plate_axis=-1,
mu=GaussianARD(3, 4,
shape=(2,),
plates=(3,)))
# With mu broadcasted
test((2,), (3,), axis=-1, plate_axis=-1,
mu=GaussianARD(3, 4,
shape=(2,),
plates=(1,)))
test((2,), (3,), axis=-1, plate_axis=-1,
mu=GaussianARD(3, 4,
shape=(2,),
plates=()))
# With mu multiple plates
test((2,), (3,4,5), axis=-1, plate_axis=-2,
mu=GaussianARD(3, 4,
shape=(2,),
plates=(3,4,5)))
# With mu multiple dims
test((2,3,4), (5,), axis=-2, plate_axis=-1,
mu=GaussianARD(3, 4,
shape=(2,3,4),
plates=(5,)))
#
# With alpha
#
print("Test: Plate rotation with alpha. Scalars.")
test((1,), (1,), axis=-1, plate_axis=-1,
alpha_plates=(1,1),
mu=0)
print("Test: Plate rotation with alpha. Plates.")
test((1,), (3,), axis=-1, plate_axis=-1,
alpha_plates=(3,1),
mu=0)
print("Test: Plate rotation with alpha. Dims.")
test((3,), (1,), axis=-1, plate_axis=-1,
alpha_plates=(1,3),
mu=0)
print("Test: Plate rotation with alpha. Broadcast alpha over rotated plates.")
test((1,), (3,), axis=-1, plate_axis=-1,
alpha_plates=(1,1),
mu=0)
test((1,), (3,), axis=-1, plate_axis=-1,
alpha_plates=(1,),
mu=0)
print("Test: Plate rotation with alpha. Broadcast alpha over dims.")
test((3,), (1,), axis=-1, plate_axis=-1,
alpha_plates=(1,1),
mu=0)
test((3,), (1,), axis=-1, plate_axis=-1,
alpha_plates=(),
mu=0)
print("Test: Plate rotation with alpha. Multiple dims.")
test((2,3,4,5), (6,), axis=-2, plate_axis=-1,
alpha_plates=(6,2,3,4,5),
mu=0)
print("Test: Plate rotation with alpha. Multiple plates.")
test((2,), (3,4,5), axis=-1, plate_axis=-1,
alpha_plates=(3,4,5,2),
mu=0)
test((2,), (3,4,5), axis=-1, plate_axis=-2,
alpha_plates=(3,4,5,2),
mu=0)
test((2,), (3,4,5), axis=-1, plate_axis=-3,
alpha_plates=(3,4,5,2),
mu=0)
#
# With alpha and mu
#
print("Test: Plate rotation with alpha and mu. Scalars.")
test((1,), (1,), axis=-1, plate_axis=-1,
alpha_plates=(1,1),
mu=GaussianARD(2, 3,
shape=(1,),
plates=(1,)))
print("Test: Plate rotation with alpha and mu. Plates.")
test((1,), (3,), axis=-1, plate_axis=-1,
alpha_plates=(3,1),
mu=GaussianARD(2, 3,
shape=(1,),
plates=(3,)))
print("Test: Plate rotation with alpha and mu. Dims.")
test((3,), (1,), axis=-1, plate_axis=-1,
alpha_plates=(1,3),
mu=GaussianARD(2, 3,
shape=(3,),
plates=(1,)))
print("Test: Plate rotation with alpha and mu. Broadcast over rotated "
"plates.")
test((1,), (3,), axis=-1, plate_axis=-1,
alpha_plates=(1,1),
mu=GaussianARD(2, 3,
shape=(1,),
plates=(1,)))
test((1,), (3,), axis=-1, plate_axis=-1,
alpha_plates=(1,),
mu=GaussianARD(2, 3,
shape=(1,),
plates=()))
print("Test: Plate rotation with alpha and mu. Broadcast over dims.")
test((3,), (1,), axis=-1, plate_axis=-1,
alpha_plates=(1,1),
mu=GaussianARD(2, 3,
shape=(1,),
plates=(1,)))
test((3,), (1,), axis=-1, plate_axis=-1,
alpha_plates=(),
mu=GaussianARD(2, 3,
shape=(),
plates=(1,)))
print("Test: Plate rotation with alpha and mu. Multiple dims.")
test((2,3,4,5), (6,), axis=-2, plate_axis=-1,
alpha_plates=(6,2,3,4,5),
mu=GaussianARD(2, 3,
shape=(2,3,4,5),
plates=(6,)))
print("Test: Plate rotation with alpha and mu. Multiple plates.")
test((2,), (3,4,5), axis=-1, plate_axis=-1,
alpha_plates=(3,4,5,2),
mu=GaussianARD(2, 3,
shape=(2,),
plates=(3,4,5,)))
test((2,), (3,4,5), axis=-1, plate_axis=-2,
alpha_plates=(3,4,5,2),
mu=GaussianARD(2, 3,
shape=(2,),
plates=(3,4,5,)))
test((2,), (3,4,5), axis=-1, plate_axis=-3,
alpha_plates=(3,4,5,2),
mu=GaussianARD(2, 3,
shape=(2,),
plates=(3,4,5,)))
# TODO: With missing values
pass
class TestRotateGaussianMarkovChain(TestCase):
def test_cost_function(self):
"""
Test the cost function of the speed-up rotation for Markov chain
"""
np.random.seed(42)
def check(D, N, mu=None, Lambda=None, rho=None, A=None):
if mu is None:
mu = np.zeros(D)
if Lambda is None:
Lambda = np.identity(D)
if rho is None:
rho = np.ones(D)
if A is None:
A = GaussianARD(3, 5,
shape=(D,),
plates=(D,))
V = np.identity(D) + np.ones((D,D))
# Construct model
X = GaussianMarkovChain(mu,
Lambda,
A,
rho,
n=N+1,
initialize=False)
Y = Gaussian(X,
V,
initialize=False)
# Posterior estimation
Y.observe(np.random.randn(*(Y.get_shape(0))))
X.update()
try:
A.update()
except:
pass
try:
mu.update()
except:
pass
try:
Lambda.update()
except:
pass
try:
rho.update()
except:
pass
# Construct rotator
rotA = RotateGaussianARD(A, axis=-1)
rotX = RotateGaussianMarkovChain(X, rotA)
# Rotation
true_cost0 = X.lower_bound_contribution()
rotX.setup()
I = np.identity(D)
R = np.random.randn(D, D)
rot_cost0 = rotX.get_bound_terms(I)
rot_cost1 = rotX.get_bound_terms(R)
self.assertAllClose(sum(rot_cost0.values()),
rotX.bound(I)[0],
msg="Bound terms and total bound differ")
self.assertAllClose(sum(rot_cost1.values()),
rotX.bound(R)[0],
msg="Bound terms and total bound differ")
rotX.rotate(R)
true_cost1 = X.lower_bound_contribution()
self.assertAllClose(true_cost1 - true_cost0,
rot_cost1[X] - rot_cost0[X],
msg="Incorrect rotation cost for X")
return
self._run_checks(check)
pass
def test_cost_gradient(self):
"""
Test the gradient of the speed-up rotation for Markov chain
"""
# Use seed for deterministic testing
np.random.seed(42)
def check(D, N, mu=None, Lambda=None, rho=None, A=None):
if mu is None:
mu = np.zeros(D)
if Lambda is None:
Lambda = np.identity(D)
if rho is None:
rho = np.ones(D)
if A is None:
A = GaussianARD(3, 5,
shape=(D,),
plates=(D,))
V = np.identity(D) + np.ones((D,D))
# Construct model
X = GaussianMarkovChain(mu,
Lambda,
A,
rho,
n=N+1,
initialize=False)
Y = Gaussian(X,
V,
initialize=False)
# Posterior estimation
Y.observe(np.random.randn(*(Y.get_shape(0))))
X.update()
try:
A.update()
except:
pass
try:
mu.update()
except:
pass
try:
Lambda.update()
except:
pass
try:
rho.update()
except:
pass
# Construct rotator
rotA = RotateGaussianARD(A, axis=-1)
rotX = RotateGaussianMarkovChain(X, rotA)
rotX.setup()
# Check gradient with respect to R
R = np.random.randn(D, D)
def cost(r):
(b, dr) = rotX.bound(np.reshape(r, np.shape(R)))
return (b, np.ravel(dr))
err = optimize.check_gradient(cost,
np.ravel(R),
verbose=False)
self.assertAllClose(err, 0,
atol=1e-5,
msg="Gradient incorrect")
return
self._run_checks(check)
pass
def _run_checks(self, check):
# Basic test
check(2, 3)
# Test mu
check(2, 3,
mu=GaussianARD(2, 4,
shape=(2,),
plates=()))
check(2, 3,
mu=GaussianARD(2, 4,
shape=(2,),
plates=(5,)))
# Test Lambda
check(2, 3,
Lambda=Wishart(3, random.covariance(2)))
check(2, 3,
Lambda=Wishart(3, random.covariance(2),
plates=(5,)))
# Test A
check(2, 3,
A=GaussianARD(2, 4,
shape=(2,),
plates=(2,)))
check(2, 3,
A=GaussianARD(2, 4,
shape=(2,),
plates=(3,2)))
check(2, 3,
A=GaussianARD(2, 4,
shape=(2,),
plates=(5,3,2)))
# Test Lambda and mu
check(2, 3,
mu=GaussianARD(2, 4,
shape=(2,),
plates=()),
Lambda=Wishart(2, random.covariance(2)))
check(2, 3,
mu=GaussianARD(2, 4,
shape=(2,),
plates=(5,)),
Lambda=Wishart(2, random.covariance(2),
plates=(5,)))
# Test mu and A
check(2, 3,
mu=GaussianARD(2, 4,
shape=(2,),
plates=()),
A=GaussianARD(2, 4,
shape=(2,),
plates=(2,)))
check(2, 3,
mu=GaussianARD(2, 4,
shape=(2,),
plates=(5,)),
A=GaussianARD(2, 4,
shape=(2,),
plates=(5,1,2,)))
# Test Lambda and A
check(2, 3,
Lambda=Wishart(2, random.covariance(2)),
A=GaussianARD(2, 4,
shape=(2,),
plates=(2,)))
check(2, 3,
Lambda=Wishart(2, random.covariance(2),
plates=(5,)),
A=GaussianARD(2, 4,
shape=(2,),
plates=(5,1,2,)))
# Test mu, Lambda and A
check(2, 3,
mu=GaussianARD(2, 4,
shape=(2,),
plates=()),
Lambda=Wishart(2, random.covariance(2)),
A=GaussianARD(2, 4,
shape=(2,),
plates=(2,)))
check(2, 3,
mu=GaussianARD(2, 4,
shape=(2,),
plates=(5,)),
Lambda=Wishart(2, random.covariance(2),
plates=(5,)),
A=GaussianARD(2, 4,
shape=(2,),
plates=(5,1,2,)))
pass
class TestRotateVaryingMarkovChain(TestCase):
def test_cost_function(self):
"""
Test the speed-up rotation of Markov chain with time-varying dynamics
"""
# Use seed for deterministic testing
np.random.seed(42)
def check(D, N, K,
mu=None,
Lambda=None,
rho=None):
if mu is None:
mu = np.zeros(D)
if Lambda is None:
Lambda = np.identity(D)
if rho is None:
rho = np.ones(D)
V = np.identity(D) + np.ones((D,D))
# Construct model
B = GaussianARD(3, 5,
shape=(D,K),
plates=(1,D))
S = GaussianARD(2, 4,
shape=(K,),
plates=(N,1))
A = SumMultiply('dk,k->d', B, S)
X = GaussianMarkovChain(mu,
Lambda,
A,
rho,
n=N+1,
initialize=False)
Y = Gaussian(X,
V,
initialize=False)
# Posterior estimation
Y.observe(np.random.randn(N+1,D))
X.update()
B.update()
S.update()
try:
mu.update()
except:
pass
try:
Lambda.update()
except:
pass
try:
rho.update()
except:
pass
# Construct rotator
rotB = RotateGaussianARD(B, axis=-2)
rotX = RotateVaryingMarkovChain(X, B, S, rotB)
# Rotation
true_cost0 = X.lower_bound_contribution()
rotX.setup()
I = np.identity(D)
R = np.random.randn(D, D)
rot_cost0 = rotX.get_bound_terms(I)
rot_cost1 = rotX.get_bound_terms(R)
self.assertAllClose(sum(rot_cost0.values()),
rotX.bound(I)[0],
msg="Bound terms and total bound differ")
self.assertAllClose(sum(rot_cost1.values()),
rotX.bound(R)[0],
msg="Bound terms and total bound differ")
rotX.rotate(R)
true_cost1 = X.lower_bound_contribution()
self.assertAllClose(true_cost1 - true_cost0,
rot_cost1[X] - rot_cost0[X],
msg="Incorrect rotation cost for X")
return
self._run_checks(check)
pass
def test_cost_gradient(self):
"""
Test the gradient of the rotation for MC with time-varying dynamics
"""
# Use seed for deterministic testing
np.random.seed(42)
def check(D, N, K,
mu=None,
Lambda=None,
rho=None):
if mu is None:
mu = np.zeros(D)
if Lambda is None:
Lambda = np.identity(D)
if rho is None:
rho = np.ones(D)
V = np.identity(D) + np.ones((D,D))
# Construct model
B = GaussianARD(3, 5,
shape=(D,K),
plates=(1,D))
S = GaussianARD(2, 4,
shape=(K,),
plates=(N,1))
A = SumMultiply('dk,k->d', B, S)
X = GaussianMarkovChain(mu,
Lambda,
A,
rho,
n=N+1,
initialize=False)
Y = Gaussian(X,
V,
initialize=False)
# Posterior estimation
Y.observe(np.random.randn(N+1,D))
X.update()
B.update()
S.update()
try:
mu.update()
except:
pass
try:
Lambda.update()
except:
pass
try:
rho.update()
except:
pass
# Construct rotator
rotB = RotateGaussianARD(B, axis=-2)
rotX = RotateVaryingMarkovChain(X, B, S, rotB)
rotX.setup()
# Check gradient with respect to R
R = np.random.randn(D, D)
def cost(r):
(b, dr) = rotX.bound(np.reshape(r, np.shape(R)))
return (b, np.ravel(dr))
err = optimize.check_gradient(cost,
np.ravel(R),
verbose=False)
self.assertAllClose(err, 0,
atol=1e-6,
msg="Gradient incorrect")
return
self._run_checks(check)
pass
def _run_checks(self, check):
# Basic test
check(1, 1, 1)
check(2, 1, 1)
check(1, 2, 1)
check(1, 1, 2)
check(3, 4, 2)
# Test mu
check(2, 3, 4,
mu=GaussianARD(2, 4,
shape=(2,),
plates=()))
# Test Lambda
check(2, 3, 4,
Lambda=Wishart(3, random.covariance(2)))
# Test Lambda and mu
check(2, 3, 4,
mu=GaussianARD(2, 4,
shape=(2,),
plates=()),
Lambda=Wishart(2, random.covariance(2)))
# TODO: Test plates
pass
bayespy-0.2.3/bayespy/inference/vmp/__init__.py 0000664 5104112 5104112 00000000000 12121570320 023216 0 ustar jluttine jluttine 0000000 0000000 bayespy-0.2.3/bayespy/inference/vmp/transformations.py 0000664 5104112 5104112 00000151655 12374370351 024753 0 ustar jluttine jluttine 0000000 0000000 ######################################################################
# Copyright (C) 2013-2014 Jaakko Luttinen
#
# This file is licensed under Version 3.0 of the GNU General Public
# License. See LICENSE for a text of the license.
######################################################################
######################################################################
# This file is part of BayesPy.
#
# BayesPy is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# BayesPy is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with BayesPy. If not, see .
######################################################################
import numpy as np
import warnings
import scipy
from bayespy.utils import optimize
from bayespy.utils import random
from bayespy.utils import linalg
from bayespy.utils import misc
from bayespy.utils.linalg import dot, tracedot
from .nodes import gaussian
from .nodes.categorical import CategoricalMoments
class RotationOptimizer():
r"""
Optimizer for rotation parameter expansion in state-space models
Rotates one model block with :math:`\mathbf{R}` and one model block with
:math:`\mathbf{R}^{-1}`.
Parameters
----------
block1 : rotator object
The first rotation parameter expansion object
block2 : rotator object
The second rotation parameter expansion object
D : int
Dimensionality of the latent space
References
----------
:cite:`Luttinen:2010`, :cite:`Luttinen:2013`
"""
def __init__(self, block1, block2, D):
self.block1 = block1
self.block2 = block2
self.D = D
def rotate(self,
maxiter=10,
check_gradient=False,
verbose=False,
check_bound=False):
"""
Optimize the rotation of two separate model blocks jointly.
If some variable is the dot product of two Gaussians, rotating the two
Gaussians optimally can make the inference algorithm orders of magnitude
faster.
First block is rotated with :math:`\mathbf{R}` and the second with
:math:`\mathbf{R}^{-T}`.
Blocks must have methods: `bound(U,s,V)` and `rotate(R)`.
"""
I = np.identity(self.D)
piv = np.arange(self.D)
def cost(r):
# Make vector-r into matrix-R
R = np.reshape(r, (self.D,self.D))
# Compute SVD
invR = np.linalg.inv(R)
logdetR = np.linalg.slogdet(R)[1]
# Compute lower bound terms
(b1,db1) = self.block1.bound(R, logdet=logdetR, inv=invR)
(b2,db2) = self.block2.bound(invR.T, logdet=-logdetR, inv=R.T)
# Apply chain rule for the second gradient:
# d b(invR.T)
# = tr(db.T * d(invR.T))
# = tr(db * d(invR))
# = -tr(db * invR * (dR) * invR)
# = -tr(invR * db * invR * dR)
db2 = -dot(invR.T, db2.T, invR.T)
# Compute the cost function
c = -(b1+b2)
dc = -(db1+db2)
return (c, np.ravel(dc))
def get_bound_terms(r, gradient=False):
"""
Returns a dictionary of bound terms for the nodes.
"""
# Gradient not yet implemented..
if gradient:
raise NotImplementedError()
# Make vector-r into matrix-R
R = np.reshape(r, (self.D,self.D))
# Compute SVD
invR = np.linalg.inv(R)
logdetR = np.linalg.slogdet(R)[1]
# Compute lower bound terms
dict1 = self.block1.get_bound_terms(R,
logdet=logdetR,
inv=invR)
dict2 = self.block2.get_bound_terms(invR.T,
logdet=-logdetR,
inv=R.T)
if not gradient:
dict1.update(dict2)
return dict1
else:
terms = dict1[0].copy()
terms = terms.update(dict2[0])
grad = dict1[1].copy()
grad = grad.update(dict2[1])
return (terms, grad)
def get_true_bound_terms():
nodes = set(self.block1.nodes()) | set(self.block2.nodes())
D = {}
# TODO/FIXME: Also compute bound for child nodes as they could be
# affected in practice although they shouldn't. Just checking that.
for node in nodes:
L = node.lower_bound_contribution()
D[node] = L
return D
self.block1.setup()
self.block2.setup()
if check_gradient:
R = np.random.randn(self.D, self.D)
err = optimize.check_gradient(cost, np.ravel(R),
verbose=verbose)
if err > 1e-5:
warnings.warn("Rotation gradient has relative error %g" % err)
# Initial rotation is identity matrix
r0 = np.ravel(np.identity(self.D))
(cost_begin, _) = cost(r0)
if check_bound:
bound_terms_begin = get_bound_terms(r0)
true_bound_terms_begin = get_true_bound_terms()
# Run optimization
r = optimize.minimize(cost, r0, maxiter=maxiter, verbose=verbose)
(cost_end, _) = cost(r)
if check_bound:
bound_terms_end = get_bound_terms(r)
# Apply the optimal rotation
R = np.reshape(r, (self.D,self.D))
invR = np.linalg.inv(R)
logdetR = np.linalg.slogdet(R)[1]
self.block1.rotate(R, inv=invR, logdet=logdetR)
self.block2.rotate(invR.T, inv=R.T, logdet=-logdetR)
# Check that the cost function and the true lower bound changed equally
cost_change = cost_end - cost_begin
# Check that we really have improved the bound.
if cost_change > 0:
warnings.warn("Rotation optimization made the cost function worse "
"by %g. Probably a bug in the gradient of the "
"rotation functions."
% (cost_change,))
if check_bound:
true_bound_terms_end = get_true_bound_terms()
bound_change = 0
for node in bound_terms_begin.keys():
node_bound_change = (bound_terms_end[node]
- bound_terms_begin[node])
bound_change += node_bound_change
true_node_bound_change = 0
try:
true_node_bound_change += (true_bound_terms_end[node]
- true_bound_terms_begin[node])
except KeyError:
raise Exception("The node %s is part of the "
"transformation but not part of the "
"model. Check your VB construction."
% node.name)
if not np.allclose(node_bound_change, true_node_bound_change):
warnings.warn("Rotation cost function is not consistent "
"with the true lower bound for node %s. "
"Bound changed %g but optimized function "
"changed %g."
% (node.name,
true_node_bound_change,
node_bound_change))
# Check that we really have improved the bound.
# TODO/FIXME: Also compute bound for child nodes as they could be
# affected in practice although they shouldn't. Just checking that.
if bound_change < 0:
warnings.warn("Rotation made the true lower bound worse by %g. "
"Probably a bug in the rotation functions."
% (bound_change,))
class RotateGaussian():
r"""
Rotation parameter expansion for :class:`bayespy.nodes.Gaussian`
"""
def __init__(self, X):
self.X = X
def rotate(self, R, inv=None, logdet=None):
self.X.rotate(R, inv=inv, logdet=logdet)
def setup(self):
"""
This method should be called just before optimization.
"""
mask = self.X.mask[...,np.newaxis,np.newaxis]
# Number of plates
self.N = self.X.plates[0] #np.sum(mask)
# Compute the sum over plates
self.XX = misc.sum_multiply(self.X.get_moments()[1],
mask,
axis=(-1,-2),
sumaxis=False,
keepdims=False)
# Parent's moments
self.Lambda = self.X.parents[1].get_moments()[0]
def _compute_bound(self, R, logdet=None, inv=None, gradient=False):
"""
Rotate q(X) as X->RX: q(X)=N(R*mu, R*Cov*R')
Assume:
:math:`p(\mathbf{X}) = \prod^M_{m=1}
N(\mathbf{x}_m|0, \mathbf{\Lambda})`
"""
# TODO/FIXME: X and alpha should NOT contain observed values!! Check
# that.
# TODO/FIXME: Allow non-zero prior mean!
# Assume constant mean and precision matrix over plates..
# Compute rotated moments
XX_R = dot(R, self.XX, R.T)
inv_R = inv
logdet_R = logdet
# Compute entropy H(X)
logH_X = random.gaussian_entropy(-2*self.N*logdet_R,
0)
# Compute
logp_X = random.gaussian_logpdf(np.vdot(XX_R, self.Lambda),
0,
0,
0,
0)
# Compute the bound
if terms:
bound = {self.X: bound}
else:
bound = logp_X + logH_X
if not gradient:
return bound
# Compute dH(X)
dlogH_X = random.gaussian_entropy(-2*self.N*inv_R.T,
0)
# Compute d
dXX = 2*dot(self.Lambda, R, self.XX)
dlogp_X = random.gaussian_logpdf(dXX,
0,
0,
0,
0)
if terms:
d_bound = {self.X: dlogp_X + dlogH_X}
else:
d_bound = dlogp_X + dlogH_X
return (bound, d_bound)
def bound(self, R, logdet=None, inv=None):
return self._compute_bound(R,
logdet=logdet,
inv=inv,
gradient=True)
def get_bound_terms(self, R, logdet=None, inv=None):
return self._compute_bound(R,
logdet=logdet,
inv=inv,
gradient=False,
terms=True)
def nodes(self):
return [self.X]
def covariance_to_variance(C, ndim=1, covariance_axis=None):
# Force None to empty list
if covariance_axis is None:
covariance_axis = []
# Force a list from integer
if isinstance(covariance_axis, int):
covariance_axis = [covariance_axis]
# Force positive axis indices
covariance_axis = [axis + ndim if axis < 0 else axis
for axis in covariance_axis]
# Make a set of the axes
covariance_axis = set(covariance_axis)
keys = [i+ndim if i in covariance_axis else i for i in range(ndim)]
keys += [i+2*ndim if i in covariance_axis else i for i in range(ndim)]
out_keys = sorted(list(set(keys)))
return np.einsum(C, [Ellipsis]+keys, [Ellipsis]+out_keys)
def sum_to_plates(V, plates_to, plates_from=None, ndim=0):
if ndim == 0:
if plates_from is not None:
r = gaussian.Gaussian._plate_multiplier(plates_from,
np.shape(V))
else:
r = 1
return r * misc.sum_to_shape(V, plates_to)
else:
dims_V = np.shape(V)[-ndim:]
plates_V = np.shape(V)[:-ndim]
shape_to = tuple(plates_to) + dims_V
if plates_from is not None:
r = gaussian.Gaussian._plate_multiplier(plates_from, plates_V)
else:
r = 1
return r * misc.sum_to_shape(V, shape_to)
class RotateGaussianARD():
"""
Rotation parameter expansion for :class:`bayespy.nodes.GaussianARD`
The model:
alpha ~ N(a, b)
X ~ N(mu, alpha)
X can be an array (e.g., GaussianARD).
Transform q(X) and q(alpha) by rotating X.
Requirements:
* X and alpha do not contain any observed values
"""
def __init__(self, X, *alpha, axis=-1, precompute=False):
"""
Precompute tells whether to compute some moments once in the setup
function instead of every time in the bound function. However, they are
computed a bit differently in the bound function so it can be useful
too. Precomputation is probably beneficial only when there are large
axes that are not rotated (by R nor Q) and they are not contained in the
plates of alpha, and the dimensions for R and Q are quite small.
"""
self.precompute = precompute
self.node_parent = X.parents[0]
if len(alpha) == 0:
self.update_alpha = False
elif len(alpha) == 1:
self.node_alpha = alpha[0]
self.update_alpha = True
else:
raise ValueError("Too many arguments")
self.node_X = X
#self.node_mu = X.parents[0]
self.ndim = len(X.dims[0])
# Force negative rotation axis indexing
if not isinstance(axis, int):
raise ValueError("Axis must be integer")
if axis >= 0:
axis -= self.ndim
if axis < -self.ndim or axis >= 0:
raise ValueError("Axis out of bounds")
self.axis = axis
def nodes(self):
if self.update_alpha:
return [self.node_X, self.node_alpha]
else:
return [self.node_X]
def rotate(self, R, inv=None, logdet=None, Q=None):
self.node_X.rotate(R,
inv=inv,
logdet=logdet,
axis=self.axis)
if self.plate_axis is not None:
self.node_X.rotate_plates(Q, plate_axis=self.plate_axis)
if self.update_alpha:
self.node_alpha.update()
def setup(self, plate_axis=None):
"""
This method should be called just before optimization.
For efficiency, sum over axes that are not in mu, alpha nor rotation.
If using Q, set rotate_plates to True.
"""
# Store the original plate_axis parameter for later use in other methods
self.plate_axis = plate_axis
# Manipulate the plate_axis parameter to suit the needs of this method
if plate_axis is not None:
if not isinstance(plate_axis, int):
raise ValueError("Plate axis must be integer")
if plate_axis >= 0:
plate_axis -= len(self.node_X.plates)
if plate_axis < -len(self.node_X.plates) or plate_axis >= 0:
raise ValueError("Axis out of bounds")
plate_axis -= self.ndim - 1 # Why -1? Because one axis is preserved!
# Get the mean parameter. It will not be rotated. This assumes that mu
# and alpha are really independent.
(alpha_mu, alpha_mu2, alpha, _) = self.node_parent.get_moments()
mu = alpha_mu / alpha
mu2 = alpha_mu2 / alpha
# For simplicity, force mu to have the same shape as X
mu = mu * np.ones(self.node_X.dims[0])
mu2 = mu2 * np.ones(self.node_X.dims[0])
## (mu, mumu) = gaussian.reshape_gaussian_array(self.node_mu.dims[0],
## self.node_X.dims[0],
## mu,
## mumu)
(X, XX) = self.node_X.get_moments()
# Take diagonal of covariances to variances for axes that are not in R
# (and move those axes to be the last)
XX = covariance_to_variance(XX,
ndim=self.ndim,
covariance_axis=self.axis)
## mumu = covariance_to_variance(mumu,
## ndim=self.ndim,
## covariance_axis=self.axis)
# Move axes of X and mu and compute their outer product
X = misc.moveaxis(X, self.axis, -1)
mu = misc.moveaxis(mu, self.axis, -1)
mu2 = misc.moveaxis(mu2, self.axis, -1)
Xmu = linalg.outer(X, mu, ndim=1)
D = np.shape(X)[-1]
# Move axes of alpha related variables
def safe_move_axis(x):
if np.ndim(x) >= -self.axis:
return misc.moveaxis(x, self.axis, -1)
else:
return x[...,np.newaxis]
if self.update_alpha:
a = safe_move_axis(self.node_alpha.phi[1])
a0 = safe_move_axis(self.node_alpha.parents[0].get_moments()[0])
b0 = safe_move_axis(self.node_alpha.parents[1].get_moments()[0])
plates_alpha = list(self.node_alpha.plates)
else:
alpha = safe_move_axis(self.node_parent.get_moments()[2])
plates_alpha = list(self.node_parent.get_shape(2))
# Move plates of alpha for R
if len(plates_alpha) >= -self.axis:
plate = plates_alpha.pop(self.axis)
plates_alpha.append(plate)
else:
plates_alpha.append(1)
plates_X = list(self.node_X.get_shape(0))
plates_X.pop(self.axis)
def sum_to_alpha(V, ndim=2):
# TODO/FIXME: This could be improved so that it is not required to
# explicitly repeat to alpha plates. Multiplying by ones was just a
# simple bug fix.
return sum_to_plates(V * np.ones(plates_alpha[:-1]+ndim*[1]),
plates_alpha[:-1],
ndim=ndim,
plates_from=plates_X)
if plate_axis is not None:
# Move plate axis just before the rotated dimensions (which are
# last)
def safe_move_plate_axis(x, ndim):
if np.ndim(x)-ndim >= -plate_axis:
return misc.moveaxis(x,
plate_axis-ndim,
-ndim-1)
else:
inds = (Ellipsis,None) + ndim*(slice(None),)
return x[inds]
X = safe_move_plate_axis(X, 1)
mu = safe_move_plate_axis(mu, 1)
XX = safe_move_plate_axis(XX, 2)
mu2 = safe_move_plate_axis(mu2, 1)
if self.update_alpha:
a = safe_move_plate_axis(a, 1)
a0 = safe_move_plate_axis(a0, 1)
b0 = safe_move_plate_axis(b0, 1)
else:
alpha = safe_move_plate_axis(alpha, 1)
# Move plates of X and alpha
plate = plates_X.pop(plate_axis)
plates_X.append(plate)
if len(plates_alpha) >= -plate_axis+1:
plate = plates_alpha.pop(plate_axis-1)
else:
plate = 1
plates_alpha = plates_alpha[:-1] + [plate] + plates_alpha[-1:]
CovX = XX - linalg.outer(X, X)
self.CovX = sum_to_plates(CovX,
plates_alpha[:-2],
ndim=3,
plates_from=plates_X[:-1])
# Broadcast mumu to ensure shape
#mumu = np.ones(np.shape(XX)[-3:]) * mumu
mu2 = mu2 * np.ones(np.shape(X)[-2:])
self.mu2 = sum_to_alpha(mu2, ndim=1)
if self.precompute:
# Precompute some stuff for the gradient of plate rotation
#
# NOTE: These terms may require a lot of memory if alpha has the
# same or almost the same plates as X.
self.X_X = sum_to_plates(X[...,:,:,None,None] *
X[...,None,None,:,:],
plates_alpha[:-2],
ndim=4,
plates_from=plates_X[:-1])
self.X_mu = sum_to_plates(X[...,:,:,None,None] *
mu[...,None,None,:,:],
plates_alpha[:-2],
ndim=4,
plates_from=plates_X[:-1])
else:
self.X = X
self.mu = mu
else:
# Sum axes that are not in the plates of alpha
self.XX = sum_to_alpha(XX)
self.mu2 = sum_to_alpha(mu2, ndim=1)
self.Xmu = sum_to_alpha(Xmu)
if self.update_alpha:
self.a = a
self.a0 = a0
self.b0 = b0
else:
self.alpha = alpha
self.plates_X = plates_X
self.plates_alpha = plates_alpha
def _compute_bound(self, R, logdet=None, inv=None, Q=None, gradient=False, terms=False):
"""
Rotate q(X) and q(alpha).
Assume:
p(X|alpha) = prod_m N(x_m|0,diag(alpha))
p(alpha) = prod_d G(a_d,b_d)
"""
#
# Transform the distributions and moments
#
plates_alpha = self.plates_alpha
plates_X = self.plates_X
# Compute rotated second moment
if self.plate_axis is not None:
# The plate axis has been moved to be the last plate axis
if Q is None:
raise ValueError("Plates should be rotated but no Q give")
# Transform covariance
sumQ = np.sum(Q, axis=0)
QCovQ = sumQ[:,None,None]**2 * self.CovX
# Rotate plates
if self.precompute:
QX_QX = np.einsum('...kalb,...ik,...il->...iab', self.X_X, Q, Q)
XX = QX_QX + QCovQ
XX = sum_to_plates(XX,
plates_alpha[:-1],
ndim=2)
Xmu = np.einsum('...kaib,...ik->...iab', self.X_mu, Q)
Xmu = sum_to_plates(Xmu,
plates_alpha[:-1],
ndim=2)
else:
X = self.X
mu = self.mu
QX = np.einsum('...ik,...kj->...ij', Q, X)
XX = (sum_to_plates(QCovQ,
plates_alpha[:-1],
ndim=2) +
sum_to_plates(linalg.outer(QX, QX),
plates_alpha[:-1],
ndim=2,
plates_from=plates_X))
Xmu = sum_to_plates(linalg.outer(QX, self.mu),
plates_alpha[:-1],
ndim=2,
plates_from=plates_X)
mu2 = self.mu2
D = np.shape(XX)[-1]
logdet_Q = D * np.log(np.abs(sumQ))
else:
XX = self.XX
mu2 = self.mu2
Xmu = self.Xmu
logdet_Q = 0
# Compute transformed moments
#mu2 = np.einsum('...ii->...i', mu2)
RXmu = np.einsum('...ik,...ki->...i', R, Xmu)
RXX = np.einsum('...ik,...kj->...ij', R, XX)
RXXR = np.einsum('...ik,...ik->...i', RXX, R)
# <(X-mu) * (X-mu)'>_R
XmuXmu = (RXXR - 2*RXmu + mu2)
D = np.shape(R)[0]
# Compute q(alpha)
if self.update_alpha:
# Parameters
a0 = self.a0
b0 = self.b0
a = self.a
b = b0 + 0.5*sum_to_plates(XmuXmu,
plates_alpha,
plates_from=None,
ndim=0)
# Some expectations
alpha = a / b
logb = np.log(b)
logalpha = -logb # + const
b0_alpha = b0 * alpha
a0_logalpha = a0 * logalpha
else:
alpha = self.alpha
logalpha = 0
#
# Compute the cost
#
def sum_plates(V, *plates):
full_plates = misc.broadcasted_shape(*plates)
r = self.node_X._plate_multiplier(full_plates, np.shape(V))
return r * np.sum(V)
XmuXmu_alpha = XmuXmu * alpha
if logdet is None:
logdet_R = np.linalg.slogdet(R)[1]
inv_R = np.linalg.inv(R)
else:
logdet_R = logdet
inv_R = inv
# Compute entropy H(X)
logH_X = random.gaussian_entropy(-2*sum_plates(logdet_R + logdet_Q,
plates_X),
0)
# Compute
logp_X = random.gaussian_logpdf(sum_plates(XmuXmu_alpha,
plates_alpha[:-1] + [D]),
0,
0,
sum_plates(logalpha,
plates_X + [D]),
0)
if self.update_alpha:
# Compute entropy H(alpha)
# This cancels out with the log(alpha) term in log(p(alpha))
logH_alpha = 0
# Compute
logp_alpha = random.gamma_logpdf(sum_plates(b0_alpha,
plates_alpha),
0,
sum_plates(a0_logalpha,
plates_alpha),
0,
0)
else:
logH_alpha = 0
logp_alpha = 0
# Compute the bound
if terms:
bound = {self.node_X: logp_X + logH_X}
if self.update_alpha:
bound.update({self.node_alpha: logp_alpha + logH_alpha})
else:
bound = (0
+ logp_X
+ logp_alpha
+ logH_X
+ logH_alpha
)
if not gradient:
return bound
#
# Compute the gradient with respect R
#
plate_multiplier = self.node_X._plate_multiplier
def sum_plates(V, plates):
ones = np.ones(np.shape(R))
r = plate_multiplier(plates, np.shape(V)[:-2])
return r * misc.sum_multiply(V, ones,
axis=(-1,-2),
sumaxis=False,
keepdims=False)
D_XmuXmu = 2*RXX - 2*gaussian.transpose_covariance(Xmu)
DXmuXmu_alpha = np.einsum('...i,...ij->...ij',
alpha,
D_XmuXmu)
if self.update_alpha:
D_b = 0.5 * D_XmuXmu
XmuXmu_Dalpha = np.einsum('...i,...i,...i,...ij->...ij',
sum_to_plates(XmuXmu,
plates_alpha,
plates_from=None,
ndim=0),
alpha,
-1/b,
D_b)
D_b0_alpha = np.einsum('...i,...i,...i,...ij->...ij',
b0,
alpha,
-1/b,
D_b)
D_logb = np.einsum('...i,...ij->...ij',
1/b,
D_b)
D_logalpha = -D_logb
D_a0_logalpha = a0 * D_logalpha
else:
XmuXmu_Dalpha = 0
D_logalpha = 0
D_XmuXmu_alpha = DXmuXmu_alpha + XmuXmu_Dalpha
D_logR = inv_R.T
# Compute dH(X)
dlogH_X = random.gaussian_entropy(-2*sum_plates(D_logR,
plates_X),
0)
# Compute d
dlogp_X = random.gaussian_logpdf(sum_plates(D_XmuXmu_alpha,
plates_alpha[:-1]),
0,
0,
(sum_plates(D_logalpha,
plates_X)
* plate_multiplier((D,),
plates_alpha[-1:])),
0)
if self.update_alpha:
# Compute dH(alpha)
# This cancels out with the log(alpha) term in log(p(alpha))
dlogH_alpha = 0
# Compute d
dlogp_alpha = random.gamma_logpdf(sum_plates(D_b0_alpha,
plates_alpha[:-1]),
0,
sum_plates(D_a0_logalpha,
plates_alpha[:-1]),
0,
0)
else:
dlogH_alpha = 0
dlogp_alpha = 0
if terms:
raise NotImplementedError()
dR_bound = {self.node_X: dlogp_X + dlogH_X}
if self.update_alpha:
dR_bound.update({self.node_alpha: dlogp_alpha + dlogH_alpha})
else:
dR_bound = (0*dlogp_X
+ dlogp_X
+ dlogp_alpha
+ dlogH_X
+ dlogH_alpha
)
if self.plate_axis is None:
return (bound, dR_bound)
#
# Compute the gradient with respect to Q (if Q given)
#
# Some pre-computations
Q_RCovR = np.einsum('...ik,...kl,...il,...->...i',
R,
self.CovX,
R,
sumQ)
if self.precompute:
Xr_rX = np.einsum('...abcd,...jb,...jd->...jac',
self.X_X,
R,
R)
QXr_rX = np.einsum('...akj,...ik->...aij',
Xr_rX,
Q)
RX_mu = np.einsum('...jk,...akbj->...jab',
R,
self.X_mu)
else:
RX = np.einsum('...ik,...k->...i', R, X)
QXR = np.einsum('...ik,...kj->...ij', Q, RX)
QXr_rX = np.einsum('...ik,...jk->...kij', QXR, RX)
RX_mu = np.einsum('...ik,...jk->...kij', RX, mu)
QXr_rX = sum_to_plates(QXr_rX,
plates_alpha[:-2],
ndim=3,
plates_from=plates_X[:-1])
RX_mu = sum_to_plates(RX_mu,
plates_alpha[:-2],
ndim=3,
plates_from=plates_X[:-1])
def psi(v):
"""
Compute: d/dQ 1/2*trace(diag(v)*<(X-mu)*(X-mu)>)
= Q*'*R'*diag(v)*R* + ones * Q diag( tr(R'*diag(v)*R*Cov) )
+ mu*diag(v)*R*
"""
# Precompute all terms to plates_alpha because v has shape
# plates_alpha.
# Gradient of 0.5*v**
v_QXrrX = np.einsum('...kij,...ik->...ij', QXr_rX, v)
# Gradient of 0.5*v*Cov
Q_tr_R_v_R_Cov = np.einsum('...k,...k->...', Q_RCovR, v)[...,None,:]
# Gradient of mu*v*x
mu_v_R_X = np.einsum('...ik,...kji->...ij', v, RX_mu)
return v_QXrrX + Q_tr_R_v_R_Cov - mu_v_R_X
def sum_plates(V, plates):
ones = np.ones(np.shape(Q))
r = self.node_X._plate_multiplier(plates,
np.shape(V)[:-2])
return r * misc.sum_multiply(V, ones,
axis=(-1,-2),
sumaxis=False,
keepdims=False)
if self.update_alpha:
D_logb = psi(1/b)
XX_Dalpha = -psi(alpha/b * sum_to_plates(XmuXmu, plates_alpha))
D_logalpha = -D_logb
else:
XX_Dalpha = 0
D_logalpha = 0
DXX_alpha = 2*psi(alpha)
D_XX_alpha = DXX_alpha + XX_Dalpha
D_logdetQ = D / sumQ
N = np.shape(Q)[-1]
# Compute dH(X)
dQ_logHX = random.gaussian_entropy(-2*sum_plates(D_logdetQ,
plates_X[:-1]),
0)
# Compute d
dQ_logpX = random.gaussian_logpdf(sum_plates(D_XX_alpha,
plates_alpha[:-2]),
0,
0,
(sum_plates(D_logalpha,
plates_X[:-1])
* plate_multiplier((N,D),
plates_alpha[-2:])),
0)
if self.update_alpha:
D_alpha = -psi(alpha/b)
D_b0_alpha = b0 * D_alpha
D_a0_logalpha = a0 * D_logalpha
# Compute dH(alpha)
# This cancels out with the log(alpha) term in log(p(alpha))
dQ_logHalpha = 0
# Compute d
dQ_logpalpha = random.gamma_logpdf(sum_plates(D_b0_alpha,
plates_alpha[:-2]),
0,
sum_plates(D_a0_logalpha,
plates_alpha[:-2]),
0,
0)
else:
dQ_logHalpha = 0
dQ_logpalpha = 0
if terms:
raise NotImplementedError()
dQ_bound = {self.node_X: dQ_logpX + dQ_logHX}
if self.update_alpha:
dQ_bound.update({self.node_alpha: dQ_logpalpha + dQ_logHalpha})
else:
dQ_bound = (0*dQ_logpX
+ dQ_logpX
+ dQ_logpalpha
+ dQ_logHX
+ dQ_logHalpha
)
return (bound, dR_bound, dQ_bound)
def bound(self, R, logdet=None, inv=None, Q=None):
return self._compute_bound(R,
logdet=logdet,
inv=inv,
Q=Q,
gradient=True)
def get_bound_terms(self, R, logdet=None, inv=None, Q=None):
return self._compute_bound(R,
logdet=logdet,
inv=inv,
Q=Q,
gradient=False,
terms=True)
class RotateGaussianMarkovChain():
r"""
Rotation parameter expansion for :class:`bayespy.nodes.GaussianMarkovChain`
Assume the following model.
Constant, unit isotropic innovation noise. Unit variance only?
Maybe: Assume innovation noise with unit variance? Would it help make this
function more general with respect to A.
TODO: Allow constant A or not rotating A.
:math:`A` may vary in time.
Shape of A: (N,D,D)
Shape of AA: (N,D,D,D)
No plates for X.
"""
def __init__(self, X, *args):
self.X_node = X
self.A_node = X.parents[2]
if len(args) == 0:
raise NotImplementedError()
elif len(args) == 1:
self.A_rotator = args[0]
else:
raise ValueError("Wrong number of arguments")
self.N = X.dims[0][0]
def nodes(self):
return [self.X_node] + self.A_rotator.nodes()
def rotate(self, R, inv=None, logdet=None):
if inv is None:
inv = np.linalg.inv(R)
if logdet is None:
logdet = np.linalg.slogdet(R)[1]
self.X_node.rotate(R, inv=inv, logdet=logdet)
self.A_rotator.rotate(inv.T, inv=R.T, logdet=-logdet, Q=R)
def _computations_for_A_and_X(self, XpXn, XpXp):
# Get moments of A (and make sure they include time axis)
(A, AA) = self.A_node.get_moments()
A = misc.atleast_nd(A, 3)
AA = misc.atleast_nd(AA, 4)
CovA = AA - A[...,:,np.newaxis]*A[...,np.newaxis,:]
#
# Expectations with respect to A and X
#
# TODO: In case A does not depend on time, use a bit more efficient
# formulas
# Compute: \sum_n
A_XpXn = np.einsum('...nik,...nkj->...ij',
A,
XpXn)
A_XpXn = sum_to_plates(A_XpXn,
(),
ndim=2,
plates_from=self.X_node.plates)
# Compute: \sum_n ^T
A_XpXp = np.einsum('...nik,...nkj->...nij',
A,
XpXp)
A_XpXp_A = np.einsum('...nik,...njk->...ij',
A_XpXp,
A)
A_XpXp_A = sum_to_plates(A_XpXp_A,
(),
ndim=2,
plates_from=self.X_node.plates)
# Compute: \sum_n tr(CovA_n )
CovA_XpXp = np.einsum('...ndij,...nij->...d',
CovA,
XpXp)
CovA_XpXp = sum_to_plates(CovA_XpXp,
(),
ndim=1,
plates_from=self.X_node.plates)
return (A_XpXn, A_XpXp_A, CovA_XpXp)
def setup(self):
"""
This method should be called just before optimization.
"""
# Get moments of X
(X, XnXn, XpXn) = self.X_node.get_moments()
# TODO/FIXME: Sum to plates of A/CovA
XpXp = XnXn[...,:-1,:,:]
#
# Expectations with respect to X
#
self.X0 = X[...,0,:]
self.X0X0 = XnXn[...,0,:,:]
#self.XnXn = np.sum(XnXn[...,1:,:,:], axis=-3)
self.XnXn = sum_to_plates(XnXn[...,1:,:,:],
(),
plates_from=self.X_node.plates + (self.N-1,),
ndim=2)
# Get moments of the fixed parameter nodes
mu = self.X_node.parents[0].get_moments()[0]
self.Lambda = self.X_node.parents[1].get_moments()[0]
self.Lambda_mu_X0 = linalg.outer(np.einsum('...ik,...k->...i',
self.Lambda,
mu),
self.X0)
self.Lambda_mu_X0 = sum_to_plates(self.Lambda_mu_X0,
(),
plates_from=self.X_node.plates,
ndim=2)
#
# Prepare the rotation for A
#
(self.A_XpXn,
self.A_XpXp_A,
self.CovA_XpXp) = self._computations_for_A_and_X(XpXn, XpXp)
self.A_rotator.setup(plate_axis=-1)
# Innovation noise is assumed to be I
#self.v = self.X_node.parents[3].get_moments()[0]
def _compute_bound(self, R, logdet=None, inv=None, gradient=False, terms=False):
"""
Rotate q(X) as X->RX: q(X)=N(R*mu, R*Cov*R')
Assume:
:math:`p(\mathbf{X}) = \prod^M_{m=1}
N(\mathbf{x}_m|0, \mathbf{\Lambda})`
Assume unit innovation noise covariance.
"""
# TODO/FIXME: X and alpha should NOT contain observed values!! Check
# that.
# Assume constant mean and precision matrix over plates..
if inv is None:
invR = np.linalg.inv(R)
else:
invR = inv
if logdet is None:
logdetR = np.linalg.slogdet(R)[1]
else:
logdetR = logdet
# Transform moments of X and A:
Lambda_R_X0X0 = sum_to_plates(dot(self.Lambda, R, self.X0X0),
(),
plates_from=self.X_node.plates,
ndim=2)
R_XnXn = dot(R, self.XnXn)
RA_XpXp_A = dot(R, self.A_XpXp_A)
sumr = np.sum(R, axis=0)
R_CovA_XpXp = sumr * self.CovA_XpXp
# Compute entropy H(X)
M = self.N*np.prod(self.X_node.plates) # total number of rotated vectors
logH_X = random.gaussian_entropy(-2 * M * logdetR,
0)
# Compute
yy = tracedot(R_XnXn, R.T) + tracedot(Lambda_R_X0X0, R.T)
yz = tracedot(dot(R,self.A_XpXn),R.T) + tracedot(self.Lambda_mu_X0, R.T)
zz = tracedot(RA_XpXp_A, R.T) + np.einsum('...k,...k->...',
R_CovA_XpXp,
sumr)
logp_X = random.gaussian_logpdf(yy,
yz,
zz,
0,
0)
# Compute the bound
if terms:
bound = {self.X_node: logp_X + logH_X}
else:
bound = logp_X + logH_X
if not gradient:
return bound
# Compute dH(X)
dlogH_X = random.gaussian_entropy(-2 * M * invR.T,
0)
# Compute d
dyy = 2 * (R_XnXn + Lambda_R_X0X0)
dyz = dot(R, self.A_XpXn + self.A_XpXn.T) + self.Lambda_mu_X0
dzz = 2 * (RA_XpXp_A + R_CovA_XpXp[None,:])
dlogp_X = random.gaussian_logpdf(dyy,
dyz,
dzz,
0,
0)
if terms:
d_bound = {self.X_node: dlogp_X + dlogH_X}
else:
d_bound = (
+ dlogp_X
+ dlogH_X
)
return (bound, d_bound)
def bound(self, R, logdet=None, inv=None):
if inv is None:
inv = np.linalg.inv(R)
if logdet is None:
logdet = np.linalg.slogdet(R)[1]
(bound_X, d_bound_X) = self._compute_bound(R,
logdet=logdet,
inv=inv,
gradient=True)
# Compute cost and gradient from A
(bound_A, dR_bound_A, dQ_bound_A) = self.A_rotator.bound(inv.T,
inv=R.T,
logdet=-logdet,
Q=R)
dR_bound_A = -dot(inv.T, dR_bound_A.T, inv.T)
# Compute the bound
bound = bound_X + bound_A
d_bound = d_bound_X + dR_bound_A + dQ_bound_A
return (bound, d_bound)
def get_bound_terms(self, R, logdet=None, inv=None):
if inv is None:
inv = np.linalg.inv(R)
if logdet is None:
logdet = np.linalg.slogdet(R)[1]
terms_A = self.A_rotator.get_bound_terms(inv.T,
inv=R.T,
logdet=-logdet,
Q=R)
terms_X = self._compute_bound(R,
logdet=logdet,
inv=inv,
gradient=False,
terms=True)
terms_X.update(terms_A)
return terms_X
class RotateVaryingMarkovChain(RotateGaussianMarkovChain):
r"""
Rotation for :class:`bayespy.nodes.SwitchingGaussianMarkovChain`
Assume the following model.
Constant, unit isotropic innovation noise.
:math:`A_n = \sum_k B_k s_{kn}`
Gaussian B: (1,D) x (D,K)
Gaussian S: (N,1) x (K)
MC X: () x (N+1,D)
No plates for X.
"""
def __init__(self, X, B, S, B_rotator):
self.X_node = X
self.B_node = B
self.S_node = S
self.B_rotator = B_rotator
if len(S.plates) > 0 and S.plates[-1] > 1:
raise ValueError("The length of the last plate of S must be 1.")
if len(B.plates) > 1 and B.plates[-2] > 1:
raise ValueError("The length of the last plate of B must be 1.")
if len(S.dims[0]) != 1:
raise ValueError("S should have exactly one variable axis")
if len(B.dims[0]) != 2:
raise ValueError("B should have exactly two variable axes")
super().__init__(X, B_rotator)
def _computations_for_A_and_X(self, XpXn, XpXp):
# Get moments of B and S
(B, BB) = self.B_node.get_moments()
CovB = BB - B[...,:,:,None,None]*B[...,None,None,:,:]
u_S = self.S_node.get_moments()
S = u_S[0]
SS = u_S[1]
#
# Expectations with respect to A and X
#
# TODO/FIXME: If S and B have overlapping plates, then these will give
# wrong results, because those plates of S are summed before multiplying
# by the plates of B. There should be some "smart einsum" function which
# would compute sum-multiplys intelligently given a number of inputs.
# Compute: \sum_n
# Axes: (N, D, D, D, K)
S_XpXn = misc.sum_multiply(S[...,None,None,:],
XpXn[...,:,None,:,:,None],
axis=(-3,-2,-1),
sumaxis=False)
A_XpXn = misc.sum_multiply(B[...,:,:,None,:],
S_XpXn[...,:,:,:],
axis=(-4,-2),
sumaxis=False)
# Compute: \sum_n ^T
# Axes: (N, D, D, D, K, D, K)
SS_XpXp = misc.sum_multiply(SS[...,None,:,None,:],
XpXp[...,None,:,None,:,None],
axis=(-4,-3,-2,-1),
sumaxis=False)
B_SS_XpXp = misc.sum_multiply(B[...,:,:,:,None,None],
SS_XpXp[...,:,:,:,:],
axis=(-4,-3),
sumaxis=True)
A_XpXp_A = misc.sum_multiply(B_SS_XpXp[...,:,None,:,:],
B[...,None,:,:,:],
axis=(-4,-3),
sumaxis=False)
# Compute: \sum_n tr(CovA_n )
# Axes: (D,D,K,D,K)
CovA_XpXp = misc.sum_multiply(CovB,
SS_XpXp,
axis=(-5,),
sumaxis=False)
return (A_XpXn, A_XpXp_A, CovA_XpXp)
class RotateSwitchingMarkovChain(RotateGaussianMarkovChain):
"""
Rotation for :class:`bayespy.nodes.VaryingGaussianMarkovChain`
Assume the following model.
Constant, unit isotropic innovation noise.
:math:`A_n = B_{z_n}`
Gaussian B: (..., K, D) x (D)
Categorical Z: (..., N-1) x (K)
GaussianMarkovChain X: (...) x (N,D)
No plates for X.
"""
def __init__(self, X, B, Z, B_rotator):
self.X_node = X
self.B_node = B
self.Z_node = Z._convert(CategoricalMoments)
self.B_rotator = B_rotator
(N,D) = self.X_node.dims[0]
K = self.Z_node.dims[0][0]
if len(self.Z_node.plates) == 0 and self.Z_node.plates[-1] != N-1:
raise ValueError("Incorrect plate length in Z")
if self.B_node.plates[-2:] != (K,D):
raise ValueError("Incorrect plates in B")
if len(self.Z_node.dims[0]) != 1:
raise ValueError("Z should have exactly one variable axis")
if len(self.B_node.dims[0]) != 1:
raise ValueError("B should have exactly one variable axes")
super().__init__(X, B_rotator)
def _computations_for_A_and_X(self, XpXn, XpXp):
# Get moments of B and Z
(B, BB) = self.B_node.get_moments()
CovB = BB - B[...,:,None]*B[...,None,:]
u_Z = self.Z_node.get_moments()
Z = u_Z[0]
#
# Expectations with respect to A and X
#
# Compute: \sum_n
Z_XpXn = np.einsum('...nij,...nk->...kij',
XpXn,
Z)
A_XpXn = np.einsum('...kil,...klj->...ij',
B,
Z_XpXn)
A_XpXn = sum_to_plates(A_XpXn,
(),
ndim=2,
plates_from=self.X_node.plates)
# Compute: \sum_n ^T
Z_XpXp = np.einsum('...nij,...nk->...kij',
XpXp,
Z)
B_Z_XpXp = np.einsum('...kil,...klj->...kij',
B,
Z_XpXp)
A_XpXp_A = np.einsum('...kil,...kjl->...ij',
B_Z_XpXp,
B)
A_XpXp_A = sum_to_plates(A_XpXp_A,
(),
ndim=2,
plates_from=self.X_node.plates)
# Compute: \sum_n tr(CovA_n )
CovA_XpXp = np.einsum('...kij,...kdij->...d',
Z_XpXp,
CovB)
CovA_XpXp = sum_to_plates(CovA_XpXp,
(),
ndim=1,
plates_from=self.X_node.plates)
return (A_XpXn, A_XpXp_A, CovA_XpXp)
class RotateMultiple():
r"""
Identical parameter expansion for several nodes simultaneously
Performs the same rotation for multiple nodes and combines the cost
effect.
"""
def __init__(self, *rotators):
self.rotators = rotators
def nodes(self):
return [node
for node in rotator.nodes()
for rotator in self.rotators]
def rotate(self, R, inv=None, logdet=None):
for rotator in self.rotators:
rotator.rotate(R, inv=inv, logdet=logdet)
def setup(self):
for rotator in self.rotators:
rotator.setup()
def bound(self, R, logdet=None, inv=None):
bound = 0
dbound = 0
for rotator in self.rotators:
(b, db) = rotator.bound(R, logdet=logdet, inv=inv)
bound = bound + b
dbound = dbound + db
return (bound, dbound)
def get_bound_terms(self, R, logdet=None, inv=None):
return {node: terms
for (node, terms) in rotator.items()
for rotator in self.rotators}
bayespy-0.2.3/bayespy/inference/vmp/vmp.py 0000664 5104112 5104112 00000027561 12430651206 022314 0 ustar jluttine jluttine 0000000 0000000 ######################################################################
# Copyright (C) 2011-2013 Jaakko Luttinen
#
# This file is licensed under Version 3.0 of the GNU General Public
# License. See LICENSE for a text of the license.
######################################################################
######################################################################
# This file is part of BayesPy.
#
# BayesPy is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# BayesPy is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with BayesPy. If not, see .
######################################################################
import numpy as np
import matplotlib.pyplot as plt
import warnings
import time
import h5py
import datetime
import tempfile
from bayespy.utils import misc
from bayespy.inference.vmp.nodes.node import Node
class VB():
r"""
Variational Bayesian (VB) inference engine
Parameters
----------
nodes : nodes
Nodes that form the model. Must include all at least all stochastic
nodes of the model.
tol : double, optional
Convergence criterion. Tolerance for the relative change in the VB
lower bound.
autosave_filename : string, optional
Filename for automatic saving
autosave_iterations : int, optional
Iteration interval between each automatic saving
callback : callable, optional
Function which is called after each update iteration step
"""
def __init__(self,
*nodes,
tol=1e-5,
autosave_filename=None,
autosave_iterations=0,
callback=None):
for (ind, node) in enumerate(nodes):
if not isinstance(node, Node):
raise ValueError("Argument number %d is not a node" % (ind+1))
# Remove duplicate nodes
self.model = misc.unique(nodes)
self._figures = {}
self.iter = 0
self.L = np.array(())
self.l = dict(zip(self.model,
len(self.model)*[np.array([])]))
self.autosave_iterations = autosave_iterations
if not autosave_filename:
date = datetime.datetime.today().strftime('%Y%m%d%H%M%S')
prefix = 'vb_autosave_%s_' % date
tmpfile = tempfile.NamedTemporaryFile(prefix=prefix,
suffix='.hdf5')
self.autosave_filename = tmpfile.name
self.filename = None
else:
self.autosave_filename = autosave_filename
self.filename = autosave_filename
# Check uniqueness of the node names
names = [node.name for node in self.model]
if len(names) != len(self.model):
raise Exception("Use unique names for nodes.")
self.callback = callback
self.callback_output = None
self.tol = tol
def set_autosave(self, filename, iterations=None):
self.autosave_filename = filename
self.filename = filename
if iterations is not None:
self.autosave_iterations = iterations
def set_callback(self, callback):
self.callback = callback
def update(self, *nodes, repeat=1, plot=False, tol=None, verbose=True):
# TODO/FIXME:
#
# If no nodes are given and thus everything is updated, the update order
# should be from down to bottom. Or something similar..
# Append the cost arrays
self.L = np.append(self.L, misc.nans(repeat))
for (node, l) in self.l.items():
self.l[node] = np.append(l, misc.nans(repeat))
# By default, update all nodes
if len(nodes) == 0:
nodes = self.model
converged = False
for i in range(repeat):
t = time.clock()
# Update nodes
for node in nodes:
X = self[node]
if hasattr(X, 'update') and callable(X.update):
X.update()
if plot:
self.plot(X)
# Call the custom function provided by the user
if callable(self.callback):
z = self.callback()
if z is not None:
z = np.array(z)[...,np.newaxis]
if self.callback_output is None:
self.callback_output = z
else:
self.callback_output = np.concatenate((self.callback_output,z),
axis=-1)
# Compute lower bound
L = self.loglikelihood_lowerbound()
if verbose:
print("Iteration %d: loglike=%e (%.3f seconds)"
% (self.iter+1, L, time.clock()-t))
# Check the progress of the iteration
if self.iter > 0:
# Check for errors
if self.L[self.iter-1] - L > 1e-6:
L_diff = (self.L[self.iter-1] - L)
warnings.warn("Lower bound decreased %e! Bug somewhere or "
"numerical inaccuracy?" % L_diff)
# Check for convergence
if tol is None:
tol = self.tol
div = 0.5 * (abs(L) + abs(self.L[self.iter-1]))
if (L - self.L[self.iter-1]) / div < tol or L - self.L[self.iter-1] <= 0:
converged = True
if verbose:
print("Converged at iteration %d." % (self.iter+1))
self.L[self.iter] = L
self.iter += 1
# Auto-save, if requested
if (self.autosave_iterations > 0
and np.mod(self.iter, self.autosave_iterations) == 0):
self.save(self.autosave_filename)
if verbose:
print('Auto-saved to %s' % self.autosave_filename)
if converged:
return
def compute_lowerbound(self):
L = 0
for node in self.model:
L += node.lower_bound_contribution()
return L
def compute_lowerbound_terms(self, *nodes):
if len(nodes) == 0:
nodes = self.model
return {node: node.lower_bound_contribution()
for node in nodes}
def loglikelihood_lowerbound(self):
L = 0
for node in self.model:
lp = node.lower_bound_contribution()
L += lp
self.l[node][self.iter] = lp
return L
def plot_iteration_by_nodes(self, axes=None):
"""
Plot the cost function per node during the iteration.
Handy tool for debugging.
"""
if axes is None:
axes = plt.gca()
D = len(self.l)
N = self.iter
L = np.empty((N,D))
legends = []
for (d, node) in enumerate(self.l):
L[:,d] = self.l[node]
legends += [node.name]
axes.plot(np.arange(N)+1, L)
axes.legend(legends, loc='lower right')
axes.set_title('Lower bound contributions by nodes')
axes.set_xlabel('Iteration')
def get_iteration_by_nodes(self):
return self.l
def save(self, filename=None):
if self.iter == 0:
# Check HDF5 version.
if h5py.version.hdf5_version_tuple < (1,8,7):
warnings.warn("WARNING! Your HDF5 version is %s. HDF5 versions "
"<1.8.7 are not able to save empty arrays, thus "
"you may experience problems if you for instance "
"try to save before running any iteration steps."
% str(h5py.version.hdf5_version_tuple))
# By default, use the same file as for auto-saving
if not filename:
if self.filename:
filename = self.filename
else:
raise Exception("Filename must be given.")
# Open HDF5 file
h5f = h5py.File(filename, 'w')
try:
# Write each node
nodegroup = h5f.create_group('nodes')
for node in self.model:
if node.name == '':
raise Exception("In order to save nodes, they must have "
"(unique) names.")
if hasattr(node, 'save') and callable(node.save):
node.save(nodegroup.create_group(node.name))
# Write iteration statistics
misc.write_to_hdf5(h5f, self.L, 'L')
misc.write_to_hdf5(h5f, self.iter, 'iter')
if self.callback_output is not None:
misc.write_to_hdf5(h5f,
self.callback_output,
'callback_output')
boundgroup = h5f.create_group('boundterms')
for node in self.model:
misc.write_to_hdf5(boundgroup, self.l[node], node.name)
finally:
# Close file
h5f.close()
def load(self, *nodes, filename=None):
# By default, use the same file as for auto-saving
if not filename:
if self.filename:
filename = self.filename
else:
raise Exception("Filename must be given.")
# Open HDF5 file
h5f = h5py.File(filename, 'r')
try:
# Get nodes to load
if len(nodes) == 0:
nodes = self.model
else:
nodes = [self[node] for node in nodes if node is not None]
# Read each node
for node_id in nodes:
node = self[node_id]
if node.name == '':
h5f.close()
raise Exception("In order to load nodes, they must have "
"(unique) names.")
if hasattr(node, 'load') and callable(node.load):
try:
node.load(h5f['nodes'][node.name])
except KeyError:
h5f.close()
raise Exception("File does not contain variable %s"
% node.name)
# Read iteration statistics
self.L = h5f['L'][...]
self.iter = h5f['iter'][...]
for node in self.model:
self.l[node] = h5f['boundterms'][node.name][...]
try:
self.callback_output = h5f['callback_output'][...]
except KeyError:
pass
finally:
# Close file
h5f.close()
def __getitem__(self, name):
if name in self.model:
return name
else:
# Dictionary for mapping node names to nodes
dictionary = {node.name: node for node in self.model}
return dictionary[name]
def plot(self, *nodes):
"""
Plot the distribution of the given nodes (or all nodes)
"""
if len(nodes) == 0:
nodes = self.model
for node in nodes:
node = self[node]
if node.has_plotter():
try:
fignum = self._figures[node]
except KeyError:
fig = plt.figure()
self._figures[node] = fig.number
else:
fig = plt.figure(num=fignum)
fig.clf()
node.plot(fig=fig)
fig.canvas.draw()
bayespy-0.2.3/bayespy/inference/__init__.py 0000664 5104112 5104112 00000003055 12374370350 022444 0 ustar jluttine jluttine 0000000 0000000 ######################################################################
# Copyright (C) 2013 Jaakko Luttinen
#
# This file is licensed under Version 3.0 of the GNU General Public
# License. See LICENSE for a text of the license.
######################################################################
######################################################################
# This file is part of BayesPy.
#
# BayesPy is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# BayesPy is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with BayesPy. If not, see .
######################################################################
"""
Package for Bayesian inference engines
Inference engines
-----------------
.. autosummary::
:toctree: generated/
VB
Parameter expansions
--------------------
.. autosummary::
:toctree: generated/
vmp.transformations.RotationOptimizer
vmp.transformations.RotateGaussian
vmp.transformations.RotateGaussianARD
vmp.transformations.RotateGaussianMarkovChain
vmp.transformations.RotateSwitchingMarkovChain
vmp.transformations.RotateVaryingMarkovChain
vmp.transformations.RotateMultiple
"""
from .vmp.vmp import VB
bayespy-0.2.3/bayespy/nodes/ 0000775 5104112 5104112 00000000000 12437607670 017512 5 ustar jluttine jluttine 0000000 0000000 bayespy-0.2.3/bayespy/nodes/__init__.py 0000664 5104112 5104112 00000004521 12374370351 021616 0 ustar jluttine jluttine 0000000 0000000 ######################################################################
# Copyright (C) 2013 Jaakko Luttinen
#
# This file is licensed under Version 3.0 of the GNU General Public
# License. See LICENSE for a text of the license.
######################################################################
######################################################################
# This file is part of BayesPy.
#
# BayesPy is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# BayesPy is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with BayesPy. If not, see .
######################################################################
"""
Package for nodes used to construct the model.
Stochastic nodes
================
.. currentmodule:: bayespy.nodes
Nodes for Gaussian variables:
.. autosummary::
:toctree: generated/
Gaussian
GaussianARD
Nodes for precision and scale variables:
.. autosummary::
:toctree: generated/
Gamma
Wishart
Exponential
Nodes for modelling Gaussian and precision variables jointly (useful as prior
for Gaussian nodes):
.. autosummary::
:toctree: generated/
GaussianGammaISO
GaussianGammaARD
GaussianWishart
Nodes for discrete count variables:
.. autosummary::
:toctree: generated/
Bernoulli
Binomial
Categorical
Multinomial
Poisson
Nodes for probabilities:
.. autosummary::
:toctree: generated/
Beta
Dirichlet
Nodes for dynamic variables:
.. autosummary::
:toctree: generated/
CategoricalMarkovChain
GaussianMarkovChain
SwitchingGaussianMarkovChain
VaryingGaussianMarkovChain
Other stochastic nodes:
.. autosummary::
:toctree: generated/
Mixture
Deterministic nodes
===================
.. autosummary::
:toctree: generated/
Dot
SumMultiply
Gate
"""
# Currently, model construction and the inference network are not separated so
# the model is constructed using variational message passing nodes.
from bayespy.inference.vmp.nodes import *
bayespy-0.2.3/bayespy/utils/ 0000775 5104112 5104112 00000000000 12437607670 017542 5 ustar jluttine jluttine 0000000 0000000 bayespy-0.2.3/bayespy/utils/covfunc/ 0000775 5104112 5104112 00000000000 12437607670 021205 5 ustar jluttine jluttine 0000000 0000000 bayespy-0.2.3/bayespy/utils/covfunc/sparse_distance/ 0000775 5104112 5104112 00000000000 12437607670 024354 5 ustar jluttine jluttine 0000000 0000000 bayespy-0.2.3/bayespy/utils/covfunc/sparse_distance/sparse_distance.c 0000664 5104112 5104112 00000015542 12374370351 027667 0 ustar jluttine jluttine 0000000 0000000
/* ========================================================================== */
/* === sparse_distance.c ==================================================== */
/* ========================================================================== */
/* -----------------------------------------------------------------------------
* Copyright (C) 2012 Jaakko Luttinen
*
* cholmod_spdist.c is licensed under Version 3.0 of the GNU General
* Public License. See LICENSE for a text of the license.
* -------------------------------------------------------------------------- */
/* -----------------------------------------------------------------------------
* This file is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License version 3 as
* published by the Free Software Foundation.
*
* This file is distributed in the hope that it will be useful, but
* WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
* General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this file. If not, see .
* -------------------------------------------------------------------------- */
/* -----------------------------------------------------------------------------
*
* Computes a sparse squared distance matrix. Squared distances that
* are larger than a given threshold are ignored in the sparsity
* structure. Zero distances are marked with explicit zeros. If X2 is
* NULL, then a symmetric distance matrix (in lower triangular form)
* is computed for X1.
*
* -------------------------------------------------------------------------- */
#include
#include
#include "sparse_distance.h"
#define EXPAND_TRIPLET_IF_NEEDED(Dx,Dij,nzmax,nz) \
{ \
if (nz == nzmax) \
{ \
nzmax *= 2 ; \
Dij = realloc(Dij, 2*nzmax*sizeof(int)) ; \
Dx = realloc(Dx, nzmax*sizeof(double)) ; \
} \
}
#define FORM_HAS_DIAGONAL(f) (((f) == FULL) || ((f) == LOWER) || ((f) == UPPER))
#define FORM_HAS_LOWER(f) (((f) == LOWER) || ((f) == STRICTLY_LOWER) || ((f) == FULL))
#define FORM_HAS_UPPER(f) (((f) == UPPER) || ((f) == STRICTLY_UPPER) || ((f) == FULL))
/* Compute squared Euclidean distance of two vectors */
inline double sqeuclidean_distance
(
const double *x1,
const double *x2,
int n
)
{
int i ;
double d, d2 = 0 ;
for (i = 0; i < n; i++)
{
d = x1[i]-x2[i] ;
d2 += d*d ;
}
return d2 ;
}
/* ========================================================================== */
/* === sppdist_sqeuclidean ================================================== */
/* ========================================================================== */
void sppdist_sqeuclidean
(
/* ---- input ---- */
const double *X, /* input matrix, m-by-n in row-major form */
int m, /* m vectors */
int n, /* n-dimensional vectors */
double threshold, /* distance threshold */
int form, /* full / lower / strictly lower */
/* ---- output ---- */
double **out_Dx, /* distances */
int **out_Dij, /* row and column indices */
int *out_nzmax /* number of non-zeros, i.e., length of Di and Dx */
)
{
int i, j ;
int nz, nzmax;
double d ;
const double *x1, *x2 ;
int *Dij ;
double *Dx ;
// Initial size for the number of non-zero elements
nzmax = 2*m ;
// Allocate initial memory
Dij = realloc(*out_Dij, 2*nzmax*sizeof(int)) ;
Dx = realloc(*out_Dx, nzmax*sizeof(double)) ;
// Number of non-zeros thus far
nz = 0 ;
for (j = 0; j < m; j++)
{
if (FORM_HAS_DIAGONAL(form))
{
Dij[2*nz] = j ;
Dij[2*nz+1] = j ;
Dx[nz] = 0.0 ;
nz++ ;
EXPAND_TRIPLET_IF_NEEDED(Dx, Dij, nzmax, nz) ;
}
for (i = j+1; i < m; i++)
{
// Compute distance between X1[:,i] and X2[:,j]
x1 = X + i*n ;
x2 = X + j*n ;
d = sqeuclidean_distance(x1, x2, n) ;
// Store distances that are small enough
if (d <= threshold)
{
// Store strictly lower triangular part
if (FORM_HAS_LOWER(form))
{
Dij[2*nz] = i ;
Dij[2*nz+1] = j ;
Dx[nz] = d ;
nz++ ;
EXPAND_TRIPLET_IF_NEEDED(Dx, Dij, nzmax, nz) ;
}
if (FORM_HAS_UPPER(form))
{
// Store strictly upper triangular part
Dij[2*nz] = j ;
Dij[2*nz+1] = i ;
Dx[nz] = d ;
nz++ ;
EXPAND_TRIPLET_IF_NEEDED(Dx, Dij, nzmax, nz) ;
}
}
}
}
// Store the matrix compactly
nzmax = nz ;
Dij = realloc(Dij, 2*nzmax*sizeof(int)) ;
Dx = realloc(Dx, nzmax*sizeof(double)) ;
// Set output variables
*out_Dij = Dij ;
*out_Dx = Dx ;
*out_nzmax = nzmax ;
return ;
}
void spcdist_sqeuclidean
(
/* ---- input ---- */
const double *X1, /* input matrix, m-by-n in row-major form */
int m1, /* m1 vectors */
const double *X2, /* input matrix, m-by-n in row-major form */
int m2, /* m2 vectors */
int n, /* n-dimensional vectors */
double threshold, /* distance threshold */
/* ---- output ---- */
double **out_Dx, /* distances */
int **out_Dij, /* row and column indices */
int *out_nzmax /* number of non-zeros */
)
{
int i, j ;
int nz, nzmax;
double d ;
const double *x1, *x2 ;
int *Dij ;
double *Dx ;
// Initial size for the number of non-zero elements
nzmax = 2 * (m1 > m2 ? m1 : m2) ;
// Allocate initial memory
Dij = realloc(*out_Dij, 2*nzmax*sizeof(int)) ;
Dx = realloc(*out_Dx, nzmax*sizeof(double)) ;
// Number of non-zeros thus far
nz = 0 ;
for (i = 0; i < m1; i++)
{
for (j = 0; j < m2; j++)
{
// Compute distance between X1[:,i] and X2[:,j]
x1 = X1 + i*n ;
x2 = X2 + j*n ;
d = sqeuclidean_distance(x1, x2, n) ;
// Store distances that are small enough
if (d <= threshold)
{
Dij[2*nz] = i ;
Dij[2*nz+1] = j ;
Dx[nz] = d ;
nz++ ;
EXPAND_TRIPLET_IF_NEEDED(Dx, Dij, nzmax, nz) ;
}
}
}
// Store the matrix compactly
nzmax = nz ;
Dij = realloc(Dij, 2*nzmax*sizeof(int)) ;
Dx = realloc(Dx, nzmax*sizeof(double)) ;
// Set output variables
*out_Dij = Dij ;
*out_Dx = Dx ;
*out_nzmax = nzmax ;
return ;
}
bayespy-0.2.3/bayespy/utils/covfunc/sparse_distance/sparse_distance.h 0000664 5104112 5104112 00000004555 12374370351 027676 0 ustar jluttine jluttine 0000000 0000000 /* ========================================================================== */
/* === sparse_distance.h ==================================================== */
/* ========================================================================== */
/* -----------------------------------------------------------------------------
* Copyright (C) 2012 Jaakko Luttinen
*
* cholmod_spdist.h is licensed under Version 3.0 of the GNU General
* Public License. See LICENSE for a text of the license.
* -------------------------------------------------------------------------- */
/* -----------------------------------------------------------------------------
* This file is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License version 3 as
* published by the Free Software Foundation.
*
* This file is distributed in the hope that it will be useful, but
* WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
* General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this file. If not, see .
* -------------------------------------------------------------------------- */
#ifndef SPARSE_DISTANCE_H
#define SPARSE_DISTANCE_H
#define FULL 0
#define LOWER 1
#define UPPER 2
#define STRICTLY_LOWER 3
#define STRICTLY_UPPER 4
void sppdist_sqeuclidean
(
/* ---- input ---- */
const double *X, /* input matrix, m-by-n in row-major form */
int m, /* m vectors */
int n, /* n-dimensional vectors */
double threshold, /* distance threshold */
int form, /* full / lower / strictly lower */
/* ---- output ---- */
double **out_Dx, /* distances */
int **out_Dij, /* row and column indices */
int *out_nzmax /* number of non-zeros */
) ;
void spcdist_sqeuclidean
(
/* ---- input ---- */
const double *X1, /* input matrix, m-by-n in row-major form */
int m1, /* m1 vectors */
const double *X2, /* input matrix, m-by-n in row-major form */
int m2, /* m2 vectors */
int n, /* n-dimensional vectors */
double threshold, /* distance threshold */
/* ---- output ---- */
double **out_Dx, /* distances */
int **out_Dij, /* row and column indices */
int *out_nzmax /* number of non-zeros */
) ;
#endif
bayespy-0.2.3/bayespy/utils/covfunc/tests/ 0000775 5104112 5104112 00000000000 12437607670 022347 5 ustar jluttine jluttine 0000000 0000000 bayespy-0.2.3/bayespy/utils/covfunc/tests/__init__.py 0000664 5104112 5104112 00000000000 12121570321 024424 0 ustar jluttine jluttine 0000000 0000000 bayespy-0.2.3/bayespy/utils/covfunc/tests/test_distance.py 0000664 5104112 5104112 00000005575 12374370351 025557 0 ustar jluttine jluttine 0000000 0000000 ######################################################################
# Copyright (C) 2012-2013 Jaakko Luttinen
#
# This file is licensed under Version 3.0 of the GNU General Public
# License. See LICENSE for a text of the license.
######################################################################
######################################################################
# This file is part of BayesPy.
#
# BayesPy is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# BayesPy is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with BayesPy. If not, see .
######################################################################
"""
Unit tests for distance module.
"""
import unittest
import numpy as np
import scipy.sparse as sp
import scipy.spatial.distance as dist
#from .. import distance as spdist
#import scikits.sparse.distance as spdist
import numpy.random as rand
## class TestDistance(unittest.TestCase):
## def test_pdist(self):
## N = 50
## D = 3
## x = rand.uniform(size=(N,D))
## threshold = 0.1
## i = np.arange(N)[:,np.newaxis]
## j = np.arange(N)[np.newaxis,:]
## # Compute full&dense distance matrix
## Dd = dist.squareform(dist.pdist(x, metric="sqeuclidean"))
## for form in ["lower", "strictly_lower", "upper", "strictly_upper", "full"]:
## Ds = spdist.sparse_pdist(x, threshold, form=form)
## self.assertTrue(sp.issparse(Ds))
## Ds = Ds.tocsr()
## Ds.sort_indices()
## if form == "lower":
## D0 = Dd[i>=j]
## elif form == "strictly_lower":
## D0 = Dd[i>j]
## elif form == "upper":
## D0 = Dd[i<=j]
## elif form == "strictly_upper":
## D0 = Dd[i.
######################################################################
import itertools
import numpy as np
#import scipy as sp
import scipy.sparse as sp # prefer CSC format
#import scipy.spatial.distance as dist
from bayespy.utils import misc
#from bayespy.utils.covfunc import distance
# Covariance matrices can be either arrays or matrices so be careful
# with products and powers! Use explicit multiply or dot instead of
# *-operator.
def gp_cov_se(D2, overwrite=False):
if overwrite:
K = D2
K *= -0.5
np.exp(K, out=K)
else:
K = np.exp(-0.5*D2)
return K
def gp_cov_pp2_new(r, d, derivative=False):
# Dimension dependent parameter
q = 2
j = np.floor(d/2) + q + 1
# Polynomial coefficients
a2 = j**2 + 4*j + 3
a1 = 3*j + 6
a0 = 3
# Two parts of the covariance function
k1 = (1-r) ** (j+2)
k2 = (a2*r**2 + a1*r + 3)
# TODO: Check that derivative is 0, 1 or 2!
if derivative == 0:
# Return covariance
return k1 * k2 / 3
dk1 = - (j+2) * (1-r)**(j+1)
dk2 = 2*a2*r + a1
if derivative == 1:
# Return first derivative of the covariance
return (k1 * dk2 + dk1 * k2) / 3
ddk1 = (j+2) * (j+1) * (1-r)**j
ddk2 = 2*a2
if derivative == 2:
# Return second derivative of the covariance
return (ddk1*k2 + 2*dk1*dk2 + k1*ddk2) / 3
def gp_cov_pp2(r, d, gradient=False):
# Dimension dependent parameter
j = np.floor(d/2) + 2 + 1
# Polynomial coefficients
a2 = j**2 + 4*j + 3
a1 = 3*j + 6
a0 = 3
# Two parts of the covariance function
k1 = (1-r) ** (j+2)
k2 = (a2*r**2 + a1*r + 3)
# The covariance function
k = k1 * k2 / 3
if gradient:
# The gradient w.r.t. r
dk = k * (j+2) / (r-1) + k1 * (2*a2*r + a1) / 3
return (k, dk)
else:
return k
def gp_cov_delta(N):
# TODO: Use sparse matrices here!
if N > 0:
#print('in gpcovdelta', N, sp.identity(N).shape)
return sp.identity(N)
else:
# Sparse matrices do not allow zero-length dimensions
return np.identity(N)
#return np.identity(N)
#return np.asmatrix(np.identity(N))
def squared_distance(x1, x2):
## # Reshape arrays to 2-D arrays
## sh1 = np.shape(x1)[:-1]
## sh2 = np.shape(x2)[:-1]
## d = np.shape(x1)[-1]
## x1 = np.reshape(x1, (-1,d))
## x2 = np.reshape(x2, (-1,d))
(m1,n1) = x1.shape
(m2,n2) = x2.shape
if m1 == 0 or m2 == 0:
D2 = np.empty((m1,m2))
else:
D2 = distance.cdist(x1, x2, metric='sqeuclidean')
#D2 = np.asmatrix(D2)
# Reshape the result
#D2 = np.reshape(D2, sh1 + sh2)
return D2
# General rule for the parameters for covariance functions:
#
# (value, [ [dvalue1, ...], [dvalue2, ...], [dvalue3, ...], ...])
#
# For instance,
#
# k = covfunc_se((1.0, []), (15, [ [1,update_grad] ]))
# K = k((x1, [ [dx1,update_grad] ]), (x2, []))
#
# Plain values are converted as:
# value -> (value, [])
def gp_standardize_input(x):
if np.size(x) == 0:
x = np.reshape(x, (0,0))
elif np.ndim(x) == 0:
x = np.reshape(x, (1,1))
elif np.ndim(x) == 1:
x = np.reshape(x, (-1,1))
elif np.ndim(x) == 2:
x = np.atleast_2d(x)
else:
raise Exception("Standard GP inputs must be 2-dimensional")
return x
def gp_preprocess_inputs(x1,x2=None):
#args = list(args)
#if len(args) < 1 or len(args) > 2:
#raise Exception("Number of inputs must be one or two")
if x2 is None:
x1 = gp_standardize_input(x1)
return x1
else:
if x1 is x2:
x1 = gp_standardize_input(x1)
x2 = x1
else:
x1 = gp_standardize_input(x1)
x2 = gp_standardize_input(x2)
return (x1, x2)
#return args
## def gp_preprocess_inputs(x1,x2=None):
## #args = list(args)
## #if len(args) < 1 or len(args) > 2:
## #raise Exception("Number of inputs must be one or two")
## if x2 is not None: len(args) == 2:
## if args[0] is args[1]:
## args[0] = gp_standardize_input(args[0])
## args[1] = args[0]
## else:
## args[1] = gp_standardize_input(args[1])
## args[0] = gp_standardize_input(args[0])
## else:
## args[0] = gp_standardize_input(args[0])
## return args
# TODO:
# General syntax for these covariance functions:
# covfunc(hyper1,
# hyper2,
# ...
# hyperN,
# x1,
# x2=None,
# gradient=list_of_booleans_for_each_hyperparameter)
def covfunc_zeros(x1, x2=None, gradient=False):
inputs = gp_preprocess_inputs(*inputs)
# Compute distance and covariance matrix
if x2 is None:
x1 = gp_preprocess_inputs(x1)
# Only variance vector asked
N = np.shape(x1)[0]
# TODO: Use sparse matrices!
K = np.zeros(N)
#K = np.asmatrix(np.zeros((N,1)))
else:
(x1,x2) = gp_preprocess_inputs(x1,x2)
# Full covariance matrix asked
#x1 = inputs[0]
#x2 = inputs[1]
# Number of inputs x1
N1 = np.shape(x1)[0]
N2 = np.shape(x2)[0]
# TODO: Use sparse matrices!
K = np.zeros((N1,N2))
#K = np.asmatrix(np.zeros((N1,N2)))
if gradient is not False:
return (K, [])
else:
return K
def covfunc_delta(amplitude, x1, x2=None, gradient=False):
# Make sure that amplitude is a scalar, not an array object
amplitude = misc.array_to_scalar(amplitude)
## if gradient:
## gradient_amplitude = gradient[0]
## else:
## gradient_amplitude = []
## inputs = gp_preprocess_inputs(*inputs)
# Compute distance and covariance matrix
if x2 is None:
x1 = gp_preprocess_inputs(x1)
# Only variance vector asked
#x = inputs[0]
N = np.shape(x1)[0]
K = np.ones(N) * amplitude**2
else:
(x1,x2) = gp_preprocess_inputs(x1,x2)
# Full covariance matrix asked
#x1 = inputs[0]
#x2 = inputs[1]
# Number of inputs x1
N1 = np.shape(x1)[0]
# x1 == x2?
if x1 is x2:
delta = True
# Delta covariance
#
# FIXME: Broadcasting doesn't work with sparse matrices,
# so must use scalar multiplication
K = gp_cov_delta(N1) * amplitude**2
#K = gp_cov_delta(N1).multiply(amplitude**2)
else:
delta = False
# Number of inputs x2
N2 = np.shape(x2)[0]
# Zero covariance
if N1 > 0 and N2 > 0:
K = sp.csc_matrix((N1,N2))
else:
K = np.zeros((N1,N2))
# Gradient w.r.t. amplitude
if gradient:
# FIXME: Broadcasting doesn't work with sparse matrices,
# so must use scalar multiplication
gradient_amplitude = K*(2/amplitude)
print("noise grad", gradient_amplitude)
return (K, (gradient_amplitude,))
else:
return K
def covfunc_pp2(amplitude, lengthscale, x1, x2=None, gradient=False):
# Make sure that hyperparameters are scalars, not an array objects
amplitude = misc.array_to_scalar(amplitude)
lengthscale = misc.array_to_scalar(lengthscale)
#amplitude = theta[0]
#lengthscale = theta[1]
## if gradient:
## gradient_amplitude = gradient[0]
## gradient_lengthscale = gradient[1]
## else:
## gradient_amplitude = []
## gradient_lengthscale = []
## inputs = gp_preprocess_inputs(*inputs)
# Compute covariance matrix
if x2 is None:
x1 = gp_preprocess_inputs(x1)
# Compute variance vector
K = np.ones(np.shape(x)[:-1])
K *= amplitude**2
# Compute gradient w.r.t. lengthscale
if gradient:
gradient_lengthscale = np.zeros(np.shape(x1)[:-1])
else:
(x1,x2) = gp_preprocess_inputs(x1,x2)
# Compute (sparse) distance matrix
if x1 is x2:
x1 = x1 / (lengthscale)
x2 = x1
D2 = distance.sparse_pdist(x1, 1.0, form="full", format="csc")
else:
x1 = x1 / (lengthscale)
x2 = x2 / (lengthscale)
D2 = distance.sparse_cdist(x1, x2, 1.0, format="csc")
r = np.sqrt(D2.data)
N1 = np.shape(x1)[0]
N2 = np.shape(x2)[0]
# Compute the covariances
if gradient:
(k, dk) = gp_cov_pp2(r, np.shape(x1)[-1], gradient=True)
else:
k = gp_cov_pp2(r, np.shape(x1)[-1])
k *= amplitude**2
# Compute gradient w.r.t. lengthscale
if gradient:
if N1 >= 1 and N2 >= 1:
dk *= r * (-amplitude**2 / lengthscale)
gradient_lengthscale = sp.csc_matrix((dk, D2.indices, D2.indptr),
shape=(N1,N2))
else:
gradient_lengthscale = np.empty((N1,N2))
# Form sparse covariance matrix
if N1 >= 1 and N2 >= 1:
## K = sp.csc_matrix((k, ij), shape=(N1,N2))
K = sp.csc_matrix((k, D2.indices, D2.indptr), shape=(N1,N2))
else:
K = np.empty((N1, N2))
#print(K.__class__)
# Gradient w.r.t. amplitude
if gradient:
gradient_amplitude = K * (2 / amplitude)
# Return values
if gradient:
print("pp2 grad", gradient_lengthscale)
return (K, (gradient_amplitude, gradient_lengthscale))
else:
return K
def covfunc_se(amplitude, lengthscale, x1, x2=None, gradient=False):
# Make sure that hyperparameters are scalars, not an array objects
amplitude = misc.array_to_scalar(amplitude)
lengthscale = misc.array_to_scalar(lengthscale)
# Compute covariance matrix
if x2 is None:
x1 = gp_preprocess_inputs(x1)
#x = inputs[0]
# Compute variance vector
N = np.shape(x1)[0]
K = np.ones(N)
np.multiply(K, amplitude**2, out=K)
# Compute gradient w.r.t. lengthscale
if gradient:
# TODO: Use sparse matrices?
gradient_lengthscale = np.zeros(N)
else:
(x1,x2) = gp_preprocess_inputs(x1,x2)
x1 = x1 / (lengthscale)
x2 = x2 / (lengthscale)
# Compute distance matrix
K = squared_distance(x1, x2)
# Compute gradient partly
if gradient:
gradient_lengthscale = np.divide(K, lengthscale)
# Compute covariance matrix
gp_cov_se(K, overwrite=True)
np.multiply(K, amplitude**2, out=K)
# Compute gradient w.r.t. lengthscale
if gradient:
gradient_lengthscale *= K
# Gradient w.r.t. amplitude
if gradient:
gradient_amplitude = K * (2 / amplitude)
# Return values
if gradient:
print("se grad", gradient_amplitude, gradient_lengthscale)
return (K, (gradient_amplitude, gradient_lengthscale))
else:
return K
bayespy-0.2.3/bayespy/utils/covfunc/distance.pyx 0000664 5104112 5104112 00000021325 12374370351 023535 0 ustar jluttine jluttine 0000000 0000000 # Copyright (C) 2009 Nathaniel Smith
# Copyright (C) 2012 Jaakko Luttinen
# Released under the terms of the GNU GPL v3
import warnings
cimport stdlib
cimport python as py
import numpy as np
cimport numpy as np
from scipy import sparse
# Put all relevant distance functions under this namespace
from scipy.spatial.distance import pdist, cdist, squareform
np.import_array()
cdef extern from "numpy/arrayobject.h":
# Cython 0.12 complains about PyTypeObject being an "incomplete type" on
# this line:
#py.PyTypeObject PyArray_Type
# So use a hack:
struct MyHackReallyPyTypeObject:
pass
MyHackReallyPyTypeObject PyArray_Type
object PyArray_NewFromDescr(MyHackReallyPyTypeObject * subtype,
np.dtype descr,
int nd,
np.npy_intp * dims,
np.npy_intp * strides,
void * data,
int flags,
object obj)
# This is ridiculous: the description of PyArrayObject in numpy.pxd does
# not mention the 'base' member, so we need a separate wrapper just to
# expose it:
ctypedef struct ndarray_with_base "PyArrayObject":
void * base
# In Cython 0.14.1, np.NPY_F_CONTIGUOUS is broken, because numpy.pxd
# claims that it is of a non-existent type called 'enum requirements', and
# new versions of Cython attempt to stash it in a temporary variable of
# this type, which then annoys the C compiler.
enum:
NPY_F_CONTIGUOUS
cdef inline np.ndarray set_base(np.ndarray arr, object base):
cdef ndarray_with_base * hack = arr
py.Py_INCREF(base)
hack.base = base
return arr
cdef extern from "sparse_distance.h":
cdef enum:
FULL, LOWER, UPPER, STRICTLY_LOWER, STRICTLY_UPPER
void sppdist_sqeuclidean(double *X,
int m,
int n,
double threshold,
int form,
double **out_Dx,
int **out_Dij,
int *out_nzmax)
void spcdist_sqeuclidean(double *X1,
int m1,
double *X2,
int m2,
int n,
double threshold,
double **out_Dx,
int **out_Dij,
int *out_nzmax)
cdef object _integer_py_dtype = np.dtype(np.int32)
assert sizeof(int) == _integer_py_dtype.itemsize == 4
cdef object _real_py_dtype = np.dtype(np.float64)
assert sizeof(double) == _real_py_dtype.itemsize == 8
cdef object _complex_py_dtype = np.dtype(np.complex128)
assert _complex_py_dtype.itemsize == 2 * sizeof(double) == 16
cdef class _CleanupCOO(object):
cdef int * _ij
cdef double * _x
def __dealloc__(self):
stdlib.free(self._ij)
stdlib.free(self._x)
cdef class _CleanupCSC(object):
cdef int * _p
cdef int * _i
cdef double * _x
def __dealloc__(self):
stdlib.free(self._p)
stdlib.free(self._i)
stdlib.free(self._x)
cdef _py_sparse_coo(double *x, int *ij, int m, int n, int nzmax):
cdef _CleanupCOO cleaner = _CleanupCOO()
cleaner._ij = ij
cleaner._x = x
# PyObject *PyArray_SimpleNewFromData(int nd, npy_intp* dims, int typenum, void* data)
shape = (m, n)
cdef np.npy_intp nz = nzmax
cdef np.npy_intp nz2[2]
nz2[0] = 2
nz2[1] = nzmax
py.Py_INCREF(_integer_py_dtype)
ijnd = set_base(PyArray_NewFromDescr(&PyArray_Type,
_integer_py_dtype, 2,
nz2,
NULL,
ij,
NPY_F_CONTIGUOUS, None),
cleaner)
py.Py_INCREF(_real_py_dtype)
data = set_base(PyArray_NewFromDescr(&PyArray_Type,
_real_py_dtype, 1,
&nz,
NULL,
x,
NPY_F_CONTIGUOUS, None),
cleaner)
return sparse.coo_matrix((data, ijnd), shape=shape)
cdef _py_sparse_csc(int *p, int *i, double *x, int m, int n, int nzmax):
cdef _CleanupCSC cleaner = _CleanupCSC()
cleaner._p = p
cleaner._i = i
cleaner._x = x
# PyObject *PyArray_SimpleNewFromData(int nd, npy_intp* dims, int typenum, void* data)
shape = (m, n)
py.Py_INCREF(_integer_py_dtype)
cdef np.npy_intp ncol_plus_1 = n + 1
indptr = set_base(PyArray_NewFromDescr(&PyArray_Type,
_integer_py_dtype, 1,
&ncol_plus_1,
NULL,
p,
NPY_F_CONTIGUOUS, None),
cleaner)
py.Py_INCREF(_integer_py_dtype)
cdef np.npy_intp nz = nzmax
indices = set_base(PyArray_NewFromDescr(&PyArray_Type,
_integer_py_dtype, 1,
&nz,
NULL,
i,
NPY_F_CONTIGUOUS, None),
cleaner)
py.Py_INCREF(_real_py_dtype)
data = set_base(PyArray_NewFromDescr(&PyArray_Type,
_real_py_dtype, 1,
&nz,
NULL,
x,
NPY_F_CONTIGUOUS, None),
cleaner)
return sparse.csc_matrix((data, indices, indptr), shape=shape)
def sparse_pdist(X, threshold, form="strictly_lower", format="csc"):
# Outputs
cdef double *Dx = NULL
cdef int *Dij = NULL
cdef int nzmax
cdef int uplo
if np.ndim(X) == 0:
X = np.atleast_2d(X)
elif np.ndim(X) == 1:
X = X[:,np.newaxis]
(m,n) = X.shape
# Check empty cases
if m == 0:
return np.empty((m,m))
#if n == 0:
# return sp.csc_matrix((m,m)).asformat(format)
cdef np.ndarray[np.double_t, ndim=2, mode="c"] X_c
X_c = np.ascontiguousarray(X, dtype=np.double)
if form.lower() == "strictly_lower":
uplo = STRICTLY_LOWER
elif form.lower() == "lower":
uplo = LOWER
elif form.lower() == "strictly_upper":
uplo = STRICTLY_UPPER
elif form.lower() == "upper":
uplo = UPPER
elif form.lower() == "full":
uplo = FULL
else:
raise Exception, "Unknown form requested"
sppdist_sqeuclidean( X_c.data,
m,
n,
threshold,
uplo,
&Dx,
&Dij,
&nzmax)
return _py_sparse_coo(Dx, Dij, m, m, nzmax).asformat(format)
def sparse_cdist(X1, X2, threshold, format="csc"):
# Outputs
cdef double *Dx = NULL
cdef int *Dij = NULL
cdef int nzmax
if np.ndim(X1) == 0:
X1 = np.atleast_2d(X1)
elif np.ndim(X1) == 1:
X1 = X1[:,np.newaxis]
elif np.ndim(X1) > 2:
raise Exception, "Input matrices must be 0-2 -dimensional"
if np.ndim(X2) == 0:
X2 = np.atleast_2d(X2)
elif np.ndim(X2) == 1:
X2 = X2[:,np.newaxis]
elif np.ndim(X2) > 2:
raise Exception, "Input matrices must be 0-2 -dimensional"
(m1,n1) = X1.shape
(m2,n2) = X2.shape
# Check empty cases
if m1 == 0 or m2 == 0:
return np.empty((m1,m2))
# Check that inputs have the same dimensionality
if n1 != n2:
raise Exception, "Matrices must have the same number of columns"
n = n1
cdef np.ndarray[np.double_t, ndim=2, mode="c"] X1_c
cdef np.ndarray[np.double_t, ndim=2, mode="c"] X2_c
X1_c = np.ascontiguousarray(X1, dtype=np.double)
X2_c = np.ascontiguousarray(X2, dtype=np.double)
spcdist_sqeuclidean( X1_c.data,
m1,
X2_c.data,
m2,
n,
threshold,
&Dx,
&Dij,
&nzmax)
return _py_sparse_coo(Dx, Dij, m1, m2, nzmax).asformat(format)
__all__ = ["pdist", "cdist"]
bayespy-0.2.3/bayespy/utils/tests/ 0000775 5104112 5104112 00000000000 12437607670 020704 5 ustar jluttine jluttine 0000000 0000000 bayespy-0.2.3/bayespy/utils/tests/__init__.py 0000664 5104112 5104112 00000000000 12231156672 022774 0 ustar jluttine jluttine 0000000 0000000 bayespy-0.2.3/bayespy/utils/tests/test_linalg.py 0000664 5104112 5104112 00000015470 12374370351 023563 0 ustar jluttine jluttine 0000000 0000000 ######################################################################
# Copyright (C) 2013 Jaakko Luttinen
#
# This file is licensed under Version 3.0 of the GNU General Public
# License. See LICENSE for a text of the license.
######################################################################
######################################################################
# This file is part of BayesPy.
#
# BayesPy is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# BayesPy is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with BayesPy. If not, see .
######################################################################
"""
Unit tests for bayespy.utils.linalg module.
"""
import numpy as np
from .. import misc
from .. import linalg
class TestDot(misc.TestCase):
def test_dot(self):
"""
Test dot product multiple multi-dimensional arrays.
"""
# If no arrays, return 0
self.assertAllClose(linalg.dot(),
0)
# If only one array, return itself
self.assertAllClose(linalg.dot([[1,2,3],
[4,5,6]]),
[[1,2,3],
[4,5,6]])
# Basic test of two arrays: (2,3) * (3,2)
self.assertAllClose(linalg.dot([[1,2,3],
[4,5,6]],
[[7,8],
[9,1],
[2,3]]),
[[31,19],
[85,55]])
# Basic test of four arrays: (2,3) * (3,2) * (2,1) * (1,2)
self.assertAllClose(linalg.dot([[1,2,3],
[4,5,6]],
[[7,8],
[9,1],
[2,3]],
[[4],
[5]],
[[6,7]]),
[[1314,1533],
[3690,4305]])
# Test broadcasting: (2,2,2) * (2,2,2,2)
self.assertAllClose(linalg.dot([[[1,2],
[3,4]],
[[5,6],
[7,8]]],
[[[[1,2],
[3,4]],
[[5,6],
[7,8]]],
[[[9,1],
[2,3]],
[[4,5],
[6,7]]]]),
[[[[ 7, 10],
[ 15, 22]],
[[ 67, 78],
[ 91, 106]]],
[[[ 13, 7],
[ 35, 15]],
[[ 56, 67],
[ 76, 91]]]])
# Inconsistent shapes: (2,3) * (2,3)
self.assertRaises(ValueError,
linalg.dot,
[[1,2,3],
[4,5,6]],
[[1,2,3],
[4,5,6]])
# Other axes do not broadcast: (2,2,2) * (3,2,2)
self.assertRaises(ValueError,
linalg.dot,
[[[1,2],
[3,4]],
[[5,6],
[7,8]]],
[[[1,2],
[3,4]],
[[5,6],
[7,8]],
[[9,1],
[2,3]]])
# Do not broadcast matrix axes: (2,1) * (3,2)
self.assertRaises(ValueError,
linalg.dot,
[[1],
[2]],
[[1,2,3],
[4,5,6]])
# Do not accept less than 2-D arrays: (2) * (2,2)
self.assertRaises(ValueError,
linalg.dot,
[1,2],
[[1,2,3],
[4,5,6]])
class TestBandedSolve(misc.TestCase):
def test_block_banded_solve(self):
"""
Test the Gaussian elimination algorithm for block-banded matrices.
"""
#
# Create a block-banded matrix
#
# Number of blocks
N = 40
# Random sizes of the blocks
#D = np.random.randint(5, 10, size=N)
# Fixed sizes of the blocks
D = 5*np.ones(N)
# Some helpful variables to create the covariances
W = [np.random.randn(D[i], 2*D[i])
for i in range(N)]
# The diagonal blocks (covariances)
A = [np.dot(W[i], W[i].T) for i in range(N)]
# The superdiagonal blocks (cross-covariances)
B = [np.dot(W[i][:,-1:], W[i+1][:,:1].T) for i in range(N-1)]
C = misc.block_banded(A, B)
# Create the system to be solved: y=C*x
x_true = np.random.randn(np.sum(D))
y = np.dot(C, x_true)
x_true = np.reshape(x_true, (N, -1))
y = np.reshape(y, (N, -1))
#
# Run tests
#
# The correct inverse
invC = np.linalg.inv(C)
# Inverse from the function that is tested
(invA, invB, x, ldet) = linalg.block_banded_solve(np.asarray(A),
np.asarray(B),
np.asarray(y))
# Check that you get the correct number of blocks
self.assertEqual(len(invA), N)
self.assertEqual(len(invB), N-1)
# Check each block
i0 = 0
for i in range(N-1):
i1 = i0 + D[i]
i2 = i1 + D[i+1]
# Check diagonal block
self.assertTrue(np.allclose(invA[i], invC[i0:i1, i0:i1]))
# Check super-diagonal block
self.assertTrue(np.allclose(invB[i], invC[i0:i1, i1:i2]))
i0 = i1
# Check last block
self.assertTrue(np.allclose(invA[-1], invC[i0:, i0:]))
# Check the solution of the system
self.assertTrue(np.allclose(x_true, x))
# Check the log determinant
self.assertAlmostEqual(ldet/np.linalg.slogdet(C)[1], 1)
bayespy-0.2.3/bayespy/utils/tests/test_misc.py 0000664 5104112 5104112 00000036170 12374370351 023250 0 ustar jluttine jluttine 0000000 0000000 ######################################################################
# Copyright (C) 2013 Jaakko Luttinen
#
# This file is licensed under Version 3.0 of the GNU General Public
# License. See LICENSE for a text of the license.
######################################################################
######################################################################
# This file is part of BayesPy.
#
# BayesPy is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# BayesPy is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with BayesPy. If not, see .
######################################################################
"""
Unit tests for bayespy.utils.misc module.
"""
import unittest
import warnings
import numpy as np
from numpy import testing
from .. import misc
class TestCeilDiv(misc.TestCase):
def test_ceildiv(self):
"""
Test the ceil division
"""
self.assertEqual(misc.ceildiv(3, 1),
3)
self.assertEqual(misc.ceildiv(6, 3),
2)
self.assertEqual(misc.ceildiv(7, 3),
3)
self.assertEqual(misc.ceildiv(8, 3),
3)
self.assertEqual(misc.ceildiv(-6, 3),
-2)
self.assertEqual(misc.ceildiv(-7, 3),
-2)
self.assertEqual(misc.ceildiv(-8, 3),
-2)
self.assertEqual(misc.ceildiv(-9, 3),
-3)
self.assertEqual(misc.ceildiv(6, -3),
-2)
self.assertEqual(misc.ceildiv(7, -3),
-2)
self.assertEqual(misc.ceildiv(8, -3),
-2)
self.assertEqual(misc.ceildiv(9, -3),
-3)
class TestAddAxes(misc.TestCase):
def test_add_axes(self):
"""
Test the add_axes method.
"""
f = lambda X, **kwargs: np.shape(misc.add_axes(X, **kwargs))
# By default, add one leading axis
self.assertEqual(f(np.ones((3,))),
(1,3))
# By default, add leading axes
self.assertEqual(f(np.ones((3,)), num=3),
(1,1,1,3))
# By default, add one axis
self.assertEqual(f(np.ones((3,)), axis=1),
(3,1))
# Add axes to the beginning
self.assertEqual(f(np.ones((2,3,4,)), axis=0, num=3),
(1,1,1,2,3,4))
# Add axes to the middle
self.assertEqual(f(np.ones((2,3,4,)), axis=1, num=3),
(2,1,1,1,3,4))
# Test negative axis index
self.assertEqual(f(np.ones((2,3,4,)), axis=-4, num=3),
(1,1,1,2,3,4))
self.assertEqual(f(np.ones((2,3,4,)), axis=-1, num=1),
(2,3,4,1))
self.assertEqual(f(np.ones((2,3,4,)), axis=-2, num=3),
(2,3,1,1,1,4))
# Add axes to the end
self.assertEqual(f(np.ones((2,3,4,)), axis=3, num=3),
(2,3,4,1,1,1))
class TestBroadcasting(unittest.TestCase):
def test_is_shape_subset(self):
f = misc.is_shape_subset
self.assertTrue(f( (), () ))
self.assertTrue(f( (), (3,) ))
self.assertTrue(f( (1,), (1,) ))
self.assertTrue(f( (1,), (3,) ))
self.assertTrue(f( (1,), (4,1) ))
self.assertTrue(f( (1,), (4,3) ))
self.assertTrue(f( (1,), (1,3) ))
self.assertTrue(f( (3,), (1,3) ))
self.assertTrue(f( (3,), (4,3) ))
self.assertTrue(f( (5,1,3), (6,5,4,3) ))
self.assertTrue(f( (5,4,3), (6,5,4,3) ))
self.assertFalse(f( (1,), () ))
self.assertFalse(f( (3,), (1,) ))
self.assertFalse(f( (4,3,), (3,) ))
self.assertFalse(f( (4,3,), (1,3,) ))
self.assertFalse(f( (6,1,4,3,), (6,1,1,3,) ))
class TestMultiplyShapes(unittest.TestCase):
def test_multiply_shapes(self):
f = lambda *shapes: tuple(misc.multiply_shapes(*shapes))
# Basic test
self.assertEqual(f((2,),
(3,)),
(6,))
# Test multiple arguments
self.assertEqual(f((2,),
(3,),
(4,)),
(24,))
# Test different lengths and multiple arguments
self.assertEqual(f(( 2,3,),
(4,5,6,),
( 7,)),
(4,10,126,))
# Test empty shapes
self.assertEqual(f((),
()),
())
self.assertEqual(f((),
(5,)),
(5,))
class TestSumMultiply(unittest.TestCase):
def check_sum_multiply(self, *shapes, **kwargs):
# The set of arrays
x = list()
for (ind, shape) in enumerate(shapes):
x += [np.random.randn(*shape)]
# Result from the function
yh = misc.sum_multiply(*x,
**kwargs)
axis = kwargs.get('axis', None)
sumaxis = kwargs.get('sumaxis', True)
keepdims = kwargs.get('keepdims', False)
# Compute the product
y = 1
for xi in x:
y = y * xi
# Compute the sum
if sumaxis:
y = np.sum(y, axis=axis, keepdims=keepdims)
else:
axes = np.arange(np.ndim(y))
# TODO/FIXME: np.delete has a bug that it doesn't accept negative
# indices. Thus, transform negative axes to positive axes.
if len(axis) > 0:
axis = [i if i >= 0
else i+np.ndim(y)
for i in axis]
elif axis < 0:
axis += np.ndim(y)
axes = np.delete(axes, axis)
axes = tuple(axes)
if len(axes) > 0:
y = np.sum(y, axis=axes, keepdims=keepdims)
# Check the result
testing.assert_allclose(yh, y,
err_msg="Incorrect value.")
def test_sum_multiply(self):
"""
Test misc.sum_multiply.
"""
# Check empty list returns error
self.assertRaises(ValueError,
self.check_sum_multiply)
# Check scalars
self.check_sum_multiply(())
self.check_sum_multiply((), (), ())
# Check doing no summation
self.check_sum_multiply((3,),
axis=())
self.check_sum_multiply((3,1,5),
( 4,1),
( 5,),
( ),
axis=(),
keepdims=True)
# Check AXES_TO_SUM
self.check_sum_multiply((3,1),
(1,4),
(3,4),
axis=(1,))
self.check_sum_multiply((3,1),
(1,4),
(3,4),
axis=(-2,))
self.check_sum_multiply((3,1),
(1,4),
(3,4),
axis=(1,-2))
# Check AXES_TO_SUM and KEEPDIMS
self.check_sum_multiply((3,1),
(1,4),
(3,4),
axis=(1,),
keepdims=True)
self.check_sum_multiply((3,1),
(1,4),
(3,4),
axis=(-2,),
keepdims=True)
self.check_sum_multiply((3,1),
(1,4),
(3,4),
axis=(1,-2,),
keepdims=True)
self.check_sum_multiply((3,1,5,6),
( 4,1,6),
( 4,1,1),
( ),
axis=(1,-2),
keepdims=True)
# Check AXES_TO_KEEP
self.check_sum_multiply((3,1),
(1,4),
(3,4),
sumaxis=False,
axis=(1,))
self.check_sum_multiply((3,1),
(1,4),
(3,4),
sumaxis=False,
axis=(-2,))
self.check_sum_multiply((3,1),
(1,4),
(3,4),
sumaxis=False,
axis=(1,-2))
# Check AXES_TO_KEEP and KEEPDIMS
self.check_sum_multiply((3,1),
(1,4),
(3,4),
sumaxis=False,
axis=(1,),
keepdims=True)
self.check_sum_multiply((3,1),
(1,4),
(3,4),
sumaxis=False,
axis=(-2,),
keepdims=True)
self.check_sum_multiply((3,1),
(1,4),
(3,4),
sumaxis=False,
axis=(1,-2,),
keepdims=True)
self.check_sum_multiply((3,1,5,6),
( 4,1,6),
( 4,1,1),
( ),
sumaxis=False,
axis=(1,-2,),
keepdims=True)
# Check errors
# Inconsistent shapes
self.assertRaises(ValueError,
self.check_sum_multiply,
(3,4),
(3,5))
# Axis index out of bounds
self.assertRaises(ValueError,
self.check_sum_multiply,
(3,4),
(3,4),
axis=(-3,))
self.assertRaises(ValueError,
self.check_sum_multiply,
(3,4),
(3,4),
axis=(2,))
self.assertRaises(ValueError,
self.check_sum_multiply,
(3,4),
(3,4),
sumaxis=False,
axis=(-3,))
self.assertRaises(ValueError,
self.check_sum_multiply,
(3,4),
(3,4),
sumaxis=False,
axis=(2,))
# Same axis several times
self.assertRaises(ValueError,
self.check_sum_multiply,
(3,4),
(3,4),
axis=(1,-1))
self.assertRaises(ValueError,
self.check_sum_multiply,
(3,4),
(3,4),
sumaxis=False,
axis=(1,-1))
class TestLogSumExp(misc.TestCase):
def test_logsumexp(self):
"""
Test the ceil division
"""
self.assertAllClose(misc.logsumexp(3),
np.log(np.sum(np.exp(3))))
with warnings.catch_warnings(record=True) as w:
self.assertAllClose(misc.logsumexp(-np.inf),
-np.inf)
self.assertAllClose(misc.logsumexp(np.inf),
np.inf)
self.assertAllClose(misc.logsumexp(np.nan),
np.nan)
self.assertAllClose(misc.logsumexp([-np.inf, -np.inf]),
-np.inf)
self.assertAllClose(misc.logsumexp([[1e10, 1e-10],
[-1e10, -np.inf]], axis=-1),
[1e10, -1e10])
# Test keeping dimensions
self.assertAllClose(misc.logsumexp([[1e10, 1e-10],
[-1e10, -np.inf]],
axis=-1,
keepdims=True),
[[1e10], [-1e10]])
# Test multiple axes
self.assertAllClose(misc.logsumexp([[1e10, 1e-10],
[-1e10, -np.inf]],
axis=(-1,-2)),
1e10)
pass
class TestMean(misc.TestCase):
def test_mean(self):
"""
Test the ceil division
"""
self.assertAllClose(misc.mean(3),
3)
with warnings.catch_warnings(record=True) as w:
self.assertAllClose(misc.mean(np.nan),
np.nan)
self.assertAllClose(misc.mean([[2,3],
[np.nan,np.nan]],
axis=-1),
[2.5,np.nan])
self.assertAllClose(misc.mean([[2,3],
[np.nan,np.nan]],
axis=-1,
keepdims=True),
[[2.5],[np.nan]])
self.assertAllClose(misc.mean([[2,3],
[np.nan,np.nan]],
axis=-2),
[2,3])
self.assertAllClose(misc.mean([[2,3],
[np.nan,np.nan]],
axis=-2,
keepdims=True),
[[2,3]])
self.assertAllClose(misc.mean([[2,3],
[np.nan,np.nan]]),
2.5)
self.assertAllClose(misc.mean([[2,3],
[np.nan,np.nan]],
axis=(-1,-2)),
2.5)
self.assertAllClose(misc.mean([[2,3],
[np.nan,np.nan]],
keepdims=True),
[[2.5]])
pass
bayespy-0.2.3/bayespy/utils/tests/test_random.py 0000664 5104112 5104112 00000024357 12374370351 023601 0 ustar jluttine jluttine 0000000 0000000 ######################################################################
# Copyright (C) 2014 Jaakko Luttinen
#
# This file is licensed under Version 3.0 of the GNU General Public
# License. See LICENSE for a text of the license.
######################################################################
######################################################################
# This file is part of BayesPy.
#
# BayesPy is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# BayesPy is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with BayesPy. If not, see .
######################################################################
"""
Unit tests for bayespy.utils.random module.
"""
import numpy as np
from .. import misc
from .. import random
class TestCeilDiv(misc.TestCase):
def test_categorical(self):
# Test dummy one category
y = random.categorical([1])
self.assertEqual(y, 0)
# Test multiple categories
y = random.categorical([1,0,0])
self.assertEqual(y, 0)
y = random.categorical([0,1,0])
self.assertEqual(y, 1)
y = random.categorical([0,0,1])
self.assertEqual(y, 2)
# Test un-normalized probabilities
y = random.categorical([0,0.1234])
self.assertEqual(y, 1)
# Test multiple distributions
y = random.categorical([ [1,0,0], [0,0,1], [0,1,0] ])
self.assertArrayEqual(y, [0,2,1])
# Test multiple samples
y = random.categorical([0,1,0], size=(4,))
self.assertArrayEqual(y, [1,1,1,1])
#
# ERRORS
#
# Negative probablities
self.assertRaises(ValueError,
random.categorical,
[0, -1])
# Requested size and probability array size mismatch
self.assertRaises(ValueError,
random.categorical,
[[1,0],[0,1]],
size=(3,))
pass
class TestDirichlet(misc.TestCase):
"""
Unit tests for the Dirichlet random sampling
"""
def test(self):
"""
Test random sampling from the Dirichlet distribution.
"""
# Test computations
p = random.dirichlet([1e-10, 1e-10, 1e10, 1e-10])
self.assertAllClose(p,
[0, 0, 1, 0],
atol=1e-5)
p = random.dirichlet([1e20, 1e20, 1e20, 5*1e20])
self.assertAllClose(p,
[0.125, 0.125, 0.125, 0.625])
# Test array
p = random.dirichlet([ [1e20, 1e-20], [3*1e20, 1e20] ])
self.assertAllClose(p,
[[1.0, 0.0], [0.75, 0.25]])
# Test size argument
p = random.dirichlet([ [1e20, 1e-20] ], size=3)
self.assertAllClose(p,
[[1, 0], [1, 0], [1, 0]])
p = random.dirichlet([ [3*1e20, 1e20] ], size=(2,3))
self.assertAllClose(p,
[ [[0.75, 0.25], [0.75, 0.25], [0.75, 0.25]],
[[0.75, 0.25], [0.75, 0.25], [0.75, 0.25]] ])
pass
class TestAlphaBetaRecursion(misc.TestCase):
def test(self):
"""
Test the results of alpha-beta recursion for Markov chains
"""
np.seterr(divide='ignore')
# Deterministic oscillator
p0 = np.array([1.0, 0.0])
P = np.array(3*[[[0.0, 1.0],
[1.0, 0.0]]])
(z0, zz, g) = random.alpha_beta_recursion(np.log(p0),
np.log(P))
self.assertAllClose(z0,
[1.0, 0])
self.assertAllClose(zz,
[ [[0.0, 1.0],
[0.0, 0.0]],
[[0.0, 0.0],
[1.0, 0.0]],
[[0.0, 1.0],
[0.0, 0.0]] ])
self.assertAllClose(g,
-np.log(np.einsum('a,ab,bc,cd->',
p0, P[0], P[1], P[2])),
msg="Cumulant generating function incorrect")
# Maximum randomness
p0 = np.array([0.5, 0.5])
P = np.array(3*[[[0.5, 0.5],
[0.5, 0.5]]])
(z0, zz, g) = random.alpha_beta_recursion(np.log(p0),
np.log(P))
self.assertAllClose(z0,
[0.5, 0.5])
self.assertAllClose(zz,
[ [[0.25, 0.25],
[0.25, 0.25]],
[[0.25, 0.25],
[0.25, 0.25]],
[[0.25, 0.25],
[0.25, 0.25]] ])
self.assertAllClose(g,
-np.log(np.einsum('a,ab,bc,cd->',
p0, P[0], P[1], P[2])),
msg="Cumulant generating function incorrect")
# Unnormalized probabilities
p0 = np.array([2, 2])
P = np.array([ [[4, 4],
[4, 4]],
[[8, 8],
[8, 8]],
[[20, 20],
[20, 20]] ])
(z0, zz, g) = random.alpha_beta_recursion(np.log(p0),
np.log(P))
self.assertAllClose(z0,
[0.5, 0.5])
self.assertAllClose(zz,
[ [[0.25, 0.25],
[0.25, 0.25]],
[[0.25, 0.25],
[0.25, 0.25]],
[[0.25, 0.25],
[0.25, 0.25]] ])
self.assertAllClose(g,
-np.log(np.einsum('a,ab,bc,cd->',
p0, P[0], P[1], P[2])),
msg="Cumulant generating function incorrect")
p0 = np.array([2, 6])
P = np.array([ [[0, 3],
[4, 1]],
[[3, 5],
[6, 4]],
[[9, 2],
[8, 1]] ])
(z0, zz, g) = random.alpha_beta_recursion(np.log(p0),
np.log(P))
y0 = np.einsum('a,ab,bc,cd->a', p0, P[0], P[1], P[2])
y1 = np.einsum('a,ab,bc,cd->ab', p0, P[0], P[1], P[2])
y2 = np.einsum('a,ab,bc,cd->bc', p0, P[0], P[1], P[2])
y3 = np.einsum('a,ab,bc,cd->cd', p0, P[0], P[1], P[2])
self.assertAllClose(z0,
y0 / np.sum(y0))
self.assertAllClose(zz,
[ y1 / np.sum(y1),
y2 / np.sum(y2),
y3 / np.sum(y3) ])
self.assertAllClose(g,
-np.log(np.einsum('a,ab,bc,cd->',
p0, P[0], P[1], P[2])),
msg="Cumulant generating function incorrect")
# Test plates
p0 = np.array([ [1.0, 0.0],
[0.5, 0.5] ])
P = np.array([ [ [[0.0, 1.0],
[1.0, 0.0]] ],
[ [[0.5, 0.5],
[0.5, 0.5]] ] ])
(z0, zz, g) = random.alpha_beta_recursion(np.log(p0),
np.log(P))
self.assertAllClose(z0,
[[1.0, 0.0],
[0.5, 0.5]])
self.assertAllClose(zz,
[ [ [[0.0, 1.0],
[0.0, 0.0]] ],
[ [[0.25, 0.25],
[0.25, 0.25]] ] ])
self.assertAllClose(g,
-np.log(np.einsum('...a,...ab->...',
p0, P[...,0,:,:])),
msg="Cumulant generating function incorrect")
# Test overflow
logp0 = np.array([1e5, -np.inf])
logP = np.array([[[-np.inf, 1e5],
[-np.inf, 1e5]]])
(z0, zz, g) = random.alpha_beta_recursion(logp0,
logP)
self.assertAllClose(z0,
[1.0, 0])
self.assertAllClose(zz,
[ [[0.0, 1.0],
[0.0, 0.0]] ])
## self.assertAllClose(g,
## -np.log(np.einsum('a,ab,bc,cd->',
## p0, P[0], P[1], P[2])))
# Test underflow
logp0 = np.array([-1e5, -np.inf])
logP = np.array([[[-np.inf, -1e5],
[-np.inf, -1e5]]])
(z0, zz, g) = random.alpha_beta_recursion(logp0,
logP)
self.assertAllClose(z0,
[1.0, 0])
self.assertAllClose(zz,
[ [[0.0, 1.0],
[0.0, 0.0]] ])
## self.assertAllClose(g,
## -np.log(np.einsum('a,ab,bc,cd->',
## p0, P[0], P[1], P[2])))
# Test stability of the algorithm
logp0 = np.array([-1e5, -np.inf])
logP = np.array(10*[[[-np.inf, 1e5],
[1e0, -np.inf]]])
(z0, zz, g) = random.alpha_beta_recursion(logp0,
logP)
self.assertTrue(np.all(~np.isnan(z0)),
msg="Nans in results, algorithm not stable")
self.assertTrue(np.all(~np.isnan(zz)),
msg="Nans in results, algorithm not stable")
self.assertTrue(np.all(~np.isnan(g)),
msg="Nans in results, algorithm not stable")
pass
bayespy-0.2.3/bayespy/utils/__init__.py 0000664 5104112 5104112 00000000124 12374370351 021641 0 ustar jluttine jluttine 0000000 0000000 from . import misc
from . import linalg
from . import random
from . import optimize
bayespy-0.2.3/bayespy/utils/linalg.py 0000664 5104112 5104112 00000037473 12412543071 021363 0 ustar jluttine jluttine 0000000 0000000 ######################################################################
# Copyright (C) 2011-2014 Jaakko Luttinen
#
# This file is licensed under Version 3.0 of the GNU General Public
# License. See LICENSE for a text of the license.
######################################################################
######################################################################
# This file is part of BayesPy.
#
# BayesPy is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# BayesPy is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with BayesPy. If not, see .
######################################################################
"""
General numerical functions and methods.
"""
import itertools
import numpy as np
import scipy as sp
#import scipy.linalg.decomp_cholesky as decomp
import scipy.linalg as linalg
import scipy.special as special
import scipy.optimize as optimize
import scipy.sparse as sparse
#import scikits.sparse.cholmod as cholmod
# THIS IS SOME NEW GENERALIZED UFUNC FOR LINALG FEATURE, NOT IN OFFICIAL NUMPY
# REPO YET
#import numpy.linalg._gufuncs_linalg as gula
#import numpy.core.gufuncs_linalg as gula
from . import misc
def chol(C):
if sparse.issparse(C):
# Sparse Cholesky decomposition (returns a Factor object)
return cholmod.cholesky(C)
else:
# Computes Cholesky decomposition for a collection of matrices.
# The last two axes of C are considered as the matrix.
C = np.atleast_2d(C)
U = np.empty(np.shape(C))
for i in misc.nested_iterator(np.shape(U)[:-2]):
try:
U[i] = linalg.cho_factor(C[i])[0]
except np.linalg.linalg.LinAlgError:
print(C[i])
raise Exception("Matrix not positive definite")
return U
def chol_solve(U, b, out=None, matrix=False):
if isinstance(U, np.ndarray):
if sparse.issparse(b):
b = b.toarray()
if matrix:
if np.ndim(b) < 2:
raise ValueError("b is not a matrix")
b = np.swapaxes(b, -1, -2)
U = U[...,None,:,:]
# Allocate memory
U = np.atleast_2d(U)
B = np.atleast_1d(b)
sh_u = U.shape[:-2]
sh_b = B.shape[:-1]
l_u = len(sh_u)
l_b = len(sh_b)
# Check which axis are iterated over with B along with U
ind_b = [Ellipsis] * l_b
l_min = min(l_u, l_b)
jnd_b = tuple(i for i in range(-l_min,0) if sh_b[i]==sh_u[i])
if out == None:
# Shape of the result (broadcasting rules)
sh = misc.broadcasted_shape(sh_u, sh_b)
#out = np.zeros(np.shape(B))
out = np.zeros(sh + B.shape[-1:])
for i in misc.nested_iterator(np.shape(U)[:-2]):
# The goal is to run Cholesky solver once for all vectors of B
# for which the matrices of U are the same (according to the
# broadcasting rules). Thus, we collect all the axes of B for
# which U is singleton and form them as a 2-D matrix and then
# run the solver once.
# Select those axes of B for which U and B are not singleton
for j in jnd_b:
ind_b[j] = i[j]
# Collect all the axes for which U is singleton
b = B[tuple(ind_b) + (Ellipsis,)]
# Reshape it to a 2-D (or 1-D) array
orig_shape = b.shape
if b.ndim > 1:
b = b.reshape((-1, b.shape[-1]))
# Ellipsis to all preceeding axes and ellipsis for the last
# axis:
if len(ind_b) < len(sh):
ind_out = (Ellipsis,) + tuple(ind_b) + (Ellipsis,)
else:
ind_out = tuple(ind_b) + (Ellipsis,)
out[ind_out] = linalg.cho_solve((U[i], False),
b.T).T.reshape(orig_shape)
if matrix:
out = np.swapaxes(out, -1, -2)
return out
elif isinstance(U, cholmod.Factor):
if matrix:
raise NotImplementedError()
if sparse.issparse(b):
b = b.toarray()
return U.solve_A(b)
else:
raise ValueError("Unknown type of Cholesky factor")
def chol_inv(U):
if isinstance(U, np.ndarray):
# Allocate memory
V = np.tile(np.identity(np.shape(U)[-1]), np.shape(U)[:-2]+(1,1))
for i in misc.nested_iterator(np.shape(U)[:-2]):
V[i] = linalg.cho_solve((U[i], False),
V[i],
overwrite_b=True) # This would need Fortran order
return V
elif isinstance(U, cholmod.Factor):
raise NotImplementedError
## if sparse.issparse(b):
## b = b.toarray()
## return U.solve_A(b)
else:
raise ValueError("Unknown type of Cholesky factor")
def chol_logdet(U):
if isinstance(U, np.ndarray):
return 2*np.sum(np.log(np.einsum('...ii->...i',U)), axis=-1)
elif isinstance(U, cholmod.Factor):
return np.sum(np.log(U.D()))
else:
raise ValueError("Unknown type of Cholesky factor")
def logdet_chol(U):
if isinstance(U, np.ndarray):
# Computes Cholesky decomposition for a collection of matrices.
return 2*np.sum(np.log(np.einsum('...ii->...i', U)), axis=(-1,))
elif isinstance(U, cholmod.Factor):
return np.sum(np.log(U.D()))
def logdet_tri(R):
"""
Logarithm of the absolute value of the determinant of a triangular matrix.
"""
return np.sum(np.log(np.abs(np.einsum('...ii->...i', R))))
def logdet_cov(C):
return logdet_chol(chol(C))
def solve_triangular(U, B, **kwargs):
# Allocate memory
U = np.atleast_2d(U)
B = np.atleast_1d(B)
sh_u = U.shape[:-2]
sh_b = B.shape[:-1]
l_u = len(sh_u)
l_b = len(sh_b)
# Check which axis are iterated over with B along with U
ind_b = [Ellipsis] * l_b
l_min = min(l_u, l_b)
jnd_b = tuple(i for i in range(-l_min,0) if sh_b[i]==sh_u[i])
# Shape of the result (broadcasting rules)
sh = misc.broadcasted_shape(sh_u, sh_b)
out = np.zeros(sh + B.shape[-1:])
for i in misc.nested_iterator(np.shape(U)[:-2]):
# The goal is to run triangular solver once for all vectors of
# B for which the matrices of U are the same (according to the
# broadcasting rules). Thus, we collect all the axes of B for
# which U is singleton and form them as a 2-D matrix and then
# run the solver once.
# Select those axes of B for which U and B are not singleton
for j in jnd_b:
ind_b[j] = i[j]
# Collect all the axes for which U is singleton
b = B[tuple(ind_b) + (Ellipsis,)]
# Reshape it to a 2-D (or 1-D) array
orig_shape = b.shape
if b.ndim > 1:
b = b.reshape((-1, b.shape[-1]))
# Ellipsis to all preceeding axes and ellipsis for the last
# axis:
if len(ind_b) < len(sh):
ind_out = (Ellipsis,) + tuple(ind_b) + (Ellipsis,)
else:
ind_out = tuple(ind_b) + (Ellipsis,)
out[ind_out] = linalg.solve_triangular(U[i],
b.T,
**kwargs).T.reshape(orig_shape)
return out
def inner(*args, ndim=1):
"""
Compute inner product.
The number of arrays is arbitrary. The number of dimensions is arbitrary.
"""
axes = tuple(range(-ndim,0))
return misc.sum_product(*args, axes_to_sum=axes)
def outer(A, B, ndim=1):
"""
Computes outer product over the last axes of A and B.
The other axes are broadcasted. Thus, if A has shape (..., N) and B has
shape (..., M), then the result has shape (..., N, M).
Using the argument `ndim` it is possible to change that how many axes
trailing axes are used for the outer product. For instance, if ndim=3, A and
B have shapes (...,N1,N2,N3) and (...,M1,M2,M3), the result has shape
(...,N1,M1,N2,M2,N3,M3).
"""
if not isinstance(ndim, int) or ndim < 0:
raise ValueError('ndim must be non-negative integer')
if ndim > 0:
if ndim > np.ndim(A):
raise ValueError('Argument ndim larger than ndim of the first '
'parameter')
if ndim > np.ndim(B):
raise ValueError('Argument ndim larger than ndim of the second '
'parameter')
shape_A = np.shape(A) + (1,)*ndim
shape_B = np.shape(B)[:-ndim] + (1,)*ndim + np.shape(B)[-ndim:]
A = np.reshape(A, shape_A)
B = np.reshape(B, shape_B)
return np.asanyarray(A) * np.asanyarray(B)
def _dot(A, B):
"""
Dot product which handles broadcasting properly.
Future NumPy will have a better built-in implementation for this.
"""
A_plates = np.shape(A)[:-2]
B_plates = np.shape(B)[:-2]
M = np.shape(A)[-2]
N = np.shape(B)[-1]
Y_plates = misc.broadcasted_shape(A_plates, B_plates)
if Y_plates == ():
return np.dot(A, B)
indices = misc.nested_iterator(Y_plates)
Y_shape = Y_plates + (M, N)
Y = np.zeros(Y_shape)
for i in indices:
Y[i] = np.dot(A[misc.safe_indices(i, A_plates)],
B[misc.safe_indices(i, B_plates)])
return Y
def dot(*arrays):
"""
Compute matrix-matrix product.
You can give multiple arrays, the dot product is computed from left to
right: A1*A2*A3*...*AN. The dot product is computed over the last two axes
of each arrays. All other axes must be broadcastable.
"""
if len(arrays) == 0:
return 0
else:
Y = np.asanyarray(arrays[0])
for X in arrays[1:]:
X = np.asanyarray(X)
if np.ndim(Y) < 2 or np.ndim(X) < 2:
raise ValueError("Must be at least 2-D arrays")
if np.shape(Y)[-1] != np.shape(X)[-2]:
raise ValueError("Dimensions do not match")
# Replace this with numpy.dot when NumPy implements broadcasting in dot
Y = _dot(Y, X)
#Y = np.einsum('...ik,...kj->...ij', Y, X)
#Y = gula.matrix_multiply(Y, X)
return Y
def tracedot(A, B):
"""
Computes trace(A*B).
"""
return np.einsum('...ij,...ji->...', A, B)
def inv(A, ndim=1):
"""
General array inversion.
Supports broadcasting and inversion of multidimensional arrays. For
instance, an array with shape (4,3,2,3,2) could mean that there are four
(3*2) x (3*2) matrices to be inverted. This can be done by inv(A, ndim=2).
For inverting scalars, ndim=0. For inverting matrices, ndim=1.
"""
A = np.asanyarray(A)
if ndim == 0:
return 1 / A
elif ndim == 1:
return np.linalg.inv(A)
else:
raise NotImplementedError()
def mvdot(A, b):
"""
Compute matrix-vector product.
Applies broadcasting.
"""
# TODO/FIXME: A bug in inner1d:
# https://github.com/numpy/numpy/issues/3338
#
# b = np.asanyarray(b)
# return gula.inner1d(A, b[...,np.newaxis,:])
#
# Use einsum instead:
return _dot(A, b[...,None])[...,0]
#return np.einsum('...ik,...k->...i', A, b)
def mmdot(A, B):
"""
Compute matrix-matrix product.
Applies broadcasting.
"""
return _dot(A, B)
#return np.einsum('...ik,...kj->...ij', A, B)
def m_dot(A,b):
raise DeprecationWarning()
# Compute matrix-vector product over the last two axes of A and
# the last axes of b. Other axes are broadcasted. If A has shape
# (..., M, N) and b has shape (..., N), then the result has shape
# (..., M)
#b = reshape(b, shape(b)[:-1] + (1,) + shape(b)[-1:])
#return np.dot(A, b)
return np.einsum('...ik,...k->...i', A, b)
# TODO: Use einsum!!
#return np.sum(A*b[...,np.newaxis,:], axis=(-1,))
def block_banded_solve(A, B, y):
"""
Invert symmetric, banded, positive-definite matrix.
A contains the diagonal blocks.
B contains the superdiagonal blocks (their transposes are the
subdiagonal blocks).
Shapes:
A: (..., N, D, D)
B: (..., N-1, D, D)
y: (..., N, D)
The algorithm is basically LU decomposition.
Computes only the diagonal and super-diagonal blocks of the
inverse. The true inverse is dense, in general.
Assume each block has the same size.
Return:
* inverse blocks
* solution to the system
* log-determinant
"""
# Number of time instance and dimensionality
N = np.shape(y)[-2]
D = np.shape(y)[-1]
# Check the shape of the diagonal blocks
if np.shape(A)[-3] != N:
raise ValueError("The number of diagonal blocks is incorrect")
if np.shape(A)[-2:] != (D,D):
raise ValueError("The diagonal blocks have wrong shape")
# Check the shape of the super-diagonal blocks
if np.shape(B)[-3] != N-1:
raise ValueError("The number of super-diagonal blocks is incorrect")
if np.shape(B)[-2:] != (D,D):
raise ValueError("The diagonal blocks have wrong shape")
plates_VC = misc.broadcasted_shape(np.shape(A)[:-3],
np.shape(B)[:-3])
plates_y = misc.broadcasted_shape(plates_VC,
np.shape(y)[:-2])
V = np.empty(plates_VC+(N,D,D))
C = np.empty(plates_VC+(N-1,D,D))
x = np.empty(plates_y+(N,D))
#
# Forward recursion
#
# In the forward recursion, store the Cholesky factor in V. So you
# don't need to recompute them in the backward recursion.
# TODO: This whole algorithm could be implemented as in-place operation.
# Might be a nice feature (optional?)
x[...,0,:] = y[...,0,:]
V[...,0,:,:] = chol(A[...,0,:,:])
ldet = chol_logdet(V[...,0,:,:])
for n in range(N-1):
# Compute the solution of the system
x[...,n+1,:] = (y[...,n+1,:]
- mvdot(misc.T(B[...,n,:,:]),
chol_solve(V[...,n,:,:],
x[...,n,:])))
# Compute the superdiagonal block of the inverse
C[...,n,:,:] = chol_solve(V[...,n,:,:],
B[...,n,:,:],
matrix=True)
# Compute the diagonal block
V[...,n+1,:,:] = (A[...,n+1,:,:]
- mmdot(misc.T(B[...,n,:,:]), C[...,n,:,:]))
# Ensure symmetry by 0.5*(V+V.T)
V[...,n+1,:,:] = 0.5 * (V[...,n+1,:,:] + misc.T(V[...,n+1,:,:]))
# Compute and store the Cholesky factor of the diagonal block
V[...,n+1,:,:] = chol(V[...,n+1,:,:])
# Compute the log-det term here, too
ldet += chol_logdet(V[...,n+1,:,:])
#
# Backward recursion
#
x[...,-1,:] = chol_solve(V[...,-1,:,:], x[...,-1,:])
V[...,-1,:,:] = chol_inv(V[...,-1,:,:])
for n in reversed(range(N-1)):
# Compute the solution of the system
x[...,n,:] = chol_solve(V[...,n,:,:],
x[...,n,:] - mvdot(B[...,n,:,:],
x[...,n+1,:]))
# Compute the diagonal block of the inverse
V[...,n,:,:] = (chol_inv(V[...,n,:,:])
+ mmdot(C[...,n,:,:],
mmdot(V[...,n+1,:,:],
misc.T(C[...,n,:,:]))))
C[...,n,:,:] = - mmdot(C[...,n,:,:], V[...,n+1,:,:])
# Ensure symmetry by 0.5*(V+V.T)
V[...,n,:,:] = 0.5 * (V[...,n,:,:] + misc.T(V[...,n,:,:]))
return (V, C, x, ldet)
bayespy-0.2.3/bayespy/utils/misc.py 0000664 5104112 5104112 00000107267 12412543071 021047 0 ustar jluttine jluttine 0000000 0000000 ######################################################################
# Copyright (C) 2011-2013 Jaakko Luttinen
#
# This file is licensed under Version 3.0 of the GNU General Public
# License. See LICENSE for a text of the license.
######################################################################
######################################################################
# This file is part of BayesPy.
#
# BayesPy is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# BayesPy is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with BayesPy. If not, see .
######################################################################
"""
General numerical functions and methods.
"""
import functools
import itertools
import numpy as np
import scipy as sp
import scipy.linalg as linalg
import scipy.special as special
import scipy.optimize as optimize
import scipy.sparse as sparse
import tempfile as tmp
import unittest
from numpy import testing
def composite_function(function_list):
"""
Construct a function composition from a list of functions.
Given a list of functions [f,g,h], constructs a function :math:`h \circ g
\circ f`. That is, returns a function :math:`z`, for which :math:`z(x) =
h(g(f(x)))`.
"""
def composite(X):
for function in function_list:
X = function(X)
return X
return composite
def ceildiv(a, b):
"""
Compute a divided by b and rounded up.
"""
return -(-a // b)
def rmse(y1, y2, axis=None):
return np.sqrt(np.mean((y1-y2)**2, axis=axis))
def is_callable(f):
return hasattr(f, '__call__')
def atleast_nd(X, d):
if np.ndim(X) < d:
sh = (d-np.ndim(X))*(1,) + np.shape(X)
X = np.reshape(X, sh)
return X
def T(X):
"""
Transpose the matrix.
"""
return np.swapaxes(X, -1, -2)
class TestCase(unittest.TestCase):
"""
Simple base class for unit testing.
Adds NumPy's features to Python's unittest.
"""
def assertAllClose(self, A, B,
msg="Arrays not almost equal",
rtol=1e-4,
atol=0):
self.assertEqual(np.shape(A), np.shape(B), msg=msg)
testing.assert_allclose(A, B, err_msg=msg, rtol=rtol, atol=atol)
pass
def assertArrayEqual(self, A, B, msg="Arrays not equal"):
self.assertEqual(np.shape(A), np.shape(B), msg=msg)
testing.assert_array_equal(A, B, err_msg=msg)
pass
def assertMessage(self, M1, M2):
if len(M1) != len(M2):
self.fail("Message lists have different lengths")
for (m1, m2) in zip(M1, M2):
self.assertAllClose(m1, m2)
pass
def assertMessageToChild(self, X, u):
self.assertMessage(X._message_to_child(), u)
pass
def symm(X):
"""
Make X symmetric.
"""
return 0.5 * (X + np.swapaxes(X, -1, -2))
def unique(l):
"""
Remove duplicate items from a list while preserving order.
"""
seen = set()
seen_add = seen.add
return [ x for x in l if x not in seen and not seen_add(x)]
def tempfile(prefix='', suffix=''):
return tmp.NamedTemporaryFile(prefix=prefix, suffix=suffix).name
def write_to_hdf5(group, data, name):
"""
Writes the given array into the HDF5 file.
"""
try:
# Try using compression. It doesn't work for scalars.
group.create_dataset(name,
data=data,
compression='gzip')
except TypeError:
group.create_dataset(name,
data=data)
except ValueError:
raise ValueError('Could not write %s' % data)
def nans(size=()):
return np.tile(np.nan, size)
def trues(shape):
return np.ones(shape, dtype=np.bool)
def identity(*shape):
return np.reshape(np.identity(np.prod(shape)), shape+shape)
def array_to_scalar(x):
# This transforms an N-dimensional array to a scalar. It's most
# useful when you know that the array has only one element and you
# want it out as a scalar.
return np.ravel(x)[0]
#def diag(x):
def grid(x1, x2):
""" Returns meshgrid as a (M*N,2)-shape array. """
(X1, X2) = np.meshgrid(x1, x2)
return np.hstack((X1.reshape((-1,1)),X2.reshape((-1,1))))
class CholeskyDense():
def __init__(self, K):
self.U = linalg.cho_factor(K)
def solve(self, b):
if sparse.issparse(b):
b = b.toarray()
return linalg.cho_solve(self.U, b)
def logdet(self):
return 2*np.sum(np.log(np.diag(self.U[0])))
def trace_solve_gradient(self, dK):
return np.trace(self.solve(dK))
class CholeskySparse():
def __init__(self, K):
self.LD = cholmod.cholesky(K)
def solve(self, b):
if sparse.issparse(b):
b = b.toarray()
return self.LD.solve_A(b)
def logdet(self):
return self.LD.logdet()
#np.sum(np.log(LD.D()))
def trace_solve_gradient(self, dK):
# WTF?! numpy.multiply doesn't work for two sparse
# matrices.. It returns a result but it is incorrect!
# Use the identity trace(K\dK)=sum(inv(K).*dK) by computing
# the sparse inverse (lower triangular part)
iK = self.LD.spinv(form='lower')
return (2*iK.multiply(dK).sum()
- iK.diagonal().dot(dK.diagonal()))
# Multiply by two because of symmetry (remove diagonal once
# because it was taken into account twice)
#return np.multiply(self.LD.inv().todense(),dK.todense()).sum()
#return self.LD.inv().multiply(dK).sum() # THIS WORKS
#return np.multiply(self.LD.inv(),dK).sum() # THIS NOT WORK!! WTF??
iK = self.LD.spinv()
return iK.multiply(dK).sum()
#return (2*iK.multiply(dK).sum()
# - iK.diagonal().dot(dK.diagonal()))
#return (2*np.multiply(iK, dK).sum()
# - iK.diagonal().dot(dK.diagonal())) # THIS NOT WORK!!
#return np.trace(self.solve(dK))
def cholesky(K):
if isinstance(K, np.ndarray):
return CholeskyDense(K)
elif sparse.issparse(K):
return CholeskySparse(K)
else:
raise Exception("Unsupported covariance matrix type")
def vb_optimize(x0, set_values, lowerbound, gradient=None):
# Function for computing the lower bound
def func(x):
# Set the value of the nodes
set_values(x)
# Compute lower bound (and gradient terms)
return -lowerbound()
#return f
# Function for computing the gradient of the lower bound
def funcprime(x):
# Collect the gradients from the nodes
set_values(x)
# Compute lower bound (and gradient terms)
#lowerbound()
return -gradient()
#return df
# Optimize
if gradient != None:
check_gradient(x0, func, funcprime, 1e-6)
xopt = optimize.fmin_bfgs(func, x0, fprime=funcprime, maxiter=100)
#xopt = optimize.fmin_ncg(func, x0, fprime=funcprime, maxiter=50)
else:
xopt = optimize.fmin_bfgs(func, x0, maxiter=100)
#xopt = optimize.fmin_ncg(func, x0, maxiter=50)
# Set optimal values to the nodes
set_values(xopt)
# Optimizes the parameters of the given nodes.
def vb_optimize_nodes(*nodes):
# Get cost functions
lbs = set()
for node in nodes:
# Add node's cost function
lbs |= node.get_all_vb_terms()
#.lower_bound_contribution)
# Add child nodes' cost functions
#for lb in node.get_children_vb_bound():
#lbs.add(lb)
# Uniqify nodes?
nodes = set(nodes)
# Get initial value and transformation/update function
ind = 0
ind_all = list()
transform_all = list()
gradient_all = list()
x0_all = np.array([])
for node in nodes:
(x0, transform, gradient) = node.start_optimization()
# Vector of initial values
x0 = np.atleast_1d(x0)
x0_all = np.concatenate((x0_all, x0))
# Indices of the vector elements that correspond to this node
sz = np.size(x0)
ind_all.append((ind, ind+sz))
ind += sz
# Function for setting the value of this node
transform_all.append(transform)
# Gradients
gradient_all.append(gradient)
# Function for changing the values of the nodes
def set_value(x):
for (ind, transform) in zip(ind_all, transform_all):
# Transform/update variable
transform(x[ind[0]:ind[1]])
# Compute the lower bound (and the gradient)
def lowerbound():
l = 0
# TODO: Put gradients to zero!
for lb in lbs:
l += lb(gradient=False)
return l
# Compute (or get) the gradient
def gradient():
for lb in lbs:
lb(gradient=True)
dl = np.zeros(np.shape(x0_all))
for (ind, gradient) in zip(ind_all, gradient_all):
dl[ind[0]:ind[1]] = gradient()
return dl
#vb_optimize(x0_all, set_value, lowerbound)
vb_optimize(x0_all, set_value, lowerbound, gradient=gradient)
for node in nodes:
node.stop_optimization()
# Computes log probability density function of the Gaussian
# distribution
def gaussian_logpdf(y_invcov_y,
y_invcov_mu,
mu_invcov_mu,
logdetcov,
D):
return (-0.5*D*np.log(2*np.pi)
-0.5*logdetcov
-0.5*y_invcov_y
+y_invcov_mu
-0.5*mu_invcov_mu)
def check_gradient(x0, f, df, eps):
#f0 = f(x0)
grad = np.zeros(np.shape(x0))
for ind in range(len(x0)):
xmin = x0.copy()
xmax = x0.copy()
xmin[ind] -= eps
xmax[ind] += eps
fmin = f(xmin)
fmax = f(xmax)
grad[ind] = (fmax-fmin) / (2*eps)
print('x: ' + str(x0))
print('Numerical gradient: ' + str(grad))
print('Exact gradient: ' + str(df(x0)))
def zipper_merge(*lists):
"""
Combines lists by alternating elements from them.
Combining lists [1,2,3], ['a','b','c'] and [42,666,99] results in
[1,'a',42,2,'b',666,3,'c',99]
The lists should have equal length or they are assumed to have the length of
the shortest list.
This is known as alternating merge or zipper merge.
"""
return list(sum(zip(*lists), ()))
def remove_whitespace(s):
return ''.join(s.split())
def is_numeric(a):
return (np.isscalar(a) or
isinstance(a, list) or
isinstance(a, np.ndarray))
def isinteger(x):
t = np.asanyarray(x).dtype.type
return ( issubclass(t, np.integer) or issubclass(t, np.bool_) )
def is_string(s):
return isinstance(s, str)
def multiply_shapes(*shapes):
"""
Compute element-wise product of lists/tuples.
Shorter lists are concatenated with leading 1s in order to get lists with
the same length.
"""
# Make the shapes equal length
shapes = make_equal_length(*shapes)
# Compute element-wise product
f = lambda X,Y: (x*y for (x,y) in zip(X,Y))
shape = functools.reduce(f, shapes)
return tuple(shape)
def make_equal_length(*shapes):
"""
Make tuples equal length.
Add leading 1s to shorter tuples.
"""
# Get maximum length
max_len = max((len(shape) for shape in shapes))
# Make the shapes equal length
shapes = ((1,)*(max_len-len(shape)) + tuple(shape) for shape in shapes)
return shapes
def make_equal_ndim(*arrays):
"""
Add trailing unit axes so that arrays have equal ndim
"""
shapes = [np.shape(array) for array in arrays]
shapes = make_equal_length(*shapes)
arrays = [np.reshape(array, shape)
for (array, shape) in zip(arrays, shapes)]
return arrays
def sum_to_dim(A, dim):
"""
Sum leading axes of A such that A has dim dimensions.
"""
dimdiff = np.ndim(A) - dim
if dimdiff > 0:
axes = np.arange(dimdiff)
A = np.sum(A, axis=axes)
return A
def sum_multiply(*args, axis=None, sumaxis=True, keepdims=False):
# Computes sum(arg[0]*arg[1]*arg[2]*..., axis=axes_to_sum) without
# explicitly computing the intermediate product
if len(args) == 0:
raise ValueError("You must give at least one input array")
# Dimensionality of the result
max_dim = 0
for k in range(len(args)):
max_dim = max(max_dim, np.ndim(args[k]))
if sumaxis:
if axis is None:
# Sum all axes
axes = []
else:
if np.isscalar(axis):
axis = [axis]
axes = [i
for i in range(max_dim)
if i not in axis and (-max_dim+i) not in axis]
else:
if axis is None:
# Keep all axes
axes = range(max_dim)
else:
# Find axes that are kept
if np.isscalar(axis):
axes = [axis]
axes = [i if i >= 0
else i+max_dim
for i in axis]
axes = sorted(axes)
if len(axes) > 0 and (min(axes) < 0 or max(axes) >= max_dim):
raise ValueError("Axis index out of bounds")
# Form a list of pairs: the array in the product and its axes
pairs = list()
for i in range(len(args)):
a = args[i]
a_dim = np.ndim(a)
pairs.append(a)
pairs.append(range(max_dim-a_dim, max_dim))
# Output axes are those which are not summed
pairs.append(axes)
# Compute the sum-product
try:
y = np.einsum(*pairs)
except ValueError as err:
if str(err) == ("If 'op_axes' or 'itershape' is not NULL in "
"theiterator constructor, 'oa_ndim' must be greater "
"than zero"):
# TODO/FIXME: Handle a bug in NumPy. If all arguments to einsum are
# scalars, it raises an error. For scalars we can just use multiply
# and forget about summing. Hopefully, in the future, einsum handles
# scalars properly and this try-except becomes unnecessary.
y = functools.reduce(np.multiply, args)
else:
raise err
# Restore summed axes as singleton axes
if keepdims:
d = 0
s = ()
for k in range(max_dim):
if k in axes:
# Axis not summed
s = s + (np.shape(y)[d],)
d += 1
else:
# Axis was summed
s = s + (1,)
y = np.reshape(y, s)
return y
def sum_product(*args, axes_to_keep=None, axes_to_sum=None, keepdims=False):
if axes_to_keep is not None:
return sum_multiply(*args,
axis=axes_to_keep,
sumaxis=False,
keepdims=keepdims)
else:
return sum_multiply(*args,
axis=axes_to_sum,
sumaxis=True,
keepdims=keepdims)
def moveaxis(A, axis_from, axis_to):
"""
Move the axis `axis_from` to position `axis_to`.
"""
if ((axis_from < 0 and abs(axis_from) > np.ndim(A)) or
(axis_from >= 0 and axis_from >= np.ndim(A)) or
(axis_to < 0 and abs(axis_to) > np.ndim(A)) or
(axis_to >= 0 and axis_to >= np.ndim(A))):
raise ValueError("Can't move axis %d to position %d. Axis index out of "
"bounds for array with shape %s"
% (axis_from,
axis_to,
np.shape(A)))
axes = np.arange(np.ndim(A))
axes[axis_from:axis_to] += 1
axes[axis_from:axis_to:-1] -= 1
axes[axis_to] = axis_from
return np.transpose(A, axes=axes)
def safe_indices(inds, shape):
"""
Makes sure that indices are valid for given shape.
The shorter shape determines the length.
For instance,
>>> safe_indices( (3, 4, 5), (1, 6) )
(0, 5)
"""
m = min(len(inds), len(shape))
if m == 0:
return ()
inds = inds[-m:]
maxinds = np.array(shape[-m:]) - 1
return tuple(np.fmin(inds, maxinds))
def broadcasted_shape(*shapes):
"""
Computes the resulting broadcasted shape for a given set of shapes.
Uses the broadcasting rules of NumPy. Raises an exception if the shapes do
not broadcast.
"""
dim = 0
for a in shapes:
dim = max(dim, len(a))
S = ()
for i in range(-dim,0):
s = 1
for a in shapes:
if -i <= len(a):
if s == 1:
s = a[i]
elif a[i] != 1 and a[i] != s:
raise ValueError("Shapes %s do not broadcast" % (shapes,))
S = S + (s,)
return S
def broadcasted_shape_from_arrays(*arrays):
"""
Computes the resulting broadcasted shape for a given set of arrays.
Raises an exception if the shapes do not broadcast.
"""
shapes = [np.shape(array) for array in arrays]
return broadcasted_shape(*shapes)
def is_shape_subset(sub_shape, full_shape):
"""
"""
if len(sub_shape) > len(full_shape):
return False
for i in range(len(sub_shape)):
ind = -1 - i
if sub_shape[ind] != 1 and sub_shape[ind] != full_shape[ind]:
return False
return True
def add_axes(X, num=1, axis=0):
for i in range(num):
X = np.expand_dims(X, axis=axis)
return X
shape = np.shape(X)[:axis] + num*(1,) + np.shape(X)[axis:]
return np.reshape(X, shape)
def add_leading_axes(x, n):
return add_axes(x, axis=0, num=n)
def add_trailing_axes(x, n):
return add_axes(x, axis=-1, num=n)
## def add_axes(x, lead, trail):
## shape = (1,)*lead + np.shape(x) + (1,)*trail
## return np.reshape(x, shape)
def nested_iterator(max_inds):
s = [range(i) for i in max_inds]
return itertools.product(*s)
def first(L):
"""
"""
for (n,l) in enumerate(L):
if l:
return n
return None
def squeeze(X):
"""
Remove leading axes that have unit length.
For instance, a shape (1,1,4,1,3) will be reshaped to (4,1,3).
"""
shape = np.array(np.shape(X))
inds = np.nonzero(shape != 1)[0]
if len(inds) == 0:
shape = ()
else:
shape = shape[inds[0]:]
return np.reshape(X, shape)
def squeeze_to_dim(X, dim):
s = tuple(range(np.ndim(X)-dim))
return np.squeeze(X, axis=s)
def axes_to_collapse(shape_x, shape_to):
# Solves which axes of shape shape_x need to be collapsed in order
# to get the shape shape_to
s = ()
for j in range(-len(shape_x), 0):
if shape_x[j] != 1:
if -j > len(shape_to) or shape_to[j] == 1:
s += (j,)
elif shape_to[j] != shape_x[j]:
print('Shape from: ' + str(shape_x))
print('Shape to: ' + str(shape_to))
raise Exception('Incompatible shape to squeeze')
return tuple(s)
def sum_to_shape(X, s):
"""
Sum axes of the array such that the resulting shape is as given.
Thus, the shape of the result will be s or an error is raised.
"""
# First, sum and remove axes that are not in s
if np.ndim(X) > len(s):
axes = tuple(range(-np.ndim(X), -len(s)))
else:
axes = ()
Y = np.sum(X, axis=axes)
# Second, sum axes that are 1 in s but keep the axes
axes = ()
for i in range(-np.ndim(Y), 0):
if s[i] == 1:
if np.shape(Y)[i] > 1:
axes = axes + (i,)
else:
if np.shape(Y)[i] != s[i]:
raise ValueError("Shape %s can't be summed to shape %s" %
(np.shape(X), s))
Y = np.sum(Y, axis=axes, keepdims=True)
return Y
def repeat_to_shape(A, s):
# Current shape
t = np.shape(A)
if len(t) > len(s):
raise Exception("Can't repeat to a smaller shape")
# Add extra axis
t = tuple([1]*(len(s)-len(t))) + t
A = np.reshape(A,t)
# Repeat
for i in reversed(range(len(s))):
if s[i] != t[i]:
if t[i] != 1:
raise Exception("Can't repeat non-singular dimensions")
else:
A = np.repeat(A, s[i], axis=i)
return A
#def spinv_chol(L):
def chol(C):
if sparse.issparse(C):
# Sparse Cholesky decomposition (returns a Factor object)
return cholmod.cholesky(C)
else:
# Dense Cholesky decomposition
return linalg.cho_factor(C)[0]
def chol_solve(U, b):
if isinstance(U, np.ndarray):
if sparse.issparse(b):
b = b.toarray()
return linalg.cho_solve((U, False), b)
elif isinstance(U, cholmod.Factor):
if sparse.issparse(b):
b = b.toarray()
return U.solve_A(b)
else:
raise ValueError("Unknown type of Cholesky factor")
def chol_inv(U):
if isinstance(U, np.ndarray):
I = np.identity(np.shape(U)[-1])
return linalg.cho_solve((U, False), I)
elif isinstance(U, cholmod.Factor):
raise NotImplementedError
## if sparse.issparse(b):
## b = b.toarray()
## return U.solve_A(b)
else:
raise ValueError("Unknown type of Cholesky factor")
def chol_logdet(U):
if isinstance(U, np.ndarray):
return 2*np.sum(np.log(np.diag(U)))
elif isinstance(U, cholmod.Factor):
return np.sum(np.log(U.D()))
else:
raise ValueError("Unknown type of Cholesky factor")
def logdet_chol(U):
if isinstance(U, np.ndarray):
return 2*np.sum(np.log(np.diag(U)))
elif isinstance(U, cholmod.Factor):
return np.sum(np.log(U.D()))
def m_solve_triangular(U, B, **kwargs):
# Allocate memory
U = np.atleast_2d(U)
B = np.atleast_1d(B)
sh_u = U.shape[:-2]
sh_b = B.shape[:-1]
l_u = len(sh_u)
l_b = len(sh_b)
# Check which axis are iterated over with B along with U
ind_b = [Ellipsis] * l_b
l_min = min(l_u, l_b)
jnd_b = tuple(i for i in range(-l_min,0) if sh_b[i]==sh_u[i])
# Shape of the result (broadcasting rules)
sh = broadcasted_shape(sh_u, sh_b)
#out = np.zeros(np.shape(B))
out = np.zeros(sh + B.shape[-1:])
## if out == None:
## # Shape of the result (broadcasting rules)
## sh = broadcasted_shape(sh_u, sh_b)
## #out = np.zeros(np.shape(B))
## out = np.zeros(sh + B.shape[-1:])
for i in nested_iterator(np.shape(U)[:-2]):
# The goal is to run triangular solver once for all vectors of
# B for which the matrices of U are the same (according to the
# broadcasting rules). Thus, we collect all the axes of B for
# which U is singleton and form them as a 2-D matrix and then
# run the solver once.
# Select those axes of B for which U and B are not singleton
for j in jnd_b:
ind_b[j] = i[j]
# Collect all the axes for which U is singleton
b = B[tuple(ind_b) + (Ellipsis,)]
# Reshape it to a 2-D (or 1-D) array
orig_shape = b.shape
if b.ndim > 1:
b = b.reshape((-1, b.shape[-1]))
# Ellipsis to all preceeding axes and ellipsis for the last
# axis:
if len(ind_b) < len(sh):
ind_out = (Ellipsis,) + tuple(ind_b) + (Ellipsis,)
else:
ind_out = tuple(ind_b) + (Ellipsis,)
#print('utils.m_solve_triangular', np.shape(U[i]), np.shape(b))
out[ind_out] = linalg.solve_triangular(U[i],
b.T,
**kwargs).T.reshape(orig_shape)
#out[ind_out] = out[ind_out].T.reshape(orig_shape)
return out
def m_chol(C):
# Computes Cholesky decomposition for a collection of matrices.
# The last two axes of C are considered as the matrix.
C = np.atleast_2d(C)
U = np.empty(np.shape(C))
#print('m_chol', C)
for i in nested_iterator(np.shape(U)[:-2]):
try:
U[i] = linalg.cho_factor(C[i])[0]
except np.linalg.linalg.LinAlgError:
print(C[i])
raise Exception("Matrix not positive definite")
return U
def m_chol_solve(U, B, out=None):
# Allocate memory
U = np.atleast_2d(U)
B = np.atleast_1d(B)
sh_u = U.shape[:-2]
sh_b = B.shape[:-1]
l_u = len(sh_u)
l_b = len(sh_b)
# Check which axis are iterated over with B along with U
ind_b = [Ellipsis] * l_b
l_min = min(l_u, l_b)
jnd_b = tuple(i for i in range(-l_min,0) if sh_b[i]==sh_u[i])
if out == None:
# Shape of the result (broadcasting rules)
sh = broadcasted_shape(sh_u, sh_b)
#out = np.zeros(np.shape(B))
out = np.zeros(sh + B.shape[-1:])
for i in nested_iterator(np.shape(U)[:-2]):
# The goal is to run Cholesky solver once for all vectors of B
# for which the matrices of U are the same (according to the
# broadcasting rules). Thus, we collect all the axes of B for
# which U is singleton and form them as a 2-D matrix and then
# run the solver once.
# Select those axes of B for which U and B are not singleton
for j in jnd_b:
ind_b[j] = i[j]
# Collect all the axes for which U is singleton
b = B[tuple(ind_b) + (Ellipsis,)]
# Reshape it to a 2-D (or 1-D) array
orig_shape = b.shape
if b.ndim > 1:
b = b.reshape((-1, b.shape[-1]))
# Ellipsis to all preceeding axes and ellipsis for the last
# axis:
if len(ind_b) < len(sh):
ind_out = (Ellipsis,) + tuple(ind_b) + (Ellipsis,)
else:
ind_out = tuple(ind_b) + (Ellipsis,)
out[ind_out] = linalg.cho_solve((U[i], False),
b.T).T.reshape(orig_shape)
return out
def m_chol_inv(U):
# Allocate memory
V = np.tile(np.identity(np.shape(U)[-1]), np.shape(U)[:-2]+(1,1))
for i in nested_iterator(np.shape(U)[:-2]):
V[i] = linalg.cho_solve((U[i], False),
V[i],
overwrite_b=True) # This would need Fortran order
return V
def m_chol_logdet(U):
# Computes Cholesky decomposition for a collection of matrices.
return 2*np.sum(np.log(np.einsum('...ii->...i', U)), axis=(-1,))
def m_digamma(a, d):
y = 0
for i in range(d):
y += special.digamma(a + 0.5*(1-i))
return y
def m_outer(A,B):
# Computes outer product over the last axes of A and B. The other
# axes are broadcasted. Thus, if A has shape (..., N) and B has
# shape (..., M), then the result has shape (..., N, M)
A = np.asanyarray(A)
B = np.asanyarray(B)
return A[...,np.newaxis]*B[...,np.newaxis,:]
def diagonal(A):
return np.diagonal(A, axis1=-2, axis2=-1)
def get_diag(X, ndim=1):
"""
Get the diagonal of an array.
If ndim>1, take the diagonal of the last 2*ndim axes.
"""
if ndim == 0:
return X
if ndim < 0:
raise ValueError("Parameter ndim must be non-negative integer")
if np.ndim(X) < 2*ndim:
raise ValueError("The array does not have enough axes")
if np.shape(X)[-ndim:] != np.shape(X)[-2*ndim:-ndim]:
raise ValueError("The array X is not square")
axes_out = tuple(range(np.ndim(X)-ndim, 0, -1))
axes_dim = tuple(range(ndim, 0, -1))
return np.einsum(X, axes_out+axes_dim, axes_out)
def diag(X, ndim=1):
"""
Create a diagonal array given the diagonal elements.
The diagonal array can be multi-dimensional. By default, the last axis is
transformed to two axes (diagonal matrix) but this can be changed using ndim
keyword. For instance, an array with shape (K,L,M,N) can be transformed to a
set of diagonal 4-D tensors with shape (K,L,M,N,M,N) by giving ndim=2. If
ndim=3, the result has shape (K,L,M,N,L,M,N), and so on.
Diagonality means that for the resulting array Y holds:
Y[...,i_1,i_2,..,i_ndim,j_1,j_2,..,j_ndim] is zero if i_n!=j_n for any n.
"""
X = atleast_nd(X, ndim)
if ndim > 0:
I = identity(*(np.shape(X)[-ndim:]))
X = add_axes(X, axis=np.ndim(X), num=ndim)
X = I * X
return X
def m_dot(A,b):
# Compute matrix-vector product over the last two axes of A and
# the last axes of b. Other axes are broadcasted. If A has shape
# (..., M, N) and b has shape (..., N), then the result has shape
# (..., M)
#b = reshape(b, shape(b)[:-1] + (1,) + shape(b)[-1:])
#return np.dot(A, b)
return np.einsum('...ik,...k->...i', A, b)
# TODO: Use einsum!!
#return np.sum(A*b[...,np.newaxis,:], axis=(-1,))
def block_banded(D, B):
"""
Construct a symmetric block-banded matrix.
`D` contains square diagonal blocks.
`B` contains super-diagonal blocks.
The resulting matrix is:
D[0], B[0], 0, 0, ..., 0, 0, 0
B[0].T, D[1], B[1], 0, ..., 0, 0, 0
0, B[1].T, D[2], B[2], ..., ..., ..., ...
... ... ... ... ..., B[N-2].T, D[N-1], B[N-1]
0, 0, 0, 0, ..., 0, B[N-1].T, D[N]
"""
D = [np.atleast_2d(d) for d in D]
B = [np.atleast_2d(b) for b in B]
# Number of diagonal blocks
N = len(D)
if len(B) != N-1:
raise ValueError("The number of super-diagonal blocks must contain "
"exactly one block less than the number of diagonal "
"blocks")
# Compute the size of the full matrix
M = 0
for i in range(N):
if np.ndim(D[i]) != 2:
raise ValueError("Blocks must be 2 dimensional arrays")
d = np.shape(D[i])
if d[0] != d[1]:
raise ValueError("Diagonal blocks must be square")
M += d[0]
for i in range(N-1):
if np.ndim(B[i]) != 2:
raise ValueError("Blocks must be 2 dimensional arrays")
b = np.shape(B[i])
if b[0] != np.shape(D[i])[1] or b[1] != np.shape(D[i+1])[0]:
raise ValueError("Shapes of the super-diagonal blocks do not match "
"the shapes of the diagonal blocks")
A = np.zeros((M,M))
k = 0
for i in range(N-1):
(d0, d1) = np.shape(B[i])
# Diagonal block
A[k:k+d0, k:k+d0] = D[i]
# Super-diagonal block
A[k:k+d0, k+d0:k+d0+d1] = B[i]
# Sub-diagonal block
A[k+d0:k+d0+d1, k:k+d0] = B[i].T
k += d0
A[k:,k:] = D[-1]
return A
def kalman_filter(y, U, A, V, mu0, Cov0, out=None):
"""
Perform Kalman filtering to obtain filtered mean and covariance.
The parameters of the process may vary in time, thus they are
given as iterators instead of fixed values.
Parameters
----------
y : (N,D) array
"Normalized" noisy observations of the states, that is, the
observations multiplied by the precision matrix U (and possibly
other transformation matrices).
U : (N,D,D) array or N-list of (D,D) arrays
Precision matrix (i.e., inverse covariance matrix) of the observation
noise for each time instance.
A : (N-1,D,D) array or (N-1)-list of (D,D) arrays
Dynamic matrix for each time instance.
V : (N-1,D,D) array or (N-1)-list of (D,D) arrays
Covariance matrix of the innovation noise for each time instance.
Returns
-------
mu : array
Filtered mean of the states.
Cov : array
Filtered covariance of the states.
See also
--------
rts_smoother
"""
mu = mu0
Cov = Cov0
# Allocate memory for the results
(N,D) = np.shape(y)
X = np.empty((N,D))
CovX = np.empty((N,D,D))
# Update step for t=0
M = np.dot(np.dot(Cov, U[0]), Cov) + Cov
L = chol(M)
mu = np.dot(Cov, chol_solve(L, np.dot(Cov,y[0]) + mu))
Cov = np.dot(Cov, chol_solve(L, Cov))
X[0,:] = mu
CovX[0,:,:] = Cov
#for (yn, Un, An, Vn) in zip(y, U, A, V):
for n in range(len(y)-1): #(yn, Un, An, Vn) in zip(y, U, A, V):
# Prediction step
mu = np.dot(A[n], mu)
Cov = np.dot(np.dot(A[n], Cov), A[n].T) + V[n]
# Update step
M = np.dot(np.dot(Cov, U[n+1]), Cov) + Cov
L = chol(M)
mu = np.dot(Cov, chol_solve(L, np.dot(Cov,y[n+1]) + mu))
Cov = np.dot(Cov, chol_solve(L, Cov))
# Force symmetric covariance (for numeric inaccuracy)
Cov = 0.5*Cov + 0.5*Cov.T
# Store results
X[n+1,:] = mu
CovX[n+1,:,:] = Cov
return (X, CovX)
def rts_smoother(mu, Cov, A, V, removethis=None):
"""
Perform Rauch-Tung-Striebel smoothing to obtain the posterior.
The function returns the posterior mean and covariance of each
state. The parameters of the process may vary in time, thus they
are given as iterators instead of fixed values.
Parameters
----------
mu : (N,D) array
Mean of the states from Kalman filter.
Cov : (N,D,D) array
Covariance of the states from Kalman filter.
A : (N-1,D,D) array or (N-1)-list of (D,D) arrays
Dynamic matrix for each time instance.
V : (N-1,D,D) array or (N-1)-list of (D,D) arrays
Covariance matrix of the innovation noise for each time instance.
Returns
-------
mu : array
Posterior mean of the states.
Cov : array
Posterior covariance of the states.
See also
--------
kalman_filter
"""
N = len(mu)
#n = N-1
# Start from the last time instance and smoothen backwards
x = mu[-1,:]
Covx = Cov[-1,:,:]
for n in reversed(range(N-1)):#(An, Vn) in zip(reversed(A), reversed(V)):
#n = n - 1
#if n <= 0:
# break
# The predicted value of n
x_p = np.dot(A[n], mu[n,:])
Cov_p = np.dot(np.dot(A[n], Cov[n,:,:]), A[n].T) + V[n]
# Temporary variable
S = np.linalg.solve(Cov_p, np.dot(A[n], Cov[n,:,:]))
# Smoothed value of n
x = mu[n,:] + np.dot(S.T, x-x_p)
Covx = Cov[n,:,:] + np.dot(np.dot(S.T, Covx-Cov_p), S)
# Force symmetric covariance (for numeric inaccuracy)
Covx = 0.5*Covx + 0.5*Covx.T
# Store results
mu[n,:] = x
Cov[n,:] = Covx
return (mu, Cov)
def dist_haversine(c1, c2, radius=6372795):
# Convert coordinates to radians
lat1 = np.atleast_1d(c1[0])[...,:,None] * np.pi / 180
lon1 = np.atleast_1d(c1[1])[...,:,None] * np.pi / 180
lat2 = np.atleast_1d(c2[0])[...,None,:] * np.pi / 180
lon2 = np.atleast_1d(c2[1])[...,None,:] * np.pi / 180
dlat = lat2 - lat1
dlon = lon2 - lon1
A = np.sin(dlat/2)**2 + np.cos(lat1)*np.cos(lat2)*(np.sin(dlon/2)**2)
C = 2 * np.arctan2(np.sqrt(A), np.sqrt(1-A))
return radius * C
def logsumexp(X, axis=None, keepdims=False):
"""
Compute log(sum(exp(X)) in a numerically stable way
"""
X = np.asanyarray(X)
maxX = np.amax(X, axis=axis, keepdims=True)
if np.ndim(maxX) > 0:
maxX[~np.isfinite(maxX)] = 0
elif not np.isfinite(maxX):
maxX = 0
X = X - maxX
if not keepdims:
maxX = np.squeeze(maxX, axis=axis)
return np.log(np.sum(np.exp(X), axis=axis, keepdims=keepdims)) + maxX
def mean(X, axis=None, keepdims=False):
"""
Compute the mean, ignoring NaNs.
"""
if np.ndim(X) == 0:
if axis is not None:
raise ValueError("Axis out of bounds")
return X
X = np.asanyarray(X)
nans = np.isnan(X)
X = X.copy()
X[nans] = 0
m = (np.sum(X, axis=axis, keepdims=keepdims) /
np.sum(~nans, axis=axis, keepdims=keepdims))
return m
bayespy-0.2.3/bayespy/utils/optimize.py 0000664 5104112 5104112 00000004347 12374370351 021755 0 ustar jluttine jluttine 0000000 0000000 ######################################################################
# Copyright (C) 2011-2013 Jaakko Luttinen
#
# This file is licensed under Version 3.0 of the GNU General Public
# License. See LICENSE for a text of the license.
######################################################################
######################################################################
# This file is part of BayesPy.
#
# BayesPy is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# BayesPy is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with BayesPy. If not, see .
######################################################################
import numpy as np
from scipy import optimize
def minimize(f, x0, maxiter=None, verbose=False):
"""
Simple wrapper for SciPy's optimize.
The given function must return a tuple: (value, gradient).
"""
options = {'disp': verbose}
if maxiter is not None:
options['maxiter'] = maxiter
opt = optimize.minimize(f, x0, jac=True, method='CG', options=options)
return opt.x
def check_gradient(f, x0, verbose=True):
"""
Simple wrapper for SciPy's gradient checker.
The given function must return a tuple: (value, gradient).
Returns relative
"""
df = f(x0)[1]
df_num = optimize.approx_fprime(x0,
lambda x: f(x)[0],
optimize.optimize._epsilon)
abserr = np.linalg.norm(df-df_num)
norm_num = np.linalg.norm(df_num)
if abserr == 0 and norm_num == 0:
err = 0
else:
err = abserr / norm_num
if verbose:
print("Norm of numerical gradient: %g" % np.linalg.norm(df_num))
print("Norm of function gradient: %g" % np.linalg.norm(df))
print("Gradient relative error = %g and absolute error = %g" %
(err,
abserr))
return err
bayespy-0.2.3/bayespy/utils/random.py 0000664 5104112 5104112 00000025542 12430650652 021373 0 ustar jluttine jluttine 0000000 0000000 ######################################################################
# Copyright (C) 2013 Jaakko Luttinen
#
# This file is licensed under Version 3.0 of the GNU General Public
# License. See LICENSE for a text of the license.
######################################################################
######################################################################
# This file is part of BayesPy.
#
# BayesPy is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# BayesPy is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with BayesPy. If not, see .
######################################################################
r"""
General functions random sampling and distributions.
"""
import numpy as np
from scipy import special
from . import linalg
from . import misc
def intervals(N, length, amount=1, gap=0):
r"""
Return random non-overlapping parts of a sequence.
For instance, N=16, length=2 and amount=4:
[0, |1, 2|, 3, 4, 5, |6, 7|, 8, 9, |10, 11|, |12, 13|, 14, 15]
that is,
[1,2,6,7,10,11,12,13]
However, the function returns only the indices of the beginning of the
sequences, that is, in the example:
[1,6,10,12]
"""
if length * amount + gap * (amount-1) > N:
raise ValueError("Too short sequence")
# In practice, we draw the sizes of the gaps between the sequences
total_gap = N - length*amount - gap*(amount-1)
gaps = np.random.multinomial(total_gap, np.ones(amount+1)/(amount+1))
# And then we get the beginning index of each sequence
intervals = np.cumsum(gaps[:-1]) + np.arange(amount)*(length+gap)
return intervals
def mask(*shape, p=0.5):
r"""
Return a boolean array of the given shape.
Parameters
----------
d0, d1, ..., dn : int
Shape of the output.
p : value in range [0,1]
A probability that the elements are `True`.
"""
return np.random.rand(*shape) < p
def wishart_rand(nu, V):
r"""
Draw a random sample from the Wishart distribution.
Parameters
----------
nu : int
"""
# TODO/FIXME: Are these correct..
D = np.shape(V)[0]
if nu < D:
raise ValueError("Degrees of freedom must be equal or greater than the "
"dimensionality of the matrix.")
X = np.random.multivariate_normal(np.zeros(D), V, size=nu)
return np.dot(X, X.T)
def invwishart_rand(nu, V):
# TODO/FIXME: Are these correct..
return np.linalg.inv(wishart_rand(nu, V))
def covariance(D, size=()):
r"""
Draw a random covariance matrix.
Draws from inverse-Wishart distribution. The distribution of each element is
independent of the dimensionality of the matrix.
C ~ Inv-W(I, D)
Parameters
----------
D : int
Dimensionality of the covariance matrix.
Returns:
--------
C : (D,D) ndarray
Positive-definite symmetric :math:`D\times D` matrix.
"""
if isinstance(size, int):
size = (size,)
shape = tuple(size) + (D,D)
C = np.random.randn(*shape)
C = linalg.dot(C, np.swapaxes(C, -1, -2))
return linalg.inv(C)
#return np.linalg.inv(np.dot(C, C.T))
def correlation(D):
r"""
Draw a random correlation matrix.
"""
X = np.random.randn(D,D);
s = np.sqrt(np.sum(X**2, axis=-1, keepdims=True))
X = X / s
return np.dot(X, X.T)
def gaussian_logpdf(yVy, yVmu, muVmu, logdet_V, D):
r"""
Log-density of a Gaussian distribution.
:math:`\mathcal{G}(\mathbf{y}|\boldsymbol{\mu},\mathbf{V}^{-1})`
Parameters
-----------
yVy : ndarray or double
:math:`\mathbf{y}^T\mathbf{Vy}`
yVmu : ndarray or double
:math:`\mathbf{y}^T\mathbf{V}\boldsymbol{\mu}`
muVmu : ndarray or double
:math:`\boldsymbol{\mu}^T\mathbf{V}\boldsymbol{\mu}`
logdet_V : ndarray or double
Log-determinant of the precision matrix, :math:`\log|\mathbf{V}|`.
D : int
Dimensionality of the distribution.
"""
return -0.5*yVy + yVmu - 0.5*muVmu + 0.5*logdet_V - 0.5*D*np.log(2*np.pi)
def gaussian_entropy(logdet_V, D):
r"""
Compute the entropy of a Gaussian distribution.
If you want to get the gradient, just let each parameter be a gradient of
that term.
Parameters
----------
logdet_V : ndarray or double
The log-determinant of the precision matrix.
D : int
The dimensionality of the distribution.
"""
return -0.5*logdet_V + 0.5*D + 0.5*D*np.log(2*np.pi)
def gamma_logpdf(bx, logx, a_logx, a_logb, gammaln_a):
r"""
Log-density of :math:`\mathcal{G}(x|a,b)`.
If you want to get the gradient, just let each parameter be a gradient of
that term.
Parameters
----------
bx : ndarray
:math:`bx`
logx : ndarray
:math:`\log(x)`
a_logx : ndarray
:math:`a \log(x)`
a_logb : ndarray
:math:`a \log(b)`
gammaln_a : ndarray
:math:`\log\Gamma(a)`
"""
return a_logb - gammaln_a + a_logx - logx - bx
#def gamma_logpdf(a, log_b, gammaln_a,
def gamma_entropy(a, log_b, gammaln_a, psi_a, a_psi_a):
r"""
Entropy of :math:`\mathcal{G}(a,b)`.
If you want to get the gradient, just let each parameter be a gradient of
that term.
Parameters
----------
a : ndarray
:math:`a`
log_b : ndarray
:math:`\log(b)`
gammaln_a : ndarray
:math:`\log\Gamma(a)`
psi_a : ndarray
:math:`\psi(a)`
a_psi_a : ndarray
:math:`a\psi(a)`
"""
return a - log_b + gammaln_a + psi_a - a_psi_a
def orth(D):
r"""
Draw random orthogonal matrix.
"""
Q = np.random.randn(D,D)
(Q, _) = np.linalg.qr(Q)
return Q
def svd(s):
r"""
Draw a random matrix given its singular values.
"""
D = len(s)
U = orth(D) * s
V = orth(D)
return np.dot(U, V.T)
def sphere(N=1):
r"""
Draw random points uniformly on a unit sphere.
Returns (latitude,longitude) in degrees.
"""
lon = np.random.uniform(-180, 180, N)
lat = (np.arccos(np.random.uniform(-1, 1, N)) * 180 / np.pi) - 90
return (lat, lon)
def bernoulli(p, size=None):
r"""
Draw random samples from the Bernoulli distribution.
"""
if isinstance(size, int):
size = (size,)
if size is None:
size = np.shape(p)
return (np.random.rand(*size) < p)
def categorical(p, size=None):
r"""
Draw random samples from a categorical distribution.
"""
if size is None:
size = np.shape(p)[:-1]
if isinstance(size, int):
size = (size,)
if np.any(np.asanyarray(p)<0):
raise ValueError("Array contains negative probabilities")
if not misc.is_shape_subset(np.shape(p)[:-1], size):
raise ValueError("Probability array shape and requested size are "
"inconsistent")
size = tuple(size)
# Normalize probabilities
p = p / np.sum(p, axis=-1, keepdims=True)
# Compute cumulative probabilities (p_1, p_1+p_2, ..., p_1+...+p_N):
P = np.cumsum(p, axis=-1)
# Draw samples from interval [0,1]
x = np.random.rand(*size)
# For simplicity, repeat p to the size of the output (plus probability axis)
K = np.shape(p)[-1]
P = P * np.ones(tuple(size)+(K,))
if size == ():
z = np.searchsorted(P, x)
else:
# Seach the indices
z = np.zeros(size)
inds = misc.nested_iterator(size)
for ind in inds:
z[ind] = np.searchsorted(P[ind], x[ind])
return z.astype(np.int)
def dirichlet(alpha, size=None):
r"""
Draw random samples from the Dirichlet distribution.
"""
if isinstance(size, int):
size = (size,)
if size is None:
size = np.shape(alpha)
else:
size = size + np.shape(alpha)[-1:]
p = np.random.gamma(alpha, size=size)
p /= np.sum(p, axis=-1, keepdims=True)
return p
def logodds_to_probability(x):
r"""
Solves p from log(p/(1-p))
"""
return 1 / (1 + np.exp(-x))
def alpha_beta_recursion(logp0, logP):
r"""
Compute alpha-beta recursion for Markov chain
Initial state log-probabilities are in `p0` and state transition
log-probabilities are in P. The probabilities do not need to be scaled to
sum to one, but they are interpreted as below:
logp0 = log P(z_0) + log P(y_0|z_0)
logP[...,n,:,:] = log P(z_{n+1}|z_n) + log P(y_{n+1}|z_{n+1})
"""
logp0 = misc.atleast_nd(logp0, 1)
logP = misc.atleast_nd(logP, 3)
D = np.shape(logp0)[-1]
N = np.shape(logP)[-3]
plates = misc.broadcasted_shape(np.shape(logp0)[:-1], np.shape(logP)[:-3])
if np.shape(logP)[-2:] != (D,D):
raise ValueError("Dimension mismatch %s != %s"
% (np.shape(logP)[-2:],
(D,D)))
#
# Run the recursion algorithm
#
# Allocate memory
logalpha = np.zeros(plates+(N,D))
logbeta = np.zeros(plates+(N,D))
g = np.zeros(plates)
# Forward recursion
logalpha[...,0,:] = logp0
for n in range(1,N):
# Compute: P(z_{n-1},z_n|x_1,...,x_n)
v = logalpha[...,n-1,:,None] + logP[...,n-1,:,:]
c = misc.logsumexp(v, axis=(-1,-2))
# Sum over z_{n-1} to get: log P(z_n|x_1,...,x_n)
logalpha[...,n,:] = misc.logsumexp(v - c[...,None,None], axis=-2)
g -= c
# Compute the normalization of the last term
v = logalpha[...,N-1,:,None] + logP[...,N-1,:,:]
g -= misc.logsumexp(v, axis=(-1,-2))
# Backward recursion
logbeta[...,N-1,:] = 0
for n in reversed(range(N-1)):
v = logbeta[...,n+1,None,:] + logP[...,n+1,:,:]
c = misc.logsumexp(v, axis=(-1,-2))
logbeta[...,n,:] = misc.logsumexp(v - c[...,None,None], axis=-1)
v = logalpha[...,:,:,None] + logbeta[...,:,None,:] + logP[...,:,:,:]
c = misc.logsumexp(v, axis=(-1,-2))
zz = np.exp(v - c[...,None,None])
# The logsumexp normalization is not numerically accurate, so do
# normalization again:
zz /= np.sum(zz, axis=(-1,-2), keepdims=True)
z0 = np.sum(zz[...,0,:,:], axis=-1)
z0 /= np.sum(z0, axis=-1, keepdims=True)
return (z0, zz, g)
def gaussian_gamma_to_t(mu, Cov, a, b, ndim=1):
r"""
Integrates gamma distribution to obtain parameters of t distribution
"""
alpha = a/b
nu = 2*a
S = Cov / misc.add_trailing_axes(alpha, 2*ndim)
return (mu, S, nu)
def t_logpdf(z2, logdet_cov, nu, D):
r"""
"""
return (special.gammaln((nu+D)/2)
- special.gammaln(nu/2)
- 0.5 * D * np.log(nu*np.pi)
- 0.5 * logdet_cov
- 0.5 * (nu+D) * np.log(1 + z2/nu))
bayespy-0.2.3/bayespy/__init__.py 0000664 5104112 5104112 00000002140 12374370350 020500 0 ustar jluttine jluttine 0000000 0000000 ######################################################################
# Copyright (C) 2011-2013 Jaakko Luttinen
#
# This file is licensed under Version 3.0 of the GNU General Public
# License. See LICENSE for a text of the license.
######################################################################
######################################################################
# This file is part of BayesPy.
#
# BayesPy is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# BayesPy is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with BayesPy. If not, see .
######################################################################
from . import utils
from . import inference
from . import nodes
from . import plot
bayespy-0.2.3/bayespy/plot.py 0000664 5104112 5104112 00000076516 12430651206 017734 0 ustar jluttine jluttine 0000000 0000000 ######################################################################
# Copyright (C) 2011-2013 Jaakko Luttinen
#
# This file is licensed under Version 3.0 of the GNU General Public
# License. See LICENSE for a text of the license.
######################################################################
######################################################################
# This file is part of BayesPy.
#
# BayesPy is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# BayesPy is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with BayesPy. If not, see .
######################################################################
"""
Functions for plotting nodes.
Functions
=========
.. currentmodule:: bayespy.plot
.. autosummary::
:toctree: generated/
pdf
contour
plot
hinton
gaussian_mixture
Plotters
========
.. autosummary::
:toctree: generated/
Plotter
PDFPlotter
ContourPlotter
HintonPlotter
FunctionPlotter
GaussianTimeseriesPlotter
CategoricalMarkovChainPlotter
"""
import os, sys
############################################################################
# A STUPID WORKAROUND FOR A MATPLOTLIB 1.4.0 BUG RELATED TO INTERACTIVE MODE
# See: https://github.com/matplotlib/matplotlib/issues/3505
import __main__
if hasattr(__main__, '__file__'):
sys.ps1 = ('WORKAROUND FOR A BUG #3505 IN MATPLOTLIB.\n'
'IF YOU SEE THIS MESSAGE, TRY MATPLOTLIB!=1.4.0.')
# This workaround does not work on Python shell, only on stand-alone scripts
# and IPython. A better solution: require MPL!=1.4.0.
#############################################################################
import numpy as np
import scipy.sparse as sp
import scipy
from scipy import special
import matplotlib.pyplot as plt
from matplotlib import animation
#from matplotlib.pyplot import *
from bayespy.inference.vmp.nodes.categorical import CategoricalMoments
from bayespy.inference.vmp.nodes.gaussian import (GaussianMoments,
GaussianWishartMoments)
from bayespy.inference.vmp.nodes.beta import BetaMoments
from bayespy.inference.vmp.nodes.beta import DirichletMoments
from bayespy.inference.vmp.nodes.bernoulli import BernoulliMoments
from bayespy.inference.vmp.nodes.categorical import CategoricalMoments
from bayespy.inference.vmp.nodes.node import Node
from bayespy.utils import (misc,
random,
linalg)
# Users can use pyplot via this module
import matplotlib
mpl = matplotlib
pyplot = plt
def interactive(function):
"""A decorator for forcing functions to use the interactive mode.
Parameters
----------
function : callable
The function to be decorated
"""
def new_function(*args, **kwargs):
if mpl.is_interactive():
was_interactive = True
else:
was_interactive = False
mpl.interactive(True)
retval = function(*args, **kwargs)
if not was_interactive:
mpl.interactive(False)
return retval
return new_function
def _subplots(plotfunc, *args, fig=None, kwargs={}):
"""Create a collection of subplots
Each subplot is created with the same plotting function.
Inputs are given as pairs:
(x, 3), (y, 2), ...
where x,y,... are the input arrays and 3,2,... are the ndim
parameters. The last ndim axes of each array are interpreted as a
single element to the plotting function.
All high-level plotting functions should wrap low-level plotting
functions with this function in order to generate subplots for
plates.
"""
if fig is None:
fig = plt.gcf()
# Parse shape and plates of each input array
shapes = [np.shape(x)[-n:] if n > 0 else ()
for (x,n) in args]
plates = [np.shape(x)[:-n] if n > 0 else np.shape(x)
for (x,n) in args]
# Get the full grid shape of the subplots
broadcasted_plates = misc.broadcasted_shape(*plates)
# Subplot indexing layout
M = np.prod(broadcasted_plates[-2::-2])
N = np.prod(broadcasted_plates[-1::-2])
strides_subplot = [np.prod(broadcasted_plates[(j+2)::2]) * N
if ((len(broadcasted_plates)-j) % 2) == 0 else
np.prod(broadcasted_plates[(j+2)::2])
for j in range(len(broadcasted_plates))]
# Plot each subplot
for ind in misc.nested_iterator(broadcasted_plates):
# Get the list of inputs for this subplot
broadcasted_args = []
for n in range(len(args)):
i = misc.safe_indices(ind, plates[n])
broadcasted_args.append(args[n][0][i])
# Plot the subplot using the given function
ind_subplot = np.einsum('i,i', ind, strides_subplot)
axes = fig.add_subplot(M, N, ind_subplot+1)
plotfunc(*broadcasted_args, axes=axes, **kwargs)
def pdf(Z, x, *args, name=None, axes=None, fig=None, **kwargs):
"""
Plot probability density function of a scalar variable.
Parameters
----------
Z : node or function
Stochastic node or log pdf function
x : array
Grid points
"""
# TODO: Make it possible to plot a plated variable using _subplots function.
if axes is None and fig is None:
axes = plt.gca()
else:
if fig is None:
fig = plt.gcf()
axes = fig.add_subplot(111)
try:
lpdf = Z.logpdf(x)
except AttributeError:
lpdf = Z(x)
p = np.exp(lpdf)
retval = axes.plot(x, p, *args, **kwargs)
if name is None:
try:
name = Z.name
except AttributeError:
pass
if name:
axes.set_title(r'$q(%s)$' % (name))
axes.set_xlabel(r'$%s$' % (name))
return retval
def contour(Z, x, y, n=None, axes=None, fig=None, **kwargs):
"""
Plot 2-D probability density function of a 2-D variable.
Parameters
----------
Z : node or function
Stochastic node or log pdf function
x : array
Grid points on x axis
y : array
Grid points on y axis
"""
# TODO: Make it possible to plot a plated variable using _subplots function.
if axes is None and fig is None:
axes = plt.gca()
else:
if fig is None:
fig = plt.gcf()
axes = fig.add_subplot(111)
XY = misc.grid(x, y)
try:
lpdf = Z.logpdf(XY)
except AttributeError:
lpdf = Z(XY)
p = np.exp(lpdf)
shape = (np.size(x), np.size(y))
X = np.reshape(XY[:,0], shape)
Y = np.reshape(XY[:,1], shape)
P = np.reshape(p, shape)
if n is not None:
levels = np.linspace(0, np.amax(P), num=n+2)[1:-1]
return axes.contour(X, Y, P, levels, **kwargs)
else:
return axes.contour(X, Y, P, **kwargs)
def plot_gaussian_mc(X, scale=2, **kwargs):
"""
Plot Gaussian Markov chain as a 1-D function
Parameters
----------
X : node
Node with Gaussian Markov chain moments.
"""
timeseries_gaussian(X, axis=-2, scale=scale, **kwargs)
def plot_bernoulli(X, axis=-1, scale=2, **kwargs):
"""
Plot Bernoulli node as a 1-D function
"""
X = X._convert(BernoulliMoments)
u_X = X.get_moments()
z = u_X[0]
return _timeseries_mean_and_error(z, None, axis=axis, **kwargs)
def plot_gaussian(X, axis=-1, scale=2, **kwargs):
"""
Plot Gaussian node as a 1-D function
Parameters
----------
X : node
Node with Gaussian moments.
axis : int
The index of the time axis.
"""
X = X._convert(GaussianMoments)
u_X = X.get_moments()
x = u_X[0]
xx = misc.get_diag(u_X[1], ndim=len(X.dims[0]))
std = scale * np.sqrt(xx - x**2)
#std = scale * np.sqrt(np.einsum('...ii->...i', xx) - x**2)
return _timeseries_mean_and_error(x, std, axis=axis, **kwargs)
def plot(Y, axis=-1, scale=2, center=False, **kwargs):
"""
Plot a variable or an array as 1-D function with errorbars
"""
if misc.is_numeric(Y):
return _timeseries_mean_and_error(Y, None, axis=axis, center=center, **kwargs)
if isinstance(Y, Node):
# Try Bernoulli plotting
try:
Y = Y._convert(BernoulliMoments)
except BernoulliMoments.NoConverterError:
pass
else:
return plot_bernoulli(Y, axis=axis, scale=scale, center=center, **kwargs)
# Try Gaussian plotting
try:
Y = Y._convert(GaussianMoments)
except GaussianMoments.NoConverterError:
pass
else:
return plot_gaussian(Y, axis=axis, scale=scale, center=center, **kwargs)
(mu, var) = Y.get_mean_and_variance()
std = np.sqrt(var)
return _timeseries_mean_and_error(mu, std,
axis=axis,
scale=scale,
center=center,
**kwargs)
# Some backward compatibility
def timeseries_gaussian_mc(*args, center=True, **kwargs):
return plot_gaussian_mc(*args, center=center, **kwargs)
def timeseries_gaussian(*args, center=True, **kwargs):
return plot_gaussian(*args, center=center, **kwargs)
timeseries_normal = timeseries_gaussian
def timeseries(*args, center=True, **kwargs):
return plot(*args, center=center, **kwargs)
def _timeseries_mean_and_error(y, std, *args, axis=-1, center=True, fig=None, **kwargs):
# TODO/FIXME: You must multiply by ones(plates) in order to plot
# broadcasted plates properly
if fig is None:
fig = plt.gcf()
y = np.atleast_1d(y)
shape = list(np.shape(y))
# Get and remove the length of the time axis
T = shape.pop(axis)
# Move time axis to first
y = np.rollaxis(y, axis)
if std is not None:
std = np.rollaxis(std, axis)
y = np.reshape(y, (T, -1))
if std is not None:
std = np.reshape(std, (T, -1))
# Remove 1s
shape = [s for s in shape if s > 1]
# Calculate number of rows and columns
shape = misc.multiply_shapes(shape, (1,1))
if len(shape) > 2:
raise Exception("Can plot only in 2 dimensions (rows and columns)")
(M, N) = shape
# Prefer plotting to rows
if M == 1:
M = N
N = 1
# Plot each timeseries
ax0 = fig.add_subplot(M, N, 1)
for i in range(M*N):
if i > 0:
# Share x axis between all subplots
ax = fig.add_subplot(M, N, i+1, sharex=ax0)
else:
ax = ax0
# Autoscale the axes to data and use tight y and x axes
ax.autoscale(enable=True, tight=True)
ax.set_ylim(auto=True)
if i < (M-1)*N:
# Remove x tick labels from other than the last row
plt.setp(ax.get_xticklabels(), visible=False)
if std is None:
errorplot(y=y[:,i], axes=ax, **kwargs)
else:
if len(args) > 0:
raise Exception("Can't handle extra arguments")
errorplot(y=y[:,i], error=std[:,i], axes=ax, **kwargs)
if center:
# Center the zero level on y-axis
ylim = ax.get_ylim()
vmax = np.max(np.abs(ylim))
ax.set_ylim([-vmax, vmax])
# Remove height space between subplots
fig.subplots_adjust(hspace=0)
def _blob(axes, x, y, area, colour):
"""
Draws a square-shaped blob with the given area (< 1) at
the given coordinates.
"""
hs = np.sqrt(area) / 2
xcorners = np.array([x - hs, x + hs, x + hs, x - hs])
ycorners = np.array([y - hs, y - hs, y + hs, y + hs])
axes.fill(xcorners, ycorners, colour, edgecolor=colour)
def _rectangle(axes, x, y, width, height, **kwargs):
_x = x - width/2
_y = y - height/2
rectangle = plt.Rectangle((_x, _y),
width,
height,
**kwargs)
axes.add_patch(rectangle)
return
def gaussian_mixture(X, scale=1, fill=False, axes=None, **kwargs):
"""
Plot Gaussian mixture as ellipses in 2-D
"""
if axes is None:
axes = plt.gca()
mu_Lambda = X.parents[1]._convert(GaussianWishartMoments)
(mu, _, Lambda, _) = mu_Lambda.get_moments()
mu = np.linalg.solve(Lambda, mu)
if len(mu_Lambda.plates) != 1:
raise NotImplementedError("Not yet implemented for more plates")
K = mu_Lambda.plates[0]
for k in range(K):
m = mu[k]
L = Lambda[k]
(u, W) = scipy.linalg.eigh(L)
u[0] = np.sqrt(1/u[0])
u[1] = np.sqrt(1/u[1])
width = 2*u[0]
height = 2*u[1]
angle = np.arctan(W[0,1] / W[0,0])
angle = 180 * angle / np.pi
ell = mpl.patches.Ellipse(m, scale*width, scale*height,
angle=(180+angle),
fill=fill,
**kwargs)
axes.add_artist(ell)
return
def _hinton(W, error=None, vmax=None, square=True, axes=None):
"""
Draws a Hinton diagram for visualizing a weight matrix.
Temporarily disables matplotlib interactive mode if it is on,
otherwise this takes forever.
Originally copied from
http://wiki.scipy.org/Cookbook/Matplotlib/HintonDiagrams
"""
if axes is None:
axes = plt.gca()
W = misc.atleast_nd(W, 2)
(height, width) = W.shape
if not vmax:
#vmax = 2**np.ceil(np.log(np.max(np.abs(W)))/np.log(2))
if error is not None:
vmax = np.max(np.abs(W) + error)
else:
vmax = np.max(np.abs(W))
axes.fill(0.5+np.array([0,width,width,0]),
0.5+np.array([0,0,height,height]),
'gray')
axes.axis('off')
if square:
axes.axis('equal')
axes.invert_yaxis()
for x in range(width):
for y in range(height):
_x = x+1
_y = y+1
w = W[y,x]
_w = np.abs(w)
if w > 0:
_c = 'white'
else:
_c = 'black'
if error is not None:
e = error[y,x]
if e < 0:
print(e, _w, vmax)
raise Exception("BUG? Negative error")
if _w + e > vmax:
print(e, _w, vmax)
raise Exception("BUG? Value+error greater than max")
_rectangle(axes,
_x,
_y,
min(1, np.sqrt((_w+e)/vmax)),
min(1, np.sqrt((_w+e)/vmax)),
edgecolor=_c,
fill=False)
_blob(axes, _x, _y, min(1, _w/vmax), _c)
def matrix(A, axes=None):
if axes is None:
axes = plt.gca()
A = np.atleast_2d(A)
vmax = np.max(np.abs(A))
return axes.imshow(A,
interpolation='nearest',
cmap='RdBu_r',
vmin=-vmax,
vmax=vmax)
def new_matrix(A, vmax=None):
A = np.atleast_2d(A)
if vmax is None:
vmax = np.max(np.abs(A))
(M, N) = np.shape(A)
for i in range(M):
for j in range(N):
pass
def gaussian_hinton(X, rows=None, cols=None, scale=1, fig=None):
"""
Plot the Hinton diagram of a Gaussian node
"""
if fig is None:
fig = plt.gcf()
# Get mean and second moment
X = X._convert(GaussianMoments)
(x, xx) = X.get_moments()
ndim = len(X.dims[0])
shape = X.get_shape(0)
size = len(X.get_shape(0))
# Compute standard deviation
xx = misc.get_diag(xx, ndim=ndim)
std = np.sqrt(xx - x**2)
# Force explicit elements when broadcasting
x = x * np.ones(shape)
std = std * np.ones(shape)
if rows is None:
rows = np.nan
if cols is None:
cols = np.nan
# Preprocess the axes to 0,...,ndim
if rows < 0:
rows += size
if cols < 0:
cols += size
if rows < 0 or rows >= size:
raise ValueError("Row axis invalid")
if cols < 0 or cols >= size:
raise ValueError("Column axis invalid")
# Remove non-row and non-column axes that have length 1
squeezed_shape = list(shape)
for i in reversed(range(len(shape))):
if shape[i] == 1 and i != rows and i != cols:
squeezed_shape.pop(i)
if i < cols:
cols -= 1
if i < rows:
rows -= 1
x = np.reshape(x, squeezed_shape)
std = np.reshape(std, squeezed_shape)
size = np.ndim(x)
if np.isnan(cols):
if rows != size - 1:
cols = size - 1
else:
cols = size - 2
if np.isnan(rows):
if cols != size - 1:
rows = size - 1
else:
rows = size - 2
# Put the row and column axes to the end
axes = [i for i in range(size) if i not in (rows, cols)] + [rows, cols]
x = np.transpose(x, axes=axes)
std = np.transpose(std, axes=axes)
vmax = np.max(np.abs(x) + scale*std)
if scale == 0:
_subplots(_hinton, (x, 2), fig=fig, kwargs=dict(vmax=vmax))
else:
def plotfunc(z, e, **kwargs):
return _hinton(z, error=e, **kwargs)
_subplots(plotfunc, (x, 2), (scale*std, 2), fig=fig, kwargs=dict(vmax=vmax))
# For backwards compatibility:
gaussian_array = gaussian_hinton
def timeseries_categorical_mc(Z, fig=None):
if fig is None:
fig = plt.gcf()
# Make sure that the node is categorical
Z = Z._convert(CategoricalMoments)
# Get expectations (and broadcast explicitly)
z = Z._message_to_child()[0] * np.ones(Z.get_shape(0))
# Compute the subplot layout
z = misc.atleast_nd(z, 4)
if np.ndim(z) != 4:
raise ValueError("Can not plot arrays with over 4 axes")
M = np.shape(z)[0]
N = np.shape(z)[1]
# Plot Hintons
for i in range(M):
for j in range(N):
axes = fig.add_subplot(M, N, i*N+j+1)
_hinton(z[i,j].T, vmax=1.0, square=False, axes=axes)
def beta_hinton(P, square=True):
"""
Plot a beta distributed random variable as a Hinton diagram
"""
# Make sure that the node is beta
P = P._convert(BetaMoments)
# Compute exp( )
p = np.exp(P._message_to_child()[0][...,0])
# Explicit broadcasting
p = p * np.ones(P.plates)
# Plot Hinton diagram
return _hinton(p, vmax=1.0, square=square)
def dirichlet_hinton(P, square=True):
"""
Plot a beta distributed random variable as a Hinton diagram
"""
# Make sure that the node is beta
P = P._convert(DirichletMoments)
# Compute exp( )
p = np.exp(P._message_to_child()[0])
# Explicit broadcasting
p = p * np.ones(P.plates+(1,))
# Plot Hinton diagram
return _hinton(p, vmax=1.0, square=square)
def bernoulli_hinton(Z, square=True):
"""
Plot a Bernoulli distributed random variable as a Hinton diagram
"""
# Make sure that the node is Bernoulli
Z = Z._convert(BernoulliMoments)
# Get
z = Z._message_to_child()[0]
# Explicit broadcasting
z = z * np.ones(Z.plates)
# Plot Hinton diagram
return _hinton(z, vmax=1.0, square=square)
def categorical_hinton(Z, square=True):
"""
Plot a Bernoulli distributed random variable as a Hinton diagram
"""
# Make sure that the node is Bernoulli
Z = Z._convert(CategoricalMoments)
# Get
z = Z._message_to_child()[0]
# Explicit broadcasting
z = z * np.ones(Z.plates+(1,))
# Plot Hinton diagram
return _hinton(np.squeeze(z), vmax=1.0, square=square)
def hinton(X, **kwargs):
r"""
Plot the Hinton diagram of a node
The keyword arguments depend on the node type. For some node types, the
diagram also shows uncertainty with non-filled rectangles. Currently,
beta-like, Gaussian-like and Dirichlet-like nodes are supported.
Parameters
----------
X : node
"""
try:
X = X._convert(GaussianMoments)
except:
pass
else:
return gaussian_hinton(X, **kwargs)
try:
X = X._convert(BetaMoments)
except:
pass
else:
return beta_hinton(X, **kwargs)
try:
X = X._convert(DirichletMoments)
except:
pass
else:
return dirichlet_hinton(X, **kwargs)
try:
X = X._convert(BernoulliMoments)
except:
pass
else:
return bernoulli_hinton(X, **kwargs)
try:
X = X._convert(CategoricalMoments)
except:
pass
else:
return categorical_hinton(X, **kwargs)
return _hinton(X, **kwargs)
class Plotter():
r"""
Wrapper for plotting functions and base class for node plotters
The purpose of this class is to collect all the parameters needed by a
plotting function and provide a callable interface which needs only the node
as the input.
Plotter instances are callable objects that plot a given node using a
specified plotting function.
Parameters
----------
plotter : function
Plotting function to use
args : defined by the plotting function
Additional inputs needed by the plotting function
kwargs : defined by the plotting function
Additional keyword arguments supported by the plotting function
Examples
--------
First, create a gamma variable:
>>> import numpy as np
>>> from bayespy.nodes import Gamma
>>> x = Gamma(4, 5)
The probability density function can be plotted as:
>>> import bayespy.plot as bpplt
>>> bpplt.pdf(x, np.linspace(0.1, 10, num=100)) # doctest: +ELLIPSIS
[]
However, this can be problematic when one needs to provide a
plotting function for the inference engine as the inference engine
gives only the node as input. Thus, we need to create a simple
plotter wrapper:
>>> p = bpplt.Plotter(bpplt.pdf, np.linspace(0.1, 10, num=100))
Now, this callable object ``p`` needs only the node as the input:
>>> p(x) # doctest: +ELLIPSIS
[]
Thus, it can be given to the inference engine to use as a plotting function:
>>> x = Gamma(4, 5, plotter=p)
>>> x.plot() # doctest: +ELLIPSIS
[]
"""
def __init__(self, plotter, *args, **kwargs):
self._args = args
self._kwargs = kwargs
self._plotter = plotter
def __call__(self, X, fig=None):
"""
Plot the node using the specified plotting function
Parameters
----------
X : node
The plotted node
"""
return self._plotter(X, *self._args, fig=fig, **self._kwargs)
class PDFPlotter(Plotter):
r"""
Plotter of probability density function of a scalar node
Parameters
----------
x_grid : array
Numerical grid on which the density function is computed and
plotted
See also
--------
pdf
"""
def __init__(self, x_grid, **kwargs):
super().__init__(pdf, x_grid, **kwargs)
class ContourPlotter(Plotter):
r"""
Plotter of probability density function of a two-dimensional node
Parameters
----------
x1_grid : array
Grid for the first dimension
x2_grid : array
Grid for the second dimension
See also
--------
contour
"""
def __init__(self, x1_grid, x2_grid, **kwargs):
super().__init__(contour, x1_grid, x2_grid, **kwargs)
class HintonPlotter(Plotter):
r"""
Plotter of the Hinton diagram of a node
See also
--------
hinton
"""
def __init__(self, **kwargs):
super().__init__(hinton, **kwargs)
class FunctionPlotter(Plotter):
r"""
Plotter of a node as a 1-dimensional function
See also
--------
plot
"""
def __init__(self, **kwargs):
super().__init__(plot, **kwargs)
class GaussianMarkovChainPlotter(Plotter):
r"""
Plotter of a Gaussian Markov chain as a timeseries
"""
def __init__(self, **kwargs):
super().__init__(timeseries_gaussian_mc, **kwargs)
class GaussianTimeseriesPlotter(Plotter):
r"""
Plotter of a Gaussian node as a timeseries
"""
def __init__(self, **kwargs):
super().__init__(timeseries_gaussian, **kwargs)
class GaussianHintonPlotter(Plotter):
r"""
Plotter of a Gaussian node as a Hinton diagram
"""
def __init__(self, **kwargs):
super().__init__(gaussian_array, **kwargs)
class CategoricalMarkovChainPlotter(Plotter):
r"""
Plotter of a Categorical timeseries
"""
def __init__(self, **kwargs):
super().__init__(timeseries_categorical_mc, **kwargs)
def matrix_animation(A, filename=None, fps=25, fig=None, **kwargs):
if fig is None:
fig = plt.gcf()
axes = fig.add_subplot(111)
A = np.atleast_3d(A)
vmax = np.max(np.abs(A))
x = axes.imshow(A[0],
interpolation='nearest',
cmap='RdBu_r',
vmin=-vmax,
vmax=vmax,
**kwargs)
s = axes.set_title('t = %d' % 0)
def animate(nframe):
s.set_text('t = %d' % nframe)
x.set_array(A[nframe])
return (x, s)
anim = animation.FuncAnimation(fig, animate,
frames=np.shape(A)[0],
interval=1000/fps,
blit=False,
repeat=False)
return anim
def save_animation(anim, filename, fps=25, bitrate=5000, fig=None):
# A bug in numpy/matplotlib causes this not to work in python3.3:
# https://github.com/matplotlib/matplotlib/issues/1891
#
# So the following command does not work currently..
#
# anim.save(filename, fps=fps)
if fig is None:
fig = plt.gcf()
writer = animation.FFMpegFileWriter(fps=fps, bitrate=bitrate)
writer.setup(fig, filename, 100)
anim.save(filename,
fps=fps,
writer=writer,
bitrate=bitrate)
return
def binary_matrix(A, axes=None):
if axes is None:
axes = plt.gca()
A = np.atleast_2d(A)
G = np.zeros(np.shape(A) + (3,))
G[A] = [0,0,0]
G[np.logical_not(A)] = [1,1,1]
axes.imshow(G, interpolation='nearest')
def gaussian_mixture_logpdf(x, w, mu, Sigma):
# Shape(x) = (N, D)
# Shape(w) = (K,)
# Shape(mu) = (K, D)
# Shape(Sigma) = (K, D, D)
# Shape(result) = (N,)
# Dimensionality
D = np.shape(x)[-1]
# Cholesky decomposition of the covariance matrix
U = misc.m_chol(Sigma)
# Reshape x:
# Shape(x) = (N, 1, D)
x = np.expand_dims(x, axis=-2)
# (x-mu) and (x-mu)'*inv(Sigma)*(x-mu):
# Shape(v) = (N, K, D)
# Shape(z) = (N, K)
v = x - mu
z = np.einsum('...i,...i', v, misc.m_chol_solve(U, v))
# Log-determinant of Sigma:
# Shape(ldet) = (K,)
ldet = misc.m_chol_logdet(U)
# Compute log pdf for each cluster:
# Shape(lpdf) = (N, K)
lpdf = misc.gaussian_logpdf(z, 0, 0, ldet, D)
def matrixplot(A, colorbar=False, axes=None):
if axes is None:
axes = plt.gca()
if sp.issparse(A):
A = A.toarray()
axes.imshow(A, interpolation='nearest')
if colorbar:
plt.colorbar(ax=axes)
def contourplot(x1, x2, y, colorbar=False, filled=True, axes=None):
""" Plots 2D contour plot. x1 and x2 are 1D vectors, y contains
the function values. y.size must be x1.size*x2.size. """
if axes is None:
axes = plt.gca()
y = np.reshape(y, (len(x2),len(x1)))
if filled:
axes.contourf(x1, x2, y)
else:
axes.contour(x1, x2, y)
if colorbar:
plt.colorbar(ax=axes)
def errorplot(y=None, error=None, x=None, lower=None, upper=None,
color=(0,0,0,1), fillcolor=(0,0,0,0.4), axes=None, **kwargs):
if axes is None:
axes = plt.gca()
# Default inputs
if x is None:
x = np.arange(np.size(y))
# Parse errors (lower=lower/error/upper, upper=upper/error/lower)
if lower is None:
if error is not None:
lower = error
elif upper is not None:
lower = upper
if upper is None:
if error is not None:
upper = error
elif lower is not None:
upper = lower
# Plot errors
if (lower is not None) and (upper is not None):
l = y - lower
u = y + upper
axes.fill_between(x,
l,
u,
facecolor=fillcolor,
edgecolor=(0, 0, 0, 0),
linewidth=1,
interpolate=True)
# Plot function
axes.plot(x, y, color=color, **kwargs)
def plotmatrix(X):
"""
Creates a matrix of marginal plots.
On diagonal, are marginal plots of each variable. Off-diagonal plot (i,j)
shows the joint marginal density of x_i and x_j.
"""
return X.plotmatrix()
def _pdf_t(mu, s2, nu, axes=None, scale=4, color='k'):
"""
"""
if axes is None:
axes = plt.gca()
s = np.sqrt(s2)
x = np.linspace(mu-scale*s, mu+scale*s, num=100)
y2 = (x-mu)**2 / s2
lpdf = random.t_logpdf(y2, np.log(s2), nu, 1)
p = np.exp(lpdf)
return axes.plot(x, p, color=color)
def _pdf_gamma(a, b, axes=None, scale=4, color='k'):
"""
"""
if axes is None:
axes = plt.gca()
if np.size(a) != 1 or np.size(b) != 1:
raise ValueError("Parameters must be scalars")
mean = a/b
v = scale*np.sqrt(a/b**2)
m = max(0, mean-v)
n = mean + v
x = np.linspace(m, n, num=100)
logx = np.log(x)
lpdf = random.gamma_logpdf(b*x,
logx,
a*logx,
a*np.log(b),
special.gammaln(a))
p = np.exp(lpdf)
return axes.plot(x, p, color=color)
def _contour_t(mu, Cov, nu, axes=None, scale=4, transpose=False, colors='k'):
"""
"""
if axes is None:
axes = plt.gca()
if np.shape(mu) != (2,) or np.shape(Cov) != (2,2) or np.shape(nu) != ():
print(np.shape(mu), np.shape(Cov), np.shape(nu))
raise ValueError("Only 2-d t-distribution allowed")
if transpose:
mu = mu[[1,0]]
Cov = Cov[np.ix_([1,0],[1,0])]
s = np.sqrt(np.diag(Cov))
x0 = np.linspace(mu[0]-scale*s[0], mu[0]+scale*s[0], num=100)
x1 = np.linspace(mu[1]-scale*s[1], mu[1]+scale*s[1], num=100)
X0X1 = misc.grid(x0, x1)
Y = X0X1 - mu
L = linalg.chol(Cov)
logdet_Cov = linalg.chol_logdet(L)
Z = linalg.chol_solve(L, Y)
Z = linalg.inner(Y, Z, ndim=1)
lpdf = random.t_logpdf(Z, logdet_Cov, nu, 2)
p = np.exp(lpdf)
shape = (np.size(x0), np.size(x1))
X0 = np.reshape(X0X1[:,0], shape)
X1 = np.reshape(X0X1[:,1], shape)
P = np.reshape(p, shape)
return axes.contour(X0, X1, P, colors=colors)
def _contour_gaussian_gamma(mu, s2, a, b, axes=None, transpose=False):
"""
"""
pass
bayespy-0.2.3/bayespy.egg-info/ 0000775 5104112 5104112 00000000000 12437607670 020074 5 ustar jluttine jluttine 0000000 0000000 bayespy-0.2.3/bayespy.egg-info/PKG-INFO 0000664 5104112 5104112 00000001600 12437607622 021163 0 ustar jluttine jluttine 0000000 0000000 Metadata-Version: 1.1
Name: bayespy
Version: 0.2.3
Summary: Bayesian inference tools for Python
Home-page: http://bayespy.org
Author: Jaakko Luttinen
Author-email: jaakko.luttinen@iki.fi
License: GPLv3
Description:
Bayesian inference tools for Python.
The package provides tools for building models and performing
posterior inference using variational Bayesian message passing.
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Development Status :: 3 - Alpha
Classifier: Environment :: Console
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Information Analysis
bayespy-0.2.3/bayespy.egg-info/SOURCES.txt 0000664 5104112 5104112 00000420253 12437607665 021772 0 ustar jluttine jluttine 0000000 0000000 INSTALL.rst
LICENSE
MANIFEST.in
README.rst
setup.cfg
setup.py
bayespy/__init__.py
bayespy/plot.py
bayespy.egg-info/PKG-INFO
bayespy.egg-info/SOURCES.txt
bayespy.egg-info/dependency_links.txt
bayespy.egg-info/requires.txt
bayespy.egg-info/top_level.txt
bayespy/demos/__init__.py
bayespy/demos/categorical.py
bayespy/demos/hmm.py
bayespy/demos/lssm.py
bayespy/demos/lssm_sd.py
bayespy/demos/lssm_tvd.py
bayespy/demos/mog.py
bayespy/demos/pca.py
bayespy/demos/saving.py
bayespy/inference/__init__.py
bayespy/inference/vmp/__init__.py
bayespy/inference/vmp/transformations.py
bayespy/inference/vmp/vmp.py
bayespy/inference/vmp/nodes/CovarianceFunctions.py
bayespy/inference/vmp/nodes/GaussianProcesses.py
bayespy/inference/vmp/nodes/__init__.py
bayespy/inference/vmp/nodes/bernoulli.py
bayespy/inference/vmp/nodes/beta.py
bayespy/inference/vmp/nodes/binomial.py
bayespy/inference/vmp/nodes/categorical.py
bayespy/inference/vmp/nodes/categorical_markov_chain.py
bayespy/inference/vmp/nodes/constant.py
bayespy/inference/vmp/nodes/deterministic.py
bayespy/inference/vmp/nodes/dirichlet.py
bayespy/inference/vmp/nodes/dot.py
bayespy/inference/vmp/nodes/expfamily.py
bayespy/inference/vmp/nodes/exponential.py
bayespy/inference/vmp/nodes/gamma.py
bayespy/inference/vmp/nodes/gate.py
bayespy/inference/vmp/nodes/gaussian.py
bayespy/inference/vmp/nodes/gaussian_markov_chain.py
bayespy/inference/vmp/nodes/gp.py
bayespy/inference/vmp/nodes/mixture.py
bayespy/inference/vmp/nodes/multinomial.py
bayespy/inference/vmp/nodes/node.py
bayespy/inference/vmp/nodes/poisson.py
bayespy/inference/vmp/nodes/stochastic.py
bayespy/inference/vmp/nodes/wishart.py
bayespy/inference/vmp/nodes/tests/__init__.py
bayespy/inference/vmp/nodes/tests/test_bernoulli.py
bayespy/inference/vmp/nodes/tests/test_beta.py
bayespy/inference/vmp/nodes/tests/test_binomial.py
bayespy/inference/vmp/nodes/tests/test_categorical.py
bayespy/inference/vmp/nodes/tests/test_categorical_markov_chain.py
bayespy/inference/vmp/nodes/tests/test_deterministic.py
bayespy/inference/vmp/nodes/tests/test_dirichlet.py
bayespy/inference/vmp/nodes/tests/test_dot.py
bayespy/inference/vmp/nodes/tests/test_gate.py
bayespy/inference/vmp/nodes/tests/test_gaussian.py
bayespy/inference/vmp/nodes/tests/test_gaussian_markov_chain.py
bayespy/inference/vmp/nodes/tests/test_mixture.py
bayespy/inference/vmp/nodes/tests/test_multinomial.py
bayespy/inference/vmp/nodes/tests/test_node.py
bayespy/inference/vmp/nodes/tests/test_poisson.py
bayespy/inference/vmp/tests/__init__.py
bayespy/inference/vmp/tests/test_transformations.py
bayespy/nodes/__init__.py
bayespy/utils/__init__.py
bayespy/utils/linalg.py
bayespy/utils/misc.py
bayespy/utils/optimize.py
bayespy/utils/random.py
bayespy/utils/covfunc/__init__.py
bayespy/utils/covfunc/covariance.py
bayespy/utils/covfunc/distance.pyx
bayespy/utils/covfunc/sparse_distance/sparse_distance.c
bayespy/utils/covfunc/sparse_distance/sparse_distance.h
bayespy/utils/covfunc/tests/__init__.py
bayespy/utils/covfunc/tests/test_distance.py
bayespy/utils/tests/__init__.py
bayespy/utils/tests/test_linalg.py
bayespy/utils/tests/test_misc.py
bayespy/utils/tests/test_random.py
doc/Makefile
doc/source/conf.py
doc/source/demos.rst
doc/source/index.rst
doc/source/intro.rst
doc/source/nodes.rst
doc/source/references.rst
doc/source/_templates/autosummary/class.rst
doc/source/_templates/autosummary/module.rst
doc/source/_templates/autosummary/short_module.rst
doc/source/dev_api/dev_api.rst
doc/source/dev_api/distributions.rst
doc/source/dev_api/moments.rst
doc/source/dev_api/nodes.rst
doc/source/dev_api/utils.rst
doc/source/dev_api/generated/bayespy.inference.vmp.nodes.bernoulli.BernoulliDistribution.rst
doc/source/dev_api/generated/bayespy.inference.vmp.nodes.bernoulli.BernoulliMoments.rst
doc/source/dev_api/generated/bayespy.inference.vmp.nodes.beta.BetaDistribution.rst
doc/source/dev_api/generated/bayespy.inference.vmp.nodes.beta.BetaMoments.rst
doc/source/dev_api/generated/bayespy.inference.vmp.nodes.binomial.BinomialDistribution.rst
doc/source/dev_api/generated/bayespy.inference.vmp.nodes.binomial.BinomialMoments.rst
doc/source/dev_api/generated/bayespy.inference.vmp.nodes.categorical.CategoricalDistribution.rst
doc/source/dev_api/generated/bayespy.inference.vmp.nodes.categorical.CategoricalMoments.rst
doc/source/dev_api/generated/bayespy.inference.vmp.nodes.categorical_markov_chain.CategoricalMarkovChainDistribution.rst
doc/source/dev_api/generated/bayespy.inference.vmp.nodes.categorical_markov_chain.CategoricalMarkovChainMoments.rst
doc/source/dev_api/generated/bayespy.inference.vmp.nodes.constant.Constant.rst
doc/source/dev_api/generated/bayespy.inference.vmp.nodes.deterministic.Deterministic.rst
doc/source/dev_api/generated/bayespy.inference.vmp.nodes.dirichlet.DirichletDistribution.rst
doc/source/dev_api/generated/bayespy.inference.vmp.nodes.dirichlet.DirichletMoments.rst
doc/source/dev_api/generated/bayespy.inference.vmp.nodes.expfamily.ExponentialFamily.rst
doc/source/dev_api/generated/bayespy.inference.vmp.nodes.expfamily.ExponentialFamilyDistribution.rst
doc/source/dev_api/generated/bayespy.inference.vmp.nodes.gamma.GammaDistribution.rst
doc/source/dev_api/generated/bayespy.inference.vmp.nodes.gamma.GammaMoments.rst
doc/source/dev_api/generated/bayespy.inference.vmp.nodes.gaussian.GaussianARDDistribution.rst
doc/source/dev_api/generated/bayespy.inference.vmp.nodes.gaussian.GaussianDistribution.rst
doc/source/dev_api/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaARDDistribution.rst
doc/source/dev_api/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaARDMoments.rst
doc/source/dev_api/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaARDToGaussianWishart.rst
doc/source/dev_api/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaISODistribution.rst
doc/source/dev_api/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaISOMoments.rst
doc/source/dev_api/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaISOToGaussianGammaARD.rst
doc/source/dev_api/generated/bayespy.inference.vmp.nodes.gaussian.GaussianMoments.rst
doc/source/dev_api/generated/bayespy.inference.vmp.nodes.gaussian.GaussianToGaussianGammaISO.rst
doc/source/dev_api/generated/bayespy.inference.vmp.nodes.gaussian.GaussianWishartDistribution.rst
doc/source/dev_api/generated/bayespy.inference.vmp.nodes.gaussian.GaussianWishartMoments.rst
doc/source/dev_api/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaussianGammaARD.rst
doc/source/dev_api/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaussianGammaISO.rst
doc/source/dev_api/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaussianWishart.rst
doc/source/dev_api/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.GaussianMarkovChainDistribution.rst
doc/source/dev_api/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.GaussianMarkovChainMoments.rst
doc/source/dev_api/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.SwitchingGaussianMarkovChainDistribution.rst
doc/source/dev_api/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.VaryingGaussianMarkovChainDistribution.rst
doc/source/dev_api/generated/bayespy.inference.vmp.nodes.multinomial.MultinomialDistribution.rst
doc/source/dev_api/generated/bayespy.inference.vmp.nodes.multinomial.MultinomialMoments.rst
doc/source/dev_api/generated/bayespy.inference.vmp.nodes.node.Moments.rst
doc/source/dev_api/generated/bayespy.inference.vmp.nodes.node.Node.rst
doc/source/dev_api/generated/bayespy.inference.vmp.nodes.poisson.PoissonDistribution.rst
doc/source/dev_api/generated/bayespy.inference.vmp.nodes.poisson.PoissonMoments.rst
doc/source/dev_api/generated/bayespy.inference.vmp.nodes.stochastic.Distribution.rst
doc/source/dev_api/generated/bayespy.inference.vmp.nodes.stochastic.Stochastic.rst
doc/source/dev_api/generated/bayespy.inference.vmp.nodes.wishart.WishartDistribution.rst
doc/source/dev_api/generated/bayespy.inference.vmp.nodes.wishart.WishartMoments.rst
doc/source/dev_api/generated/bayespy.utils.linalg.rst
doc/source/dev_api/generated/bayespy.utils.misc.rst
doc/source/dev_api/generated/bayespy.utils.optimize.rst
doc/source/dev_api/generated/bayespy.utils.random.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.bernoulli.BernoulliDistribution.__init__.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.bernoulli.BernoulliDistribution.compute_cgf_from_parents.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.bernoulli.BernoulliDistribution.compute_fixed_moments_and_f.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.bernoulli.BernoulliDistribution.compute_logpdf.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.bernoulli.BernoulliDistribution.compute_mask_to_parent.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.bernoulli.BernoulliDistribution.compute_message_to_parent.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.bernoulli.BernoulliDistribution.compute_moments_and_cgf.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.bernoulli.BernoulliDistribution.compute_phi_from_parents.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.bernoulli.BernoulliDistribution.plates_from_parent.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.bernoulli.BernoulliDistribution.plates_to_parent.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.bernoulli.BernoulliDistribution.random.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.bernoulli.BernoulliMoments.__init__.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.bernoulli.BernoulliMoments.add_converter.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.bernoulli.BernoulliMoments.compute_dims_from_values.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.bernoulli.BernoulliMoments.compute_fixed_moments.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.bernoulli.BernoulliMoments.get_converter.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.beta.BetaDistribution.compute_cgf_from_parents.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.beta.BetaDistribution.compute_fixed_moments_and_f.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.beta.BetaDistribution.compute_logpdf.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.beta.BetaDistribution.compute_mask_to_parent.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.beta.BetaDistribution.compute_message_to_parent.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.beta.BetaDistribution.compute_moments_and_cgf.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.beta.BetaDistribution.compute_phi_from_parents.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.beta.BetaDistribution.plates_from_parent.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.beta.BetaDistribution.plates_to_parent.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.beta.BetaDistribution.random.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.beta.BetaMoments.add_converter.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.beta.BetaMoments.compute_dims_from_values.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.beta.BetaMoments.compute_fixed_moments.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.beta.BetaMoments.get_converter.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.binomial.BinomialDistribution.__init__.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.binomial.BinomialDistribution.compute_cgf_from_parents.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.binomial.BinomialDistribution.compute_fixed_moments_and_f.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.binomial.BinomialDistribution.compute_logpdf.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.binomial.BinomialDistribution.compute_mask_to_parent.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.binomial.BinomialDistribution.compute_message_to_parent.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.binomial.BinomialDistribution.compute_moments_and_cgf.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.binomial.BinomialDistribution.compute_phi_from_parents.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.binomial.BinomialDistribution.plates_from_parent.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.binomial.BinomialDistribution.plates_to_parent.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.binomial.BinomialDistribution.random.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.binomial.BinomialMoments.__init__.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.binomial.BinomialMoments.add_converter.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.binomial.BinomialMoments.compute_dims_from_values.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.binomial.BinomialMoments.compute_fixed_moments.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.binomial.BinomialMoments.get_converter.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical.CategoricalDistribution.__init__.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical.CategoricalDistribution.compute_cgf_from_parents.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical.CategoricalDistribution.compute_fixed_moments_and_f.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical.CategoricalDistribution.compute_logpdf.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical.CategoricalDistribution.compute_mask_to_parent.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical.CategoricalDistribution.compute_message_to_parent.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical.CategoricalDistribution.compute_moments_and_cgf.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical.CategoricalDistribution.compute_phi_from_parents.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical.CategoricalDistribution.plates_from_parent.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical.CategoricalDistribution.plates_to_parent.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical.CategoricalDistribution.random.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical.CategoricalMoments.__init__.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical.CategoricalMoments.add_converter.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical.CategoricalMoments.compute_dims_from_values.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical.CategoricalMoments.compute_fixed_moments.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical.CategoricalMoments.get_converter.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical_markov_chain.CategoricalMarkovChainDistribution.__init__.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical_markov_chain.CategoricalMarkovChainDistribution.compute_cgf_from_parents.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical_markov_chain.CategoricalMarkovChainDistribution.compute_fixed_moments_and_f.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical_markov_chain.CategoricalMarkovChainDistribution.compute_logpdf.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical_markov_chain.CategoricalMarkovChainDistribution.compute_mask_to_parent.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical_markov_chain.CategoricalMarkovChainDistribution.compute_message_to_parent.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical_markov_chain.CategoricalMarkovChainDistribution.compute_moments_and_cgf.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical_markov_chain.CategoricalMarkovChainDistribution.compute_phi_from_parents.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical_markov_chain.CategoricalMarkovChainDistribution.plates_from_parent.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical_markov_chain.CategoricalMarkovChainDistribution.plates_to_parent.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical_markov_chain.CategoricalMarkovChainDistribution.random.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical_markov_chain.CategoricalMarkovChainMoments.__init__.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical_markov_chain.CategoricalMarkovChainMoments.add_converter.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical_markov_chain.CategoricalMarkovChainMoments.compute_dims_from_values.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical_markov_chain.CategoricalMarkovChainMoments.compute_fixed_moments.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical_markov_chain.CategoricalMarkovChainMoments.get_converter.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.constant.Constant.__init__.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.constant.Constant.add_plate_axis.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.constant.Constant.delete.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.constant.Constant.get_mask.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.constant.Constant.get_moments.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.constant.Constant.get_shape.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.constant.Constant.has_plotter.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.constant.Constant.move_plates.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.constant.Constant.plates.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.constant.Constant.plot.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.constant.Constant.set_plotter.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.deterministic.Deterministic.__init__.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.deterministic.Deterministic.add_plate_axis.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.deterministic.Deterministic.delete.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.deterministic.Deterministic.get_mask.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.deterministic.Deterministic.get_moments.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.deterministic.Deterministic.get_shape.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.deterministic.Deterministic.has_plotter.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.deterministic.Deterministic.lower_bound_contribution.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.deterministic.Deterministic.move_plates.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.deterministic.Deterministic.plates.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.deterministic.Deterministic.plot.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.deterministic.Deterministic.set_plotter.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.dirichlet.DirichletDistribution.compute_cgf_from_parents.rst
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doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.ExponentialFamily.__init__.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.ExponentialFamily.add_plate_axis.rst
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doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.ExponentialFamily.initialize_from_value.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.ExponentialFamily.load.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.ExponentialFamily.logpdf.rst
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doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.ExponentialFamily.lowerbound.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.ExponentialFamily.move_plates.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.ExponentialFamily.observe.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.ExponentialFamily.pdf.rst
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doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.ExponentialFamily.plot.rst
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doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.ExponentialFamily.unobserve.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.ExponentialFamily.update.rst
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doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gamma.GammaDistribution.compute_cgf_from_parents.rst
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doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianARDDistribution.__init__.rst
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doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianARDDistribution.compute_logpdf.rst
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doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaARDToGaussianWishart.__init__.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaARDToGaussianWishart.add_plate_axis.rst
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doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaISOToGaussianGammaARD.__init__.rst
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doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaISOToGaussianGammaARD.plot.rst
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doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianToGaussianGammaISO.__init__.rst
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doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianWishartDistribution.compute_cgf_from_parents.rst
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doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaussianWishart.__init__.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaussianWishart.add_plate_axis.rst
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doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaussianWishart.get_mask.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaussianWishart.get_moments.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaussianWishart.get_shape.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaussianWishart.has_plotter.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaussianWishart.lower_bound_contribution.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaussianWishart.move_plates.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaussianWishart.plates.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaussianWishart.plot.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaussianWishart.set_plotter.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.GaussianMarkovChainDistribution.__init__.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.GaussianMarkovChainDistribution.compute_cgf_from_parents.rst
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doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.GaussianMarkovChainDistribution.compute_logpdf.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.GaussianMarkovChainDistribution.compute_mask_to_parent.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.GaussianMarkovChainDistribution.compute_message_to_parent.rst
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doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.GaussianMarkovChainDistribution.compute_phi_from_parents.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.GaussianMarkovChainDistribution.plates_from_parent.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.GaussianMarkovChainDistribution.plates_to_parent.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.GaussianMarkovChainDistribution.random.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.GaussianMarkovChainMoments.add_converter.rst
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doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.GaussianMarkovChainMoments.compute_fixed_moments.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.GaussianMarkovChainMoments.get_converter.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.SwitchingGaussianMarkovChainDistribution.__init__.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.SwitchingGaussianMarkovChainDistribution.compute_cgf_from_parents.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.SwitchingGaussianMarkovChainDistribution.compute_fixed_moments_and_f.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.SwitchingGaussianMarkovChainDistribution.compute_logpdf.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.SwitchingGaussianMarkovChainDistribution.compute_mask_to_parent.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.SwitchingGaussianMarkovChainDistribution.compute_message_to_parent.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.SwitchingGaussianMarkovChainDistribution.compute_moments_and_cgf.rst
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doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.SwitchingGaussianMarkovChainDistribution.plates_from_parent.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.SwitchingGaussianMarkovChainDistribution.plates_to_parent.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.SwitchingGaussianMarkovChainDistribution.random.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.VaryingGaussianMarkovChainDistribution.__init__.rst
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doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.VaryingGaussianMarkovChainDistribution.compute_logpdf.rst
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doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.VaryingGaussianMarkovChainDistribution.compute_moments_and_cgf.rst
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doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.VaryingGaussianMarkovChainDistribution.plates_from_parent.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.VaryingGaussianMarkovChainDistribution.plates_to_parent.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.VaryingGaussianMarkovChainDistribution.random.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.multinomial.MultinomialDistribution.__init__.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.multinomial.MultinomialDistribution.compute_cgf_from_parents.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.multinomial.MultinomialDistribution.compute_fixed_moments_and_f.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.multinomial.MultinomialDistribution.compute_logpdf.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.multinomial.MultinomialDistribution.compute_mask_to_parent.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.multinomial.MultinomialDistribution.compute_message_to_parent.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.multinomial.MultinomialDistribution.compute_moments_and_cgf.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.multinomial.MultinomialDistribution.compute_phi_from_parents.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.multinomial.MultinomialDistribution.plates_from_parent.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.multinomial.MultinomialDistribution.plates_to_parent.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.multinomial.MultinomialDistribution.random.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.multinomial.MultinomialMoments.add_converter.rst
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doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.multinomial.MultinomialMoments.compute_fixed_moments.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.multinomial.MultinomialMoments.get_converter.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.node.Moments.add_converter.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.node.Moments.compute_dims_from_values.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.node.Moments.compute_fixed_moments.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.node.Moments.get_converter.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.node.Node.__init__.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.node.Node.add_plate_axis.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.node.Node.delete.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.node.Node.get_mask.rst
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doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.node.Node.move_plates.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.node.Node.plates.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.node.Node.plot.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.node.Node.set_plotter.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.poisson.PoissonDistribution.compute_cgf_from_parents.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.poisson.PoissonDistribution.compute_fixed_moments_and_f.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.poisson.PoissonDistribution.compute_logpdf.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.poisson.PoissonDistribution.compute_mask_to_parent.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.poisson.PoissonDistribution.compute_message_to_parent.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.poisson.PoissonDistribution.compute_moments_and_cgf.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.poisson.PoissonDistribution.compute_phi_from_parents.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.poisson.PoissonDistribution.plates_from_parent.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.poisson.PoissonDistribution.plates_to_parent.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.poisson.PoissonDistribution.random.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.poisson.PoissonMoments.add_converter.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.poisson.PoissonMoments.compute_dims_from_values.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.poisson.PoissonMoments.compute_fixed_moments.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.poisson.PoissonMoments.get_converter.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.stochastic.Distribution.compute_mask_to_parent.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.stochastic.Distribution.compute_message_to_parent.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.stochastic.Distribution.plates_from_parent.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.stochastic.Distribution.plates_to_parent.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.stochastic.Distribution.random.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.stochastic.Stochastic.__init__.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.stochastic.Stochastic.add_plate_axis.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.stochastic.Stochastic.delete.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.stochastic.Stochastic.get_mask.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.stochastic.Stochastic.get_moments.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.stochastic.Stochastic.get_shape.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.stochastic.Stochastic.has_plotter.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.stochastic.Stochastic.load.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.stochastic.Stochastic.lowerbound.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.stochastic.Stochastic.move_plates.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.stochastic.Stochastic.observe.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.stochastic.Stochastic.plates.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.stochastic.Stochastic.plot.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.stochastic.Stochastic.random.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.stochastic.Stochastic.save.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.stochastic.Stochastic.set_plotter.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.stochastic.Stochastic.unobserve.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.stochastic.Stochastic.update.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.wishart.WishartDistribution.compute_cgf_from_parents.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.wishart.WishartDistribution.compute_fixed_moments_and_f.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.wishart.WishartDistribution.compute_logpdf.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.wishart.WishartDistribution.compute_mask_to_parent.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.wishart.WishartDistribution.compute_message_to_parent.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.wishart.WishartDistribution.compute_moments_and_cgf.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.wishart.WishartDistribution.compute_phi_from_parents.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.wishart.WishartDistribution.plates_from_parent.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.wishart.WishartDistribution.plates_to_parent.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.wishart.WishartDistribution.random.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.wishart.WishartMoments.add_converter.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.wishart.WishartMoments.compute_dims_from_values.rst
doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.wishart.WishartMoments.compute_fixed_moments.rst
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doc/source/dev_api/generated/generated/bayespy.utils.linalg.block_banded_solve.rst
doc/source/dev_api/generated/generated/bayespy.utils.linalg.chol.rst
doc/source/dev_api/generated/generated/bayespy.utils.linalg.chol_inv.rst
doc/source/dev_api/generated/generated/bayespy.utils.linalg.chol_logdet.rst
doc/source/dev_api/generated/generated/bayespy.utils.linalg.chol_solve.rst
doc/source/dev_api/generated/generated/bayespy.utils.linalg.dot.rst
doc/source/dev_api/generated/generated/bayespy.utils.linalg.inner.rst
doc/source/dev_api/generated/generated/bayespy.utils.linalg.inv.rst
doc/source/dev_api/generated/generated/bayespy.utils.linalg.logdet_chol.rst
doc/source/dev_api/generated/generated/bayespy.utils.linalg.logdet_cov.rst
doc/source/dev_api/generated/generated/bayespy.utils.linalg.logdet_tri.rst
doc/source/dev_api/generated/generated/bayespy.utils.linalg.m_dot.rst
doc/source/dev_api/generated/generated/bayespy.utils.linalg.mmdot.rst
doc/source/dev_api/generated/generated/bayespy.utils.linalg.mvdot.rst
doc/source/dev_api/generated/generated/bayespy.utils.linalg.outer.rst
doc/source/dev_api/generated/generated/bayespy.utils.linalg.solve_triangular.rst
doc/source/dev_api/generated/generated/bayespy.utils.linalg.tracedot.rst
doc/source/dev_api/generated/generated/bayespy.utils.misc.CholeskyDense.rst
doc/source/dev_api/generated/generated/bayespy.utils.misc.CholeskySparse.rst
doc/source/dev_api/generated/generated/bayespy.utils.misc.T.rst
doc/source/dev_api/generated/generated/bayespy.utils.misc.TestCase.rst
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doc/source/dev_api/generated/generated/bayespy.utils.misc.atleast_nd.rst
doc/source/dev_api/generated/generated/bayespy.utils.misc.axes_to_collapse.rst
doc/source/dev_api/generated/generated/bayespy.utils.misc.block_banded.rst
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doc/source/dev_api/generated/generated/bayespy.utils.misc.ceildiv.rst
doc/source/dev_api/generated/generated/bayespy.utils.misc.check_gradient.rst
doc/source/dev_api/generated/generated/bayespy.utils.misc.chol.rst
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doc/source/dev_api/generated/generated/bayespy.utils.misc.chol_logdet.rst
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doc/source/dev_api/generated/generated/bayespy.utils.misc.composite_function.rst
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doc/source/dev_api/generated/generated/bayespy.utils.misc.dist_haversine.rst
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doc/source/dev_api/generated/generated/bayespy.utils.misc.gaussian_logpdf.rst
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doc/source/dev_api/generated/generated/bayespy.utils.misc.kalman_filter.rst
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doc/source/dev_api/generated/generated/bayespy.utils.misc.logsumexp.rst
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doc/source/dev_api/generated/generated/bayespy.utils.misc.m_digamma.rst
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doc/source/dev_api/generated/generated/bayespy.utils.misc.make_equal_length.rst
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doc/source/dev_api/generated/generated/bayespy.utils.misc.symm.rst
doc/source/dev_api/generated/generated/bayespy.utils.misc.tempfile.rst
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doc/source/dev_api/generated/generated/bayespy.utils.misc.unique.rst
doc/source/dev_api/generated/generated/bayespy.utils.misc.vb_optimize.rst
doc/source/dev_api/generated/generated/bayespy.utils.misc.vb_optimize_nodes.rst
doc/source/dev_api/generated/generated/bayespy.utils.misc.write_to_hdf5.rst
doc/source/dev_api/generated/generated/bayespy.utils.misc.zipper_merge.rst
doc/source/dev_api/generated/generated/bayespy.utils.optimize.check_gradient.rst
doc/source/dev_api/generated/generated/bayespy.utils.optimize.minimize.rst
doc/source/dev_api/generated/generated/bayespy.utils.random.alpha_beta_recursion.rst
doc/source/dev_api/generated/generated/bayespy.utils.random.bernoulli.rst
doc/source/dev_api/generated/generated/bayespy.utils.random.categorical.rst
doc/source/dev_api/generated/generated/bayespy.utils.random.correlation.rst
doc/source/dev_api/generated/generated/bayespy.utils.random.covariance.rst
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h5py bayespy-0.2.3/bayespy.egg-info/top_level.txt 0000664 5104112 5104112 00000000010 12437607622 022612 0 ustar jluttine jluttine 0000000 0000000 bayespy
bayespy-0.2.3/doc/ 0000775 5104112 5104112 00000000000 12437607670 015473 5 ustar jluttine jluttine 0000000 0000000 bayespy-0.2.3/doc/source/ 0000775 5104112 5104112 00000000000 12437607670 016773 5 ustar jluttine jluttine 0000000 0000000 bayespy-0.2.3/doc/source/_templates/ 0000775 5104112 5104112 00000000000 12437607670 021130 5 ustar jluttine jluttine 0000000 0000000 bayespy-0.2.3/doc/source/_templates/autosummary/ 0000775 5104112 5104112 00000000000 12437607670 023516 5 ustar jluttine jluttine 0000000 0000000 bayespy-0.2.3/doc/source/_templates/autosummary/class.rst 0000664 5104112 5104112 00000001105 12374370351 025343 0 ustar jluttine jluttine 0000000 0000000 {{ fullname }}
{{ underline }}
.. currentmodule:: {{ module }}
.. autoclass:: {{ objname }}
{% block methods %}
.. automethod:: __init__
{% if methods %}
.. rubric:: Methods
.. autosummary::
:toctree: generated/
{% for item in methods %}
~{{ name }}.{{ item }}
{%- endfor %}
{% endif %}
{% endblock %}
{% block attributes %}
{% if attributes %}
.. rubric:: Attributes
.. autosummary::
:toctree: generated/
{% for item in attributes %}
~{{ name }}.{{ item }}
{%- endfor %}
{% endif %}
{% endblock %}
bayespy-0.2.3/doc/source/_templates/autosummary/module.rst 0000664 5104112 5104112 00000001314 12374370351 025525 0 ustar jluttine jluttine 0000000 0000000 {{ fullname }}
{{ underline }}
.. automodule:: {{ fullname }}
{% block functions %}
{% if functions %}
.. rubric:: Functions
.. autosummary::
:toctree: generated/
{% for item in functions %}
{{ item }}
{%- endfor %}
{% endif %}
{% endblock %}
{% block classes %}
{% if classes %}
.. rubric:: Classes
.. autosummary::
:toctree: generated/
{% for item in classes %}
{{ item }}
{%- endfor %}
{% endif %}
{% endblock %}
{% block exceptions %}
{% if exceptions %}
.. rubric:: Exceptions
.. autosummary::
:toctree: generated/
{% for item in exceptions %}
{{ item }}
{%- endfor %}
{% endif %}
{% endblock %}
bayespy-0.2.3/doc/source/_templates/autosummary/short_module.rst 0000664 5104112 5104112 00000000077 12374370351 026751 0 ustar jluttine jluttine 0000000 0000000 {{ fullname }}
{{ underline }}
.. automodule:: {{ fullname }}
bayespy-0.2.3/doc/source/dev_api/ 0000775 5104112 5104112 00000000000 12437607670 020402 5 ustar jluttine jluttine 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/ 0000775 5104112 5104112 00000000000 12437607670 022340 5 ustar jluttine jluttine 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/ 0000775 5104112 5104112 00000000000 12437607671 024277 5 ustar jluttine jluttine 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/ 0000775 5104112 5104112 00000000000 12437607671 026235 5 ustar jluttine jluttine 0000000 0000000 ././@LongLink 0000000 0000000 0000000 00000000155 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.CholeskyDense.__init__.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.CholeskyDense.__in0000664 5104112 5104112 00000000242 12356463442 035361 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.CholeskyDense.__init__
=========================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: CholeskyDense.__init__ ././@LongLink 0000000 0000000 0000000 00000000153 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.CholeskyDense.logdet.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.CholeskyDense.logd0000664 5104112 5104112 00000000234 12356463442 035403 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.CholeskyDense.logdet
=======================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: CholeskyDense.logdet ././@LongLink 0000000 0000000 0000000 00000000152 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.CholeskyDense.solve.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.CholeskyDense.solv0000664 5104112 5104112 00000000231 12356463442 035436 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.CholeskyDense.solve
======================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: CholeskyDense.solve ././@LongLink 0000000 0000000 0000000 00000000171 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.CholeskyDense.trace_solve_gradient.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.CholeskyDense.trac0000664 5104112 5104112 00000000306 12356463442 035407 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.CholeskyDense.trace_solve_gradient
=====================================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: CholeskyDense.trace_solve_gradient ././@LongLink 0000000 0000000 0000000 00000000156 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.CholeskySparse.__init__.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.CholeskySparse.__i0000664 5104112 5104112 00000000245 12356463442 035405 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.CholeskySparse.__init__
==========================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: CholeskySparse.__init__ ././@LongLink 0000000 0000000 0000000 00000000154 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.CholeskySparse.logdet.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.CholeskySparse.log0000664 5104112 5104112 00000000237 12356463442 035441 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.CholeskySparse.logdet
========================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: CholeskySparse.logdet ././@LongLink 0000000 0000000 0000000 00000000153 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.CholeskySparse.solve.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.CholeskySparse.sol0000664 5104112 5104112 00000000234 12356463442 035452 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.CholeskySparse.solve
=======================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: CholeskySparse.solve ././@LongLink 0000000 0000000 0000000 00000000172 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.CholeskySparse.trace_solve_gradient.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.CholeskySparse.tra0000664 5104112 5104112 00000000311 12356463442 035437 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.CholeskySparse.trace_solve_gradient
======================================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: CholeskySparse.trace_solve_gradient ././@LongLink 0000000 0000000 0000000 00000000150 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.__init__.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.__init__.0000664 5104112 5104112 00000000223 12356463442 035244 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.TestCase.__init__
====================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: TestCase.__init__ ././@LongLink 0000000 0000000 0000000 00000000152 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.addCleanup.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.addCleanu0000664 5104112 5104112 00000000231 12356463442 035306 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.TestCase.addCleanup
======================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: TestCase.addCleanup ././@LongLink 0000000 0000000 0000000 00000000163 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.addTypeEqualityFunc.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.addTypeEq0000664 5104112 5104112 00000000264 12356463442 035314 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.TestCase.addTypeEqualityFunc
===============================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: TestCase.addTypeEqualityFunc ././@LongLink 0000000 0000000 0000000 00000000156 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertAllClose.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertAll0000664 5104112 5104112 00000000245 12356463442 035365 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.TestCase.assertAllClose
==========================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: TestCase.assertAllClose ././@LongLink 0000000 0000000 0000000 00000000161 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertAlmostEqual.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertAlm0000664 5104112 5104112 00000000256 12356463442 035370 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.TestCase.assertAlmostEqual
=============================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: TestCase.assertAlmostEqual ././@LongLink 0000000 0000000 0000000 00000000162 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertAlmostEquals.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertAlm0000664 5104112 5104112 00000000261 12356463442 035364 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.TestCase.assertAlmostEquals
==============================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: TestCase.assertAlmostEquals ././@LongLink 0000000 0000000 0000000 00000000160 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertArrayEqual.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertArr0000664 5104112 5104112 00000000253 12356463442 035400 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.TestCase.assertArrayEqual
============================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: TestCase.assertArrayEqual ././@LongLink 0000000 0000000 0000000 00000000160 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertCountEqual.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertCou0000664 5104112 5104112 00000000253 12356463442 035402 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.TestCase.assertCountEqual
============================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: TestCase.assertCountEqual ././@LongLink 0000000 0000000 0000000 00000000170 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertDictContainsSubset.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertDic0000664 5104112 5104112 00000000303 12356463442 035347 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.TestCase.assertDictContainsSubset
====================================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: TestCase.assertDictContainsSubset ././@LongLink 0000000 0000000 0000000 00000000157 00000000000 011220 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertDictEqual.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertDic0000664 5104112 5104112 00000000250 12356463442 035350 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.TestCase.assertDictEqual
===========================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: TestCase.assertDictEqual ././@LongLink 0000000 0000000 0000000 00000000153 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertEqual.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertEqu0000664 5104112 5104112 00000000234 12356463442 035405 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.TestCase.assertEqual
=======================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: TestCase.assertEqual ././@LongLink 0000000 0000000 0000000 00000000154 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertEquals.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertEqu0000664 5104112 5104112 00000000237 12356463442 035410 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.TestCase.assertEquals
========================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: TestCase.assertEquals ././@LongLink 0000000 0000000 0000000 00000000153 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertFalse.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertFal0000664 5104112 5104112 00000000234 12356463442 035355 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.TestCase.assertFalse
=======================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: TestCase.assertFalse ././@LongLink 0000000 0000000 0000000 00000000155 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertGreater.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertGre0000664 5104112 5104112 00000000242 12356463442 035367 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.TestCase.assertGreater
=========================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: TestCase.assertGreater ././@LongLink 0000000 0000000 0000000 00000000162 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertGreaterEqual.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertGre0000664 5104112 5104112 00000000261 12356463442 035370 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.TestCase.assertGreaterEqual
==============================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: TestCase.assertGreaterEqual ././@LongLink 0000000 0000000 0000000 00000000150 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertIn.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertIn.0000664 5104112 5104112 00000000223 12356463442 035275 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.TestCase.assertIn
====================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: TestCase.assertIn ././@LongLink 0000000 0000000 0000000 00000000150 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertIs.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertIs.0000664 5104112 5104112 00000000223 12356463442 035302 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.TestCase.assertIs
====================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: TestCase.assertIs ././@LongLink 0000000 0000000 0000000 00000000160 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertIsInstance.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertIsI0000664 5104112 5104112 00000000253 12356463442 035340 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.TestCase.assertIsInstance
============================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: TestCase.assertIsInstance ././@LongLink 0000000 0000000 0000000 00000000154 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertIsNone.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertIsN0000664 5104112 5104112 00000000237 12356463442 035347 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.TestCase.assertIsNone
========================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: TestCase.assertIsNone ././@LongLink 0000000 0000000 0000000 00000000153 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertIsNot.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertIsN0000664 5104112 5104112 00000000234 12356463442 035344 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.TestCase.assertIsNot
=======================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: TestCase.assertIsNot ././@LongLink 0000000 0000000 0000000 00000000157 00000000000 011220 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertIsNotNone.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertIsN0000664 5104112 5104112 00000000250 12356463442 035342 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.TestCase.assertIsNotNone
===========================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: TestCase.assertIsNotNone ././@LongLink 0000000 0000000 0000000 00000000152 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertLess.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertLes0000664 5104112 5104112 00000000231 12356463442 035373 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.TestCase.assertLess
======================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: TestCase.assertLess ././@LongLink 0000000 0000000 0000000 00000000157 00000000000 011220 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertLessEqual.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertLes0000664 5104112 5104112 00000000250 12356463442 035374 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.TestCase.assertLessEqual
===========================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: TestCase.assertLessEqual ././@LongLink 0000000 0000000 0000000 00000000157 00000000000 011220 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertListEqual.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertLis0000664 5104112 5104112 00000000250 12356463442 035400 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.TestCase.assertListEqual
===========================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: TestCase.assertListEqual ././@LongLink 0000000 0000000 0000000 00000000155 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertMessage.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertMes0000664 5104112 5104112 00000000242 12356463442 035376 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.TestCase.assertMessage
=========================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: TestCase.assertMessage bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.fail.rst 0000664 5104112 5104112 00000000207 12356463443 035154 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.TestCase.fail
================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: TestCase.fail ././@LongLink 0000000 0000000 0000000 00000000164 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertMessageToChild.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertMes0000664 5104112 5104112 00000000267 12356463442 035405 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.TestCase.assertMessageToChild
================================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: TestCase.assertMessageToChild ././@LongLink 0000000 0000000 0000000 00000000164 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertMultiLineEqual.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertMul0000664 5104112 5104112 00000000267 12356463442 035416 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.TestCase.assertMultiLineEqual
================================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: TestCase.assertMultiLineEqual ././@LongLink 0000000 0000000 0000000 00000000164 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertNotAlmostEqual.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertNot0000664 5104112 5104112 00000000267 12356463442 035421 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.TestCase.assertNotAlmostEqual
================================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: TestCase.assertNotAlmostEqual ././@LongLink 0000000 0000000 0000000 00000000165 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertNotAlmostEquals.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertNot0000664 5104112 5104112 00000000272 12356463442 035415 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.TestCase.assertNotAlmostEquals
=================================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: TestCase.assertNotAlmostEquals ././@LongLink 0000000 0000000 0000000 00000000156 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertNotEqual.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertNot0000664 5104112 5104112 00000000245 12356463442 035415 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.TestCase.assertNotEqual
==========================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: TestCase.assertNotEqual ././@LongLink 0000000 0000000 0000000 00000000157 00000000000 011220 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertNotEquals.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertNot0000664 5104112 5104112 00000000250 12356463442 035411 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.TestCase.assertNotEquals
===========================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: TestCase.assertNotEquals ././@LongLink 0000000 0000000 0000000 00000000153 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertNotIn.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertNot0000664 5104112 5104112 00000000234 12356463442 035413 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.TestCase.assertNotIn
=======================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: TestCase.assertNotIn ././@LongLink 0000000 0000000 0000000 00000000163 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertNotIsInstance.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertNot0000664 5104112 5104112 00000000264 12356463442 035416 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.TestCase.assertNotIsInstance
===============================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: TestCase.assertNotIsInstance ././@LongLink 0000000 0000000 0000000 00000000156 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertNotRegex.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertNot0000664 5104112 5104112 00000000245 12356463442 035415 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.TestCase.assertNotRegex
==========================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: TestCase.assertNotRegex ././@LongLink 0000000 0000000 0000000 00000000154 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertRaises.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertRai0000664 5104112 5104112 00000000237 12356463442 035371 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.TestCase.assertRaises
========================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: TestCase.assertRaises ././@LongLink 0000000 0000000 0000000 00000000161 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertRaisesRegex.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertRai0000664 5104112 5104112 00000000256 12356463442 035372 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.TestCase.assertRaisesRegex
=============================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: TestCase.assertRaisesRegex ././@LongLink 0000000 0000000 0000000 00000000162 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertRaisesRegexp.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertRai0000664 5104112 5104112 00000000261 12356463442 035366 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.TestCase.assertRaisesRegexp
==============================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: TestCase.assertRaisesRegexp ././@LongLink 0000000 0000000 0000000 00000000153 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertRegex.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertReg0000664 5104112 5104112 00000000234 12356463442 035370 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.TestCase.assertRegex
=======================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: TestCase.assertRegex ././@LongLink 0000000 0000000 0000000 00000000163 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertRegexpMatches.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertReg0000664 5104112 5104112 00000000264 12356463442 035373 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.TestCase.assertRegexpMatches
===============================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: TestCase.assertRegexpMatches ././@LongLink 0000000 0000000 0000000 00000000163 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertSequenceEqual.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertSeq0000664 5104112 5104112 00000000264 12356463442 035406 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.TestCase.assertSequenceEqual
===============================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: TestCase.assertSequenceEqual ././@LongLink 0000000 0000000 0000000 00000000156 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertSetEqual.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertSet0000664 5104112 5104112 00000000245 12356463442 035410 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.TestCase.assertSetEqual
==========================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: TestCase.assertSetEqual ././@LongLink 0000000 0000000 0000000 00000000152 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertTrue.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertTru0000664 5104112 5104112 00000000231 12356463442 035422 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.TestCase.assertTrue
======================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: TestCase.assertTrue ././@LongLink 0000000 0000000 0000000 00000000160 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertTupleEqual.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertTup0000664 5104112 5104112 00000000253 12356463442 035424 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.TestCase.assertTupleEqual
============================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: TestCase.assertTupleEqual ././@LongLink 0000000 0000000 0000000 00000000153 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertWarns.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertWar0000664 5104112 5104112 00000000234 12356463442 035404 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.TestCase.assertWarns
=======================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: TestCase.assertWarns ././@LongLink 0000000 0000000 0000000 00000000160 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertWarnsRegex.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assertWar0000664 5104112 5104112 00000000253 12356463442 035405 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.TestCase.assertWarnsRegex
============================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: TestCase.assertWarnsRegex ././@LongLink 0000000 0000000 0000000 00000000147 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assert_.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.assert_.r0000664 5104112 5104112 00000000220 12356463442 035324 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.TestCase.assert_
===================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: TestCase.assert_ ././@LongLink 0000000 0000000 0000000 00000000156 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.countTestCases.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.countTest0000664 5104112 5104112 00000000245 12356463442 035423 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.TestCase.countTestCases
==========================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: TestCase.countTestCases bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.debug.rst0000664 5104112 5104112 00000000212 12356463442 035322 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.TestCase.debug
=================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: TestCase.debug ././@LongLink 0000000 0000000 0000000 00000000161 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.defaultTestResult.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.defaultTe0000664 5104112 5104112 00000000256 12356463443 035353 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.TestCase.defaultTestResult
=============================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: TestCase.defaultTestResult ././@LongLink 0000000 0000000 0000000 00000000152 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.doCleanups.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.doCleanup0000664 5104112 5104112 00000000231 12356463443 035341 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.TestCase.doCleanups
======================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: TestCase.doCleanups ././@LongLink 0000000 0000000 0000000 00000000146 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.failIf.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.failIf.rs0000664 5104112 5104112 00000000215 12356463443 035246 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.TestCase.failIf
==================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: TestCase.failIf ././@LongLink 0000000 0000000 0000000 00000000161 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.failIfAlmostEqual.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.failIfAlm0000664 5104112 5104112 00000000256 12356463443 035262 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.TestCase.failIfAlmostEqual
=============================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: TestCase.failIfAlmostEqual ././@LongLink 0000000 0000000 0000000 00000000153 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.failIfEqual.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.failIfEqu0000664 5104112 5104112 00000000234 12356463443 035277 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.TestCase.failIfEqual
=======================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: TestCase.failIfEqual ././@LongLink 0000000 0000000 0000000 00000000152 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.failUnless.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.failUnles0000664 5104112 5104112 00000000231 12356463443 035351 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.TestCase.failUnless
======================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: TestCase.failUnless ././@LongLink 0000000 0000000 0000000 00000000165 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.failUnlessAlmostEqual.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.failUnles0000664 5104112 5104112 00000000272 12356463443 035356 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.TestCase.failUnlessAlmostEqual
=================================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: TestCase.failUnlessAlmostEqual ././@LongLink 0000000 0000000 0000000 00000000157 00000000000 011220 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.failUnlessEqual.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.failUnles0000664 5104112 5104112 00000000250 12356463443 035352 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.TestCase.failUnlessEqual
===========================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: TestCase.failUnlessEqual ././@LongLink 0000000 0000000 0000000 00000000160 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.failUnlessRaises.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.failUnles0000664 5104112 5104112 00000000253 12356463443 035355 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.TestCase.failUnlessRaises
============================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: TestCase.failUnlessRaises bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.id.rst 0000664 5104112 5104112 00000000201 12356463443 034627 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.TestCase.id
==============================
.. currentmodule:: bayespy.utils.misc
.. automethod:: TestCase.id bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.run.rst 0000664 5104112 5104112 00000000204 12356463443 035042 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.TestCase.run
===============================
.. currentmodule:: bayespy.utils.misc
.. automethod:: TestCase.run ././@LongLink 0000000 0000000 0000000 00000000153 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.longMessage.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.longMessa0000664 5104112 5104112 00000000237 12356463443 035365 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.TestCase.longMessage
=======================================
.. currentmodule:: bayespy.utils.misc
.. autoattribute:: TestCase.longMessage ././@LongLink 0000000 0000000 0000000 00000000147 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.maxDiff.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.maxDiff.r0000664 5104112 5104112 00000000223 12356463443 035246 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.TestCase.maxDiff
===================================
.. currentmodule:: bayespy.utils.misc
.. autoattribute:: TestCase.maxDiff bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.setUp.rst0000664 5104112 5104112 00000000212 12356463443 035335 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.TestCase.setUp
=================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: TestCase.setUp ././@LongLink 0000000 0000000 0000000 00000000152 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.setUpClass.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.setUpClas0000664 5104112 5104112 00000000231 12356463443 035332 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.TestCase.setUpClass
======================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: TestCase.setUpClass ././@LongLink 0000000 0000000 0000000 00000000160 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.shortDescription.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.shortDesc0000664 5104112 5104112 00000000253 12356463443 035371 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.TestCase.shortDescription
============================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: TestCase.shortDescription ././@LongLink 0000000 0000000 0000000 00000000150 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.skipTest.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.skipTest.0000664 5104112 5104112 00000000223 12356463443 035314 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.TestCase.skipTest
====================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: TestCase.skipTest ././@LongLink 0000000 0000000 0000000 00000000150 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.tearDown.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.tearDown.0000664 5104112 5104112 00000000223 12356463443 035271 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.TestCase.tearDown
====================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: TestCase.tearDown ././@LongLink 0000000 0000000 0000000 00000000155 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.tearDownClass.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/generated/bayespy.utils.misc.TestCase.tearDownC0000664 5104112 5104112 00000000242 12356463443 035317 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.TestCase.tearDownClass
=========================================
.. currentmodule:: bayespy.utils.misc
.. automethod:: TestCase.tearDownClass ././@LongLink 0000000 0000000 0000000 00000000176 00000000000 011221 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.bernoulli.BernoulliDistribution.__init__.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.bernoulli.Bernoulli0000664 5104112 5104112 00000000363 12356463433 035465 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.bernoulli.BernoulliDistribution.__init__
====================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.bernoulli
.. automethod:: BernoulliDistribution.__init__ ././@LongLink 0000000 0000000 0000000 00000000216 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.bernoulli.BernoulliDistribution.compute_cgf_from_parents.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.bernoulli.Bernoulli0000664 5104112 5104112 00000000443 12356463433 035464 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.bernoulli.BernoulliDistribution.compute_cgf_from_parents
====================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.bernoulli
.. automethod:: BernoulliDistribution.compute_cgf_from_parents ././@LongLink 0000000 0000000 0000000 00000000221 00000000000 011210 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.bernoulli.BernoulliDistribution.compute_fixed_moments_and_f.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.bernoulli.Bernoulli0000664 5104112 5104112 00000000454 12356463433 035466 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.bernoulli.BernoulliDistribution.compute_fixed_moments_and_f
=======================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.bernoulli
.. automethod:: BernoulliDistribution.compute_fixed_moments_and_f ././@LongLink 0000000 0000000 0000000 00000000204 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.bernoulli.BernoulliDistribution.compute_logpdf.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.bernoulli.Bernoulli0000664 5104112 5104112 00000000405 12356463433 035462 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.bernoulli.BernoulliDistribution.compute_logpdf
==========================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.bernoulli
.. automethod:: BernoulliDistribution.compute_logpdf ././@LongLink 0000000 0000000 0000000 00000000214 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.bernoulli.BernoulliDistribution.compute_mask_to_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.bernoulli.Bernoulli0000664 5104112 5104112 00000000435 12356463433 035465 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.bernoulli.BernoulliDistribution.compute_mask_to_parent
==================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.bernoulli
.. automethod:: BernoulliDistribution.compute_mask_to_parent ././@LongLink 0000000 0000000 0000000 00000000217 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.bernoulli.BernoulliDistribution.compute_message_to_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.bernoulli.Bernoulli0000664 5104112 5104112 00000000446 12356463433 035467 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.bernoulli.BernoulliDistribution.compute_message_to_parent
=====================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.bernoulli
.. automethod:: BernoulliDistribution.compute_message_to_parent ././@LongLink 0000000 0000000 0000000 00000000215 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.bernoulli.BernoulliDistribution.compute_moments_and_cgf.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.bernoulli.Bernoulli0000664 5104112 5104112 00000000440 12356463433 035461 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.bernoulli.BernoulliDistribution.compute_moments_and_cgf
===================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.bernoulli
.. automethod:: BernoulliDistribution.compute_moments_and_cgf bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.linalg.chol.rst 0000664 5104112 5104112 00000000164 12356463435 032034 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.linalg.chol
=========================
.. currentmodule:: bayespy.utils.linalg
.. autofunction:: chol ././@LongLink 0000000 0000000 0000000 00000000216 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.bernoulli.BernoulliDistribution.compute_phi_from_parents.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.bernoulli.Bernoulli0000664 5104112 5104112 00000000443 12356463433 035464 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.bernoulli.BernoulliDistribution.compute_phi_from_parents
====================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.bernoulli
.. automethod:: BernoulliDistribution.compute_phi_from_parents ././@LongLink 0000000 0000000 0000000 00000000210 00000000000 011206 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.bernoulli.BernoulliDistribution.plates_from_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.bernoulli.Bernoulli0000664 5104112 5104112 00000000421 12356463433 035460 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.bernoulli.BernoulliDistribution.plates_from_parent
==============================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.bernoulli
.. automethod:: BernoulliDistribution.plates_from_parent ././@LongLink 0000000 0000000 0000000 00000000206 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.bernoulli.BernoulliDistribution.plates_to_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.bernoulli.Bernoulli0000664 5104112 5104112 00000000413 12356463433 035461 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.bernoulli.BernoulliDistribution.plates_to_parent
============================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.bernoulli
.. automethod:: BernoulliDistribution.plates_to_parent ././@LongLink 0000000 0000000 0000000 00000000174 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.bernoulli.BernoulliDistribution.random.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.bernoulli.Bernoulli0000664 5104112 5104112 00000000355 12356463433 035466 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.bernoulli.BernoulliDistribution.random
==================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.bernoulli
.. automethod:: BernoulliDistribution.random ././@LongLink 0000000 0000000 0000000 00000000171 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.bernoulli.BernoulliMoments.__init__.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.bernoulli.Bernoulli0000664 5104112 5104112 00000000344 12356463433 035464 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.bernoulli.BernoulliMoments.__init__
===============================================================
.. currentmodule:: bayespy.inference.vmp.nodes.bernoulli
.. automethod:: BernoulliMoments.__init__ ././@LongLink 0000000 0000000 0000000 00000000176 00000000000 011221 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.bernoulli.BernoulliMoments.add_converter.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.bernoulli.Bernoulli0000664 5104112 5104112 00000000363 12356463433 035465 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.bernoulli.BernoulliMoments.add_converter
====================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.bernoulli
.. automethod:: BernoulliMoments.add_converter ././@LongLink 0000000 0000000 0000000 00000000211 00000000000 011207 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.bernoulli.BernoulliMoments.compute_dims_from_values.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.bernoulli.Bernoulli0000664 5104112 5104112 00000000424 12356463433 035463 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.bernoulli.BernoulliMoments.compute_dims_from_values
===============================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.bernoulli
.. automethod:: BernoulliMoments.compute_dims_from_values ././@LongLink 0000000 0000000 0000000 00000000206 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.bernoulli.BernoulliMoments.compute_fixed_moments.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.bernoulli.Bernoulli0000664 5104112 5104112 00000000413 12356463433 035461 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.bernoulli.BernoulliMoments.compute_fixed_moments
============================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.bernoulli
.. automethod:: BernoulliMoments.compute_fixed_moments bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.linalg.chol_inv.rst 0000664 5104112 5104112 00000000200 12356463436 032700 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.linalg.chol_inv
=============================
.. currentmodule:: bayespy.utils.linalg
.. autofunction:: chol_inv ././@LongLink 0000000 0000000 0000000 00000000176 00000000000 011221 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.bernoulli.BernoulliMoments.get_converter.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.bernoulli.Bernoulli0000664 5104112 5104112 00000000363 12356463433 035465 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.bernoulli.BernoulliMoments.get_converter
====================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.bernoulli
.. automethod:: BernoulliMoments.get_converter ././@LongLink 0000000 0000000 0000000 00000000204 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.beta.BetaDistribution.compute_cgf_from_parents.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.beta.BetaDistributi0000664 5104112 5104112 00000000405 12356463433 035365 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.beta.BetaDistribution.compute_cgf_from_parents
==========================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.beta
.. automethod:: BetaDistribution.compute_cgf_from_parents ././@LongLink 0000000 0000000 0000000 00000000207 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.beta.BetaDistribution.compute_fixed_moments_and_f.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.beta.BetaDistributi0000664 5104112 5104112 00000000416 12356463433 035367 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.beta.BetaDistribution.compute_fixed_moments_and_f
=============================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.beta
.. automethod:: BetaDistribution.compute_fixed_moments_and_f ././@LongLink 0000000 0000000 0000000 00000000172 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.beta.BetaDistribution.compute_logpdf.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.beta.BetaDistributi0000664 5104112 5104112 00000000347 12356463433 035372 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.beta.BetaDistribution.compute_logpdf
================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.beta
.. automethod:: BetaDistribution.compute_logpdf ././@LongLink 0000000 0000000 0000000 00000000202 00000000000 011207 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.beta.BetaDistribution.compute_mask_to_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.beta.BetaDistributi0000664 5104112 5104112 00000000377 12356463433 035375 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.beta.BetaDistribution.compute_mask_to_parent
========================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.beta
.. automethod:: BetaDistribution.compute_mask_to_parent ././@LongLink 0000000 0000000 0000000 00000000205 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.beta.BetaDistribution.compute_message_to_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.beta.BetaDistributi0000664 5104112 5104112 00000000410 12356463433 035361 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.beta.BetaDistribution.compute_message_to_parent
===========================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.beta
.. automethod:: BetaDistribution.compute_message_to_parent ././@LongLink 0000000 0000000 0000000 00000000203 00000000000 011210 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.beta.BetaDistribution.compute_moments_and_cgf.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.beta.BetaDistributi0000664 5104112 5104112 00000000402 12356463433 035362 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.beta.BetaDistribution.compute_moments_and_cgf
=========================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.beta
.. automethod:: BetaDistribution.compute_moments_and_cgf ././@LongLink 0000000 0000000 0000000 00000000204 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.beta.BetaDistribution.compute_phi_from_parents.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.beta.BetaDistributi0000664 5104112 5104112 00000000405 12356463433 035365 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.beta.BetaDistribution.compute_phi_from_parents
==========================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.beta
.. automethod:: BetaDistribution.compute_phi_from_parents bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.linalg.block_banded_solve.rst 0000664 5104112 5104112 00000000236 12356463435 034706 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.linalg.block_banded_solve
=======================================
.. currentmodule:: bayespy.utils.linalg
.. autofunction:: block_banded_solve ././@LongLink 0000000 0000000 0000000 00000000176 00000000000 011221 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.beta.BetaDistribution.plates_from_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.beta.BetaDistributi0000664 5104112 5104112 00000000363 12356463433 035370 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.beta.BetaDistribution.plates_from_parent
====================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.beta
.. automethod:: BetaDistribution.plates_from_parent ././@LongLink 0000000 0000000 0000000 00000000174 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.beta.BetaDistribution.plates_to_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.beta.BetaDistributi0000664 5104112 5104112 00000000355 12356463433 035371 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.beta.BetaDistribution.plates_to_parent
==================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.beta
.. automethod:: BetaDistribution.plates_to_parent ././@LongLink 0000000 0000000 0000000 00000000162 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.beta.BetaDistribution.random.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.beta.BetaDistributi0000664 5104112 5104112 00000000317 12356463433 035367 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.beta.BetaDistribution.random
========================================================
.. currentmodule:: bayespy.inference.vmp.nodes.beta
.. automethod:: BetaDistribution.random ././@LongLink 0000000 0000000 0000000 00000000164 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.beta.BetaMoments.add_converter.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.beta.BetaMoments.ad0000664 5104112 5104112 00000000325 12356463433 035251 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.beta.BetaMoments.add_converter
==========================================================
.. currentmodule:: bayespy.inference.vmp.nodes.beta
.. automethod:: BetaMoments.add_converter ././@LongLink 0000000 0000000 0000000 00000000177 00000000000 011222 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.beta.BetaMoments.compute_dims_from_values.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.beta.BetaMoments.co0000664 5104112 5104112 00000000366 12356463433 035273 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.beta.BetaMoments.compute_dims_from_values
=====================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.beta
.. automethod:: BetaMoments.compute_dims_from_values ././@LongLink 0000000 0000000 0000000 00000000174 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.beta.BetaMoments.compute_fixed_moments.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.beta.BetaMoments.co0000664 5104112 5104112 00000000355 12356463433 035271 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.beta.BetaMoments.compute_fixed_moments
==================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.beta
.. automethod:: BetaMoments.compute_fixed_moments ././@LongLink 0000000 0000000 0000000 00000000164 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.beta.BetaMoments.get_converter.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.beta.BetaMoments.ge0000664 5104112 5104112 00000000325 12356463434 035261 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.beta.BetaMoments.get_converter
==========================================================
.. currentmodule:: bayespy.inference.vmp.nodes.beta
.. automethod:: BetaMoments.get_converter ././@LongLink 0000000 0000000 0000000 00000000174 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.binomial.BinomialDistribution.__init__.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.binomial.BinomialDi0000664 5104112 5104112 00000000355 12356463434 035342 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.binomial.BinomialDistribution.__init__
==================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.binomial
.. automethod:: BinomialDistribution.__init__ ././@LongLink 0000000 0000000 0000000 00000000214 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.binomial.BinomialDistribution.compute_cgf_from_parents.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.binomial.BinomialDi0000664 5104112 5104112 00000000435 12356463434 035341 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.binomial.BinomialDistribution.compute_cgf_from_parents
==================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.binomial
.. automethod:: BinomialDistribution.compute_cgf_from_parents ././@LongLink 0000000 0000000 0000000 00000000217 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.binomial.BinomialDistribution.compute_fixed_moments_and_f.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.binomial.BinomialDi0000664 5104112 5104112 00000000446 12356463434 035343 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.binomial.BinomialDistribution.compute_fixed_moments_and_f
=====================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.binomial
.. automethod:: BinomialDistribution.compute_fixed_moments_and_f ././@LongLink 0000000 0000000 0000000 00000000202 00000000000 011207 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.binomial.BinomialDistribution.compute_logpdf.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.binomial.BinomialDi0000664 5104112 5104112 00000000377 12356463434 035346 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.binomial.BinomialDistribution.compute_logpdf
========================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.binomial
.. automethod:: BinomialDistribution.compute_logpdf ././@LongLink 0000000 0000000 0000000 00000000212 00000000000 011210 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.binomial.BinomialDistribution.compute_mask_to_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.binomial.BinomialDi0000664 5104112 5104112 00000000427 12356463434 035342 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.binomial.BinomialDistribution.compute_mask_to_parent
================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.binomial
.. automethod:: BinomialDistribution.compute_mask_to_parent ././@LongLink 0000000 0000000 0000000 00000000215 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.binomial.BinomialDistribution.compute_message_to_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.binomial.BinomialDi0000664 5104112 5104112 00000000440 12356463434 035335 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.binomial.BinomialDistribution.compute_message_to_parent
===================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.binomial
.. automethod:: BinomialDistribution.compute_message_to_parent ././@LongLink 0000000 0000000 0000000 00000000213 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.binomial.BinomialDistribution.compute_moments_and_cgf.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.binomial.BinomialDi0000664 5104112 5104112 00000000432 12356463434 035336 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.binomial.BinomialDistribution.compute_moments_and_cgf
=================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.binomial
.. automethod:: BinomialDistribution.compute_moments_and_cgf ././@LongLink 0000000 0000000 0000000 00000000214 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.binomial.BinomialDistribution.compute_phi_from_parents.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.binomial.BinomialDi0000664 5104112 5104112 00000000435 12356463434 035341 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.binomial.BinomialDistribution.compute_phi_from_parents
==================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.binomial
.. automethod:: BinomialDistribution.compute_phi_from_parents ././@LongLink 0000000 0000000 0000000 00000000206 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.binomial.BinomialDistribution.plates_from_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.binomial.BinomialDi0000664 5104112 5104112 00000000413 12356463434 035335 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.binomial.BinomialDistribution.plates_from_parent
============================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.binomial
.. automethod:: BinomialDistribution.plates_from_parent ././@LongLink 0000000 0000000 0000000 00000000204 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.binomial.BinomialDistribution.plates_to_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.binomial.BinomialDi0000664 5104112 5104112 00000000405 12356463434 035336 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.binomial.BinomialDistribution.plates_to_parent
==========================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.binomial
.. automethod:: BinomialDistribution.plates_to_parent ././@LongLink 0000000 0000000 0000000 00000000172 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.binomial.BinomialDistribution.random.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.binomial.BinomialDi0000664 5104112 5104112 00000000347 12356463434 035343 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.binomial.BinomialDistribution.random
================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.binomial
.. automethod:: BinomialDistribution.random ././@LongLink 0000000 0000000 0000000 00000000167 00000000000 011221 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.binomial.BinomialMoments.__init__.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.binomial.BinomialMo0000664 5104112 5104112 00000000336 12356463434 035360 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.binomial.BinomialMoments.__init__
=============================================================
.. currentmodule:: bayespy.inference.vmp.nodes.binomial
.. automethod:: BinomialMoments.__init__ ././@LongLink 0000000 0000000 0000000 00000000174 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.binomial.BinomialMoments.add_converter.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.binomial.BinomialMo0000664 5104112 5104112 00000000355 12356463434 035361 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.binomial.BinomialMoments.add_converter
==================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.binomial
.. automethod:: BinomialMoments.add_converter ././@LongLink 0000000 0000000 0000000 00000000207 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.binomial.BinomialMoments.compute_dims_from_values.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.binomial.BinomialMo0000664 5104112 5104112 00000000416 12356463434 035357 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.binomial.BinomialMoments.compute_dims_from_values
=============================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.binomial
.. automethod:: BinomialMoments.compute_dims_from_values ././@LongLink 0000000 0000000 0000000 00000000204 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.binomial.BinomialMoments.compute_fixed_moments.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.binomial.BinomialMo0000664 5104112 5104112 00000000405 12356463434 035355 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.binomial.BinomialMoments.compute_fixed_moments
==========================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.binomial
.. automethod:: BinomialMoments.compute_fixed_moments ././@LongLink 0000000 0000000 0000000 00000000174 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.binomial.BinomialMoments.get_converter.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.binomial.BinomialMo0000664 5104112 5104112 00000000355 12356463434 035361 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.binomial.BinomialMoments.get_converter
==================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.binomial
.. automethod:: BinomialMoments.get_converter ././@LongLink 0000000 0000000 0000000 00000000202 00000000000 011207 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical.CategoricalDistribution.__init__.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical.Categor0000664 5104112 5104112 00000000377 12356463434 035406 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.categorical.CategoricalDistribution.__init__
========================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.categorical
.. automethod:: CategoricalDistribution.__init__ ././@LongLink 0000000 0000000 0000000 00000000222 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical.CategoricalDistribution.compute_cgf_from_parents.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical.Categor0000664 5104112 5104112 00000000457 12356463434 035405 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.categorical.CategoricalDistribution.compute_cgf_from_parents
========================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.categorical
.. automethod:: CategoricalDistribution.compute_cgf_from_parents ././@LongLink 0000000 0000000 0000000 00000000150 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.node.Node.__init__.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.node.Node.__init__.0000664 5104112 5104112 00000000261 12356463435 035164 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.node.Node.__init__
==============================================
.. currentmodule:: bayespy.inference.vmp.nodes.node
.. automethod:: Node.__init__ ././@LongLink 0000000 0000000 0000000 00000000225 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical.CategoricalDistribution.compute_fixed_moments_and_f.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical.Categor0000664 5104112 5104112 00000000470 12356463434 035400 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.categorical.CategoricalDistribution.compute_fixed_moments_and_f
===========================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.categorical
.. automethod:: CategoricalDistribution.compute_fixed_moments_and_f ././@LongLink 0000000 0000000 0000000 00000000210 00000000000 011206 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical.CategoricalDistribution.compute_logpdf.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical.Categor0000664 5104112 5104112 00000000421 12356463434 035374 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.categorical.CategoricalDistribution.compute_logpdf
==============================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.categorical
.. automethod:: CategoricalDistribution.compute_logpdf ././@LongLink 0000000 0000000 0000000 00000000220 00000000000 011207 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical.CategoricalDistribution.compute_mask_to_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical.Categor0000664 5104112 5104112 00000000451 12356463434 035377 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.categorical.CategoricalDistribution.compute_mask_to_parent
======================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.categorical
.. automethod:: CategoricalDistribution.compute_mask_to_parent ././@LongLink 0000000 0000000 0000000 00000000223 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical.CategoricalDistribution.compute_message_to_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical.Categor0000664 5104112 5104112 00000000462 12356463434 035401 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.categorical.CategoricalDistribution.compute_message_to_parent
=========================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.categorical
.. automethod:: CategoricalDistribution.compute_message_to_parent ././@LongLink 0000000 0000000 0000000 00000000221 00000000000 011210 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical.CategoricalDistribution.compute_moments_and_cgf.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical.Categor0000664 5104112 5104112 00000000454 12356463434 035402 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.categorical.CategoricalDistribution.compute_moments_and_cgf
=======================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.categorical
.. automethod:: CategoricalDistribution.compute_moments_and_cgf ././@LongLink 0000000 0000000 0000000 00000000222 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical.CategoricalDistribution.compute_phi_from_parents.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical.Categor0000664 5104112 5104112 00000000457 12356463434 035405 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.categorical.CategoricalDistribution.compute_phi_from_parents
========================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.categorical
.. automethod:: CategoricalDistribution.compute_phi_from_parents ././@LongLink 0000000 0000000 0000000 00000000214 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical.CategoricalDistribution.plates_from_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical.Categor0000664 5104112 5104112 00000000435 12356463434 035401 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.categorical.CategoricalDistribution.plates_from_parent
==================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.categorical
.. automethod:: CategoricalDistribution.plates_from_parent ././@LongLink 0000000 0000000 0000000 00000000160 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.constant.Constant.__init__.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.constant.Constant._0000664 5104112 5104112 00000000311 12356463434 035370 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.constant.Constant.__init__
======================================================
.. currentmodule:: bayespy.inference.vmp.nodes.constant
.. automethod:: Constant.__init__ ././@LongLink 0000000 0000000 0000000 00000000212 00000000000 011210 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical.CategoricalDistribution.plates_to_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical.Categor0000664 5104112 5104112 00000000427 12356463434 035402 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.categorical.CategoricalDistribution.plates_to_parent
================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.categorical
.. automethod:: CategoricalDistribution.plates_to_parent ././@LongLink 0000000 0000000 0000000 00000000200 00000000000 011205 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical.CategoricalDistribution.random.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical.Categor0000664 5104112 5104112 00000000371 12356463434 035400 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.categorical.CategoricalDistribution.random
======================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.categorical
.. automethod:: CategoricalDistribution.random ././@LongLink 0000000 0000000 0000000 00000000175 00000000000 011220 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical.CategoricalMoments.__init__.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical.Categor0000664 5104112 5104112 00000000360 12356463434 035376 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.categorical.CategoricalMoments.__init__
===================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.categorical
.. automethod:: CategoricalMoments.__init__ ././@LongLink 0000000 0000000 0000000 00000000202 00000000000 011207 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical.CategoricalMoments.add_converter.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical.Categor0000664 5104112 5104112 00000000377 12356463434 035406 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.categorical.CategoricalMoments.add_converter
========================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.categorical
.. automethod:: CategoricalMoments.add_converter ././@LongLink 0000000 0000000 0000000 00000000215 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical.CategoricalMoments.compute_dims_from_values.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical.Categor0000664 5104112 5104112 00000000440 12356463434 035375 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.categorical.CategoricalMoments.compute_dims_from_values
===================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.categorical
.. automethod:: CategoricalMoments.compute_dims_from_values ././@LongLink 0000000 0000000 0000000 00000000212 00000000000 011210 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical.CategoricalMoments.compute_fixed_moments.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical.Categor0000664 5104112 5104112 00000000427 12356463434 035402 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.categorical.CategoricalMoments.compute_fixed_moments
================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.categorical
.. automethod:: CategoricalMoments.compute_fixed_moments ././@LongLink 0000000 0000000 0000000 00000000202 00000000000 011207 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical.CategoricalMoments.get_converter.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical.Categor0000664 5104112 5104112 00000000377 12356463434 035406 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.categorical.CategoricalMoments.get_converter
========================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.categorical
.. automethod:: CategoricalMoments.get_converter ././@LongLink 0000000 0000000 0000000 00000000232 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical_markov_chain.CategoricalMarkovChainDistribution.__init__.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical_markov_0000664 5104112 5104112 00000000507 12356463434 035534 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.categorical_markov_chain.CategoricalMarkovChainDistribution.__init__
================================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.categorical_markov_chain
.. automethod:: CategoricalMarkovChainDistribution.__init__ ././@LongLink 0000000 0000000 0000000 00000000252 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical_markov_chain.CategoricalMarkovChainDistribution.compute_cgf_from_parents.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical_markov_0000664 5104112 5104112 00000000567 12356463434 035542 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.categorical_markov_chain.CategoricalMarkovChainDistribution.compute_cgf_from_parents
================================================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.categorical_markov_chain
.. automethod:: CategoricalMarkovChainDistribution.compute_cgf_from_parents ././@LongLink 0000000 0000000 0000000 00000000255 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical_markov_chain.CategoricalMarkovChainDistribution.compute_fixed_moments_and_f.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical_markov_0000664 5104112 5104112 00000000600 12356463434 035526 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.categorical_markov_chain.CategoricalMarkovChainDistribution.compute_fixed_moments_and_f
===================================================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.categorical_markov_chain
.. automethod:: CategoricalMarkovChainDistribution.compute_fixed_moments_and_f ././@LongLink 0000000 0000000 0000000 00000000240 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical_markov_chain.CategoricalMarkovChainDistribution.compute_logpdf.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical_markov_0000664 5104112 5104112 00000000531 12356463434 035531 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.categorical_markov_chain.CategoricalMarkovChainDistribution.compute_logpdf
======================================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.categorical_markov_chain
.. automethod:: CategoricalMarkovChainDistribution.compute_logpdf ././@LongLink 0000000 0000000 0000000 00000000250 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical_markov_chain.CategoricalMarkovChainDistribution.compute_mask_to_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical_markov_0000664 5104112 5104112 00000000561 12356463434 035534 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.categorical_markov_chain.CategoricalMarkovChainDistribution.compute_mask_to_parent
==============================================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.categorical_markov_chain
.. automethod:: CategoricalMarkovChainDistribution.compute_mask_to_parent ././@LongLink 0000000 0000000 0000000 00000000253 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical_markov_chain.CategoricalMarkovChainDistribution.compute_message_to_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical_markov_0000664 5104112 5104112 00000000572 12356463434 035536 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.categorical_markov_chain.CategoricalMarkovChainDistribution.compute_message_to_parent
=================================================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.categorical_markov_chain
.. automethod:: CategoricalMarkovChainDistribution.compute_message_to_parent ././@LongLink 0000000 0000000 0000000 00000000251 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical_markov_chain.CategoricalMarkovChainDistribution.compute_moments_and_cgf.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical_markov_0000664 5104112 5104112 00000000564 12356463434 035537 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.categorical_markov_chain.CategoricalMarkovChainDistribution.compute_moments_and_cgf
===============================================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.categorical_markov_chain
.. automethod:: CategoricalMarkovChainDistribution.compute_moments_and_cgf bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.linalg.logdet_chol.rst 0000664 5104112 5104112 00000000211 12356463436 033364 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.linalg.logdet_chol
================================
.. currentmodule:: bayespy.utils.linalg
.. autofunction:: logdet_chol ././@LongLink 0000000 0000000 0000000 00000000252 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical_markov_chain.CategoricalMarkovChainDistribution.compute_phi_from_parents.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical_markov_0000664 5104112 5104112 00000000567 12356463434 035542 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.categorical_markov_chain.CategoricalMarkovChainDistribution.compute_phi_from_parents
================================================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.categorical_markov_chain
.. automethod:: CategoricalMarkovChainDistribution.compute_phi_from_parents ././@LongLink 0000000 0000000 0000000 00000000244 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical_markov_chain.CategoricalMarkovChainDistribution.plates_from_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical_markov_0000664 5104112 5104112 00000000545 12356463434 035536 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.categorical_markov_chain.CategoricalMarkovChainDistribution.plates_from_parent
==========================================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.categorical_markov_chain
.. automethod:: CategoricalMarkovChainDistribution.plates_from_parent ././@LongLink 0000000 0000000 0000000 00000000242 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical_markov_chain.CategoricalMarkovChainDistribution.plates_to_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical_markov_0000664 5104112 5104112 00000000537 12356463434 035537 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.categorical_markov_chain.CategoricalMarkovChainDistribution.plates_to_parent
========================================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.categorical_markov_chain
.. automethod:: CategoricalMarkovChainDistribution.plates_to_parent ././@LongLink 0000000 0000000 0000000 00000000230 00000000000 011210 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical_markov_chain.CategoricalMarkovChainDistribution.random.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical_markov_0000664 5104112 5104112 00000000501 12356463434 035526 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.categorical_markov_chain.CategoricalMarkovChainDistribution.random
==============================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.categorical_markov_chain
.. automethod:: CategoricalMarkovChainDistribution.random ././@LongLink 0000000 0000000 0000000 00000000225 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical_markov_chain.CategoricalMarkovChainMoments.__init__.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical_markov_0000664 5104112 5104112 00000000470 12356463434 035533 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.categorical_markov_chain.CategoricalMarkovChainMoments.__init__
===========================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.categorical_markov_chain
.. automethod:: CategoricalMarkovChainMoments.__init__ ././@LongLink 0000000 0000000 0000000 00000000232 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical_markov_chain.CategoricalMarkovChainMoments.add_converter.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical_markov_0000664 5104112 5104112 00000000507 12356463434 035534 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.categorical_markov_chain.CategoricalMarkovChainMoments.add_converter
================================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.categorical_markov_chain
.. automethod:: CategoricalMarkovChainMoments.add_converter ././@LongLink 0000000 0000000 0000000 00000000166 00000000000 011220 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.constant.Constant.add_plate_axis.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.constant.Constant.a0000664 5104112 5104112 00000000333 12356463434 035376 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.constant.Constant.add_plate_axis
============================================================
.. currentmodule:: bayespy.inference.vmp.nodes.constant
.. automethod:: Constant.add_plate_axis ././@LongLink 0000000 0000000 0000000 00000000245 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical_markov_chain.CategoricalMarkovChainMoments.compute_dims_from_values.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical_markov_0000664 5104112 5104112 00000000550 12356463434 035532 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.categorical_markov_chain.CategoricalMarkovChainMoments.compute_dims_from_values
===========================================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.categorical_markov_chain
.. automethod:: CategoricalMarkovChainMoments.compute_dims_from_values ././@LongLink 0000000 0000000 0000000 00000000242 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical_markov_chain.CategoricalMarkovChainMoments.compute_fixed_moments.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical_markov_0000664 5104112 5104112 00000000537 12356463434 035537 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.categorical_markov_chain.CategoricalMarkovChainMoments.compute_fixed_moments
========================================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.categorical_markov_chain
.. automethod:: CategoricalMarkovChainMoments.compute_fixed_moments ././@LongLink 0000000 0000000 0000000 00000000232 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical_markov_chain.CategoricalMarkovChainMoments.get_converter.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.categorical_markov_0000664 5104112 5104112 00000000507 12356463434 035534 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.categorical_markov_chain.CategoricalMarkovChainMoments.get_converter
================================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.categorical_markov_chain
.. automethod:: CategoricalMarkovChainMoments.get_converter ././@LongLink 0000000 0000000 0000000 00000000156 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.constant.Constant.delete.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.constant.Constant.d0000664 5104112 5104112 00000000303 12356463434 035376 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.constant.Constant.delete
====================================================
.. currentmodule:: bayespy.inference.vmp.nodes.constant
.. automethod:: Constant.delete ././@LongLink 0000000 0000000 0000000 00000000160 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.constant.Constant.get_mask.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.constant.Constant.g0000664 5104112 5104112 00000000311 12356463434 035400 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.constant.Constant.get_mask
======================================================
.. currentmodule:: bayespy.inference.vmp.nodes.constant
.. automethod:: Constant.get_mask ././@LongLink 0000000 0000000 0000000 00000000163 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.constant.Constant.get_moments.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.constant.Constant.g0000664 5104112 5104112 00000000322 12356463434 035402 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.constant.Constant.get_moments
=========================================================
.. currentmodule:: bayespy.inference.vmp.nodes.constant
.. automethod:: Constant.get_moments ././@LongLink 0000000 0000000 0000000 00000000161 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.constant.Constant.get_shape.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.constant.Constant.g0000664 5104112 5104112 00000000314 12356463434 035403 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.constant.Constant.get_shape
=======================================================
.. currentmodule:: bayespy.inference.vmp.nodes.constant
.. automethod:: Constant.get_shape ././@LongLink 0000000 0000000 0000000 00000000163 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.constant.Constant.has_plotter.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.constant.Constant.h0000664 5104112 5104112 00000000322 12356463434 035403 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.constant.Constant.has_plotter
=========================================================
.. currentmodule:: bayespy.inference.vmp.nodes.constant
.. automethod:: Constant.has_plotter bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.linalg.chol_logdet.rst 0000664 5104112 5104112 00000000211 12356463436 033364 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.linalg.chol_logdet
================================
.. currentmodule:: bayespy.utils.linalg
.. autofunction:: chol_logdet ././@LongLink 0000000 0000000 0000000 00000000163 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.constant.Constant.move_plates.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.constant.Constant.m0000664 5104112 5104112 00000000322 12356463434 035410 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.constant.Constant.move_plates
=========================================================
.. currentmodule:: bayespy.inference.vmp.nodes.constant
.. automethod:: Constant.move_plates ././@LongLink 0000000 0000000 0000000 00000000156 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.constant.Constant.plates.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.constant.Constant.p0000664 5104112 5104112 00000000306 12356463434 035415 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.constant.Constant.plates
====================================================
.. currentmodule:: bayespy.inference.vmp.nodes.constant
.. autoattribute:: Constant.plates ././@LongLink 0000000 0000000 0000000 00000000154 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.constant.Constant.plot.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.constant.Constant.p0000664 5104112 5104112 00000000275 12356463434 035422 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.constant.Constant.plot
==================================================
.. currentmodule:: bayespy.inference.vmp.nodes.constant
.. automethod:: Constant.plot ././@LongLink 0000000 0000000 0000000 00000000163 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.constant.Constant.set_plotter.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.constant.Constant.s0000664 5104112 5104112 00000000322 12356463434 035416 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.constant.Constant.set_plotter
=========================================================
.. currentmodule:: bayespy.inference.vmp.nodes.constant
.. automethod:: Constant.set_plotter ././@LongLink 0000000 0000000 0000000 00000000172 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.deterministic.Deterministic.__init__.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.deterministic.Deter0000664 5104112 5104112 00000000347 12356463434 035450 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.deterministic.Deterministic.__init__
================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.deterministic
.. automethod:: Deterministic.__init__ ././@LongLink 0000000 0000000 0000000 00000000200 00000000000 011205 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.deterministic.Deterministic.add_plate_axis.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.deterministic.Deter0000664 5104112 5104112 00000000371 12356463434 035445 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.deterministic.Deterministic.add_plate_axis
======================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.deterministic
.. automethod:: Deterministic.add_plate_axis ././@LongLink 0000000 0000000 0000000 00000000170 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.deterministic.Deterministic.delete.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.deterministic.Deter0000664 5104112 5104112 00000000341 12356463434 035442 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.deterministic.Deterministic.delete
==============================================================
.. currentmodule:: bayespy.inference.vmp.nodes.deterministic
.. automethod:: Deterministic.delete ././@LongLink 0000000 0000000 0000000 00000000172 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.deterministic.Deterministic.get_mask.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.deterministic.Deter0000664 5104112 5104112 00000000347 12356463434 035450 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.deterministic.Deterministic.get_mask
================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.deterministic
.. automethod:: Deterministic.get_mask ././@LongLink 0000000 0000000 0000000 00000000175 00000000000 011220 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.deterministic.Deterministic.get_moments.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.deterministic.Deter0000664 5104112 5104112 00000000360 12356463434 035443 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.deterministic.Deterministic.get_moments
===================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.deterministic
.. automethod:: Deterministic.get_moments bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.linalg.dot.rst 0000664 5104112 5104112 00000000161 12356463436 031673 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.linalg.dot
========================
.. currentmodule:: bayespy.utils.linalg
.. autofunction:: dot ././@LongLink 0000000 0000000 0000000 00000000173 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.deterministic.Deterministic.get_shape.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.deterministic.Deter0000664 5104112 5104112 00000000352 12356463434 035444 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.deterministic.Deterministic.get_shape
=================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.deterministic
.. automethod:: Deterministic.get_shape ././@LongLink 0000000 0000000 0000000 00000000175 00000000000 011220 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.deterministic.Deterministic.has_plotter.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.deterministic.Deter0000664 5104112 5104112 00000000360 12356463434 035443 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.deterministic.Deterministic.has_plotter
===================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.deterministic
.. automethod:: Deterministic.has_plotter ././@LongLink 0000000 0000000 0000000 00000000212 00000000000 011210 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.deterministic.Deterministic.lower_bound_contribution.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.deterministic.Deter0000664 5104112 5104112 00000000427 12356463434 035447 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.deterministic.Deterministic.lower_bound_contribution
================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.deterministic
.. automethod:: Deterministic.lower_bound_contribution ././@LongLink 0000000 0000000 0000000 00000000175 00000000000 011220 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.deterministic.Deterministic.move_plates.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.deterministic.Deter0000664 5104112 5104112 00000000360 12356463434 035443 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.deterministic.Deterministic.move_plates
===================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.deterministic
.. automethod:: Deterministic.move_plates ././@LongLink 0000000 0000000 0000000 00000000170 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.deterministic.Deterministic.plates.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.deterministic.Deter0000664 5104112 5104112 00000000344 12356463434 035445 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.deterministic.Deterministic.plates
==============================================================
.. currentmodule:: bayespy.inference.vmp.nodes.deterministic
.. autoattribute:: Deterministic.plates ././@LongLink 0000000 0000000 0000000 00000000166 00000000000 011220 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.deterministic.Deterministic.plot.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.deterministic.Deter0000664 5104112 5104112 00000000333 12356463434 035443 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.deterministic.Deterministic.plot
============================================================
.. currentmodule:: bayespy.inference.vmp.nodes.deterministic
.. automethod:: Deterministic.plot ././@LongLink 0000000 0000000 0000000 00000000175 00000000000 011220 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.deterministic.Deterministic.set_plotter.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.deterministic.Deter0000664 5104112 5104112 00000000360 12356463434 035443 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.deterministic.Deterministic.set_plotter
===================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.deterministic
.. automethod:: Deterministic.set_plotter ././@LongLink 0000000 0000000 0000000 00000000216 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.dirichlet.DirichletDistribution.compute_cgf_from_parents.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.dirichlet.Dirichlet0000664 5104112 5104112 00000000443 12356463434 035415 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.dirichlet.DirichletDistribution.compute_cgf_from_parents
====================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.dirichlet
.. automethod:: DirichletDistribution.compute_cgf_from_parents ././@LongLink 0000000 0000000 0000000 00000000172 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.ExponentialFamily.__init__.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.Exponenti0000664 5104112 5104112 00000000347 12356463434 035511 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.expfamily.ExponentialFamily.__init__
================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.expfamily
.. automethod:: ExponentialFamily.__init__ ././@LongLink 0000000 0000000 0000000 00000000221 00000000000 011210 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.dirichlet.DirichletDistribution.compute_fixed_moments_and_f.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.dirichlet.Dirichlet0000664 5104112 5104112 00000000454 12356463434 035417 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.dirichlet.DirichletDistribution.compute_fixed_moments_and_f
=======================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.dirichlet
.. automethod:: DirichletDistribution.compute_fixed_moments_and_f ././@LongLink 0000000 0000000 0000000 00000000204 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.dirichlet.DirichletDistribution.compute_logpdf.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.dirichlet.Dirichlet0000664 5104112 5104112 00000000405 12356463434 035413 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.dirichlet.DirichletDistribution.compute_logpdf
==========================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.dirichlet
.. automethod:: DirichletDistribution.compute_logpdf ././@LongLink 0000000 0000000 0000000 00000000214 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.dirichlet.DirichletDistribution.compute_mask_to_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.dirichlet.Dirichlet0000664 5104112 5104112 00000000435 12356463434 035416 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.dirichlet.DirichletDistribution.compute_mask_to_parent
==================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.dirichlet
.. automethod:: DirichletDistribution.compute_mask_to_parent ././@LongLink 0000000 0000000 0000000 00000000217 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.dirichlet.DirichletDistribution.compute_message_to_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.dirichlet.Dirichlet0000664 5104112 5104112 00000000446 12356463434 035420 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.dirichlet.DirichletDistribution.compute_message_to_parent
=====================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.dirichlet
.. automethod:: DirichletDistribution.compute_message_to_parent ././@LongLink 0000000 0000000 0000000 00000000215 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.dirichlet.DirichletDistribution.compute_moments_and_cgf.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.dirichlet.Dirichlet0000664 5104112 5104112 00000000440 12356463434 035412 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.dirichlet.DirichletDistribution.compute_moments_and_cgf
===================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.dirichlet
.. automethod:: DirichletDistribution.compute_moments_and_cgf ././@LongLink 0000000 0000000 0000000 00000000216 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.dirichlet.DirichletDistribution.compute_phi_from_parents.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.dirichlet.Dirichlet0000664 5104112 5104112 00000000443 12356463434 035415 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.dirichlet.DirichletDistribution.compute_phi_from_parents
====================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.dirichlet
.. automethod:: DirichletDistribution.compute_phi_from_parents ././@LongLink 0000000 0000000 0000000 00000000210 00000000000 011206 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.dirichlet.DirichletDistribution.plates_from_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.dirichlet.Dirichlet0000664 5104112 5104112 00000000421 12356463434 035411 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.dirichlet.DirichletDistribution.plates_from_parent
==============================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.dirichlet
.. automethod:: DirichletDistribution.plates_from_parent ././@LongLink 0000000 0000000 0000000 00000000206 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.dirichlet.DirichletDistribution.plates_to_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.dirichlet.Dirichlet0000664 5104112 5104112 00000000413 12356463434 035412 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.dirichlet.DirichletDistribution.plates_to_parent
============================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.dirichlet
.. automethod:: DirichletDistribution.plates_to_parent ././@LongLink 0000000 0000000 0000000 00000000174 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.dirichlet.DirichletDistribution.random.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.dirichlet.Dirichlet0000664 5104112 5104112 00000000355 12356463434 035417 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.dirichlet.DirichletDistribution.random
==================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.dirichlet
.. automethod:: DirichletDistribution.random ././@LongLink 0000000 0000000 0000000 00000000176 00000000000 011221 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.dirichlet.DirichletMoments.add_converter.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.dirichlet.Dirichlet0000664 5104112 5104112 00000000363 12356463434 035416 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.dirichlet.DirichletMoments.add_converter
====================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.dirichlet
.. automethod:: DirichletMoments.add_converter ././@LongLink 0000000 0000000 0000000 00000000211 00000000000 011207 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.dirichlet.DirichletMoments.compute_dims_from_values.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.dirichlet.Dirichlet0000664 5104112 5104112 00000000424 12356463434 035414 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.dirichlet.DirichletMoments.compute_dims_from_values
===============================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.dirichlet
.. automethod:: DirichletMoments.compute_dims_from_values ././@LongLink 0000000 0000000 0000000 00000000206 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.dirichlet.DirichletMoments.compute_fixed_moments.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.dirichlet.Dirichlet0000664 5104112 5104112 00000000413 12356463434 035412 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.dirichlet.DirichletMoments.compute_fixed_moments
============================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.dirichlet
.. automethod:: DirichletMoments.compute_fixed_moments ././@LongLink 0000000 0000000 0000000 00000000176 00000000000 011221 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.dirichlet.DirichletMoments.get_converter.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.dirichlet.Dirichlet0000664 5104112 5104112 00000000363 12356463434 035416 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.dirichlet.DirichletMoments.get_converter
====================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.dirichlet
.. automethod:: DirichletMoments.get_converter ././@LongLink 0000000 0000000 0000000 00000000200 00000000000 011205 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.ExponentialFamily.add_plate_axis.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.Exponenti0000664 5104112 5104112 00000000371 12356463434 035506 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.expfamily.ExponentialFamily.add_plate_axis
======================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.expfamily
.. automethod:: ExponentialFamily.add_plate_axis ././@LongLink 0000000 0000000 0000000 00000000170 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.ExponentialFamily.delete.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.Exponenti0000664 5104112 5104112 00000000341 12356463434 035503 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.expfamily.ExponentialFamily.delete
==============================================================
.. currentmodule:: bayespy.inference.vmp.nodes.expfamily
.. automethod:: ExponentialFamily.delete ././@LongLink 0000000 0000000 0000000 00000000166 00000000000 011220 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.ExponentialFamily.dims.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.Exponenti0000664 5104112 5104112 00000000336 12356463434 035507 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.expfamily.ExponentialFamily.dims
============================================================
.. currentmodule:: bayespy.inference.vmp.nodes.expfamily
.. autoattribute:: ExponentialFamily.dims ././@LongLink 0000000 0000000 0000000 00000000172 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.ExponentialFamily.get_mask.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.Exponenti0000664 5104112 5104112 00000000347 12356463434 035511 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.expfamily.ExponentialFamily.get_mask
================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.expfamily
.. automethod:: ExponentialFamily.get_mask ././@LongLink 0000000 0000000 0000000 00000000175 00000000000 011220 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.ExponentialFamily.get_moments.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.Exponenti0000664 5104112 5104112 00000000360 12356463434 035504 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.expfamily.ExponentialFamily.get_moments
===================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.expfamily
.. automethod:: ExponentialFamily.get_moments ././@LongLink 0000000 0000000 0000000 00000000173 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.ExponentialFamily.get_shape.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.Exponenti0000664 5104112 5104112 00000000352 12356463434 035505 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.expfamily.ExponentialFamily.get_shape
=================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.expfamily
.. automethod:: ExponentialFamily.get_shape ././@LongLink 0000000 0000000 0000000 00000000175 00000000000 011220 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.ExponentialFamily.has_plotter.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.Exponenti0000664 5104112 5104112 00000000360 12356463434 035504 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.expfamily.ExponentialFamily.has_plotter
===================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.expfamily
.. automethod:: ExponentialFamily.has_plotter ././@LongLink 0000000 0000000 0000000 00000000214 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.ExponentialFamily.initialize_from_parameters.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.Exponenti0000664 5104112 5104112 00000000435 12356463434 035507 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.expfamily.ExponentialFamily.initialize_from_parameters
==================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.expfamily
.. automethod:: ExponentialFamily.initialize_from_parameters ././@LongLink 0000000 0000000 0000000 00000000207 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.ExponentialFamily.initialize_from_prior.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.Exponenti0000664 5104112 5104112 00000000416 12356463434 035506 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.expfamily.ExponentialFamily.initialize_from_prior
=============================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.expfamily
.. automethod:: ExponentialFamily.initialize_from_prior ././@LongLink 0000000 0000000 0000000 00000000210 00000000000 011206 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.ExponentialFamily.initialize_from_random.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.Exponenti0000664 5104112 5104112 00000000421 12356463434 035502 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.expfamily.ExponentialFamily.initialize_from_random
==============================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.expfamily
.. automethod:: ExponentialFamily.initialize_from_random ././@LongLink 0000000 0000000 0000000 00000000207 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.ExponentialFamily.initialize_from_value.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.Exponenti0000664 5104112 5104112 00000000416 12356463434 035506 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.expfamily.ExponentialFamily.initialize_from_value
=============================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.expfamily
.. automethod:: ExponentialFamily.initialize_from_value ././@LongLink 0000000 0000000 0000000 00000000166 00000000000 011220 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.ExponentialFamily.load.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.Exponenti0000664 5104112 5104112 00000000333 12356463434 035504 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.expfamily.ExponentialFamily.load
============================================================
.. currentmodule:: bayespy.inference.vmp.nodes.expfamily
.. automethod:: ExponentialFamily.load bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.linalg.logdet_cov.rst 0000664 5104112 5104112 00000000206 12356463436 033232 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.linalg.logdet_cov
===============================
.. currentmodule:: bayespy.utils.linalg
.. autofunction:: logdet_cov ././@LongLink 0000000 0000000 0000000 00000000170 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.ExponentialFamily.logpdf.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.Exponenti0000664 5104112 5104112 00000000341 12356463434 035503 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.expfamily.ExponentialFamily.logpdf
==============================================================
.. currentmodule:: bayespy.inference.vmp.nodes.expfamily
.. automethod:: ExponentialFamily.logpdf ././@LongLink 0000000 0000000 0000000 00000000212 00000000000 011210 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.ExponentialFamily.lower_bound_contribution.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.Exponenti0000664 5104112 5104112 00000000427 12356463434 035510 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.expfamily.ExponentialFamily.lower_bound_contribution
================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.expfamily
.. automethod:: ExponentialFamily.lower_bound_contribution ././@LongLink 0000000 0000000 0000000 00000000174 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.ExponentialFamily.lowerbound.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.Exponenti0000664 5104112 5104112 00000000355 12356463434 035510 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.expfamily.ExponentialFamily.lowerbound
==================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.expfamily
.. automethod:: ExponentialFamily.lowerbound ././@LongLink 0000000 0000000 0000000 00000000175 00000000000 011220 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.ExponentialFamily.move_plates.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.Exponenti0000664 5104112 5104112 00000000360 12356463434 035504 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.expfamily.ExponentialFamily.move_plates
===================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.expfamily
.. automethod:: ExponentialFamily.move_plates ././@LongLink 0000000 0000000 0000000 00000000171 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.ExponentialFamily.observe.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.Exponenti0000664 5104112 5104112 00000000344 12356463434 035506 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.expfamily.ExponentialFamily.observe
===============================================================
.. currentmodule:: bayespy.inference.vmp.nodes.expfamily
.. automethod:: ExponentialFamily.observe ././@LongLink 0000000 0000000 0000000 00000000165 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.ExponentialFamily.pdf.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.Exponenti0000664 5104112 5104112 00000000330 12356463434 035501 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.expfamily.ExponentialFamily.pdf
===========================================================
.. currentmodule:: bayespy.inference.vmp.nodes.expfamily
.. automethod:: ExponentialFamily.pdf ././@LongLink 0000000 0000000 0000000 00000000170 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.ExponentialFamily.plates.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.Exponenti0000664 5104112 5104112 00000000344 12356463434 035506 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.expfamily.ExponentialFamily.plates
==============================================================
.. currentmodule:: bayespy.inference.vmp.nodes.expfamily
.. autoattribute:: ExponentialFamily.plates ././@LongLink 0000000 0000000 0000000 00000000166 00000000000 011220 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.ExponentialFamily.plot.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.Exponenti0000664 5104112 5104112 00000000333 12356463434 035504 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.expfamily.ExponentialFamily.plot
============================================================
.. currentmodule:: bayespy.inference.vmp.nodes.expfamily
.. automethod:: ExponentialFamily.plot ././@LongLink 0000000 0000000 0000000 00000000170 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.ExponentialFamily.random.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.Exponenti0000664 5104112 5104112 00000000341 12356463434 035503 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.expfamily.ExponentialFamily.random
==============================================================
.. currentmodule:: bayespy.inference.vmp.nodes.expfamily
.. automethod:: ExponentialFamily.random ././@LongLink 0000000 0000000 0000000 00000000166 00000000000 011220 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.ExponentialFamily.save.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.Exponenti0000664 5104112 5104112 00000000333 12356463434 035504 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.expfamily.ExponentialFamily.save
============================================================
.. currentmodule:: bayespy.inference.vmp.nodes.expfamily
.. automethod:: ExponentialFamily.save ././@LongLink 0000000 0000000 0000000 00000000175 00000000000 011220 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.ExponentialFamily.set_plotter.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.Exponenti0000664 5104112 5104112 00000000360 12356463434 035504 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.expfamily.ExponentialFamily.set_plotter
===================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.expfamily
.. automethod:: ExponentialFamily.set_plotter ././@LongLink 0000000 0000000 0000000 00000000173 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.ExponentialFamily.unobserve.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.Exponenti0000664 5104112 5104112 00000000352 12356463434 035505 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.expfamily.ExponentialFamily.unobserve
=================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.expfamily
.. automethod:: ExponentialFamily.unobserve ././@LongLink 0000000 0000000 0000000 00000000170 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.ExponentialFamily.update.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.Exponenti0000664 5104112 5104112 00000000341 12356463434 035503 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.expfamily.ExponentialFamily.update
==============================================================
.. currentmodule:: bayespy.inference.vmp.nodes.expfamily
.. automethod:: ExponentialFamily.update ././@LongLink 0000000 0000000 0000000 00000000226 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.ExponentialFamilyDistribution.compute_cgf_from_parents.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.Exponenti0000664 5104112 5104112 00000000473 12356463434 035511 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.expfamily.ExponentialFamilyDistribution.compute_cgf_from_parents
============================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.expfamily
.. automethod:: ExponentialFamilyDistribution.compute_cgf_from_parents ././@LongLink 0000000 0000000 0000000 00000000231 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.ExponentialFamilyDistribution.compute_fixed_moments_and_f.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.Exponenti0000664 5104112 5104112 00000000504 12356463434 035504 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.expfamily.ExponentialFamilyDistribution.compute_fixed_moments_and_f
===============================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.expfamily
.. automethod:: ExponentialFamilyDistribution.compute_fixed_moments_and_f ././@LongLink 0000000 0000000 0000000 00000000214 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.ExponentialFamilyDistribution.compute_logpdf.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.Exponenti0000664 5104112 5104112 00000000435 12356463434 035507 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.expfamily.ExponentialFamilyDistribution.compute_logpdf
==================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.expfamily
.. automethod:: ExponentialFamilyDistribution.compute_logpdf ././@LongLink 0000000 0000000 0000000 00000000224 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.ExponentialFamilyDistribution.compute_mask_to_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.Exponenti0000664 5104112 5104112 00000000465 12356463434 035512 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.expfamily.ExponentialFamilyDistribution.compute_mask_to_parent
==========================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.expfamily
.. automethod:: ExponentialFamilyDistribution.compute_mask_to_parent ././@LongLink 0000000 0000000 0000000 00000000227 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.ExponentialFamilyDistribution.compute_message_to_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.Exponenti0000664 5104112 5104112 00000000476 12356463434 035514 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.expfamily.ExponentialFamilyDistribution.compute_message_to_parent
=============================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.expfamily
.. automethod:: ExponentialFamilyDistribution.compute_message_to_parent ././@LongLink 0000000 0000000 0000000 00000000225 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.ExponentialFamilyDistribution.compute_moments_and_cgf.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.Exponenti0000664 5104112 5104112 00000000470 12356463434 035506 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.expfamily.ExponentialFamilyDistribution.compute_moments_and_cgf
===========================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.expfamily
.. automethod:: ExponentialFamilyDistribution.compute_moments_and_cgf ././@LongLink 0000000 0000000 0000000 00000000226 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.ExponentialFamilyDistribution.compute_phi_from_parents.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.Exponenti0000664 5104112 5104112 00000000473 12356463434 035511 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.expfamily.ExponentialFamilyDistribution.compute_phi_from_parents
============================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.expfamily
.. automethod:: ExponentialFamilyDistribution.compute_phi_from_parents ././@LongLink 0000000 0000000 0000000 00000000220 00000000000 011207 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.ExponentialFamilyDistribution.plates_from_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.Exponenti0000664 5104112 5104112 00000000451 12356463434 035505 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.expfamily.ExponentialFamilyDistribution.plates_from_parent
======================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.expfamily
.. automethod:: ExponentialFamilyDistribution.plates_from_parent ././@LongLink 0000000 0000000 0000000 00000000216 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.ExponentialFamilyDistribution.plates_to_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.Exponenti0000664 5104112 5104112 00000000443 12356463434 035506 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.expfamily.ExponentialFamilyDistribution.plates_to_parent
====================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.expfamily
.. automethod:: ExponentialFamilyDistribution.plates_to_parent ././@LongLink 0000000 0000000 0000000 00000000204 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.ExponentialFamilyDistribution.random.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.expfamily.Exponenti0000664 5104112 5104112 00000000405 12356463434 035504 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.expfamily.ExponentialFamilyDistribution.random
==========================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.expfamily
.. automethod:: ExponentialFamilyDistribution.random ././@LongLink 0000000 0000000 0000000 00000000206 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gamma.GammaDistribution.compute_cgf_from_parents.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gamma.GammaDistribu0000664 5104112 5104112 00000000413 12356463434 035346 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gamma.GammaDistribution.compute_cgf_from_parents
============================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gamma
.. automethod:: GammaDistribution.compute_cgf_from_parents ././@LongLink 0000000 0000000 0000000 00000000164 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gamma.GammaDistribution.random.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gamma.GammaDistribu0000664 5104112 5104112 00000000325 12356463434 035350 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gamma.GammaDistribution.random
==========================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gamma
.. automethod:: GammaDistribution.random ././@LongLink 0000000 0000000 0000000 00000000211 00000000000 011207 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gamma.GammaDistribution.compute_fixed_moments_and_f.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gamma.GammaDistribu0000664 5104112 5104112 00000000424 12356463434 035350 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gamma.GammaDistribution.compute_fixed_moments_and_f
===============================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gamma
.. automethod:: GammaDistribution.compute_fixed_moments_and_f ././@LongLink 0000000 0000000 0000000 00000000174 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gamma.GammaDistribution.compute_logpdf.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gamma.GammaDistribu0000664 5104112 5104112 00000000355 12356463434 035353 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gamma.GammaDistribution.compute_logpdf
==================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gamma
.. automethod:: GammaDistribution.compute_logpdf ././@LongLink 0000000 0000000 0000000 00000000204 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gamma.GammaDistribution.compute_mask_to_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gamma.GammaDistribu0000664 5104112 5104112 00000000405 12356463434 035347 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gamma.GammaDistribution.compute_mask_to_parent
==========================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gamma
.. automethod:: GammaDistribution.compute_mask_to_parent ././@LongLink 0000000 0000000 0000000 00000000207 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gamma.GammaDistribution.compute_message_to_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gamma.GammaDistribu0000664 5104112 5104112 00000000416 12356463434 035351 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gamma.GammaDistribution.compute_message_to_parent
=============================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gamma
.. automethod:: GammaDistribution.compute_message_to_parent ././@LongLink 0000000 0000000 0000000 00000000205 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gamma.GammaDistribution.compute_moments_and_cgf.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gamma.GammaDistribu0000664 5104112 5104112 00000000410 12356463434 035343 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gamma.GammaDistribution.compute_moments_and_cgf
===========================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gamma
.. automethod:: GammaDistribution.compute_moments_and_cgf ././@LongLink 0000000 0000000 0000000 00000000206 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gamma.GammaDistribution.compute_phi_from_parents.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gamma.GammaDistribu0000664 5104112 5104112 00000000413 12356463434 035346 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gamma.GammaDistribution.compute_phi_from_parents
============================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gamma
.. automethod:: GammaDistribution.compute_phi_from_parents ././@LongLink 0000000 0000000 0000000 00000000200 00000000000 011205 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gamma.GammaDistribution.plates_from_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gamma.GammaDistribu0000664 5104112 5104112 00000000371 12356463434 035351 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gamma.GammaDistribution.plates_from_parent
======================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gamma
.. automethod:: GammaDistribution.plates_from_parent ././@LongLink 0000000 0000000 0000000 00000000176 00000000000 011221 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gamma.GammaDistribution.plates_to_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gamma.GammaDistribu0000664 5104112 5104112 00000000363 12356463434 035352 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gamma.GammaDistribution.plates_to_parent
====================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gamma
.. automethod:: GammaDistribution.plates_to_parent bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.linalg.chol_solve.rst 0000664 5104112 5104112 00000000206 12356463436 033242 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.linalg.chol_solve
===============================
.. currentmodule:: bayespy.utils.linalg
.. autofunction:: chol_solve ././@LongLink 0000000 0000000 0000000 00000000166 00000000000 011220 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gamma.GammaMoments.add_converter.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gamma.GammaMoments.0000664 5104112 5104112 00000000333 12356463434 035262 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gamma.GammaMoments.add_converter
============================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gamma
.. automethod:: GammaMoments.add_converter ././@LongLink 0000000 0000000 0000000 00000000201 00000000000 011206 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gamma.GammaMoments.compute_dims_from_values.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gamma.GammaMoments.0000664 5104112 5104112 00000000374 12356463434 035267 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gamma.GammaMoments.compute_dims_from_values
=======================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gamma
.. automethod:: GammaMoments.compute_dims_from_values ././@LongLink 0000000 0000000 0000000 00000000176 00000000000 011221 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gamma.GammaMoments.compute_fixed_moments.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gamma.GammaMoments.0000664 5104112 5104112 00000000363 12356463434 035265 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gamma.GammaMoments.compute_fixed_moments
====================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gamma
.. automethod:: GammaMoments.compute_fixed_moments ././@LongLink 0000000 0000000 0000000 00000000166 00000000000 011220 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gamma.GammaMoments.get_converter.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gamma.GammaMoments.0000664 5104112 5104112 00000000333 12356463434 035262 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gamma.GammaMoments.get_converter
============================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gamma
.. automethod:: GammaMoments.get_converter ././@LongLink 0000000 0000000 0000000 00000000177 00000000000 011222 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianARDDistribution.__init__.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianAR0000664 5104112 5104112 00000000366 12356463434 035352 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianARDDistribution.__init__
=====================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianARDDistribution.__init__ ././@LongLink 0000000 0000000 0000000 00000000217 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianARDDistribution.compute_cgf_from_parents.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianAR0000664 5104112 5104112 00000000446 12356463434 035351 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianARDDistribution.compute_cgf_from_parents
=====================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianARDDistribution.compute_cgf_from_parents ././@LongLink 0000000 0000000 0000000 00000000222 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianARDDistribution.compute_fixed_moments_and_f.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianAR0000664 5104112 5104112 00000000457 12356463434 035353 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianARDDistribution.compute_fixed_moments_and_f
========================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianARDDistribution.compute_fixed_moments_and_f ././@LongLink 0000000 0000000 0000000 00000000205 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianARDDistribution.compute_logpdf.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianAR0000664 5104112 5104112 00000000410 12356463434 035340 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianARDDistribution.compute_logpdf
===========================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianARDDistribution.compute_logpdf bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.linalg.logdet_tri.rst 0000664 5104112 5104112 00000000206 12356463436 033241 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.linalg.logdet_tri
===============================
.. currentmodule:: bayespy.utils.linalg
.. autofunction:: logdet_tri ././@LongLink 0000000 0000000 0000000 00000000215 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianARDDistribution.compute_mask_to_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianAR0000664 5104112 5104112 00000000440 12356463434 035343 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianARDDistribution.compute_mask_to_parent
===================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianARDDistribution.compute_mask_to_parent ././@LongLink 0000000 0000000 0000000 00000000220 00000000000 011207 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianARDDistribution.compute_message_to_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianAR0000664 5104112 5104112 00000000451 12356463434 035345 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianARDDistribution.compute_message_to_parent
======================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianARDDistribution.compute_message_to_parent ././@LongLink 0000000 0000000 0000000 00000000216 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianARDDistribution.compute_moments_and_cgf.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianAR0000664 5104112 5104112 00000000443 12356463434 035346 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianARDDistribution.compute_moments_and_cgf
====================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianARDDistribution.compute_moments_and_cgf ././@LongLink 0000000 0000000 0000000 00000000217 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianARDDistribution.compute_phi_from_parents.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianAR0000664 5104112 5104112 00000000446 12356463434 035351 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianARDDistribution.compute_phi_from_parents
=====================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianARDDistribution.compute_phi_from_parents ././@LongLink 0000000 0000000 0000000 00000000211 00000000000 011207 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianARDDistribution.plates_from_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianAR0000664 5104112 5104112 00000000424 12356463434 035345 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianARDDistribution.plates_from_parent
===============================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianARDDistribution.plates_from_parent ././@LongLink 0000000 0000000 0000000 00000000207 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianARDDistribution.plates_to_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianAR0000664 5104112 5104112 00000000416 12356463434 035346 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianARDDistribution.plates_to_parent
=============================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianARDDistribution.plates_to_parent ././@LongLink 0000000 0000000 0000000 00000000175 00000000000 011220 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianARDDistribution.random.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianAR0000664 5104112 5104112 00000000360 12356463434 035344 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianARDDistribution.random
===================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianARDDistribution.random ././@LongLink 0000000 0000000 0000000 00000000214 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianDistribution.compute_cgf_from_parents.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianDi0000664 5104112 5104112 00000000435 12356463434 035401 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianDistribution.compute_cgf_from_parents
==================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianDistribution.compute_cgf_from_parents ././@LongLink 0000000 0000000 0000000 00000000217 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianDistribution.compute_fixed_moments_and_f.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianDi0000664 5104112 5104112 00000000446 12356463434 035403 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianDistribution.compute_fixed_moments_and_f
=====================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianDistribution.compute_fixed_moments_and_f ././@LongLink 0000000 0000000 0000000 00000000202 00000000000 011207 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianDistribution.compute_logpdf.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianDi0000664 5104112 5104112 00000000377 12356463434 035406 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianDistribution.compute_logpdf
========================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianDistribution.compute_logpdf ././@LongLink 0000000 0000000 0000000 00000000212 00000000000 011210 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianDistribution.compute_mask_to_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianDi0000664 5104112 5104112 00000000427 12356463434 035402 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianDistribution.compute_mask_to_parent
================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianDistribution.compute_mask_to_parent ././@LongLink 0000000 0000000 0000000 00000000215 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianDistribution.compute_message_to_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianDi0000664 5104112 5104112 00000000440 12356463434 035375 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianDistribution.compute_message_to_parent
===================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianDistribution.compute_message_to_parent ././@LongLink 0000000 0000000 0000000 00000000213 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianDistribution.compute_moments_and_cgf.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianDi0000664 5104112 5104112 00000000432 12356463434 035376 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianDistribution.compute_moments_and_cgf
=================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianDistribution.compute_moments_and_cgf ././@LongLink 0000000 0000000 0000000 00000000214 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianDistribution.compute_phi_from_parents.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianDi0000664 5104112 5104112 00000000435 12356463434 035401 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianDistribution.compute_phi_from_parents
==================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianDistribution.compute_phi_from_parents ././@LongLink 0000000 0000000 0000000 00000000206 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianDistribution.plates_from_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianDi0000664 5104112 5104112 00000000413 12356463434 035375 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianDistribution.plates_from_parent
============================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianDistribution.plates_from_parent ././@LongLink 0000000 0000000 0000000 00000000204 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianDistribution.plates_to_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianDi0000664 5104112 5104112 00000000405 12356463434 035376 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianDistribution.plates_to_parent
==========================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianDistribution.plates_to_parent ././@LongLink 0000000 0000000 0000000 00000000172 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianDistribution.random.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianDi0000664 5104112 5104112 00000000347 12356463434 035403 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianDistribution.random
================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianDistribution.random ././@LongLink 0000000 0000000 0000000 00000000204 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaARDDistribution.__init__.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGa0000664 5104112 5104112 00000000405 12356463434 035371 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianGammaARDDistribution.__init__
==========================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianGammaARDDistribution.__init__ ././@LongLink 0000000 0000000 0000000 00000000224 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaARDDistribution.compute_cgf_from_parents.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGa0000664 5104112 5104112 00000000465 12356463434 035377 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianGammaARDDistribution.compute_cgf_from_parents
==========================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianGammaARDDistribution.compute_cgf_from_parents ././@LongLink 0000000 0000000 0000000 00000000227 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaARDDistribution.compute_fixed_moments_and_f.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGa0000664 5104112 5104112 00000000476 12356463434 035401 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianGammaARDDistribution.compute_fixed_moments_and_f
=============================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianGammaARDDistribution.compute_fixed_moments_and_f ././@LongLink 0000000 0000000 0000000 00000000212 00000000000 011210 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaARDDistribution.compute_logpdf.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGa0000664 5104112 5104112 00000000427 12356463434 035375 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianGammaARDDistribution.compute_logpdf
================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianGammaARDDistribution.compute_logpdf ././@LongLink 0000000 0000000 0000000 00000000222 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaARDDistribution.compute_mask_to_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGa0000664 5104112 5104112 00000000457 12356463434 035400 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianGammaARDDistribution.compute_mask_to_parent
========================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianGammaARDDistribution.compute_mask_to_parent ././@LongLink 0000000 0000000 0000000 00000000225 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaARDDistribution.compute_message_to_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGa0000664 5104112 5104112 00000000470 12356463434 035373 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianGammaARDDistribution.compute_message_to_parent
===========================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianGammaARDDistribution.compute_message_to_parent ././@LongLink 0000000 0000000 0000000 00000000202 00000000000 011207 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaARDDistribution.random.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGa0000664 5104112 5104112 00000000377 12356463434 035401 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianGammaARDDistribution.random
========================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianGammaARDDistribution.random ././@LongLink 0000000 0000000 0000000 00000000223 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaARDDistribution.compute_moments_and_cgf.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGa0000664 5104112 5104112 00000000462 12356463434 035374 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianGammaARDDistribution.compute_moments_and_cgf
=========================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianGammaARDDistribution.compute_moments_and_cgf ././@LongLink 0000000 0000000 0000000 00000000224 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaARDDistribution.compute_phi_from_parents.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGa0000664 5104112 5104112 00000000465 12356463434 035377 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianGammaARDDistribution.compute_phi_from_parents
==========================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianGammaARDDistribution.compute_phi_from_parents ././@LongLink 0000000 0000000 0000000 00000000216 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaARDDistribution.plates_from_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGa0000664 5104112 5104112 00000000443 12356463434 035373 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianGammaARDDistribution.plates_from_parent
====================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianGammaARDDistribution.plates_from_parent ././@LongLink 0000000 0000000 0000000 00000000214 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaARDDistribution.plates_to_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGa0000664 5104112 5104112 00000000435 12356463434 035374 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianGammaARDDistribution.plates_to_parent
==================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianGammaARDDistribution.plates_to_parent ././@LongLink 0000000 0000000 0000000 00000000177 00000000000 011222 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaARDMoments.__init__.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGa0000664 5104112 5104112 00000000366 12356463434 035377 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianGammaARDMoments.__init__
=====================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianGammaARDMoments.__init__ ././@LongLink 0000000 0000000 0000000 00000000204 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaARDMoments.add_converter.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGa0000664 5104112 5104112 00000000405 12356463434 035371 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianGammaARDMoments.add_converter
==========================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianGammaARDMoments.add_converter ././@LongLink 0000000 0000000 0000000 00000000217 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaARDMoments.compute_dims_from_values.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGa0000664 5104112 5104112 00000000446 12356463434 035376 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianGammaARDMoments.compute_dims_from_values
=====================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianGammaARDMoments.compute_dims_from_values ././@LongLink 0000000 0000000 0000000 00000000204 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaARDMoments.get_converter.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGa0000664 5104112 5104112 00000000405 12356463434 035371 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianGammaARDMoments.get_converter
==========================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianGammaARDMoments.get_converter ././@LongLink 0000000 0000000 0000000 00000000214 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaARDMoments.compute_fixed_moments.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGa0000664 5104112 5104112 00000000435 12356463434 035374 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianGammaARDMoments.compute_fixed_moments
==================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianGammaARDMoments.compute_fixed_moments ././@LongLink 0000000 0000000 0000000 00000000211 00000000000 011207 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaARDToGaussianWishart.__init__.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGa0000664 5104112 5104112 00000000424 12356463434 035372 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianGammaARDToGaussianWishart.__init__
===============================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianGammaARDToGaussianWishart.__init__ ././@LongLink 0000000 0000000 0000000 00000000217 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaARDToGaussianWishart.add_plate_axis.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGa0000664 5104112 5104112 00000000446 12356463434 035376 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianGammaARDToGaussianWishart.add_plate_axis
=====================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianGammaARDToGaussianWishart.add_plate_axis ././@LongLink 0000000 0000000 0000000 00000000207 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaARDToGaussianWishart.delete.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGa0000664 5104112 5104112 00000000416 12356463434 035373 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianGammaARDToGaussianWishart.delete
=============================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianGammaARDToGaussianWishart.delete ././@LongLink 0000000 0000000 0000000 00000000211 00000000000 011207 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaARDToGaussianWishart.get_mask.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGa0000664 5104112 5104112 00000000424 12356463434 035372 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianGammaARDToGaussianWishart.get_mask
===============================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianGammaARDToGaussianWishart.get_mask ././@LongLink 0000000 0000000 0000000 00000000214 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaARDToGaussianWishart.get_moments.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGa0000664 5104112 5104112 00000000435 12356463434 035374 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianGammaARDToGaussianWishart.get_moments
==================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianGammaARDToGaussianWishart.get_moments ././@LongLink 0000000 0000000 0000000 00000000212 00000000000 011210 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaARDToGaussianWishart.get_shape.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGa0000664 5104112 5104112 00000000427 12356463434 035375 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianGammaARDToGaussianWishart.get_shape
================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianGammaARDToGaussianWishart.get_shape ././@LongLink 0000000 0000000 0000000 00000000214 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaARDToGaussianWishart.has_plotter.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGa0000664 5104112 5104112 00000000435 12356463434 035374 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianGammaARDToGaussianWishart.has_plotter
==================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianGammaARDToGaussianWishart.has_plotter ././@LongLink 0000000 0000000 0000000 00000000231 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaARDToGaussianWishart.lower_bound_contribution.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGa0000664 5104112 5104112 00000000504 12356463434 035371 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianGammaARDToGaussianWishart.lower_bound_contribution
===============================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianGammaARDToGaussianWishart.lower_bound_contribution ././@LongLink 0000000 0000000 0000000 00000000214 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaARDToGaussianWishart.move_plates.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGa0000664 5104112 5104112 00000000435 12356463434 035374 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianGammaARDToGaussianWishart.move_plates
==================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianGammaARDToGaussianWishart.move_plates ././@LongLink 0000000 0000000 0000000 00000000207 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaARDToGaussianWishart.plates.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGa0000664 5104112 5104112 00000000421 12356463434 035367 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianGammaARDToGaussianWishart.plates
=============================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. autoattribute:: GaussianGammaARDToGaussianWishart.plates ././@LongLink 0000000 0000000 0000000 00000000205 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaARDToGaussianWishart.plot.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGa0000664 5104112 5104112 00000000410 12356463434 035365 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianGammaARDToGaussianWishart.plot
===========================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianGammaARDToGaussianWishart.plot ././@LongLink 0000000 0000000 0000000 00000000214 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaARDToGaussianWishart.set_plotter.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGa0000664 5104112 5104112 00000000435 12356463434 035374 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianGammaARDToGaussianWishart.set_plotter
==================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianGammaARDToGaussianWishart.set_plotter ././@LongLink 0000000 0000000 0000000 00000000224 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaISODistribution.compute_cgf_from_parents.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGa0000664 5104112 5104112 00000000465 12356463434 035377 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianGammaISODistribution.compute_cgf_from_parents
==========================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianGammaISODistribution.compute_cgf_from_parents ././@LongLink 0000000 0000000 0000000 00000000227 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaISODistribution.compute_fixed_moments_and_f.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGa0000664 5104112 5104112 00000000476 12356463434 035401 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianGammaISODistribution.compute_fixed_moments_and_f
=============================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianGammaISODistribution.compute_fixed_moments_and_f ././@LongLink 0000000 0000000 0000000 00000000167 00000000000 011221 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianMoments.__init__.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianMo0000664 5104112 5104112 00000000336 12356463434 035420 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianMoments.__init__
=============================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianMoments.__init__ ././@LongLink 0000000 0000000 0000000 00000000212 00000000000 011210 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaISODistribution.compute_logpdf.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGa0000664 5104112 5104112 00000000427 12356463434 035375 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianGammaISODistribution.compute_logpdf
================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianGammaISODistribution.compute_logpdf ././@LongLink 0000000 0000000 0000000 00000000222 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaISODistribution.compute_mask_to_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGa0000664 5104112 5104112 00000000457 12356463434 035400 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianGammaISODistribution.compute_mask_to_parent
========================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianGammaISODistribution.compute_mask_to_parent ././@LongLink 0000000 0000000 0000000 00000000225 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaISODistribution.compute_message_to_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGa0000664 5104112 5104112 00000000470 12356463434 035373 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianGammaISODistribution.compute_message_to_parent
===========================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianGammaISODistribution.compute_message_to_parent ././@LongLink 0000000 0000000 0000000 00000000223 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaISODistribution.compute_moments_and_cgf.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGa0000664 5104112 5104112 00000000462 12356463434 035374 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianGammaISODistribution.compute_moments_and_cgf
=========================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianGammaISODistribution.compute_moments_and_cgf ././@LongLink 0000000 0000000 0000000 00000000224 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaISODistribution.compute_phi_from_parents.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGa0000664 5104112 5104112 00000000465 12356463434 035377 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianGammaISODistribution.compute_phi_from_parents
==========================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianGammaISODistribution.compute_phi_from_parents ././@LongLink 0000000 0000000 0000000 00000000216 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaISODistribution.plates_from_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGa0000664 5104112 5104112 00000000443 12356463434 035373 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianGammaISODistribution.plates_from_parent
====================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianGammaISODistribution.plates_from_parent ././@LongLink 0000000 0000000 0000000 00000000214 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaISODistribution.plates_to_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGa0000664 5104112 5104112 00000000435 12356463434 035374 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianGammaISODistribution.plates_to_parent
==================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianGammaISODistribution.plates_to_parent ././@LongLink 0000000 0000000 0000000 00000000160 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.node.Moments.add_converter.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.node.Moments.add_co0000664 5104112 5104112 00000000311 12356463435 035311 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.node.Moments.add_converter
======================================================
.. currentmodule:: bayespy.inference.vmp.nodes.node
.. automethod:: Moments.add_converter ././@LongLink 0000000 0000000 0000000 00000000202 00000000000 011207 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaISODistribution.random.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGa0000664 5104112 5104112 00000000377 12356463434 035401 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianGammaISODistribution.random
========================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianGammaISODistribution.random ././@LongLink 0000000 0000000 0000000 00000000177 00000000000 011222 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaISOMoments.__init__.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGa0000664 5104112 5104112 00000000366 12356463434 035377 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianGammaISOMoments.__init__
=====================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianGammaISOMoments.__init__ ././@LongLink 0000000 0000000 0000000 00000000204 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaISOMoments.add_converter.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGa0000664 5104112 5104112 00000000405 12356463434 035371 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianGammaISOMoments.add_converter
==========================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianGammaISOMoments.add_converter ././@LongLink 0000000 0000000 0000000 00000000217 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaISOMoments.compute_dims_from_values.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGa0000664 5104112 5104112 00000000446 12356463434 035376 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianGammaISOMoments.compute_dims_from_values
=====================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianGammaISOMoments.compute_dims_from_values ././@LongLink 0000000 0000000 0000000 00000000214 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaISOMoments.compute_fixed_moments.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGa0000664 5104112 5104112 00000000435 12356463434 035374 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianGammaISOMoments.compute_fixed_moments
==================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianGammaISOMoments.compute_fixed_moments ././@LongLink 0000000 0000000 0000000 00000000204 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaISOMoments.get_converter.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGa0000664 5104112 5104112 00000000405 12356463434 035371 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianGammaISOMoments.get_converter
==========================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianGammaISOMoments.get_converter ././@LongLink 0000000 0000000 0000000 00000000212 00000000000 011210 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaISOToGaussianGammaARD.__init__.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGa0000664 5104112 5104112 00000000427 12356463434 035375 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianGammaISOToGaussianGammaARD.__init__
================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianGammaISOToGaussianGammaARD.__init__ ././@LongLink 0000000 0000000 0000000 00000000220 00000000000 011207 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaISOToGaussianGammaARD.add_plate_axis.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGa0000664 5104112 5104112 00000000451 12356463434 035372 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianGammaISOToGaussianGammaARD.add_plate_axis
======================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianGammaISOToGaussianGammaARD.add_plate_axis ././@LongLink 0000000 0000000 0000000 00000000210 00000000000 011206 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaISOToGaussianGammaARD.delete.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGa0000664 5104112 5104112 00000000421 12356463434 035367 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianGammaISOToGaussianGammaARD.delete
==============================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianGammaISOToGaussianGammaARD.delete ././@LongLink 0000000 0000000 0000000 00000000212 00000000000 011210 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaISOToGaussianGammaARD.get_mask.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGa0000664 5104112 5104112 00000000427 12356463434 035375 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianGammaISOToGaussianGammaARD.get_mask
================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianGammaISOToGaussianGammaARD.get_mask ././@LongLink 0000000 0000000 0000000 00000000215 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaISOToGaussianGammaARD.get_moments.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGa0000664 5104112 5104112 00000000440 12356463434 035370 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianGammaISOToGaussianGammaARD.get_moments
===================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianGammaISOToGaussianGammaARD.get_moments ././@LongLink 0000000 0000000 0000000 00000000213 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaISOToGaussianGammaARD.get_shape.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGa0000664 5104112 5104112 00000000432 12356463434 035371 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianGammaISOToGaussianGammaARD.get_shape
=================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianGammaISOToGaussianGammaARD.get_shape ././@LongLink 0000000 0000000 0000000 00000000215 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaISOToGaussianGammaARD.has_plotter.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGa0000664 5104112 5104112 00000000440 12356463434 035370 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianGammaISOToGaussianGammaARD.has_plotter
===================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianGammaISOToGaussianGammaARD.has_plotter ././@LongLink 0000000 0000000 0000000 00000000232 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaISOToGaussianGammaARD.lower_bound_contribution.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGa0000664 5104112 5104112 00000000507 12356463434 035374 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianGammaISOToGaussianGammaARD.lower_bound_contribution
================================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianGammaISOToGaussianGammaARD.lower_bound_contribution ././@LongLink 0000000 0000000 0000000 00000000215 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaISOToGaussianGammaARD.move_plates.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGa0000664 5104112 5104112 00000000440 12356463434 035370 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianGammaISOToGaussianGammaARD.move_plates
===================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianGammaISOToGaussianGammaARD.move_plates ././@LongLink 0000000 0000000 0000000 00000000210 00000000000 011206 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaISOToGaussianGammaARD.plates.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGa0000664 5104112 5104112 00000000424 12356463434 035372 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianGammaISOToGaussianGammaARD.plates
==============================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. autoattribute:: GaussianGammaISOToGaussianGammaARD.plates ././@LongLink 0000000 0000000 0000000 00000000206 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaISOToGaussianGammaARD.plot.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGa0000664 5104112 5104112 00000000413 12356463434 035370 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianGammaISOToGaussianGammaARD.plot
============================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianGammaISOToGaussianGammaARD.plot ././@LongLink 0000000 0000000 0000000 00000000215 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaISOToGaussianGammaARD.set_plotter.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGa0000664 5104112 5104112 00000000440 12356463434 035370 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianGammaISOToGaussianGammaARD.set_plotter
===================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianGammaISOToGaussianGammaARD.set_plotter ././@LongLink 0000000 0000000 0000000 00000000174 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianMoments.add_converter.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianMo0000664 5104112 5104112 00000000355 12356463434 035421 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianMoments.add_converter
==================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianMoments.add_converter ././@LongLink 0000000 0000000 0000000 00000000207 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianMoments.compute_dims_from_values.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianMo0000664 5104112 5104112 00000000416 12356463434 035417 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianMoments.compute_dims_from_values
=============================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianMoments.compute_dims_from_values ././@LongLink 0000000 0000000 0000000 00000000204 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianMoments.compute_fixed_moments.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianMo0000664 5104112 5104112 00000000405 12356463434 035415 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianMoments.compute_fixed_moments
==========================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianMoments.compute_fixed_moments ././@LongLink 0000000 0000000 0000000 00000000174 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianMoments.get_converter.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianMo0000664 5104112 5104112 00000000355 12356463434 035421 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianMoments.get_converter
==================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianMoments.get_converter ././@LongLink 0000000 0000000 0000000 00000000202 00000000000 011207 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianToGaussianGammaISO.__init__.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianTo0000664 5104112 5104112 00000000377 12356463434 035434 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianToGaussianGammaISO.__init__
========================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianToGaussianGammaISO.__init__ ././@LongLink 0000000 0000000 0000000 00000000210 00000000000 011206 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianToGaussianGammaISO.add_plate_axis.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianTo0000664 5104112 5104112 00000000421 12356463434 035422 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianToGaussianGammaISO.add_plate_axis
==============================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianToGaussianGammaISO.add_plate_axis bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.linalg.inv.rst 0000664 5104112 5104112 00000000161 12356463436 031701 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.linalg.inv
========================
.. currentmodule:: bayespy.utils.linalg
.. autofunction:: inv ././@LongLink 0000000 0000000 0000000 00000000200 00000000000 011205 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianToGaussianGammaISO.delete.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianTo0000664 5104112 5104112 00000000371 12356463434 035426 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianToGaussianGammaISO.delete
======================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianToGaussianGammaISO.delete ././@LongLink 0000000 0000000 0000000 00000000202 00000000000 011207 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianToGaussianGammaISO.get_mask.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianTo0000664 5104112 5104112 00000000377 12356463434 035434 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianToGaussianGammaISO.get_mask
========================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianToGaussianGammaISO.get_mask ././@LongLink 0000000 0000000 0000000 00000000205 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianToGaussianGammaISO.get_moments.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianTo0000664 5104112 5104112 00000000410 12356463434 035420 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianToGaussianGammaISO.get_moments
===========================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianToGaussianGammaISO.get_moments ././@LongLink 0000000 0000000 0000000 00000000203 00000000000 011210 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianToGaussianGammaISO.get_shape.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianTo0000664 5104112 5104112 00000000402 12356463434 035421 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianToGaussianGammaISO.get_shape
=========================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianToGaussianGammaISO.get_shape ././@LongLink 0000000 0000000 0000000 00000000205 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianToGaussianGammaISO.has_plotter.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianTo0000664 5104112 5104112 00000000410 12356463434 035420 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianToGaussianGammaISO.has_plotter
===========================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianToGaussianGammaISO.has_plotter ././@LongLink 0000000 0000000 0000000 00000000222 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianToGaussianGammaISO.lower_bound_contribution.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianTo0000664 5104112 5104112 00000000457 12356463434 035433 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianToGaussianGammaISO.lower_bound_contribution
========================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianToGaussianGammaISO.lower_bound_contribution ././@LongLink 0000000 0000000 0000000 00000000205 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianToGaussianGammaISO.move_plates.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianTo0000664 5104112 5104112 00000000410 12356463434 035420 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianToGaussianGammaISO.move_plates
===========================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianToGaussianGammaISO.move_plates ././@LongLink 0000000 0000000 0000000 00000000200 00000000000 011205 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianToGaussianGammaISO.plates.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianTo0000664 5104112 5104112 00000000374 12356463434 035431 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianToGaussianGammaISO.plates
======================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. autoattribute:: GaussianToGaussianGammaISO.plates ././@LongLink 0000000 0000000 0000000 00000000176 00000000000 011221 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianToGaussianGammaISO.plot.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianTo0000664 5104112 5104112 00000000363 12356463434 035427 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianToGaussianGammaISO.plot
====================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianToGaussianGammaISO.plot ././@LongLink 0000000 0000000 0000000 00000000205 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianToGaussianGammaISO.set_plotter.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianTo0000664 5104112 5104112 00000000410 12356463434 035420 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianToGaussianGammaISO.set_plotter
===========================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianToGaussianGammaISO.set_plotter ././@LongLink 0000000 0000000 0000000 00000000223 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianWishartDistribution.compute_cgf_from_parents.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianWi0000664 5104112 5104112 00000000462 12356463434 035424 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianWishartDistribution.compute_cgf_from_parents
=========================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianWishartDistribution.compute_cgf_from_parents ././@LongLink 0000000 0000000 0000000 00000000226 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianWishartDistribution.compute_fixed_moments_and_f.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianWi0000664 5104112 5104112 00000000473 12356463434 035426 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianWishartDistribution.compute_fixed_moments_and_f
============================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianWishartDistribution.compute_fixed_moments_and_f ././@LongLink 0000000 0000000 0000000 00000000211 00000000000 011207 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianWishartDistribution.compute_logpdf.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianWi0000664 5104112 5104112 00000000424 12356463434 035422 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianWishartDistribution.compute_logpdf
===============================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianWishartDistribution.compute_logpdf ././@LongLink 0000000 0000000 0000000 00000000221 00000000000 011210 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianWishartDistribution.compute_mask_to_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianWi0000664 5104112 5104112 00000000454 12356463434 035425 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianWishartDistribution.compute_mask_to_parent
=======================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianWishartDistribution.compute_mask_to_parent ././@LongLink 0000000 0000000 0000000 00000000224 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianWishartDistribution.compute_message_to_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianWi0000664 5104112 5104112 00000000465 12356463434 035427 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianWishartDistribution.compute_message_to_parent
==========================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianWishartDistribution.compute_message_to_parent ././@LongLink 0000000 0000000 0000000 00000000201 00000000000 011206 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianWishartDistribution.random.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianWi0000664 5104112 5104112 00000000374 12356463434 035426 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianWishartDistribution.random
=======================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianWishartDistribution.random ././@LongLink 0000000 0000000 0000000 00000000222 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianWishartDistribution.compute_moments_and_cgf.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianWi0000664 5104112 5104112 00000000457 12356463434 035430 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianWishartDistribution.compute_moments_and_cgf
========================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianWishartDistribution.compute_moments_and_cgf ././@LongLink 0000000 0000000 0000000 00000000223 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianWishartDistribution.compute_phi_from_parents.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianWi0000664 5104112 5104112 00000000462 12356463434 035424 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianWishartDistribution.compute_phi_from_parents
=========================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianWishartDistribution.compute_phi_from_parents ././@LongLink 0000000 0000000 0000000 00000000215 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianWishartDistribution.plates_from_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianWi0000664 5104112 5104112 00000000440 12356463434 035420 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianWishartDistribution.plates_from_parent
===================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianWishartDistribution.plates_from_parent ././@LongLink 0000000 0000000 0000000 00000000213 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianWishartDistribution.plates_to_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianWi0000664 5104112 5104112 00000000432 12356463434 035421 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianWishartDistribution.plates_to_parent
=================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianWishartDistribution.plates_to_parent ././@LongLink 0000000 0000000 0000000 00000000203 00000000000 011210 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianWishartMoments.add_converter.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianWi0000664 5104112 5104112 00000000402 12356463434 035416 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianWishartMoments.add_converter
=========================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianWishartMoments.add_converter ././@LongLink 0000000 0000000 0000000 00000000216 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianWishartMoments.compute_dims_from_values.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianWi0000664 5104112 5104112 00000000443 12356463434 035423 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianWishartMoments.compute_dims_from_values
====================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianWishartMoments.compute_dims_from_values ././@LongLink 0000000 0000000 0000000 00000000213 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianWishartMoments.compute_fixed_moments.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianWi0000664 5104112 5104112 00000000432 12356463434 035421 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianWishartMoments.compute_fixed_moments
=================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianWishartMoments.compute_fixed_moments ././@LongLink 0000000 0000000 0000000 00000000203 00000000000 011210 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianWishartMoments.get_converter.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.GaussianWi0000664 5104112 5104112 00000000402 12356463434 035416 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianWishartMoments.get_converter
=========================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: GaussianWishartMoments.get_converter ././@LongLink 0000000 0000000 0000000 00000000176 00000000000 011221 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaussianGammaARD.__init__.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaus0000664 5104112 5104112 00000000363 12356463434 035406 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.WrapToGaussianGammaARD.__init__
====================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: WrapToGaussianGammaARD.__init__ ././@LongLink 0000000 0000000 0000000 00000000204 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaussianGammaARD.add_plate_axis.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaus0000664 5104112 5104112 00000000405 12356463434 035403 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.WrapToGaussianGammaARD.add_plate_axis
==========================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: WrapToGaussianGammaARD.add_plate_axis ././@LongLink 0000000 0000000 0000000 00000000174 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaussianGammaARD.delete.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaus0000664 5104112 5104112 00000000355 12356463434 035407 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.WrapToGaussianGammaARD.delete
==================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: WrapToGaussianGammaARD.delete ././@LongLink 0000000 0000000 0000000 00000000176 00000000000 011221 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaussianGammaARD.get_mask.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaus0000664 5104112 5104112 00000000363 12356463434 035406 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.WrapToGaussianGammaARD.get_mask
====================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: WrapToGaussianGammaARD.get_mask ././@LongLink 0000000 0000000 0000000 00000000201 00000000000 011206 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaussianGammaARD.get_moments.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaus0000664 5104112 5104112 00000000374 12356463434 035410 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.WrapToGaussianGammaARD.get_moments
=======================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: WrapToGaussianGammaARD.get_moments ././@LongLink 0000000 0000000 0000000 00000000177 00000000000 011222 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaussianGammaARD.get_shape.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaus0000664 5104112 5104112 00000000366 12356463434 035411 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.WrapToGaussianGammaARD.get_shape
=====================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: WrapToGaussianGammaARD.get_shape ././@LongLink 0000000 0000000 0000000 00000000201 00000000000 011206 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaussianGammaARD.has_plotter.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaus0000664 5104112 5104112 00000000374 12356463434 035410 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.WrapToGaussianGammaARD.has_plotter
=======================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: WrapToGaussianGammaARD.has_plotter ././@LongLink 0000000 0000000 0000000 00000000216 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaussianGammaARD.lower_bound_contribution.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaus0000664 5104112 5104112 00000000443 12356463434 035405 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.WrapToGaussianGammaARD.lower_bound_contribution
====================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: WrapToGaussianGammaARD.lower_bound_contribution ././@LongLink 0000000 0000000 0000000 00000000146 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.node.Node.delete.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.node.Node.delete.rs0000664 5104112 5104112 00000000253 12356463435 035235 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.node.Node.delete
============================================
.. currentmodule:: bayespy.inference.vmp.nodes.node
.. automethod:: Node.delete ././@LongLink 0000000 0000000 0000000 00000000201 00000000000 011206 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaussianGammaARD.move_plates.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaus0000664 5104112 5104112 00000000374 12356463434 035410 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.WrapToGaussianGammaARD.move_plates
=======================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: WrapToGaussianGammaARD.move_plates ././@LongLink 0000000 0000000 0000000 00000000174 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaussianGammaARD.plates.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaus0000664 5104112 5104112 00000000360 12356463434 035403 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.WrapToGaussianGammaARD.plates
==================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. autoattribute:: WrapToGaussianGammaARD.plates ././@LongLink 0000000 0000000 0000000 00000000172 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaussianGammaARD.plot.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaus0000664 5104112 5104112 00000000347 12356463434 035410 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.WrapToGaussianGammaARD.plot
================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: WrapToGaussianGammaARD.plot ././@LongLink 0000000 0000000 0000000 00000000201 00000000000 011206 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaussianGammaARD.set_plotter.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaus0000664 5104112 5104112 00000000374 12356463434 035410 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.WrapToGaussianGammaARD.set_plotter
=======================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: WrapToGaussianGammaARD.set_plotter ././@LongLink 0000000 0000000 0000000 00000000176 00000000000 011221 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaussianGammaISO.__init__.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaus0000664 5104112 5104112 00000000363 12356463434 035406 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.WrapToGaussianGammaISO.__init__
====================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: WrapToGaussianGammaISO.__init__ ././@LongLink 0000000 0000000 0000000 00000000204 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaussianGammaISO.add_plate_axis.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaus0000664 5104112 5104112 00000000405 12356463434 035403 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.WrapToGaussianGammaISO.add_plate_axis
==========================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: WrapToGaussianGammaISO.add_plate_axis ././@LongLink 0000000 0000000 0000000 00000000174 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaussianGammaISO.delete.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaus0000664 5104112 5104112 00000000355 12356463435 035410 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.WrapToGaussianGammaISO.delete
==================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: WrapToGaussianGammaISO.delete ././@LongLink 0000000 0000000 0000000 00000000176 00000000000 011221 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaussianGammaISO.get_mask.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaus0000664 5104112 5104112 00000000363 12356463435 035407 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.WrapToGaussianGammaISO.get_mask
====================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: WrapToGaussianGammaISO.get_mask ././@LongLink 0000000 0000000 0000000 00000000201 00000000000 011206 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaussianGammaISO.get_moments.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaus0000664 5104112 5104112 00000000374 12356463435 035411 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.WrapToGaussianGammaISO.get_moments
=======================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: WrapToGaussianGammaISO.get_moments ././@LongLink 0000000 0000000 0000000 00000000177 00000000000 011222 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaussianGammaISO.get_shape.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaus0000664 5104112 5104112 00000000366 12356463435 035412 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.WrapToGaussianGammaISO.get_shape
=====================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: WrapToGaussianGammaISO.get_shape ././@LongLink 0000000 0000000 0000000 00000000201 00000000000 011206 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaussianGammaISO.has_plotter.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaus0000664 5104112 5104112 00000000374 12356463435 035411 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.WrapToGaussianGammaISO.has_plotter
=======================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: WrapToGaussianGammaISO.has_plotter ././@LongLink 0000000 0000000 0000000 00000000216 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaussianGammaISO.lower_bound_contribution.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaus0000664 5104112 5104112 00000000443 12356463435 035406 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.WrapToGaussianGammaISO.lower_bound_contribution
====================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: WrapToGaussianGammaISO.lower_bound_contribution ././@LongLink 0000000 0000000 0000000 00000000201 00000000000 011206 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaussianGammaISO.move_plates.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaus0000664 5104112 5104112 00000000374 12356463435 035411 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.WrapToGaussianGammaISO.move_plates
=======================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: WrapToGaussianGammaISO.move_plates ././@LongLink 0000000 0000000 0000000 00000000174 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaussianGammaISO.plates.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaus0000664 5104112 5104112 00000000360 12356463435 035404 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.WrapToGaussianGammaISO.plates
==================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. autoattribute:: WrapToGaussianGammaISO.plates ././@LongLink 0000000 0000000 0000000 00000000172 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaussianGammaISO.plot.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaus0000664 5104112 5104112 00000000347 12356463435 035411 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.WrapToGaussianGammaISO.plot
================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: WrapToGaussianGammaISO.plot ././@LongLink 0000000 0000000 0000000 00000000201 00000000000 011206 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaussianGammaISO.set_plotter.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaus0000664 5104112 5104112 00000000374 12356463435 035411 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.WrapToGaussianGammaISO.set_plotter
=======================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: WrapToGaussianGammaISO.set_plotter ././@LongLink 0000000 0000000 0000000 00000000175 00000000000 011220 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaussianWishart.__init__.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaus0000664 5104112 5104112 00000000360 12356463435 035404 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.WrapToGaussianWishart.__init__
===================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: WrapToGaussianWishart.__init__ ././@LongLink 0000000 0000000 0000000 00000000160 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.node.Moments.get_converter.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.node.Moments.get_co0000664 5104112 5104112 00000000311 12356463435 035340 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.node.Moments.get_converter
======================================================
.. currentmodule:: bayespy.inference.vmp.nodes.node
.. automethod:: Moments.get_converter ././@LongLink 0000000 0000000 0000000 00000000203 00000000000 011210 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaussianWishart.add_plate_axis.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaus0000664 5104112 5104112 00000000402 12356463435 035401 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.WrapToGaussianWishart.add_plate_axis
=========================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: WrapToGaussianWishart.add_plate_axis ././@LongLink 0000000 0000000 0000000 00000000173 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaussianWishart.delete.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaus0000664 5104112 5104112 00000000352 12356463435 035405 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.WrapToGaussianWishart.delete
=================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: WrapToGaussianWishart.delete ././@LongLink 0000000 0000000 0000000 00000000175 00000000000 011220 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaussianWishart.get_mask.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaus0000664 5104112 5104112 00000000360 12356463435 035404 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.WrapToGaussianWishart.get_mask
===================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: WrapToGaussianWishart.get_mask ././@LongLink 0000000 0000000 0000000 00000000200 00000000000 011205 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaussianWishart.get_moments.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaus0000664 5104112 5104112 00000000371 12356463435 035406 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.WrapToGaussianWishart.get_moments
======================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: WrapToGaussianWishart.get_moments ././@LongLink 0000000 0000000 0000000 00000000176 00000000000 011221 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaussianWishart.get_shape.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaus0000664 5104112 5104112 00000000363 12356463435 035407 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.WrapToGaussianWishart.get_shape
====================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: WrapToGaussianWishart.get_shape ././@LongLink 0000000 0000000 0000000 00000000200 00000000000 011205 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaussianWishart.has_plotter.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaus0000664 5104112 5104112 00000000371 12356463435 035406 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.WrapToGaussianWishart.has_plotter
======================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: WrapToGaussianWishart.has_plotter ././@LongLink 0000000 0000000 0000000 00000000215 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaussianWishart.lower_bound_contribution.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaus0000664 5104112 5104112 00000000440 12356463435 035403 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.WrapToGaussianWishart.lower_bound_contribution
===================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: WrapToGaussianWishart.lower_bound_contribution ././@LongLink 0000000 0000000 0000000 00000000200 00000000000 011205 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaussianWishart.move_plates.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaus0000664 5104112 5104112 00000000371 12356463435 035406 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.WrapToGaussianWishart.move_plates
======================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: WrapToGaussianWishart.move_plates ././@LongLink 0000000 0000000 0000000 00000000150 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.node.Node.get_mask.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.node.Node.get_mask.0000664 5104112 5104112 00000000261 12356463435 035217 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.node.Node.get_mask
==============================================
.. currentmodule:: bayespy.inference.vmp.nodes.node
.. automethod:: Node.get_mask ././@LongLink 0000000 0000000 0000000 00000000173 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaussianWishart.plates.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaus0000664 5104112 5104112 00000000355 12356463435 035410 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.WrapToGaussianWishart.plates
=================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. autoattribute:: WrapToGaussianWishart.plates ././@LongLink 0000000 0000000 0000000 00000000171 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaussianWishart.plot.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaus0000664 5104112 5104112 00000000344 12356463435 035406 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.WrapToGaussianWishart.plot
===============================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: WrapToGaussianWishart.plot ././@LongLink 0000000 0000000 0000000 00000000200 00000000000 011205 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaussianWishart.set_plotter.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaus0000664 5104112 5104112 00000000371 12356463435 035406 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.WrapToGaussianWishart.set_plotter
======================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. automethod:: WrapToGaussianWishart.set_plotter ././@LongLink 0000000 0000000 0000000 00000000224 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.GaussianMarkovChainDistribution.__init__.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_cha0000664 5104112 5104112 00000000465 12356463435 035551 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian_markov_chain.GaussianMarkovChainDistribution.__init__
==========================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian_markov_chain
.. automethod:: GaussianMarkovChainDistribution.__init__ ././@LongLink 0000000 0000000 0000000 00000000244 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.GaussianMarkovChainDistribution.compute_cgf_from_parents.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_cha0000664 5104112 5104112 00000000545 12356463435 035550 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian_markov_chain.GaussianMarkovChainDistribution.compute_cgf_from_parents
==========================================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian_markov_chain
.. automethod:: GaussianMarkovChainDistribution.compute_cgf_from_parents ././@LongLink 0000000 0000000 0000000 00000000247 00000000000 011220 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.GaussianMarkovChainDistribution.compute_fixed_moments_and_f.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_cha0000664 5104112 5104112 00000000556 12356463435 035552 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian_markov_chain.GaussianMarkovChainDistribution.compute_fixed_moments_and_f
=============================================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian_markov_chain
.. automethod:: GaussianMarkovChainDistribution.compute_fixed_moments_and_f ././@LongLink 0000000 0000000 0000000 00000000232 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.GaussianMarkovChainDistribution.compute_logpdf.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_cha0000664 5104112 5104112 00000000507 12356463435 035546 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian_markov_chain.GaussianMarkovChainDistribution.compute_logpdf
================================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian_markov_chain
.. automethod:: GaussianMarkovChainDistribution.compute_logpdf ././@LongLink 0000000 0000000 0000000 00000000156 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.node.Node.add_plate_axis.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.node.Node.add_plate0000664 5104112 5104112 00000000303 12356463435 035261 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.node.Node.add_plate_axis
====================================================
.. currentmodule:: bayespy.inference.vmp.nodes.node
.. automethod:: Node.add_plate_axis ././@LongLink 0000000 0000000 0000000 00000000242 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.GaussianMarkovChainDistribution.compute_mask_to_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_cha0000664 5104112 5104112 00000000537 12356463435 035551 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian_markov_chain.GaussianMarkovChainDistribution.compute_mask_to_parent
========================================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian_markov_chain
.. automethod:: GaussianMarkovChainDistribution.compute_mask_to_parent ././@LongLink 0000000 0000000 0000000 00000000245 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.GaussianMarkovChainDistribution.compute_message_to_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_cha0000664 5104112 5104112 00000000550 12356463435 035544 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian_markov_chain.GaussianMarkovChainDistribution.compute_message_to_parent
===========================================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian_markov_chain
.. automethod:: GaussianMarkovChainDistribution.compute_message_to_parent ././@LongLink 0000000 0000000 0000000 00000000243 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.GaussianMarkovChainDistribution.compute_moments_and_cgf.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_cha0000664 5104112 5104112 00000000542 12356463435 035545 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian_markov_chain.GaussianMarkovChainDistribution.compute_moments_and_cgf
=========================================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian_markov_chain
.. automethod:: GaussianMarkovChainDistribution.compute_moments_and_cgf ././@LongLink 0000000 0000000 0000000 00000000244 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.GaussianMarkovChainDistribution.compute_phi_from_parents.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_cha0000664 5104112 5104112 00000000545 12356463435 035550 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian_markov_chain.GaussianMarkovChainDistribution.compute_phi_from_parents
==========================================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian_markov_chain
.. automethod:: GaussianMarkovChainDistribution.compute_phi_from_parents ././@LongLink 0000000 0000000 0000000 00000000236 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.GaussianMarkovChainDistribution.plates_from_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_cha0000664 5104112 5104112 00000000523 12356463435 035544 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian_markov_chain.GaussianMarkovChainDistribution.plates_from_parent
====================================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian_markov_chain
.. automethod:: GaussianMarkovChainDistribution.plates_from_parent ././@LongLink 0000000 0000000 0000000 00000000234 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.GaussianMarkovChainDistribution.plates_to_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_cha0000664 5104112 5104112 00000000515 12356463435 035545 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian_markov_chain.GaussianMarkovChainDistribution.plates_to_parent
==================================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian_markov_chain
.. automethod:: GaussianMarkovChainDistribution.plates_to_parent ././@LongLink 0000000 0000000 0000000 00000000222 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.GaussianMarkovChainDistribution.random.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_cha0000664 5104112 5104112 00000000457 12356463435 035552 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian_markov_chain.GaussianMarkovChainDistribution.random
========================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian_markov_chain
.. automethod:: GaussianMarkovChainDistribution.random ././@LongLink 0000000 0000000 0000000 00000000224 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.GaussianMarkovChainMoments.add_converter.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_cha0000664 5104112 5104112 00000000465 12356463435 035551 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian_markov_chain.GaussianMarkovChainMoments.add_converter
==========================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian_markov_chain
.. automethod:: GaussianMarkovChainMoments.add_converter ././@LongLink 0000000 0000000 0000000 00000000237 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.GaussianMarkovChainMoments.compute_dims_from_values.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_cha0000664 5104112 5104112 00000000526 12356463435 035547 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian_markov_chain.GaussianMarkovChainMoments.compute_dims_from_values
=====================================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian_markov_chain
.. automethod:: GaussianMarkovChainMoments.compute_dims_from_values ././@LongLink 0000000 0000000 0000000 00000000234 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.GaussianMarkovChainMoments.compute_fixed_moments.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_cha0000664 5104112 5104112 00000000515 12356463435 035545 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian_markov_chain.GaussianMarkovChainMoments.compute_fixed_moments
==================================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian_markov_chain
.. automethod:: GaussianMarkovChainMoments.compute_fixed_moments ././@LongLink 0000000 0000000 0000000 00000000224 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.GaussianMarkovChainMoments.get_converter.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_cha0000664 5104112 5104112 00000000465 12356463435 035551 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian_markov_chain.GaussianMarkovChainMoments.get_converter
==========================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian_markov_chain
.. automethod:: GaussianMarkovChainMoments.get_converter ././@LongLink 0000000 0000000 0000000 00000000235 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.SwitchingGaussianMarkovChainDistribution.__init__.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_cha0000664 5104112 5104112 00000000520 12356463435 035541 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian_markov_chain.SwitchingGaussianMarkovChainDistribution.__init__
===================================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian_markov_chain
.. automethod:: SwitchingGaussianMarkovChainDistribution.__init__ ././@LongLink 0000000 0000000 0000000 00000000255 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.SwitchingGaussianMarkovChainDistribution.compute_cgf_from_parents.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_cha0000664 5104112 5104112 00000000600 12356463435 035540 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian_markov_chain.SwitchingGaussianMarkovChainDistribution.compute_cgf_from_parents
===================================================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian_markov_chain
.. automethod:: SwitchingGaussianMarkovChainDistribution.compute_cgf_from_parents ././@LongLink 0000000 0000000 0000000 00000000202 00000000000 011207 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.multinomial.MultinomialDistribution.__init__.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.multinomial.Multino0000664 5104112 5104112 00000000377 12356463435 035527 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.multinomial.MultinomialDistribution.__init__
========================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.multinomial
.. automethod:: MultinomialDistribution.__init__ ././@LongLink 0000000 0000000 0000000 00000000260 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.SwitchingGaussianMarkovChainDistribution.compute_fixed_moments_and_f.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_cha0000664 5104112 5104112 00000000611 12356463435 035542 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian_markov_chain.SwitchingGaussianMarkovChainDistribution.compute_fixed_moments_and_f
======================================================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian_markov_chain
.. automethod:: SwitchingGaussianMarkovChainDistribution.compute_fixed_moments_and_f ././@LongLink 0000000 0000000 0000000 00000000243 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.SwitchingGaussianMarkovChainDistribution.compute_logpdf.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_cha0000664 5104112 5104112 00000000542 12356463435 035545 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian_markov_chain.SwitchingGaussianMarkovChainDistribution.compute_logpdf
=========================================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian_markov_chain
.. automethod:: SwitchingGaussianMarkovChainDistribution.compute_logpdf ././@LongLink 0000000 0000000 0000000 00000000253 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.SwitchingGaussianMarkovChainDistribution.compute_mask_to_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_cha0000664 5104112 5104112 00000000572 12356463435 035550 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian_markov_chain.SwitchingGaussianMarkovChainDistribution.compute_mask_to_parent
=================================================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian_markov_chain
.. automethod:: SwitchingGaussianMarkovChainDistribution.compute_mask_to_parent ././@LongLink 0000000 0000000 0000000 00000000256 00000000000 011220 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.SwitchingGaussianMarkovChainDistribution.compute_message_to_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_cha0000664 5104112 5104112 00000000603 12356463435 035543 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian_markov_chain.SwitchingGaussianMarkovChainDistribution.compute_message_to_parent
====================================================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian_markov_chain
.. automethod:: SwitchingGaussianMarkovChainDistribution.compute_message_to_parent ././@LongLink 0000000 0000000 0000000 00000000254 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.SwitchingGaussianMarkovChainDistribution.compute_moments_and_cgf.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_cha0000664 5104112 5104112 00000000575 12356463435 035553 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian_markov_chain.SwitchingGaussianMarkovChainDistribution.compute_moments_and_cgf
==================================================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian_markov_chain
.. automethod:: SwitchingGaussianMarkovChainDistribution.compute_moments_and_cgf ././@LongLink 0000000 0000000 0000000 00000000255 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.SwitchingGaussianMarkovChainDistribution.compute_phi_from_parents.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_cha0000664 5104112 5104112 00000000600 12356463435 035540 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian_markov_chain.SwitchingGaussianMarkovChainDistribution.compute_phi_from_parents
===================================================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian_markov_chain
.. automethod:: SwitchingGaussianMarkovChainDistribution.compute_phi_from_parents bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.T.rst 0000664 5104112 5104112 00000000145 12356463436 030777 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.T
====================
.. currentmodule:: bayespy.utils.misc
.. autofunction:: T ././@LongLink 0000000 0000000 0000000 00000000247 00000000000 011220 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.SwitchingGaussianMarkovChainDistribution.plates_from_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_cha0000664 5104112 5104112 00000000556 12356463435 035552 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian_markov_chain.SwitchingGaussianMarkovChainDistribution.plates_from_parent
=============================================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian_markov_chain
.. automethod:: SwitchingGaussianMarkovChainDistribution.plates_from_parent ././@LongLink 0000000 0000000 0000000 00000000245 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.SwitchingGaussianMarkovChainDistribution.plates_to_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_cha0000664 5104112 5104112 00000000550 12356463435 035544 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian_markov_chain.SwitchingGaussianMarkovChainDistribution.plates_to_parent
===========================================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian_markov_chain
.. automethod:: SwitchingGaussianMarkovChainDistribution.plates_to_parent ././@LongLink 0000000 0000000 0000000 00000000233 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.SwitchingGaussianMarkovChainDistribution.random.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_cha0000664 5104112 5104112 00000000512 12356463435 035542 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian_markov_chain.SwitchingGaussianMarkovChainDistribution.random
=================================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian_markov_chain
.. automethod:: SwitchingGaussianMarkovChainDistribution.random ././@LongLink 0000000 0000000 0000000 00000000233 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.VaryingGaussianMarkovChainDistribution.__init__.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_cha0000664 5104112 5104112 00000000512 12356463435 035542 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian_markov_chain.VaryingGaussianMarkovChainDistribution.__init__
=================================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian_markov_chain
.. automethod:: VaryingGaussianMarkovChainDistribution.__init__ ././@LongLink 0000000 0000000 0000000 00000000253 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.VaryingGaussianMarkovChainDistribution.compute_cgf_from_parents.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_cha0000664 5104112 5104112 00000000572 12356463435 035550 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian_markov_chain.VaryingGaussianMarkovChainDistribution.compute_cgf_from_parents
=================================================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian_markov_chain
.. automethod:: VaryingGaussianMarkovChainDistribution.compute_cgf_from_parents ././@LongLink 0000000 0000000 0000000 00000000256 00000000000 011220 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.VaryingGaussianMarkovChainDistribution.compute_fixed_moments_and_f.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_cha0000664 5104112 5104112 00000000603 12356463435 035543 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian_markov_chain.VaryingGaussianMarkovChainDistribution.compute_fixed_moments_and_f
====================================================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian_markov_chain
.. automethod:: VaryingGaussianMarkovChainDistribution.compute_fixed_moments_and_f ././@LongLink 0000000 0000000 0000000 00000000170 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.node.Moments.compute_fixed_moments.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.node.Moments.comput0000664 5104112 5104112 00000000341 12356463435 035412 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.node.Moments.compute_fixed_moments
==============================================================
.. currentmodule:: bayespy.inference.vmp.nodes.node
.. automethod:: Moments.compute_fixed_moments ././@LongLink 0000000 0000000 0000000 00000000241 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.VaryingGaussianMarkovChainDistribution.compute_logpdf.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_cha0000664 5104112 5104112 00000000534 12356463435 035546 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian_markov_chain.VaryingGaussianMarkovChainDistribution.compute_logpdf
=======================================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian_markov_chain
.. automethod:: VaryingGaussianMarkovChainDistribution.compute_logpdf ././@LongLink 0000000 0000000 0000000 00000000251 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.VaryingGaussianMarkovChainDistribution.compute_mask_to_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_cha0000664 5104112 5104112 00000000564 12356463435 035551 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian_markov_chain.VaryingGaussianMarkovChainDistribution.compute_mask_to_parent
===============================================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian_markov_chain
.. automethod:: VaryingGaussianMarkovChainDistribution.compute_mask_to_parent ././@LongLink 0000000 0000000 0000000 00000000254 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.VaryingGaussianMarkovChainDistribution.compute_message_to_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_cha0000664 5104112 5104112 00000000575 12356463435 035553 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian_markov_chain.VaryingGaussianMarkovChainDistribution.compute_message_to_parent
==================================================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian_markov_chain
.. automethod:: VaryingGaussianMarkovChainDistribution.compute_message_to_parent ././@LongLink 0000000 0000000 0000000 00000000252 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.VaryingGaussianMarkovChainDistribution.compute_moments_and_cgf.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_cha0000664 5104112 5104112 00000000567 12356463435 035554 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian_markov_chain.VaryingGaussianMarkovChainDistribution.compute_moments_and_cgf
================================================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian_markov_chain
.. automethod:: VaryingGaussianMarkovChainDistribution.compute_moments_and_cgf ././@LongLink 0000000 0000000 0000000 00000000253 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.VaryingGaussianMarkovChainDistribution.compute_phi_from_parents.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_cha0000664 5104112 5104112 00000000572 12356463435 035550 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian_markov_chain.VaryingGaussianMarkovChainDistribution.compute_phi_from_parents
=================================================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian_markov_chain
.. automethod:: VaryingGaussianMarkovChainDistribution.compute_phi_from_parents ././@LongLink 0000000 0000000 0000000 00000000245 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.VaryingGaussianMarkovChainDistribution.plates_from_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_cha0000664 5104112 5104112 00000000550 12356463435 035544 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian_markov_chain.VaryingGaussianMarkovChainDistribution.plates_from_parent
===========================================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian_markov_chain
.. automethod:: VaryingGaussianMarkovChainDistribution.plates_from_parent ././@LongLink 0000000 0000000 0000000 00000000151 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.node.Node.get_shape.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.node.Node.get_shape0000664 5104112 5104112 00000000264 12356463435 035311 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.node.Node.get_shape
===============================================
.. currentmodule:: bayespy.inference.vmp.nodes.node
.. automethod:: Node.get_shape ././@LongLink 0000000 0000000 0000000 00000000243 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.VaryingGaussianMarkovChainDistribution.plates_to_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_cha0000664 5104112 5104112 00000000542 12356463435 035545 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian_markov_chain.VaryingGaussianMarkovChainDistribution.plates_to_parent
=========================================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian_markov_chain
.. automethod:: VaryingGaussianMarkovChainDistribution.plates_to_parent ././@LongLink 0000000 0000000 0000000 00000000231 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.VaryingGaussianMarkovChainDistribution.random.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.gaussian_markov_cha0000664 5104112 5104112 00000000504 12356463435 035543 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian_markov_chain.VaryingGaussianMarkovChainDistribution.random
===============================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian_markov_chain
.. automethod:: VaryingGaussianMarkovChainDistribution.random ././@LongLink 0000000 0000000 0000000 00000000222 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.multinomial.MultinomialDistribution.compute_cgf_from_parents.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.multinomial.Multino0000664 5104112 5104112 00000000457 12356463435 035526 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.multinomial.MultinomialDistribution.compute_cgf_from_parents
========================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.multinomial
.. automethod:: MultinomialDistribution.compute_cgf_from_parents ././@LongLink 0000000 0000000 0000000 00000000225 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.multinomial.MultinomialDistribution.compute_fixed_moments_and_f.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.multinomial.Multino0000664 5104112 5104112 00000000470 12356463435 035521 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.multinomial.MultinomialDistribution.compute_fixed_moments_and_f
===========================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.multinomial
.. automethod:: MultinomialDistribution.compute_fixed_moments_and_f ././@LongLink 0000000 0000000 0000000 00000000210 00000000000 011206 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.multinomial.MultinomialDistribution.compute_logpdf.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.multinomial.Multino0000664 5104112 5104112 00000000421 12356463435 035515 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.multinomial.MultinomialDistribution.compute_logpdf
==============================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.multinomial
.. automethod:: MultinomialDistribution.compute_logpdf ././@LongLink 0000000 0000000 0000000 00000000220 00000000000 011207 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.multinomial.MultinomialDistribution.compute_mask_to_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.multinomial.Multino0000664 5104112 5104112 00000000451 12356463435 035520 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.multinomial.MultinomialDistribution.compute_mask_to_parent
======================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.multinomial
.. automethod:: MultinomialDistribution.compute_mask_to_parent ././@LongLink 0000000 0000000 0000000 00000000223 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.multinomial.MultinomialDistribution.compute_message_to_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.multinomial.Multino0000664 5104112 5104112 00000000462 12356463435 035522 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.multinomial.MultinomialDistribution.compute_message_to_parent
=========================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.multinomial
.. automethod:: MultinomialDistribution.compute_message_to_parent bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.chol.rst 0000664 5104112 5104112 00000000156 12356463436 031523 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.chol
=======================
.. currentmodule:: bayespy.utils.misc
.. autofunction:: chol ././@LongLink 0000000 0000000 0000000 00000000221 00000000000 011210 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.multinomial.MultinomialDistribution.compute_moments_and_cgf.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.multinomial.Multino0000664 5104112 5104112 00000000454 12356463435 035523 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.multinomial.MultinomialDistribution.compute_moments_and_cgf
=======================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.multinomial
.. automethod:: MultinomialDistribution.compute_moments_and_cgf ././@LongLink 0000000 0000000 0000000 00000000222 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.multinomial.MultinomialDistribution.compute_phi_from_parents.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.multinomial.Multino0000664 5104112 5104112 00000000457 12356463435 035526 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.multinomial.MultinomialDistribution.compute_phi_from_parents
========================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.multinomial
.. automethod:: MultinomialDistribution.compute_phi_from_parents ././@LongLink 0000000 0000000 0000000 00000000214 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.multinomial.MultinomialDistribution.plates_from_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.multinomial.Multino0000664 5104112 5104112 00000000435 12356463435 035522 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.multinomial.MultinomialDistribution.plates_from_parent
==================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.multinomial
.. automethod:: MultinomialDistribution.plates_from_parent ././@LongLink 0000000 0000000 0000000 00000000212 00000000000 011210 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.multinomial.MultinomialDistribution.plates_to_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.multinomial.Multino0000664 5104112 5104112 00000000427 12356463435 035523 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.multinomial.MultinomialDistribution.plates_to_parent
================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.multinomial
.. automethod:: MultinomialDistribution.plates_to_parent ././@LongLink 0000000 0000000 0000000 00000000200 00000000000 011205 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.multinomial.MultinomialDistribution.random.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.multinomial.Multino0000664 5104112 5104112 00000000371 12356463435 035521 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.multinomial.MultinomialDistribution.random
======================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.multinomial
.. automethod:: MultinomialDistribution.random ././@LongLink 0000000 0000000 0000000 00000000202 00000000000 011207 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.multinomial.MultinomialMoments.add_converter.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.multinomial.Multino0000664 5104112 5104112 00000000377 12356463435 035527 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.multinomial.MultinomialMoments.add_converter
========================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.multinomial
.. automethod:: MultinomialMoments.add_converter ././@LongLink 0000000 0000000 0000000 00000000215 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.multinomial.MultinomialMoments.compute_dims_from_values.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.multinomial.Multino0000664 5104112 5104112 00000000440 12356463435 035516 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.multinomial.MultinomialMoments.compute_dims_from_values
===================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.multinomial
.. automethod:: MultinomialMoments.compute_dims_from_values ././@LongLink 0000000 0000000 0000000 00000000212 00000000000 011210 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.multinomial.MultinomialMoments.compute_fixed_moments.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.multinomial.Multino0000664 5104112 5104112 00000000427 12356463435 035523 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.multinomial.MultinomialMoments.compute_fixed_moments
================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.multinomial
.. automethod:: MultinomialMoments.compute_fixed_moments ././@LongLink 0000000 0000000 0000000 00000000202 00000000000 011207 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.multinomial.MultinomialMoments.get_converter.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.multinomial.Multino0000664 5104112 5104112 00000000377 12356463435 035527 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.multinomial.MultinomialMoments.get_converter
========================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.multinomial
.. automethod:: MultinomialMoments.get_converter ././@LongLink 0000000 0000000 0000000 00000000173 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.node.Moments.compute_dims_from_values.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.node.Moments.comput0000664 5104112 5104112 00000000352 12356463435 035414 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.node.Moments.compute_dims_from_values
=================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.node
.. automethod:: Moments.compute_dims_from_values ././@LongLink 0000000 0000000 0000000 00000000153 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.node.Node.get_moments.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.node.Node.get_momen0000664 5104112 5104112 00000000272 12356463435 035323 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.node.Node.get_moments
=================================================
.. currentmodule:: bayespy.inference.vmp.nodes.node
.. automethod:: Node.get_moments ././@LongLink 0000000 0000000 0000000 00000000153 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.node.Node.has_plotter.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.node.Node.has_plott0000664 5104112 5104112 00000000272 12356463435 035346 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.node.Node.has_plotter
=================================================
.. currentmodule:: bayespy.inference.vmp.nodes.node
.. automethod:: Node.has_plotter ././@LongLink 0000000 0000000 0000000 00000000153 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.node.Node.move_plates.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.node.Node.move_plat0000664 5104112 5104112 00000000272 12356463435 035337 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.node.Node.move_plates
=================================================
.. currentmodule:: bayespy.inference.vmp.nodes.node
.. automethod:: Node.move_plates ././@LongLink 0000000 0000000 0000000 00000000146 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.node.Node.plates.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.node.Node.plates.rs0000664 5104112 5104112 00000000256 12356463435 035266 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.node.Node.plates
============================================
.. currentmodule:: bayespy.inference.vmp.nodes.node
.. autoattribute:: Node.plates bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.node.Node.plot.rst 0000664 5104112 5104112 00000000245 12356463435 035136 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.node.Node.plot
==========================================
.. currentmodule:: bayespy.inference.vmp.nodes.node
.. automethod:: Node.plot ././@LongLink 0000000 0000000 0000000 00000000153 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.node.Node.set_plotter.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.node.Node.set_plott0000664 5104112 5104112 00000000272 12356463435 035366 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.node.Node.set_plotter
=================================================
.. currentmodule:: bayespy.inference.vmp.nodes.node
.. automethod:: Node.set_plotter ././@LongLink 0000000 0000000 0000000 00000000212 00000000000 011210 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.poisson.PoissonDistribution.compute_cgf_from_parents.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.poisson.PoissonDist0000664 5104112 5104112 00000000427 12356463435 035512 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.poisson.PoissonDistribution.compute_cgf_from_parents
================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.poisson
.. automethod:: PoissonDistribution.compute_cgf_from_parents ././@LongLink 0000000 0000000 0000000 00000000170 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.poisson.PoissonDistribution.random.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.poisson.PoissonDist0000664 5104112 5104112 00000000341 12356463435 035505 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.poisson.PoissonDistribution.random
==============================================================
.. currentmodule:: bayespy.inference.vmp.nodes.poisson
.. automethod:: PoissonDistribution.random ././@LongLink 0000000 0000000 0000000 00000000215 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.poisson.PoissonDistribution.compute_fixed_moments_and_f.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.poisson.PoissonDist0000664 5104112 5104112 00000000440 12356463435 035505 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.poisson.PoissonDistribution.compute_fixed_moments_and_f
===================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.poisson
.. automethod:: PoissonDistribution.compute_fixed_moments_and_f ././@LongLink 0000000 0000000 0000000 00000000200 00000000000 011205 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.poisson.PoissonDistribution.compute_logpdf.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.poisson.PoissonDist0000664 5104112 5104112 00000000371 12356463435 035510 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.poisson.PoissonDistribution.compute_logpdf
======================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.poisson
.. automethod:: PoissonDistribution.compute_logpdf ././@LongLink 0000000 0000000 0000000 00000000210 00000000000 011206 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.poisson.PoissonDistribution.compute_mask_to_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.poisson.PoissonDist0000664 5104112 5104112 00000000421 12356463435 035504 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.poisson.PoissonDistribution.compute_mask_to_parent
==============================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.poisson
.. automethod:: PoissonDistribution.compute_mask_to_parent ././@LongLink 0000000 0000000 0000000 00000000213 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.poisson.PoissonDistribution.compute_message_to_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.poisson.PoissonDist0000664 5104112 5104112 00000000432 12356463435 035506 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.poisson.PoissonDistribution.compute_message_to_parent
=================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.poisson
.. automethod:: PoissonDistribution.compute_message_to_parent ././@LongLink 0000000 0000000 0000000 00000000211 00000000000 011207 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.poisson.PoissonDistribution.compute_moments_and_cgf.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.poisson.PoissonDist0000664 5104112 5104112 00000000424 12356463435 035507 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.poisson.PoissonDistribution.compute_moments_and_cgf
===============================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.poisson
.. automethod:: PoissonDistribution.compute_moments_and_cgf ././@LongLink 0000000 0000000 0000000 00000000212 00000000000 011210 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.poisson.PoissonDistribution.compute_phi_from_parents.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.poisson.PoissonDist0000664 5104112 5104112 00000000427 12356463435 035512 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.poisson.PoissonDistribution.compute_phi_from_parents
================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.poisson
.. automethod:: PoissonDistribution.compute_phi_from_parents ././@LongLink 0000000 0000000 0000000 00000000204 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.poisson.PoissonDistribution.plates_from_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.poisson.PoissonDist0000664 5104112 5104112 00000000405 12356463435 035506 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.poisson.PoissonDistribution.plates_from_parent
==========================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.poisson
.. automethod:: PoissonDistribution.plates_from_parent ././@LongLink 0000000 0000000 0000000 00000000202 00000000000 011207 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.poisson.PoissonDistribution.plates_to_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.poisson.PoissonDist0000664 5104112 5104112 00000000377 12356463435 035516 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.poisson.PoissonDistribution.plates_to_parent
========================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.poisson
.. automethod:: PoissonDistribution.plates_to_parent ././@LongLink 0000000 0000000 0000000 00000000172 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.poisson.PoissonMoments.add_converter.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.poisson.PoissonMome0000664 5104112 5104112 00000000347 12356463435 035505 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.poisson.PoissonMoments.add_converter
================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.poisson
.. automethod:: PoissonMoments.add_converter ././@LongLink 0000000 0000000 0000000 00000000205 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.poisson.PoissonMoments.compute_dims_from_values.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.poisson.PoissonMome0000664 5104112 5104112 00000000410 12356463435 035474 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.poisson.PoissonMoments.compute_dims_from_values
===========================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.poisson
.. automethod:: PoissonMoments.compute_dims_from_values ././@LongLink 0000000 0000000 0000000 00000000202 00000000000 011207 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.poisson.PoissonMoments.compute_fixed_moments.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.poisson.PoissonMome0000664 5104112 5104112 00000000377 12356463435 035510 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.poisson.PoissonMoments.compute_fixed_moments
========================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.poisson
.. automethod:: PoissonMoments.compute_fixed_moments ././@LongLink 0000000 0000000 0000000 00000000172 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.poisson.PoissonMoments.get_converter.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.poisson.PoissonMome0000664 5104112 5104112 00000000347 12356463435 035505 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.poisson.PoissonMoments.get_converter
================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.poisson
.. automethod:: PoissonMoments.get_converter ././@LongLink 0000000 0000000 0000000 00000000204 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.stochastic.Distribution.compute_mask_to_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.stochastic.Distribu0000664 5104112 5104112 00000000405 12356463435 035467 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.stochastic.Distribution.compute_mask_to_parent
==========================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.stochastic
.. automethod:: Distribution.compute_mask_to_parent ././@LongLink 0000000 0000000 0000000 00000000207 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.stochastic.Distribution.compute_message_to_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.stochastic.Distribu0000664 5104112 5104112 00000000416 12356463435 035471 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.stochastic.Distribution.compute_message_to_parent
=============================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.stochastic
.. automethod:: Distribution.compute_message_to_parent ././@LongLink 0000000 0000000 0000000 00000000200 00000000000 011205 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.stochastic.Distribution.plates_from_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.stochastic.Distribu0000664 5104112 5104112 00000000371 12356463435 035471 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.stochastic.Distribution.plates_from_parent
======================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.stochastic
.. automethod:: Distribution.plates_from_parent ././@LongLink 0000000 0000000 0000000 00000000176 00000000000 011221 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.stochastic.Distribution.plates_to_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.stochastic.Distribu0000664 5104112 5104112 00000000363 12356463435 035472 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.stochastic.Distribution.plates_to_parent
====================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.stochastic
.. automethod:: Distribution.plates_to_parent ././@LongLink 0000000 0000000 0000000 00000000162 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.stochastic.Stochastic.delete.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.stochastic.Stochast0000664 5104112 5104112 00000000317 12356463435 035474 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.stochastic.Stochastic.delete
========================================================
.. currentmodule:: bayespy.inference.vmp.nodes.stochastic
.. automethod:: Stochastic.delete ././@LongLink 0000000 0000000 0000000 00000000164 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.stochastic.Distribution.random.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.stochastic.Distribu0000664 5104112 5104112 00000000325 12356463435 035470 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.stochastic.Distribution.random
==========================================================
.. currentmodule:: bayespy.inference.vmp.nodes.stochastic
.. automethod:: Distribution.random ././@LongLink 0000000 0000000 0000000 00000000164 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.stochastic.Stochastic.__init__.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.stochastic.Stochast0000664 5104112 5104112 00000000325 12356463435 035473 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.stochastic.Stochastic.__init__
==========================================================
.. currentmodule:: bayespy.inference.vmp.nodes.stochastic
.. automethod:: Stochastic.__init__ ././@LongLink 0000000 0000000 0000000 00000000172 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.stochastic.Stochastic.add_plate_axis.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.stochastic.Stochast0000664 5104112 5104112 00000000347 12356463435 035477 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.stochastic.Stochastic.add_plate_axis
================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.stochastic
.. automethod:: Stochastic.add_plate_axis ././@LongLink 0000000 0000000 0000000 00000000164 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.stochastic.Stochastic.get_mask.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.stochastic.Stochast0000664 5104112 5104112 00000000325 12356463435 035473 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.stochastic.Stochastic.get_mask
==========================================================
.. currentmodule:: bayespy.inference.vmp.nodes.stochastic
.. automethod:: Stochastic.get_mask ././@LongLink 0000000 0000000 0000000 00000000167 00000000000 011221 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.stochastic.Stochastic.get_moments.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.stochastic.Stochast0000664 5104112 5104112 00000000336 12356463435 035475 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.stochastic.Stochastic.get_moments
=============================================================
.. currentmodule:: bayespy.inference.vmp.nodes.stochastic
.. automethod:: Stochastic.get_moments ././@LongLink 0000000 0000000 0000000 00000000165 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.stochastic.Stochastic.get_shape.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.stochastic.Stochast0000664 5104112 5104112 00000000330 12356463435 035467 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.stochastic.Stochastic.get_shape
===========================================================
.. currentmodule:: bayespy.inference.vmp.nodes.stochastic
.. automethod:: Stochastic.get_shape ././@LongLink 0000000 0000000 0000000 00000000167 00000000000 011221 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.stochastic.Stochastic.has_plotter.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.stochastic.Stochast0000664 5104112 5104112 00000000336 12356463435 035475 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.stochastic.Stochastic.has_plotter
=============================================================
.. currentmodule:: bayespy.inference.vmp.nodes.stochastic
.. automethod:: Stochastic.has_plotter ././@LongLink 0000000 0000000 0000000 00000000160 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.stochastic.Stochastic.load.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.stochastic.Stochast0000664 5104112 5104112 00000000311 12356463435 035466 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.stochastic.Stochastic.load
======================================================
.. currentmodule:: bayespy.inference.vmp.nodes.stochastic
.. automethod:: Stochastic.load ././@LongLink 0000000 0000000 0000000 00000000166 00000000000 011220 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.stochastic.Stochastic.lowerbound.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.stochastic.Stochast0000664 5104112 5104112 00000000333 12356463435 035472 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.stochastic.Stochastic.lowerbound
============================================================
.. currentmodule:: bayespy.inference.vmp.nodes.stochastic
.. automethod:: Stochastic.lowerbound bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.linalg.inner.rst 0000664 5104112 5104112 00000000167 12356463436 032226 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.linalg.inner
==========================
.. currentmodule:: bayespy.utils.linalg
.. autofunction:: inner ././@LongLink 0000000 0000000 0000000 00000000167 00000000000 011221 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.stochastic.Stochastic.move_plates.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.stochastic.Stochast0000664 5104112 5104112 00000000336 12356463435 035475 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.stochastic.Stochastic.move_plates
=============================================================
.. currentmodule:: bayespy.inference.vmp.nodes.stochastic
.. automethod:: Stochastic.move_plates ././@LongLink 0000000 0000000 0000000 00000000163 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.stochastic.Stochastic.observe.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.stochastic.Stochast0000664 5104112 5104112 00000000322 12356463435 035470 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.stochastic.Stochastic.observe
=========================================================
.. currentmodule:: bayespy.inference.vmp.nodes.stochastic
.. automethod:: Stochastic.observe ././@LongLink 0000000 0000000 0000000 00000000162 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.stochastic.Stochastic.plates.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.stochastic.Stochast0000664 5104112 5104112 00000000322 12356463435 035470 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.stochastic.Stochastic.plates
========================================================
.. currentmodule:: bayespy.inference.vmp.nodes.stochastic
.. autoattribute:: Stochastic.plates ././@LongLink 0000000 0000000 0000000 00000000160 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.stochastic.Stochastic.plot.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.stochastic.Stochast0000664 5104112 5104112 00000000311 12356463435 035466 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.stochastic.Stochastic.plot
======================================================
.. currentmodule:: bayespy.inference.vmp.nodes.stochastic
.. automethod:: Stochastic.plot ././@LongLink 0000000 0000000 0000000 00000000162 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.stochastic.Stochastic.random.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.stochastic.Stochast0000664 5104112 5104112 00000000317 12356463435 035474 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.stochastic.Stochastic.random
========================================================
.. currentmodule:: bayespy.inference.vmp.nodes.stochastic
.. automethod:: Stochastic.random ././@LongLink 0000000 0000000 0000000 00000000160 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.stochastic.Stochastic.save.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.stochastic.Stochast0000664 5104112 5104112 00000000311 12356463435 035466 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.stochastic.Stochastic.save
======================================================
.. currentmodule:: bayespy.inference.vmp.nodes.stochastic
.. automethod:: Stochastic.save ././@LongLink 0000000 0000000 0000000 00000000167 00000000000 011221 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.stochastic.Stochastic.set_plotter.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.stochastic.Stochast0000664 5104112 5104112 00000000336 12356463435 035475 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.stochastic.Stochastic.set_plotter
=============================================================
.. currentmodule:: bayespy.inference.vmp.nodes.stochastic
.. automethod:: Stochastic.set_plotter ././@LongLink 0000000 0000000 0000000 00000000165 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.stochastic.Stochastic.unobserve.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.stochastic.Stochast0000664 5104112 5104112 00000000330 12356463435 035467 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.stochastic.Stochastic.unobserve
===========================================================
.. currentmodule:: bayespy.inference.vmp.nodes.stochastic
.. automethod:: Stochastic.unobserve ././@LongLink 0000000 0000000 0000000 00000000162 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.stochastic.Stochastic.update.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.stochastic.Stochast0000664 5104112 5104112 00000000317 12356463435 035474 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.stochastic.Stochastic.update
========================================================
.. currentmodule:: bayespy.inference.vmp.nodes.stochastic
.. automethod:: Stochastic.update bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.linalg.m_dot.rst 0000664 5104112 5104112 00000000167 12356463436 032215 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.linalg.m_dot
==========================
.. currentmodule:: bayespy.utils.linalg
.. autofunction:: m_dot ././@LongLink 0000000 0000000 0000000 00000000212 00000000000 011210 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.wishart.WishartDistribution.compute_cgf_from_parents.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.wishart.WishartDist0000664 5104112 5104112 00000000427 12356463435 035470 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.wishart.WishartDistribution.compute_cgf_from_parents
================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.wishart
.. automethod:: WishartDistribution.compute_cgf_from_parents ././@LongLink 0000000 0000000 0000000 00000000215 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.wishart.WishartDistribution.compute_fixed_moments_and_f.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.wishart.WishartDist0000664 5104112 5104112 00000000440 12356463435 035463 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.wishart.WishartDistribution.compute_fixed_moments_and_f
===================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.wishart
.. automethod:: WishartDistribution.compute_fixed_moments_and_f ././@LongLink 0000000 0000000 0000000 00000000200 00000000000 011205 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.wishart.WishartDistribution.compute_logpdf.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.wishart.WishartDist0000664 5104112 5104112 00000000371 12356463435 035466 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.wishart.WishartDistribution.compute_logpdf
======================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.wishart
.. automethod:: WishartDistribution.compute_logpdf ././@LongLink 0000000 0000000 0000000 00000000210 00000000000 011206 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.wishart.WishartDistribution.compute_mask_to_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.wishart.WishartDist0000664 5104112 5104112 00000000421 12356463435 035462 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.wishart.WishartDistribution.compute_mask_to_parent
==============================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.wishart
.. automethod:: WishartDistribution.compute_mask_to_parent ././@LongLink 0000000 0000000 0000000 00000000213 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.wishart.WishartDistribution.compute_message_to_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.wishart.WishartDist0000664 5104112 5104112 00000000432 12356463435 035464 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.wishart.WishartDistribution.compute_message_to_parent
=================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.wishart
.. automethod:: WishartDistribution.compute_message_to_parent ././@LongLink 0000000 0000000 0000000 00000000211 00000000000 011207 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.wishart.WishartDistribution.compute_moments_and_cgf.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.wishart.WishartDist0000664 5104112 5104112 00000000424 12356463435 035465 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.wishart.WishartDistribution.compute_moments_and_cgf
===============================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.wishart
.. automethod:: WishartDistribution.compute_moments_and_cgf ././@LongLink 0000000 0000000 0000000 00000000212 00000000000 011210 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.wishart.WishartDistribution.compute_phi_from_parents.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.wishart.WishartDist0000664 5104112 5104112 00000000427 12356463435 035470 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.wishart.WishartDistribution.compute_phi_from_parents
================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.wishart
.. automethod:: WishartDistribution.compute_phi_from_parents ././@LongLink 0000000 0000000 0000000 00000000204 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.wishart.WishartDistribution.plates_from_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.wishart.WishartDist0000664 5104112 5104112 00000000405 12356463435 035464 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.wishart.WishartDistribution.plates_from_parent
==========================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.wishart
.. automethod:: WishartDistribution.plates_from_parent ././@LongLink 0000000 0000000 0000000 00000000202 00000000000 011207 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.wishart.WishartDistribution.plates_to_parent.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.wishart.WishartDist0000664 5104112 5104112 00000000377 12356463435 035474 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.wishart.WishartDistribution.plates_to_parent
========================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.wishart
.. automethod:: WishartDistribution.plates_to_parent ././@LongLink 0000000 0000000 0000000 00000000170 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.wishart.WishartDistribution.random.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.wishart.WishartDist0000664 5104112 5104112 00000000341 12356463435 035463 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.wishart.WishartDistribution.random
==============================================================
.. currentmodule:: bayespy.inference.vmp.nodes.wishart
.. automethod:: WishartDistribution.random ././@LongLink 0000000 0000000 0000000 00000000172 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.wishart.WishartMoments.add_converter.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.wishart.WishartMome0000664 5104112 5104112 00000000347 12356463435 035463 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.wishart.WishartMoments.add_converter
================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.wishart
.. automethod:: WishartMoments.add_converter ././@LongLink 0000000 0000000 0000000 00000000205 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.wishart.WishartMoments.compute_dims_from_values.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.wishart.WishartMome0000664 5104112 5104112 00000000410 12356463435 035452 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.wishart.WishartMoments.compute_dims_from_values
===========================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.wishart
.. automethod:: WishartMoments.compute_dims_from_values ././@LongLink 0000000 0000000 0000000 00000000202 00000000000 011207 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.wishart.WishartMoments.compute_fixed_moments.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.wishart.WishartMome0000664 5104112 5104112 00000000377 12356463435 035466 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.wishart.WishartMoments.compute_fixed_moments
========================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.wishart
.. automethod:: WishartMoments.compute_fixed_moments ././@LongLink 0000000 0000000 0000000 00000000172 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.wishart.WishartMoments.get_converter.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.inference.vmp.nodes.wishart.WishartMome0000664 5104112 5104112 00000000347 12356463435 035463 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.wishart.WishartMoments.get_converter
================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.wishart
.. automethod:: WishartMoments.get_converter bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.linalg.mmdot.rst 0000664 5104112 5104112 00000000167 12356463436 032233 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.linalg.mmdot
==========================
.. currentmodule:: bayespy.utils.linalg
.. autofunction:: mmdot bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.linalg.mvdot.rst 0000664 5104112 5104112 00000000167 12356463436 032244 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.linalg.mvdot
==========================
.. currentmodule:: bayespy.utils.linalg
.. autofunction:: mvdot bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.linalg.outer.rst 0000664 5104112 5104112 00000000167 12356463436 032251 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.linalg.outer
==========================
.. currentmodule:: bayespy.utils.linalg
.. autofunction:: outer bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.linalg.solve_triangular.rst 0000664 5104112 5104112 00000000230 12356463436 034462 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.linalg.solve_triangular
=====================================
.. currentmodule:: bayespy.utils.linalg
.. autofunction:: solve_triangular bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.linalg.tracedot.rst 0000664 5104112 5104112 00000000200 12356463436 032704 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.linalg.tracedot
=============================
.. currentmodule:: bayespy.utils.linalg
.. autofunction:: tracedot bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.CholeskyDense.rst 0000664 5104112 5104112 00000000613 12356463436 033334 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.CholeskyDense
================================
.. currentmodule:: bayespy.utils.misc
.. autoclass:: CholeskyDense
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~CholeskyDense.__init__
~CholeskyDense.logdet
~CholeskyDense.solve
~CholeskyDense.trace_solve_gradient
bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.CholeskySparse.rst 0000664 5104112 5104112 00000000622 12356463436 033533 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.CholeskySparse
=================================
.. currentmodule:: bayespy.utils.misc
.. autoclass:: CholeskySparse
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~CholeskySparse.__init__
~CholeskySparse.logdet
~CholeskySparse.solve
~CholeskySparse.trace_solve_gradient
bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.TestCase.rst 0000664 5104112 5104112 00000004525 12356463436 032315 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.TestCase
===========================
.. currentmodule:: bayespy.utils.misc
.. autoclass:: TestCase
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~TestCase.__init__
~TestCase.addCleanup
~TestCase.addTypeEqualityFunc
~TestCase.assertAllClose
~TestCase.assertAlmostEqual
~TestCase.assertAlmostEquals
~TestCase.assertArrayEqual
~TestCase.assertCountEqual
~TestCase.assertDictContainsSubset
~TestCase.assertDictEqual
~TestCase.assertEqual
~TestCase.assertEquals
~TestCase.assertFalse
~TestCase.assertGreater
~TestCase.assertGreaterEqual
~TestCase.assertIn
~TestCase.assertIs
~TestCase.assertIsInstance
~TestCase.assertIsNone
~TestCase.assertIsNot
~TestCase.assertIsNotNone
~TestCase.assertLess
~TestCase.assertLessEqual
~TestCase.assertListEqual
~TestCase.assertMessage
~TestCase.assertMessageToChild
~TestCase.assertMultiLineEqual
~TestCase.assertNotAlmostEqual
~TestCase.assertNotAlmostEquals
~TestCase.assertNotEqual
~TestCase.assertNotEquals
~TestCase.assertNotIn
~TestCase.assertNotIsInstance
~TestCase.assertNotRegex
~TestCase.assertRaises
~TestCase.assertRaisesRegex
~TestCase.assertRaisesRegexp
~TestCase.assertRegex
~TestCase.assertRegexpMatches
~TestCase.assertSequenceEqual
~TestCase.assertSetEqual
~TestCase.assertTrue
~TestCase.assertTupleEqual
~TestCase.assertWarns
~TestCase.assertWarnsRegex
~TestCase.assert_
~TestCase.countTestCases
~TestCase.debug
~TestCase.defaultTestResult
~TestCase.doCleanups
~TestCase.fail
~TestCase.failIf
~TestCase.failIfAlmostEqual
~TestCase.failIfEqual
~TestCase.failUnless
~TestCase.failUnlessAlmostEqual
~TestCase.failUnlessEqual
~TestCase.failUnlessRaises
~TestCase.id
~TestCase.run
~TestCase.setUp
~TestCase.setUpClass
~TestCase.shortDescription
~TestCase.skipTest
~TestCase.tearDown
~TestCase.tearDownClass
.. rubric:: Attributes
.. autosummary::
:toctree: generated/
~TestCase.longMessage
~TestCase.maxDiff
bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.add_axes.rst 0000664 5104112 5104112 00000000172 12356463436 032344 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.add_axes
===========================
.. currentmodule:: bayespy.utils.misc
.. autofunction:: add_axes bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.add_leading_axes.rst 0000664 5104112 5104112 00000000222 12356463436 034023 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.add_leading_axes
===================================
.. currentmodule:: bayespy.utils.misc
.. autofunction:: add_leading_axes bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.add_trailing_axes.rst 0000664 5104112 5104112 00000000225 12356463436 034234 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.add_trailing_axes
====================================
.. currentmodule:: bayespy.utils.misc
.. autofunction:: add_trailing_axes bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.array_to_scalar.rst 0000664 5104112 5104112 00000000217 12356463436 033741 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.array_to_scalar
==================================
.. currentmodule:: bayespy.utils.misc
.. autofunction:: array_to_scalar bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.atleast_nd.rst 0000664 5104112 5104112 00000000200 12356463436 032702 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.atleast_nd
=============================
.. currentmodule:: bayespy.utils.misc
.. autofunction:: atleast_nd bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.axes_to_collapse.rst 0000664 5104112 5104112 00000000222 12356463436 034114 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.axes_to_collapse
===================================
.. currentmodule:: bayespy.utils.misc
.. autofunction:: axes_to_collapse bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.block_banded.rst 0000664 5104112 5104112 00000000206 12356463436 033161 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.block_banded
===============================
.. currentmodule:: bayespy.utils.misc
.. autofunction:: block_banded bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.broadcasted_shape.rst 0000664 5104112 5104112 00000000225 12356463436 034226 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.broadcasted_shape
====================================
.. currentmodule:: bayespy.utils.misc
.. autofunction:: broadcasted_shape ././@LongLink 0000000 0000000 0000000 00000000152 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.broadcasted_shape_from_arrays.rst bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.broadcasted_shape_from_array0000664 5104112 5104112 00000000271 12356463436 035641 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.broadcasted_shape_from_arrays
================================================
.. currentmodule:: bayespy.utils.misc
.. autofunction:: broadcasted_shape_from_arrays bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.ceildiv.rst 0000664 5104112 5104112 00000000167 12356463436 032217 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.ceildiv
==========================
.. currentmodule:: bayespy.utils.misc
.. autofunction:: ceildiv bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.check_gradient.rst 0000664 5104112 5104112 00000000214 12356463436 033523 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.check_gradient
=================================
.. currentmodule:: bayespy.utils.misc
.. autofunction:: check_gradient bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.chol_inv.rst 0000664 5104112 5104112 00000000172 12356463436 032375 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.chol_inv
===========================
.. currentmodule:: bayespy.utils.misc
.. autofunction:: chol_inv bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.chol_logdet.rst 0000664 5104112 5104112 00000000203 12356463436 033052 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.chol_logdet
==============================
.. currentmodule:: bayespy.utils.misc
.. autofunction:: chol_logdet bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.chol_solve.rst 0000664 5104112 5104112 00000000200 12356463436 032721 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.chol_solve
=============================
.. currentmodule:: bayespy.utils.misc
.. autofunction:: chol_solve bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.cholesky.rst 0000664 5104112 5104112 00000000172 12356463436 032415 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.cholesky
===========================
.. currentmodule:: bayespy.utils.misc
.. autofunction:: cholesky bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.composite_function.rst 0000664 5104112 5104112 00000000230 12356463436 034476 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.composite_function
=====================================
.. currentmodule:: bayespy.utils.misc
.. autofunction:: composite_function bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.diag.rst 0000664 5104112 5104112 00000000156 12356463436 031502 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.diag
=======================
.. currentmodule:: bayespy.utils.misc
.. autofunction:: diag bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.diagonal.rst 0000664 5104112 5104112 00000000172 12356463436 032352 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.diagonal
===========================
.. currentmodule:: bayespy.utils.misc
.. autofunction:: diagonal bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.dist_haversine.rst 0000664 5104112 5104112 00000000214 12356463436 033600 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.dist_haversine
=================================
.. currentmodule:: bayespy.utils.misc
.. autofunction:: dist_haversine bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.first.rst 0000664 5104112 5104112 00000000161 12356463436 031721 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.first
========================
.. currentmodule:: bayespy.utils.misc
.. autofunction:: first bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.gaussian_logpdf.rst 0000664 5104112 5104112 00000000217 12356463436 033741 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.gaussian_logpdf
==================================
.. currentmodule:: bayespy.utils.misc
.. autofunction:: gaussian_logpdf bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.get_diag.rst 0000664 5104112 5104112 00000000172 12356463436 032337 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.get_diag
===========================
.. currentmodule:: bayespy.utils.misc
.. autofunction:: get_diag bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.grid.rst 0000664 5104112 5104112 00000000156 12356463436 031523 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.grid
=======================
.. currentmodule:: bayespy.utils.misc
.. autofunction:: grid bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.identity.rst 0000664 5104112 5104112 00000000172 12356463436 032425 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.identity
===========================
.. currentmodule:: bayespy.utils.misc
.. autofunction:: identity bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.is_callable.rst 0000664 5104112 5104112 00000000203 12356463436 033021 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.is_callable
==============================
.. currentmodule:: bayespy.utils.misc
.. autofunction:: is_callable bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.is_numeric.rst 0000664 5104112 5104112 00000000200 12356463436 032721 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.is_numeric
=============================
.. currentmodule:: bayespy.utils.misc
.. autofunction:: is_numeric bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.is_shape_subset.rst 0000664 5104112 5104112 00000000217 12356463436 033754 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.is_shape_subset
==================================
.. currentmodule:: bayespy.utils.misc
.. autofunction:: is_shape_subset bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.is_string.rst 0000664 5104112 5104112 00000000175 12356463436 032600 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.is_string
============================
.. currentmodule:: bayespy.utils.misc
.. autofunction:: is_string bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.isinteger.rst 0000664 5104112 5104112 00000000175 12356463436 032570 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.isinteger
============================
.. currentmodule:: bayespy.utils.misc
.. autofunction:: isinteger bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.kalman_filter.rst 0000664 5104112 5104112 00000000211 12356463436 033376 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.kalman_filter
================================
.. currentmodule:: bayespy.utils.misc
.. autofunction:: kalman_filter bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.logdet_chol.rst 0000664 5104112 5104112 00000000203 12356463436 033052 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.logdet_chol
==============================
.. currentmodule:: bayespy.utils.misc
.. autofunction:: logdet_chol bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.logsumexp.rst 0000664 5104112 5104112 00000000175 12356463436 032622 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.logsumexp
============================
.. currentmodule:: bayespy.utils.misc
.. autofunction:: logsumexp bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.m_chol.rst 0000664 5104112 5104112 00000000164 12356463436 032036 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.m_chol
=========================
.. currentmodule:: bayespy.utils.misc
.. autofunction:: m_chol bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.m_chol_inv.rst 0000664 5104112 5104112 00000000200 12356463436 032701 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.m_chol_inv
=============================
.. currentmodule:: bayespy.utils.misc
.. autofunction:: m_chol_inv bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.m_chol_logdet.rst 0000664 5104112 5104112 00000000211 12356463436 033365 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.m_chol_logdet
================================
.. currentmodule:: bayespy.utils.misc
.. autofunction:: m_chol_logdet bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.m_chol_solve.rst 0000664 5104112 5104112 00000000206 12356463436 033243 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.m_chol_solve
===============================
.. currentmodule:: bayespy.utils.misc
.. autofunction:: m_chol_solve bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.m_digamma.rst 0000664 5104112 5104112 00000000175 12356463436 032512 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.m_digamma
============================
.. currentmodule:: bayespy.utils.misc
.. autofunction:: m_digamma bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.m_dot.rst 0000664 5104112 5104112 00000000161 12356463436 031674 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.m_dot
========================
.. currentmodule:: bayespy.utils.misc
.. autofunction:: m_dot bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.m_outer.rst 0000664 5104112 5104112 00000000167 12356463436 032252 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.m_outer
==========================
.. currentmodule:: bayespy.utils.misc
.. autofunction:: m_outer bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.m_solve_triangular.rst 0000664 5104112 5104112 00000000230 12356463436 034463 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.m_solve_triangular
=====================================
.. currentmodule:: bayespy.utils.misc
.. autofunction:: m_solve_triangular bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.make_equal_length.rst 0000664 5104112 5104112 00000000225 12356463436 034240 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.make_equal_length
====================================
.. currentmodule:: bayespy.utils.misc
.. autofunction:: make_equal_length bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.make_equal_ndim.rst 0000664 5104112 5104112 00000000217 12356463436 033707 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.make_equal_ndim
==================================
.. currentmodule:: bayespy.utils.misc
.. autofunction:: make_equal_ndim bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.mean.rst 0000664 5104112 5104112 00000000156 12356463436 031516 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.mean
=======================
.. currentmodule:: bayespy.utils.misc
.. autofunction:: mean bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.moveaxis.rst 0000664 5104112 5104112 00000000172 12356463436 032427 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.moveaxis
===========================
.. currentmodule:: bayespy.utils.misc
.. autofunction:: moveaxis bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.multiply_shapes.rst 0000664 5104112 5104112 00000000217 12356463436 034016 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.multiply_shapes
==================================
.. currentmodule:: bayespy.utils.misc
.. autofunction:: multiply_shapes bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.nans.rst 0000664 5104112 5104112 00000000156 12356463436 031535 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.nans
=======================
.. currentmodule:: bayespy.utils.misc
.. autofunction:: nans bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.nested_iterator.rst 0000664 5104112 5104112 00000000217 12356463436 033767 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.nested_iterator
==================================
.. currentmodule:: bayespy.utils.misc
.. autofunction:: nested_iterator bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.remove_whitespace.rst 0000664 5104112 5104112 00000000225 12356463436 034304 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.remove_whitespace
====================================
.. currentmodule:: bayespy.utils.misc
.. autofunction:: remove_whitespace bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.repeat_to_shape.rst 0000664 5104112 5104112 00000000217 12356463436 033736 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.repeat_to_shape
==================================
.. currentmodule:: bayespy.utils.misc
.. autofunction:: repeat_to_shape bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.rmse.rst 0000664 5104112 5104112 00000000156 12356463436 031544 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.rmse
=======================
.. currentmodule:: bayespy.utils.misc
.. autofunction:: rmse bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.rts_smoother.rst 0000664 5104112 5104112 00000000206 12356463436 033322 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.rts_smoother
===============================
.. currentmodule:: bayespy.utils.misc
.. autofunction:: rts_smoother bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.squeeze.rst 0000664 5104112 5104112 00000000167 12356463436 032261 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.squeeze
==========================
.. currentmodule:: bayespy.utils.misc
.. autofunction:: squeeze bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.squeeze_to_dim.rst 0000664 5104112 5104112 00000000214 12356463436 033605 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.squeeze_to_dim
=================================
.. currentmodule:: bayespy.utils.misc
.. autofunction:: squeeze_to_dim bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.sum_multiply.rst 0000664 5104112 5104112 00000000206 12356463436 033335 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.sum_multiply
===============================
.. currentmodule:: bayespy.utils.misc
.. autofunction:: sum_multiply bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.sum_product.rst 0000664 5104112 5104112 00000000203 12356463436 033133 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.sum_product
==============================
.. currentmodule:: bayespy.utils.misc
.. autofunction:: sum_product bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.sum_to_dim.rst 0000664 5104112 5104112 00000000200 12356463436 032723 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.sum_to_dim
=============================
.. currentmodule:: bayespy.utils.misc
.. autofunction:: sum_to_dim bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.sum_to_shape.rst 0000664 5104112 5104112 00000000206 12356463436 033260 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.sum_to_shape
===============================
.. currentmodule:: bayespy.utils.misc
.. autofunction:: sum_to_shape bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.symm.rst 0000664 5104112 5104112 00000000156 12356463436 031563 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.symm
=======================
.. currentmodule:: bayespy.utils.misc
.. autofunction:: symm bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.tempfile.rst 0000664 5104112 5104112 00000000172 12356463436 032401 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.tempfile
===========================
.. currentmodule:: bayespy.utils.misc
.. autofunction:: tempfile bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.trues.rst 0000664 5104112 5104112 00000000161 12356463436 031734 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.trues
========================
.. currentmodule:: bayespy.utils.misc
.. autofunction:: trues bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.unique.rst 0000664 5104112 5104112 00000000164 12356463436 032103 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.unique
=========================
.. currentmodule:: bayespy.utils.misc
.. autofunction:: unique bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.vb_optimize.rst 0000664 5104112 5104112 00000000203 12356463436 033116 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.vb_optimize
==============================
.. currentmodule:: bayespy.utils.misc
.. autofunction:: vb_optimize bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.vb_optimize_nodes.rst 0000664 5104112 5104112 00000000225 12356463436 034312 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.vb_optimize_nodes
====================================
.. currentmodule:: bayespy.utils.misc
.. autofunction:: vb_optimize_nodes bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.write_to_hdf5.rst 0000664 5104112 5104112 00000000211 12356463436 033330 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.write_to_hdf5
================================
.. currentmodule:: bayespy.utils.misc
.. autofunction:: write_to_hdf5 bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.misc.zipper_merge.rst 0000664 5104112 5104112 00000000206 12356463436 033262 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc.zipper_merge
===============================
.. currentmodule:: bayespy.utils.misc
.. autofunction:: zipper_merge bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.optimize.check_gradient.rst 0000664 5104112 5104112 00000000230 12356463436 034426 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.optimize.check_gradient
=====================================
.. currentmodule:: bayespy.utils.optimize
.. autofunction:: check_gradient bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.optimize.minimize.rst 0000664 5104112 5104112 00000000206 12356463436 033320 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.optimize.minimize
===============================
.. currentmodule:: bayespy.utils.optimize
.. autofunction:: minimize bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.random.alpha_beta_recursion.rst 0000664 5104112 5104112 00000000244 12356463436 035272 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.random.alpha_beta_recursion
=========================================
.. currentmodule:: bayespy.utils.random
.. autofunction:: alpha_beta_recursion bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.random.bernoulli.rst 0000664 5104112 5104112 00000000203 12356463436 033107 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.random.bernoulli
==============================
.. currentmodule:: bayespy.utils.random
.. autofunction:: bernoulli bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.random.categorical.rst 0000664 5104112 5104112 00000000211 12356463436 033370 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.random.categorical
================================
.. currentmodule:: bayespy.utils.random
.. autofunction:: categorical bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.random.correlation.rst 0000664 5104112 5104112 00000000211 12356463436 033434 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.random.correlation
================================
.. currentmodule:: bayespy.utils.random
.. autofunction:: correlation bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.random.covariance.rst 0000664 5104112 5104112 00000000206 12356463436 033231 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.random.covariance
===============================
.. currentmodule:: bayespy.utils.random
.. autofunction:: covariance bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.random.dirichlet.rst 0000664 5104112 5104112 00000000203 12356463436 033063 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.random.dirichlet
==============================
.. currentmodule:: bayespy.utils.random
.. autofunction:: dirichlet bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.random.gamma_entropy.rst 0000664 5104112 5104112 00000000217 12356463436 033763 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.random.gamma_entropy
==================================
.. currentmodule:: bayespy.utils.random
.. autofunction:: gamma_entropy bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.random.gamma_logpdf.rst 0000664 5104112 5104112 00000000214 12356463436 033533 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.random.gamma_logpdf
=================================
.. currentmodule:: bayespy.utils.random
.. autofunction:: gamma_logpdf bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.random.gaussian_entropy.rst 0000664 5104112 5104112 00000000230 12356463436 034506 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.random.gaussian_entropy
=====================================
.. currentmodule:: bayespy.utils.random
.. autofunction:: gaussian_entropy bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.random.gaussian_gamma_to_t.rst 0000664 5104112 5104112 00000000241 12356463436 035117 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.random.gaussian_gamma_to_t
========================================
.. currentmodule:: bayespy.utils.random
.. autofunction:: gaussian_gamma_to_t bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.random.gaussian_logpdf.rst 0000664 5104112 5104112 00000000225 12356463436 034265 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.random.gaussian_logpdf
====================================
.. currentmodule:: bayespy.utils.random
.. autofunction:: gaussian_logpdf bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.random.intervals.rst 0000664 5104112 5104112 00000000203 12356463436 033123 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.random.intervals
==============================
.. currentmodule:: bayespy.utils.random
.. autofunction:: intervals bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.random.invwishart_rand.rst 0000664 5104112 5104112 00000000225 12356463436 034322 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.random.invwishart_rand
====================================
.. currentmodule:: bayespy.utils.random
.. autofunction:: invwishart_rand bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.random.mask.rst 0000664 5104112 5104112 00000000164 12356463436 032055 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.random.mask
=========================
.. currentmodule:: bayespy.utils.random
.. autofunction:: mask bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.random.orth.rst 0000664 5104112 5104112 00000000164 12356463436 032076 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.random.orth
=========================
.. currentmodule:: bayespy.utils.random
.. autofunction:: orth bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.random.sphere.rst 0000664 5104112 5104112 00000000172 12356463436 032407 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.random.sphere
===========================
.. currentmodule:: bayespy.utils.random
.. autofunction:: sphere bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.random.svd.rst 0000664 5104112 5104112 00000000161 12356463436 031713 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.random.svd
========================
.. currentmodule:: bayespy.utils.random
.. autofunction:: svd bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.random.t_logpdf.rst 0000664 5104112 5104112 00000000200 12356463436 032707 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.random.t_logpdf
=============================
.. currentmodule:: bayespy.utils.random
.. autofunction:: t_logpdf bayespy-0.2.3/doc/source/dev_api/generated/generated/bayespy.utils.random.wishart_rand.rst 0000664 5104112 5104112 00000000214 12356463436 033603 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.random.wishart_rand
=================================
.. currentmodule:: bayespy.utils.random
.. autofunction:: wishart_rand ././@LongLink 0000000 0000000 0000000 00000000153 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.bernoulli.BernoulliDistribution.rst bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.bernoulli.BernoulliDistributi0000664 5104112 5104112 00000001566 12356463433 035600 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.bernoulli.BernoulliDistribution
===========================================================
.. currentmodule:: bayespy.inference.vmp.nodes.bernoulli
.. autoclass:: BernoulliDistribution
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~BernoulliDistribution.__init__
~BernoulliDistribution.compute_cgf_from_parents
~BernoulliDistribution.compute_fixed_moments_and_f
~BernoulliDistribution.compute_logpdf
~BernoulliDistribution.compute_mask_to_parent
~BernoulliDistribution.compute_message_to_parent
~BernoulliDistribution.compute_moments_and_cgf
~BernoulliDistribution.compute_phi_from_parents
~BernoulliDistribution.plates_from_parent
~BernoulliDistribution.plates_to_parent
~BernoulliDistribution.random
././@LongLink 0000000 0000000 0000000 00000000146 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.bernoulli.BernoulliMoments.rst bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.bernoulli.BernoulliMoments.rs0000664 5104112 5104112 00000001032 12356463433 035507 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.bernoulli.BernoulliMoments
======================================================
.. currentmodule:: bayespy.inference.vmp.nodes.bernoulli
.. autoclass:: BernoulliMoments
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~BernoulliMoments.__init__
~BernoulliMoments.add_converter
~BernoulliMoments.compute_dims_from_values
~BernoulliMoments.compute_fixed_moments
~BernoulliMoments.get_converter
bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.beta.BetaDistribution.rst 0000664 5104112 5104112 00000001400 12356463433 034567 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.beta.BetaDistribution
=================================================
.. currentmodule:: bayespy.inference.vmp.nodes.beta
.. autoclass:: BetaDistribution
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~BetaDistribution.compute_cgf_from_parents
~BetaDistribution.compute_fixed_moments_and_f
~BetaDistribution.compute_logpdf
~BetaDistribution.compute_mask_to_parent
~BetaDistribution.compute_message_to_parent
~BetaDistribution.compute_moments_and_cgf
~BetaDistribution.compute_phi_from_parents
~BetaDistribution.plates_from_parent
~BetaDistribution.plates_to_parent
~BetaDistribution.random
bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.beta.BetaMoments.rst 0000664 5104112 5104112 00000000707 12356463433 033543 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.beta.BetaMoments
============================================
.. currentmodule:: bayespy.inference.vmp.nodes.beta
.. autoclass:: BetaMoments
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~BetaMoments.add_converter
~BetaMoments.compute_dims_from_values
~BetaMoments.compute_fixed_moments
~BetaMoments.get_converter
././@LongLink 0000000 0000000 0000000 00000000151 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.binomial.BinomialDistribution.rst bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.binomial.BinomialDistribution0000664 5104112 5104112 00000001545 12356463433 035530 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.binomial.BinomialDistribution
=========================================================
.. currentmodule:: bayespy.inference.vmp.nodes.binomial
.. autoclass:: BinomialDistribution
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~BinomialDistribution.__init__
~BinomialDistribution.compute_cgf_from_parents
~BinomialDistribution.compute_fixed_moments_and_f
~BinomialDistribution.compute_logpdf
~BinomialDistribution.compute_mask_to_parent
~BinomialDistribution.compute_message_to_parent
~BinomialDistribution.compute_moments_and_cgf
~BinomialDistribution.compute_phi_from_parents
~BinomialDistribution.plates_from_parent
~BinomialDistribution.plates_to_parent
~BinomialDistribution.random
bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.binomial.BinomialMoments.rst 0000664 5104112 5104112 00000001017 12356463433 035274 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.binomial.BinomialMoments
====================================================
.. currentmodule:: bayespy.inference.vmp.nodes.binomial
.. autoclass:: BinomialMoments
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~BinomialMoments.__init__
~BinomialMoments.add_converter
~BinomialMoments.compute_dims_from_values
~BinomialMoments.compute_fixed_moments
~BinomialMoments.get_converter
././@LongLink 0000000 0000000 0000000 00000000157 00000000000 011220 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.categorical.CategoricalDistribution.rst bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.categorical.CategoricalDistri0000664 5104112 5104112 00000001630 12356463433 035450 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.categorical.CategoricalDistribution
===============================================================
.. currentmodule:: bayespy.inference.vmp.nodes.categorical
.. autoclass:: CategoricalDistribution
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~CategoricalDistribution.__init__
~CategoricalDistribution.compute_cgf_from_parents
~CategoricalDistribution.compute_fixed_moments_and_f
~CategoricalDistribution.compute_logpdf
~CategoricalDistribution.compute_mask_to_parent
~CategoricalDistribution.compute_message_to_parent
~CategoricalDistribution.compute_moments_and_cgf
~CategoricalDistribution.compute_phi_from_parents
~CategoricalDistribution.plates_from_parent
~CategoricalDistribution.plates_to_parent
~CategoricalDistribution.random
././@LongLink 0000000 0000000 0000000 00000000152 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.categorical.CategoricalMoments.rst bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.categorical.CategoricalMoment0000664 5104112 5104112 00000001060 12356463433 035446 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.categorical.CategoricalMoments
==========================================================
.. currentmodule:: bayespy.inference.vmp.nodes.categorical
.. autoclass:: CategoricalMoments
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~CategoricalMoments.__init__
~CategoricalMoments.add_converter
~CategoricalMoments.compute_dims_from_values
~CategoricalMoments.compute_fixed_moments
~CategoricalMoments.get_converter
././@LongLink 0000000 0000000 0000000 00000000207 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.categorical_markov_chain.CategoricalMarkovChainDistribution.rst bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.categorical_markov_chain.Cate0000664 5104112 5104112 00000002131 12356463433 035446 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.categorical_markov_chain.CategoricalMarkovChainDistribution
=======================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.categorical_markov_chain
.. autoclass:: CategoricalMarkovChainDistribution
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~CategoricalMarkovChainDistribution.__init__
~CategoricalMarkovChainDistribution.compute_cgf_from_parents
~CategoricalMarkovChainDistribution.compute_fixed_moments_and_f
~CategoricalMarkovChainDistribution.compute_logpdf
~CategoricalMarkovChainDistribution.compute_mask_to_parent
~CategoricalMarkovChainDistribution.compute_message_to_parent
~CategoricalMarkovChainDistribution.compute_moments_and_cgf
~CategoricalMarkovChainDistribution.compute_phi_from_parents
~CategoricalMarkovChainDistribution.plates_from_parent
~CategoricalMarkovChainDistribution.plates_to_parent
~CategoricalMarkovChainDistribution.random
././@LongLink 0000000 0000000 0000000 00000000202 00000000000 011207 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.categorical_markov_chain.CategoricalMarkovChainMoments.rst bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.categorical_markov_chain.Cate0000664 5104112 5104112 00000001257 12356463433 035456 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.categorical_markov_chain.CategoricalMarkovChainMoments
==================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.categorical_markov_chain
.. autoclass:: CategoricalMarkovChainMoments
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~CategoricalMarkovChainMoments.__init__
~CategoricalMarkovChainMoments.add_converter
~CategoricalMarkovChainMoments.compute_dims_from_values
~CategoricalMarkovChainMoments.compute_fixed_moments
~CategoricalMarkovChainMoments.get_converter
bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.constant.Constant.rst 0000664 5104112 5104112 00000001243 12356463433 034030 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.constant.Constant
=============================================
.. currentmodule:: bayespy.inference.vmp.nodes.constant
.. autoclass:: Constant
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~Constant.__init__
~Constant.add_plate_axis
~Constant.delete
~Constant.get_mask
~Constant.get_moments
~Constant.get_shape
~Constant.has_plotter
~Constant.move_plates
~Constant.plot
~Constant.set_plotter
.. rubric:: Attributes
.. autosummary::
:toctree: generated/
~Constant.plates
././@LongLink 0000000 0000000 0000000 00000000147 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.deterministic.Deterministic.rst bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.deterministic.Deterministic.r0000664 5104112 5104112 00000001446 12356463433 035512 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.deterministic.Deterministic
=======================================================
.. currentmodule:: bayespy.inference.vmp.nodes.deterministic
.. autoclass:: Deterministic
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~Deterministic.__init__
~Deterministic.add_plate_axis
~Deterministic.delete
~Deterministic.get_mask
~Deterministic.get_moments
~Deterministic.get_shape
~Deterministic.has_plotter
~Deterministic.lower_bound_contribution
~Deterministic.move_plates
~Deterministic.plot
~Deterministic.set_plotter
.. rubric:: Attributes
.. autosummary::
:toctree: generated/
~Deterministic.plates
././@LongLink 0000000 0000000 0000000 00000000153 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.dirichlet.DirichletDistribution.rst bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.dirichlet.DirichletDistributi0000664 5104112 5104112 00000001520 12356463433 035516 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.dirichlet.DirichletDistribution
===========================================================
.. currentmodule:: bayespy.inference.vmp.nodes.dirichlet
.. autoclass:: DirichletDistribution
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~DirichletDistribution.compute_cgf_from_parents
~DirichletDistribution.compute_fixed_moments_and_f
~DirichletDistribution.compute_logpdf
~DirichletDistribution.compute_mask_to_parent
~DirichletDistribution.compute_message_to_parent
~DirichletDistribution.compute_moments_and_cgf
~DirichletDistribution.compute_phi_from_parents
~DirichletDistribution.plates_from_parent
~DirichletDistribution.plates_to_parent
~DirichletDistribution.random
././@LongLink 0000000 0000000 0000000 00000000146 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.dirichlet.DirichletMoments.rst bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.dirichlet.DirichletMoments.rs0000664 5104112 5104112 00000000771 12356463433 035450 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.dirichlet.DirichletMoments
======================================================
.. currentmodule:: bayespy.inference.vmp.nodes.dirichlet
.. autoclass:: DirichletMoments
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~DirichletMoments.add_converter
~DirichletMoments.compute_dims_from_values
~DirichletMoments.compute_fixed_moments
~DirichletMoments.get_converter
././@LongLink 0000000 0000000 0000000 00000000147 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.expfamily.ExponentialFamily.rst bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.expfamily.ExponentialFamily.r0000664 5104112 5104112 00000002527 12356463433 035473 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.expfamily.ExponentialFamily
=======================================================
.. currentmodule:: bayespy.inference.vmp.nodes.expfamily
.. autoclass:: ExponentialFamily
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~ExponentialFamily.__init__
~ExponentialFamily.add_plate_axis
~ExponentialFamily.delete
~ExponentialFamily.get_mask
~ExponentialFamily.get_moments
~ExponentialFamily.get_shape
~ExponentialFamily.has_plotter
~ExponentialFamily.initialize_from_parameters
~ExponentialFamily.initialize_from_prior
~ExponentialFamily.initialize_from_random
~ExponentialFamily.initialize_from_value
~ExponentialFamily.load
~ExponentialFamily.logpdf
~ExponentialFamily.lower_bound_contribution
~ExponentialFamily.lowerbound
~ExponentialFamily.move_plates
~ExponentialFamily.observe
~ExponentialFamily.pdf
~ExponentialFamily.plot
~ExponentialFamily.random
~ExponentialFamily.save
~ExponentialFamily.set_plotter
~ExponentialFamily.unobserve
~ExponentialFamily.update
.. rubric:: Attributes
.. autosummary::
:toctree: generated/
~ExponentialFamily.dims
~ExponentialFamily.plates
././@LongLink 0000000 0000000 0000000 00000000163 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.expfamily.ExponentialFamilyDistribution.rst bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.expfamily.ExponentialFamilyDi0000664 5104112 5104112 00000001670 12356463433 035506 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.expfamily.ExponentialFamilyDistribution
===================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.expfamily
.. autoclass:: ExponentialFamilyDistribution
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~ExponentialFamilyDistribution.compute_cgf_from_parents
~ExponentialFamilyDistribution.compute_fixed_moments_and_f
~ExponentialFamilyDistribution.compute_logpdf
~ExponentialFamilyDistribution.compute_mask_to_parent
~ExponentialFamilyDistribution.compute_message_to_parent
~ExponentialFamilyDistribution.compute_moments_and_cgf
~ExponentialFamilyDistribution.compute_phi_from_parents
~ExponentialFamilyDistribution.plates_from_parent
~ExponentialFamilyDistribution.plates_to_parent
~ExponentialFamilyDistribution.random
bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.gamma.GammaDistribution.rst 0000664 5104112 5104112 00000001420 12356463433 035107 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gamma.GammaDistribution
===================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gamma
.. autoclass:: GammaDistribution
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~GammaDistribution.compute_cgf_from_parents
~GammaDistribution.compute_fixed_moments_and_f
~GammaDistribution.compute_logpdf
~GammaDistribution.compute_mask_to_parent
~GammaDistribution.compute_message_to_parent
~GammaDistribution.compute_moments_and_cgf
~GammaDistribution.compute_phi_from_parents
~GammaDistribution.plates_from_parent
~GammaDistribution.plates_to_parent
~GammaDistribution.random
bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.gamma.GammaMoments.rst 0000664 5104112 5104112 00000000721 12356463433 034055 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gamma.GammaMoments
==============================================
.. currentmodule:: bayespy.inference.vmp.nodes.gamma
.. autoclass:: GammaMoments
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~GammaMoments.add_converter
~GammaMoments.compute_dims_from_values
~GammaMoments.compute_fixed_moments
~GammaMoments.get_converter
bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.gaussian.GaussianMoments.rst 0000664 5104112 5104112 00000001017 12356463433 035334 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianMoments
====================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. autoclass:: GaussianMoments
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~GaussianMoments.__init__
~GaussianMoments.add_converter
~GaussianMoments.compute_dims_from_values
~GaussianMoments.compute_fixed_moments
~GaussianMoments.get_converter
././@LongLink 0000000 0000000 0000000 00000000154 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.gaussian.GaussianARDDistribution.rst bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.gaussian.GaussianARDDistribut0000664 5104112 5104112 00000001617 12356463433 035411 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianARDDistribution
============================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. autoclass:: GaussianARDDistribution
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~GaussianARDDistribution.__init__
~GaussianARDDistribution.compute_cgf_from_parents
~GaussianARDDistribution.compute_fixed_moments_and_f
~GaussianARDDistribution.compute_logpdf
~GaussianARDDistribution.compute_mask_to_parent
~GaussianARDDistribution.compute_message_to_parent
~GaussianARDDistribution.compute_moments_and_cgf
~GaussianARDDistribution.compute_phi_from_parents
~GaussianARDDistribution.plates_from_parent
~GaussianARDDistribution.plates_to_parent
~GaussianARDDistribution.random
././@LongLink 0000000 0000000 0000000 00000000151 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.gaussian.GaussianDistribution.rst bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.gaussian.GaussianDistribution0000664 5104112 5104112 00000001500 12356463433 035557 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianDistribution
=========================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. autoclass:: GaussianDistribution
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~GaussianDistribution.compute_cgf_from_parents
~GaussianDistribution.compute_fixed_moments_and_f
~GaussianDistribution.compute_logpdf
~GaussianDistribution.compute_mask_to_parent
~GaussianDistribution.compute_message_to_parent
~GaussianDistribution.compute_moments_and_cgf
~GaussianDistribution.compute_phi_from_parents
~GaussianDistribution.plates_from_parent
~GaussianDistribution.plates_to_parent
~GaussianDistribution.random
././@LongLink 0000000 0000000 0000000 00000000161 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaARDDistribution.rst bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaARDDist0000664 5104112 5104112 00000001725 12356463433 035306 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianGammaARDDistribution
=================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. autoclass:: GaussianGammaARDDistribution
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~GaussianGammaARDDistribution.__init__
~GaussianGammaARDDistribution.compute_cgf_from_parents
~GaussianGammaARDDistribution.compute_fixed_moments_and_f
~GaussianGammaARDDistribution.compute_logpdf
~GaussianGammaARDDistribution.compute_mask_to_parent
~GaussianGammaARDDistribution.compute_message_to_parent
~GaussianGammaARDDistribution.compute_moments_and_cgf
~GaussianGammaARDDistribution.compute_phi_from_parents
~GaussianGammaARDDistribution.plates_from_parent
~GaussianGammaARDDistribution.plates_to_parent
~GaussianGammaARDDistribution.random
././@LongLink 0000000 0000000 0000000 00000000154 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaARDMoments.rst bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaARDMome0000664 5104112 5104112 00000001117 12356463433 035273 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianGammaARDMoments
============================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. autoclass:: GaussianGammaARDMoments
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~GaussianGammaARDMoments.__init__
~GaussianGammaARDMoments.add_converter
~GaussianGammaARDMoments.compute_dims_from_values
~GaussianGammaARDMoments.compute_fixed_moments
~GaussianGammaARDMoments.get_converter
././@LongLink 0000000 0000000 0000000 00000000166 00000000000 011220 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaARDToGaussianWishart.rst bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaARDToGa0000664 5104112 5104112 00000002103 12356463433 035224 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianGammaARDToGaussianWishart
======================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. autoclass:: GaussianGammaARDToGaussianWishart
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~GaussianGammaARDToGaussianWishart.__init__
~GaussianGammaARDToGaussianWishart.add_plate_axis
~GaussianGammaARDToGaussianWishart.delete
~GaussianGammaARDToGaussianWishart.get_mask
~GaussianGammaARDToGaussianWishart.get_moments
~GaussianGammaARDToGaussianWishart.get_shape
~GaussianGammaARDToGaussianWishart.has_plotter
~GaussianGammaARDToGaussianWishart.lower_bound_contribution
~GaussianGammaARDToGaussianWishart.move_plates
~GaussianGammaARDToGaussianWishart.plot
~GaussianGammaARDToGaussianWishart.set_plotter
.. rubric:: Attributes
.. autosummary::
:toctree: generated/
~GaussianGammaARDToGaussianWishart.plates
././@LongLink 0000000 0000000 0000000 00000000161 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaISODistribution.rst bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaISODist0000664 5104112 5104112 00000001650 12356463433 035327 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianGammaISODistribution
=================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. autoclass:: GaussianGammaISODistribution
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~GaussianGammaISODistribution.compute_cgf_from_parents
~GaussianGammaISODistribution.compute_fixed_moments_and_f
~GaussianGammaISODistribution.compute_logpdf
~GaussianGammaISODistribution.compute_mask_to_parent
~GaussianGammaISODistribution.compute_message_to_parent
~GaussianGammaISODistribution.compute_moments_and_cgf
~GaussianGammaISODistribution.compute_phi_from_parents
~GaussianGammaISODistribution.plates_from_parent
~GaussianGammaISODistribution.plates_to_parent
~GaussianGammaISODistribution.random
././@LongLink 0000000 0000000 0000000 00000000154 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaISOMoments.rst bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaISOMome0000664 5104112 5104112 00000001117 12356463433 035317 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianGammaISOMoments
============================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. autoclass:: GaussianGammaISOMoments
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~GaussianGammaISOMoments.__init__
~GaussianGammaISOMoments.add_converter
~GaussianGammaISOMoments.compute_dims_from_values
~GaussianGammaISOMoments.compute_fixed_moments
~GaussianGammaISOMoments.get_converter
././@LongLink 0000000 0000000 0000000 00000000167 00000000000 011221 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaISOToGaussianGammaARD.rst bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.gaussian.GaussianGammaISOToGa0000664 5104112 5104112 00000002122 12356463433 035251 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianGammaISOToGaussianGammaARD
=======================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. autoclass:: GaussianGammaISOToGaussianGammaARD
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~GaussianGammaISOToGaussianGammaARD.__init__
~GaussianGammaISOToGaussianGammaARD.add_plate_axis
~GaussianGammaISOToGaussianGammaARD.delete
~GaussianGammaISOToGaussianGammaARD.get_mask
~GaussianGammaISOToGaussianGammaARD.get_moments
~GaussianGammaISOToGaussianGammaARD.get_shape
~GaussianGammaISOToGaussianGammaARD.has_plotter
~GaussianGammaISOToGaussianGammaARD.lower_bound_contribution
~GaussianGammaISOToGaussianGammaARD.move_plates
~GaussianGammaISOToGaussianGammaARD.plot
~GaussianGammaISOToGaussianGammaARD.set_plotter
.. rubric:: Attributes
.. autosummary::
:toctree: generated/
~GaussianGammaISOToGaussianGammaARD.plates
././@LongLink 0000000 0000000 0000000 00000000157 00000000000 011220 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.gaussian.GaussianToGaussianGammaISO.rst bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.gaussian.GaussianToGaussianGa0000664 5104112 5104112 00000001732 12356463433 035434 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianToGaussianGammaISO
===============================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. autoclass:: GaussianToGaussianGammaISO
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~GaussianToGaussianGammaISO.__init__
~GaussianToGaussianGammaISO.add_plate_axis
~GaussianToGaussianGammaISO.delete
~GaussianToGaussianGammaISO.get_mask
~GaussianToGaussianGammaISO.get_moments
~GaussianToGaussianGammaISO.get_shape
~GaussianToGaussianGammaISO.has_plotter
~GaussianToGaussianGammaISO.lower_bound_contribution
~GaussianToGaussianGammaISO.move_plates
~GaussianToGaussianGammaISO.plot
~GaussianToGaussianGammaISO.set_plotter
.. rubric:: Attributes
.. autosummary::
:toctree: generated/
~GaussianToGaussianGammaISO.plates
././@LongLink 0000000 0000000 0000000 00000000160 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.gaussian.GaussianWishartDistribution.rst bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.gaussian.GaussianWishartDistr0000664 5104112 5104112 00000001633 12356463433 035536 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianWishartDistribution
================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. autoclass:: GaussianWishartDistribution
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~GaussianWishartDistribution.compute_cgf_from_parents
~GaussianWishartDistribution.compute_fixed_moments_and_f
~GaussianWishartDistribution.compute_logpdf
~GaussianWishartDistribution.compute_mask_to_parent
~GaussianWishartDistribution.compute_message_to_parent
~GaussianWishartDistribution.compute_moments_and_cgf
~GaussianWishartDistribution.compute_phi_from_parents
~GaussianWishartDistribution.plates_from_parent
~GaussianWishartDistribution.plates_to_parent
~GaussianWishartDistribution.random
././@LongLink 0000000 0000000 0000000 00000000153 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.gaussian.GaussianWishartMoments.rst bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.gaussian.GaussianWishartMomen0000664 5104112 5104112 00000001040 12356463433 035514 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.GaussianWishartMoments
===========================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. autoclass:: GaussianWishartMoments
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~GaussianWishartMoments.add_converter
~GaussianWishartMoments.compute_dims_from_values
~GaussianWishartMoments.compute_fixed_moments
~GaussianWishartMoments.get_converter
././@LongLink 0000000 0000000 0000000 00000000153 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaussianGammaARD.rst bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaussianGammaA0000664 5104112 5104112 00000001636 12356463433 035372 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.WrapToGaussianGammaARD
===========================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. autoclass:: WrapToGaussianGammaARD
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~WrapToGaussianGammaARD.__init__
~WrapToGaussianGammaARD.add_plate_axis
~WrapToGaussianGammaARD.delete
~WrapToGaussianGammaARD.get_mask
~WrapToGaussianGammaARD.get_moments
~WrapToGaussianGammaARD.get_shape
~WrapToGaussianGammaARD.has_plotter
~WrapToGaussianGammaARD.lower_bound_contribution
~WrapToGaussianGammaARD.move_plates
~WrapToGaussianGammaARD.plot
~WrapToGaussianGammaARD.set_plotter
.. rubric:: Attributes
.. autosummary::
:toctree: generated/
~WrapToGaussianGammaARD.plates
././@LongLink 0000000 0000000 0000000 00000000153 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaussianGammaISO.rst bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaussianGammaI0000664 5104112 5104112 00000001636 12356463433 035402 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.WrapToGaussianGammaISO
===========================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. autoclass:: WrapToGaussianGammaISO
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~WrapToGaussianGammaISO.__init__
~WrapToGaussianGammaISO.add_plate_axis
~WrapToGaussianGammaISO.delete
~WrapToGaussianGammaISO.get_mask
~WrapToGaussianGammaISO.get_moments
~WrapToGaussianGammaISO.get_shape
~WrapToGaussianGammaISO.has_plotter
~WrapToGaussianGammaISO.lower_bound_contribution
~WrapToGaussianGammaISO.move_plates
~WrapToGaussianGammaISO.plot
~WrapToGaussianGammaISO.set_plotter
.. rubric:: Attributes
.. autosummary::
:toctree: generated/
~WrapToGaussianGammaISO.plates
././@LongLink 0000000 0000000 0000000 00000000152 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaussianWishart.rst bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.gaussian.WrapToGaussianWishar0000664 5104112 5104112 00000001617 12356463433 035503 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian.WrapToGaussianWishart
==========================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian
.. autoclass:: WrapToGaussianWishart
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~WrapToGaussianWishart.__init__
~WrapToGaussianWishart.add_plate_axis
~WrapToGaussianWishart.delete
~WrapToGaussianWishart.get_mask
~WrapToGaussianWishart.get_moments
~WrapToGaussianWishart.get_shape
~WrapToGaussianWishart.has_plotter
~WrapToGaussianWishart.lower_bound_contribution
~WrapToGaussianWishart.move_plates
~WrapToGaussianWishart.plot
~WrapToGaussianWishart.set_plotter
.. rubric:: Attributes
.. autosummary::
:toctree: generated/
~WrapToGaussianWishart.plates
././@LongLink 0000000 0000000 0000000 00000000201 00000000000 011206 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.GaussianMarkovChainDistribution.rst bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.Gaussia0000664 5104112 5104112 00000002046 12356463433 035530 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian_markov_chain.GaussianMarkovChainDistribution
=================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian_markov_chain
.. autoclass:: GaussianMarkovChainDistribution
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~GaussianMarkovChainDistribution.__init__
~GaussianMarkovChainDistribution.compute_cgf_from_parents
~GaussianMarkovChainDistribution.compute_fixed_moments_and_f
~GaussianMarkovChainDistribution.compute_logpdf
~GaussianMarkovChainDistribution.compute_mask_to_parent
~GaussianMarkovChainDistribution.compute_message_to_parent
~GaussianMarkovChainDistribution.compute_moments_and_cgf
~GaussianMarkovChainDistribution.compute_phi_from_parents
~GaussianMarkovChainDistribution.plates_from_parent
~GaussianMarkovChainDistribution.plates_to_parent
~GaussianMarkovChainDistribution.random
././@LongLink 0000000 0000000 0000000 00000000174 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.GaussianMarkovChainMoments.rst bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.Gaussia0000664 5104112 5104112 00000001143 12356463433 035525 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian_markov_chain.GaussianMarkovChainMoments
============================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian_markov_chain
.. autoclass:: GaussianMarkovChainMoments
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~GaussianMarkovChainMoments.add_converter
~GaussianMarkovChainMoments.compute_dims_from_values
~GaussianMarkovChainMoments.compute_fixed_moments
~GaussianMarkovChainMoments.get_converter
././@LongLink 0000000 0000000 0000000 00000000212 00000000000 011210 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.SwitchingGaussianMarkovChainDistribution.rst bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.Switchi0000664 5104112 5104112 00000002244 12356463433 035546 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian_markov_chain.SwitchingGaussianMarkovChainDistribution
==========================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian_markov_chain
.. autoclass:: SwitchingGaussianMarkovChainDistribution
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~SwitchingGaussianMarkovChainDistribution.__init__
~SwitchingGaussianMarkovChainDistribution.compute_cgf_from_parents
~SwitchingGaussianMarkovChainDistribution.compute_fixed_moments_and_f
~SwitchingGaussianMarkovChainDistribution.compute_logpdf
~SwitchingGaussianMarkovChainDistribution.compute_mask_to_parent
~SwitchingGaussianMarkovChainDistribution.compute_message_to_parent
~SwitchingGaussianMarkovChainDistribution.compute_moments_and_cgf
~SwitchingGaussianMarkovChainDistribution.compute_phi_from_parents
~SwitchingGaussianMarkovChainDistribution.plates_from_parent
~SwitchingGaussianMarkovChainDistribution.plates_to_parent
~SwitchingGaussianMarkovChainDistribution.random
././@LongLink 0000000 0000000 0000000 00000000157 00000000000 011220 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.multinomial.MultinomialDistribution.rst bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.multinomial.MultinomialDistri0000664 5104112 5104112 00000001630 12356463433 035602 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.multinomial.MultinomialDistribution
===============================================================
.. currentmodule:: bayespy.inference.vmp.nodes.multinomial
.. autoclass:: MultinomialDistribution
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~MultinomialDistribution.__init__
~MultinomialDistribution.compute_cgf_from_parents
~MultinomialDistribution.compute_fixed_moments_and_f
~MultinomialDistribution.compute_logpdf
~MultinomialDistribution.compute_mask_to_parent
~MultinomialDistribution.compute_message_to_parent
~MultinomialDistribution.compute_moments_and_cgf
~MultinomialDistribution.compute_phi_from_parents
~MultinomialDistribution.plates_from_parent
~MultinomialDistribution.plates_to_parent
~MultinomialDistribution.random
././@LongLink 0000000 0000000 0000000 00000000210 00000000000 011206 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.VaryingGaussianMarkovChainDistribution.rst bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.gaussian_markov_chain.Varying0000664 5104112 5104112 00000002210 12356463433 035544 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.gaussian_markov_chain.VaryingGaussianMarkovChainDistribution
========================================================================================
.. currentmodule:: bayespy.inference.vmp.nodes.gaussian_markov_chain
.. autoclass:: VaryingGaussianMarkovChainDistribution
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~VaryingGaussianMarkovChainDistribution.__init__
~VaryingGaussianMarkovChainDistribution.compute_cgf_from_parents
~VaryingGaussianMarkovChainDistribution.compute_fixed_moments_and_f
~VaryingGaussianMarkovChainDistribution.compute_logpdf
~VaryingGaussianMarkovChainDistribution.compute_mask_to_parent
~VaryingGaussianMarkovChainDistribution.compute_message_to_parent
~VaryingGaussianMarkovChainDistribution.compute_moments_and_cgf
~VaryingGaussianMarkovChainDistribution.compute_phi_from_parents
~VaryingGaussianMarkovChainDistribution.plates_from_parent
~VaryingGaussianMarkovChainDistribution.plates_to_parent
~VaryingGaussianMarkovChainDistribution.random
././@LongLink 0000000 0000000 0000000 00000000152 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.multinomial.MultinomialMoments.rst bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.multinomial.MultinomialMoment0000664 5104112 5104112 00000001015 12356463433 035600 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.multinomial.MultinomialMoments
==========================================================
.. currentmodule:: bayespy.inference.vmp.nodes.multinomial
.. autoclass:: MultinomialMoments
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~MultinomialMoments.add_converter
~MultinomialMoments.compute_dims_from_values
~MultinomialMoments.compute_fixed_moments
~MultinomialMoments.get_converter
bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.node.Moments.rst 0000664 5104112 5104112 00000000653 12356463433 032761 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.node.Moments
========================================
.. currentmodule:: bayespy.inference.vmp.nodes.node
.. autoclass:: Moments
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~Moments.add_converter
~Moments.compute_dims_from_values
~Moments.compute_fixed_moments
~Moments.get_converter
bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.node.Node.rst 0000664 5104112 5104112 00000001137 12356463433 032222 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.node.Node
=====================================
.. currentmodule:: bayespy.inference.vmp.nodes.node
.. autoclass:: Node
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~Node.__init__
~Node.add_plate_axis
~Node.delete
~Node.get_mask
~Node.get_moments
~Node.get_shape
~Node.has_plotter
~Node.move_plates
~Node.plot
~Node.set_plotter
.. rubric:: Attributes
.. autosummary::
:toctree: generated/
~Node.plates
././@LongLink 0000000 0000000 0000000 00000000147 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.poisson.PoissonDistribution.rst bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.poisson.PoissonDistribution.r0000664 5104112 5104112 00000001460 12356463433 035564 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.poisson.PoissonDistribution
=======================================================
.. currentmodule:: bayespy.inference.vmp.nodes.poisson
.. autoclass:: PoissonDistribution
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~PoissonDistribution.compute_cgf_from_parents
~PoissonDistribution.compute_fixed_moments_and_f
~PoissonDistribution.compute_logpdf
~PoissonDistribution.compute_mask_to_parent
~PoissonDistribution.compute_message_to_parent
~PoissonDistribution.compute_moments_and_cgf
~PoissonDistribution.compute_phi_from_parents
~PoissonDistribution.plates_from_parent
~PoissonDistribution.plates_to_parent
~PoissonDistribution.random
bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.poisson.PoissonMoments.rst 0000664 5104112 5104112 00000000745 12356463433 035103 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.poisson.PoissonMoments
==================================================
.. currentmodule:: bayespy.inference.vmp.nodes.poisson
.. autoclass:: PoissonMoments
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~PoissonMoments.add_converter
~PoissonMoments.compute_dims_from_values
~PoissonMoments.compute_fixed_moments
~PoissonMoments.get_converter
bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.stochastic.Distribution.rst 0000664 5104112 5104112 00000001005 12356463433 035225 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.stochastic.Distribution
===================================================
.. currentmodule:: bayespy.inference.vmp.nodes.stochastic
.. autoclass:: Distribution
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~Distribution.compute_mask_to_parent
~Distribution.compute_message_to_parent
~Distribution.plates_from_parent
~Distribution.plates_to_parent
~Distribution.random
bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.stochastic.Stochastic.rst 0000664 5104112 5104112 00000001570 12356463433 034661 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.stochastic.Stochastic
=================================================
.. currentmodule:: bayespy.inference.vmp.nodes.stochastic
.. autoclass:: Stochastic
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~Stochastic.__init__
~Stochastic.add_plate_axis
~Stochastic.delete
~Stochastic.get_mask
~Stochastic.get_moments
~Stochastic.get_shape
~Stochastic.has_plotter
~Stochastic.load
~Stochastic.lowerbound
~Stochastic.move_plates
~Stochastic.observe
~Stochastic.plot
~Stochastic.random
~Stochastic.save
~Stochastic.set_plotter
~Stochastic.unobserve
~Stochastic.update
.. rubric:: Attributes
.. autosummary::
:toctree: generated/
~Stochastic.plates
././@LongLink 0000000 0000000 0000000 00000000147 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.wishart.WishartDistribution.rst bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.wishart.WishartDistribution.r0000664 5104112 5104112 00000001460 12356463433 035542 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.wishart.WishartDistribution
=======================================================
.. currentmodule:: bayespy.inference.vmp.nodes.wishart
.. autoclass:: WishartDistribution
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~WishartDistribution.compute_cgf_from_parents
~WishartDistribution.compute_fixed_moments_and_f
~WishartDistribution.compute_logpdf
~WishartDistribution.compute_mask_to_parent
~WishartDistribution.compute_message_to_parent
~WishartDistribution.compute_moments_and_cgf
~WishartDistribution.compute_phi_from_parents
~WishartDistribution.plates_from_parent
~WishartDistribution.plates_to_parent
~WishartDistribution.random
bayespy-0.2.3/doc/source/dev_api/generated/bayespy.inference.vmp.nodes.wishart.WishartMoments.rst 0000664 5104112 5104112 00000000745 12356463433 035061 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.nodes.wishart.WishartMoments
==================================================
.. currentmodule:: bayespy.inference.vmp.nodes.wishart
.. autoclass:: WishartMoments
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~WishartMoments.add_converter
~WishartMoments.compute_dims_from_values
~WishartMoments.compute_fixed_moments
~WishartMoments.get_converter
bayespy-0.2.3/doc/source/dev_api/generated/bayespy.utils.linalg.rst 0000664 5104112 5104112 00000000707 12356463433 027153 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.linalg
====================
.. automodule:: bayespy.utils.linalg
.. rubric:: Functions
.. autosummary::
:toctree: generated/
block_banded_solve
chol
chol_inv
chol_logdet
chol_solve
dot
inner
inv
logdet_chol
logdet_cov
logdet_tri
m_dot
mmdot
mvdot
outer
solve_triangular
tracedot
bayespy-0.2.3/doc/source/dev_api/generated/bayespy.utils.misc.rst 0000664 5104112 5104112 00000002757 12356463433 026647 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.misc
==================
.. automodule:: bayespy.utils.misc
.. rubric:: Functions
.. autosummary::
:toctree: generated/
T
add_axes
add_leading_axes
add_trailing_axes
array_to_scalar
atleast_nd
axes_to_collapse
block_banded
broadcasted_shape
broadcasted_shape_from_arrays
ceildiv
check_gradient
chol
chol_inv
chol_logdet
chol_solve
cholesky
composite_function
diag
diagonal
dist_haversine
first
gaussian_logpdf
get_diag
grid
identity
is_callable
is_numeric
is_shape_subset
is_string
isinteger
kalman_filter
logdet_chol
logsumexp
m_chol
m_chol_inv
m_chol_logdet
m_chol_solve
m_digamma
m_dot
m_outer
m_solve_triangular
make_equal_length
make_equal_ndim
mean
moveaxis
multiply_shapes
nans
nested_iterator
remove_whitespace
repeat_to_shape
rmse
rts_smoother
squeeze
squeeze_to_dim
sum_multiply
sum_product
sum_to_dim
sum_to_shape
symm
tempfile
trues
unique
vb_optimize
vb_optimize_nodes
write_to_hdf5
zipper_merge
.. rubric:: Classes
.. autosummary::
:toctree: generated/
CholeskyDense
CholeskySparse
TestCase
bayespy-0.2.3/doc/source/dev_api/generated/bayespy.utils.optimize.rst 0000664 5104112 5104112 00000000361 12356463433 027541 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.optimize
======================
.. automodule:: bayespy.utils.optimize
.. rubric:: Functions
.. autosummary::
:toctree: generated/
check_gradient
minimize
bayespy-0.2.3/doc/source/dev_api/generated/bayespy.utils.random.rst 0000664 5104112 5104112 00000001032 12356463433 027155 0 ustar jluttine jluttine 0000000 0000000 bayespy.utils.random
====================
.. automodule:: bayespy.utils.random
.. rubric:: Functions
.. autosummary::
:toctree: generated/
alpha_beta_recursion
bernoulli
categorical
correlation
covariance
dirichlet
gamma_entropy
gamma_logpdf
gaussian_entropy
gaussian_gamma_to_t
gaussian_logpdf
intervals
invwishart_rand
mask
orth
sphere
svd
t_logpdf
wishart_rand
bayespy-0.2.3/doc/source/dev_api/dev_api.rst 0000664 5104112 5104112 00000001726 12374370351 022542 0 ustar jluttine jluttine 0000000 0000000 ..
Copyright (C) 2014 Jaakko Luttinen
This file is licensed under Version 3.0 of the GNU General Public
License. See LICENSE for a text of the license.
This file is part of BayesPy.
BayesPy is free software: you can redistribute it and/or modify it
under the terms of the GNU General Public License version 3 as
published by the Free Software Foundation.
BayesPy is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
General Public License for more details.
You should have received a copy of the GNU General Public License
along with BayesPy. If not, see .
Developer API
=============
This chapter contains API specifications which are relevant to BayesPy
developers and contributors.
.. toctree::
:maxdepth: 1
nodes
moments
distributions
utils
bayespy-0.2.3/doc/source/dev_api/distributions.rst 0000664 5104112 5104112 00000003226 12374370351 024032 0 ustar jluttine jluttine 0000000 0000000 ..
Copyright (C) 2014 Jaakko Luttinen
This file is licensed under Version 3.0 of the GNU General Public
License. See LICENSE for a text of the license.
This file is part of BayesPy.
BayesPy is free software: you can redistribute it and/or modify it
under the terms of the GNU General Public License version 3 as
published by the Free Software Foundation.
BayesPy is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
General Public License for more details.
You should have received a copy of the GNU General Public License
along with BayesPy. If not, see .
Distributions
=============
.. currentmodule:: bayespy.inference.vmp.nodes
.. autosummary::
:toctree: generated/
stochastic.Distribution
expfamily.ExponentialFamilyDistribution
gaussian.GaussianDistribution
gaussian.GaussianARDDistribution
gaussian.GaussianGammaISODistribution
gaussian.GaussianGammaARDDistribution
gaussian.GaussianWishartDistribution
gaussian_markov_chain.GaussianMarkovChainDistribution
gaussian_markov_chain.SwitchingGaussianMarkovChainDistribution
gaussian_markov_chain.VaryingGaussianMarkovChainDistribution
gamma.GammaDistribution
wishart.WishartDistribution
beta.BetaDistribution
dirichlet.DirichletDistribution
bernoulli.BernoulliDistribution
binomial.BinomialDistribution
categorical.CategoricalDistribution
categorical_markov_chain.CategoricalMarkovChainDistribution
multinomial.MultinomialDistribution
poisson.PoissonDistribution
bayespy-0.2.3/doc/source/dev_api/moments.rst 0000664 5104112 5104112 00000002543 12374370351 022613 0 ustar jluttine jluttine 0000000 0000000 ..
Copyright (C) 2014 Jaakko Luttinen
This file is licensed under Version 3.0 of the GNU General Public
License. See LICENSE for a text of the license.
This file is part of BayesPy.
BayesPy is free software: you can redistribute it and/or modify it
under the terms of the GNU General Public License version 3 as
published by the Free Software Foundation.
BayesPy is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
General Public License for more details.
You should have received a copy of the GNU General Public License
along with BayesPy. If not, see .
Moments
=======
.. currentmodule:: bayespy.inference.vmp.nodes
.. autosummary::
:toctree: generated/
node.Moments
gaussian.GaussianMoments
gaussian_markov_chain.GaussianMarkovChainMoments
gaussian.GaussianGammaISOMoments
gaussian.GaussianGammaARDMoments
gaussian.GaussianWishartMoments
gamma.GammaMoments
wishart.WishartMoments
beta.BetaMoments
dirichlet.DirichletMoments
bernoulli.BernoulliMoments
binomial.BinomialMoments
categorical.CategoricalMoments
categorical_markov_chain.CategoricalMarkovChainMoments
multinomial.MultinomialMoments
poisson.PoissonMoments
bayespy-0.2.3/doc/source/dev_api/nodes.rst 0000664 5104112 5104112 00000002675 12374370351 022247 0 ustar jluttine jluttine 0000000 0000000 ..
Copyright (C) 2014 Jaakko Luttinen
This file is licensed under Version 3.0 of the GNU General Public
License. See LICENSE for a text of the license.
This file is part of BayesPy.
BayesPy is free software: you can redistribute it and/or modify it
under the terms of the GNU General Public License version 3 as
published by the Free Software Foundation.
BayesPy is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
General Public License for more details.
You should have received a copy of the GNU General Public License
along with BayesPy. If not, see .
Developer nodes
===============
.. currentmodule:: bayespy.inference.vmp.nodes
The following base classes are useful if writing new nodes:
.. autosummary::
:toctree: generated/
node.Node
stochastic.Stochastic
expfamily.ExponentialFamily
deterministic.Deterministic
The following nodes are examples of special nodes that remain hidden for the
user although they are often implicitly used:
.. autosummary::
:toctree: generated/
constant.Constant
gaussian.GaussianToGaussianGammaISO
gaussian.GaussianGammaISOToGaussianGammaARD
gaussian.GaussianGammaARDToGaussianWishart
gaussian.WrapToGaussianGammaISO
gaussian.WrapToGaussianGammaARD
gaussian.WrapToGaussianWishart
bayespy-0.2.3/doc/source/dev_api/utils.rst 0000664 5104112 5104112 00000001622 12374370351 022266 0 ustar jluttine jluttine 0000000 0000000 ..
Copyright (C) 2014 Jaakko Luttinen
This file is licensed under Version 3.0 of the GNU General Public
License. See LICENSE for a text of the license.
This file is part of BayesPy.
BayesPy is free software: you can redistribute it and/or modify it
under the terms of the GNU General Public License version 3 as
published by the Free Software Foundation.
BayesPy is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
General Public License for more details.
You should have received a copy of the GNU General Public License
along with BayesPy. If not, see .
Utility functions
=================
.. currentmodule:: bayespy.utils
.. autosummary::
:toctree: generated/
linalg
random
optimize
misc
bayespy-0.2.3/doc/source/dev_guide/ 0000775 5104112 5104112 00000000000 12437607671 020727 5 ustar jluttine jluttine 0000000 0000000 bayespy-0.2.3/doc/source/dev_guide/vmp/ 0000775 5104112 5104112 00000000000 12437607671 021531 5 ustar jluttine jluttine 0000000 0000000 bayespy-0.2.3/doc/source/dev_guide/vmp/vmp_gamma.rst 0000664 5104112 5104112 00000000046 12374370351 024217 0 ustar jluttine jluttine 0000000 0000000 Gamma distribution
------------------
bayespy-0.2.3/doc/source/dev_guide/vmp/vmp_gaussian.rst 0000664 5104112 5104112 00000004641 12374370351 024754 0 ustar jluttine jluttine 0000000 0000000 Multivariate normal distribution
--------------------------------
.. math::
\mathbf{x} &\sim \mathcal{N}(\boldsymbol{\mu}, \mathbf{\Lambda}),
.. math::
\mathbf{x},\boldsymbol{\mu} \in \mathbb{R}^{D},
\quad \mathbf{\Lambda} \in \mathbb{R}^{D \times D},
\quad \mathbf{\Lambda} \text{ symmetric positive definite}
.. math::
\log\mathcal{N}( \mathbf{x} | \boldsymbol{\mu}, \mathbf{\Lambda} )
&=
- \frac{1}{2} \mathbf{x}^{\mathrm{T}} \mathbf{\Lambda} \mathbf{x}
+ \mathbf{x}^{\mathrm{T}} \mathbf{\Lambda} \boldsymbol{\mu}
- \frac{1}{2} \boldsymbol{\mu}^{\mathrm{T}} \mathbf{\Lambda}
\boldsymbol{\mu}
+ \frac{1}{2} \log |\mathbf{\Lambda}|
- \frac{D}{2} \log (2\pi)
.. math::
\mathbf{u} (\mathbf{x})
&=
\left[ \begin{matrix}
\mathbf{x}
\\
\mathbf{xx}^{\mathrm{T}}
\end{matrix} \right]
\\
\boldsymbol{\phi} (\boldsymbol{\mu}, \mathbf{\Lambda})
&=
\left[ \begin{matrix}
\mathbf{\Lambda} \boldsymbol{\mu}
\\
- \frac{1}{2} \mathbf{\Lambda}
\end{matrix} \right]
\\
\boldsymbol{\phi}_{\boldsymbol{\mu}} (\mathbf{x}, \mathbf{\Lambda})
&=
\left[ \begin{matrix}
\mathbf{\Lambda} \mathbf{x}
\\
- \frac{1}{2} \mathbf{\Lambda}
\end{matrix} \right]
\\
\boldsymbol{\phi}_{\mathbf{\Lambda}} (\mathbf{x}, \boldsymbol{\mu})
&=
\left[ \begin{matrix}
- \frac{1}{2} \mathbf{xx}^{\mathrm{T}}
+ \frac{1}{2} \mathbf{x}\boldsymbol{\mu}^{\mathrm{T}}
+ \frac{1}{2} \boldsymbol{\mu}\mathbf{x}^{\mathrm{T}}
- \frac{1}{2} \boldsymbol{\mu\mu}^{\mathrm{T}}
\\
\frac{1}{2}
\end{matrix} \right]
\\
g (\boldsymbol{\mu}, \mathbf{\Lambda})
&=
- \frac{1}{2} \operatorname{tr}(\boldsymbol{\mu\mu}^{\mathrm{T}}
\mathbf{\Lambda} )
+ \frac{1}{2} \log |\mathbf{\Lambda}|
\\
g_{\boldsymbol{\phi}} (\boldsymbol{\phi})
&=
\frac{1}{4} \boldsymbol{\phi}^{\mathrm{T}}_1 \boldsymbol{\phi}^{-1}_2
\boldsymbol{\phi}_1
+ \frac{1}{2} \log | -2 \boldsymbol{\phi}_2 |
\\
f(\mathbf{x})
&= - \frac{D}{2} \log(2\pi)
\\
\overline{\mathbf{u}} (\boldsymbol{\phi})
&=
\left[ \begin{matrix}
- \frac{1}{2} \boldsymbol{\phi}^{-1}_2 \boldsymbol{\phi}_1
\\
\frac{1}{4} \boldsymbol{\phi}^{-1}_2 \boldsymbol{\phi}_1
\boldsymbol{\phi}^{\mathrm{T}}_1 \boldsymbol{\phi}^{-1}_2
- \frac{1}{2} \boldsymbol{\phi}^{-1}_2
\end{matrix} \right]
bayespy-0.2.3/doc/source/dev_guide/vmp/vmp_gaussian_gamma.rst 0000664 5104112 5104112 00000000071 12374370351 026107 0 ustar jluttine jluttine 0000000 0000000
Gaussian-Gamma distribution
---------------------------
bayespy-0.2.3/doc/source/dev_guide/vmp/vmp_gaussian_wishart.rst 0000664 5104112 5104112 00000000075 12374370351 026512 0 ustar jluttine jluttine 0000000 0000000 Gaussian-Wishart distribution
-----------------------------
bayespy-0.2.3/doc/source/dev_guide/vmp/vmp_mixture.rst 0000664 5104112 5104112 00000006326 12374370351 024641 0 ustar jluttine jluttine 0000000 0000000 Mixture distribution
--------------------
.. math::
\mathbf{x}
&\sim
\mathrm{Mix}_{\mathcal{D}}
\left(
\lambda,
\left\{ \mathbf{\Theta}^{(n)}_1, \ldots, \mathbf{\Theta}^{(n)}_K \right\}^N_{n=1}
\right)
.. math::
\lambda \in \{1, \ldots, N\},
\quad \mathcal{D} \text{ is an exp.fam. distribution},
\quad \mathbf{\Theta}^{(n)}_k \text{ are parameters of } \mathcal{D}
.. math::
\log\mathrm{Mix}_{\mathcal{D}}
\left(
\mathbf{x}
\left| \lambda,
\left\{ \mathbf{\Theta}^{(n)}_1, \ldots, \mathbf{\Theta}^{(n)}_K \right\}^N_{n=1}
\right.
\right)
&=
\sum^N_{n=1} [\lambda=n]
\mathbf{u}_{\mathcal{D}}(\mathbf{x})^{\mathrm{T}}
\boldsymbol{\phi}_{\mathcal{D}}
\left(
\mathbf{\Theta}^{(n)}_1, \ldots, \mathbf{\Theta}^{(n)}_K
\right)
\\
& \quad +
\sum^N_{n=1} [\lambda=n]
g_{\mathcal{D}}
\left(
\mathbf{\Theta}^{(n)}_1, \ldots, \mathbf{\Theta}^{(n)}_K
\right)
+ f_{\mathcal{D}} (\mathbf{x})
.. math::
\mathbf{u} (\mathbf{x})
&=
\mathbf{u}_{\mathcal{D}} (\mathbf{x})
\\
\boldsymbol{\phi}
\left(
\lambda,
\left\{ \mathbf{\Theta}^{(n)}_1, \ldots, \mathbf{\Theta}^{(n)}_K \right\}^N_{n=1}
\right)
&=
\sum^N_{n=1} [\lambda=n]
\boldsymbol{\phi}_{\mathcal{D}}
\left(
\mathbf{\Theta}^{(n)}_1, \ldots, \mathbf{\Theta}^{(n)}_K
\right)
%
\\
%
\boldsymbol{\phi}_{\lambda}
\left(
\mathbf{x},
\left\{ \mathbf{\Theta}^{(n)}_1, \ldots, \mathbf{\Theta}^{(n)}_K \right\}^N_{n=1}
\right)
&=
\left[\begin{matrix}
\mathbf{u}_{\mathcal{D}} (\mathbf{x})^{\mathrm{T}}
\boldsymbol{\phi}_{\mathcal{D}}
\left(
\mathbf{\Theta}^{(1)}_1, \ldots, \mathbf{\Theta}^{(1)}_K
\right)
+ g_{\mathcal{D}}
\left(
\mathbf{\Theta}^{(1)}_1, \ldots, \mathbf{\Theta}^{(1)}_K
\right)
\\
\vdots
\\
\mathbf{u}_{\mathcal{D}} (\mathbf{x})^{\mathrm{T}}
\boldsymbol{\phi}_{\mathcal{D}}
\left(
\mathbf{\Theta}^{(N)}_1, \ldots, \mathbf{\Theta}^{(N)}_K
\right)
+ g_{\mathcal{D}}
\left(
\mathbf{\Theta}^{(N)}_1, \ldots, \mathbf{\Theta}^{(N)}_K
\right)
\end{matrix}\right]
%
\\
%
\boldsymbol{\phi}_{\mathbf{\Theta}^{(m)}_l}
\left(
\mathbf{x},
\lambda,
\left\{ \mathbf{\Theta}^{(n)}_1, \ldots, \mathbf{\Theta}^{(n)}_K \right\}^N_{n=1}
\setminus \left\{ \mathbf{\Theta}^{(m)}_l \right\}
\right)
&=
[\lambda=m] \boldsymbol{\phi}_{\mathcal{D}\rightarrow\mathbf{\Theta}_l}
\left(
\mathbf{x},
\left\{ \mathbf{\Theta}^{(m)}_k \right\}_{k\neq l}
\right)
%
\\
%
g
\left(
\lambda,
\left\{ \mathbf{\Theta}^{(n)}_1, \ldots, \mathbf{\Theta}^{(n)}_K \right\}^N_{n=1}
\right)
&=
\sum^N_{n=1} [\lambda=n]
g_{\mathcal{D}}
\left(
\mathbf{\Theta}^{(n)}_1, \ldots, \mathbf{\Theta}^{(n)}_K
\right)
\\
g (\boldsymbol{\phi})
&=
g_{\mathcal{D}} (\boldsymbol{\phi})
\\
f(\mathbf{x})
&=
f_{\mathcal{D}} (\mathbf{x})
\\
\overline{\mathbf{u}} (\boldsymbol{\phi})
&=
\overline{\mathbf{u}}_{\mathcal{D}} (\boldsymbol{\phi})
bayespy-0.2.3/doc/source/dev_guide/vmp/vmp_normal.rst 0000664 5104112 5104112 00000000076 12374370351 024430 0 ustar jluttine jluttine 0000000 0000000 Univariate normal distribution
------------------------------
bayespy-0.2.3/doc/source/dev_guide/vmp/vmp_normal_gamma.rst 0000664 5104112 5104112 00000000064 12374370351 025567 0 ustar jluttine jluttine 0000000 0000000 Normal-Gamma distribution
-------------------------
bayespy-0.2.3/doc/source/dev_guide/vmp/vmp_wishart.rst 0000664 5104112 5104112 00000003602 12374370351 024617 0 ustar jluttine jluttine 0000000 0000000 Wishart distribution
--------------------
.. math::
\mathbf{\Lambda}
&\sim \mathcal{W}(n, \mathbf{V}),
.. math::
n > D-1,
\quad \mathbf{\Lambda}, \mathbf{V} \in \mathbb{R}^{D \times D},
\quad \mathbf{\Lambda}, \mathbf{V} \text{ symmetric positive definite}
.. math::
\log\mathcal{W}( \mathbf{\Lambda} | n, \mathbf{V} )
&=
- \frac{1}{2} \operatorname{tr} (\mathbf{\Lambda V})
+ \frac{n}{2} \log |\mathbf{\Lambda}|
+ \frac{n}{2} \log |\mathbf{V}|
- \frac{D+1}{2} \log |\mathbf{\Lambda}|
- \frac{nD}{2} \log 2
- \log \Gamma_D \left(\frac{n}{2}\right)
.. math::
\mathbf{u} (\mathbf{\Lambda})
&=
\left[ \begin{matrix}
\mathbf{\Lambda}
\\
\log |\mathbf{\Lambda}|
\end{matrix} \right]
\\
\boldsymbol{\phi} (n, \mathbf{V})
&=
\left[ \begin{matrix}
- \frac{1}{2} \mathbf{V}
\\
\frac{1}{2} n
\end{matrix} \right]
\\
\boldsymbol{\phi}_{n} (\mathbf{\Lambda}, \mathbf{V})
&=
\left[ \begin{matrix}
\frac{1}{2}\log|\mathbf{\Lambda}|
+ \frac{1}{2}\log|\mathbf{V}|
+ \frac{D}{2} \log 2
\\
-1
\end{matrix} \right]
\\
\boldsymbol{\phi}_{\mathbf{V}} (\mathbf{\Lambda}, n)
&=
\left[ \begin{matrix}
- \frac{1}{2} \mathbf{\Lambda}
\\
\frac{1}{2} n
\end{matrix} \right]
\\
g (n, \mathbf{V})
&=
\frac{n}{2} \log|\mathbf{V}|
- \frac{nD}{2}\log 2
- \log \Gamma_D \left(\frac{n}{2}\right)
\\
g_{\boldsymbol{\phi}} (\boldsymbol{\phi})
&=
\boldsymbol{\phi}_2 \log|-\boldsymbol{\phi}_1|
- \log \Gamma_D (\boldsymbol{\phi}_2)
\\
f(\mathbf{\Lambda})
&=
- \frac{D+1}{2} \log|\mathbf{\Lambda}|
\\
\overline{\mathbf{u}} (\boldsymbol{\phi})
&=
\left[ \begin{matrix}
- \boldsymbol{\phi}_2 \boldsymbol{\phi}^{-1}_1
\\
- \log|-\boldsymbol{\phi}_1|
+ \psi_D(\boldsymbol{\phi}_2)
\end{matrix} \right]
bayespy-0.2.3/doc/source/dev_guide/dev_guide.rst 0000664 5104112 5104112 00000002075 12374370351 023410 0 ustar jluttine jluttine 0000000 0000000 ..
Copyright (C) 2011,2012 Jaakko Luttinen
This file is licensed under Version 3.0 of the GNU General Public
License. See LICENSE for a text of the license.
This file is part of BayesPy.
BayesPy is free software: you can redistribute it and/or modify it
under the terms of the GNU General Public License version 3 as
published by the Free Software Foundation.
BayesPy is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
General Public License for more details.
You should have received a copy of the GNU General Public License
along with BayesPy. If not, see .
Developer guide
***************
This chapter provides basic information for developers about contributing, the
theoretical background and the core structure. It is assumed that the reader
has read and is familiar with :ref:`sec-user-guide`.
.. toctree::
:maxdepth: 2
workflow
vmp
engine
writingnodes
bayespy-0.2.3/doc/source/dev_guide/engine.rst 0000664 5104112 5104112 00000002455 12374370351 022724 0 ustar jluttine jluttine 0000000 0000000 ..
Copyright (C) 2014 Jaakko Luttinen
This file is licensed under Version 3.0 of the GNU General Public License.
See LICENSE for a text of the license.
This file is part of BayesPy.
BayesPy is free software: you can redistribute it and/or modify it under the
terms of the GNU General Public License version 3 as published by the Free
Software Foundation.
BayesPy is distributed in the hope that it will be useful, but WITHOUT ANY
WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR
A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with
BayesPy. If not, see .
Implementing inference engines
==============================
Currently, only variational Bayesian inference engine is implemented. This
implementation is not very modular, that is, the inference engine is not well
separated from the model construction. Thus, it is not straightforward to
implement other inference engines at the moment. Improving the modularity of
the inference engine and model construction is future work with high priority.
In any case, BayesPy aims to be an efficient, simple and modular Bayesian
package for variational inference at least.
bayespy-0.2.3/doc/source/dev_guide/vmp.rst 0000664 5104112 5104112 00000026213 12374370351 022257 0 ustar jluttine jluttine 0000000 0000000 ..
Copyright (C) 2012 Jaakko Luttinen
This file is licensed under Version 3.0 of the GNU General Public
License. See LICENSE for a text of the license.
This file is part of BayesPy.
BayesPy is free software: you can redistribute it and/or modify it
under the terms of the GNU General Public License version 3 as
published by the Free Software Foundation.
BayesPy is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
General Public License for more details.
You should have received a copy of the GNU General Public License
along with BayesPy. If not, see .
Variational message passing
===========================
This section briefly describes the variational message passing (VMP) framework,
which is currently the only implemented inference engine in BayesPy. The
variational Bayesian (VB) inference engine in BayesPy assumes that the posterior
approximation factorizes with respect to nodes and plates. VMP is based on
updating one node at a time (the plates in one node can be updated
simultaneously) and iteratively updating all nodes in turns until convergence.
Standard update equation
------------------------
The general update equation for the factorized approximation of node
:math:`\boldsymbol{\theta}` is the following:
.. math::
:label: vmp_general_update
\log q(\boldsymbol{\theta})
&=
\langle
\log p\left( \boldsymbol{\theta} |
\mathrm{pa}(\boldsymbol{\theta}) \right)
\rangle
+ \sum_{\mathbf{x} \in \mathrm{ch}(\boldsymbol{\theta})}
\langle \log p(\mathbf{x}|\mathrm{pa}(\mathbf{x})) \rangle
+ \mathrm{const},
where :math:`\mathrm{pa}(\boldsymbol{\theta})` and
:math:`\mathrm{ch}(\boldsymbol{\theta})` are the set of parents and children of
:math:`\boldsymbol{\theta}`, respectively. Thus, the posterior approximation of
a node is updated by taking a sum of the expectations of all log densities in
which the node variable appears. The expectations are over the approximate
distribution of all other variables than :math:`\boldsymbol{\theta}`. Actually,
not all the variables are needed, because the non-constant part depends only on
the Markov blanket of :math:`\boldsymbol{\theta}`. This leads to a local
optimization scheme, which uses messages from neighbouring nodes.
The messages are simple for conjugate exponential family models. An exponential
family distribution has the following log probability density function:
.. math::
:label: likelihood
\log p(\mathbf{x}|\mathbf{\Theta})
&=
\mathbf{u}_{\mathbf{x}}(\mathbf{x})^{\mathrm{T}}
\boldsymbol{\phi}_{\mathbf{x}}(\mathbf{\Theta})
+ g_{\mathbf{x}}(\mathbf{\Theta})
+ f_{\mathbf{x}}(\mathbf{x}),
where :math:`\mathbf{\Theta}=\{\boldsymbol{\theta}_j\}` is the set of parents,
:math:`\mathbf{u}` is the sufficient statistic vector, :math:`\boldsymbol{\phi}`
is the natural parameter vector, :math:`g` is the negative log normalizer, and
:math:`f` is the log base function. Note that the log density is linear with
respect to the terms that are functions of :math:`\mathbf{x}`:
:math:`\mathbf{u}` and :math:`f`. If a parent has a conjugate prior,
:eq:`likelihood` is also linear with respect to the parent's sufficient
statistic vector. Thus, :eq:`likelihood` can be re-organized with respect to a
parent :math:`\boldsymbol{\theta}_j` as
.. math::
\log p(\mathbf{x}|\mathbf{\Theta})
&=
\mathbf{u}_{\boldsymbol{\theta}_j}(\boldsymbol{\theta}_j)^{\mathrm{T}}
\boldsymbol{\phi}_{\mathbf{x}\rightarrow\boldsymbol{\theta}_j}
(\mathbf{x}, \{\boldsymbol{\theta}_k\}_{k\neq j})
+ \mathrm{const},
where :math:`\mathbf{u}_{\boldsymbol{\theta}_j}` is the sufficient statistic
vector of :math:`\boldsymbol{\theta}_j` and the constant part is constant with
respect to :math:`\boldsymbol{\theta}_j`. Thus, the update equation
:eq:`vmp_general_update` for :math:`\boldsymbol{\theta}_j` can be written as
.. math::
\log q(\boldsymbol{\theta}_j)
&=
\mathbf{u}_{\boldsymbol{\theta}_j}(\boldsymbol{\theta}_j)^{\mathrm{T}}
\langle \boldsymbol{\phi}_{\boldsymbol{\theta}_j} \rangle
+ f_{\boldsymbol{\theta}_j}(\boldsymbol{\theta}_j)
+
\mathbf{u}_{\boldsymbol{\theta}_j}(\boldsymbol{\theta}_j)^{\mathrm{T}}
\sum_{\mathbf{x} \in \mathrm{ch}(\boldsymbol{\theta}_j)}
\langle \boldsymbol{\phi}_{\mathbf{x}\rightarrow\boldsymbol{\theta}_j} \rangle
+ \mathrm{const},
\\
&=
\mathbf{u}_{\boldsymbol{\theta}_j}(\boldsymbol{\theta}_j)^{\mathrm{T}}
\left(
\langle \boldsymbol{\phi}_{\boldsymbol{\theta}_j} \rangle
+ \sum_{\mathbf{x} \in \mathrm{ch}(\boldsymbol{\theta}_j)}
\langle \boldsymbol{\phi}_{\mathbf{x}\rightarrow\boldsymbol{\theta}_j} \rangle
\right)
+ f_{\boldsymbol{\theta}_j}(\boldsymbol{\theta}_j)
+ \mathrm{const},
where the summation is over all the child nodes of
:math:`\boldsymbol{\theta}_j`. Because of the conjugacy,
:math:`\langle\boldsymbol{\phi}_{\boldsymbol{\theta}_j}\rangle` depends
(multi)linearly on the parents' sufficient statistic vector. Similarly,
:math:`\langle \boldsymbol{\phi}_{\mathbf{x}\rightarrow\boldsymbol{\theta}_j}
\rangle` depends (multi)linearly on the expectations of the children's and
co-parents' sufficient statistics. This gives the following update equation for
the natural parameter vector of the posterior approximation
:math:`q(\boldsymbol{\phi}_j)`:
.. math::
:label: update_phi
\tilde{\boldsymbol{\phi}}_j &= \langle \boldsymbol{\phi}_{\boldsymbol{\theta}_j} \rangle
+ \sum_{\mathbf{x} \in \mathrm{ch}(\boldsymbol{\theta}_j)} \langle
\boldsymbol{\phi}_{\mathbf{x}\rightarrow\boldsymbol{\theta}_j} \rangle.
Variational messages
--------------------
The update equation :eq:`update_phi` leads to a message passing scheme: the term
:math:`\langle \boldsymbol{\phi}_{\boldsymbol{\theta}_j} \rangle` is a function
of the parents' sufficient statistic vector and the term :math:`\langle
\boldsymbol{\phi}_{\mathbf{x}\rightarrow\boldsymbol{\theta}_j} \rangle` can be
interpreted as a message from the child node :math:`\mathbf{x}`. Thus, the
message from the child node :math:`\mathbf{x}` to the parent node
:math:`\boldsymbol{\theta}` is
.. math::
\mathbf{m}_{\mathbf{x}\rightarrow\boldsymbol{\theta}}
&\equiv
\langle \boldsymbol{\phi}_{\mathbf{x}\rightarrow\boldsymbol{\theta}} \rangle,
which can be computed as a function of the sufficient statistic vector of the
co-parent nodes of :math:`\boldsymbol{\theta}` and the sufficient statistic
vector of the child node :math:`\mathbf{x}`. The message from the parent node
:math:`\boldsymbol{\theta}` to the child node :math:`\mathbf{x}` is simply the
expectation of the sufficient statistic vector:
.. math::
\mathbf{m}_{\mathbf{\boldsymbol{\theta}}\rightarrow\mathbf{x}}
&\equiv
\langle \mathbf{u}_{\boldsymbol{\theta}} \rangle.
In order to compute the expectation of the sufficient statistic vector we need
to write :math:`q(\boldsymbol{\theta})` as
.. math::
\log q(\boldsymbol{\theta}) &=
\mathbf{u}(\boldsymbol{\theta})^{\mathrm{T}}
\tilde{\boldsymbol{\phi}}
+ \tilde{g}(\tilde{\boldsymbol{\phi}})
+ f(\boldsymbol{\theta}),
where :math:`\tilde{\boldsymbol{\phi}}` is the natural
parameter vector of :math:`q(\boldsymbol{\theta})`. Now, the expectation of the
sufficient statistic vector is defined as
.. math::
:label: moments
\langle \mathbf{u}_{\boldsymbol{\theta}} \rangle
&= - \frac{\partial \tilde{g}}{\partial
\tilde{\boldsymbol{\phi}}_{\boldsymbol{\theta}}}
(\tilde{\boldsymbol{\phi}}_{\boldsymbol{\theta}}).
We call this expectation of the sufficient statistic vector as the moments
vector.
Lower bound
-----------
Computing the VB lower bound is not necessary in order to find the posterior
approximation, although it is extremely useful in monitoring convergence and
possible bugs. The VB lower bound can be written as
.. math::
\mathcal{L} = \langle \log p(\mathbf{Y}, \mathbf{X}) \rangle - \langle \log
q(\mathbf{X}) \rangle,
where :math:`\mathbf{Y}` is the set of all observed variables and
:math:`\mathbf{X}` is the set of all latent variables. It can also be written as
.. math::
\mathcal{L} = \sum_{\mathbf{y} \in \mathbf{Y}} \langle \log p(\mathbf{y} |
\mathrm{pa}(\mathbf{y})) \rangle
+ \sum_{\mathbf{x} \in \mathbf{X}} \left[ \langle \log p(\mathbf{x} |
\mathrm{pa}(\mathbf{x})) \rangle - \langle \log q(\mathbf{x}) \right],
which shows that observed and latent variables contribute differently to the
lower bound. These contributions have simple forms for exponential family
nodes. Observed exponential family nodes contribute to the lower bound as
follows:
.. math::
\langle \log p(\mathbf{y}|\mathrm{pa}(\mathbf{y})) \rangle &=
\mathbf{u}(\mathbf{y})^T \langle \boldsymbol{\phi} \rangle
+ \langle g \rangle + f(\mathbf{x}),
where :math:`\mathbf{y}` is the observed data. On the other hand, latent
exponential family nodes contribute to the lower bound as follows:
.. math::
\langle \log p(\mathbf{x}|\boldsymbol{\theta}) \rangle
- \langle \log q(\mathbf{x}) \rangle &= \langle \mathbf{u} \rangle^T (\langle
\boldsymbol{\phi} \rangle - \tilde{\boldsymbol{\phi}} )
+ \langle g \rangle - \tilde{g}.
If a node is partially observed and partially unobserved, these formulas are
applied plate-wise appropriately.
.. _sec-vmp-terms:
Terms
-----
To summarize, implementing VMP requires one to write for each stochastic
exponential family node:
:math:`\langle \boldsymbol{\phi} \rangle` : the expectation of the prior
natural parameter vector
Computed as a function of the messages from parents.
:math:`\tilde{\boldsymbol{\phi}}` : natural parameter vector of the
posterior approximation
Computed as a sum of :math:`\langle \boldsymbol{\phi} \rangle` and the
messages from children.
:math:`\langle \mathbf{u} \rangle` : the posterior moments vector
Computed as a function of :math:`\tilde{\boldsymbol{\phi}}` as defined
in :eq:`moments`.
:math:`\mathbf{u}(\mathbf{x})` : the moments vector for given data
Computed as a function of of the observed data :math:`\mathbf{x}`.
:math:`\langle g \rangle` : the expectation of the negative log normalizer
of the prior
Computed as a function of parent moments.
:math:`\tilde{g}` : the negative log normalizer of the posterior
approximation
Computed as a function of :math:`\tilde{\boldsymbol{\phi}}`.
:math:`f(\mathbf{x})` : the log base measure for given data
Computed as a function of the observed data :math:`\mathbf{x}`.
:math:`\langle \boldsymbol{\phi}_{\mathbf{x}\rightarrow\boldsymbol{\theta}}
\rangle` : the message to parent :math:`\boldsymbol{\theta}`
Computed as a function of the moments of this node and the other
parents.
Deterministic nodes require only the following terms:
:math:`\langle \mathbf{u} \rangle` : the posterior moments vector
Computed as a function of the messages from the parents.
:math:`\mathbf{m}` : the message to a parent
Computed as a function of the messages from the other parents and all
children.
bayespy-0.2.3/doc/source/dev_guide/workflow.rst 0000664 5104112 5104112 00000004055 12374370351 023327 0 ustar jluttine jluttine 0000000 0000000 ..
Copyright (C) 2014 Jaakko Luttinen
This file is licensed under Version 3.0 of the GNU General Public
License. See LICENSE for a text of the license.
This file is part of BayesPy.
BayesPy is free software: you can redistribute it and/or modify it
under the terms of the GNU General Public License version 3 as
published by the Free Software Foundation.
BayesPy is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
General Public License for more details.
You should have received a copy of the GNU General Public License
along with BayesPy. If not, see .
Workflow
========
The main forum for BayesPy development is `GitHub
`_. Bugs and other issues can be reported
at https://github.com/bayespy/bayespy/issues. Contributions to the code and
documentation are welcome and should be given as pull requests at
https://github.com/bayespy/bayespy/pulls. In order to create pull requests, it
is recommended to fork the git repository, make local changes and submit these
changes as a pull request. The style guide for writing docstrings follows the
style guide of NumPy, available at
https://github.com/numpy/numpy/blob/master/doc/HOWTO_DOCUMENT.rst.txt. Detailed
instructions on development workflow can be read from NumPy guide, available at
http://docs.scipy.org/doc/numpy/dev/gitwash/development_workflow.html. BayesPy
uses the following acronyms to start the commit message:
* API: an (incompatible) API change
* BLD: change related to building numpy
* BUG: bug fix
* DEMO: modification in demo code
* DEP: deprecate something, or remove a deprecated object
* DEV: development tool or utility
* DOC: documentation
* ENH: enhancement
* MAINT: maintenance commit (refactoring, typos, etc.)
* REV: revert an earlier commit
* STY: style fix (whitespace, PEP8)
* TST: addition or modification of tests
* REL: related to releasing numpy
bayespy-0.2.3/doc/source/dev_guide/writingnodes.rst 0000664 5104112 5104112 00000037456 12374370351 024204 0 ustar jluttine jluttine 0000000 0000000 ..
Copyright (C) 2014 Jaakko Luttinen
This file is licensed under Version 3.0 of the GNU General Public License.
See LICENSE for a text of the license.
This file is part of BayesPy.
BayesPy is free software: you can redistribute it and/or modify it under the
terms of the GNU General Public License version 3 as published by the Free
Software Foundation.
BayesPy is distributed in the hope that it will be useful, but WITHOUT ANY
WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR
A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with
BayesPy. If not, see .
Implementing nodes
==================
The main goal of BayesPy is to provide a package which enables easy and flexible
construction of simple and complex models with efficient inference. However,
users may sometimes be unable to construct their models because the built-in
nodes do not implement some specific features. Thus, one may need to implement
new nodes in order to construct the model. BayesPy aims to make the
implementation of new nodes both simple and fast. Probably, a large complex
model can be constructed almost completely with the built-in nodes and the user
needs to implement only a few nodes.
Moments
-------
.. currentmodule:: bayespy.inference.vmp.nodes.node
In order to implement nodes, it is important to understand the messaging
framework of the nodes. A node is a unit of calculation which communicates to
its parent and child nodes using messages. These messages have types that need
to match between nodes, that is, the child node needs to understand the messages
its parents are sending and vice versa. Thus, a node defines which message type
it requires from each of its parents, and only nodes that have that type of
output message (i.e., the message to a child node) are valid parent nodes for
that node.
The message type is defined by the moments of the parent node. The moments are
a collection of expectations: :math:`\{ \langle f_1(X) \rangle, \ldots, \langle
f_N(X) \rangle \}`. The functions :math:`f_1, \ldots, f_N` (and the number of
the functions) define the message type and they are the sufficient statistic as
discussed in the previous section. Different message types are represented by
:class:`Moments` class hierarchy. For instance, :class:`GaussianMoments`
represents a message type with parent moments :math:`\{\langle \mathbf{x}
\rangle, \langle \mathbf{xx}^T \rangle \}` and :class:`WishartMoments` a message
type with parent moments :math:`\{\langle \mathbf{\Lambda} \rangle, \langle \log
|\mathbf{\Lambda}| \rangle\}`.
.. currentmodule:: bayespy.nodes
Let us give an example: :class:`Gaussian` node outputs :class:`GaussianMoments`
messages and :class:`Wishart` node outputs :class:`WishartMoments` messages.
:class:`Gaussian` node requires that it receives :class:`GaussianMoments`
messages from the mean parent node and :class:`WishartMoments` messages from the
precision parent node. Thus, :class:`Gaussian` and :class:`Wishart` are valid
node classes as the mean and precision parent nodes of :class:`Gaussian` node.
Note that several nodes may have the same output message type and some message
types can be transformed to other message types using deterministic converter
nodes. For instance, :class:`Gaussian` and :class:`GaussianARD` nodes both
output :class:`GaussianMoments` messages, deterministic :class:`SumMultiply`
also outputs :class:`GaussianMoments` messages, and deterministic converter
:class:`_MarkovChainToGaussian` converts :class:`GaussianMarkovChainMoments` to
:class:`GaussianMoments`.
.. currentmodule:: bayespy.inference.vmp.nodes.node
Each node specifies the message type requirements of its parents by
:attr:`Node._parent_moments` attribute which is a list of :class:`Moments`
sub-class instances. These moments objects have a few purpose when creating the
node: 1) check that parents are sending proper messages; 2) if parents use
different message type, try to add a converter which converts the messages to
the correct type if possible; 3) if given parents are not nodes but numeric
arrays, convert them to constant nodes with correct output message type.
When implementing a new node, it is not always necessary to implement a new
moments class. If another node has the same sufficient statistic vector, thus
the same moments, that moments class can be used. Otherwise, one must implement
a simple moments class which has the following methods:
* :func:`Moments.compute_fixed_moments`
Computes the moments for a known value. This is used to compute the
moments of constant numeric arrays and wrap them into constant nodes.
* :func:`Moments.compute_dims_from_values`
Given a known value of the variable, return the shape of the variable
dimensions in the moments. This is used to solve the shape of the moments
array for constant nodes.
Distributions
-------------
.. currentmodule:: bayespy.inference.vmp.stochastic
In order to implement a stochastic exponential family node, one must first write
down the log probability density function of the node and derive the terms
discussed in section :ref:`sec-vmp-terms`. These terms are implemented and
collected as a class which is a subclass of :class:`Distribution`. The main
reason to implement these methods in another class instead of the node class
itself is that these methods can be used without creating a node, for instance,
in :class:`Mixture` class.
.. currentmodule:: bayespy.inference.vmp.nodes.expfamily
For exponential family distributions, the distribution class is a subclass of
:class:`ExponentialFamilyDistribution`, and the relation between the terms in
section :ref:`sec-vmp-terms` and the methods is as follows:
* :func:`ExponentialFamilyDistribution.compute_phi_from_parents`
Computes the expectation of the natural parameters :math:`\langle
\boldsymbol{\phi} \rangle` in the prior distribution given the moments of
the parents.
* :func:`ExponentialFamilyDistribution.compute_cgf_from_parents`
Computes the expectation of the negative log normalizer :math:`\langle g
\rangle` of the prior distribution given the moments of the parents.
* :func:`ExponentialFamilyDistribution.compute_moments_and_cgf`
Computes the moments :math:`\langle \mathbf{u} \rangle` and the negative
log normalizer :math:`\tilde{g}` of the posterior distribution
given the natural parameters :math:`\tilde{\boldsymbol{\phi}}`.
* :func:`ExponentialFamilyDistribution.compute_message_to_parent`
Computes the message :math:`\langle
\boldsymbol{\phi}_{\mathbf{x}\rightarrow\boldsymbol{\theta}} \rangle` from
the node :math:`\mathbf{x}` to its parent node :math:`\boldsymbol{\theta}`
given the moments of the node and the other parents.
* :func:`ExponentialFamilyDistribution.compute_fixed_moments_and_f`
Computes :math:`\mathbf{u}(\mathbf{x})` and :math:`f(\mathbf{x})` for
given observed value :math:`\mathbf{x}`. Without this method, variables
from this distribution cannot be observed.
For each stochastic exponential family node, one must write a distribution class
which implements these methods. After that, the node class is basically a
simple wrapper and it also stores the moments and the natural parameters of the
current posterior approximation. Note that the distribution classes do not
store node-specific information, they are more like static collections of
methods. However, sometimes the implementations depend on some information,
such as the dimensionality of the variable, and this information must be
provided, if needed, when constructing the distribution object.
In addition to the methods listed above, it is necessary to implement a few more
methods in some cases. This happens when the plates of the parent do not map to
the plates directly as discussed in section :ref:`sec-irregular-plates`. Then,
one must write methods that implement this plate mapping and apply the same
mapping to the mask array:
* :func:`ExponentialFamilyDistribution.plates_from_parent`
Given the plates of the parent, return the resulting plates of the child.
* :func:`ExponentialFamilyDistribution.plates_to_parent`
Given the plates of the child, return the plates of the parent that would
have resulted them.
* :func:`ExponentialFamilyDistribution.compute_mask_to_parent`
Given the mask array of the child, apply the plate mapping.
It is important to understand when one must implement these methods, because the
default implementations in the base class will lead to errors or weird results.
Stochastic exponential family nodes
-----------------------------------
After implementing the distribution class, the next task is to implement the
node class. First, we need to explain a few important attributes before we can
explain how to implement a node class.
Stochastic exponential family nodes have two attributes that store the state of
the posterior distribution:
* ``phi``
The natural parameter vector :math:`\tilde{\boldsymbol{\phi}}` of the
posterior approximation.
* ``u``
The moments :math:`\langle \mathbf{u} \rangle` of the posterior
approximation.
Instead of storing these two variables as vectors (as in the mathematical
formulas), they are stored as lists of arrays with convenient shapes. For
instance, :class:`Gaussian` node stores the moments as a list consisting of a
vector :math:`\langle \mathbf{x} \rangle` and a matrix :math:`\langle
\mathbf{xx}^T \rangle` instead of reshaping and concatenating these into a
single vector. The same applies for the natural parameters ``phi`` because it
has the same shape as ``u``.
The shapes of the arrays in the lists ``u`` and ``phi`` consist of the shape
caused by the plates and the shape caused by the variable itself. For instance,
the moments of :class:`Gaussian` node have shape ``(D,)`` and ``(D, D)``, where
``D`` is the dimensionality of the Gaussian vector. In addition, if the node
has plates, they are added to these shapes. Thus, for instance, if the
:class:`Gaussian` node has plates ``(3, 7)`` and ``D`` is 5, the shape of
``u[0]`` and ``phi[0]`` would be ``(3, 7, 5)`` and the shape of ``u[1]`` and
``phi[1]`` would be ``(3, 7, 5, 5)``. This shape information is stored in the
following attributes:
* ``plates`` : a tuple
The plates of the node. In our example, ``(3, 7)``.
* ``dims`` : a list of tuples
The shape of each of the moments arrays (or natural parameter arrays) without
plates. In our example, ``[ (5,), (5, 5) ]``.
Finally, three attributes define VMP for the node:
* ``_moments`` : :class:`Moments` sub-class instance
An object defining the moments of the node.
* ``_parent_moments`` : list of :class:`Moments` sub-class instances
A list defining the moments requirements for each parent.
* ``_distribution`` : :class:`Distribution` sub-class instance
An object implementing the VMP formulas.
Basically, a node class is a collection of the above attributes. When a node is
created, these attributes are defined. The base class for exponential family
nodes, :class:`ExponentialFamily`, provides a simple default constructor which
does not need to be overwritten if ``dims``, ``_moments``, ``_parent_moments``
and ``_distribution`` can be provided as static class attributes. For instance,
:class:`Gamma` node defines these attributes statically. However, usually at
least one of these attributes cannot be defined statically in the class. In
that case, one must implement a class method which overloads
:func:`ExponentialFamily._constructor`. The purpose of this method is to define
all the attributes given the parent nodes. These are defined using a class
method instead of ``__init__`` method in order to be able to use the class
constructors statically, for instance, in :class:`Mixture` class. This
construction allows users to create mixtures of any exponential family
distribution with simple syntax.
The parents of a node must be converted so that they have a correct message
type, because the user may have provided numeric arrays or nodes with incorrect
message type. Numeric arrays should be converted to constant nodes with correct
message type. Incorrect message type nodes should be converted to correct
message type nodes if possible. Thus, the constructor should use
``Node._ensure_moments`` method to make sure the parent is a node with correct
message type. Instead of calling this method for each parent node in the
constructor, one can use ``ensureparents`` decorator to do this automatically.
However, the decorator requires that ``_parent_moments`` attribute has already
been defined statically. If this is not possible, the parent nodes must be
converted manually in the constructor, because one should never assume that the
parent nodes given to the constructor are nodes with correct message type or
even nodes at all.
Deterministic nodes
-------------------
Deterministic nodes are nodes that do not correspond to any probability
distribution but rather a deterministic function. It does not have any moments
or natural parameters to store. A deterministic node is implemented as a
subclass of :class:`Deterministic` base class. The new node class must
implement the following methods:
* :func:`Deterministic._compute_moments`
Computes the moments given the moments of the parents.
* :func:`Deterministic._compute_message_to_parent`
Computes the message to a parent node given the message from the children and
the moments of the other parents. In some cases, one may want to implement
:func:`Deterministic._compute_message_and_mask_to_parent` or
:func:`Deterministic._message_to_parent` instead in order to gain more
control over efficient computation.
Similarly as in :class:`Distribution` class, if the node handles plates
irregularly, it is important to implement the following methods:
* :func:`Deterministic._plates_from_parent`
Given the plates of the parent, return the resulting plates of the child.
* :func:`Deterministic._plates_to_parent`
Given the plates of the child, return the plates of the parent that would
have resulted them.
* :func:`Deterministic._compute_mask_to_parent`
Given the mask array, convert it to a plate mask of the parent.
Converter nodes
+++++++++++++++
Sometimes a node has incorrect message type but the message can be converted
into a correct type. For instance, :class:`GaussianMarkovChain` has
:class:`GaussianMarkovChainMoments` message type, which means moments :math:`\{
\langle \mathbf{x}_n \rangle, \langle \mathbf{x}_n \mathbf{x}_n^T \rangle,
\langle \mathbf{x}_n \mathbf{x}_{n-1}^T \rangle \}^N_{n=1}`. These moments can
be converted to :class:`GaussianMoments` by ignoring the third element and
considering the time axis as a plate axis. Thus, if a node requires
:class:`GaussianMoments` message from its parent, :class:`GaussianMarkovChain`
is a valid parent if its messages are modified properly. This conversion is
implemented in :class:`_MarkovChainToGaussian` converter class. Converter nodes
are simple deterministic nodes that have one parent node and they convert the
messages to another message type.
For the user, it is not convenient if the exact message type has to be known and
an explicit converter node needs to be created. Thus, the conversions are done
automatically and the user will be unaware of them. In order to enable this
automatization, when writing a converter node, one should register the converter
to the moments class using :func:`Moments.add_converter`. For instance, a class
``X`` which converts moments ``A`` to moments ``B`` is registered as
``A.add_conveter(B, X)``. After that, :func:`Node._ensure_moments` and
:func:`Node._convert` methods are used to perform the conversions automatically.
The conversion can consist of several consecutive converter nodes, and the least
number of conversions is used.
bayespy-0.2.3/doc/source/examples/ 0000775 5104112 5104112 00000000000 12437607671 020612 5 ustar jluttine jluttine 0000000 0000000 bayespy-0.2.3/doc/source/examples/additive_fhmm.rst 0000664 5104112 5104112 00000006634 12374370351 024145 0 ustar jluttine jluttine 0000000 0000000
Additive factorial hidden Markov model
======================================
.. code:: python
import numpy as np
from bayespy.nodes import (Dirichlet,
CategoricalMarkovChain,
GaussianARD,
Gate,
SumMultiply,
Gamma)
D = 4 # dimensionality of the data vectors (and mu vectors)
N = 5 # number of chains
K = 3 # number of states in each chain
T = 100 # length of each chain
# Markov chain parameters.
# Use known values
p0 = np.ones(K) / K
P = np.ones((K,K)) / K
# Or set Dirichlet prior.
p0 = Dirichlet(np.ones(K), plates=(N,))
P = Dirichlet(np.ones(K), plates=(N,1,K))
# N Markov chains with K possible states, and length T
X = CategoricalMarkovChain(p0, P, states=T, plates=(N,))
# For each of the N chains, have K different D-dimensional mu's
# Unknown mu's
mu = GaussianARD(0, 1e-3, plates=(D,1,1,K), shape=(N,))
# Gate/select mu's
print(mu.plates, mu.dims[0], X.plates, X.dims[1])
Z = Gate(X, mu)
# Sum the mu's of different chains
F = SumMultiply('i->', Z)
print(mu.plates, mu.dims[0], X.plates, X.dims[0], Z.plates, Z.dims[0], F.plates, F.dims[0])
# Known observation noise inverse covariance
tau = np.ones(D)
# or unknown observation noise inverse covariance
tau = Gamma(1e-3, 1e-3, plates=(D,))
# Observed process
Y = GaussianARD(F, tau)
# Data
data = np.random.randn(T, D)
Y.observe(data)
from bayespy.inference import VB
Q = VB(Y, X, p0, P, mu, tau)
Q.update(repeat=10)
.. parsed-literal::
(4, 1, 1, 3) (5,) (5,) (99, 3, 3)
(4, 1, 1, 3) (5,) (5,) (3,) (4, 5, 100) (5,) (4, 5, 100) ()
::
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
in ()
41
42 # Observed process
---> 43 Y = GaussianARD(F, tau)
44
45 # Data
/home/jluttine/workspace/bayespy/bayespy/inference/vmp/nodes/expfamily.py in constructor_decorator(self, *args, **kwargs)
81
82 (args, kwargs, dims, plates, dist, stats, pstats) = \
---> 83 self._constructor(*args, **kwargs)
84
85 self.dims = dims
/home/jluttine/workspace/bayespy/bayespy/inference/vmp/nodes/gaussian.py in _constructor(cls, mu, alpha, ndim, shape, **kwargs)
890 plates = cls._total_plates(kwargs.get('plates'),
891 distribution.plates_from_parent(0, mu.plates),
--> 892 distribution.plates_from_parent(1, alpha.plates))
893
894 parents = [mu, alpha]
/home/jluttine/workspace/bayespy/bayespy/inference/vmp/nodes/node.py in _total_plates(cls, plates, *parent_plates)
244 return utils.broadcasted_shape(*parent_plates)
245 except ValueError:
--> 246 raise ValueError("The plates of the parents do not broadcast.")
247 else:
248 # Check that the parent_plates are a subset of plates.
ValueError: The plates of the parents do not broadcast.
bayespy-0.2.3/doc/source/examples/bmm.rst 0000664 5104112 5104112 00000021350 12374370351 022110 0 ustar jluttine jluttine 0000000 0000000 ..
Copyright (C) 2014 Jaakko Luttinen
This file is licensed under Version 3.0 of the GNU General Public
License. See LICENSE for a text of the license.
This file is part of BayesPy.
BayesPy is free software: you can redistribute it and/or modify it
under the terms of the GNU General Public License version 3 as
published by the Free Software Foundation.
BayesPy is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
General Public License for more details.
You should have received a copy of the GNU General Public License
along with BayesPy. If not, see .
.. testsetup::
import numpy
numpy.random.seed(1)
Bernoulli mixture model
=======================
This example considers data generated from a Bernoulli mixture model. One
simple example process could be a questionnaire for election candidates. We
observe a set of binary vectors, where each vector represents a candidate in the
election and each element in these vectors correspond to a candidate's answer to
a yes-or-no question. The goal is to find groups of similar candidates and
analyze the answer patterns of these groups.
Data
----
First, we generate artificial data to analyze. Let us assume that the
questionnaire contains ten yes-or-no questions. We assume that there are three
groups with similar opinions. These groups could represent parties. These
groups have the following answering patterns, which are represented by vectors
with probabilities of a candidate answering yes to the questions:
>>> p0 = [0.1, 0.9, 0.1, 0.9, 0.1, 0.9, 0.1, 0.9, 0.1, 0.9]
>>> p1 = [0.1, 0.1, 0.1, 0.1, 0.1, 0.9, 0.9, 0.9, 0.9, 0.9]
>>> p2 = [0.9, 0.9, 0.9, 0.9, 0.9, 0.1, 0.1, 0.1, 0.1, 0.1]
Thus, the candidates in the first group are likely to answer no to questions 1,
3, 5, 7 and 9, and yes to questions 2, 4, 6, 8, 10. The candidates in the
second group are likely to answer yes to the last five questions, whereas the
candidates in the third group are likely to answer yes to the first five
questions. For convenience, we form a NumPy array of these vectors:
>>> import numpy as np
>>> p = np.array([p0, p1, p2])
Next, we generate a hundred candidates. First, we randomly select the group for
each candidate:
>>> from bayespy.utils import random
>>> z = random.categorical([1/3, 1/3, 1/3], size=100)
Using the group patterns, we generate yes-or-no answers for the candidates:
>>> x = random.bernoulli(p[z])
This is our simulated data to be analyzed.
Model
-----
Now, we construct a model for learning the structure in the data. We have a
dataset of hundred 10-dimensional binary vectors:
>>> N = 100
>>> D = 10
We will create a Bernoulli mixture model. We assume that the true number of
groups is unknown to us, so we use a large enough number of clusters:
>>> K = 10
We use the categorical distribution for the group assignments and give the group
assignment probabilities an uninformative Dirichlet prior:
>>> from bayespy.nodes import Categorical, Dirichlet
>>> R = Dirichlet(K*[1e-5],
... name='R')
>>> Z = Categorical(R,
... plates=(N,1),
... name='Z')
Each group has a probability of a yes answer for each question. These
probabilities are given beta priors:
>>> from bayespy.nodes import Beta
>>> P = Beta([0.5, 0.5],
... plates=(D,K),
... name='P')
The answers of the candidates are modelled with the Bernoulli distribution:
>>> from bayespy.nodes import Mixture, Bernoulli
>>> X = Mixture(Z, Bernoulli, P)
Here, ``Z`` defines the group assignments and ``P`` the answering probability
patterns for each group. Note how the plates of the nodes are matched: ``Z``
has plates ``(N,1)`` and ``P`` has plates ``(D,K)``, but in the mixture node the
last plate axis of ``P`` is discarded and thus the node broadcasts plates
``(N,1)`` and ``(D,)`` resulting in plates ``(N,D)`` for ``X``.
Inference
---------
In order to infer the variables in our model, we construct a variational
Bayesian inference engine:
>>> from bayespy.inference import VB
>>> Q = VB(Z, R, X, P)
This also gives the default update order of the nodes. In order to find
different groups, they must be initialized differently, thus we use random
initialization for the group probability patterns:
>>> P.initialize_from_random()
We provide our simulated data:
>>> X.observe(x)
Now, we can run inference:
>>> Q.update(repeat=1000)
Iteration 1: loglike=-6.872145e+02 (... seconds)
...
Iteration 17: loglike=-5.236921e+02 (... seconds)
Converged at iteration 17.
The algorithm converges in 17 iterations.
Results
-------
Now we can examine the approximate posterior distribution. First, let us plot
the group assignment probabilities:
>>> import bayespy.plot as bpplt
>>> bpplt.hinton(R)
.. plot::
import numpy
numpy.random.seed(1)
p0 = [0.1, 0.9, 0.1, 0.9, 0.1, 0.9, 0.1, 0.9, 0.1, 0.9]
p1 = [0.1, 0.1, 0.1, 0.1, 0.1, 0.9, 0.9, 0.9, 0.9, 0.9]
p2 = [0.9, 0.9, 0.9, 0.9, 0.9, 0.1, 0.1, 0.1, 0.1, 0.1]
import numpy as np
p = np.array([p0, p1, p2])
from bayespy.utils import random
z = random.categorical([1/3, 1/3, 1/3], size=100)
x = random.bernoulli(p[z])
N = 100
D = 10
K = 10
from bayespy.nodes import Categorical, Dirichlet
R = Dirichlet(K*[1e-5],
name='R')
Z = Categorical(R,
plates=(N,1),
name='Z')
from bayespy.nodes import Beta
P = Beta([0.5, 0.5],
plates=(D,K),
name='P')
from bayespy.nodes import Mixture, Bernoulli
X = Mixture(Z, Bernoulli, P)
from bayespy.inference import VB
Q = VB(Z, R, X, P)
P.initialize_from_random()
X.observe(x)
Q.update(repeat=1000)
import bayespy.plot as bpplt
bpplt.hinton(R)
bpplt.pyplot.show()
This plot shows that there are three dominant groups, which is equal to the true
number of groups used to generate the data. However, there are still two
smaller groups as the data does not give enough evidence to prune them out. The
yes-or-no answer probability patterns for the groups can be plotted as:
>>> bpplt.hinton(P)
.. plot::
import numpy
numpy.random.seed(1)
p0 = [0.1, 0.9, 0.1, 0.9, 0.1, 0.9, 0.1, 0.9, 0.1, 0.9]
p1 = [0.1, 0.1, 0.1, 0.1, 0.1, 0.9, 0.9, 0.9, 0.9, 0.9]
p2 = [0.9, 0.9, 0.9, 0.9, 0.9, 0.1, 0.1, 0.1, 0.1, 0.1]
import numpy as np
p = np.array([p0, p1, p2])
from bayespy.utils import random
z = random.categorical([1/3, 1/3, 1/3], size=100)
x = random.bernoulli(p[z])
N = 100
D = 10
K = 10
from bayespy.nodes import Categorical, Dirichlet
R = Dirichlet(K*[1e-5],
name='R')
Z = Categorical(R,
plates=(N,1),
name='Z')
from bayespy.nodes import Beta
P = Beta([0.5, 0.5],
plates=(D,K),
name='P')
from bayespy.nodes import Mixture, Bernoulli
X = Mixture(Z, Bernoulli, P)
from bayespy.inference import VB
Q = VB(Z, R, X, P)
P.initialize_from_random()
X.observe(x)
Q.update(repeat=1000)
import bayespy.plot as bpplt
bpplt.hinton(P)
bpplt.pyplot.show()
The three dominant groups have found the true patterns accurately. The patterns
of the two minor groups some kind of mixtures of the three groups and they exist
because the generated data happened to contain a few samples giving evidence for
these groups. Finally, we can plot the group assignment probabilities for the
candidates:
>>> bpplt.hinton(Z)
.. plot::
import numpy
numpy.random.seed(1)
p0 = [0.1, 0.9, 0.1, 0.9, 0.1, 0.9, 0.1, 0.9, 0.1, 0.9]
p1 = [0.1, 0.1, 0.1, 0.1, 0.1, 0.9, 0.9, 0.9, 0.9, 0.9]
p2 = [0.9, 0.9, 0.9, 0.9, 0.9, 0.1, 0.1, 0.1, 0.1, 0.1]
import numpy as np
p = np.array([p0, p1, p2])
from bayespy.utils import random
z = random.categorical([1/3, 1/3, 1/3], size=100)
x = random.bernoulli(p[z])
N = 100
D = 10
K = 10
from bayespy.nodes import Categorical, Dirichlet
R = Dirichlet(K*[1e-5],
name='R')
Z = Categorical(R,
plates=(N,1),
name='Z')
from bayespy.nodes import Beta
P = Beta([0.5, 0.5],
plates=(D,K),
name='P')
from bayespy.nodes import Mixture, Bernoulli
X = Mixture(Z, Bernoulli, P)
from bayespy.inference import VB
Q = VB(Z, R, X, P)
P.initialize_from_random()
X.observe(x)
Q.update(repeat=1000)
import bayespy.plot as bpplt
bpplt.hinton(Z)
bpplt.pyplot.show()
.. currentmodule:: bayespy.plot
This plot shows the clustering of the candidates. It is possible to use
:class:`HintonPlotter` to enable monitoring during the VB iteration by providing
``plotter=HintonPlotter()`` for ``Z``, ``P`` and ``R`` when creating the nodes.
bayespy-0.2.3/doc/source/examples/examples.rst 0000664 5104112 5104112 00000001534 12374370351 023155 0 ustar jluttine jluttine 0000000 0000000 ..
Copyright (C) 2014 Jaakko Luttinen
This file is licensed under Version 3.0 of the GNU General Public
License. See LICENSE for a text of the license.
This file is part of BayesPy.
BayesPy is free software: you can redistribute it and/or modify it
under the terms of the GNU General Public License version 3 as
published by the Free Software Foundation.
BayesPy is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
General Public License for more details.
You should have received a copy of the GNU General Public License
along with BayesPy. If not, see .
Examples
********
.. toctree::
:maxdepth: 1
regression
gmm
bmm
hmm
pca
lssm
bayespy-0.2.3/doc/source/examples/gmm.rst 0000664 5104112 5104112 00000014447 12374370351 022126 0 ustar jluttine jluttine 0000000 0000000 ..
Copyright (C) 2014 Jaakko Luttinen
This file is licensed under Version 3.0 of the GNU General Public
License. See LICENSE for a text of the license.
This file is part of BayesPy.
BayesPy is free software: you can redistribute it and/or modify it
under the terms of the GNU General Public License version 3 as
published by the Free Software Foundation.
BayesPy is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
General Public License for more details.
You should have received a copy of the GNU General Public License
along with BayesPy. If not, see .
.. testsetup::
import numpy
numpy.random.seed(1)
Gaussian mixture model
======================
This example demonstrates the use of Gaussian mixture model for flexible density
estimation, clustering or classification.
Data
----
First, let us generate some artificial data for the analysis. The data are
two-dimensional vectors from one of the four different Gaussian distributions:
>>> import numpy as np
>>> y0 = np.random.multivariate_normal([0, 0], [[2, 0], [0, 0.1]], size=50)
>>> y1 = np.random.multivariate_normal([0, 0], [[0.1, 0], [0, 2]], size=50)
>>> y2 = np.random.multivariate_normal([2, 2], [[2, -1.5], [-1.5, 2]], size=50)
>>> y3 = np.random.multivariate_normal([-2, -2], [[0.5, 0], [0, 0.5]], size=50)
>>> y = np.vstack([y0, y1, y2, y3])
Thus, there are 200 data vectors in total. The data looks as follows:
>>> import bayespy.plot as bpplt
>>> bpplt.pyplot.plot(y[:,0], y[:,1], 'rx')
[]
.. plot::
import numpy
numpy.random.seed(1)
y0 = np.random.multivariate_normal([0, 0], [[1, 0], [0, 0.02]], size=50)
y1 = np.random.multivariate_normal([0, 0], [[0.02, 0], [0, 1]], size=50)
y2 = np.random.multivariate_normal([2, 2], [[1, -0.9], [-0.9, 1]], size=50)
y3 = np.random.multivariate_normal([-2, -2], [[0.1, 0], [0, 0.1]], size=50)
y = np.vstack([y0, y1, y2, y3])
import bayespy.plot as bpplt
bpplt.pyplot.plot(y[:,0], y[:,1], 'rx')
bpplt.pyplot.show()
Model
-----
For clarity, let us denote the number of the data vectors with ``N``
>>> N = 200
and the dimensionality of the data vectors with ``D``:
>>> D = 2
We will use a "large enough" number of Gaussian clusters in our model:
>>> K = 10
Cluster assignments ``Z`` and the prior for the cluster assignment probabilities
``alpha``:
>>> from bayespy.nodes import Dirichlet, Categorical
>>> alpha = Dirichlet(1e-5*np.ones(K),
... name='alpha')
>>> Z = Categorical(alpha,
... plates=(N,),
... name='z')
The mean vectors and the precision matrices of the clusters:
>>> from bayespy.nodes import Gaussian, Wishart
>>> mu = Gaussian(np.zeros(D), 1e-5*np.identity(D),
... plates=(K,),
... name='mu')
>>> Lambda = Wishart(D, 1e-5*np.identity(D),
... plates=(K,),
... name='Lambda')
If either the mean or precision should be shared between clusters, then that
node should not have plates, that is, ``plates=()``. The data vectors are from
a Gaussian mixture with cluster assignments ``Z`` and Gaussian component
parameters ``mu`` and ``Lambda``:
>>> from bayespy.nodes import Mixture
>>> Y = Mixture(Z, Gaussian, mu, Lambda,
... name='Y')
>>> Z.initialize_from_random()
>>> from bayespy.inference import VB
>>> Q = VB(Y, mu, Lambda, Z, alpha)
Inference
---------
Before running the inference algorithm, we provide the data:
>>> Y.observe(y)
Then, run VB iteration until convergence:
>>> Q.update(repeat=1000)
Iteration 1: loglike=-1.401968e+03 (... seconds)
...
Iteration 48: loglike=-1.017893e+03 (... seconds)
Converged at iteration 48.
The algorithm converges very quickly. Note that the default update order of the
nodes was such that ``mu`` and ``Lambda`` were updated before ``Z``, which is
what we wanted because ``Z`` was initialized randomly.
Results
-------
.. currentmodule:: bayespy.plot
For two-dimensional Gaussian mixtures, the mixture components can be plotted
using :func:`gaussian_mixture`:
>>> bpplt.gaussian_mixture(Y, scale=2)
.. plot::
import numpy
numpy.random.seed(1)
import numpy as np
y0 = np.random.multivariate_normal([0, 0], [[1, 0], [0, 0.02]], size=50)
y1 = np.random.multivariate_normal([0, 0], [[0.02, 0], [0, 1]], size=50)
y2 = np.random.multivariate_normal([2, 2], [[1, -0.9], [-0.9, 1]], size=50)
y3 = np.random.multivariate_normal([-2, -2], [[0.1, 0], [0, 0.1]], size=50)
y = np.vstack([y0, y1, y2, y3])
import bayespy.plot as bpplt
bpplt.pyplot.plot(y[:,0], y[:,1], 'rx')
N = 200
D = 2
K = 10
from bayespy.nodes import Dirichlet, Categorical
alpha = Dirichlet(1e-5*np.ones(K),
name='alpha')
Z = Categorical(alpha,
plates=(N,),
name='z')
from bayespy.nodes import Gaussian, Wishart
mu = Gaussian(np.zeros(D), 1e-5*np.identity(D),
plates=(K,),
name='mu')
Lambda = Wishart(D, 1e-5*np.identity(D),
plates=(K,),
name='Lambda')
from bayespy.nodes import Mixture
Y = Mixture(Z, Gaussian, mu, Lambda,
name='Y')
Z.initialize_from_random()
from bayespy.inference import VB
Q = VB(Y, mu, Lambda, Z, alpha)
Y.observe(y)
Q.update(repeat=1000)
bpplt.gaussian_mixture(Y, scale=2)
bpplt.pyplot.show()
The function is called with ``scale=2`` which means that each ellipse shows two
standard deviations. From the ten cluster components, the model uses
effectively the correct number of clusters (4). These clusters capture the true
density accurately.
Next steps
----------
.. currentmodule:: bayespy.nodes
The next step for improving the results could be to use :class:`GaussianWishart`
node for modelling the mean vectors ``mu`` and precision matrices ``Lambda``
jointly without factorization. This should improve the accuracy of the
posterior approximation and the speed of the VB estimation. However, the
implementation is a bit more complex.
In addition to clustering and density estimation, this model could also be used
for classification by setting the known class assignments as observed.
bayespy-0.2.3/doc/source/examples/hmm.rst 0000664 5104112 5104112 00000031165 12374370351 022123 0 ustar jluttine jluttine 0000000 0000000 ..
Copyright (C) 2014 Jaakko Luttinen
This file is licensed under Version 3.0 of the GNU General Public
License. See LICENSE for a text of the license.
This file is part of BayesPy.
BayesPy is free software: you can redistribute it and/or modify it
under the terms of the GNU General Public License version 3 as
published by the Free Software Foundation.
BayesPy is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
General Public License for more details.
You should have received a copy of the GNU General Public License
along with BayesPy. If not, see .
.. testsetup::
import numpy
numpy.random.seed(1)
Hidden Markov model
===================
In this example, we will demonstrate the use of hidden Markov model in the case
of known and unknown parameters. We will also use two different emission
distributions to demonstrate the flexibility of the model construction.
Known parameters
----------------
This example follows the one presented in `Wikipedia
`__.
Model
+++++
Each day, the state of the weather is either 'rainy' or 'sunny'. The weather
follows a first-order discrete Markov process. It has the following initial
state probabilities
>>> a0 = [0.6, 0.4] # p(rainy)=0.6, p(sunny)=0.4
and state transition probabilities:
>>> A = [[0.7, 0.3], # p(rainy->rainy)=0.7, p(rainy->sunny)=0.3
... [0.4, 0.6]] # p(sunny->rainy)=0.4, p(sunny->sunny)=0.6
We will be observing one hundred samples:
>>> N = 100
The discrete first-order Markov chain is constructed as:
>>> from bayespy.nodes import CategoricalMarkovChain
>>> Z = CategoricalMarkovChain(a0, A, states=N)
However, instead of observing this process directly, we observe whether Bob is
'walking', 'shopping' or 'cleaning'. The probability of each activity depends on
the current weather as follows:
>>> P = [[0.1, 0.4, 0.5],
... [0.6, 0.3, 0.1]]
where the first row contains activity probabilities on a rainy weather and the
second row contains activity probabilities on a sunny weather. Using these
emission probabilities, the observed process is constructed as:
>>> from bayespy.nodes import Categorical, Mixture
>>> Y = Mixture(Z, Categorical, P)
Data
++++
In order to test our method, we'll generate artificial data from the model
itself. First, draw realization of the weather process:
>>> weather = Z.random()
Then, using this weather, draw realizations of the activities:
>>> activity = Mixture(weather, Categorical, P).random()
Inference
+++++++++
Now, using this data, we set our variable :math:`Y` to be observed:
>>> Y.observe(activity)
In order to run inference, we construct variational Bayesian inference engine:
>>> from bayespy.inference import VB
>>> Q = VB(Y, Z)
Note that we need to give all random variables to ``VB``. In this case, the only
random variables were ``Y`` and ``Z``. Next we run the inference, that is,
compute our posterior distribution:
>>> Q.update()
Iteration 1: loglike=-1.095883e+02 (... seconds)
In this case, because there is only one unobserved random variable, we
recover the exact posterior distribution and there is no need to iterate
more than one step.
Results
+++++++
.. currentmodule:: bayespy.plot
One way to plot a 2-class categorical timeseries is to use the basic
:func:`plot` function:
>>> import bayespy.plot as bpplt
>>> bpplt.plot(Z)
>>> bpplt.plot(1-weather, color='r', marker='x')
.. plot::
import numpy
numpy.random.seed(1)
from bayespy.nodes import CategoricalMarkovChain
a0 = [0.6, 0.4] # p(rainy)=0.6, p(sunny)=0.4
A = [[0.7, 0.3], # p(rainy->rainy)=0.7, p(rainy->sunny)=0.3
[0.4, 0.6]] # p(sunny->rainy)=0.4, p(sunny->sunny)=0.6
N = 100
Z = CategoricalMarkovChain(a0, A, states=N)
from bayespy.nodes import Categorical, Mixture
P = [[0.1, 0.4, 0.5],
[0.6, 0.3, 0.1]]
Y = Mixture(Z, Categorical, P)
weather = Z.random()
activity = Mixture(weather, Categorical, P).random()
Y.observe(activity)
from bayespy.inference import VB
Q = VB(Y, Z)
Q.update()
import bayespy.plot as bpplt
bpplt.plot(Z)
bpplt.plot(1-weather, color='r', marker='x')
bpplt.pyplot.show()
The black line shows the posterior probability of rain and the red line and
crosses show the true state. Clearly, the method is not able to infer the
weather very accurately in this case because the activies do not give that much
information about the weather.
Unknown parameters
------------------
In this example, we consider unknown parameters for the Markov process and
different emission distribution.
Data
++++
We generate data from three 2-dimensional Gaussian distributions with different
mean vectors and common standard deviation:
>>> import numpy as np
>>> mu = np.array([ [0,0], [3,4], [6,0] ])
>>> std = 2.0
Thus, the number of clusters is three:
>>> K = 3
And the number of samples is 200:
>>> N = 200
Each initial state is equally probable:
>>> p0 = np.ones(K) / K
State transition matrix is such that with probability 0.9 the process stays in
the same state. The probability to move one of the other two states is 0.05 for
both of those states.
>>> q = 0.9
>>> r = (1-q) / (K-1)
>>> P = q*np.identity(K) + r*(np.ones((3,3))-np.identity(3))
Simulate the data:
>>> y = np.zeros((N,2))
>>> z = np.zeros(N)
>>> state = np.random.choice(K, p=p0)
>>> for n in range(N):
... z[n] = state
... y[n,:] = std*np.random.randn(2) + mu[state]
... state = np.random.choice(K, p=P[state])
Then, let us visualize the data:
>>> bpplt.pyplot.figure()
>>> bpplt.pyplot.axis('equal')
(...)
>>> colors = [ [[1,0,0], [0,1,0], [0,0,1]][int(state)] for state in z ]
>>> bpplt.pyplot.plot(y[:,0], y[:,1], 'k-', zorder=-10)
[]
>>> bpplt.pyplot.scatter(y[:,0], y[:,1], c=colors, s=40)
.. plot::
import numpy
numpy.random.seed(1)
from bayespy.nodes import CategoricalMarkovChain
a0 = [0.6, 0.4] # p(rainy)=0.6, p(sunny)=0.4
A = [[0.7, 0.3], # p(rainy->rainy)=0.7, p(rainy->sunny)=0.3
[0.4, 0.6]] # p(sunny->rainy)=0.4, p(sunny->sunny)=0.6
N = 100
Z = CategoricalMarkovChain(a0, A, states=N)
from bayespy.nodes import Categorical, Mixture
P = [[0.1, 0.4, 0.5],
[0.6, 0.3, 0.1]]
Y = Mixture(Z, Categorical, P)
weather = Z.random()
from bayespy.inference import VB
import bayespy.plot as bpplt
import numpy as np
mu = np.array([ [0,0], [3,4], [6,0] ])
std = 2.0
K = 3
N = 200
p0 = np.ones(K) / K
q = 0.9
r = (1-q)/(K-1)
P = q*np.identity(K) + r*(np.ones((3,3))-np.identity(3))
y = np.zeros((N,2))
z = np.zeros(N)
state = np.random.choice(K, p=p0)
for n in range(N):
z[n] = state
y[n,:] = std*np.random.randn(2) + mu[state]
state = np.random.choice(K, p=P[state])
bpplt.pyplot.figure()
bpplt.pyplot.axis('equal')
colors = [ [[1,0,0], [0,1,0], [0,0,1]][int(state)] for state in z ]
bpplt.pyplot.plot(y[:,0], y[:,1], 'k-', zorder=-10)
bpplt.pyplot.scatter(y[:,0], y[:,1], c=colors, s=40)
bpplt.pyplot.show()
Consecutive states are connected by a solid black line and the dot color shows
the true class.
Model
+++++
Now, assume that we do not know the parameters of the process (initial state
probability and state transition probabilities). We give these parameters quite
non-informative priors, but it is possible to provide more informative priors if
such information is available:
>>> from bayespy.nodes import Dirichlet
>>> a0 = Dirichlet(1e-3*np.ones(K))
>>> A = Dirichlet(1e-3*np.ones((K,K)))
The discrete Markov chain is constructed as:
>>> Z = CategoricalMarkovChain(a0, A, states=N)
Now, instead of using categorical emission distribution as before, we'll use
Gaussian distribution. For simplicity, we use the true parameters of the
Gaussian distributions instead of giving priors and estimating them. The known
standard deviation can be converted to a precision matrix as:
>>> Lambda = std**(-2) * np.identity(2)
Thus, the observed process is a Gaussian mixture with cluster assignments from
the hidden Markov process ``Z``:
>>> from bayespy.nodes import Gaussian
>>> Y = Mixture(Z, Gaussian, mu, Lambda)
Note that ``Lambda`` does not have cluster plate axis because it is shared
between the clusters.
Inference
+++++++++
Let us use the simulated data:
>>> Y.observe(y)
Because ``VB`` takes all the random variables, we need to provide ``A`` and
``a0`` also:
>>> Q = VB(Y, Z, A, a0)
Then, run VB iteration until convergence:
>>> Q.update(repeat=1000)
Iteration 1: loglike=-9.963054e+02 (... seconds)
...
Iteration 8: loglike=-9.235053e+02 (... seconds)
Converged at iteration 8.
Results
+++++++
Plot the classification of the data similarly as the data:
>>> bpplt.pyplot.figure()
>>> bpplt.pyplot.axis('equal')
(...)
>>> colors = Y.parents[0].get_moments()[0]
>>> bpplt.pyplot.plot(y[:,0], y[:,1], 'k-', zorder=-10)
[]
>>> bpplt.pyplot.scatter(y[:,0], y[:,1], c=colors, s=40)
.. plot::
import numpy
numpy.random.seed(1)
from bayespy.nodes import CategoricalMarkovChain
a0 = [0.6, 0.4] # p(rainy)=0.6, p(sunny)=0.4
A = [[0.7, 0.3], # p(rainy->rainy)=0.7, p(rainy->sunny)=0.3
[0.4, 0.6]] # p(sunny->rainy)=0.4, p(sunny->sunny)=0.6
N = 100
Z = CategoricalMarkovChain(a0, A, states=N)
from bayespy.nodes import Categorical, Mixture
P = [[0.1, 0.4, 0.5],
[0.6, 0.3, 0.1]]
Y = Mixture(Z, Categorical, P)
weather = Z.random()
from bayespy.inference import VB
import bayespy.plot as bpplt
import numpy as np
mu = np.array([ [0,0], [3,4], [6,0] ])
std = 2.0
K = 3
N = 200
p0 = np.ones(K) / K
q = 0.9
r = (1-q)/(K-1)
P = q*np.identity(K) + r*(np.ones((3,3))-np.identity(3))
y = np.zeros((N,2))
z = np.zeros(N)
state = np.random.choice(K, p=p0)
for n in range(N):
z[n] = state
y[n,:] = std*np.random.randn(2) + mu[state]
state = np.random.choice(K, p=P[state])
from bayespy.nodes import Dirichlet
a0 = Dirichlet(1e-3*np.ones(K))
A = Dirichlet(1e-3*np.ones((K,K)))
Z = CategoricalMarkovChain(a0, A, states=N)
Lambda = std**(-2) * np.identity(2)
from bayespy.nodes import Gaussian
Y = Mixture(Z, Gaussian, mu, Lambda)
Y.observe(y)
Q = VB(Y, Z, A, a0)
Q.update(repeat=1000)
bpplt.pyplot.figure()
bpplt.pyplot.axis('equal')
colors = Y.parents[0].get_moments()[0]
bpplt.pyplot.plot(y[:,0], y[:,1], 'k-', zorder=-10)
bpplt.pyplot.scatter(y[:,0], y[:,1], c=colors, s=40)
bpplt.pyplot.show()
The data has been classified quite correctly. Even samples that are more in the
region of another cluster are classified correctly if the previous and next
sample provide enough evidence for the correct class. We can also plot the
state transition matrix:
>>> bpplt.hinton(A)
.. plot::
import numpy
numpy.random.seed(1)
from bayespy.nodes import CategoricalMarkovChain
a0 = [0.6, 0.4] # p(rainy)=0.6, p(sunny)=0.4
A = [[0.7, 0.3], # p(rainy->rainy)=0.7, p(rainy->sunny)=0.3
[0.4, 0.6]] # p(sunny->rainy)=0.4, p(sunny->sunny)=0.6
N = 100
Z = CategoricalMarkovChain(a0, A, states=N)
from bayespy.nodes import Categorical, Mixture
P = [[0.1, 0.4, 0.5],
[0.6, 0.3, 0.1]]
Y = Mixture(Z, Categorical, P)
weather = Z.random()
from bayespy.inference import VB
import bayespy.plot as bpplt
import numpy as np
mu = np.array([ [0,0], [3,4], [6,0] ])
std = 2.0
K = 3
N = 200
p0 = np.ones(K) / K
q = 0.9
r = (1-q)/(K-1)
P = q*np.identity(K) + r*(np.ones((3,3))-np.identity(3))
y = np.zeros((N,2))
z = np.zeros(N)
state = np.random.choice(K, p=p0)
for n in range(N):
z[n] = state
y[n,:] = std*np.random.randn(2) + mu[state]
state = np.random.choice(K, p=P[state])
from bayespy.nodes import Dirichlet
a0 = Dirichlet(1e-3*np.ones(K))
A = Dirichlet(1e-3*np.ones((K,K)))
Z = CategoricalMarkovChain(a0, A, states=N)
Lambda = std**(-2) * np.identity(2)
from bayespy.nodes import Gaussian
Y = Mixture(Z, Gaussian, mu, Lambda)
Y.observe(y)
Q = VB(Y, Z, A, a0)
Q.update(repeat=1000)
bpplt.hinton(A)
bpplt.pyplot.show()
Clearly, the learned state transition matrix is close to the true matrix. The
models described above could also be used for classification by providing the
known class assignments as observed data to ``Z`` and the unknown class
assignments as missing data.
bayespy-0.2.3/doc/source/examples/lda.rst 0000664 5104112 5104112 00000000113 12374370351 022067 0 ustar jluttine jluttine 0000000 0000000
Latent Dirichlet allocation
===========================
blaa blaa blaa..
bayespy-0.2.3/doc/source/examples/lssm.rst 0000664 5104112 5104112 00000034516 12430651206 022315 0 ustar jluttine jluttine 0000000 0000000 ..
Copyright (C) 2014 Jaakko Luttinen
This file is licensed under Version 3.0 of the GNU General Public
License. See LICENSE for a text of the license.
This file is part of BayesPy.
BayesPy is free software: you can redistribute it and/or modify it
under the terms of the GNU General Public License version 3 as
published by the Free Software Foundation.
BayesPy is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
General Public License for more details.
You should have received a copy of the GNU General Public License
along with BayesPy. If not, see .
.. testsetup::
import numpy as np
np.random.seed(1)
Linear state-space model
========================
Model
-----
.. currentmodule:: bayespy.nodes
In linear state-space models a sequence of :math:`M`-dimensional observations
:math:`\mathbf{Y}=(\mathbf{y}_1,\ldots,\mathbf{y}_N)` is assumed to be generated
from latent :math:`D`-dimensional states
:math:`\mathbf{X}=(\mathbf{x}_1,\ldots,\mathbf{x}_N)` which follow a first-order
Markov process:
.. math::
\begin{aligned}
\mathbf{x}_{n} &= \mathbf{A}\mathbf{x}_{n-1} + \text{noise} \,,
\\
\mathbf{y}_{n} &= \mathbf{C}\mathbf{x}_{n} + \text{noise} \,,
\end{aligned}
where the noise is Gaussian, :math:`\mathbf{A}` is the :math:`D\times D` state
dynamics matrix and :math:`\mathbf{C}` is the :math:`M\times D` loading
matrix. Usually, the latent space dimensionality :math:`D` is assumed to be much
smaller than the observation space dimensionality :math:`M` in order to model
the dependencies of high-dimensional observations efficiently.
In order to construct the model in BayesPy, first import relevant nodes:
>>> from bayespy.nodes import GaussianARD, GaussianMarkovChain, Gamma, Dot
The data vectors will be 30-dimensional:
>>> M = 30
There will be 400 data vectors:
>>> N = 400
Let us use 10-dimensional latent space:
>>> D = 10
The state dynamics matrix :math:`\mathbf{A}` has ARD prior:
>>> alpha = Gamma(1e-5,
... 1e-5,
... plates=(D,),
... name='alpha')
>>> A = GaussianARD(0,
... alpha,
... shape=(D,),
... plates=(D,),
... name='A')
Note that :math:`\mathbf{A}` is a :math:`D\times{}D`-dimensional matrix.
However, in BayesPy it is modelled as a collection (``plates=(D,)``) of
:math:`D`-dimensional vectors (``shape=(D,)``) because this is how the variables
factorize in the posterior approximation of the state dynamics matrix in
:class:`GaussianMarkovChain`. The latent states are constructed as
>>> X = GaussianMarkovChain(np.zeros(D),
... 1e-3*np.identity(D),
... A,
... np.ones(D),
... n=N,
... name='X')
where the first two arguments are the mean and precision matrix of the initial
state, the third argument is the state dynamics matrix and the fourth argument
is the diagonal elements of the precision matrix of the innovation noise. The
node also needs the length of the chain given as the keyword argument ``n=N``.
Thus, the shape of this node is ``(N,D)``.
The linear mapping from the latent space to the observation space is modelled
with the loading matrix which has ARD prior:
>>> gamma = Gamma(1e-5,
... 1e-5,
... plates=(D,),
... name='gamma')
>>> C = GaussianARD(0,
... gamma,
... shape=(D,),
... plates=(M,1),
... name='C')
Note that the plates for ``C`` are ``(M,1)``, thus the full shape of the node is
``(M,1,D)``. The unit plate axis is added so that ``C`` broadcasts with ``X``
when computing the dot product:
>>> F = Dot(C,
... X,
... name='F')
This dot product is computed over the :math:`D`-dimensional latent space, thus
the result is a :math:`M\times{}N`-dimensional matrix which is now represented
with plates ``(M,N)`` in BayesPy:
>>> F.plates
(30, 400)
We also need to use random initialization either for ``C`` or ``X`` in order to
find non-zero latent space because by default both ``C`` and ``X`` are
initialized to zero because of their prior distributions. We use random
initialization for ``C`` and then we must update ``X`` the first time before
updating ``C``:
>>> C.initialize_from_random()
The precision of the observation noise is given gamma prior:
>>> tau = Gamma(1e-5,
... 1e-5,
... name='tau')
The observations are noisy versions of the dot products:
>>> Y = GaussianARD(F,
... tau,
... name='Y')
The variational Bayesian inference engine is then construced as:
>>> from bayespy.inference import VB
>>> Q = VB(X, C, gamma, A, alpha, tau, Y)
Note that ``X`` is given before ``C``, thus ``X`` is updated before ``C`` by
default.
Data
----
Now, let us generate some toy data for our model. Our true latent space is four
dimensional with two noisy oscillator components, one random walk component and
one white noise component.
>>> w = 0.3
>>> a = np.array([[np.cos(w), -np.sin(w), 0, 0],
... [np.sin(w), np.cos(w), 0, 0],
... [0, 0, 1, 0],
... [0, 0, 0, 0]])
The true linear mapping is just random:
>>> c = np.random.randn(M,4)
Then, generate the latent states and the observations using the model equations:
>>> x = np.empty((N,4))
>>> f = np.empty((M,N))
>>> y = np.empty((M,N))
>>> x[0] = 10*np.random.randn(4)
>>> f[:,0] = np.dot(c,x[0])
>>> y[:,0] = f[:,0] + 3*np.random.randn(M)
>>> for n in range(N-1):
... x[n+1] = np.dot(a,x[n]) + [1,1,10,10]*np.random.randn(4)
... f[:,n+1] = np.dot(c,x[n+1])
... y[:,n+1] = f[:,n+1] + 3*np.random.randn(M)
We want to simulate missing values, thus we create a mask which randomly removes
80% of the data:
>>> from bayespy.utils import random
>>> mask = random.mask(M, N, p=0.2)
>>> Y.observe(y, mask=mask)
Inference
---------
As we did not define plotters for our nodes when creating the model, it is done
now for some of the nodes:
>>> import bayespy.plot as bpplt
>>> X.set_plotter(bpplt.FunctionPlotter(center=True, axis=-2))
>>> A.set_plotter(bpplt.HintonPlotter())
>>> C.set_plotter(bpplt.HintonPlotter())
>>> tau.set_plotter(bpplt.PDFPlotter(np.linspace(0.02, 0.5, num=1000)))
This enables plotting of the approximate posterior distributions during VB
learning. The inference engine can be run using :func:`VB.update` method:
>>> Q.update(repeat=10)
Iteration 1: loglike=-1.439704e+05 (... seconds)
...
Iteration 10: loglike=-1.051441e+04 (... seconds)
The iteration progresses a bit slowly, thus we'll consider parameter expansion
to speed it up.
Parameter expansion
+++++++++++++++++++
Section :ref:`sec-parameter-expansion` discusses parameter expansion for
state-space models to speed up inference. It is based on a rotating the latent
space such that the posterior in the observation space is not affected:
.. math::
\mathbf{y}_n = \mathbf{C}\mathbf{x}_n =
(\mathbf{C}\mathbf{R}^{-1}) (\mathbf{R}\mathbf{x}_n) \,.
Thus, the transformation is
:math:`\mathbf{C}\rightarrow\mathbf{C}\mathbf{R}^{-1}` and
:math:`\mathbf{X}\rightarrow\mathbf{R}\mathbf{X}`. In order to keep the
dynamics of the latent states unaffected by the transformation, the state
dynamics matrix :math:`\mathbf{A}` must be transformed accordingly:
.. math::
\mathbf{R}\mathbf{x}_n = \mathbf{R}\mathbf{A}\mathbf{R}^{-1}
\mathbf{R}\mathbf{x}_{n-1} \,,
resulting in a transformation
:math:`\mathbf{A}\rightarrow\mathbf{R}\mathbf{A}\mathbf{R}^{-1}`. For more
details, refer to :cite:`Luttinen:2013` and :cite:`Luttinen:2010`. In BayesPy,
the transformations are available in
:mod:`bayespy.inference.vmp.transformations`:
>>> from bayespy.inference.vmp import transformations
The rotation of the loading matrix along with the ARD parameters is defined as:
>>> rotC = transformations.RotateGaussianARD(C, gamma)
For rotating ``X``, we first need to define the rotation of the state dynamics
matrix:
>>> rotA = transformations.RotateGaussianARD(A, alpha)
Now we can define the rotation of the latent states:
>>> rotX = transformations.RotateGaussianMarkovChain(X, rotA)
The optimal rotation for all these variables is found using rotation optimizer:
>>> R = transformations.RotationOptimizer(rotX, rotC, D)
Set the parameter expansion to be applied after each iteration:
>>> Q.callback = R.rotate
Now, run iterations until convergence:
>>> Q.update(repeat=1000)
Iteration 11: loglike=-1.010806e+04 (... seconds)
...
Iteration 60: loglike=-8.906259e+03 (... seconds)
Converged at iteration 60.
Results
-------
Because we have set the plotters, we can plot those nodes as:
>>> Q.plot(X, A, C, tau)
.. plot::
import numpy as np
np.random.seed(1)
from bayespy.nodes import GaussianARD, GaussianMarkovChain, Gamma, Dot
M = 30
N = 400
D = 10
alpha = Gamma(1e-5,
1e-5,
plates=(D,),
name='alpha')
A = GaussianARD(0,
alpha,
shape=(D,),
plates=(D,),
name='A')
X = GaussianMarkovChain(np.zeros(D),
1e-3*np.identity(D),
A,
np.ones(D),
n=N,
name='X')
gamma = Gamma(1e-5,
1e-5,
plates=(D,),
name='gamma')
C = GaussianARD(0,
gamma,
shape=(D,),
plates=(M,1),
name='C')
F = Dot(C,
X,
name='F')
C.initialize_from_random()
tau = Gamma(1e-5,
1e-5,
name='tau')
Y = GaussianARD(F,
tau,
name='Y')
from bayespy.inference import VB
Q = VB(X, C, gamma, A, alpha, tau, Y)
w = 0.3
a = np.array([[np.cos(w), -np.sin(w), 0, 0],
[np.sin(w), np.cos(w), 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 0]])
c = np.random.randn(M,4)
x = np.empty((N,4))
f = np.empty((M,N))
y = np.empty((M,N))
x[0] = 10*np.random.randn(4)
f[:,0] = np.dot(c,x[0])
y[:,0] = f[:,0] + 3*np.random.randn(M)
for n in range(N-1):
x[n+1] = np.dot(a,x[n]) + [1,1,10,10]*np.random.randn(4)
f[:,n+1] = np.dot(c,x[n+1])
y[:,n+1] = f[:,n+1] + 3*np.random.randn(M)
from bayespy.utils import random
mask = random.mask(M, N, p=0.2)
Y.observe(y, mask=mask)
import bayespy.plot as bpplt
X.set_plotter(bpplt.FunctionPlotter(center=True, axis=-2))
A.set_plotter(bpplt.HintonPlotter())
C.set_plotter(bpplt.HintonPlotter())
tau.set_plotter(bpplt.PDFPlotter(np.linspace(0.02, 0.5, num=1000)))
Q.update(repeat=10)
from bayespy.inference.vmp import transformations
rotC = transformations.RotateGaussianARD(C, gamma)
rotA = transformations.RotateGaussianARD(A, alpha)
rotX = transformations.RotateGaussianMarkovChain(X, rotA)
R = transformations.RotationOptimizer(rotX, rotC, D)
Q.callback = R.rotate
Q.update(repeat=1000)
Q.plot(X, A, C, tau)
bpplt.pyplot.show()
There are clearly four effective components in ``X``: random walk (component
number 1), random oscillation (7 and 10), and white noise (9). These dynamics
are also visible in the state dynamics matrix Hinton diagram. Note that the
white noise component does not have any dynamics. Also ``C`` shows only four
effective components. The posterior of ``tau`` captures the true value
:math:`3^{-2}\approx0.111` accurately. We can also plot predictions in the
observation space:
>>> bpplt.plot(F, center=True)
.. plot::
import numpy as np
np.random.seed(1)
from bayespy.nodes import GaussianARD, GaussianMarkovChain, Gamma, Dot
M = 30
N = 400
D = 10
alpha = Gamma(1e-5,
1e-5,
plates=(D,),
name='alpha')
A = GaussianARD(0,
alpha,
shape=(D,),
plates=(D,),
name='A')
X = GaussianMarkovChain(np.zeros(D),
1e-3*np.identity(D),
A,
np.ones(D),
n=N,
name='X')
gamma = Gamma(1e-5,
1e-5,
plates=(D,),
name='gamma')
C = GaussianARD(0,
gamma,
shape=(D,),
plates=(M,1),
name='C')
F = Dot(C,
X,
name='F')
C.initialize_from_random()
tau = Gamma(1e-5,
1e-5,
name='tau')
Y = GaussianARD(F,
tau,
name='Y')
from bayespy.inference import VB
Q = VB(X, C, gamma, A, alpha, tau, Y)
w = 0.3
a = np.array([[np.cos(w), -np.sin(w), 0, 0],
[np.sin(w), np.cos(w), 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 0]])
c = np.random.randn(M,4)
x = np.empty((N,4))
f = np.empty((M,N))
y = np.empty((M,N))
x[0] = 10*np.random.randn(4)
f[:,0] = np.dot(c,x[0])
y[:,0] = f[:,0] + 3*np.random.randn(M)
for n in range(N-1):
x[n+1] = np.dot(a,x[n]) + [1,1,10,10]*np.random.randn(4)
f[:,n+1] = np.dot(c,x[n+1])
y[:,n+1] = f[:,n+1] + 3*np.random.randn(M)
from bayespy.utils import random
mask = random.mask(M, N, p=0.2)
Y.observe(y, mask=mask)
import bayespy.plot as bpplt
Q.update(repeat=10)
from bayespy.inference.vmp import transformations
rotC = transformations.RotateGaussianARD(C, gamma)
rotA = transformations.RotateGaussianARD(A, alpha)
rotX = transformations.RotateGaussianMarkovChain(X, rotA)
R = transformations.RotationOptimizer(rotX, rotC, D)
Q.callback = R.rotate
Q.update(repeat=1000)
bpplt.plot(F, center=True)
bpplt.pyplot.show()
We can also measure the performance numerically by computing root-mean-square
error (RMSE) of the missing values:
>>> from bayespy.utils import misc
>>> misc.rmse(y[~mask], F.get_moments()[0][~mask])
5.182...
This is relatively close to the standard deviation of the noise (3), so the
predictions are quite good considering that only 20% of the data was used.
bayespy-0.2.3/doc/source/examples/pca.rst 0000664 5104112 5104112 00000015073 12430651206 022077 0 ustar jluttine jluttine 0000000 0000000 ..
Copyright (C) 2014 Jaakko Luttinen
This file is licensed under Version 3.0 of the GNU General Public
License. See LICENSE for a text of the license.
This file is part of BayesPy.
BayesPy is free software: you can redistribute it and/or modify it
under the terms of the GNU General Public License version 3 as
published by the Free Software Foundation.
BayesPy is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
General Public License for more details.
You should have received a copy of the GNU General Public License
along with BayesPy. If not, see .
.. testsetup::
import numpy
numpy.random.seed(1)
Principal component analysis
============================
This example uses a simple principal component analysis to find a
two-dimensional latent subspace in a higher dimensional dataset.
Data
----
Let us create a Gaussian dataset with latent space dimensionality two and some
observation noise:
>>> M = 20
>>> N = 100
>>> import numpy as np
>>> x = np.random.randn(N, 2)
>>> w = np.random.randn(M, 2)
>>> f = np.einsum('ik,jk->ij', w, x)
>>> y = f + 0.1*np.random.randn(M, N)
Model
-----
We will use 10-dimensional latent space in our model and let it learn the true
dimensionality:
>>> D = 10
Import relevant nodes:
>>> from bayespy.nodes import GaussianARD, Gamma, SumMultiply
The latent states:
>>> X = GaussianARD(0, 1, plates=(1,N), shape=(D,))
The loading matrix with automatic relevance determination (ARD) prior:
>>> alpha = Gamma(1e-5, 1e-5, plates=(D,))
>>> C = GaussianARD(0, alpha, plates=(M,1), shape=(D,))
Compute the dot product of the latent states and the loading matrix:
>>> F = SumMultiply('d,d->', X, C)
The observation noise:
>>> tau = Gamma(1e-5, 1e-5)
The observed variable:
>>> Y = GaussianARD(F, tau)
Inference
---------
Observe the data:
>>> Y.observe(y)
We do not have missing data now, but they could be easily handled with ``mask``
keyword argument. Construct variational Bayesian (VB) inference engine:
>>> from bayespy.inference import VB
>>> Q = VB(Y, X, C, alpha, tau)
Initialize the latent subspace randomly, otherwise both ``X`` and ``C`` would
converge to zero:
>>> C.initialize_from_random()
.. currentmodule:: bayespy.inference
Now we could use :func:`VB.update` to run the inference. However, let us first
create a parameter expansion to speed up the inference. The expansion is based
on rotating the latent subspace optimally. This is optional but will usually
improve the speed of the inference significantly, especially in high-dimensional
problems:
>>> from bayespy.inference.vmp.transformations import RotateGaussianARD
>>> rot_X = RotateGaussianARD(X)
>>> rot_C = RotateGaussianARD(C, alpha)
By giving ``alpha`` for ``rot_C``, the rotation will also optimize ``alpha``
jointly with ``C``. Now that we have defined the rotations for our variables,
we need to construct an optimizer:
>>> from bayespy.inference.vmp.transformations import RotationOptimizer
>>> R = RotationOptimizer(rot_X, rot_C, D)
In order to use the rotations automatically, we need to set it as a callback
function:
>>> Q.set_callback(R.rotate)
For more information about the rotation parameter expansion, see
:cite:`Luttinen:2010` and :cite:`Luttinen:2013`. Now we can run the actual
inference until convergence:
>>> Q.update(repeat=1000)
Iteration 1: loglike=-2.339710e+03 (... seconds)
...
Iteration 22: loglike=6.500773e+02 (... seconds)
Converged at iteration 22.
Results
-------
The results can be visualized, for instance, by plotting the Hinton diagram of
the loading matrix:
>>> import bayespy.plot as bpplt
>>> bpplt.pyplot.figure()
>>> bpplt.hinton(C)
.. plot::
import numpy
numpy.random.seed(1)
M = 20
N = 100
import numpy as np
x = np.random.randn(N, 2)
w = np.random.randn(M, 2)
f = np.einsum('ik,jk->ij', w, x)
y = f + 0.1*np.random.randn(M, N)
D = 10
from bayespy.nodes import GaussianARD, Gamma, SumMultiply
X = GaussianARD(0, 1, plates=(1,N), shape=(D,))
alpha = Gamma(1e-5, 1e-5, plates=(D,))
C = GaussianARD(0, alpha, plates=(M,1), shape=(D,))
F = SumMultiply('d,d->', X, C)
tau = Gamma(1e-5, 1e-5)
Y = GaussianARD(F, tau)
Y.observe(y)
from bayespy.inference import VB
Q = VB(Y, X, C, alpha, tau)
C.initialize_from_random()
from bayespy.inference.vmp.transformations import RotateGaussianARD
rot_X = RotateGaussianARD(X)
rot_C = RotateGaussianARD(C, alpha)
from bayespy.inference.vmp.transformations import RotationOptimizer
R = RotationOptimizer(rot_X, rot_C, D)
Q.set_callback(R.rotate)
Q.update(repeat=1000)
import bayespy.plot as bpplt
bpplt.hinton(C)
The method has been able to prune out unnecessary latent dimensions and keep two
components, which is the true number of components.
>>> bpplt.pyplot.figure()
>>> bpplt.plot(F)
>>> bpplt.plot(f, color='r', marker='x', linestyle='None')
.. plot::
import numpy
numpy.random.seed(1)
M = 20
N = 100
import numpy as np
x = np.random.randn(N, 2)
w = np.random.randn(M, 2)
f = np.einsum('ik,jk->ij', w, x)
y = f + 0.1*np.random.randn(M, N)
D = 10
from bayespy.nodes import GaussianARD, Gamma, SumMultiply
X = GaussianARD(0, 1, plates=(1,N), shape=(D,))
alpha = Gamma(1e-5, 1e-5, plates=(D,))
C = GaussianARD(0, alpha, plates=(M,1), shape=(D,))
F = SumMultiply('d,d->', X, C)
tau = Gamma(1e-5, 1e-5)
Y = GaussianARD(F, tau)
Y.observe(y)
from bayespy.inference import VB
Q = VB(Y, X, C, alpha, tau)
C.initialize_from_random()
from bayespy.inference.vmp.transformations import RotateGaussianARD
rot_X = RotateGaussianARD(X)
rot_C = RotateGaussianARD(C, alpha)
from bayespy.inference.vmp.transformations import RotationOptimizer
R = RotationOptimizer(rot_X, rot_C, D)
Q.set_callback(R.rotate)
Q.update(repeat=1000)
import bayespy.plot as bpplt
bpplt.plot(F)
bpplt.plot(f, color='r', marker='x', linestyle='None')
.. currentmodule:: bayespy.nodes
The reconstruction of the noiseless function values are practically perfect in
this simple example. Larger noise variance, more latent space dimensions and
missing values would make this problem more difficult. The model construction
could also be improved by having, for instance, ``C`` and ``tau`` in the same
node without factorizing between them in the posterior approximation. This can
be achieved by using :class:`GaussianGammaISO` node.
bayespy-0.2.3/doc/source/examples/regression.rst 0000664 5104112 5104112 00000021377 12374370351 023526 0 ustar jluttine jluttine 0000000 0000000 ..
Copyright (C) 2014 Jaakko Luttinen
This file is licensed under Version 3.0 of the GNU General Public
License. See LICENSE for a text of the license.
This file is part of BayesPy.
BayesPy is free software: you can redistribute it and/or modify it
under the terms of the GNU General Public License version 3 as
published by the Free Software Foundation.
BayesPy is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
General Public License for more details.
You should have received a copy of the GNU General Public License
along with BayesPy. If not, see .
.. testsetup::
import numpy
numpy.random.seed(1)
Linear regression
=================
Data
----
The true parameters of the linear regression:
>>> k = 2 # slope
>>> c = 5 # bias
>>> s = 2 # noise standard deviation
Generate data:
>>> import numpy as np
>>> x = np.arange(10)
>>> y = k*x + c + s*np.random.randn(10)
Model
-----
The regressors, that is, the input data:
>>> X = np.vstack([x, np.ones(len(x))]).T
Note that we added a column of ones to the regressor matrix for the bias term.
We model the slope and the bias term in the same node so we do not factorize
between them:
>>> from bayespy.nodes import GaussianARD
>>> B = GaussianARD(0, 1e-6, shape=(2,))
The first element is the slope which multiplies ``x`` and the second element is
the bias term which multiplies the constant ones. Now we compute the dot
product of ``X`` and ``B``:
>>> from bayespy.nodes import SumMultiply
>>> F = SumMultiply('i,i', B, X)
The noise parameter:
>>> from bayespy.nodes import Gamma
>>> tau = Gamma(1e-3, 1e-3)
The noisy observations:
>>> Y = GaussianARD(F, tau)
Inference
---------
Observe the data:
>>> Y.observe(y)
Construct the variational Bayesian (VB) inference engine by giving all
stochastic nodes:
>>> from bayespy.inference import VB
>>> Q = VB(Y, B, tau)
Iterate until convergence:
>>> Q.update(repeat=1000)
Iteration 1: loglike=-4.595948e+01 (... seconds)
...
Iteration 5: loglike=-4.495017e+01 (... seconds)
Converged at iteration 5.
Results
-------
Create a simple predictive model for new inputs:
>>> xh = np.linspace(-5, 15, 100)
>>> Xh = np.vstack([xh, np.ones(len(xh))]).T
>>> Fh = SumMultiply('i,i', B, Xh)
Note that we use the learned node ``B`` but create a new regressor array for
predictions. Plot the predictive distribution of noiseless function values:
>>> import bayespy.plot as bpplt
>>> bpplt.pyplot.figure()
>>> bpplt.plot(Fh, x=xh, scale=2)
>>> bpplt.plot(y, x=x, color='r', marker='x', linestyle='None')
>>> bpplt.plot(k*xh+c, x=xh, color='r');
.. plot::
import numpy
numpy.random.seed(1)
k = 2 # slope
c = 5 # bias
s = 2 # noise standard deviation
import numpy as np
x = np.arange(10)
y = k*x + c + s*np.random.randn(10)
X = np.vstack([x, np.ones(len(x))]).T
from bayespy.nodes import GaussianARD
B = GaussianARD(0, 1e-6, shape=(2,))
from bayespy.nodes import SumMultiply
F = SumMultiply('i,i', B, X)
from bayespy.nodes import Gamma
tau = Gamma(1e-3, 1e-3)
Y = GaussianARD(F, tau)
Y.observe(y)
from bayespy.inference import VB
Q = VB(Y, B, tau)
Q.update(repeat=1000)
xh = np.linspace(-5, 15, 100)
Xh = np.vstack([xh, np.ones(len(xh))]).T
Fh = SumMultiply('i,i', B, Xh)
import bayespy.plot as bpplt
bpplt.plot(Fh, x=xh, scale=2)
bpplt.plot(y, x=x, color='r', marker='x', linestyle='None')
bpplt.plot(k*xh+c, x=xh, color='r');
bpplt.pyplot.show()
Note that the above plot shows two standard deviation of the posterior of the
noiseless function, thus the data points may lie well outside this range. The
red line shows the true linear function. Next, plot the distribution of the
noise parameter and the true value, :math:`2^{-2}=0.25`:
>>> bpplt.pyplot.figure()
>>> bpplt.pdf(tau, np.linspace(1e-6,1,100), color='k')
[]
>>> bpplt.pyplot.axvline(s**(-2), color='r')
.. plot::
import numpy
numpy.random.seed(1)
k = 2 # slope
c = 5 # bias
s = 2 # noise standard deviation
import numpy as np
x = np.arange(10)
y = k*x + c + s*np.random.randn(10)
X = np.vstack([x, np.ones(len(x))]).T
from bayespy.nodes import GaussianARD
B = GaussianARD(0, 1e-6, shape=(2,))
from bayespy.nodes import SumMultiply
F = SumMultiply('i,i', B, X)
from bayespy.nodes import Gamma
tau = Gamma(1e-3, 1e-3)
Y = GaussianARD(F, tau)
Y.observe(y)
from bayespy.inference import VB
Q = VB(Y, B, tau)
Q.update(repeat=1000)
xh = np.linspace(-5, 15, 100)
Xh = np.vstack([xh, np.ones(len(xh))]).T
Fh = SumMultiply('i,i', B, Xh)
import bayespy.plot as bpplt
bpplt.pdf(tau, np.linspace(1e-6,1,100), color='k')
bpplt.pyplot.axvline(s**(-2), color='r')
bpplt.pyplot.show()
The noise level is captured quite well, although the posterior has more mass on
larger noise levels (smaller precision parameter values). Finally, plot the
distribution of the regression parameters and mark the true value:
>>> bpplt.pyplot.figure()
>>> bpplt.contour(B, np.linspace(1,3,1000), np.linspace(1,9,1000),
... n=10, colors='k')
>>> bpplt.plot(c, x=k, color='r', marker='x', linestyle='None',
... markersize=10, markeredgewidth=2)
>>> bpplt.pyplot.xlabel(r'$k$')
>>> bpplt.pyplot.ylabel(r'$c$');
.. plot::
import numpy
numpy.random.seed(1)
k = 2 # slope
c = 5 # bias
s = 2 # noise standard deviation
import numpy as np
x = np.arange(10)
y = k*x + c + s*np.random.randn(10)
X = np.vstack([x, np.ones(len(x))]).T
from bayespy.nodes import GaussianARD
B = GaussianARD(0, 1e-6, shape=(2,))
from bayespy.nodes import SumMultiply
F = SumMultiply('i,i', B, X)
from bayespy.nodes import Gamma
tau = Gamma(1e-3, 1e-3)
Y = GaussianARD(F, tau)
Y.observe(y)
from bayespy.inference import VB
Q = VB(Y, B, tau)
Q.update(repeat=1000)
xh = np.linspace(-5, 15, 100)
Xh = np.vstack([xh, np.ones(len(xh))]).T
Fh = SumMultiply('i,i', B, Xh)
import bayespy.plot as bpplt
bpplt.contour(B, np.linspace(1,3,1000), np.linspace(1,9,1000),
n=10, colors='k')
bpplt.plot(c, x=k, color='r', marker='x', linestyle='None',
markersize=10, markeredgewidth=2)
bpplt.pyplot.xlabel(r'$k$')
bpplt.pyplot.ylabel(r'$c$');
bpplt.pyplot.show()
In this case, the true parameters are captured well by the posterior
distribution.
Improving accuracy
------------------
.. currentmodule:: bayespy.nodes
The model can be improved by not factorizing between ``B`` and ``tau`` but
learning their joint posterior distribution. This requires a slight
modification to the model by using :class:`GaussianGammaISO` node:
>>> from bayespy.nodes import GaussianGammaISO
>>> B_tau = GaussianGammaISO(np.zeros(2), 1e-6*np.identity(2), 1e-3, 1e-3)
This node contains both the regression parameter vector and the noise parameter.
We compute the dot product similarly as before:
>>> F_tau = SumMultiply('i,i', B_tau, X)
However, ``Y`` is constructed as follows:
>>> Y = GaussianARD(F_tau, 1)
Because the noise parameter is already in ``F_tau`` we can give a constant one
as the second argument. The total noise parameter for ``Y`` is the product of
the noise parameter in ``F_tau`` and one. Now, inference is run similarly as
before:
>>> Y.observe(y)
>>> Q = VB(Y, B_tau)
>>> Q.update(repeat=1000)
Iteration 1: loglike=-4.678478e+01 (... seconds)
Iteration 2: loglike=-4.678478e+01 (... seconds)
Converged at iteration 2.
Note that the method converges immediately. This happens because there is only
one unobserved stochastic node so there is no need for iteration and the result
is actually the exact true posterior distribution, not an approximation.
Currently, the main drawback of using this approach is that BayesPy does not yet
contain any plotting utilities for nodes that contain both Gaussian and gamma
variables jointly.
Further extensions
------------------
The approach discussed in this example can easily be extended to non-linear
regression and multivariate regression. For non-linear regression, the inputs
are first transformed by some known non-linear functions and then linear
regression is applied to this transformed data. For multivariate regression,
``X`` and ``B`` are concatenated appropriately: If there are more regressors,
add more columns to both ``X`` and ``B``. If there are more output dimensions,
add plates to ``B``.
bayespy-0.2.3/doc/source/user_api/ 0000775 5104112 5104112 00000000000 12437607671 020603 5 ustar jluttine jluttine 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/ 0000775 5104112 5104112 00000000000 12437607671 022541 5 ustar jluttine jluttine 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/ 0000775 5104112 5104112 00000000000 12437607671 024477 5 ustar jluttine jluttine 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/ 0000775 5104112 5104112 00000000000 12437607672 026436 5 ustar jluttine jluttine 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.inference.VB.__init__.rst 0000664 5104112 5104112 00000000176 12356463436 034647 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.VB.__init__
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.. currentmodule:: bayespy.nodes
.. automethod:: Bernoulli.add_plate_axis ././@LongLink 0000000 0000000 0000000 00000000202 00000000000 011207 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.inference.vmp.transformations.RotateMultiple.__init__.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.inference.vmp.transformation0000664 5104112 5104112 00000000336 12356463437 035641 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.transformations.RotateMultiple.__init__
=============================================================
.. currentmodule:: bayespy.inference.vmp.transformations
.. automethod:: RotateMultiple.__init__ ././@LongLink 0000000 0000000 0000000 00000000177 00000000000 011222 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.inference.vmp.transformations.RotateMultiple.bound.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.inference.vmp.transformation0000664 5104112 5104112 00000000325 12356463437 035637 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.transformations.RotateMultiple.bound
==========================================================
.. currentmodule:: bayespy.inference.vmp.transformations
.. automethod:: RotateMultiple.bound ././@LongLink 0000000 0000000 0000000 00000000211 00000000000 011207 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.inference.vmp.transformations.RotateMultiple.get_bound_terms.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.inference.vmp.transformation0000664 5104112 5104112 00000000363 12356463437 035641 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.transformations.RotateMultiple.get_bound_terms
====================================================================
.. currentmodule:: bayespy.inference.vmp.transformations
.. automethod:: RotateMultiple.get_bound_terms ././@LongLink 0000000 0000000 0000000 00000000177 00000000000 011222 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.inference.vmp.transformations.RotateMultiple.nodes.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.inference.vmp.transformation0000664 5104112 5104112 00000000325 12356463437 035637 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.transformations.RotateMultiple.nodes
==========================================================
.. currentmodule:: bayespy.inference.vmp.transformations
.. automethod:: RotateMultiple.nodes ././@LongLink 0000000 0000000 0000000 00000000200 00000000000 011205 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.inference.vmp.transformations.RotateMultiple.rotate.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.inference.vmp.transformation0000664 5104112 5104112 00000000330 12356463437 035633 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.transformations.RotateMultiple.rotate
===========================================================
.. currentmodule:: bayespy.inference.vmp.transformations
.. automethod:: RotateMultiple.rotate ././@LongLink 0000000 0000000 0000000 00000000177 00000000000 011222 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.inference.vmp.transformations.RotateMultiple.setup.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.inference.vmp.transformation0000664 5104112 5104112 00000000325 12356463437 035637 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.transformations.RotateMultiple.setup
==========================================================
.. currentmodule:: bayespy.inference.vmp.transformations
.. automethod:: RotateMultiple.setup ././@LongLink 0000000 0000000 0000000 00000000216 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.inference.vmp.transformations.RotateSwitchingMarkovChain.__init__.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.inference.vmp.transformation0000664 5104112 5104112 00000000402 12356463437 035633 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.transformations.RotateSwitchingMarkovChain.__init__
=========================================================================
.. currentmodule:: bayespy.inference.vmp.transformations
.. automethod:: RotateSwitchingMarkovChain.__init__ ././@LongLink 0000000 0000000 0000000 00000000213 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.inference.vmp.transformations.RotateSwitchingMarkovChain.bound.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.inference.vmp.transformation0000664 5104112 5104112 00000000371 12356463437 035640 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.transformations.RotateSwitchingMarkovChain.bound
======================================================================
.. currentmodule:: bayespy.inference.vmp.transformations
.. automethod:: RotateSwitchingMarkovChain.bound ././@LongLink 0000000 0000000 0000000 00000000225 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.inference.vmp.transformations.RotateSwitchingMarkovChain.get_bound_terms.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.inference.vmp.transformation0000664 5104112 5104112 00000000427 12356463437 035642 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.transformations.RotateSwitchingMarkovChain.get_bound_terms
================================================================================
.. currentmodule:: bayespy.inference.vmp.transformations
.. automethod:: RotateSwitchingMarkovChain.get_bound_terms ././@LongLink 0000000 0000000 0000000 00000000213 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.inference.vmp.transformations.RotateSwitchingMarkovChain.nodes.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.inference.vmp.transformation0000664 5104112 5104112 00000000371 12356463437 035640 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.transformations.RotateSwitchingMarkovChain.nodes
======================================================================
.. currentmodule:: bayespy.inference.vmp.transformations
.. automethod:: RotateSwitchingMarkovChain.nodes ././@LongLink 0000000 0000000 0000000 00000000214 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.inference.vmp.transformations.RotateSwitchingMarkovChain.rotate.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.inference.vmp.transformation0000664 5104112 5104112 00000000374 12356463437 035643 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.transformations.RotateSwitchingMarkovChain.rotate
=======================================================================
.. currentmodule:: bayespy.inference.vmp.transformations
.. automethod:: RotateSwitchingMarkovChain.rotate ././@LongLink 0000000 0000000 0000000 00000000213 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.inference.vmp.transformations.RotateSwitchingMarkovChain.setup.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.inference.vmp.transformation0000664 5104112 5104112 00000000371 12356463437 035640 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.transformations.RotateSwitchingMarkovChain.setup
======================================================================
.. currentmodule:: bayespy.inference.vmp.transformations
.. automethod:: RotateSwitchingMarkovChain.setup ././@LongLink 0000000 0000000 0000000 00000000214 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.inference.vmp.transformations.RotateVaryingMarkovChain.__init__.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.inference.vmp.transformation0000664 5104112 5104112 00000000374 12356463437 035643 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.transformations.RotateVaryingMarkovChain.__init__
=======================================================================
.. currentmodule:: bayespy.inference.vmp.transformations
.. automethod:: RotateVaryingMarkovChain.__init__ ././@LongLink 0000000 0000000 0000000 00000000211 00000000000 011207 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.inference.vmp.transformations.RotateVaryingMarkovChain.bound.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.inference.vmp.transformation0000664 5104112 5104112 00000000363 12356463437 035641 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.transformations.RotateVaryingMarkovChain.bound
====================================================================
.. currentmodule:: bayespy.inference.vmp.transformations
.. automethod:: RotateVaryingMarkovChain.bound ././@LongLink 0000000 0000000 0000000 00000000223 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.inference.vmp.transformations.RotateVaryingMarkovChain.get_bound_terms.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.inference.vmp.transformation0000664 5104112 5104112 00000000421 12356463437 035634 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.transformations.RotateVaryingMarkovChain.get_bound_terms
==============================================================================
.. currentmodule:: bayespy.inference.vmp.transformations
.. automethod:: RotateVaryingMarkovChain.get_bound_terms ././@LongLink 0000000 0000000 0000000 00000000211 00000000000 011207 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.inference.vmp.transformations.RotateVaryingMarkovChain.nodes.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.inference.vmp.transformation0000664 5104112 5104112 00000000363 12356463437 035641 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.transformations.RotateVaryingMarkovChain.nodes
====================================================================
.. currentmodule:: bayespy.inference.vmp.transformations
.. automethod:: RotateVaryingMarkovChain.nodes ././@LongLink 0000000 0000000 0000000 00000000212 00000000000 011210 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.inference.vmp.transformations.RotateVaryingMarkovChain.rotate.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.inference.vmp.transformation0000664 5104112 5104112 00000000366 12356463437 035644 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.transformations.RotateVaryingMarkovChain.rotate
=====================================================================
.. currentmodule:: bayespy.inference.vmp.transformations
.. automethod:: RotateVaryingMarkovChain.rotate bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Bernoulli.__init__.rst0000664 5104112 5104112 00000000207 12356463437 035441 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Bernoulli.__init__
================================
.. currentmodule:: bayespy.nodes
.. automethod:: Bernoulli.__init__ ././@LongLink 0000000 0000000 0000000 00000000211 00000000000 011207 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.inference.vmp.transformations.RotateVaryingMarkovChain.setup.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.inference.vmp.transformation0000664 5104112 5104112 00000000363 12356463437 035641 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.transformations.RotateVaryingMarkovChain.setup
====================================================================
.. currentmodule:: bayespy.inference.vmp.transformations
.. automethod:: RotateVaryingMarkovChain.setup ././@LongLink 0000000 0000000 0000000 00000000205 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.inference.vmp.transformations.RotationOptimizer.__init__.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.inference.vmp.transformation0000664 5104112 5104112 00000000347 12356463437 035643 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.transformations.RotationOptimizer.__init__
================================================================
.. currentmodule:: bayespy.inference.vmp.transformations
.. automethod:: RotationOptimizer.__init__ ././@LongLink 0000000 0000000 0000000 00000000203 00000000000 011210 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.inference.vmp.transformations.RotationOptimizer.rotate.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.inference.vmp.transformation0000664 5104112 5104112 00000000341 12356463437 035635 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.transformations.RotationOptimizer.rotate
==============================================================
.. currentmodule:: bayespy.inference.vmp.transformations
.. automethod:: RotationOptimizer.rotate bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Bernoulli.delete.rst 0000664 5104112 5104112 00000000201 12356463437 035136 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Bernoulli.delete
==============================
.. currentmodule:: bayespy.nodes
.. automethod:: Bernoulli.delete bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Bernoulli.dims.rst 0000664 5104112 5104112 00000000176 12356463437 034643 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Bernoulli.dims
============================
.. currentmodule:: bayespy.nodes
.. autoattribute:: Bernoulli.dims bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Bernoulli.get_mask.rst0000664 5104112 5104112 00000000207 12356463437 035474 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Bernoulli.get_mask
================================
.. currentmodule:: bayespy.nodes
.. automethod:: Bernoulli.get_mask ././@LongLink 0000000 0000000 0000000 00000000150 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Bernoulli.get_moments.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Bernoulli.get_moments.0000664 5104112 5104112 00000000220 12356463437 035465 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Bernoulli.get_moments
===================================
.. currentmodule:: bayespy.nodes
.. automethod:: Bernoulli.get_moments ././@LongLink 0000000 0000000 0000000 00000000146 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Bernoulli.get_shape.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Bernoulli.get_shape.rs0000664 5104112 5104112 00000000212 12356463437 035451 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Bernoulli.get_shape
=================================
.. currentmodule:: bayespy.nodes
.. automethod:: Bernoulli.get_shape ././@LongLink 0000000 0000000 0000000 00000000150 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Bernoulli.has_plotter.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Bernoulli.has_plotter.0000664 5104112 5104112 00000000220 12356463437 035470 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Bernoulli.has_plotter
===================================
.. currentmodule:: bayespy.nodes
.. automethod:: Bernoulli.has_plotter ././@LongLink 0000000 0000000 0000000 00000000167 00000000000 011221 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Bernoulli.initialize_from_parameters.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Bernoulli.initialize_f0000664 5104112 5104112 00000000275 12356463437 035546 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Bernoulli.initialize_from_parameters
==================================================
.. currentmodule:: bayespy.nodes
.. automethod:: Bernoulli.initialize_from_parameters ././@LongLink 0000000 0000000 0000000 00000000162 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Bernoulli.initialize_from_prior.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Bernoulli.initialize_f0000664 5104112 5104112 00000000256 12356463437 035545 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Bernoulli.initialize_from_prior
=============================================
.. currentmodule:: bayespy.nodes
.. automethod:: Bernoulli.initialize_from_prior ././@LongLink 0000000 0000000 0000000 00000000163 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Bernoulli.initialize_from_random.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Bernoulli.initialize_f0000664 5104112 5104112 00000000261 12356463437 035541 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Bernoulli.initialize_from_random
==============================================
.. currentmodule:: bayespy.nodes
.. automethod:: Bernoulli.initialize_from_random ././@LongLink 0000000 0000000 0000000 00000000162 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Bernoulli.initialize_from_value.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Bernoulli.initialize_f0000664 5104112 5104112 00000000256 12356463437 035545 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Bernoulli.initialize_from_value
=============================================
.. currentmodule:: bayespy.nodes
.. automethod:: Bernoulli.initialize_from_value bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Bernoulli.load.rst 0000664 5104112 5104112 00000000173 12356463437 034623 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Bernoulli.load
============================
.. currentmodule:: bayespy.nodes
.. automethod:: Bernoulli.load bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Bernoulli.logpdf.rst 0000664 5104112 5104112 00000000201 12356463437 035147 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Bernoulli.logpdf
==============================
.. currentmodule:: bayespy.nodes
.. automethod:: Bernoulli.logpdf ././@LongLink 0000000 0000000 0000000 00000000165 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Bernoulli.lower_bound_contribution.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Bernoulli.lower_bound_0000664 5104112 5104112 00000000267 12356463437 035557 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Bernoulli.lower_bound_contribution
================================================
.. currentmodule:: bayespy.nodes
.. automethod:: Bernoulli.lower_bound_contribution ././@LongLink 0000000 0000000 0000000 00000000147 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Bernoulli.lowerbound.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Bernoulli.lowerbound.r0000664 5104112 5104112 00000000215 12356463437 035512 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Bernoulli.lowerbound
==================================
.. currentmodule:: bayespy.nodes
.. automethod:: Bernoulli.lowerbound ././@LongLink 0000000 0000000 0000000 00000000150 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Bernoulli.move_plates.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Bernoulli.move_plates.0000664 5104112 5104112 00000000220 12356463437 035462 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Bernoulli.move_plates
===================================
.. currentmodule:: bayespy.nodes
.. automethod:: Bernoulli.move_plates bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Bernoulli.observe.rst 0000664 5104112 5104112 00000000204 12356463437 035344 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Bernoulli.observe
===============================
.. currentmodule:: bayespy.nodes
.. automethod:: Bernoulli.observe bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Bernoulli.pdf.rst 0000664 5104112 5104112 00000000170 12356463437 034452 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Bernoulli.pdf
===========================
.. currentmodule:: bayespy.nodes
.. automethod:: Bernoulli.pdf bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Bernoulli.plates.rst 0000664 5104112 5104112 00000000204 12356463437 035167 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Bernoulli.plates
==============================
.. currentmodule:: bayespy.nodes
.. autoattribute:: Bernoulli.plates bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Bernoulli.plot.rst 0000664 5104112 5104112 00000000173 12356463437 034662 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Bernoulli.plot
============================
.. currentmodule:: bayespy.nodes
.. automethod:: Bernoulli.plot bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Bernoulli.random.rst 0000664 5104112 5104112 00000000201 12356463437 035154 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Bernoulli.random
==============================
.. currentmodule:: bayespy.nodes
.. automethod:: Bernoulli.random bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Beta.dims.rst 0000664 5104112 5104112 00000000157 12356463437 033562 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Beta.dims
=======================
.. currentmodule:: bayespy.nodes
.. autoattribute:: Beta.dims bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Bernoulli.save.rst 0000664 5104112 5104112 00000000173 12356463437 034642 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Bernoulli.save
============================
.. currentmodule:: bayespy.nodes
.. automethod:: Bernoulli.save ././@LongLink 0000000 0000000 0000000 00000000150 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Bernoulli.set_plotter.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Bernoulli.set_plotter.0000664 5104112 5104112 00000000220 12356463437 035510 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Bernoulli.set_plotter
===================================
.. currentmodule:: bayespy.nodes
.. automethod:: Bernoulli.set_plotter bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Bernoulli.show.rst 0000664 5104112 5104112 00000000173 12356463437 034664 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Bernoulli.show
============================
.. currentmodule:: bayespy.nodes
.. automethod:: Bernoulli.show ././@LongLink 0000000 0000000 0000000 00000000146 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Bernoulli.unobserve.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Bernoulli.unobserve.rs0000664 5104112 5104112 00000000212 12356463437 035522 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Bernoulli.unobserve
=================================
.. currentmodule:: bayespy.nodes
.. automethod:: Bernoulli.unobserve bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Bernoulli.update.rst 0000664 5104112 5104112 00000000201 12356463437 035156 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Bernoulli.update
==============================
.. currentmodule:: bayespy.nodes
.. automethod:: Bernoulli.update bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Beta.__init__.rst 0000664 5104112 5104112 00000000170 12356463437 034360 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Beta.__init__
===========================
.. currentmodule:: bayespy.nodes
.. automethod:: Beta.__init__ ././@LongLink 0000000 0000000 0000000 00000000146 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Beta.add_plate_axis.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Beta.add_plate_axis.rs0000664 5104112 5104112 00000000212 12356463437 035373 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Beta.add_plate_axis
=================================
.. currentmodule:: bayespy.nodes
.. automethod:: Beta.add_plate_axis bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Beta.delete.rst 0000664 5104112 5104112 00000000162 12356463437 034064 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Beta.delete
=========================
.. currentmodule:: bayespy.nodes
.. automethod:: Beta.delete bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Beta.get_mask.rst 0000664 5104112 5104112 00000000170 12356463437 034413 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Beta.get_mask
===========================
.. currentmodule:: bayespy.nodes
.. automethod:: Beta.get_mask bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Beta.get_moments.rst 0000664 5104112 5104112 00000000201 12356463437 035135 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Beta.get_moments
==============================
.. currentmodule:: bayespy.nodes
.. automethod:: Beta.get_moments bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Beta.get_shape.rst 0000664 5104112 5104112 00000000173 12356463437 034563 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Beta.get_shape
============================
.. currentmodule:: bayespy.nodes
.. automethod:: Beta.get_shape bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Beta.has_plotter.rst 0000664 5104112 5104112 00000000201 12356463437 035140 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Beta.has_plotter
==============================
.. currentmodule:: bayespy.nodes
.. automethod:: Beta.has_plotter ././@LongLink 0000000 0000000 0000000 00000000162 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Beta.initialize_from_parameters.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Beta.initialize_from_p0000664 5104112 5104112 00000000256 12356463437 035522 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Beta.initialize_from_parameters
=============================================
.. currentmodule:: bayespy.nodes
.. automethod:: Beta.initialize_from_parameters ././@LongLink 0000000 0000000 0000000 00000000155 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Beta.initialize_from_prior.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Beta.initialize_from_p0000664 5104112 5104112 00000000237 12356463437 035521 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Beta.initialize_from_prior
========================================
.. currentmodule:: bayespy.nodes
.. automethod:: Beta.initialize_from_prior ././@LongLink 0000000 0000000 0000000 00000000156 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Beta.initialize_from_random.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Beta.initialize_from_r0000664 5104112 5104112 00000000242 12356463437 035517 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Beta.initialize_from_random
=========================================
.. currentmodule:: bayespy.nodes
.. automethod:: Beta.initialize_from_random ././@LongLink 0000000 0000000 0000000 00000000155 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Beta.initialize_from_value.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Beta.initialize_from_v0000664 5104112 5104112 00000000237 12356463437 035527 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Beta.initialize_from_value
========================================
.. currentmodule:: bayespy.nodes
.. automethod:: Beta.initialize_from_value bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Beta.load.rst 0000664 5104112 5104112 00000000154 12356463437 033542 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Beta.load
=======================
.. currentmodule:: bayespy.nodes
.. automethod:: Beta.load bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Beta.logpdf.rst 0000664 5104112 5104112 00000000162 12356463437 034075 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Beta.logpdf
=========================
.. currentmodule:: bayespy.nodes
.. automethod:: Beta.logpdf ././@LongLink 0000000 0000000 0000000 00000000160 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Beta.lower_bound_contribution.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Beta.lower_bound_contr0000664 5104112 5104112 00000000250 12356463437 035535 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Beta.lower_bound_contribution
===========================================
.. currentmodule:: bayespy.nodes
.. automethod:: Beta.lower_bound_contribution bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Beta.lowerbound.rst 0000664 5104112 5104112 00000000176 12356463437 035007 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Beta.lowerbound
=============================
.. currentmodule:: bayespy.nodes
.. automethod:: Beta.lowerbound bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Beta.move_plates.rst 0000664 5104112 5104112 00000000201 12356463437 035132 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Beta.move_plates
==============================
.. currentmodule:: bayespy.nodes
.. automethod:: Beta.move_plates bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Beta.observe.rst 0000664 5104112 5104112 00000000165 12356463437 034272 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Beta.observe
==========================
.. currentmodule:: bayespy.nodes
.. automethod:: Beta.observe bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Beta.pdf.rst 0000664 5104112 5104112 00000000151 12356463437 033371 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Beta.pdf
======================
.. currentmodule:: bayespy.nodes
.. automethod:: Beta.pdf bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Beta.plates.rst 0000664 5104112 5104112 00000000165 12356463437 034115 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Beta.plates
=========================
.. currentmodule:: bayespy.nodes
.. autoattribute:: Beta.plates bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Beta.plot.rst 0000664 5104112 5104112 00000000154 12356463437 033601 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Beta.plot
=======================
.. currentmodule:: bayespy.nodes
.. automethod:: Beta.plot bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Beta.random.rst 0000664 5104112 5104112 00000000162 12356463437 034102 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Beta.random
=========================
.. currentmodule:: bayespy.nodes
.. automethod:: Beta.random bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Beta.save.rst 0000664 5104112 5104112 00000000154 12356463437 033561 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Beta.save
=======================
.. currentmodule:: bayespy.nodes
.. automethod:: Beta.save bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Beta.set_plotter.rst 0000664 5104112 5104112 00000000201 12356463437 035160 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Beta.set_plotter
==============================
.. currentmodule:: bayespy.nodes
.. automethod:: Beta.set_plotter bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Beta.show.rst 0000664 5104112 5104112 00000000154 12356463437 033603 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Beta.show
=======================
.. currentmodule:: bayespy.nodes
.. automethod:: Beta.show bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Beta.unobserve.rst 0000664 5104112 5104112 00000000173 12356463437 034634 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Beta.unobserve
============================
.. currentmodule:: bayespy.nodes
.. automethod:: Beta.unobserve bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Beta.update.rst 0000664 5104112 5104112 00000000162 12356463437 034104 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Beta.update
=========================
.. currentmodule:: bayespy.nodes
.. automethod:: Beta.update bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Binomial.__init__.rst 0000664 5104112 5104112 00000000204 12356463437 035235 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Binomial.__init__
===============================
.. currentmodule:: bayespy.nodes
.. automethod:: Binomial.__init__ ././@LongLink 0000000 0000000 0000000 00000000152 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Binomial.add_plate_axis.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Binomial.add_plate_axi0000664 5104112 5104112 00000000226 12356463437 035451 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Binomial.add_plate_axis
=====================================
.. currentmodule:: bayespy.nodes
.. automethod:: Binomial.add_plate_axis bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Binomial.delete.rst 0000664 5104112 5104112 00000000176 12356463437 034750 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Binomial.delete
=============================
.. currentmodule:: bayespy.nodes
.. automethod:: Binomial.delete bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Binomial.dims.rst 0000664 5104112 5104112 00000000173 12356463437 034437 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Binomial.dims
===========================
.. currentmodule:: bayespy.nodes
.. autoattribute:: Binomial.dims bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Binomial.get_mask.rst 0000664 5104112 5104112 00000000204 12356463437 035270 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Binomial.get_mask
===============================
.. currentmodule:: bayespy.nodes
.. automethod:: Binomial.get_mask ././@LongLink 0000000 0000000 0000000 00000000147 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Binomial.get_moments.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Binomial.get_moments.r0000664 5104112 5104112 00000000215 12356463437 035452 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Binomial.get_moments
==================================
.. currentmodule:: bayespy.nodes
.. automethod:: Binomial.get_moments bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Binomial.get_shape.rst0000664 5104112 5104112 00000000207 12356463437 035440 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Binomial.get_shape
================================
.. currentmodule:: bayespy.nodes
.. automethod:: Binomial.get_shape ././@LongLink 0000000 0000000 0000000 00000000147 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Binomial.has_plotter.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Binomial.has_plotter.r0000664 5104112 5104112 00000000215 12356463437 035455 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Binomial.has_plotter
==================================
.. currentmodule:: bayespy.nodes
.. automethod:: Binomial.has_plotter ././@LongLink 0000000 0000000 0000000 00000000166 00000000000 011220 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Binomial.initialize_from_parameters.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Binomial.initialize_fr0000664 5104112 5104112 00000000272 12356463437 035524 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Binomial.initialize_from_parameters
=================================================
.. currentmodule:: bayespy.nodes
.. automethod:: Binomial.initialize_from_parameters ././@LongLink 0000000 0000000 0000000 00000000161 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Binomial.initialize_from_prior.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Binomial.initialize_fr0000664 5104112 5104112 00000000253 12356463437 035523 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Binomial.initialize_from_prior
============================================
.. currentmodule:: bayespy.nodes
.. automethod:: Binomial.initialize_from_prior ././@LongLink 0000000 0000000 0000000 00000000162 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Binomial.initialize_from_random.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Binomial.initialize_fr0000664 5104112 5104112 00000000256 12356463437 035526 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Binomial.initialize_from_random
=============================================
.. currentmodule:: bayespy.nodes
.. automethod:: Binomial.initialize_from_random ././@LongLink 0000000 0000000 0000000 00000000161 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Binomial.initialize_from_value.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Binomial.initialize_fr0000664 5104112 5104112 00000000253 12356463437 035523 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Binomial.initialize_from_value
============================================
.. currentmodule:: bayespy.nodes
.. automethod:: Binomial.initialize_from_value bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Binomial.load.rst 0000664 5104112 5104112 00000000170 12356463437 034417 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Binomial.load
===========================
.. currentmodule:: bayespy.nodes
.. automethod:: Binomial.load bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Binomial.logpdf.rst 0000664 5104112 5104112 00000000176 12356463437 034761 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Binomial.logpdf
=============================
.. currentmodule:: bayespy.nodes
.. automethod:: Binomial.logpdf ././@LongLink 0000000 0000000 0000000 00000000164 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Binomial.lower_bound_contribution.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Binomial.lower_bound_c0000664 5104112 5104112 00000000264 12356463437 035516 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Binomial.lower_bound_contribution
===============================================
.. currentmodule:: bayespy.nodes
.. automethod:: Binomial.lower_bound_contribution ././@LongLink 0000000 0000000 0000000 00000000146 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Binomial.lowerbound.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Binomial.lowerbound.rs0000664 5104112 5104112 00000000212 12356463437 035471 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Binomial.lowerbound
=================================
.. currentmodule:: bayespy.nodes
.. automethod:: Binomial.lowerbound ././@LongLink 0000000 0000000 0000000 00000000147 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Binomial.move_plates.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Binomial.move_plates.r0000664 5104112 5104112 00000000215 12356463437 035447 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Binomial.move_plates
==================================
.. currentmodule:: bayespy.nodes
.. automethod:: Binomial.move_plates bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Binomial.observe.rst 0000664 5104112 5104112 00000000201 12356463437 035140 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Binomial.observe
==============================
.. currentmodule:: bayespy.nodes
.. automethod:: Binomial.observe bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Binomial.pdf.rst 0000664 5104112 5104112 00000000165 12356463437 034255 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Binomial.pdf
==========================
.. currentmodule:: bayespy.nodes
.. automethod:: Binomial.pdf bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Binomial.plates.rst 0000664 5104112 5104112 00000000201 12356463437 034763 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Binomial.plates
=============================
.. currentmodule:: bayespy.nodes
.. autoattribute:: Binomial.plates bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Binomial.plot.rst 0000664 5104112 5104112 00000000170 12356463437 034456 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Binomial.plot
===========================
.. currentmodule:: bayespy.nodes
.. automethod:: Binomial.plot bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Binomial.random.rst 0000664 5104112 5104112 00000000176 12356463437 034766 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Binomial.random
=============================
.. currentmodule:: bayespy.nodes
.. automethod:: Binomial.random bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Binomial.save.rst 0000664 5104112 5104112 00000000170 12356463437 034436 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Binomial.save
===========================
.. currentmodule:: bayespy.nodes
.. automethod:: Binomial.save ././@LongLink 0000000 0000000 0000000 00000000147 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Binomial.set_plotter.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Binomial.set_plotter.r0000664 5104112 5104112 00000000215 12356463437 035475 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Binomial.set_plotter
==================================
.. currentmodule:: bayespy.nodes
.. automethod:: Binomial.set_plotter bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Binomial.show.rst 0000664 5104112 5104112 00000000170 12356463437 034460 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Binomial.show
===========================
.. currentmodule:: bayespy.nodes
.. automethod:: Binomial.show bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Binomial.unobserve.rst0000664 5104112 5104112 00000000207 12356463437 035511 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Binomial.unobserve
================================
.. currentmodule:: bayespy.nodes
.. automethod:: Binomial.unobserve bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Binomial.update.rst 0000664 5104112 5104112 00000000176 12356463437 034770 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Binomial.update
=============================
.. currentmodule:: bayespy.nodes
.. automethod:: Binomial.update ././@LongLink 0000000 0000000 0000000 00000000147 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Categorical.__init__.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Categorical.__init__.r0000664 5104112 5104112 00000000215 12356463437 035353 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Categorical.__init__
==================================
.. currentmodule:: bayespy.nodes
.. automethod:: Categorical.__init__ ././@LongLink 0000000 0000000 0000000 00000000155 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Categorical.add_plate_axis.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Categorical.add_plate_0000664 5104112 5104112 00000000237 12356463437 035434 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Categorical.add_plate_axis
========================================
.. currentmodule:: bayespy.nodes
.. automethod:: Categorical.add_plate_axis bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Categorical.delete.rst0000664 5104112 5104112 00000000207 12356463437 035426 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Categorical.delete
================================
.. currentmodule:: bayespy.nodes
.. automethod:: Categorical.delete bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Categorical.dims.rst 0000664 5104112 5104112 00000000204 12356463437 035115 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Categorical.dims
==============================
.. currentmodule:: bayespy.nodes
.. autoattribute:: Categorical.dims ././@LongLink 0000000 0000000 0000000 00000000147 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Categorical.get_mask.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Categorical.get_mask.r0000664 5104112 5104112 00000000215 12356463437 035406 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Categorical.get_mask
==================================
.. currentmodule:: bayespy.nodes
.. automethod:: Categorical.get_mask ././@LongLink 0000000 0000000 0000000 00000000152 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Categorical.get_moments.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Categorical.get_moment0000664 5104112 5104112 00000000226 12356463437 035514 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Categorical.get_moments
=====================================
.. currentmodule:: bayespy.nodes
.. automethod:: Categorical.get_moments ././@LongLink 0000000 0000000 0000000 00000000150 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Categorical.get_shape.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Categorical.get_shape.0000664 5104112 5104112 00000000220 12356463437 035365 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Categorical.get_shape
===================================
.. currentmodule:: bayespy.nodes
.. automethod:: Categorical.get_shape ././@LongLink 0000000 0000000 0000000 00000000152 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Categorical.has_plotter.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Categorical.has_plotte0000664 5104112 5104112 00000000226 12356463437 035520 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Categorical.has_plotter
=====================================
.. currentmodule:: bayespy.nodes
.. automethod:: Categorical.has_plotter ././@LongLink 0000000 0000000 0000000 00000000171 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Categorical.initialize_from_parameters.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Categorical.initialize0000664 5104112 5104112 00000000303 12356463437 035513 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Categorical.initialize_from_parameters
====================================================
.. currentmodule:: bayespy.nodes
.. automethod:: Categorical.initialize_from_parameters ././@LongLink 0000000 0000000 0000000 00000000164 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Categorical.initialize_from_prior.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Categorical.initialize0000664 5104112 5104112 00000000264 12356463437 035521 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Categorical.initialize_from_prior
===============================================
.. currentmodule:: bayespy.nodes
.. automethod:: Categorical.initialize_from_prior ././@LongLink 0000000 0000000 0000000 00000000165 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Categorical.initialize_from_random.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Categorical.initialize0000664 5104112 5104112 00000000267 12356463437 035524 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Categorical.initialize_from_random
================================================
.. currentmodule:: bayespy.nodes
.. automethod:: Categorical.initialize_from_random ././@LongLink 0000000 0000000 0000000 00000000164 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Categorical.initialize_from_value.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Categorical.initialize0000664 5104112 5104112 00000000264 12356463437 035521 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Categorical.initialize_from_value
===============================================
.. currentmodule:: bayespy.nodes
.. automethod:: Categorical.initialize_from_value bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Categorical.load.rst 0000664 5104112 5104112 00000000201 12356463437 035075 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Categorical.load
==============================
.. currentmodule:: bayespy.nodes
.. automethod:: Categorical.load bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Categorical.logpdf.rst0000664 5104112 5104112 00000000207 12356463437 035437 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Categorical.logpdf
================================
.. currentmodule:: bayespy.nodes
.. automethod:: Categorical.logpdf ././@LongLink 0000000 0000000 0000000 00000000167 00000000000 011221 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Categorical.lower_bound_contribution.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Categorical.lower_boun0000664 5104112 5104112 00000000275 12356463437 035535 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Categorical.lower_bound_contribution
==================================================
.. currentmodule:: bayespy.nodes
.. automethod:: Categorical.lower_bound_contribution ././@LongLink 0000000 0000000 0000000 00000000151 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Categorical.lowerbound.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Categorical.lowerbound0000664 5104112 5104112 00000000223 12356463437 035533 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Categorical.lowerbound
====================================
.. currentmodule:: bayespy.nodes
.. automethod:: Categorical.lowerbound ././@LongLink 0000000 0000000 0000000 00000000152 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Categorical.move_plates.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Categorical.move_plate0000664 5104112 5104112 00000000226 12356463437 035511 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Categorical.move_plates
=====================================
.. currentmodule:: bayespy.nodes
.. automethod:: Categorical.move_plates bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Categorical.pdf.rst 0000664 5104112 5104112 00000000176 12356463437 034742 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Categorical.pdf
=============================
.. currentmodule:: bayespy.nodes
.. automethod:: Categorical.pdf ././@LongLink 0000000 0000000 0000000 00000000146 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Categorical.observe.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Categorical.observe.rs0000664 5104112 5104112 00000000212 12356463437 035441 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Categorical.observe
=================================
.. currentmodule:: bayespy.nodes
.. automethod:: Categorical.observe bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Categorical.plates.rst0000664 5104112 5104112 00000000212 12356463437 035450 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Categorical.plates
================================
.. currentmodule:: bayespy.nodes
.. autoattribute:: Categorical.plates bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Categorical.plot.rst 0000664 5104112 5104112 00000000201 12356463437 035134 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Categorical.plot
==============================
.. currentmodule:: bayespy.nodes
.. automethod:: Categorical.plot bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Categorical.random.rst0000664 5104112 5104112 00000000207 12356463437 035444 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Categorical.random
================================
.. currentmodule:: bayespy.nodes
.. automethod:: Categorical.random bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Categorical.save.rst 0000664 5104112 5104112 00000000201 12356463437 035114 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Categorical.save
==============================
.. currentmodule:: bayespy.nodes
.. automethod:: Categorical.save ././@LongLink 0000000 0000000 0000000 00000000152 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Categorical.set_plotter.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Categorical.set_plotte0000664 5104112 5104112 00000000226 12356463437 035540 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Categorical.set_plotter
=====================================
.. currentmodule:: bayespy.nodes
.. automethod:: Categorical.set_plotter bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Categorical.show.rst 0000664 5104112 5104112 00000000201 12356463437 035136 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Categorical.show
==============================
.. currentmodule:: bayespy.nodes
.. automethod:: Categorical.show ././@LongLink 0000000 0000000 0000000 00000000150 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Categorical.unobserve.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Categorical.unobserve.0000664 5104112 5104112 00000000220 12356463437 035436 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Categorical.unobserve
===================================
.. currentmodule:: bayespy.nodes
.. automethod:: Categorical.unobserve bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Categorical.update.rst0000664 5104112 5104112 00000000207 12356463437 035446 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Categorical.update
================================
.. currentmodule:: bayespy.nodes
.. automethod:: Categorical.update ././@LongLink 0000000 0000000 0000000 00000000162 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.CategoricalMarkovChain.__init__.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.CategoricalMarkovChain0000664 5104112 5104112 00000000256 12356463437 035505 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.CategoricalMarkovChain.__init__
=============================================
.. currentmodule:: bayespy.nodes
.. automethod:: CategoricalMarkovChain.__init__ ././@LongLink 0000000 0000000 0000000 00000000170 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.CategoricalMarkovChain.add_plate_axis.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.CategoricalMarkovChain0000664 5104112 5104112 00000000300 12356463437 035473 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.CategoricalMarkovChain.add_plate_axis
===================================================
.. currentmodule:: bayespy.nodes
.. automethod:: CategoricalMarkovChain.add_plate_axis ././@LongLink 0000000 0000000 0000000 00000000160 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.CategoricalMarkovChain.delete.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.CategoricalMarkovChain0000664 5104112 5104112 00000000250 12356463437 035477 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.CategoricalMarkovChain.delete
===========================================
.. currentmodule:: bayespy.nodes
.. automethod:: CategoricalMarkovChain.delete ././@LongLink 0000000 0000000 0000000 00000000156 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.CategoricalMarkovChain.dims.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.CategoricalMarkovChain0000664 5104112 5104112 00000000245 12356463437 035503 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.CategoricalMarkovChain.dims
=========================================
.. currentmodule:: bayespy.nodes
.. autoattribute:: CategoricalMarkovChain.dims ././@LongLink 0000000 0000000 0000000 00000000162 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.CategoricalMarkovChain.get_mask.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.CategoricalMarkovChain0000664 5104112 5104112 00000000256 12356463437 035505 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.CategoricalMarkovChain.get_mask
=============================================
.. currentmodule:: bayespy.nodes
.. automethod:: CategoricalMarkovChain.get_mask ././@LongLink 0000000 0000000 0000000 00000000165 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.CategoricalMarkovChain.get_moments.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.CategoricalMarkovChain0000664 5104112 5104112 00000000267 12356463437 035507 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.CategoricalMarkovChain.get_moments
================================================
.. currentmodule:: bayespy.nodes
.. automethod:: CategoricalMarkovChain.get_moments ././@LongLink 0000000 0000000 0000000 00000000163 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.CategoricalMarkovChain.get_shape.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.CategoricalMarkovChain0000664 5104112 5104112 00000000261 12356463437 035501 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.CategoricalMarkovChain.get_shape
==============================================
.. currentmodule:: bayespy.nodes
.. automethod:: CategoricalMarkovChain.get_shape ././@LongLink 0000000 0000000 0000000 00000000165 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.CategoricalMarkovChain.has_plotter.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.CategoricalMarkovChain0000664 5104112 5104112 00000000267 12356463437 035507 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.CategoricalMarkovChain.has_plotter
================================================
.. currentmodule:: bayespy.nodes
.. automethod:: CategoricalMarkovChain.has_plotter ././@LongLink 0000000 0000000 0000000 00000000204 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.CategoricalMarkovChain.initialize_from_parameters.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.CategoricalMarkovChain0000664 5104112 5104112 00000000344 12356463437 035503 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.CategoricalMarkovChain.initialize_from_parameters
===============================================================
.. currentmodule:: bayespy.nodes
.. automethod:: CategoricalMarkovChain.initialize_from_parameters ././@LongLink 0000000 0000000 0000000 00000000177 00000000000 011222 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.CategoricalMarkovChain.initialize_from_prior.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.CategoricalMarkovChain0000664 5104112 5104112 00000000325 12356463437 035502 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.CategoricalMarkovChain.initialize_from_prior
==========================================================
.. currentmodule:: bayespy.nodes
.. automethod:: CategoricalMarkovChain.initialize_from_prior ././@LongLink 0000000 0000000 0000000 00000000200 00000000000 011205 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.CategoricalMarkovChain.initialize_from_random.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.CategoricalMarkovChain0000664 5104112 5104112 00000000330 12356463437 035476 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.CategoricalMarkovChain.initialize_from_random
===========================================================
.. currentmodule:: bayespy.nodes
.. automethod:: CategoricalMarkovChain.initialize_from_random ././@LongLink 0000000 0000000 0000000 00000000177 00000000000 011222 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.CategoricalMarkovChain.initialize_from_value.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.CategoricalMarkovChain0000664 5104112 5104112 00000000325 12356463437 035502 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.CategoricalMarkovChain.initialize_from_value
==========================================================
.. currentmodule:: bayespy.nodes
.. automethod:: CategoricalMarkovChain.initialize_from_value ././@LongLink 0000000 0000000 0000000 00000000156 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.CategoricalMarkovChain.load.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.CategoricalMarkovChain0000664 5104112 5104112 00000000242 12356463437 035500 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.CategoricalMarkovChain.load
=========================================
.. currentmodule:: bayespy.nodes
.. automethod:: CategoricalMarkovChain.load ././@LongLink 0000000 0000000 0000000 00000000160 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.CategoricalMarkovChain.logpdf.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.CategoricalMarkovChain0000664 5104112 5104112 00000000250 12356463437 035477 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.CategoricalMarkovChain.logpdf
===========================================
.. currentmodule:: bayespy.nodes
.. automethod:: CategoricalMarkovChain.logpdf ././@LongLink 0000000 0000000 0000000 00000000202 00000000000 011207 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.CategoricalMarkovChain.lower_bound_contribution.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.CategoricalMarkovChain0000664 5104112 5104112 00000000336 12356463437 035504 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.CategoricalMarkovChain.lower_bound_contribution
=============================================================
.. currentmodule:: bayespy.nodes
.. automethod:: CategoricalMarkovChain.lower_bound_contribution ././@LongLink 0000000 0000000 0000000 00000000164 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.CategoricalMarkovChain.lowerbound.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.CategoricalMarkovChain0000664 5104112 5104112 00000000264 12356463437 035504 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.CategoricalMarkovChain.lowerbound
===============================================
.. currentmodule:: bayespy.nodes
.. automethod:: CategoricalMarkovChain.lowerbound ././@LongLink 0000000 0000000 0000000 00000000165 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.CategoricalMarkovChain.move_plates.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.CategoricalMarkovChain0000664 5104112 5104112 00000000267 12356463437 035507 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.CategoricalMarkovChain.move_plates
================================================
.. currentmodule:: bayespy.nodes
.. automethod:: CategoricalMarkovChain.move_plates ././@LongLink 0000000 0000000 0000000 00000000161 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.CategoricalMarkovChain.observe.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.CategoricalMarkovChain0000664 5104112 5104112 00000000253 12356463437 035502 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.CategoricalMarkovChain.observe
============================================
.. currentmodule:: bayespy.nodes
.. automethod:: CategoricalMarkovChain.observe ././@LongLink 0000000 0000000 0000000 00000000155 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.CategoricalMarkovChain.pdf.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.CategoricalMarkovChain0000664 5104112 5104112 00000000237 12356463437 035504 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.CategoricalMarkovChain.pdf
========================================
.. currentmodule:: bayespy.nodes
.. automethod:: CategoricalMarkovChain.pdf ././@LongLink 0000000 0000000 0000000 00000000160 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.CategoricalMarkovChain.plates.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.CategoricalMarkovChain0000664 5104112 5104112 00000000253 12356463440 035474 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.CategoricalMarkovChain.plates
===========================================
.. currentmodule:: bayespy.nodes
.. autoattribute:: CategoricalMarkovChain.plates ././@LongLink 0000000 0000000 0000000 00000000156 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.CategoricalMarkovChain.plot.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.CategoricalMarkovChain0000664 5104112 5104112 00000000242 12356463440 035472 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.CategoricalMarkovChain.plot
=========================================
.. currentmodule:: bayespy.nodes
.. automethod:: CategoricalMarkovChain.plot ././@LongLink 0000000 0000000 0000000 00000000160 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.CategoricalMarkovChain.random.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.CategoricalMarkovChain0000664 5104112 5104112 00000000250 12356463440 035471 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.CategoricalMarkovChain.random
===========================================
.. currentmodule:: bayespy.nodes
.. automethod:: CategoricalMarkovChain.random ././@LongLink 0000000 0000000 0000000 00000000156 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.CategoricalMarkovChain.save.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.CategoricalMarkovChain0000664 5104112 5104112 00000000242 12356463440 035472 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.CategoricalMarkovChain.save
=========================================
.. currentmodule:: bayespy.nodes
.. automethod:: CategoricalMarkovChain.save ././@LongLink 0000000 0000000 0000000 00000000165 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.CategoricalMarkovChain.set_plotter.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.CategoricalMarkovChain0000664 5104112 5104112 00000000267 12356463440 035501 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.CategoricalMarkovChain.set_plotter
================================================
.. currentmodule:: bayespy.nodes
.. automethod:: CategoricalMarkovChain.set_plotter ././@LongLink 0000000 0000000 0000000 00000000156 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.CategoricalMarkovChain.show.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.CategoricalMarkovChain0000664 5104112 5104112 00000000242 12356463440 035472 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.CategoricalMarkovChain.show
=========================================
.. currentmodule:: bayespy.nodes
.. automethod:: CategoricalMarkovChain.show ././@LongLink 0000000 0000000 0000000 00000000163 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.CategoricalMarkovChain.unobserve.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.CategoricalMarkovChain0000664 5104112 5104112 00000000261 12356463440 035473 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.CategoricalMarkovChain.unobserve
==============================================
.. currentmodule:: bayespy.nodes
.. automethod:: CategoricalMarkovChain.unobserve ././@LongLink 0000000 0000000 0000000 00000000160 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.CategoricalMarkovChain.update.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.CategoricalMarkovChain0000664 5104112 5104112 00000000250 12356463440 035471 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.CategoricalMarkovChain.update
===========================================
.. currentmodule:: bayespy.nodes
.. automethod:: CategoricalMarkovChain.update bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Dirichlet.__init__.rst0000664 5104112 5104112 00000000207 12356463440 035407 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Dirichlet.__init__
================================
.. currentmodule:: bayespy.nodes
.. automethod:: Dirichlet.__init__ ././@LongLink 0000000 0000000 0000000 00000000153 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Dirichlet.add_plate_axis.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Dirichlet.add_plate_ax0000664 5104112 5104112 00000000231 12356463440 035443 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Dirichlet.add_plate_axis
======================================
.. currentmodule:: bayespy.nodes
.. automethod:: Dirichlet.add_plate_axis bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Dirichlet.delete.rst 0000664 5104112 5104112 00000000201 12356463440 035104 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Dirichlet.delete
==============================
.. currentmodule:: bayespy.nodes
.. automethod:: Dirichlet.delete bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Dirichlet.dims.rst 0000664 5104112 5104112 00000000176 12356463440 034611 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Dirichlet.dims
============================
.. currentmodule:: bayespy.nodes
.. autoattribute:: Dirichlet.dims bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Dirichlet.get_mask.rst0000664 5104112 5104112 00000000207 12356463440 035442 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Dirichlet.get_mask
================================
.. currentmodule:: bayespy.nodes
.. automethod:: Dirichlet.get_mask ././@LongLink 0000000 0000000 0000000 00000000150 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Dirichlet.get_moments.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Dirichlet.get_moments.0000664 5104112 5104112 00000000220 12356463440 035433 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Dirichlet.get_moments
===================================
.. currentmodule:: bayespy.nodes
.. automethod:: Dirichlet.get_moments ././@LongLink 0000000 0000000 0000000 00000000146 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Dirichlet.get_shape.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Dirichlet.get_shape.rs0000664 5104112 5104112 00000000212 12356463440 035417 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Dirichlet.get_shape
=================================
.. currentmodule:: bayespy.nodes
.. automethod:: Dirichlet.get_shape ././@LongLink 0000000 0000000 0000000 00000000150 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Dirichlet.has_plotter.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Dirichlet.has_plotter.0000664 5104112 5104112 00000000220 12356463440 035436 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Dirichlet.has_plotter
===================================
.. currentmodule:: bayespy.nodes
.. automethod:: Dirichlet.has_plotter ././@LongLink 0000000 0000000 0000000 00000000167 00000000000 011221 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Dirichlet.initialize_from_parameters.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Dirichlet.initialize_f0000664 5104112 5104112 00000000275 12356463440 035514 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Dirichlet.initialize_from_parameters
==================================================
.. currentmodule:: bayespy.nodes
.. automethod:: Dirichlet.initialize_from_parameters ././@LongLink 0000000 0000000 0000000 00000000162 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Dirichlet.initialize_from_prior.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Dirichlet.initialize_f0000664 5104112 5104112 00000000256 12356463440 035513 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Dirichlet.initialize_from_prior
=============================================
.. currentmodule:: bayespy.nodes
.. automethod:: Dirichlet.initialize_from_prior ././@LongLink 0000000 0000000 0000000 00000000163 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Dirichlet.initialize_from_random.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Dirichlet.initialize_f0000664 5104112 5104112 00000000261 12356463440 035507 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Dirichlet.initialize_from_random
==============================================
.. currentmodule:: bayespy.nodes
.. automethod:: Dirichlet.initialize_from_random ././@LongLink 0000000 0000000 0000000 00000000162 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Dirichlet.initialize_from_value.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Dirichlet.initialize_f0000664 5104112 5104112 00000000256 12356463440 035513 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Dirichlet.initialize_from_value
=============================================
.. currentmodule:: bayespy.nodes
.. automethod:: Dirichlet.initialize_from_value bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Dirichlet.load.rst 0000664 5104112 5104112 00000000173 12356463440 034571 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Dirichlet.load
============================
.. currentmodule:: bayespy.nodes
.. automethod:: Dirichlet.load bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Dirichlet.logpdf.rst 0000664 5104112 5104112 00000000201 12356463440 035115 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Dirichlet.logpdf
==============================
.. currentmodule:: bayespy.nodes
.. automethod:: Dirichlet.logpdf ././@LongLink 0000000 0000000 0000000 00000000165 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Dirichlet.lower_bound_contribution.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Dirichlet.lower_bound_0000664 5104112 5104112 00000000267 12356463440 035525 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Dirichlet.lower_bound_contribution
================================================
.. currentmodule:: bayespy.nodes
.. automethod:: Dirichlet.lower_bound_contribution ././@LongLink 0000000 0000000 0000000 00000000147 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Dirichlet.lowerbound.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Dirichlet.lowerbound.r0000664 5104112 5104112 00000000215 12356463440 035460 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Dirichlet.lowerbound
==================================
.. currentmodule:: bayespy.nodes
.. automethod:: Dirichlet.lowerbound ././@LongLink 0000000 0000000 0000000 00000000150 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Dirichlet.move_plates.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Dirichlet.move_plates.0000664 5104112 5104112 00000000220 12356463440 035430 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Dirichlet.move_plates
===================================
.. currentmodule:: bayespy.nodes
.. automethod:: Dirichlet.move_plates bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Dirichlet.observe.rst 0000664 5104112 5104112 00000000204 12356463440 035312 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Dirichlet.observe
===============================
.. currentmodule:: bayespy.nodes
.. automethod:: Dirichlet.observe bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Dirichlet.pdf.rst 0000664 5104112 5104112 00000000170 12356463440 034420 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Dirichlet.pdf
===========================
.. currentmodule:: bayespy.nodes
.. automethod:: Dirichlet.pdf bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Dirichlet.plates.rst 0000664 5104112 5104112 00000000204 12356463440 035135 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Dirichlet.plates
==============================
.. currentmodule:: bayespy.nodes
.. autoattribute:: Dirichlet.plates bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Dirichlet.plot.rst 0000664 5104112 5104112 00000000173 12356463440 034630 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Dirichlet.plot
============================
.. currentmodule:: bayespy.nodes
.. automethod:: Dirichlet.plot bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Dirichlet.random.rst 0000664 5104112 5104112 00000000201 12356463440 035122 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Dirichlet.random
==============================
.. currentmodule:: bayespy.nodes
.. automethod:: Dirichlet.random bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Dirichlet.save.rst 0000664 5104112 5104112 00000000173 12356463440 034610 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Dirichlet.save
============================
.. currentmodule:: bayespy.nodes
.. automethod:: Dirichlet.save ././@LongLink 0000000 0000000 0000000 00000000150 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Dirichlet.set_plotter.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Dirichlet.set_plotter.0000664 5104112 5104112 00000000220 12356463440 035456 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Dirichlet.set_plotter
===================================
.. currentmodule:: bayespy.nodes
.. automethod:: Dirichlet.set_plotter bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Dirichlet.show.rst 0000664 5104112 5104112 00000000173 12356463440 034632 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Dirichlet.show
============================
.. currentmodule:: bayespy.nodes
.. automethod:: Dirichlet.show ././@LongLink 0000000 0000000 0000000 00000000146 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Dirichlet.unobserve.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Dirichlet.unobserve.rs0000664 5104112 5104112 00000000212 12356463440 035470 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Dirichlet.unobserve
=================================
.. currentmodule:: bayespy.nodes
.. automethod:: Dirichlet.unobserve bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Dirichlet.update.rst 0000664 5104112 5104112 00000000201 12356463440 035124 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Dirichlet.update
==============================
.. currentmodule:: bayespy.nodes
.. automethod:: Dirichlet.update ././@LongLink 0000000 0000000 0000000 00000000147 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Exponential.__init__.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Exponential.__init__.r0000664 5104112 5104112 00000000215 12356463440 035416 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Exponential.__init__
==================================
.. currentmodule:: bayespy.nodes
.. automethod:: Exponential.__init__ ././@LongLink 0000000 0000000 0000000 00000000155 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Exponential.add_plate_axis.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Exponential.add_plate_0000664 5104112 5104112 00000000237 12356463440 035477 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Exponential.add_plate_axis
========================================
.. currentmodule:: bayespy.nodes
.. automethod:: Exponential.add_plate_axis ././@LongLink 0000000 0000000 0000000 00000000162 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Exponential.as_diagonal_wishart.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Exponential.as_diagona0000664 5104112 5104112 00000000256 12356463440 035511 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Exponential.as_diagonal_wishart
=============================================
.. currentmodule:: bayespy.nodes
.. automethod:: Exponential.as_diagonal_wishart bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Exponential.delete.rst0000664 5104112 5104112 00000000207 12356463440 035471 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Exponential.delete
================================
.. currentmodule:: bayespy.nodes
.. automethod:: Exponential.delete bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Exponential.dims.rst 0000664 5104112 5104112 00000000204 12356463440 035160 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Exponential.dims
==============================
.. currentmodule:: bayespy.nodes
.. autoattribute:: Exponential.dims ././@LongLink 0000000 0000000 0000000 00000000147 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Exponential.get_mask.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Exponential.get_mask.r0000664 5104112 5104112 00000000215 12356463440 035451 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Exponential.get_mask
==================================
.. currentmodule:: bayespy.nodes
.. automethod:: Exponential.get_mask bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Exponential.load.rst 0000664 5104112 5104112 00000000201 12356463440 035140 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Exponential.load
==============================
.. currentmodule:: bayespy.nodes
.. automethod:: Exponential.load ././@LongLink 0000000 0000000 0000000 00000000152 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Exponential.get_moments.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Exponential.get_moment0000664 5104112 5104112 00000000226 12356463440 035557 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Exponential.get_moments
=====================================
.. currentmodule:: bayespy.nodes
.. automethod:: Exponential.get_moments ././@LongLink 0000000 0000000 0000000 00000000150 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Exponential.get_shape.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Exponential.get_shape.0000664 5104112 5104112 00000000220 12356463440 035430 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Exponential.get_shape
===================================
.. currentmodule:: bayespy.nodes
.. automethod:: Exponential.get_shape ././@LongLink 0000000 0000000 0000000 00000000152 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Exponential.has_plotter.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Exponential.has_plotte0000664 5104112 5104112 00000000226 12356463440 035563 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Exponential.has_plotter
=====================================
.. currentmodule:: bayespy.nodes
.. automethod:: Exponential.has_plotter ././@LongLink 0000000 0000000 0000000 00000000171 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Exponential.initialize_from_parameters.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Exponential.initialize0000664 5104112 5104112 00000000303 12356463440 035556 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Exponential.initialize_from_parameters
====================================================
.. currentmodule:: bayespy.nodes
.. automethod:: Exponential.initialize_from_parameters ././@LongLink 0000000 0000000 0000000 00000000164 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Exponential.initialize_from_prior.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Exponential.initialize0000664 5104112 5104112 00000000264 12356463440 035564 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Exponential.initialize_from_prior
===============================================
.. currentmodule:: bayespy.nodes
.. automethod:: Exponential.initialize_from_prior ././@LongLink 0000000 0000000 0000000 00000000165 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Exponential.initialize_from_random.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Exponential.initialize0000664 5104112 5104112 00000000267 12356463440 035567 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Exponential.initialize_from_random
================================================
.. currentmodule:: bayespy.nodes
.. automethod:: Exponential.initialize_from_random ././@LongLink 0000000 0000000 0000000 00000000164 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Exponential.initialize_from_value.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Exponential.initialize0000664 5104112 5104112 00000000264 12356463440 035564 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Exponential.initialize_from_value
===============================================
.. currentmodule:: bayespy.nodes
.. automethod:: Exponential.initialize_from_value bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Exponential.logpdf.rst0000664 5104112 5104112 00000000207 12356463440 035502 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Exponential.logpdf
================================
.. currentmodule:: bayespy.nodes
.. automethod:: Exponential.logpdf ././@LongLink 0000000 0000000 0000000 00000000167 00000000000 011221 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Exponential.lower_bound_contribution.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Exponential.lower_boun0000664 5104112 5104112 00000000275 12356463440 035600 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Exponential.lower_bound_contribution
==================================================
.. currentmodule:: bayespy.nodes
.. automethod:: Exponential.lower_bound_contribution ././@LongLink 0000000 0000000 0000000 00000000151 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Exponential.lowerbound.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Exponential.lowerbound0000664 5104112 5104112 00000000223 12356463440 035576 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Exponential.lowerbound
====================================
.. currentmodule:: bayespy.nodes
.. automethod:: Exponential.lowerbound ././@LongLink 0000000 0000000 0000000 00000000152 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Exponential.move_plates.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Exponential.move_plate0000664 5104112 5104112 00000000226 12356463440 035554 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Exponential.move_plates
=====================================
.. currentmodule:: bayespy.nodes
.. automethod:: Exponential.move_plates bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gamma.dims.rst 0000664 5104112 5104112 00000000162 12356463440 033717 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gamma.dims
========================
.. currentmodule:: bayespy.nodes
.. autoattribute:: Gamma.dims ././@LongLink 0000000 0000000 0000000 00000000146 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Exponential.observe.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Exponential.observe.rs0000664 5104112 5104112 00000000212 12356463440 035504 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Exponential.observe
=================================
.. currentmodule:: bayespy.nodes
.. automethod:: Exponential.observe bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Exponential.pdf.rst 0000664 5104112 5104112 00000000176 12356463440 035005 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Exponential.pdf
=============================
.. currentmodule:: bayespy.nodes
.. automethod:: Exponential.pdf bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Exponential.plates.rst0000664 5104112 5104112 00000000212 12356463440 035513 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Exponential.plates
================================
.. currentmodule:: bayespy.nodes
.. autoattribute:: Exponential.plates bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Exponential.plot.rst 0000664 5104112 5104112 00000000201 12356463440 035177 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Exponential.plot
==============================
.. currentmodule:: bayespy.nodes
.. automethod:: Exponential.plot bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Exponential.random.rst0000664 5104112 5104112 00000000207 12356463440 035507 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Exponential.random
================================
.. currentmodule:: bayespy.nodes
.. automethod:: Exponential.random bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Exponential.save.rst 0000664 5104112 5104112 00000000201 12356463440 035157 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Exponential.save
==============================
.. currentmodule:: bayespy.nodes
.. automethod:: Exponential.save ././@LongLink 0000000 0000000 0000000 00000000152 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Exponential.set_plotter.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Exponential.set_plotte0000664 5104112 5104112 00000000226 12356463440 035603 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Exponential.set_plotter
=====================================
.. currentmodule:: bayespy.nodes
.. automethod:: Exponential.set_plotter bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Exponential.show.rst 0000664 5104112 5104112 00000000201 12356463440 035201 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Exponential.show
==============================
.. currentmodule:: bayespy.nodes
.. automethod:: Exponential.show ././@LongLink 0000000 0000000 0000000 00000000150 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Exponential.unobserve.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Exponential.unobserve.0000664 5104112 5104112 00000000220 12356463440 035501 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Exponential.unobserve
===================================
.. currentmodule:: bayespy.nodes
.. automethod:: Exponential.unobserve bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Exponential.update.rst0000664 5104112 5104112 00000000207 12356463440 035511 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Exponential.update
================================
.. currentmodule:: bayespy.nodes
.. automethod:: Exponential.update bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gamma.__init__.rst 0000664 5104112 5104112 00000000173 12356463440 034524 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gamma.__init__
============================
.. currentmodule:: bayespy.nodes
.. automethod:: Gamma.__init__ ././@LongLink 0000000 0000000 0000000 00000000147 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gamma.add_plate_axis.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gamma.add_plate_axis.r0000664 5104112 5104112 00000000215 12356463440 035354 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gamma.add_plate_axis
==================================
.. currentmodule:: bayespy.nodes
.. automethod:: Gamma.add_plate_axis ././@LongLink 0000000 0000000 0000000 00000000154 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gamma.as_diagonal_wishart.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gamma.as_diagonal_wish0000664 5104112 5104112 00000000234 12356463440 035447 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gamma.as_diagonal_wishart
=======================================
.. currentmodule:: bayespy.nodes
.. automethod:: Gamma.as_diagonal_wishart bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gamma.delete.rst 0000664 5104112 5104112 00000000165 12356463440 034230 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gamma.delete
==========================
.. currentmodule:: bayespy.nodes
.. automethod:: Gamma.delete bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gamma.get_mask.rst 0000664 5104112 5104112 00000000173 12356463440 034557 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gamma.get_mask
============================
.. currentmodule:: bayespy.nodes
.. automethod:: Gamma.get_mask bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gamma.get_moments.rst 0000664 5104112 5104112 00000000204 12356463440 035301 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gamma.get_moments
===============================
.. currentmodule:: bayespy.nodes
.. automethod:: Gamma.get_moments bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gamma.get_shape.rst 0000664 5104112 5104112 00000000176 12356463440 034727 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gamma.get_shape
=============================
.. currentmodule:: bayespy.nodes
.. automethod:: Gamma.get_shape bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gamma.has_plotter.rst 0000664 5104112 5104112 00000000204 12356463440 035304 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gamma.has_plotter
===============================
.. currentmodule:: bayespy.nodes
.. automethod:: Gamma.has_plotter ././@LongLink 0000000 0000000 0000000 00000000163 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gamma.initialize_from_parameters.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gamma.initialize_from_0000664 5104112 5104112 00000000261 12356463440 035477 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gamma.initialize_from_parameters
==============================================
.. currentmodule:: bayespy.nodes
.. automethod:: Gamma.initialize_from_parameters ././@LongLink 0000000 0000000 0000000 00000000156 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gamma.initialize_from_prior.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gamma.initialize_from_0000664 5104112 5104112 00000000242 12356463440 035476 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gamma.initialize_from_prior
=========================================
.. currentmodule:: bayespy.nodes
.. automethod:: Gamma.initialize_from_prior ././@LongLink 0000000 0000000 0000000 00000000157 00000000000 011220 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gamma.initialize_from_random.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gamma.initialize_from_0000664 5104112 5104112 00000000245 12356463440 035501 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gamma.initialize_from_random
==========================================
.. currentmodule:: bayespy.nodes
.. automethod:: Gamma.initialize_from_random ././@LongLink 0000000 0000000 0000000 00000000156 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gamma.initialize_from_value.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gamma.initialize_from_0000664 5104112 5104112 00000000242 12356463440 035476 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gamma.initialize_from_value
=========================================
.. currentmodule:: bayespy.nodes
.. automethod:: Gamma.initialize_from_value bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gamma.load.rst 0000664 5104112 5104112 00000000157 12356463440 033706 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gamma.load
========================
.. currentmodule:: bayespy.nodes
.. automethod:: Gamma.load bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gamma.logpdf.rst 0000664 5104112 5104112 00000000165 12356463440 034241 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gamma.logpdf
==========================
.. currentmodule:: bayespy.nodes
.. automethod:: Gamma.logpdf ././@LongLink 0000000 0000000 0000000 00000000161 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gamma.lower_bound_contribution.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gamma.lower_bound_cont0000664 5104112 5104112 00000000253 12356463440 035517 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gamma.lower_bound_contribution
============================================
.. currentmodule:: bayespy.nodes
.. automethod:: Gamma.lower_bound_contribution bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gamma.pdf.rst 0000664 5104112 5104112 00000000154 12356463440 033535 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gamma.pdf
=======================
.. currentmodule:: bayespy.nodes
.. automethod:: Gamma.pdf bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gamma.lowerbound.rst 0000664 5104112 5104112 00000000201 12356463440 035135 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gamma.lowerbound
==============================
.. currentmodule:: bayespy.nodes
.. automethod:: Gamma.lowerbound bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gamma.move_plates.rst 0000664 5104112 5104112 00000000204 12356463440 035276 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gamma.move_plates
===============================
.. currentmodule:: bayespy.nodes
.. automethod:: Gamma.move_plates bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gamma.observe.rst 0000664 5104112 5104112 00000000170 12356463440 034427 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gamma.observe
===========================
.. currentmodule:: bayespy.nodes
.. automethod:: Gamma.observe bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gamma.plates.rst 0000664 5104112 5104112 00000000170 12356463440 034252 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gamma.plates
==========================
.. currentmodule:: bayespy.nodes
.. autoattribute:: Gamma.plates bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gamma.plot.rst 0000664 5104112 5104112 00000000157 12356463440 033745 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gamma.plot
========================
.. currentmodule:: bayespy.nodes
.. automethod:: Gamma.plot bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gamma.random.rst 0000664 5104112 5104112 00000000165 12356463440 034246 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gamma.random
==========================
.. currentmodule:: bayespy.nodes
.. automethod:: Gamma.random bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gamma.save.rst 0000664 5104112 5104112 00000000157 12356463440 033725 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gamma.save
========================
.. currentmodule:: bayespy.nodes
.. automethod:: Gamma.save bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gamma.set_plotter.rst 0000664 5104112 5104112 00000000204 12356463440 035324 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gamma.set_plotter
===============================
.. currentmodule:: bayespy.nodes
.. automethod:: Gamma.set_plotter bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gamma.show.rst 0000664 5104112 5104112 00000000157 12356463440 033747 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gamma.show
========================
.. currentmodule:: bayespy.nodes
.. automethod:: Gamma.show bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gamma.unobserve.rst 0000664 5104112 5104112 00000000176 12356463440 035000 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gamma.unobserve
=============================
.. currentmodule:: bayespy.nodes
.. automethod:: Gamma.unobserve bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gamma.update.rst 0000664 5104112 5104112 00000000165 12356463440 034250 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gamma.update
==========================
.. currentmodule:: bayespy.nodes
.. automethod:: Gamma.update bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gate.__init__.rst 0000664 5104112 5104112 00000000170 12356463440 034357 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gate.__init__
===========================
.. currentmodule:: bayespy.nodes
.. automethod:: Gate.__init__ ././@LongLink 0000000 0000000 0000000 00000000146 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gate.add_plate_axis.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gate.add_plate_axis.rs0000664 5104112 5104112 00000000212 12356463440 035372 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gate.add_plate_axis
=================================
.. currentmodule:: bayespy.nodes
.. automethod:: Gate.add_plate_axis bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gate.delete.rst 0000664 5104112 5104112 00000000162 12356463440 034063 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gate.delete
=========================
.. currentmodule:: bayespy.nodes
.. automethod:: Gate.delete bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gate.get_mask.rst 0000664 5104112 5104112 00000000170 12356463440 034412 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gate.get_mask
===========================
.. currentmodule:: bayespy.nodes
.. automethod:: Gate.get_mask bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gate.get_moments.rst 0000664 5104112 5104112 00000000201 12356463440 035134 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gate.get_moments
==============================
.. currentmodule:: bayespy.nodes
.. automethod:: Gate.get_moments bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gate.get_shape.rst 0000664 5104112 5104112 00000000173 12356463440 034562 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gate.get_shape
============================
.. currentmodule:: bayespy.nodes
.. automethod:: Gate.get_shape bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gate.has_plotter.rst 0000664 5104112 5104112 00000000201 12356463440 035137 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gate.has_plotter
==============================
.. currentmodule:: bayespy.nodes
.. automethod:: Gate.has_plotter ././@LongLink 0000000 0000000 0000000 00000000160 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gate.lower_bound_contribution.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gate.lower_bound_contr0000664 5104112 5104112 00000000250 12356463440 035534 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gate.lower_bound_contribution
===========================================
.. currentmodule:: bayespy.nodes
.. automethod:: Gate.lower_bound_contribution bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gate.move_plates.rst 0000664 5104112 5104112 00000000201 12356463440 035131 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gate.move_plates
==============================
.. currentmodule:: bayespy.nodes
.. automethod:: Gate.move_plates bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gate.plates.rst 0000664 5104112 5104112 00000000165 12356463440 034114 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gate.plates
=========================
.. currentmodule:: bayespy.nodes
.. autoattribute:: Gate.plates bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gate.plot.rst 0000664 5104112 5104112 00000000154 12356463440 033600 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gate.plot
=======================
.. currentmodule:: bayespy.nodes
.. automethod:: Gate.plot bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gate.set_plotter.rst 0000664 5104112 5104112 00000000201 12356463440 035157 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gate.set_plotter
==============================
.. currentmodule:: bayespy.nodes
.. automethod:: Gate.set_plotter bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gaussian.__init__.rst 0000664 5104112 5104112 00000000204 12356463440 035247 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gaussian.__init__
===============================
.. currentmodule:: bayespy.nodes
.. automethod:: Gaussian.__init__ ././@LongLink 0000000 0000000 0000000 00000000152 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gaussian.add_plate_axis.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gaussian.add_plate_axi0000664 5104112 5104112 00000000226 12356463440 035463 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gaussian.add_plate_axis
=====================================
.. currentmodule:: bayespy.nodes
.. automethod:: Gaussian.add_plate_axis bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gaussian.delete.rst 0000664 5104112 5104112 00000000176 12356463440 034762 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gaussian.delete
=============================
.. currentmodule:: bayespy.nodes
.. automethod:: Gaussian.delete bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gaussian.dims.rst 0000664 5104112 5104112 00000000173 12356463440 034451 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gaussian.dims
===========================
.. currentmodule:: bayespy.nodes
.. autoattribute:: Gaussian.dims bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gaussian.get_mask.rst 0000664 5104112 5104112 00000000204 12356463440 035302 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gaussian.get_mask
===============================
.. currentmodule:: bayespy.nodes
.. automethod:: Gaussian.get_mask ././@LongLink 0000000 0000000 0000000 00000000147 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gaussian.get_moments.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gaussian.get_moments.r0000664 5104112 5104112 00000000215 12356463440 035464 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gaussian.get_moments
==================================
.. currentmodule:: bayespy.nodes
.. automethod:: Gaussian.get_moments bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gaussian.get_shape.rst0000664 5104112 5104112 00000000207 12356463440 035452 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gaussian.get_shape
================================
.. currentmodule:: bayespy.nodes
.. automethod:: Gaussian.get_shape ././@LongLink 0000000 0000000 0000000 00000000147 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gaussian.has_plotter.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gaussian.has_plotter.r0000664 5104112 5104112 00000000215 12356463440 035467 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gaussian.has_plotter
==================================
.. currentmodule:: bayespy.nodes
.. automethod:: Gaussian.has_plotter ././@LongLink 0000000 0000000 0000000 00000000166 00000000000 011220 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gaussian.initialize_from_parameters.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gaussian.initialize_fr0000664 5104112 5104112 00000000272 12356463440 035536 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gaussian.initialize_from_parameters
=================================================
.. currentmodule:: bayespy.nodes
.. automethod:: Gaussian.initialize_from_parameters ././@LongLink 0000000 0000000 0000000 00000000161 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gaussian.initialize_from_prior.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gaussian.initialize_fr0000664 5104112 5104112 00000000253 12356463440 035535 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gaussian.initialize_from_prior
============================================
.. currentmodule:: bayespy.nodes
.. automethod:: Gaussian.initialize_from_prior ././@LongLink 0000000 0000000 0000000 00000000162 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gaussian.initialize_from_random.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gaussian.initialize_fr0000664 5104112 5104112 00000000256 12356463440 035540 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gaussian.initialize_from_random
=============================================
.. currentmodule:: bayespy.nodes
.. automethod:: Gaussian.initialize_from_random ././@LongLink 0000000 0000000 0000000 00000000161 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gaussian.initialize_from_value.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gaussian.initialize_fr0000664 5104112 5104112 00000000253 12356463440 035535 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gaussian.initialize_from_value
============================================
.. currentmodule:: bayespy.nodes
.. automethod:: Gaussian.initialize_from_value bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gaussian.load.rst 0000664 5104112 5104112 00000000170 12356463440 034431 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gaussian.load
===========================
.. currentmodule:: bayespy.nodes
.. automethod:: Gaussian.load bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gaussian.logpdf.rst 0000664 5104112 5104112 00000000176 12356463440 034773 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gaussian.logpdf
=============================
.. currentmodule:: bayespy.nodes
.. automethod:: Gaussian.logpdf ././@LongLink 0000000 0000000 0000000 00000000164 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gaussian.lower_bound_contribution.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gaussian.lower_bound_c0000664 5104112 5104112 00000000264 12356463440 035530 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gaussian.lower_bound_contribution
===============================================
.. currentmodule:: bayespy.nodes
.. automethod:: Gaussian.lower_bound_contribution ././@LongLink 0000000 0000000 0000000 00000000146 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gaussian.lowerbound.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gaussian.lowerbound.rs0000664 5104112 5104112 00000000212 12356463440 035503 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gaussian.lowerbound
=================================
.. currentmodule:: bayespy.nodes
.. automethod:: Gaussian.lowerbound ././@LongLink 0000000 0000000 0000000 00000000147 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gaussian.move_plates.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gaussian.move_plates.r0000664 5104112 5104112 00000000215 12356463440 035461 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gaussian.move_plates
==================================
.. currentmodule:: bayespy.nodes
.. automethod:: Gaussian.move_plates bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gaussian.observe.rst 0000664 5104112 5104112 00000000201 12356463440 035152 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gaussian.observe
==============================
.. currentmodule:: bayespy.nodes
.. automethod:: Gaussian.observe bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gaussian.pdf.rst 0000664 5104112 5104112 00000000165 12356463440 034267 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gaussian.pdf
==========================
.. currentmodule:: bayespy.nodes
.. automethod:: Gaussian.pdf bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gaussian.plates.rst 0000664 5104112 5104112 00000000201 12356463440 034775 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gaussian.plates
=============================
.. currentmodule:: bayespy.nodes
.. autoattribute:: Gaussian.plates bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gaussian.plot.rst 0000664 5104112 5104112 00000000170 12356463440 034470 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gaussian.plot
===========================
.. currentmodule:: bayespy.nodes
.. automethod:: Gaussian.plot bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gaussian.random.rst 0000664 5104112 5104112 00000000176 12356463440 035000 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gaussian.random
=============================
.. currentmodule:: bayespy.nodes
.. automethod:: Gaussian.random bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gaussian.rotate.rst 0000664 5104112 5104112 00000000176 12356463440 035016 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gaussian.rotate
=============================
.. currentmodule:: bayespy.nodes
.. automethod:: Gaussian.rotate ././@LongLink 0000000 0000000 0000000 00000000151 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gaussian.rotate_matrix.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gaussian.rotate_matrix0000664 5104112 5104112 00000000223 12356463440 035564 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gaussian.rotate_matrix
====================================
.. currentmodule:: bayespy.nodes
.. automethod:: Gaussian.rotate_matrix bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gaussian.save.rst 0000664 5104112 5104112 00000000170 12356463440 034450 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gaussian.save
===========================
.. currentmodule:: bayespy.nodes
.. automethod:: Gaussian.save ././@LongLink 0000000 0000000 0000000 00000000147 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gaussian.set_plotter.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gaussian.set_plotter.r0000664 5104112 5104112 00000000215 12356463440 035507 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gaussian.set_plotter
==================================
.. currentmodule:: bayespy.nodes
.. automethod:: Gaussian.set_plotter bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gaussian.show.rst 0000664 5104112 5104112 00000000170 12356463440 034472 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gaussian.show
===========================
.. currentmodule:: bayespy.nodes
.. automethod:: Gaussian.show bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gaussian.unobserve.rst0000664 5104112 5104112 00000000207 12356463440 035523 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gaussian.unobserve
================================
.. currentmodule:: bayespy.nodes
.. automethod:: Gaussian.unobserve bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Gaussian.update.rst 0000664 5104112 5104112 00000000176 12356463440 035002 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gaussian.update
=============================
.. currentmodule:: bayespy.nodes
.. automethod:: Gaussian.update ././@LongLink 0000000 0000000 0000000 00000000147 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianARD.__init__.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianARD.__init__.r0000664 5104112 5104112 00000000215 12356463440 035231 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianARD.__init__
==================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianARD.__init__ ././@LongLink 0000000 0000000 0000000 00000000155 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianARD.add_plate_axis.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianARD.add_plate_0000664 5104112 5104112 00000000237 12356463440 035312 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianARD.add_plate_axis
========================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianARD.add_plate_axis bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianARD.delete.rst0000664 5104112 5104112 00000000207 12356463440 035304 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianARD.delete
================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianARD.delete bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianARD.dims.rst 0000664 5104112 5104112 00000000204 12356463440 034773 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianARD.dims
==============================
.. currentmodule:: bayespy.nodes
.. autoattribute:: GaussianARD.dims ././@LongLink 0000000 0000000 0000000 00000000147 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianARD.get_mask.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianARD.get_mask.r0000664 5104112 5104112 00000000215 12356463440 035264 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianARD.get_mask
==================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianARD.get_mask ././@LongLink 0000000 0000000 0000000 00000000152 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianARD.get_moments.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianARD.get_moment0000664 5104112 5104112 00000000226 12356463440 035372 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianARD.get_moments
=====================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianARD.get_moments ././@LongLink 0000000 0000000 0000000 00000000150 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianARD.get_shape.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianARD.get_shape.0000664 5104112 5104112 00000000220 12356463440 035243 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianARD.get_shape
===================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianARD.get_shape ././@LongLink 0000000 0000000 0000000 00000000152 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianARD.has_plotter.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianARD.has_plotte0000664 5104112 5104112 00000000226 12356463440 035376 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianARD.has_plotter
=====================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianARD.has_plotter ././@LongLink 0000000 0000000 0000000 00000000202 00000000000 011207 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianARD.initialize_from_mean_and_covariance.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianARD.initialize0000664 5104112 5104112 00000000336 12356463440 035377 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianARD.initialize_from_mean_and_covariance
=============================================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianARD.initialize_from_mean_and_covariance ././@LongLink 0000000 0000000 0000000 00000000171 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianARD.initialize_from_parameters.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianARD.initialize0000664 5104112 5104112 00000000303 12356463440 035371 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianARD.initialize_from_parameters
====================================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianARD.initialize_from_parameters ././@LongLink 0000000 0000000 0000000 00000000164 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianARD.initialize_from_prior.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianARD.initialize0000664 5104112 5104112 00000000264 12356463440 035377 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianARD.initialize_from_prior
===============================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianARD.initialize_from_prior bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianARD.load.rst 0000664 5104112 5104112 00000000201 12356463440 034753 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianARD.load
==============================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianARD.load ././@LongLink 0000000 0000000 0000000 00000000165 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianARD.initialize_from_random.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianARD.initialize0000664 5104112 5104112 00000000267 12356463440 035402 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianARD.initialize_from_random
================================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianARD.initialize_from_random ././@LongLink 0000000 0000000 0000000 00000000164 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianARD.initialize_from_value.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianARD.initialize0000664 5104112 5104112 00000000264 12356463440 035377 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianARD.initialize_from_value
===============================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianARD.initialize_from_value bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianARD.logpdf.rst0000664 5104112 5104112 00000000207 12356463440 035315 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianARD.logpdf
================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianARD.logpdf ././@LongLink 0000000 0000000 0000000 00000000167 00000000000 011221 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianARD.lower_bound_contribution.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianARD.lower_boun0000664 5104112 5104112 00000000275 12356463440 035413 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianARD.lower_bound_contribution
==================================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianARD.lower_bound_contribution ././@LongLink 0000000 0000000 0000000 00000000151 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianARD.lowerbound.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianARD.lowerbound0000664 5104112 5104112 00000000223 12356463440 035411 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianARD.lowerbound
====================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianARD.lowerbound ././@LongLink 0000000 0000000 0000000 00000000152 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianARD.move_plates.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianARD.move_plate0000664 5104112 5104112 00000000226 12356463440 035367 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianARD.move_plates
=====================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianARD.move_plates ././@LongLink 0000000 0000000 0000000 00000000146 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianARD.observe.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianARD.observe.rs0000664 5104112 5104112 00000000212 12356463440 035317 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianARD.observe
=================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianARD.observe bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianARD.pdf.rst 0000664 5104112 5104112 00000000176 12356463440 034620 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianARD.pdf
=============================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianARD.pdf bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianARD.plates.rst0000664 5104112 5104112 00000000212 12356463440 035326 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianARD.plates
================================
.. currentmodule:: bayespy.nodes
.. autoattribute:: GaussianARD.plates bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianARD.plot.rst 0000664 5104112 5104112 00000000201 12356463440 035012 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianARD.plot
==============================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianARD.plot bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianARD.random.rst0000664 5104112 5104112 00000000207 12356463440 035322 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianARD.random
================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianARD.random bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianARD.rotate.rst0000664 5104112 5104112 00000000207 12356463440 035340 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianARD.rotate
================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianARD.rotate ././@LongLink 0000000 0000000 0000000 00000000154 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianARD.rotate_plates.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianARD.rotate_pla0000664 5104112 5104112 00000000234 12356463440 035365 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianARD.rotate_plates
=======================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianARD.rotate_plates bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianARD.save.rst 0000664 5104112 5104112 00000000201 12356463440 034772 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianARD.save
==============================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianARD.save ././@LongLink 0000000 0000000 0000000 00000000152 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianARD.set_plotter.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianARD.set_plotte0000664 5104112 5104112 00000000226 12356463440 035416 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianARD.set_plotter
=====================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianARD.set_plotter bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianARD.show.rst 0000664 5104112 5104112 00000000201 12356463440 035014 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianARD.show
==============================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianARD.show ././@LongLink 0000000 0000000 0000000 00000000150 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianARD.unobserve.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianARD.unobserve.0000664 5104112 5104112 00000000220 12356463440 035314 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianARD.unobserve
===================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianARD.unobserve bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianARD.update.rst0000664 5104112 5104112 00000000207 12356463440 035324 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianARD.update
================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianARD.update ././@LongLink 0000000 0000000 0000000 00000000154 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaARD.__init__.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaARD.__ini0000664 5104112 5104112 00000000234 12356463440 035253 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianGammaARD.__init__
=======================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianGammaARD.__init__ ././@LongLink 0000000 0000000 0000000 00000000162 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaARD.add_plate_axis.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaARD.add_p0000664 5104112 5104112 00000000256 12356463440 035251 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianGammaARD.add_plate_axis
=============================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianGammaARD.add_plate_axis ././@LongLink 0000000 0000000 0000000 00000000152 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaARD.delete.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaARD.delet0000664 5104112 5104112 00000000226 12356463440 035274 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianGammaARD.delete
=====================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianGammaARD.delete ././@LongLink 0000000 0000000 0000000 00000000150 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaARD.dims.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaARD.dims.0000664 5104112 5104112 00000000223 12356463440 035206 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianGammaARD.dims
===================================
.. currentmodule:: bayespy.nodes
.. autoattribute:: GaussianGammaARD.dims ././@LongLink 0000000 0000000 0000000 00000000154 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaARD.get_mask.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaARD.get_m0000664 5104112 5104112 00000000234 12356463440 035271 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianGammaARD.get_mask
=======================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianGammaARD.get_mask ././@LongLink 0000000 0000000 0000000 00000000157 00000000000 011220 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaARD.get_moments.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaARD.get_m0000664 5104112 5104112 00000000245 12356463440 035273 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianGammaARD.get_moments
==========================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianGammaARD.get_moments ././@LongLink 0000000 0000000 0000000 00000000155 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaARD.get_shape.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaARD.get_s0000664 5104112 5104112 00000000237 12356463440 035302 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianGammaARD.get_shape
========================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianGammaARD.get_shape ././@LongLink 0000000 0000000 0000000 00000000157 00000000000 011220 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaARD.has_plotter.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaARD.has_p0000664 5104112 5104112 00000000245 12356463440 035272 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianGammaARD.has_plotter
==========================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianGammaARD.has_plotter ././@LongLink 0000000 0000000 0000000 00000000176 00000000000 011221 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaARD.initialize_from_parameters.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaARD.initi0000664 5104112 5104112 00000000322 12356463440 035310 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianGammaARD.initialize_from_parameters
=========================================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianGammaARD.initialize_from_parameters ././@LongLink 0000000 0000000 0000000 00000000171 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaARD.initialize_from_prior.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaARD.initi0000664 5104112 5104112 00000000303 12356463440 035307 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianGammaARD.initialize_from_prior
====================================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianGammaARD.initialize_from_prior ././@LongLink 0000000 0000000 0000000 00000000172 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaARD.initialize_from_random.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaARD.initi0000664 5104112 5104112 00000000306 12356463440 035312 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianGammaARD.initialize_from_random
=====================================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianGammaARD.initialize_from_random ././@LongLink 0000000 0000000 0000000 00000000171 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaARD.initialize_from_value.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaARD.initi0000664 5104112 5104112 00000000303 12356463440 035307 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianGammaARD.initialize_from_value
====================================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianGammaARD.initialize_from_value ././@LongLink 0000000 0000000 0000000 00000000150 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaARD.load.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaARD.load.0000664 5104112 5104112 00000000220 12356463440 035166 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianGammaARD.load
===================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianGammaARD.load ././@LongLink 0000000 0000000 0000000 00000000152 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaARD.logpdf.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaARD.logpd0000664 5104112 5104112 00000000226 12356463440 035304 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianGammaARD.logpdf
=====================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianGammaARD.logpdf ././@LongLink 0000000 0000000 0000000 00000000174 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaARD.lower_bound_contribution.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaARD.lower0000664 5104112 5104112 00000000314 12356463440 035325 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianGammaARD.lower_bound_contribution
=======================================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianGammaARD.lower_bound_contribution ././@LongLink 0000000 0000000 0000000 00000000156 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaARD.lowerbound.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaARD.lower0000664 5104112 5104112 00000000242 12356463440 035325 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianGammaARD.lowerbound
=========================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianGammaARD.lowerbound ././@LongLink 0000000 0000000 0000000 00000000157 00000000000 011220 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaARD.move_plates.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaARD.move_0000664 5104112 5104112 00000000245 12356463440 035305 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianGammaARD.move_plates
==========================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianGammaARD.move_plates ././@LongLink 0000000 0000000 0000000 00000000153 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaARD.observe.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaARD.obser0000664 5104112 5104112 00000000231 12356463440 035305 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianGammaARD.observe
======================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianGammaARD.observe ././@LongLink 0000000 0000000 0000000 00000000147 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaARD.pdf.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaARD.pdf.r0000664 5104112 5104112 00000000215 12356463440 035206 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianGammaARD.pdf
==================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianGammaARD.pdf ././@LongLink 0000000 0000000 0000000 00000000152 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaARD.plates.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaARD.plate0000664 5104112 5104112 00000000231 12356463440 035300 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianGammaARD.plates
=====================================
.. currentmodule:: bayespy.nodes
.. autoattribute:: GaussianGammaARD.plates ././@LongLink 0000000 0000000 0000000 00000000150 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaARD.plot.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaARD.plot.0000664 5104112 5104112 00000000220 12356463440 035225 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianGammaARD.plot
===================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianGammaARD.plot ././@LongLink 0000000 0000000 0000000 00000000152 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaARD.random.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaARD.rando0000664 5104112 5104112 00000000226 12356463440 035302 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianGammaARD.random
=====================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianGammaARD.random ././@LongLink 0000000 0000000 0000000 00000000150 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaARD.save.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaARD.save.0000664 5104112 5104112 00000000220 12356463440 035205 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianGammaARD.save
===================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianGammaARD.save ././@LongLink 0000000 0000000 0000000 00000000157 00000000000 011220 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaARD.set_plotter.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaARD.set_p0000664 5104112 5104112 00000000245 12356463440 035312 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianGammaARD.set_plotter
==========================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianGammaARD.set_plotter ././@LongLink 0000000 0000000 0000000 00000000155 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaARD.unobserve.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaARD.unobs0000664 5104112 5104112 00000000237 12356463440 035327 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianGammaARD.unobserve
========================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianGammaARD.unobserve ././@LongLink 0000000 0000000 0000000 00000000152 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaARD.update.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaARD.updat0000664 5104112 5104112 00000000226 12356463440 035314 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianGammaARD.update
=====================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianGammaARD.update ././@LongLink 0000000 0000000 0000000 00000000154 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaISO.__init__.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaISO.__ini0000664 5104112 5104112 00000000234 12356463440 035277 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianGammaISO.__init__
=======================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianGammaISO.__init__ ././@LongLink 0000000 0000000 0000000 00000000162 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaISO.add_plate_axis.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaISO.add_p0000664 5104112 5104112 00000000256 12356463440 035275 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianGammaISO.add_plate_axis
=============================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianGammaISO.add_plate_axis ././@LongLink 0000000 0000000 0000000 00000000152 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaISO.delete.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaISO.delet0000664 5104112 5104112 00000000226 12356463440 035320 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianGammaISO.delete
=====================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianGammaISO.delete ././@LongLink 0000000 0000000 0000000 00000000150 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaISO.dims.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaISO.dims.0000664 5104112 5104112 00000000223 12356463440 035232 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianGammaISO.dims
===================================
.. currentmodule:: bayespy.nodes
.. autoattribute:: GaussianGammaISO.dims ././@LongLink 0000000 0000000 0000000 00000000202 00000000000 011207 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaISO.get_gaussian_mean_and_variance.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaISO.get_g0000664 5104112 5104112 00000000336 12356463440 035312 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianGammaISO.get_gaussian_mean_and_variance
=============================================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianGammaISO.get_gaussian_mean_and_variance ././@LongLink 0000000 0000000 0000000 00000000167 00000000000 011221 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaISO.get_marginal_logpdf.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaISO.get_m0000664 5104112 5104112 00000000275 12356463440 035322 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianGammaISO.get_marginal_logpdf
==================================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianGammaISO.get_marginal_logpdf ././@LongLink 0000000 0000000 0000000 00000000154 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaISO.get_mask.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaISO.get_m0000664 5104112 5104112 00000000234 12356463440 035315 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianGammaISO.get_mask
=======================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianGammaISO.get_mask ././@LongLink 0000000 0000000 0000000 00000000157 00000000000 011220 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaISO.get_moments.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaISO.get_m0000664 5104112 5104112 00000000245 12356463440 035317 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianGammaISO.get_moments
==========================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianGammaISO.get_moments ././@LongLink 0000000 0000000 0000000 00000000155 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaISO.get_shape.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaISO.get_s0000664 5104112 5104112 00000000237 12356463440 035326 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianGammaISO.get_shape
========================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianGammaISO.get_shape ././@LongLink 0000000 0000000 0000000 00000000157 00000000000 011220 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaISO.has_plotter.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaISO.has_p0000664 5104112 5104112 00000000245 12356463440 035316 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianGammaISO.has_plotter
==========================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianGammaISO.has_plotter ././@LongLink 0000000 0000000 0000000 00000000176 00000000000 011221 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaISO.initialize_from_parameters.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaISO.initi0000664 5104112 5104112 00000000322 12356463440 035334 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianGammaISO.initialize_from_parameters
=========================================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianGammaISO.initialize_from_parameters ././@LongLink 0000000 0000000 0000000 00000000171 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaISO.initialize_from_prior.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaISO.initi0000664 5104112 5104112 00000000303 12356463440 035333 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianGammaISO.initialize_from_prior
====================================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianGammaISO.initialize_from_prior ././@LongLink 0000000 0000000 0000000 00000000172 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaISO.initialize_from_random.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaISO.initi0000664 5104112 5104112 00000000306 12356463440 035336 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianGammaISO.initialize_from_random
=====================================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianGammaISO.initialize_from_random ././@LongLink 0000000 0000000 0000000 00000000150 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaISO.load.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaISO.load.0000664 5104112 5104112 00000000220 12356463440 035212 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianGammaISO.load
===================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianGammaISO.load ././@LongLink 0000000 0000000 0000000 00000000171 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaISO.initialize_from_value.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaISO.initi0000664 5104112 5104112 00000000303 12356463440 035333 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianGammaISO.initialize_from_value
====================================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianGammaISO.initialize_from_value ././@LongLink 0000000 0000000 0000000 00000000152 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaISO.logpdf.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaISO.logpd0000664 5104112 5104112 00000000226 12356463440 035330 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianGammaISO.logpdf
=====================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianGammaISO.logpdf ././@LongLink 0000000 0000000 0000000 00000000174 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaISO.lower_bound_contribution.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaISO.lower0000664 5104112 5104112 00000000314 12356463440 035351 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianGammaISO.lower_bound_contribution
=======================================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianGammaISO.lower_bound_contribution ././@LongLink 0000000 0000000 0000000 00000000156 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaISO.lowerbound.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaISO.lower0000664 5104112 5104112 00000000242 12356463440 035351 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianGammaISO.lowerbound
=========================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianGammaISO.lowerbound ././@LongLink 0000000 0000000 0000000 00000000157 00000000000 011220 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaISO.move_plates.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaISO.move_0000664 5104112 5104112 00000000245 12356463440 035331 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianGammaISO.move_plates
==========================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianGammaISO.move_plates ././@LongLink 0000000 0000000 0000000 00000000153 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaISO.observe.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaISO.obser0000664 5104112 5104112 00000000231 12356463440 035331 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianGammaISO.observe
======================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianGammaISO.observe ././@LongLink 0000000 0000000 0000000 00000000147 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaISO.pdf.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaISO.pdf.r0000664 5104112 5104112 00000000215 12356463440 035232 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianGammaISO.pdf
==================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianGammaISO.pdf ././@LongLink 0000000 0000000 0000000 00000000152 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaISO.plates.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaISO.plate0000664 5104112 5104112 00000000231 12356463440 035324 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianGammaISO.plates
=====================================
.. currentmodule:: bayespy.nodes
.. autoattribute:: GaussianGammaISO.plates ././@LongLink 0000000 0000000 0000000 00000000150 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaISO.plot.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaISO.plot.0000664 5104112 5104112 00000000220 12356463440 035251 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianGammaISO.plot
===================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianGammaISO.plot ././@LongLink 0000000 0000000 0000000 00000000156 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaISO.plotmatrix.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaISO.plotm0000664 5104112 5104112 00000000242 12356463440 035354 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianGammaISO.plotmatrix
=========================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianGammaISO.plotmatrix ././@LongLink 0000000 0000000 0000000 00000000152 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaISO.random.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaISO.rando0000664 5104112 5104112 00000000226 12356463440 035326 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianGammaISO.random
=====================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianGammaISO.random ././@LongLink 0000000 0000000 0000000 00000000150 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaISO.save.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaISO.save.0000664 5104112 5104112 00000000220 12356463440 035231 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianGammaISO.save
===================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianGammaISO.save ././@LongLink 0000000 0000000 0000000 00000000157 00000000000 011220 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaISO.set_plotter.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaISO.set_p0000664 5104112 5104112 00000000245 12356463440 035336 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianGammaISO.set_plotter
==========================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianGammaISO.set_plotter ././@LongLink 0000000 0000000 0000000 00000000150 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaISO.show.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaISO.show.0000664 5104112 5104112 00000000220 12356463440 035253 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianGammaISO.show
===================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianGammaISO.show ././@LongLink 0000000 0000000 0000000 00000000155 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaISO.unobserve.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaISO.unobs0000664 5104112 5104112 00000000237 12356463440 035353 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianGammaISO.unobserve
========================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianGammaISO.unobserve ././@LongLink 0000000 0000000 0000000 00000000152 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaISO.update.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianGammaISO.updat0000664 5104112 5104112 00000000226 12356463440 035340 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianGammaISO.update
=====================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianGammaISO.update ././@LongLink 0000000 0000000 0000000 00000000157 00000000000 011220 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianMarkovChain.__init__.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianMarkovChain.__0000664 5104112 5104112 00000000245 12356463440 035406 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianMarkovChain.__init__
==========================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianMarkovChain.__init__ ././@LongLink 0000000 0000000 0000000 00000000165 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianMarkovChain.add_plate_axis.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianMarkovChain.ad0000664 5104112 5104112 00000000267 12356463440 035421 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianMarkovChain.add_plate_axis
================================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianMarkovChain.add_plate_axis ././@LongLink 0000000 0000000 0000000 00000000155 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianMarkovChain.delete.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianMarkovChain.de0000664 5104112 5104112 00000000237 12356463440 035422 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianMarkovChain.delete
========================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianMarkovChain.delete ././@LongLink 0000000 0000000 0000000 00000000153 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianMarkovChain.dims.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianMarkovChain.di0000664 5104112 5104112 00000000234 12356463440 035423 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianMarkovChain.dims
======================================
.. currentmodule:: bayespy.nodes
.. autoattribute:: GaussianMarkovChain.dims ././@LongLink 0000000 0000000 0000000 00000000157 00000000000 011220 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianMarkovChain.get_mask.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianMarkovChain.ge0000664 5104112 5104112 00000000245 12356463440 035424 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianMarkovChain.get_mask
==========================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianMarkovChain.get_mask ././@LongLink 0000000 0000000 0000000 00000000162 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianMarkovChain.get_moments.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianMarkovChain.ge0000664 5104112 5104112 00000000256 12356463440 035426 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianMarkovChain.get_moments
=============================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianMarkovChain.get_moments bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Mixture.__init__.rst 0000664 5104112 5104112 00000000201 12356463441 035130 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Mixture.__init__
==============================
.. currentmodule:: bayespy.nodes
.. automethod:: Mixture.__init__ ././@LongLink 0000000 0000000 0000000 00000000160 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianMarkovChain.get_shape.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianMarkovChain.ge0000664 5104112 5104112 00000000250 12356463440 035420 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianMarkovChain.get_shape
===========================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianMarkovChain.get_shape ././@LongLink 0000000 0000000 0000000 00000000162 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianMarkovChain.has_plotter.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianMarkovChain.ha0000664 5104112 5104112 00000000256 12356463440 035423 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianMarkovChain.has_plotter
=============================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianMarkovChain.has_plotter ././@LongLink 0000000 0000000 0000000 00000000201 00000000000 011206 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianMarkovChain.initialize_from_parameters.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianMarkovChain.in0000664 5104112 5104112 00000000333 12356463440 035435 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianMarkovChain.initialize_from_parameters
============================================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianMarkovChain.initialize_from_parameters ././@LongLink 0000000 0000000 0000000 00000000174 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianMarkovChain.initialize_from_prior.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianMarkovChain.in0000664 5104112 5104112 00000000314 12356463440 035434 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianMarkovChain.initialize_from_prior
=======================================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianMarkovChain.initialize_from_prior ././@LongLink 0000000 0000000 0000000 00000000175 00000000000 011220 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianMarkovChain.initialize_from_random.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianMarkovChain.in0000664 5104112 5104112 00000000317 12356463440 035437 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianMarkovChain.initialize_from_random
========================================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianMarkovChain.initialize_from_random ././@LongLink 0000000 0000000 0000000 00000000174 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianMarkovChain.initialize_from_value.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianMarkovChain.in0000664 5104112 5104112 00000000314 12356463440 035434 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianMarkovChain.initialize_from_value
=======================================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianMarkovChain.initialize_from_value ././@LongLink 0000000 0000000 0000000 00000000153 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianMarkovChain.load.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianMarkovChain.lo0000664 5104112 5104112 00000000231 12356463440 035436 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianMarkovChain.load
======================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianMarkovChain.load ././@LongLink 0000000 0000000 0000000 00000000155 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianMarkovChain.logpdf.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianMarkovChain.lo0000664 5104112 5104112 00000000237 12356463440 035444 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianMarkovChain.logpdf
========================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianMarkovChain.logpdf ././@LongLink 0000000 0000000 0000000 00000000177 00000000000 011222 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianMarkovChain.lower_bound_contribution.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianMarkovChain.lo0000664 5104112 5104112 00000000325 12356463440 035442 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianMarkovChain.lower_bound_contribution
==========================================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianMarkovChain.lower_bound_contribution ././@LongLink 0000000 0000000 0000000 00000000161 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianMarkovChain.lowerbound.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianMarkovChain.lo0000664 5104112 5104112 00000000253 12356463440 035442 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianMarkovChain.lowerbound
============================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianMarkovChain.lowerbound bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Mixture.delete.rst 0000664 5104112 5104112 00000000173 12356463441 034643 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Mixture.delete
============================
.. currentmodule:: bayespy.nodes
.. automethod:: Mixture.delete ././@LongLink 0000000 0000000 0000000 00000000162 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianMarkovChain.move_plates.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianMarkovChain.mo0000664 5104112 5104112 00000000256 12356463440 035446 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianMarkovChain.move_plates
=============================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianMarkovChain.move_plates ././@LongLink 0000000 0000000 0000000 00000000156 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianMarkovChain.observe.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianMarkovChain.ob0000664 5104112 5104112 00000000242 12356463440 035426 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianMarkovChain.observe
=========================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianMarkovChain.observe ././@LongLink 0000000 0000000 0000000 00000000152 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianMarkovChain.pdf.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianMarkovChain.pd0000664 5104112 5104112 00000000226 12356463440 035433 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianMarkovChain.pdf
=====================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianMarkovChain.pdf ././@LongLink 0000000 0000000 0000000 00000000155 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianMarkovChain.plates.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianMarkovChain.pl0000664 5104112 5104112 00000000242 12356463440 035441 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianMarkovChain.plates
========================================
.. currentmodule:: bayespy.nodes
.. autoattribute:: GaussianMarkovChain.plates ././@LongLink 0000000 0000000 0000000 00000000153 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianMarkovChain.plot.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianMarkovChain.pl0000664 5104112 5104112 00000000231 12356463440 035437 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianMarkovChain.plot
======================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianMarkovChain.plot ././@LongLink 0000000 0000000 0000000 00000000155 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianMarkovChain.random.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianMarkovChain.ra0000664 5104112 5104112 00000000237 12356463440 035434 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianMarkovChain.random
========================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianMarkovChain.random ././@LongLink 0000000 0000000 0000000 00000000155 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianMarkovChain.rotate.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianMarkovChain.ro0000664 5104112 5104112 00000000237 12356463440 035452 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianMarkovChain.rotate
========================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianMarkovChain.rotate ././@LongLink 0000000 0000000 0000000 00000000153 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianMarkovChain.save.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianMarkovChain.sa0000664 5104112 5104112 00000000231 12356463440 035427 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianMarkovChain.save
======================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianMarkovChain.save ././@LongLink 0000000 0000000 0000000 00000000162 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianMarkovChain.set_plotter.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianMarkovChain.se0000664 5104112 5104112 00000000256 12356463441 035443 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianMarkovChain.set_plotter
=============================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianMarkovChain.set_plotter ././@LongLink 0000000 0000000 0000000 00000000153 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianMarkovChain.show.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianMarkovChain.sh0000664 5104112 5104112 00000000231 12356463441 035437 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianMarkovChain.show
======================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianMarkovChain.show ././@LongLink 0000000 0000000 0000000 00000000160 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianMarkovChain.unobserve.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianMarkovChain.un0000664 5104112 5104112 00000000250 12356463441 035450 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianMarkovChain.unobserve
===========================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianMarkovChain.unobserve bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Mixture.dims.rst 0000664 5104112 5104112 00000000170 12356463441 034332 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Mixture.dims
==========================
.. currentmodule:: bayespy.nodes
.. autoattribute:: Mixture.dims ././@LongLink 0000000 0000000 0000000 00000000155 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianMarkovChain.update.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianMarkovChain.up0000664 5104112 5104112 00000000237 12356463441 035457 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianMarkovChain.update
========================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianMarkovChain.update ././@LongLink 0000000 0000000 0000000 00000000153 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianWishart.__init__.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianWishart.__init0000664 5104112 5104112 00000000231 12356463441 035505 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianWishart.__init__
======================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianWishart.__init__ ././@LongLink 0000000 0000000 0000000 00000000161 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianWishart.add_plate_axis.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianWishart.add_pl0000664 5104112 5104112 00000000253 12356463441 035473 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianWishart.add_plate_axis
============================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianWishart.add_plate_axis ././@LongLink 0000000 0000000 0000000 00000000151 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianWishart.delete.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianWishart.delete0000664 5104112 5104112 00000000223 12356463441 035507 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianWishart.delete
====================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianWishart.delete ././@LongLink 0000000 0000000 0000000 00000000147 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianWishart.dims.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianWishart.dims.r0000664 5104112 5104112 00000000220 12356463441 035436 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianWishart.dims
==================================
.. currentmodule:: bayespy.nodes
.. autoattribute:: GaussianWishart.dims ././@LongLink 0000000 0000000 0000000 00000000153 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianWishart.get_mask.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianWishart.get_ma0000664 5104112 5104112 00000000231 12356463441 035500 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianWishart.get_mask
======================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianWishart.get_mask ././@LongLink 0000000 0000000 0000000 00000000156 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianWishart.get_moments.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianWishart.get_mo0000664 5104112 5104112 00000000242 12356463441 035520 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianWishart.get_moments
=========================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianWishart.get_moments ././@LongLink 0000000 0000000 0000000 00000000154 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianWishart.get_shape.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianWishart.get_sh0000664 5104112 5104112 00000000234 12356463441 035520 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianWishart.get_shape
=======================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianWishart.get_shape ././@LongLink 0000000 0000000 0000000 00000000156 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianWishart.has_plotter.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianWishart.has_pl0000664 5104112 5104112 00000000242 12356463441 035514 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianWishart.has_plotter
=========================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianWishart.has_plotter ././@LongLink 0000000 0000000 0000000 00000000175 00000000000 011220 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianWishart.initialize_from_parameters.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianWishart.initia0000664 5104112 5104112 00000000317 12356463441 035526 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianWishart.initialize_from_parameters
========================================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianWishart.initialize_from_parameters ././@LongLink 0000000 0000000 0000000 00000000170 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianWishart.initialize_from_prior.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianWishart.initia0000664 5104112 5104112 00000000300 12356463441 035516 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianWishart.initialize_from_prior
===================================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianWishart.initialize_from_prior ././@LongLink 0000000 0000000 0000000 00000000171 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianWishart.initialize_from_random.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianWishart.initia0000664 5104112 5104112 00000000303 12356463441 035521 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianWishart.initialize_from_random
====================================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianWishart.initialize_from_random ././@LongLink 0000000 0000000 0000000 00000000170 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianWishart.initialize_from_value.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianWishart.initia0000664 5104112 5104112 00000000300 12356463441 035516 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianWishart.initialize_from_value
===================================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianWishart.initialize_from_value ././@LongLink 0000000 0000000 0000000 00000000147 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianWishart.load.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianWishart.load.r0000664 5104112 5104112 00000000215 12356463441 035425 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianWishart.load
==================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianWishart.load ././@LongLink 0000000 0000000 0000000 00000000151 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianWishart.logpdf.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianWishart.logpdf0000664 5104112 5104112 00000000223 12356463441 035520 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianWishart.logpdf
====================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianWishart.logpdf ././@LongLink 0000000 0000000 0000000 00000000173 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianWishart.lower_bound_contribution.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianWishart.lower_0000664 5104112 5104112 00000000311 12356463441 035532 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianWishart.lower_bound_contribution
======================================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianWishart.lower_bound_contribution ././@LongLink 0000000 0000000 0000000 00000000155 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianWishart.lowerbound.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianWishart.lowerb0000664 5104112 5104112 00000000237 12356463441 035544 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianWishart.lowerbound
========================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianWishart.lowerbound ././@LongLink 0000000 0000000 0000000 00000000156 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianWishart.move_plates.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianWishart.move_p0000664 5104112 5104112 00000000242 12356463441 035533 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianWishart.move_plates
=========================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianWishart.move_plates ././@LongLink 0000000 0000000 0000000 00000000152 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianWishart.observe.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianWishart.observ0000664 5104112 5104112 00000000226 12356463441 035550 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianWishart.observe
=====================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianWishart.observe ././@LongLink 0000000 0000000 0000000 00000000146 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianWishart.pdf.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianWishart.pdf.rs0000664 5104112 5104112 00000000212 12356463441 035437 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianWishart.pdf
=================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianWishart.pdf ././@LongLink 0000000 0000000 0000000 00000000151 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianWishart.plates.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianWishart.plates0000664 5104112 5104112 00000000226 12356463441 035540 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianWishart.plates
====================================
.. currentmodule:: bayespy.nodes
.. autoattribute:: GaussianWishart.plates ././@LongLink 0000000 0000000 0000000 00000000147 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianWishart.plot.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianWishart.plot.r0000664 5104112 5104112 00000000215 12356463441 035464 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianWishart.plot
==================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianWishart.plot bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Mixture.get_mask.rst 0000664 5104112 5104112 00000000201 12356463441 035163 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Mixture.get_mask
==============================
.. currentmodule:: bayespy.nodes
.. automethod:: Mixture.get_mask ././@LongLink 0000000 0000000 0000000 00000000151 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianWishart.random.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianWishart.random0000664 5104112 5104112 00000000223 12356463441 035525 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianWishart.random
====================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianWishart.random ././@LongLink 0000000 0000000 0000000 00000000147 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianWishart.save.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianWishart.save.r0000664 5104112 5104112 00000000215 12356463441 035444 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianWishart.save
==================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianWishart.save ././@LongLink 0000000 0000000 0000000 00000000156 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianWishart.set_plotter.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianWishart.set_pl0000664 5104112 5104112 00000000242 12356463441 035534 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianWishart.set_plotter
=========================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianWishart.set_plotter ././@LongLink 0000000 0000000 0000000 00000000147 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianWishart.show.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianWishart.show.r0000664 5104112 5104112 00000000215 12356463441 035466 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianWishart.show
==================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianWishart.show ././@LongLink 0000000 0000000 0000000 00000000154 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianWishart.unobserve.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianWishart.unobse0000664 5104112 5104112 00000000234 12356463441 035542 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianWishart.unobserve
=======================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianWishart.unobserve ././@LongLink 0000000 0000000 0000000 00000000151 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianWishart.update.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.GaussianWishart.update0000664 5104112 5104112 00000000223 12356463441 035527 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianWishart.update
====================================
.. currentmodule:: bayespy.nodes
.. automethod:: GaussianWishart.update ././@LongLink 0000000 0000000 0000000 00000000151 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Mixture.add_plate_axis.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Mixture.add_plate_axis0000664 5104112 5104112 00000000223 12356463441 035527 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Mixture.add_plate_axis
====================================
.. currentmodule:: bayespy.nodes
.. automethod:: Mixture.add_plate_axis ././@LongLink 0000000 0000000 0000000 00000000146 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Mixture.get_moments.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Mixture.get_moments.rs0000664 5104112 5104112 00000000212 12356463441 035530 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Mixture.get_moments
=================================
.. currentmodule:: bayespy.nodes
.. automethod:: Mixture.get_moments bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Mixture.get_shape.rst 0000664 5104112 5104112 00000000204 12356463441 035333 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Mixture.get_shape
===============================
.. currentmodule:: bayespy.nodes
.. automethod:: Mixture.get_shape ././@LongLink 0000000 0000000 0000000 00000000146 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Mixture.has_plotter.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Mixture.has_plotter.rs0000664 5104112 5104112 00000000212 12356463441 035533 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Mixture.has_plotter
=================================
.. currentmodule:: bayespy.nodes
.. automethod:: Mixture.has_plotter ././@LongLink 0000000 0000000 0000000 00000000165 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Mixture.initialize_from_parameters.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Mixture.initialize_fro0000664 5104112 5104112 00000000267 12356463441 035605 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Mixture.initialize_from_parameters
================================================
.. currentmodule:: bayespy.nodes
.. automethod:: Mixture.initialize_from_parameters ././@LongLink 0000000 0000000 0000000 00000000160 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Mixture.initialize_from_prior.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Mixture.initialize_fro0000664 5104112 5104112 00000000250 12356463441 035575 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Mixture.initialize_from_prior
===========================================
.. currentmodule:: bayespy.nodes
.. automethod:: Mixture.initialize_from_prior ././@LongLink 0000000 0000000 0000000 00000000161 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Mixture.initialize_from_random.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Mixture.initialize_fro0000664 5104112 5104112 00000000253 12356463441 035600 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Mixture.initialize_from_random
============================================
.. currentmodule:: bayespy.nodes
.. automethod:: Mixture.initialize_from_random ././@LongLink 0000000 0000000 0000000 00000000160 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Mixture.initialize_from_value.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Mixture.initialize_fro0000664 5104112 5104112 00000000250 12356463441 035575 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Mixture.initialize_from_value
===========================================
.. currentmodule:: bayespy.nodes
.. automethod:: Mixture.initialize_from_value ././@LongLink 0000000 0000000 0000000 00000000171 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Mixture.integrated_logpdf_from_parents.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Mixture.integrated_log0000664 5104112 5104112 00000000303 12356463441 035554 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Mixture.integrated_logpdf_from_parents
====================================================
.. currentmodule:: bayespy.nodes
.. automethod:: Mixture.integrated_logpdf_from_parents bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Mixture.load.rst 0000664 5104112 5104112 00000000165 12356463441 034321 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Mixture.load
==========================
.. currentmodule:: bayespy.nodes
.. automethod:: Mixture.load bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Mixture.logpdf.rst 0000664 5104112 5104112 00000000173 12356463441 034654 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Mixture.logpdf
============================
.. currentmodule:: bayespy.nodes
.. automethod:: Mixture.logpdf ././@LongLink 0000000 0000000 0000000 00000000163 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Mixture.lower_bound_contribution.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Mixture.lower_bound_co0000664 5104112 5104112 00000000261 12356463441 035570 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Mixture.lower_bound_contribution
==============================================
.. currentmodule:: bayespy.nodes
.. automethod:: Mixture.lower_bound_contribution bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Mixture.lowerbound.rst0000664 5104112 5104112 00000000207 12356463441 035557 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Mixture.lowerbound
================================
.. currentmodule:: bayespy.nodes
.. automethod:: Mixture.lowerbound ././@LongLink 0000000 0000000 0000000 00000000146 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Mixture.move_plates.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Mixture.move_plates.rs0000664 5104112 5104112 00000000212 12356463441 035525 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Mixture.move_plates
=================================
.. currentmodule:: bayespy.nodes
.. automethod:: Mixture.move_plates bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Mixture.observe.rst 0000664 5104112 5104112 00000000176 12356463441 035051 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Mixture.observe
=============================
.. currentmodule:: bayespy.nodes
.. automethod:: Mixture.observe bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Mixture.pdf.rst 0000664 5104112 5104112 00000000162 12356463441 034150 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Mixture.pdf
=========================
.. currentmodule:: bayespy.nodes
.. automethod:: Mixture.pdf bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Mixture.plates.rst 0000664 5104112 5104112 00000000176 12356463441 034674 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Mixture.plates
============================
.. currentmodule:: bayespy.nodes
.. autoattribute:: Mixture.plates bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Mixture.plot.rst 0000664 5104112 5104112 00000000165 12356463441 034360 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Mixture.plot
==========================
.. currentmodule:: bayespy.nodes
.. automethod:: Mixture.plot bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Mixture.random.rst 0000664 5104112 5104112 00000000173 12356463441 034661 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Mixture.random
============================
.. currentmodule:: bayespy.nodes
.. automethod:: Mixture.random bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Mixture.save.rst 0000664 5104112 5104112 00000000165 12356463441 034340 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Mixture.save
==========================
.. currentmodule:: bayespy.nodes
.. automethod:: Mixture.save ././@LongLink 0000000 0000000 0000000 00000000146 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Mixture.set_plotter.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Mixture.set_plotter.rs0000664 5104112 5104112 00000000212 12356463441 035553 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Mixture.set_plotter
=================================
.. currentmodule:: bayespy.nodes
.. automethod:: Mixture.set_plotter bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Mixture.unobserve.rst 0000664 5104112 5104112 00000000204 12356463441 035404 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Mixture.unobserve
===============================
.. currentmodule:: bayespy.nodes
.. automethod:: Mixture.unobserve bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Mixture.update.rst 0000664 5104112 5104112 00000000173 12356463441 034663 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Mixture.update
============================
.. currentmodule:: bayespy.nodes
.. automethod:: Mixture.update ././@LongLink 0000000 0000000 0000000 00000000147 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Multinomial.__init__.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Multinomial.__init__.r0000664 5104112 5104112 00000000215 12356463441 035423 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Multinomial.__init__
==================================
.. currentmodule:: bayespy.nodes
.. automethod:: Multinomial.__init__ ././@LongLink 0000000 0000000 0000000 00000000155 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Multinomial.add_plate_axis.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Multinomial.add_plate_0000664 5104112 5104112 00000000237 12356463441 035504 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Multinomial.add_plate_axis
========================================
.. currentmodule:: bayespy.nodes
.. automethod:: Multinomial.add_plate_axis bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Multinomial.delete.rst0000664 5104112 5104112 00000000207 12356463441 035476 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Multinomial.delete
================================
.. currentmodule:: bayespy.nodes
.. automethod:: Multinomial.delete bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Multinomial.dims.rst 0000664 5104112 5104112 00000000204 12356463441 035165 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Multinomial.dims
==============================
.. currentmodule:: bayespy.nodes
.. autoattribute:: Multinomial.dims ././@LongLink 0000000 0000000 0000000 00000000147 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Multinomial.get_mask.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Multinomial.get_mask.r0000664 5104112 5104112 00000000215 12356463441 035456 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Multinomial.get_mask
==================================
.. currentmodule:: bayespy.nodes
.. automethod:: Multinomial.get_mask ././@LongLink 0000000 0000000 0000000 00000000152 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Multinomial.get_moments.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Multinomial.get_moment0000664 5104112 5104112 00000000226 12356463441 035564 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Multinomial.get_moments
=====================================
.. currentmodule:: bayespy.nodes
.. automethod:: Multinomial.get_moments ././@LongLink 0000000 0000000 0000000 00000000150 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Multinomial.get_shape.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Multinomial.get_shape.0000664 5104112 5104112 00000000220 12356463441 035435 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Multinomial.get_shape
===================================
.. currentmodule:: bayespy.nodes
.. automethod:: Multinomial.get_shape ././@LongLink 0000000 0000000 0000000 00000000152 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Multinomial.has_plotter.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Multinomial.has_plotte0000664 5104112 5104112 00000000226 12356463441 035570 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Multinomial.has_plotter
=====================================
.. currentmodule:: bayespy.nodes
.. automethod:: Multinomial.has_plotter ././@LongLink 0000000 0000000 0000000 00000000171 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Multinomial.initialize_from_parameters.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Multinomial.initialize0000664 5104112 5104112 00000000303 12356463441 035563 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Multinomial.initialize_from_parameters
====================================================
.. currentmodule:: bayespy.nodes
.. automethod:: Multinomial.initialize_from_parameters ././@LongLink 0000000 0000000 0000000 00000000164 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Multinomial.initialize_from_prior.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Multinomial.initialize0000664 5104112 5104112 00000000264 12356463441 035571 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Multinomial.initialize_from_prior
===============================================
.. currentmodule:: bayespy.nodes
.. automethod:: Multinomial.initialize_from_prior ././@LongLink 0000000 0000000 0000000 00000000165 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Multinomial.initialize_from_random.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Multinomial.initialize0000664 5104112 5104112 00000000267 12356463441 035574 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Multinomial.initialize_from_random
================================================
.. currentmodule:: bayespy.nodes
.. automethod:: Multinomial.initialize_from_random ././@LongLink 0000000 0000000 0000000 00000000164 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Multinomial.initialize_from_value.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Multinomial.initialize0000664 5104112 5104112 00000000264 12356463441 035571 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Multinomial.initialize_from_value
===============================================
.. currentmodule:: bayespy.nodes
.. automethod:: Multinomial.initialize_from_value bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Multinomial.load.rst 0000664 5104112 5104112 00000000201 12356463441 035145 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Multinomial.load
==============================
.. currentmodule:: bayespy.nodes
.. automethod:: Multinomial.load bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Multinomial.logpdf.rst0000664 5104112 5104112 00000000207 12356463441 035507 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Multinomial.logpdf
================================
.. currentmodule:: bayespy.nodes
.. automethod:: Multinomial.logpdf ././@LongLink 0000000 0000000 0000000 00000000167 00000000000 011221 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Multinomial.lower_bound_contribution.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Multinomial.lower_boun0000664 5104112 5104112 00000000275 12356463441 035605 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Multinomial.lower_bound_contribution
==================================================
.. currentmodule:: bayespy.nodes
.. automethod:: Multinomial.lower_bound_contribution ././@LongLink 0000000 0000000 0000000 00000000151 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Multinomial.lowerbound.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Multinomial.lowerbound0000664 5104112 5104112 00000000223 12356463441 035603 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Multinomial.lowerbound
====================================
.. currentmodule:: bayespy.nodes
.. automethod:: Multinomial.lowerbound ././@LongLink 0000000 0000000 0000000 00000000152 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Multinomial.move_plates.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Multinomial.move_plate0000664 5104112 5104112 00000000226 12356463441 035561 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Multinomial.move_plates
=====================================
.. currentmodule:: bayespy.nodes
.. automethod:: Multinomial.move_plates ././@LongLink 0000000 0000000 0000000 00000000146 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Multinomial.observe.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Multinomial.observe.rs0000664 5104112 5104112 00000000212 12356463441 035511 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Multinomial.observe
=================================
.. currentmodule:: bayespy.nodes
.. automethod:: Multinomial.observe bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Multinomial.pdf.rst 0000664 5104112 5104112 00000000176 12356463441 035012 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Multinomial.pdf
=============================
.. currentmodule:: bayespy.nodes
.. automethod:: Multinomial.pdf bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Multinomial.plates.rst0000664 5104112 5104112 00000000212 12356463441 035520 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Multinomial.plates
================================
.. currentmodule:: bayespy.nodes
.. autoattribute:: Multinomial.plates bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Multinomial.plot.rst 0000664 5104112 5104112 00000000201 12356463441 035204 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Multinomial.plot
==============================
.. currentmodule:: bayespy.nodes
.. automethod:: Multinomial.plot bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Multinomial.random.rst0000664 5104112 5104112 00000000207 12356463441 035514 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Multinomial.random
================================
.. currentmodule:: bayespy.nodes
.. automethod:: Multinomial.random bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Multinomial.save.rst 0000664 5104112 5104112 00000000201 12356463441 035164 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Multinomial.save
==============================
.. currentmodule:: bayespy.nodes
.. automethod:: Multinomial.save ././@LongLink 0000000 0000000 0000000 00000000152 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Multinomial.set_plotter.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Multinomial.set_plotte0000664 5104112 5104112 00000000226 12356463441 035610 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Multinomial.set_plotter
=====================================
.. currentmodule:: bayespy.nodes
.. automethod:: Multinomial.set_plotter bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Multinomial.show.rst 0000664 5104112 5104112 00000000201 12356463441 035206 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Multinomial.show
==============================
.. currentmodule:: bayespy.nodes
.. automethod:: Multinomial.show ././@LongLink 0000000 0000000 0000000 00000000150 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Multinomial.unobserve.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Multinomial.unobserve.0000664 5104112 5104112 00000000220 12356463441 035506 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Multinomial.unobserve
===================================
.. currentmodule:: bayespy.nodes
.. automethod:: Multinomial.unobserve bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Multinomial.update.rst0000664 5104112 5104112 00000000207 12356463441 035516 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Multinomial.update
================================
.. currentmodule:: bayespy.nodes
.. automethod:: Multinomial.update bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Poisson.__init__.rst 0000664 5104112 5104112 00000000201 12356463441 035125 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Poisson.__init__
==============================
.. currentmodule:: bayespy.nodes
.. automethod:: Poisson.__init__ ././@LongLink 0000000 0000000 0000000 00000000151 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Poisson.add_plate_axis.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Poisson.add_plate_axis0000664 5104112 5104112 00000000223 12356463441 035524 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Poisson.add_plate_axis
====================================
.. currentmodule:: bayespy.nodes
.. automethod:: Poisson.add_plate_axis bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Poisson.delete.rst 0000664 5104112 5104112 00000000173 12356463441 034640 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Poisson.delete
============================
.. currentmodule:: bayespy.nodes
.. automethod:: Poisson.delete bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Poisson.dims.rst 0000664 5104112 5104112 00000000170 12356463441 034327 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Poisson.dims
==========================
.. currentmodule:: bayespy.nodes
.. autoattribute:: Poisson.dims bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Poisson.get_mask.rst 0000664 5104112 5104112 00000000201 12356463441 035160 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Poisson.get_mask
==============================
.. currentmodule:: bayespy.nodes
.. automethod:: Poisson.get_mask ././@LongLink 0000000 0000000 0000000 00000000146 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Poisson.get_moments.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Poisson.get_moments.rs0000664 5104112 5104112 00000000212 12356463441 035525 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Poisson.get_moments
=================================
.. currentmodule:: bayespy.nodes
.. automethod:: Poisson.get_moments bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Poisson.get_shape.rst 0000664 5104112 5104112 00000000204 12356463441 035330 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Poisson.get_shape
===============================
.. currentmodule:: bayespy.nodes
.. automethod:: Poisson.get_shape ././@LongLink 0000000 0000000 0000000 00000000146 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Poisson.has_plotter.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Poisson.has_plotter.rs0000664 5104112 5104112 00000000212 12356463441 035530 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Poisson.has_plotter
=================================
.. currentmodule:: bayespy.nodes
.. automethod:: Poisson.has_plotter ././@LongLink 0000000 0000000 0000000 00000000165 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Poisson.initialize_from_parameters.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Poisson.initialize_fro0000664 5104112 5104112 00000000267 12356463441 035602 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Poisson.initialize_from_parameters
================================================
.. currentmodule:: bayespy.nodes
.. automethod:: Poisson.initialize_from_parameters ././@LongLink 0000000 0000000 0000000 00000000160 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Poisson.initialize_from_prior.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Poisson.initialize_fro0000664 5104112 5104112 00000000250 12356463441 035572 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Poisson.initialize_from_prior
===========================================
.. currentmodule:: bayespy.nodes
.. automethod:: Poisson.initialize_from_prior ././@LongLink 0000000 0000000 0000000 00000000161 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Poisson.initialize_from_random.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Poisson.initialize_fro0000664 5104112 5104112 00000000253 12356463441 035575 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Poisson.initialize_from_random
============================================
.. currentmodule:: bayespy.nodes
.. automethod:: Poisson.initialize_from_random ././@LongLink 0000000 0000000 0000000 00000000160 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Poisson.initialize_from_value.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Poisson.initialize_fro0000664 5104112 5104112 00000000250 12356463441 035572 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Poisson.initialize_from_value
===========================================
.. currentmodule:: bayespy.nodes
.. automethod:: Poisson.initialize_from_value bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Poisson.load.rst 0000664 5104112 5104112 00000000165 12356463441 034316 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Poisson.load
==========================
.. currentmodule:: bayespy.nodes
.. automethod:: Poisson.load bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Poisson.logpdf.rst 0000664 5104112 5104112 00000000173 12356463441 034651 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Poisson.logpdf
============================
.. currentmodule:: bayespy.nodes
.. automethod:: Poisson.logpdf ././@LongLink 0000000 0000000 0000000 00000000163 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Poisson.lower_bound_contribution.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Poisson.lower_bound_co0000664 5104112 5104112 00000000261 12356463441 035565 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Poisson.lower_bound_contribution
==============================================
.. currentmodule:: bayespy.nodes
.. automethod:: Poisson.lower_bound_contribution bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Poisson.lowerbound.rst0000664 5104112 5104112 00000000207 12356463441 035554 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Poisson.lowerbound
================================
.. currentmodule:: bayespy.nodes
.. automethod:: Poisson.lowerbound ././@LongLink 0000000 0000000 0000000 00000000146 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Poisson.move_plates.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Poisson.move_plates.rs0000664 5104112 5104112 00000000212 12356463441 035522 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Poisson.move_plates
=================================
.. currentmodule:: bayespy.nodes
.. automethod:: Poisson.move_plates bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Poisson.observe.rst 0000664 5104112 5104112 00000000176 12356463441 035046 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Poisson.observe
=============================
.. currentmodule:: bayespy.nodes
.. automethod:: Poisson.observe bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Poisson.pdf.rst 0000664 5104112 5104112 00000000162 12356463441 034145 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Poisson.pdf
=========================
.. currentmodule:: bayespy.nodes
.. automethod:: Poisson.pdf bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Poisson.plates.rst 0000664 5104112 5104112 00000000176 12356463441 034671 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Poisson.plates
============================
.. currentmodule:: bayespy.nodes
.. autoattribute:: Poisson.plates bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Poisson.plot.rst 0000664 5104112 5104112 00000000165 12356463441 034355 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Poisson.plot
==========================
.. currentmodule:: bayespy.nodes
.. automethod:: Poisson.plot bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Poisson.random.rst 0000664 5104112 5104112 00000000173 12356463441 034656 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Poisson.random
============================
.. currentmodule:: bayespy.nodes
.. automethod:: Poisson.random bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Poisson.save.rst 0000664 5104112 5104112 00000000165 12356463441 034335 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Poisson.save
==========================
.. currentmodule:: bayespy.nodes
.. automethod:: Poisson.save ././@LongLink 0000000 0000000 0000000 00000000146 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Poisson.set_plotter.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Poisson.set_plotter.rs0000664 5104112 5104112 00000000212 12356463441 035550 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Poisson.set_plotter
=================================
.. currentmodule:: bayespy.nodes
.. automethod:: Poisson.set_plotter bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Poisson.show.rst 0000664 5104112 5104112 00000000165 12356463441 034357 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Poisson.show
==========================
.. currentmodule:: bayespy.nodes
.. automethod:: Poisson.show bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Poisson.unobserve.rst 0000664 5104112 5104112 00000000204 12356463441 035401 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Poisson.unobserve
===============================
.. currentmodule:: bayespy.nodes
.. automethod:: Poisson.unobserve bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Poisson.update.rst 0000664 5104112 5104112 00000000173 12356463441 034660 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Poisson.update
============================
.. currentmodule:: bayespy.nodes
.. automethod:: Poisson.update ././@LongLink 0000000 0000000 0000000 00000000147 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SumMultiply.__init__.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SumMultiply.__init__.r0000664 5104112 5104112 00000000215 12356463441 035435 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.SumMultiply.__init__
==================================
.. currentmodule:: bayespy.nodes
.. automethod:: SumMultiply.__init__ ././@LongLink 0000000 0000000 0000000 00000000155 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SumMultiply.add_plate_axis.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SumMultiply.add_plate_0000664 5104112 5104112 00000000237 12356463441 035516 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.SumMultiply.add_plate_axis
========================================
.. currentmodule:: bayespy.nodes
.. automethod:: SumMultiply.add_plate_axis bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SumMultiply.delete.rst0000664 5104112 5104112 00000000207 12356463441 035510 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.SumMultiply.delete
================================
.. currentmodule:: bayespy.nodes
.. automethod:: SumMultiply.delete ././@LongLink 0000000 0000000 0000000 00000000147 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SumMultiply.get_mask.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SumMultiply.get_mask.r0000664 5104112 5104112 00000000215 12356463441 035470 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.SumMultiply.get_mask
==================================
.. currentmodule:: bayespy.nodes
.. automethod:: SumMultiply.get_mask ././@LongLink 0000000 0000000 0000000 00000000152 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SumMultiply.get_moments.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SumMultiply.get_moment0000664 5104112 5104112 00000000226 12356463441 035576 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.SumMultiply.get_moments
=====================================
.. currentmodule:: bayespy.nodes
.. automethod:: SumMultiply.get_moments ././@LongLink 0000000 0000000 0000000 00000000155 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SumMultiply.get_parameters.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SumMultiply.get_parame0000664 5104112 5104112 00000000237 12356463441 035546 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.SumMultiply.get_parameters
========================================
.. currentmodule:: bayespy.nodes
.. automethod:: SumMultiply.get_parameters ././@LongLink 0000000 0000000 0000000 00000000150 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SumMultiply.get_shape.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SumMultiply.get_shape.0000664 5104112 5104112 00000000220 12356463441 035447 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.SumMultiply.get_shape
===================================
.. currentmodule:: bayespy.nodes
.. automethod:: SumMultiply.get_shape ././@LongLink 0000000 0000000 0000000 00000000152 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SumMultiply.has_plotter.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SumMultiply.has_plotte0000664 5104112 5104112 00000000226 12356463441 035602 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.SumMultiply.has_plotter
=====================================
.. currentmodule:: bayespy.nodes
.. automethod:: SumMultiply.has_plotter ././@LongLink 0000000 0000000 0000000 00000000167 00000000000 011221 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SumMultiply.lower_bound_contribution.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SumMultiply.lower_boun0000664 5104112 5104112 00000000275 12356463441 035617 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.SumMultiply.lower_bound_contribution
==================================================
.. currentmodule:: bayespy.nodes
.. automethod:: SumMultiply.lower_bound_contribution ././@LongLink 0000000 0000000 0000000 00000000152 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SumMultiply.move_plates.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SumMultiply.move_plate0000664 5104112 5104112 00000000226 12356463441 035573 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.SumMultiply.move_plates
=====================================
.. currentmodule:: bayespy.nodes
.. automethod:: SumMultiply.move_plates bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SumMultiply.plates.rst0000664 5104112 5104112 00000000212 12356463441 035532 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.SumMultiply.plates
================================
.. currentmodule:: bayespy.nodes
.. autoattribute:: SumMultiply.plates bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SumMultiply.plot.rst 0000664 5104112 5104112 00000000201 12356463441 035216 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.SumMultiply.plot
==============================
.. currentmodule:: bayespy.nodes
.. automethod:: SumMultiply.plot ././@LongLink 0000000 0000000 0000000 00000000152 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SumMultiply.set_plotter.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SumMultiply.set_plotte0000664 5104112 5104112 00000000226 12356463441 035622 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.SumMultiply.set_plotter
=====================================
.. currentmodule:: bayespy.nodes
.. automethod:: SumMultiply.set_plotter ././@LongLink 0000000 0000000 0000000 00000000170 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SwitchingGaussianMarkovChain.__init__.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SwitchingGaussianMarko0000664 5104112 5104112 00000000300 12356463441 035552 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.SwitchingGaussianMarkovChain.__init__
===================================================
.. currentmodule:: bayespy.nodes
.. automethod:: SwitchingGaussianMarkovChain.__init__ ././@LongLink 0000000 0000000 0000000 00000000176 00000000000 011221 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SwitchingGaussianMarkovChain.add_plate_axis.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SwitchingGaussianMarko0000664 5104112 5104112 00000000322 12356463441 035556 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.SwitchingGaussianMarkovChain.add_plate_axis
=========================================================
.. currentmodule:: bayespy.nodes
.. automethod:: SwitchingGaussianMarkovChain.add_plate_axis bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Wishart.__init__.rst 0000664 5104112 5104112 00000000201 12356463442 035115 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Wishart.__init__
==============================
.. currentmodule:: bayespy.nodes
.. automethod:: Wishart.__init__ ././@LongLink 0000000 0000000 0000000 00000000166 00000000000 011220 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SwitchingGaussianMarkovChain.delete.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SwitchingGaussianMarko0000664 5104112 5104112 00000000272 12356463441 035562 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.SwitchingGaussianMarkovChain.delete
=================================================
.. currentmodule:: bayespy.nodes
.. automethod:: SwitchingGaussianMarkovChain.delete ././@LongLink 0000000 0000000 0000000 00000000164 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SwitchingGaussianMarkovChain.dims.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SwitchingGaussianMarko0000664 5104112 5104112 00000000267 12356463441 035566 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.SwitchingGaussianMarkovChain.dims
===============================================
.. currentmodule:: bayespy.nodes
.. autoattribute:: SwitchingGaussianMarkovChain.dims ././@LongLink 0000000 0000000 0000000 00000000170 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SwitchingGaussianMarkovChain.get_mask.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SwitchingGaussianMarko0000664 5104112 5104112 00000000300 12356463441 035552 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.SwitchingGaussianMarkovChain.get_mask
===================================================
.. currentmodule:: bayespy.nodes
.. automethod:: SwitchingGaussianMarkovChain.get_mask ././@LongLink 0000000 0000000 0000000 00000000173 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SwitchingGaussianMarkovChain.get_moments.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SwitchingGaussianMarko0000664 5104112 5104112 00000000311 12356463441 035554 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.SwitchingGaussianMarkovChain.get_moments
======================================================
.. currentmodule:: bayespy.nodes
.. automethod:: SwitchingGaussianMarkovChain.get_moments ././@LongLink 0000000 0000000 0000000 00000000171 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SwitchingGaussianMarkovChain.get_shape.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SwitchingGaussianMarko0000664 5104112 5104112 00000000303 12356463441 035555 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.SwitchingGaussianMarkovChain.get_shape
====================================================
.. currentmodule:: bayespy.nodes
.. automethod:: SwitchingGaussianMarkovChain.get_shape ././@LongLink 0000000 0000000 0000000 00000000173 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SwitchingGaussianMarkovChain.has_plotter.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SwitchingGaussianMarko0000664 5104112 5104112 00000000311 12356463441 035554 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.SwitchingGaussianMarkovChain.has_plotter
======================================================
.. currentmodule:: bayespy.nodes
.. automethod:: SwitchingGaussianMarkovChain.has_plotter ././@LongLink 0000000 0000000 0000000 00000000212 00000000000 011210 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SwitchingGaussianMarkovChain.initialize_from_parameters.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SwitchingGaussianMarko0000664 5104112 5104112 00000000366 12356463441 035566 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.SwitchingGaussianMarkovChain.initialize_from_parameters
=====================================================================
.. currentmodule:: bayespy.nodes
.. automethod:: SwitchingGaussianMarkovChain.initialize_from_parameters ././@LongLink 0000000 0000000 0000000 00000000205 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SwitchingGaussianMarkovChain.initialize_from_prior.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SwitchingGaussianMarko0000664 5104112 5104112 00000000347 12356463441 035565 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.SwitchingGaussianMarkovChain.initialize_from_prior
================================================================
.. currentmodule:: bayespy.nodes
.. automethod:: SwitchingGaussianMarkovChain.initialize_from_prior ././@LongLink 0000000 0000000 0000000 00000000206 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SwitchingGaussianMarkovChain.initialize_from_random.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SwitchingGaussianMarko0000664 5104112 5104112 00000000352 12356463441 035561 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.SwitchingGaussianMarkovChain.initialize_from_random
=================================================================
.. currentmodule:: bayespy.nodes
.. automethod:: SwitchingGaussianMarkovChain.initialize_from_random ././@LongLink 0000000 0000000 0000000 00000000205 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SwitchingGaussianMarkovChain.initialize_from_value.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SwitchingGaussianMarko0000664 5104112 5104112 00000000347 12356463441 035565 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.SwitchingGaussianMarkovChain.initialize_from_value
================================================================
.. currentmodule:: bayespy.nodes
.. automethod:: SwitchingGaussianMarkovChain.initialize_from_value ././@LongLink 0000000 0000000 0000000 00000000164 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SwitchingGaussianMarkovChain.load.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SwitchingGaussianMarko0000664 5104112 5104112 00000000264 12356463441 035563 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.SwitchingGaussianMarkovChain.load
===============================================
.. currentmodule:: bayespy.nodes
.. automethod:: SwitchingGaussianMarkovChain.load ././@LongLink 0000000 0000000 0000000 00000000166 00000000000 011220 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SwitchingGaussianMarkovChain.logpdf.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SwitchingGaussianMarko0000664 5104112 5104112 00000000272 12356463441 035562 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.SwitchingGaussianMarkovChain.logpdf
=================================================
.. currentmodule:: bayespy.nodes
.. automethod:: SwitchingGaussianMarkovChain.logpdf ././@LongLink 0000000 0000000 0000000 00000000210 00000000000 011206 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SwitchingGaussianMarkovChain.lower_bound_contribution.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SwitchingGaussianMarko0000664 5104112 5104112 00000000360 12356463441 035560 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.SwitchingGaussianMarkovChain.lower_bound_contribution
===================================================================
.. currentmodule:: bayespy.nodes
.. automethod:: SwitchingGaussianMarkovChain.lower_bound_contribution ././@LongLink 0000000 0000000 0000000 00000000172 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SwitchingGaussianMarkovChain.lowerbound.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SwitchingGaussianMarko0000664 5104112 5104112 00000000306 12356463441 035560 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.SwitchingGaussianMarkovChain.lowerbound
=====================================================
.. currentmodule:: bayespy.nodes
.. automethod:: SwitchingGaussianMarkovChain.lowerbound ././@LongLink 0000000 0000000 0000000 00000000173 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SwitchingGaussianMarkovChain.move_plates.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SwitchingGaussianMarko0000664 5104112 5104112 00000000311 12356463441 035554 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.SwitchingGaussianMarkovChain.move_plates
======================================================
.. currentmodule:: bayespy.nodes
.. automethod:: SwitchingGaussianMarkovChain.move_plates ././@LongLink 0000000 0000000 0000000 00000000167 00000000000 011221 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SwitchingGaussianMarkovChain.observe.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SwitchingGaussianMarko0000664 5104112 5104112 00000000275 12356463441 035565 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.SwitchingGaussianMarkovChain.observe
==================================================
.. currentmodule:: bayespy.nodes
.. automethod:: SwitchingGaussianMarkovChain.observe ././@LongLink 0000000 0000000 0000000 00000000163 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SwitchingGaussianMarkovChain.pdf.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SwitchingGaussianMarko0000664 5104112 5104112 00000000261 12356463441 035560 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.SwitchingGaussianMarkovChain.pdf
==============================================
.. currentmodule:: bayespy.nodes
.. automethod:: SwitchingGaussianMarkovChain.pdf ././@LongLink 0000000 0000000 0000000 00000000166 00000000000 011220 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SwitchingGaussianMarkovChain.plates.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SwitchingGaussianMarko0000664 5104112 5104112 00000000275 12356463441 035565 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.SwitchingGaussianMarkovChain.plates
=================================================
.. currentmodule:: bayespy.nodes
.. autoattribute:: SwitchingGaussianMarkovChain.plates ././@LongLink 0000000 0000000 0000000 00000000164 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SwitchingGaussianMarkovChain.plot.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SwitchingGaussianMarko0000664 5104112 5104112 00000000264 12356463441 035563 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.SwitchingGaussianMarkovChain.plot
===============================================
.. currentmodule:: bayespy.nodes
.. automethod:: SwitchingGaussianMarkovChain.plot ././@LongLink 0000000 0000000 0000000 00000000166 00000000000 011220 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SwitchingGaussianMarkovChain.random.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SwitchingGaussianMarko0000664 5104112 5104112 00000000272 12356463441 035562 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.SwitchingGaussianMarkovChain.random
=================================================
.. currentmodule:: bayespy.nodes
.. automethod:: SwitchingGaussianMarkovChain.random ././@LongLink 0000000 0000000 0000000 00000000166 00000000000 011220 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SwitchingGaussianMarkovChain.rotate.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SwitchingGaussianMarko0000664 5104112 5104112 00000000272 12356463441 035562 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.SwitchingGaussianMarkovChain.rotate
=================================================
.. currentmodule:: bayespy.nodes
.. automethod:: SwitchingGaussianMarkovChain.rotate ././@LongLink 0000000 0000000 0000000 00000000164 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SwitchingGaussianMarkovChain.save.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SwitchingGaussianMarko0000664 5104112 5104112 00000000264 12356463441 035563 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.SwitchingGaussianMarkovChain.save
===============================================
.. currentmodule:: bayespy.nodes
.. automethod:: SwitchingGaussianMarkovChain.save ././@LongLink 0000000 0000000 0000000 00000000173 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SwitchingGaussianMarkovChain.set_plotter.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SwitchingGaussianMarko0000664 5104112 5104112 00000000311 12356463441 035554 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.SwitchingGaussianMarkovChain.set_plotter
======================================================
.. currentmodule:: bayespy.nodes
.. automethod:: SwitchingGaussianMarkovChain.set_plotter ././@LongLink 0000000 0000000 0000000 00000000164 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SwitchingGaussianMarkovChain.show.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SwitchingGaussianMarko0000664 5104112 5104112 00000000264 12356463441 035563 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.SwitchingGaussianMarkovChain.show
===============================================
.. currentmodule:: bayespy.nodes
.. automethod:: SwitchingGaussianMarkovChain.show ././@LongLink 0000000 0000000 0000000 00000000171 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SwitchingGaussianMarkovChain.unobserve.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SwitchingGaussianMarko0000664 5104112 5104112 00000000303 12356463441 035555 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.SwitchingGaussianMarkovChain.unobserve
====================================================
.. currentmodule:: bayespy.nodes
.. automethod:: SwitchingGaussianMarkovChain.unobserve ././@LongLink 0000000 0000000 0000000 00000000166 00000000000 011220 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SwitchingGaussianMarkovChain.update.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.SwitchingGaussianMarko0000664 5104112 5104112 00000000272 12356463441 035562 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.SwitchingGaussianMarkovChain.update
=================================================
.. currentmodule:: bayespy.nodes
.. automethod:: SwitchingGaussianMarkovChain.update ././@LongLink 0000000 0000000 0000000 00000000166 00000000000 011220 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.VaryingGaussianMarkovChain.__init__.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.VaryingGaussianMarkovC0000664 5104112 5104112 00000000272 12356463441 035533 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.VaryingGaussianMarkovChain.__init__
=================================================
.. currentmodule:: bayespy.nodes
.. automethod:: VaryingGaussianMarkovChain.__init__ ././@LongLink 0000000 0000000 0000000 00000000174 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.VaryingGaussianMarkovChain.add_plate_axis.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.VaryingGaussianMarkovC0000664 5104112 5104112 00000000314 12356463441 035530 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.VaryingGaussianMarkovChain.add_plate_axis
=======================================================
.. currentmodule:: bayespy.nodes
.. automethod:: VaryingGaussianMarkovChain.add_plate_axis ././@LongLink 0000000 0000000 0000000 00000000164 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.VaryingGaussianMarkovChain.delete.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.VaryingGaussianMarkovC0000664 5104112 5104112 00000000264 12356463441 035534 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.VaryingGaussianMarkovChain.delete
===============================================
.. currentmodule:: bayespy.nodes
.. automethod:: VaryingGaussianMarkovChain.delete ././@LongLink 0000000 0000000 0000000 00000000162 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.VaryingGaussianMarkovChain.dims.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.VaryingGaussianMarkovC0000664 5104112 5104112 00000000261 12356463441 035531 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.VaryingGaussianMarkovChain.dims
=============================================
.. currentmodule:: bayespy.nodes
.. autoattribute:: VaryingGaussianMarkovChain.dims ././@LongLink 0000000 0000000 0000000 00000000166 00000000000 011220 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.VaryingGaussianMarkovChain.get_mask.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.VaryingGaussianMarkovC0000664 5104112 5104112 00000000272 12356463441 035533 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.VaryingGaussianMarkovChain.get_mask
=================================================
.. currentmodule:: bayespy.nodes
.. automethod:: VaryingGaussianMarkovChain.get_mask ././@LongLink 0000000 0000000 0000000 00000000171 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.VaryingGaussianMarkovChain.get_moments.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.VaryingGaussianMarkovC0000664 5104112 5104112 00000000303 12356463441 035526 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.VaryingGaussianMarkovChain.get_moments
====================================================
.. currentmodule:: bayespy.nodes
.. automethod:: VaryingGaussianMarkovChain.get_moments ././@LongLink 0000000 0000000 0000000 00000000167 00000000000 011221 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.VaryingGaussianMarkovChain.get_shape.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.VaryingGaussianMarkovC0000664 5104112 5104112 00000000275 12356463441 035536 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.VaryingGaussianMarkovChain.get_shape
==================================================
.. currentmodule:: bayespy.nodes
.. automethod:: VaryingGaussianMarkovChain.get_shape ././@LongLink 0000000 0000000 0000000 00000000171 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.VaryingGaussianMarkovChain.has_plotter.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.VaryingGaussianMarkovC0000664 5104112 5104112 00000000303 12356463441 035526 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.VaryingGaussianMarkovChain.has_plotter
====================================================
.. currentmodule:: bayespy.nodes
.. automethod:: VaryingGaussianMarkovChain.has_plotter ././@LongLink 0000000 0000000 0000000 00000000210 00000000000 011206 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.VaryingGaussianMarkovChain.initialize_from_parameters.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.VaryingGaussianMarkovC0000664 5104112 5104112 00000000360 12356463442 035532 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.VaryingGaussianMarkovChain.initialize_from_parameters
===================================================================
.. currentmodule:: bayespy.nodes
.. automethod:: VaryingGaussianMarkovChain.initialize_from_parameters ././@LongLink 0000000 0000000 0000000 00000000203 00000000000 011210 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.VaryingGaussianMarkovChain.initialize_from_prior.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.VaryingGaussianMarkovC0000664 5104112 5104112 00000000341 12356463442 035531 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.VaryingGaussianMarkovChain.initialize_from_prior
==============================================================
.. currentmodule:: bayespy.nodes
.. automethod:: VaryingGaussianMarkovChain.initialize_from_prior ././@LongLink 0000000 0000000 0000000 00000000204 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.VaryingGaussianMarkovChain.initialize_from_random.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.VaryingGaussianMarkovC0000664 5104112 5104112 00000000344 12356463442 035534 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.VaryingGaussianMarkovChain.initialize_from_random
===============================================================
.. currentmodule:: bayespy.nodes
.. automethod:: VaryingGaussianMarkovChain.initialize_from_random ././@LongLink 0000000 0000000 0000000 00000000203 00000000000 011210 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.VaryingGaussianMarkovChain.initialize_from_value.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.VaryingGaussianMarkovC0000664 5104112 5104112 00000000341 12356463442 035531 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.VaryingGaussianMarkovChain.initialize_from_value
==============================================================
.. currentmodule:: bayespy.nodes
.. automethod:: VaryingGaussianMarkovChain.initialize_from_value ././@LongLink 0000000 0000000 0000000 00000000162 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.VaryingGaussianMarkovChain.load.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.VaryingGaussianMarkovC0000664 5104112 5104112 00000000256 12356463442 035536 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.VaryingGaussianMarkovChain.load
=============================================
.. currentmodule:: bayespy.nodes
.. automethod:: VaryingGaussianMarkovChain.load ././@LongLink 0000000 0000000 0000000 00000000164 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.VaryingGaussianMarkovChain.logpdf.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.VaryingGaussianMarkovC0000664 5104112 5104112 00000000264 12356463442 035535 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.VaryingGaussianMarkovChain.logpdf
===============================================
.. currentmodule:: bayespy.nodes
.. automethod:: VaryingGaussianMarkovChain.logpdf ././@LongLink 0000000 0000000 0000000 00000000206 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.VaryingGaussianMarkovChain.lower_bound_contribution.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.VaryingGaussianMarkovC0000664 5104112 5104112 00000000352 12356463442 035533 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.VaryingGaussianMarkovChain.lower_bound_contribution
=================================================================
.. currentmodule:: bayespy.nodes
.. automethod:: VaryingGaussianMarkovChain.lower_bound_contribution ././@LongLink 0000000 0000000 0000000 00000000170 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.VaryingGaussianMarkovChain.lowerbound.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.VaryingGaussianMarkovC0000664 5104112 5104112 00000000300 12356463442 035524 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.VaryingGaussianMarkovChain.lowerbound
===================================================
.. currentmodule:: bayespy.nodes
.. automethod:: VaryingGaussianMarkovChain.lowerbound ././@LongLink 0000000 0000000 0000000 00000000171 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.VaryingGaussianMarkovChain.move_plates.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.VaryingGaussianMarkovC0000664 5104112 5104112 00000000303 12356463442 035527 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.VaryingGaussianMarkovChain.move_plates
====================================================
.. currentmodule:: bayespy.nodes
.. automethod:: VaryingGaussianMarkovChain.move_plates ././@LongLink 0000000 0000000 0000000 00000000165 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.VaryingGaussianMarkovChain.observe.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.VaryingGaussianMarkovC0000664 5104112 5104112 00000000267 12356463442 035540 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.VaryingGaussianMarkovChain.observe
================================================
.. currentmodule:: bayespy.nodes
.. automethod:: VaryingGaussianMarkovChain.observe ././@LongLink 0000000 0000000 0000000 00000000161 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.VaryingGaussianMarkovChain.pdf.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.VaryingGaussianMarkovC0000664 5104112 5104112 00000000253 12356463442 035533 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.VaryingGaussianMarkovChain.pdf
============================================
.. currentmodule:: bayespy.nodes
.. automethod:: VaryingGaussianMarkovChain.pdf ././@LongLink 0000000 0000000 0000000 00000000164 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.VaryingGaussianMarkovChain.plates.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.VaryingGaussianMarkovC0000664 5104112 5104112 00000000267 12356463442 035540 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.VaryingGaussianMarkovChain.plates
===============================================
.. currentmodule:: bayespy.nodes
.. autoattribute:: VaryingGaussianMarkovChain.plates ././@LongLink 0000000 0000000 0000000 00000000162 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.VaryingGaussianMarkovChain.plot.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.VaryingGaussianMarkovC0000664 5104112 5104112 00000000256 12356463442 035536 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.VaryingGaussianMarkovChain.plot
=============================================
.. currentmodule:: bayespy.nodes
.. automethod:: VaryingGaussianMarkovChain.plot ././@LongLink 0000000 0000000 0000000 00000000164 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.VaryingGaussianMarkovChain.random.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.VaryingGaussianMarkovC0000664 5104112 5104112 00000000264 12356463442 035535 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.VaryingGaussianMarkovChain.random
===============================================
.. currentmodule:: bayespy.nodes
.. automethod:: VaryingGaussianMarkovChain.random ././@LongLink 0000000 0000000 0000000 00000000164 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.VaryingGaussianMarkovChain.rotate.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.VaryingGaussianMarkovC0000664 5104112 5104112 00000000264 12356463442 035535 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.VaryingGaussianMarkovChain.rotate
===============================================
.. currentmodule:: bayespy.nodes
.. automethod:: VaryingGaussianMarkovChain.rotate ././@LongLink 0000000 0000000 0000000 00000000162 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.VaryingGaussianMarkovChain.save.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.VaryingGaussianMarkovC0000664 5104112 5104112 00000000256 12356463442 035536 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.VaryingGaussianMarkovChain.save
=============================================
.. currentmodule:: bayespy.nodes
.. automethod:: VaryingGaussianMarkovChain.save bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Wishart.delete.rst 0000664 5104112 5104112 00000000173 12356463442 034630 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Wishart.delete
============================
.. currentmodule:: bayespy.nodes
.. automethod:: Wishart.delete ././@LongLink 0000000 0000000 0000000 00000000171 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.VaryingGaussianMarkovChain.set_plotter.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.VaryingGaussianMarkovC0000664 5104112 5104112 00000000303 12356463442 035527 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.VaryingGaussianMarkovChain.set_plotter
====================================================
.. currentmodule:: bayespy.nodes
.. automethod:: VaryingGaussianMarkovChain.set_plotter ././@LongLink 0000000 0000000 0000000 00000000162 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.VaryingGaussianMarkovChain.show.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.VaryingGaussianMarkovC0000664 5104112 5104112 00000000256 12356463442 035536 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.VaryingGaussianMarkovChain.show
=============================================
.. currentmodule:: bayespy.nodes
.. automethod:: VaryingGaussianMarkovChain.show ././@LongLink 0000000 0000000 0000000 00000000167 00000000000 011221 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.VaryingGaussianMarkovChain.unobserve.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.VaryingGaussianMarkovC0000664 5104112 5104112 00000000275 12356463442 035537 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.VaryingGaussianMarkovChain.unobserve
==================================================
.. currentmodule:: bayespy.nodes
.. automethod:: VaryingGaussianMarkovChain.unobserve ././@LongLink 0000000 0000000 0000000 00000000164 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.VaryingGaussianMarkovChain.update.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.VaryingGaussianMarkovC0000664 5104112 5104112 00000000264 12356463442 035535 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.VaryingGaussianMarkovChain.update
===============================================
.. currentmodule:: bayespy.nodes
.. automethod:: VaryingGaussianMarkovChain.update ././@LongLink 0000000 0000000 0000000 00000000151 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Wishart.add_plate_axis.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Wishart.add_plate_axis0000664 5104112 5104112 00000000223 12356463442 035514 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Wishart.add_plate_axis
====================================
.. currentmodule:: bayespy.nodes
.. automethod:: Wishart.add_plate_axis bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Wishart.dims.rst 0000664 5104112 5104112 00000000170 12356463442 034317 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Wishart.dims
==========================
.. currentmodule:: bayespy.nodes
.. autoattribute:: Wishart.dims bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Wishart.get_mask.rst 0000664 5104112 5104112 00000000201 12356463442 035150 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Wishart.get_mask
==============================
.. currentmodule:: bayespy.nodes
.. automethod:: Wishart.get_mask ././@LongLink 0000000 0000000 0000000 00000000146 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Wishart.get_moments.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Wishart.get_moments.rs0000664 5104112 5104112 00000000212 12356463442 035515 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Wishart.get_moments
=================================
.. currentmodule:: bayespy.nodes
.. automethod:: Wishart.get_moments bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Wishart.get_shape.rst 0000664 5104112 5104112 00000000204 12356463442 035320 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Wishart.get_shape
===============================
.. currentmodule:: bayespy.nodes
.. automethod:: Wishart.get_shape ././@LongLink 0000000 0000000 0000000 00000000146 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Wishart.has_plotter.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Wishart.has_plotter.rs0000664 5104112 5104112 00000000212 12356463442 035520 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Wishart.has_plotter
=================================
.. currentmodule:: bayespy.nodes
.. automethod:: Wishart.has_plotter ././@LongLink 0000000 0000000 0000000 00000000165 00000000000 011217 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Wishart.initialize_from_parameters.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Wishart.initialize_fro0000664 5104112 5104112 00000000267 12356463442 035572 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Wishart.initialize_from_parameters
================================================
.. currentmodule:: bayespy.nodes
.. automethod:: Wishart.initialize_from_parameters ././@LongLink 0000000 0000000 0000000 00000000160 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Wishart.initialize_from_prior.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Wishart.initialize_fro0000664 5104112 5104112 00000000250 12356463442 035562 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Wishart.initialize_from_prior
===========================================
.. currentmodule:: bayespy.nodes
.. automethod:: Wishart.initialize_from_prior ././@LongLink 0000000 0000000 0000000 00000000161 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Wishart.initialize_from_random.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Wishart.initialize_fro0000664 5104112 5104112 00000000253 12356463442 035565 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Wishart.initialize_from_random
============================================
.. currentmodule:: bayespy.nodes
.. automethod:: Wishart.initialize_from_random ././@LongLink 0000000 0000000 0000000 00000000160 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Wishart.initialize_from_value.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Wishart.initialize_fro0000664 5104112 5104112 00000000250 12356463442 035562 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Wishart.initialize_from_value
===========================================
.. currentmodule:: bayespy.nodes
.. automethod:: Wishart.initialize_from_value bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Wishart.load.rst 0000664 5104112 5104112 00000000165 12356463442 034306 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Wishart.load
==========================
.. currentmodule:: bayespy.nodes
.. automethod:: Wishart.load bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Wishart.logpdf.rst 0000664 5104112 5104112 00000000173 12356463442 034641 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Wishart.logpdf
============================
.. currentmodule:: bayespy.nodes
.. automethod:: Wishart.logpdf ././@LongLink 0000000 0000000 0000000 00000000163 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Wishart.lower_bound_contribution.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Wishart.lower_bound_co0000664 5104112 5104112 00000000261 12356463442 035555 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Wishart.lower_bound_contribution
==============================================
.. currentmodule:: bayespy.nodes
.. automethod:: Wishart.lower_bound_contribution bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Wishart.lowerbound.rst0000664 5104112 5104112 00000000207 12356463442 035544 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Wishart.lowerbound
================================
.. currentmodule:: bayespy.nodes
.. automethod:: Wishart.lowerbound ././@LongLink 0000000 0000000 0000000 00000000146 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Wishart.move_plates.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Wishart.move_plates.rs0000664 5104112 5104112 00000000212 12356463442 035512 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Wishart.move_plates
=================================
.. currentmodule:: bayespy.nodes
.. automethod:: Wishart.move_plates bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Wishart.observe.rst 0000664 5104112 5104112 00000000176 12356463442 035036 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Wishart.observe
=============================
.. currentmodule:: bayespy.nodes
.. automethod:: Wishart.observe bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Wishart.pdf.rst 0000664 5104112 5104112 00000000162 12356463442 034135 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Wishart.pdf
=========================
.. currentmodule:: bayespy.nodes
.. automethod:: Wishart.pdf bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Wishart.plates.rst 0000664 5104112 5104112 00000000176 12356463442 034661 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Wishart.plates
============================
.. currentmodule:: bayespy.nodes
.. autoattribute:: Wishart.plates bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Wishart.plot.rst 0000664 5104112 5104112 00000000165 12356463442 034345 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Wishart.plot
==========================
.. currentmodule:: bayespy.nodes
.. automethod:: Wishart.plot bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Wishart.random.rst 0000664 5104112 5104112 00000000173 12356463442 034646 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Wishart.random
============================
.. currentmodule:: bayespy.nodes
.. automethod:: Wishart.random bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Wishart.save.rst 0000664 5104112 5104112 00000000165 12356463442 034325 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Wishart.save
==========================
.. currentmodule:: bayespy.nodes
.. automethod:: Wishart.save ././@LongLink 0000000 0000000 0000000 00000000146 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Wishart.set_plotter.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Wishart.set_plotter.rs0000664 5104112 5104112 00000000212 12356463442 035540 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Wishart.set_plotter
=================================
.. currentmodule:: bayespy.nodes
.. automethod:: Wishart.set_plotter bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Wishart.show.rst 0000664 5104112 5104112 00000000165 12356463442 034347 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Wishart.show
==========================
.. currentmodule:: bayespy.nodes
.. automethod:: Wishart.show bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Wishart.unobserve.rst 0000664 5104112 5104112 00000000204 12356463442 035371 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Wishart.unobserve
===============================
.. currentmodule:: bayespy.nodes
.. automethod:: Wishart.unobserve bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.nodes.Wishart.update.rst 0000664 5104112 5104112 00000000173 12356463442 034650 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Wishart.update
============================
.. currentmodule:: bayespy.nodes
.. automethod:: Wishart.update ././@LongLink 0000000 0000000 0000000 00000000170 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.plot.CategoricalMarkovChainPlotter.__init__.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.plot.CategoricalMarkovChainP0000664 5104112 5104112 00000000300 12356463442 035455 0 ustar jluttine jluttine 0000000 0000000 bayespy.plot.CategoricalMarkovChainPlotter.__init__
===================================================
.. currentmodule:: bayespy.plot
.. automethod:: CategoricalMarkovChainPlotter.__init__ ././@LongLink 0000000 0000000 0000000 00000000151 00000000000 011212 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.plot.ContourPlotter.__init__.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.plot.ContourPlotter.__init__0000664 5104112 5104112 00000000223 12356463442 035542 0 ustar jluttine jluttine 0000000 0000000 bayespy.plot.ContourPlotter.__init__
====================================
.. currentmodule:: bayespy.plot
.. automethod:: ContourPlotter.__init__ ././@LongLink 0000000 0000000 0000000 00000000152 00000000000 011213 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.plot.FunctionPlotter.__init__.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.plot.FunctionPlotter.__init_0000664 5104112 5104112 00000000226 12356463442 035542 0 ustar jluttine jluttine 0000000 0000000 bayespy.plot.FunctionPlotter.__init__
=====================================
.. currentmodule:: bayespy.plot
.. automethod:: FunctionPlotter.__init__ ././@LongLink 0000000 0000000 0000000 00000000164 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.plot.GaussianTimeseriesPlotter.__init__.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.plot.GaussianTimeseriesPlott0000664 5104112 5104112 00000000264 12356463442 035635 0 ustar jluttine jluttine 0000000 0000000 bayespy.plot.GaussianTimeseriesPlotter.__init__
===============================================
.. currentmodule:: bayespy.plot
.. automethod:: GaussianTimeseriesPlotter.__init__ ././@LongLink 0000000 0000000 0000000 00000000150 00000000000 011211 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.plot.HintonPlotter.__init__.rst bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.plot.HintonPlotter.__init__.0000664 5104112 5104112 00000000220 12356463442 035423 0 ustar jluttine jluttine 0000000 0000000 bayespy.plot.HintonPlotter.__init__
===================================
.. currentmodule:: bayespy.plot
.. automethod:: HintonPlotter.__init__ bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.plot.PDFPlotter.__init__.rst0000664 5104112 5104112 00000000207 12356463442 035333 0 ustar jluttine jluttine 0000000 0000000 bayespy.plot.PDFPlotter.__init__
================================
.. currentmodule:: bayespy.plot
.. automethod:: PDFPlotter.__init__ bayespy-0.2.3/doc/source/user_api/generated/generated/generated/bayespy.plot.Plotter.__init__.rst 0000664 5104112 5104112 00000000176 12356463442 035006 0 ustar jluttine jluttine 0000000 0000000 bayespy.plot.Plotter.__init__
=============================
.. currentmodule:: bayespy.plot
.. automethod:: Plotter.__init__ bayespy-0.2.3/doc/source/user_api/generated/generated/bayespy.inference.VB.rst 0000664 5104112 5104112 00000000766 12356463433 031155 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.VB
====================
.. currentmodule:: bayespy.inference
.. autoclass:: VB
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~VB.__init__
~VB.compute_lowerbound
~VB.compute_lowerbound_terms
~VB.get_iteration_by_nodes
~VB.load
~VB.loglikelihood_lowerbound
~VB.plot
~VB.plot_iteration_by_nodes
~VB.save
~VB.set_autosave
~VB.update
././@LongLink 0000000 0000000 0000000 00000000157 00000000000 011220 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/bayespy.inference.vmp.transformations.RotateGaussian.rst bayespy-0.2.3/doc/source/user_api/generated/generated/bayespy.inference.vmp.transformations.RotateGa0000664 5104112 5104112 00000000776 12356463435 035601 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.transformations.RotateGaussian
====================================================
.. currentmodule:: bayespy.inference.vmp.transformations
.. autoclass:: RotateGaussian
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~RotateGaussian.__init__
~RotateGaussian.bound
~RotateGaussian.get_bound_terms
~RotateGaussian.nodes
~RotateGaussian.rotate
~RotateGaussian.setup
././@LongLink 0000000 0000000 0000000 00000000162 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/bayespy.inference.vmp.transformations.RotateGaussianARD.rst bayespy-0.2.3/doc/source/user_api/generated/generated/bayespy.inference.vmp.transformations.RotateGa0000664 5104112 5104112 00000001031 12356463435 035562 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.transformations.RotateGaussianARD
=======================================================
.. currentmodule:: bayespy.inference.vmp.transformations
.. autoclass:: RotateGaussianARD
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~RotateGaussianARD.__init__
~RotateGaussianARD.bound
~RotateGaussianARD.get_bound_terms
~RotateGaussianARD.nodes
~RotateGaussianARD.rotate
~RotateGaussianARD.setup
././@LongLink 0000000 0000000 0000000 00000000172 00000000000 011215 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/bayespy.inference.vmp.transformations.RotateGaussianMarkovChain.rst bayespy-0.2.3/doc/source/user_api/generated/generated/bayespy.inference.vmp.transformations.RotateGa0000664 5104112 5104112 00000001141 12356463435 035564 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.transformations.RotateGaussianMarkovChain
===============================================================
.. currentmodule:: bayespy.inference.vmp.transformations
.. autoclass:: RotateGaussianMarkovChain
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~RotateGaussianMarkovChain.__init__
~RotateGaussianMarkovChain.bound
~RotateGaussianMarkovChain.get_bound_terms
~RotateGaussianMarkovChain.nodes
~RotateGaussianMarkovChain.rotate
~RotateGaussianMarkovChain.setup
././@LongLink 0000000 0000000 0000000 00000000157 00000000000 011220 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/bayespy.inference.vmp.transformations.RotateMultiple.rst bayespy-0.2.3/doc/source/user_api/generated/generated/bayespy.inference.vmp.transformations.RotateMu0000664 5104112 5104112 00000000776 12356463435 035633 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.transformations.RotateMultiple
====================================================
.. currentmodule:: bayespy.inference.vmp.transformations
.. autoclass:: RotateMultiple
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~RotateMultiple.__init__
~RotateMultiple.bound
~RotateMultiple.get_bound_terms
~RotateMultiple.nodes
~RotateMultiple.rotate
~RotateMultiple.setup
././@LongLink 0000000 0000000 0000000 00000000173 00000000000 011216 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/bayespy.inference.vmp.transformations.RotateSwitchingMarkovChain.rst bayespy-0.2.3/doc/source/user_api/generated/generated/bayespy.inference.vmp.transformations.RotateSw0000664 5104112 5104112 00000001152 12356463435 035630 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.transformations.RotateSwitchingMarkovChain
================================================================
.. currentmodule:: bayespy.inference.vmp.transformations
.. autoclass:: RotateSwitchingMarkovChain
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~RotateSwitchingMarkovChain.__init__
~RotateSwitchingMarkovChain.bound
~RotateSwitchingMarkovChain.get_bound_terms
~RotateSwitchingMarkovChain.nodes
~RotateSwitchingMarkovChain.rotate
~RotateSwitchingMarkovChain.setup
././@LongLink 0000000 0000000 0000000 00000000171 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/bayespy.inference.vmp.transformations.RotateVaryingMarkovChain.rst bayespy-0.2.3/doc/source/user_api/generated/generated/bayespy.inference.vmp.transformations.RotateVa0000664 5104112 5104112 00000001130 12356463435 035601 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.transformations.RotateVaryingMarkovChain
==============================================================
.. currentmodule:: bayespy.inference.vmp.transformations
.. autoclass:: RotateVaryingMarkovChain
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~RotateVaryingMarkovChain.__init__
~RotateVaryingMarkovChain.bound
~RotateVaryingMarkovChain.get_bound_terms
~RotateVaryingMarkovChain.nodes
~RotateVaryingMarkovChain.rotate
~RotateVaryingMarkovChain.setup
././@LongLink 0000000 0000000 0000000 00000000162 00000000000 011214 L ustar 0000000 0000000 bayespy-0.2.3/doc/source/user_api/generated/generated/bayespy.inference.vmp.transformations.RotationOptimizer.rst bayespy-0.2.3/doc/source/user_api/generated/generated/bayespy.inference.vmp.transformations.Rotation0000664 5104112 5104112 00000000623 12356463435 035661 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference.vmp.transformations.RotationOptimizer
=======================================================
.. currentmodule:: bayespy.inference.vmp.transformations
.. autoclass:: RotationOptimizer
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~RotationOptimizer.__init__
~RotationOptimizer.rotate
bayespy-0.2.3/doc/source/user_api/generated/generated/bayespy.nodes.Bernoulli.rst 0000664 5104112 5104112 00000002075 12356463435 031750 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Bernoulli
=======================
.. currentmodule:: bayespy.nodes
.. autoclass:: Bernoulli
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~Bernoulli.__init__
~Bernoulli.add_plate_axis
~Bernoulli.delete
~Bernoulli.get_mask
~Bernoulli.get_moments
~Bernoulli.get_shape
~Bernoulli.has_plotter
~Bernoulli.initialize_from_parameters
~Bernoulli.initialize_from_prior
~Bernoulli.initialize_from_random
~Bernoulli.initialize_from_value
~Bernoulli.load
~Bernoulli.logpdf
~Bernoulli.lower_bound_contribution
~Bernoulli.lowerbound
~Bernoulli.move_plates
~Bernoulli.observe
~Bernoulli.pdf
~Bernoulli.plot
~Bernoulli.random
~Bernoulli.save
~Bernoulli.set_plotter
~Bernoulli.show
~Bernoulli.unobserve
~Bernoulli.update
.. rubric:: Attributes
.. autosummary::
:toctree: generated/
~Bernoulli.dims
~Bernoulli.plates
bayespy-0.2.3/doc/source/user_api/generated/generated/bayespy.nodes.Beta.rst 0000664 5104112 5104112 00000001647 12356463435 030674 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Beta
==================
.. currentmodule:: bayespy.nodes
.. autoclass:: Beta
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~Beta.__init__
~Beta.add_plate_axis
~Beta.delete
~Beta.get_mask
~Beta.get_moments
~Beta.get_shape
~Beta.has_plotter
~Beta.initialize_from_parameters
~Beta.initialize_from_prior
~Beta.initialize_from_random
~Beta.initialize_from_value
~Beta.load
~Beta.logpdf
~Beta.lower_bound_contribution
~Beta.lowerbound
~Beta.move_plates
~Beta.observe
~Beta.pdf
~Beta.plot
~Beta.random
~Beta.save
~Beta.set_plotter
~Beta.show
~Beta.unobserve
~Beta.update
.. rubric:: Attributes
.. autosummary::
:toctree: generated/
~Beta.dims
~Beta.plates
bayespy-0.2.3/doc/source/user_api/generated/generated/bayespy.nodes.Binomial.rst 0000664 5104112 5104112 00000002037 12356463435 031545 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Binomial
======================
.. currentmodule:: bayespy.nodes
.. autoclass:: Binomial
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~Binomial.__init__
~Binomial.add_plate_axis
~Binomial.delete
~Binomial.get_mask
~Binomial.get_moments
~Binomial.get_shape
~Binomial.has_plotter
~Binomial.initialize_from_parameters
~Binomial.initialize_from_prior
~Binomial.initialize_from_random
~Binomial.initialize_from_value
~Binomial.load
~Binomial.logpdf
~Binomial.lower_bound_contribution
~Binomial.lowerbound
~Binomial.move_plates
~Binomial.observe
~Binomial.pdf
~Binomial.plot
~Binomial.random
~Binomial.save
~Binomial.set_plotter
~Binomial.show
~Binomial.unobserve
~Binomial.update
.. rubric:: Attributes
.. autosummary::
:toctree: generated/
~Binomial.dims
~Binomial.plates
bayespy-0.2.3/doc/source/user_api/generated/generated/bayespy.nodes.Categorical.rst 0000664 5104112 5104112 00000002171 12356463435 032227 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Categorical
=========================
.. currentmodule:: bayespy.nodes
.. autoclass:: Categorical
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~Categorical.__init__
~Categorical.add_plate_axis
~Categorical.delete
~Categorical.get_mask
~Categorical.get_moments
~Categorical.get_shape
~Categorical.has_plotter
~Categorical.initialize_from_parameters
~Categorical.initialize_from_prior
~Categorical.initialize_from_random
~Categorical.initialize_from_value
~Categorical.load
~Categorical.logpdf
~Categorical.lower_bound_contribution
~Categorical.lowerbound
~Categorical.move_plates
~Categorical.observe
~Categorical.pdf
~Categorical.plot
~Categorical.random
~Categorical.save
~Categorical.set_plotter
~Categorical.show
~Categorical.unobserve
~Categorical.update
.. rubric:: Attributes
.. autosummary::
:toctree: generated/
~Categorical.dims
~Categorical.plates
bayespy-0.2.3/doc/source/user_api/generated/generated/bayespy.nodes.CategoricalMarkovChain.rst 0000664 5104112 5104112 00000002703 12356463435 034353 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.CategoricalMarkovChain
====================================
.. currentmodule:: bayespy.nodes
.. autoclass:: CategoricalMarkovChain
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~CategoricalMarkovChain.__init__
~CategoricalMarkovChain.add_plate_axis
~CategoricalMarkovChain.delete
~CategoricalMarkovChain.get_mask
~CategoricalMarkovChain.get_moments
~CategoricalMarkovChain.get_shape
~CategoricalMarkovChain.has_plotter
~CategoricalMarkovChain.initialize_from_parameters
~CategoricalMarkovChain.initialize_from_prior
~CategoricalMarkovChain.initialize_from_random
~CategoricalMarkovChain.initialize_from_value
~CategoricalMarkovChain.load
~CategoricalMarkovChain.logpdf
~CategoricalMarkovChain.lower_bound_contribution
~CategoricalMarkovChain.lowerbound
~CategoricalMarkovChain.move_plates
~CategoricalMarkovChain.observe
~CategoricalMarkovChain.pdf
~CategoricalMarkovChain.plot
~CategoricalMarkovChain.random
~CategoricalMarkovChain.save
~CategoricalMarkovChain.set_plotter
~CategoricalMarkovChain.show
~CategoricalMarkovChain.unobserve
~CategoricalMarkovChain.update
.. rubric:: Attributes
.. autosummary::
:toctree: generated/
~CategoricalMarkovChain.dims
~CategoricalMarkovChain.plates
bayespy-0.2.3/doc/source/user_api/generated/generated/bayespy.nodes.Dirichlet.rst 0000664 5104112 5104112 00000002075 12356463435 031724 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Dirichlet
=======================
.. currentmodule:: bayespy.nodes
.. autoclass:: Dirichlet
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~Dirichlet.__init__
~Dirichlet.add_plate_axis
~Dirichlet.delete
~Dirichlet.get_mask
~Dirichlet.get_moments
~Dirichlet.get_shape
~Dirichlet.has_plotter
~Dirichlet.initialize_from_parameters
~Dirichlet.initialize_from_prior
~Dirichlet.initialize_from_random
~Dirichlet.initialize_from_value
~Dirichlet.load
~Dirichlet.logpdf
~Dirichlet.lower_bound_contribution
~Dirichlet.lowerbound
~Dirichlet.move_plates
~Dirichlet.observe
~Dirichlet.pdf
~Dirichlet.plot
~Dirichlet.random
~Dirichlet.save
~Dirichlet.set_plotter
~Dirichlet.show
~Dirichlet.unobserve
~Dirichlet.update
.. rubric:: Attributes
.. autosummary::
:toctree: generated/
~Dirichlet.dims
~Dirichlet.plates
bayespy-0.2.3/doc/source/user_api/generated/generated/bayespy.nodes.Dot.rst 0000664 5104112 5104112 00000000134 12356463435 030535 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Dot
=================
.. currentmodule:: bayespy.nodes
.. autofunction:: Dot bayespy-0.2.3/doc/source/user_api/generated/generated/bayespy.nodes.Exponential.rst 0000664 5104112 5104112 00000002240 12356463435 032275 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Exponential
=========================
.. currentmodule:: bayespy.nodes
.. autoclass:: Exponential
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~Exponential.__init__
~Exponential.add_plate_axis
~Exponential.as_diagonal_wishart
~Exponential.delete
~Exponential.get_mask
~Exponential.get_moments
~Exponential.get_shape
~Exponential.has_plotter
~Exponential.initialize_from_parameters
~Exponential.initialize_from_prior
~Exponential.initialize_from_random
~Exponential.initialize_from_value
~Exponential.load
~Exponential.logpdf
~Exponential.lower_bound_contribution
~Exponential.lowerbound
~Exponential.move_plates
~Exponential.observe
~Exponential.pdf
~Exponential.plot
~Exponential.random
~Exponential.save
~Exponential.set_plotter
~Exponential.show
~Exponential.unobserve
~Exponential.update
.. rubric:: Attributes
.. autosummary::
:toctree: generated/
~Exponential.dims
~Exponential.plates
bayespy-0.2.3/doc/source/user_api/generated/generated/bayespy.nodes.Gamma.rst 0000664 5104112 5104112 00000001746 12356463435 031043 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gamma
===================
.. currentmodule:: bayespy.nodes
.. autoclass:: Gamma
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~Gamma.__init__
~Gamma.add_plate_axis
~Gamma.as_diagonal_wishart
~Gamma.delete
~Gamma.get_mask
~Gamma.get_moments
~Gamma.get_shape
~Gamma.has_plotter
~Gamma.initialize_from_parameters
~Gamma.initialize_from_prior
~Gamma.initialize_from_random
~Gamma.initialize_from_value
~Gamma.load
~Gamma.logpdf
~Gamma.lower_bound_contribution
~Gamma.lowerbound
~Gamma.move_plates
~Gamma.observe
~Gamma.pdf
~Gamma.plot
~Gamma.random
~Gamma.save
~Gamma.set_plotter
~Gamma.show
~Gamma.unobserve
~Gamma.update
.. rubric:: Attributes
.. autosummary::
:toctree: generated/
~Gamma.dims
~Gamma.plates
bayespy-0.2.3/doc/source/user_api/generated/generated/bayespy.nodes.Gate.rst 0000664 5104112 5104112 00000001113 12356463435 030665 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gate
==================
.. currentmodule:: bayespy.nodes
.. autoclass:: Gate
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~Gate.__init__
~Gate.add_plate_axis
~Gate.delete
~Gate.get_mask
~Gate.get_moments
~Gate.get_shape
~Gate.has_plotter
~Gate.lower_bound_contribution
~Gate.move_plates
~Gate.plot
~Gate.set_plotter
.. rubric:: Attributes
.. autosummary::
:toctree: generated/
~Gate.plates
bayespy-0.2.3/doc/source/user_api/generated/generated/bayespy.nodes.Gaussian.rst 0000664 5104112 5104112 00000002124 12356463435 031562 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Gaussian
======================
.. currentmodule:: bayespy.nodes
.. autoclass:: Gaussian
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~Gaussian.__init__
~Gaussian.add_plate_axis
~Gaussian.delete
~Gaussian.get_mask
~Gaussian.get_moments
~Gaussian.get_shape
~Gaussian.has_plotter
~Gaussian.initialize_from_parameters
~Gaussian.initialize_from_prior
~Gaussian.initialize_from_random
~Gaussian.initialize_from_value
~Gaussian.load
~Gaussian.logpdf
~Gaussian.lower_bound_contribution
~Gaussian.lowerbound
~Gaussian.move_plates
~Gaussian.observe
~Gaussian.pdf
~Gaussian.plot
~Gaussian.random
~Gaussian.rotate
~Gaussian.rotate_matrix
~Gaussian.save
~Gaussian.set_plotter
~Gaussian.show
~Gaussian.unobserve
~Gaussian.update
.. rubric:: Attributes
.. autosummary::
:toctree: generated/
~Gaussian.dims
~Gaussian.plates
bayespy-0.2.3/doc/source/user_api/generated/generated/bayespy.nodes.GaussianARD.rst 0000664 5104112 5104112 00000002353 12356463435 032115 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianARD
=========================
.. currentmodule:: bayespy.nodes
.. autoclass:: GaussianARD
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~GaussianARD.__init__
~GaussianARD.add_plate_axis
~GaussianARD.delete
~GaussianARD.get_mask
~GaussianARD.get_moments
~GaussianARD.get_shape
~GaussianARD.has_plotter
~GaussianARD.initialize_from_mean_and_covariance
~GaussianARD.initialize_from_parameters
~GaussianARD.initialize_from_prior
~GaussianARD.initialize_from_random
~GaussianARD.initialize_from_value
~GaussianARD.load
~GaussianARD.logpdf
~GaussianARD.lower_bound_contribution
~GaussianARD.lowerbound
~GaussianARD.move_plates
~GaussianARD.observe
~GaussianARD.pdf
~GaussianARD.plot
~GaussianARD.random
~GaussianARD.rotate
~GaussianARD.rotate_plates
~GaussianARD.save
~GaussianARD.set_plotter
~GaussianARD.show
~GaussianARD.unobserve
~GaussianARD.update
.. rubric:: Attributes
.. autosummary::
:toctree: generated/
~GaussianARD.dims
~GaussianARD.plates
bayespy-0.2.3/doc/source/user_api/generated/generated/bayespy.nodes.GaussianGammaARD.rst 0000664 5104112 5104112 00000002362 12356463435 033060 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianGammaARD
==============================
.. currentmodule:: bayespy.nodes
.. autoclass:: GaussianGammaARD
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~GaussianGammaARD.__init__
~GaussianGammaARD.add_plate_axis
~GaussianGammaARD.delete
~GaussianGammaARD.get_mask
~GaussianGammaARD.get_moments
~GaussianGammaARD.get_shape
~GaussianGammaARD.has_plotter
~GaussianGammaARD.initialize_from_parameters
~GaussianGammaARD.initialize_from_prior
~GaussianGammaARD.initialize_from_random
~GaussianGammaARD.initialize_from_value
~GaussianGammaARD.load
~GaussianGammaARD.logpdf
~GaussianGammaARD.lower_bound_contribution
~GaussianGammaARD.lowerbound
~GaussianGammaARD.move_plates
~GaussianGammaARD.observe
~GaussianGammaARD.pdf
~GaussianGammaARD.plot
~GaussianGammaARD.random
~GaussianGammaARD.save
~GaussianGammaARD.set_plotter
~GaussianGammaARD.unobserve
~GaussianGammaARD.update
.. rubric:: Attributes
.. autosummary::
:toctree: generated/
~GaussianGammaARD.dims
~GaussianGammaARD.plates
bayespy-0.2.3/doc/source/user_api/generated/generated/bayespy.nodes.GaussianGammaISO.rst 0000664 5104112 5104112 00000002625 12356463435 033106 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianGammaISO
==============================
.. currentmodule:: bayespy.nodes
.. autoclass:: GaussianGammaISO
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~GaussianGammaISO.__init__
~GaussianGammaISO.add_plate_axis
~GaussianGammaISO.delete
~GaussianGammaISO.get_gaussian_mean_and_variance
~GaussianGammaISO.get_marginal_logpdf
~GaussianGammaISO.get_mask
~GaussianGammaISO.get_moments
~GaussianGammaISO.get_shape
~GaussianGammaISO.has_plotter
~GaussianGammaISO.initialize_from_parameters
~GaussianGammaISO.initialize_from_prior
~GaussianGammaISO.initialize_from_random
~GaussianGammaISO.initialize_from_value
~GaussianGammaISO.load
~GaussianGammaISO.logpdf
~GaussianGammaISO.lower_bound_contribution
~GaussianGammaISO.lowerbound
~GaussianGammaISO.move_plates
~GaussianGammaISO.observe
~GaussianGammaISO.pdf
~GaussianGammaISO.plot
~GaussianGammaISO.plotmatrix
~GaussianGammaISO.random
~GaussianGammaISO.save
~GaussianGammaISO.set_plotter
~GaussianGammaISO.show
~GaussianGammaISO.unobserve
~GaussianGammaISO.update
.. rubric:: Attributes
.. autosummary::
:toctree: generated/
~GaussianGammaISO.dims
~GaussianGammaISO.plates
bayespy-0.2.3/doc/source/user_api/generated/generated/bayespy.nodes.GaussianMarkovChain.rst 0000664 5104112 5104112 00000002613 12356463435 033710 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianMarkovChain
=================================
.. currentmodule:: bayespy.nodes
.. autoclass:: GaussianMarkovChain
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~GaussianMarkovChain.__init__
~GaussianMarkovChain.add_plate_axis
~GaussianMarkovChain.delete
~GaussianMarkovChain.get_mask
~GaussianMarkovChain.get_moments
~GaussianMarkovChain.get_shape
~GaussianMarkovChain.has_plotter
~GaussianMarkovChain.initialize_from_parameters
~GaussianMarkovChain.initialize_from_prior
~GaussianMarkovChain.initialize_from_random
~GaussianMarkovChain.initialize_from_value
~GaussianMarkovChain.load
~GaussianMarkovChain.logpdf
~GaussianMarkovChain.lower_bound_contribution
~GaussianMarkovChain.lowerbound
~GaussianMarkovChain.move_plates
~GaussianMarkovChain.observe
~GaussianMarkovChain.pdf
~GaussianMarkovChain.plot
~GaussianMarkovChain.random
~GaussianMarkovChain.rotate
~GaussianMarkovChain.save
~GaussianMarkovChain.set_plotter
~GaussianMarkovChain.show
~GaussianMarkovChain.unobserve
~GaussianMarkovChain.update
.. rubric:: Attributes
.. autosummary::
:toctree: generated/
~GaussianMarkovChain.dims
~GaussianMarkovChain.plates
bayespy-0.2.3/doc/source/user_api/generated/generated/bayespy.nodes.GaussianWishart.rst 0000664 5104112 5104112 00000002361 12356463435 033127 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.GaussianWishart
=============================
.. currentmodule:: bayespy.nodes
.. autoclass:: GaussianWishart
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~GaussianWishart.__init__
~GaussianWishart.add_plate_axis
~GaussianWishart.delete
~GaussianWishart.get_mask
~GaussianWishart.get_moments
~GaussianWishart.get_shape
~GaussianWishart.has_plotter
~GaussianWishart.initialize_from_parameters
~GaussianWishart.initialize_from_prior
~GaussianWishart.initialize_from_random
~GaussianWishart.initialize_from_value
~GaussianWishart.load
~GaussianWishart.logpdf
~GaussianWishart.lower_bound_contribution
~GaussianWishart.lowerbound
~GaussianWishart.move_plates
~GaussianWishart.observe
~GaussianWishart.pdf
~GaussianWishart.plot
~GaussianWishart.random
~GaussianWishart.save
~GaussianWishart.set_plotter
~GaussianWishart.show
~GaussianWishart.unobserve
~GaussianWishart.update
.. rubric:: Attributes
.. autosummary::
:toctree: generated/
~GaussianWishart.dims
~GaussianWishart.plates
bayespy-0.2.3/doc/source/user_api/generated/generated/bayespy.nodes.Mixture.rst 0000664 5104112 5104112 00000002033 12356463435 031444 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Mixture
=====================
.. currentmodule:: bayespy.nodes
.. autoclass:: Mixture
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~Mixture.__init__
~Mixture.add_plate_axis
~Mixture.delete
~Mixture.get_mask
~Mixture.get_moments
~Mixture.get_shape
~Mixture.has_plotter
~Mixture.initialize_from_parameters
~Mixture.initialize_from_prior
~Mixture.initialize_from_random
~Mixture.initialize_from_value
~Mixture.integrated_logpdf_from_parents
~Mixture.load
~Mixture.logpdf
~Mixture.lower_bound_contribution
~Mixture.lowerbound
~Mixture.move_plates
~Mixture.observe
~Mixture.pdf
~Mixture.plot
~Mixture.random
~Mixture.save
~Mixture.set_plotter
~Mixture.unobserve
~Mixture.update
.. rubric:: Attributes
.. autosummary::
:toctree: generated/
~Mixture.dims
~Mixture.plates
bayespy-0.2.3/doc/source/user_api/generated/generated/bayespy.nodes.Multinomial.rst 0000664 5104112 5104112 00000002171 12356463435 032304 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Multinomial
=========================
.. currentmodule:: bayespy.nodes
.. autoclass:: Multinomial
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~Multinomial.__init__
~Multinomial.add_plate_axis
~Multinomial.delete
~Multinomial.get_mask
~Multinomial.get_moments
~Multinomial.get_shape
~Multinomial.has_plotter
~Multinomial.initialize_from_parameters
~Multinomial.initialize_from_prior
~Multinomial.initialize_from_random
~Multinomial.initialize_from_value
~Multinomial.load
~Multinomial.logpdf
~Multinomial.lower_bound_contribution
~Multinomial.lowerbound
~Multinomial.move_plates
~Multinomial.observe
~Multinomial.pdf
~Multinomial.plot
~Multinomial.random
~Multinomial.save
~Multinomial.set_plotter
~Multinomial.show
~Multinomial.unobserve
~Multinomial.update
.. rubric:: Attributes
.. autosummary::
:toctree: generated/
~Multinomial.dims
~Multinomial.plates
bayespy-0.2.3/doc/source/user_api/generated/generated/bayespy.nodes.Poisson.rst 0000664 5104112 5104112 00000002001 12356463435 031434 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Poisson
=====================
.. currentmodule:: bayespy.nodes
.. autoclass:: Poisson
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~Poisson.__init__
~Poisson.add_plate_axis
~Poisson.delete
~Poisson.get_mask
~Poisson.get_moments
~Poisson.get_shape
~Poisson.has_plotter
~Poisson.initialize_from_parameters
~Poisson.initialize_from_prior
~Poisson.initialize_from_random
~Poisson.initialize_from_value
~Poisson.load
~Poisson.logpdf
~Poisson.lower_bound_contribution
~Poisson.lowerbound
~Poisson.move_plates
~Poisson.observe
~Poisson.pdf
~Poisson.plot
~Poisson.random
~Poisson.save
~Poisson.set_plotter
~Poisson.show
~Poisson.unobserve
~Poisson.update
.. rubric:: Attributes
.. autosummary::
:toctree: generated/
~Poisson.dims
~Poisson.plates
bayespy-0.2.3/doc/source/user_api/generated/generated/bayespy.nodes.SumMultiply.rst 0000664 5104112 5104112 00000001326 12356463435 032317 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.SumMultiply
=========================
.. currentmodule:: bayespy.nodes
.. autoclass:: SumMultiply
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~SumMultiply.__init__
~SumMultiply.add_plate_axis
~SumMultiply.delete
~SumMultiply.get_mask
~SumMultiply.get_moments
~SumMultiply.get_parameters
~SumMultiply.get_shape
~SumMultiply.has_plotter
~SumMultiply.lower_bound_contribution
~SumMultiply.move_plates
~SumMultiply.plot
~SumMultiply.set_plotter
.. rubric:: Attributes
.. autosummary::
:toctree: generated/
~SumMultiply.plates
bayespy-0.2.3/doc/source/user_api/generated/generated/bayespy.nodes.SwitchingGaussianMarkovChain.rst0000664 5104112 5104112 00000003242 12356463435 035567 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.SwitchingGaussianMarkovChain
==========================================
.. currentmodule:: bayespy.nodes
.. autoclass:: SwitchingGaussianMarkovChain
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~SwitchingGaussianMarkovChain.__init__
~SwitchingGaussianMarkovChain.add_plate_axis
~SwitchingGaussianMarkovChain.delete
~SwitchingGaussianMarkovChain.get_mask
~SwitchingGaussianMarkovChain.get_moments
~SwitchingGaussianMarkovChain.get_shape
~SwitchingGaussianMarkovChain.has_plotter
~SwitchingGaussianMarkovChain.initialize_from_parameters
~SwitchingGaussianMarkovChain.initialize_from_prior
~SwitchingGaussianMarkovChain.initialize_from_random
~SwitchingGaussianMarkovChain.initialize_from_value
~SwitchingGaussianMarkovChain.load
~SwitchingGaussianMarkovChain.logpdf
~SwitchingGaussianMarkovChain.lower_bound_contribution
~SwitchingGaussianMarkovChain.lowerbound
~SwitchingGaussianMarkovChain.move_plates
~SwitchingGaussianMarkovChain.observe
~SwitchingGaussianMarkovChain.pdf
~SwitchingGaussianMarkovChain.plot
~SwitchingGaussianMarkovChain.random
~SwitchingGaussianMarkovChain.rotate
~SwitchingGaussianMarkovChain.save
~SwitchingGaussianMarkovChain.set_plotter
~SwitchingGaussianMarkovChain.show
~SwitchingGaussianMarkovChain.unobserve
~SwitchingGaussianMarkovChain.update
.. rubric:: Attributes
.. autosummary::
:toctree: generated/
~SwitchingGaussianMarkovChain.dims
~SwitchingGaussianMarkovChain.plates
bayespy-0.2.3/doc/source/user_api/generated/generated/bayespy.nodes.VaryingGaussianMarkovChain.rst 0000664 5104112 5104112 00000003144 12356463435 035250 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.VaryingGaussianMarkovChain
========================================
.. currentmodule:: bayespy.nodes
.. autoclass:: VaryingGaussianMarkovChain
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~VaryingGaussianMarkovChain.__init__
~VaryingGaussianMarkovChain.add_plate_axis
~VaryingGaussianMarkovChain.delete
~VaryingGaussianMarkovChain.get_mask
~VaryingGaussianMarkovChain.get_moments
~VaryingGaussianMarkovChain.get_shape
~VaryingGaussianMarkovChain.has_plotter
~VaryingGaussianMarkovChain.initialize_from_parameters
~VaryingGaussianMarkovChain.initialize_from_prior
~VaryingGaussianMarkovChain.initialize_from_random
~VaryingGaussianMarkovChain.initialize_from_value
~VaryingGaussianMarkovChain.load
~VaryingGaussianMarkovChain.logpdf
~VaryingGaussianMarkovChain.lower_bound_contribution
~VaryingGaussianMarkovChain.lowerbound
~VaryingGaussianMarkovChain.move_plates
~VaryingGaussianMarkovChain.observe
~VaryingGaussianMarkovChain.pdf
~VaryingGaussianMarkovChain.plot
~VaryingGaussianMarkovChain.random
~VaryingGaussianMarkovChain.rotate
~VaryingGaussianMarkovChain.save
~VaryingGaussianMarkovChain.set_plotter
~VaryingGaussianMarkovChain.show
~VaryingGaussianMarkovChain.unobserve
~VaryingGaussianMarkovChain.update
.. rubric:: Attributes
.. autosummary::
:toctree: generated/
~VaryingGaussianMarkovChain.dims
~VaryingGaussianMarkovChain.plates
bayespy-0.2.3/doc/source/user_api/generated/generated/bayespy.nodes.Wishart.rst 0000664 5104112 5104112 00000002001 12356463435 031423 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes.Wishart
=====================
.. currentmodule:: bayespy.nodes
.. autoclass:: Wishart
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~Wishart.__init__
~Wishart.add_plate_axis
~Wishart.delete
~Wishart.get_mask
~Wishart.get_moments
~Wishart.get_shape
~Wishart.has_plotter
~Wishart.initialize_from_parameters
~Wishart.initialize_from_prior
~Wishart.initialize_from_random
~Wishart.initialize_from_value
~Wishart.load
~Wishart.logpdf
~Wishart.lower_bound_contribution
~Wishart.lowerbound
~Wishart.move_plates
~Wishart.observe
~Wishart.pdf
~Wishart.plot
~Wishart.random
~Wishart.save
~Wishart.set_plotter
~Wishart.show
~Wishart.unobserve
~Wishart.update
.. rubric:: Attributes
.. autosummary::
:toctree: generated/
~Wishart.dims
~Wishart.plates
bayespy-0.2.3/doc/source/user_api/generated/generated/bayespy.plot.CategoricalMarkovChainPlotter.rst0000664 5104112 5104112 00000000530 12356463435 035567 0 ustar jluttine jluttine 0000000 0000000 bayespy.plot.CategoricalMarkovChainPlotter
==========================================
.. currentmodule:: bayespy.plot
.. autoclass:: CategoricalMarkovChainPlotter
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~CategoricalMarkovChainPlotter.__init__
bayespy-0.2.3/doc/source/user_api/generated/generated/bayespy.plot.ContourPlotter.rst 0000664 5104112 5104112 00000000434 12356463435 032663 0 ustar jluttine jluttine 0000000 0000000 bayespy.plot.ContourPlotter
===========================
.. currentmodule:: bayespy.plot
.. autoclass:: ContourPlotter
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~ContourPlotter.__init__
bayespy-0.2.3/doc/source/user_api/generated/generated/bayespy.plot.FunctionPlotter.rst 0000664 5104112 5104112 00000000440 12356463435 033014 0 ustar jluttine jluttine 0000000 0000000 bayespy.plot.FunctionPlotter
============================
.. currentmodule:: bayespy.plot
.. autoclass:: FunctionPlotter
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~FunctionPlotter.__init__
bayespy-0.2.3/doc/source/user_api/generated/generated/bayespy.plot.GaussianTimeseriesPlotter.rst 0000664 5104112 5104112 00000000510 12356463435 035031 0 ustar jluttine jluttine 0000000 0000000 bayespy.plot.GaussianTimeseriesPlotter
======================================
.. currentmodule:: bayespy.plot
.. autoclass:: GaussianTimeseriesPlotter
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~GaussianTimeseriesPlotter.__init__
bayespy-0.2.3/doc/source/user_api/generated/generated/bayespy.plot.HintonPlotter.rst 0000664 5104112 5104112 00000000430 12356463435 032465 0 ustar jluttine jluttine 0000000 0000000 bayespy.plot.HintonPlotter
==========================
.. currentmodule:: bayespy.plot
.. autoclass:: HintonPlotter
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~HintonPlotter.__init__
bayespy-0.2.3/doc/source/user_api/generated/generated/bayespy.plot.PDFPlotter.rst 0000664 5104112 5104112 00000000414 12356463435 031641 0 ustar jluttine jluttine 0000000 0000000 bayespy.plot.PDFPlotter
=======================
.. currentmodule:: bayespy.plot
.. autoclass:: PDFPlotter
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~PDFPlotter.__init__
bayespy-0.2.3/doc/source/user_api/generated/generated/bayespy.plot.Plotter.rst 0000664 5104112 5104112 00000000400 12356463435 031302 0 ustar jluttine jluttine 0000000 0000000 bayespy.plot.Plotter
====================
.. currentmodule:: bayespy.plot
.. autoclass:: Plotter
.. automethod:: __init__
.. rubric:: Methods
.. autosummary::
:toctree: generated/
~Plotter.__init__
bayespy-0.2.3/doc/source/user_api/generated/generated/bayespy.plot.contour.rst 0000664 5104112 5104112 00000000145 12356463435 031350 0 ustar jluttine jluttine 0000000 0000000 bayespy.plot.contour
====================
.. currentmodule:: bayespy.plot
.. autofunction:: contour bayespy-0.2.3/doc/source/user_api/generated/generated/bayespy.plot.gaussian_mixture.rst 0000664 5104112 5104112 00000000200 12373144473 033233 0 ustar jluttine jluttine 0000000 0000000 bayespy.plot.gaussian_mixture
=============================
.. currentmodule:: bayespy.plot
.. autofunction:: gaussian_mixture bayespy-0.2.3/doc/source/user_api/generated/generated/bayespy.plot.hinton.rst 0000664 5104112 5104112 00000000142 12356463435 031153 0 ustar jluttine jluttine 0000000 0000000 bayespy.plot.hinton
===================
.. currentmodule:: bayespy.plot
.. autofunction:: hinton bayespy-0.2.3/doc/source/user_api/generated/generated/bayespy.plot.pdf.rst 0000664 5104112 5104112 00000000131 12356463435 030423 0 ustar jluttine jluttine 0000000 0000000 bayespy.plot.pdf
================
.. currentmodule:: bayespy.plot
.. autofunction:: pdf bayespy-0.2.3/doc/source/user_api/generated/generated/bayespy.plot.plot.rst 0000664 5104112 5104112 00000000134 12356463435 030633 0 ustar jluttine jluttine 0000000 0000000 bayespy.plot.plot
=================
.. currentmodule:: bayespy.plot
.. autofunction:: plot bayespy-0.2.3/doc/source/user_api/generated/bayespy.inference.rst 0000664 5104112 5104112 00000000106 12356463433 026675 0 ustar jluttine jluttine 0000000 0000000 bayespy.inference
=================
.. automodule:: bayespy.inference bayespy-0.2.3/doc/source/user_api/generated/bayespy.nodes.rst 0000664 5104112 5104112 00000000072 12356463433 026051 0 ustar jluttine jluttine 0000000 0000000 bayespy.nodes
=============
.. automodule:: bayespy.nodes bayespy-0.2.3/doc/source/user_api/generated/bayespy.plot.rst 0000664 5104112 5104112 00000000067 12356463433 025723 0 ustar jluttine jluttine 0000000 0000000 bayespy.plot
============
.. automodule:: bayespy.plot bayespy-0.2.3/doc/source/user_api/user_api.rst 0000664 5104112 5104112 00000001665 12374370351 023144 0 ustar jluttine jluttine 0000000 0000000 ..
Copyright (C) 2011-2013 Jaakko Luttinen
This file is licensed under Version 3.0 of the GNU General Public
License. See LICENSE for a text of the license.
This file is part of BayesPy.
BayesPy is free software: you can redistribute it and/or modify it
under the terms of the GNU General Public License version 3 as
published by the Free Software Foundation.
BayesPy is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
General Public License for more details.
You should have received a copy of the GNU General Public License
along with BayesPy. If not, see .
.. _sec-user-api:
User API
========
.. autosummary::
:toctree: generated/
:template: autosummary/short_module.rst
bayespy.nodes
bayespy.inference
bayespy.plot
bayespy-0.2.3/doc/source/user_guide/ 0000775 5104112 5104112 00000000000 12437607672 021130 5 ustar jluttine jluttine 0000000 0000000 bayespy-0.2.3/doc/source/user_guide/inference.rst 0000664 5104112 5104112 00000035057 12374370351 023621 0 ustar jluttine jluttine 0000000 0000000 ..
Copyright (C) 2014 Jaakko Luttinen
This file is licensed under Version 3.0 of the GNU General Public
License. See LICENSE for a text of the license.
This file is part of BayesPy.
BayesPy is free software: you can redistribute it and/or modify it
under the terms of the GNU General Public License version 3 as
published by the Free Software Foundation.
BayesPy is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
General Public License for more details.
You should have received a copy of the GNU General Public License
along with BayesPy. If not, see .
.. testsetup::
# This is the PCA model from the previous section
import numpy as np
np.random.seed(1)
from bayespy.nodes import GaussianARD, Gamma, Dot
D = 3
X = GaussianARD(0, 1,
shape=(D,),
plates=(1,100),
name='X')
alpha = Gamma(1e-3, 1e-3,
plates=(D,),
name='alpha')
C = GaussianARD(0, alpha,
shape=(D,),
plates=(10,1),
name='C')
F = Dot(C, X)
tau = Gamma(1e-3, 1e-3, name='tau')
Y = GaussianARD(F, tau)
Performing inference
====================
Approximation of the posterior distribution can be divided into several steps:
- Observe some nodes
- Choose the inference engine
- Initialize the posterior approximation
- Run the inference algorithm
In order to illustrate these steps, we'll be using the PCA model constructed in
the previous section.
Observing nodes
---------------
First, let us generate some toy data:
>>> c = np.random.randn(10, 2)
>>> x = np.random.randn(2, 100)
>>> data = np.dot(c, x) + 0.1*np.random.randn(10, 100)
The data is provided by simply calling ``observe`` method of a stochastic node:
>>> Y.observe(data)
It is important that the shape of the ``data`` array matches the plates and
shape of the node ``Y``. For instance, if ``Y`` was :class:`Wishart` node for
:math:`3\times 3` matrices with plates ``(5,1,10)``, the full shape of ``Y``
would be ``(5,1,10,3,3)``. The ``data`` array should have this shape exactly,
that is, no broadcasting rules are applied.
Missing values
++++++++++++++
It is possible to mark missing values by providing a mask which is a boolean
array:
>>> Y.observe(data, mask=[[True], [False], [False], [True], [True],
... [False], [True], [True], [True], [False]])
``True`` means that the value is observed and ``False`` means that the value is
missing. The shape of the above mask is ``(10,1)``, which broadcasts to the
plates of Y, ``(10,100)``. Thus, the above mask means that the second, third,
sixth and tenth rows of the :math:`10\times 100` data matrix are missing.
The mask is applied to the *plates*, not to the data array directly. This means
that it is not possible to observe a random variable partially, each repetition
defined by the plates is either fully observed or fully missing. Thus, the mask
is applied to the plates. It is often possible to circumvent this seemingly
tight restriction by adding an observable child node which factorizes more.
The shape of the mask is broadcasted to plates using standard NumPy broadcasting
rules. So, if the variable has plates ``(5,1,10)``, the mask could have a shape
``()``, ``(1,)``, ``(1,1)``, ``(1,1,1)``, ``(10,)``, ``(1,10)``, ``(1,1,10)``,
``(5,1,1)`` or ``(5,1,10)``. In order to speed up the inference, missing values
are automatically integrated out if they are not needed as latent variables to
child nodes. This leads to faster convergence and more accurate approximations.
Choosing the inference method
-----------------------------
Inference methods can be found in :mod:`bayespy.inference` package. Currently,
only variational Bayesian approximation is implemented
(:class:`bayespy.inference.VB`). The inference engine is constructed by giving
the stochastic nodes of the model.
>>> from bayespy.inference import VB
>>> Q = VB(Y, C, X, alpha, tau)
There is no need to give any deterministic nodes. Currently, the inference
engine does not automatically search for stochastic parents and children, thus
it is important that all stochastic nodes of the model are given. This should
be made more robust in future versions.
A node of the model can be obtained by using the name of the node as a key:
>>> Q['X']
Note that the returned object is the same as the node object itself:
>>> Q['X'] is X
True
Thus, one may use the object ``X`` when it is available. However, if the model
and the inference engine are constructed in another function or module, the node
object may not be available directly and this feature becomes useful.
Initializing the posterior approximation
----------------------------------------
The inference engines give some initialization to the stochastic nodes by
default. However, the inference algorithms can be sensitive to the
initialization, thus it is sometimes necessary to have better control over the
initialization. For VB, the following initialization methods are available:
- ``initialize_from_prior``: Use the current states of the parent nodes to
update the node. This is the default initialization.
- ``initialize_from_parameters``: Use the given parameter values for the
distribution.
- ``initialize_from_value``: Use the given value for the variable.
- ``initialize_from_random``: Draw a random value for the variable. The random
sample is drawn from the current state of the node's distribution.
Note that ``initialize_from_value`` and ``initialize_from_random`` initialize
the distribution with a value of the variable instead of parameters of the
distribution. Thus, the distribution is actually a delta distribution with a
peak on the value after the initialization. This state of the distribution does
not have proper natural parameter values nor normalization, thus the VB lower
bound terms are ``np.nan`` for this initial state.
These initialization methods can be used to perform even a bit more complex
initializations. For instance, a Gaussian distribution could be initialized
with a random mean and variance 0.1. In our PCA model, this can be obtained by
>>> X.initialize_from_parameters(np.random.randn(1, 100, D), 10)
Note that the shape of the random mean is the sum of the plates ``(1, 100)`` and
the variable shape ``(D,)``. In addition, instead of variance,
:class:`GaussianARD` uses precision as the second parameter, thus we initialized
the variance to :math:`\frac{1}{10}`. This random initialization is important
in our PCA model because the default initialization gives ``C`` and ``X`` zero
mean. If the mean of the other variable was zero when the other is updated, the
other variable gets zero mean too. This would lead to an update algorithm where
both means remain zeros and effectively no latent space is found. Thus, it is
important to give non-zero random initialization for ``X`` if ``C`` is updated
before ``X`` the first time. It is typical that at least some nodes need be
initialized with some randomness.
By default, nodes are initialized with the method ``initialize_from_prior``.
The method is not very time consuming but if for any reason you want to avoid
that default initialization computation, you can provide ``initialize=False``
when creating the stochastic node. However, the node does not have a proper
state in that case, which leads to errors in VB learning unless the distribution
is initialized using the above methods.
Running the inference algorithm
-------------------------------
The approximation methods are based on iterative algorithms, which can
be run using ``update`` method. By default, it takes one iteration step
updating all nodes once:
>>> Q.update()
Iteration 1: loglike=-9.305259e+02 (... seconds)
The ``loglike`` tells the VB lower bound. The order in which the nodes are
updated is the same as the order in which the nodes were given when creating
``Q``. If you want to change the order or update only some of the nodes, you
can give as arguments the nodes you want to update and they are updated in the
given order:
>>> Q.update(C, X)
Iteration 2: loglike=-8.818976e+02 (... seconds)
It is also possible to give the same node several times:
>>> Q.update(C, X, C, tau)
Iteration 3: loglike=-8.071222e+02 (... seconds)
Note that each call to ``update`` is counted as one iteration step although not
variables are necessarily updated. Instead of doing one iteration step,
``repeat`` keyword argument can be used to perform several iteration steps:
>>> Q.update(repeat=10)
Iteration 4: loglike=-7.167588e+02 (... seconds)
Iteration 5: loglike=-6.827873e+02 (... seconds)
Iteration 6: loglike=-6.259477e+02 (... seconds)
Iteration 7: loglike=-4.725400e+02 (... seconds)
Iteration 8: loglike=-3.270816e+02 (... seconds)
Iteration 9: loglike=-2.208865e+02 (... seconds)
Iteration 10: loglike=-1.658761e+02 (... seconds)
Iteration 11: loglike=-1.469468e+02 (... seconds)
Iteration 12: loglike=-1.420311e+02 (... seconds)
Iteration 13: loglike=-1.405139e+02 (... seconds)
The VB algorithm stops automatically if it converges, that is, the relative
change in the lower bound is below some threshold:
>>> Q.update(repeat=1000)
Iteration 14: loglike=-1.396481e+02 (... seconds)
...
Iteration 488: loglike=-1.224106e+02 (... seconds)
Converged at iteration 488.
Now the algorithm stopped before taking 1000 iteration steps because it
converged. The relative tolerance can be adjusted by providing ``tol`` keyword
argument to the ``update`` method:
>>> Q.update(repeat=10000, tol=1e-6)
Iteration 489: loglike=-1.224094e+02 (... seconds)
...
Iteration 847: loglike=-1.222506e+02 (... seconds)
Converged at iteration 847.
Making the tolerance smaller, may improve the result but it may also
significantly increase the iteration steps until convergence.
Instead of using ``update`` method of the inference engine ``VB``, it is
possible to use the ``update`` methods of the nodes directly as
>>> C.update()
or
>>> Q['C'].update()
However, this is not recommended, because the ``update`` method of the inference
engine ``VB`` is a wrapper which, in addition to calling the nodes' ``update``
methods, checks for convergence and does a few other useful minor things. But
if for any reason these direct update methods are needed, they can be used.
.. _sec-parameter-expansion:
Parameter expansion
+++++++++++++++++++
Sometimes the VB algorithm converges very slowly. This may happen when the
variables are strongly coupled in the true posterior but factorized in the
approximate posterior. This coupling leads to zigzagging of the variational
parameters which progresses slowly. One solution to this problem is to use
parameter expansion. The idea is to add an auxiliary variable which
parameterizes the posterior approximation of several variables. Then optimizing
this auxiliary variable actually optimizes several posterior approximations
jointly leading to faster convergence.
The parameter expansion is model specific. Currently in BayesPy, only
state-space models have built-in parameter expansions available. These
state-space models contain a variable which is a dot product of two variables
(plus some noise):
.. math::
y = \mathbf{c}^T\mathbf{x} + \mathrm{noise}
The parameter expansion can be motivated by noticing that we can add an
auxiliary variable which rotates the variables :math:`\mathbf{c}` and
:math:`\mathbf{x}` so that the dot product is unaffected:
.. math::
y &= \mathbf{c}^T\mathbf{x} + \mathrm{noise}
= \mathbf{c}^T \mathbf{R} \mathbf{R}^{-1}\mathbf{x} + \mathrm{noise}
= (\mathbf{R}^T\mathbf{c})^T(\mathbf{R}^{-1}\mathbf{x}) + \mathrm{noise}
Now, applying this rotation to the posterior approximations
:math:`q(\mathbf{c})` and :math:`q(\mathbf{x})`, and optimizing the VB lower
bound with respect to the rotation leads to parameterized joint optimization of
:math:`\mathbf{c}` and :math:`\mathbf{x}`.
The available parameter expansion methods are in module ``transformations``:
>>> from bayespy.inference.vmp import transformations
First, you create the rotation transformations for the two variables:
>>> rotX = transformations.RotateGaussianARD(X)
>>> rotC = transformations.RotateGaussianARD(C, alpha)
.. currentmodule:: bayespy.inference.vmp.transformations
Here, the rotation for ``C`` provides the ARD parameters ``alpha`` so they are
updated simultaneously. In addition to :class:`RotateGaussianARD`, there are a
few other built-in rotations defined, for instance, :class:`RotateGaussian` and
:class:`RotateGaussianMarkovChain`. It is extremely important that the model
satisfies the assumptions made by the rotation class and the user is mostly
responsible for this. The optimizer for the rotations is constructed by giving
the two rotations and the dimensionality of the rotated space:
.. currentmodule:: bayespy.nodes
>>> R = transformations.RotationOptimizer(rotC, rotX, D)
Now, calling ``rotate`` method will find optimal rotation and update the
relevant nodes (``X``, ``C`` and ``alpha``) accordingly:
>>> R.rotate()
Let us see how our iteration would have gone if we had used this parameter
expansion. First, let us re-initialize our nodes and VB algorithm:
>>> alpha.initialize_from_prior()
>>> C.initialize_from_prior()
>>> X.initialize_from_parameters(np.random.randn(1, 100, D), 10)
>>> tau.initialize_from_prior()
>>> Q = VB(Y, C, X, alpha, tau)
Then, the rotation is set to run after each iteration step:
>>> Q.callback = R.rotate
Now the iteration converges to the relative tolerance :math:`10^{-6}` much
faster:
>>> Q.update(repeat=1000, tol=1e-6)
Iteration 1: loglike=-9.363500e+02 (... seconds)
...
Iteration 18: loglike=-1.221354e+02 (... seconds)
Converged at iteration 18.
The convergence took 18 iterations with rotations and 488 or 847 iterations
without the parameter expansion. In addition, the lower bound is improved
slightly. One can compare the number of iteration steps in this case because
the cost per iteration step with or without parameter expansion is approximately
the same. Sometimes the parameter expansion can have the drawback that it
converges to a bad local optimum. Usually, this can be solved by updating the
nodes near the observations a few times before starting to update the
hyperparameters and to use parameter expansion. In any case, the parameter
expansion is practically necessary when using state-space models in order to
converge to a proper solution in a reasonable time.
bayespy-0.2.3/doc/source/user_guide/inference_backup.py 0000664 5104112 5104112 00000013236 12346272001 024751 0 ustar jluttine jluttine 0000000 0000000
# coding: utf-8
## Test title
# Some text. I'd like HTML to make a page break here.
## Performing inference
# Approximation of the posterior distribution can be divided into several steps:
#
# * Observe some nodes
#
# * Choose the inference engine
#
# * Initialize the posterior approximation
#
# * Run the inference algorithm
#
# In order to illustrate these steps, we'll consider a principal component analysis model:
# In[ ]:
import numpy as np
from bayespy.nodes import GaussianARD, Gamma, Dot
D = 3
X = GaussianARD(0, 1,
shape=(D,),
plates=(1,100),
name='X')
alpha = Gamma(1e-3, 1e-3,
plates=(D,),
name='alpha')
C = GaussianARD(0, alpha,
shape=(D,),
plates=(10,1),
name='C')
F = Dot(C, X)
tau = Gamma(1e-3, 1e-3)
Y = GaussianARD(F, tau)
.. bayesnet::
\node[latent] (y) {$\mathbf{y}_{mn}$} ;
\node[det, above left=1 and 2 of y] (dot) {dot} ;
\node[latent, above right=1 and 2 of y] (tau) {$\tau$} ;
\node[latent, above left=1 and 2 of dot] (C) {$c_{md}$} ;
\node[latent, above=of C] (alpha) {$\alpha_d$} ;
\node[latent, above right=1 and 1 of dot] (X) {$x_{nd}$} ;
\factor[above=of y] {y-f} {above:$\mathcal{N}$} {dot,tau} {y};
\factor[above=of C] {C-f} {above:$\mathcal{N}$} {alpha} {C};
\factor[above=of X] {X-f} {above:$\mathcal{N}$} {} {X};
\factor[above=of alpha] {alpha-f} {above:$\mathcal{G}$} {} {alpha};
\factor[above=of tau] {tau-f} {above:$\mathcal{G}$} {} {tau};
\plate {d-plate} {(X)(X-f)(X-f-caption)(C)(C-f)(C-f-caption)(alpha)(alpha-f)(alpha-f-caption)} {$d=0,\ldots,2$} ;
\plate {m-plate} {(y)(y-f)(y-f-caption)(C)(C-f)(C-f-caption)} {$m=0,\ldots,9$} ;
\plate {n-plate} {(y)(y-f)(y-f-caption)(X)(X-f)(X-f-caption)(m-plate-caption)(m-plate.north east)} {$n=0,\ldots,99$} ;
### Observing nodes
# The data is provided by simply calling `observe` method of a stochastic node:
# In[ ]:
from bayespy.nodes import GaussianARD
y = GaussianARD(0, 1, plates=(10,))
data = [2.3, 1.1, 0.3, 3.1, 1.5, 2.3, 1.9, 0.6, 1.2, -0.1]
y.observe(data)
#### Missing values
# It is possible to mark missing values by providing a mask:
# In[ ]:
y.observe(data, mask=[True, False, False, True, True,
False, True, True, True, False])
# `True` means that the value is observed and `False` means that the value is missing. The mask is applied to the *plates*, not to the data array directly. This means that it is not possible to observe a random variable partially, each repetition defined by the plates is either fully observed or fully missing. Thus, the mask is applied to the plates. It is often possible to circumvent this seemingly tight restriction by adding an observable child node which factorizes more.
#
# The shape of the mask is broadcasted to plates using standard NumPy broadcasting rules. So, if the variable has plates `(5,1,10)`, the mask could have a shape `()`, `(1,)`, `(1,1)`, `(1,1,1)`, `(10,)`, `(1,10)`, `(1,1,10)`, `(5,1,1)` or `(5,1,10)`. In order to speed up the inference, missing plates are automatically ignored by the inference algorithm if they are not needed. Thus, the missing values are integrated out giving more accurate approximations faster.
### Choosing the inference method
# In[ ]:
from bayespy.inference import VB
Q = VB(node1, node2, node3, node4)
### Initializing the inference
# The inference engines give some initialization to the nodes by default. However, the inference algorithms can be sensitive to the initialization, thus it is sometimes necessary to have full control over the initialization. There may be different initialization methods, but for VB you can, for instance, initialize in one of the following ways:
#
# * `initialize_from_prior`: Use only parent nodes to update the node.
#
# * `initialize_from_parameters`: Use the given parameter values for the distribution.
#
# A random initialization for VB has to be performed manually, because it is not obvious what is actually wanted. For instance, one way to achieve it is to first update from the parents, then to draw a random sample from that distribution and to set the values of the parameters based on that. For ``Normal`` node, one could draw the mean parameter randomly and choose the precision parameter arbitrarily:
# In[ ]:
x = bp.nodes.Normal(mu, tau, plates=(10,))
x.initialize_from_prior()
x.initialize_from_parameters(x.random(), 1)
# In this case, the precision was set to one. The default initialization method is `initialization_from_prior`, which is performed when the node is created. If the initialization uses the values of the parents, they should be initialized before the children.
### Running the inference algorithm
# The approximation methods are based on iterative algorithms, which can be run using `update` method. By default, it takes one iteration step updating all nodes once. However, you can give as arguments the nodes you want to update and they are updated in the given order. It is possible to give same nodes several times, for instance:
# In[ ]:
Q.update(node1, node3, node1, node4)
# This would update ``node3`` and ``node4`` once, and ``node1`` twice.
# In order to update several times, one can use the optional argument
# ``repeat``.
# In[ ]:
Q.update(node3, node4, repeat=5)
Q.update(node1, node2, node3, node4, repeat=10)
# This first updates ``node3`` and ``node4`` five times and then all the
# nodes ten times. This might be useful, for instance, if updating some
# nodes is expensive and should be done rarely or if updating some nodes
# in the beginning would cause the algorithm to converge to a bad
# solution.
bayespy-0.2.3/doc/source/user_guide/inference_backup.rst 0000664 5104112 5104112 00000017371 12346272000 025134 0 ustar jluttine jluttine 0000000 0000000
..
Copyright (C) 2014 Jaakko Luttinen
This file is licensed under Version 3.0 of the GNU General Public
License. See LICENSE for a text of the license.
This file is part of BayesPy.
BayesPy is free software: you can redistribute it and/or modify it
under the terms of the GNU General Public License version 3 as
published by the Free Software Foundation.
BayesPy is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
General Public License for more details.
You should have received a copy of the GNU General Public License
along with BayesPy. If not, see .
Test title
==========
Some text. I'd like HTML to make a page break here.
.. raw:: html
Performing inference
====================
Approximation of the posterior distribution can be divided into several
steps:
- Observe some nodes
- Choose the inference engine
- Initialize the posterior approximation
- Run the inference algorithm
In order to illustrate these steps, we'll consider a principal component
analysis model:
.. code:: python
import numpy as np
from bayespy.nodes import GaussianARD, Gamma, Dot
D = 3
X = GaussianARD(0, 1,
shape=(D,),
plates=(1,100),
name='X')
alpha = Gamma(1e-3, 1e-3,
plates=(D,),
name='alpha')
C = GaussianARD(0, alpha,
shape=(D,),
plates=(10,1),
name='C')
F = Dot(C, X)
tau = Gamma(1e-3, 1e-3)
Y = GaussianARD(F, tau)
.. bayesnet::
\node[latent] (y) {$\mathbf{y}_{mn}$} ;
\node[det, above left=1 and 2 of y] (dot) {dot} ;
\node[latent, above right=1 and 2 of y] (tau) {$\tau$} ;
\node[latent, above left=1 and 2 of dot] (C) {$c_{md}$} ;
\node[latent, above=of C] (alpha) {$\alpha_d$} ;
\node[latent, above right=1 and 1 of dot] (X) {$x_{nd}$} ;
\factor[above=of y] {y-f} {above:$\mathcal{N}$} {dot,tau} {y};
\factor[above=of C] {C-f} {above:$\mathcal{N}$} {alpha} {C};
\factor[above=of X] {X-f} {above:$\mathcal{N}$} {} {X};
\factor[above=of alpha] {alpha-f} {above:$\mathcal{G}$} {} {alpha};
\factor[above=of tau] {tau-f} {above:$\mathcal{G}$} {} {tau};
\plate {d-plate} {(X)(X-f)(X-f-caption)(C)(C-f)(C-f-caption)(alpha)(alpha-f)(alpha-f-caption)} {$d=0,\ldots,2$} ;
\plate {m-plate} {(y)(y-f)(y-f-caption)(C)(C-f)(C-f-caption)} {$m=0,\ldots,9$} ;
\plate {n-plate} {(y)(y-f)(y-f-caption)(X)(X-f)(X-f-caption)(m-plate-caption)(m-plate.north east)} {$n=0,\ldots,99$} ;
Observing nodes
---------------
The data is provided by simply calling ``observe`` method of a
stochastic node:
.. code:: python
from bayespy.nodes import GaussianARD
y = GaussianARD(0, 1, plates=(10,))
data = [2.3, 1.1, 0.3, 3.1, 1.5, 2.3, 1.9, 0.6, 1.2, -0.1]
y.observe(data)
It is important that the shape of the ``data`` array matches the plates and shape
of the node ``y`` For instance, if ``y`` is :class:`Wishart` node for
:math:`3\times 3` matrices with plates ``(5,1,10)``, the full shape
of ``y`` would be ``(5,1,10,3,3)``. The data array must have this
shape exactly, that is, no broadcasting rules are applied.
Missing values
~~~~~~~~~~~~~~
It is possible to mark missing values by providing a mask:
.. code:: python
y.observe(data, mask=[True, False, False, True, True,
False, True, True, True, False])
``True`` means that the value is observed and ``False`` means that the
value is missing. The mask is applied to the *plates*, not to the data
array directly. This means that it is not possible to observe a random
variable partially, each repetition defined by the plates is either
fully observed or fully missing. Thus, the mask is applied to the
plates. It is often possible to circumvent this seemingly tight
restriction by adding an observable child node which factorizes more.
The shape of the mask is broadcasted to plates using standard NumPy
broadcasting rules. So, if the variable has plates ``(5,1,10)``, the
mask could have a shape ``()``, ``(1,)``, ``(1,1)``, ``(1,1,1)``,
``(10,)``, ``(1,10)``, ``(1,1,10)``, ``(5,1,1)`` or ``(5,1,10)``. In
order to speed up the inference, missing plates are automatically
ignored by the inference algorithm if they are not needed. Thus, the
missing values are integrated out giving more accurate approximations
faster.
Choosing the inference method
-----------------------------
Inference methods can be found in :mod:`bayespy.inference` package.
Currently, only variational Bayesian approximation is implemented
(:class:`bayespy.inference.VB`). The inference engine is constructed by
giving the nodes of the model.
.. code:: python
from bayespy.inference import VB
Q = VB(node1, node2, node3, node4)
Initializing the inference
--------------------------
The inference engines give some initialization to the nodes by default.
However, the inference algorithms can be sensitive to the
initialization, thus it is sometimes necessary to have full control over
the initialization. There may be different initialization methods, but
for VB you can, for instance, initialize in one of the following ways:
- ``initialize_from_prior``: Use only parent nodes to update the node.
- ``initialize_from_parameters``: Use the given parameter values for
the distribution.
A random initialization for VB has to be performed manually, because it
is not obvious what is actually wanted. For instance, one way to achieve
it is to first update from the parents, then to draw a random sample
from that distribution and to set the values of the parameters based on
that. For ``Normal`` node, one could draw the mean parameter randomly
and choose the precision parameter arbitrarily:
.. code:: python
x = bp.nodes.Normal(mu, tau, plates=(10,))
x.initialize_from_prior()
x.initialize_from_parameters(x.random(), 1)
In this case, the precision was set to one. The default initialization
method is ``initialization_from_prior``, which is performed when the
node is created. If the initialization uses the values of the parents,
they should be initialized before the children.
Running the inference algorithm
-------------------------------
The approximation methods are based on iterative algorithms, which can
be run using ``update`` method. By default, it takes one iteration step
updating all nodes once. However, you can give as arguments the nodes
you want to update and they are updated in the given order. It is
possible to give same nodes several times, for instance:
.. code:: python
Q.update(node1, node3, node1, node4)
This would update ``node3`` and ``node4`` once, and ``node1`` twice. In
order to update several times, one can use the optional argument
``repeat``.
.. code:: python
Q.update(node3, node4, repeat=5)
Q.update(node1, node2, node3, node4, repeat=10)
This first updates ``node3`` and ``node4`` five times and then all the
nodes ten times. This might be useful, for instance, if updating some
nodes is expensive and should be done rarely or if updating some nodes
in the beginning would cause the algorithm to converge to a bad
solution.
.. warning::
Ideally, one constructs the model and then chooses the inference
method to be used - possibly trying several different methods.
However, the model construction is not yet separated from the model
construction, that is, the constructed model network is also the
variational message passing network for VB inference.
bayespy-0.2.3/doc/source/user_guide/install.rst 0000664 5104112 5104112 00000001441 12374370351 023317 0 ustar jluttine jluttine 0000000 0000000 ..
Copyright (C) 2011,2012 Jaakko Luttinen
This file is licensed under Version 3.0 of the GNU General Public
License. See LICENSE for a text of the license.
This file is part of BayesPy.
BayesPy is free software: you can redistribute it and/or modify it
under the terms of the GNU General Public License version 3 as
published by the Free Software Foundation.
BayesPy is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
General Public License for more details.
You should have received a copy of the GNU General Public License
along with BayesPy. If not, see .
.. include:: ../../../INSTALL.rst
bayespy-0.2.3/doc/source/user_guide/modelconstruct.rst 0000664 5104112 5104112 00000052051 12374370351 024721 0 ustar jluttine jluttine 0000000 0000000 ..
Copyright (C) 2014 Jaakko Luttinen
This file is licensed under Version 3.0 of the GNU General Public
License. See LICENSE for a text of the license.
This file is part of BayesPy.
BayesPy is free software: you can redistribute it and/or modify it
under the terms of the GNU General Public License version 3 as
published by the Free Software Foundation.
BayesPy is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
General Public License for more details.
You should have received a copy of the GNU General Public License
along with BayesPy. If not, see .
.. currentmodule:: bayespy.nodes
Constructing the model
======================
In BayesPy, the model is constructed by creating nodes which form a directed
network. There are two types of nodes: stochastic and deterministic. A
stochastic node corresponds to a random variable (or a set of random variables)
from a specific probability distribution. A deterministic node corresponds to a
deterministic function of its parents. For a list of built-in nodes, see the
:ref:`sec-user-api`.
Creating nodes
--------------
Creating a node is basically like writing the conditional prior distribution of
the variable in Python. The node is constructed by giving the parent nodes,
that is, the conditioning variables as arguments. The number of parents and
their meaning depend on the node. For instance, a :class:`Gaussian` node is
created by giving the mean vector and the precision matrix. These parents can be
constant numerical arrays if they are known:
>>> from bayespy.nodes import Gaussian
>>> X = Gaussian([2, 5], [[1.0, 0.3], [0.3, 1.0]])
or other nodes if they are unknown and given prior distributions:
>>> from bayespy.nodes import Gaussian, Wishart
>>> mu = Gaussian([0, 0], [[1e-6, 0],[0, 1e-6]])
>>> Lambda = Wishart(2, [[1, 0], [0, 1]])
>>> X = Gaussian(mu, Lambda)
Nodes can also be named by providing ``name`` keyword argument:
>>> X = Gaussian(mu, Lambda, name='x')
The name may be useful when referring to the node using an inference engine.
For the parent nodes, there are two main restrictions: non-constant parent nodes
must be conjugate and the parent nodes must be mutually independent in the
posterior approximation.
Conjugacy of the parents
~~~~~~~~~~~~~~~~~~~~~~~~
In Bayesian framework in general, one can give quite arbitrary probability
distributions for variables. However, one often uses distributions that are easy
to handle in practice. Quite often this means that the parents are given
conjugate priors. This is also one of the limitations in BayesPy: only conjugate
family prior distributions are accepted currently. Thus, although in principle
one could give, for instance, gamma prior for the mean parameter ``mu``, only
Gaussian-family distributions are accepted because of the conjugacy. If the
parent is not of a proper type, an error is raised. This conjugacy is checked
automatically by BayesPy and ``NoConverterError`` is raised if a parent cannot
be interpreted as being from a conjugate distribution.
Independence of the parents
~~~~~~~~~~~~~~~~~~~~~~~~~~~
Another a bit rarely encountered limitation is that the parents must be mutually
independent (in the posterior factorization). Thus, a node cannot have the same
stochastic node as several parents without intermediate stochastic nodes. For
instance, the following leads to an error:
>>> from bayespy.nodes import Dot
>>> Y = Dot(X, X)
Traceback (most recent call last):
...
ValueError: Parent nodes are not independent
The error is raised because ``X`` is given as two parents for ``Y``, and
obviously ``X`` is not independent of ``X`` in the posterior approximation. Even
if ``X`` is not given several times directly but there are some intermediate
deterministic nodes, an error is raised because the deterministic nodes depend
on their parents and thus the parents of ``Y`` would not be independent.
However, it is valid that a node is a parent of another node via several paths
if all the paths or all except one path has intermediate stochastic nodes. This
is valid because the intermediate stochastic nodes have independent posterior
approximations. Thus, for instance, the following construction does not raise
errors:
>>> from bayespy.nodes import Dot
>>> Z = Gaussian(X, [[1,0], [0,1]])
>>> Y = Dot(X, Z)
This works because there is now an intermediate stochastic node ``Z`` on the
other path from ``X`` node to ``Y`` node.
Effects of the nodes on inference
---------------------------------
When constructing the network with nodes, the stochastic nodes actually define
three important aspects:
1. the prior probability distribution for the variables,
2. the factorization of the posterior approximation,
3. the functional form of the posterior approximation for the variables.
Prior probability distribution
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
First, the most intuitive feature of the nodes is that they define the prior
distribution. In the previous example, ``mu`` was a stochastic
:class:`GaussianARD` node corresponding to :math:`\mu` from the normal
distribution, ``tau`` was a stochastic :class:`Gamma` node corresponding to
:math:`\tau` from the gamma distribution, and ``y`` was a stochastic
:class:`GaussianARD` node corresponding to :math:`y` from the normal
distribution with mean :math:`\mu` and precision :math:`\tau`. If we denote the
set of all stochastic nodes by :math:`\Omega`, and by :math:`\pi_X` the set of
parents of a node :math:`X`, the model is defined as
.. math::
p(\Omega) = \prod_{X \in \Omega} p(X|\pi_X),
where nodes correspond to the terms :math:`p(X|\pi_X)`\ .
Posterior factorization
~~~~~~~~~~~~~~~~~~~~~~~
Second, the nodes define the structure of the posterior approximation. The
variational Bayesian approximation factorizes with respect to nodes, that is,
each node corresponds to an independent probability distribution in the
posterior approximation. In the previous example, ``mu`` and ``tau`` were
separate nodes, thus the posterior approximation factorizes with respect to
them: :math:`q(\mu)q(\tau)`\ . Thus, the posterior approximation can be written
as:
.. math::
p(\tilde{\Omega}|\hat{\Omega}) \approx \prod_{X \in \tilde{\Omega}} q(X),
where :math:`\tilde{\Omega}` is the set of latent stochastic nodes and
:math:`\hat{\Omega}` is the set of observed stochastic nodes. Sometimes one may
want to avoid the factorization between some variables. For this purpose, there
are some nodes which model several variables jointly without factorization. For
instance, :class:`GaussianGammaISO` is a joint node for :math:`\mu` and
:math:`\tau` variables from the normal-gamma distribution and the posterior
approximation does not factorize between :math:`\mu` and :math:`\tau`, that is,
the posterior approximation is :math:`q(\mu,\tau)`.
Functional form of the posterior
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Last, the nodes define the functional form of the posterior approximation.
Usually, the posterior approximation has the same or similar functional form as
the prior. For instance, :class:`Gamma` uses gamma distribution to also
approximate the posterior distribution. Similarly, :class:`GaussianARD` uses
Gaussian distribution for the posterior. However, the posterior approximation
of :class:`GaussianARD` uses a full covariance matrix although the prior assumes
a diagonal covariance matrix. Thus, there can be slight differences in the
exact functional form of the posterior approximation but the rule of thumb is
that the functional form of the posterior approximation is the same as or more
general than the functional form of the prior.
Using plate notation
--------------------
Defining plates
~~~~~~~~~~~~~~~
Stochastic nodes take the optional parameter ``plates``, which can be used to
define plates of the variable. A plate defines the number of repetitions of a
set of variables. For instance, a set of random variables
:math:`\mathbf{y}_{mn}` could be defined as
.. math::
\mathbf{y}_{mn} \sim \mathcal{N}(\boldsymbol{\mu}, \mathbf{\Lambda}),\qquad m=0,\ldots,9, \quad n=0,\ldots,29.
This can also be visualized as a graphical model:
.. bayesnet::
\node[latent] (y) {$\mathbf{y}_{mn}$} ;
\node[latent, above left=1.8 and 0.4 of y] (mu) {$\boldsymbol{\mu}$} ;
\node[latent, above right=1.8 and 0.4 of y] (Lambda) {$\mathbf{\Lambda}$} ;
\factor[above=of y] {y-f} {left:$\mathcal{N}$} {mu,Lambda} {y};
\plate {m-plate} {(y)(y-f)(y-f-caption)} {$m=0,\ldots,9$} ;
\plate {n-plate} {(m-plate)(m-plate-caption)} {$n=0,\ldots,29$} ;
The variable has two plates: one for the index :math:`m` and one for the
index :math:`n`\ . In BayesPy, this random variable can be constructed
as:
>>> y = Gaussian(mu, Lambda, plates=(10,30))
.. note:: The plates are always given as a tuple of positive integers.
Plates also define indexing for the nodes, thus you can use simple NumPy-style
slice indexing to obtain a subset of the plates:
>>> y_0 = y[0]
>>> y_0.plates
(30,)
>>> y_even = y[:,::2]
>>> y_even.plates
(10, 15)
>>> y_complex = y[:5, 10:20:5]
>>> y_complex.plates
(5, 2)
Note that this indexing is for the plates only, not for the random variable
dimensions.
Sharing and broadcasting plates
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Instead of having a common mean and precision matrix for all
:math:`\mathbf{y}_{mn}`\ , it is also possible to share plates with parents. For
instance, the mean could be different for each index :math:`m` and the precision
for each index :math:`n`\ :
.. math::
\mathbf{y}_{mn} \sim \mathcal{N}(\boldsymbol{\mu}_m,
\mathbf{\Lambda}_n),\qquad m=0,\ldots,9, \quad n=0,\ldots,29.
which has the following graphical representation:
.. bayesnet::
\node[latent] (y) {$\mathbf{y}_{mn}$} ;
\node[latent, above left=1 and 2 of y] (mu) {$\boldsymbol{\mu}_m$} ;
\node[latent, above right=1 and 1 of y] (Lambda) {$\mathbf{\Lambda}_n$} ;
\factor[above=of y] {y-f} {above:$\mathcal{N}$} {mu,Lambda} {y};
\plate {m-plate} {(mu)(y)(y-f)(y-f-caption)} {$m=0,\ldots,9$} ;
\plate {n-plate} {(Lambda)(y)(y-f)(y-f-caption)(m-plate-caption)(m-plate.north east)} {$n=0,\ldots,29$} ;
This can be constructed in BayesPy, for instance, as:
>>> from bayespy.nodes import Gaussian, Wishart
>>> mu = Gaussian([0, 0], [[1e-6, 0],[0, 1e-6]], plates=(10,1))
>>> Lambda = Wishart(2, [[1, 0], [0, 1]], plates=(1,30))
>>> X = Gaussian(mu, Lambda)
There are a few things to notice here. First, the plates are defined similarly
as shapes in NumPy, that is, they use similar broadcasting rules. For instance,
the plates ``(10,1)`` and ``(1,30)`` broadcast to ``(10,30)``. In fact, one
could use plates ``(10,1)`` and ``(30,)`` to get the broadcasted plates
``(10,30)`` because broadcasting compares the plates from right to left starting
from the last axis. Second, ``X`` is not given ``plates`` keyword argument
because the default plates are the plates broadcasted from the parents and that
was what we wanted so it was not necessary to provide the keyword argument. If
we wanted, for instance, plates ``(20,10,30)`` for ``X``, then we would have
needed to provide ``plates=(20,10,30)``.
The validity of the plates between a child and its parents is checked as
follows. The plates are compared plate-wise starting from the last axis and
working the way forward. A plate of the child is compatible with a plate of the
parent if either of the following conditions is met:
1. The two plates have equal size
2. The parent has size 1 (or no plate)
Table below shows an example of compatible plates for a child node and
its two parent nodes:
+---------+----------------------------+
| node | plates |
+=========+===+===+===+===+===+===+====+
| parent1 | | 3 | 1 | 1 | 1 | 8 | 10 |
+---------+---+---+---+---+---+---+----+
| parent2 | | | 1 | 1 | 5 | 1 | 10 |
+---------+---+---+---+---+---+---+----+
| child | 5 | 3 | 1 | 7 | 5 | 8 | 10 |
+---------+---+---+---+---+---+---+----+
Plates in deterministic nodes
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Note that plates can be defined explicitly only for stochastic nodes. For
deterministic nodes, the plates are defined implicitly by the plate broadcasting
rules from the parents. Deterministic nodes do not need more plates than this
because there is no randomness. The deterministic node would just have the same
value over the extra plates, but it is not necessary to do this explicitly
because the child nodes of the deterministic node can utilize broadcasting
anyway. Thus, there is no point in having extra plates in deterministic nodes,
and for this reason, deterministic nodes do not use ``plates`` keyword argument.
Plates in constants
~~~~~~~~~~~~~~~~~~~
It is useful to understand how the plates and the shape of a random variable are
connected. The shape of an array which contains all the plates of a random
variable is the concatenation of the plates and the shape of the variable. For
instance, consider a 2-dimensional Gaussian variable with plates ``(3,)``. If
you want the value of the constant mean vector and constant precision matrix to
vary between plates, they are given as ``(3,2)``-shape and ``(3,2,2)``-shape
arrays, respectively:
>>> import numpy as np
>>> mu = [ [0,0], [1,1], [2,2] ]
>>> Lambda = [ [[1.0, 0.0],
... [0.0, 1.0]],
... [[1.0, 0.9],
... [0.9, 1.0]],
... [[1.0, -0.3],
... [-0.3, 1.0]] ]
>>> X = Gaussian(mu, Lambda)
>>> np.shape(mu)
(3, 2)
>>> np.shape(Lambda)
(3, 2, 2)
>>> X.plates
(3,)
Thus, the leading axes of an array are the plate axes and the trailing axes are
the random variable axes. In the example above, the mean vector has plates
``(3,)`` and shape ``(2,)``, and the precision matrix has plates ``(3,)`` and
shape ``(2,2)``.
Factorization of plates
~~~~~~~~~~~~~~~~~~~~~~~
It is important to undestand the independency structure the plates induce for
the model. First, the repetitions defined by a plate are independent a priori
given the parents. Second, the repetitions are independent in the posterior
approximation, that is, the posterior approximation factorizes with respect to
plates. Thus, the plates also have an effect on the independence structure of
the posterior approximation, not only prior. If dependencies between a set of
variables need to be handled, that set must be handled as a some kind of
multi-dimensional variable.
.. _sec-irregular-plates:
Irregular plates
~~~~~~~~~~~~~~~~
The handling of plates is not always as simple as described above. There are
cases in which the plates of the parents do not map directly to the plates of
the child node. The user API should mention such irregularities.
For instance, the parents of a mixture distribution have a plate which contains
the different parameters for each cluster, but the variable from the mixture
distribution does not have that plate:
>>> from bayespy.nodes import Gaussian, Wishart, Categorical, Mixture
>>> mu = Gaussian([[0], [0], [0]], [ [[1]], [[1]], [[1]] ])
>>> Lambda = Wishart(1, [ [[1]], [[1]], [[1]]])
>>> Z = Categorical([1/3, 1/3, 1/3], plates=(100,))
>>> X = Mixture(Z, Gaussian, mu, Lambda)
>>> mu.plates
(3,)
>>> Lambda.plates
(3,)
>>> Z.plates
(100,)
>>> X.plates
(100,)
The plates ``(3,)`` and ``(100,)`` should not broadcast according to the rules
mentioned above. However, when validating the plates, :class:`Mixture` removes
the plate which corresponds to the clusters in ``mu`` and ``Lambda``. Thus,
``X`` has plates which are the result of broadcasting plates ``()`` and
``(100,)`` which equals ``(100,)``.
Also, sometimes the plates of the parents may be mapped to the variable
axes. For instance, an automatic relevance determination (ARD) prior for a
Gaussian variable is constructed by giving the diagonal elements of the
precision matrix (or tensor). The Gaussian variable itself can be a scalar, a
vector, a matrix or a tensor. A set of five :math:`4 \times 3`
-dimensional Gaussian matrices with ARD prior is constructed as:
>>> from bayespy.nodes import GaussianARD, Gamma
>>> tau = Gamma(1, 1, plates=(5,4,3))
>>> X = GaussianARD(0, tau, shape=(4,3))
>>> tau.plates
(5, 4, 3)
>>> X.plates
(5,)
Note how the last two plate axes of ``tau`` are mapped to the variable axes of
``X`` with shape ``(4,3)`` and the plates of ``X`` are obtained by taking the
remaining leading plate axes of ``tau``.
Example model: Principal component analysis
-------------------------------------------
Now, we'll construct a bit more complex model which will be used in the
following sections. The model is a probabilistic version of principal component
analysis (PCA):
.. math::
\mathbf{Y} = \mathbf{C}\mathbf{X}^T + \mathrm{noise}
where :math:`\mathbf{Y}` is :math:`M\times N` data matrix, :math:`\mathbf{C}` is
:math:`M\times D` loading matrix, :math:`\mathbf{X}` is :math:`N\times D` state
matrix, and noise is isotropic Gaussian. The dimensionality :math:`D` is
usually assumed to be much smaller than :math:`M` and :math:`N`.
A probabilistic formulation can be written as:
.. math::
p(\mathbf{Y}) &= \prod^{M-1}_{m=0} \prod^{N-1}_{n=0} \mathcal{N}(y_{mn} |
\mathbf{c}_m^T \mathbf{x}_n, \tau)
\\
p(\mathbf{X}) &= \prod^{N-1}_{n=0} \prod^{D-1}_{d=0} \mathcal{N}(x_{nd} |
0, 1)
\\
p(\mathbf{C}) &= \prod^{M-1}_{m=0} \prod^{D-1}_{d=0} \mathcal{N}(c_{md} |
0, \alpha_d)
\\
p(\boldsymbol{\alpha}) &= \prod^{D-1}_{d=0} \mathcal{G} (\alpha_d | 10^{-3},
10^{-3})
\\
p(\tau) &= \mathcal{G} (\tau | 10^{-3}, 10^{-3})
where we have given automatic relevance determination (ARD) prior for
:math:`\mathbf{C}`. This can be visualized as a graphical model:
.. bayesnet::
\node[latent] (y) {$\mathbf{y}_{mn}$} ;
\node[det, above=of y] (dot) {dot} ;
\node[latent, right=2 of dot] (tau) {$\tau$} ;
\node[latent, above left=1 and 2 of dot] (C) {$c_{md}$} ;
\node[latent, above=of C] (alpha) {$\alpha_d$} ;
\node[latent, above right=1 and 1 of dot] (X) {$x_{nd}$} ;
\factor[above=of y] {y-f} {left:$\mathcal{N}$} {dot,tau} {y};
\factor[above=of C] {C-f} {left:$\mathcal{N}$} {alpha} {C};
\factor[above=of X] {X-f} {above:$\mathcal{N}$} {} {X};
\factor[above=of alpha] {alpha-f} {above:$\mathcal{G}$} {} {alpha};
\factor[above=of tau] {tau-f} {above:$\mathcal{G}$} {} {tau};
\edge {C,X} {dot};
\tikzstyle{plate caption} += [below left=0pt and 0pt of #1.north east] ;
\plate {d-plate} {(X)(X-f)(X-f-caption)(C)(C-f)(C-f-caption)(alpha)(alpha-f)(alpha-f-caption)} {$d=0,\ldots,2$} ;
\tikzstyle{plate caption} += [below left=5pt and 0pt of #1.south east] ;
\plate {m-plate} {(y)(y-f)(y-f-caption)(C)(C-f)(C-f-caption)(d-plate.south west)} {$m=0,\ldots,9$} ;
\plate {n-plate} {(y)(y-f)(y-f-caption)(X)(X-f)(X-f-caption)(m-plate-caption)(m-plate.north east)(d-plate.south east)} {$n=0,\ldots,99$} ;
Now, let us construct this model in BayesPy. First, we'll define the
dimensionality of the latent space in our model:
>>> D = 3
Then the prior for the latent states :math:`\mathbf{X}`:
>>> X = GaussianARD(0, 1,
... shape=(D,),
... plates=(1,100),
... name='X')
Note that the shape of ``X`` is ``(D,)``, although the latent dimensions are
marked with a plate in the graphical model and they are conditionally
independent in the prior. However, we want to (and need to) model the posterior
dependency of the latent dimensions, thus we cannot factorize them, which would
happen if we used ``plates=(1,100,D)`` and ``shape=()``. The first plate axis
with size 1 is given just for clarity.
The prior for the ARD parameters :math:`\boldsymbol{\alpha}` of the loading
matrix:
>>> alpha = Gamma(1e-3, 1e-3,
... plates=(D,),
... name='alpha')
The prior for the loading matrix :math:`\mathbf{C}`:
>>> C = GaussianARD(0, alpha,
... shape=(D,),
... plates=(10,1),
... name='C')
Again, note that the shape is the same as for ``X`` for the same reason. Also,
the plates of ``alpha``, ``(D,)``, are mapped to the full shape of the node
``C``, ``(10,1,D)``, using standard broadcasting rules.
The dot product is just a deterministic node:
>>> F = Dot(C, X)
However, note that ``Dot`` requires that the input Gaussian nodes have the same
shape and that this shape has exactly one axis, that is, the variables are
vectors. This the reason why we used shape ``(D,)`` for ``X`` and ``C`` but
from a bit different perspective. The node computes the inner product of
:math:`D`-dimensional vectors resulting in plates ``(10,100)`` broadcasted from
the plates ``(1,100)`` and ``(10,1)``:
>>> F.plates
(10, 100)
The prior for the observation noise :math:`\tau`:
>>> tau = Gamma(1e-3, 1e-3, name='tau')
Finally, the observations are conditionally independent Gaussian scalars:
>>> Y = GaussianARD(F, tau, name='Y')
Now we have defined our model and the next step is to observe some data and to
perform inference.
bayespy-0.2.3/doc/source/user_guide/modelconstruct_backup.py 0000664 5104112 5104112 00000024362 12346272001 026062 0 ustar jluttine jluttine 0000000 0000000
# coding: utf-8
## Constructing the model
### Creating nodes
# In[34]:
from bayespy.nodes import Gaussian
X = Gaussian([2, 5], [[1.0, 0.3], [0.3, 1.0]])
# or other nodes if they are unknown and given prior distributions:
# In[35]:
from bayespy.nodes import Gaussian, Wishart
mu = Gaussian([0, 0], [[1e-6, 0],[0, 1e-6]])
Lambda = Wishart(2, [[1, 0], [0, 1]])
X = Gaussian(mu, Lambda)
# However, there are two main restrictions for the parent nodes: non-constant parent nodes must be conjugate and the parent nodes must be independent in the posterior approximation.
#### Conjugacy of the parents
# In Bayesian framework in general, one can give quite arbitrary probability distributions for variables. However, one often uses distributions that are easy to handle in practice. Quite often this means that the parents are given conjugate priors. This is also one of the limitations in BayesPy: only conjugate family prior distributions are accepted currently. Thus, although in principle one could give, for instance, gamma prior for the mean parameter `mu`, only Gaussian-family distributions are accepted because of the conjugacy. If the parent is not of a proper type, an error is raised. This conjugacy is checked automatically by BayesPy and `NoConverterError` is raised if a parent cannot be interpreted as being from a conjugate distribution.
#### Independence of the parents
# Another a bit rarely encountered limitation is that the parents must be independent (in the posterior factorization). Thus, a node cannot have the same stochastic node as several parents without intermediate stochastic nodes. For instance, the following would lead to an error:
.. code-block:: python3
from bayespy.nodes import Dot
Y = Dot(X, X)
.. parsed-literal::
ValueError: Parent nodes are not independent
# The error is raised because `X` is given as two parents for `Y`, and obviously `X` is not independent of `X` in the posterior approximation. Even if `X` is not given several times directly but there are some intermediate deterministic nodes, an error is raised because the deterministic nodes depend on their parents and thus the parents of `Y` would not be independent. However, it is valid that a node is a parent of another node via several paths if all the paths or all except one path has intermediate stochastic nodes. This is valid because the intermediate stochastic nodes have independent posterior approximations. Thus, for instance, the following construction does not raise errors:
# In[36]:
from bayespy.nodes import Dot
Z = Gaussian(X, [[1,0], [0,1]])
Y = Dot(X, Z)
# This works because there is now an intermediate stochastic node `Z` on the other path from `X` node to `Y` node.
### Effects of the nodes on inference
# When constructing the network with nodes, the stochastic nodes actually define three important aspects:
#
# 1. the prior probability distribution for the variables,
#
# 2. the factorization of the posterior approximation,
#
# 3. the functional form of the posterior approximation for the variables.
#### Prior probability distribution
# $$
# p(\Omega) = \prod_{X \in \Omega} p(X|\pi_X),
# $$
#
# where nodes correspond to the terms $p(X|\pi_X)$.
#### Posterior factorization
# Second, the nodes define the structure of the posterior approximation. The variational Bayesian approximation factorizes with respect to nodes, that is, each node corresponds to an independent probability distribution in the posterior approximation. In the previous example, `mu` and `tau` were separate nodes, thus the posterior approximation factorizes with respect to them: $q(\mu)q(\tau)$. Thus, the posterior approximation can be written as:
#
# $$
# p(\tilde{\Omega}|\hat{\Omega}) \approx \prod_{X \in \tilde{\Omega}} q(X),
# $$
#### Functional form of the posterior
### Using plate notation
#### Defining plates
# Stochastic nodes take the optional parameter `plates`, which can be used to define plates of the variable. A plate defines the number of repetitions of a set of variables. For instance, a set of random variables $\mathbf{y}_{mn}$ could be defined as
#
# $$
# \mathbf{y}_{mn} \sim \mathcal{N}(\boldsymbol{\mu}, \mathbf{\Lambda}),\qquad m=0,\ldots,9, \quad n=0,\ldots,29.
# $$
#
# This can also be visualized as a graphical model:
# The variable has two plates: one for the index $m$ and one for the index $n$. In BayesPy, this random variable can be constructed as:
# In[37]:
y = Gaussian(mu, Lambda, plates=(10,30))
#### Sharing and broadcasting plates
# Instead of having a common mean and precision matrix for all $\mathbf{y}_{mn}$, it is also possible to share plates with parents. For instance, the mean could be different for each index $m$ and the precision for each index $n$:
#
# $$
# \mathbf{y}_{mn} \sim \mathcal{N}(\boldsymbol{\mu}_m, \mathbf{\Lambda}_n),\qquad m=0,\ldots,9, \quad n=0,\ldots,29.
# $$
#
# which has the following graphical representation:
# This can be constructed in BayesPy, for instance, as:
# In[38]:
from bayespy.nodes import Gaussian, Wishart
mu = Gaussian([0, 0], [[1e-6, 0],[0, 1e-6]], plates=(10,1))
Lambda = Wishart(2, [[1, 0], [0, 1]], plates=(1,30))
X = Gaussian(mu, Lambda)
# There are a few things to notice here. First, the plates are defined similarly as shapes in NumPy, that is, they use similar broadcasting rules. For instance, the plates `(10,1)` and `(1,30)` broadcast to `(10,30)`. In fact, one could use plates `(10,1)` and `(30,)` to get the broadcasted plates `(10,30)` because broadcasting compares the plates from right to left starting from the last axis. Second, `X` is not given `plates` keyword argument because the default plates are the plates broadcasted from the parents and that was what we wanted so it was not necessary to provide the keyword argument. If we wanted, for instance, plates `(20,10,30)` for `X`, then we would have needed to provide `plates=(20,10,30)`.
#
# The validity of the plates between a child and its parents is checked as follows. The plates are compared plate-wise starting from the last axis and working the way forward. A plate of the child is compatible with a plate of the parent if either of the following conditions is met:
#
# 1. The two plates have equal size
# 2. The parent has size 1 (or no plate)
#
# Table below shows an example of compatible plates for a child node and its two parent nodes:
#### Plates in deterministic nodes
# Note that plates can be defined explicitly only for stochastic nodes. For deterministic nodes, the plates are defined implicitly by the plate broadcasting rules from the parents. Deterministic nodes do not need more plates than this because there is no randomness. The deterministic node would just have the same value over the extra plates, but it is not necessary to do this explicitly because the child nodes of the deterministic node can utilize broadcasting anyway. Thus, there is no point in having extra plates in deterministic nodes, and for this reason, deterministic nodes do not use `plates` keyword argument.
#### Plates and shape
# It is useful to understand how the plates and the shape of a random variable are connected. The shape of an array which contains all the plates of a random variable is the concatenation of the plates and the shape of the variable. For instance, consider a 2-dimensional Gaussian variable with plates `(3,)`. If you want the value of the constant mean vector and constant precision matrix to vary between plates, they are given as `(3,2)`-shape and `(3,2,2)`-shape arrays, respectively:
# In[39]:
import numpy as np
mu = [ [0,0], [1,1], [2,2] ]
Lambda = [ [[1.0, 0.0],
[0.0, 1.0]],
[[1.0, 0.9],
[0.9, 1.0]],
[[1.0, -0.3],
[-0.3, 1.0]] ]
X = Gaussian(mu, Lambda)
print("Shape of mu:", np.shape(mu))
print("Shape of Lambda:", np.shape(Lambda))
print("Plates of X:", X.plates)
# Thus, the leading axes of an array are the plate axes and the trailing axes are the random variable axes. In the example above, the mean vector has plates `(3,)` and shape `(2,)`, and the precision matrix has plates `(3,)` and shape `(2,2)`.
#### Factorization of plates
# It is important to undestand the independency structure the plates induce for the model. First, the repetitions defined by a plate are independent a priori given the parents. Second, the repetitions are independent in the posterior approximation, that is, the posterior approximation factorizes with respect to plates. Thus, the plates also have an effect on the independence structure of the posterior approximation, not only prior. If dependencies between a set of variables need to be handled, that set must be handled as a some kind of multi-dimensional variable.
#### Irregular plates
# The handling of plates is not always as simple as described above. There are cases in which the plates of the parents do not map directly to the plates of the child node. The user API should mention such irregularities.
#
# For instance, the parents of a mixture distribution have a plate which contains the different parameters for each cluster, but the variable from the mixture distribution does not have that plate:
# In[40]:
from bayespy.nodes import Gaussian, Wishart, Categorical, Mixture
mu = Gaussian([[0], [0], [0]], [ [[1]], [[1]], [[1]] ])
Lambda = Wishart(1, [ [[1]], [[1]], [[1]]])
Z = Categorical([1/3, 1/3, 1/3], plates=(100,))
X = Mixture(Z, Gaussian, mu, Lambda)
print("Plates of mu:", mu.plates)
print("Plates of Lambda:", Lambda.plates)
print("Plates of Z:", Z.plates)
print("Plates of X:", X.plates)
# Also, sometimes the plates of the parents may be mapped to the variable axes. For instance, an automatic relevance determination (ARD) prior for a Gaussian variable is constructed by giving the diagonal elements of the precision matrix (or tensor). The Gaussian variable itself can be a scalar, a vector, a matrix or a tensor. A set of five $4 \times 3$ -dimensional Gaussian matrices with ARD prior is constructed as:
# In[41]:
from bayespy.nodes import GaussianARD, Gamma
tau = Gamma(1, 1, plates=(5,4,3))
X = GaussianARD(0, tau, shape=(4,3))
print("Plates of tau:", tau.plates)
print("Plates of X:", X.plates)
# Note how the last two plate axes of `tau` are mapped to the variable axes of `X` with shape `(4,3)` and the plates of `X` are obtained by taking the remaining leading plate axes of `tau`.
bayespy-0.2.3/doc/source/user_guide/modelconstruct_backup.rst 0000664 5104112 5104112 00000041060 12346272000 026233 0 ustar jluttine jluttine 0000000 0000000
..
Copyright (C) 2014 Jaakko Luttinen
This file is licensed under Version 3.0 of the GNU General Public
License. See LICENSE for a text of the license.
This file is part of BayesPy.
BayesPy is free software: you can redistribute it and/or modify it
under the terms of the GNU General Public License version 3 as
published by the Free Software Foundation.
BayesPy is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
General Public License for more details.
You should have received a copy of the GNU General Public License
along with BayesPy. If not, see .
.. currentmodule:: bayespy.nodes
Constructing the model
======================
In BayesPy, the model is constructed by creating nodes which form a directed network. There are two types of nodes: stochastic and deterministic. A stochastic node corresponds to a random variable (or a set of random variables) from a specific probability distribution. A deterministic node corresponds to a deterministic function of its parents. For a list of built-in nodes, see the :ref:`sec-user-api`.
Creating nodes
--------------
Creating a node is basically like writing the conditional prior distribution of
the variable in Python. The node is constructed by giving the parent nodes,
that is, the conditioning variables as arguments. The number of parents and
their meaning depend on the node. For instance, a :class:`Gaussian` node is
created by giving the mean vector and the precision matrix. These parents can be
constant numerical arrays if they are known:
.. code:: python
from bayespy.nodes import Gaussian
X = Gaussian([2, 5], [[1.0, 0.3], [0.3, 1.0]])
or other nodes if they are unknown and given prior distributions:
.. code:: python
from bayespy.nodes import Gaussian, Wishart
mu = Gaussian([0, 0], [[1e-6, 0],[0, 1e-6]])
Lambda = Wishart(2, [[1, 0], [0, 1]])
X = Gaussian(mu, Lambda)
However, there are two main restrictions for the parent nodes:
non-constant parent nodes must be conjugate and the parent nodes must be
independent in the posterior approximation.
Conjugacy of the parents
~~~~~~~~~~~~~~~~~~~~~~~~
In Bayesian framework in general, one can give quite arbitrary
probability distributions for variables. However, one often uses
distributions that are easy to handle in practice. Quite often this
means that the parents are given conjugate priors. This is also one of
the limitations in BayesPy: only conjugate family prior distributions
are accepted currently. Thus, although in principle one could give, for
instance, gamma prior for the mean parameter ``mu``, only
Gaussian-family distributions are accepted because of the conjugacy. If
the parent is not of a proper type, an error is raised. This conjugacy
is checked automatically by BayesPy and ``NoConverterError`` is raised
if a parent cannot be interpreted as being from a conjugate
distribution.
Independence of the parents
~~~~~~~~~~~~~~~~~~~~~~~~~~~
Another a bit rarely encountered limitation is that the parents must be
independent (in the posterior factorization). Thus, a node cannot have
the same stochastic node as several parents without intermediate
stochastic nodes. For instance, the following would lead to an error:
.. code-block:: python3
from bayespy.nodes import Dot
Y = Dot(X, X)
.. parsed-literal::
ValueError: Parent nodes are not independent
The error is raised because ``X`` is given as two parents for ``Y``, and
obviously ``X`` is not independent of ``X`` in the posterior
approximation. Even if ``X`` is not given several times directly but
there are some intermediate deterministic nodes, an error is raised
because the deterministic nodes depend on their parents and thus the
parents of ``Y`` would not be independent. However, it is valid that a
node is a parent of another node via several paths if all the paths or
all except one path has intermediate stochastic nodes. This is valid
because the intermediate stochastic nodes have independent posterior
approximations. Thus, for instance, the following construction does not
raise errors:
.. code:: python
from bayespy.nodes import Dot
Z = Gaussian(X, [[1,0], [0,1]])
Y = Dot(X, Z)
This works because there is now an intermediate stochastic node ``Z`` on
the other path from ``X`` node to ``Y`` node.
Effects of the nodes on inference
---------------------------------
When constructing the network with nodes, the stochastic nodes actually
define three important aspects:
1. the prior probability distribution for the variables,
2. the factorization of the posterior approximation,
3. the functional form of the posterior approximation for the variables.
Prior probability distribution
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
First, the most intuitive feature of the nodes is that they define the prior
distribution. In the previous example, ``mu`` was a stochastic
:class:`GaussianARD` node corresponding to :math:`\mu` from the normal
distribution, ``tau`` was a stochastic :class:`Gamma` node corresponding to
:math:`\tau` from the gamma distribution, and ``y`` was a stochastic
:class:`GaussianARD` node corresponding to :math:`y` from the normal
distribution with mean :math:`\mu` and precision :math:`\tau`. If we denote the
set of all stochastic nodes by :math:`\Omega`, and by :math:`\pi_X` the set of parents of a
node :math:`X`, the model is defined as
.. math::
p(\Omega) = \prod_{X \in \Omega} p(X|\pi_X),
where nodes correspond to the terms :math:`p(X|\pi_X)`\ .
Posterior factorization
~~~~~~~~~~~~~~~~~~~~~~~
Second, the nodes define the structure of the posterior approximation.
The variational Bayesian approximation factorizes with respect to nodes,
that is, each node corresponds to an independent probability
distribution in the posterior approximation. In the previous example,
``mu`` and ``tau`` were separate nodes, thus the posterior approximation
factorizes with respect to them: :math:`q(\mu)q(\tau)`\ . Thus, the
posterior approximation can be written as:
.. math::
p(\tilde{\Omega}|\hat{\Omega}) \approx \prod_{X \in \tilde{\Omega}} q(X),
where :math:`\tilde{\Omega}` is the set of latent stochastic nodes and :math:`\hat{\Omega}`
is the set of observed stochastic nodes. Sometimes one may want to avoid the factorization
between some variables. For this purpose, there are some nodes which model
several variables jointly without factorization. For instance,
:class:`GaussianGammaISO` is a joint node for :math:`\mu` and :math:`\tau`
variables from the normal-gamma distribution and the posterior approximation
does not factorize between :math:`\mu` and :math:`\tau`, that is, the posterior
approximation is :math:`q(\mu,\tau)`.
Functional form of the posterior
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Last, the nodes define the functional form of the posterior approximation.
Usually, the posterior approximation has the same or similar functional form as
the prior. For instance, :class:`Gamma` uses gamma distribution to also
approximate the posterior distribution. Similarly, :class:`GaussianARD` uses
Gaussian distribution for the posterior. However, the posterior approximation
of :class:`GaussianARD` uses a full covariance matrix although the prior assumes
a diagonal covariance matrix. Thus, there can be slight differences in the
exact functional form of the posterior approximation but the rule of thumb is
that the functional form of the posterior approximation is the same as or more general than the
functional form of the prior.
Using plate notation
--------------------
Defining plates
~~~~~~~~~~~~~~~
Stochastic nodes take the optional parameter ``plates``, which can be
used to define plates of the variable. A plate defines the number of
repetitions of a set of variables. For instance, a set of random
variables :math:`\mathbf{y}_{mn}` could be defined as
.. math::
\mathbf{y}_{mn} \sim \mathcal{N}(\boldsymbol{\mu}, \mathbf{\Lambda}),\qquad m=0,\ldots,9, \quad n=0,\ldots,29.
This can also be visualized as a graphical model:
.. bayesnet::
\node[latent] (y) {$\mathbf{y}_{mn}$} ;
\node[latent, above left=1.8 and 0.4 of y] (mu) {$\boldsymbol{\mu}$} ;
\node[latent, above right=1.8 and 0.4 of y] (Lambda) {$\mathbf{\Lambda}$} ;
\factor[above=of y] {y-f} {left:$\mathcal{N}$} {mu,Lambda} {y};
\plate {m-plate} {(y)(y-f)(y-f-caption)} {$m=0,\ldots,9$} ;
\plate {n-plate} {(m-plate)(m-plate-caption)} {$n=0,\ldots,29$} ;
The variable has two plates: one for the index :math:`m` and one for the
index :math:`n`\ . In BayesPy, this random variable can be constructed
as:
.. code:: python
y = Gaussian(mu, Lambda, plates=(10,30))
.. note:: The plates are always given as a tuple of positive integers.
Sharing and broadcasting plates
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Instead of having a common mean and precision matrix for all
:math:`\mathbf{y}_{mn}`\ , it is also possible to share plates with
parents. For instance, the mean could be different for each index
:math:`m` and the precision for each index :math:`n`\ :
.. math::
\mathbf{y}_{mn} \sim \mathcal{N}(\boldsymbol{\mu}_m, \mathbf{\Lambda}_n),\qquad m=0,\ldots,9, \quad n=0,\ldots,29.
which has the following graphical representation:
.. bayesnet::
\node[latent] (y) {$\mathbf{y}_{mn}$} ;
\node[latent, above left=1 and 2 of y] (mu) {$\boldsymbol{\mu}_m$} ;
\node[latent, above right=1 and 1 of y] (Lambda) {$\mathbf{\Lambda}_n$} ;
\factor[above=of y] {y-f} {above:$\mathcal{N}$} {mu,Lambda} {y};
\plate {m-plate} {(mu)(y)(y-f)(y-f-caption)} {$m=0,\ldots,9$} ;
\plate {n-plate} {(Lambda)(y)(y-f)(y-f-caption)(m-plate-caption)(m-plate.north east)} {$n=0,\ldots,29$} ;
This can be constructed in BayesPy, for instance, as:
.. code:: python
from bayespy.nodes import Gaussian, Wishart
mu = Gaussian([0, 0], [[1e-6, 0],[0, 1e-6]], plates=(10,1))
Lambda = Wishart(2, [[1, 0], [0, 1]], plates=(1,30))
X = Gaussian(mu, Lambda)
There are a few things to notice here. First, the plates are defined
similarly as shapes in NumPy, that is, they use similar broadcasting
rules. For instance, the plates ``(10,1)`` and ``(1,30)`` broadcast to
``(10,30)``. In fact, one could use plates ``(10,1)`` and ``(30,)`` to
get the broadcasted plates ``(10,30)`` because broadcasting compares the
plates from right to left starting from the last axis. Second, ``X`` is
not given ``plates`` keyword argument because the default plates are the
plates broadcasted from the parents and that was what we wanted so it
was not necessary to provide the keyword argument. If we wanted, for
instance, plates ``(20,10,30)`` for ``X``, then we would have needed to
provide ``plates=(20,10,30)``.
The validity of the plates between a child and its parents is checked as
follows. The plates are compared plate-wise starting from the last axis
and working the way forward. A plate of the child is compatible with a
plate of the parent if either of the following conditions is met:
1. The two plates have equal size
2. The parent has size 1 (or no plate)
Table below shows an example of compatible plates for a child node and
its two parent nodes:
+---------+----------------------------+
| node | plates |
+=========+===+===+===+===+===+===+====+
| parent1 | | 3 | 1 | 1 | 1 | 8 | 10 |
+---------+---+---+---+---+---+---+----+
| parent2 | | | 1 | 1 | 5 | 1 | 10 |
+---------+---+---+---+---+---+---+----+
| child | 5 | 3 | 1 | 7 | 5 | 8 | 10 |
+---------+---+---+---+---+---+---+----+
Plates in deterministic nodes
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Note that plates can be defined explicitly only for stochastic nodes.
For deterministic nodes, the plates are defined implicitly by the plate
broadcasting rules from the parents. Deterministic nodes do not need
more plates than this because there is no randomness. The deterministic
node would just have the same value over the extra plates, but it is not
necessary to do this explicitly because the child nodes of the
deterministic node can utilize broadcasting anyway. Thus, there is no
point in having extra plates in deterministic nodes, and for this
reason, deterministic nodes do not use ``plates`` keyword argument.
Plates and shape
~~~~~~~~~~~~~~~~
It is useful to understand how the plates and the shape of a random
variable are connected. The shape of an array which contains all the
plates of a random variable is the concatenation of the plates and the
shape of the variable. For instance, consider a 2-dimensional Gaussian
variable with plates ``(3,)``. If you want the value of the constant
mean vector and constant precision matrix to vary between plates, they
are given as ``(3,2)``-shape and ``(3,2,2)``-shape arrays, respectively:
.. code:: python
import numpy as np
mu = [ [0,0], [1,1], [2,2] ]
Lambda = [ [[1.0, 0.0],
[0.0, 1.0]],
[[1.0, 0.9],
[0.9, 1.0]],
[[1.0, -0.3],
[-0.3, 1.0]] ]
X = Gaussian(mu, Lambda)
print("Shape of mu:", np.shape(mu))
print("Shape of Lambda:", np.shape(Lambda))
print("Plates of X:", X.plates)
.. parsed-literal::
Shape of mu: (3, 2)
Shape of Lambda: (3, 2, 2)
Plates of X: (3,)
Thus, the leading axes of an array are the plate axes and the trailing
axes are the random variable axes. In the example above, the mean vector
has plates ``(3,)`` and shape ``(2,)``, and the precision matrix has
plates ``(3,)`` and shape ``(2,2)``.
Factorization of plates
~~~~~~~~~~~~~~~~~~~~~~~
It is important to undestand the independency structure the plates
induce for the model. First, the repetitions defined by a plate are
independent a priori given the parents. Second, the repetitions are
independent in the posterior approximation, that is, the posterior
approximation factorizes with respect to plates. Thus, the plates also
have an effect on the independence structure of the posterior
approximation, not only prior. If dependencies between a set of
variables need to be handled, that set must be handled as a some kind of
multi-dimensional variable.
Irregular plates
~~~~~~~~~~~~~~~~
The handling of plates is not always as simple as described above. There
are cases in which the plates of the parents do not map directly to the
plates of the child node. The user API should mention such
irregularities.
For instance, the parents of a mixture distribution have a plate which
contains the different parameters for each cluster, but the variable
from the mixture distribution does not have that plate:
.. code:: python
from bayespy.nodes import Gaussian, Wishart, Categorical, Mixture
mu = Gaussian([[0], [0], [0]], [ [[1]], [[1]], [[1]] ])
Lambda = Wishart(1, [ [[1]], [[1]], [[1]]])
Z = Categorical([1/3, 1/3, 1/3], plates=(100,))
X = Mixture(Z, Gaussian, mu, Lambda)
print("Plates of mu:", mu.plates)
print("Plates of Lambda:", Lambda.plates)
print("Plates of Z:", Z.plates)
print("Plates of X:", X.plates)
.. parsed-literal::
Plates of mu: (3,)
Plates of Lambda: (3,)
Plates of Z: (100,)
Plates of X: (100,)
The plates `(3,)` and `(100,)` should not broadcast according to the rules mentioned above. However, when validating the plates, :class:`Mixture` removes the plate which corresponds to the clusters in `mu` and `Lambda`. Thus, `X` has plates which are the result of broadcasting plates `()` and `(100,)` which equals `(100,)`.
Also, sometimes the plates of the parents may be mapped to the variable
axes. For instance, an automatic relevance determination (ARD) prior for
a Gaussian variable is constructed by giving the diagonal elements of
the precision matrix (or tensor). The Gaussian variable itself can be a
scalar, a vector, a matrix or a tensor. A set of five :math:`4 \times 3`
-dimensional Gaussian matrices with ARD prior is constructed as:
.. code:: python
from bayespy.nodes import GaussianARD, Gamma
tau = Gamma(1, 1, plates=(5,4,3))
X = GaussianARD(0, tau, shape=(4,3))
print("Plates of tau:", tau.plates)
print("Plates of X:", X.plates)
.. parsed-literal::
Plates of tau: (5, 4, 3)
Plates of X: (5,)
Note how the last two plate axes of ``tau`` are mapped to the variable
axes of ``X`` with shape ``(4,3)`` and the plates of ``X`` are obtained
by taking the remaining leading plate axes of ``tau``.
bayespy-0.2.3/doc/source/user_guide/plot.rst 0000664 5104112 5104112 00000037442 12374370351 022641 0 ustar jluttine jluttine 0000000 0000000 ..
Copyright (C) 2014 Jaakko Luttinen
This file is licensed under Version 3.0 of the GNU General Public
License. See LICENSE for a text of the license.
This file is part of BayesPy.
BayesPy is free software: you can redistribute it and/or modify it
under the terms of the GNU General Public License version 3 as
published by the Free Software Foundation.
BayesPy is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
General Public License for more details.
You should have received a copy of the GNU General Public License
along with BayesPy. If not, see .
.. testsetup::
# This is the PCA model from the previous sections
import numpy as np
np.random.seed(1)
from bayespy.nodes import GaussianARD, Gamma, Dot
D = 3
X = GaussianARD(0, 1,
shape=(D,),
plates=(1,100),
name='X')
alpha = Gamma(1e-3, 1e-3,
plates=(D,),
name='alpha')
C = GaussianARD(0, alpha,
shape=(D,),
plates=(10,1),
name='C')
F = Dot(C, X)
tau = Gamma(1e-3, 1e-3, name='tau')
Y = GaussianARD(F, tau, name='Y')
c = np.random.randn(10, 2)
x = np.random.randn(2, 100)
data = np.dot(c, x) + 0.1*np.random.randn(10, 100)
Y.observe(data)
Y.observe(data, mask=[[True], [False], [False], [True], [True],
[False], [True], [True], [True], [False]])
X.initialize_from_parameters(np.random.randn(1, 100, D), 10)
from bayespy.inference import VB
Q = VB(Y, C, X, alpha, tau)
X.initialize_from_parameters(np.random.randn(1, 100, D), 10)
from bayespy.inference.vmp import transformations
rotX = transformations.RotateGaussianARD(X)
rotC = transformations.RotateGaussianARD(C, alpha)
R = transformations.RotationOptimizer(rotC, rotX, D)
Q = VB(Y, C, X, alpha, tau)
Q.callback = R.rotate
Q.update(repeat=1000, tol=1e-6, verbose=False)
from bayespy.nodes import Gaussian
Examining the results
=====================
After the results have been obtained, it is important to be able to examine the
results easily. The results can be examined either numerically by inspecting
numerical arrays or visually by plotting distributions of the nodes. In
addition, the posterior distributions can be visualized during the learning
algorithm and the results can saved into a file.
Plotting the results
--------------------
.. currentmodule:: bayespy
The module :mod:`plot` offers some plotting basic functionality:
>>> import bayespy.plot as bpplt
.. currentmodule:: bayespy.plot
The module contains ``matplotlib.pyplot`` module if the user needs that. For
instance, interactive plotting can be enabled as:
>>> bpplt.pyplot.ion()
The :mod:`plot` module contains some functions but it is not a very
comprehensive collection, thus the user may need to write some problem- or
model-specific plotting functions. The current collection is:
* :func:`pdf`: show probability density function of a scalar
* :func:`contour`: show probability density function of two-element vector
* :func:`hinton`: show the Hinton diagram
* :func:`plot`: show value as a function
The probability density function of a scalar random variable can be plotted
using the function :func:`pdf`:
>>> bpplt.pyplot.figure()
>>> bpplt.pdf(Q['tau'], np.linspace(60, 140, num=100))
[]
.. plot::
# This is the PCA model from the previous sections
import numpy as np
np.random.seed(1)
from bayespy.nodes import GaussianARD, Gamma, Dot
D = 3
X = GaussianARD(0, 1,
shape=(D,),
plates=(1,100),
name='X')
alpha = Gamma(1e-3, 1e-3,
plates=(D,),
name='alpha')
C = GaussianARD(0, alpha,
shape=(D,),
plates=(10,1),
name='C')
F = Dot(C, X)
tau = Gamma(1e-3, 1e-3, name='tau')
Y = GaussianARD(F, tau)
c = np.random.randn(10, 2)
x = np.random.randn(2, 100)
data = np.dot(c, x) + 0.1*np.random.randn(10, 100)
Y.observe(data)
Y.observe(data, mask=[[True], [False], [False], [True], [True],
[False], [True], [True], [True], [False]])
from bayespy.inference import VB
Q = VB(Y, C, X, alpha, tau)
X.initialize_from_parameters(np.random.randn(1, 100, D), 10)
from bayespy.inference.vmp import transformations
rotX = transformations.RotateGaussianARD(X)
rotC = transformations.RotateGaussianARD(C, alpha)
R = transformations.RotationOptimizer(rotC, rotX, D)
Q = VB(Y, C, X, alpha, tau)
Q.callback = R.rotate
Q.update(repeat=1000, tol=1e-6)
Q.update(repeat=50, tol=np.nan)
import bayespy.plot as bpplt
bpplt.pdf(Q['tau'], np.linspace(60, 140, num=100))
The variable ``tau`` models the inverse variance of the noise, for which the
true value is :math:`0.1^{-2}=100`. Thus, the posterior captures the true value
quite accurately. Similarly, the function :func:`contour` can be used to plot
the probability density function of a 2-dimensional variable, for instance:
>>> V = Gaussian([3, 5], [[4, 2], [2, 5]])
>>> bpplt.pyplot.figure()
>>> bpplt.contour(V, np.linspace(1, 5, num=100), np.linspace(3, 7, num=100))
.. plot::
# This is the PCA model from the previous sections
import numpy as np
np.random.seed(1)
from bayespy.nodes import GaussianARD, Gamma, Dot
D = 3
X = GaussianARD(0, 1,
shape=(D,),
plates=(1,100),
name='X')
alpha = Gamma(1e-3, 1e-3,
plates=(D,),
name='alpha')
C = GaussianARD(0, alpha,
shape=(D,),
plates=(10,1),
name='C')
F = Dot(C, X)
tau = Gamma(1e-3, 1e-3, name='tau')
Y = GaussianARD(F, tau)
c = np.random.randn(10, 2)
x = np.random.randn(2, 100)
data = np.dot(c, x) + 0.1*np.random.randn(10, 100)
Y.observe(data)
Y.observe(data, mask=[[True], [False], [False], [True], [True],
[False], [True], [True], [True], [False]])
from bayespy.inference import VB
Q = VB(Y, C, X, alpha, tau)
X.initialize_from_parameters(np.random.randn(1, 100, D), 10)
from bayespy.inference.vmp import transformations
rotX = transformations.RotateGaussianARD(X)
rotC = transformations.RotateGaussianARD(C, alpha)
R = transformations.RotationOptimizer(rotC, rotX, D)
Q = VB(Y, C, X, alpha, tau)
Q.callback = R.rotate
Q.update(repeat=1000, tol=1e-6)
Q.update(repeat=50, tol=np.nan)
import bayespy.plot as bpplt
from bayespy.nodes import Gaussian
V = Gaussian([3, 5], [[4, 2], [2, 5]])
bpplt.contour(V, np.linspace(1, 5, num=100), np.linspace(3, 7, num=100))
Both :func:`pdf` and :func:`contour` require that the user provides the grid on
which the probability density function is computed. They also support several
keyword arguments for modifying the output, similarly as ``plot`` and
``contour`` in ``matplotlib.pyplot``. These functions can be used only for
stochastic nodes. A few other plot types are also available as built-in
functions. A Hinton diagram can be plotted as:
>>> bpplt.pyplot.figure()
>>> bpplt.hinton(C)
.. plot::
# This is the PCA model from the previous sections
import numpy as np
np.random.seed(1)
from bayespy.nodes import GaussianARD, Gamma, Dot
D = 3
X = GaussianARD(0, 1,
shape=(D,),
plates=(1,100),
name='X')
alpha = Gamma(1e-3, 1e-3,
plates=(D,),
name='alpha')
C = GaussianARD(0, alpha,
shape=(D,),
plates=(10,1),
name='C')
F = Dot(C, X)
tau = Gamma(1e-3, 1e-3, name='tau')
Y = GaussianARD(F, tau)
c = np.random.randn(10, 2)
x = np.random.randn(2, 100)
data = np.dot(c, x) + 0.1*np.random.randn(10, 100)
Y.observe(data)
Y.observe(data, mask=[[True], [False], [False], [True], [True],
[False], [True], [True], [True], [False]])
from bayespy.inference import VB
Q = VB(Y, C, X, alpha, tau)
X.initialize_from_parameters(np.random.randn(1, 100, D), 10)
from bayespy.inference.vmp import transformations
rotX = transformations.RotateGaussianARD(X)
rotC = transformations.RotateGaussianARD(C, alpha)
R = transformations.RotationOptimizer(rotC, rotX, D)
Q = VB(Y, C, X, alpha, tau)
Q.callback = R.rotate
Q.update(repeat=1000, tol=1e-6)
Q.update(repeat=50, tol=np.nan)
import bayespy.plot as bpplt
from bayespy.nodes import Gaussian
bpplt.hinton(C)
The diagram shows the elements of the matrix :math:`C`. The size of the filled
rectangle corresponds to the absolute value of the element mean, and white and
black correspond to positive and negative values, respectively. The non-filled
rectangle shows standard deviation. From this diagram it is clear that the
third column of :math:`C` has been pruned out and the rows that were missing in
the data have zero mean and column-specific variance. The function
:func:`hinton` is a simple wrapper for node-specific Hinton diagram plotters,
such as :func:`gaussian_hinton` and :func:`dirichlet_hinton`. Thus, the keyword
arguments depend on the node which is plotted.
Another plotting function is :func:`plot`, which just plots the values of the
node over one axis as a function:
>>> bpplt.pyplot.figure()
>>> bpplt.plot(X, axis=-2)
.. plot::
# This is the PCA model from the previous sections
import numpy as np
np.random.seed(1)
from bayespy.nodes import GaussianARD, Gamma, Dot
D = 3
X = GaussianARD(0, 1,
shape=(D,),
plates=(1,100),
name='X')
alpha = Gamma(1e-3, 1e-3,
plates=(D,),
name='alpha')
C = GaussianARD(0, alpha,
shape=(D,),
plates=(10,1),
name='C')
F = Dot(C, X)
tau = Gamma(1e-3, 1e-3, name='tau')
Y = GaussianARD(F, tau)
c = np.random.randn(10, 2)
x = np.random.randn(2, 100)
data = np.dot(c, x) + 0.1*np.random.randn(10, 100)
Y.observe(data)
Y.observe(data, mask=[[True], [False], [False], [True], [True],
[False], [True], [True], [True], [False]])
from bayespy.inference import VB
Q = VB(Y, C, X, alpha, tau)
X.initialize_from_parameters(np.random.randn(1, 100, D), 10)
from bayespy.inference.vmp import transformations
rotX = transformations.RotateGaussianARD(X)
rotC = transformations.RotateGaussianARD(C, alpha)
R = transformations.RotationOptimizer(rotC, rotX, D)
Q = VB(Y, C, X, alpha, tau)
Q.callback = R.rotate
Q.update(repeat=1000, tol=1e-6)
Q.update(repeat=50, tol=np.nan)
import bayespy.plot as bpplt
from bayespy.nodes import Gaussian
bpplt.plot(X, axis=-2)
Now, the ``axis`` is the second last axis which corresponds to
:math:`n=0,\ldots,N-1`. As :math:`D=3`, there are three subplots. For Gaussian
variables, the function shows the mean and two standard deviations. The plot
shows that the third component has been pruned out, thus the method has been
able to recover the true dimensionality of the latent space. It also has
similar keyword arguments to ``plot`` function in ``matplotlib.pyplot``. Again,
:func:`plot` is a simple wrapper over node-specific plotting functions, thus it
supports only some node classes.
Monitoring during the inference algorithm
-----------------------------------------
It is possible to plot the distribution of the nodes during the learning
algorithm. This is useful when the user is interested to see how the
distributions evolve during learning and what is happening to the distributions.
In order to utilize monitoring, the user must set plotters for the nodes that he
or she wishes to monitor. This can be done either when creating the node or
later at any time.
The plotters are set by creating a plotter object and providing this object to
the node. The plotter is a wrapper of one of the plotting functions mentioned
above: :class:`PDFPlotter`, :class:`ContourPlotter`, :class:`HintonPlotter` or
:class:`FunctionPlotter`. Thus, our example model could use the following
plotters:
>>> tau.set_plotter(bpplt.PDFPlotter(np.linspace(60, 140, num=100)))
>>> C.set_plotter(bpplt.HintonPlotter())
>>> X.set_plotter(bpplt.FunctionPlotter(axis=-2))
These could have been given at node creation as a keyword argument ``plotter``:
>>> V = Gaussian([3, 5], [[4, 2], [2, 5]],
... plotter=bpplt.ContourPlotter(np.linspace(1, 5, num=100),
... np.linspace(3, 7, num=100)))
When the plotter is set, one can use the ``plot`` method of the node to perform
plotting:
>>> V.plot()
Nodes can also be plotted using the ``plot`` method of the inference engine:
>>> Q.plot('C')
This method remembers the figure in which a node has been plotted and uses that
every time it plots the same node. In order to monitor the nodes during
learning, it is possible to use the keyword argument ``plot``:
>>> Q.update(repeat=5, plot=True, tol=np.nan)
Iteration 19: loglike=-1.221354e+02 (... seconds)
Iteration 20: loglike=-1.221354e+02 (... seconds)
Iteration 21: loglike=-1.221354e+02 (... seconds)
Iteration 22: loglike=-1.221354e+02 (... seconds)
Iteration 23: loglike=-1.221354e+02 (... seconds)
Each node which has a plotter set will be plotted after it is updated. Note
that this may slow down the inference significantly if the plotting operation is
time consuming.
Posterior parameters and moments
--------------------------------
If the built-in plotting functions are not sufficient, it is possible to use
``matplotlib.pyplot`` for custom plotting. Each node has ``get_moments`` method
which returns the moments and they can be used for plotting. Stochastic
exponential family nodes have natural parameter vectors which can also be used.
In addition to plotting, it is also possible to just print the moments or
parameters in the console.
Saving and loading results
--------------------------
.. currentmodule:: bayespy.inference
The results of the inference engine can be easily saved and loaded using
:func:`VB.save` and :func:`VB.load` methods:
>>> Q.save(filename='tmp.hdf5')
>>> Q.load(filename='tmp.hdf5')
The results are stored in a HDF5 file. The user may set an autosave file in
which the results are automatically saved regularly. Autosave filename can be
set at creation time by ``autosave_filename`` keyword argument or later using
:func:`VB.set_autosave` method. If autosave file has been set, the
:func:`VB.save` and :func:`VB.load` methods use that file by default. In order
for the saving to work, all stochastic nodes must have been given (unique)
names.
However, note that these methods do *not* save nor load the node definitions.
It means that the user must create the nodes and the inference engine and then
use :func:`VB.load` to set the state of the nodes and the inference engine. If
there are any differences in the model that was saved and the one which is tried
to update using loading, then loading does not work. Thus, the user should keep
the model construction unmodified in a Python file in order to be able to load
the results later. Or if the user wishes to share the results, he or she must
share the model construction Python file with the HDF5 results file.
bayespy-0.2.3/doc/source/user_guide/quickstart.rst 0000664 5104112 5104112 00000014574 12430651206 024050 0 ustar jluttine jluttine 0000000 0000000 ..
Copyright (C) 2014 Jaakko Luttinen
This file is licensed under Version 3.0 of the GNU General Public
License. See LICENSE for a text of the license.
This file is part of BayesPy.
BayesPy is free software: you can redistribute it and/or modify it
under the terms of the GNU General Public License version 3 as
published by the Free Software Foundation.
BayesPy is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
General Public License for more details.
You should have received a copy of the GNU General Public License
along with BayesPy. If not, see .
.. testsetup::
from matplotlib import pyplot
pyplot.ion()
import numpy
numpy.random.seed(1)
Quick start guide
=================
This short guide shows the key steps in using BayesPy for variational
Bayesian inference by applying BayesPy to a simple problem. The key
steps in using BayesPy are the following:
- Construct the model
- Observe some of the variables by providing the data in a proper
format
- Run variational Bayesian inference
- Examine the resulting posterior approximation
To demonstrate BayesPy, we'll consider a very simple problem: we have a
set of observations from a Gaussian distribution with unknown mean and
variance, and we want to learn these parameters. In this case, we do not
use any real-world data but generate some artificial data. The dataset
consists of ten samples from a Gaussian distribution with mean 5 and
standard deviation 10. This dataset can be generated with NumPy as
follows:
>>> import numpy as np
>>> data = np.random.normal(5, 10, size=(10,))
Constructing the model
----------------------
Now, given this data we would like to estimate the mean and the standard
deviation as if we didn't know their values. The model can be defined as
follows:
.. math::
\begin{split}
p(\mathbf{y}|\mu,\tau) &= \prod^{9}_{n=0} \mathcal{N}(y_n|\mu,\tau) \\
p(\mu) &= \mathcal{N}(\mu|0,10^{-6}) \\
p(\tau) &= \mathcal{G}(\tau|10^{-6},10^{-6})
\end{split}
where :math:`\mathcal{N}` is the Gaussian distribution parameterized by
its mean and precision (i.e., inverse variance), and :math:`\mathcal{G}`
is the gamma distribution parameterized by its shape and rate
parameters. Note that we have given quite uninformative priors for the
variables :math:`\mu` and :math:`\tau`\ . This simple model can also be
shown as a directed factor graph:
.. bayesnet:: Directed factor graph of the example model.
\node[obs] (y) {$y_n$} ;
\node[latent, above left=1.5 and 0.5 of y] (mu) {$\mu$} ;
\node[latent, above right=1.5 and 0.5 of y] (tau) {$\tau$} ;
\factor[above=of mu] {mu-f} {left:$\mathcal{N}$} {} {mu} ;
\factor[above=of tau] {tau-f} {left:$\mathcal{G}$} {} {tau} ;
\factor[above=of y] {y-f} {left:$\mathcal{N}$} {mu,tau} {y};
\plate {} {(y)(y-f)(y-f-caption)} {$n=0,\ldots,9$} ;
This model can be constructed in BayesPy as follows:
>>> from bayespy.nodes import GaussianARD, Gamma
>>> mu = GaussianARD(0, 1e-6)
>>> tau = Gamma(1e-6, 1e-6)
>>> y = GaussianARD(mu, tau, plates=(10,))
.. currentmodule:: bayespy.nodes
This is quite self-explanatory given the model definitions above. We have used
two types of nodes :class:`GaussianARD` and :class:`Gamma` to represent Gaussian
and gamma distributions, respectively. There are much more distributions in
:mod:`bayespy.nodes` so you can construct quite complex conjugate exponential
family models. The node :code:`y` uses keyword argument :code:`plates` to define
the plates :math:`n=0,\ldots,9`.
Performing inference
--------------------
Now that we have created the model, we can provide our data by setting
``y`` as observed:
>>> y.observe(data)
Next we want to estimate the posterior distribution. In principle, we could use
different inference engines (e.g., MCMC or EP) but currently only variational
Bayesian (VB) engine is implemented. The engine is initialized by giving all the
nodes of the model:
>>> from bayespy.inference import VB
>>> Q = VB(mu, tau, y)
The inference algorithm can be run as long as wanted (max. 20 iterations
in this case):
>>> Q.update(repeat=20)
Iteration 1: loglike=-6.020956e+01 (... seconds)
Iteration 2: loglike=-5.820527e+01 (... seconds)
Iteration 3: loglike=-5.820290e+01 (... seconds)
Iteration 4: loglike=-5.820288e+01 (... seconds)
Converged at iteration 4.
Now the algorithm converged after four iterations, before the requested 20
iterations. VB approximates the true posterior :math:`p(\mu,\tau|\mathbf{y})`
with a distribution which factorizes with respect to the nodes:
:math:`q(\mu)q(\tau)`\ .
Examining posterior approximation
---------------------------------
The resulting approximate posterior distributions :math:`q(\mu)` and
:math:`q(\tau)` can be examined, for instance, by plotting the marginal
probability density functions:
>>> import bayespy.plot as bpplt
>>> bpplt.pyplot.subplot(2, 1, 1)
>>> bpplt.pdf(mu, np.linspace(-10, 20, num=100), color='k', name=r'\mu')
[]
>>> bpplt.pyplot.subplot(2, 1, 2)
>>> bpplt.pdf(tau, np.linspace(1e-6, 0.08, num=100), color='k', name=r'\tau')
[]
>>> bpplt.pyplot.tight_layout()
>>> bpplt.pyplot.show()
.. plot::
import numpy as np
np.random.seed(1)
data = np.random.normal(5, 10, size=(10,))
from bayespy.nodes import GaussianARD, Gamma
mu = GaussianARD(0, 1e-6)
tau = Gamma(1e-6, 1e-6)
y = GaussianARD(mu, tau, plates=(10,))
y.observe(data)
from bayespy.inference import VB
Q = VB(mu, tau, y)
Q.update(repeat=20)
import bayespy.plot as bpplt
bpplt.pyplot.subplot(2, 1, 1)
bpplt.pdf(mu, np.linspace(-10, 20, num=100), color='k', name=r'\mu')
bpplt.pyplot.subplot(2, 1, 2)
bpplt.pdf(tau, np.linspace(1e-6, 0.08, num=100), color='k', name=r'\tau')
bpplt.pyplot.tight_layout()
bpplt.pyplot.show()
This example was a very simple introduction to using BayesPy. The model
can be much more complex and each phase contains more options to give
the user more control over the inference. The following sections give
more details about the phases.
bayespy-0.2.3/doc/source/user_guide/quickstartbackup.py 0000664 5104112 5104112 00000006464 12374370351 025063 0 ustar jluttine jluttine 0000000 0000000
# coding: utf-8
## Quick start guide
# This short guide shows the key steps in using BayesPy for variational Bayesian inference by applying BayesPy to a simple problem. The key steps in using BayesPy are the following:
#
# * Construct the model
#
# * Observe some of the variables by providing the data in a proper format
#
# * Run variational Bayesian inference
#
# * Examine the resulting posterior approximation
#
# To demonstrate BayesPy, we'll consider a very simple problem: we have a set of observations from a Gaussian distribution with unknown mean and variance, and we want to learn these parameters. In this case, we do not use any real-world data but generate some artificial data. The dataset consists of ten samples from a Gaussian distribution with mean 5 and standard deviation 10. This dataset can be generated with NumPy as follows:
# In[1]:
import numpy as np
data = np.random.normal(5, 10, size=(10,))
# Now, given this data we would like to estimate the mean and the standard deviation as if we didn't know their values. The model can be defined as follows:
#
# $$
# \begin{split}
# p(\mathbf{y}|\mu,\tau) &= \prod^{9}_{n=0} \mathcal{N}(y_n|\mu,\tau) \\
# p(\mu) &= \mathcal{N}(\mu|0,10^{-6}) \\
# p(\tau) &= \mathcal{G}(\tau|10^{-6},10^{-6})
# \end{split}
# $$
#
# where $\mathcal{N}$ is the Gaussian distribution parameterized by its mean and precision (i.e., inverse variance), and $\mathcal{G}$ is the gamma distribution parameterized by its shape and rate parameters. Note that we have given quite uninformative priors for the variables $\mu$ and $\tau$. This simple model can also be shown as a directed factor graph:
# This model can be constructed in BayesPy as follows:
# In[2]:
from bayespy.nodes import GaussianARD, Gamma
mu = GaussianARD(0, 1e-6)
tau = Gamma(1e-6, 1e-6)
y = GaussianARD(mu, tau, plates=(10,))
# In[3]:
y.observe(data)
# Next we want to estimate the posterior distribution. In principle, we could use different inference engines (e.g., MCMC or EP) but currently only variational Bayesian (VB) engine is implemented. The engine is initialized by giving all the nodes of the model:
# In[4]:
from bayespy.inference import VB
Q = VB(mu, tau, y)
# The inference algorithm can be run as long as wanted (max. 20 iterations in this case):
# In[5]:
Q.update(repeat=20)
# Now the algorithm converged after four iterations, before the requested 20 iterations.
#
# VB approximates the true posterior $p(\mu,\tau|\mathbf{y})$ with a distribution which factorizes with respect to the nodes: $q(\mu)q(\tau)$. The resulting approximate posterior distributions $q(\mu)$ and $q(\tau)$ can be examined, for instance, by plotting the marginal probability density functions:
# In[6]:
import bayespy.plot as bpplt
# The following two two lines are just for enabling matplotlib plotting in notebooks
get_ipython().magic('matplotlib inline')
bpplt.pyplot.plot([])
bpplt.pyplot.subplot(2, 1, 1)
bpplt.pdf(mu, np.linspace(-10, 20, num=100), color='k', name=r'\mu')
bpplt.pyplot.subplot(2, 1, 2)
bpplt.pdf(tau, np.linspace(1e-6, 0.08, num=100), color='k', name=r'\tau');
# This example was a very simple introduction to using BayesPy. The model can be much more complex and each phase contains more options to give the user more control over the inference. The following sections give more details about the phases.
bayespy-0.2.3/doc/source/user_guide/quickstartbackup.rst 0000664 5104112 5104112 00000011507 12374370351 025235 0 ustar jluttine jluttine 0000000 0000000
Quick start guide
=================
This short guide shows the key steps in using BayesPy for variational
Bayesian inference by applying BayesPy to a simple problem. The key
steps in using BayesPy are the following:
- Construct the model
- Observe some of the variables by providing the data in a proper
format
- Run variational Bayesian inference
- Examine the resulting posterior approximation
To demonstrate BayesPy, we'll consider a very simple problem: we have a
set of observations from a Gaussian distribution with unknown mean and
variance, and we want to learn these parameters. In this case, we do not
use any real-world data but generate some artificial data. The dataset
consists of ten samples from a Gaussian distribution with mean 5 and
standard deviation 10. This dataset can be generated with NumPy as
follows:
.. code:: python
import numpy as np
data = np.random.normal(5, 10, size=(10,))
Now, given this data we would like to estimate the mean and the standard
deviation as if we didn't know their values. The model can be defined as
follows:
.. math::
\begin{split}
p(\mathbf{y}|\mu,\tau) &= \prod^{9}_{n=0} \mathcal{N}(y_n|\mu,\tau) \\
p(\mu) &= \mathcal{N}(\mu|0,10^{-6}) \\
p(\tau) &= \mathcal{G}(\tau|10^{-6},10^{-6})
\end{split}
where :math:`\mathcal{N}` is the Gaussian distribution parameterized by
its mean and precision (i.e., inverse variance), and :math:`\mathcal{G}`
is the gamma distribution parameterized by its shape and rate
parameters. Note that we have given quite uninformative priors for the
variables :math:`\mu` and :math:`\tau`\ . This simple model can also be
shown as a directed factor graph:
.. bayesnet:: Directed factor graph of the example model.
\node[obs] (y) {$y_n$} ;
\node[latent, above left=1.5 and 0.5 of y] (mu) {$\mu$} ;
\node[latent, above right=1.5 and 0.5 of y] (tau) {$\tau$} ;
\factor[above=of mu] {mu-f} {left:$\mathcal{N}$} {} {mu} ;
\factor[above=of tau] {tau-f} {left:$\mathcal{G}$} {} {tau} ;
\factor[above=of y] {y-f} {left:$\mathcal{N}$} {mu,tau} {y};
\plate {} {(y)(y-f)(y-f-caption)} {$n=0,\ldots,9$} ;
This model can be constructed in BayesPy as follows:
.. code:: python
from bayespy.nodes import GaussianARD, Gamma
mu = GaussianARD(0, 1e-6)
tau = Gamma(1e-6, 1e-6)
y = GaussianARD(mu, tau, plates=(10,))
.. currentmodule:: bayespy.nodes
This is quite self-explanatory given the model definitions above. We have used two types of nodes :class:`GaussianARD` and :class:`Gamma` to represent Gaussian and gamma distributions, respectively. There are much more distributions in :mod:`bayespy.nodes` so you can construct quite complex conjugate exponential family models. The node :code:`y` uses keyword argument :code:`plates` to define the plates :math:`n=0,\ldots,9`.
Now that we have created the model, we can provide our data by setting :code:`y` as observed:
.. code:: python
y.observe(data)
Next we want to estimate the posterior distribution. In principle, we
could use different inference engines (e.g., MCMC or EP) but currently
only variational Bayesian (VB) engine is implemented. The engine is
initialized by giving all the nodes of the model:
.. code:: python
from bayespy.inference import VB
Q = VB(mu, tau, y)
The inference algorithm can be run as long as wanted (max. 20 iterations
in this case):
.. code:: python
Q.update(repeat=20)
.. parsed-literal::
Iteration 1: loglike=-5.789562e+01 (0.010 seconds)
Iteration 2: loglike=-5.612083e+01 (0.000 seconds)
Iteration 3: loglike=-5.611848e+01 (0.010 seconds)
Iteration 4: loglike=-5.611846e+01 (0.000 seconds)
Converged.
Now the algorithm converged after four iterations, before the requested
20 iterations.
VB approximates the true posterior :math:`p(\mu,\tau|\mathbf{y})` with a
distribution which factorizes with respect to the nodes:
:math:`q(\mu)q(\tau)`\ . The resulting approximate posterior
distributions :math:`q(\mu)` and :math:`q(\tau)` can be examined, for
instance, by plotting the marginal probability density functions:
.. code:: python
import bayespy.plot as bpplt
# The following two two lines are just for enabling matplotlib plotting in notebooks
%matplotlib inline
bpplt.pyplot.plot([])
bpplt.pyplot.subplot(2, 1, 1)
bpplt.pdf(mu, np.linspace(-10, 20, num=100), color='k', name=r'\mu')
bpplt.pyplot.subplot(2, 1, 2)
bpplt.pdf(tau, np.linspace(1e-6, 0.08, num=100), color='k', name=r'\tau');
.. image:: quickstartbackup_files/quickstartbackup_14_0.png
This example was a very simple introduction to using BayesPy. The model
can be much more complex and each phase contains more options to give
the user more control over the inference. The following sections give
more details about the phases.
bayespy-0.2.3/doc/source/user_guide/user_guide.rst 0000664 5104112 5104112 00000001575 12374370351 024014 0 ustar jluttine jluttine 0000000 0000000 ..
Copyright (C) 2011,2012 Jaakko Luttinen
This file is licensed under Version 3.0 of the GNU General Public
License. See LICENSE for a text of the license.
This file is part of BayesPy.
BayesPy is free software: you can redistribute it and/or modify it
under the terms of the GNU General Public License version 3 as
published by the Free Software Foundation.
BayesPy is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
General Public License for more details.
You should have received a copy of the GNU General Public License
along with BayesPy. If not, see .
.. _sec-user-guide:
User guide
**********
.. toctree::
install
quickstart
modelconstruct
inference
plot
bayespy-0.2.3/doc/source/conf.py 0000664 5104112 5104112 00000024435 12430651206 020265 0 ustar jluttine jluttine 0000000 0000000 # -*- coding: utf-8 -*-
#
# BayesPy documentation build configuration file, created by
# sphinx-quickstart on Mon Aug 27 12:22:11 2012.
#
# This file is execfile()d with the current directory set to its containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# All configuration values have a default; values that are commented out
# serve to show the default.
import sys, os
# If extensions (or modules to document with autodoc) are in another directory,
# add these directories to sys.path here. If the directory is relative to the
# documentation root, use os.path.abspath to make it absolute, like shown here.
#sys.path.insert(0, os.path.abspath('.'))
# Import some information from the setup.py script.
sys.path.insert(0, os.path.abspath('../..'))
import setup as setupfile
# -- General configuration -----------------------------------------------------
# If your documentation needs a minimal Sphinx version, state it here.
#needs_sphinx = '1.0'
# Add any Sphinx extension module names here, as strings. They can be extensions
# coming with Sphinx (named 'sphinx.ext.*') or your custom ones.
extensions = [
'sphinx.ext.autodoc',
'sphinx.ext.mathjax',
'sphinx.ext.todo',
'sphinx.ext.viewcode',
'sphinx.ext.doctest',
'numpydoc',
'matplotlib.sphinxext.plot_directive',
'sphinx.ext.autosummary',
'sphinxcontrib.tikz',
'sphinxcontrib.bayesnet',
'sphinxcontrib.bibtex',
]
numpydoc_show_class_members = False
# Include TODOs in the documentation?
todo_include_todos = True
# Add a path to binary files that are necessary for readthedocs.org to
# build HTML with tikz extension
if os.environ.get('READTHEDOCS', None) == 'True':
os.environ["PATH"] += os.pathsep + os.path.abspath('bin')
os.environ["LD_LIBRARY_PATH"] = os.path.abspath('bin')
# Generate autosummary stub pages automatically
# Or manually: sphinx-autogen -o source/generated source/*.rst
#autosummary_generate = False
import glob
autosummary_generate = glob.glob("*.rst") + glob.glob("*/*.rst") + glob.glob("*/*/*.rst") + glob.glob("*/*/*/*.rst")
# Add any paths that contain templates here, relative to this directory.
templates_path = ['_templates']
# The suffix of source filenames.
source_suffix = '.rst'
# The encoding of source files.
#source_encoding = 'utf-8-sig'
# The master toctree document.
master_doc = 'index'
# General information about the project.
project = setupfile.NAME
copyright = u'2011-2014, Jaakko Luttinen, GPLv3'
# The version info for the project you're documenting, acts as replacement for
# |version| and |release|, also used in various other places throughout the
# built documents.
#
# The short X.Y version.
version = setupfile.VERSION
# The full version, including alpha/beta/rc tags.
release = setupfile.VERSION
# The language for content autogenerated by Sphinx. Refer to documentation
# for a list of supported languages.
#language = None
# There are two options for replacing |today|: either, you set today to some
# non-false value, then it is used:
#today = ''
# Else, today_fmt is used as the format for a strftime call.
#today_fmt = '%B %d, %Y'
# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
exclude_patterns = []
# The reST default role (used for this markup: `text`) to use for all documents.
#default_role = None
# If true, '()' will be appended to :func: etc. cross-reference text.
#add_function_parentheses = True
# If true, the current module name will be prepended to all description
# unit titles (such as .. function::).
#add_module_names = True
# If true, sectionauthor and moduleauthor directives will be shown in the
# output. They are ignored by default.
#show_authors = False
# The name of the Pygments (syntax highlighting) style to use.
pygments_style = 'sphinx'
# A list of ignored prefixes for module index sorting.
#modindex_common_prefix = []
# -- Options for HTML output ---------------------------------------------------
# Sphinx-TikZ extension
tikz_latex_preamble = """
\usepackage{amsmath}
"""
# The theme to use for HTML and HTML Help pages. See the documentation for
# a list of builtin themes.
html_theme = 'nature'
#html_theme = 'default'
# Theme options are theme-specific and customize the look and feel of a theme
# further. For a list of options available for each theme, see the
# documentation.
#html_theme_options = {}
# Add any paths that contain custom themes here, relative to this directory.
#html_theme_path = []
# The name for this set of Sphinx documents. If None, it defaults to
# " v documentation".
html_title = "BayesPy v%s Documentation" % (version)
# A shorter title for the navigation bar. Default is the same as html_title.
#html_short_title = None
# The name of an image file (relative to this directory) to place at the top
# of the sidebar.
#html_logo = None
# The name of an image file (within the static path) to use as favicon of the
# docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32
# pixels large.
#html_favicon = None
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
html_static_path = ['_static']
# If not '', a 'Last updated on:' timestamp is inserted at every page bottom,
# using the given strftime format.
#html_last_updated_fmt = '%b %d, %Y'
# If true, SmartyPants will be used to convert quotes and dashes to
# typographically correct entities.
#html_use_smartypants = True
# Custom sidebar templates, maps document names to template names.
#html_sidebars = {}
# Additional templates that should be rendered to pages, maps page names to
# template names.
#html_additional_pages = {}
# If false, no module index is generated.
#html_domain_indices = True
# If false, no index is generated.
#html_use_index = True
# If true, the index is split into individual pages for each letter.
#html_split_index = False
# If true, links to the reST sources are added to the pages.
#html_show_sourcelink = True
# If true, "Created using Sphinx" is shown in the HTML footer. Default is True.
#html_show_sphinx = True
# If true, "(C) Copyright ..." is shown in the HTML footer. Default is True.
#html_show_copyright = True
# If true, an OpenSearch description file will be output, and all pages will
# contain a tag referring to it. The value of this option must be the
# base URL from which the finished HTML is served.
#html_use_opensearch = ''
# This is the file name suffix for HTML files (e.g. ".xhtml").
#html_file_suffix = None
# Output file base name for HTML help builder.
htmlhelp_basename = 'BayesPydoc'
# -- Options for LaTeX output --------------------------------------------------
latex_elements = {
# The paper size ('letterpaper' or 'a4paper').
#'papersize': 'letterpaper',
# The font size ('10pt', '11pt' or '12pt').
#'pointsize': '10pt',
# Additional stuff for the LaTeX preamble.
'preamble': r'''
\usepackage{tikz}
\usepackage{amssymb}
\usepackage{amsmath}
\usetikzlibrary{shapes}
\usetikzlibrary{fit}
\usetikzlibrary{chains}
\usetikzlibrary{arrows}
''',
# Do not use [T1]{fontenc} because it does not work on libre systems
'fontenc': ''
}
#latex_additional_files = ['images/bayesnet.sty',]
# Grouping the document tree into LaTeX files. List of tuples
# (source start file, target name, title, author, documentclass [howto/manual]).
latex_documents = [
('index', 'BayesPy.tex', u'BayesPy Documentation',
u'Jaakko Luttinen', 'manual'),
]
# The name of an image file (relative to this directory) to place at the top of
# the title page.
#latex_logo = None
# For "manual" documents, if this is true, then toplevel headings are parts,
# not chapters.
#latex_use_parts = False
# If true, show page references after internal links.
#latex_show_pagerefs = False
# If true, show URL addresses after external links.
#latex_show_urls = False
# Documents to append as an appendix to all manuals.
#latex_appendices = []
# If false, no module index is generated.
#latex_domain_indices = True
# -- Options for manual page output --------------------------------------------
# One entry per manual page. List of tuples
# (source start file, name, description, authors, manual section).
man_pages = [
('index', 'bayespy', u'BayesPy Documentation',
[u'Jaakko Luttinen'], 1)
]
# If true, show URL addresses after external links.
#man_show_urls = False
# -- Options for Texinfo output ------------------------------------------------
# Grouping the document tree into Texinfo files. List of tuples
# (source start file, target name, title, author,
# dir menu entry, description, category)
texinfo_documents = [
('index', 'BayesPy', u'BayesPy Documentation',
u'Jaakko Luttinen', 'BayesPy', 'One line description of project.',
'Miscellaneous'),
]
# Documents to append as an appendix to all manuals.
#texinfo_appendices = []
# If false, no module index is generated.
#texinfo_domain_indices = True
# How to display URL addresses: 'footnote', 'no', or 'inline'.
#texinfo_show_urls = 'footnote'
# -- Options for Epub output ---------------------------------------------------
# Bibliographic Dublin Core info.
epub_title = u'BayesPy'
epub_author = u'Jaakko Luttinen'
epub_publisher = u'Jaakko Luttinen'
epub_copyright = u'2012, Jaakko Luttinen'
# The language of the text. It defaults to the language option
# or en if the language is not set.
#epub_language = ''
# The scheme of the identifier. Typical schemes are ISBN or URL.
#epub_scheme = ''
# The unique identifier of the text. This can be a ISBN number
# or the project homepage.
#epub_identifier = ''
# A unique identification for the text.
#epub_uid = ''
# A tuple containing the cover image and cover page html template filenames.
#epub_cover = ()
# HTML files that should be inserted before the pages created by sphinx.
# The format is a list of tuples containing the path and title.
#epub_pre_files = []
# HTML files shat should be inserted after the pages created by sphinx.
# The format is a list of tuples containing the path and title.
#epub_post_files = []
# A list of files that should not be packed into the epub file.
#epub_exclude_files = []
# The depth of the table of contents in toc.ncx.
#epub_tocdepth = 3
# Allow duplicate toc entries.
#epub_tocdup = True
bayespy-0.2.3/doc/source/demos.rst 0000664 5104112 5104112 00000001510 12374370351 020622 0 ustar jluttine jluttine 0000000 0000000 ..
Copyright (C) 2014 Jaakko Luttinen
This file is licensed under Version 3.0 of the GNU General Public
License. See LICENSE for a text of the license.
This file is part of BayesPy.
BayesPy is free software: you can redistribute it and/or modify it
under the terms of the GNU General Public License version 3 as
published by the Free Software Foundation.
BayesPy is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
General Public License for more details.
You should have received a copy of the GNU General Public License
along with BayesPy. If not, see .
Examples
********
.. toctree::
_notebooks/mog.rst
_notebooks/pca.rst
bayespy-0.2.3/doc/source/index.rst 0000664 5104112 5104112 00000002063 12374370351 020626 0 ustar jluttine jluttine 0000000 0000000 ..
Copyright (C) 2011,2012 Jaakko Luttinen
This file is licensed under Version 3.0 of the GNU General Public
License. See LICENSE for a text of the license.
This file is part of BayesPy.
BayesPy is free software: you can redistribute it and/or modify it
under the terms of the GNU General Public License version 3 as
published by the Free Software Foundation.
BayesPy is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
General Public License for more details.
You should have received a copy of the GNU General Public License
along with BayesPy. If not, see .
BayesPy -- Bayesian Python
==========================
.. toctree::
:maxdepth: 2
intro
user_guide/user_guide
examples/examples
dev_guide/dev_guide
user_api/user_api
dev_api/dev_api
references
* :doc:`Bibliography `
* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`
bayespy-0.2.3/doc/source/intro.rst 0000664 5104112 5104112 00000001615 12374373332 020656 0 ustar jluttine jluttine 0000000 0000000 ..
Copyright (C) 2011,2012 Jaakko Luttinen
This file is licensed under Version 3.0 of the GNU General Public
License. See LICENSE for a text of the license.
This file is part of BayesPy.
BayesPy is free software: you can redistribute it and/or modify it
under the terms of the GNU General Public License version 3 as
published by the Free Software Foundation.
BayesPy is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
General Public License for more details.
You should have received a copy of the GNU General Public License
along with BayesPy. If not, see .
Introduction
============
.. include:: ../../README.rst
:start-line: 2
Version history
---------------
.. include:: ../../CHANGELOG.rst
bayespy-0.2.3/doc/source/nodes.rst 0000664 5104112 5104112 00000002200 12231156672 020620 0 ustar jluttine jluttine 0000000 0000000 ..
Copyright (C) 2011,2012 Jaakko Luttinen
This file is licensed under Version 3.0 of the GNU General Public
License. See LICENSE for a text of the license.
This file is part of BayesPy.
BayesPy is free software: you can redistribute it and/or modify it
under the terms of the GNU General Public License version 3 as
published by the Free Software Foundation.
BayesPy is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
General Public License for more details.
You should have received a copy of the GNU General Public License
along with BayesPy. If not, see .
Nodes
=====
Stochastic nodes
----------------
Normal
++++++
Gaussian
++++++++
Gamma
+++++
Wishart
+++++++
Bernoulli
+++++++++
Categorical
+++++++++++
Binomial
++++++++
Multinomial
+++++++++++
Beta
++++
Dirichlet
+++++++++
Mixture
+++++++
Constant
++++++++
Deterministic nodes
-------------------
Dot product (of Gaussian variables)
+++++++++++++++++++++++++++++++++++
bayespy-0.2.3/doc/source/references.rst 0000664 5104112 5104112 00000001516 12374370351 021642 0 ustar jluttine jluttine 0000000 0000000 ..
Copyright (C) 2014 Jaakko Luttinen
This file is licensed under Version 3.0 of the GNU General Public
License. See LICENSE for a text of the license.
This file is part of BayesPy.
BayesPy is free software: you can redistribute it and/or modify it
under the terms of the GNU General Public License version 3 as
published by the Free Software Foundation.
BayesPy is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
General Public License for more details.
You should have received a copy of the GNU General Public License
along with BayesPy. If not, see .
.. rubric:: Bibliography
.. bibliography:: references.bib
:all:
:style: plain
bayespy-0.2.3/doc/Makefile 0000664 5104112 5104112 00000016003 12374370351 017124 0 ustar jluttine jluttine 0000000 0000000 # Makefile for Sphinx documentation
#
# You can set these variables from the command line.
SPHINXOPTS =
SPHINXBUILD = sphinx-build
PAPER =
BUILDDIR = build
# User-friendly check for sphinx-build
ifeq ($(shell which $(SPHINXBUILD) >/dev/null 2>&1; echo $$?), 1)
$(error The '$(SPHINXBUILD)' command was not found. Make sure you have Sphinx installed, then set the SPHINXBUILD environment variable to point to the full path of the '$(SPHINXBUILD)' executable. Alternatively you can add the directory with the executable to your PATH. If you don't have Sphinx installed, grab it from http://sphinx-doc.org/)
endif
# Internal variables.
PAPEROPT_a4 = -D latex_paper_size=a4
PAPEROPT_letter = -D latex_paper_size=letter
ALLSPHINXOPTS = -d $(BUILDDIR)/doctrees $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) source
# the i18n builder cannot share the environment and doctrees with the others
I18NSPHINXOPTS = $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) source
.PHONY: help clean html dirhtml singlehtml pickle json htmlhelp qthelp devhelp epub latex latexpdf text man changes linkcheck doctest gettext
help:
@echo "Please use \`make ' where is one of"
@echo " html to make standalone HTML files"
@echo " dirhtml to make HTML files named index.html in directories"
@echo " singlehtml to make a single large HTML file"
@echo " pickle to make pickle files"
@echo " json to make JSON files"
@echo " htmlhelp to make HTML files and a HTML help project"
@echo " qthelp to make HTML files and a qthelp project"
@echo " devhelp to make HTML files and a Devhelp project"
@echo " epub to make an epub"
@echo " latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter"
@echo " latexpdf to make LaTeX files and run them through pdflatex"
@echo " latexpdfja to make LaTeX files and run them through platex/dvipdfmx"
@echo " text to make text files"
@echo " man to make manual pages"
@echo " texinfo to make Texinfo files"
@echo " info to make Texinfo files and run them through makeinfo"
@echo " gettext to make PO message catalogs"
@echo " changes to make an overview of all changed/added/deprecated items"
@echo " xml to make Docutils-native XML files"
@echo " pseudoxml to make pseudoxml-XML files for display purposes"
@echo " linkcheck to check all external links for integrity"
@echo " doctest to run all doctests embedded in the documentation (if enabled)"
clean:
rm -rf $(BUILDDIR)/*
gh-pages:
rm -rf build/html
cd build && git clone https://github.com/bayespy/bayespy.git -b gh-pages html
make html
cd build/html && git add -f *.png *.html *.txt *.js *.pdf *.py *.css * && git commit -a && git push
html: latexpdf
$(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html
cp $(BUILDDIR)/latex/BayesPy.pdf $(BUILDDIR)/html/_static/
cp -r build/latex/user_guide/* build/html/user_guide/
cp -r build/latex/examples/* build/html/examples/
@echo
@echo "Build finished. The HTML pages are in $(BUILDDIR)/html."
dirhtml:
$(SPHINXBUILD) -b dirhtml $(ALLSPHINXOPTS) $(BUILDDIR)/dirhtml
@echo
@echo "Build finished. The HTML pages are in $(BUILDDIR)/dirhtml."
singlehtml:
$(SPHINXBUILD) -b singlehtml $(ALLSPHINXOPTS) $(BUILDDIR)/singlehtml
@echo
@echo "Build finished. The HTML page is in $(BUILDDIR)/singlehtml."
pickle:
$(SPHINXBUILD) -b pickle $(ALLSPHINXOPTS) $(BUILDDIR)/pickle
@echo
@echo "Build finished; now you can process the pickle files."
json:
$(SPHINXBUILD) -b json $(ALLSPHINXOPTS) $(BUILDDIR)/json
@echo
@echo "Build finished; now you can process the JSON files."
htmlhelp:
$(SPHINXBUILD) -b htmlhelp $(ALLSPHINXOPTS) $(BUILDDIR)/htmlhelp
@echo
@echo "Build finished; now you can run HTML Help Workshop with the" \
".hhp project file in $(BUILDDIR)/htmlhelp."
qthelp:
$(SPHINXBUILD) -b qthelp $(ALLSPHINXOPTS) $(BUILDDIR)/qthelp
@echo
@echo "Build finished; now you can run "qcollectiongenerator" with the" \
".qhcp project file in $(BUILDDIR)/qthelp, like this:"
@echo "# qcollectiongenerator $(BUILDDIR)/qthelp/BayesPy.qhcp"
@echo "To view the help file:"
@echo "# assistant -collectionFile $(BUILDDIR)/qthelp/BayesPy.qhc"
devhelp:
$(SPHINXBUILD) -b devhelp $(ALLSPHINXOPTS) $(BUILDDIR)/devhelp
@echo
@echo "Build finished."
@echo "To view the help file:"
@echo "# mkdir -p $$HOME/.local/share/devhelp/BayesPy"
@echo "# ln -s $(BUILDDIR)/devhelp $$HOME/.local/share/devhelp/BayesPy"
@echo "# devhelp"
epub:
$(SPHINXBUILD) -b epub $(ALLSPHINXOPTS) $(BUILDDIR)/epub
@echo
@echo "Build finished. The epub file is in $(BUILDDIR)/epub."
latex:
$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
@echo
@echo "Build finished; the LaTeX files are in $(BUILDDIR)/latex."
@echo "Run \`make' in that directory to run these through (pdf)latex" \
"(use \`make latexpdf' here to do that automatically)."
latexpdf:
$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
@echo "Running LaTeX files through pdflatex..."
$(MAKE) -C $(BUILDDIR)/latex all-pdf
@echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex."
latexpdfja:
$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
@echo "Running LaTeX files through platex and dvipdfmx..."
$(MAKE) -C $(BUILDDIR)/latex all-pdf-ja
@echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex."
text:
$(SPHINXBUILD) -b text $(ALLSPHINXOPTS) $(BUILDDIR)/text
@echo
@echo "Build finished. The text files are in $(BUILDDIR)/text."
man:
$(SPHINXBUILD) -b man $(ALLSPHINXOPTS) $(BUILDDIR)/man
@echo
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texinfo:
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bayespy-0.2.3/INSTALL.rst 0000664 5104112 5104112 00000012724 12437606256 016573 0 ustar jluttine jluttine 0000000 0000000 ..
Copyright (C) 2011,2012,2014 Jaakko Luttinen
This file is licensed under Version 3.0 of the GNU General Public
License. See LICENSE for a text of the license.
This file is part of BayesPy.
BayesPy is free software: you can redistribute it and/or modify it
under the terms of the GNU General Public License version 3 as
published by the Free Software Foundation.
BayesPy is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
General Public License for more details.
You should have received a copy of the GNU General Public License
along with BayesPy. If not, see .
Installation
============
BayesPy is a Python 3 package and it can be installed from PyPI or the latest
development version from GitHub. The instructions below explain how to set up
the system by installing required packages, how to install BayesPy and how to
compile this documentation yourself. However, if these instructions contain
errors or some relevant details are missing, please file a bug report at
https://github.com/bayespy/bayespy/issues.
Installing BayesPy
------------------
BayesPy can be installed easily by using Pip if the system has been properly set
up. First, you may want to set up a virtual environment. Using virtual
environment is optional but recommended. To create and activate a new virtual
environment, run
.. code-block:: console
virtualenv -p python3 --system-site-packages ENV
source ENV/bin/activate
The latest release of BayesPy can be installed from PyPI simply as
.. code-block:: console
pip install bayespy
If you want to install the latest development version of BayesPy, use GitHub
instead:
.. code-block:: console
pip install https://github.com/bayespy/bayespy/archive/master.zip
If you have problems installing BayesPy, read the next section for more details.
It is recommended to run the unit tests in order to check that BayesPy is
working properly. Thus, install Nose and run the unit tests:
.. code-block:: console
pip install nose
nosetests bayespy
Installing requirements
-----------------------
BayesPy requires Python 3.2 (or later) and the following packages:
* NumPy (>=1.8.0),
* SciPy (>=0.13.0)
* matplotlib (>=1.2)
* h5py
Ideally, Pip should install the necessary requirements and a manual installation
of these dependencies is not required. However, there are several reasons why
the installation of these dependencies needs to be done manually in some cases.
Thus, this section tries to give some details on how to set up your system. A
proper installation of the dependencies for Python 3 can be a bit tricky and you
may refer to http://www.scipy.org/install.html for more detailed instructions
about the SciPy stack. Detailed instructions on installing recent SciPy stack
for various platforms is out of the scope of these instructions, but we provide
some general guidance here. There are basically three ways to install the
dependencies:
1. Install a Python distribution which includes the packages. For Windows,
Mac and Linux, there are several Python distributions which include all the
necessary packages:
http://www.scipy.org/install.html#scientific-python-distributions. For
instance, you may try `Anaconda `_ or
`Enthought `_.
2. Install the packages using the system package manager. On Linux, the
packages might be called something like ``python-scipy`` or ``scipy``.
However, it is possible that these system packages are not recent enough
for BayesPy.
3. Install the packages using Pip: ``pip install "numpy>=1.8.0"
"scipy>=0.13.0" "matplotlib>=1.2" h5py``. However, this may require that
the system has the libraries needed for compiling (e.g., C compiler, Python
development files, BLAS/LAPACK). For instance, on Ubuntu (>= 12.10), you
may install the required system libraries for each package as:
.. code-block:: console
sudo apt-get build-dep python3-numpy
sudo apt-get build-dep python3-scipy
sudo apt-get build-dep python3-matplotlib
sudo apt-get build-dep python-h5py
Then installation using Pip should work. Also, make sure you have recent
enough version of Distribute (required by Matplotlib): ``pip install
"distribute>=0.6.28"``.
Compiling documentation
-----------------------
This documentation can be found at http://bayespy.org/ in HTML and PDF formats.
The documentation source files are also readable as such in reStructuredText
format in ``doc/source/`` directory. It is possible to compile the
documentation into HTML or PDF yourself. In order to compile the documentation,
Sphinx is required and a few extensions for it. Those can be installed as:
.. code-block:: console
pip install sphinx sphinxcontrib-tikz sphinxcontrib-bayesnet sphinxcontrib-bibtex "numpydoc>=0.5"
In order to visualize graphical models in HTML, you need to have ``ImageMagick``
or ``Netpbm`` installed. The documentation can be compiled to HTML and PDF by
running the following commands in the ``doc`` directory:
.. code-block:: console
make html
make latexpdf
You can also run doctest to test code snippets in the documentation:
.. code-block:: console
make doctest
or in the docstrings:
.. code-block:: console
nosetests --with-doctest bayespy
bayespy-0.2.3/LICENSE 0000664 5104112 5104112 00000104513 12121570320 015714 0 ustar jluttine jluttine 0000000 0000000 GNU GENERAL PUBLIC LICENSE
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.
bayespy-0.2.3/MANIFEST.in 0000664 5104112 5104112 00000000410 12231156732 016445 0 ustar jluttine jluttine 0000000 0000000 include README*
include INSTALL*
include LICENSE
# Include header files
recursive-include bayespy *.h
# Add documentation to sdist-generated tarballs
recursive-include doc/source *.rst
recursive-include doc/source *.py
include doc/Makefile
#include doc/make.bat
bayespy-0.2.3/README.rst 0000664 5104112 5104112 00000007260 12412543071 016405 0 ustar jluttine jluttine 0000000 0000000 BayesPy - Bayesian Python
=========================
BayesPy provides tools for Bayesian inference with Python. The user
constructs a model as a Bayesian network, observes data and runs
posterior inference. The goal is to provide a tool which is
efficient, flexible and extendable enough for expert use but also
accessible for more casual users.
Currently, only variational Bayesian inference for
conjugate-exponential family (variational message passing) has been
implemented. Future work includes variational approximations for
other types of distributions and possibly other approximate inference
methods such as expectation propagation, Laplace approximations,
Markov chain Monte Carlo (MCMC) and other methods. Contributions are
welcome.
Project information
-------------------
Copyright (C) 2011-2014 Jaakko Luttinen, Aalto University
BayesPy including the documentation is licensed under Version 3.0 of
the GNU General Public License. See LICENSE file for a text of the
license or visit http://www.gnu.org/copyleft/gpl.html.
* Documentation:
* http://bayespy.org
* `PDF file <_static/BayesPy.pdf>`_
* RST format in ``doc`` directory
* Repository: https://github.com/bayespy/bayespy.git
* Bug reports: https://github.com/bayespy/bayespy/issues
* Mailing list: bayespy@googlegroups.com
* IRC: #bayespy @ `freenode `_
* Author: Jaakko Luttinen jaakko.luttinen@iki.fi
* Latest release:
.. image:: https://pypip.in/v/bayespy/badge.png
:target: https://pypi.python.org/pypi/bayespy
* Build status:
.. image:: https://travis-ci.org/bayespy/bayespy.png?branch=master
:target: https://travis-ci.org/bayespy/bayespy/
* Unit test coverage:
.. image:: https://coveralls.io/repos/bayespy/bayespy/badge.png?branch=master
:target: https://coveralls.io/r/bayespy/bayespy?branch=master
Similar projects
----------------
`VIBES `_
(http://vibes.sourceforge.net/) allows variational inference to be
performed automatically on a Bayesian network. It is implemented in
Java and released under revised BSD license.
`Bayes Blocks `_
(http://research.ics.aalto.fi/bayes/software/) is a C++/Python
implementation of the variational building block framework. The
framework allows easy learning of a wide variety of models using
variational Bayesian learning. It is available as free software under
the GNU General Public License.
`Infer.NET `_
(http://research.microsoft.com/infernet/) is a .NET framework for
machine learning. It provides message-passing algorithms and
statistical routines for performing Bayesian inference. It is partly
closed source and licensed for non-commercial use only.
`PyMC `_
(https://github.com/pymc-devs/pymc) provides MCMC methods in Python.
It is released under the Academic Free License.
`OpenBUGS `_ (http://www.openbugs.info) is a
software package for performing Bayesian inference using Gibbs
sampling. It is released under the GNU General Public License.
`Dimple `_ (http://dimple.probprog.org/) provides
Gibbs sampling, belief propagation and a few other inference algorithms for
Matlab and Java. It is released under the Apache License.
`Stan `_ (http://mc-stan.org/) provides inference using
MCMC with an interface for R and Python. It is released under the New BSD
License.
`PBNT - Python Bayesian Network Toolbox `_
(http://pbnt.berlios.de/) is Bayesian network library in Python supporting
static networks with discrete variables. There was no information about the
license.
bayespy-0.2.3/setup.py 0000664 5104112 5104112 00000005406 12437606775 016452 0 ustar jluttine jluttine 0000000 0000000 #!/usr/bin/env python
######################################################################
# Copyright (C) 2011,2012,2014 Jaakko Luttinen
#
# This file is licensed under Version 3.0 of the GNU General Public
# License. See LICENSE for a text of the license.
######################################################################
######################################################################
# This file is part of BayesPy.
#
# BayesPy is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# BayesPy is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with BayesPy. If not, see .
######################################################################
LONG_DESCRIPTION = \
"""
Bayesian inference tools for Python.
The package provides tools for building models and performing
posterior inference using variational Bayesian message passing.
"""
NAME = 'bayespy'
DESCRIPTION = 'Bayesian inference tools for Python'
AUTHOR = 'Jaakko Luttinen'
AUTHOR_EMAIL = 'jaakko.luttinen@iki.fi'
URL = 'http://bayespy.org'
LICENSE = 'GPLv3'
VERSION = '0.2.3'
if __name__ == "__main__":
from setuptools import setup, find_packages
# Setup for BayesPy
setup(
install_requires = ['numpy>=1.8.0', # 1.8 implements broadcasting in numpy.linalg
'scipy>=0.13.0', # <0.13 have a bug in special.multigammaln
'matplotlib>=1.2.0',
'h5py'],
packages = find_packages(),
name = NAME,
version = VERSION,
author = AUTHOR,
author_email = AUTHOR_EMAIL,
description = DESCRIPTION,
license = LICENSE,
url = URL,
long_description = LONG_DESCRIPTION,
classifiers =
[
'Programming Language :: Python :: 3',
'Development Status :: 3 - Alpha',
'Environment :: Console',
'Intended Audience :: Developers',
'Intended Audience :: Science/Research',
'License :: OSI Approved :: GNU General Public License v3 (GPLv3)',
'Operating System :: OS Independent',
'Topic :: Scientific/Engineering',
'Topic :: Scientific/Engineering :: Information Analysis'
]
)
bayespy-0.2.3/PKG-INFO 0000664 5104112 5104112 00000001600 12437607672 016022 0 ustar jluttine jluttine 0000000 0000000 Metadata-Version: 1.1
Name: bayespy
Version: 0.2.3
Summary: Bayesian inference tools for Python
Home-page: http://bayespy.org
Author: Jaakko Luttinen
Author-email: jaakko.luttinen@iki.fi
License: GPLv3
Description:
Bayesian inference tools for Python.
The package provides tools for building models and performing
posterior inference using variational Bayesian message passing.
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Development Status :: 3 - Alpha
Classifier: Environment :: Console
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Information Analysis
bayespy-0.2.3/setup.cfg 0000664 5104112 5104112 00000000216 12437607672 016550 0 ustar jluttine jluttine 0000000 0000000 [egg_info]
tag_build =
tag_date = 0
tag_svn_revision = 0
[aliases]
release = egg_info -RDb ''
release_pypi = release register sdist upload