ctbnrle/ 0000755 0004642 0000777 00000000000 11264651744 012614 5 ustar cshelton csprofs ctbnrle/demo/ 0000755 0004642 0000777 00000000000 11262435040 013523 5 ustar cshelton csprofs ctbnrle/demo/learnparams.cc 0000644 0004642 0000777 00000003677 11257240650 016361 0 ustar cshelton csprofs /* Continuous Time Bayesian Network Reasoning and Learning Engine
* Copyright (C) 2009 The Regents of the University of California
*
* Authored by Yu Fan, William Lam, Joon Lee, Christian Shelton, and Jing Xu
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* This program 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 program. If not, see .
*/
//This code learns the parameters of a CTBN model
//given fully observed data.
#include "markov.h"
#include "trajectory.h"
#include "params.h"
#include
using namespace std;
int main(int argc, char**argv) {
InitParams(argc, argv);
string ctbnfile = ParamStr("ctbn", "");
if(ctbnfile == "") {
cout << "CTBN model is missing" << endl;
exit(1);
}
int n = ParamInt("numofsample", 1000);
double begintime = ParamDouble("begintime" , 0.0);
double endtime = ParamDouble("endtime", 5.0);
ifstream fin(ctbnfile.c_str());
if(!fin.good()) {
cout << "bad CTBN file" << endl;
exit(1);
}
//load CTBN model from the file
Markov ctbn(fin);
//generate data
vector data;
for(int i=0; i w(n, 1.0);
SS *ss = ctbn.SuffStats(data, w);
//random choose parameters
ctbn.Scramble();
//learn parameters from complete data
ctbn.Maximize(ss);
ctbn.Save(cout);
if(ss!=NULL) delete ss;
return 0;
}
ctbnrle/demo/queryinput.data 0000644 0004642 0000777 00000004306 11257222633 016614 0 ustar cshelton csprofs BN 12 3 0 2 1 2 2 3 1 0 3 MultiRV 2 1 0 2 1 0 16RVCondSimpleCompI12MultiZSimpleE 1 2 0.5 0.5 0 MultiRV 2 1 1 2 2 1 0 2 16RVCondSimpleCompI12MultiZSimpleE 2 2 0.8 0.2 0 2 0.3 0.7 0 MultiRV 3 1 2 3 1 0 16RVCondSimpleCompI12MultiZSimpleE 1 3 0.2 0.3 0.5 0 CTBNDyn 12 3 0 2 1 2 2 3 1 0 3 MarkovDyn 2 1 0 2 1 0 MarkovSimple 2 2
-1 1
2 -2 MarkovDyn 2 1 1 2 2 1 0 2 MarkovSimple 2 2
-3 3
4 -4 MarkovSimple 2 2
-5 5
6 -6 MarkovDyn 3 1 2 3 1 0 MarkovSimple 3 3
-6 3 3
6 -12 6
9 9 -18
0 20 3 0 16 0 1 0.77263 -1 4.77263 0 5.1174 -1 9.1174 0 9.21886 1 9.60155 -1 16.7236 0 17.4769 1 17.484 0 17.5554 1 17.7718 0 18.8884 1 19.1432 0 19.4411 1 19.8002 0 1 38 0 0 0.316272 1 0.365676 0 0.645966 1 0.764025 0 0.774797 1 0.837957 0 0.92683 1 1.01157 0 1.06317 1 1.28621 0 2.16463 1 2.24661 0 2.32091 1 2.33814 0 2.62128 1 2.71801 0 2.83202 1 2.85891 0 2.87445 1 3.31781 0 3.93952 1 4.0815 0 4.72941 1 5.45909 0 5.47341 1 5.57328 0 5.65995 1 5.97848 0 5.99476 1 6.15639 0 6.2641 1 6.34758 0 6.62033 -1 18.6065 1 18.7449 0 19.4524 1 19.4663 0 2 110 0 2 0.0206161 1 0.0348029 2 0.0804483 1 0.11676 0 0.269922 1 0.369986 0 0.571562 2 0.631984 1 0.67498 2 0.685927 0 0.754802 2 0.801601 1 0.805742 0 0.866722 2 0.894783 0 1.04049 2 1.04329 1 1.06369 2 1.06588 0 1.11438 2 1.2948 1 1.37324 0 1.6805 1 1.74205 2 1.94763 1 2.05266 2 2.08956 1 2.18388 2 2.26138 0 2.36928 2 2.45702 0 2.53206 1 2.60557 0 2.61942 2 2.62044 0 2.94362 1 2.99217 2 3.07686 0 3.38743 1 3.42291 0 3.44245 2 3.70943 1 3.775 0 3.84005 -1 9.18293 0 9.28835 2 9.29098 1 9.34743 2 9.34861 0 9.35482 2 9.36261 0 9.78396 2 9.80645 0 10.187 2 10.1913 1 10.1961 0 10.508 1 10.5325 2 10.5538 0 10.6383 2 10.668 1 10.9786 2 10.9979 0 11.0182 2 11.0873 1 11.3193 2 11.4397 0 11.6594 2 11.666 1 11.8316 0 12.0717 2 12.0901 1 12.1525 0 12.1835 2 12.2837 0 12.4861 2 12.5001 1 12.5867 0 12.6844 2 12.8064 1 12.8247 0 13.0476 -1 17.0476 1 17.0654 0 17.0735 2 17.1063 1 17.1237 2 17.1485 0 17.2547 2 17.2726 1 17.3225 0 17.5154 1 17.5386 0 17.6988 1 17.7316 2 17.8683 1 18.015 0 18.4749 2 18.4953 0 18.5531 1 18.5995 2 18.6154 0 18.7448 2 18.8395 0 19.1138 2 19.1151 0 19.3385 1 19.4282 0 19.8278 2
8.16498 41.9283
ctbnrle/demo/drug.ctbn 0000644 0004642 0000777 00000004314 11257222633 015344 0 ustar cshelton csprofs BN 864 8 0 2 1 3 2 2 3 2 4 3 5 3 6 2 7 2 1 0 8 MultiRV 2 1 0 2 1 0 16RVCondSimpleCompI12MultiZSimpleE 1 2 1 0 0 MultiRV 3 1 1 3 1 0 16RVCondSimpleCompI12MultiZSimpleE 1 3 1 0 0 0 MultiRV 2 1 2 2 1 0 16RVCondSimpleCompI12MultiZSimpleE 1 2 1 0 0 MultiRV 2 1 3 2 1 0 16RVCondSimpleCompI12MultiZSimpleE 1 2 0 1 0 MultiRV 3 1 4 3 1 0 16RVCondSimpleCompI12MultiZSimpleE 1 3 1 0 0 0 MultiRV 3 1 5 3 1 0 16RVCondSimpleCompI12MultiZSimpleE 1 3 0 1 0 0 MultiRV 2 1 6 2 1 0 16RVCondSimpleCompI12MultiZSimpleE 1 2 0 1 0 MultiRV 2 1 7 2 1 0 16RVCondSimpleCompI12MultiZSimpleE 1 2 1 0 0 CTBNDyn 864 8 0 2 1 3 2 2 3 2 4 3 5 3 6 2 7 2 1 0 8 MarkovDyn 2 1 0 2 2 1 2 2 MarkovSimple 2 2
-0.01 0.01
10 -10 MarkovSimple 2 2
-2 2
1 -1 MarkovDyn 3 1 1 3 2 1 0 2 MarkovSimple 3 3
-0.02 0.01 0.01
0.3 -0.31 0.01
0.01 1 -1.01 MarkovSimple 3 3
-6.01 6 0.01
0.01 -3.01 3
0.01 0.01 -0.02 MarkovDyn 2 1 2 2 3 1 1 3 MarkovSimple 2 2
-2 2
0.01 -0.01 MarkovSimple 2 2
-0.01 0.01
0.01 -0.01 MarkovSimple 2 2
-0.01 0.01
10 -10 MarkovDyn 2 1 3 2 1 0 MarkovSimple 2 2
-0 0
0.5 -0.5 MarkovDyn 3 1 4 3 6 2 1 3 3 2 MarkovSimple 3 3
-0.02 0.01 0.01
0.25 -0.26 0.01
0.01 0.5 -0.51 MarkovSimple 3 3
-0.02 0.01 0.01
0.25 -0.26 0.01
0.01 0.5 -0.51 MarkovSimple 3 3
-0.02 0.01 0.01
0.25 -0.26 0.01
0.01 0.5 -0.51 MarkovSimple 3 3
-2.01 2 0.01
0.01 -1.01 1
0.01 0.01 -0.02 MarkovSimple 3 3
-1.01 1 0.01
0.01 -2.01 2
0.01 0.01 -0.02 MarkovSimple 3 3
-0.51 0.5 0.01
0.01 -4.01 4
0.01 0.01 -0.02 MarkovDyn 3 1 5 3 1 0 MarkovSimple 3 3
-0.21 0.2 0.01
0.05 -0.1 0.05
0.01 0.2 -0.21 MarkovDyn 2 1 6 2 9 2 4 3 5 3 MarkovSimple 2 2
-6 6
0.1 -0.1 MarkovSimple 2 2
-1 1
0.1 -0.1 MarkovSimple 2 2
-6 6
0.1 -0.1 MarkovSimple 2 2
-1 1
0.3 -0.3 MarkovSimple 2 2
-0.3 0.3
0.3 -0.3 MarkovSimple 2 2
-1 1
0.3 -0.3 MarkovSimple 2 2
-0.01 0.01
2 -2 MarkovSimple 2 2
-0.01 0.01
2 -2 MarkovSimple 2 2
-0.01 0.01
2 -2 MarkovDyn 2 1 7 2 3 1 4 3 MarkovSimple 2 2
-0.17 0.17
0.5 -0.5 MarkovSimple 2 2
-0.33 0.33
0.33 -0.33 MarkovSimple 2 2
-1 1
ctbnrle/demo/using_graphic.cc 0000644 0004642 0000777 00000004717 11257240650 016672 0 ustar cshelton csprofs /* Continuous Time Bayesian Network Reasoning and Learning Engine
* Copyright (C) 2009 The Regents of the University of California
*
* Authored by Yu Fan, William Lam, Joon Lee, Christian Shelton, and Jing Xu
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* This program 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 program. If not, see .
*/
#include
#include "graphic.h"
#include "markov.h"
#include "params.h"
using namespace std;
int main(int argc, char **argv)
{
//to initialize or use the params.h class
InitParams(argc, argv);
//to see if the file opens or not
string File;
if(argc < 2)
{
cout << " you need to include a ctbn file" << endl;
return 0;
}
File = ParamStr("File", "fail");
ifstream ls(File.c_str());
if(!ls.is_open()) return 0;
//building the Markov to use in graphic program
Markov ctbn(ls);
//several different file to be written
//dot extention for graphviz and eps extention for post-script
ofstream file("output2.eps");
ofstream file2("output.dot");
ofstream file3("forthetest.eps");
ofstream file4("forthetest2.eps");
//creating the Graphic class name test
Graphic test;
//setting the font for the trajectory graph
test.SetFontTrajectory(16, 10);
//drawing the CTBN graph with rank system intergrated
string filename = "output.dot";
test.DrawCTBNRank(ctbn,file2, filename, file);
file.close();
//to create the Trajectory::Index
const CTBNDyn * B = dynamic_cast(ctbn.GetDynamics());
Context context = Context();
for (int i = 0; i < B->NumofNodes(); ++i)
context = context + B->Node(i)->Domain();
string name;
Trajectory tr;
tr.SetBeginTime(0);
tr.SetEndTime(3);
ctbn.Sample(tr);
ofstream os("unit_test_traj.graph");
tr.Save(os);
//to draw the Index into trajectory graph
test.DrawTrajectory(tr, context ,file3);
test.DrawTrajectory(tr,context ,file4);
//end of the file
cout << "done" << endl;
file.close();
file2.close();
file3.close();
file4.close();
return 0;
}
ctbnrle/demo/makechain.cc 0000644 0004642 0000777 00000005536 11257240650 015770 0 ustar cshelton csprofs /* Continuous Time Bayesian Network Reasoning and Learning Engine
* Copyright (C) 2009 The Regents of the University of California
*
* Authored by Yu Fan, William Lam, Joon Lee, Christian Shelton, and Jing Xu
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* This program 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 program. If not, see .
*/
// This code generates a chain network.
#include "markovdyn.h"
#include "markov.h"
#include "streamextra.h"
#include "context.h"
#include
#include "multirv.h"
#include "trajectory.h"
#include "exactmarkovinf.h"
#include "params.h"
#include "bn.h"
#include "ctbndyn.h"
#include "em.h"
#include "ctbn.h"
using namespace std;
int main(int argc, char **argv) {
InitParams(argc, argv);
cout << setprecision(4);
// Set various parameters including the number of nodes, states per node,
// initial distribution values, and Q matrix values.
string paramval;
int nnode = ParamInt("numofnodes", 3);
int nstate = ParamInt("numofstates", 2);
double headrate = ParamDouble("headrate", 1.0);
double chaserate = ParamDouble("chaserate", 1.0);
double randrate = ParamDouble("randrate", 0.1 );
// Generate the head node
Context nullc;
Context headc;
headc.AddVar(0, nstate);
MarkovDyn Phead(headc, nullc);
MultiRV P0head(headc, nullc);
vectr p0(nstate, 0.0);
for (int i=0; iIntensity()[i][j] = (i==j ? -headrate : headrate/(nstate-1));
p0[i] = (i==0 ? 1.0 : 0.0);
}
P0head[0].SetDist(p0);
CTBNDyn ctbndyn;
ctbndyn.AddNode(Phead.Clone());
BN bn;
bn.AddNode(P0head.Clone());
// Generate the rest of the nodes
for (int i=1; iIntensity()[k][l] = (j==k ?
((k==l) ? -randrate*(nstate-1) : randrate)
: ((k==l) ? -chaserate-randrate*(nstate-2) :
(l==j ? chaserate : randrate)));
ctbndyn.AddNode(node.Clone());
Context initp;
initp.AddVar(i, nstate);
MultiRV P0node(initp, nullc);
P0node[0].SetDist(p0);
bn.AddNode(P0node.Clone());
}
// Create the final object to represent the chain network
Markov m(bn.Clone(), ctbndyn.Clone());
m.Save(cout);
}
ctbnrle/demo/sampleprocess.cc 0000644 0004642 0000777 00000003171 11257240650 016721 0 ustar cshelton csprofs /* Continuous Time Bayesian Network Reasoning and Learning Engine
* Copyright (C) 2009 The Regents of the University of California
*
* Authored by Yu Fan, William Lam, Joon Lee, Christian Shelton, and Jing Xu
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* This program 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 program. If not, see .
*/
// this file reads in a model file and outputs a trajectory sampled from it
// from time 0 to time as given by the first argument
#include "process.h"
#include
#include "params.h"
using namespace std;
int main(int argc, char **argv)
{
InitParams(argc, argv);
if (argc < 2) {
cerr << "Not enough arguments! ./sampleprocess ending_time" << endl;
exit(1);
}
addnan(cout);
addnan(cin);
double endt = atof(argv[1]);
if (endt<0.0) {
cerr << "time must be non-negative" << endl;
exit(1);
}
Process *p = dynamic_cast(StreamObj::LoadPtr(cin));
if (p==NULL) {
cerr << "stdin is not a saved process pointer" << endl;
exit(1);
}
Trajectory tr;
tr.SetBeginTime(0.0);
tr.SetEndTime(endt);
p->Sample(tr);
tr.Save(cout);
cout << endl;
return 0;
}
ctbnrle/demo/gibbsquery.cc 0000644 0004642 0000777 00000005553 11257240650 016223 0 ustar cshelton csprofs /* Continuous Time Bayesian Network Reasoning and Learning Engine
* Copyright (C) 2009 The Regents of the University of California
*
* Authored by Yu Fan, William Lam, Joon Lee, Christian Shelton, and Jing Xu
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* This program 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 program. If not, see .
*/
// this file reads in a model file and an evidence file, do the query using gibbs sampling
#include "markovdyn.h"
#include "streamextra.h"
#include "context.h"
#include "multirv.h"
#include "samplinginf.h"
#include "gibbssampler.h"
#include "params.h"
#include "bn.h"
#include "ctbndyn.h"
#include "em.h"
#include "utils.h"
#include
using namespace std;
namespace {
int testing_result = 0;
double kEpsilon = 0.15;
};
#define EXPECT(cond) do { \
if (!(cond)) { \
std::cerr << "Test failure at " << __FILE__ << ":" << __LINE__ \
<< " in " << __func__ \
<< ", failed condition is " #cond << std::endl; \
testing_result = 1; \
} \
} while (0)
int main(int argc, char **argv)
{
randomizer.Reset(1);
const char *input_file = "queryinput.data";
addnan(cout);
InitParams(argc, argv);
ifstream fin(input_file);
Markov m(fin);
const CTBNDyn *ctbndyn = dynamic_cast(m.GetDynamics());
const BN *bn = dynamic_cast(m.GetStartDist());
Context context = ctbndyn->Domain() + ctbndyn->CondDomain();
Trajectory evid;
fin >> evid;
double expect_q1, expect_q2;
fin >> expect_q1 >> expect_q2;
fin.close();
Instantiation x(context, -1);
int querynode = ParamInt("QueryNode", 1);
int querytimeval = ParamInt("QueryTimeVal", 1);
Context c = ctbndyn->Node(querynode)->Domain();
x.SetVal(querynode, querytimeval);
QueryTime query1(x);
QueryTransition query2(c, 0, 1);
SamplingInf inf;
int numofburniniter = 1000;
int numofsamples = 1000;
Sampler *sampler;
sampler = new GibbsSampler(&m, &evid, numofburniniter);
inf.SetSampler(sampler);
inf.SetProcess(&m);
inf.SetNumSample(numofsamples);
double error1 = (inf.CalcQuery(query1) - expect_q1) / expect_q1;
double error2 = (inf.CalcQuery(query2) - expect_q2) / expect_q2;
EXPECT(fabs(error1) < kEpsilon);
EXPECT(fabs(error2) < kEpsilon);
if (testing_result == 0)
cout << "PASS gibbsquery unittest." << endl;
return 0;
}
ctbnrle/demo/makequeryinput.cc 0000644 0004642 0000777 00000006100 11257240650 017117 0 ustar cshelton csprofs /* Continuous Time Bayesian Network Reasoning and Learning Engine
* Copyright (C) 2009 The Regents of the University of California
*
* Authored by Yu Fan, William Lam, Joon Lee, Christian Shelton, and Jing Xu
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* This program 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 program. If not, see .
*/
#include "markovdyn.h"
#include "streamextra.h"
#include "context.h"
#include "multirv.h"
#include "exactmarkovinf.h"
#include "params.h"
#include "bn.h"
#include "ctbndyn.h"
#include "em.h"
#include "utils.h"
#include
using namespace std;
int main (int argc, char *argv) {
// We do not want randomness in unittests.
randomizer.Reset(1);
Context X,Y,Z;
Context Null;
X.AddVar(0,2);
Y.AddVar(1,2);
Z.AddVar(2,3);
MarkovDyn PX(X,Null); // process of X
MarkovDyn PY(Y,X); // process of Y given X
MarkovDyn PZ(Z,Null);
PX(0)->Intensity()[0][0] = -1;
PX(0)->Intensity()[0][1] = 1;
PX(0)->Intensity()[1][0] = 2;
PX(0)->Intensity()[1][1] = -2;
PY(0)->Intensity()[0][0] = -3;
PY(0)->Intensity()[0][1] = 3;
PY(0)->Intensity()[1][0] = 4;
PY(0)->Intensity()[1][1] = -4;
PY(1)->Intensity()[0][0] = -5;
PY(1)->Intensity()[0][1] = 5;
PY(1)->Intensity()[1][0] = 6;
PY(1)->Intensity()[1][1] = -6;
PZ(0)->Intensity()[0][0] = -6;
PZ(0)->Intensity()[0][1] = 3;
PZ(0)->Intensity()[0][2] = 3;
PZ(0)->Intensity()[1][1] = -12;
PZ(0)->Intensity()[1][0] = 6;
PZ(0)->Intensity()[1][2] = 6;
PZ(0)->Intensity()[2][2] = -18;
PZ(0)->Intensity()[2][0] = 9;
PZ(0)->Intensity()[2][1] = 9;
MultiRV P0X(X,Null);
MultiRV P0Y(Y,X);
MultiRV P0Z(Z,Null);
vectr p0(2,0.0);
p0[0] = 0.5; p0[1] = 0.5;
P0X[0].SetDist(p0);
p0[0] = 0.8; p0[1] = 0.2;
P0Y[0].SetDist(p0);
p0[0] = 0.3; p0[1] = 0.7;
P0Y[1].SetDist(p0);
vectr pz(3,0.0);
pz[0] = 0.2; pz[1] = 0.3; pz[2] = 0.5;
P0Z[0].SetDist(pz);
// set up CTBN:
CTBNDyn ctbndyn;
ctbndyn.AddNode(PX.Clone());
ctbndyn.AddNode(PY.Clone());
ctbndyn.AddNode(PZ.Clone());
BN bn;
bn.AddNode(P0X.Clone());
bn.AddNode(P0Y.Clone());
bn.AddNode(P0Z.Clone());
Markov ctbn(bn.Clone(),ctbndyn.Clone());
Context context = ctbndyn.Domain() + ctbndyn.CondDomain();
double begintime = ParamDouble("BeginTime", 0.0);
double endtime = ParamDouble("EndTime", 20.0);
Trajectory evid;
evid.SetBeginTime(begintime);
evid.SetEndTime(endtime);
ctbn.Sample(evid);
RemoveNodesInformation(evid, context, ctbndyn.NumofNodes(), ctbndyn.NumofNodes()*4 , 0.2);
ctbn.Save(cout);
cout << endl << evid << endl;
// The expected query results (exact):
cout << 8.16498 << " " << 41.9283 << endl;
return 0;
}
ctbnrle/demo/makedrug2.cc 0000644 0004642 0000777 00000031030 11257240650 015715 0 ustar cshelton csprofs /* Continuous Time Bayesian Network Reasoning and Learning Engine
* Copyright (C) 2009 The Regents of the University of California
*
* Authored by Yu Fan, William Lam, Joon Lee, Christian Shelton, and Jing Xu
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* This program 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 program. If not, see .
*/
// This code generates the drug effect network as first seen in
// Continuous Time Bayesian Networks by Nodelman et al., 2002.
#include "markovdyn.h"
#include "markov.h"
#include "streamextra.h"
#include "context.h"
#include
#include "multirv.h"
#include "trajectory.h"
#include "exactmarkovinf.h"
#include "params.h"
#include "ctbndyn.h"
#include "bn.h"
#include "em.h"
#include "ctbn.h"
using namespace std;
int main(int argc, char **argv) {
InitParams(argc, argv);
cout << setprecision(4);
//*******************************************************
// Create contexts for each variable
Context nullc;
Context eatc, fullc, hungryc, uptakec, concentrationc, barometerc, painc, drowsyc;
eatc.AddVar(0, 2);
fullc.AddVar(1,3);
hungryc.AddVar(2,2);
uptakec.AddVar(3, 2);
concentrationc.AddVar(4,3);
barometerc.AddVar(5,3);
painc.AddVar(6, 2);
drowsyc.AddVar(7,2);
// Define the connections between variables. For a variable with multiple
// parents, the context used is the union of the two parent contexts
MarkovDyn eat(eatc, hungryc);
MarkovDyn full(fullc, eatc);
MarkovDyn hungry(hungryc, fullc);
MarkovDyn uptake(uptakec, nullc);
Context full_and_uptakec(fullc, uptakec);
MarkovDyn concentration(concentrationc, full_and_uptakec);
MarkovDyn barometer(barometerc, nullc);
MarkovDyn pain(painc, Context(barometerc, concentrationc));
MarkovDyn drowsy(drowsyc, concentrationc);
// Generate starting distributions for each variable
MultiRV eatrv(eatc, nullc);
MultiRV fullrv(fullc, eatc);
MultiRV hungryrv(hungryc, eatc);
MultiRV uptakerv(uptakec, nullc);
MultiRV concentrationrv(concentrationc, uptakec+hungryc);
MultiRV barometerrv(barometerc, nullc);
MultiRV painrv(painc, barometerc);
MultiRV drowsyrv(drowsyc, concentrationc+painc);
// The following fills the conditional intensity matrices for each
// variable and the initial distributions (multinomial distributions).
//------eat--------
eat(0)->Intensity()[0][0] = -0.01;
eat(0)->Intensity()[0][1] = 0.01;
eat(0)->Intensity()[1][0] = 10.0;
eat(0)->Intensity()[1][1] = -10.0;
eat(1)->Intensity()[0][0] = -2.0;
eat(1)->Intensity()[0][1] = 2.0;
eat(1)->Intensity()[1][0] = 1.0;
eat(1)->Intensity()[1][1] = -1.0;
vectr peat(2, 0.0);
peat[0] = 0.3;
peat[1] = 0.7;
eatrv[0].SetDist(peat);
//------full--------
full(0)->Intensity()[0][0] = -0.02;
full(0)->Intensity()[0][1] = 0.01;
full(0)->Intensity()[0][2] = 0.01;
full(0)->Intensity()[1][0] = 0.3;
full(0)->Intensity()[1][1] = -0.31;
full(0)->Intensity()[1][2] = 0.01;
full(0)->Intensity()[2][0] = 0.01;
full(0)->Intensity()[2][1] = 1.0;
full(0)->Intensity()[2][2] = -1.01;
full(1)->Intensity()[0][0] = -6.01;
full(1)->Intensity()[0][1] = 6.0;
full(1)->Intensity()[0][2] = 0.01;
full(1)->Intensity()[1][0] = 0.01;
full(1)->Intensity()[1][1] = -3.01;
full(1)->Intensity()[1][2] = 3.0;
full(1)->Intensity()[2][0] = 0.01;
full(1)->Intensity()[2][1] = 0.01;
full(1)->Intensity()[2][2] = -0.02;
vectr pfull(3, 0.0);
pfull[0] = 0.5;
pfull[1] = 0.25;
pfull[2] = 0.25;
fullrv[0].SetDist(pfull);
pfull[0] = 0.2;
pfull[1] = 0.25;
pfull[2] = 0.55;
fullrv[1].SetDist(pfull);
//------hungry--------
hungry(0)->Intensity()[0][0] = -2.0;
hungry(0)->Intensity()[0][1] = 2.0;
hungry(0)->Intensity()[1][0] = 0.01;
hungry(0)->Intensity()[1][1] = -0.01;
hungry(1)->Intensity()[0][0] = -0.01;
hungry(1)->Intensity()[0][1] = 0.01;
hungry(1)->Intensity()[1][0] = 0.01;
hungry(1)->Intensity()[1][1] = -0.01;
hungry(2)->Intensity()[0][0] = -0.01;
hungry(2)->Intensity()[0][1] = 0.01;
hungry(2)->Intensity()[1][0] = 10.0;
hungry(2)->Intensity()[1][1] = -10.0;
vectr phungry(2, 0.0);
phungry[0] = 0.35;
phungry[1] = 0.65;
hungryrv[0].SetDist(phungry);
phungry[0] = 0.45;
phungry[1] = 0.55;
hungryrv[1].SetDist(phungry);
//------uptake--------
uptake(0)->Intensity()[0][0] = -0.0;
uptake(0)->Intensity()[0][1] = 0.0;
uptake(0)->Intensity()[1][0] = 0.5;
uptake(0)->Intensity()[1][1] = -0.5;
vectr puptake(2, 0.0);
puptake[1] = 1;
uptakerv[0].SetDist(puptake);
// For this variable there is more than one parent.
// The CIM is filled by creating an Instantiation object that represents
// the current intensity matrix we are entering values for. The
// MarkovDyn objects can be then indexed with Instantiation objects.
//------concentration------
Instantiation i(Context(fullc, uptakec));
// Set uptake to state 0; full to state 0
i.SetVal(3,0); i.SetVal(1,0);
concentration(i)->Intensity()[0][0] = -0.02;
concentration(i)->Intensity()[0][1] = 0.01;
concentration(i)->Intensity()[0][2] = 0.01;
concentration(i)->Intensity()[1][0] = 0.25;
concentration(i)->Intensity()[1][1] = -0.26;
concentration(i)->Intensity()[1][2] = 0.01;
concentration(i)->Intensity()[2][0] = 0.01;
concentration(i)->Intensity()[2][1] = 0.5;
concentration(i)->Intensity()[2][2] = -0.51;
// Set uptake to state 1; full to state 0
// (We repeat this process for each parent instantiation.)
i.SetVal(3,1); i.SetVal(1,0);
concentration(i)->Intensity()[0][0] = -2.01;
concentration(i)->Intensity()[0][1] = 2.0;
concentration(i)->Intensity()[0][2] = 0.01;
concentration(i)->Intensity()[1][0] = 0.01;
concentration(i)->Intensity()[1][1] = -1.01;
concentration(i)->Intensity()[1][2] = 1.0;
concentration(i)->Intensity()[2][0] = 0.01;
concentration(i)->Intensity()[2][1] = 0.01;
concentration(i)->Intensity()[2][2] = -0.02;
i.SetVal(3,0); i.SetVal(1,1);
concentration(i)->Intensity()[0][0] = -0.02;
concentration(i)->Intensity()[0][1] = 0.01;
concentration(i)->Intensity()[0][2] = 0.01;
concentration(i)->Intensity()[1][0] = 0.25;
concentration(i)->Intensity()[1][1] = -0.26;
concentration(i)->Intensity()[1][2] = 0.01;
concentration(i)->Intensity()[2][0] = 0.01;
concentration(i)->Intensity()[2][1] = 0.5;
concentration(i)->Intensity()[2][2] = -0.51;
i.SetVal(3,1); i.SetVal(1,1);
concentration(i)->Intensity()[0][0] = -1.01;
concentration(i)->Intensity()[0][1] = 1.0;
concentration(i)->Intensity()[0][2] = 0.01;
concentration(i)->Intensity()[1][0] = 0.01;
concentration(i)->Intensity()[1][1] = -2.01;
concentration(i)->Intensity()[1][2] = 2.0;
concentration(i)->Intensity()[2][0] = 0.01;
concentration(i)->Intensity()[2][1] = 0.01;
concentration(i)->Intensity()[2][2] = -0.02;
i.SetVal(3,0); i.SetVal(1,2);
concentration(i)->Intensity()[0][0] = -0.02;
concentration(i)->Intensity()[0][1] = 0.01;
concentration(i)->Intensity()[0][2] = 0.01;
concentration(i)->Intensity()[1][0] = 0.25;
concentration(i)->Intensity()[1][1] = -0.26;
concentration(i)->Intensity()[1][2] = 0.01;
concentration(i)->Intensity()[2][0] = 0.01;
concentration(i)->Intensity()[2][1] = 0.5;
concentration(i)->Intensity()[2][2] = -0.51;
i.SetVal(3,1); i.SetVal(1,2);
concentration(i)->Intensity()[0][0] = -0.51;
concentration(i)->Intensity()[0][1] = 0.5;
concentration(i)->Intensity()[0][2] = 0.01;
concentration(i)->Intensity()[1][0] = 0.01;
concentration(i)->Intensity()[1][1] = -4.01;
concentration(i)->Intensity()[1][2] = 4.0;
concentration(i)->Intensity()[2][0] = 0.01;
concentration(i)->Intensity()[2][1] = 0.01;
concentration(i)->Intensity()[2][2] = -0.02;
vectr pconcentration(3, 0.0);
pconcentration[0] = 0.1;
pconcentration[1] = 0.3;
pconcentration[2] = 0.6;
concentrationrv[0].SetDist(pconcentration);
pconcentration[0] = 0.1;
pconcentration[1] = 0.4;
pconcentration[2] = 0.5;
concentrationrv[1].SetDist(pconcentration);
pconcentration[0] = 0.4;
pconcentration[1] = 0.2;
pconcentration[2] = 0.4;
concentrationrv[2].SetDist(pconcentration);
pconcentration[0] = 0.5;
pconcentration[1] = 0.4;
pconcentration[2] = 0.1;
concentrationrv[3].SetDist(pconcentration);
//------barometer------
barometer(0)->Intensity()[0][0] = -0.21;
barometer(0)->Intensity()[0][1] = 0.2;
barometer(0)->Intensity()[0][2] = 0.01;
barometer(0)->Intensity()[1][0] = 0.05;
barometer(0)->Intensity()[1][1] = -0.1;
barometer(0)->Intensity()[1][2] = 0.05;
barometer(0)->Intensity()[2][0] = 0.01;
barometer(0)->Intensity()[2][1] = 0.2;
barometer(0)->Intensity()[2][2] = -0.21;
vectr pbarometer(3, 0.0);
pbarometer[1] = 1;
barometerrv[0].SetDist(pbarometer);
//------pain------
Instantiation j(Context(barometerc, concentrationc));
j.SetVal(4,0); j.SetVal(5,0);
pain(j)->Intensity()[0][0] = -6.0;
pain(j)->Intensity()[0][1] = 6.0;
pain(j)->Intensity()[1][0] = 0.1;
pain(j)->Intensity()[1][1] = -0.1;
j.SetVal(4,1); j.SetVal(5,0);
pain(j)->Intensity()[0][0] = -1.0;
pain(j)->Intensity()[0][1] = 1.0;
pain(j)->Intensity()[1][0] = 0.1;
pain(j)->Intensity()[1][1] = -0.1;
j.SetVal(4,2); j.SetVal(5,0);
pain(j)->Intensity()[0][0] = -6.0;
pain(j)->Intensity()[0][1] = 6.0;
pain(j)->Intensity()[1][0] = 0.1;
pain(j)->Intensity()[1][1] = -0.1;
j.SetVal(4,0); j.SetVal(5,1);
pain(j)->Intensity()[0][0] = -1.0;
pain(j)->Intensity()[0][1] = 1.0;
pain(j)->Intensity()[1][0] = 0.3;
pain(j)->Intensity()[1][1] = -0.3;
j.SetVal(4,1); j.SetVal(5,1);
pain(j)->Intensity()[0][0] = -0.3;
pain(j)->Intensity()[0][1] = 0.3;
pain(j)->Intensity()[1][0] = 0.3;
pain(j)->Intensity()[1][1] = -0.3;
j.SetVal(4,2); j.SetVal(5,1);
pain(j)->Intensity()[0][0] = -1.0;
pain(j)->Intensity()[0][1] = 1.0;
pain(j)->Intensity()[1][0] = 0.3;
pain(j)->Intensity()[1][1] = -0.3;
j.SetVal(4,0); j.SetVal(5,2);
pain(j)->Intensity()[0][0] = -0.01;
pain(j)->Intensity()[0][1] = 0.01;
pain(j)->Intensity()[1][0] = 2.0;
pain(j)->Intensity()[1][1] = -2.0;
j.SetVal(4,1); j.SetVal(5,2);
pain(j)->Intensity()[0][0] = -0.01;
pain(j)->Intensity()[0][1] = 0.01;
pain(j)->Intensity()[1][0] = 2.0;
pain(j)->Intensity()[1][1] = -2.0;
j.SetVal(4,2); j.SetVal(5,2);
pain(j)->Intensity()[0][0] = -0.01;
pain(j)->Intensity()[0][1] = 0.01;
pain(j)->Intensity()[1][0] = 2.0;
pain(j)->Intensity()[1][1] = -2.0;
vectr ppain(2, 0.0);
ppain[0] = 0.1;
ppain[1] = 0.9;
painrv[0].SetDist(ppain);
ppain[0] = 0.3;
ppain[1] = 0.7;
painrv[1].SetDist(ppain);
ppain[0] = 0.9;
ppain[1] = 0.1;
painrv[2].SetDist(ppain);
//------drowsy------
drowsy(0)->Intensity()[0][0] = -0.17;
drowsy(0)->Intensity()[0][1] = 0.17;
drowsy(0)->Intensity()[1][0] = 0.5;
drowsy(0)->Intensity()[1][1] = -0.5;
drowsy(1)->Intensity()[0][0] = -0.33;
drowsy(1)->Intensity()[0][1] = 0.33;
drowsy(1)->Intensity()[1][0] = 0.33;
drowsy(1)->Intensity()[1][1] = -0.33;
drowsy(2)->Intensity()[0][0] = -1.0;
drowsy(2)->Intensity()[0][1] = 1.0;
drowsy(2)->Intensity()[1][0] = 0.1;
drowsy(2)->Intensity()[1][1] = -0.1;
vectr pdrowsy(2, 0.0);
pdrowsy[0] = 0.8;
pdrowsy[1] = 0.2;
drowsyrv[0].SetDist(pdrowsy);
pdrowsy[0] = 0.7;
pdrowsy[1] = 0.3;
drowsyrv[1].SetDist(pdrowsy);
pdrowsy[0] = 0.3;
pdrowsy[1] = 0.7;
drowsyrv[2].SetDist(pdrowsy);
pdrowsy[0] = 0.4;
pdrowsy[1] = 0.6;
drowsyrv[3].SetDist(pdrowsy);
pdrowsy[0] = 0.2;
pdrowsy[1] = 0.8;
drowsyrv[4].SetDist(pdrowsy);
pdrowsy[0] = 0.8;
pdrowsy[1] = 0.2;
drowsyrv[5].SetDist(pdrowsy);
// The dynamics of a CTBN are defined by the dynamics of each conditional
// Markov process. Here it is the MarkovDyn objects created above.
CTBNDyn ctbndyn;
ctbndyn.AddNode(eat.Clone());
ctbndyn.AddNode(full.Clone());
ctbndyn.AddNode(hungry.Clone());
ctbndyn.AddNode(uptake.Clone());
ctbndyn.AddNode(concentration.Clone());
ctbndyn.AddNode(barometer.Clone());
ctbndyn.AddNode(pain.Clone());
ctbndyn.AddNode(drowsy.Clone());
// The initial distribution of a CTBN is defined by the underlying
// Bayesian network containing a multinomial distribution for each
// variable.
BN bn;
bn.AddNode(eatrv.Clone());
bn.AddNode(fullrv.Clone());
bn.AddNode(hungryrv.Clone());
bn.AddNode(uptakerv.Clone());
bn.AddNode(concentrationrv.Clone());
bn.AddNode(barometerrv.Clone());
bn.AddNode(painrv.Clone());
bn.AddNode(drowsyrv.Clone());
// Construct the final object that represents the CTBN.
Markov m(bn.Clone(), ctbndyn.Clone());
// Output the CTBN to the cout strem
m.Save(cout);
}
ctbnrle/demo/makedrug.cc 0000644 0004642 0000777 00000026646 11257240650 015654 0 ustar cshelton csprofs /* Continuous Time Bayesian Network Reasoning and Learning Engine
* Copyright (C) 2009 The Regents of the University of California
*
* Authored by Yu Fan, William Lam, Joon Lee, Christian Shelton, and Jing Xu
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* This program 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 program. If not, see .
*/
// This code generates the drug effect network as first seen in
// Continuous Time Bayesian Networks by Nodelman et al., 2002.
#include "markovdyn.h"
#include "markov.h"
#include "streamextra.h"
#include "context.h"
#include
#include "multirv.h"
#include "trajectory.h"
#include "exactmarkovinf.h"
#include "params.h"
#include "ctbndyn.h"
#include "bn.h"
#include "em.h"
#include "ctbn.h"
using namespace std;
int main(int argc, char **argv) {
InitParams(argc, argv);
cout << setprecision(4);
//*******************************************************
// Create contexts for each variable
Context nullc;
Context eatc, fullc, hungryc, uptakec, concentrationc, barometerc, painc, drowsyc;
eatc.AddVar(0, 2);
fullc.AddVar(1,3);
hungryc.AddVar(2,2);
uptakec.AddVar(3, 2);
concentrationc.AddVar(4,3);
barometerc.AddVar(5,3);
painc.AddVar(6, 2);
drowsyc.AddVar(7,2);
// Define the connections between variables. For a variable with multiple
// parents, the context used is the union of the two parent contexts
MarkovDyn eat(eatc, hungryc);
MarkovDyn full(fullc, eatc);
MarkovDyn hungry(hungryc, fullc);
MarkovDyn uptake(uptakec, nullc);
Context full_and_uptakec(fullc, uptakec);
MarkovDyn concentration(concentrationc, full_and_uptakec);
MarkovDyn barometer(barometerc, nullc);
MarkovDyn pain(painc, Context(barometerc, concentrationc));
MarkovDyn drowsy(drowsyc, concentrationc);
// Generate starting distributions for each variable
MultiRV eatrv(eatc, nullc);
MultiRV fullrv(fullc, nullc);
MultiRV hungryrv(hungryc, nullc);
MultiRV uptakerv(uptakec, nullc);
MultiRV concentrationrv(concentrationc, nullc);
MultiRV barometerrv(barometerc, nullc);
MultiRV painrv(painc, nullc);
MultiRV drowsyrv(drowsyc, nullc);
// The following fills the conditional intensity matrices for each
// variable and the initial distributions (multinomial distributions).
//------eat--------
eat(0)->Intensity()[0][0] = -0.01;
eat(0)->Intensity()[0][1] = 0.01;
eat(0)->Intensity()[1][0] = 10.0;
eat(0)->Intensity()[1][1] = -10.0;
eat(1)->Intensity()[0][0] = -2.0;
eat(1)->Intensity()[0][1] = 2.0;
eat(1)->Intensity()[1][0] = 1.0;
eat(1)->Intensity()[1][1] = -1.0;
vectr peat(2, 0.0);
peat[0] = 1;
peat[1] = 0;
eatrv[0].SetDist(peat);
//------full--------
full(0)->Intensity()[0][0] = -0.02;
full(0)->Intensity()[0][1] = 0.01;
full(0)->Intensity()[0][2] = 0.01;
full(0)->Intensity()[1][0] = 0.3;
full(0)->Intensity()[1][1] = -0.31;
full(0)->Intensity()[1][2] = 0.01;
full(0)->Intensity()[2][0] = 0.01;
full(0)->Intensity()[2][1] = 1.0;
full(0)->Intensity()[2][2] = -1.01;
full(1)->Intensity()[0][0] = -6.01;
full(1)->Intensity()[0][1] = 6.0;
full(1)->Intensity()[0][2] = 0.01;
full(1)->Intensity()[1][0] = 0.01;
full(1)->Intensity()[1][1] = -3.01;
full(1)->Intensity()[1][2] = 3.0;
full(1)->Intensity()[2][0] = 0.01;
full(1)->Intensity()[2][1] = 0.01;
full(1)->Intensity()[2][2] = -0.02;
vectr pfull(3, 0.0);
pfull[0] = 1;
//pfull[1] = 0.25;
//pfull[2] = 0.25;
fullrv[0].SetDist(pfull);
//------hungry--------
hungry(0)->Intensity()[0][0] = -2.0;
hungry(0)->Intensity()[0][1] = 2.0;
hungry(0)->Intensity()[1][0] = 0.01;
hungry(0)->Intensity()[1][1] = -0.01;
hungry(1)->Intensity()[0][0] = -0.01;
hungry(1)->Intensity()[0][1] = 0.01;
hungry(1)->Intensity()[1][0] = 0.01;
hungry(1)->Intensity()[1][1] = -0.01;
hungry(2)->Intensity()[0][0] = -0.01;
hungry(2)->Intensity()[0][1] = 0.01;
hungry(2)->Intensity()[1][0] = 10.0;
hungry(2)->Intensity()[1][1] = -10.0;
vectr phungry(2, 0.0);
phungry[0] = 1;
hungryrv[0].SetDist(phungry);
//------uptake--------
uptake(0)->Intensity()[0][0] = -0.0;
uptake(0)->Intensity()[0][1] = 0.0;
uptake(0)->Intensity()[1][0] = 0.5;
uptake(0)->Intensity()[1][1] = -0.5;
vectr puptake(2, 0.0);
puptake[1] = 1;
uptakerv[0].SetDist(puptake);
// For this variable there is more than one parent.
// The CIM is filled by creating an Instantiation object that represents
// the current intensity matrix we are entering values for. The
// MarkovDyn objects can be then indexed with Instantiation objects.
//------concentration------
Instantiation i(Context(fullc, uptakec));
// Set uptake to state 0; full to state 0
i.SetVal(3,0); i.SetVal(1,0);
concentration(i)->Intensity()[0][0] = -0.02;
concentration(i)->Intensity()[0][1] = 0.01;
concentration(i)->Intensity()[0][2] = 0.01;
concentration(i)->Intensity()[1][0] = 0.25;
concentration(i)->Intensity()[1][1] = -0.26;
concentration(i)->Intensity()[1][2] = 0.01;
concentration(i)->Intensity()[2][0] = 0.01;
concentration(i)->Intensity()[2][1] = 0.5;
concentration(i)->Intensity()[2][2] = -0.51;
// Set uptake to state 1; full to state 0
// (We repeat this process for each parent instantiation.)
i.SetVal(3,1); i.SetVal(1,0);
concentration(i)->Intensity()[0][0] = -2.01;
concentration(i)->Intensity()[0][1] = 2.0;
concentration(i)->Intensity()[0][2] = 0.01;
concentration(i)->Intensity()[1][0] = 0.01;
concentration(i)->Intensity()[1][1] = -1.01;
concentration(i)->Intensity()[1][2] = 1.0;
concentration(i)->Intensity()[2][0] = 0.01;
concentration(i)->Intensity()[2][1] = 0.01;
concentration(i)->Intensity()[2][2] = -0.02;
i.SetVal(3,0); i.SetVal(1,1);
concentration(i)->Intensity()[0][0] = -0.02;
concentration(i)->Intensity()[0][1] = 0.01;
concentration(i)->Intensity()[0][2] = 0.01;
concentration(i)->Intensity()[1][0] = 0.25;
concentration(i)->Intensity()[1][1] = -0.26;
concentration(i)->Intensity()[1][2] = 0.01;
concentration(i)->Intensity()[2][0] = 0.01;
concentration(i)->Intensity()[2][1] = 0.5;
concentration(i)->Intensity()[2][2] = -0.51;
i.SetVal(3,1); i.SetVal(1,1);
concentration(i)->Intensity()[0][0] = -1.01;
concentration(i)->Intensity()[0][1] = 1.0;
concentration(i)->Intensity()[0][2] = 0.01;
concentration(i)->Intensity()[1][0] = 0.01;
concentration(i)->Intensity()[1][1] = -2.01;
concentration(i)->Intensity()[1][2] = 2.0;
concentration(i)->Intensity()[2][0] = 0.01;
concentration(i)->Intensity()[2][1] = 0.01;
concentration(i)->Intensity()[2][2] = -0.02;
i.SetVal(3,0); i.SetVal(1,2);
concentration(i)->Intensity()[0][0] = -0.02;
concentration(i)->Intensity()[0][1] = 0.01;
concentration(i)->Intensity()[0][2] = 0.01;
concentration(i)->Intensity()[1][0] = 0.25;
concentration(i)->Intensity()[1][1] = -0.26;
concentration(i)->Intensity()[1][2] = 0.01;
concentration(i)->Intensity()[2][0] = 0.01;
concentration(i)->Intensity()[2][1] = 0.5;
concentration(i)->Intensity()[2][2] = -0.51;
i.SetVal(3,1); i.SetVal(1,2);
concentration(i)->Intensity()[0][0] = -0.51;
concentration(i)->Intensity()[0][1] = 0.5;
concentration(i)->Intensity()[0][2] = 0.01;
concentration(i)->Intensity()[1][0] = 0.01;
concentration(i)->Intensity()[1][1] = -4.01;
concentration(i)->Intensity()[1][2] = 4.0;
concentration(i)->Intensity()[2][0] = 0.01;
concentration(i)->Intensity()[2][1] = 0.01;
concentration(i)->Intensity()[2][2] = -0.02;
vectr pconcentration(3, 0.0);
pconcentration[0] = 1;
concentrationrv[0].SetDist(pconcentration);
//------barometer------
barometer(0)->Intensity()[0][0] = -0.21;
barometer(0)->Intensity()[0][1] = 0.2;
barometer(0)->Intensity()[0][2] = 0.01;
barometer(0)->Intensity()[1][0] = 0.05;
barometer(0)->Intensity()[1][1] = -0.1;
barometer(0)->Intensity()[1][2] = 0.05;
barometer(0)->Intensity()[2][0] = 0.01;
barometer(0)->Intensity()[2][1] = 0.2;
barometer(0)->Intensity()[2][2] = -0.21;
vectr pbarometer(3, 0.0);
pbarometer[1] = 1;
barometerrv[0].SetDist(pbarometer);
//------pain------
Instantiation j(Context(barometerc, concentrationc));
j.SetVal(4,0); j.SetVal(5,0);
pain(j)->Intensity()[0][0] = -6.0;
pain(j)->Intensity()[0][1] = 6.0;
pain(j)->Intensity()[1][0] = 0.1;
pain(j)->Intensity()[1][1] = -0.1;
j.SetVal(4,1); j.SetVal(5,0);
pain(j)->Intensity()[0][0] = -1.0;
pain(j)->Intensity()[0][1] = 1.0;
pain(j)->Intensity()[1][0] = 0.1;
pain(j)->Intensity()[1][1] = -0.1;
j.SetVal(4,2); j.SetVal(5,0);
pain(j)->Intensity()[0][0] = -6.0;
pain(j)->Intensity()[0][1] = 6.0;
pain(j)->Intensity()[1][0] = 0.1;
pain(j)->Intensity()[1][1] = -0.1;
j.SetVal(4,0); j.SetVal(5,1);
pain(j)->Intensity()[0][0] = -1.0;
pain(j)->Intensity()[0][1] = 1.0;
pain(j)->Intensity()[1][0] = 0.3;
pain(j)->Intensity()[1][1] = -0.3;
j.SetVal(4,1); j.SetVal(5,1);
pain(j)->Intensity()[0][0] = -0.3;
pain(j)->Intensity()[0][1] = 0.3;
pain(j)->Intensity()[1][0] = 0.3;
pain(j)->Intensity()[1][1] = -0.3;
j.SetVal(4,2); j.SetVal(5,1);
pain(j)->Intensity()[0][0] = -1.0;
pain(j)->Intensity()[0][1] = 1.0;
pain(j)->Intensity()[1][0] = 0.3;
pain(j)->Intensity()[1][1] = -0.3;
j.SetVal(4,0); j.SetVal(5,2);
pain(j)->Intensity()[0][0] = -0.01;
pain(j)->Intensity()[0][1] = 0.01;
pain(j)->Intensity()[1][0] = 2.0;
pain(j)->Intensity()[1][1] = -2.0;
j.SetVal(4,1); j.SetVal(5,2);
pain(j)->Intensity()[0][0] = -0.01;
pain(j)->Intensity()[0][1] = 0.01;
pain(j)->Intensity()[1][0] = 2.0;
pain(j)->Intensity()[1][1] = -2.0;
j.SetVal(4,2); j.SetVal(5,2);
pain(j)->Intensity()[0][0] = -0.01;
pain(j)->Intensity()[0][1] = 0.01;
pain(j)->Intensity()[1][0] = 2.0;
pain(j)->Intensity()[1][1] = -2.0;
vectr ppain(2, 0.0);
ppain[1] = 1;
painrv[0].SetDist(ppain);
//------drowsy------
drowsy(0)->Intensity()[0][0] = -0.17;
drowsy(0)->Intensity()[0][1] = 0.17;
drowsy(0)->Intensity()[1][0] = 0.5;
drowsy(0)->Intensity()[1][1] = -0.5;
drowsy(1)->Intensity()[0][0] = -0.33;
drowsy(1)->Intensity()[0][1] = 0.33;
drowsy(1)->Intensity()[1][0] = 0.33;
drowsy(1)->Intensity()[1][1] = -0.33;
drowsy(2)->Intensity()[0][0] = -1.0;
drowsy(2)->Intensity()[0][1] = 1.0;
drowsy(2)->Intensity()[1][0] = 0.1;
drowsy(2)->Intensity()[1][1] = -0.1;
vectr pdrowsy(2, 0.0);
pdrowsy[0] = 1;
drowsyrv[0].SetDist(pdrowsy);
// The dynamics of a CTBN are defined by the dynamics of each conditional
// Markov process. Here it is the MarkovDyn objects created above.
CTBNDyn ctbndyn;
ctbndyn.AddNode(eat.Clone());
ctbndyn.AddNode(full.Clone());
ctbndyn.AddNode(hungry.Clone());
ctbndyn.AddNode(uptake.Clone());
ctbndyn.AddNode(concentration.Clone());
ctbndyn.AddNode(barometer.Clone());
ctbndyn.AddNode(pain.Clone());
ctbndyn.AddNode(drowsy.Clone());
// The initial distribution of a CTBN is defined by the underlying
// Bayesian network containing a multinomial distribution for each
// variable.
BN bn;
bn.AddNode(eatrv.Clone());
bn.AddNode(fullrv.Clone());
bn.AddNode(hungryrv.Clone());
bn.AddNode(uptakerv.Clone());
bn.AddNode(concentrationrv.Clone());
bn.AddNode(barometerrv.Clone());
bn.AddNode(painrv.Clone());
bn.AddNode(drowsyrv.Clone());
// Construct the final object that represents the CTBN.
Markov m(bn.Clone(), ctbndyn.Clone());
// Output the CTBN to the cout strem
m.Save(cout);
}
ctbnrle/demo/importancequery.cc 0000644 0004642 0000777 00000005603 11257240650 017272 0 ustar cshelton csprofs /* Continuous Time Bayesian Network Reasoning and Learning Engine
* Copyright (C) 2009 The Regents of the University of California
*
* Authored by Yu Fan, William Lam, Joon Lee, Christian Shelton, and Jing Xu
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* This program 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 program. If not, see .
*/
// this file reads in a model file and an evidence file, do the query using importance sampling
#include "markovdyn.h"
#include "streamextra.h"
#include "context.h"
#include "multirv.h"
#include "samplinginf.h"
#include "importancesampler.h"
#include "expmethod.h"
#include "params.h"
#include "bn.h"
#include "ctbndyn.h"
#include "em.h"
#include "utils.h"
#include
using namespace std;
namespace {
int testing_result = 0;
double kEpsilon = 0.1;
};
#define EXPECT(cond) do { \
if (!(cond)) { \
std::cerr << "Test failure at " << __FILE__ << ":" << __LINE__ \
<< " in " << __func__ \
<< ", failed condition is " #cond << std::endl; \
testing_result = 1; \
} \
} while (0)
int main(int argc, char **argv)
{
randomizer.Reset(1);
const char *input_file = "queryinput.data";
addnan(cout);
InitParams(argc, argv);
ifstream fin(input_file);
Markov m(fin);
const CTBNDyn *ctbndyn = dynamic_cast(m.GetDynamics());
const BN *bn = dynamic_cast(m.GetStartDist());
Context context = ctbndyn->Domain() + ctbndyn->CondDomain();
Trajectory evid;
fin >> evid;
double expect_q1, expect_q2;
fin >> expect_q1 >> expect_q2;
fin.close();
Instantiation x(context, -1);
int querynode = ParamInt("QueryNode", 1);
int querytimeval = ParamInt("QueryTimeVal", 1);
Context c = ctbndyn->Node(querynode)->Domain();
x.SetVal(querynode, querytimeval);
QueryTime query1(x);
QueryTransition query2(c, 0, 1);
SamplingInf inf;
int numofsamples = 1000;
Sampler *sampler;
ExpMethod method;
sampler = new ImportanceSampler(&m, &evid, &method);
inf.SetSampler(sampler);
inf.SetProcess(&m);
inf.SetNumSample(numofsamples);
double error1 = (inf.CalcQuery(query1) - expect_q1) / expect_q1;
double error2 = (inf.CalcQuery(query2) - expect_q2) / expect_q2;
EXPECT(fabs(error1) < kEpsilon);
EXPECT(fabs(error2) < kEpsilon);
if (testing_result == 0)
cout << "PASS importancequery unittest." << endl;
return 0;
}
ctbnrle/demo/traj2ascii 0000755 0004642 0000777 00002476666 11262433130 015535 0 ustar cshelton csprofs ELF P4 4 ( 4 44 4 44 $
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_ZZ8suffstatRK5vectrS1_R6matrixRKS2_dddE10defaulteps _ZGVZ8suffstatRK5vectrS1_R6matrixRKS2_dddE3dc1 _ZGVZ8suffstatRK5vectrS1_R6matrixRKS2_dddE3dc3 _ZGVZ8suffstatRK5vectrS1_R6matrixRKS2_dddE3dc4 _ZGVZ8suffstatRK5vectrS1_R6matrixRKS2_dddE3dc6 _ZZ8suffstatRK5vectrS1_R6matrixRKS2_dddE3b21 _ZZ8suffstatRK5vectrS1_R6matrixRKS2_dddE3b32 _ZZ8suffstatRK5vectrS1_R6matrixRKS2_dddE3b31 _ZZ8suffstatRK5vectrS1_R6matrixRKS2_dddE3b43 _ZZ8suffstatRK5vectrS1_R6matrixRKS2_dddE3b42 _ZZ8suffstatRK5vectrS1_R6matrixRKS2_dddE3b41 _ZZ8suffstatRK5vectrS1_R6matrixRKS2_dddE3b54 _ZZ8suffstatRK5vectrS1_R6matrixRKS2_dddE3b53 _ZZ8suffstatRK5vectrS1_R6matrixRKS2_dddE3b52 _ZZ8suffstatRK5vectrS1_R6matrixRKS2_dddE3b51 _ZZ8suffstatRK5vectrS1_R6matrixRKS2_dddE3b65 _ZZ8suffstatRK5vectrS1_R6matrixRKS2_dddE3b64 _ZZ8suffstatRK5vectrS1_R6matrixRKS2_dddE3b63 _ZZ8suffstatRK5vectrS1_R6matrixRKS2_dddE3b62 _ZZ8suffstatRK5vectrS1_R6matrixRKS2_dddE3b61 _ZZ8suffstatRK5vectrS1_R6matrixRKS2_dddE3dc6 _ZZ8suffstatRK5vectrS1_R6matrixRKS2_dddE3dc5 _ZZ8suffstatRK5vectrS1_R6matrixRKS2_dddE3dc4 _ZZ8suffstatRK5vectrS1_R6matrixRKS2_dddE3dc3 _ZZ8suffstatRK5vectrS1_R6matrixRKS2_dddE3dc1 _ZZ8suffstatRK5vectrS1_R6matrixRKS2_dddE2c6 _ZZ8suffstatRK5vectrS1_R6matrixRKS2_dddE2c4 _ZZ8suffstatRK5vectrS1_R6matrixRKS2_dddE2c1 _ZZ8suffstatRK5vectrS1_R6matrixRKS2_dddE2c3 ctbndyn.cc _GLOBAL__I__ZN7CTBNDyn6IDnameE _ZZN7CTBNDyn4MultEPK8DynamicsE19__PRETTY_FUNCTION__ markovdyn.cc _GLOBAL__I__ZN9MarkovDyn6IDnameE rvcomp.cc _GLOBAL__I__ZN6RVComp6IDnameE _ZZNK6RVComp5AddSSEPK2SSPK2RVPS0_dE19__PRETTY_FUNCTION__ ctbn.cc _GLOBAL__I__ZN4CTBN6IDnameE markov.cc _GLOBAL__I__ZN6Markov6IDnameE rv.cc _GLOBAL__I__ZN2RVC2ERK7ContextS2_ defines.cc markovsimple.cc _GLOBAL__I__ZN12MarkovSimple6IDnameE _ZZN12MarkovSimple4MultEPK9DynSimpleE19__PRETTY_FUNCTION__ _ZZNK12MarkovSimple5AddSSEPK2SSPK9DynSimpleRKSt6vectorIS6_IiSaIiEESaIS8_EEPS0_dE19__PRETTY_FUNCTION__ _ZGVZN12MarkovSimple8MaximizeEPK2SSE8divzeroq _ZZN12MarkovSimple8MaximizeEPK2SSE8divzeroq _ZGVZN18MarkovSimpleToggle8MaximizeEPK2SSE8divzeroq _ZZN18MarkovSimpleToggle8MaximizeEPK2SSE8divzeroq _ZZNK12MarkovSimple13AddExpTransSSEPK8RVSimpleS2_dP2SSdE19__PRETTY_FUNCTION__ _ZZNK12MarkovSimple8AddExpSSEPK8RVSimpleS2_ddP2SSdE19__PRETTY_FUNCTION__ rvsimple.cc _GLOBAL__I__ZN8RVSimpleC2Ev dynamics.cc _GLOBAL__I__ZN8DynamicsC2ERK7ContextS2_ matrix.cc _GLOBAL__I__ZNK6matrix13sparsepremultER5vectr ss.cc _GLOBAL__I__ZN2SSC2Ev dyncomp.cc _GLOBAL__I__ZN9DynCompSS6IDnameE _ZZNK7DynComp5AddSSEPK2SSPK8DynamicsPS0_dE19__PRETTY_FUNCTION__ _ZZN7DynComp4MultEPK8DynamicsE19__PRETTY_FUNCTION__ multirv.cc _GLOBAL__I__ZN7MultiRV6IDnameE _ZZNK16RVCondSimpleCompI12MultiZSimpleE5RMultERP8RVSimpleE19__PRETTY_FUNCTION__ streamextra.cc _GLOBAL__I__ZN9StreamObj9loadtableEv _ZN10nannum_getIcXadL_ZN36_GLOBAL__N__ZN9StreamObj9loadtableEv9infstringEEEXadL_ZNS0_9nanstringEEESt19istreambuf_iteratorIcSt11char_traitsIcEEED1Ev 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_ZNSt8_Rb_treeIiSt4pairIKiSt6vectorIiSaIiEEESt10_Select1stIS5_ESt4lessIiESaIS5_EE13insert_uniqueESt17_Rb_tree_iteratorIS5_ERKS5_ _ZNK12GibbsSampler22SampleVariableIntervalEiiiddR6Random _ZNSt8_Rb_treeIiSt4pairIKi7ContextESt10_Select1stIS3_ESt4lessIiESaIS3_EE9_M_insertEPSt18_Rb_tree_node_baseSB_RKS3_ _ZN17ExpSuffStatsQuery7GetP0SSEv _ZN7Graphic11SetFontCTBNERKi _ZN9StructureC1Ev _ZN9StructureD0Ev _ZTI18SparseMultiZSimple _ZSt24__uninitialized_fill_n_aIPSt6vectorIiSaIiEEjS2_S2_EvT_T0_RKT1_SaIT2_E _ZNSt6vectorIiSaIiEE5eraseEN9__gnu_cxx17__normal_iteratorIPiS1_EES5_ _ZN18SparseMultiZSimpleD0Ev _ZN16SparseCondTransQI6matrixE8ScrambleEddR6Random _ZNK10RVCSCompSS4SaveERSo _ZN10CliqueTree10AddVarSizeEii _ZN12GibbsSampler13SetTrajectoryEPK10Trajectory _ZTS17ExpSuffStatsQuery _ZNK11MultiSimple7GetDistER5vectrRd _ZTS15LookAheadMethod _ZN13MultiSimpleSS6CreateERSi _ZN9CTBNDynSSC2ERKS_ _ZN9MarkovDyn6IDnameE _ZN17ExpSuffStatsQueryD1Ev _ZSt4fillIN9__gnu_cxx17__normal_iteratorIPP12RVCondSimpleSt6vectorIS3_SaIS3_EEEES3_EvT_S9_RKT0_ _ZN10CondTransQIRK6matrixE4LoadERSi _ZN7MultiRVD2Ev _ZNK13MultiSimpleSS4SaveERSo _ZN8FamScoreC2EddP14SuffStatsQuery _ZNK2BN4SaveERSo _ZN12MultiZSimpleC2ERSi ctbnrle/demo/traj2ascii.cc 0000644 0004642 0000777 00000002336 11257240650 016076 0 ustar cshelton csprofs /* Continuous Time Bayesian Network Reasoning and Learning Engine
* Copyright (C) 2009 The Regents of the University of California
*
* Authored by Yu Fan, William Lam, Joon Lee, Christian Shelton, and Jing Xu
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* This program 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 program. If not, see .
*/
// this file reads in a trajectory from stdin and "draws" an
// ASCII representation of it on stdout
#include "trajectory.h"
#include
#include "params.h"
#include "streamextra.h"
using namespace std;
int main(int argc, char **argv)
{
InitParams(argc, argv);
addnan(cout);
addnan(cin);
Trajectory tr;
tr.Load(cin);
tr.Draw(cout);
cout << endl;
return 0;
}
ctbnrle/demo/drug.ctbndyn 0000644 0004642 0000777 00000003157 11257222633 016063 0 ustar cshelton csprofs 864 8 0 2 1 3 2 2 3 2 4 3 5 3 6 2 7 2 1 0 8 MarkovDyn 2 1 0 2 2 1 2 2 MarkovSimple 2 2
-0.01 0.01
10 -10 MarkovSimple 2 2
-2 2
1 -1 MarkovDyn 3 1 1 3 2 1 0 2 MarkovSimple 3 3
-0.02 0.01 0.01
0.3 -0.31 0.01
0.01 1 -1.01 MarkovSimple 3 3
-6.01 6 0.01
0.01 -3.01 3
0.01 0.01 -0.02 MarkovDyn 2 1 2 2 3 1 1 3 MarkovSimple 2 2
-2 2
0.01 -0.01 MarkovSimple 2 2
-0.01 0.01
0.01 -0.01 MarkovSimple 2 2
-0.01 0.01
10 -10 MarkovDyn 2 1 3 2 1 0 MarkovSimple 2 2
-0 0
0.5 -0.5 MarkovDyn 3 1 4 3 6 2 1 3 3 2 MarkovSimple 3 3
-0.02 0.01 0.01
0.25 -0.26 0.01
0.01 0.5 -0.51 MarkovSimple 3 3
-0.02 0.01 0.01
0.25 -0.26 0.01
0.01 0.5 -0.51 MarkovSimple 3 3
-0.02 0.01 0.01
0.25 -0.26 0.01
0.01 0.5 -0.51 MarkovSimple 3 3
-2.01 2 0.01
0.01 -1.01 1
0.01 0.01 -0.02 MarkovSimple 3 3
-1.01 1 0.01
0.01 -2.01 2
0.01 0.01 -0.02 MarkovSimple 3 3
-0.51 0.5 0.01
0.01 -4.01 4
0.01 0.01 -0.02 MarkovDyn 3 1 5 3 1 0 MarkovSimple 3 3
-0.21 0.2 0.01
0.05 -0.1 0.05
0.01 0.2 -0.21 MarkovDyn 2 1 6 2 9 2 4 3 5 3 MarkovSimple 2 2
-6 6
0.1 -0.1 MarkovSimple 2 2
-1 1
0.1 -0.1 MarkovSimple 2 2
-6 6
0.1 -0.1 MarkovSimple 2 2
-1 1
0.3 -0.3 MarkovSimple 2 2
-0.3 0.3
0.3 -0.3 MarkovSimple 2 2
-1 1
0.3 -0.3 MarkovSimple 2 2
-0.01 0.01
2 -2 MarkovSimple 2 2
-0.01 0.01
2 -2 MarkovSimple 2 2
-0.01 0.01
2 -2 MarkovDyn 2 1 7 2 3 1 4 3 MarkovSimple 2 2
-0.17 0.17
0.5 -0.5 MarkovSimple 2 2
-0.33 0.33
0.33 -0.33 MarkovSimple 2 2
-1 1
0.1 -0.1 ctbnrle/demo/Makefile 0000644 0004642 0000777 00000004632 11262433012 015165 0 ustar cshelton csprofs LIBOBJ = ../src/bn.o ../src/extramath.o ../src/random.o ../src/context.o ../src/inference.o ../src/rk.o ../src/ctbndyn.o ../src/markovdyn.o ../src/rvcomp.o ../src/ctbn.o ../src/markov.o ../src/rv.o ../src/defines.o ../src/markovsimple.o ../src/rvsimple.o ../src/dynamics.o ../src/matrix.o ../src/ss.o ../src/dyncomp.o ../src/multirv.o ../src/streamextra.o ../src/dynsimple.o ../src/multisimple.o ../src/trajectory.o ../src/em.o ../src/params.o ../src/exactmarkovinf.o ../src/process.o ../src/sampler.o ../src/structure.o ../src/varsample.o ../src/expmethod.o ../src/gibbssampler.o ../src/samplinginf.o ../src/importancesampler.o ../src/utils.o ../src/impbase.o ../src/clique.o ../src/graphic.o ../src/samplequeue.o ../src/suffstatsquery.o ../src/nonexpsuffstatsquery.o ../src/expsuffstatsquery.o ../src/searchqueue.o ../src/structuresearch.o ../src/brutestructuresearch.o ../src/grapheditsearch.o ../src/famscore.o
CXX = g++
OFLAG = -O2
#OFLAG = -ggdb
CXXFLAGS += $(OFLAG) -I../hdr
DEPEND = makedepend -- $(CXXFLAGS) --
all: clique_example makechain makedrug learnparams exactquery gibbsquery sampleprocess traj2ascii
using_graphic: using_graphic.o $(LIBOBJ)
$(CXX) $(CXXFLAGS) $(LDFLAGS) -o using_graphic using_graphic.o $(LIBOBJ)
clique_example: clique_example.o $(LIBOBJ)
$(CXX) $(CXXFLAGS) $(LDFLAGS) -o clique_example clique_example.o $(LIBOBJ)
makechain: makechain.o $(LIBOBJ)
$(CXX) $(CXXFLAGS) $(LDFLAGS) -o makechain makechain.o $(LIBOBJ)
makedrug: makedrug.o $(LIBOBJ)
$(CXX) $(CXXFLAGS) $(LDFLAGS) -o makedrug makedrug.o $(LIBOBJ)
makedrug2: makedrug2.o $(LIBOBJ)
$(CXX) $(CXXFLAGS) $(LDFLAGS) -o makedrug2 makedrug2.o $(LIBOBJ)
learnparams: learnparams.o $(LIBOBJ)
$(CXX) $(CXXFLAGS) $(LDFLAGS) -o learnparams learnparams.o $(LIBOBJ)
exactquery: exactquery.o $(LIBOBJ)
$(CXX) $(CXXFLAGS) $(LDFLAGS) -o exactquery exactquery.o $(LIBOBJ)
gibbsquery: gibbsquery.o $(LIBOBJ)
$(CXX) $(CXXFLAGS) $(LDFLAGS) -o gibbsquery gibbsquery.o $(LIBOBJ)
importancequery: importancequery.o $(LIBOBJ)
$(CXX) $(CXXFLAGS) $(LDFLAGS) -o importancequery importancequery.o $(LIBOBJ)
sampleprocess: sampleprocess.o $(LIBOBJ)
$(CXX) $(CXXFLAGS) $(LDFLAGS) -o sampleprocess sampleprocess.o $(LIBOBJ)
traj2ascii: traj2ascii.o $(LIBOBJ)
$(CXX) $(CXXFLAGS) $(LDFLAGS) -o traj2ascii traj2ascii.o $(LIBOBJ)
makequeryinput: makequeryinput.o $(LIBOBJ)
$(CXX) $(CXXFLAGS) $(LDFLAGS) -o makequeryinput makequeryinput.o $(LIBOBJ)
clean:
rm -rf *.o *~
ctbnrle/demo/exactquery.cc 0000644 0004642 0000777 00000005245 11257240650 016237 0 ustar cshelton csprofs /* Continuous Time Bayesian Network Reasoning and Learning Engine
* Copyright (C) 2009 The Regents of the University of California
*
* Authored by Yu Fan, William Lam, Joon Lee, Christian Shelton, and Jing Xu
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* This program 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 program. If not, see .
*/
// this file reads in a model file and an evidence file, do the query using exact inference
#include "markovdyn.h"
#include "streamextra.h"
#include "context.h"
#include "multirv.h"
#include "exactmarkovinf.h"
#include "params.h"
#include "bn.h"
#include "ctbndyn.h"
#include "em.h"
#include "utils.h"
#include
using namespace std;
namespace {
int testing_result = 0;
double kEpsilon = 1e-4;
};
#define EXPECT(cond) do { \
if (!(cond)) { \
std::cerr << "Test failure at " << __FILE__ << ":" << __LINE__ \
<< " in " << __func__ \
<< ", failed condition is " #cond << std::endl; \
testing_result = 1; \
} \
} while (0)
int main(int argc, char **argv) {
const char *input_file = "queryinput.data";
addnan(cout);
InitParams(argc, argv);
ifstream fin(input_file);
Markov m(fin);
const CTBNDyn *ctbndyn = dynamic_cast(m.GetDynamics());
const BN *bn = dynamic_cast(m.GetStartDist());
Context context = ctbndyn->Domain() + ctbndyn->CondDomain();
Trajectory evid;
fin >> evid;
double expect_q1, expect_q2;
fin >> expect_q1 >> expect_q2;
fin.close();
Instantiation x(context, -1);
int querynode = ParamInt("QueryNode", 1);
int querytimeval = ParamInt("QueryTimeVal", 1);
Context c = ctbndyn->Node(querynode)->Domain();
x.SetVal(querynode, querytimeval);
QueryTime query1(x);
QueryTransition query2(c, 0, 1);
ExactMarkovInf einf;
einf.SetProcess(&m);
einf.SetTrajectory(&evid);
double error1 = (einf.CalcQuery(query1) - expect_q1) / expect_q1;
double error2 = (einf.CalcQuery(query2) - expect_q2) / expect_q2;
EXPECT(fabs(error1) < kEpsilon);
EXPECT(fabs(error2) < kEpsilon);
if (testing_result == 0)
cout << "PASS exactquery unittest." << endl;
return 0;
}
ctbnrle/demo/clique_example.cc 0000644 0004642 0000777 00000005125 11257240650 017037 0 ustar cshelton csprofs /* Continuous Time Bayesian Network Reasoning and Learning Engine
* Copyright (C) 2009 The Regents of the University of California
*
* Authored by Yu Fan, William Lam, Joon Lee, Christian Shelton, and Jing Xu
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* This program 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 program. If not, see .
*/
#include
#include
#include "clique.h"
#include "markov.h"
using namespace std;
/*
void print(set printall)
{
set ::iterator i;
for(i = printall.begin(); i != printall.end(); i++)
{
cout << *i << " ";
}
cout << endl;
}
*/
int main(int argc, char **argv)
{
if(argc < 2)
{
cout << " you need to include a ctbn file" << endl;
return 0;
}
//to initialize the CTBNDyn
ifstream ls(argv[1]);
Markov ctbn(ls);
//CTBNDyn * Dyn = new CTBNDyn(ls);
CliqueTree check;
//this commented part is for the load the saved clique tree
//ifstream is("test.clique");
//check.Load(is);
//build the clique tree
check.BuildClique(ctbn);
cout << "test " << endl;
cout << "Outputting the clique tree" << endl << endl;
//print out the clique tree in shell
check.CliquePrint(cout);
//recieve the clique that been created by vector of
vector test2 = check.ReturnCliques();
cout << "received item" << endl;
//this will output clique::cliques
//conn_context is whatthis contextes is connected to
//contextes is contexts
for(unsigned int i = 0; i < test2.size(); i++)
{
cout << "clique " << i << ": " << endl;
cout << " variable ids(size): ";
vector varlist = test2[i].vars.VarList();
for (unsigned int j=0;j
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.
ctbnrle/tst/ 0000755 0004642 0000777 00000000000 11260221632 013407 5 ustar cshelton csprofs ctbnrle/tst/run_burnin_error.pl 0000755 0004642 0000777 00000002152 11257241230 017343 0 ustar cshelton csprofs #!/usr/bin/perl -w
# Continuous Time Bayesian Network Reasoning and Learning Engine
# Copyright (C) 2009 The Regents of the University of California
#
# Authored by Yu Fan, William Lam, Joon Lee, Christian Shelton, and Jing Xu
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program 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 program. If not, see .
sub main {
system("rm -fr burnin_error_results");
system("mkdir burnin_error_results");
for (my $burnin = 100; $burnin <= 1000; $burnin += 100) {
system("./burnin_error_n1000 chain.ctbn traj_q.txt $burnin >burnin_error_results/$burnin.txt &");
}
}
&main;
ctbnrle/tst/queryinput.data 0000644 0004642 0000777 00000004306 11257222634 016503 0 ustar cshelton csprofs BN 12 3 0 2 1 2 2 3 1 0 3 MultiRV 2 1 0 2 1 0 16RVCondSimpleCompI12MultiZSimpleE 1 2 0.5 0.5 0 MultiRV 2 1 1 2 2 1 0 2 16RVCondSimpleCompI12MultiZSimpleE 2 2 0.8 0.2 0 2 0.3 0.7 0 MultiRV 3 1 2 3 1 0 16RVCondSimpleCompI12MultiZSimpleE 1 3 0.2 0.3 0.5 0 CTBNDyn 12 3 0 2 1 2 2 3 1 0 3 MarkovDyn 2 1 0 2 1 0 MarkovSimple 2 2
-1 1
2 -2 MarkovDyn 2 1 1 2 2 1 0 2 MarkovSimple 2 2
-3 3
4 -4 MarkovSimple 2 2
-5 5
6 -6 MarkovDyn 3 1 2 3 1 0 MarkovSimple 3 3
-6 3 3
6 -12 6
9 9 -18
0 20 3 0 16 0 1 0.77263 -1 4.77263 0 5.1174 -1 9.1174 0 9.21886 1 9.60155 -1 16.7236 0 17.4769 1 17.484 0 17.5554 1 17.7718 0 18.8884 1 19.1432 0 19.4411 1 19.8002 0 1 38 0 0 0.316272 1 0.365676 0 0.645966 1 0.764025 0 0.774797 1 0.837957 0 0.92683 1 1.01157 0 1.06317 1 1.28621 0 2.16463 1 2.24661 0 2.32091 1 2.33814 0 2.62128 1 2.71801 0 2.83202 1 2.85891 0 2.87445 1 3.31781 0 3.93952 1 4.0815 0 4.72941 1 5.45909 0 5.47341 1 5.57328 0 5.65995 1 5.97848 0 5.99476 1 6.15639 0 6.2641 1 6.34758 0 6.62033 -1 18.6065 1 18.7449 0 19.4524 1 19.4663 0 2 110 0 2 0.0206161 1 0.0348029 2 0.0804483 1 0.11676 0 0.269922 1 0.369986 0 0.571562 2 0.631984 1 0.67498 2 0.685927 0 0.754802 2 0.801601 1 0.805742 0 0.866722 2 0.894783 0 1.04049 2 1.04329 1 1.06369 2 1.06588 0 1.11438 2 1.2948 1 1.37324 0 1.6805 1 1.74205 2 1.94763 1 2.05266 2 2.08956 1 2.18388 2 2.26138 0 2.36928 2 2.45702 0 2.53206 1 2.60557 0 2.61942 2 2.62044 0 2.94362 1 2.99217 2 3.07686 0 3.38743 1 3.42291 0 3.44245 2 3.70943 1 3.775 0 3.84005 -1 9.18293 0 9.28835 2 9.29098 1 9.34743 2 9.34861 0 9.35482 2 9.36261 0 9.78396 2 9.80645 0 10.187 2 10.1913 1 10.1961 0 10.508 1 10.5325 2 10.5538 0 10.6383 2 10.668 1 10.9786 2 10.9979 0 11.0182 2 11.0873 1 11.3193 2 11.4397 0 11.6594 2 11.666 1 11.8316 0 12.0717 2 12.0901 1 12.1525 0 12.1835 2 12.2837 0 12.4861 2 12.5001 1 12.5867 0 12.6844 2 12.8064 1 12.8247 0 13.0476 -1 17.0476 1 17.0654 0 17.0735 2 17.1063 1 17.1237 2 17.1485 0 17.2547 2 17.2726 1 17.3225 0 17.5154 1 17.5386 0 17.6988 1 17.7316 2 17.8683 1 18.015 0 18.4749 2 18.4953 0 18.5531 1 18.5995 2 18.6154 0 18.7448 2 18.8395 0 19.1138 2 19.1151 0 19.3385 1 19.4282 0 19.8278 2
8.16498 41.9283
ctbnrle/tst/unit_test_traj.graph 0000644 0004642 0000777 00000000376 11257222634 017507 0 ustar cshelton csprofs 0 3 8 0 2 0 0 0.563505 1 1 10 0 0 0.837845 1 1.50088 0 1.67087 1 2.08898 0 2.21837 1 2.23882 0 2.34252 1 2.67114 0 2.85411 1 2 2 0 0 0.129662 1 3 1 0 1 4 3 0 0 0.811089 1 1.16406 2 5 3 0 1 0.937131 2 1.80199 1 6 2 0 1 1.37333 0 7 2 0 0 2.25742 1 ctbnrle/tst/drug.ctbn 0000644 0004642 0000777 00000004325 11257222634 015235 0 ustar cshelton csprofs BN 864 8 0 2 1 3 2 2 3 2 4 3 5 3 6 2 7 2 1 0 8 MultiRV 2 1 0 2 1 0 16RVCondSimpleCompI12MultiZSimpleE 1 2 1 0 0 MultiRV 3 1 1 3 1 0 16RVCondSimpleCompI12MultiZSimpleE 1 3 1 0 0 0 MultiRV 2 1 2 2 1 0 16RVCondSimpleCompI12MultiZSimpleE 1 2 1 0 0 MultiRV 2 1 3 2 1 0 16RVCondSimpleCompI12MultiZSimpleE 1 2 0 1 0 MultiRV 3 1 4 3 1 0 16RVCondSimpleCompI12MultiZSimpleE 1 3 1 0 0 0 MultiRV 3 1 5 3 1 0 16RVCondSimpleCompI12MultiZSimpleE 1 3 0 1 0 0 MultiRV 2 1 6 2 1 0 16RVCondSimpleCompI12MultiZSimpleE 1 2 0 1 0 MultiRV 2 1 7 2 1 0 16RVCondSimpleCompI12MultiZSimpleE 1 2 1 0 0 CTBNDyn 864 8 0 2 1 3 2 2 3 2 4 3 5 3 6 2 7 2 1 0 8 MarkovDyn 2 1 0 2 2 1 2 2 MarkovSimple 2 2
-0.01 0.01
10 -10 MarkovSimple 2 2
-2 2
1 -1 MarkovDyn 3 1 1 3 2 1 0 2 MarkovSimple 3 3
-0.02 0.01 0.01
0.3 -0.31 0.01
0.01 1 -1.01 MarkovSimple 3 3
-6.01 6 0.01
0.01 -3.01 3
0.01 0.01 -0.02 MarkovDyn 2 1 2 2 3 1 1 3 MarkovSimple 2 2
-2 2
0.01 -0.01 MarkovSimple 2 2
-0.01 0.01
0.01 -0.01 MarkovSimple 2 2
-0.01 0.01
10 -10 MarkovDyn 2 1 3 2 1 0 MarkovSimple 2 2
-0 0
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0.01 0.5 -0.51 MarkovSimple 3 3
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0.01 0.5 -0.51 MarkovSimple 3 3
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0.01 -1.01 1
0.01 0.01 -0.02 MarkovSimple 3 3
-1.01 1 0.01
0.01 -2.01 2
0.01 0.01 -0.02 MarkovSimple 3 3
-0.51 0.5 0.01
0.01 -4.01 4
0.01 0.01 -0.02 MarkovDyn 3 1 5 3 1 0 MarkovSimple 3 3
-0.21 0.2 0.01
0.05 -0.1 0.05
0.01 0.2 -0.21 MarkovDyn 2 1 6 2 9 2 4 3 5 3 MarkovSimple 2 2
-6 6
0.1 -0.1 MarkovSimple 2 2
-1 1
0.1 -0.1 MarkovSimple 2 2
-6 6
0.1 -0.1 MarkovSimple 2 2
-1 1
0.3 -0.3 MarkovSimple 2 2
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0.3 -0.3 MarkovSimple 2 2
-1 1
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2 -2 MarkovSimple 2 2
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2 -2 MarkovSimple 2 2
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2 -2 MarkovDyn 2 1 7 2 3 1 4 3 MarkovSimple 2 2
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0.5 -0.5 MarkovSimple 2 2
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0.1 -0.1
ctbnrle/tst/unit_test_ctbn.dot 0000644 0004642 0000777 00000001402 11257222634 017151 0 ustar cshelton csprofs digraph g {
ordering=out;
graph [
rankdir = "LR"
];
node [
fontsize = "16"
shape = "eclipse"];
edge [
];
"node0"[
label = "0"
shape = "record"
];
"node1"[
label = "1"
shape = "record"
];
"node2"[
label = "2"
shape = "record"
];
"node3"[
label = "3"
shape = "record"
];
"node4"[
label = "4"
shape = "record"
];
"node5"[
label = "5"
shape = "record"
];
"node6"[
label = "6"
shape = "record"
];
"node7"[
label = "7"
shape = "record"
];
"node0" -> "node2" [ dir=back, id = 0];
"node1" -> "node0" [ dir=back, id = 1];
"node2" -> "node1" [ dir=back, id = 2];
"node4" -> "node1" [ dir=back, id = 4];
"node4" -> "node3" [ dir=back, id = 5];
"node6" -> "node4" [ dir=back, id = 7];
"node6" -> "node5" [ dir=back, id = 8];
"node7" -> "node4" [ dir=back, id = 9];
overlap=false
}
ctbnrle/tst/unit_test.py 0000644 0004642 0000777 00000004304 11257241254 016010 0 ustar cshelton csprofs #! /usr/bin/python
# Continuous Time Bayesian Network Reasoning and Learning Engine
# Copyright (C) 2009 The Regents of the University of California
#
# Authored by Yu Fan, William Lam, Joon Lee, Christian Shelton, and Jing Xu
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program 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 program. If not, see .
import os;
# execute the Makefile
os.system("make")
# Run the all the file from all: in Makefile
#open the Makefile file to read
filename = "Makefile"
FILE = open(filename, "r")
#start the loop
while 1:
#read one line from the file
sLine = FILE.readline()
#check if the all: is in the line just being passed in
if "all:" in sLine:
while 1:
#seperate the every word by space
thisline = sLine.split(" ")
#go through the array of string
for element in thisline:
# if it isn't all: run those file
if "all:" not in element:
if '\\' not in element:
if " " not in element:
#take out new line
oPipe = os.popen("./" + element.strip('\n'))
#start other loop
while 1:
#read the out put of the file
osLine = oPipe.read()
#check if pass is in the output first line
if "PASS" in osLine:
print "\n" + element.strip('\n') + " has PASSED."
break
else:
print "\n" + element.strip('\n') + " has FAILED."
print osLine
#output everything form the program
for osLine in oPipe.readlines():
print osLine
break
#read one more time if the sLine end with \\
if '\\' not in sLine:
sline = ""
break
else:
sLine = FILE.readline()
#same as eof()
if len(sLine) is 0:
break
#new line
print "\n"
ctbnrle/tst/unit_testing_graphic.cc 0000644 0004642 0000777 00000007001 11257240650 020134 0 ustar cshelton csprofs /* Continuous Time Bayesian Network Reasoning and Learning Engine
* Copyright (C) 2009 The Regents of the University of California
*
* Authored by Yu Fan, William Lam, Joon Lee, Christian Shelton, and Jing Xu
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* This program 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 program. If not, see .
*/
#include
#include "graphic.h"
#include "markov.h"
#include "params.h"
using namespace std;
int main(int argc, char **argv)
{
//to initialize or use the params.h class
InitParams(argc, argv);
//to see if the file opens or not
string File;
ifstream ls("drug.ctbn");
if(!ls.is_open())
{
cout << "drug.ctbn is not presented "
<< "in current directory. " << endl;
return 0;
}
ifstream output1;
ifstream output2;
//building the Markov to use in graphic program
Markov ctbn(ls);
//several different file to be written
//dot extention for graphviz and eps extention for post-script
ofstream file("output_test_ctbn.dot");
//creating the Graphic class name test
Graphic test;
//setting the font for the trajectory graph
test.SetFontTrajectory(16, 10);
//drawing the CTBN graph with rank system intergrated
test.DrawCTBN(ctbn, file);
file.close();
char compare1, compare2;
output1.open("unit_test_ctbn.dot");
output2.open("output_test_ctbn.dot");
if(output1.is_open() and output2.is_open())
{
while(output1 >> compare1 and output2 >> compare2)
{
if(compare1 != compare2)
{
cout << "CTBN graphic it "
<< "generated is wrong. " << endl;
return 0;
}
}
output1.close();
output2.close();
}
ofstream testoutput1("output.dot");
ofstream testoutput2("output2.eps");
string filename = "output.dot";
test.DrawCTBNRank(ctbn,testoutput1, filename, testoutput2);
const CTBNDyn * B = dynamic_cast(ctbn.GetDynamics());
Context context = Context();
for (int i = 0; i < B->NumofNodes(); ++i)
context = context + B->Node(i)->Domain();
//to create the Trajectory::Index
Trajectory tr;
ls.close();
ls.open("unit_test_traj.graph");
if(!ls.is_open())
{
cout << "unit_test_traj.graph is"
<< "not presented in current directory. " << endl;
return 0;
}
tr.Load(ls);
ofstream file2("output_unit_test.eps");
//to draw the Index into trajectory graph
test.DrawTrajectory(tr, context, file2);
file2.close();
//end of the file
output1.open("unit_test_traj.eps");
output2.open("output_unit_test.eps");
if(output1.is_open() and output2.is_open())
{
string buffer;
while(output1 >> compare1 and output2 >> compare2)
{
if(compare1 != compare2)
{
cout << compare1 << " " << compare2 << endl;
cout << "Trajectory graphic "
<< "it generated is wrong. " << endl;
return 0;
}
if(compare1 == '%')
{
getline(output1, buffer);
getline(output2, buffer);
}
}
output1.close();
output2.close();
}
else
{
cout << "Error on trajectory graphic test."
<< "\ncouldn't find all the necessary file." << endl;
return 0;
}
cout << "PASS" << endl;
return 0;
}
ctbnrle/tst/gibbsquery.cc 0000644 0004642 0000777 00000005551 11257240650 016107 0 ustar cshelton csprofs /* Continuous Time Bayesian Network Reasoning and Learning Engine
* Copyright (C) 2009 The Regents of the University of California
*
* Authored by Yu Fan, William Lam, Joon Lee, Christian Shelton, and Jing Xu
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* This program 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 program. If not, see .
*/
// this file reads in a model file and an evidence file, do the query using gibbs sampling
#include "markovdyn.h"
#include "streamextra.h"
#include "context.h"
#include "multirv.h"
#include "samplinginf.h"
#include "gibbssampler.h"
#include "params.h"
#include "bn.h"
#include "ctbndyn.h"
#include "em.h"
#include "utils.h"
#include
using namespace std;
namespace {
int testing_result = 0;
double kEpsilon = 0.15;
};
#define EXPECT(cond) do { \
if (!(cond)) { \
std::cerr << "Test failure at " << __FILE__ << ":" << __LINE__ \
<< " in " << __func__ \
<< ", failed condition is " #cond << std::endl; \
testing_result = 1; \
} \
} while (0)
int main(int argc, char **argv)
{
randomizer.Reset(1);
const char *input_file = "queryinput.data";
addnan(cout);
InitParams(argc, argv);
ifstream fin(input_file);
Markov m(fin);
const CTBNDyn *ctbndyn = dynamic_cast(m.GetDynamics());
const BN *bn = dynamic_cast(m.GetStartDist());
Context context = ctbndyn->Domain() + ctbndyn->CondDomain();
Trajectory evid;
fin >> evid;
double expect_q1, expect_q2;
fin >> expect_q1 >> expect_q2;
fin.close();
Instantiation x(context, -1);
int querynode = ParamInt("QueryNode", 1);
int querytimeval = ParamInt("QueryTimeVal", 1);
Context c = ctbndyn->Node(querynode)->Domain();
x.SetVal(querynode, querytimeval);
QueryTime query1(x);
QueryTransition query2(c, 0, 1);
SamplingInf inf;
int numofburniniter = 100;
int numofsamples = 100;
Sampler *sampler;
sampler = new GibbsSampler(&m, &evid, numofburniniter);
inf.SetSampler(sampler);
inf.SetProcess(&m);
inf.SetNumSample(numofsamples);
double error1 = (inf.CalcQuery(query1) - expect_q1) / expect_q1;
double error2 = (inf.CalcQuery(query2) - expect_q2) / expect_q2;
EXPECT(fabs(error1) < kEpsilon);
EXPECT(fabs(error2) < kEpsilon);
if (testing_result == 0)
cout << "PASS gibbsquery unittest." << endl;
return 0;
}
ctbnrle/tst/makequeryinput.cc 0000644 0004642 0000777 00000006100 11257240650 017005 0 ustar cshelton csprofs /* Continuous Time Bayesian Network Reasoning and Learning Engine
* Copyright (C) 2009 The Regents of the University of California
*
* Authored by Yu Fan, William Lam, Joon Lee, Christian Shelton, and Jing Xu
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* This program 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 program. If not, see .
*/
#include "markovdyn.h"
#include "streamextra.h"
#include "context.h"
#include "multirv.h"
#include "exactmarkovinf.h"
#include "params.h"
#include "bn.h"
#include "ctbndyn.h"
#include "em.h"
#include "utils.h"
#include
using namespace std;
int main (int argc, char *argv) {
// We do not want randomness in unittests.
randomizer.Reset(1);
Context X,Y,Z;
Context Null;
X.AddVar(0,2);
Y.AddVar(1,2);
Z.AddVar(2,3);
MarkovDyn PX(X,Null); // process of X
MarkovDyn PY(Y,X); // process of Y given X
MarkovDyn PZ(Z,Null);
PX(0)->Intensity()[0][0] = -1;
PX(0)->Intensity()[0][1] = 1;
PX(0)->Intensity()[1][0] = 2;
PX(0)->Intensity()[1][1] = -2;
PY(0)->Intensity()[0][0] = -3;
PY(0)->Intensity()[0][1] = 3;
PY(0)->Intensity()[1][0] = 4;
PY(0)->Intensity()[1][1] = -4;
PY(1)->Intensity()[0][0] = -5;
PY(1)->Intensity()[0][1] = 5;
PY(1)->Intensity()[1][0] = 6;
PY(1)->Intensity()[1][1] = -6;
PZ(0)->Intensity()[0][0] = -6;
PZ(0)->Intensity()[0][1] = 3;
PZ(0)->Intensity()[0][2] = 3;
PZ(0)->Intensity()[1][1] = -12;
PZ(0)->Intensity()[1][0] = 6;
PZ(0)->Intensity()[1][2] = 6;
PZ(0)->Intensity()[2][2] = -18;
PZ(0)->Intensity()[2][0] = 9;
PZ(0)->Intensity()[2][1] = 9;
MultiRV P0X(X,Null);
MultiRV P0Y(Y,X);
MultiRV P0Z(Z,Null);
vectr p0(2,0.0);
p0[0] = 0.5; p0[1] = 0.5;
P0X[0].SetDist(p0);
p0[0] = 0.8; p0[1] = 0.2;
P0Y[0].SetDist(p0);
p0[0] = 0.3; p0[1] = 0.7;
P0Y[1].SetDist(p0);
vectr pz(3,0.0);
pz[0] = 0.2; pz[1] = 0.3; pz[2] = 0.5;
P0Z[0].SetDist(pz);
// set up CTBN:
CTBNDyn ctbndyn;
ctbndyn.AddNode(PX.Clone());
ctbndyn.AddNode(PY.Clone());
ctbndyn.AddNode(PZ.Clone());
BN bn;
bn.AddNode(P0X.Clone());
bn.AddNode(P0Y.Clone());
bn.AddNode(P0Z.Clone());
Markov ctbn(bn.Clone(),ctbndyn.Clone());
Context context = ctbndyn.Domain() + ctbndyn.CondDomain();
double begintime = ParamDouble("BeginTime", 0.0);
double endtime = ParamDouble("EndTime", 20.0);
Trajectory evid;
evid.SetBeginTime(begintime);
evid.SetEndTime(endtime);
ctbn.Sample(evid);
RemoveNodesInformation(evid, context, ctbndyn.NumofNodes(), ctbndyn.NumofNodes()*4 , 0.2);
ctbn.Save(cout);
cout << endl << evid << endl;
// The expected query results (exact):
cout << 8.16498 << " " << 41.9283 << endl;
return 0;
}
ctbnrle/tst/importancequery.cc 0000644 0004642 0000777 00000005603 11257240650 017160 0 ustar cshelton csprofs /* Continuous Time Bayesian Network Reasoning and Learning Engine
* Copyright (C) 2009 The Regents of the University of California
*
* Authored by Yu Fan, William Lam, Joon Lee, Christian Shelton, and Jing Xu
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* This program 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 program. If not, see .
*/
// this file reads in a model file and an evidence file, do the query using importance sampling
#include "markovdyn.h"
#include "streamextra.h"
#include "context.h"
#include "multirv.h"
#include "samplinginf.h"
#include "importancesampler.h"
#include "expmethod.h"
#include "params.h"
#include "bn.h"
#include "ctbndyn.h"
#include "em.h"
#include "utils.h"
#include
using namespace std;
namespace {
int testing_result = 0;
double kEpsilon = 0.1;
};
#define EXPECT(cond) do { \
if (!(cond)) { \
std::cerr << "Test failure at " << __FILE__ << ":" << __LINE__ \
<< " in " << __func__ \
<< ", failed condition is " #cond << std::endl; \
testing_result = 1; \
} \
} while (0)
int main(int argc, char **argv)
{
randomizer.Reset(1);
const char *input_file = "queryinput.data";
addnan(cout);
InitParams(argc, argv);
ifstream fin(input_file);
Markov m(fin);
const CTBNDyn *ctbndyn = dynamic_cast(m.GetDynamics());
const BN *bn = dynamic_cast(m.GetStartDist());
Context context = ctbndyn->Domain() + ctbndyn->CondDomain();
Trajectory evid;
fin >> evid;
double expect_q1, expect_q2;
fin >> expect_q1 >> expect_q2;
fin.close();
Instantiation x(context, -1);
int querynode = ParamInt("QueryNode", 1);
int querytimeval = ParamInt("QueryTimeVal", 1);
Context c = ctbndyn->Node(querynode)->Domain();
x.SetVal(querynode, querytimeval);
QueryTime query1(x);
QueryTransition query2(c, 0, 1);
SamplingInf inf;
int numofsamples = 1000;
Sampler *sampler;
ExpMethod method;
sampler = new ImportanceSampler(&m, &evid, &method);
inf.SetSampler(sampler);
inf.SetProcess(&m);
inf.SetNumSample(numofsamples);
double error1 = (inf.CalcQuery(query1) - expect_q1) / expect_q1;
double error2 = (inf.CalcQuery(query2) - expect_q2) / expect_q2;
EXPECT(fabs(error1) < kEpsilon);
EXPECT(fabs(error2) < kEpsilon);
if (testing_result == 0)
cout << "PASS importancequery unittest." << endl;
return 0;
}
ctbnrle/tst/run_n_error.pl 0000755 0004642 0000777 00000002276 11257241230 016312 0 ustar cshelton csprofs #!/usr/bin/perl -w
# Continuous Time Bayesian Network Reasoning and Learning Engine
# Copyright (C) 2009 The Regents of the University of California
#
# Authored by Yu Fan, William Lam, Joon Lee, Christian Shelton, and Jing Xu
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program 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 program. If not, see .
sub main {
#system("rm -fr bhps_query_results");
#system("mkdir bhps_query_results");
@nsample = (10,20,50,100,200,500,1000); #,2000,5000,10000,20000,50000,100000,200000,500000);
for (my $i = 0; $i <= $#nsample; $i++) {
system("./approxinf drug.ctbn drug.traj_q.txt $nsample[$i] >drug_query_results/gibbs.$nsample[$i].txt &");
}
}
&main;
ctbnrle/tst/unit_test_traj.eps 0000644 0004642 0000777 00000013151 11257222634 017170 0 ustar cshelton csprofs %!PS-Adobe-3.0 EPSF-3.0
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%%Creator: UCR Research project
%%CreationDate: Mon Jul 6 15:42:54 2009
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end ctbnrle/tst/drug.ctbndyn 0000644 0004642 0000777 00000003157 11257222634 015752 0 ustar cshelton csprofs 864 8 0 2 1 3 2 2 3 2 4 3 5 3 6 2 7 2 1 0 8 MarkovDyn 2 1 0 2 2 1 2 2 MarkovSimple 2 2
-0.01 0.01
10 -10 MarkovSimple 2 2
-2 2
1 -1 MarkovDyn 3 1 1 3 2 1 0 2 MarkovSimple 3 3
-0.02 0.01 0.01
0.3 -0.31 0.01
0.01 1 -1.01 MarkovSimple 3 3
-6.01 6 0.01
0.01 -3.01 3
0.01 0.01 -0.02 MarkovDyn 2 1 2 2 3 1 1 3 MarkovSimple 2 2
-2 2
0.01 -0.01 MarkovSimple 2 2
-0.01 0.01
0.01 -0.01 MarkovSimple 2 2
-0.01 0.01
10 -10 MarkovDyn 2 1 3 2 1 0 MarkovSimple 2 2
-0 0
0.5 -0.5 MarkovDyn 3 1 4 3 6 2 1 3 3 2 MarkovSimple 3 3
-0.02 0.01 0.01
0.25 -0.26 0.01
0.01 0.5 -0.51 MarkovSimple 3 3
-0.02 0.01 0.01
0.25 -0.26 0.01
0.01 0.5 -0.51 MarkovSimple 3 3
-0.02 0.01 0.01
0.25 -0.26 0.01
0.01 0.5 -0.51 MarkovSimple 3 3
-2.01 2 0.01
0.01 -1.01 1
0.01 0.01 -0.02 MarkovSimple 3 3
-1.01 1 0.01
0.01 -2.01 2
0.01 0.01 -0.02 MarkovSimple 3 3
-0.51 0.5 0.01
0.01 -4.01 4
0.01 0.01 -0.02 MarkovDyn 3 1 5 3 1 0 MarkovSimple 3 3
-0.21 0.2 0.01
0.05 -0.1 0.05
0.01 0.2 -0.21 MarkovDyn 2 1 6 2 9 2 4 3 5 3 MarkovSimple 2 2
-6 6
0.1 -0.1 MarkovSimple 2 2
-1 1
0.1 -0.1 MarkovSimple 2 2
-6 6
0.1 -0.1 MarkovSimple 2 2
-1 1
0.3 -0.3 MarkovSimple 2 2
-0.3 0.3
0.3 -0.3 MarkovSimple 2 2
-1 1
0.3 -0.3 MarkovSimple 2 2
-0.01 0.01
2 -2 MarkovSimple 2 2
-0.01 0.01
2 -2 MarkovSimple 2 2
-0.01 0.01
2 -2 MarkovDyn 2 1 7 2 3 1 4 3 MarkovSimple 2 2
-0.17 0.17
0.5 -0.5 MarkovSimple 2 2
-0.33 0.33
0.33 -0.33 MarkovSimple 2 2
-1 1
0.1 -0.1 ctbnrle/tst/Makefile 0000644 0004642 0000777 00000003550 11257222634 015063 0 ustar cshelton csprofs SRC = learnparams.cc clique_unit_test.cc
OBJ = learnparams.o clique_unit_test.o
LIBOBJ = ../src/bn.o ../src/extramath.o ../src/random.o ../src/context.o ../src/inference.o ../src/rk.o ../src/ctbndyn.o ../src/markovdyn.o ../src/rvcomp.o ../src/ctbn.o ../src/markov.o ../src/rv.o ../src/defines.o ../src/markovsimple.o ../src/rvsimple.o ../src/dynamics.o ../src/matrix.o ../src/ss.o ../src/dyncomp.o ../src/multirv.o ../src/streamextra.o ../src/dynsimple.o ../src/multisimple.o ../src/trajectory.o ../src/em.o ../src/params.o ../src/exactmarkovinf.o ../src/process.o ../src/sampler.o ../src/structure.o ../src/varsample.o ../src/expmethod.o ../src/gibbssampler.o ../src/samplinginf.o ../src/importancesampler.o ../src/utils.o ../src/impbase.o ../src/clique.o ../src/nodesampler.o ../src/samplequeue.o ../src/suffstatsquery.o ../src/nonexpsuffstatsquery.o ../src/expsuffstatsquery.o ../src/graphic.o ../src/searchqueue.o ../src/structuresearch.o ../src/brutestructuresearch.o ../src/grapheditsearch.o ../src/famscore.o
CXX = g++
OFLAG = -O2
#OFLAG = -ggdb -I../hdr
CXXFLAGS += $(OFLAG) -I../hdr
DEPEND = makedepend -- $(CXXFLAGS) --
all: clique_unit_test unit_testing_graphic exactquery importancequery gibbsquery
unit_testing_graphic: unit_testing_graphic.o $(LIBOBJ)
$(CXX) $(CXXFLAGS) $(LDFLAGS) -o unit_testing_graphic unit_testing_graphic.o $(LIBOBJ)
clique_unit_test: clique_unit_test.o $(LIBOBJ)
$(CXX) $(CXXFLAGS) $(LDFLAGS) -o clique_unit_test clique_unit_test.o $(LIBOBJ)
exactquery: exactquery.o $(LIBOBJ)
$(CXX) $(CXXFLAGS) $(LDFLAGS) -o exactquery exactquery.o $(LIBOBJ)
gibbsquery: gibbsquery.o $(LIBOBJ)
$(CXX) $(CXXFLAGS) $(LDFLAGS) -o gibbsquery gibbsquery.o $(LIBOBJ)
importancequery: importancequery.o $(LIBOBJ)
$(CXX) $(CXXFLAGS) $(LDFLAGS) -o importancequery importancequery.o $(LIBOBJ)
depend: $(SRC)
$(DEPEND) $(SRC)
clean:
rm -rf *.o *~
# DO NOT DELETE
ctbnrle/tst/exactquery.cc 0000644 0004642 0000777 00000005246 11257240650 016126 0 ustar cshelton csprofs /* Continuous Time Bayesian Network Reasoning and Learning Engine
* Copyright (C) 2009 The Regents of the University of California
*
* Authored by Yu Fan, William Lam, Joon Lee, Christian Shelton, and Jing Xu
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* This program 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 program. If not, see .
*/
// this file reads in a model file and an evidence file, do the query using exact inference
#include "markovdyn.h"
#include "streamextra.h"
#include "context.h"
#include "multirv.h"
#include "exactmarkovinf.h"
#include "params.h"
#include "bn.h"
#include "ctbndyn.h"
#include "em.h"
#include "utils.h"
#include
using namespace std;
namespace {
int testing_result = 0;
double kEpsilon = 1e-4;
};
#define EXPECT(cond) do { \
if (!(cond)) { \
std::cerr << "Test failure at " << __FILE__ << ":" << __LINE__ \
<< " in " << __func__ \
<< ", failed condition is " #cond << std::endl; \
testing_result = 1; \
} \
} while (0)
int main(int argc, char **argv) {
const char *input_file = "queryinput.data";
addnan(cout);
InitParams(argc, argv);
ifstream fin(input_file);
Markov m(fin);
const CTBNDyn *ctbndyn = dynamic_cast(m.GetDynamics());
const BN *bn = dynamic_cast(m.GetStartDist());
Context context = ctbndyn->Domain() + ctbndyn->CondDomain();
Trajectory evid;
fin >> evid;
double expect_q1, expect_q2;
fin >> expect_q1 >> expect_q2;
fin.close();
Instantiation x(context, -1);
int querynode = ParamInt("QueryNode", 1);
int querytimeval = ParamInt("QueryTimeVal", 1);
Context c = ctbndyn->Node(querynode)->Domain();
x.SetVal(querynode, querytimeval);
QueryTime query1(x);
QueryTransition query2(c, 0, 1);
ExactMarkovInf einf;
einf.SetProcess(&m);
einf.SetTrajectory(&evid);
double error1 = (einf.CalcQuery(query1) - expect_q1) / expect_q1;
double error2 = (einf.CalcQuery(query2) - expect_q2) / expect_q2;
EXPECT(fabs(error1) < kEpsilon);
EXPECT(fabs(error2) < kEpsilon);
if (testing_result == 0)
cout << "PASS exactquery unit test." << endl;
return 0;
}
ctbnrle/tst/clique_unit_test.cc 0000644 0004642 0000777 00000011122 11257240650 017302 0 ustar cshelton csprofs /* Continuous Time Bayesian Network Reasoning and Learning Engine
* Copyright (C) 2009 The Regents of the University of California
*
* Authored by Yu Fan, William Lam, Joon Lee, Christian Shelton, and Jing Xu
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* This program 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 program. If not, see .
*/
#include
#include
#include "clique.h"
#include "markov.h"
#include "bn.h"
using namespace std;
const int unit_test_check [4][3] = {{1}, {0, 2, 3} , {1} , {1}};
const int unit_test_check1 [4][3] = {{0,1,2}, {1, 3, 4}, {4,5,6}, {4,7}};
const int unit_test_save [3][80] = {{0, 2, -1, 0, 1, -1, 1, 2,
-1, 3, -1, 1, 3, 4, -1, 5, -1, 4, 5, 6,
-1, 4, 7, -1, -100, 0, 0, 1, 2, -1, 1,
1, 3, 4, -1, 2, 4, 5, 6, -1, 2, 4, 7, -1},
{0, 2, -1, 0, 1, -1, 1, 2, -1, 3, -1, 1,
3, 4, -1, 5, -1, 4, 5, 6, -1, 4, 7, -1,
0, -1, 0, 1, -1, 2, -1, 2, 3, -1, 4, -1,
5, -1, 4, 6, -1, 7, -1, -100, 0, 1, 2, 3,
-1, 1, 1, 3, 4, -1, 2, 4, 5, 6, -1, 2, 4,
7, -1, 1, 0, 1, 2, -1 }};
bool checkingSAVE (ifstream &, const int &);
int main(int argc, char **argv)
{
//to initialize the CTBNDyn
ifstream ls("drug.ctbn");
if(!ls.is_open())
{
cout << "error: can not find the drug.ctbndyn file";
return 0;
}
Markov ctbn(ls);
CliqueTree check;
//set the random generated value to always be 0
check.IgnoreRand(true);
const Dynamics * d = ctbn.GetDynamics();
const CTBNDyn * Dyn = dynamic_cast(d);
//build the clique tree
check.BuildClique(Dyn);
//recieve the clique that been created by
//vector of
vector test2 = check.ReturnCliques();
//use the receive clique to test the correctness
if(test2.size() != 4)
{
cout << "error: wrong number of clique item" << endl;
cout << test2.size() << endl;
return 0;
}
set ::iterator it;
int count;
for(unsigned int i = 0; i < test2.size(); i++)
{
vector varlist = test2[i].vars.VarList();
for (unsigned int j=0;j varlist = test2[i].vars.VarList();
for (unsigned int j=0;j> save_testing)
{
if(save_testing != unit_test_save[j][count_])
{
cout << "error: wrong saved data been created" << endl;
return false;
}
count_ ++;
}
return true;
}
ctbnrle/hdr/ 0000755 0004642 0000777 00000000000 11262422676 013367 5 ustar cshelton csprofs ctbnrle/hdr/trajectory.h 0000644 0004642 0000777 00000013733 11261504014 015717 0 ustar cshelton csprofs /* Continuous Time Bayesian Network Reasoning and Learning Engine
* Copyright (C) 2009 The Regents of the University of California
*
* Authored by Yu Fan, William Lam, Joon Lee, Christian Shelton, and Jing Xu
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* This program 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 program. If not, see .
*/
#ifndef _TRAJECTORY_H_
#define _TRAJECTORY_H_
#include