ctbnrle.1.0.2/0000755000464200007770000000000011344302770013240 5ustar csheltoncsprofsctbnrle.1.0.2/doc/0000755000464200007770000000000011344303214013777 5ustar csheltoncsprofsctbnrle.1.0.2/doc/readme.txt0000644000464200007770000000562611344302770016014 0ustar csheltoncsprofsHow to cite: ============ If you wish to cite this code, please cite the JMLR paper Christian R. Shelton, Yu Fan, William Lam, Joon Lee, and Jing Xu. Continuous Time Bayesian Network Reasoning and Learning Engine. Journal of Machine Learning Research, 2010. to appear. a suitable bibtex entry would be @article{CTBNRLE, author = "Christian R. Shelton and Yu Fan and William Lam and Joon Lee and Jing Xu", title = "Continuous Time {B}ayesian Network Reasoning and Learning Engine", journal = "Journal of Machine Learning Research", note = "to appear", year = 2010, } How to compile: =============== This code has been tested under Unix with the Gnu tools and Windows with Visual C++. You may want to read the "Installing Graphviz" section first if you plan to use the visualization portion of the libraries. UNIX: The Makefile in src/ will make ctbn.a (a library of all of the object files). "make" in demo/ will make the demos (including making the objects in src/ if necessary), and "make" tst/ will make all of the unit/regression tests. Running the python script tst/unit_test.py will run each of the test scripts to confirm that the calculations are being performed correctly. Windows: Under Visual C++, you must create your own project. Add all of the source files in src/ and all of the header files in hdr/. Adding one "main" file (from tst/ or demo/) should allow the entire project to compile. You may have a number of compile warnings about conversions from "size_t" to "int." These may be safely ignored. Installing Graphviz: ==================== graphiviz is a graph layout program available from www.graphviz.org. The CTBNRLE uses the "dot" executable from it. We have successfully tested CTBNRLE with graphviz version 2.14.1 (Oct 20, 2007) and version 2.26.3 (Jan 26, 2010). Install graphviz as per the instructions on their website. If the executable is not in your path, or you would like to explicitly state where to find it, you should edit src/graphic.cc and change the definition of DOTCOMMAND to the string containing the executable (with path) to be executed to run the "dot" program of graphviz. Structure: ========== The base directory is split into multiple directorys: demo/ This contains example programs on how to use the libraries. They are often useful on their own (e.g. sample from a CTBN, learn a CTBN from samples, etc). The README.txt file also has information on the file extensions used for data files. doc/ This directory contains information on how to read the code and use it, literature to read, and funding sources that made this code base possible. src/ This directory contains all of the non-header files. hdr/ This directory contains all of the header files. This, plus the ctbn.a created in /src are all that is necessary to link a new program to the libraries here tst/ This directory contains regression tests that are used to verify that the code is working ctbnrle.1.0.2/doc/code_intro.txt0000644000464200007770000002324611344302770016702 0ustar csheltoncsprofsThis file serves as an introduction to the code. The first section is on how to use the code as a black box. The second is a brief introduction to the logic behind the class organization. Throughout this document, it is assumed that the reader is familiar with continuous time Bayesian networks (CTBNs) in at least their formulation and basic theory. See the file ctbn_papers.txt for a list of reading on the subject. HOW TO USE: ----------- In general, the header files (in .hdr) explain what the class' methods do. This is a general primer for orientation. Context & Instantiation (in context.h) A context is a collection of variables. Each is identified by a number (variable 0, 1, etc.) but need not be consecutively ordered. Each variable has a size (number of values it can take on). An instantiation is a context plus an assignment to each of the variables. However, the assignment -1 indicates that the value is unknown. If the assignment is complete (no -1 values), an instantiation also has an index which maps the assignment to a unique integer. The indexes for a particular context are consecutive, starting at 0. Inc() moves an instantiation to the next consecutive index's assignment. Trajectory (in trajectory.h) This class stores temporal data. It has a set of variables, a beginning and ending time, and a set of events. Each event is the assignment of a value to one variable. The value -1 is used to indicate that the variable is unobserved. Therefore a "transition" of a variable *to* -1 means that it has gone from being observed to being unobserved. Conversely a "transition" of a variable *from* -1 (to a non-negative value) means that the variable has started to be observed at this instant. Notable is the inner class Index (and RIndex) which allow for the iteration through a trajectories events (or a subset of them). They are like iterators from STL (but not exactly the same, hence the different name). Process (in process.h) An abstract class, all processes are subclasses of it Dynamics (in dynamics.h) An abstract class, all dynamics are subclasses of it. A "dynamics" is the part of a Markov process describing how it changes (as compared to its starting distribution at time 0). RV (in rv.h) An abstract class, all random variables are subclasses of it. An RV is the part of a Markov process describing how it starts (at time 0). It can also be used for other purposes. We make a distinction between random variables and processes. Markov (in markov.h) Markov is a dynamic process with a starting distribution and a dynamics. CTBNDyn (in ctbndyn.h) This is a "factored" dynamics class, the essence of a CTBN BN (in bn.h) This is a "factored" RV, the starting distribution for a CTBN CTBN (in ctbn.h) A really small class purely for notational convenience. It is a Markov class with CTBNDyn as the dynamics and BN as the starting distribution -- How to make a CTBN: The demo/ directory has a number of examples (makedrug.cc and makechain.cc for example) on how to construct a CTBN by hand. They involve creating MarkovDyn and MultiRV for each variable, describing the conditional dynamics and starting distribution for each (conditioned on other variables -- its parents) and then adding them all into a CTBNDyn variable and a BN variable and then placing the two into a CTBN. -- How to sample from a CTBN: The method Sample will generate a sample trajectory, provided the trajectory input already has a starting and ending time set. -- How to learn the parameters of a CTBN from data: The demo/ directory has an example learnparams.cc of how to learn parameters. It takes a CTBN as input, samples from it, and then learns the parameters back again (hopefully similar to the original ones). For more general case, you would replace the sampled trajectories with real data. The essential method is to feed the data into the SuffStats method for the CTBN, thus generating the sufficient statistics necessary for learning parameters (the CTBN need only have the correct structure, the parameters are ignored). Then, these sufficient statistics are fed into the CTBN's Maximize method which calculates the maximum likelihood parameters. If you have incomplete data, you will want to run the EM function in em.h. It requires the data, the process to optimize, and an inference method (for the E-step). inference.h has the abstract superclass for inference. exactmarkovinf.h has ExactMarkovInf which will perform exact inference. samplinginf.h has SamplingInf which performs sampling-based inference. It takes in a Sampler (see sampler.h) as the method for generating samples from the posterior distribution. GibbsSampler (gibbssampler.h), ImportanceSampler (importancesampler.h), and MetropolisSampler (metropolissampler.h) are all possible sampling methods. If you also do not know the structure, you will want to run the SEM function in em.h. It requires the data, the process to optimize, and an inference method (just as EM). -- How to perform inference for a CTBN: exactquery (and others) in the demo/ directory demonstrate the basics of inference. They read in a trajectory (a partial trajectory with missing values for some variables for some periods of time) and query the expected amount of time a variable spent in a particular state and the expected number of transitions from one state to another state. Other queries are possible (like the distribution of a variable at a point -- either smoothed or filtered -- see inference.h). CODING NOTES: ------------- Unless stated in the header file, all pointers passed into a function or method are still "owned" by the calling function afterward. All returned pointers are owned by the calling function. A few constructors ask for pointers and do not own them, but assume that the pointers are valid for the lifetime of the object. This is noted in the comments in the header file. Comments describing the purpose and usage of each method are described once at the most base class's header file. STL is assumed, but no other external libraries are required. Almost all classes implement Clone() as a virtual copy constructor. The code is contained in the ctbn namespace, with the exception of a few items that cannot be placed there (iostream related items that must be in std, for instance). CLASS ORGANIZATION: ------------------- The basic organization of the classes is reasonably straight-forward. There are a few small self-contained modules: random.h/.cc have code for drawing random numbers, matrix.h/.cc hold matrix operations, define.h holds differences between compilation environments, streamextra.h/.cc contains stream operations (it has extensive comments) extramath.h has random math functions (like log-gamma) rk.h/.cc contain the Runga-Kutta methods for computing the alpha and beta (forward and backward) passes for inference and for computing expected sufficient statistics of a flat Markov process clique.h/.cc hold the code for building clique trees (not needed by anything currently) graphic.h/.cc hold the code for drawing trajectories or CTBNs graphically samplequeue.h/.cc is an internal class for sampling In general, the class Process holds any process. It has a subclass Markov that holds any Markov process. Markov consists of two variables: an initial distribution and a "dynamics." The former is of type RV (or rather a subclass of RV) and the latter describes the evolution of the system (and is a Dynamics or subclass). All of these classes also hold a Context (see above) describing the set of variables in the event space (and the variables' sizes) as well as a second Context describing the set of variable upon which the variable or process is conditioned. If the conditional context is not empty, then any requests (for sampling, probability, etc) to the class must specify all values for these conditioning variables. Working from the "bottom up," classes ending with "simple" represent random variables or processes with no conditioning and where the event space is "flat" (just the first n non-negative integers). They are used in the lower levels of all of the algorithms to describe things when the event space is known or implied and need not be "carried" by all variables. Classes ending with "comp" make a composition of "simple" classes to construct a full RV, Process, or Dynamics. That is, they have conditioning contexts and event contexts and translate their method calls to those of "simple" classes that perform the computations. They are usually templates (where the template argument is the simple class out of which to construct a full version). All processes, dynamics, and random variables have their own sufficient statistics class. Each sufficient statistics class is a subclass of the (empty) class SS. With these distinctions understood, the rest of the organization is pretty easily read from the code itself. Here is a class inheritance diagram: SS -+- MarkovSS +- CTBNDynSS +- DynCompSS +- MarkovSimpleSS +- MultiSimpleSS +- BNSS \- BNCompSS Process --- Markov Sampler -+- GibbsSampler \- ImpBase --- ImportanceSampler VarSample --- ExpMethod DynSimple --- MarkovSimple --- MarkovSimpleToggle Dynamics -+- CTBNDyn \- DynComp --- MarkovDyn RV -+- BN \- RVComp --- MultiRV Inference -+- ExactMarkovInf \- SamplingInf QueryCalculator -+- QueryTransition \- QueryTime RVSimple -+- MultiLogSimple +- MultiSimple \- MultiZSimple --- SparseMultiZSimple RVCondSimple -+- RVCondSimpleComp +- SparseCondTransQ2 +- CondTransQ2 +- SparseCondTransQ +- CondTransQ StructureSearch -+- BruteStructureSearch \- GraphEditSearch ctbnrle.1.0.2/doc/Nodelman-etal-2002.pdf0000644000464200007770000067437611344302770017570 0ustar csheltoncsprofs%PDF-1.3 %쏢 5 0 obj <> stream x\YsƑޘ|JCu-W4 t-#w7ʬ=Z1ˣb7/iV֕MSkVwot~oonx ωvǛkLVB*~mXuq~8<㭖ԍ{~]hRET6W틿(*n]jMҝUVFxu> eQJH8VVqV&&b^0z"q;ö)*-;%Op?N{_U)S7Ae0,c(iO^ ϗ!vs5WUWWQJ+ϧ"+Ck"p7ԕfMlVnyƵ}>=ކES)֤Mٛ4NnޱsRum$+q}_m-a7xxޗwijwp?7`y~@p[gط! 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0000169005 00000 n 0000170148 00000 n 0000170726 00000 n 0000171693 00000 n 0000187181 00000 n 0000197753 00000 n 0000215123 00000 n trailer << /Size 194 /Root 1 0 R /Info 2 0 R >> startxref 223620 %%EOF ctbnrle.1.0.2/doc/sponsorship.txt0000644000464200007770000000040411344302770017133 0ustar csheltoncsprofsThis code base was made possible by two grants: Continuous Time Structured Stochastic Processes AFOSR Award No FA9550-07-1-0076 Reasoning in Dynamic Real Time Systems DARPA Award No HR0011-09-1-0030 We thank them for their generous support of this project. ctbnrle.1.0.2/doc/ctbn_papers.txt0000644000464200007770000001534711344302770017060 0ustar csheltoncsprofsThe following are the academic papers we know about that pertain to continuous time Bayesian networks. The code assumes knowledge of at least the first paper which is included in this directory. BASIC FORMULATION: Uri Nodelman, Christian R. Shelton, and Daphne Koller (2002). "Continuous Time Bayesian Networks." Proceedings of the Eighteenth International Conference on Uncertainty in Artificial Intelligence (pp. 378-387). http://www.cs.ucr.edu/~cshelton/papers/index.cgi?NodSheKol02 [First paper formulating CTBNs. It defines a CTBN's syntax and semantics and briefly discusses an approximate inference method.] Tal El-Hay, Nir Friedman, Daphne Koller and Raz Kupferman (2006). "Continuous Time Markov Networks", Proceedings of the Twenty-Second Conference on Uncertainty in Artificial Intelligence (pp. 155-164). http://ai.stanford.edu/~koller/Papers/El-Hay+al:UAI06.pdf [An undirected version of CTBNs that represents the subset of reversable Markov processes.] LEARNING: Uri Nodelman, Christian R. Shelton, and Daphne Koller (2003). "Learning Continuous Time Bayesian Networks." Proceedings of the Nineteenth International Conference on Uncertainty in Artificial Intelligence (pp. 451-458). http://www.cs.ucr.edu/~cshelton/papers/index.cgi?NodSheKol03 [Introduces the sufficient statistics for a CTBN and formulates the maximum likelihood and maximum a posteriori parameter learning formulas for the complete data case. It also formulates a structure learning method based on a Bayesian scoring function.] Uri Nodelman, Christian R. Shelton, and Daphne Koller (2005). "Expectation Maximization and Complex Duration Distributions for Continuous Time Bayesian Networks." Proceedings of the Twenty-First International Conference on Uncertainty in Artificial Intelligence (pp. 421-430). http://www.cs.ucr.edu/~cshelton/papers/index.cgi?NodSheKol05 [Introduces expectation maximization for CTBNs to extend the parameter and structure learning methods of the previous paper to the incomplete data case.] INFERENCE: (The first paper from 2002 also discusses this to some degree) Uri Nodelman, Daphne Koller, and Christian R. Shelton (2005). "Expectation Propagation for Continuous Time Bayesian Networks." Proceedings of the Twenty-First International Conference on Uncertainty in Artificial Intelligence (pp. 431-440). http://www.cs.ucr.edu/~cshelton/papers/index.cgi?NodKolShe05 [An approximate inference method based on expectation propagation.] Brenda Ng, Avi Pfeffer, and Richard Dearden (2005). "Continuous Time Particle Filtering." Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence (pp. 1360-1365). [A particle filter for the case of an unobserved CTBN that is driving stchastic differential equations whose values are observed at discrete time points.] Suchi Saria, Uri Nodelman, and Daphne Koller (2007). "Reasoning at the Right Time Granularity." Proceedings of the Twenty-third Conference on Uncertainty in Artificial Intelligence" (pp. 421-430). http://ai.stanford.edu/~nodelman/papers/dynamic-EP.pdf [An improved approximate inference method based on expectation propagation.] Yu Fan and Christian R. Shelton (2008). "Sampling for Approximate Inference in Continuous Time Bayesian Networks." Proceedings of the Tenth International Symposium on Artificial Intelligence and Mathematics. http://www.cs.ucr.edu/~cshelton/papers/index.cgi?FanShe08 [An importance sampling method and its extension to particle filtering and smoothing.] Tal El-Hay, Nir Friedman, and Raz Kupferman (2008). "Gibbs Sampling in Factorized Continuous-Time Markov Processes." Proceedings of the Twenty-Fourth Conference on Uncertainty in Artificial Intelligence (pp. 169-178). http://uai2008.cs.helsinki.fi/UAI_camera_ready/el-hay.pdf [A method for Gibbs sampling for a CTBN.] Ido Cohn, Tal El-Hay, Raz Kupferman, and Nir Friedman (2009). "Mean Field Variational Approximation for Continuous-Time Bayesian Networks." Proceedings of the Twenty-Fifth International Conference on Uncertainty in Artificial Intelligence. http://www.cs.mcgill.ca/~uai2009/papers/ UAI2009_0134_c9c16e478d8e82ecc3848c5dc76b4925.pdf [A mean field variational method for a CTBN.] OTHER EXTENSIONS: Karthik Gopalratnam, Henry Kautz, and Daniel S. Weld (2005). "Extending Continuous Time Bayesian Networks." Proceedings of 20th National Conference on Artificial Intelligence-AAAI 2005 (pp. 981-986). http://www.cs.washington.edu/homes/weld/papers/aaai05_CTBN.pdf [This presents an alternative to exponential duration distributions.] Kin Fai Kan and Christian R. Shelton (2008). "Solving Structured Continuous-Time Markov Decision Processes." Tenth International Symposium on Artificial Intelligence and Mathematics. http://www.cs.ucr.edu/~cshelton/papers/index.cgi?KanShe08 [This presents an approximate continuous Markov decision process planning solution using CTBN.] Luigi Portinale and Daniele Codetta-Raiteri (2009). "Generalizing Continuous Time Bayesian Networks with Immediate Nodes." Proceedings on the Workshop on Graph Structured for Knowledge Representation and Reasoning. http://web.unipmn.it/%7Eraiteri/papers/gkr.pdf [Presents an extension for describing simultaneous transitions in a CTBN and links it to generalized stochastic Petri nets.] APPLICATIONS: (most of the papers above have at least one application of a CTBN. These papers' main contributions are in applications, rather than theory.) Uri Nodelman and Eric Horvitz (2003). "Continuous Time Bayesian Networks for Inferring Users' Presence and Activities with Extensions for Modeling and Evaluation." MSR-TR-2003-97, Microsoft Research. http://research.microsoft.com/en-us/um/people/horvitz/uri_eh.pdf [Applies CTBNs to modeling user's use of desktop applications.] Ralf Herbrich, Thore Graepel, and Brendan Murphy (2007). "Structure from Failure." SYSML '07: Proceedings of the 2nd USENIX workshop on tackling computer system problems with machine learning. pp. 1-6. http://www.usenix.org/event/sysml07/tech/full_papers/herbrich/herbrich.pdf [Applies CTBNs to modeling failures in server farms.] Jing Xu and Christian R. Shelton (2008). "Continuous Time Bayesian Networks for Host Level Network Intrusion Detection." Machine Learning and Knowledge Discovery in Databases (ECML/PKDD) (LNAI, vol 5212) (pp. 613-627). http://www.cs.ucr.edu/~cshelton/papers/index.cgi?XuShe08 [Applies CTBNs to host-level computer network intrusion detection. Introduces "toggle" variables.] Yu Fan and Christian R. Shelton (2009). "Learning Continuous-Time Social Network Dynamics." Proceedings of the Twenty-Fifth International Conference on Uncertainty in Artificial Intelligence. http://www.cs.ucr.edu/~cshelton/papers/index.cgi?YuShe09 [Applies CTBNs to social network dynamics. Introduces MCMC inference method.] ctbnrle.1.0.2/hdr/0000755000464200007770000000000011344303152014010 5ustar csheltoncsprofsctbnrle.1.0.2/hdr/impbase.h0000644000464200007770000000650111344302770015610 0ustar csheltoncsprofs/* 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 CTBNRLE_IMPBASE_H #define CTBNRLE_IMPBASE_H #include "sampler.h" #include "markovdyn.h" #include "ctbn.h" #include "trajectory.h" #include "structure.h" #include namespace ctbn { //ImpBase is the base class for importance sampling, particle //filtering and particle smoothing algorithm class ImpBase : public Sampler{ public: virtual ~ImpBase(){}; //virtual ImpBase *Conle() const; virtual void SetProcess(const Process *pr); //virtual void SetMethod(const VarSample *m); virtual void SampleTrajectories(std::vector &tr, std::vector &w, int numsamples, Random &rand=randomizer) = 0; protected: // returns the log of the weight of a sample from the initial distribution // (sampled via importance sampling) completing "initval" virtual double SampleInitial(Instantiation &initval, Random &rand=randomizer); virtual double SampleDyn(Trajectory &tr, Random &rand=randomizer) = 0; //sample the next transition time for a variable virtual double SampleTime(const Instantiation &curri, int id, int parindex, int nodeval, int evidencetype, Trajectory::Index &nextindex, int e, double t, Random &rand=randomizer) ; //weight contribution of sampling transition time virtual double SampleTimeWeight(const Instantiation &curri, int nodeid, int transitionid, int parindex, int nodeval, int evidencetype, Trajectory::Index &nextindex, int e, double currt, double nextt); //sample the next state for a variable and //update the corresponding weight contribution virtual int SampleTransition(Instantiation &inst, int id, int parindex, int nodeval, int evidencetype, Trajectory::Index &nextindex, int e, double t, double &weight, Random &rand=randomizer); //weight contribution of sampling the next state /*virtual double SampleTransitionWeight(const Instantiation &nextinst, int transitionid, int oldval, int parindex, int nodeval, int evidencetype, Trajectory::Index &nextindex, int e, double currt, double nextt);*/ virtual void UndefineTime(int id, std::vector &undefinelist); //generate index of the evidence trajectory for each variable virtual void MakeIndex(); //double EndTime() const {return endtime;} virtual void SetCpuTime(double t){times.clear(); times.push_back(t);} Structure str; std::vector > markovblanket; std::vector nodes; std::vector evidindex; bool forceflip; std::vector changeindex; }; } // end of ctbn namespace #endif ctbnrle.1.0.2/hdr/nonexpsuffstatsquery.h0000644000464200007770000000250011344302770020523 0ustar csheltoncsprofs/* 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 CTBNRLE_NONEXPSUFFSTATSQUERY_H #define CTBNRLE_NONEXPSUFFSTATSQUERY_H // A class for performing queries for sufficient statistics where we have // complete trajectories. #include "suffstatsquery.h" #include "trajectory.h" #include namespace ctbn { class NonExpSuffStatsQuery : public SuffStatsQuery { public: NonExpSuffStatsQuery(); virtual ~NonExpSuffStatsQuery(); virtual SS* GetP0SS(); virtual SS* GetDSS(); }; } // end of ctbn namespace #endif ctbnrle.1.0.2/hdr/ctbn.h0000644000464200007770000000320111344302770015110 0ustar csheltoncsprofs/* 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 CTBNRLE_CTBN_H #define CTBNRLE_CTBN_H #include "markov.h" #include "structure.h" #include "markovdyn.h" #include "bn.h" namespace ctbn { // A CTBN is just a Markov process in which the starting // distribution is a BN and the dynamics are described by a CTBNDyn // This class exists only for naming convenience class CTBN : public Markov { SOBJCLASSDECL(CTBN) public: // startdist and dyn are now owned by this class! CTBN(RV *startdist, Dynamics *dyn); CTBN(std::istream &is); CTBN(const Markov &m); CTBN &operator=(const CTBN &ctbn); virtual ~CTBN() {}; CTBN *Clone() const; // These are new methods for CTBNs only // They are just short cuts to the CTBNDyn const MarkovDyn* Node(int i) const; const CTBNDyn *CTBNDynamic() const; }; } // end of ctbn namespace #endif ctbnrle.1.0.2/hdr/importancesampler.h0000644000464200007770000000406411344302770017717 0ustar csheltoncsprofs/* 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 CTBNRLE_IMPORTANCESAMPLER_H #define CTBNRLE_IMPORTANCESAMPLER_H #include "impbase.h" #include "sampler.h" #include "markovdyn.h" #include "ctbn.h" #include "trajectory.h" #include "structure.h" #include namespace ctbn { class ImportanceSampler : public ImpBase{ public: //The caller of the function owns all the pointers // but they must remain valie for the lifetime of this object ImportanceSampler(const Process *pr, const Trajectory *traj, const VarSample *m); virtual ~ImportanceSampler(); void SampleTrajectories(std::vector &tr, std::vector &w, int numsamples, Random &rand=randomizer) ; ImportanceSampler *Clone() const; void DiscardBnWeight() {discard_bn_weight = true;} protected: double SampleSingleTrajectory(Trajectory &traj, bool logscale=1, Random &rand=randomizer); double SampleDyn(Trajectory &traj, Random &rand=randomizer); void CleanCache(); void MakeCache(const Instantiation &inst, const std::vector &nextevid); bool discard_bn_weight; std::vector nodeindex; std::vector nodeval; //std::vector incindex; //std::vector currevidval; std::vector nextevidval; }; } // end of ctbn namespace #endif ctbnrle.1.0.2/hdr/structure.h0000644000464200007770000000501611344302770016230 0ustar csheltoncsprofs/* 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 CTBNRLE_STRUCTURE_H #define CTBNRLE_STRUCTURE_H #include #include "ctbndyn.h" #include "context.h" namespace ctbn { //The Structure class copies node2var, var2node and children //from class CTBNDyn. It can be used when the structure //cannot be accessed directly. //It represents the graph structure of a factored model, like a BN // or CTBNDyn //It also understands that the variable IDs involved might not be //consecutive and so it keeps a mapping from varid <-> array index class Structure { friend class CTBNDyn; friend class BN; friend class StructureSearch; friend class GraphEditSearch; public: Structure(); virtual ~Structure(); const std::vector& GetParent(int child) const; const std::vector& GetChildren(int parent) const; int Node2Var(int index) const; int Var2Node(int index) const; const std::vector >& GetAdjLists() const; int HammingDist(const Structure &other); std::vector GetVarList() const; int GetNumParams(const Context& c) const; bool IsCyclic() const; void Print(std::ostream& os) const; protected: //mapping from index into "nodes" to the variable id represented std::vector node2var; //parents of nodes //cache the parents variable id of each node index std::vector > parentlist; //mapping from variable id to the index into "nodes" std::map var2node; // cache of // the indexes into "nodes" // of the children for a particular variable id // (Note this one is slightly odd as it maps a variable id // into a list of node-indexes (not back into varids) std::map > children; std::vector empty; }; } // end of ctbn namespace #endif ctbnrle.1.0.2/hdr/markovdyn.h0000644000464200007770000000373011344302770016203 0ustar csheltoncsprofs/* 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 CTBNRLE_MARKOVDYN_H #define CTBNRLE_MARKOVDYN_H #include "markovsimple.h" #include "dyncomp.h" namespace ctbn { // just a wrapper to make things simpler // A Markov Dynamics is a composition of a set of MarkovSimple processes // (either toggle variables, or -- by default -- regular Markov processes) class MarkovDyn : public DynComp { SOBJCLASSDECL(MarkovDyn) public: MarkovDyn(const Context &var, const Context &cvar, bool is_toggle = false); MarkovDyn(std::istream &is); virtual ~MarkovDyn(); virtual MarkovDyn *Clone() const; inline const MarkovSimple *operator()(const Instantiation &i) const { return dynamic_cast((*this)[i]); } inline MarkovSimple *operator()(const Instantiation &i) { return dynamic_cast((*this)[i]); } inline const MarkovSimple *operator()(int i) const { return dynamic_cast((*this)[i]); } inline MarkovSimple *operator()(int i) { return dynamic_cast((*this)[i]); } private: const static MarkovSimple *mbase; const static MarkovSimpleToggle *tmbase; }; } // end of ctbn namespace #endif ctbnrle.1.0.2/hdr/markovsimple.h0000644000464200007770000003255511344302770016711 0ustar csheltoncsprofs/* 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 CTBNRLE_MARKOVSIMPLE_H #define CTBNRLE_MARKOVSIMPLE_H #include "dynsimple.h" namespace ctbn { // This main class in this file (see the end) is Markov Simple // It describes a Markov process over the state space 0..n-1 // with no "conditioning" variables (that is the dynamics do not // depend on anything else) class MarkovSimple; // The sufficient statistics for MarkovSimple: class MarkovSimpleSS : public SS { SOBJCLASSDECL(MarkovSimpleSS) public: friend class MarkovSimple; friend class MarkovSimpleToggle; MarkovSimpleSS(int n); MarkovSimpleSS(std::istream &is); virtual ~MarkovSimpleSS(); virtual MarkovSimpleSS *Clone() const; virtual void Load(std::istream &is); virtual void Save(std::ostream &os) const; virtual void Scale(double w); virtual void AddSS(const SS* nss, double w=1.0); virtual double Element(int s1, int s2) const {return c[s1][s2];} private: // diagonal elements hold the amount of time in each state // off-diagonal element (i,j) is the number of transitions from i to j matrix c; }; // The next 4 classes are special versions of conditional random variables // (mapping from a domain to the same domain). They are used by MarkovSimple // and are not designed to be learned from data or really interact with // sufficient statistics. Therefore all of the suffstats methods are // blank. For similar reasons they do not have serialization (load/save) // currently. // The next 2 classes represent the conditional distribution of the value of a // Markov process at one time point given its value at a previous time point // They store the Q matrix and the time interval instead of the stochastic // matrix explicitly. // When "Mult" is called, they generate the marginal distribution at the // later point given the previous point (and the reverse for RMult) using // numeric integration (see rk.h) // This one is directly as described above. template class CondTransQ : public RVCondSimple { //SOBJCLASSDECL(CondTransQ) public: CondTransQ(const matrix &QQ, double deltat); //CondTransQ(std::istream &is); virtual ~CondTransQ(); virtual CondTransQ *Clone() const; virtual double Prob(int ind, int cind, bool log=false) const; virtual RVSimple *Condition(int ind) const; virtual void Mult(RVSimple *&x) const; virtual void RMult(RVSimple *&x) const; virtual RVSimple *Convert(const RVSimple *rvs) const; virtual void Load(std::istream &is) {} virtual void Save(std::ostream &os) const {} virtual void Normalize() {} virtual void Restrict(const std::vector &ind, const std::vector &cind) {} virtual void Reindex(int n, int nc, const RVCondSimple::RemapT &ind) {} virtual void MultBy(const RVCondSimple *x) {} virtual SS *BlankSS() const { return NULL; }; virtual void AddSS(int cind, int ind, SS *ss, double w=1.0) const {} virtual void AddExpSS(int cind, SS *ss, double w=1.0) const {} virtual void AddExpSS(int cind, const RVSimple *rvs, SS *ss, double w=1.0) const {} virtual void AddSS(const SS *toadd, const RVCondSimple* rv, const std::vector > &mapping, int mycondi, int rvccondi, SS *ss, double w=1.0) const {} virtual int Sample(int cx, Random &rand = randomizer) const; virtual void Maximize(const SS *ss) {} virtual void Scramble(double alpha=1.0, double degree=1.0, Random &rand=randomizer) {} virtual double LLH(const SS *ss) const {return 0.0;}; virtual double GetScore(double numTrans, const SS *ss) const {return 0.0;}; protected: QTYPE Q; double t; }; // This one assumes that the system is constrained to a subset of the // states (ind) and it only represents the submatrix of this state. // It should be used with SparseMultiZSimple (as the RVSimple for Mult and // RMult) to also capture this sparseness. The methods will error if // used with other versions of RVSimple template class SparseCondTransQ : public RVCondSimple { //SOBJCLASSDECL(SparseCondTransQ) public: SparseCondTransQ(const matrix &QQ, double deltat, const std::vector &ind); virtual ~SparseCondTransQ(); virtual SparseCondTransQ *Clone() const; virtual double Prob(int ind, int cind, bool log=false) const; virtual RVSimple *Condition(int ind) const; virtual void Mult(RVSimple *&x) const; virtual void RMult(RVSimple *&x) const; virtual RVSimple *Convert(const RVSimple *rvs) const; virtual void Load(std::istream &is) {} virtual void Save(std::ostream &os) const {} virtual void Normalize() {} virtual void Restrict(const std::vector &ind, const std::vector &cind) {} virtual void Reindex(int n, int nc, const RVCondSimple::RemapT &ind) {} virtual void MultBy(const RVCondSimple *x) {} virtual SS *BlankSS() const { return NULL; }; virtual void AddSS(int cind, int ind, SS *ss, double w=1.0) const {} virtual void AddExpSS(int cind, SS *ss, double w=1.0) const {} virtual void AddExpSS(int cind, const RVSimple *rvs, SS *ss, double w=1.0) const {} virtual void AddSS(const SS *toadd, const RVCondSimple* rv, const std::vector > &mapping, int mycondi, int rvccondi, SS *ss, double w=1.0) const {} virtual int Sample(int cx, Random &rand = randomizer) const {return 0;} virtual void Maximize(const SS *ss) {} virtual void Scramble(double alpha=1.0, double degree=1.0, Random &rand=randomizer) {} virtual double LLH(const SS *ss) const {return 0.0;} virtual double GetScore(double numTrans, const SS *ss) const {return 0.0;} protected: QTYPE Q; double t; std::vector ix; }; // The next two classes do the same thing (and again, for an "unconstrained" // system and then for a "constrained" system). However, they represent // the conditional density of a transition at a time instance (therefore // they map from "just before" the transition to "just after" the transition // instead of across any non-zero interval of time). // Here is the unconstrained (non-sparse) version. template class CondTransQ2 : public RVCondSimple { //SOBJCLASSDECL(CondTransQ2) public: CondTransQ2(const matrix &QQ); virtual ~CondTransQ2(); virtual CondTransQ2 *Clone() const; virtual double Prob(int ind, int cind, bool log=false) const; virtual RVSimple *Condition(int ind) const; virtual void Mult(RVSimple *&x) const; virtual void RMult(RVSimple *&x) const; virtual RVSimple *Convert(const RVSimple *rvs) const; virtual void Load(std::istream &is) {} virtual void Save(std::ostream &os) const {} virtual void Normalize() {} virtual void Restrict(const std::vector &ind, const std::vector &cind) {} virtual void Reindex(int n, int nc, const RVCondSimple::RemapT &ind) {} virtual void MultBy(const RVCondSimple *x) {} virtual SS *BlankSS() const { return NULL; }; virtual void AddSS(int cind, int ind, SS *ss, double w=1.0) const {} virtual void AddExpSS(int cind, SS *ss, double w=1.0) const {} virtual void AddExpSS(int cind, const RVSimple *rvs, SS *ss, double w=1.0) const {} virtual void AddSS(const SS *toadd, const RVCondSimple* rv, const std::vector > &mapping, int mycondi, int rvccondi, SS *ss, double w=1.0) const {} virtual int Sample(int cx, Random &rand = randomizer) const {return 0;} virtual void Maximize(const SS *ss) {} virtual void Scramble(double alpha=1.0, double degree=1.0, Random &rand=randomizer) {} virtual double LLH(const SS *ss) const {return 0.0;}; virtual double GetScore(double numTrans, const SS *ss) const {return 0.0;} protected: QTYPE Q; }; // Here is the constrained version. It is assumed that the state // before transition is conditioned to be one of the cind set. // The state after the transition is conditioned to be one of the ind set. template class SparseCondTransQ2 : public RVCondSimple { //SOBJCLASSDECL(SparseCondTransQ2) public: SparseCondTransQ2(const matrix &QQ, const std::vector &cind, const std::vector &ind); virtual ~SparseCondTransQ2(); virtual SparseCondTransQ2 *Clone() const; virtual double Prob(int ind, int cind, bool log=false) const; virtual RVSimple *Condition(int ind) const; virtual void Mult(RVSimple *&x) const; virtual void RMult(RVSimple *&x) const; virtual RVSimple *Convert(const RVSimple *rvs) const; virtual void Load(std::istream &is) {} virtual void Save(std::ostream &os) const {} virtual void Normalize() {} virtual void Restrict(const std::vector &ind, const std::vector &cind) {} virtual void Reindex(int n, int nc, const RVCondSimple::RemapT &ind) {} virtual void MultBy(const RVCondSimple *x) {} virtual SS *BlankSS() const { return NULL; }; virtual void AddSS(int cind, int ind, SS *ss, double w=1.0) const {} virtual void AddExpSS(int cind, SS *ss, double w=1.0) const {} virtual void AddExpSS(int cind, const RVSimple *rvs, SS *ss, double w=1.0) const {} virtual void AddSS(const SS *toadd, const RVCondSimple* rv, const std::vector > &mapping, int mycondi, int rvccondi, SS *ss, double w=1.0) const {} virtual int Sample(int cx, Random &rand = randomizer) const {return 0;} virtual void Maximize(const SS *ss) {} virtual void Scramble(double alpha=1.0, double degree=1.0, Random &rand=randomizer) {} virtual double LLH(const SS *ss) const {return 0.0;} virtual double GetScore(double numTrans, const SS *ss) const {return 0.0;} protected: QTYPE Q; std::vector cix,ix; }; // see dynsimple.h for a full description of the methods' uses class MarkovSimple : public DynSimple { SOBJCLASSDECL(MarkovSimple) public: MarkovSimple(int n=1); MarkovSimple(std::istream &is); virtual ~MarkovSimple(); virtual void Load(std::istream &is); virtual void Save(std::ostream &os) const; virtual MarkovSimple *Clone() const; virtual MarkovSimple *MakeNew(int nstates) const; virtual void Normalize(); virtual void Restrict(const std::vector &ind); virtual void Expand(const std::vector > &ind, int newn); virtual CondTransQ *Cond(double t0, double t1) const; virtual SparseCondTransQ *CondRestrict(double t0, double t1, const std::vector &ind) const; virtual CondTransQ2 *Cond(double t) const; virtual SparseCondTransQ2 *CondRestrict(double t, const std::vector &fromind, const std::vector &toind, bool transition=true) const; virtual MarkovSimpleSS *BlankSS() const; virtual void Mult(const DynSimple *x); virtual void AddSS(int x, double t0, double t1, SS *ss, double w=1.0) const; virtual void AddTransSS(int x1, int x2, double t, SS *ss, double w=1.0) const; virtual void AddExpSS(const RVSimple *alpha, const RVSimple *beta, double t0, double deltat, SS *ss, double w=1.0) const; virtual void AddExpTransSS(const RVSimple *x1, const RVSimple *x2, double t, SS *ss, double w=1.0) const; virtual void AddSS(const SS *toadd, const DynSimple *dyn, const std::vector > &mapping, SS *ss, double w=1.0) const; virtual void SampleNextEvent(int ind, double t, double &newt, int &newind, Random &rand=randomizer) const; virtual void Maximize(const SS *ss); inline matrix &Intensity() { return Q; } inline const matrix &Intensity() const { return Q; } virtual void Scramble(double a=1.0, double b=1.0, double alpha=1.0, double degree=1.0, Random &rand=randomizer); virtual double LLH(const SS *ss) const; virtual double GetScore(int parentCard, double numTrans, double amtTime, const SS* ss) const; private: matrix Q; }; // MarkovSimpleToggle represents a variable that "toggles" or "flips" // as an extention to the MarkovSimple, but has parameter tieing; // e.g Q = [-2 1 1; // 1 -2 1; // 1 1 -2]; // a transition of a toggle variable only indicates a state changes; // like a flip variable, it does not differentiate between changing from // which to which; // the probabilities of transit from one state to the other are all the same; // for a fixed size of the toggle variable, only 1 free paratmeter: // the intensity of the state changes class MarkovSimpleToggle : public MarkovSimple { public: MarkovSimpleToggle(int n=1); MarkovSimpleToggle(std::istream &is); virtual ~MarkovSimpleToggle(); // calls MarkovSimple:MakeNew(); virtual MarkovSimpleToggle *MakeNew(int nstates) const; // calls MarkovSimple:Clone(); virtual MarkovSimpleToggle *Clone() const; // get the ML estimate from sufficient statistics ss; // the state change intensity is estimated by total number of // transitions between all pair of states divided by total number of // time staying at all states virtual void Maximize(const SS *ss); }; } // end of ctbn namespace #endif ctbnrle.1.0.2/hdr/graphic.h0000644000464200007770000001417311344302770015611 0ustar csheltoncsprofs/* 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 CTBNRLE_GRAPHIC_H #define CTBNRLE_GRAPHIC_H #include #include #include #include #include #include #include #include #include #include "markov.h" #include "dynamics.h" #include "context.h" #include "ctbndyn.h" #include "matrix.h" #include "markovdyn.h" #include "trajectory.h" namespace ctbn { /* * this class is devoted to drawing a ctbn graph and a trajectory * given the start time and ending time it will generate the trajectory graph * or output the Trajectory::Index graph. * * * In int main you need to use InitParams(argc, argv); before anything else * params.h * to change the window x coordinate size use the "traj_win_size" * to change the font on trajectory graph use the "traj_font" * to change the font on ctbn graph use the "ctbn_font" * to change the font on trajectory graph ruler use the "traj_ruler" */ class Graphic { public: Graphic(); //output CTBN graph without the matrices void DrawCTBN(const Markov &, std::ofstream &); //output trajectory graph void DrawTrajectory(Trajectory,const Context &, std::ofstream &); //output trajectory graph by generate new trajectory void DrawTrajectory(const Markov &, std::ofstream &); //output CTBN graph with the matrices void DrawCTBNMatrix (const Markov &, std::ofstream &); //output both graph (i.e. it require name of ctbn graph as string) void DrawCTBNRank(const Markov &, std::ofstream &, std::string &, std::ofstream &); //output both graph (i.e. it require name of ctbn graph as string) void DrawBoth(const Markov &, Trajectory &, std::ofstream &,std::string &, std::ofstream &); //change size for name, and time of change void SetFontTrajectory(const int & change_font = 15 , const int & change_font1 = 5 ); //set size of page of trajectory void SetSizeTrajectory(const int &); //set font size on CTBN graph void SetFontCTBN(const int &); //change name of the variable. void Changes(std::vector ); //clearing the changed name void ClearName(); private: //class use for save up the trajectory graphic class GraphicEvent { public: GraphicEvent(); void SetChanges(int, double, const std::vector &); void SetName(std::string); std::string GetName() const; int GetChange() const; double GetTime() const; std::vector GetState() const; private: std::vector state; std::string name; int change_node; double time; }; //class use for save up the ctbn graphic class GraphicNode { public: GraphicNode(); void Add(const int &); void Add(const std::string &); std::string GetName(); int GetSize(); void SetName(const std::string &); void SetMatrix(const matrix &, int &, int &); void SetPlace(int); void SetMatrix(const matrix &); void SetBool(); void SetMatching(std::vector ); void SetMatching(std::vector ); std::vector GetPlace(); std::vector GetMatching(); bool GetExist(); matrix GetMatrix(const int &); bool CheckStrings(); private: bool exist; bool check_string; std::string a; int b; std::vector connect; std::vector dyn; std::vector matching; std::vector place; }; //generate CTBN process with time limit for trajectory void Create(const Markov &, double &, double &); //create the vector to generate CTBN graph void ExtractInfo(const Markov &); void GetLocation(std::string); void Create(Trajectory::Index &); //function created for attempt align each number together void Arrange(std::ofstream& ls, int); void ArrangeFind(matrix); //get largest value int Largest(std::vector); //get larger value void Largest(int); //get the different color for each number std::vector< std::vector > setcolor(); //get size of text int BiggestText(); //get size of number int BiggestNumber(); //change name of the variable. void Changes(); //build graph of CTBN with matrixes void BuildM(std::ofstream & ls); //build graph of CTBN without matrixes void Build(std::ofstream & ls); //build graph of CTBN without matrixes with rank void BuildRank(std::ofstream & ls); //build trajectory void BuildState(std::ofstream & ls); //part of the buildm which generate matrix on the graph void BuildMatrix(matrix, std::ofstream& ls); void BuildStateRank(std::ofstream & ls); void CTBNGraphicIntro(std::ofstream & ls); std::map graph; std::vector graph_change; std::vector change_names; int size; //size of x coordinate the trajectory visualization int size_x; //size of the font on trajectory int change_point; //size in trajectory change mark int size_distance; //start time of the trajectory double start_point; //ending time of the trajectory double ending_point; double large_point; int largest_tr; //size of the font on ctbn graph int size_font; double start; bool change_name; double finish; std::vector< std::vector > getlocation; std::vector < std::vector > arrangement; std::vector < std::vector > troject; std::vector int_size; //They draw the pointers D and nodes form the CTBN which are own by this class const Dynamics *d; std::vector nodes; }; } // end of ctbn namespace #endif /*GRAPHIC_H_*/ ctbnrle.1.0.2/hdr/dynsimple.h0000644000464200007770000001205711344302770016177 0ustar csheltoncsprofs/* 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 CTBNRLE_DYNSIMPLE_H #define CTBNRLE_DYNSIMPLE_H #include "streamextra.h" #include "random.h" #include #include "ss.h" #include "rvsimple.h" namespace ctbn { // DynSimple is the ABC for continuous-time semi-Markov dynamics // Just like RVSimple, its domain is implied // So this is an abstract representation of a semi-Markov process // dynamics over the state space 0..n-1 with no conditioning class DynSimple : public StreamObj { public: DynSimple(); DynSimple(std::istream &is); virtual ~DynSimple(); virtual void Load(std::istream &is); virtual void Save(std::ostream &os) const; // virtual copy constructor virtual DynSimple *Clone() const = 0; // virtual blank constructor virtual DynSimple *MakeNew(int nstates) const = 0; // Conditions the system being closed virtual void Normalize() = 0; // Sets to zero all probability of leaving the set of ind virtual void Restrict(const std::vector &ind) = 0; // Changes the indexing. // newn is the new number of states // (let n be the old number of states) // ind is a list of size n // each element of ind is a list of size s (s is a constant) // each of these sublists is a set of states that were // indistinguishable from the original dynamics point of view virtual void Expand(const std::vector > &ind, int newn) = 0; // Returns the conditional distribution of going from t0 to t1 virtual RVCondSimple *Cond(double t0, double t1) const = 0; // same, but with the dynamics constrainted to the subsystem // described by ind virtual RVCondSimple *CondRestrict(double t0, double t1, const std::vector &ind) const = 0; // Returns the density of instantaneous transitioning virtual RVCondSimple *Cond(double t) const = 0; // Same, but constrained (see same method in Dynamics) virtual RVCondSimple *CondRestrict(double t, const std::vector &fromind, const std::vector &toind, bool transition=true) const = 0; // returns a suitable sufficient statistics object virtual SS *BlankSS() const = 0; // the amalgamation operation... assumes that indices // already agree... virtual void Mult(const DynSimple *x) = 0; // add interval to ss virtual void AddSS(int x, double t0, double t1, SS *ss, double w=1.0) const = 0; // add transition to ss virtual void AddTransSS(int x1, int x2, double t, SS *ss, double w=1.0) const = 0; // adds expected ss (assumes that alpha and beta have the // same domain and that domain is the one to which the data // restricts transitions) virtual void AddExpSS(const RVSimple *alpha, const RVSimple *beta, double t0, double deltat, SS *ss, double w=1.0) const = 0; // adds a transition from x1 to x2 virtual void AddExpTransSS(const RVSimple *x1, const RVSimple *x2, double t, SS *ss, double w=1.0) const = 0; // Add the sufficient statistics in "toadd" to those in "ss" // However, using "mapping" to map state-indexes for toadd to // those to ss. "ss" should be for "this" while "toadd" should be // for "dyn." Currently dyn needs to be of type MarkovSimple. // mapping[i] is a list of the states from toadd/dyn that correspond // to state i for ss/this. // [The code might also clarify -- see .cc file.] virtual void AddSS(const SS *toadd, const DynSimple *dyn, const std::vector > &mapping, SS *ss, double w=1.0) const = 0; // sample the next event - time: newt, state index: newind virtual void SampleNextEvent(int ind, double t, double &newt, int &newind, Random &rand=randomizer) const = 0; // sets parameters from ML estimate virtual void Maximize(const SS *ss) = 0; // Randomize the parameters virtual void Scramble(double a=1.0, double b=1.0, double alpha=1.0, double degree=1.0, Random &rand=randomizer) = 0; // calculate the log-likelihood of the sufficient SS virtual double LLH(const SS *ss) const = 0; // Returns the structure search score where parentCard is // the number of parents this node has (this object represents // the process for just one parent instantiation, presumably) // numTrans and amtTime are the BDe prior parameters virtual double GetScore(int parentCard, double numTrans, double amtTime, const SS* ss) const = 0; }; } // end of ctbn namespace #endif ctbnrle.1.0.2/hdr/context.h0000644000464200007770000002004011344302770015646 0ustar csheltoncsprofs/* 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 CTBNRLE_CONTEXT_H #define CTBNRLE_CONTEXT_H #include #include #include #include namespace ctbn { // The two classes here (context and instantiation) are coupled and // represent the event space of a set of random variables and // an assignment to the same, respectively class Instantiation; // Represents the event space of a set of random variables // That is, it represents the cross product of a set of dicrete sets. // Each event space (for a random variable) is assumed to be the // integers from 0 through n-1 (for a variable with n possible values). // The variables are also indexed by integers (varid), which need not // be consecutive. class Context { public: Context(); // assumes null context enum setop {UNION, INTERSECTION, DIFFERENCE}; Context(const Context &c1, const Context &c2, setop op=UNION); // creates a context of the variables that are difference between // the two instantiations (variables that are "unmentioned" // in one or the other are not included) Context(const Instantiation &i1, const Instantiation &i2); Context(std::istream &is); virtual ~Context(); // Maps an assignment to all variables onto an integer from // 0 to Size(). If Instantiation is missing some values, returns -1 int Index(const Instantiation &ind) const; // Reverse mapping from above Instantiation Index(int i) const; // assumes the varid is not already there... // if not true, future behavior of the object is undefined void AddVar(int varid, int cardinality) { size*=cardinality; card[varid]=cardinality; } // Lists the set of variable IDs std::vector VarList() const; // Total number of possible assignments to these variables // (product of the cardinalities of each constituent variable) int Size() const { return size; } int NumVars() const { return card.size(); } // smallest varid int MinVar() const { return card.empty() ? -1 : card.begin()->first; } // largest varid int MaxVar() const { return card.empty() ? -1 : card.end()->first; } // does this varid exist in this context? bool HasId(int varid) const { return card.find(varid) != card.end(); } // total number of values varid can take on (from 0 - (n-1)) int Cardinality(int varid) const { std::map::const_iterator l = card.find(varid); return l != card.end() ? l->second : 0; } // returns, in l, all of the indexes (like those that would be found // from calling Index()) that are consistant with the (partial) // assignment v void ConsistentIndexes(std::vector &l, const Instantiation &v) const; // Like above, but returns all of the indexes that are *not* // consistant. void InconsistentIndexes(std::vector &l, const Instantiation &v) const; virtual std::ostream &Save(std::ostream &os) const; virtual std::istream &Load(std::istream &is); // returns whether c is a subset (not proper) of *this bool IsSubset(const Context &c) const; // Does *this and c share any variables in common? bool IsOverlap(const Context &c) const; protected: std::map card; // maps id to max value int size; // These "complete" the calculation (and are reuseable in other // similar calculations) void CConsistentIndexes(std::vector &l, const std::vector &maxind, const std::vector &incind, int startindex) const; void CInconsistentIndexes(std::vector &l, const std::vector &lneg) const; }; // forms union: inline Context operator+(const Context &c1, const Context &c2) { return Context(c1,c2,Context::UNION); } // forms difference inline Context operator-(const Context &c1, const Context &c2) { return Context(c1,c2,Context::DIFFERENCE); } // forms intersection inline Context operator/(const Context &c1, const Context &c2) { return Context(c1,c2,Context::INTERSECTION); } // This class is a context *plus* a, possibly partial, assignment // to the variables in the context // -1 is used to indicate a non-assigned variable class Instantiation : public Context { friend class Context; public: Instantiation(); // assumes null context Instantiation(const Context &c, int defaultval=-1); Instantiation(const Context &c, const Instantiation &v); virtual ~Instantiation(); // returns the value of the variable varid // (from 0 to Cardinality(varid)-1), or -1 if the variable is unassigned int Value(int varid) const { std::map::const_iterator l = vals.find(varid); return l != vals.end() ? l->second : -1; } // the following three Inc methods increment the instantiation // they return false iff the increment has "wrapped" // increments only varid -- simple bool Inc(int varid); // increments all variables lexographically bool Inc(); // increments only those variables in *this that are also in c // (again, lexographically) bool Inc(const Context &c); // val needs to be either -1 (to indicate the value is not assigned) // or in [0,n-1] where n is the cardinality of varid bool SetVal(int varid, int val) { // returns whether it was added std::map::iterator l = vals.find(varid); if (l != vals.end()) { ChangeVar(varid,l->second,val); l->second = val; return true; } else return false; } // Set the value of variables mentioned in val. If add is // true, then variables mentioned in val but not in *this are // added to *this. If add is false, they are not. void SetVal(const Instantiation &val, bool add=false) { for(std::map::const_iterator l = val.vals.begin(), ll = val.card.begin(); l!=val.vals.end();++l,++ll) if (!SetVal(l->first,l->second) && add) AddVar(l->first,ll->second,l->second); } // Set every variable to value val void SetAllVal(int val); // Add variable varid with cardinality cardinality to the set // initialize the value to val (-1 = unassigned) void AddVar(int varid, int cardinality, int val=-1); virtual std::ostream &Save(std::ostream &os) const; virtual std::istream &Load(std::istream &is); // Returns the index (see Context::Index()) for the current // instantiation (like calling Index(*this), but faster) // Returns -1 if any variables are unassigned. int Index() const { return numunknown ? -1 : index; } // Makes the index equal to i. i may *not* be -1 void SetIndex(int i); // used with ZeroOut from IMatrix (and ZeroOut above) // See same method names from Context void InconsistentIndexes(std::vector &l) const; void ConsistentIndexes(std::vector &l) const; // Returns a Context of all the variables that are assigned Context KnownVars() const; // Same for variables that are not assigned Context MissingVars() const; int NumMissingVars() const { return numunknown; } void PrintVal(std::ostream &os) const; //debug use only protected: void ChangeVar(int varid, int oldval, int newval) { if (oldval==-1) { numunknown--; oldval = 0; } if (newval==-1) { numunknown++; newval = 0; } index += indexinc[varid]*(newval-oldval); } void Recalc(); std::map vals; std::map indexinc; int index, numunknown; }; std::ostream& operator<< (std::ostream& os, const Instantiation& inst); std::istream& operator>> (std::istream& is, Instantiation& inst); std::ostream& operator<< (std::ostream& os, const Context& con); std::istream& operator>> (std::istream& is, Context& con); } // end of ctbn namespace #endif ctbnrle.1.0.2/hdr/multisimple.h0000644000464200007770000002106411344302770016535 0ustar csheltoncsprofs/* 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 CTBNRLE_MULTISIMPLE_H #define CTBNRLE_MULTISIMPLE_H #include "rvsimple.h" #include "matrix.h" namespace ctbn { // Four different versions of a RVSimple (see rvsimple.h) // Each one stores the multinomial distribution differently // However, all assume a general multinomial form (no parameter tieing) class MultiSimple; class MultiLogSimple; class MultiZSimple; class SparseMultiZSimple; // This is the Sufficient Statistics for any of the RVSimple // subclasses in this file class MultiSimpleSS : public SS { SOBJCLASSDECL(MultiSimpleSS) public: friend class MultiSimple; friend class MultiLogSimple; friend class MultiZSimple; friend class SparseMultiZSimple; friend class RVSimple; public: MultiSimpleSS(int n=1); MultiSimpleSS(std::istream &is); MultiSimpleSS(const MultiSimpleSS &msss); virtual ~MultiSimpleSS(); virtual MultiSimpleSS *Clone() const; virtual void Load(std::istream &is); virtual void Save(std::ostream &os) const; virtual void Scale(double w); virtual void AddSS(const SS* nss, double w=1.0); private: vectr c; // vector of counts }; // This class stores the probabilties directly (as theta, below) class MultiSimple : public RVSimple { SOBJCLASSDECL(MultiSimple) public: MultiSimple(int n=1); MultiSimple(std::istream &is); virtual ~MultiSimple(); virtual MultiSimple *Clone() const; virtual void Load(std::istream &is); virtual void Save(std::ostream &os) const; virtual double Normalize(); virtual double Prob(int ind, bool log=false) const; virtual double Sum(bool log=false) const; virtual void Restrict(const std::vector &ind); virtual void Reindex(const std::vector > &ind); virtual void MultBy(const RVSimple *x); virtual void Add(const RVSimple *x); virtual void Add(const RVSimple *x, double w); virtual void Mult(double x); virtual MultiSimpleSS *BlankSS() const; virtual void AddSS(int x, SS *ss, double w=1.0) const; virtual void AddExpSS(SS *ss, double w=1.0) const; virtual void AddSS(const SS *toadd, const RVSimple *rvs, const std::vector > &mapping, SS *ss, double w=1.0) const; virtual int Sample(Random &rand = randomizer) const; virtual void Maximize(const SS *ss); virtual void GetDist(vectr &d, double &logfactor) const; virtual void SetDist(const vectr &d, double logfactor=0.0); virtual void MakeUniform(); virtual void Scramble(double alpha=1.0, double degree=1.0, Random &rand = randomizer); virtual double LLH(const SS *ss) const; protected: vectr theta; // parameters of the multinomial distribution }; // same as above, but log of parameters is stored class MultiLogSimple : public RVSimple { SOBJCLASSDECL(MultiLogSimple) public: MultiLogSimple(int n=1); MultiLogSimple(std::istream &is); virtual ~MultiLogSimple(); virtual MultiLogSimple *Clone() const; virtual void Load(std::istream &is); virtual void Save(std::ostream &os) const; virtual double Normalize(); virtual double Prob(int ind, bool log=false) const; virtual double Sum(bool log=false) const; virtual void Restrict(const std::vector &ind); virtual void Reindex(const std::vector > &ind); virtual void MultBy(const RVSimple *x); virtual void Add(const RVSimple *x); virtual void Add(const RVSimple *x, double w); virtual void Mult(double x); virtual MultiSimpleSS *BlankSS() const; virtual void AddSS(int x, SS *ss, double w=1.0) const; virtual void AddExpSS(SS *ss, double w=1.0) const; virtual void AddSS(const SS *toadd, const RVSimple *rvs, const std::vector > &mapping, SS *ss, double w=1.0) const; virtual int Sample(Random &rand = randomizer) const; virtual void Maximize(const SS *ss); virtual void GetDist(vectr &d, double &logfactor) const; virtual void SetDist(const vectr &d, double logfactor=0.0); virtual void MakeUniform(); virtual void Scramble(double alpha=1.0, double degree=1.0, Random &rand = randomizer); virtual double LLH(const SS *ss) const; protected: vectr logtheta; // parameters of the multinomial distribution }; // Same as MultiSimple, but normalization constant is kept separately // (in log-space) class MultiZSimple : public RVSimple { SOBJCLASSDECL(MultiZSimple) public: MultiZSimple(int n=1); MultiZSimple(const vectr &dist, double logfactor=0.0); MultiZSimple(std::istream &is); virtual ~MultiZSimple(); virtual MultiZSimple *Clone() const; virtual void Load(std::istream &is); virtual void Save(std::ostream &os) const; virtual double Normalize(); virtual double Prob(int ind, bool log=false) const; virtual double Sum(bool log=false) const; virtual void Restrict(const std::vector &ind); virtual void Reindex(const std::vector > &ind); virtual void MultBy(const RVSimple *x); virtual void Add(const RVSimple *x); virtual void Add(const RVSimple *x, double w); virtual void Mult(double x); virtual MultiSimpleSS *BlankSS() const; virtual void AddSS(int x, SS *ss, double w=1.0) const; virtual void AddExpSS(SS *ss, double w=1.0) const; virtual void AddSS(const SS *toadd, const RVSimple *rvs, const std::vector > &mapping, SS *ss, double w=1.0) const; virtual int Sample(Random &rand = randomizer) const; virtual void Maximize(const SS *ss); virtual void GetDist(vectr &d, double &logfactor) const; virtual void SetDist(const vectr &d, double logfactor=0.0); virtual void MakeUniform(); virtual void Scramble(double alpha=1.0, double degree=1.0, Random &rand = randomizer); virtual double LLH(const SS *ss) const; protected: void SetZ(); vectr theta; // parameters of the multinomial distribution double logz; // the normalizing factor: true theta = exp(logz)*theta }; // Same as MultiZSimple, but only a sparse number of non-zero elements // are kept class SparseMultiZSimple : public MultiZSimple { SOBJCLASSDECL(SparseMultiZSimple) public: SparseMultiZSimple(int n=1); // indexes need to be in sorted order SparseMultiZSimple(const vectr &dist, const std::vector &indexes, double logfactor=0.0); SparseMultiZSimple(std::istream &is); virtual ~SparseMultiZSimple(); virtual SparseMultiZSimple *Clone() const; virtual void Load(std::istream &is); virtual void Save(std::ostream &os) const; // These work as before: using MultiZSimple::Normalize; using MultiZSimple::Sum; using MultiZSimple::Mult; using MultiZSimple::MultBy; // this one is a bit dangerous // as it assumes the represented indices are the same using MultiZSimple::Add; // as is this one using MultiZSimple::BlankSS; //using MultiZSimple::AddSS; using MultiZSimple::MakeUniform; using MultiZSimple::Scramble; // note that this means that // scramble will not "unzero" zero elements (unlike Maximize) // These are modified: virtual double Prob(int ind, bool log=false) const; virtual void Restrict(const std::vector &ind); virtual void Reindex(const std::vector > &ind); virtual void AddSS(int x, SS *ss, double w=1.0) const; virtual void AddExpSS(SS *ss, double w=1.0) const; virtual void AddSS(const SS *toadd, const RVSimple *rvs, const std::vector > &mapping, SS *ss, double w=1.0) const; virtual int Sample(Random &rand = randomizer) const; virtual void Maximize(const SS *ss); virtual void GetDist(vectr &d, double &logfactor) const; virtual void SetDist(const vectr &d, double logfactor=0.0); virtual void SetSparseDist(const vectr &d, double logfactor=0.0); virtual void GetSparseDist(vectr &d, double &logfactor) const; virtual void SetSparseIndexes(const std::vector &ind); virtual double LLH(const SS *ss) const; inline const std::vector &Indexes() const { return ix; } inline double LogFactor() const { return logz; } inline const vectr &Dist() const { return theta; } protected: std::vector ix; }; } // end of ctbn namespace #endif ctbnrle.1.0.2/hdr/random.h0000644000464200007770000000607711344302770015460 0ustar csheltoncsprofs/* 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 CTBNRLE_RANDOM_H #define CTBNRLE_RANDOM_H #include #include namespace ctbn { // Class for generating (pseduo-)random numbers. Keeps the state of the // generator so that different instantiations can be used for different // parts of the code and be predictable (their pseudo-random draws don't // affect each other) // Class also includes method for drawing from non uniform distributions class Random { public: typedef unsigned long uint32; // just needs to be at least 32 bits Random (uint32=-1); void Reset(uint32 in_seed=-1); // returns [0.0, 1.0) double RandReal(); // same, but only uses 32 bits of randomness (instead of 64) double RandRealFast(); // returns [0, range-1] int RandInt (int range); // returns ~N(0,1) double SampleNormal (void); // returns ~Beta(a,b) where a and b are positive reals double SampleBeta (double a, double b); // returns ~Gamma(a,b) where a and b are positive reals double SampleGamma (double a, double b); /// samples from the Dirichlet joint distribution over thetas // theta ~ theta[0]^(alpha[0]-1) * ... * theta[n]^(alpha[n]-1) void SampleDirichlet(double const *alpha, int n, double *theta); // samples from Exponential Dist for intensity a = 1/mean double SampleExp (double a); //sample from Exponential dist for intensity q, given value is // less than t double SampleTruncateExp(double q, double t); // samples from a set of additive weights (for multi) int SampleAddWeights (const std::vector& additive_weights); // samples from a weighted distribution int SampleMultinomial (const std::vector& weights, double sum=1.0); int SampleMultinomial (const double *weights, int n, double sum=1.0); //sample n samples a time void SampleMultinomial(const std::vector &weights, std::vector &samples, int n); private: double DblGammaLessThanOne(double dblaphla); double DblGammaGreaterThanOne(double dblaphla); uint32 Rand32(); void generateMore(); private: // starting seed number uint32 seed; // state of the generator: uint32 MT[624]; int loc; static const double Conv32Double, Conv64Double; }; extern Random randomizer; } // end of ctbn namespace #endif ctbnrle.1.0.2/hdr/searchqueue.h0000644000464200007770000000515711344302770016510 0ustar csheltoncsprofs/* 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 CTBNRLE_SEARCHQUEUE_H #define CTBNRLE_SEARCHQUEUE_H #include #include namespace ctbn { // A priority queue for the GraphEditSearch algorithm. // All the actions possible in the search at each node in the // search tree are enqueued into this. // There is no dequeue operation. Instead, a remove operation used will // remove all actions associated with a specific child. We can do this // since taking a certain action with a node invalidates the scores // associated with that node. The possible operations also change. class SearchQueue { public: enum searchop {ADD,REMOVE,REVERSE}; // a class to store an action // pretty much just a structure class Action { public: Action(int ch, int pa, searchop op, double g, double gr) : child(ch), parent(pa), oper(op), gain(g), gainrev(gr) {} Action() { } friend std::ostream& operator << (std::ostream& out, const Action &rhs) { return out << "(C=" << rhs.child << ",P=" << rhs.parent << ",O=" << rhs.oper << ",G=" << rhs.gain << ",GR=" << rhs.gainrev << ")"; } int child; int parent; searchop oper; double gain; double gainrev; }; // create a queue with a maximum of maxn actions SearchQueue(int maxn, int nvars); ~SearchQueue(); // place the earliest action in a // return false if there are no actions // Note: this action is NOT removed from the queue bool Head(Action &a); // add an action to the queue void Add(const Action &a); // remove the actions of a specific node void Remove(int child); // empty the entire queue void Clear(); void Print(); void PrintPlaceList(); private: int Heapify(int i, double tempk); Action *heap; std::deque * places; int n; int nv; }; } // end of ctbn namespace #endif ctbnrle.1.0.2/hdr/gibbssampler.h0000644000464200007770000001432111344302770016641 0ustar csheltoncsprofs/* 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 CTBNRLE_GIBBSSAMPLER_H #define CTBNRLE_GIBBSSAMPLER_H #include #include #include "sampler.h" namespace ctbn { class matrix; class MultiSimple; class RV; class MarkovDyn; // GibbsSampler is an implementation of the paper: // Tal El-Hay, Nir Friedman, and Raz Kupferman (2008). "Gibbs Sampling in // Factorized Continuous-Time Markov Processes." // This class will take in an evidence trajectory, a markov process, // the number of samples needed as parameters. // It will first burn in for several iterations (number of burnin_iter is // specified in construction function), then generate a set of sampled // trajectories. It is an approximate inference method used for learning // and inference on CTBNs. // Sample usage: // // Markov ctbn; // Trajectory evidence; // GibbsSampler sampler(&ctbn, &evidence); // Trajectory sample_traj; // sampler.SampleTrajectory(sample_traj); class GibbsSampler : public Sampler { public: // the caller of the function owns the process and evidence pointers // but they must remain valid for the life of the GibbsSampler object GibbsSampler(const Process *process, const Trajectory *evidence, int burnin); virtual ~GibbsSampler() { if (init_traj) delete init_traj; } // inherited interface functions from base class virtual Sampler *Clone() const { return new GibbsSampler(p, evid, numBurninIter); } // the caller of the function owns the process pointer // but it must remain valid for the life of the GibbsSampler object virtual void SetProcess(const Process *process) { p = process; Initialize(); } // the caller of the function owns the evidence pointer // but it must remain valid for the life of the GibbsSampler object virtual void SetTrajectory(const Trajectory *evidence) { evid = evidence; } // the caller of the function owns the m pointer // but it must remain valid for the life of the GibbsSampler object virtual void SetMethod(const VarSample *m) { method = m; } virtual void SampleTrajectories(std::vector &traj, std::vector &w, int numsamples, Random &rand = randomizer); protected: // specific public interfaces for GibbsSampler // burn in, do numBurninIter steps of markov transitions void BurnIn(Random &rand = randomizer) const; // one step in the markov chain state transition; virtual void Next(int num_iter = 1, Random &rand = randomizer) const; // get the current trajectory const Trajectory &Get() const { return tr; } // get the start point - the initial trajectory const Trajectory &GetInitTraj() const { return *init_traj; } // set the initial trajectory void SetInitTraj(const Trajectory &t); // delete the initial trajetcory void ClearInitTraj(); int numBurninIter; // # of burn in iterations Context context; std::vector own_var_list; const RV *p0; const MarkovDyn *md; // mutable so that we can access parents[v] // etc. easily in const member fuctions. // each variable's parents and children mutable std::map parents, children; // each variable's markovdyn mutable std::map mds; // initial trajectory mutable Trajectory *init_traj; // the current sample mutable Trajectory tr; // inherited functions from base class, currently do nothing double SampleInitial(Instantiation &inst, Random &rand=randomizer){return 0.0;}; double SampleDyn(Trajectory &traj, Random &rand=randomizer){return 0.0;}; // precompute context, p0, dynamics, parents/children and markovdyn // for each variable; void Initialize(); // returns true if c has variable in v's Markov blanket bool IsInBlanket(const Context &c, int v) const; // Find the {tau} splitting points (timing, full instantiation) for // transitioned variables who are in v's Markov blanket void GetTau(const Trajectory &t, double begin_time, double end_time, int v, std::vector &tau, std::vector &inst) const; // Resample the entire trajectory of v given all the other variables' // full trajectory. virtual void SampleVariable(int v, Random &rand = randomizer) const; // Sample initial trajectory that agrees with evidence *evid. virtual void SampleInitialTrajectory(Random &rand = randomizer) const; private: // find evidence on v, and split into "free" intervals for v // there is no v's evidence inside each [t0, tT] interval void SplitFreeIntervals(int v, std::vector &x0, std::vector &xT, std::vector &t0, std::vector &tT, const Instantiation &inst0, Random &rand = randomizer) const; // sampler v's trajectory for interval [t0, tT]; void SampleVariableInterval(int v, int x0, int xT, double t0, double tT, Random &rand = randomizer) const; // equivalent to Qss; matrix GetRMatrix(int v, const Instantiation &inst) const; // equivalent to Qss'; matrix GetTMatrix(int v, const Instantiation &oldinst, const Instantiation &newinst) const; // search the exact time points between t0 and t1 using binary search; double SearchPoint(double ksi, int x0, const matrix &R, double log_p_future0, double log_p_past, MultiSimple p_future, double log_z, double t0, double t1, double epsilon = 1e-6) const; // sample the initial bn conditioned on evindence "partial" Instantiation SampleConditionalRv(const Instantiation &partial, int v, Random &rand = randomizer) const; }; } // end of ctbn namespace #endif ctbnrle.1.0.2/hdr/markov.h0000644000464200007770000000577011344302770015476 0ustar csheltoncsprofs/* 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 CTBNRLE_MARKOV_H #define CTBNRLE_MARKOV_H #include "process.h" #include "dynamics.h" #include "rv.h" #include "ss.h" #include "structure.h" #include "extramath.h" namespace ctbn { class ExactMarkovInf; // See process.h for a full description of the method's uses class Markov : public Process { friend class ExactMarkovInf; friend class SamplingInf; SOBJCLASSDECL(Markov) public: // this object now "owns" these two pointers Markov(RV *startdist, Dynamics *dyn); Markov(std::istream &is); Markov(const Markov &m); Markov &operator=(const Markov &m); virtual ~Markov(); virtual Markov *Clone() const; virtual void Mult(const Process *p); virtual SS *BlankSS() const; virtual void Maximize(const SS *ss); virtual void Load(std::istream &is); virtual void Save(std::ostream &os) const; virtual void Sample(Trajectory &tr, Random &rand=randomizer) const; const Dynamics *GetDynamics() const { return d; } const RV *GetStartDist() const {return p0;} double LLH(const Trajectory &tr, double w, double &p0llh, double &dynllh) const; double LLH(const std::vector &tr, const std::vector &w) const; virtual double LLH(const SS *ss) const; SS* SuffStats(const std::vector &tr, const std::vector &w) const; virtual void Scramble(double a=1.0, double b=1.0, double alpha=1.0, double degree=1.0, Random &rand=randomizer); virtual double GetScore(double numTrans, double amtTime, const SS* ss) const; protected: RV *p0; Dynamics *d; }; class MarkovSS : public SS { friend class Markov; friend class ExactMarkovInf; friend class SamplingInf; SOBJCLASSDECL(MarkovSS) public: MarkovSS(std::istream &is); MarkovSS(SS *rvss, SS *dynss); MarkovSS(const MarkovSS &mss); MarkovSS &operator=(const MarkovSS &mss); virtual ~MarkovSS(); virtual MarkovSS *Clone() const; virtual void Load(std::istream &is); virtual void Save(std::ostream &os) const; virtual void Scale(double w); virtual void AddSS(const SS* nss, double w=1.0); virtual double NodeSS(int id, int val1, int val2, const Instantiation &cond) const; private: SS *p0ss,*dss; }; } // end of ctbn namespace #endif ctbnrle.1.0.2/hdr/multirv.h0000644000464200007770000000436511344302770015700 0ustar csheltoncsprofs/* 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 CTBNRLE_MULTIRV_H #define CTBNRLE_MULTIRV_H #include "rvcomp.h" #include "rvsimple.h" #include "multisimple.h" namespace ctbn { // just a wrapper to make things simpler // A general multinomial random variable can be described as // a composition (a set) of RVSimple objects, one for each value // of the conditioning set. Each RVSimple is just a standard // multinomial random variable class MultiRV : public RVComp { SOBJCLASSDECL(MultiRV) public: MultiRV(const Context &var, const Context &cvar); MultiRV(std::istream &is); virtual ~MultiRV(); virtual MultiRV *Clone() const; MultiZSimple &operator[](int i) { RVCondSimpleComp *ptr = dynamic_cast *>(Base()); return ptr->operator[](i); } const MultiZSimple &operator[](int i) const { const RVCondSimpleComp *ptr = dynamic_cast *>(Base()); return ptr->operator[](i); } MultiZSimple &operator[](const Instantiation &i) { RVCondSimpleComp *ptr = dynamic_cast *>(Base()); return ptr->operator[](CondDomain().Index(i)); } const MultiZSimple &operator[](const Instantiation &i) const { const RVCondSimpleComp *ptr = dynamic_cast *>(Base()); return ptr->operator[](CondDomain().Index(i)); } }; } // end of ctbn namespace #endif ctbnrle.1.0.2/hdr/brutestructuresearch.h0000644000464200007770000000270411344302770020461 0ustar csheltoncsprofs/* 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 CTBNRLE_BRUTESTRUCTURESEARCH_H #define CTBNRLE_BRUTESTRUCTURESEARCH_H #include "structuresearch.h" namespace ctbn { // Class to perform exhaustive "brute-force" structure search. class BruteStructureSearch : public StructureSearch { public: // The FamScore pointer is not owned by this class. It is owned by // the caller of the constructor. BruteStructureSearch(const Context &c, FamScore* fs, int mp = 1); ~BruteStructureSearch(); virtual Structure LearnStructure(); void SetMaxParents(int mp) {maxParents = mp;} protected: int maxParents; }; } // end of ctbn namespace #endif ctbnrle.1.0.2/hdr/dynamics.h0000644000464200007770000001551111344302770016000 0ustar csheltoncsprofs/* 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 CTBNRLE_DYNAMICS_H #define CTBNRLE_DYNAMICS_H #include "context.h" #include "trajectory.h" #include "rvsimple.h" #include "rv.h" namespace ctbn { // Dynamics is a continuous-time semi-Markov process except for the // starting distribution // The domain is explicit class Dynamics : public StreamObj { public: // var is the set of variables (state space of the process) // cvar is the set of variables on which the process is // conditioned. Let cvar be a null context (just create a // context without any arguments to the constructor) if this // process is not dependent on any others. Dynamics(const Context &var, const Context &cvar); // load from file.. Dynamics(std::istream &is); // destructor virtual ~Dynamics(); // load and save... virtual void Load(std::istream &is); virtual void Save(std::ostream &os) const; // virtual copy constructor virtual Dynamics *Clone() const = 0; // Returns the state space const Context &Domain() const { return v; } // Returns the conditioning variables const Context &CondDomain() const {return cv; } // Cause the dynamics to be "closed" (no probability of leaving // the system) virtual void Normalize() = 0; // Add evidence that the system stays within the states // defined in x virtual void Restrict(const Instantiation &x) = 0; // the version here just sets up the contexts (v and cv) virtual void Mult(const Dynamics *x); // The next two return a conditional random variable that // describes the distribution at one time point conditioned on // a previous time point. They are returned as RVCondSimple // because the domains are implied // the implied domain for RVCondSimple is the Context // of this dynamic process // x gives the values for the conditional context // as well as any subsystem restrictions for the process's context virtual RVCondSimple *Cond(double t0, double t1, const Instantiation &x) const = 0; // same, but instantaneous intensity of transitioning // if transition==false, this doesn't correspond to a // transition, but rather just a change of support virtual RVCondSimple *Cond(double t, const Instantiation &from, const Instantiation &to, bool transition=true) const = 0; // Return a blank sufficient statistics object suitable for // this dynamics virtual SS *BlankSS() const = 0; // Adds sufficient statistics for being in state x from t0 // to t0+deltat by adjusting ss. Addition can be weighted by w virtual void AddSS(const Instantiation &x, double t0, double deltat, SS *ss, double w=1.0) const = 0; // Same, but adds ss for a transition from x1 to x2 at time t virtual void AddTransSS(const Instantiation &x1, const Instantiation &x2, double t, SS *ss, double w=1.0) const = 0; // Adds expected sufficient statistics for the interval t0 to // to+deltat if alpha is the distribution at t0 given evidence // before t0 and beta is the distribution at t0+deltat given // evidence after t0+deltat. virtual void AddExpSS(const RV *alpha, const RV *beta, double t0, double deltat, SS *ss, double w=1.0) const = 0; // Same as above, but for a transition at t. x1 is the // distribution just before the transition (conditioned on evidence // before t) and x2 is the same just after t (conditioned on // evidence after t) // changevar is the variable that changed value virtual void AddExpTransSS(const RV *x1, const RV *x2, const Context &changevar, double t, SS *ss, double w=1.0) const = 0; // These are the same as above, but where the context is implied // and the conditioning variables (cvar) takes on a known value (not // a distribution over them) -- either its index or a full // instantiation is supplied... virtual void AddExpSS(const Instantiation &condi, const RVSimple *alpha, const RVSimple *beta, double t0, double deltat, SS *ss, double w=1.0) const = 0; virtual void AddExpSS(int condi, const RVSimple *alpha, const RVSimple *beta, double t0, double deltat, SS *ss, double w=1.0) const = 0; virtual void AddExpTransSS(const Instantiation &condi, const RVSimple *x1, const RVSimple *x2, const Context &changevar, double t, SS *ss, double w=1.0) const = 0; virtual void AddExpTransSS(int condi, const RVSimple *x1, const RVSimple *x2, const Context &changevar, double t, SS *ss, double w=1.0) const = 0; // This adds the sufficient statistics of a different process // to this process's sufficient statistics virtual void AddSS(const SS *toadd, const Dynamics *dyn, SS *ss, double w=1.0) const = 0; // i is the assignment at t. nextt becomes the next time when // a transition occurs. nexti is the next assignment // assumes that the context of nexti equals Domain virtual void SampleNextEvent(const Instantiation &i, double t, double &nextt, Instantiation &nexti, Random &rand=randomizer) const = 0; // completes the trajectory, starting at t and going until // tr.EndTime() // the method implemented here repeatedly calls SampleNextEvent // override it if you can do something more efficient virtual void SampleTrajectory(Trajectory &tr, double t, Random &rand=randomizer) const; // perform ML by adjusting the parameters, using the sufficient // statistics in ss (which you may assume are of the correct type) virtual void Maximize(const SS *ss) = 0; // Randomize the parameters virtual void Scramble(double a=1.0, double b=1.0, double alpha=1.0, double degree=1.0, Random &rand=randomizer) = 0; // return the log-likelihood from the sufficient statistics ss virtual double LLH(const SS *ss) const = 0; // returnn the sufficient statistics from the set of trajectories // tr, each weighted by the weight w (w is *not* in log space) SS* SuffStats(const std::vector &tr, const std::vector &w) const; // returns a structure search score based on ss // numTrans and amtTime are the Bayesian prior parameters // for a BDe prior virtual double GetScore(double numTrans, double amtTime, const SS *ss) const = 0; protected: Context v,cv; }; } // end of ctbn namespace #endif ctbnrle.1.0.2/hdr/grapheditsearch.h0000644000464200007770000000424211344302770017325 0ustar csheltoncsprofs/* 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 CTBNRLE_GRAPHEDITSEARCH_H #define CTBNRLE_GRAPHEDITSEARCH_H #include "structuresearch.h" #include "markovdyn.h" #include "searchqueue.h" #include #include namespace ctbn { // Performs the graph edit search algorithm (a greedy search) // The user has the option to disallow cycles (generally for BNs) class GraphEditSearch : public StructureSearch { public: // The FamScore pointer is owned by the caller of the constructor // but must remain valid for the lifetime of the GraphEditSearch object GraphEditSearch(const Context &c, FamScore* fs, bool noCyc=false); ~GraphEditSearch(); virtual Structure LearnStructure(); virtual void SetStructure(const Structure& s); protected: // Called by constructor to setup the nodes used to score // and determine initial scores. virtual void Initialize(); // Enqueues the operators performed by the variable of nodeid i virtual void EnqueueOps(int i); // Checks if adding nodeid j to the parent set of i produces a cycle virtual bool ProducesCycle(int i, int j); // Keeps the current scores std::vector scores; // Priority queue designed for this algorithm // This class owns this pointer and creates the object for it upon // construction. SearchQueue* sQ; bool noCycles; bool learnCalled; }; } // end of ctbn namespace #endif ctbnrle.1.0.2/hdr/sampler.h0000644000464200007770000000463111344302770015635 0ustar csheltoncsprofs/* 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 CTBNRLE_SAMPLER_H #define CTBNRLE_SAMPLER_H #include "process.h" #include "trajectory.h" #include "random.h" #include "varsample.h" #include "structure.h" namespace ctbn { //Sampler abstract class for sampling method for dynamic process class Sampler{ public: virtual ~Sampler(); virtual Sampler *Clone() const = 0; //set the dynamic process, the caller of the function //owns the pointer of the process variable, // but it must remain valid for the lifetime of this object virtual void SetProcess(const Process *pr)=0; //set the evidence, the caller of the function owns the pointer // but it must remain valid for the lifetime of this object virtual void SetTrajectory(const Trajectory *traj); //set the sampling method, the caller of the function owns the pointer // but it must remain valid for the lifetime of this object virtual void SetMethod(const VarSample *m); //sampling trajectories given the evidence virtual void SampleTrajectories(std::vector &tr, std::vector &w, int numsamples, Random &rand=randomizer) = 0; protected: //sample the initial values virtual double SampleInitial(Instantiation &inst, Random &rand=randomizer) = 0; //sample the dynamics (complete the trajectory assuming the initial // state has been completed) virtual double SampleDyn(Trajectory &tr, Random &rand=randomizer) = 0; //The class does not own these pointers const Process *p; const Trajectory *evid; const VarSample *method; double begintime; double endtime; std::vector times; }; } // end of ctbn namespace #endif ctbnrle.1.0.2/hdr/samplinginf.h0000644000464200007770000000511111344302770016473 0ustar csheltoncsprofs/* 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 CTBNRLE_SAMPLINGINF_H #define CTBNRLE_SAMPLINGINF_H #include "inference.h" #include "sampler.h" #include "trajectory.h" #include "context.h" #include "markov.h" #include "trajectory.h" #include "ss.h" #include namespace ctbn { // See inference.h for a description of the methods class SamplingInf : public Inference{ public: SamplingInf(); virtual ~SamplingInf(); virtual SamplingInf *Clone() const; //The caller of the function owns the pointers // (but they must remain valid for the lifetime of the object) virtual void SetProcess(const Process *pr); virtual void SetTrajectory(const Trajectory *tr); virtual void SetSampler(const Sampler *s); void SetNumSample(int n); void SetTimeLimit(double t, bool isgibbs=false); virtual double Filter(const Instantiation &x, double t, bool log=false); virtual double Smooth(const Instantiation &x, double t, bool log=false); virtual double Prob(double t, bool log=false); //the caller of the function own the pointer virtual void AddExpSuffStats(SS *ss, double w=1.0); //The caller of the function own the pointers virtual void AddExpSuffStats(const Dynamics *dyn, SS *ss, double w=1.0); //The caller of the function own the pointers virtual void AddExpSuffStats(const RV *p0, SS *ss, double w=1.0); //double LLH(const std::vector &tr, // const std::vector &w) const; double CalcQuery(QueryCalculator &calc); protected: void Sample(); void CleanSamples(); const Markov *p; //the class does not own this pointer Sampler *sampler; //SamplingInf own this pointer int numofsamples; double timelimit; bool isgibbs; std::vector traj; std::vector weights; }; } // end of ctbn namespace #endif ctbnrle.1.0.2/hdr/clique.h0000644000464200007770000001022211344302770015445 0ustar csheltoncsprofs/* 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 CTBNRLE_CLIQUE_H_ #define CTBNRLE_CLIQUE_H_ #include "bn.h" #include "markov.h" #include "dynamics.h" #include "context.h" #include "ctbndyn.h" #include "markovdyn.h" #include "streamextra.h" #include "markov.h" #include #include #include #include #include #include #include #include #include "random.h" namespace ctbn { // Class to build a clique tree from a set of factors, // either defined from a BN, a CTBNDyn, or both (Markov process) class CliqueTree: public StreamObj{ public: CliqueTree(); //build the clique tree for a markov (both CTBNDyn and BN factors) void BuildClique(const Markov &process, Random &rand = randomizer); //build the clique tree for a CTBNDyn // caller retains ownership of dyn void BuildClique(const CTBNDyn *dyn, Random &rand = randomizer); //build the clique tree for a BN // caller retains ownership of bn void BuildClique(const BN *bn, Random &rand = randomizer); //print the clique tree void CliquePrint(std::ostream &os); //save the current state virtual void Save(std::ostream &os) const; //loading the value into the class so it will have all the data virtual void Load(std::istream &is); //for testing: //if true is passed in random number generator is ignored //and factors are processed in a deterministic order void IgnoreRand(bool ignore) { unit_test = ignore; } //change the number of random restarts void SetNumRestarts(int num) { numrandrestarts = num; } // stores a node in the clique tree class Node { public: Context vars; // variables that are part of this factor std::vector adj; // index of adjacent nodes in tree }; //return the clique tree std::vector ReturnCliques() const; virtual ~CliqueTree(); private: //all the private function with pointers own its pointers class CliqueNode { friend class CliqueTree; public: CliqueNode(); void AddVar(const int &); void print (std::ostream &); ~CliqueNode(); private: std::set connected; std::vector next; CliqueNode * prev; }; void CliqueTrees(Random &rand); void CliqueTrees(int); void CliqueNodePrint(CliqueNode *, int, std::ostream &); static void Destroy(CliqueNode *); void ReturnClique(CliqueNode *, int, std::vector &) const; void InsertCliqueNode(CliqueNode *, int, std::vector &) const; Context Clique2Context(CliqueNode *) const; void CliqueSave(std::ostream &os, CliqueNode * print, int size) const; void Eliminate(); bool EliminateDups(CliqueNode *); void EliminatePrev(CliqueNode *); static void RemoveElem(std::vector &, int); void prints(std::set , std::ostream &) const; double MaxSize() const; double MaxSize(CliqueNode *) const; void Dequeue(std::vector remove, CliqueNode * newitem); void BuildIt(Random &); void AddFromBN(const BN *); void AddFromCTBNDyn(const CTBNDyn *); std::vector data; std::vector save_data; std::vector clique_builded; //those pointers on bottom are own by this class CliqueNode * Head_compare; bool exist; int numrandrestarts; CliqueNode * Head; bool unit_test; int VarSize(int varid) const; void AddVarSize(int varid, int size); // maps varid -> size std::map context_size; }; } // end of ctbn namespace #endif /* CLIQUE_H_ */ ctbnrle.1.0.2/hdr/extramath.h0000644000464200007770000000243411344302770016166 0ustar csheltoncsprofs/* 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 CTBNRLE_EXTRAMATH_H #define CTBNRLE_EXTRAMATH_H #include namespace ctbn { double lngamma(double x); // Generates a list of combinations std::vector > combo(int nItems, int nChoose); // Returns the number of elements not in the intersection of the two lists // (used for Hamming Distance) int listDiff(const std::vector &listA, const std::vector &listB); } // end of ctbn namespace #endif ctbnrle.1.0.2/hdr/exactmarkovinf.h0000644000464200007770000000623611344302770017216 0ustar csheltoncsprofs/* 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 CTBNRLE_EXACTMARKOVINF_H #define CTBNRLE_EXACTMARKOVINF_H #include "markov.h" #include "ctbndyn.h" #include "dyncomp.h" #include "bn.h" #include "rvcomp.h" #include "inference.h" #include namespace ctbn { // This class performs exact inference by using the conditional distributions // supplied by the process's (assumed to be a Markov process) dynamics's // method "Cond." For compact structures (like a CTBN) this will probably // require generation of the full "flattened" dynamics which is exponentially // (in the number of variables) large. However, for some processes (like a // fully factored process), it might be possible to return a conditional // object that can represent this more compactly. // // The dynamics's "cond" methods should return RVCondSimple objects that // are compatible with the RVSimple object's "MakeSimple" method (in that // the RVCondSimple can accept the output of the MakeSimple method). class ExactMarkovInf : public Inference { public: ExactMarkovInf(); virtual ~ExactMarkovInf(); virtual ExactMarkovInf *Clone() const; // See inference.h for ownership of p and tr in the following functions virtual void SetProcess(const Process *p); virtual void SetTrajectory(const Trajectory *tr); virtual double Filter(const Instantiation &x, double t, bool log=false); virtual double Smooth(const Instantiation &x, double t, bool log=false); virtual double Prob(double t, bool log=false); virtual void AddExpSuffStats(SS *ss, double w=1.0); virtual void AddExpSuffStats(const Dynamics *dyn, SS *ss, double w=1.0); virtual void AddExpSuffStats(const RV *p0, SS *ss, double w=1.0); virtual void printdist(std::ostream &os) const {p->Save(os);}; double CalcQuery(QueryCalculator &calc); private: double FilterReal(const Instantiation &x, double t, bool log=false, bool normalize=true); const Markov *p; const Trajectory *tr; void ClearCache(); // make up until t void MakeCache(); void GetProp(); void AlphaPass(); void BetaPass(); // the cache: std::vector times; std::vector alpha; std::vector beta; std::vector prop; std::vector condi; std::vector transtype; std::vector changevars; // Cache the joint SS SS* jointP0SS; SS* jointDSS; }; } // end of ctbn namespace #endif ctbnrle.1.0.2/hdr/expmethod.h0000644000464200007770000000510111344302770016160 0ustar csheltoncsprofs/* 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 CTBNRLE_EXPMETHOD_H #define CTBNRLE_EXPMETHOD_H #include "varsample.h" namespace ctbn { //importance sampling method for CTBN // see Fan & Shelton 2008 class ExpMethod : public VarSample { public: ExpMethod(); virtual ~ExpMethod(); virtual double SampleTime(const matrix &Q, int id, int val, const Instantiation &currinst, double currt, const Trajectory *evid, double te, int e, Random &rand=randomizer) const; virtual int SampleTransition(const matrix &Q, int id, int val, const Instantiation &inst, double currt, const Trajectory *evid, double te, int e, double &weight, Random &rand=randomizer) const; virtual double TimeWeight(const matrix &Q, const Instantiation &currinst, int id, int transitionid, int val, double currt, double deltat, const Trajectory *evid, double te, int e) const; /* virtual double TransitionWeight(const matrix &Q, int transitionid, int val, const Instantiation &currinst, const Instantiation &nextinst, double currt, double deltat, const Trajectory *evid, double te, int e, bool logscale=1) const;*/ }; //lookahead version class LookAheadMethod : public ExpMethod{ public: LookAheadMethod(); virtual ~LookAheadMethod(){} virtual int SampleTransition(const matrix &Q, int id, int val, const Instantiation &inst, double currt, const Trajectory *evid, double te, int e, double &weight, Random &rand=randomizer) const; /* virtual double TransitionWeight(const matrix &Q, int transitionid, int val, const Instantiation &currinst, const Instantiation &nextinst, double currt, double deltat, const Trajectory *evid, double te, int e) const; */ protected: mutable double transitionprob; }; } // end of ctbn namespace #endif ctbnrle.1.0.2/hdr/samplequeue.h0000644000464200007770000000474611344302770016527 0ustar csheltoncsprofs/* 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 CTBNRLE_SAMPLEQUEUE_H #define CTBNRLE_SAMPLEQUEUE_H namespace ctbn { // a priority queue just for sampling quickly // It keeps track of a set of event associated with a set of variables // will return the next event (earliest event) and can remove an event // associated with a particular variable even if that event is not next // Different from STL's pqueue in that we need to be able // to remove an event from the middle of the queue without having // to search for it. Also, we know the total number of events to be stored // (this could be done by storing a structure with a copy operator that // kept track of its position. However, this is simpler and faster.) // Note: the "var" stored in an event is assume to be one of a // set of consecutive integers starting at 0. Therefore, you may need // a map to map your varid onto this value. class SampleQueue { public: // a class to store an event // pretty much just a structure class Event { public: Event(int v,int val, double t) { var = v; value = val; time = t; } Event() { } int var; int value; double time; }; // create a queue with a maximum of maxn variables SampleQueue(int maxn); ~SampleQueue(); // place the earliest event in e // return false if there are no events // Note: this event is NOT removed from the queue bool Head(Event &e); // add an event to the queue void Add(const Event &e); // remove the event for variable var from the queue void Remove(int var); private: int Heapify(int i, double tempk); Event *heap; int *places; int n; }; } // end of ctbn namespace #endif ctbnrle.1.0.2/hdr/structuresearch.h0000644000464200007770000000507511344302770017423 0ustar csheltoncsprofs/* 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 CTBNRLE_STRUCTURESEARCH_H #define CTBNRLE_STRUCTURESEARCH_H #include "context.h" #include "structure.h" #include "famscore.h" #include namespace ctbn { // An abstract class for performing structure searches. // Constructed using a Context containing the relevant variables // and a FamScore object that takes care of the scoring. // (see famscore.h for more details.) class StructureSearch { public: // A generic node to keep track of the different // Domains and CondDomains in the network class Node { public: Node(const Context &var, const Context &cvar) : v(var), cv(cvar) {} Node* Clone() const {return new Node(*this);} const Context &Domain() const {return v;} const Context &CondDomain() const {return cv;} private: Context v; Context cv; }; // This assumes each variable in the context is a node in the network // Also, takes in a scoring object to choose between different // models (BN and CTBNDyn). This scoring object's pointer is // owned by the caller, but must remain valid for the lifetime of // the StructureSearch object. StructureSearch(const Context &c, FamScore* fs); ~StructureSearch(); virtual Structure LearnStructure() = 0; // Use the node vector below to fill a structure object void GetStructure(Structure &s) const; protected: // Stores each variable of the Context passed in std::vector vars; // After running LearnStructure(), this contains the nodes that // describe the learned network std::vector nodes; // Mappings from nodeids to varids and vice versa mutable std::vector node2var; mutable std::map var2node; // This class does not own this pointer FamScore* fScore; }; } // end of ctbn namespace #endif ctbnrle.1.0.2/hdr/defines.h0000644000464200007770000000470011344302770015604 0ustar csheltoncsprofs/* 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 CTBNRLE_DEFINES_H #define CTBNRLE_DEFINES_H /* This file describes changes that need to be made for compilation * on different platforms. */ #include #include #include #include #include #include typedef long long int longlong; #if defined __linux__ || __CYGWIN__ // Linux/Cygwin section... #define TIME_STRUCT longlong #define GET_TIME GetLinuxTime() TIME_STRUCT GET_TIME; #elif defined _WIN32 // Windows section... #include #define NOMINMAX #define WIN32_LEAN_AND_MEAN #include #ifdef UNION #undef UNION #endif #ifdef INTERSECTION #undef INTERSECTION #endif #ifdef DIFFERENCE #undef DIFFERENCE #endif #define TIME_STRUCT longlong #define GET_TIME GetWindowsTime() /* #define TIME_STRUCT longlong #define GET_TIME GetTime() */ TIME_STRUCT GET_TIME; #else // Mac OS section... // #define TIME_STRUCT hrtime_t // #define GET_TIME gethrtime() // for mac os x #include #define GET_TIME mach_absolute_time() #endif #if defined(_WIN32) // more Windows section... #ifndef finite #define finite(x) (_finite (x)) #endif #ifndef isfinite #define isfinite(x) (_finite (x)) #endif #ifndef isnan #define isnan(d) (_isnan(d)) #endif #ifndef __func__ #define __func__ __FUNCTION__ #endif #ifndef isinf #define isinf(x) (!_finite(x) && !_isnan(x)) #endif #ifndef INFINITY #define INFINITY (std::numeric_limits::infinity()) #endif extern const unsigned long win32nan[2]; double nan(const char *); float nanf(const char *); long double nanl(const char *); #endif #endif // _DEFINES_H ctbnrle.1.0.2/hdr/rk.h0000644000464200007770000000453211344302770014606 0ustar csheltoncsprofs/* 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 CTBNRLE_RK_H #define CTBNRLE_RK_H #include "matrix.h" #include "defines.h" namespace ctbn { // eps is the desired tolerance of the answer // (EPS below is the default value) which can be overridden // by the parameter (see params.h) RKEPS // (the default of -1.0 below forces the code to check for // the default parameter) #define RKEPS 1e-8 // h is a "recommended starting step size" // If h<0, then it uses 0.1/max(|diag(Q)|) as h // computes a * exp(Qt), via integration with desired error of eps // (h, if non-negative, gives a starting estimate of step size) // a is replaced with the result // the return value is the log of the factor by which a should // be multiplied for the true result (the returned result is normalized // to sum to 1) double vexpmt(vectr &a, const matrix &Q, double t, double eps=-1.0, double h=-1.0); // same as above, but calculates exp(Qt) * b double expmtv(vectr &b, const matrix &Q, double t, double eps=-1.0, double h=-1.0); // calculates \int_u=0^t a*exp(Q*u)*e_i e_j*exp(Q*(t-u))*b du // [this is a matrix indexed by (i,j)] // the transpose of this calculation is returned in c // (it is natural to calculate the transpose... the callee can // transpose it back) // Again, the returned value is the log of the factor by which the // result should be multiplied to find the "true result" double suffstat(const vectr &a, const vectr &b, matrix &c, const matrix &Q, double t, double eps=-1.0, double h=-1.0); } // end of ctbn namespace #endif ctbnrle.1.0.2/hdr/process.h0000644000464200007770000000310011344302770015636 0ustar csheltoncsprofs/* 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 CTBNRLE_PROCESS_H #define CTBNRLE_PROCESS_H #include "dynamics.h" #include "rv.h" namespace ctbn { // Process is a ABC for any general dynamic process class Process : public StreamObj { //SOBJCLASSDECL(Process) public: virtual ~Process(); // virtual copy constructor virtual Process *Clone() const = 0; // the amalgamtion operation virtual void Mult(const Process *p) = 0; // returns a suitable sufficient statistics object virtual SS *BlankSS() const = 0; // sets parameters from ML estimate virtual void Maximize(const SS *ss) = 0; // sample a trajectory of events from the process virtual void Sample(Trajectory &tr, Random &rand=randomizer) const = 0; }; } // end of ctbn namespace #endif ctbnrle.1.0.2/hdr/rv.h0000644000464200007770000001066211344302770014622 0ustar csheltoncsprofs/* 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 CTBNRLE_RV_H #define CTBNRLE_RV_H #include "rvsimple.h" #include "context.h" #include "trajectory.h" namespace ctbn { // RV is a random variable. It can be conditional and joint // It keeps track of its context class RV : public StreamObj { public: // var = varaibles, cvar = conditioning variables RV(const Context &var, const Context &cvar); RV(std::istream &is); virtual ~RV(); virtual void Load(std::istream &is); virtual void Save(std::ostream &os) const; // virtual copy constructor virtual RV *Clone() const = 0; const Context &Domain() const { return v; } const Context &CondDomain() const { return cv; } // makes sum to 1 for each conditional instantiation virtual void Normalize() = 0; // returns the probability (or log thereof) virtual double Prob(const Instantiation &x, bool log=false) const = 0; // Sets to zero all probabilities that are not consistent with v // (followed by a normalize, this is the same as conditioning // except that it doesn't change the domain of the variable) // (if again followed by a Project, then you have conditioning) virtual void Restrict(const Instantiation &x) = 0; // Marginalizes to c // ids not mentioned in c are marginalized out // can also be used to transfer ids to/from conditioning virtual void Project(const Context &c, const Context &cc) { v = c; cv = cc; } // Multiplies by another measure (point-wise) // Should expand context to include union virtual void MultBy(const RV *x) = 0; // returns a suitable sufficient statistics object virtual SS *BlankSS() const =0; // adds the instantiation to ss virtual void AddSS(const Instantiation &x, SS *ss, double w=1.0) const = 0; // adds the expected sufficient statistic to ss // (ie x can be incomplete -- but not in the conditioning variables virtual void AddExpSS(const Instantiation &x, SS *ss, double w=1.0) const = 0; // adds the distribution to the sufficient statistics // (x only gives the conditional context. rvs gives a distribution // over the domain of this variable) virtual void AddExpSS(const Instantiation &x, const RVSimple *rvs, SS *ss, double w=1.0) const = 0; //This adds the sufficient statistics of a different random variable //to this random variable's sufficient statistics virtual void AddSS(const SS* toadd, const RV *rv, SS *ss, double w=1.0) const = 0; // Completes the instantiation v with a conditional sample virtual void Sample(Instantiation &x, Random &rand = randomizer) const = 0; // sets parameters to ML estimate virtual void Maximize(const SS *ss) = 0; // returns an RVSimple object (owned by caller) that represents // the distribution conditioned on x (note that x must include // a complete assignment to all variables in the CondDomain() // context) without explicit reference to the domain // (normalize indicates whether the RV should be renormalized // -- in the case where x has assignments in Domain()) virtual RVSimple *MakeSimple(const Instantiation &x, bool normalize=true) const = 0; virtual void Scramble(double alpha=1.0, double degree=1.0, Random &rand=randomizer) = 0; // computes the log-likelihood from the sufficient statistics passed in virtual double LLH(const SS *ss) const = 0; // scores this variable (and the implied parent assignment given // by the conditional context) by the Bayesian structure score // with a uniform BDe prior with numTrans strength. virtual double GetScore(double numTrans, const SS *aa) const = 0; SS* SuffStats(const std::vector &tr, const std::vector &w) const; protected: Context v,cv; }; } // end of ctbn namespace #endif ctbnrle.1.0.2/hdr/trajectory.h0000644000464200007770000001672311344302770016365 0ustar csheltoncsprofs/* 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 CTBNRLE_TRAJECTORY_H #define CTBNRLE_TRAJECTORY_H #include #include #include #include "context.h" #include // This file describes a Trajectory... a mapping over the values // of variables over time. A Trajectory might be incomplete in that // the values of certain variables are not known are certain times (or // durations). -1 (just like in context.h) represents an unknown // value. The variable IDs need not be consecutive, however, their // values are assumed to be non-negative integers. // Trajectories may start and end at any time (provided the former // is before the latter). // "point evidence" (evidence of the value of a variable at exactly // one time instant but not before or after that) is not currently // handled exactly. Instead, a small window of time is used. // See AddPointEvidence() namespace ctbn { // A single variable trajectory is a map from a time to // the new value of the variable at that time // (we might want to make this a sorted vector for speed // purposes later) typedef std::map VarTrajectory; // A trajectory over multiple variables is... class Trajectory { public: Trajectory(); virtual ~Trajectory(); double TimeBegin() const { return ts; } double TimeEnd() const { return te; } void SetBeginTime(double t) { ts = t; } void SetEndTime(double t) { te = t; } int Value(int varid, double time, bool inclusive=true) const; Instantiation Values(const Context &c, double time, bool inclusive=true) const; // Adding a transition to -1 causes the variable to be unobserved void AddTransition(int varid, double time, int newval); void AddTransition(const Instantiation &i, double time); // adds point evidence. This is currently handled by adding // a very short window of evidence (deltime below) void AddPointEvidence(int varid, double time, int value, double deltime = 1e-10); void Save(std::ostream &os) const; void Load(std::istream &is); // Removes all information about variable varid void SetUnknown(int varid); // Same as SetUnknown, except that initial value is not set to -1 // (i.e. variable is never even mentioned void RemoveNodeTraj(int varid); // replace varid's trajectory in *this with varid's trajectory in tr void ReplaceNodeTraj(int varid, const Trajectory &tr); // Index is a way of moving through a trajectory (similar to an // iterator). It is constructed by calling Trajectory::Begin() class Index { public: ~Index(); // moves to the next transition of any variable Index &operator++() { TestInc(Context()); return *this; } const Index operator++(int) { Trajectory::Index ret(*this); ++(*this); return ret; } // moves to the next transition of the context void Inc(const Context &c) { while(!done && !TestInc(c)); } // returns the current "absolute" time double Time() const { return t; }; // returns the time until the next event double DeltaT() const { return nextvar.empty() ? (endt-t) : (-nextvar.top().first-t); } // returned reference is invalid if Index is changed const Instantiation &Values() const { return v; } bool Done() const { return done; } // I really want these to be private... hmmm... Index(const Trajectory &tr, const Context &c, double t0, double tend); // increments to next transition and returns whether context // was changed // return == 0 if nothing in c is changed // return == 1 if only change is from known value to hidden // value (or the reverse) // return == 2 if change at least one variable in c changes // from one known value to another int TestInc(const Context &c); int NextVar() const {return nextvar.empty() ? (-1) : (nextvar.top().second);} private: Instantiation v; double t,endt; // maps varid onto the pair of next trans and last trans std::map > vari; std::priority_queue > nextvar; bool done; }; // a reverse index, build from Trajectory::End() class RIndex { public: ~RIndex(); // moves to the previous transition of any variable RIndex &operator--() { TestDec(Context()); return *this; } const RIndex operator--(int) { Trajectory::RIndex ret(*this); --(*this); return ret; } // moves to the next transition of the context void Dec(const Context &c) { while(!done && !TestDec(c)); } // returns the current "absolute" time double Time() const { return t; }; // returns the time until the previous event double DeltaT() const { return nextvar.empty() ? (t-startt) : (t-nextvar.top().first); } // returned reference is invalid if Index is changed // Note that *despite* the trajectory being left continuous // this is the value just *before* the current transition const Instantiation &Values() const { return v; } bool Done() const { return done; } // I really want these to be private... hmmm... RIndex(const Trajectory &tr, const Context &c, double t0, double tstart); // increments to next transition and returns whether context // was changed // return == 0 if nothing in c is changed // return == 1 if only change is from known value to hidden // value (or the reverse) // return == 2 if change at least one variable in c changes // from one known value to another int TestDec(const Context &c); private: Instantiation v; double t,startt; // maps varid onto the pair of next trans and last trans std::map > vari; std::priority_queue > nextvar; bool done; }; // Return an Index (see above) over only the variables mentioned // in c Index Begin(const Context &c) const { return Index(*this,c,TimeBegin(),TimeEnd()); } // Same as above, but the Index only runs from startt to endt Index Begin(const Context &c, double startt, double endt) const { return Index(*this,c,startt,endt); } // Same as above, but run through the trajectory in reverse: RIndex End(const Context &c) const { return RIndex(*this,c,TimeEnd(),TimeBegin()); } // firstt < startt RIndex End(const Context &c, double startt, double firstt) const { return RIndex(*this,c,startt,firstt); } // makes a nicer looking (but unloadable) ASCII version void Draw(std::ostream &os) const; private: double ts,te; std::map traj; int Value(const std::map::const_iterator &vari, double time, bool inclusive=true) const; }; std::ostream &operator<<(std::ostream &os, const Trajectory &tr); std::istream &operator>>(std::istream &is, Trajectory &tr); } // end of ctbn namespace #endif ctbnrle.1.0.2/hdr/expsuffstatsquery.h0000644000464200007770000000424211344302770020015 0ustar csheltoncsprofs/* 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 CTBNRLE_EXPSUFFSTATSQUERY_H #define CTBNRLE_EXPSUFFSTATSQUERY_H // A class for performing queries for sufficient statistics where we have // incomplete trajectories. #include "suffstatsquery.h" #include "trajectory.h" #include "process.h" #include "inference.h" #include namespace ctbn { class ExpSuffStatsQuery : public SuffStatsQuery { public: // Expected sufficient statistics need a process for its expectation, // along with an inference method. The pointer of the inference // object is owned by the caller. ExpSuffStatsQuery(Inference* i, bool cloneInf = false); virtual ~ExpSuffStatsQuery(); // In the event of a high number of queries with the same process // and data, caching the inference objects eliminates the need to // set the same trajectories multiple times. void InitInfCache(); // changes the process (associated with the Inference object // passed into the constructor) from which the expectaction is to // be taken // (SetInitRV and SetDynamics from SuffStatsQuery change the // process who sufficient statistics are taken) void SetProcess(const Process* p); // Calculate and return sufficient statistics. virtual SS* GetP0SS(); virtual SS* GetDSS(); protected: Inference* inf; std::vector infCache; bool useInfCache; }; } // end of ctbn namespace #endif ctbnrle.1.0.2/hdr/rvsimple.tcc0000644000464200007770000001235511344302770016357 0ustar csheltoncsprofs// implementation of RVCondSimpleComp template SOBJCLASSDEFTEMPL1(RVCondSimpleComp,RVS) template RVCondSimpleComp::RVCondSimpleComp(int n, int nc) : RVCondSimple(), impl(nc,RVS(n)){ } template RVCondSimpleComp::RVCondSimpleComp(std::istream &is) : RVCondSimple(is) { int nc; is >> nc; impl.resize(nc); for(int i=0;i RVCondSimpleComp::~RVCondSimpleComp() { } template void RVCondSimpleComp::Load(std::istream &is) { RVCondSimple::Load(is); int nc; is >> nc; impl.resize(nc,RVS(0)); for(int i=0;i void RVCondSimpleComp::Save(std::ostream &os) const { RVCondSimple::Save(os); int nc = impl.size(); os << os.fill() << nc; for(int i=0;i RVCondSimpleComp *RVCondSimpleComp::Clone() const { return new RVCondSimpleComp(*this); } template double RVCondSimpleComp::Prob(int ind, int cind, bool log) const { return impl[cind].Prob(ind,log); } template void RVCondSimpleComp::Normalize() { int nc = impl.size(); for(int i=0;i RVS *RVCondSimpleComp::Condition(int ind) const { return impl[ind].Clone(); } template void RVCondSimpleComp::Restrict(const std::vector &ind, const std::vector &cind) { int nc = impl.size(); std::vector blank; int k=0; for(int i=0;i void RVCondSimpleComp::Reindex(int n, int nc, const RVCondSimple::RemapT &ind) { std::vector newimpl(nc,RVS(n)); for (RVCondSimple::RemapT::const_iterator i=ind.begin();i!=ind.end();++i) { int newci = i->first.first; int oldci = i->first.second; RVS cpy(impl[oldci]); cpy.Reindex(i->second); newimpl[newci].Add(&cpy); } impl = newimpl; } template void RVCondSimpleComp::MultBy(const RVCondSimple *x) { const RVCondSimpleComp *xx = dynamic_cast *>(x); int nc = impl.size(); for(int i=0;iimpl[i])); } template SS *RVCondSimpleComp::BlankSS() const { int nc = impl.size(); RVCSCompSS *ret = new RVCSCompSS(nc); for(int i=0;iss[i] = impl[i].BlankSS(); return ret; } template void RVCondSimpleComp::AddSS(int cind, int ind, SS *ss, double w) const { RVCSCompSS *rss = dynamic_cast(ss); impl[cind].AddSS(ind,rss->ss[cind],w); } template void RVCondSimpleComp::AddExpSS(int cind, SS *ss, double w) const { RVCSCompSS *rss = dynamic_cast(ss); impl[cind].AddExpSS(rss->ss[cind],w); } template void RVCondSimpleComp::AddExpSS(int cind, const RVSimple *rvs, SS *ss, double w) const { RVCSCompSS *rss = dynamic_cast(ss); rvs->AddExpSS(rss->ss[cind],w); } template void RVCondSimpleComp::AddSS(const SS *toadd, const RVCondSimple *rv, const std::vector > &mapping, int mycondi, int rvccondi, SS *ss, double w) const { const RVCondSimpleComp *rvcsc = dynamic_cast *>(rv); const RVCSCompSS* toaddrvcss = dynamic_cast(toadd); RVCSCompSS* rvcss = dynamic_cast(ss); impl[mycondi].AddSS(toaddrvcss->ss[rvccondi], &(rvcsc->impl[rvccondi]), mapping,rvcss->ss[mycondi]); } template int RVCondSimpleComp::Sample(int cx, Random &rand) const { return impl[cx].Sample(rand); } template void RVCondSimpleComp::Maximize(const SS *ss) { const RVCSCompSS *sss = dynamic_cast(ss); int nc = impl.size(); for(int i=0;iss[i]); } template void RVCondSimpleComp::Mult(RVSimple *&x) const { int nc = impl.size(); RVSimple *ret = impl[0].Clone(); ret->Mult(x->Prob(0)); for(int i=1;iAdd(&(impl[i]),x->Prob(i)); delete x; x = ret; } template void RVCondSimpleComp::RMult(RVSimple *&x) const { assert(0); // not easily doable (and probably not important) } template RVSimple *RVCondSimpleComp::Convert(const RVSimple *rvs) const { // can't really multiply anyway... so we'll just call Clone return rvs->Clone(); } template void RVCondSimpleComp::Scramble(double alpha, double degree, Random &rand) { int nc = impl.size(); for(int i=0; i double RVCondSimpleComp::LLH(const SS *ss) const { double llh = 0.0; const RVCSCompSS *sss = dynamic_cast(ss); int nc = impl.size(); for(int i=0; iss[i]); return llh; } template double RVCondSimpleComp::GetScore(double numTrans, const SS* ss) const { double localScore(0); const RVCSCompSS *rvss = dynamic_cast (ss); int nc = impl.size(); for(int i = 0; i < nc; i++) localScore += impl[i].GetScore(numTrans/nc, rvss->ss[i]); return localScore; } ctbnrle.1.0.2/hdr/suffstatsquery.h0000644000464200007770000000335111344302770017300 0ustar csheltoncsprofs/* 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 CTBNRLE_SUFFSTATSQUERY_H #define CTBNRLE_SUFFSTATSQUERY_H // A class to perform queries for sufficient statistics // The object is to be passed into a function as an empty object. // The member variables are set by the function using this class. #include "ss.h" #include "rv.h" #include "dynamics.h" #include "trajectory.h" #include namespace ctbn { class SuffStatsQuery { public: SuffStatsQuery(); virtual ~SuffStatsQuery(); virtual SS* GetP0SS() = 0; virtual SS* GetDSS() = 0; // The caller of these functions own the pointers. void SetInitRV(const RV* p0) {this->p0 = p0;} void SetDynamics(const Dynamics* d) {this->d = d;} virtual void SetData(const std::vector* data) {tr = data;} protected: // This class does not own any of these pointers. const RV* p0; const Dynamics* d; const std::vector* tr; }; } // end of ctbn namespace #endif ctbnrle.1.0.2/hdr/varsample.h0000644000464200007770000000461611344302770016167 0ustar csheltoncsprofs/* 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 CTBNRLE_VARSAMPLE_H #define CTBNRLE_VARSAMPLE_H #include "dynamics.h" #include "context.h" #include "trajectory.h" namespace ctbn { //VarSample is abstract class to define sample method //for Markov process, including how to sample the transition //time, how to sample the next state and the corresponding //sampling weights. //*weights are all in log scale!* class VarSample{ public: VarSample(); virtual ~VarSample(); //sample the next transition time for virtual double SampleTime(const matrix &Q, int id, int val, const Instantiation &currinst, double currt, const Trajectory *evid, double te, int e, Random &rand=randomizer) const = 0; //sample the next state //and update the corresponding weight contribution virtual int SampleTransition(const matrix &Q, int id, int val, const Instantiation &inst, double currt, const Trajectory *evid, double te, int e, double &weight, Random &rand=randomizer) const = 0; //calculate the weight contribution of sampling time virtual double TimeWeight(const matrix &Q, const Instantiation &currinst, int id, int transitionid, int val, double currt, double deltat, const Trajectory *evid, double te, int e) const = 0; //calculate the weight contribution of sampling next state /* virtual double TransitionWeight(const matrix &Q, int transitionid, int val, const Instantiation &currinst, const Instantiation &nextinst, double currt, double deltat, const Trajectory *evid, double te, int e, bool logscale = 1) const = 0; */ }; } // end of ctbn namespace #endif ctbnrle.1.0.2/hdr/rvsimple.h0000644000464200007770000002334211344302770016033 0ustar csheltoncsprofs/* 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 CTBNRLE_RVSIMPLE_H #define CTBNRLE_RVSIMPLE_H #include "streamextra.h" #include "random.h" #include #include #include "ss.h" #include "matrix.h" namespace ctbn { // RVSimple is the ABC (Abstract Base Class) for measures. // It can be thought of as a random variable that is not conditional // and whose domain is implied (or just indexed by integers from 0 to n-1) class RVSimple : public StreamObj { public: RVSimple(); RVSimple(std::istream &is); virtual ~RVSimple(); virtual void Load(std::istream &is); virtual void Save(std::ostream &os) const; // virtual copy constructor virtual RVSimple *Clone() const = 0; // Makes sum to 1 -- returns sum prior to normalization virtual double Normalize() = 0; // returns the probability of an individual event (index) virtual double Prob(int ind, bool log=false) const = 0; // Returns the sum (or log thereof) virtual double Sum(bool log=false) const = 0; // Sets to zero all indexes that are not listed in ind // Restrict followed by Normalize is the same as // conditioning the RV to the set of ind virtual void Restrict(const std::vector &ind) = 0; // Marginalizes, reindexes, or expands depending on the argument // ind is a list, for each new index, of the list of old indexes // which should be summed virtual void Reindex(const std::vector > &ind) = 0; // Multiplies by another measure (point-wise) virtual void MultBy(const RVSimple *x) = 0; // Adds in another measure virtual void Add(const RVSimple *x) = 0; virtual void Add(const RVSimple *x, double w) = 0; // Multiplies measure by constant virtual void Mult(double x) = 0; // return suitable sufficient statistics object virtual SS *BlankSS() const = 0; // add independent draw of x to ss virtual void AddSS(int x, SS *ss, double w=1.0) const = 0; // add average of independent draws of x to ss virtual void AddExpSS(SS *ss, double w=1.0) const = 0; virtual void AddSS(const SS *toadd, const RVSimple* rvs, const std::vector > &mapping, SS *ss, double w=1.0) const = 0; // returns a sample virtual int Sample(Random &rand = randomizer) const = 0; // sets parameters to ML estimate virtual void Maximize(const SS *ss) = 0; // set measure to be 1 everywhere it has support virtual void MakeUniform() = 0; virtual void GetDist(vectr &d, double &logfactor) const = 0; virtual void SetDist(const vectr &d, double logfactor=0.0) = 0; // Draws parameters from Dirichlet distribution // with all alphas the same (equal to alpha) // If degree equals 1, all parameters are replaced // If degree is between 0 and 1, the old parameters are // mixed with the new one (linear combination, degree controls // the mixing). virtual void Scramble(double alpha=1.0, double degree=1.0, Random &rand=randomizer) = 0; // Returns the log-likelihood of a set of sufficient statistics virtual double LLH(const SS *ss) const = 0; virtual double GetScore(double numTrans, const SS* ss) const; }; // This is the same, but with conditioning // That is, it represents a conditional measure (indexed by 0..n-1) // conditioned on an integer from 0..m-1 class RVCondSimple : public StreamObj { public: RVCondSimple(); RVCondSimple(std::istream &is); virtual ~RVCondSimple(); virtual void Load(std::istream &is); virtual void Save(std::ostream &os) const; // virtual copy constructor virtual RVCondSimple *Clone() const = 0; // returns the probability of an individual conditional event virtual double Prob(int ind, int cind, bool log=false) const = 0; // Makes sum to 1 for each conditioning value virtual void Normalize() = 0; // returns an object representing the conditioning // on that value virtual RVSimple *Condition(int ind) const = 0; // Sets to zero all indexes that are not listed in ind and cind // That is, for conditions mentioned in cind, the measure is // restricted to the indexes in ind. For conditions not in cind, // the measure is made to be zero everywhere. virtual void Restrict(const std::vector &ind, const std::vector &cind) = 0; // Marginalizes, reindexes, or expands depending on the // argument // n = # new values nc = # new conditioning values // ind[] is a mapping that needs to be applied to old // conditioning index j and then added to i // (see Reindex for RVSimple above to see how a mapping is // applied) // (Really, this is probably too confusing to be "simple.") typedef std::map, std::vector > > RemapT; virtual void Reindex(int n, int nc, const RemapT &ind) = 0; // Multiplies by another measure (point-wise) virtual void MultBy(const RVCondSimple *x) = 0; // return suitable sufficient statistics object virtual SS *BlankSS() const = 0; // add independent draw of (cind,ind) (conditioning value, value) to ss virtual void AddSS(int cind, int ind, SS *ss, double w=1.0) const = 0; // add average of independent draws to ss virtual void AddExpSS(int cind, SS *ss, double w=1.0) const = 0; virtual void AddExpSS(int cind, const RVSimple *rvs, SS *ss, double w) const = 0; virtual void AddSS(const SS *toadd, const RVCondSimple* rv, const std::vector > &mapping, int mycondi, int rvccondi, SS *ss, double w=1.0) const = 0; // returns a sample virtual int Sample(int cx, Random &rand = randomizer) const = 0; // sets parameters to ML estimate virtual void Maximize(const SS *ss) = 0; // assumes that x is over the conditional range // multiplies x by *this and then sums out over the conditional // range to produce a distribution over the domain // x is changed to the result // Think of *this as p(x|y) and the input as p(y) // This function changes the input to be the resulting // margin p(x) virtual void Mult(RVSimple *&x) const = 0; // this is the reverse (not a generally used operation except // in the forward-backward algorithm) // it assumes x is over the range of the non-conditioning variable // and returns a measure over the conditioning variables virtual void RMult(RVSimple *&x) const = 0; // Returns a new object that can be multiplied into this // object (as per Mult and RMult) virtual RVSimple *Convert(const RVSimple *rvs) const = 0; // see above class for description virtual void Scramble(double alpha=1.0, double degree=1.0, Random &rand=randomizer) = 0; //by Yu virtual double LLH(const SS *ss) const = 0; virtual double GetScore(double numTrans, const SS *ss) const = 0; }; // This is a template. It takes in a subclass of RVSimple and // creates a RVCondSimple from it (by making one copy for each // conditioning value) template class RVCondSimpleComp : public RVCondSimple { SOBJCLASSDECL(RVCondSimpleComp) public: RVCondSimpleComp(int n, int nc); RVCondSimpleComp(std::istream &is); virtual ~RVCondSimpleComp(); virtual void Load(std::istream &is); virtual void Save(std::ostream &os) const; virtual RVCondSimpleComp *Clone() const; virtual double Prob(int ind, int cind, bool log=false) const; virtual void Normalize(); virtual RVS *Condition(int ind) const; virtual void Restrict(const std::vector &ind, const std::vector &cind); virtual void Reindex(int n, int nc, const RVCondSimple::RemapT &ind); virtual void MultBy(const RVCondSimple *x); virtual SS *BlankSS() const; virtual void AddSS(int cind, int ind, SS *ss, double w=1.0) const; virtual void AddExpSS(int cind, SS *ss, double w=1.0) const; virtual void AddExpSS(int cind, const RVSimple *rvs, SS *ss, double w) const; virtual void AddSS(const SS *toadd, const RVCondSimple* rv, const std::vector > &mapping, int mycondi, int rvccondi, SS* ss, double w=1.0) const; virtual int Sample(int cx, Random &rand = randomizer) const; virtual void Maximize(const SS *ss); virtual void Mult(RVSimple *&x) const; virtual void RMult(RVSimple *&x) const; virtual RVSimple *Convert(const RVSimple *rvs) const; RVS &operator[](int i) { return impl[i]; } const RVS &operator[](int i) const { return impl[i]; } virtual void Scramble(double alpha=1.0, double degree=1.0, Random &rand=randomizer); //by Yu virtual double LLH(const SS *ss) const; virtual double GetScore(double numTrans, const SS* ss) const; private: std::vector impl; }; // This is the corresponding sufficient statistics for RVCondSimpleComp // (for any template instantiation) class RVCSCompSS : public SS { SOBJCLASSDECL(RVCSCompSS) public: RVCSCompSS(int nc); RVCSCompSS(const RVCSCompSS &x); RVCSCompSS(std::istream &is); virtual ~RVCSCompSS(); virtual void Load(std::istream &is); virtual void Save(std::ostream &os) const; virtual RVCSCompSS *Clone() const; RVCSCompSS &operator=(const RVCSCompSS &x); virtual void Scale(double w); virtual void AddSS(const SS* nss, double w=1.0); // can't make the general set of RVCondSimpleComp<*> friends // for some reason in g++ //private: std::vector ss; }; #include "rvsimple.tcc" } // end of ctbn namespace #endif ctbnrle.1.0.2/hdr/inference.h0000644000464200007770000001003311344302770016121 0ustar csheltoncsprofs/* 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 CTBNRLE_INFERENCE_H #define CTBNRLE_INFERENCE_H #include "streamextra.h" #include "context.h" #include "bn.h" #include "ss.h" #include "process.h" namespace ctbn { // QueryCalculator is the base class to answer any query of // a process given a trajectory or suffcient statistics. class QueryCalculator { public: // virtual function that calculates queries, // to be implemented by the inheriting classes // QueryTime and QueryTransition // calculate query given a trajectory virtual double Calculate(const Trajectory &tr) = 0; // calculate query given sufficient statistics virtual double Calculate(const SS &ss) = 0; }; // QueryTime is used to answer query that the total time // a process stays in some state. The state is passed through // constructor using an Instantiation c. class QueryTime : public QueryCalculator { public: QueryTime(const Instantiation &c); double Calculate(const Trajectory &tr); double Calculate(const SS &ss); virtual ~QueryTime(){}; private: Instantiation condi; int queryindex; }; // QueryTransition is used to answer query that the total number // of times a process transitions from one state to another. class QueryTransition : public QueryCalculator { public: QueryTransition(const Context &c, int from, int to); double Calculate(const Trajectory &tr); double Calculate(const SS &ss); virtual ~QueryTransition(){}; private: Context querycontext; int queryfrom; int queryto; }; // Inference is not streamable for many reasons // (although it could potentially be useful to be able // to serialize it) class Inference { // : public StreamObj { //SOBJCLASSDECL(Inference) public: virtual ~Inference(); virtual Inference *Clone() const = 0; // The Inference object owns neither of these // (they are still owned by the caller, but the // pointers passed in must remain valid (and unchanging) // for the life of the Inference object, or at least // until the next "SetProcess" or "SetTrajectory" (respectively) // is called virtual void SetProcess(const Process *p) = 0; virtual void SetTrajectory(const Trajectory *tr) = 0; // returns the probability of x at t given the trajectory up to time t virtual double Filter(const Instantiation &x, double t, bool log=false) = 0; //double log=false) = 0; // returns the probability of x at t given the whole trajectory virtual double Smooth(const Instantiation &x, double t, bool log=false) = 0; // returns the probability of the trajectory up to time t virtual double Prob(double t, bool log=false) = 0; // adds the Expected Sufficient Statistics for the process // given the trajectory virtual void AddExpSuffStats(SS *ss, double w=1.0) = 0; // adds the ESS for the dynamics given by dyn to ss // (note that the expectation is wrt to the process set // in "SetProcess" but the sufficient statistics are those // for the dynamics dyn) // used primarily for structure search virtual void AddExpSuffStats(const Dynamics *dyn, SS *ss, double w=1.0) = 0; // same as above, but for an initial starting distribution p0 virtual void AddExpSuffStats(const RV *p0, SS *ss, double w=1.0) = 0; virtual double CalcQuery(QueryCalculator &calc) = 0; }; } // end of ctbn namespace #endif ctbnrle.1.0.2/hdr/matrix.h0000644000464200007770000007243711344302770015507 0ustar csheltoncsprofs/* 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 CTBNRLE_MATRIX_H #define CTBNRLE_MATRIX_H #include #include #include #include #include #include #include #include #include "defines.h" namespace ctbn { // undefine to cause code to check for OOB conditions #define NO_ARRAY_CHECK // This code defines a vector (class name vectr) // and a matrix (class name matrix) that have most of the // common vector-matrix operations. // Code is kept inline for speed for most of the operations class matrix; // Vector class class vectr { friend class matrix; public: // default vector is length 1 (a scalar) inline vectr() { m = 1; x = new double[1]; } // new vector (contents not set) inline vectr(int m) { this->m = m; x = new double[m]; } inline ~vectr() { delete []x; } // copy constructor inline vectr(const vectr &v) { m = v.m; x = new double[m]; //for(int i=0;im = m; x = new double[m]; for(int i=0;im = m; if (keep) x = v; else { x = new double[m]; //for(int i=0;i &v) { m = v.size(); x = new double[m]; for(int i=0;i &ind) { m = ind.size(); x = new double[m]; for(int i=0;ima) ma = t; return ma; } // returns minimum value in vector inline double min() const { double t,mi = x[0]; for(int i=1;i0?x[0]:-x[0]; for(int i=1;i0?x[i]:-x[i]))>ma) ma = t; return ma; } // returns minimum absolute value in vector inline double absmin() const { double t,mi = x[0]>0?x[0]:-x[0]; for(int i=1;i0?x[i]:-x[i]))>(std::istream &s, vectr &v); // same as length, but added inline int getm() const { return m; } // a nicer way of displaying the vector (than <<) for humans // cannot be loaded (size not explicitly given) inline std::ostream &niceprint(std::ostream &s) const { for(int i=0;im?m:newm)*sizeof(double)); delete []x; x = newx; m = newm; } private: int m; double *x; }; // implementations of binary operators in terms of inplace operators // (see Effective C++ for why this is a good idea) inline vectr operator+(const vectr &a, const vectr &b) { return vectr(a)+=b; } inline vectr operator-(const vectr &a, const vectr &b) { return vectr(a)-=b; } inline vectr operator+(const vectr &a, const double &b) { return vectr(a)+=b; } inline vectr operator-(const vectr &a, const double &b) { return vectr(a)-=b; } inline vectr operator+(const double &a, const vectr &b) { return vectr(b)+=a; } inline vectr operator-(const double &a, const vectr &b) { return vectr(b.getm(),a)-=b; } inline vectr operator*(const vectr &a, const double &b) { return vectr(a)*=b; } inline vectr operator*(const double &a, const vectr &b) { return vectr(b)*=a; } inline vectr operator/(const vectr &a, const double &b) { return vectr(a)/=b; } // saving a loading uses the format: ... inline std::ostream &operator<<(std::ostream &s, const vectr& v) { s << v.m; for(int i=0;i>(std::istream &s, vectr& v) { int tm; s >> tm; if (tm!=v.m) { delete []v.x; v.m = tm; v.x = new double[tm]; } for(int i=0;i> v.x[i]; return s; } class matrix { friend class vectr; public: // default matrix is 1-by-1 inline matrix(int m=1, int n=1) { this->m = m; this->n = n; s = m*n; x = new double[s]; } inline ~matrix() { delete []x; } // copy constructor and ability to construct transpose // matrix a; // ... // matrix b(a,true); // b is now transpose of a inline matrix(const matrix &ma, bool transpose=false) { s = ma.m*ma.n; x = new double[s]; if (transpose) { int i,j,c; n = ma.m; m = ma.n; c = 0; for(i=0;im = m; this->n = n; s = m*n; x = new double[s]; if (diaonly) { //for(i=0;i=m) for(int i=0;im = m; this->n = n; s = m*n; x = new double[s]; //for(int i=0;im = m; this->n = n; s = m*n; //int i; x = new double[s]; //for(i=0;i &ind) { n = m = ind.size(); s = n*m; x = new double[s]; int c= 0; for(int i=0;i &ind1, const std::vector &ind2) { m = ind1.size(); n = ind2.size(); s = m*n; x = new double[s]; int c = 0; for(int i=0;ima) ma=t; return ma; } // returns the minimal element inline double min() const { double t,mi = x[0]; for(int i=1;i0?x[0]:-x[0]; for(int i=1;i0?x[i]:-x[i]))0?x[0]:-x[0]; for(int i=1;i0?x[i]:-x[i]))>ma) ma=t; return ma; } // returns the sum of all elements inline double sum() const { double ret = 0.0; for(int i=0;i>(std::istream& s, matrix& ma); // inplace version of unary - inline void negate() { for(int i = 0; i < s; i++) x[i] = -x[i]; } // return the trace of the matrix (sum of diagonal elements) inline double trace(); // dest = this*source (but we do not need to allocate memory for dest inline void multiply(const vectr &source, vectr &dest) { #ifndef NO_ARRAY_CHECK assert(n == source.m && m == dest.m); #endif for(int i = 0,c=0; i < m; i++) { dest[i] = 0; for(int j = 0; j < n; j++,c++) dest[i] += x[c] * source[j]; } } // inplace pre-mult for sparse vector v // v = v*this (is faster if many elements of v are zero) void sparsepremult(vectr &v) const; // prints out the matrix is a some-what nicer form // keeps m and n at the top so that it can be read by operator>> inline std::ostream &niceprint(std::ostream& s) const { s << m << ' ' << n << std::endl; for(int i=0;i <[0][0]> <[0][1]> ... <[m][n]> inline std::ostream& operator<<(std::ostream& s, const matrix& ma) { s << ma.m << ' ' << ma.n << ' '; for(int i=0;i>(std::istream& s, matrix& ma) { int tn,tm; s >> tm >> tn; if (tm!=ma.m || tn!=ma.n) { delete []ma.x; ma.s = tm*tn; ma.x = new double[ma.s]; ma.m = tm; ma.n = tn; } for(int i=0;i> ma.x[i]; } return s; } } // end of ctbn namespace #endif ctbnrle.1.0.2/hdr/dyncomp.h0000644000464200007770000001200011344302770015630 0ustar csheltoncsprofs/* 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 CTBNRLE_DYNCOMP_H #define CTBNRLE_DYNCOMP_H #include "streamextra.h" #include "dynamics.h" #include "dynsimple.h" namespace ctbn { class DynComp; // sufficient statistics for a DynComp class DynCompSS : public SS { SOBJCLASSDECL(DynCompSS); friend class DynComp; public: DynCompSS(int n); DynCompSS(const DynCompSS &dcss); DynCompSS(std::istream &is); DynCompSS &operator=(const DynCompSS &dcss); virtual ~DynCompSS(); virtual DynCompSS *Clone() const; virtual void Load(std::istream &is); virtual void Save(std::ostream &os) const; virtual void Scale(double w); virtual void AddSS(const SS* nss, double w=1.0); virtual double Element(std::vector index, int s1, int s2) const; private: std::vector ss; }; // a class that creates a Dynamics object out of a DynSimple class // see dynamics.h for a description of the methods // see dynsimple.h for a description of DynSimple // This class is essentially just a set of DynSimple objects (one // for each possible value of the conditioning context), with // wrappers to map from instantiations to the raw indexes used by // DynSimple class DynComp : public Dynamics { SOBJCLASSDECL(DynComp) public: // the base argument will only be used for Clone() // it is still owned by the caller DynComp(const Context &var, const Context &cvar, const DynSimple *base); DynComp(std::istream &is); DynComp(const DynComp &dc); DynComp &operator=(const DynComp &dc); virtual ~DynComp(); virtual void Load(std::istream &is); virtual void Save(std::ostream &os) const; virtual DynComp *Clone() const; virtual void Normalize(); virtual void Restrict(const Instantiation &x); virtual void Mult(const Dynamics *x); virtual RVCondSimple *Cond(double t0, double t1, const Instantiation &x) const; virtual RVCondSimple *Cond(double t, const Instantiation &from, const Instantiation &to, bool transition=true) const; virtual DynCompSS *BlankSS() const; virtual void AddSS(const Instantiation &x, double t0, double deltat, SS *ss, double w=1.0) const; virtual void AddTransSS(const Instantiation &x1, const Instantiation &x2, double t, SS *ss, double w=1.0) const; virtual void AddExpSS(const RV *alpha, const RV *beta, double t0, double deltat, SS *ss, double w=1.0) const; virtual void AddExpTransSS(const RV *x1, const RV *x2, const Context &changevar, double t, SS *ss, double w=1.0) const; virtual void AddExpSS(const Instantiation &condi, const RVSimple *alpha, const RVSimple *beta, double t0, double deltat, SS *ss, double w=1.0) const; virtual void AddExpSS(int condi, const RVSimple *alpha, const RVSimple *beta, double t0, double deltat, SS *ss, double w=1.0) const; virtual void AddExpTransSS(const Instantiation &condi, const RVSimple *x1, const RVSimple *x2, const Context &changevar, double t, SS *ss, double w=1.0) const; virtual void AddExpTransSS(int condi, const RVSimple *x1, const RVSimple *x2, const Context &changevar, double t, SS *ss, double w=1.0) const; virtual void AddSS(const SS *toadd, const Dynamics *dyn, SS *ss, double w=1.0) const; virtual void SampleNextEvent(const Instantiation &i, double t, double &nextt, Instantiation &nexti, Random &rand=randomizer) const; virtual void Maximize(const SS *ss); inline DynSimple *operator[](const Instantiation &i) { return impl[CondDomain().Index(i)]; } inline const DynSimple *operator[](const Instantiation &i) const { return impl[CondDomain().Index(i)]; } virtual void Scramble(double a=1.0, double b=1.0, double alpha=1.0, double degree=1.0, Random &rand=randomizer); virtual double LLH(const SS *ss) const; virtual double GetScore(double numTrans, double amtTime, const SS* ss) const; // It is questionable as to whether this breaks the abstraction... // note that the pointer returned is still owned by this object inline DynSimple *operator[](int i) { return impl[i]; } inline const DynSimple *operator[](int i) const { return impl[i]; } protected: inline int CondIndex(const Instantiation &x) const { if (impl.size()<2) return 0; else return CondDomain().Index(x); } std::vector impl; }; } // end of ctbn namespace #endif ctbnrle.1.0.2/hdr/params.h0000644000464200007770000000635111344302770015456 0ustar csheltoncsprofs/* 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 CTBNRLE_PARAMS_H #define CTBNRLE_PARAMS_H #include namespace ctbn { // This file creates a method for setting global parameters in a variety // of ways and preventing the values from needing to be passed all around // if they are more-or-less "assumed." // To make this work, the function "InitParams" (below) should be the // first thing called by the main function. This will initialize the // parameters (argc and argv should be the corresponding arguments to // the main function). void InitParams(int argc, char **argv); // Then, whenever the program needs a parameter, it can call one of the // three functions immediately following. The first argument is the // name of the parameter. The second value is the value that should be // returned in case the parameter was not specified. std::string ParamStr(const std::string &name, const std::string &dflt=""); int ParamInt(const std::string &name, int dflt=0); double ParamDouble(const std::string &name, double dflt=0.0); // How this works: // InitParams sets up a global variable (see top of .cc file) who scope // is hidden from all except the functions above. // It reads parameters from two places: parameter files and the command // line. Parameter files take the form of one parameter per line. // Lines beginning with "#" or ";" are ignored. Whitespace at the // beginning of a line is ignored. The first token on the line // (delimited by whitespace) is the name of the parameter. The remainder // of the line (after the separating whitespace) is the parameter value. // // So, the file // #----------- // parameter1 3.0 // par two words // #do not use // q 4 // #----------- // defines three parameters: // "parameter1" => 3.0 // "par" => "two words" // "q" => 4 // // InitParams reads the parameters in in the following order with more // recent values overriding older values: // 1. Parameter file "params" (in the current directory) // 2. Parameter file ".params" (where is the executed name of // the program) // 3. Command line // // On the command line, -D defines paramname to have // value value. The argument "-param " reads in file filename. // And the argument "-listparams" will cause the program to read in all // of the parameters, list them all to stdout, and then quit // with error code 0. } // end of ctbn namespace #endif ctbnrle.1.0.2/hdr/em.h0000644000464200007770000000304411344302770014570 0ustar csheltoncsprofs/* 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 CTBNRLE_EM_H #define CTBNRLE_EM_H #include "inference.h" #include "process.h" #include "trajectory.h" #include "structure.h" // All of the pointers in these functions are owned by the caller namespace ctbn { // Perform Expectation-Maximization with the data data // on the Process dist with inference method inf void EM(const std::vector &data, Process *dist, Inference *inf); // Perform Structural Expectation-Maximization // In this case, dist may need to be changed in which case the original // one will be "deleted" (freed) and replaced with a new one void SEM(const std::vector &data, Process *&dist, Inference *inf); } // end of ctbn namespace #endif ctbnrle.1.0.2/hdr/ss.h0000644000464200007770000000273311344302770014620 0ustar csheltoncsprofs/* 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 CTBNRLE_SS_H #define CTBNRLE_SS_H #include "streamextra.h" namespace ctbn { // ABC for Sufficient Statistics // Really just a blank class as what goes in here really depends // greatly and the methods are all part of the relevant RVs and Processes class SS : public StreamObj { public: SS(); SS(std::istream &is); virtual ~SS(); // virtual copy constructor virtual SS *Clone() const = 0; virtual void Load(std::istream &is); virtual void Save(std::ostream &os) const; virtual void Scale(double w) = 0; virtual void AddSS(const SS* nss, double w=1.0)=0; }; } // end of ctbn namespace #endif ctbnrle.1.0.2/hdr/rvcomp.h0000644000464200007770000000551211344302770015477 0ustar csheltoncsprofs/* 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 CTBNRLE_RVCOMP_H #define CTBNRLE_RVCOMP_H #include "streamextra.h" #include "rv.h" #include "ss.h" #include namespace ctbn { // a class that creates a RV out of an RVCondSimple // essentially, it adds mappings from Instantiations to integers // (Context::Index()) to wrap the RVCondSimple class RVComp : public RV { SOBJCLASSDECL(RVComp) public: // touse becomes the property of RVComp. Call with touse->Clone() // to avoid this RVComp(const Context &var, const Context &cvar, RVCondSimple *touse); RVComp(std::istream &is); RVComp(const RVComp &rvc); RVComp &operator=(const RVComp &rvc); virtual ~RVComp(); virtual void Load(std::istream &is); virtual void Save(std::ostream &os) const; virtual RVComp *Clone() const; virtual void Normalize(); virtual double Prob(const Instantiation &x, bool log=false) const; virtual void Restrict(const Instantiation &x); virtual void Project(const Context &c, const Context &cc); virtual void MultBy(const RV *x); virtual SS *BlankSS() const; virtual void AddSS(const Instantiation &x, SS *ss, double w=1.0) const; virtual void AddExpSS(const Instantiation &x, SS *ss, double w=1.0) const; virtual void AddExpSS(const Instantiation &x, const RVSimple *rvs, SS *ss, double w=1.0) const; //This adds the sufficient statistics of a different random variable //to this random variable's sufficient statistics virtual void AddSS(const SS* toadd, const RV *rv, SS *ss, double w=1.0) const; virtual void Sample(Instantiation &x, Random &rand = randomizer) const; virtual void Maximize(const SS *ss); RVCondSimple *Base() { return impl; } const RVCondSimple *Base() const { return impl; } RVSimple *MakeSimple(const Instantiation &x, bool normalize=true) const; virtual void Scramble(double alpha=1.0, double degree=1.0, Random &rand=randomizer); virtual double LLH(const SS *ss) const; virtual double GetScore(double numTrans, const SS* ss) const; private: RVCondSimple *impl; }; } // end of ctbn namespace #endif ctbnrle.1.0.2/hdr/ctbndyn.h0000644000464200007770000001524211344302770015633 0ustar csheltoncsprofs/* 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 CTBNRLE_CTBNDYN_H #define CTBNRLE_CTBNDYN_H #include "dynamics.h" #include "structure.h" #include "suffstatsquery.h" namespace ctbn { class CTBNDyn; class Structure; // These are the sufficient statistics for a CTBN Dynamics // (that is everything in a CTBN, except the initial distribution) // It consists of a vector (for each node in the graph) of its // sufficient statistics. class CTBNDynSS : public SS { friend class CTBNDyn; SOBJCLASSDECL(CTBNDynSS) public: CTBNDynSS(std::istream &is); CTBNDynSS(const CTBNDynSS &ctbnss); CTBNDynSS &operator=(const CTBNDynSS &ctbnss); virtual CTBNDynSS *Clone() const; virtual ~CTBNDynSS(); virtual void Load(std::istream &is); virtual void Save(std::ostream &os) const; virtual void Scale(double w); virtual void AddSS(const SS* nss, double w=1.0); virtual double NodeSS(int id, int val1, int val2, const Instantiation &cond) const; const SS* GetNodeSS(int id) const {return dynss[id];} private: CTBNDynSS(); std::vector dynss; }; // This is a CTBN Dynamics (that is, everything except the // initial distribution). All methods work just as described in // dynamics.h class CTBNDyn : public Dynamics { SOBJCLASSDECL(CTBNDyn) public: CTBNDyn(); CTBNDyn(std::istream &is); CTBNDyn(const CTBNDyn &ctbn); CTBNDyn(const Structure &s, const Context& vars); CTBNDyn &operator=(const CTBNDyn &ctbn); virtual CTBNDyn *Clone() const; virtual ~CTBNDyn(); virtual void Load(std::istream &is); virtual void Save(std::ostream &os) const; virtual void Normalize(); virtual void Restrict(const Instantiation &x); virtual void Mult(const Dynamics *x); virtual RVCondSimple *Cond(double t0, double t1, const Instantiation &x) const; virtual RVCondSimple *Cond(double t, const Instantiation &from, const Instantiation &to, bool transition=true) const; virtual CTBNDynSS *BlankSS() const; virtual void AddSS(const Instantiation &x, double t0, double deltat, SS *ss, double w=1.0) const; virtual void AddTransSS(const Instantiation &x1, const Instantiation &x2, double t, SS *ss, double w=1.0) const; virtual void AddExpSS(const RV *alpha, const RV *beta, double t0, double deltat, SS *ss, double w=1.0) const; virtual void AddExpTransSS(const RV *x1, const RV *x2, const Context &changevar, double t, SS *ss, double w=1.0) const; virtual void AddExpSS(const Instantiation &condi, const RVSimple *alpha, const RVSimple *beta, double t0, double deltat, SS *ss, double w=1.0) const; virtual void AddExpSS(int condi, const RVSimple *alpha, const RVSimple *beta, double t0, double deltat, SS *ss, double w=1.0) const; virtual void AddExpTransSS(const Instantiation &condi, const RVSimple *x1, const RVSimple *x2, const Context &changevar, double t, SS *ss, double w=1.0) const; virtual void AddExpTransSS(int condi, const RVSimple *x1, const RVSimple *x2, const Context &changevar, double t, SS *ss, double w=1.0) const; virtual void AddSS(const SS *toadd, const Dynamics *dyn, SS *ss, double w=1.0) const; virtual void SampleNextEvent(const Instantiation &i, double t, double &nextt, Instantiation &nexti, Random &rand=randomizer) const; virtual void SampleTrajectory(Trajectory &tr, double t, Random &rand=randomizer) const; virtual void Maximize(const SS *ss); virtual double LLH(const SS *ss) const; // These methods are new to a CTBNDyn: // CTBNDyn now owns the pointer (call ->Clone() to give it a copy) void AddNode(Dynamics *d); //Places the structure of the graph in s void GetStructure(Structure &s) const; // returns the number of nodes in the graph int NumofNodes() const{return nodes.size();}; // returns the ith node const Dynamics *Node(int i) const {return nodes[i];}; // returns the node for the variable with ID i const Dynamics *NodeByVar(int i) const; // randomizes the parameters of the network virtual void Scramble(double a=1.0, double b=1.0, double alpha=1.0, double degree=1.0, Random &rand=randomizer); // returns a joint intensity matrix for the entire // CTBN (not recommended if your CTBN is large!) // the rows (and columns) are ordered as by the Inc() method // for Instantiation (that is, construct an instantiation // from the context of this CTBNDyn and increment it to iterate // through the assignments for the rows). matrix JointMatrix() const; // returns a dynamics that represents the entire process // (flattened) -- again not recommended for large CTBNs const Dynamics *GetJoint() const {return Joint();} // returns the Bayesian structure score for the CTBN (given // the sufficient statistics) with the BDe prior with numTrans // and amtTime as the two meta-parameters virtual double GetScore(double numTrans, double amtTime, const SS* ss) const; // fills in the parameters from a different CTBNDyn. Used // in structural EM to initial the parameters after the // structure has been changed void FillParams(const CTBNDyn &src); // returns the sufficient statistics just for variable ID i const SS *NodeSSByVar(int i, SS* ss) const; protected: // cannot delete returned value const Dynamics *Joint() const; std::vector nodes; mutable Dynamics *joint; // cache of joint RV // generates the items below // call before use -- will do nothing if they already exist void PrepNode2IdMaps() const; // clears them (call if nodes changes) void ClearNode2IdMaps() const; // cache of mapping from index into "nodes" to the variable id represented mutable std::vector node2var; // cache of mapping from variable id to the index into "nodes" mutable std::map var2node; // cache of the indexes into "nodes" of the children for a particular // variable id (Note this one is slightly odd as it maps a variable id into // a list of node-indexes (not back into varids) mutable std::map > children; }; } // end of ctbn namespace #endif ctbnrle.1.0.2/hdr/bn.h0000644000464200007770000001111111344302770014560 0ustar csheltoncsprofs/* 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 CTBNRLE_BN_H #define CTBNRLE_BN_H #include "rv.h" #include "structure.h" namespace ctbn { class BN; // sufficient statistics object for a Bayesian network class BNSS : public SS { friend class BN; SOBJCLASSDECL(BNSS) public: BNSS(std::istream &is); BNSS(const BNSS &bnss); BNSS &operator=(const BNSS &bnss); virtual BNSS *Clone() const; virtual ~BNSS(); virtual void Load(std::istream &is); virtual void Save(std::ostream &os) const; virtual void Scale(double w); virtual void AddSS(const SS* nss, double w=1.0); private: BNSS(); std::vector rvss; }; // BN is a class to represent a Bayesian Network // See RV for description of most of the methods class BN : public RV { SOBJCLASSDECL(BN) friend class ExactMarkovInf; public: BN(); BN(const BN &bn); // load from stream BN(std::istream &is); // build with structure and variables given BN(const Structure &s, const Context &vars); BN &operator=(const BN &bn); virtual BN *Clone() const; virtual ~BN(); // next set: standard RV methods: virtual void Load(std::istream &is); virtual void Save(std::ostream &os) const; virtual void Normalize(); virtual double Prob(const Instantiation &x, bool log=false) const; virtual void Restrict(const Instantiation &x); virtual void Project(const Context &c, const Context &cc); virtual void MultBy(const RV *x); virtual BNSS *BlankSS() const; virtual void AddSS(const Instantiation &x, SS *ss, double w=1.0) const; virtual void AddExpSS(const Instantiation &x, SS *ss, double w=1.0) const; virtual void AddExpSS(const Instantiation &x, const RVSimple *rvs, SS *ss, double w=1.0) const; virtual void AddSS(const SS* toadd, const RV *rv, SS *ss, double w=1.0) const {} virtual void Sample(Instantiation &x, Random &rand=randomizer) const; virtual void Maximize(const SS *ss); virtual RVSimple *MakeSimple(const Instantiation &x, bool normalize=true) const; virtual void Scramble(double alpha=1.0, double degree=1.0, Random &rand=randomizer); virtual double LLH(const SS *ss) const; virtual double GetScore(double numTrans, const SS* ss) const; // these are specific to a BN: // the BN now owns the pointer rv (call with rv->Clone()) if the // caller doesn't want to give up the variable void AddNode(RV *rv); // implements importance sampling and returns the weight of the // sample double ImportanceSample(Instantiation &x, bool log=false, Random &rand=randomizer) const; // get parameters from a BN that might not have the same // structure (say to initialize the E step inside SEM) void FillParams(const BN &src); // i is the index of the node (might not be variable "i") const RV *Node(int i) const {return nodes[i]; } // i is the "name" of a variable const RV *NodeByVar(int i) const; int NumofNodes() const { return nodes.size(); } void GetStructure(Structure &s) const; protected: void SetSampleOrder() const; std::vector nodes; mutable std::vector sampleorder; // as a cache // returns joint distribution restricted to x RV *Joint(const Instantiation &x) const; // generates the items below // call before use -- will do nothing if they already exist void PrepNode2IdMaps() const; // clears them (call if nodes changes) void ClearNode2IdMaps() const; // cache of mapping from index into "nodes" to the variable id represented mutable std::vector node2var; // cache of mapping from variable id to the index into "nodes" mutable std::map var2node; // cache of the indexes into "nodes" of the children for a particular // variable id (Note this one is slightly odd as it maps a variable id into // a list of node-indexes (not back into varids) mutable std::map > children; }; } // end of ctbn namespace #endif ctbnrle.1.0.2/hdr/utils.h0000644000464200007770000000435011344302770015330 0ustar csheltoncsprofs/* 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 CTBNRLE_UTILS_H #define CTBNRLE_UTILS_H #include #include #include #include // A set of useful utility functions namespace ctbn { class Trajectory; class Context; bool IsTrajSame(const Trajectory &tr1, const Trajectory &tr2, const Context &c); bool IsTrajValid(const Trajectory &tr, const Context &c); void RemoveInformation(Trajectory &tr, const Context &c, int var, double start, double end); void RemoveInformation(Trajectory &tr, const Context &c, int nvar, int nit, double frac); void RemoveNodesInformation(Trajectory &tr, const Context &c, int nvar, int nit, double frac); void SelectNodesInformation(Trajectory &tr, const Context &c, int nvar, double thresh, double frac); double SelectTime(std::priority_queue &select, double ratio, double begint, double endt, double thresh ); double getcputime(void); void Normalize(std::vector &logw); double mean(const std::vector &v); double relative_bias(const std::vector &v, double vtrue); double relative_std(const std::vector &v, double vtrue); bool LoadNetworkTraj(std::istream &in, std::vector &results); bool SplitPortTraj(const std::vector &whole, std::vector > &splitted); std::vector PartitionTraj(const Trajectory &whole); Trajectory LoadTraj(std::ifstream &in); } // end of ctbn namespace #endif ctbnrle.1.0.2/hdr/streamextra.h0000644000464200007770000003546711344302770016544 0ustar csheltoncsprofs/* 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 CTBNRLE_STREAMEXTRA_H #define CTBNRLE_STREAMEXTRA_H #include #include #include #include "defines.h" /* This header file does three separated (but related) things: 1. It defines a way to save and load via streams (ie serialize) maps and vectors from STL. Because of scoping issues it has to be defined in the std namespace (which is unfortunate) 2. It defines a function addnan which can be called on any input or output character stream. It makes sure that stream can read or write "NaN" and infinite values properly. 3. It defines a SteamObj class. This class can save and load any of its descendents to or from a stream without knowing the type at compile time. That is, you can load and save pointers without having the object "sliced" to the type of the pointer. Using it is slightly complex. Basically, you need to define a class that inherits (directly or indirectly) from StreamObj. This class needs two things: i. a constructor with parameter "std::istream &", and ii. a virtual method Save that returns void and takes in a "std::ostream &os" The former serves as a "load" method and the later as a save method. In the class declaration (on the first line) you need to add the macro "SOBJCLASSDECL(xxx)" where xxx is the name of the class. Similarly, in the definition (.cc) file, you need to add the macro "SOBJCLASSDEF(xxx)" Below is a simple example. The first constructor is not required by the class. -------------in the .h file: class A : public StreamObj { SOBJCLASSDECL(A) public: A(int xx, int yy); A(std::istream &is); virtual void Save(std::ostream &os) const; private: int x,y; }; --------------in the .cc file: SOBJCLASSDEF(A) A::A(int xx, int yy) { x = xx; y = yy; } A::A(std::istream &is) { is >> x >> y; } void A::Save(std::ostream &os) const { os << x << os.fill() << y; } --------------- To use the capabilities, simply save pointers to objects by calling "SavePtr" and load them using StreamObj::LoadPtr. For example: A *aptr = new A(2,3); aptr->SavePtr(cout); // save object pointed to by aptr to cout // Note that if aptr pointed to a derived class of A, // provided it also had the form above, it would be saved // in its entirety (not just the "class A" portion). StreamObj *objptr = StreamObj::LoadPtr(cin); // load obj from cin // Note that the resulting object will be of the // "correct" type and not necessarily a StreamObj object. */ #include #include #include #include #include #include // first declaration of some helper methods to push maps and vectors // automatically through operator<< and operator>> // It would be *great* if this could be taken out of the std namespace! // Unfortunately, with current C++ compilers (March 2010), that is not // possible. namespace std { template ostream &operator<<(ostream &os, const pair &p) { return os << p.first << os.fill() << p.second; } template istream &operator>>(istream &is, pair &p) { return is >> p.first >> p.second; } // basically the same as istream_iterator, but with a limit // on the number of items that can be read. template, typename Dist = ptrdiff_t> class istream_lim_iterator : public iterator { private: int togo; basic_istream *s; T t; public: istream_lim_iterator() { togo = -1; s = NULL; } istream_lim_iterator(basic_istream &is, int limit) : s(&is), togo(limit) { read(); } istream_lim_iterator& operator++() { read(); return *this; } const istream_lim_iterator operator++(int) { istream_lim_iterator tmp = *this; (*this)++; return tmp; } const T operator*() const { return t; } const T* operator->() const { return &(operator*()); } bool isequal(const istream_lim_iterator &ili) const { return (togo==-1 && ili.togo==-1) || (togo!=-1 && ili.togo!=-1); } private: void read() { if (togo>=0) { if (togo>0 && s && s->good()) { *s >> t; togo--; if (!s || !s->good()) togo = -1; } else togo=-1; } } }; template inline bool operator==( const istream_lim_iterator &x, const istream_lim_iterator &y) { return x.isequal(y); } template inline bool operator!=( const istream_lim_iterator &x, const istream_lim_iterator &y) { return !x.isequal(y); } // And now we have the definitions of the shift operators: template ostream &operator<<(ostream &os, const map &m) { os << m.size() << os.fill(); copy(m.begin(),m.end(),ostream_iterator >(os," ")); return os; } template istream &operator>>(istream &is, map &m) { m.clear(); int s; is >> s; m.insert(istream_lim_iterator >(is,s), istream_lim_iterator >()); return is; } template ostream &operator<<(ostream &os, const vector &v) { os << v.size() << os.fill(); copy(v.begin(),v.end(),ostream_iterator(os," ")); return os; } template istream &operator>>(istream &is, vector &v) { int s; is >> s; v.assign(istream_lim_iterator(is,s), istream_lim_iterator()); return is; } } // Here is some code to define a new locale so that floating point // numbers like -inf and Nan can be read and written. // You can forget about all of it. What you need to do is call // this function (addnan) on any input or output stream before // using it. It will make sure that stream reads or writes "inf" // and "NaN" values properly void addnan(std::basic_ios &str); // This should work, but someone didn't add num_put_byname // (there is time_put_byname however!) //template //class nannum_put : public std::num_put_byname { // so instead we do... template > class nannum_put : public std::num_put { public: explicit nannum_put(const char *locname) //: std::num_put_byname(locname) {} : std::num_put() {} protected: virtual OutputIterator do_put(OutputIterator itt, std::ios_base &iob, charT fill, double d) const { if (!isfinite(d)) { itt = writeit(itt,d); } else { //itt = std::num_put_byname:: // do_put(itt,iob,fill,d); itt = std::num_put:: do_put(itt,iob,fill,d); } return itt; } virtual OutputIterator do_put(OutputIterator itt, std::ios_base &iob, charT fill, long double d) const { if (!isfinite(d)) { itt = writeit(itt,d); } else { //itt = std::num_put_byname:: // do_put(itt,iob,fill,d); itt = std::num_put:: do_put(itt,iob,fill,d); } return itt; } virtual OutputIterator do_put(OutputIterator itt, std::ios_base &iob, charT fill, float f) const { if (!isfinite(f)) { itt = writeit(itt,f); } else { //itt = std::num_put_byname:: // do_put(itt,iob,fill,f); itt = std::num_put:: do_put(itt,iob,fill,f); } return itt; } private: template static OutputIterator writeit(OutputIterator itt, FP f) { static const char *minus = "-"; // commented-out code works for g++'s STL, but // not for others. Uncommented-out code is fine // more generally if (isinf(f)) { if (f<0) //itt = std::__write(itt,"-",1); std::copy(minus,minus+1,itt); std::copy(infstr, infstr +std::char_traits::length(infstr), itt); /* itt = std::__write(itt, infstr, std:: char_traits:: length(infstr)); */ } else std::copy(nanstr, nanstr +std::char_traits::length(nanstr), itt); /* itt = std::__write(itt, nanstr, std:: char_traits:: length(nanstr)); */ return itt; } }; // This should work, but someone didn't add num_get_byname // (there is time_get_byname however!) //template //class nannum_get : public std::num_put_byname { // so instead we do... template > class nannum_get : public std::num_get { public: typedef std::basic_string strtype; explicit nannum_get(const char *locname) //: std::num_get_byname(locname) {} : std::num_get() {} protected: virtual InputIterator do_get(InputIterator beg, InputIterator end, std::ios_base &iob, std::ios_base::iostate &iost, double &d) const { bool negate = false; if (*beg == '-') { negate=true; ++beg; } if (*beg == '+') { negate=false; ++beg; } if (*beg==*infstr) { if (checkstr(beg,end,infstr,iost)) d = negate ? -INFINITY : INFINITY; return beg; } else if (*beg==*nanstr) { if (checkstr(beg,end,nanstr,iost)) d = nan(""); return beg; } else { InputIterator ret = std::num_get:: do_get(beg,end,iob,iost,d); if (negate) d = -d; return ret; } } virtual InputIterator do_get(InputIterator beg, InputIterator end, std::ios_base &iob, std::ios_base::iostate &iost, long double &d) const { bool negate = false; if (*beg == '-') { negate = true; ++beg; } if (*beg == '+') { negate = false; ++beg; } if (*beg==*infstr) { if (checkstr(beg,end,infstr,iost)) d = negate ? -INFINITY : INFINITY; return beg; } else if (*beg==*nanstr) { if (checkstr(beg,end,nanstr,iost)) d = nanl(""); return beg; } else { InputIterator ret = std::num_get:: do_get(beg,end,iob,iost,d); if (negate) d = -d; return ret; } } virtual InputIterator do_get(InputIterator beg, InputIterator end, std::ios_base &iob, std::ios_base::iostate &iost, float &f) const { bool negate = false; if (*beg == '-') { negate = true; ++beg; } if (*beg == '+') { negate = false; ++beg; } if (*beg==*infstr) { if (checkstr(beg,end,infstr,iost)) f = negate ? -INFINITY : INFINITY; return beg; } else if (*beg==*nanstr) { if (checkstr(beg,end,nanstr,iost)) f = nanf(""); return beg; } else { InputIterator ret = std::num_get:: do_get(beg,end,iob,iost,f); if (negate) f = -f; return ret; } } private: static bool checkstr(InputIterator &beg, InputIterator end, charT *str, std::ios_base::iostate &iost) { while(*str && beg!=end) { if (*beg==*str) { ++str; ++beg; } else { iost |= std::ios_base::failbit; return false; } } return *str==0; } }; // End of stuff for addnan namespace ctbn { // This is the StreamObj. The macros below take care of all of the // internals for inherited classes. Do NOT overload any of these // methods except Save! Just define Save and a constructor (from // an input stream) for your new class and ignore the rest. // // The top of this file has instructions and an example class StreamObj { public: StreamObj(); virtual ~StreamObj(); // This is the one that saves the info and should // be called on all ptrs void SavePtr(std::ostream &os) const; // This is standard (and the one you should overwrite) virtual void Save(std::ostream &os) const; // this one does all of the "RTTI-ish" stuff // this is the one you want to call to "load a ptr" static StreamObj *LoadPtr(std::istream &is); // this is the straight-forward one static StreamObj *Create(std::istream &is); typedef StreamObj* (*CreatorFn)(std::istream &); static const char *addID(const char *classname, CreatorFn fn); static const char *addID(const char *classname, const char *classname2, CreatorFn fn); static char *killSpaces(const char *classname); static const char *IDname; protected: virtual const char *myID() const; private: static std::map &loadtable(); }; #define SOBJCLASSDECL(classname) \ protected: \ virtual const char *myID() const; \ public: \ static const char *IDname; \ static StreamObj *Create(std::istream &is); #define SOBJCLASSDEF(classname) \ const char *classname::IDname = StreamObj::addID(#classname,classname::Create); \ StreamObj *classname::Create(std::istream &is) { \ return new classname(is); \ } \ const char *classname::myID() const { return IDname; } \ //StreamObj::addID(#classname,tclass::IDname,classname::Create); // This isn't that portable as typeid( ).name() isn't gauranteed to // return anything useful and it certainly doesn't return the same thing // across compilers. // Another fix doesn't really seem possible just now. // (asking class tclass for its name gets into a circular mess // where that name might not be set up yet) #define SOBJCLASSDEFTEMPL1(classname,tclass) \ template \ const char *classname::IDname = \ StreamObj::addID(typeid(classname).name(),classname::Create); \ template \ StreamObj *classname::Create(std::istream &is) { \ return new classname(is); \ } \ template \ const char *classname::myID() const { return IDname; } \ } // end of ctbn namespace #endif ctbnrle.1.0.2/hdr/famscore.h0000644000464200007770000000436311344302770015773 0ustar csheltoncsprofs/* 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 CTBNRLE_FAMSCORE_H #define CTBNRLE_FAMSCORE_H #include "suffstatsquery.h" #include "context.h" namespace ctbn { // Classes to handle the scoring done in structure search. // Each class takes in the relevant prior parameters and a // SuffStatsQuery object that provides the sufficient statistics // for scoring. class FamScore { public: // The pointer is owned by the caller of the constructor // but the pointer must remain valid for the life of the FamScore object FamScore(double nTrans, double aTime, SuffStatsQuery* ssQ); ~FamScore(); virtual double GetScore(const Context& v, const Context& cv) = 0; protected: double numTrans; double amtTime; // This class does not own this pointer SuffStatsQuery* ssQuery; }; class BNFamScore : public FamScore { public: // The pointer is owned by the caller of the constructor // but the pointer must remain valid for the life of the object BNFamScore(double nTrans, SuffStatsQuery* ssQ); ~BNFamScore(); virtual double GetScore(const Context& v, const Context& cv); }; class CTBNFamScore : public FamScore { public: // The pointer is owned by the caller of the constructor // but the pointer must remain valid for the life of the object CTBNFamScore(double nTrans, double aTime, SuffStatsQuery* ssQ); ~CTBNFamScore(); virtual double GetScore(const Context& v, const Context& cv); }; } // end of ctbn namespace #endif ctbnrle.1.0.2/README0000644000464200007770000000311411344302770014117 0ustar csheltoncsprofs--------------------------------------------------------------------------- |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 . | --------------------------------------------------------------------------- The doc/ directory contains more information including sponsorship, code documentation, and references to related academic papers. License information is in COPYING ctbnrle.1.0.2/tst/0000755000464200007770000000000011344303125014045 5ustar csheltoncsprofsctbnrle.1.0.2/tst/unit_test.py0000644000464200007770000000435411344302770016450 0ustar csheltoncsprofs#! /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 EXES the file from EXES= 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 EXES= is in the line just being passed in if sLine.startswith("EXES="): 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 EXES= run those file if "EXES=" 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" #os.system("make clean") ctbnrle.1.0.2/tst/unit_test_ctbn.dot0000644000464200007770000000140211344302770017603 0ustar csheltoncsprofsdigraph 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.1.0.2/tst/makedrug.cc0000777000464200007770000000000011344302770021422 2../demo/makedrug.ccustar csheltoncsprofsctbnrle.1.0.2/tst/clique_unit_test.cc0000644000464200007770000001114511344302770017743 0ustar csheltoncsprofs/* 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; using namespace ctbn; 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.ctbn 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.1.0.2/tst/run_burnin_error.pl0000755000464200007770000000215211344302770020004 0ustar csheltoncsprofs#!/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.1.0.2/tst/run_n_error.pl0000755000464200007770000000227611344302770016753 0ustar csheltoncsprofs#!/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.1.0.2/tst/gibbsquery.cc0000644000464200007770000000610111344302770016533 0ustar csheltoncsprofs/* 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 "samplinginf.h" #include "gibbssampler.h" #include "params.h" #include "bn.h" #include "ctbndyn.h" #include "em.h" #include "utils.h" #include using namespace std; using namespace ctbn; // this file reads in the input model & evidence & true query results from "queryinput.data" // it calculate the queries using gibbs sampling and compare the results with the true ones // if the error is within the threshold, this unittest successes, o.w.error message will pop out 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 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.1.0.2/tst/unit_test_traj.graph0000644000464200007770000000037611344302770020141 0ustar csheltoncsprofs0 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.1.0.2/tst/Makefile0000644000464200007770000000506711344302770015522 0ustar csheltoncsprofsSRC = 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/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 -Y -- $(CXXFLAGS) -- # keep line below as "EXES=" and not "EXES =" so that python script parses # it correctly EXES= clique_unit_test unit_testing_graphic exactquery importancequery gibbsquery DATA = queryinput.data drug.ctbn drug.ctbndyn all: $(EXES) $(DATA) queryinput.data: makequeryinput (rm -f queryinput.data; ./makequeryinput > queryinput.data) drug.ctbn: makedrug (rm -f drug.ctbn; ./makedrug > drug.ctbn) drug.ctbndyn: makedrug (rm -f drug.ctbndyn; ./makedrug -Dsavedyn 1 > drug.ctbndyn) 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) makequeryinput: makequeryinput.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o makequeryinput makequeryinput.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) makedrug: makedrug.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o makedrug makedrug.o $(LIBOBJ) depend: $(SRC) $(DEPEND) $(SRC) clean: rm -rf *.o *~ output2.eps output.dot output_test_ctbn.dot output_unit_test.eps unit_testing_graphic makedrug $(EXES) # DO NOT DELETE ctbnrle.1.0.2/tst/exactquery.cc0000644000464200007770000000560011344302770016554 0ustar csheltoncsprofs/* 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; using namespace ctbn; // this file reads in the input model & evidence & true query results from "queryinput.data" // it calculate the queries using exact inference and compare the results with the true ones // if the error is within the threshold, this unittest successes, o.w.error message will pop out 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.1.0.2/tst/unit_testing_graphic.cc0000644000464200007770000000702711344302770020600 0ustar csheltoncsprofs/* 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; using namespace ctbn; 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. 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110 newpath moveto (2.854) show showpage end ctbnrle.1.0.2/tst/makequeryinput.cc0000644000464200007770000000744711344302770017460 0ustar csheltoncsprofs/* 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; using namespace ctbn; // this file is used to create an example CTBN model and a partial trajectory sampled from the model // the model, the evidence, and the true results of queries will be saved to a file "queryinput.data" // "queryinput.data" will be feeded as an input to: // exactquery.cc, importancequery.cc and gibbsquery.cc // the above three .cc file will do the queries, and compare the results with the true ones in "queryinput.data" // the queries will be specified in these three files // In case you want to change the example model, you can modify this file and rewrite the output to "queryinput.data" // and then do the exactquery.cc/importancequery.cc/gibbsquery.cc 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(); // sample a partial trajectory as the input evidence 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 << 9.09241 << " " << 42.1912 << endl; return 0; } ctbnrle.1.0.2/tst/importancequery.cc0000644000464200007770000000613211344302770017612 0ustar csheltoncsprofs/* 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 "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; using namespace ctbn; // this file reads in the input model & evidence & true query results from "queryinput.data" // it calculate the queries using impotrance sampling and compare the results with the true ones // if the error is within the threshold, this unittest successes, o.w.error message will pop out 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.1.0.2/demo/0000755000464200007770000000000011344303120014152 5ustar csheltoncsprofsctbnrle.1.0.2/demo/README.txt0000644000464200007770000001006611344302767015673 0ustar csheltoncsprofsThe excutables in this folder give examples of how to use the CTBN-RLE libraries. CTBN models are usually loaded from files in the excutables. The following is a quick description of the file formats and their extensions File Ext Description .ctbn A CTBN model saved by method Save() .ctbnptr A CTBN model saved by method SavePtr() .ctbndyn A CTBNDyn model saved by method Save() .ctbndynptr A pointer of CTBNDyn model saved by method SavePtr() .traj A single trajectory saved by method Save() The example files drug.ctbn, drug.ctnbptr, and queryinput.data are created by the Makefile. This is because, for this version of the code, the file formats depend on the RTTI information supplied by the compiler, which differs across compilers. Demo executables ================ Constructing a network ---------------------- makedrug: use as "makedrug [options] > " Creates the drug network as used in Nodelman et al. 2002 and saves it to stdout. See makedrug.cc for details on optional parameters. makechain: use as "makechain [option] > " Creates a chain structured network and saves it to stdout. See makechain.cc for details on optional parameters. Generating trajectories ------------------------- gen_fulldata: use as "gen_fulldata <.ctbn file> [option]" Generates complete trajectories from a given CTBN and saves the trajectories in a file. The trajectories can be used as fully observed data for learning CTBN parameters. See gen_fulldata.cc for details on optional paramters. gen_partialdata: use as "gen_partialdata <.ctbn file> [option]" Generates incomplete trajectories from a given CTBN and save the trajectories in a file. The trajectories can be used as partially observed data for learning CTBN parameters using EM algorithm. See gen_partialdata.cc for details on optional patameters. Queries ------- exactquery: use as "exactquery" Load in model, evidence, true query answers from the file "queryinput.data" and compute queries using exact Markov inference and compare to the true answers. See exactquery.cc for details. importancequery: use as "importancequery" Load in model, evidence, true query answers from the file "queryinput.data" Compute queries using approximate inference via importance sampler and compare to the true answers. See importancequery.cc for details. gibbsquery: use as "gibbsquery" Load in model, evidence, true query answers from the file "queryinput.data" and compute queries using approximate inference via gibbs sampler and compare to the true answers. See gibbsquery.cc for details. Parameter learning ------------------ learnparams: use as "learnparams <.ctbn file> " Demonstates parameter learning of a given CTBN with complete data. The algorithm was described in Nodelman et al., 2003. The data can be generated using gen_fulldata. emlearn: use as "emlearn <.ctbn file> " Demonstate parameter learning of a given CTBN with incomplete data. The algorithm was described in Nodelman et al., 2005. The data can be generated using gen_partialdata. Structure learning ------------------ structurelearn: use as "structurelearn" Demonstrates structure learning on the drug network used in Nodelman et al. 2002. Reports on the difference in number of parameters for the learned BN and Hamming distance for the learned CTBN. See structurelearn.cc for details on optional parameters. sem: use as "sem" Demonstrates structural EM on the drug network used in Nodelman et al. 2002. Reports on the difference in number of parameters for the learned BN and Hamming distance for the learned CTBN. See sem.cc for details on optional parameters. Constructing a clique tree -------------------------- clique_example: use as "clique_example <.ctbn file>" Generates a clique tree from a CTBN. Visualization ------------- using_graphic: use as "using_graphic" Produces a .dot file for graphviz from a CTBN Draws a trajectory as a postscript file. see using_graphic.cc for details on optional parameters. ctbnrle.1.0.2/demo/makedrug.cc0000644000464200007770000002775011344302767016313 0ustar csheltoncsprofs/* 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 creates the drug effect network as first seen in // Continuous Time Bayesian Networks by Nodelman et al., 2002 and // save it to stdout // // Usage: makedrug [options] > // options: // // Set whether to save only the dynamics (as opposed to the full CTBN): // -Dsavedyn (default: 0 [=false]) // // Set whether to save as a pointer (which can be loaded into any superclass // -Dsaveptr (default: 0 [=false]) #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; using namespace ctbn; int main(int argc, char **argv) { InitParams(argc, argv); bool savedyn = ParamInt("savedyn",false); bool saveasptr = ParamInt("saveptr",false); 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 if (savedyn) { if (saveasptr) ctbndyn.SavePtr(cout); else ctbndyn.Save(cout); } else { if (saveasptr) m.SavePtr(cout); else m.Save(cout); } } ctbnrle.1.0.2/demo/sampleprocess.cc0000644000464200007770000000321711344302767017364 0ustar csheltoncsprofs/* 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; using namespace ctbn; 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.1.0.2/demo/sem.cc0000644000464200007770000004274411344302767015300 0ustar csheltoncsprofs/* 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. // To demonstrate structure search on incomplete data, trajectories // are sampled from the network and evidence is removed. SEM is then // used (with an underlying inference method) to learn the structure // and parameters of the model. /* Command line parameters: * * Set the number of trajectories * -Dnumtraj (default: 1000) * * Set the length of each trajectory * -Dtlen (default: 5.0) * * Set the alpha prior (prior belief for number of transitions) * -Dalpha (default: 1.0) * * Set the tau prior (prior belief for amount of time spent) * -Dtau (default: 1.0) * * Set the amount of loss in the trajectory data * -Dloss (default: 0.25) */ #include "markovdyn.h" #include "markov.h" #include "streamextra.h" #include "context.h" #include #include "multirv.h" #include "trajectory.h" #include "exactmarkovinf.h" #include "samplinginf.h" #include "expmethod.h" #include "importancesampler.h" #include "params.h" #include "ctbndyn.h" #include "bn.h" #include "em.h" #include "ctbn.h" #include "nonexpsuffstatsquery.h" #include "famscore.h" #include "grapheditsearch.h" #include "utils.h" using namespace std; using namespace ctbn; 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()); //Building independent structure MultiRV eatrvi(eatc, nullc); MultiRV fullrvi(fullc, nullc); MultiRV hungryrvi(hungryc, nullc); MultiRV uptakervi(uptakec, nullc); MultiRV concentrationrvi(concentrationc, nullc); MultiRV barometerrvi(barometerc, nullc); MultiRV painrvi(painc, nullc); MultiRV drowsyrvi(drowsyc, nullc); BN bnindep; bnindep.AddNode(eatrvi.Clone()); bnindep.AddNode(fullrvi.Clone()); bnindep.AddNode(hungryrvi.Clone()); bnindep.AddNode(uptakervi.Clone()); bnindep.AddNode(concentrationrvi.Clone()); bnindep.AddNode(barometerrvi.Clone()); bnindep.AddNode(painrvi.Clone()); bnindep.AddNode(drowsyrvi.Clone()); MarkovDyn eatindep(eatc, nullc); MarkovDyn fullindep(fullc, nullc); MarkovDyn hungryindep(hungryc, nullc); MarkovDyn uptakeindep(uptakec, nullc); MarkovDyn concentrationindep(concentrationc, nullc); MarkovDyn barometerindep(barometerc, nullc); MarkovDyn painindep(painc, nullc); MarkovDyn drowsyindep(drowsyc, nullc); CTBNDyn ctbndynindep; ctbndynindep.AddNode(eatindep.Clone()); ctbndynindep.AddNode(fullindep.Clone()); ctbndynindep.AddNode(hungryindep.Clone()); ctbndynindep.AddNode(uptakeindep.Clone()); ctbndynindep.AddNode(concentrationindep.Clone()); ctbndynindep.AddNode(barometerindep.Clone()); ctbndynindep.AddNode(painindep.Clone()); ctbndynindep.AddNode(drowsyindep.Clone()); Markov mindep(bnindep.Clone(), ctbndynindep.Clone()); cout << "Demo of Structural EM" << endl << "================================="<< endl; // Load the actual structure into a structure object Structure sABN; Structure sACTBN; bn.GetStructure(sABN); ctbndyn.GetStructure(sACTBN); cout << "Actual Structure (BN):\n"; sABN.Print(cout); cout << endl << endl; cout << "Actual Structure (CTBN):\n"; sABN.Print(cout); cout << endl << endl; // Set the number and length of trajectories to sample int numTrajs = ParamInt("numtraj",500); double tlen = ParamDouble("tlen",5.0); double loss = ParamDouble("loss",0.25); double factor = loss/0.25; const Context& con = ctbndyn.Domain(); int numNodes = ctbndyn.NumofNodes(); // Sample the trajectories and remove evidence randomly // in 0.25 time step segments. Also removes starting evidence // from half of the variables at random from each trajectory. cout << "Sampling trajectories...\n\n"; vector trVec; for(int k=0; k(sem_ctbn)->Scramble(); // Structure classes to load the learned structures into Structure sLBN; Structure sLCTBN; // Use importance sampling as the inference method for SEM ExpMethod method; SamplingInf inf; Sampler *sampler; sampler = new ImportanceSampler(sem_ctbn, &trVec[0], &method); inf.SetSampler(sampler); cout << "Running SEM...\n"; // Pass in data, starting process, and inference method to run SEM SEM(trVec,sem_ctbn,&inf); Markov *learned = dynamic_cast (sem_ctbn); // Get the learned Bayesian network const BN *learnedBN = dynamic_cast ( learned->GetStartDist()); learnedBN->GetStructure(sLBN); cout << "Learned structure (BN):\n"; sLBN.Print(cout); cout << endl; // Compute difference in number of parameters to measure accuracy int paramd = abs(sABN.GetNumParams(bn.Domain()) - sLBN.GetNumParams(learnedBN->Domain())); cout << "Param Distance: " << paramd << endl << endl; // Get the learned CTBN const CTBNDyn* learnedCTBN = dynamic_cast ( learned->GetDynamics()); learnedCTBN->GetStructure(sLCTBN); cout << "Learned structure (CTBN):\n"; sLCTBN.Print(cout); cout << endl; // Compute Hamming distance to measure accuracy int hamming = sACTBN.HammingDist(sLCTBN); cout << "Hamming Distance: " << hamming << endl << endl; } ctbnrle.1.0.2/demo/gibbsquery.cc0000644000464200007770000000600311344302767016654 0ustar csheltoncsprofs/* 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 "samplinginf.h" #include "gibbssampler.h" #include "params.h" #include "bn.h" #include "ctbndyn.h" #include "em.h" #include "utils.h" #include using namespace std; using namespace ctbn; // this file reads in the input model & evidence & true query results from "queryinput.data" // it calculate the queries using gibbs sampling // Usage: // no inputs to run: ./gibbsquery >output_file int main(int argc, char **argv) { // load the input model, evidence and query answers in "queryinput.data" randomizer.Reset(1); const char *input_file = "queryinput.data"; addnan(cout); InitParams(argc, argv); ifstream fin(input_file); // load model Markov m(fin); const CTBNDyn *ctbndyn = dynamic_cast(m.GetDynamics()); const BN *bn = dynamic_cast(m.GetStartDist()); Context context = ctbndyn->Domain() + ctbndyn->CondDomain(); //load evidence Trajectory evid; fin >> evid; // load true query answers double expect_q1, expect_q2; fin >> expect_q1 >> expect_q2; fin.close(); // construct the queries Instantiation x(context, -1); int querynode = ParamInt("QueryNode", 1); int querytimeval = ParamInt("QueryTimeVal", 1); Context c = ctbndyn->Node(querynode)->Domain(); x.SetVal(querynode, querytimeval); // query the amount of time node 1 spends on state 1 QueryTime query1(x); // query the number of times node 1 transition from state 0 to state 1 QueryTransition query2(c, 0, 1); // set up the approximate inference engine using gibbs sampler SamplingInf ginf; // number of burn in iterations int numofburniniter = 1000; // number of samples int numofsamples = 1000; Sampler *sampler; sampler = new GibbsSampler(&m, &evid, numofburniniter); ginf.SetSampler(sampler); ginf.SetProcess(&m); ginf.SetNumSample(numofsamples); // output the results cout << "the expected query answer is:" << endl; cout << expect_q1 << " " << expect_q2 << endl; cout << "the approximate inference answer using gibbs sampler to the query is:" << endl; cout << ginf.CalcQuery(query1) << " " << ginf.CalcQuery(query2) << endl; return 0; } ctbnrle.1.0.2/demo/gen_partialdata.cc0000644000464200007770000000556611344302767017634 0ustar csheltoncsprofs/* 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 n partially observed trajectories from //a given ctbn by sampling trajectories and randomly remove //certain fraction of information from the trajectories. // //Usage: gen_partialdata <.ctbnfile> /* Command line parameters: * * Set the begin time of the trajectory * -DBeginTime (default: 0.0) * * Set the end time of the trajectory * -DEndTime (default: 5.0) * * Set the number of trajectories * -DNumTraj (default: 1) * * Set the fraction of information removed in each iteration * -DRemoveFrac (default: 0.1) * * Set the number of iterations * -DRemoveIt (default 1) */ #include "markov.h" #include "trajectory.h" #include "params.h" #include "utils.h" #include using namespace std; using namespace ctbn; int main(int argc, char**argv) { if (argc<3) { cout << "Usage: ./gen_partialdata <.ctbn file> " << endl; exit(0); } InitParams(argc, argv); double begintime = ParamDouble("BeginTime" , 0.0); double endtime = ParamDouble("EndTime", 5.0); int n = ParamInt("NumTraj", 1); double frac = ParamDouble("RemoveFrac", 0.1); int nit = ParamInt("RemoveIt", 1); if (endtime <= begintime) { cout << "invalid length of trajectory" << endl; exit(0); } ifstream fin(argv[1]); if (!fin.good()) { cout << "bad CTBN file" << endl; exit(1); } //load CTBN model from the file Markov ctbn(fin); ofstream fdata(argv[2]); if (!fdata.good()) { cout << "bad output file" << endl; exit(1); } fdata << n << endl; //generate partially observed data Context context = ctbn.GetDynamics()->Domain(); int numvar = context.NumVars(); for(int i=0; i. */ // 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; using namespace ctbn; 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.1.0.2/demo/emlearn.cc0000644000464200007770000000401711344302767016126 0ustar csheltoncsprofs/* 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 shows how to learn the paramters of a CTBN //given partially observed data using EM algorithm. Data //are n trajectories saved in datafile. They can be generated //using gen_partialdata. Exact inference method is used in EM. #include "markov.h" #include "trajectory.h" #include "params.h" #include "em.h" #include "exactmarkovinf.h" #include using namespace std; using namespace ctbn; int main(int argc, char**argv) { InitParams(argc, argv); if (argc<3) { cout << "Usage: ./emlearn <.ctbn file> " << endl; exit(0); } ifstream fin(argv[1]); if (!fin.good()) { cout << "bad CTBN file" << endl; exit(1); } //load CTBN model from the file Markov ctbn(fin); ifstream fdata(argv[2]); if (!fdata.good()) { cout << "bad datafile" << endl; exit(1); } int n; fdata >> n; //load data vector data; for(int i=0; i. */ //This code shows how to learn the paramters of a CTBN //given fully observed data. Data are n trajectories //saved in datafile. They can be generated using //gen_fulldata #include "markov.h" #include "trajectory.h" #include "params.h" #include using namespace std; using namespace ctbn; int main(int argc, char**argv) { InitParams(argc, argv); if (argc<3) { cout << "Usage: ./learnparams <.ctbn file> " << endl; exit(0); } ifstream fin(argv[1]); if (!fin.good()) { cout << "bad CTBN file" << endl; exit(1); } //load CTBN model from the file Markov ctbn(fin); ifstream fdata(argv[2]); if (!fdata.good()) { cout << "bad datafile" << endl; exit(1); } int n; fdata >> n; //load data vector data; for(int i=0; i w(n, 1.0); //Calculate the sufficient statistics of the data SS *ss = ctbn.SuffStats(data, w); //randomly choose parameters ctbn.Scramble(); //learn the parameters from complete data ctbn.Maximize(ss); ctbn.Save(cout); if(ss!=NULL) delete ss; return 0; } ctbnrle.1.0.2/demo/gen_fulldata.cc0000644000464200007770000000437411344302767017136 0ustar csheltoncsprofs/* 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 n fully observed trajectories from //a given ctbn. // //Usage: gen_fulldata <.ctbnfile> /* Command line parameters: * * Set the begin time of the trajectory * -DBeginTime (default: 0.0) * * Set the end time of the trajectory * -DEndTime (default: 5.0) * * Set the number of trajectories * -DNumTraj (default: 1) * */ #include "markov.h" #include "trajectory.h" #include "params.h" #include using namespace std; using namespace ctbn; int main(int argc, char**argv) { if (argc<3) { cout << "Usage: ./gen_fulldata <.ctbn file> " << endl; exit(0); } InitParams(argc, argv); double begintime = ParamDouble("BeginTime" , 0.0); double endtime = ParamDouble("EndTime", 5.0); if (endtime <= begintime) { cout << "invalid length of trajectory" << endl; exit(0); } int n = ParamInt("NumTraj", 1); ifstream fin(argv[1]); if (!fin.good()) { cout << "bad CTBN file" << endl; exit(1); } //load CTBN model from the file Markov ctbn(fin); ofstream fdata(argv[2]); if (!fdata.good()) { cout << "bad output file" << endl; exit(1); } fdata << n << endl; //generate data for(int i=0; i. */ // Usage: makechain [options] > // // This code creates a chain structured network and saves // it into a file. // // The intensities of leaving each state for the head node // are the same, which can be set using the parameter "headrate". // When leaving the current state, the head node has equal // probability to jump to other states. // // For the other nodes, each node stays in its current state // if it matches its parents and otherwise transitions // to its parent’s state with a high probability (set using // parameter "chaserate".) /* Command line parameters: * * Set the number of nodes * -Dnumofnodes (default: 3) * * Set the number of states for each node * -Dnumofstates (default: 2) * * Set the intensity of leaving each state for the head node. * -Dheadrate (default: 1.0) * * Set the intensity of transitioning to the state that is the same * as the current state of the parent for nodes other than the head node * -Dchaserate (default: 1.0) * * Set the intensity of transitioning to other states * -Drandrate (default: 0.1) */ #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" using namespace std; using namespace ctbn; 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.1.0.2/demo/Makefile0000644000464200007770000000654411344302770015635 0ustar csheltoncsprofsLIBOBJ = ../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 EXES = clique_example makechain makedrug learnparams exactquery gibbsquery sampleprocess traj2ascii using_graphic structurelearn sem makequeryinput emlearn gen_fulldata gen_partialdata importancequery DATA = queryinput.data drug.ctbn drug.ctbndyn CXX = g++ OFLAG = -O2 #OFLAG = -ggdb CXXFLAGS += $(OFLAG) -I../hdr DEPEND = makedepend -Y -- $(CXXFLAGS) -- all: $(EXES) $(DATA) queryinput.data: makequeryinput (rm -f makequeryinput.data; ./makequeryinput > queryinput.data) drug.ctbn: makedrug (rm -f drug.ctbn; ./makedrug > drug.ctbn) drug.ctbndyn: makedrug (rm -f drug.ctbndyn; ./makedrug -Dsavedyn 1 > drug.ctbndyn) 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) emlearn: emlearn.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o emlearn emlearn.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) structurelearn: structurelearn.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o structurelearn structurelearn.o $(LIBOBJ) gen_fulldata: gen_fulldata.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o gen_fulldata gen_fulldata.o $(LIBOBJ) gen_partialdata: gen_partialdata.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o gen_partialdata gen_partialdata.o $(LIBOBJ) sem: sem.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o sem sem.o $(LIBOBJ) clean: rm -rf *.o *~ $(EXES) # DO NOT DELETE ctbnrle.1.0.2/demo/exactquery.cc0000644000464200007770000000531211344302770016666 0ustar csheltoncsprofs/* 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; using namespace ctbn; // this file reads in the input model & evidence & true query results from "queryinput.data" // it calculate the queries using exact inference // Usage: // no inputs to run: ./exactquery >output_file int main(int argc, char **argv) { // load the input model, evidence and query answers in "queryinput.data" const char *input_file = "queryinput.data"; addnan(cout); InitParams(argc, argv); ifstream fin(input_file); // load model Markov m(fin); const CTBNDyn *ctbndyn = dynamic_cast(m.GetDynamics()); const BN *bn = dynamic_cast(m.GetStartDist()); Context context = ctbndyn->Domain() + ctbndyn->CondDomain(); // load evidence Trajectory evid; fin >> evid; // load true query answers double expect_q1, expect_q2; fin >> expect_q1 >> expect_q2; fin.close(); // construct the queries Instantiation x(context, -1); int querynode = ParamInt("QueryNode", 1); int querytimeval = ParamInt("QueryTimeVal", 1); Context c = ctbndyn->Node(querynode)->Domain(); x.SetVal(querynode, querytimeval); // query the amount of time node 1 spends on state 1 QueryTime query1(x); // query the number of times node 1 transition from state 0 to state 1 QueryTransition query2(c, 0, 1); // set up the exact Markov inference engine ExactMarkovInf einf; einf.SetProcess(&m); einf.SetTrajectory(&evid); // output the results cout << "the expected query answer is:" << endl; cout << expect_q1 << " " << expect_q2 << endl; cout << "the exact inference answer to the query is:" << endl; cout << einf.CalcQuery(query1) << " " << einf.CalcQuery(query2) << endl; return 0; } ctbnrle.1.0.2/demo/makequeryinput.cc0000644000464200007770000000773211344302770017567 0ustar csheltoncsprofs/* 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; using namespace ctbn; // this file is used to create an example CTBN model and a partial trajectory sampled from the model // the model, the evidence, and the true results of queries will be saved to a file "queryinput.data" // "queryinput.data" will be feeded as an input to: // exactquery.cc, importancequery.cc and gibbsquery.cc // the above three .cc file will do the queries, and compare the results with the true answers in "queryinput.data" // the queries will be specified in these three files // In case you want to change the example model, you can modify this file and rewrite the output to "queryinput.data" // and then do the exactquery.cc/importancequery.cc/gibbsquery.cc // Usage: // no inputs to run: ./makequeryinput >output_file int main (int argc, char *argv) { // We do not want randomness in unittests. randomizer.Reset(1); // build up the context Context X,Y,Z; Context Null; X.AddVar(0,2); Y.AddVar(1,2); Z.AddVar(2,3); // build up the dynamics 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; // build up the start distribution 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(); // sample a partial trajectory as the input evidence 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); // output the model ctbn.Save(cout); cout << endl << evid << endl; // output the expected query results (exact): cout << 9.09241 << " " << 42.1912 << endl; return 0; } ctbnrle.1.0.2/demo/clique_example.cc0000644000464200007770000000455511344302770017501 0ustar csheltoncsprofs/* 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; using namespace ctbn; /* * Pass in ctbn file * */ int main(int argc, char **argv) { if(argc < 2) { cout << " you need to include a ctbn file" << endl; return 0; } //to initialize the CTBN ifstream ls(argv[1]); Markov ctbn(ls); //initializing the CliqueTree class CliqueTree check; //build the clique tree check.BuildClique(ctbn); 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. */ #include #include "graphic.h" #include "markov.h" #include "params.h" #include "utils.h" using namespace std; using namespace ctbn; /* * This is code for generating the illustration of CTBN and trajectory * It takes in the MARKOV file (.ctbn) to extract the information. * */ /* Command line parameters: * * Pass in CTBN file * -DFile (default: drug.ctbn) * * Set the window x coordinate size in trajectory * -Dtraj_win_size (default: 800) * * * Set the font on trajectory graph * -Dtraj_font (default: 15) * * * Set the font on ctbn graph * -Dctbn_font (default: 16) * * * Set font on trajectory graph ruler * -Dtraj_ruler (default: 5) */ 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 < 3) { cout << " you need to include a ctbn file" << endl; cout << " for example: -DFile drug.ctbn" << endl; return 0; } //Read in the -DFile File = ParamStr("File", "drug.ctbn"); 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 //do not use temp.dot as the file name ofstream file("TrajRank.eps"); ofstream file2("CTBNRank.dot"); ofstream file3("TrajWName.eps"); ofstream file4("TrajWoName.eps"); ofstream file5("CTBNMatrix.dot"); //creating the Graphic class name test Graphic test; //setting the font for the trajectory graph test.SetFontTrajectory(16, 10); //to change the size of page in X use the below function //test.SetSizeTrajectory(const int &); //setting the font size for CTBN graphic test.SetFontCTBN(20); //string_ is the vector of name which will replace the number system //automatically assigned when graphic.cc has envoked. vector string_; string_.push_back("Eating"); string_.push_back("Full Stomach"); string_.push_back("Hungry"); string_.push_back("Uptake"); string_.push_back("Concentration"); string_.push_back("Barometer"); string_.push_back("Joint pain"); string_.push_back("Drowsy"); //changing the name of the CTBN and trajectory graphics test.Changes(string_); // use DrawBoth for specific trajectory need to be draw // DrawBoth take in // const Markov &, Trajectory & // std::ofstream &,std::string &, std::ofstream & //drawing the CTBN graph with rank system intergrated //it need to know what the file2 name is string filename = "CTBNRank.dot"; test.DrawCTBNRank(ctbn,file2, filename, file); //use the DrawCTBN if CTBN without matrix is needed //drawing the CTBN with matrixs for each node test.DrawCTBNMatrix(ctbn, file5); /* / Generate the trajectory using the CTBNDyn */ //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(); Trajectory tr; //generating the trajectory starting from 0 to 3 second tr.SetBeginTime(0); tr.SetEndTime(3); ctbn.Sample(tr); //just save the trajectory which is created for later use ofstream os("using_graphic.traj"); //remove the part of the Information frmo the trajectory for(int i = 0; i < B->NumofNodes(); i ++) { RemoveInformation(tr,context, i, 1.0, 1.5); } tr.Save(os); //to draw the Index into trajectory graph test.DrawTrajectory(tr, context ,file3); //clear the changed name test.ClearName(); //draw the trajectory with name unchanged test.DrawTrajectory(tr,context ,file4); //example on DrawBoth //test.DrawBoth(ctbn,tr, file2, filename, file); //end of the file cout << "done" << endl; file.close(); file2.close(); file3.close(); file4.close(); file5.close(); return 0; } ctbnrle.1.0.2/demo/structurelearn.cc0000644000464200007770000004073511344302770017566 0ustar csheltoncsprofs/* 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. // To demonstrate structure search on complete data, trajectories // are sampled from the network and used to learn the structure. /* Command line parameters: * * Set the number of trajectories * -Dnumtraj (default: 1000) * * Set the length of each trajectory * -Dtlen (default: 5.0) * * Set the alpha prior (prior belief for number of transitions) * -Dalpha (defualt: 1.0) * * Set the tau prior (prior belief for amount of time spent) * -Dtau (defualt: 1.0) */ #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" #include "nonexpsuffstatsquery.h" #include "famscore.h" #include "grapheditsearch.h" using namespace std; using namespace ctbn; 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()); cout << "Demo of learning a CTBN structure" << endl << "================================="<< endl; // Load the actual structure into a structure object Structure sA; ctbndyn.GetStructure(sA); cout << "Actual Structure:\n"; sA.Print(cout); cout << endl << endl; // Set the number and length of trajectories to sample int numTrajs = ParamInt("numtraj",1000); double tlen = ParamDouble("tlen",5.0); // Sample the trajectories cout << "Sampling trajectories...\n\n"; vector trVec; for(int i=0; iSetData(&trVec); ssQuery->SetDynamics(&ctbndyn); // Create the scoring object by specifying priors and the // sufficient statistics object to use for scoring double alphaPrior = ParamDouble("alpha",1.0); double tauPrior = ParamDouble("tau",1.0); CTBNFamScore* cfs = new CTBNFamScore(alphaPrior,tauPrior,ssQuery); // Create the search object by providing the context to search // over and the scoring function to use GraphEditSearch ges(ctbndyn.Domain(),cfs); cout << "Learning the structure...\n\n"; // Calling LearnStructure() returns a structure object with the // learned structure Structure s = ges.LearnStructure(); cout << "Learned Structure:\n"; s.Print(cout); cout << endl; // Compared the learned structure against the actual structure // with Hamming distance cout << "Hamming Distance: " << s.HammingDist(sA) << endl; delete ssQuery, cfs; cout << endl << endl; cout << "Demo of learning a BN structure" << endl << "================================="<< endl; // Load the actual structure into a structure object Structure sABN; bn.GetStructure(sABN); cout << "Actual Structure:\n"; sABN.Print(cout); cout << endl << endl; // Sample the trajectories (point evidence for BNs) cout << "Sampling...\n\n"; vector trVecBN; for(int i=0; iSetData(&trVecBN); ssQueryBN->SetInitRV(&bn); // Create the scoring object by specifying priors and the // sufficient statistics object to use for scoring BNFamScore* bfs = new BNFamScore(alphaPrior,ssQueryBN); // Create the search object by providing the context to search // over and the scoring function to use // We need to specify true as the 3rd parameter here to restrict // to structures without cycles GraphEditSearch gesBN(bn.Domain(),bfs,true); cout << "Learning the structure...\n\n"; // Calling LearnStructure() returns a structure object with the // learned structure Structure sBN = gesBN.LearnStructure(); cout << "Learned Structure:\n"; sBN.Print(cout); cout << endl; // Compared the learned structure against the actual structure // by checking the difference between the number of parameters cout << "Difference in number of parameters: " << abs(sABN.GetNumParams(bn.Domain()) - sBN.GetNumParams(bn.Domain())) << endl; delete ssQueryBN, bfs; } ctbnrle.1.0.2/demo/importancequery.cc0000644000464200007770000000600311344302770017721 0ustar csheltoncsprofs/* 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 "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; using namespace ctbn; // this file reads in the input model & evidence & true query results from "queryinput.data" // it calculate the queries using impotrance sampling // Usage: // no inputs to run: ./importancequery >output_file int main(int argc, char **argv) { // load the input model, evidence and query answers in "queryinput.data" randomizer.Reset(1); const char *input_file = "queryinput.data"; addnan(cout); InitParams(argc, argv); ifstream fin(input_file); // load model Markov m(fin); const CTBNDyn *ctbndyn = dynamic_cast(m.GetDynamics()); const BN *bn = dynamic_cast(m.GetStartDist()); Context context = ctbndyn->Domain() + ctbndyn->CondDomain(); //load evidence Trajectory evid; fin >> evid; // load true query answers double expect_q1, expect_q2; fin >> expect_q1 >> expect_q2; fin.close(); // construct the queries Instantiation x(context, -1); int querynode = ParamInt("QueryNode", 1); int querytimeval = ParamInt("QueryTimeVal", 1); Context c = ctbndyn->Node(querynode)->Domain(); x.SetVal(querynode, querytimeval); // query the amount of time node 1 spends on state 1 QueryTime query1(x); // query the number of times node 1 transition from state 0 to state 1 QueryTransition query2(c, 0, 1); // set up the approximate inference engine using importance sampler SamplingInf iinf; // set the number of samples int numofsamples = 1000; Sampler *sampler; ExpMethod method; sampler = new ImportanceSampler(&m, &evid, &method); iinf.SetSampler(sampler); iinf.SetProcess(&m); iinf.SetNumSample(numofsamples); // output the results cout << "the expected query answer is:" << endl; cout << expect_q1 << " " << expect_q2 << endl; cout << "the approximate inference answer using importance sampler to the query is:" << endl; cout << iinf.CalcQuery(query1) << " " << iinf.CalcQuery(query2) << endl; return 0; } ctbnrle.1.0.2/demo/makedrug2.cc0000644000464200007770000003105611344302770016361 0ustar csheltoncsprofs/* 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; using namespace ctbn; 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.1.0.2/src/0000755000464200007770000000000011344303133014021 5ustar csheltoncsprofsctbnrle.1.0.2/src/bn.cc0000644000464200007770000002716411344302770014747 0ustar csheltoncsprofs/* 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 "bn.h" #include "multirv.h" namespace ctbn { using namespace std; SOBJCLASSDEF(BN) BN::BN() : RV(Context(),Context()) { } BN::BN(istream &is) : RV(is) { int n; is >> n; nodes.resize(n); for(int i=0;i(StreamObj::LoadPtr(is)); } BN::BN(const BN &bn) : RV(bn), nodes(bn.nodes.size()) { int n = nodes.size(); for(int i=0;iClone(); } BN::BN(const Structure &s, const Context &vars) : RV(Context(), Context()) { vector > parentlist = s.parentlist; int n = parentlist.size(); for(int i=0;i currentParentlist = parentlist[i]; Context v; Context cv; if(vars.HasId(i)) v.AddVar(i,vars.Cardinality(i)); else continue; for(int j=0;jClone(); sampleorder = bn.sampleorder; } return *this; } BN *BN::Clone() const { return new BN(*this); } BN::~BN() { int n = nodes.size(); for(int i=0;i> n; nodes.resize(n); for(int i=0;i(StreamObj::LoadPtr(is)); sampleorder.clear(); } void BN::Save(ostream &os) const { RV::Save(os); int n = nodes.size(); os << os.fill() << n; for(int i=0;iSavePtr(os); } } void BN::Normalize() { assert(0); // not implemented (requires changing structure of network) // below would work, but would create one large node: RV *v = Joint(Context()); // don't condition on anything v->Normalize(); sampleorder.clear(); int n = nodes.size(); for(int i=0;iClone(); ans->Restrict(x); //ans->Project(ans->Domain()/rest, ans->CondDomain()/rest); int n = nodes.size(); for(int i=1;iClone(); v->Restrict(x); //v->Project(v->Domain()/rest, v->CondDomain()/rest); ans->MultBy(v); delete v; } return ans; } // This is the variable elimination that does // not sum out until the very end (not efficient) double BN::Prob(const Instantiation &x, bool log) const { RVSimple *v = MakeSimple(x); double ret = v->Sum(log); delete v; return ret; } void BN::Restrict(const Instantiation &x) { int n= nodes.size(); for(int i=0;iRestrict(x); } void BN::Project(const Context &c, const Context &cc) { assert(0); // not yet implemented RV::Project(c,cc); } void BN::MultBy(const RV *x) { assert(0); // again, not yet implemented } BNSS *BN::BlankSS() const { BNSS *ret = new BNSS(); int n = nodes.size(); ret->rvss.resize(n); for(int i=0;irvss[i] = nodes[i]->BlankSS(); return ret; } void BN::AddSS(const Instantiation &x, SS *ss, double w) const { BNSS *bnss = dynamic_cast(ss); // x should be a complete instantiation, so this should work // fine (and it not complex at all) int n = nodes.size(); for(int i=0;iAddSS(x,bnss->rvss[i],w); } void BN::AddExpSS(const Instantiation &x, SS *ss, double w) const { RVSimple *rvs = MakeSimple(x); AddExpSS(x,rvs,ss,w); delete rvs; } // Again, this one is correct (hopefully) but in no way efficient! void BN::AddExpSS(const Instantiation &x, const RVSimple *rvs, SS *ss, double w) const { BNSS *bnss = dynamic_cast(ss); int n = nodes.size(); Instantiation knowncond(CondDomain(),x); for(int i=0;iDomain()+nodes[i]->CondDomain(); Instantiation inst(var,0); Context conditt = nodes[i]->CondDomain() - CondDomain(); inst.SetVal(knowncond); do { Instantiation cond(nodes[i]->CondDomain(),inst); vector > reind; do { vector vv; Domain().ConsistentIndexes(vv,inst); reind.push_back(vv); } while(inst.Inc(nodes[i]->Domain())); RVSimple *subrv = rvs->Clone(); subrv->Reindex(reind); nodes[i]->AddExpSS(cond,subrv,bnss->rvss[i],w); delete subrv; } while(inst.Inc(conditt)); } } void BN::Sample(Instantiation &x, Random &rand) const { RV *v = Joint(x); v->Normalize(); Instantiation sample(v->Domain()); v->Sample(sample,rand); x.SetVal(sample,true); } void BN::Maximize(const SS *ss) { const BNSS *bnss = dynamic_cast(ss); int n = nodes.size(); for(int i=0;iMaximize(bnss->rvss[i]); } RVSimple *BN::MakeSimple(const Instantiation &x, bool normalize) const { RV *v = Joint(x); RVSimple *ret = v->MakeSimple(x,normalize); delete v; return ret; } void BN::AddNode(RV *rv) { nodes.push_back(rv); v = v+rv->Domain(); cv = (cv+rv->CondDomain())-v; sampleorder.clear(); } void BN::SetSampleOrder() const { sampleorder.clear(); int numnode = nodes.size(); bool *mat = new bool[numnode*numnode]; bool *added = new bool[numnode]; int *numpar = new int[numnode]; for(int i=0; iCondDomain(); if(conddomain.VarList().size()==0) continue; for(int j=0; jDomain(); if(conddomain.IsSubset(nodedomain)) { mat[j*numnode+i] = 1; numpar[i]++; } } } bool change = true; while (change) { change = false; for(int i=0; i::const_iterator i=sampleorder.begin(); i!=sampleorder.end();++i) { // If there is only one variable per node, this is faster; if(nodes[*i]->Domain().NumVars()==1) { if (x.Value(nodes[*i]->Domain().VarList()[0])!=-1) { if (log) ret += nodes[*i]->Prob(x,true); else ret *= nodes[*i]->Prob(x,false); } else { nodes[*i]->Sample(x,rand); } } else { // If not, this is necessary: if (log) ret += nodes[*i]->Prob(x,true); else ret *= nodes[*i]->Prob(x,false); nodes[*i]->Sample(x,rand); } } return ret; } void BN::Scramble(double alpha, double degree, Random &rand) { int n = nodes.size(); for(int i=0; iScramble(alpha, degree, rand); } double BN::LLH(const SS *ss) const { double llh = 0.0; const BNSS *bnss = dynamic_cast(ss); int n = nodes.size(); for(int i=0; iLLH(bnss->rvss[i]); } return llh; } double BN::GetScore(double numTrans, const SS* ss) const { const BNSS *bnss = dynamic_cast(ss); double score(0); int numNodes = NumofNodes(); for(int i = 0; i < numNodes; i++) score += nodes[i]->GetScore(numTrans, bnss->rvss[i]); return score; } // Assumes the source BN has the same organization of nodes. void BN::FillParams(const BN &src) { for(int i = 0; i < NumofNodes(); i++) { const MultiRV *csrv = dynamic_cast(src.nodes[i]); MultiRV *crv = dynamic_cast(nodes[i]); const Context &csrc = csrv->CondDomain(); const Context &cur = crv->CondDomain(); Instantiation ins(csrc); ins.SetAllVal(0); int nstates = crv->Domain().Size(); Context diff = csrc - cur; Instantiation curins(cur); Instantiation diffins(diff); curins.SetAllVal(0); diffins.SetAllVal(0); do { vectr avgDist(nstates,0.0); int count = 0; // Compute average IM over extra vars do { ins.SetVal(curins); ins.SetVal(diffins); vectr thisDist(nstates,0.0); double logfactor = 0.0; (*csrv)[ins].GetDist(thisDist,logfactor); avgDist += thisDist; count++; } while(diffins.Inc()); avgDist /= count; (*crv)[curins].SetDist(avgDist); } while(curins.Inc()); } } void BN::PrepNode2IdMaps() const { if (node2var.size() == nodes.size()) return; ClearNode2IdMaps(); int m = nodes.size(); node2var.resize(m); for(int j=0;jDomain().VarList()[0]; var2node[node2var[j]] = j; vector par = nodes[j]->CondDomain().VarList(); for(int k=0;k())); } } void BN::ClearNode2IdMaps() const { node2var.clear(); var2node.clear(); children.clear(); } const RV* BN::NodeByVar(int i) const { PrepNode2IdMaps(); int idx = var2node[i]; return nodes[idx]; } void BN::GetStructure(Structure &s) const { PrepNode2IdMaps(); s.var2node = var2node; s.node2var = node2var; s.children = children; int n = nodes.size(); s.parentlist.clear(); for (int i=0; iCondDomain(); s.parentlist.push_back(c.VarList()); } } SOBJCLASSDEF(BNSS) BNSS::BNSS() { } BNSS::BNSS(istream &is) { int n; is >> n; rvss.resize(n); for(int i=0;i(StreamObj::LoadPtr(is)); } BNSS::BNSS(const BNSS &bnss) { int n = rvss.size(); for(int i=0;iClone(); } BNSS &BNSS::operator=(const BNSS &bnss) { if (&bnss!=this) { int n = rvss.size(); for(int i=0;iClone(); } return *this; } BNSS *BNSS::Clone() const { return new BNSS(*this); } BNSS::~BNSS() { int n = rvss.size(); for(int i=0;i> n; rvss.resize(n); for(int i=0;i(StreamObj::LoadPtr(is)); } void BNSS::Save(ostream &os) const { int n = rvss.size(); os << n; for(int i=0;iSavePtr(os); } } void BNSS::Scale(double w) { int n = rvss.size(); for(int i=0; iScale(w); } void BNSS::AddSS(const SS* nss, double w) { int n = rvss.size(); const BNSS *bss = dynamic_cast(nss); for(int i=0; iAddSS(bss->rvss[i], w); } } // end of ctbn namespace ctbnrle.1.0.2/src/em.cc0000644000464200007770000000560411344302770014744 0ustar csheltoncsprofs/* 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 "em.h" #include "params.h" #include "utils.h" #include "markov.h" #include "exactmarkovinf.h" #include "structure.h" #include "ctbndyn.h" #include "dynamics.h" #include "expsuffstatsquery.h" #include "nonexpsuffstatsquery.h" #include "brutestructuresearch.h" #include "grapheditsearch.h" #include "famscore.h" namespace ctbn { using namespace std; void EM(const vector &data, Process *dist, Inference *inf) { int nitt = ParamInt("EMItt", 5); int ndata = data.size(); for(int i = 0; i < nitt; i++) { inf->SetProcess(dist); SS *ss = dist->BlankSS(); for(int j=0;jSetTrajectory(&data[j]); inf->AddExpSuffStats(ss); } dist->Maximize(ss); delete ss; } } void SEM(const vector &data, Process *&dist, Inference *inf) { int nitt = ParamInt("SEMItt", 10); double nTrans = ParamDouble("ptrans", 1.0); double nTime = ParamDouble("ptime", 1.0); Structure s; // Initial parameter update EM(data,dist,inf); for(int i = 0; i < nitt; i++) { ExpSuffStatsQuery* essQuery = new ExpSuffStatsQuery(inf->Clone(),true); essQuery->SetData(&data); essQuery->SetProcess(dist); // Structure modification step Markov* m = dynamic_cast(dist); const BN* bnet = dynamic_cast(m->GetStartDist()); const CTBNDyn* cdyn = dynamic_cast(m->GetDynamics()); BNFamScore* bfs = new BNFamScore(nTrans,essQuery); GraphEditSearch gesBN(bnet->Domain(),bfs,true); s = gesBN.LearnStructure(); delete bfs; RV* newrv = new BN(s, bnet->Domain()); dynamic_cast(newrv)->FillParams(*bnet); CTBNFamScore* cfs = new CTBNFamScore(nTrans,nTime,essQuery); GraphEditSearch ges(cdyn->Domain(),cfs); s = ges.LearnStructure(); delete cfs; delete essQuery; Dynamics* newdyn = new CTBNDyn(s, cdyn->Domain()); dynamic_cast(newdyn)->FillParams(*cdyn); Process* newdist = new Markov(newrv, newdyn); delete dist; dist = newdist; // Inference and parameter update step EM(data, dist, inf); } } } // end of ctbn namespace ctbnrle.1.0.2/src/grapheditsearch.cc0000644000464200007770000001222711344302770017477 0ustar csheltoncsprofs/* 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 "grapheditsearch.h" namespace ctbn { using namespace std; GraphEditSearch::GraphEditSearch(const Context& c, FamScore* fs, bool noCyc) : StructureSearch(c, fs), noCycles(noCyc), learnCalled(false) { int numNodes = vars.size(); sQ = new SearchQueue(numNodes*2*(numNodes-1), numNodes); Initialize(); } GraphEditSearch::~GraphEditSearch() { delete sQ; } void GraphEditSearch::SetStructure(const Structure& s) { for(int i=0; i& curParList = s.parentlist[i]; int curvarid = s.node2var[i]; map::iterator it = var2node.find(curvarid); int ssnodeid = it != var2node.end() ? it->second : -1; if(ssnodeid == -1) continue; delete nodes[ssnodeid]; Context cv; for(int j=0; jsecond : -1; if(pnodeid == -1) continue; cv = cv + vars[pnodeid]; } double s = fScore->GetScore(vars[ssnodeid], cv); nodes[ssnodeid] = new Node(vars[ssnodeid], cv); scores[ssnodeid] = s; } } void GraphEditSearch::Initialize() { scores.clear(); for(int i=0; iGetScore(vars[i],nullc); nodes.push_back(new Node(vars[i],nullc)); scores.push_back(s); } } void GraphEditSearch::EnqueueOps(int i) { vector varLvid = nodes[i]->CondDomain().VarList(); vector varL; // Map the VarList into a new vector using node ids instead. for(int j=0; jGetScore(nodes[i]->Domain(), nodes[i]->CondDomain() + vars[j]); double deltas = s - scores[i]; sQ->Add(SearchQueue::Action(i,j, SearchQueue::ADD,deltas,0)); } // Enqueue all REMOVE and REVERSE operations for(int j=0; jGetScore(nodes[i]->Domain(), nodes[i]->CondDomain() - vars[k]); double deltasrem = srem - scores[i]; sQ->Add(SearchQueue::Action(i,k, SearchQueue::REMOVE,deltasrem,0)); if(noCycles && ProducesCycle(k,i)) continue; double saddr = fScore->GetScore(nodes[k]->Domain(), nodes[k]->CondDomain() + vars[i]); double deltasaddr = saddr - scores[k]; sQ->Add(SearchQueue::Action(i,k, SearchQueue::REVERSE, deltasrem,deltasaddr)); } } bool GraphEditSearch::ProducesCycle(int i, int j) { Node* temp = nodes[i]; nodes[i] = new Node(nodes[i]->Domain(), nodes[i]->CondDomain() + vars[j]); Structure sCycleCheck; GetStructure(sCycleCheck); delete nodes[i]; nodes[i] = temp; return sCycleCheck.IsCyclic(); } Structure GraphEditSearch::LearnStructure() { Context nullc; int numNodes = vars.size(); if(learnCalled) Initialize(); for(int i=0; iHead(nextAct); while(nextAct.gain + nextAct.gainrev > 0) { int ch = nextAct.child; int pa = nextAct.parent; SearchQueue::searchop op = nextAct.oper; if(op == SearchQueue::ADD) { Node* newNode = new Node(nodes[ch]->Domain(), nodes[ch]->CondDomain() + vars[pa]); delete nodes[ch]; nodes[ch] = newNode; scores[ch] += nextAct.gain; } else if(op == SearchQueue::REMOVE) { Node* newNode = new Node(nodes[ch]->Domain(), nodes[ch]->CondDomain() - vars[pa]); delete nodes[ch]; nodes[ch] = newNode; scores[ch] += nextAct.gain; } else if(op == SearchQueue::REVERSE) { Node* newNode = new Node(nodes[ch]->Domain(), nodes[ch]->CondDomain() - vars[pa]); delete nodes[ch]; nodes[ch] = newNode; scores[ch] += nextAct.gain; newNode = new Node(nodes[pa]->Domain(), nodes[pa]->CondDomain() + vars[ch]); delete nodes[pa]; nodes[pa] = newNode; scores[pa] += nextAct.gainrev; } if(noCycles) { sQ->Clear(); for(int i = 0; i < vars.size(); i++) EnqueueOps(i); } else { sQ->Remove(ch); EnqueueOps(ch); if(op == SearchQueue::REVERSE) EnqueueOps(pa); } sQ->Head(nextAct); } Structure s; GetStructure(s); learnCalled = true; return s; } } // end of ctbn namespace ctbnrle.1.0.2/src/searchqueue.cc0000644000464200007770000001016611344302770016654 0ustar csheltoncsprofs/* 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 "searchqueue.h" #include "defines.h" #include #include namespace ctbn { using namespace std; SearchQueue::SearchQueue(int maxn, int nvars) { heap = new Action[maxn]; places = new deque[nvars]; n = 0; nv = nvars; } SearchQueue::~SearchQueue() { delete []heap; delete []places; } bool SearchQueue::Head(Action &a) { if (n<=0) return false; a = heap[0]; return true; } void SearchQueue::Add(const Action &a) { heap[n] = a; ++n; double key = a.gain + a.gainrev; int i; for(i=n-1;i>0 && key > heap[(i-1)/2].gain + heap[(i-1)/2].gainrev; i = (i-1)/2) { heap[i] = heap[(i-1)/2]; deque &list = places[heap[i].child]; list.erase(find(list.begin(),list.end(),(i-1)/2)); list.push_front(i); } heap[i] = a; places[a.child].push_front(i); } void SearchQueue::Remove(int child) { while(places[child].size() != 0) { int i = places[child].front(); places[child].pop_front(); if (i==0) { --n; if (n>0) { heap[0] = heap[n]; deque &list = places[heap[0].child]; list.erase(find(list.begin(),list.end(),n)); list.push_front(0); Heapify(0,heap[0].gain + heap[0].gainrev); } } else { i = Heapify(i,INFINITY); --n; deque &list = places[heap[i].child]; list.erase(find(list.begin(),list.end(),i)); if (i==n) continue; Action temp = heap[n]; while(i>0 && temp.gain + temp.gainrev > heap[(i-1)/2].gain + heap[(i-1)/2].gainrev) { heap[i] = heap[(i-1)/2]; deque &list2 = places[heap[i].child]; list2.erase(find(list2.begin(),list2.end(), (i-1)/2)); list2.push_front(i); i = (i-1)/2; } heap[i] = temp; deque &list2 = places[temp.child]; list2.erase(find(list2.begin(),list2.end(),n)); list2.push_front(i); } } } int SearchQueue::Heapify(int i, double tempk) { int l,r,large,oldlarge; double larget; oldlarge = -1; while(i tempk || isinf(tempk))) { large = l; larget = heap[l].gain + heap[l].gainrev; } else { large = i; larget = tempk; } if (r < n && heap[r].gain + heap[r].gainrev > larget) { large = r; larget = heap[r].gain + heap[r].gainrev; } if (large==i) { deque &list = places[heap[i].child]; deque::iterator it = find(list.begin(),list.end(),oldlarge); if(it != list.end()) list.erase(it); list.push_front(i); return i; } Action temp = heap[i]; heap[i] = heap[large]; heap[large] = temp; deque &list = places[temp.child]; deque::iterator it = find(list.begin(),list.end(),i); if(it != list.end()) list.erase(it); list.push_front(large); deque &list2 = places[heap[i].child]; it = find(list2.begin(),list2.end(),large); if(it != list2.end()) list2.erase(it); list2.push_front(i); i = large; oldlarge = large; } return i; // shouldn't get here } void SearchQueue::Clear() { n = 0; for(int i = 0; i < nv; i++) { places[i].clear(); } } void SearchQueue::Print() { for(int i = 0; i < n; i++) { cout << i << ": " << heap[i] << endl; } } void SearchQueue::PrintPlaceList() { for(int i = 0; i < nv; i++) { cout << i << ": "; for(int j = 0; j < places[i].size(); j++) cout << places[i][j] << " "; cout << endl; } } } // end of ctbn namespace ctbnrle.1.0.2/src/samplinginf.cc0000644000464200007770000001550511344302770016653 0ustar csheltoncsprofs/* 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 "samplinginf.h" #include "markov.h" #include "markovdyn.h" #include "multirv.h" #include "ctbndyn.h" #include "utils.h" namespace ctbn { using namespace std; SamplingInf::SamplingInf() { p = NULL; numofsamples = 100; timelimit = 0.0; isgibbs = false; } SamplingInf::~SamplingInf() { if(sampler!=NULL) delete sampler; } SamplingInf* SamplingInf::Clone() const { SamplingInf* siClone = new SamplingInf(*this); siClone->SetSampler(this->sampler->Clone()); return siClone; } void SamplingInf::SetProcess(const Process *pr) { if(sampler==NULL) { cout << "Sampler not set!" << endl; exit(1); } sampler->SetProcess(pr); p = dynamic_cast(pr); CleanSamples(); } void SamplingInf::SetSampler(const Sampler *s) { sampler = s->Clone(); if(p!=NULL) sampler->SetProcess(p); } void SamplingInf::SetTrajectory(const Trajectory *tr) { if(sampler==NULL) { cout << "Sampler not set!" << endl; exit(1); } if(p==NULL) { cout << "Process not set!" << endl; exit(1); } sampler->SetTrajectory(tr); CleanSamples(); } void SamplingInf::SetNumSample(int n) { numofsamples = n; } void SamplingInf::SetTimeLimit(double t, bool gibbs) { timelimit = t; isgibbs = gibbs; } // Sample completions of given evidence trajectory (stored in sampler), and // associate them with their weights. void SamplingInf::Sample() { if(sampler==NULL || p==NULL) { cout << "Sampler or process not set!" << endl; exit(1); } if (timelimit == 0.0) { sampler->SampleTrajectories(traj, weights, numofsamples); // cerr << "+++++++ sampled trajectories +++++\n"; // for (size_t i = 0; i < traj.size(); ++i) // traj[i].Draw(cerr); // cout << "weights: " << weights << endl; } else { int totalnum = 0; double begin_time = getcputime(); double end_time = begin_time + timelimit; int kMinNumTraj= 10; if(isgibbs) kMinNumTraj= 2; do { vector t; vector w; sampler->SampleTrajectories(t, w, kMinNumTraj); for (int i = 0; i < kMinNumTraj; ++i) { traj.push_back(t[i]); weights.push_back(w[i]); } totalnum+=kMinNumTraj; } while (getcputime() < end_time); cout << "total samples: " << totalnum << endl; if (isgibbs) { vector::iterator traj_iter = traj.begin(); vector::iterator weights_iter = weights.begin(); if(traj.size()<50) { // Discard the first half (as burn-in). traj.erase(traj_iter, traj_iter + traj.size() / 2); weights.erase(weights_iter, weights_iter + weights.size() / 2); } else { traj.erase(traj_iter, traj_iter + 50); weights.erase(weights_iter, weights_iter + 50); } } } double maxweight = weights[0]; for(int i=1; imaxweight) maxweight = weights[i]; for(int i=0; iSuffStats(traj, weights); newss->Scale(sumweights); //by Yu ss->AddSS(newss); delete newss; } void SamplingInf::AddExpSuffStats(const Dynamics *dyn, SS *ss, double w) { if(sampler==NULL) { cout << "Sampler not set!" << endl; exit(1); } if(traj.size()==0) Sample(); double sumweights = 0.0; for(int i=0; iSuffStats(traj, weights); newdynss->Scale(sumweights); ss->AddSS(newdynss); delete newdynss; } void SamplingInf::AddExpSuffStats(const RV *p0, SS *ss, double w) { if(sampler==NULL) { cout << "Sampler not set!" << endl; exit(1); } if(traj.size()==0) Sample(); double sumweights = 0.0; for(int i=0; iSuffStats(traj, weights); newp0ss->Scale(sumweights); ss->AddSS(newp0ss); delete newp0ss; } double SamplingInf::CalcQuery(QueryCalculator &calc) { if(sampler==NULL) { cout << "Sampler not set!" << endl; exit(1); } if(traj.size()==0) Sample(); double sumweights = 0; for(int i=0; i. */ #include "inference.h" #include "markov.h" #include namespace ctbn { using namespace std; QueryTime::QueryTime(const Instantiation &c) { condi = Instantiation(c.KnownVars()); vector varlist = condi.VarList(); for (int i=0; i varlist = condi.VarList(); int id = varlist[0]; return dynamic_cast(ss).NodeSS(id, condi.Value(id), condi.Value(id), condi); } QueryTransition::QueryTransition(const Context &c, int from, int to) { queryfrom = from; queryto = to; querycontext = c; } double QueryTransition::Calculate(const SS &ss) { vector varlist = querycontext.VarList(); int id = varlist[0]; // condi is not used here. Just a place-holder to call ss.NodeSS. Instantiation condi = Instantiation(querycontext); return dynamic_cast(ss).NodeSS(id, queryfrom, queryto, condi); } double QueryTransition::Calculate(const Trajectory &tr) { Trajectory::Index i = tr.Begin(querycontext); double retval = 0.0; int previndex = i.Values().Index(); ++i; //tr.Draw(cout); for(; !i.Done(); ++i) { int currindex = i.Values().Index(); //cout << currindex << ' ' << previndex << " retval=" // << retval << endl; if( (currindex==queryto && previndex==queryfrom) || (currindex==queryto && queryfrom==-1) || (queryto==-1 && queryfrom==previndex)) retval += 1.0; previndex = currindex; } return retval; } //SOBJCLASSDEF(Inference) Inference::~Inference() { } } // end of ctbn namespace ctbnrle.1.0.2/src/ss.cc0000644000464200007770000000214011344302770014760 0ustar csheltoncsprofs/* 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 "ss.h" namespace ctbn { SS::SS() : StreamObj() { } SS::SS(std::istream &is) : StreamObj() { } SS::~SS() { } void SS::Load(std::istream &is) { } void SS::Save(std::ostream &os) const { StreamObj::Save(os); } } // end of ctbn namespace ctbnrle.1.0.2/src/expmethod.cc0000644000464200007770000001000711344302770016331 0ustar csheltoncsprofs/* 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 "expmethod.h" #include "markovdyn.h" #include "rk.h" namespace ctbn { using namespace std; ExpMethod::ExpMethod() : VarSample() { } ExpMethod::~ExpMethod() { } double ExpMethod::SampleTime(const matrix &Q, int id, int val, const Instantiation &currinst, double currt, const Trajectory *evid, double te, int e, Random &rand) const { double deltat; if (e==-1 || val==e) { deltat = rand.SampleExp(-Q[val][val]); } else { deltat = rand.SampleTruncateExp(-Q[val][val], te-currt); } return deltat; } int ExpMethod::SampleTransition(const matrix &Q, int id, int val, const Instantiation &inst, double currt, const Trajectory *evid, double te, int e, double &wegiht, Random &rand) const { vectr prob = Q.row(val); double sum = -prob[val]; prob[val] = 0; int newval = -1; newval = rand.SampleMultinomial(prob, prob.length(), sum); return newval; } double ExpMethod::TimeWeight(const matrix &Q, const Instantiation &currinst, int id, int transitionid, int val, double currt, double deltat, const Trajectory *evid, double te, int e) const { if (e==-1||val==e) return 0; if (!finite(1/deltat)) return -100.0; double weight = 0; double q = -Q[val][val]; double z = exp(-q * (te-currt)); if (id==transitionid) { if (te-currt>0) weight = log(1-z); else weight = log(te-currt); } else { double benifit = 1 - exp(-q*(te-currt-deltat)); if (te-currt>0) { weight = log(1-z) - log(benifit); } else { weight = log(te-currt) - log(te-currt-deltat); } } if (!finite(weight)) { exit(0); } return weight; } /* double ExpMethod::TransitionWeight(const matrix &Q, int transitionid, int val, const Instantiation &currinst, const Instantiation &nextinst, double currt, double deltat, const Trajectory *evid, double te, int e, bool logscale) const { double weight = 0; // if (te-currt<1e-8&&Q.getm()>2) // if (nextinst.Value(transitionid)==e) // weight = Q[val][e]; return weight; } */ //******************************************************** LookAheadMethod::LookAheadMethod() : ExpMethod() { } int LookAheadMethod::SampleTransition(const matrix &Q, int id, int val, const Instantiation &inst, double currt, const Trajectory *evid, double te, int e, double &weight, Random &rand) const { transitionprob = 1; vectr prob = Q.row(val); double sum = -prob[val]; prob[val] = 0; int newval = -1; int size = prob.length(); if (size<3) { newval = rand.SampleMultinomial(prob, prob.length(), sum); transitionprob = 1.0; } else { vectr b(prob.length(), 0.0); b[e] = 1; expmtv(b, Q, te-currt); b = b.dotstar(Q.row(val)); b[val] = 0.0; double bsum = b.sum(); newval = randomizer.SampleMultinomial(b, size, bsum); transitionprob = prob[newval] * bsum / (b[newval] * sum); } weight += log(transitionprob); return newval; } /* double LookAheadMethod::TransitionWeight(const matrix &Q, int transitionid, int val, const Instantiation &currinst, const Instantiation &nextinst, double currt, double deltat, const Trajectory *evid, double te, int e, bool logscale) const { // vectr prob = Q.row(val); // if (prob.length()<3) return 0; // else return log(transitionprob); } */ } // end of ctbn namespace ctbnrle.1.0.2/src/params.cc0000644000464200007770000000555311344302770015631 0ustar csheltoncsprofs/* 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 "params.h" #include #include #include #include #include #include namespace ctbn { using namespace std; map Params; void ReadParams(istream &pf) { while(!pf.eof()) { string line; getline(pf,line); if (line[0] == ';' || line[0] == '#') continue; int nstart = line.find_first_not_of(" \t\n\r",0); if (nstart==string::npos) continue; int nend = line.find_first_of(" \t\n\r",nstart); if (nend==string::npos) continue; int valstart = line.find_first_not_of(" \t\n\r",nend); if (valstart==string::npos) continue; int valend = line.size(); if (valend==string::npos) continue; Params[line.substr(nstart,nend-nstart)] = line.substr(valstart,valend-valstart); } } void InitParams(int argc, char **argv) { ifstream pf("params"); if (pf.good()) { ReadParams(pf); pf.close(); } pf.open((string(argv[0]) + string(".params")).c_str()); if (pf.good()) { ReadParams(pf); pf.close(); } bool listem = false; for(int i=1;i::iterator i = Params.begin(); i != Params.end(); ++i) { cout << i->first << " => " << i->second << endl; } exit(0); } } string ParamStr(const string &name, const string &dflt) { map::iterator i = Params.find(name); if (i==Params.end()) return dflt; return i->second; } int ParamInt(const string &name, int dflt) { map::iterator i = Params.find(name); if (i==Params.end()) return dflt; return atoi(i->second.c_str()); } double ParamDouble(const string &name, double dflt) { map::iterator i = Params.find(name); if (i==Params.end()) return dflt; return atof(i->second.c_str()); } } // end of ctbn namespace ctbnrle.1.0.2/src/nonexpsuffstatsquery.cc0000644000464200007770000000272111344302770020700 0ustar csheltoncsprofs/* 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 "nonexpsuffstatsquery.h" namespace ctbn { using namespace std; NonExpSuffStatsQuery::NonExpSuffStatsQuery() : SuffStatsQuery() { } SS* NonExpSuffStatsQuery::GetP0SS() { if(p0 && tr) { return p0->SuffStats(*tr,vector(tr->size(),1)); } else { cerr << "p0 and/or data are not initialized" << endl; return NULL; } } SS* NonExpSuffStatsQuery::GetDSS() { if(d && tr) { return d->SuffStats(*tr,vector(tr->size(),1)); } else { cerr << "d and/or data are not initialized" << endl; return NULL; } } NonExpSuffStatsQuery::~NonExpSuffStatsQuery() { } } // end of ctbn namespace ctbnrle.1.0.2/src/dynamics.cc0000644000464200007770000000470311344302770016151 0ustar csheltoncsprofs/* 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 "dynamics.h" namespace ctbn { using namespace std; Dynamics::Dynamics(const Context &var, const Context &cvar) : StreamObj(), v(var), cv(cvar) { } Dynamics::Dynamics(istream &is) : StreamObj(), v(is), cv(is) { } Dynamics::~Dynamics() { } void Dynamics::Load(istream &is) { v.Load(is); cv.Load(is); } void Dynamics::Save(ostream &os) const { v.Save(os); os << os.fill(); cv.Save(os); } void Dynamics::SampleTrajectory(Trajectory &tr, double t, Random &rand) const { double endt = tr.TimeEnd(); Instantiation newi(Domain()); Instantiation i(tr.Values(Domain(),t)); while(1) { double newt; SampleNextEvent(i,t,newt,newi,rand); if (newt>=endt) break; tr.AddTransition(newi,newt); t = newt; i = newi; } } void Dynamics::Mult(const Dynamics *x) { v = v+x->v; cv = (cv+x->cv)-v; } SS* Dynamics::SuffStats(const vector &tr, const vector &w) const { SS *ss = BlankSS(); const Context &c = this->Domain() + this->CondDomain(); const Context &owndomain = Domain(); for(int i=0; iAddSS(curri, t, deltat, ss, w[i]); int sign = index.TestInc(owndomain); if(!index.Done()) { Instantiation nexti = index.Values(); t = index.Time(); if (sign==2) this->AddTransSS(curri,nexti, t,ss,w[i]); curri = nexti; } } } return ss; } } // end of ctbn namespace ctbnrle.1.0.2/src/markovdyn.cc0000644000464200007770000000254711344302770016360 0ustar csheltoncsprofs/* 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" namespace ctbn { SOBJCLASSDEF(MarkovDyn) MarkovDyn::MarkovDyn(const Context &var, const Context &cvar, bool is_toggle) : DynComp(var,cvar,(is_toggle? tmbase:mbase)) { } MarkovDyn::MarkovDyn(std::istream &is) : DynComp(is) { } MarkovDyn::~MarkovDyn() { } MarkovDyn *MarkovDyn::Clone() const { return new MarkovDyn(*this); } const MarkovSimple *MarkovDyn::mbase = new MarkovSimple(); const MarkovSimpleToggle *MarkovDyn::tmbase = new MarkovSimpleToggle(); } // end of ctbn namespace ctbnrle.1.0.2/src/dynsimple.cc0000644000464200007770000000222011344302770016336 0ustar csheltoncsprofs/* 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 "dynsimple.h" namespace ctbn { using namespace std; DynSimple::DynSimple() : StreamObj() { } DynSimple::DynSimple(istream &is) : StreamObj() { } DynSimple::~DynSimple() { } void DynSimple::Load(istream &is) { } void DynSimple::Save(ostream &os) const { } } // end of ctbn namespace ctbnrle.1.0.2/src/gibbssampler.cc0000644000464200007770000003453411344302770017021 0ustar csheltoncsprofs/* 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 "gibbssampler.h" #include "ctbndyn.h" #include "markov.h" #include "markovdyn.h" #include "multisimple.h" #include "bn.h" #include "rk.h" #include "utils.h" #include "params.h" namespace ctbn { using namespace std; // "process" is a pointer to a (subclass of ) Process object. // "evidence" is a pointer to Trajectory object that holds the context. // Variables with value -1 means no evidence. // Interval evidence X(t:t+dt)=x is represented as X(t)=x, X(t+dt)=-1. // Point evidence X(t)=x is represented as X(t)=x, X(t+epsilon)=-1. GibbsSampler::GibbsSampler(const Process *process, const Trajectory *evidence, int burnin) { p = process; init_traj = NULL; evid = evidence; numBurninIter = burnin; Initialize(); } void GibbsSampler::Initialize() { const Markov *ctbn = dynamic_cast(p); const CTBNDyn *ctbndyn = dynamic_cast (ctbn->GetDynamics()); own_var_list = ctbndyn->Domain().VarList(); context = ctbndyn->Domain() + ctbndyn->CondDomain(); p0 = ctbn->GetStartDist(); vector var_list = context.VarList(); for (size_t j=0; j!=var_list.size(); ++j) { // markovdyn for each variable const MarkovDyn *markov_dyn = dynamic_cast (ctbndyn->NodeByVar(var_list[j])); Context domain = markov_dyn->Domain(); vector domain_var_list = domain.VarList(); // domain_var_list should have exactly one variable mds[domain_var_list[0]] = markov_dyn; // parents and children set for each variable Context parent_context = Context(); Context children_context = Context(); for (int i=0; iNumofNodes(); ++i) { Context c = ctbndyn->Node(i)->Domain(); Context cc = ctbndyn->Node(i)->CondDomain(); if (c.HasId(var_list[j])) parent_context = parent_context + cc; if (cc.HasId(var_list[j])) children_context = children_context + c; } parents[var_list[j]] = parent_context; children[var_list[j]] = children_context; } } void GibbsSampler::SampleTrajectories(vector &traj, vector &w, int numsamples, Random &rand) { double t1 = getcputime(); BurnIn(rand); double t2 = getcputime(); times.clear(); times.push_back(t2-t1); t1 = getcputime(); for (int i=0; iTimeBegin()); tr.SetEndTime(evid->TimeEnd()); // Sample a start distribution from BN. The start distribution should // be consistent with the initial values in evidence. // NOTE: start with the next event after tr.Timebegin(), // since the first one is already handled in BN's sample; Trajectory::Index i = evid->Begin(context); Instantiation inst(i.Values()); Instantiation inst0 = inst; p0->Sample(inst0, rand); tr.AddTransition(inst0, tr.TimeBegin()); double dt = i.DeltaT(); // loop over evidence for (++i; !i.Done(); ++i) { Instantiation newinst(i.Values()); for (int v=0; v diff_vars = diff.VarList(); for (size_t j=0; j!=diff_vars.size(); ++j) { double event_time = i.Time() - randomizer.RandReal() * dt; tr.AddTransition(diff_vars[j], event_time, newinst.Value(diff_vars[j])); } inst = newinst; dt = i.DeltaT(); } } void GibbsSampler::SampleVariable(int v, Random &rand) const { // Remove all v's transitions from tr. Trajectory newtr; newtr.SetBeginTime(tr.TimeBegin()); newtr.SetEndTime(tr.TimeEnd()); // deal with the first instantiation Trajectory::Index i = tr.Begin(context); Instantiation inst(i.Values()); int oldv = inst.Value(v); inst.SetVal(v, -1); newtr.AddTransition(inst, i.Time()); // looping over the whole trajectory for (++i; !i.Done(); ++i) { inst = i.Values(); int newv = inst.Value(v); if (newv != oldv) { oldv = newv; continue; } inst.SetVal(v, -1); newtr.AddTransition(inst, i.Time()); } tr = newtr; vector x0, xT; vector t0, tT; SplitFreeIntervals(v, x0, xT, t0, tT, tr.Values(context, tr.TimeBegin()), rand); if (x0.empty()) // no free interval for v return; for (size_t i=0; i!=x0.size(); ++i) SampleVariableInterval(v, x0[i], xT[i], t0[i], tT[i], rand); } void GibbsSampler::SplitFreeIntervals(int v, vector &x0, vector &xT, vector &t0, vector &tT, const Instantiation &inst0, Random &rand) const { // NOTE: if no evidence for v specifies the value of v at // tr.TimeBegin() and tr.TimeEnd(), then we must sample // x0 and xT for them. Trajectory::Index i = evid->Begin(context); Instantiation inst(i.Values()); int oldval = inst.Value(v); if (oldval == -1) { // open free interval t0.push_back(i.Time()); Instantiation init_vals = SampleConditionalRv(inst0, v, rand); x0.push_back(init_vals.Value(v)); tr.AddTransition(v, tr.TimeBegin(), init_vals.Value(v)); } else { tr.AddTransition(v, tr.TimeBegin(), oldval); } for (++i; !i.Done(); ++i) { inst = i.Values(); int newval = inst.Value(v); if (newval!=oldval && newval!=-1) tr.AddTransition(v, i.Time(), newval); if (oldval==-1 && newval!=-1) { // close free interval tT.push_back(i.Time()); xT.push_back(newval); } else if (oldval!=-1 && newval==-1) { // open free interval t0.push_back(i.Time()); x0.push_back(oldval); } oldval = newval; } if (oldval == -1) { // close free interval tT.push_back(evid->TimeEnd()); xT.push_back(-1); } } bool GibbsSampler::IsInBlanket(const Context &c, int v) const { if (parents[v].IsSubset(c) || children[v].IsSubset(c)) return true; vector children_list = children[v].VarList(); for (size_t j=0; j!=children_list.size(); ++j) { if (parents[children_list[j]].IsSubset(c)) return true; } return false; } void GibbsSampler::GetTau(const Trajectory &t, double begin_time, double end_time, int v, vector &tau, vector &inst) const { Trajectory::Index index = t.Begin(context, begin_time, end_time); Instantiation oldinst(index.Values()); tau.push_back(begin_time); inst.push_back(oldinst); vector vids = context.VarList(); for (++index; !index.Done(); ++index) { Instantiation newinst(index.Values()); Context diff(oldinst, newinst); if (IsInBlanket(diff, v)) { inst.push_back(newinst); tau.push_back(index.Time()); } oldinst = newinst; } tau.push_back(end_time); inst.push_back(oldinst); } // The implementation of the paper goes here. void GibbsSampler::SampleVariableInterval(int v, int x0, int xT, double t0, double tT, Random &rand) const { static double kEpsilon = ParamDouble("GibbsEpsilon",1e-5); // interval too small if (fabs(tT-t0) < 2.0*kEpsilon) { if (x0==xT || xT==-1) // already consistent return; tr.AddTransition(v, (t0+tT)/2.0, xT); return; } vector tau; vector inst; GetTau(tr, t0, tT, v, tau, inst); // Sample the time to stay at state x0. // Compute P(future) vectors for {tau} from K back to 0. vector p_future; // normalized distribution vectors vector log_z; // log of the normalization factors of p_future double logfactor; // required by GetDist but not used int tau_size = static_cast(tau.size()); int vdim = context.Cardinality(v); for (int i=0; i!=tau_size; ++i) { p_future.push_back(MultiSimple(vdim)); log_z.push_back(0.0); } vectr dist(vdim, 0.0); if (xT == -1) dist = 1.0; else dist[xT] = 1.0; p_future[tau_size-1].SetDist(dist); for (int i=tau_size - 2; i>=0; --i) { // compute p_future[i] from p_future[i+1] matrix R = GetRMatrix(v, inst[i]); p_future[i+1].GetDist(dist, logfactor); logfactor = expmtv(dist, R, tau[i+1] - tau[i]); if (i > 0) { matrix T = GetTMatrix(v, inst[i-1], inst[i]); dist = T * dist; } p_future[i].SetDist(dist); logfactor += log(p_future[i].Normalize()); log_z[i] = logfactor + log_z[i+1]; } double log_p_future0 = p_future[0].Prob(x0, true) + log_z[0]; double ksi = rand.RandReal(); double log_p_past = 0.0; int which_interval; for (which_interval=0; which_interval= tau_size - 1) return; matrix R = GetRMatrix(v, inst[which_interval]); double t = SearchPoint(ksi, x0, R, log_p_future0, log_p_past, p_future[which_interval+1], log_z[which_interval+1], tau[which_interval], tau[which_interval+1], kEpsilon); // stay till the end of trajectory if (fabs(tT-t) < 2.0*kEpsilon) { if (x0==xT || xT==-1) // already consistent return; tr.AddTransition(v, (t+tT)/2.0, xT); return; } // Sample the next state to transition to. p_future[which_interval+1].GetDist(dist, logfactor); expmtv(dist, R, tau[which_interval+1] - t); for (int i=0; iIntensity(); vector children_id = children[v].VarList(); for (size_t i=0; i!=children_id.size(); ++i) { int val = inst.Value(children_id[i]); Instantiation pinst = inst; const MarkovDyn *cmd = mds[children_id[i]]; for (int j=0; jIntensity()[val][val]; } } return R; } matrix GibbsSampler::GetTMatrix(int v, const Instantiation &oldinst, const Instantiation &newinst) const { int vdim = context.Cardinality(v); matrix T(vdim, vdim, 0.0); Context diff(oldinst, newinst); // NOTE: assume there is exactly one variable changed int changeid = diff.VarList()[0]; // if the changed variable is not a child of v; // simply return an identity matrix; if (!children[v].HasId(changeid)) { for (int i = 0; i < vdim; ++i) T[i][i] = 1.0; return T; } // o.w. return a diagonal matrix whose T[i][i] = q(a->a')|i int a = oldinst.Value(changeid); int b = newinst.Value(changeid); const MarkovDyn *md = mds[changeid]; Instantiation inst = oldinst; for (int i=0; iIntensity()[a][b]; //get the CIM } return T; } double GibbsSampler::SearchPoint(double ksi, int x0, const matrix &R, double log_p_future0, double log_p_past, MultiSimple p_future, double log_z, double t0, double t1, double epsilon) const { // This loop is guaranteed to be terminating since each iteration we will // reduce (t1 - t0) by a half. for (;;) { double t = (t0+t1)/2.0; double dt = (t1-t0)/2.0; if (dt < epsilon) return t; // Compute F(t). vectr dist; double logfactor; p_future.GetDist(dist, logfactor); logfactor = expmtv(dist, R, dt); logfactor += log(dist.normalize()); logfactor += log_z; double new_log_p_past = log_p_past; new_log_p_past += dt*R[x0][x0]; MultiSimple new_p_future = p_future; new_p_future.SetDist(dist); double Ft = 1.0 - exp(new_log_p_past + log(dist[x0]) + logfactor - log_p_future0); if (fabs(Ft-ksi) < epsilon) return t; if (Ft < ksi) { // search (t, t1) t0 = t; log_p_past = new_log_p_past; } else { t1 = t; // search (t0, t) p_future = new_p_future; log_z = logfactor; } } } Instantiation GibbsSampler::SampleConditionalRv(const Instantiation &partial, int v, Random &rand) const { double log_prob_e = p0->Prob(partial, true); int card_v = context.Cardinality(v); vector posterior_prob(card_v); double max_log_prob = -1e20; for (int val=0; valProb(inst, true); if (posterior_prob[val] > max_log_prob) max_log_prob = posterior_prob[val]; } double summation = 0.0; for (int val=0; val. */ #include "multisimple.h" #include "extramath.h" #include #include namespace ctbn { using namespace std; SOBJCLASSDEF(MultiSimple) SOBJCLASSDEF(MultiLogSimple) SOBJCLASSDEF(MultiZSimple) SOBJCLASSDEF(SparseMultiZSimple) SOBJCLASSDEF(MultiSimpleSS) MultiSimple::MultiSimple(int n) : RVSimple(), theta(n,0.0) { } MultiLogSimple::MultiLogSimple(int n) : RVSimple(), logtheta(n,0.0) { } MultiZSimple::MultiZSimple(int n) : RVSimple(), theta(n,0.0) { logz = 0.0; } SparseMultiZSimple::SparseMultiZSimple(int n) : MultiZSimple(n), ix(n) { //iota(ix.begin(),ix.end(),0); // set to 0, 1, 2, ... for(int i=0;i &indexes, double logfactor) : MultiZSimple(dist,logfactor), ix(indexes) { } //--- MultiSimple::MultiSimple(istream &is) : RVSimple(is) { is >> theta; } MultiLogSimple::MultiLogSimple(istream &is) : RVSimple(is) { is >> logtheta; } MultiZSimple::MultiZSimple(istream &is) : RVSimple(is) { is >> theta >> logz; } SparseMultiZSimple::SparseMultiZSimple(istream &is) : MultiZSimple(is) { is >> ix; } //--- MultiSimple::~MultiSimple() { } MultiLogSimple::~MultiLogSimple() { } MultiZSimple::~MultiZSimple() { } SparseMultiZSimple::~SparseMultiZSimple() { } //--- void MultiSimple::Load(istream &is) { RVSimple::Load(is); is >> theta; } void MultiLogSimple::Load(istream &is) { RVSimple::Load(is); is >> logtheta; } void MultiZSimple::Load(istream &is) { RVSimple::Load(is); is >> theta >> logz; } void SparseMultiZSimple::Load(istream &is) { MultiZSimple::Load(is); is >> ix; } //--- void MultiSimple::Save(ostream &os) const { RVSimple::Save(os); os << os.fill() << theta; } void MultiLogSimple::Save(ostream &os) const { RVSimple::Save(os); os << os.fill() << logtheta; } void MultiZSimple::Save(ostream &os) const { RVSimple::Save(os); os << os.fill() << theta << os.fill() << logz; } void SparseMultiZSimple::Save(ostream &os) const { MultiZSimple::Save(os); os << os.fill() << ix; } //--- MultiSimple *MultiSimple::Clone() const { return new MultiSimple(*this); } MultiLogSimple *MultiLogSimple::Clone() const { return new MultiLogSimple(*this); } MultiZSimple *MultiZSimple::Clone() const { return new MultiZSimple(*this); } SparseMultiZSimple *SparseMultiZSimple::Clone() const { return new SparseMultiZSimple(*this); } //--- double MultiSimple::Prob(int ind, bool log) const { if (log) return ::log(theta[ind]); else return theta[ind]; } double MultiLogSimple::Prob(int ind, bool log) const { if (log) return logtheta[ind]; else return exp(logtheta[ind]); } double MultiZSimple::Prob(int ind, bool log) const { if (log) return logz+::log(theta[ind]); else return exp(logz)*theta[ind]; } double SparseMultiZSimple::Prob(int ind, bool log) const { vector::const_iterator spot = lower_bound(ix.begin(),ix.end(),ind); if (spot==ix.end() || *spot!=ind) return log ? -INFINITY : 0; return MultiZSimple::Prob(spot-ix.begin(),log); } //--- double MultiSimple::Normalize() { return theta.normalize(); } double MultiLogSimple::Normalize() { double lmax = logtheta.max(); double sum = 0.0; int n = logtheta.getm(); for(int i=0;i &ind) { int n = theta.getm(); int indn = ind.size(); for(int i=0,j=0;i &ind) { int n = logtheta.getm(); int indn = ind.size(); for(int i=0,j=0;i &ind) { int n = theta.getm(); int indn = ind.size(); for(int i=0,j=0;i &ind) { int n = ix.size(); int indn = ind.size(); vectr oldt(theta); theta.resize(indn); theta = 0.0; int i=0,j=0; while(i > &ind) { vectr oldt(theta); int n = ind.size(); theta.resize(n); for(int i=0;i > &ind) { double lmax = logtheta.max(); vectr oldt(logtheta); int n = ind.size(); logtheta.resize(n); for(int i=0;i > &ind) { vectr oldt(theta); int n = ind.size(); theta.resize(n); for(int i=0;i > &ind) { vectr oldt(theta); int n = ind.size(); int oldn = ix.size(); vector oldix(ix); int newn = 0; for(int i=0;i0) ++newn; theta.resize(newn); ix.resize(newn,0); int kk = 0; for(int k=0;k(x); theta.multby(ms->theta); } void MultiLogSimple::MultBy(const RVSimple *x) { const MultiLogSimple *ms = dynamic_cast(x); logtheta += ms->logtheta; } void MultiZSimple::MultBy(const RVSimple *x) { const MultiZSimple *ms = dynamic_cast(x); theta.multby(ms->theta); logz += ms->logz; SetZ(); } //--- void MultiSimple::Add(const RVSimple *x) { const MultiSimple *ms = dynamic_cast(x); theta += ms->theta; } void MultiLogSimple::Add(const RVSimple *x) { const MultiLogSimple *ms = dynamic_cast(x); int n = logtheta.getm(); for(int i=0;ilogtheta[i]) logtheta[i] = ms->logtheta[i] + ::log(exp(logtheta[i]-ms->logtheta[i]) + 1.0); else logtheta[i] = logtheta[i] + ::log(exp(ms->logtheta[i]-logtheta[i]) + 1.0); } } void MultiZSimple::Add(const RVSimple *x) { const MultiZSimple *ms = dynamic_cast(x); if (ms->logz>logz) { theta = theta*exp(logz-ms->logz) + ms->theta; logz = ms->logz; } else { theta += ms->theta*exp(ms->logz-logz); } SetZ(); } //--- void MultiSimple::Add(const RVSimple *x, double w) { const MultiSimple *ms = dynamic_cast(x); theta.add(ms->theta,w); } void MultiLogSimple::Add(const RVSimple *x, double w) { const MultiLogSimple *ms = dynamic_cast(x); int n = logtheta.getm(); double logw = ::log(w); for(int i=0;ilogtheta[i]+logw; if (logtheta[i](x); double xlz = ms->logz+::log(w); if (xlz>logz) { theta = theta*exp(logz-xlz) + ms->theta; logz = xlz; } else { theta += ms->theta*exp(xlz-logz); } SetZ(); } //--- void MultiSimple::Mult(double x) { theta *= x; } void MultiLogSimple::Mult(double x) { logtheta += log(x); } void MultiZSimple::Mult(double x) { logz += log(x); } //--- MultiSimpleSS *MultiSimple::BlankSS() const { return new MultiSimpleSS(theta.getm()); } MultiSimpleSS *MultiLogSimple::BlankSS() const { return new MultiSimpleSS(logtheta.getm()); } MultiSimpleSS *MultiZSimple::BlankSS() const { return new MultiSimpleSS(theta.getm()); } //--- void MultiSimple::AddSS(int x, SS *ss, double w) const { MultiSimpleSS * msss = dynamic_cast(ss); msss->c[x] += w; } void MultiLogSimple::AddSS(int x, SS *ss, double w) const { MultiSimpleSS * msss = dynamic_cast(ss); msss->c[x] += w; } void MultiZSimple::AddSS(int x, SS *ss, double w) const { MultiSimpleSS * msss = dynamic_cast(ss); msss->c[x] += w; } void SparseMultiZSimple::AddSS(int x, SS *ss, double w) const { MultiSimpleSS * msss = dynamic_cast(ss); msss->c[x] += w; } //--- void MultiSimple::AddExpSS(SS *ss, double w) const { MultiSimpleSS * msss = dynamic_cast(ss); msss->c += w*theta; } void MultiLogSimple::AddExpSS(SS *ss, double w) const { MultiSimpleSS * msss = dynamic_cast(ss); int n = logtheta.getm(); for(int i=0;ic[i] += w*exp(logtheta[i]); } void MultiZSimple::AddExpSS(SS *ss, double w) const { MultiSimpleSS * msss = dynamic_cast(ss); int n = theta.getm(); w *= exp(logz); for(int i=0;ic[i] += w*theta[i]; } void SparseMultiZSimple::AddExpSS(SS *ss, double w) const { MultiSimpleSS * msss = dynamic_cast(ss); int n = theta.getm(); w *= exp(logz); for(int i=0;ic[ix[i]] += w*theta[i]; } //--- void MultiSimple::AddSS(const SS *toadd, const RVSimple *rvs, const std::vector > &mapping, SS *ss, double w) const { const MultiSimple *ms = dynamic_cast(rvs); assert(ms!=NULL); // otherwise, need double-dispatch and more code MultiSimpleSS *msss = dynamic_cast(ss); const MultiSimpleSS *addmsss = dynamic_cast(toadd); int n = theta.getm(); for(int i=0;ic[i] += addmsss->c[mapping[i][ki]]; } } } void MultiLogSimple::AddSS(const SS *toadd, const RVSimple *rvs, const std::vector > &mapping, SS *ss, double w) const { const MultiLogSimple *ms = dynamic_cast(rvs); assert(ms!=NULL); // otherwise, need double-dispatch and more code MultiSimpleSS *msss = dynamic_cast(ss); const MultiSimpleSS *addmsss = dynamic_cast(toadd); int n = logtheta.getm(); for(int i=0;ic[i] += addmsss->c[mapping[i][ki]]; } } } void MultiZSimple::AddSS(const SS *toadd, const RVSimple *rvs, const std::vector > &mapping, SS *ss, double w) const { const MultiZSimple *ms = dynamic_cast(rvs); assert(ms!=NULL); // otherwise, need double-dispatch and more code MultiSimpleSS *msss = dynamic_cast(ss); const MultiSimpleSS *addmsss = dynamic_cast(toadd); int n = theta.getm(); for(int i=0;ic[i] += addmsss->c[mapping[i][ki]]; } } } void SparseMultiZSimple::AddSS(const SS *toadd, const RVSimple *rvs, const std::vector > &mapping, SS *ss, double w) const { const SparseMultiZSimple *ms = dynamic_cast(rvs); assert(ms!=NULL); // otherwise, need double-dispatch and more code MultiSimpleSS *msss = dynamic_cast(ss); const MultiSimpleSS *addmsss = dynamic_cast(toadd); int n = theta.getm(); for(int i=0;ic[ix[i]] += addmsss->c[mapping[ix[i]][ki]]; } } } //--- int MultiSimple::Sample(Random &rand) const { return rand.SampleMultinomial(theta,theta.getm(),theta.sum()); } int MultiLogSimple::Sample(Random &rand) const { int n = logtheta.getm(); vector addw(n,0.0); double lmax = logtheta.max(); double sum = 0.0; for(int i=0;i(ss); theta = msss->c; double sum = theta.sum(); if (sum>0) theta /= sum; else theta = 1.0/theta.getm(); } void MultiLogSimple::Maximize(const SS *ss) { const MultiSimpleSS *msss = dynamic_cast(ss); logtheta = msss->c; double sum = logtheta.sum(); if (sum>0) sum = log(sum); else logtheta = 1.0/logtheta.getm(); int n = logtheta.getm(); for(int i=0;i(ss); theta = msss->c; double sum = theta.sum(); if (sum>0) logz = -log(sum); else { theta = 1.0; logz = -log((double) theta.getm()); } } void SparseMultiZSimple::Maximize(const SS *ss) { MultiZSimple::Maximize(ss); int n = theta.getm(); ix.resize(n); // iota(ix.begin(),ix.end(),0); // set to 0, 1, 2, ... for(int i=0;ilogfactor) logfactor = d[i]; for(int i=0;inn) nn = ix[i]; if (d.getm()0) theta[i] = 1.0; } void MultiLogSimple::MakeUniform() { int n = logtheta.getm(); for(int i=0;i0) theta[i] = 1.0; logz = 0.0; } //--- void MultiSimple::Scramble(double alpha, double degree, Random &rand) { int n = theta.getm(); double *in = new double[n]; double *newtheta = new double[n]; for(int i=0;i(ss); double llh = 0.0; int n = theta.length(); for(int i=0; ic[i]!=0.0) llh += rvss->c[i] * log(theta[i]); return llh; } double MultiZSimple::LLH(const SS *ss) const { const MultiSimpleSS *rvss = dynamic_cast(ss); double llh = 0.0; int n = theta.length(); for(int i=0; ic[i]>0.0) llh += rvss->c[i] * (log(theta[i]) + logz); return llh; } double MultiLogSimple::LLH(const SS *ss) const { const MultiSimpleSS *rvss = dynamic_cast(ss); double llh = 0.0; int n = logtheta.length(); for(int i=0; ic[i]!=0.0) llh += rvss->c[i] * logtheta[i]; return llh; } double SparseMultiZSimple::LLH(const SS *ss) const { const MultiSimpleSS *rvss = dynamic_cast(ss); double llh = 0.0; int n = rvss->c.length(); //int m = ix.length(); int m = ix.size(); for(int i=0,j=0; ic[i]>0.0) { if (ix[j]==i) llh += rvss->c[i] * (log(theta[j]) + logz); else return log(0.0); } } return llh; } //double MultiLogSimple::LLH(const SS *ss) const //--- void SparseMultiZSimple::SetSparseDist(const vectr &d, double logfactor) { MultiZSimple::SetDist(d,logfactor); } void SparseMultiZSimple::SetSparseIndexes(const vector &ind) { ix = ind; } void SparseMultiZSimple::GetSparseDist(vectr &d, double &logfactor) const { MultiZSimple::GetDist(d,logfactor); } //--- void MultiZSimple::SetZ() { // set so that maximum is 1 (could set so that sum=1 instead, // but this seems to work better) double s = theta.max(); if (s>0) { theta /= s; logz += log(s); } } //--- double RVSimple::GetScore(double numTrans, const SS* ss) const { double localScore(0); const MultiSimpleSS *rvss = dynamic_cast(ss); const vectr& c = rvss->c; int size = c.length(); double alpha_xx_u(numTrans / ((size-1)*size)); double alpha_x_u(alpha_xx_u*size); localScore += lngamma(alpha_x_u) - lngamma(alpha_x_u + c.sum()); for(int i = 0; i < size; i++) localScore += lngamma(alpha_xx_u + c[i]) - lngamma(alpha_xx_u); return localScore; } //--------- MultiSimpleSS::MultiSimpleSS(int n) : SS(), c(n,0.0) { } MultiSimpleSS::MultiSimpleSS(istream &is) : SS(is) { is >> c; } MultiSimpleSS::MultiSimpleSS(const MultiSimpleSS &msss) : SS(*this), c(msss.c) { } MultiSimpleSS::~MultiSimpleSS() { } MultiSimpleSS *MultiSimpleSS::Clone() const { return new MultiSimpleSS(*this); } void MultiSimpleSS::Load(istream &is) { SS::Load(is); is >> c; } void MultiSimpleSS::Save(ostream &os) const { SS::Save(os); os << os.fill() << c; } void MultiSimpleSS::Scale(double w) { int n = c.length(); for(int i=0; i(nss); int n = c.length(); for(int i=0; ic[i] * w; } } // end of ctbn namespace ctbnrle.1.0.2/src/rv.cc0000644000464200007770000000314711344302770014772 0ustar csheltoncsprofs/* 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 "rv.h" namespace ctbn { using namespace std; RV::RV(const Context &var, const Context &cvar) : StreamObj(), v(var), cv(cvar) { } RV::RV(istream &is) : StreamObj(), v(is), cv(is) { } RV::~RV() { } void RV::Load(istream &is) { v.Load(is); cv.Load(is); } void RV::Save(ostream &os) const { v.Save(os); os << os.fill(); cv.Save(os); } SS* RV::SuffStats(const vector &tr, const vector &w) const { SS* ss = this->BlankSS(); const Context &c = this->Domain() + this->CondDomain(); for(int i=0; iAddSS(curri, ss, w[i]); } return ss; } } // end of ctbn namespace ctbnrle.1.0.2/src/graphic.cc0000644000464200007770000010142111344302770015752 0ustar csheltoncsprofs#include "graphic.h" #include "params.h" namespace ctbn { using namespace std; // If the "dot" command (from graphviz) is not in your // path, or if you wish to define an explicit path to it, // change the string below accordingly #define DOTCOMMAND "/home/rlair/bin/dot" /* * GraphicEvent is class for generating trajectory graph */ //return the difference between two Instantiation vector diffvalues(const Instantiation &i1, const Instantiation &i2) { Context both(i1,i2,Context::UNION); vector vars = both.VarList(); vector ret; for(vector::iterator i=vars.begin();i!=vars.end();++i) if (i1.Value(*i) != i2.Value(*i)) ret.push_back(*i); return ret; } Graphic::GraphicEvent::GraphicEvent() { // Auto-generated constructor stub change_node = 0; time = 0; state.clear(); } void Graphic::GraphicEvent::SetChanges(int node, double change_time, const vector &change_state) { change_node = node; time = change_time; state = change_state; } double Graphic::GraphicEvent::GetTime() const { return time; } int Graphic::GraphicEvent::GetChange() const { return change_node; } vector Graphic::GraphicEvent::GetState() const { return state; } void Graphic::GraphicEvent::SetName(string insertname) { name = insertname; } string Graphic::GraphicEvent::GetName() const{ return name; } /* * GraphicNode class is for drawing the CTBN graph. * this class will save all the data need to be generate the CTBN graph */ Graphic::GraphicNode::GraphicNode() { a = ""; b = 0; check_string = false; } string Graphic::GraphicNode :: GetName() { return a; } int Graphic::GraphicNode :: GetSize() { return matching.size(); } void Graphic::GraphicNode::Add(const string & name) { a = name; } void Graphic::GraphicNode::Add(const int & name) { exist = false; check_string = true; stringstream ss; ss << name; ss >> a; ss.clear(stringstream::goodbit); } void Graphic::GraphicNode::SetMatrix(const matrix & matrix, int & conn, int & locate) { dyn.push_back(matrix); matching.push_back(conn); place.push_back(locate); } vector Graphic::GraphicNode::GetPlace() { return place; } vector Graphic::GraphicNode::GetMatching() { return matching; } void Graphic::GraphicNode::SetMatching(vector matching) { exist = true; this->matching = matching; } void Graphic::GraphicNode::SetPlace(int location) { place.push_back(location); } void Graphic::GraphicNode::SetMatrix(const matrix & matrix) { dyn.push_back(matrix); } void Graphic::GraphicNode::SetBool() { exist = true; } bool Graphic::GraphicNode::GetExist() { return exist; } matrix Graphic::GraphicNode::GetMatrix(const int & i) { return dyn[i]; } bool Graphic::GraphicNode::CheckStrings() { return check_string; } void Graphic::GraphicNode::SetName(const string & name) { a = name; check_string = true; } //initialize graphic class Graphic :: Graphic() { d = NULL; size = 0; //size of x coordinate the trajectory visualization size_x = ParamInt("traj_win_size", 800); //size of the font on trajectory change_point = ParamInt("traj_font", 15); //size of changed time mark in trajectory graph size_distance = ParamInt("traj_ruler", 5); //size of the font on ctbn graph size_font = ParamInt("ctbn_font", 16); //start time of the trajectory start = 0; //ending time of the trajectory finish = 2; largest_tr = 0; change_name = false; } //outputing both graph representation of Markov and trajectory void Graphic::DrawBoth(const Markov & A, Trajectory & traj, ofstream & ls, string & name, ofstream & lb) { this->start_point = traj.TimeBegin(); this->ending_point = traj.TimeEnd(); const CTBNDyn * B = dynamic_cast(A.GetDynamics()); size = B->NumofNodes(); Context context = Context(); for (int i = 0; i < B->NumofNodes(); ++i) context = context + B->Node(i)->Domain(); Trajectory::Index i = traj.Begin(context); ExtractInfo(A); Changes(); BuildRank(ls); Create(i); large_point = 0; getlocation.clear(); string command2 = string(DOTCOMMAND) + " -Tdot " + name + " > temp.dot"; const char * commend; commend = command2.c_str(); system(commend); GetLocation("temp.dot"); BuildStateRank(lb); } //extracting location of each placement in graph void Graphic::GetLocation(string name) { ifstream obtainlocation; double get_distance; obtainlocation.open(name.c_str()); if(!obtainlocation.is_open()) return; int count = size; vector location; string textmatch = "rects="; string::iterator it = textmatch.begin(); while(!obtainlocation.eof()) { if( it == textmatch.end()) { obtainlocation.get(); obtainlocation.get(); obtainlocation >> get_distance; obtainlocation.get(); obtainlocation >> get_distance; if(large_point < get_distance) { large_point = get_distance; } location.push_back(get_distance); obtainlocation.get(); obtainlocation >> get_distance; obtainlocation.get(); obtainlocation >> get_distance; if(large_point < get_distance) { large_point = get_distance; } location.push_back(get_distance); count--; getlocation.push_back(location); location.clear(); if(count == 0) { break; } it = textmatch.begin(); } else if(* it == obtainlocation.get()) it ++; else it = textmatch.begin(); } obtainlocation.close(); } //output Markov graph in one arrange line void Graphic::DrawCTBNRank(const Markov & A, ofstream & ls, string & name, ofstream & lb) { ExtractInfo(A); Changes(); BuildRank(ls); large_point = 0; getlocation.clear(); string command2 = string(DOTCOMMAND) + " -Tdot " + name + " > temp.dot"; const char * commend; commend = command2.c_str(); system(commend); GetLocation("temp.dot"); Create(A, start, finish); BuildStateRank(lb); } void Graphic::DrawCTBN(const Markov & A, ofstream & ls) { ExtractInfo(A); Changes(); Build(ls); } void Graphic::DrawCTBNMatrix(const Markov & A, ofstream & ls) { ExtractInfo(A); Changes(); BuildM(ls); } void Graphic ::SetSizeTrajectory(const int & size_x) { this -> size_x = size_x; } void Graphic :: SetFontTrajectory(const int & change_font, const int & change_font1) { if(change_font > 0 && change_font1 > 0) { change_point = change_font; size_distance = change_font1; } } void Graphic ::SetFontCTBN(const int & size_font) { this -> size_font = size_font; } //extract the data from Markov void Graphic :: ExtractInfo(const Markov & A) { graph.clear(); nodes.clear(); d = A.GetDynamics(); const CTBNDyn * B = dynamic_cast(d); int n = B->NumofNodes(); size = B->NumofNodes(); for(int i = 0; i < n; i++) { nodes.push_back(dynamic_cast (B->Node(i))); } Context x, y; vector domain; vector condomain; std::map::iterator it; for(int i = 0; i < n; i ++) { x = nodes[i]->Domain(); y = nodes[i]->CondDomain(); domain = x.VarList(); if(graph.find(domain[0]) != graph.end()) { it = graph.find(domain[0]); } else { GraphicNode newnode; newnode.Add(domain[0]); graph[domain[0]] = newnode; it = graph.find(domain[0]); } if(y.NumVars() > 0) { condomain = y.VarList(); it->second.SetMatching(condomain); Instantiation inst(y, 0); bool good = true; while(good) { matrix cim = (*nodes[i])(inst)->Intensity(); for(unsigned int j = 0; j < condomain.size(); j ++) { graph[domain[0]].SetPlace(inst.Value(condomain[j])); } it->second.SetMatrix(cim); good = inst.Inc(); } } else { it->second.SetBool(); Instantiation inst(y, 0); matrix cim = (*nodes[i])(inst)->Intensity(); it->second.SetMatrix(cim); it->second.SetMatching(domain); it->second.SetPlace(0); } } } void Graphic::Changes (vector new_name) { change_name = true; change_names = new_name; } void Graphic::ClearName() { change_names.clear(); change_name = false; } void Graphic::Changes() { if(change_name) { std::map::iterator it = graph.begin(); for(unsigned int i = 0; i < change_names.size() && it != graph.end(); i++, it++) { if(it->second.GetExist()) { it->second.SetName(change_names[i]); } } } } void Graphic ::DrawTrajectory(const Markov & A, ofstream & ls) { ExtractInfo(A); Create(A, start, finish); Changes(); BuildState(ls); } void Graphic::Largest(int big) { if (big > largest_tr) { largest_tr = big; } } void Graphic ::DrawTrajectory(Trajectory traj,const Context & context, ofstream & ls) { this->start_point = traj.TimeBegin(); this->ending_point = traj.TimeEnd(); Trajectory::Index i = traj.Begin(context); vector domain = context.VarList(); this ->size = domain.size(); graph.clear(); for(int k = 0; k < size; k ++) { GraphicNode newItem; newItem.Add(domain[k]); newItem.SetBool(); graph[domain[k]] = newItem; } Create(i); Changes(); BuildState(ls); } void Graphic ::Create(Trajectory::Index & i) { graph_change.clear(); stringstream ss; string name; Instantiation oldinst(i.Values()); vector state; for(int n = 0; n < size; n++) { state.push_back(oldinst.Value(n)); Largest(oldinst.Value(n)); } GraphicEvent newItem; newItem.SetChanges(size+1, i.Time(), state); newItem.SetName("0"); graph_change.push_back(newItem); int count = 1; while(!i.Done()) { ++i; GraphicEvent newItem; Instantiation newinst(i.Values()); vector change_id = diffvalues(newinst, oldinst); if(change_id.size() > 0 && ending_point != i.Time()) { for(unsigned int j = 0; j < change_id.size(); j ++ ) { state[change_id[j]] = newinst.Value(change_id[j]); newItem.SetChanges(change_id[j], i.Time(), state); Largest(newinst.Value(change_id[j])); ss << count; ss >> name; ss.clear(stringstream::goodbit); newItem.SetName(name); graph_change.push_back(newItem); count ++; } } oldinst = newinst; } } void Graphic ::Create(const Markov & A, double & start, double & finish) { largest_tr = 0; graph_change.clear(); ending_point = finish; stringstream ss; d = A.GetDynamics(); const CTBNDyn * B = dynamic_cast(d); size = B->NumofNodes(); Context context = Context(); for (int i = 0; i < B->NumofNodes(); ++i) context = context + B->Node(i)->Domain(); string name; Trajectory tr; tr.SetBeginTime(start); tr.SetEndTime(finish); A.Sample(tr); Trajectory::Index i = tr.Begin(context); Instantiation oldinst(i.Values()); vector state; for(int n = 0; n < size; n++) { state.push_back(oldinst.Value(n)); Largest(oldinst.Value(n)); } GraphicEvent newItem; newItem.SetChanges(size+1, i.Time(), state); newItem.SetName("0"); graph_change.push_back(newItem); int count = 1; while(!i.Done()) { ++i; GraphicEvent newItem; Instantiation newinst(i.Values()); vector change_id = diffvalues(newinst, oldinst); if(change_id.size() > 0 && ending_point != i.Time()) { for(unsigned int j = 0; j < change_id.size(); j ++ ) { state[change_id[j]] = newinst.Value(change_id[j]); newItem.SetChanges(change_id[j], i.Time(), state); Largest(newinst.Value(change_id[j])); ss << count; ss >> name; ss.clear(stringstream::goodbit); newItem.SetName(name); graph_change.push_back(newItem); count ++; } } oldinst = newinst; } } void Graphic::BuildMatrix(matrix cim, ofstream& ls) { int matrix_size = 0; matrix_size = cim.getm(); for(int m = 0; m < matrix_size; m++) { if(m != 0) { ls << "{"; } for (int n = 0; n < matrix_size; n++) { ls << cim[n][m]; ArrangeFind(cim); Arrange(ls,(int_size[n]- arrangement[m][n])); if(n +1 != matrix_size) ls<< " | "; } ls << "}"; if ((m+1) != matrix_size) ls << "|"; } } int Graphic::Largest(vector length) { int large = length[0]; for(unsigned int i = 1; i < length.size(); i++) { if(length[i] > large) large = length[i]; } return large; } void Graphic::Arrange(ofstream & ls, int space) { for(int i = 0; i < space; i++) ls << "\\ "; } void Graphic::ArrangeFind(matrix cim) { arrangement.clear(); int_size.clear(); vector length; int matrix_size = cim.getm(); string size; int size_count; for(int m = 0; m < matrix_size; m++) { length.clear(); for (int n = 0; n < matrix_size; n++) { stringstream ss; ss << cim[n][m]; ss >> size; size_count = 0; for(unsigned int i = 0; i < size.size(); i ++) { if(size[i] == '.') { size_count = i +1; } if(size_count == 0 && i +1 == size.size()) { size_count = i +1; } } length.push_back((size.size()-size_count)); ss.clear(stringstream::goodbit); } int_size.push_back(Largest(length)); arrangement.push_back(length); } } void Graphic::CTBNGraphicIntro(std::ofstream & ls) { ls << "digraph g {" << endl; ls << "ordering=out;" << endl; ls << "graph [ " << endl << "rankdir = " << "\"" << "LR" << "\"" << endl << "];" << endl; ls << "node [" << endl << "fontsize = " << "\"" << size_font << "\"" << "\n" << "shape = " << "\"" << "eclipse" << "\"" << "];" << endl; ls << "edge [" << endl << "];" << endl; } void Graphic::BuildM(ofstream& ls) { CTBNGraphicIntro(ls); string name = ""; string node = "node"; stringstream ss; vector temp; vector places; int count; std::map::iterator it; for(it = graph.begin(); it != graph.end(); it ++) { if(it->second.GetExist()) { node = node + it->second.GetName(); ls << "\"" << node << "\"" << "[" << endl; ls << "label = " << "\"" << it->second.GetName(); temp = it->second.GetMatching(); places = it->second.GetPlace(); count = 0; int matrix_count = 0; for(unsigned int x = 0; x < places.size(); x ++) { if (x ==0) { ls << "| " << "{"; } if(places.size() != 1) { if(count == 0) { ls << graph.find(temp[count])->second.GetName() << "= " << places[x] << " "; } else { ls << ", " << graph.find(temp[count])->second.GetName() << "= " << places[x] << " "; } } count ++; if((unsigned) count >= temp.size()) { count = 0; if((x+1) != places.size()) { ls << "| {"; matrix cim = it->second. GetMatrix(matrix_count); BuildMatrix(cim, ls); ls <<"} || {"; matrix_count++; } } } if(places.size() != 1) { ls << "| "; } ls << "{"; matrix cim = it->second.GetMatrix(matrix_count); BuildMatrix(cim, ls); ls <<"}"; ls << "\"" << endl; ls << "shape = " << "\"" << "record" << "\"" << endl; ls << "];" << endl; } node = "node"; } string nod1 ="node"; count = 0; temp.clear(); for(it = graph.begin(); it != graph.end(); it ++) { node = node + it->second.GetName(); temp = it->second.GetMatching(); for(unsigned int j = 0; j < temp.size(); j++) { if(it->second.CheckStrings()) { nod1 = nod1 + graph.find(temp[j])->second.GetName(); } else { nod1 = nod1 + name; } ss << count; ss >> name; if (nod1 != node) { ls << "\"" << nod1 << "\"" << "-> " << "\"" << node << "\"" << " [ id = " << name << "];" << endl; } nod1 = "node"; ss.clear(stringstream::goodbit); count ++; } node = "node"; } ls << "overlap=false" << endl <<"}" << endl; } void Graphic::Build(ofstream & ls) { CTBNGraphicIntro(ls); string name = ""; string node = "node"; stringstream ss; vector temp; vector places; std::map::iterator it; int count; for(it = graph.begin(); it != graph.end(); it ++) { if(it->second.GetExist()) { node = node + it->second.GetName(); ls << "\"" << node << "\"" << "[" << endl; ls << "label = " << "\"" << it->second.GetName(); temp = it->second.GetMatching(); places = it->second.GetPlace(); count = 0; ls << "\"" << endl; ls << "shape = " << "\"" << "record" << "\"" << endl; ls << "];" << endl; } node = "node"; } string nod1 ="node"; count = 0; temp.clear(); for(it = graph.begin(); it != graph.end(); it ++) { node = node + it->second.GetName(); temp = it->second.GetMatching(); for(unsigned int j = 0; j < temp.size(); j++) { if(it->second.CheckStrings()) { nod1 = nod1 + graph.find(temp[j])->second.GetName(); } else { nod1 = nod1 + name; } ss << count; ss >> name; if (nod1 != node) { ls << "\"" << node << "\"" << " -> " << "\"" << nod1 << "\"" << " [ dir=back, id = " << name << "];" << endl; } nod1 = "node"; ss.clear(stringstream::goodbit); count ++; } node = "node"; } ls << "overlap=false" << endl <<"}" << endl; } void Graphic::BuildRank(ofstream & ls) { CTBNGraphicIntro(ls); string name = ""; string node = "node"; stringstream ss; vector temp; vector places; int count; std::map::iterator it; for(it = graph.begin(); it != graph.end(); it ++) { if(it->second.GetExist()) { node = node + it->second.GetName(); ls << "\"" << node << "\"" << "[" << endl; ls << "label = " << "\"" << it->second.GetName(); temp = it->second.GetMatching(); places = it->second.GetPlace(); count = 0; ls << "\"" << endl; ls << "shape = " << "\"" << "record" << "\"" << endl; ls << "];" << endl; } node = "node"; } string nod1 ="node"; count = 0; temp.clear(); ls << "subgraph ctbn { " << endl; ls << "rank = same; " << endl; for(it = graph.begin(); it != graph.end(); it ++) { node = node + it->second.GetName(); temp = it->second.GetMatching(); for(unsigned int j = 0; j < temp.size(); j++) { if(it->second.CheckStrings()) { nod1 = nod1 + graph.find(temp[j])->second.GetName(); } else { nod1 = nod1 + name; } ss << count; ss >> name; if (nod1 != node) { ls << "\"" << node << "\"" << " -> " << "\"" << nod1 << "\"" << " [dir = back id = " << name << "];" << endl; } nod1 = "node"; ss.clear(stringstream::goodbit); count ++; } node = "node"; } ls << endl << "}"; ls << endl <<"}" << endl; } vector< vector > Graphic::setcolor() { int change; int count; vector< vector > color_set; vector color_sequence; for(int i = 0; (largest_tr - i) > -1; i ++) { change = largest_tr -i; count = 0; color_sequence.clear(); while(change > 7) { change = change -7; count++; } if(change >= 4) { change = change - 4; if(count >= 3 && count <= 5) { color_sequence.push_back( 9.00 - (.15 * count)); } else { color_sequence.push_back(1.00); } } else { if(count >= 3 && count <= 5) { color_sequence.push_back(.18 * count); } else { color_sequence.push_back(0.00); } } if(change >= 2) { change= change - 2; if(count >= 1 && count <= 3) { color_sequence.push_back( 1.00 - (.15 * count)); } else { color_sequence.push_back(1.00); } } else { if(count >= 1 && count <= 3) { color_sequence.push_back(.18 * count); } else { color_sequence.push_back(0.10); } } if(change >= 1) { change= change - 1; color_sequence.push_back(1.00 - (.15 * count)); } else { if(count >= 1) { color_sequence.push_back(.18 * count); } else { color_sequence.push_back(0.00); } } color_set.push_back(color_sequence); } return color_set; } int Graphic ::BiggestNumber() { int size_text = 0; int compare = size; while(compare >= 0) { compare = compare - 10; size_text ++; } return size_text; } int Graphic::BiggestText() { int size_text = graph.begin()->second.GetName().length(); std::map::iterator it; for(it = graph.begin()++; it != graph.end(); it ++) { if(it->second.GetName().length() > (unsigned) size_text) { size_text = it->second.GetName().length(); } } return size_text; } void Graphic::BuildState(ofstream & ls) { vector > space_place; vector space_inn; int size_text; if(graph.begin()->second.CheckStrings()) { size_text = BiggestText(); size_text = (int) ((size_text * 0.55) + 0.5); } else { size_text= BiggestNumber(); } space_inn.push_back(((change_point * size_text)) + change_point); space_inn.push_back(((size+size_distance) * change_point - ( change_point * 2.5 ))); int state_size = graph_change.size(); ls << "%!PS-Adobe-3.0 EPSF-3.0" << endl; ls << "%%Title: CTBN Graphic" << endl; ls << "%%Creator: UCR Research project" << endl; time_t timenow; struct tm * timeinfo; time ( &timenow ); timeinfo = localtime ( &timenow ); ls << "%%CreationDate: "<< asctime (timeinfo); ls << "%%BoundingBox: 0 0 " << (size_x + change_point) <<" " <<(size+size_distance)* change_point << endl; ls << "%%EndComments" << endl; ls << "%%Document-Fonts: Times-Roman" << endl; ls << "/Times-Roman findfont" << endl; ls << change_point << " scalefont" << endl; ls << "setfont \n" << endl; ls << "1 setlinewidth " << endl; int init_box = (change_point * 2); int count_line = 0; vector< vector > color_set = setcolor(); for(unsigned int i = 0; i < color_set.size(); i++) { if(init_box > size_x) { init_box = (change_point * 2); count_line++; } ls << init_box - change_point << " " << ((size+size_distance)* change_point) -(count_line * change_point ) << " " << "newpath moveto" << endl; ls << init_box << " " << ((size+size_distance)* change_point) - (count_line * change_point ) << " " << "lineto" << endl; ls << init_box << " " << ((size+size_distance)* change_point) - (count_line * change_point ) - change_point << " " << "lineto" << endl; ls << init_box - change_point<< " " << ((size+size_distance)* change_point) - (count_line * change_point ) - change_point << " " << "lineto" << endl; ls << "closepath" << endl; ls << "gsave" << endl; ls << color_set[color_set.size() - i -1][0] << " " << color_set[color_set.size() - i -1][1] << " " << color_set[color_set.size() - i -1][2] << " setrgbcolor fill" << endl; ls << "grestore" << endl; ls << "stroke" << endl; ls << init_box << " " << ((size+size_distance)* change_point) - (count_line * change_point )- change_point << " " << "newpath moveto" << endl; ls << "( =" << i << " )" << " show" << endl << endl; init_box = init_box + 4 * change_point; } for(int i = 0; i < size; i ++) { space_inn[1] = space_inn[1] - (change_point * i); space_place.push_back(space_inn); ls << space_inn[0] - (change_point * size_text) << " " << space_inn[1] << " " << "newpath moveto" << endl; ls << space_inn[0] << " " << space_inn[1] << " " << "lineto" << endl; ls << space_inn[0] << " " << space_inn[1] - change_point << " " << "lineto" << endl; ls << space_inn[0] - (change_point * size_text) << " " << space_inn[1] - change_point << " " << "lineto" << endl; ls << "closepath" << endl; ls << "stroke" << endl; ls << space_inn[0]- change_point* size_text +(change_point / 4) << " " << space_inn[1] - change_point+(change_point / 4) << " " << "newpath moveto" << endl; ls << "(" << graph[i].GetName() << ")" << " show" << endl << endl; space_inn[1] = ((size+size_distance)* change_point - ( change_point * 2.5 )); } int measure; vector check_value; for(int i = 0; i < size; i ++) { for(int j = 0; j < state_size; j ++) { if (graph_change[j].GetChange() == i) { measure = (int) (((size_x - space_inn[0])*(graph_change[j]. GetTime() / (ending_point - start_point))) + space_inn[0]); ls << space_place[i][0] << " " << space_place[i][1] << " " << "newpath moveto" << endl; ls << measure << " " << space_place[i][1] << " " << "lineto" << endl; ls << measure << " " << space_place[i][1] - change_point << " " << "lineto" << endl; ls << space_place[i][0]<< " " << space_place[i][1] - change_point << " " << "lineto" << endl; ls << "closepath" << endl; ls << "gsave" << endl; check_value = graph_change[j-1].GetState(); if(check_value[i] != -1) { ls << color_set[color_set.size() - 1 - check_value[i]][0] << " " << color_set[color_set.size() - 1 - check_value[i]][1] << " " << color_set[color_set.size() - 1 - check_value[i]][2] << " setrgbcolor fill" << endl; } ls << "grestore" << endl; ls << "stroke" << endl << endl; ls << "1 setlinewidth " << endl; space_place[i][0] = measure; } } ls << space_place[i][0] << " " << space_place[i][1] << " " << "newpath moveto" << endl; ls << size_x << " " << space_place[i][1] << " " << "lineto" << endl; ls << size_x << " " << space_place[i][1] - change_point << " " << "lineto" << endl; ls << space_place[i][0]<< " " << space_place[i][1] - change_point << " " << "lineto" << endl; ls << "closepath" << endl; ls << "gsave" << endl; check_value =graph_change[state_size-1].GetState(); if(check_value[i] != -1) { ls << color_set[color_set.size() - 1 - check_value[i]][0] << " " << color_set[color_set.size() - 1 - check_value[i]][1] << " " << color_set[color_set.size() - 1 - check_value[i]][2] << " setrgbcolor fill" << endl; } ls << "grestore" << endl; ls << "stroke" << endl << endl; } ls << "/Times-Roman findfont" << endl; ls << size_distance << " scalefont" << endl; ls << "setfont \n" << endl; int first_row = (int) (space_place[size-1][1] - (change_point + size_distance)); int next_row = size_distance; vector firstline_row; firstline_row.push_back(0); int line = 0; int present_column; int rejust; double justed; for(int i = 0; i < state_size; i ++) { if(i > 0 && graph_change[i].GetTime() != graph_change[i-1].GetTime()) { measure = (int) ((size_x - space_inn[0]) * (graph_change[i].GetTime() / (ending_point - start_point)) + space_inn[0]); line = 0; while(firstline_row[line] > measure - (size_distance * 2.5)) { line ++; if(line >= firstline_row.size()) firstline_row.push_back(0); } present_column = first_row -(next_row * line); ls << measure << " " << present_column << " " << "newpath moveto" << endl; firstline_row[line] = measure; rejust = (int) (graph_change[i].GetTime() * 1000); justed = rejust; justed = justed / 1000; ls << "(" << justed << ")" << " show" << endl << endl; } } ls << "showpage"<< endl; ls << "end"; } void Graphic::BuildStateRank(ofstream & ls) { vector space_inn; int size_text; if(graph[0].CheckStrings()) { size_text = BiggestText(); size_text = (int) ((size_text * 0.55) + 0.5); } else { size_text= BiggestNumber(); } int state_size = graph_change.size(); ls << "%!PS-Adobe-3.0 EPSF-3.0" << endl; ls << "%%Title: CTBN Graphic" << endl; ls << "%%Creator: UCR Research project" << endl; time_t timenow; struct tm * timeinfo; time ( &timenow ); timeinfo = localtime ( &timenow ); ls << "%%CreationDate: "<< asctime (timeinfo); ls << "%%BoundingBox: 0 0 " << (size_x + change_point) <<" " << large_point + (9 * change_point) << endl; ls << "%%EndComments" << endl; ls << "%%Document-Fonts: Times-Roman" << endl; ls << "/Times-Roman findfont" << endl; ls << change_point << " scalefont" << endl; ls << "setfont \n" << endl; ls << "1 setlinewidth " << endl; int init_box = (change_point* 2); double position_y = large_point + (9 * change_point); int count_line = 0; vector< vector > color_set = setcolor(); for(unsigned int i = 0; i < color_set.size(); i++) { if(init_box > size_x) { init_box = (change_point * 2); count_line++; } ls << init_box - change_point<< " " << position_y -(count_line * change_point ) << " " << "newpath moveto" << endl; ls << init_box << " " << position_y -(count_line * change_point ) << " " << "lineto" << endl; ls << init_box << " " << position_y - (count_line * change_point ) - change_point << " " << "lineto" << endl; ls << init_box - change_point << " " << position_y -(count_line * change_point ) - change_point << " " << "lineto" << endl; ls << "closepath" << endl; ls << "gsave" << endl; ls << color_set[color_set.size() - i -1][0] << " " << color_set[color_set.size() - i -1][1] << " " << color_set[color_set.size() - i -1][2] << " setrgbcolor fill" << endl; ls << "grestore" << endl; ls << "stroke" << endl; ls << init_box << " " << position_y -(count_line * change_point ) - change_point << " " << "newpath moveto" << endl; ls << "( =" << i << " )" << " show" << endl << endl; init_box = init_box + 4 * change_point; } init_box = ((change_point * size_text)) + change_point; for(int i = 0; i < size; i ++) { ls << init_box - (change_point * size_text) << " " << getlocation[i][1] + (5 * change_point) << " " << "newpath moveto" << endl; ls << init_box << " " << getlocation[i][1] + (5 * change_point) << " " << "lineto" << endl; ls << init_box << " " << getlocation[i][0] + (5 * change_point) << " " << "lineto" << endl; ls << init_box - (change_point * size_text) << " " << getlocation[i][0] + (5 * change_point) << " " << "lineto" << endl; ls << "closepath" << endl; ls << "stroke" << endl; ls << init_box - change_point * size_text +(change_point / 4) << " " << getlocation[i][0] + (5 * change_point) + ((getlocation[i][1] - getlocation[i][0]) / 4) << " " << "newpath moveto" << endl; ls << "(" << graph[i].GetName() << ")" << " show" << endl << endl; space_inn.push_back(init_box); } int measure; vector check_value; for(int i = 0; i < size; i ++) { for(int j = 0; j < state_size; j ++) { if (graph_change[j].GetChange() == i) { measure = (int) ((size_x - init_box)*(graph_change[j]. GetTime() / (ending_point - start_point)) + init_box); ls << space_inn[i] << " " << getlocation[i][1] + (5 * change_point) << " " << "newpath moveto" << endl; ls << measure << " " << getlocation[i][1] + (5 * change_point) << " " << "lineto" << endl; ls << measure << " " << getlocation[i][0] + (5 * change_point) << " " << "lineto" << endl; ls << space_inn[i] << " " << getlocation[i][0] + (5 * change_point) << " " << "lineto" << endl; ls << "closepath" << endl; ls << "gsave" << endl; check_value =graph_change[j-1].GetState(); if(check_value[i] != -1) { ls << color_set[color_set.size() - 1 - check_value[i]][0] << " " << color_set[color_set.size() - 1 - check_value[i]][1] << " " << color_set[color_set.size() - 1 - check_value[i]][2] << " setrgbcolor fill" << endl; } else { ls << 1 << " " << 1 << " " << 1 << " setrgbcolor fill" << endl; } ls << "grestore" << endl; ls << "stroke" << endl << endl; ls << "1 setlinewidth " << endl; space_inn[i] = measure; } } ls << space_inn[i]<< " " << getlocation[i][1] + (5 * change_point) << " " << "newpath moveto" << endl; ls << size_x << " " << getlocation[i][1] + (5 * change_point) << " " << "lineto" << endl; ls << size_x << " " << getlocation[i][0] + (5 * change_point) << " " << "lineto" << endl; ls << space_inn[i]<< " " << getlocation[i][0] + (5 * change_point) << " " << "lineto" << endl; ls << "closepath" << endl; ls << "gsave" << endl; check_value =graph_change[state_size-1].GetState(); if(check_value[i] != -1) { ls << color_set[color_set.size() - 1 - check_value[i]][0] << " " << color_set[color_set.size() - 1 - check_value[i]][1] << " " << color_set[color_set.size() - 1 - check_value[i]][2] << " setrgbcolor fill" << endl; } ls << "grestore" << endl; ls << "stroke" << endl << endl; } ls << "/Times-Roman findfont" << endl; ls << size_distance << " scalefont" << endl; ls << "setfont \n" << endl; int first_row = (int) getlocation[0][0] + size_distance; int next_row = size_distance; vector firstline_row; firstline_row.push_back(0); int line = 0; int present_column; int rejust; double justed; for(int i = 0; i < state_size; i ++) { if(i > 0 && graph_change[i].GetTime() != graph_change[i-1].GetTime()) { measure = (int) ((size_x - init_box) * (graph_change[i].GetTime() / (ending_point - start_point)) + init_box); line = 0; while(firstline_row[line] > measure - (size_distance * 2.5)) { line ++; if(line >= firstline_row.size()) firstline_row.push_back(0); } present_column = first_row -(next_row * line); ls << measure << " " << present_column << " " << "newpath moveto" << endl; firstline_row[line] = measure; rejust = (int) (graph_change[i].GetTime() * 1000); justed = rejust; justed = justed / 1000; ls << "(" << justed << ")" << " show" << endl << endl; } } ls << "showpage"<< endl; ls << "end"; } } // end of ctbn namespace ctbnrle.1.0.2/src/multirv.cc0000644000464200007770000000245111344302770016042 0ustar csheltoncsprofs/* 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 "multirv.h" #include "multisimple.h" namespace ctbn { SOBJCLASSDEF(MultiRV) MultiRV::MultiRV(const Context &var, const Context &cvar) : RVComp(var,cvar, new RVCondSimpleComp(var.Size(),cvar.Size())) { } MultiRV::MultiRV(std::istream &is) : RVComp(is) { } MultiRV::~MultiRV() { } MultiRV *MultiRV::Clone() const { return new MultiRV(*this); } template class RVCondSimpleComp; } // end of ctbn namespace ctbnrle.1.0.2/src/rk.cc0000644000464200007770000003501411344302770014755 0ustar csheltoncsprofs/* 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 #include #include "matrix.h" #include "rk.h" #include "params.h" namespace ctbn { // This version has a calculation cache // comment out the USECACHE definition to remove the caching //#define USECACHE #define MAXCACHE 1000 using namespace std; #ifdef USECACHE static int reuse = 0; static int tries = 0; #endif // computes a * exp(Qt), via integration with desired error of eps // (h, if non-negative, gives a starting estimate of step size) // return value replaces a // This is a version based on adaptive step sizes (and therefore // a fifth-order Runge-Kutta method with an embedded 4th order method // -- see Numerical Recipes in C) double vexpmt(vectr &a, const matrix &Q, double t, double eps, double h) { double ret = 0.0; if (t<=0.0) return ret; static double defaulteps = ParamDouble("RKEPS",RKEPS); if (eps<0.0) eps = defaulteps; #ifdef USECACHE static vector cachea(MAXCACHE); static vector cacheQ(MAXCACHE); static vector cachet(MAXCACHE); static vector cacheret(MAXCACHE); static vector cachellh(MAXCACHE); static int cachepos = 0; tries++; //if (tries%1000==0) cout << "cache: " << reuse << '/' << tries << endl; for(int i=0;i0) h = 0.1/Q.diag().absmax(); else h = 0.1; } vectr ka1(p),ka2(p),ka3(p),ka4(p),ka5(p),ka6(p); //static double a2=0.2, a3=0.3, a4=0.6, a5=1.0, a6=0.875, static double b21=0.2, b31=3.0/40.0, b32=9.0/40.0, b41=0.3, b42=-0.9, b43=1.2, b51=-11.0/54.0, b52=2.5, b53=-70.0/27.0, b54=35.0/27.0, b61=1631.0/55296.0, b62=175.0/512.0, b63=575.0/13824.0, b64=44275.0/110592.0, b65=253.0/4096.0, c1=37.0/378.0, c3=250.0/621.0, c4=125.0/594.0, c6=512.0/1771.0, dc5=-277.00/14336.0; static double dc1=c1-2825.0/27648.0, dc3=c3-18575.0/48384.0, dc4=c4-13525.0/55296.0, dc6=c6-0.25; int iter=0; double currt = 0; while(currt=t) h = t-currt; // don't overshoot! double err; for(;;) { // find stepsize // take 5th-order step: //iter++; vectr temp; Q.leftmult(a,ka1); ka1 *= h; //ka1 = (a*Q)*h; temp = a; temp.add(ka1,b21); //temp = ka1*b21+a; Q.leftmult(temp,ka2); ka2 *= h; //ka2 = (temp*Q)*h; temp = a; temp.add(ka1,b31); temp.add(ka2,b32); //temp = ka1*b31+ka2*b32+a; Q.leftmult(temp,ka3); ka3 *= h; //ka3 = (temp*Q)*h; temp = a; temp.add(ka1,b41); temp.add(ka2,b42); temp.add(ka3,b43); //temp = ka1*b41+ka2*b42+ka3*b43+a; Q.leftmult(temp,ka4); ka4 *= h; //ka4 = (temp*Q)*h; temp = a; temp.add(ka1,b51); temp.add(ka2,b52); temp.add(ka3,b53); temp.add(ka4,b54); //temp = ka1*b51+ka2*b52+ka3*b53+ka4*b54+a; Q.leftmult(temp,ka5); ka5 *= h; //ka5 = (temp*Q)*h; temp = a; temp.add(ka1,b61); temp.add(ka2,b62); temp.add(ka3,b63); temp.add(ka4,b64); temp.add(ka5,b65); //temp = ka1*b61+ka2*b62+ka3*b63+ka4*b64+ka5*b65+a; Q.leftmult(temp,ka6); ka6 *= h; //ka6 = (temp*Q)*h; vectr erra = ka1; erra *= dc1; erra.add(ka3,dc3); erra.add(ka4,dc4); erra.add(ka5,dc5); erra.add(ka6,dc6); //vectr erra = dc1*ka1+dc3*ka3+dc4*ka4+dc5*ka5+dc6*ka6; // check error: /*err = erra.absmax(); err /= eps; if (err <= 1.0) break;*/ double amin = a.absmin(); if(amin<1.0) amin = 1.0; err = erra.absmax()/amin; err /= eps; if(err<=1.0) break; // adjust step: double htemp = 0.9*h*pow(err,-0.25); if (htemp>h/10) h = htemp; else h /= 10; if (currt+h == currt) { /*cerr << "step size underflow in rk2" << endl; cerr << "in alpha pass:" << endl; cerr << Q << endl; cerr << a << endl; cerr << t << ' ' << currt << endl; exit(1);*/ return ret; } } currt += h; if (err > 1.89e-4) h = 0.9*h*pow(err,-0.2); else h *= 5; a.add(ka1,c1); a.add(ka3,c3); a.add(ka4,c4); a.add(ka6,c6); //a += c1*ka1+c3*ka3+c4*ka4+c6*ka6; double ss = a.sum(); if (ss>0.0) { ret += log(ss); a /= ss; } //ret += log(a.normalize()); } #ifdef USECACHE cacheret[cachepos] = a; cachellh[cachepos] = ret; cachepos++; //if (cachepos>MAXCACHE) { cerr << "cache overflow" << endl; cachepos=0;} if (cachepos>=MAXCACHE) cachepos=0; #endif return ret; } // computes exp(Qt) * b, via integration with desired error of eps // (h, if non-negative, gives a starting estimate of step size) // return value replaces b // This is a version based on adaptive step sizes (and therefore // a fifth-order Runge-Kutta method with an embedded 4th order-method // -- see Numerical Recipes in C) double expmtv(vectr &b, const matrix &Q, double t, double eps, double h) { double ret = 0.0; if (t<=0.0) return ret; static double defaulteps = ParamDouble("RKEPS",RKEPS); if (eps<0.0) eps = defaulteps; #ifdef USECACHE static vector cacheb(MAXCACHE); static vector cacheQ(MAXCACHE); static vector cachet(MAXCACHE); static vector cacheret(MAXCACHE); static vector cachellh(MAXCACHE); static int cachepos = 0; tries++; //if (tries%1000==0) cout << "cache: " << reuse << '/' << tries << endl; for(int i=0;i0) h = 0.1/Q.diag().absmax(); else h = 0.1; } vectr kb1(p),kb2(p),kb3(p),kb4(p),kb5(p),kb6(p); //static double a2=0.2, a3=0.3, a4=0.6, a5=1.0, a6=0.875, static double b21=0.2, b31=3.0/40.0, b32=9.0/40.0, b41=0.3, b42=-0.9, b43=1.2, b51=-11.0/54.0, b52=2.5, b53=-70.0/27.0, b54=35.0/27.0, b61=1631.0/55296.0, b62=175.0/512.0, b63=575.0/13824.0, b64=44275.0/110592.0, b65=253.0/4096.0, c1=37.0/378.0, c3=250.0/621.0, c4=125.0/594.0, c6=512.0/1771.0, dc5=-277.00/14336.0; static double dc1=c1-2825.0/27648.0, dc3=c3-18575.0/48384.0, dc4=c4-13525.0/55296.0, dc6=c6-0.25; double currt = 0; while(currt=t) h = t-currt; // don't overshoot! double err; for(;;) { // find stepsize // take 5th-order step: vectr temp; Q.rightmult(b,kb1); kb1 *= h; //kb1 = (Q*b)*h; temp = b; temp.add(kb1,b21); //temp = kb1*b21+b; Q.rightmult(temp,kb2); kb2 *= h; //kb2 = (Q*temp)*h; temp = b; temp.add(kb1,b31); temp.add(kb2,b32); //temp = kb1*b31+kb2*b32+b; Q.rightmult(temp,kb3); kb3 *= h; //kb3 = (Q*temp)*h; temp = b; temp.add(kb1,b41); temp.add(kb2,b42); temp.add(kb3,b43); //temp = kb1*b41+kb2*b42+kb3*b43+b; Q.rightmult(temp,kb4); kb4 *= h; //kb4 = (Q*temp)*h; temp = b; temp.add(kb1,b51); temp.add(kb2,b52); temp.add(kb3,b53); temp.add(kb4,b54); //temp = kb1*b51+kb2*b52+kb3*b53+kb4*b54+b; Q.rightmult(temp,kb5); kb5 *= h; //kb5 = (Q*temp)*h; temp = b; temp.add(kb1,b61); temp.add(kb2,b62); temp.add(kb3,b63); temp.add(kb4,b64); temp.add(kb5,b65); //temp = kb1*b61+kb2*b62+kb3*b63+kb4*b64+kb5*b65+b; Q.rightmult(temp,kb6); kb6 *= h; //kb6 = (Q*temp)*h; vectr errb = kb1; errb *= dc1; errb.add(kb3,dc3); errb.add(kb4,dc4); errb.add(kb5,dc5); errb.add(kb6,dc6); //vectr errb = dc1*kb1+dc3*kb3+dc4*kb4+dc5*kb5+dc6*kb6; // check error: /*err = errb.absmax(); err /= eps; if (err <= 1.0) break;*/ double bmin = b.absmin(); if(bmin<1.0) bmin = 1.0; err = errb.absmax()/bmin; err /= eps; if(err<=1.0) break; // adjust step: double htemp = 0.9*h*pow(err,-0.25); if (htemp>h/10) h = htemp; else h /= 10; if (currt+h == currt) { return ret; /*cerr << "step size underflow in rk2" << endl; cerr << "in beta pass:" << endl; cerr << Q << endl; cerr << b << endl; cerr << t << ' ' << currt << endl; exit(1);*/ } } currt += h; if (err > 1.89e-4) h = 0.9*h*pow(err,-0.2); else h *= 5; b.add(kb1,c1); b.add(kb3,c3); b.add(kb4,c4); b.add(kb6,c6); //b += c1*kb1+c3*kb3+c4*kb4+c6*kb6; double ss = b.sum(); if (ss>0.0) { ret += log(ss); b /= ss; } //ret += log(b.normalize()); } #ifdef USECACHE cacheret[cachepos] = b; cachellh[cachepos] = ret; cachepos++; //if (cachepos>MAXCACHE) { cerr << "cache overflow" << endl; cachepos=0;} if (cachepos>=MAXCACHE) cachepos=0; #endif return ret; } // calculates \int_u=0^t a*exp(Q*u)*e_i e_j*exp(Q*(t-u))*b du // [this is a matrix indexed by (i,j)] // the output is returned on c // (actually, it returns the transpose...) // This is a version based on adaptive step sizes (and therefore // a fifth-order Runge-Kutta method with an embedded 4th order method // -- see Numerical Recipes in C) double suffstat(const vectr &a, const vectr &b, matrix &c, const matrix &Q, double t, double eps, double h) { double ret = 0.0; if (t<=0.0) return ret; static double defaulteps = ParamDouble("RKEPS",RKEPS); if (eps<0.0) eps = defaulteps; #ifdef USECACHE static vector cachea(MAXCACHE); static vector cacheb(MAXCACHE); static vector cacheQ(MAXCACHE); static vector cachet(MAXCACHE); static vector cacheret(MAXCACHE); static vector cachellh(MAXCACHE); static int cachepos = 0; tries++; //if (tries%1000==0) cout << "cache: " << reuse << '/' << tries << endl; for(int i=0;i0) h = 0.1/Q.diag().absmax(); else h = 0.1; } double maxh = h, minh = h; //int evals = 0; vectr mya(a); c = matrix(p,p,0.0); vectr ka1(p),ka2(p),ka3(p),ka4(p),ka5(p),ka6(p); matrix kc1(p,p),kc2(p,p),kc3(p,p),kc4(p,p),kc5(p,p),kc6(p,p); //static double a2=0.2, a3=0.3, a4=0.6, a5=1.0, a6=0.875, static double b21=0.2, b31=3.0/40.0, b32=9.0/40.0, b41=0.3, b42=-0.9, b43=1.2, b51=-11.0/54.0, b52=2.5, b53=-70.0/27.0, b54=35.0/27.0, b61=1631.0/55296.0, b62=175.0/512.0, b63=575.0/13824.0, b64=44275.0/110592.0, b65=253.0/4096.0, c1=37.0/378.0, c3=250.0/621.0, c4=125.0/594.0, c6=512.0/1771.0, dc5=-277.00/14336.0; static double dc1=c1-2825.0/27648.0, dc3=c3-18575.0/48384.0, dc4=c4-13525.0/55296.0, dc6=c6-0.25; double currt = 0; while(currtmaxh) maxh = h; if (h=t) h = t-currt; // don't overshoot! double err; for(;;) { // find stepsize //evals++; //if(evals>MAX_INT) return; // take 5th-order step: vectr temp; ka1 = (mya*Q)*h; kc1 = (matrix(b,mya) + Q*c)*h; temp = ka1*b21+mya; ka2 = (temp*Q)*h; kc2 = (matrix(b,temp) + Q*(kc1*b21+c))*h; temp = ka1*b31+ka2*b32+mya; ka3 = (temp*Q)*h; kc3 = (matrix(b,temp) + Q*(kc1*b31+kc2*b32+c))*h; temp = ka1*b41+ka2*b42+ka3*b43+mya; ka4 = (temp*Q)*h; kc4 = (matrix(b,temp) + Q*(kc1*b41+kc2*b42+kc3*b43+c))*h; temp = ka1*b51+ka2*b52+ka3*b53+ka4*b54+mya; ka5 = (temp*Q)*h; kc5 = (matrix(b,temp) + Q*(kc1*b51+kc2*b52+kc3*b53+kc4*b54+c))*h; temp = ka1*b61+ka2*b62+ka3*b63+ka4*b64+ka5*b65+mya; ka6 = (temp*Q)*h; kc6 = (matrix(b,temp) + Q*(kc1*b61+kc2*b62+kc3*b63+kc4*b64+kc5*b65+c))*h; vectr erra = dc1*ka1+dc3*ka3+dc4*ka4+dc5*ka5+dc6*ka6; matrix errc = dc1*kc1+dc3*kc3+dc4*kc4+dc5*kc5+dc6*kc6; // check error: /*err = erra.absmax(); double terr = errc.absmax(); if (erramin) amin = bmin; err = erra.absmax()/amin; double terr = errc.absmax()/amin; if(errh/10) h = htemp; else h /= 10; if (currt+h == currt) { return ret; /*cerr << "step size underflow in rk2" << endl; cerr << "in ss pass:" << endl; cerr << Q << endl; cerr << a << endl; cerr << b << endl; cerr << t << ' ' << currt << endl; exit(1);*/ } } currt += h; if (err > 1.89e-4) h = 0.9*h*pow(err,-0.2); else h *= 5; mya += c1*ka1+c3*ka3+c4*ka4+c6*ka6; c += c1*kc1+c3*kc3+c4*kc4+c6*kc6; double ss = mya.sum(); if (ss>0.0) { ret += log(ss); c /= ss; mya /= ss; } //double z=mya.normalize(); //ret += log(z); //c/=z; } #ifdef USECACHE cacheret[cachepos] = c; cachellh[cachepos] = ret; cachepos++; //if (cachepos>MAXCACHE) { cerr << "cache overflow" << endl; cachepos=0;} if (cachepos>=MAXCACHE) cachepos=0; #endif return ret; } } // end of ctbn namespace ctbnrle.1.0.2/src/structuresearch.cc0000644000464200007770000000402311344302770017563 0ustar csheltoncsprofs/* 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 "structuresearch.h" #include "matrix.h" namespace ctbn { using namespace std; StructureSearch::StructureSearch(const Context &c, FamScore* fs) : fScore(fs) { vector varL = c.VarList(); for(int i=0; i > children; int n = nodes.size(); for(int j=0;j par = nodes[j]->CondDomain().VarList(); for(int k=0;k())); } s.children = children; s.parentlist.clear(); for (int i=0; iCondDomain(); s.parentlist.push_back(c.VarList()); } } } // end of ctbn namespace ctbnrle.1.0.2/src/varsample.cc0000644000464200007770000000176711344302770016343 0ustar csheltoncsprofs/* 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 "varsample.h" namespace ctbn { using namespace std; VarSample::VarSample() {} VarSample::~VarSample() {} } // end of ctbn namespace ctbnrle.1.0.2/src/defines.cc0000644000464200007770000000331611344302770015756 0ustar csheltoncsprofs/* 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 "defines.h" #if defined __linux__ || __CYGWIN__ #include TIME_STRUCT GetLinuxTime() { struct timeval t; struct timezone z; gettimeofday(&t, &z); return (TIME_STRUCT)t.tv_sec * (TIME_STRUCT)1000000000 + (TIME_STRUCT)t.tv_usec * (TIME_STRUCT)1000; } #endif #if defined(_WIN32) longlong GetWindowsTime() { SYSTEMTIME syst; GetSystemTime(&syst); FILETIME filet; SystemTimeToFileTime(&syst,&filet); ULARGE_INTEGER larget; larget.LowPart = filet.dwLowDateTime; larget.HighPart = filet.dwHighDateTime; return larget.QuadPart/10; } const unsigned long win32nan[2]={0xffffff, 0x7ffffff}; double nan(const char *) { return *((double *)win32nan);} float nanf(const char *) { return (float)(*((double *)win32nan));} long double nanl(const char *) { return (long double)(*((double *)win32nan));} #endif ctbnrle.1.0.2/src/sampler.cc0000644000464200007770000000222711344302770016004 0ustar csheltoncsprofs/* 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 "sampler.h" namespace ctbn { using namespace std; Sampler::~Sampler() { } void Sampler::SetMethod(const VarSample *m) { method = m; } void Sampler::SetTrajectory(const Trajectory *traj) { evid = traj; begintime = evid->TimeBegin(); endtime = evid->TimeEnd(); } } // end of ctbn namespace ctbnrle.1.0.2/src/brutestructuresearch.cc0000644000464200007770000000476511344302770020642 0ustar csheltoncsprofs/* 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 "brutestructuresearch.h" #include "extramath.h" #include namespace ctbn { using namespace std; BruteStructureSearch::BruteStructureSearch(const Context &c, FamScore* fs, int mp) : StructureSearch(c, fs), maxParents(mp) { } BruteStructureSearch::~BruteStructureSearch() { } Structure BruteStructureSearch::LearnStructure() { int numNodes = vars.size(); Context nullc; vector > exhaustIdx = combo(numNodes-1,maxParents); int numTests = exhaustIdx.size(); for(int i=0; i parentSet; parentSet.insert(parentSet.end(), vars.begin(),vars.begin() + i); parentSet.insert(parentSet.end(), vars.begin() + (i+1),vars.end()); double score; double bestScore = 1; Node* bestnode; for(int j=0; jGetScore(vars[i],cvTest); if(bestScore == 1 || score > bestScore) { if(bestScore != 1) delete bestnode; bestScore = score; bestnode = new Node(vars[i],cvTest); } } nodes.push_back(bestnode->Clone()); vector tmp = bestnode->CondDomain().VarList(); for(int k=0;k. */ #include "random.h" #include "defines.h" #include #include namespace ctbn { // Define a constant to be 2 * sqrt(2/e); used in SampleNormal() const double TWICE_A = 1.7155277699214135; using namespace std; const double Random::Conv32Double = 1.0/2/2/2/2/2/2/2/2/2/2/2/2/2/2/2/2/2/2/2/2/2/2/2/2/2/2/2/2/2/2/2/2; const double Random::Conv64Double = Random::Conv32Double*Random::Conv32Double; Random randomizer(GET_TIME % (1 << 24)); Random::Random (Random::uint32 in_seed) { static int init_seed = 5489; if (in_seed > 0) seed = in_seed; else seed = init_seed; init_seed++; Reset(seed); } void Random::Reset (Random::uint32 in_seed) { if (in_seed > 0) seed = in_seed; MT[0] = seed; for(int i=1;i<624;i++) MT[i] = (1812433253 * (MT[i-1] ^ (MT[i-1]>>30))+i) & 0xffffffff; loc = 0; } Random::uint32 Random::Rand32() { if (loc==0) generateMore(); uint32 ret = MT[loc]; ret ^= ret>11; ret ^= (ret<<7) & 0x9d2c5680; ret ^= (ret<<15) & 0xefc60000; ret ^= ret>>18; loc++; if (loc==624) loc=0; return ret&0xffffffff; } void Random::generateMore() { for(int i=0;i<624;i++) { int i1 = i==623 ? 0 : i+1; int i397 = i>226 ? i-227 : i+397; uint32 t = (MT[i]&0x80000000) + (MT[i1]&0x7fffffff); MT[i] = MT[i397] ^ (t>>1); if (t & 1) MT[i] = MT[i] ^ 0x9908b0df; MT[i] &= 0xffffffff; } } double Random::RandRealFast() { return Rand32()*Conv32Double; } double Random::RandReal() { return Rand32()*Conv32Double+Rand32()*Conv64Double; } int Random::RandInt(int range) { // for small range, this works fine return int(RandRealFast()*range); } // The algorithm is taken from "Non-Uniform Random Variate Generation" by // Luc Devroye pages 194-199 double Random::SampleNormal (void) { double u, v, x, sqr_x; while (true) { u = RandReal(); if (u == 0.0) continue; v = (RandReal() - 0.5) * TWICE_A; x = v / u; sqr_x = x*x; if (sqr_x <= 6 - 8*u + 2*u*u) return x; if (sqr_x >= 2 / u - 2 * u) continue; if (sqr_x <= -4 * ::log(u)) return x; } } // The algorithm is taken from Cheng, Generating Beta Variates // with Nonintegral Shape Parameters. Communications of the ACM // 1978 Vol 21 No 4 p317 - p322 double Random::SampleBeta (double a, double b) { double s, lambda, u, u1, u2, v, y; s = a+b; double minab = a= ::log(u1*u1*u2)) { return (y/(y+b)); } } } double Random::SampleGamma (double a, double b) { if( a < 1.0 ) return DblGammaLessThanOne(a)/b; else if( a > 1.0 ) return DblGammaGreaterThanOne(a)/b; return -::log(RandReal()); } // Code adopted from Nir who adapted it from David Heckerman //----------------------------------------------------------- // DblGammaGreaterThanOne(dblAlpha) // // routine to generate a gamma random variable with unit scale and // alpha > 1 // reference: Ripley, Stochastic Simulation, p.90 // Chang and Feast, Appl.Stat. (28) p.290 //----------------------------------------------------------- double Random::DblGammaGreaterThanOne(double dblAlpha) { double rgdbl[6]; rgdbl[1] = dblAlpha - 1.0; rgdbl[2] = (dblAlpha - (1.0 / (6.0 * dblAlpha))) / rgdbl[1]; rgdbl[3] = 2.0 / rgdbl[1]; rgdbl[4] = rgdbl[3] + 2.0; rgdbl[5] = 1.0 / sqrt(dblAlpha); for (;;) { double dblRand1; double dblRand2; do { dblRand1 = RandReal(); dblRand2 = RandReal(); if (dblAlpha > 2.5) dblRand1 = dblRand2 + rgdbl[5] * (1.0 - 1.86 * dblRand1); } while (!(0.0 < dblRand1 && dblRand1 < 1.0)); double dblTemp = rgdbl[2] * dblRand2 / dblRand1; if (rgdbl[3] * dblRand1 + dblTemp + 1.0 / dblTemp <= rgdbl[4] || rgdbl[3] * ::log(dblRand1) + dblTemp - ::log(dblTemp) < 1.0) { return dblTemp * rgdbl[1]; } } assert(false); return 0.0; } /* routine to generate a gamma random variable with unit scale and * alpha < 1 reference: Ripley, Stochastic Simulation, p.88 */ double Random::DblGammaLessThanOne(double dblAlpha) { double dblTemp; const double dblexp = exp(1.0); for (;;) { double dblRand0 = RandReal(); double dblRand1 = RandReal(); if (dblRand0 <= (dblexp / (dblAlpha + dblexp))) { dblTemp = pow(((dblAlpha + dblexp) * dblRand0) / dblexp, 1.0 / dblAlpha); if (dblRand1 <= exp(-1.0 * dblTemp)) return dblTemp; } else { dblTemp = -1.0 * ::log((dblAlpha + dblexp) * (1.0 - dblRand0) / (dblAlpha * dblexp)); if (dblRand1 <= pow(dblTemp,dblAlpha - 1.0)) return dblTemp; } } assert(false); return 0.0; } /* DblGammaLessThanOne */ void Random::SampleDirichlet(double const *alpha, int n, double *theta) { double sum = 0; for(int i =0; i < n; i++) sum += (theta[i] = SampleGamma(alpha[i], 1)); // normalize for(int i =0; i < n; i++) theta[i] /= sum; } double Random::SampleExp (double a) { // where a = intensity = 1/mean ///// ///// Need to fix so it never samples a 0? if(isfinite(1/a)) return (-::log(1.0 - RandReal()) / a); else return INFINITY; } double Random::SampleTruncateExp(double q, double t) { double u1 = 1 - exp(-q * t); double u = RandReal() * u1; double ret = (-::log(1.0 - u)/q); return ret; } int Random::SampleAddWeights (const vector& additive_weights) { double val = RandReal() * additive_weights[additive_weights.size()-1]; return lower_bound(additive_weights.begin(),additive_weights.end(),val) - additive_weights.begin(); } int Random::SampleMultinomial(const double *weights, int n, double sum) { double j = RandReal()*sum; for(int i=0;i0) { // added so that an intensity row can be used // directly j -= weights[i]; if (j<=0) return i; } } return n-1; } int Random::SampleMultinomial(const vector &weights, double sum) { double j= RandReal()*sum; int n = weights.size(); for(int i=0;i0) { // added so that an intensity row can be used // directly j -= weights[i]; if (j<=0) return i; } } return weights.size()-1; } //sample n samples from multinomial void Random::SampleMultinomial(const vector &weights, vector &samples, int n) { int length = weights.size(); double * cdf = new double [length]; cdf[0] = weights[0]; for(int i=1; icdf[idx]) idx++; samples.push_back(idx); } delete [] cdf; } } // end of ctbn namespace ctbnrle.1.0.2/src/exactmarkovinf.cc0000644000464200007770000002201611344302770017360 0ustar csheltoncsprofs/* 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 "exactmarkovinf.h" #include namespace ctbn { using namespace std; ExactMarkovInf::ExactMarkovInf() { p = NULL; tr = NULL; jointP0SS = NULL; jointDSS = NULL; } ExactMarkovInf::~ExactMarkovInf() { // don't delete anything... we don't own it (?) ClearCache(); } ExactMarkovInf *ExactMarkovInf::Clone() const { // a little dangerous, but no more so than having this // kind of object in the first place ExactMarkovInf* cloneInf = new ExactMarkovInf(*this); for(int i=0;ialpha[i] = alpha[i]->Clone(); for(int i=0;ibeta[i] = beta[i]->Clone(); for(int i=0;iprop[i] = prop[i]->Clone(); if(cloneInf->jointP0SS) cloneInf->jointP0SS = jointP0SS->Clone(); if(cloneInf->jointDSS) cloneInf->jointDSS = jointDSS->Clone(); return cloneInf; } void ExactMarkovInf::SetProcess(const Process *p) { ClearCache(); this->p=NULL; this->p = dynamic_cast(p); } void ExactMarkovInf::SetTrajectory(const Trajectory *tr) { ClearCache(); this->tr = tr; } double ExactMarkovInf::FilterReal(const Instantiation &x, double t, bool log, bool normalize) { if (p==NULL || tr==NULL) return log ? -INFINITY: 0.0; MakeCache(); int i = lower_bound(times.begin(),times.end(),t)-times.begin(); RVSimple *a; if (times[i]==t) a = alpha[i]->Clone(); else { if (i==0) return log ? -INFINITY : 0.0; a = alpha[i-1]->Clone(); RVCondSimple *trans = p->d->Cond(times[i-1],t, tr->Values(p->d->Domain()+p->d->CondDomain(),t)); trans->Mult(a); delete trans; } if (normalize) a->Normalize(); double ret; if (x.NumVars()==0) ret = a->Sum(log); else { vector ind; p->d->Domain().ConsistentIndexes(ind,x); a->Restrict(ind); ret = a->Sum(log); } delete a; return ret; } double ExactMarkovInf::Filter(const Instantiation &x, double t, bool log) { return FilterReal(x,t,log); } double ExactMarkovInf::Smooth(const Instantiation &x, double t, bool log) { if (p==NULL || tr==NULL) return log ? -INFINITY : 0.0; MakeCache(); int i = lower_bound(times.begin(),times.end(),t)-times.begin(); RVSimple *a; if (times[i]==t) { a = alpha[i]->Clone(); a->MultBy(beta[i]); } else { if (i==0 || i>=times.size()-1) return log ? -INFINITY : 0.0; a = alpha[i-1]->Clone(); Instantiation inst = tr->Values(p->d->Domain()+p->d->CondDomain(),t); RVCondSimple *trans = p->d->Cond(times[i-1],t,inst); trans->Mult(a); delete trans; RVSimple *b = beta[i]->Clone(); trans = p->d->Cond(t,times[i],inst); trans->RMult(b); delete trans; a->MultBy(b); delete b; } a->Normalize(); double ret; if (x.NumVars()==0) ret = a->Sum(log); else { vector ind; p->d->Domain().ConsistentIndexes(ind,x); a->Restrict(ind); ret = a->Sum(log); } delete a; return ret; } double ExactMarkovInf::Prob(double t, bool log) { return FilterReal(Instantiation(),t,log,false); } void ExactMarkovInf::ClearCache() { times.resize(0); for(int i=0;i0) return; //cout << "MAKE CACHE" << endl; GetProp(); AlphaPass(); BetaPass(); } // This method goes and retrieves all of the sequence of conditional // distributions (given the evidence in the trajectory) transitioning // from one event to the next and also "across" events that signify a // transition. void ExactMarkovInf::GetProp() { times.push_back(tr->TimeBegin()); Trajectory::Index i = tr->Begin(p->d->Domain()+p->d->CondDomain()); while(!i.Done()) { RVCondSimple *pr = p->d->Cond(i.Time(), i.Time()+i.DeltaT(),i.Values()); prop.push_back(pr); transtype.push_back(0); changevars.push_back(Context()); condi.push_back(p->d->CondDomain().Index(i.Values())); Instantiation oldv = i.Values(); int varchange = i.TestInc(p->d->Domain()); times.push_back(i.Time()); if (varchange>1 && !i.Done()) { pr = p->d->Cond(i.Time(),oldv,i.Values()); prop.push_back(pr); condi.push_back(p->d->CondDomain().Index(i.Values())); times.push_back(i.Time()); transtype.push_back(2); changevars.push_back(Context(oldv,i.Values())); } else if (varchange==1 && !i.Done()) { pr = p->d->Cond(i.Time(),oldv,i.Values(),false); prop.push_back(pr); condi.push_back(p->d->CondDomain().Index(i.Values())); transtype.push_back(1); changevars.push_back(Context()); times.push_back(i.Time()); } // else a change in conditioning set only... } } // This method propagates the classic "alpha pass" starting with // the initial distribution indicated by the Markov process's starting // distribution and running it forward through the conditional distributions // set up by GetProp. They are saved for easy access later. void ExactMarkovInf::AlphaPass() { int n = prop.size(); alpha.resize(n+1); RVSimple *curr = p->p0->MakeSimple( tr->Values( p->p0->Domain()+p->p0->CondDomain(), tr->TimeBegin())); if (n>0) { alpha[0] = prop[0]->Convert(curr); delete curr; curr = alpha[0]->Clone(); } else { alpha[0] = curr->Clone(); } for(int i=0;iMult(curr); alpha[i+1] = curr->Clone(); } delete curr; } // This is the same as AlphaPass, but running the distribution (starting // with a uniform one) backward through the conditional distributions. void ExactMarkovInf::BetaPass() { int i = prop.size(); beta.resize(i+1); RVSimple *curr = alpha[i]->Clone(); curr->MakeUniform(); // set to all 1s beta[i] = curr->Clone(); for(i--;i>=0;i--) { prop[i]->RMult(curr); beta[i] = curr->Clone(); } delete curr; } void ExactMarkovInf::AddExpSuffStats(SS *ss, double w) { MarkovSS *mss = dynamic_cast(ss); MakeCache(); int n = prop.size(); //cout << "n=" << n << endl; for(int i=0;id, but that's a lot of extra unneeded // work given that we know the underlying structure if (transtype[i]==0) p->d->AddExpSS(condi[i],alpha[i],beta[i+1], times[i],times[i+1]-times[i], mss->dss,w); else if (transtype[i]==2) p->d->AddExpTransSS(condi[i],alpha[i],beta[i+1], changevars[i],times[i], mss->dss,w); } if (alpha.size()>0) { // add suffstats for initial distribution RVSimple *init = alpha[0]->Clone(); init->MultBy(beta[0]); init->Normalize(); p->p0->AddExpSS(tr-> Values(p->p0->CondDomain(),tr->TimeBegin()), init,mss->p0ss,w); delete init; } } void ExactMarkovInf::AddExpSuffStats(const Dynamics *dyn, SS *ss, double w) { const CTBNDyn* cdyn = dynamic_cast(p->d); const DynComp* jcdyn = dynamic_cast(cdyn->GetJoint()); if(!jointDSS) { DynCompSS* dcss = dynamic_cast(jcdyn->BlankSS()); MakeCache(); int n = prop.size(); for(int i=0;iAddExpSS(condi[i],alpha[i],beta[i+1], times[i],times[i+1]-times[i], dcss,w); else if (transtype[i]==2) jcdyn->AddExpTransSS(condi[i],alpha[i], beta[i+1],changevars[i], times[i],dcss,w); } jointDSS = dcss; } dyn->AddSS(jointDSS,jcdyn,ss,w); } void ExactMarkovInf::AddExpSuffStats(const RV *p0, SS *ss, double w) { const BN* bn = dynamic_cast(p->p0); RVComp *jbn = dynamic_cast(bn->Joint( Instantiation(bn->Domain()+bn->CondDomain()))); if(!jointP0SS) { RVCSCompSS* rvcscss = dynamic_cast(jbn->BlankSS()); MakeCache(); if (alpha.size()>0) { RVSimple *init = alpha[0]->Clone(); init->MultBy(beta[0]); init->Normalize(); jbn->AddExpSS(tr->Values(jbn->CondDomain(), tr->TimeBegin()), init,rvcscss,w); delete init; } jointP0SS = rvcscss; } p0->AddSS(jointP0SS,jbn,ss,w); } double ExactMarkovInf::CalcQuery(QueryCalculator &calc) { SS *ss = p->BlankSS(); ClearCache(); AddExpSuffStats(ss); double ret = calc.Calculate(*ss); delete ss; return ret; } } // end of ctbn namespace ctbnrle.1.0.2/src/matrix.cc0000644000464200007770000000242711344302770015647 0ustar csheltoncsprofs/* 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 "matrix.h" #include #include namespace ctbn { using namespace std; void matrix::sparsepremult(vectr &v) const { vectr c(v); v = 0.0; for(int i=0,s=0;i. */ #include "samplequeue.h" #include "defines.h" #include namespace ctbn { SampleQueue::SampleQueue(int maxn) { heap = new Event[maxn]; places = new int[maxn]; n = 0; } SampleQueue::~SampleQueue() { delete []heap; delete []places; } bool SampleQueue::Head(Event &e) { if (n<=0) return false; e = heap[0]; return true; } void SampleQueue::Add(const Event &e) { heap[n] = e; ++n; double key = e.time; int i; for(i=n-1;i>0 && key < heap[(i-1)/2].time;i = (i-1)/2) { heap[i] = heap[(i-1)/2]; places[heap[i].var] = i; } heap[i] = e; places[e.var] = i; } void SampleQueue::Remove(int var) { int i = places[var]; if (i==0) { --n; if (n>0) { heap[0] = heap[n]; places[heap[0].var] = 0; Heapify(0,heap[0].time); } } else { i = Heapify(i,INFINITY); --n; if (i==n) return; Event temp = heap[n]; while(i>0 && temp.time < heap[(i-1)/2].time) { heap[i] = heap[(i-1)/2]; places[heap[i].var] = i; i = (i-1)/2; } heap[i] = temp; places[temp.var] = i; } } int SampleQueue::Heapify(int i, double tempk) { int l,r,small; double smallt; while(i. */ #include "importancesampler.h" #include "ctbn.h" #include "utils.h" #include "samplequeue.h" namespace ctbn { //#define DEBUG using namespace std; ImportanceSampler::ImportanceSampler(const Process *pr, const Trajectory *traj, const VarSample *m) { SetProcess(pr); SetTrajectory(traj); SetMethod(m); discard_bn_weight = false; } ImportanceSampler::~ImportanceSampler() {} ImportanceSampler *ImportanceSampler::Clone() const { return new ImportanceSampler(*this); } void ImportanceSampler::SampleTrajectories(vector &tr, vector &w, int numsamples, Random &rand) { MakeIndex(); double t1 = getcputime(); const Markov* pr = dynamic_cast(p); Instantiation x = evid->Values(pr->GetDynamics()->Domain() + pr->GetDynamics()->CondDomain(), begintime); for (int i=0; i &nextevid) { nodeindex.clear(); nodeval.clear(); nextevidval.clear(); for (int i=0; iCondDomain().Index(inst)); nodeval.push_back(inst.Value(str.Node2Var(i))); nextevidval.push_back(nextevid[i].Values().Value(str.Node2Var(i))); } } double ImportanceSampler::SampleDyn(Trajectory &traj, Random &rand) { int numofnodes = nodes.size(); vector nextindex = evidindex; //index for each node changeindex = vector(numofnodes, 0); int * nextevidtype = new int [numofnodes]; double weight = 0; double currt = begintime; const Dynamics* dyn = dynamic_cast(p)->GetDynamics(); //Get initial values Instantiation curri = traj.Values(dyn->Domain()+dyn->CondDomain(), currt); for (int i=0; iDomain()); //check conddomain if (nextindex.size()>numofnodes) ++nextindex[numofnodes]; MakeCache(curri, nextindex); int count = 1; int movecount = 0; vector undefinelist; //save the nodes that need to be resampled for (int i=0; iCondDomain().Index(curri); double newt = SampleTime(curri, i, nodeindex[i], nodeval[i], nextevidtype[i], nextindex[i], nextevidval[i], currt, rand); events.Add(SampleQueue::Event(i, 0, newt)); } SampleQueue::Event e; events.Head(e); //Get the next transition variable int nextid = e.var; double nextt = e.time; //check transitions in conddomain of the process if (nextindex.size()>numofnodes && nextt>nextindex[numofnodes].Time()) { nextt = nextindex[numofnodes].Time(); nextid = -1; } //############################################## //This part checks if the sampling process falls into //some state that will never be able to jump out if (fabs(nextt-currt)<1e-12) movecount++; else movecount = 0; if (movecount>100) { exit(1); } //############################################## if (nextt>=endtime) { nextt = endtime; for (int i=0; iDomain()); nextevidval[nextid] = nextindex[nextid].Values().Value(nextid); #ifdef DEBUG cout << "Change index: nextid: " << nextid << endl; cout << "next evid type: " << nextevidtype[nextid] << endl; cout << "nextevid val: " << nextevidval[nextid] << endl; #endif } } else { //Add the transition of the conddomain to the trajectory //we have to assume that the evidence of conddomain is fully observed //otherwise, we can't sample any trajectory traj.AddTransition(nextindex[numofnodes].Values(), nextindex[numofnodes].Time()); ++nextindex[numofnodes]; currt = nextt; } UndefineTime(nextid, undefinelist); for (int k=0; kValues(dynamic_cast(p)->GetDynamics()->Domain(), currt); //Sample initial values // notes by Jing: for network application: only use the dynamic's weight double weight = 0.0; if (discard_bn_weight) SampleInitial(curri); else weight += SampleInitial(curri); traj.AddTransition(curri, begintime); weight += SampleDyn(traj); if (logscale) return weight; else return exp(weight); } } // end of ctbn namespace ctbnrle.1.0.2/src/ctbndyn.cc0000644000464200007770000003624711344302770016013 0ustar csheltoncsprofs/* 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 "ctbndyn.h" #include "markovdyn.h" #include #include "extramath.h" #include "samplequeue.h" #include "searchqueue.h" namespace ctbn { using namespace std; SOBJCLASSDEF(CTBNDyn) CTBNDyn::CTBNDyn() : Dynamics(Context(),Context()) { joint = NULL; } CTBNDyn::CTBNDyn(std::istream &is) : Dynamics(is) { int n; is >> n; nodes.resize(n); for(int i=0;i(StreamObj::LoadPtr(is)); joint = NULL; } CTBNDyn::CTBNDyn(const CTBNDyn &ctbn) : Dynamics(ctbn) { int n = ctbn.nodes.size(); nodes.resize(n); for(int i=0;iClone(); joint = NULL; } CTBNDyn::CTBNDyn(const Structure &s, const Context &vars) : Dynamics(Context(), Context()) { joint = NULL; vector > parentlist = s.parentlist; int n = parentlist.size(); for(int i=0;i currentParentlist = parentlist[i]; Context v; Context cv; if(vars.HasId(i)) v.AddVar(i,vars.Cardinality(i)); else continue; for(int j=0;jClone(); ClearNode2IdMaps(); } return *this; } CTBNDyn *CTBNDyn::Clone() const { return new CTBNDyn(*this); } CTBNDyn::~CTBNDyn() { if (joint) delete joint; int n = nodes.size(); for(int i=0;i> n; for(int i=0;i(StreamObj::LoadPtr(is)); ClearNode2IdMaps(); } void CTBNDyn::Save(ostream &os) const { Dynamics::Save(os); int n = nodes.size(); os << os.fill() << n; for(int i=0;iSavePtr(os); } } void CTBNDyn::Normalize() { int n = nodes.size(); if (joint) delete joint; joint = NULL; for(int i=0;iNormalize(); } void CTBNDyn::Restrict(const Instantiation &x) { int n = nodes.size(); if (joint) delete joint; joint = NULL; for(int i=0;iRestrict(x); ClearNode2IdMaps(); // could change... } void CTBNDyn::Mult(const Dynamics *x) { // not really sure how to do this... assert(0); } RVCondSimple *CTBNDyn::Cond(double t0, double t1, const Instantiation &x) const { return Joint()->Cond(t0,t1,x); } RVCondSimple *CTBNDyn::Cond(double t, const Instantiation &from, const Instantiation &to, bool transition) const { return Joint()->Cond(t,from,to,transition); } CTBNDynSS *CTBNDyn::BlankSS() const { CTBNDynSS *ret = new CTBNDynSS(); int n = nodes.size(); ret->dynss.resize(n); for(int i=0;idynss[i] = nodes[i]->BlankSS(); return ret; } void CTBNDyn::AddSS(const Instantiation &x, double t0, double deltat, SS *ss, double w) const { CTBNDynSS *css = dynamic_cast(ss); int n = nodes.size(); for(int i=0;iAddSS(x,t0,deltat,css->dynss[i],w); } void CTBNDyn::AddTransSS(const Instantiation &x1, const Instantiation &x2, double t, SS *ss, double w) const { CTBNDynSS *css = dynamic_cast(ss); int n = nodes.size(); for(int i=0;iAddTransSS(x1,x2,t,css->dynss[i],w); } void CTBNDyn::AddExpSS(const RV *alpha, const RV *beta, double t0, double deltat, SS *ss, double w) const { CTBNDynSS *css = dynamic_cast(ss); int n = nodes.size(); for(int i=0;iAddExpSS(alpha,beta,t0,deltat,css->dynss[i],w); } void CTBNDyn::AddExpTransSS(const RV *x1, const RV *x2, const Context &changevar, double t, SS *ss, double w) const { if (!changevar.IsOverlap(Domain())) return; CTBNDynSS *css = dynamic_cast(ss); int n = nodes.size(); for(int i=0;iAddExpTransSS(x1,x2,changevar,t,css->dynss[i],w); } void CTBNDyn::AddExpSS(const Instantiation &condi, const RVSimple *alpha, const RVSimple *beta, double t0, double deltat, SS *ss, double w) const { SS *allss = Joint()->BlankSS(); Joint()->AddExpSS(condi,alpha,beta,t0,deltat,allss,w); CTBNDynSS *css = dynamic_cast(ss); int n = nodes.size(); for(int i=0;iAddSS(allss,Joint(),css->dynss[i]); delete allss; } void CTBNDyn::AddSS(const SS *toadd, const Dynamics *dyn, SS *ss, double w) const { int n = nodes.size(); CTBNDynSS *ctbndynss = dynamic_cast(ss); for(int i=0;iAddSS(toadd,dyn,ctbndynss->dynss[i],w); } void CTBNDyn::AddExpSS(int condi, const RVSimple *alpha, const RVSimple *beta, double t0, double deltat, SS *ss, double w) const { Instantiation i(CondDomain()); i.SetIndex(condi); AddExpSS(i,alpha,beta,t0,deltat,ss,w); } void CTBNDyn::AddExpTransSS(const Instantiation &condi, const RVSimple *x1, const RVSimple *x2, const Context &changevar, double t, SS *ss, double w) const { RVSimple *myx1 = x1->Clone(); RVSimple *myx2 = x2->Clone(); myx1->Normalize(); myx2->Normalize(); CTBNDynSS *css = dynamic_cast(ss); int n = nodes.size(); for(int i=0;iDomain().IsOverlap(changevar)) continue; Instantiation ii(nodes[i]->Domain()+nodes[i]->CondDomain(),0); ii.SetVal(condi); Context cond = nodes[i]->CondDomain()-CondDomain(); Instantiation nodeii(nodes[i]->Domain(), -1); vectr condprob(nodes[i]->CondDomain().Size(), 0.0); do { vector > mapping( nodes[i]->Domain().Size()); do { Domain().ConsistentIndexes( mapping[nodes[i]->Domain().Index(ii)], ii); } while (ii.Inc(nodes[i]->Domain())); RVSimple *t1 = myx1->Clone(); t1->Reindex(mapping); Instantiation transii(ii); transii.SetVal(nodeii); RVCondSimple *p1 = nodes[i]->Cond(t, transii, transii, true); p1->Mult(t1); RVSimple *t2 = myx2->Clone(); t2->Reindex(mapping); t1->MultBy(t2); condprob[cond.Index(ii)] = t1->Sum(); delete t2; delete t1; delete p1; } while(ii.Inc(cond)); ii = Instantiation(nodes[i]->Domain()+nodes[i]->CondDomain(),0); condprob.normalize(); do { vector > mapping( nodes[i]->Domain().Size()); do { Domain().ConsistentIndexes( mapping[nodes[i]->Domain().Index(ii)], ii); } while(ii.Inc(nodes[i]->Domain())); RVSimple *t1 = myx1->Clone(); t1->Reindex(mapping); t1->Normalize(); RVSimple *t2 = myx2->Clone(); t2->Reindex(mapping); t2->Normalize(); nodes[i]->AddExpTransSS(ii,t1,t2, changevar,t,css->dynss[i],w*condprob[cond.Index(ii)]); delete t2; delete t1; } while(ii.Inc(cond)); } delete myx1; delete myx2; } void CTBNDyn::AddExpTransSS(int condi, const RVSimple *x1, const RVSimple *x2, const Context &changevar, double t, SS *ss, double w) const { Instantiation i(CondDomain()); i.SetIndex(condi); AddExpTransSS(i,x1,x2,changevar,t,ss,w); } void CTBNDyn::SampleNextEvent(const Instantiation &i, double t, double &nextt, Instantiation &nexti, Random &rand) const { int n = nodes.size(); nextt = INFINITY; for(int j=0;jSampleNextEvent(i,t,tt,ni,rand); if (finite(tt) && tt(ss); int n = nodes.size(); for(int i=0;iMaximize(css->dynss[i]); if (joint) delete joint; joint = NULL; } void CTBNDyn::PrepNode2IdMaps() const { if (node2var.size() == nodes.size()) return; ClearNode2IdMaps(); int m = nodes.size(); node2var.resize(m); for(int j=0;jDomain().VarList()[0]; var2node[node2var[j]] = j; vector par = nodes[j]->CondDomain().VarList(); for(int k=0;k())); } } void CTBNDyn::ClearNode2IdMaps() const { node2var.clear(); var2node.clear(); children.clear(); } // We could just keep the default SampleTrajectory. However that has // a running time O(nm) where n is the number of samples and m is the number // of variables. This method is O(m+nkln(m)) where k is the maximal number of // parents in the graph. // This currently makes the assumption that each node only corresponds to // one variable void CTBNDyn::SampleTrajectory(Trajectory &tr, double t, Random &rand) const { int m = nodes.size(); SampleQueue events(m); double endt = tr.TimeEnd(); Instantiation i(tr.Values(Domain(),t)); PrepNode2IdMaps(); for(int j=0;jSampleNextEvent(i,t,newt,newv,rand); events.Add(SampleQueue::Event(j,newv.Value(node2var[j]), newt)); } while(1) { SampleQueue::Event e; if (!events.Head(e) || e.time>endt) return; int varid = node2var[e.var]; //int varid = var2node.find(e.var)->second; tr.AddTransition(varid, e.time, e.value); i.SetVal(varid,e.value); t = e.time; events.Remove(e.var); map >::iterator ci = children.find(node2var[e.var]); vector c; if (ci != children.end() && ci->first == node2var[e.var]) c = ci->second; for(int j=0;jSampleNextEvent(i,t,newt,newv,rand); events.Add(SampleQueue::Event(e.var, newv.Value(node2var[e.var]),newt)); for(int j=0;jSampleNextEvent(i,t,newt,newv,rand); events.Add(SampleQueue::Event(c[j], newv.Value(node2var[c[j]]),newt)); } } } void CTBNDyn::AddNode(Dynamics *d) { nodes.push_back(d); v = v+d->Domain(); cv = (cv+d->CondDomain())-v; if (joint!=NULL) { delete joint; joint = NULL; } ClearNode2IdMaps(); } const Dynamics *CTBNDyn::Joint() const { if (!joint) { int n = nodes.size(); if (n==0) return NULL; joint = nodes[0]->Clone(); for(int i=1;iMult(nodes[i]); } } return joint; } void CTBNDyn::GetStructure(Structure &s) const { PrepNode2IdMaps(); s.var2node = var2node; s.node2var = node2var; s.children = children; s.parentlist.clear(); int n = nodes.size(); for (int i=0; iCondDomain(); s.parentlist.push_back(c.VarList()); } } void CTBNDyn::Scramble(double a, double b, double alpha, double degree, Random &rand) { //Added by Yu int nc = nodes.size(); for(int i=0;iScramble(a, b, alpha, degree, rand); } double CTBNDyn::LLH(const SS *ss) const { const CTBNDynSS *css = dynamic_cast(ss); int nc = nodes.size(); double llh = 0.0; for(int i=0; iLLH(css->dynss[i]); return llh; } matrix CTBNDyn::JointMatrix() const { const MarkovDyn *jointp = dynamic_cast(Joint()); matrix m = (*jointp)(0)->Intensity(); return m; } double CTBNDyn::GetScore(double numTrans, double amtTime, const SS *ss) const { const CTBNDynSS *css = dynamic_cast(ss); double score(0); int vars = css->dynss.size(); double localscore; // Sum over each variable for(int i=0;i GetScore(numTrans, amtTime, css->dynss[i]); score += localscore; } return score; } // Assumes the source CTBNDyn has the same organization of nodes. void CTBNDyn::FillParams(const CTBNDyn &src) { for(int i = 0; i < NumofNodes(); i++) { const MarkovDyn *csdyn = dynamic_cast(src.nodes[i]); MarkovDyn *cdyn = dynamic_cast(nodes[i]); const Context &csrc = csdyn->CondDomain(); const Context &cur = cdyn->CondDomain(); Instantiation ins(csrc); ins.SetAllVal(0); int nstates = cdyn->Domain().Size(); Context diff = csrc - cur; Instantiation curins(cur); Instantiation diffins(diff); curins.SetAllVal(0); diffins.SetAllVal(0); do { matrix AIM(nstates,nstates,0.0); int count = 0; // Compute average IM over extra vars do { ins.SetVal(curins); ins.SetVal(diffins); AIM += (*csdyn)(ins)->Intensity(); count++; } while(diffins.Inc()); AIM /= count; (*cdyn)(curins)->Intensity() = AIM; } while(curins.Inc()); } } const Dynamics *CTBNDyn::NodeByVar(int i) const { PrepNode2IdMaps(); int idx = var2node[i]; return nodes[idx]; } const SS *CTBNDyn::NodeSSByVar(int i, SS* ss) const { PrepNode2IdMaps(); int idx = var2node[i]; return dynamic_cast(ss)->dynss[idx]; } SOBJCLASSDEF(CTBNDynSS) CTBNDynSS::CTBNDynSS(istream &is) { int n; is >> n; dynss.resize(n); for(int i=0;i(StreamObj::LoadPtr(is)); } CTBNDynSS::CTBNDynSS() { } CTBNDynSS::CTBNDynSS(const CTBNDynSS &ctbnss) { int n = ctbnss.dynss.size(); dynss.resize(n); for(int i=0;iClone(); } CTBNDynSS::~CTBNDynSS() { int n = dynss.size(); for(int i=0;iClone(); } return *this; } CTBNDynSS *CTBNDynSS::Clone() const { return new CTBNDynSS(*this); } void CTBNDynSS::Load(istream &is) { int n = dynss.size(); for(int i=0;i> n; dynss.resize(n); for(int i=0;i(StreamObj::LoadPtr(is)); } void CTBNDynSS::Save(ostream &os) const { int n = dynss.size(); os << n; for(int i=0;iSavePtr(os); } } void CTBNDynSS::Scale(double w) { int n = dynss.size(); for(int i=0; iScale(w); } double CTBNDynSS::NodeSS(int id, int val1, int val2, const Instantiation &cond) const { vector index; //cond.ConsistentIndexes(index); return dynamic_cast(dynss[id])-> Element(index, val1, val2); } void CTBNDynSS::AddSS(const SS* nss, double w) { int n = dynss.size(); const CTBNDynSS *css = dynamic_cast(nss); for(int i=0; iAddSS(css->dynss[i], w); } } // end of ctbn namespace ctbnrle.1.0.2/src/rvsimple.cc0000644000464200007770000000567211344302770016211 0ustar csheltoncsprofs/* 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 "rvsimple.h" namespace ctbn { using namespace std; RVSimple::RVSimple() : StreamObj() { } RVSimple::RVSimple(istream &is) : StreamObj() { } RVSimple::~RVSimple() { } void RVSimple::Load(istream &is) { } void RVSimple::Save(ostream &os) const { } //------ RVCondSimple::RVCondSimple() : StreamObj() { } RVCondSimple::RVCondSimple(std::istream &is) : StreamObj() { } RVCondSimple::~RVCondSimple() { } void RVCondSimple::Load(istream &is) { } void RVCondSimple::Save(ostream &os) const { } //------ SOBJCLASSDEF(RVCSCompSS) RVCSCompSS::RVCSCompSS(int nc) : ss(nc) { for(int i=0;iClone(); } RVCSCompSS::RVCSCompSS(istream &is) { int nc; is >> nc; for(int i=0;i(StreamObj::LoadPtr(is)); ss.push_back(sptr); } } RVCSCompSS::~RVCSCompSS() { int nc = ss.size(); for(int i=0;i> nc; ss.resize(nc); for(int i=0;i(StreamObj::LoadPtr(is)); } void RVCSCompSS::Save(ostream &os) const { int nc = ss.size(); os << nc; for(int i=0;iSavePtr(os); } } RVCSCompSS *RVCSCompSS::Clone() const { return new RVCSCompSS(*this); } RVCSCompSS &RVCSCompSS::operator=(const RVCSCompSS &x) { if (&x!=this) { int nc = ss.size(); for(int i=0;iClone(); } return *this; } void RVCSCompSS::Scale(double w) { int n = ss.size(); for(int i=0; iScale(w); } void RVCSCompSS::AddSS(const SS* nss, double w) { const RVCSCompSS* rss = dynamic_cast(nss); int n = ss.size(); for(int i=0; iAddSS(rss->ss[i], w); } } // end of ctbn namespace ctbnrle.1.0.2/src/markov.cc0000644000464200007770000001515711344302770015646 0ustar csheltoncsprofs/* 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 "markov.h" #include "ctbndyn.h" #include "markovdyn.h" #include "params.h" #include namespace ctbn { using namespace std; SOBJCLASSDEF(Markov) Markov::Markov(RV *startdist, Dynamics *dyn) : Process() { p0 = startdist; d = dyn; } Markov::Markov(istream &is) : Process() { p0 = NULL; d = NULL; Load(is); } Markov::Markov(const Markov &m) : Process() { p0 = m.p0->Clone(); d = m.d->Clone(); } Markov::~Markov() { if (p0!=NULL) delete p0; if (d!=NULL) delete d; } Markov &Markov::operator=(const Markov &m) { if (&m==this) return *this; if (p0!=NULL) delete p0; if (d!=NULL) delete d; p0 = m.p0->Clone(); d = m.d->Clone(); return *this; } Markov *Markov::Clone() const { return new Markov(*this); } void Markov::Mult(const Process *p) { const Markov *m = dynamic_cast(p); p0->MultBy(m->p0); d->Mult(m->d); } SS *Markov::BlankSS() const { return new MarkovSS(p0->BlankSS(),d->BlankSS()); } void Markov::Maximize(const SS *ss) { const MarkovSS *mss = dynamic_cast(ss); p0->Maximize(mss->p0ss); d->Maximize(mss->dss); } void Markov::Load(istream &is) { if (p0!=NULL) delete p0; if (d!=NULL) delete d; p0 = dynamic_cast(StreamObj::LoadPtr(is)); d = dynamic_cast(StreamObj::LoadPtr(is)); } void Markov::Save(ostream &os) const { p0->SavePtr(os); os << os.fill(); d->SavePtr(os); } void Markov::Sample(Trajectory &tr, Random &rand) const { Instantiation x0; p0->Sample(x0,rand); tr.AddTransition(x0,tr.TimeBegin()); d->SampleTrajectory(tr,tr.TimeBegin(),rand); //dynamic_cast (d)->SampleTrajectory(tr,tr.TimeBegin(),rand); } // return value is the total llh on a single trajectory // also pass out the p0llh and dynllh seperately double Markov::LLH(const Trajectory &tr, double w, double &p0llh, double &dynllh) const { MarkovSS *mss = dynamic_cast(BlankSS()); const Context &c = d->Domain() + d->CondDomain(); const Context &owndomain = d->Domain(); Instantiation curri = tr.Values(c, tr.TimeBegin()); p0->AddSS(curri, mss->p0ss, w); Trajectory::Index index = tr.Begin(c); while(!index.Done()) { double t = index.Time(); double deltat = index.DeltaT(); d->AddSS(curri, t, deltat, mss->dss, w); int sign = index.TestInc(owndomain); if (!index.Done()) { Instantiation nexti = index.Values(); t = index.Time(); if (sign==2) d->AddTransSS(curri, nexti, t, mss->dss, w); curri = nexti; } } mss->Scale(w); p0llh = p0->LLH(mss->p0ss); dynllh = d->LLH(mss->dss); delete mss; return p0llh + dynllh; } double Markov::LLH(const vector &tr, const vector &w) const { MarkovSS *mss = dynamic_cast(BlankSS()); const Context &c = d->Domain() + d->CondDomain(); const Context &owndomain = d->Domain(); double sumw = 0.0; for(int i=0; iAddSS(curri, mss->p0ss, w[i]); Trajectory::Index index = tr[i].Begin(c); while(!index.Done()) { double t = index.Time(); double deltat = index.DeltaT(); d->AddSS(curri, t, deltat, mss->dss, w[i]); int sign = index.TestInc(owndomain); if(!index.Done()) { Instantiation nexti = index.Values(); t = index.Time(); if (sign==2) d->AddTransSS(curri, nexti, t, mss->dss, w[i]); curri = nexti; } } sumw += w[i]; } mss->Scale(sumw); double llh = LLH(mss); delete mss; return llh; } double Markov::LLH(const SS *ss) const { const MarkovSS *mss = dynamic_cast(ss); return p0->LLH(mss->p0ss) + d->LLH(mss->dss); } SS* Markov::SuffStats(const vector &tr, const vector &w) const { MarkovSS *mss = dynamic_cast(BlankSS()); mss->p0ss = p0->SuffStats(tr,w); mss->dss = d->SuffStats(tr,w); for(int i=0; iScramble(a, b, alpha, degree, rand); p0->Scramble(alpha, degree, rand); } double Markov::GetScore(double numTrans, double amtTime, const SS* ss) const { double p0Score = p0->GetScore(numTrans, dynamic_cast (ss)->p0ss); double dynScore = d->GetScore(numTrans, amtTime, dynamic_cast (ss)->dss); return p0Score + dynScore; } double MarkovSS::NodeSS(int id, int val1, int val2, const Instantiation &cond) const { return dynamic_cast(dss)-> NodeSS(id, val1, val2, cond); } SOBJCLASSDEF(MarkovSS) MarkovSS::MarkovSS(std::istream &is) { p0ss = dynamic_cast(StreamObj::LoadPtr(is)); dss = dynamic_cast(StreamObj::LoadPtr(is)); } MarkovSS::MarkovSS(SS *rvss, SS *dynss) { p0ss = rvss; dss = dynss; } MarkovSS::MarkovSS(const MarkovSS &mss) { p0ss = mss.p0ss->Clone(); dss = mss.dss->Clone(); } MarkovSS &MarkovSS::operator=(const MarkovSS &mss) { if (&mss!=this) { if (p0ss != NULL) delete p0ss; if (dss != NULL) delete dss; p0ss = mss.p0ss->Clone(); dss = mss.dss->Clone(); } return *this; } MarkovSS::~MarkovSS() { if (p0ss != NULL) delete p0ss; if (dss != NULL) delete dss; } MarkovSS *MarkovSS::Clone() const { return new MarkovSS(*this); } void MarkovSS::Load(istream &is) { if (p0ss != NULL) delete p0ss; if (dss != NULL) delete dss; p0ss = dynamic_cast(StreamObj::LoadPtr(is)); dss = dynamic_cast(StreamObj::LoadPtr(is)); } void MarkovSS::Save(ostream &os) const { p0ss->SavePtr(os); os << os.fill(); dss->SavePtr(os); } void MarkovSS::Scale(double w) { p0ss->Scale(w); dss->Scale(w); } void MarkovSS::AddSS(const SS* nss, double w) { const MarkovSS* mss = dynamic_cast(nss); p0ss->AddSS(mss->p0ss, w); dss->AddSS(mss->dss, w); } } // end of ctbn namespace ctbnrle.1.0.2/src/rvcomp.cc0000644000464200007770000001361411344302770015651 0ustar csheltoncsprofs/* 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 "rvcomp.h" #include "multisimple.h" namespace ctbn { using namespace std; SOBJCLASSDEF(RVComp) RVComp::RVComp(const Context &var, const Context &cvar, RVCondSimple *touse) : RV(var,cvar) { impl = touse; } RVComp::RVComp(istream &is) : RV(is) { impl = dynamic_cast(StreamObj::LoadPtr(is)); } RVComp::RVComp(const RVComp &rvc) : RV(rvc) { impl = rvc.impl->Clone(); } RVComp &RVComp::operator=(const RVComp &rvc) { if (&rvc != this) { delete impl; impl = rvc.impl->Clone(); } return *this; } RVComp::~RVComp() { delete impl; } void RVComp::Load(std::istream &is) { RV::Load(is); delete impl; impl = dynamic_cast(StreamObj::LoadPtr(is)); } void RVComp::Save(std::ostream &os) const { RV::Save(os); os << os.fill(); impl->SavePtr(os); } RVComp *RVComp::Clone() const { return new RVComp(*this); } void RVComp::Normalize() { impl->Normalize(); } double RVComp::Prob(const Instantiation &x, bool log) const { int condi = CondDomain().Index(x); if (condi==-1) return log ? -INFINITY : 0.0; RVSimple *condrv = impl->Condition(condi); vector ids; Domain().ConsistentIndexes(ids,x); condrv->Restrict(ids); double ret = condrv->Sum(log); delete condrv; return ret; } void RVComp::Restrict(const Instantiation &x) { vector i,ci; Domain().ConsistentIndexes(i,x); CondDomain().ConsistentIndexes(ci,x); impl->Restrict(i,ci); } void RVComp::Project(const Context &c, const Context &cc) { Context all = c+cc+Domain()+CondDomain(); Context condc = cc+CondDomain(); Context nonc = all-condc; RVCondSimple::RemapT ind; Instantiation v(all,0); do { int oldci = CondDomain().Index(v); int newci = cc.Index(v); vector > remap(c.Size()); do { int oldi = Domain().Index(v); int newi = c.Index(v); remap[newi].push_back(oldi); } while(v.Inc(nonc)); ind[make_pair(newci,oldci)] = remap; } while (v.Inc(condc)); impl->Reindex(c.Size(),cc.Size(),ind); RV::Project(c,cc); } void RVComp::MultBy(const RV *x) { const RVComp *xx = dynamic_cast(x); Context newc(Domain(),xx->Domain()); Context newcc(CondDomain(),xx->CondDomain()); newcc = newcc - newc; if (newc.Size()!=Domain().Size() || newcc.Size()!=CondDomain().Size()) Project(newc,newcc); if (newc.Size()!=xx->Domain().Size() || newcc.Size()!=xx->CondDomain().Size()) { RVComp *cpy = xx->Clone(); cpy->Project(newc,newcc); impl->MultBy(cpy->impl); delete cpy; } else { impl->MultBy(xx->impl); } } SS *RVComp::BlankSS() const { return impl->BlankSS(); } void RVComp::AddSS(const Instantiation &x, SS *ss, double w) const { int condi = CondDomain().Index(x); int i = Domain().Index(x); impl->AddSS(condi,i,ss,w); } void RVComp::AddExpSS(const Instantiation &x, SS *ss, double w) const { int condi = CondDomain().Index(x); vector ids,cids; Domain().ConsistentIndexes(ids,x); cids.push_back(condi); if (ids.size()==Domain().Size()) { impl->AddExpSS(condi,ss,w); } else { RVCondSimple *condrv = impl->Clone(); condrv->Restrict(ids,cids); condrv->Normalize(); condrv->AddExpSS(condi,ss,w); delete condrv; } } void RVComp::AddExpSS(const Instantiation &x, const RVSimple *rvs, SS *ss, double w) const { // cerr << "here" << endl; // cerr << rvs << endl; int condi = CondDomain().Index(x); // cerr << condi << endl; // cerr << "impl of this "; // cerr << impl << endl; impl->AddExpSS(condi,rvs,ss,w); } void RVComp::AddSS(const SS *toadd, const RV* rv, SS *ss, double w) const { const RVComp *rvc = dynamic_cast(rv); assert(rvc!=NULL); assert(CondDomain().IsSubset(rvc->CondDomain())); Instantiation i(Domain()+CondDomain(),0); do { int mycondi = CondDomain().Index(i); int rvccondi = rvc->CondDomain().Index(i); vector > mapping(Domain().Size()); do { rvc->Domain().ConsistentIndexes( mapping[Domain().Index(i)],i); } while(i.Inc(Domain())); impl->AddSS(toadd,rvc->impl,mapping,mycondi,rvccondi,ss); } while(i.Inc(CondDomain())); } void RVComp::Sample(Instantiation &x, Random &rand) const { int condi = CondDomain().Index(x); vector ids,cids; Domain().ConsistentIndexes(ids,x); cids.push_back(condi); int i; if (ids.size()==Domain().Size()) { // sample all... i = impl->Sample(condi,rand); } else { RVCondSimple *condrv = impl->Clone(); condrv->Restrict(ids,cids); condrv->Normalize(); i = condrv->Sample(condi,rand); delete condrv; } Instantiation newi(Domain()); newi.SetIndex(i); x.SetVal(newi,true); } void RVComp::Maximize(const SS *ss) { impl->Maximize(ss); } RVSimple *RVComp::MakeSimple(const Instantiation &x, bool normalize) const { RVSimple *ret = impl->Condition(CondDomain().Index(x)); vector ids; Domain().ConsistentIndexes(ids,x); ret->Restrict(ids); if (normalize) ret->Normalize(); return ret; } void RVComp::Scramble(double alpha, double degree, Random &rand) { impl->Scramble(alpha, degree, rand); } double RVComp::LLH(const SS *ss) const { return impl->LLH(ss); } double RVComp::GetScore(double numTrans, const SS *ss) const { return impl->GetScore(numTrans,ss); } } // end of ctbn namespace ctbnrle.1.0.2/src/streamextra.cc0000644000464200007770000000626411344302770016705 0ustar csheltoncsprofs/* 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 "streamextra.h" #include namespace ctbn { // the (few) definitions for that in streamextra.h std::map &StreamObj::loadtable() { static std::map *_loadtable = new std::map(); return *_loadtable; } StreamObj::StreamObj() { } StreamObj::~StreamObj() { } const char *StreamObj::myID() const { return "StreamObj"; } const char *StreamObj::IDname = StreamObj::addID("StreamObj",StreamObj::Create); void StreamObj::Save(std::ostream &os) const { } void StreamObj::SavePtr(std::ostream &os) const { os << myID() << os.fill(); Save(os); } StreamObj *StreamObj::LoadPtr(std::istream &is) { std::string name; is >> name; //std::cout << "name : " << name << std::endl; std::map::iterator rec = loadtable().find(name); if (rec == loadtable().end()) return NULL; else return rec->second(is); } StreamObj *StreamObj::Create(std::istream &is) { return new StreamObj(); } const char *StreamObj::addID(const char *classname, CreatorFn fn) { loadtable()[std::string(classname)] = fn; //std::cout << "LOADTABLE adding " << classname << std::endl; return classname; } const char *StreamObj::addID(const char *classname, const char *classname2, CreatorFn fn) { char *newname = new char[strlen(classname)+strlen(classname2)+2]; strcpy(newname,classname); strcat(newname,"."); strcat(newname,classname2); //std::cout << "LOADTABLE adding " << newname << std::endl; loadtable()[std::string(newname)] = fn; return newname; } char *StreamObj::killSpaces(const char *classname) { char *ret = new char[strlen(classname)+1]; strcpy(ret,classname); for(int i=0;ret[i];i++) if (ret[i]==' ') ret[i]='_'; return ret; } } // end of ctbn namespace namespace { char infstring[] = "inf"; char nanstring[] = "NaN"; // the "C" means classic, and it should really be based // on the current locale, but currently the "C" is ignored anyway! nannum_put *putfacet = new nannum_put("C"); nannum_get *getfacet = new nannum_get("C"); } void addnan(std::basic_ios &str) { std::locale withread(str.getloc(),putfacet); std::locale withboth(withread,getfacet); str.imbue(withboth); } ctbnrle.1.0.2/src/famscore.cc0000644000464200007770000000403511344302770016137 0ustar csheltoncsprofs/* 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 "famscore.h" #include "markovdyn.h" #include "ss.h" #include "multirv.h" #include namespace ctbn { using namespace std; FamScore::FamScore(double nTrans, double aTime, SuffStatsQuery* ssQ) : numTrans(nTrans), amtTime(aTime), ssQuery(ssQ) { } FamScore::~FamScore() { } // Subclasses below // ================ BNFamScore::BNFamScore(double nTrans, SuffStatsQuery* ssQ) : FamScore(nTrans,0,ssQ) { } CTBNFamScore::CTBNFamScore(double nTrans, double aTime, SuffStatsQuery* ssQ) : FamScore(nTrans,aTime,ssQ) { } //--- BNFamScore::~BNFamScore() { } CTBNFamScore::~CTBNFamScore() { } //--- double BNFamScore::GetScore(const Context& v, const Context& cv) { MultiRV* test = new MultiRV(v, cv); ssQuery->SetInitRV(test); SS* ss = ssQuery->GetP0SS(); double ret = test->GetScore(numTrans,ss); delete ss; ssQuery->SetInitRV(NULL); delete test; return ret; } double CTBNFamScore::GetScore(const Context& v, const Context& cv) { MarkovDyn* test = new MarkovDyn(v, cv); ssQuery->SetDynamics(test); SS* ss = ssQuery->GetDSS(); double ret = test->GetScore(numTrans,amtTime,ss); delete ss; ssQuery->SetDynamics(NULL); delete test; return ret; } } // end of ctbn namespace ctbnrle.1.0.2/src/expsuffstatsquery.cc0000644000464200007770000000473611344302770020175 0ustar csheltoncsprofs/* 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 "expsuffstatsquery.h" namespace ctbn { using namespace std; ExpSuffStatsQuery::ExpSuffStatsQuery(Inference* i, bool cloneInf) : SuffStatsQuery(), inf(i), useInfCache(cloneInf) { } void ExpSuffStatsQuery::InitInfCache() { if(infCache.size() == tr->size()) return; for(int i = 0; i < tr->size(); i++) { infCache.push_back(inf->Clone()); infCache[i]->SetTrajectory(&(*tr)[i]); } } void ExpSuffStatsQuery::SetProcess(const Process* p) { inf->SetProcess(p); for(int i=0;iBlankSS(); if(useInfCache) { InitInfCache(); for(int i = 0; i < infCache.size(); i++) { infCache[i]->AddExpSuffStats(p0,ss); } } else { for(int i = 0; i < tr->size(); i++) { inf->SetTrajectory(&(*tr)[i]); inf->AddExpSuffStats(p0,ss); } } return ss; } else { cerr << "p0 and/or data are not initialized" << endl; exit(0); } } SS* ExpSuffStatsQuery::GetDSS() { if(d && tr) { SS* ss = d->BlankSS(); if(useInfCache) { InitInfCache(); for(int i = 0; i < infCache.size(); i++) { infCache[i]->AddExpSuffStats(d,ss); } } else { const vector &trj = *tr; for(int i = 0; i < trj.size(); i++) { inf->SetTrajectory(&trj[i]); inf->AddExpSuffStats(d,ss); } } return ss; } else { cerr << "d and/or data are not initialized" << endl; exit(0); } } ExpSuffStatsQuery::~ExpSuffStatsQuery() { for(int i = 0; i < infCache.size(); i++) if (infCache[i]!=NULL) delete infCache[i]; infCache.clear(); delete inf; } } // end of ctbn namespace ctbnrle.1.0.2/src/suffstatsquery.cc0000644000464200007770000000220711344302770017447 0ustar csheltoncsprofs/* 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 "suffstatsquery.h" namespace ctbn { using namespace std; SuffStatsQuery::SuffStatsQuery() : p0(NULL), d(NULL), tr(new vector()) { } // Nothing to delete; none of the pointers are owned by this class. SuffStatsQuery::~SuffStatsQuery() { } } // end of ctbn namespace ctbnrle.1.0.2/src/trajectory.cc0000644000464200007770000001650111344302770016527 0ustar csheltoncsprofs/* 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 "trajectory.h" #include "streamextra.h" namespace ctbn { using namespace std; Trajectory::Trajectory() { ts = te = 0; } Trajectory::~Trajectory() { } int Trajectory::Value(int varid, double time, bool inclusive) const { if (time>te || (time==te && inclusive)) return -1; if (time::const_iterator vari = traj.find(varid); if (vari==traj.end()) return -1; return Value(vari,time,inclusive); } int Trajectory::Value(const map::const_iterator &vari, double time, bool inclusive) const { VarTrajectory::const_reverse_iterator i(vari->second.upper_bound(time)); while (i!=vari->second.rend() && (i->first>time || (!inclusive && i->first==time))) { ++i; } return i==vari->second.rend() ? -1 : i->second; } Instantiation Trajectory::Values(const Context &c, double time, bool inclusive) const { Instantiation ret(c); if (time>te || (time==te && inclusive)) return ret; if (time::const_iterator vari=traj.begin(); vari!=traj.end();vari++) ret.SetVal(vari->first,Value(vari,time,inclusive)); return ret; } void Trajectory::AddTransition(int varid, double time, int newval) { if (ts==te && traj.empty()) ts=te=time; else if (ts>time) ts = time; else if (te::iterator vari = traj.find(varid); if (vari==traj.end()) vari = traj.insert(make_pair(varid,VarTrajectory())).first; vari->second.insert(make_pair(time,newval)); } void Trajectory::AddTransition(const Instantiation &i, double time) { Instantiation oldi = Values(i,time); vector varlist = i.VarList(); for(int j=0;j> ts >> te >> traj; } ostream &operator<<(ostream &os, const Trajectory &tr) { tr.Save(os); return os; } istream &operator>>(istream &is, Trajectory &tr) { tr.Load(is); return is; } Trajectory::Index::~Index() { } Trajectory::Index::Index(const Trajectory &tr, const Context &c, double t0, double tend) { done = false; t = t0; endt = tend; v = tr.Values(c,t0); for(map::const_iterator vi = tr.traj.begin(); vi != tr.traj.end(); vi++) { if(!c.HasId(vi->first)) continue; VarTrajectory::const_iterator i = vi->second.upper_bound(t0); if (i!=vi->second.end()) { vari.insert(make_pair(vi->first,make_pair(i,vi->second.end()))); if (i->firstfirst,vi->first)); } } } int Trajectory::Index::TestInc(const Context &c) { if (nextvar.empty()) { done = true; t = endt; int retval; if(v.Index()==-1) retval = 0; else retval = 1; v.SetAllVal(-1); return retval; } int ret = 0; double newt = -nextvar.top().first; while(!nextvar.empty() && -nextvar.top().first==newt) { int vi = nextvar.top().second; nextvar.pop(); std::map >::iterator spot = vari.find(vi); VarTrajectory::const_iterator &vti = spot->second.first; VarTrajectory::const_iterator &vtiend = spot->second.second; if (c.HasId(vi)) { if (vti->second != -1 && v.Value(vi)!=-1) ret = 2; else if (ret==0) ret = 1; } v.SetVal(vi,vti->second); ++vti; if (vti!=vtiend && vti->firstfirst,vi)); } } t = newt; return ret; } Trajectory::RIndex::~RIndex() { } Trajectory::RIndex::RIndex(const Trajectory &tr, const Context &c, double t0, double tstart) { done = false; t = t0; startt = tstart; v = tr.Values(c,t0,false); for(map::const_iterator vi = tr.traj.begin(); vi != tr.traj.end(); vi++) { if(!c.HasId(vi->first)) continue; VarTrajectory::const_reverse_iterator i(vi->second.lower_bound(t0)); if (i!=vi->second.rend()) { vari.insert(make_pair(vi->first,make_pair(i,vi->second.rend()))); if (i->first>startt) nextvar.push(make_pair(i->first,vi->first)); } } } int Trajectory::RIndex::TestDec(const Context &c) { if (nextvar.empty()) { done = true; t = startt; int retval; if(v.Index()==-1) retval = 0; else retval = 1; v.SetAllVal(-1); return retval; } int ret = 0; double newt = nextvar.top().first; while(!nextvar.empty() && nextvar.top().first==newt) { int vi = nextvar.top().second; nextvar.pop(); std::map >::iterator spot = vari.find(vi); VarTrajectory::const_reverse_iterator &vti = spot->second.first; VarTrajectory::const_reverse_iterator &vtiend = spot->second.second; ++vti; if (c.HasId(vi)) { if (vti->second != -1 && v.Value(vi)!=-1) ret = 2; else if (ret==0) ret = 1; } v.SetVal(vi,vti->second); if (vti!=vtiend && vti->first>startt) { nextvar.push(make_pair(vti->first,vi)); } } t = newt; return ret; } void Trajectory::Draw(ostream &os) const { ios::fmtflags oldflags = os.flags(); os.setf(ios::fixed); Context all; os << "time \\ var "; for(map::const_iterator i=traj.begin();i!=traj.end();++i) { os.width(5); os << i->first << " "; all.AddVar(i->first,2); } os << endl; Instantiation oldv(all,-1); for(Index ii = Begin(all);!ii.Done();++ii) { os << "_____________________________________________________________" << endl; os << ii.Time() << " "; for(map::const_iterator i=traj.begin();i!=traj.end();++i) { if (oldv.Value(i->first)!=ii.Values().Value(i->first)) { os.width(5); os << ii.Values().Value(i->first) << " "; } else { os << " "; } } os << endl; oldv = ii.Values(); } os.flags(oldflags); } void Trajectory::SetUnknown(int varid) { traj.erase(varid); AddTransition(varid, ts, -1); } void Trajectory::RemoveNodeTraj(int varid) { traj.erase(varid); } void Trajectory::ReplaceNodeTraj(int varid, const Trajectory &tr) { map::const_iterator iter = tr.traj.find(varid); if (iter != tr.traj.end()) traj[varid] = iter->second; } } // end of ctbn namespace ctbnrle.1.0.2/src/Makefile0000644000464200007770000003710511344302770015475 0ustar csheltoncsprofsSRC = context.cc defines.cc dynamics.cc dyncomp.cc dynsimple.cc extramath.cc \ markovsimple.cc matrix.cc multisimple.cc params.cc random.cc rk.cc \ rv.cc rvcomp.cc rvsimple.cc ss.cc streamextra.cc trajectory.cc \ markovdyn.cc inference.cc markov.cc exactmarkovinf.cc \ multirv.cc process.cc bn.cc ctbndyn.cc em.cc structure.cc sampler.cc \ varsample.cc expmethod.cc impbase.cc importancesampler.cc \ gibbssampler.cc samplinginf.cc utils.cc clique.cc \ graphic.cc samplequeue.cc \ suffstatsquery.cc nonexpsuffstatsquery.cc expsuffstatsquery.cc \ searchqueue.cc structuresearch.cc brutestructuresearch.cc \ grapheditsearch.cc famscore.cc OBJ = context.o defines.o dynamics.o dyncomp.o dynsimple.o extramath.o \ markovsimple.o matrix.o multisimple.o params.o random.o rk.o \ rv.o rvcomp.o rvsimple.o ss.o streamextra.o trajectory.o \ markovdyn.o inference.o markov.o exactmarkovinf.o \ multirv.o process.o bn.o ctbndyn.o em.o structure.o \ sampler.o varsample.o expmethod.o impbase.o importancesampler.o \ gibbssampler.o samplinginf.o utils.o clique.o \ graphic.o samplequeue.o \ suffstatsquery.o nonexpsuffstatsquery.o expsuffstatsquery.o \ searchqueue.o structuresearch.o brutestructuresearch.o \ grapheditsearch.o famscore.o CXX = g++ CXXFLAGS = -O2 -I../hdr #CXXFLAGS = -ggdb -I../hdr DEPEND = makedepend -Y -- $(CXXFLAGS) -- ctbn2.a: $(OBJ) ar rvu $@ $(OBJ) ranlib $@ depend: $(SRC) $(DEPEND) $(SRC) clean: rm -rf *.o *~ *.a # DO NOT DELETE context.o: ../hdr/context.h ../hdr/streamextra.h ../hdr/defines.h defines.o: ../hdr/defines.h dynamics.o: ../hdr/dynamics.h ../hdr/context.h ../hdr/trajectory.h dynamics.o: ../hdr/rvsimple.h ../hdr/streamextra.h ../hdr/defines.h dynamics.o: ../hdr/random.h ../hdr/ss.h ../hdr/matrix.h ../hdr/rvsimple.tcc dynamics.o: ../hdr/rv.h dyncomp.o: ../hdr/dyncomp.h ../hdr/streamextra.h ../hdr/defines.h dyncomp.o: ../hdr/dynamics.h ../hdr/context.h ../hdr/trajectory.h dyncomp.o: ../hdr/rvsimple.h ../hdr/random.h ../hdr/ss.h ../hdr/matrix.h dyncomp.o: ../hdr/rvsimple.tcc ../hdr/rv.h ../hdr/dynsimple.h dyncomp.o: ../hdr/markovsimple.h dynsimple.o: ../hdr/dynsimple.h ../hdr/streamextra.h ../hdr/defines.h dynsimple.o: ../hdr/random.h ../hdr/ss.h ../hdr/rvsimple.h ../hdr/matrix.h dynsimple.o: ../hdr/rvsimple.tcc extramath.o: ../hdr/extramath.h markovsimple.o: ../hdr/markovsimple.h ../hdr/dynsimple.h ../hdr/streamextra.h markovsimple.o: ../hdr/defines.h ../hdr/random.h ../hdr/ss.h markovsimple.o: ../hdr/rvsimple.h ../hdr/matrix.h ../hdr/rvsimple.tcc markovsimple.o: ../hdr/params.h ../hdr/multisimple.h ../hdr/rk.h markovsimple.o: ../hdr/extramath.h matrix.o: ../hdr/matrix.h ../hdr/defines.h multisimple.o: ../hdr/multisimple.h ../hdr/rvsimple.h ../hdr/streamextra.h multisimple.o: ../hdr/defines.h ../hdr/random.h ../hdr/ss.h ../hdr/matrix.h multisimple.o: ../hdr/rvsimple.tcc ../hdr/extramath.h params.o: ../hdr/params.h random.o: ../hdr/random.h ../hdr/defines.h rk.o: ../hdr/matrix.h ../hdr/defines.h ../hdr/rk.h ../hdr/params.h rv.o: ../hdr/rv.h ../hdr/rvsimple.h ../hdr/streamextra.h ../hdr/defines.h rv.o: ../hdr/random.h ../hdr/ss.h ../hdr/matrix.h ../hdr/rvsimple.tcc rv.o: ../hdr/context.h ../hdr/trajectory.h rvcomp.o: ../hdr/rvcomp.h ../hdr/streamextra.h ../hdr/defines.h ../hdr/rv.h rvcomp.o: ../hdr/rvsimple.h ../hdr/random.h ../hdr/ss.h ../hdr/matrix.h rvcomp.o: ../hdr/rvsimple.tcc ../hdr/context.h ../hdr/trajectory.h rvcomp.o: ../hdr/multisimple.h rvsimple.o: ../hdr/rvsimple.h ../hdr/streamextra.h ../hdr/defines.h rvsimple.o: ../hdr/random.h ../hdr/ss.h ../hdr/matrix.h ../hdr/rvsimple.tcc ss.o: ../hdr/ss.h ../hdr/streamextra.h ../hdr/defines.h streamextra.o: ../hdr/streamextra.h ../hdr/defines.h trajectory.o: ../hdr/trajectory.h ../hdr/context.h ../hdr/streamextra.h trajectory.o: ../hdr/defines.h markovdyn.o: ../hdr/markovdyn.h ../hdr/markovsimple.h ../hdr/dynsimple.h markovdyn.o: ../hdr/streamextra.h ../hdr/defines.h ../hdr/random.h markovdyn.o: ../hdr/ss.h ../hdr/rvsimple.h ../hdr/matrix.h markovdyn.o: ../hdr/rvsimple.tcc ../hdr/dyncomp.h ../hdr/dynamics.h markovdyn.o: ../hdr/context.h ../hdr/trajectory.h ../hdr/rv.h inference.o: ../hdr/inference.h ../hdr/streamextra.h ../hdr/defines.h inference.o: ../hdr/context.h ../hdr/bn.h ../hdr/rv.h ../hdr/rvsimple.h inference.o: ../hdr/random.h ../hdr/ss.h ../hdr/matrix.h ../hdr/rvsimple.tcc inference.o: ../hdr/trajectory.h ../hdr/structure.h ../hdr/ctbndyn.h inference.o: ../hdr/dynamics.h ../hdr/suffstatsquery.h ../hdr/process.h inference.o: ../hdr/markov.h ../hdr/extramath.h markov.o: ../hdr/markov.h ../hdr/process.h ../hdr/dynamics.h ../hdr/context.h markov.o: ../hdr/trajectory.h ../hdr/rvsimple.h ../hdr/streamextra.h markov.o: ../hdr/defines.h ../hdr/random.h ../hdr/ss.h ../hdr/matrix.h markov.o: ../hdr/rvsimple.tcc ../hdr/rv.h ../hdr/structure.h ../hdr/ctbndyn.h markov.o: ../hdr/suffstatsquery.h ../hdr/extramath.h ../hdr/markovdyn.h markov.o: ../hdr/markovsimple.h ../hdr/dynsimple.h ../hdr/dyncomp.h markov.o: ../hdr/params.h exactmarkovinf.o: ../hdr/exactmarkovinf.h ../hdr/markov.h ../hdr/process.h exactmarkovinf.o: ../hdr/dynamics.h ../hdr/context.h ../hdr/trajectory.h exactmarkovinf.o: ../hdr/rvsimple.h ../hdr/streamextra.h ../hdr/defines.h exactmarkovinf.o: ../hdr/random.h ../hdr/ss.h ../hdr/matrix.h exactmarkovinf.o: ../hdr/rvsimple.tcc ../hdr/rv.h ../hdr/structure.h exactmarkovinf.o: ../hdr/ctbndyn.h ../hdr/suffstatsquery.h ../hdr/extramath.h exactmarkovinf.o: ../hdr/dyncomp.h ../hdr/dynsimple.h ../hdr/bn.h exactmarkovinf.o: ../hdr/rvcomp.h ../hdr/inference.h multirv.o: ../hdr/multirv.h ../hdr/rvcomp.h ../hdr/streamextra.h multirv.o: ../hdr/defines.h ../hdr/rv.h ../hdr/rvsimple.h ../hdr/random.h multirv.o: ../hdr/ss.h ../hdr/matrix.h ../hdr/rvsimple.tcc ../hdr/context.h multirv.o: ../hdr/trajectory.h ../hdr/multisimple.h process.o: ../hdr/process.h ../hdr/dynamics.h ../hdr/context.h process.o: ../hdr/trajectory.h ../hdr/rvsimple.h ../hdr/streamextra.h process.o: ../hdr/defines.h ../hdr/random.h ../hdr/ss.h ../hdr/matrix.h process.o: ../hdr/rvsimple.tcc ../hdr/rv.h bn.o: ../hdr/bn.h ../hdr/rv.h ../hdr/rvsimple.h ../hdr/streamextra.h bn.o: ../hdr/defines.h ../hdr/random.h ../hdr/ss.h ../hdr/matrix.h bn.o: ../hdr/rvsimple.tcc ../hdr/context.h ../hdr/trajectory.h bn.o: ../hdr/structure.h ../hdr/ctbndyn.h ../hdr/dynamics.h bn.o: ../hdr/suffstatsquery.h ../hdr/multirv.h ../hdr/rvcomp.h bn.o: ../hdr/multisimple.h ctbndyn.o: ../hdr/ctbndyn.h ../hdr/dynamics.h ../hdr/context.h ctbndyn.o: ../hdr/trajectory.h ../hdr/rvsimple.h ../hdr/streamextra.h ctbndyn.o: ../hdr/defines.h ../hdr/random.h ../hdr/ss.h ../hdr/matrix.h ctbndyn.o: ../hdr/rvsimple.tcc ../hdr/rv.h ../hdr/structure.h ctbndyn.o: ../hdr/suffstatsquery.h ../hdr/markovdyn.h ../hdr/markovsimple.h ctbndyn.o: ../hdr/dynsimple.h ../hdr/dyncomp.h ../hdr/extramath.h ctbndyn.o: ../hdr/samplequeue.h ../hdr/searchqueue.h em.o: ../hdr/em.h ../hdr/inference.h ../hdr/streamextra.h ../hdr/defines.h em.o: ../hdr/context.h ../hdr/bn.h ../hdr/rv.h ../hdr/rvsimple.h em.o: ../hdr/random.h ../hdr/ss.h ../hdr/matrix.h ../hdr/rvsimple.tcc em.o: ../hdr/trajectory.h ../hdr/structure.h ../hdr/ctbndyn.h em.o: ../hdr/dynamics.h ../hdr/suffstatsquery.h ../hdr/process.h em.o: ../hdr/params.h ../hdr/utils.h ../hdr/markov.h ../hdr/extramath.h em.o: ../hdr/exactmarkovinf.h ../hdr/dyncomp.h ../hdr/dynsimple.h em.o: ../hdr/rvcomp.h ../hdr/expsuffstatsquery.h em.o: ../hdr/nonexpsuffstatsquery.h ../hdr/brutestructuresearch.h em.o: ../hdr/structuresearch.h ../hdr/famscore.h ../hdr/grapheditsearch.h em.o: ../hdr/markovdyn.h ../hdr/markovsimple.h ../hdr/searchqueue.h structure.o: ../hdr/structure.h ../hdr/ctbndyn.h ../hdr/dynamics.h structure.o: ../hdr/context.h ../hdr/trajectory.h ../hdr/rvsimple.h structure.o: ../hdr/streamextra.h ../hdr/defines.h ../hdr/random.h structure.o: ../hdr/ss.h ../hdr/matrix.h ../hdr/rvsimple.tcc ../hdr/rv.h structure.o: ../hdr/suffstatsquery.h ../hdr/extramath.h sampler.o: ../hdr/sampler.h ../hdr/process.h ../hdr/dynamics.h sampler.o: ../hdr/context.h ../hdr/trajectory.h ../hdr/rvsimple.h sampler.o: ../hdr/streamextra.h ../hdr/defines.h ../hdr/random.h ../hdr/ss.h sampler.o: ../hdr/matrix.h ../hdr/rvsimple.tcc ../hdr/rv.h ../hdr/varsample.h sampler.o: ../hdr/structure.h ../hdr/ctbndyn.h ../hdr/suffstatsquery.h varsample.o: ../hdr/varsample.h ../hdr/dynamics.h ../hdr/context.h varsample.o: ../hdr/trajectory.h ../hdr/rvsimple.h ../hdr/streamextra.h varsample.o: ../hdr/defines.h ../hdr/random.h ../hdr/ss.h ../hdr/matrix.h varsample.o: ../hdr/rvsimple.tcc ../hdr/rv.h expmethod.o: ../hdr/expmethod.h ../hdr/varsample.h ../hdr/dynamics.h expmethod.o: ../hdr/context.h ../hdr/trajectory.h ../hdr/rvsimple.h expmethod.o: ../hdr/streamextra.h ../hdr/defines.h ../hdr/random.h expmethod.o: ../hdr/ss.h ../hdr/matrix.h ../hdr/rvsimple.tcc ../hdr/rv.h expmethod.o: ../hdr/markovdyn.h ../hdr/markovsimple.h ../hdr/dynsimple.h expmethod.o: ../hdr/dyncomp.h ../hdr/rk.h impbase.o: ../hdr/impbase.h ../hdr/sampler.h ../hdr/process.h impbase.o: ../hdr/dynamics.h ../hdr/context.h ../hdr/trajectory.h impbase.o: ../hdr/rvsimple.h ../hdr/streamextra.h ../hdr/defines.h impbase.o: ../hdr/random.h ../hdr/ss.h ../hdr/matrix.h ../hdr/rvsimple.tcc impbase.o: ../hdr/rv.h ../hdr/varsample.h ../hdr/structure.h ../hdr/ctbndyn.h impbase.o: ../hdr/suffstatsquery.h ../hdr/markovdyn.h ../hdr/markovsimple.h impbase.o: ../hdr/dynsimple.h ../hdr/dyncomp.h ../hdr/ctbn.h ../hdr/markov.h impbase.o: ../hdr/extramath.h ../hdr/bn.h importancesampler.o: ../hdr/importancesampler.h ../hdr/impbase.h importancesampler.o: ../hdr/sampler.h ../hdr/process.h ../hdr/dynamics.h importancesampler.o: ../hdr/context.h ../hdr/trajectory.h ../hdr/rvsimple.h importancesampler.o: ../hdr/streamextra.h ../hdr/defines.h ../hdr/random.h importancesampler.o: ../hdr/ss.h ../hdr/matrix.h ../hdr/rvsimple.tcc importancesampler.o: ../hdr/rv.h ../hdr/varsample.h ../hdr/structure.h importancesampler.o: ../hdr/ctbndyn.h ../hdr/suffstatsquery.h importancesampler.o: ../hdr/markovdyn.h ../hdr/markovsimple.h importancesampler.o: ../hdr/dynsimple.h ../hdr/dyncomp.h ../hdr/ctbn.h importancesampler.o: ../hdr/markov.h ../hdr/extramath.h ../hdr/bn.h importancesampler.o: ../hdr/utils.h ../hdr/samplequeue.h gibbssampler.o: ../hdr/gibbssampler.h ../hdr/sampler.h ../hdr/process.h gibbssampler.o: ../hdr/dynamics.h ../hdr/context.h ../hdr/trajectory.h gibbssampler.o: ../hdr/rvsimple.h ../hdr/streamextra.h ../hdr/defines.h gibbssampler.o: ../hdr/random.h ../hdr/ss.h ../hdr/matrix.h gibbssampler.o: ../hdr/rvsimple.tcc ../hdr/rv.h ../hdr/varsample.h gibbssampler.o: ../hdr/structure.h ../hdr/ctbndyn.h ../hdr/suffstatsquery.h gibbssampler.o: ../hdr/markov.h ../hdr/extramath.h ../hdr/markovdyn.h gibbssampler.o: ../hdr/markovsimple.h ../hdr/dynsimple.h ../hdr/dyncomp.h gibbssampler.o: ../hdr/multisimple.h ../hdr/bn.h ../hdr/rk.h ../hdr/utils.h gibbssampler.o: ../hdr/params.h samplinginf.o: ../hdr/samplinginf.h ../hdr/inference.h ../hdr/streamextra.h samplinginf.o: ../hdr/defines.h ../hdr/context.h ../hdr/bn.h ../hdr/rv.h samplinginf.o: ../hdr/rvsimple.h ../hdr/random.h ../hdr/ss.h ../hdr/matrix.h samplinginf.o: ../hdr/rvsimple.tcc ../hdr/trajectory.h ../hdr/structure.h samplinginf.o: ../hdr/ctbndyn.h ../hdr/dynamics.h ../hdr/suffstatsquery.h samplinginf.o: ../hdr/process.h ../hdr/sampler.h ../hdr/varsample.h samplinginf.o: ../hdr/markov.h ../hdr/extramath.h ../hdr/markovdyn.h samplinginf.o: ../hdr/markovsimple.h ../hdr/dynsimple.h ../hdr/dyncomp.h samplinginf.o: ../hdr/multirv.h ../hdr/rvcomp.h ../hdr/multisimple.h samplinginf.o: ../hdr/utils.h utils.o: ../hdr/utils.h ../hdr/trajectory.h ../hdr/context.h ../hdr/random.h utils.o: ../hdr/params.h clique.o: ../hdr/clique.h ../hdr/bn.h ../hdr/rv.h ../hdr/rvsimple.h clique.o: ../hdr/streamextra.h ../hdr/defines.h ../hdr/random.h ../hdr/ss.h clique.o: ../hdr/matrix.h ../hdr/rvsimple.tcc ../hdr/context.h clique.o: ../hdr/trajectory.h ../hdr/structure.h ../hdr/ctbndyn.h clique.o: ../hdr/dynamics.h ../hdr/suffstatsquery.h ../hdr/markov.h clique.o: ../hdr/process.h ../hdr/extramath.h ../hdr/markovdyn.h clique.o: ../hdr/markovsimple.h ../hdr/dynsimple.h ../hdr/dyncomp.h graphic.o: ../hdr/graphic.h ../hdr/markov.h ../hdr/process.h graphic.o: ../hdr/dynamics.h ../hdr/context.h ../hdr/trajectory.h graphic.o: ../hdr/rvsimple.h ../hdr/streamextra.h ../hdr/defines.h graphic.o: ../hdr/random.h ../hdr/ss.h ../hdr/matrix.h ../hdr/rvsimple.tcc graphic.o: ../hdr/rv.h ../hdr/structure.h ../hdr/ctbndyn.h graphic.o: ../hdr/suffstatsquery.h ../hdr/extramath.h ../hdr/markovdyn.h graphic.o: ../hdr/markovsimple.h ../hdr/dynsimple.h ../hdr/dyncomp.h graphic.o: ../hdr/params.h samplequeue.o: ../hdr/samplequeue.h ../hdr/defines.h suffstatsquery.o: ../hdr/suffstatsquery.h ../hdr/ss.h ../hdr/streamextra.h suffstatsquery.o: ../hdr/defines.h ../hdr/rv.h ../hdr/rvsimple.h suffstatsquery.o: ../hdr/random.h ../hdr/matrix.h ../hdr/rvsimple.tcc suffstatsquery.o: ../hdr/context.h ../hdr/trajectory.h ../hdr/dynamics.h nonexpsuffstatsquery.o: ../hdr/nonexpsuffstatsquery.h ../hdr/suffstatsquery.h nonexpsuffstatsquery.o: ../hdr/ss.h ../hdr/streamextra.h ../hdr/defines.h nonexpsuffstatsquery.o: ../hdr/rv.h ../hdr/rvsimple.h ../hdr/random.h nonexpsuffstatsquery.o: ../hdr/matrix.h ../hdr/rvsimple.tcc ../hdr/context.h nonexpsuffstatsquery.o: ../hdr/trajectory.h ../hdr/dynamics.h expsuffstatsquery.o: ../hdr/expsuffstatsquery.h ../hdr/suffstatsquery.h expsuffstatsquery.o: ../hdr/ss.h ../hdr/streamextra.h ../hdr/defines.h expsuffstatsquery.o: ../hdr/rv.h ../hdr/rvsimple.h ../hdr/random.h expsuffstatsquery.o: ../hdr/matrix.h ../hdr/rvsimple.tcc ../hdr/context.h expsuffstatsquery.o: ../hdr/trajectory.h ../hdr/dynamics.h ../hdr/process.h expsuffstatsquery.o: ../hdr/inference.h ../hdr/bn.h ../hdr/structure.h expsuffstatsquery.o: ../hdr/ctbndyn.h searchqueue.o: ../hdr/searchqueue.h ../hdr/defines.h structuresearch.o: ../hdr/structuresearch.h ../hdr/context.h structuresearch.o: ../hdr/structure.h ../hdr/ctbndyn.h ../hdr/dynamics.h structuresearch.o: ../hdr/trajectory.h ../hdr/rvsimple.h ../hdr/streamextra.h structuresearch.o: ../hdr/defines.h ../hdr/random.h ../hdr/ss.h structuresearch.o: ../hdr/matrix.h ../hdr/rvsimple.tcc ../hdr/rv.h structuresearch.o: ../hdr/suffstatsquery.h ../hdr/famscore.h brutestructuresearch.o: ../hdr/brutestructuresearch.h brutestructuresearch.o: ../hdr/structuresearch.h ../hdr/context.h brutestructuresearch.o: ../hdr/structure.h ../hdr/ctbndyn.h ../hdr/dynamics.h brutestructuresearch.o: ../hdr/trajectory.h ../hdr/rvsimple.h brutestructuresearch.o: ../hdr/streamextra.h ../hdr/defines.h ../hdr/random.h brutestructuresearch.o: ../hdr/ss.h ../hdr/matrix.h ../hdr/rvsimple.tcc brutestructuresearch.o: ../hdr/rv.h ../hdr/suffstatsquery.h ../hdr/famscore.h brutestructuresearch.o: ../hdr/extramath.h grapheditsearch.o: ../hdr/grapheditsearch.h ../hdr/structuresearch.h grapheditsearch.o: ../hdr/context.h ../hdr/structure.h ../hdr/ctbndyn.h grapheditsearch.o: ../hdr/dynamics.h ../hdr/trajectory.h ../hdr/rvsimple.h grapheditsearch.o: ../hdr/streamextra.h ../hdr/defines.h ../hdr/random.h grapheditsearch.o: ../hdr/ss.h ../hdr/matrix.h ../hdr/rvsimple.tcc grapheditsearch.o: ../hdr/rv.h ../hdr/suffstatsquery.h ../hdr/famscore.h grapheditsearch.o: ../hdr/markovdyn.h ../hdr/markovsimple.h grapheditsearch.o: ../hdr/dynsimple.h ../hdr/dyncomp.h ../hdr/searchqueue.h famscore.o: ../hdr/famscore.h ../hdr/suffstatsquery.h ../hdr/ss.h famscore.o: ../hdr/streamextra.h ../hdr/defines.h ../hdr/rv.h famscore.o: ../hdr/rvsimple.h ../hdr/random.h ../hdr/matrix.h famscore.o: ../hdr/rvsimple.tcc ../hdr/context.h ../hdr/trajectory.h famscore.o: ../hdr/dynamics.h ../hdr/markovdyn.h ../hdr/markovsimple.h famscore.o: ../hdr/dynsimple.h ../hdr/dyncomp.h ../hdr/multirv.h famscore.o: ../hdr/rvcomp.h ../hdr/multisimple.h ctbnrle.1.0.2/src/context.cc0000644000464200007770000002327111344302770016027 0ustar csheltoncsprofs/* 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 "context.h" #include #include #include "streamextra.h" #include namespace ctbn { using namespace std; ostream& operator<< (ostream& os, const Instantiation& inst) { return(inst.Save(os)); } istream& operator>> (istream& is, Instantiation& inst) { return(inst.Load(is)); } ostream& operator<< (ostream& os, const Context& con) { return(con.Save(os)); } istream& operator>> (istream& is, Context& con) { return(con.Load(is)); } Context::Context() { size = 1; } Context::Context(istream &is) { Load(is); } Context::~Context() { } Context::Context(const Context &c1, const Context &c2, setop op) { switch(op) { case UNION: set_union(c1.card.begin(),c1.card.end(), c2.card.begin(),c2.card.end(), inserter(card,card.begin())); break; case INTERSECTION: set_intersection(c1.card.begin(),c1.card.end(), c2.card.begin(),c2.card.end(), inserter(card,card.begin())); break; case DIFFERENCE: set_difference(c1.card.begin(),c1.card.end(), c2.card.begin(),c2.card.end(), inserter(card,card.begin())); break; } size = 1; for(map::iterator i=card.begin();i!=card.end();++i) size *= i->second; } Context::Context(const Instantiation &i1, const Instantiation &i2) { set_intersection(i1.card.begin(),i1.card.end(), i2.card.begin(),i2.card.end(), inserter(card,card.begin())); size = 1; for(map::iterator i=card.begin();i!=card.end();) { int v1 = i1.Value(i->first); int v2 = i2.Value(i->first); if (v1 == -1 || v2==-1 || v1==v2) card.erase(i++); else { size *= i->second; ++i; } } } vector Context::VarList() const { vector ret; for(map::const_iterator i=card.begin();i!=card.end();++i) ret.push_back(i->first); return ret; } int Context::Index(const Instantiation &ind) const { int ret = 0; int inc = 1; for(map::const_iterator i=card.begin();i!=card.end();++i) { int v = ind.Value(i->first); if (v==-1) return -1; ret += inc*v; inc *= i->second; } return ret; } Instantiation Context::Index(int i) const { Instantiation ret(*this); ret.SetIndex(i); return ret; } istream &Context::Load(istream &is) { return is >> size >> card; } ostream &Context::Save(ostream &os) const { return os << size << os.fill() << card; } void Context::ConsistentIndexes(vector &l, const Instantiation &v) const { l.clear(); vector maxind,incind; int iinc = 1; int index = 0; for(map::const_iterator j=card.begin();j!=card.end();++j) { int vv = v.Value(j->first); if (vv == -1) { maxind.push_back(j->second); incind.push_back(iinc); } else { index += vv*iinc; } iinc *= j->second; } CConsistentIndexes(l,maxind,incind,index); } void Context::CConsistentIndexes(vector &l, const vector &maxind, const vector &incind, int startindex) const { int n = maxind.size(); vector v(n,0); int i=0; do { l.push_back(startindex); for(i=0;i &l, const Instantiation &v) const { l.clear(); vector lneg; ConsistentIndexes(lneg,v); CInconsistentIndexes(l,lneg); } void Context::CInconsistentIndexes(vector &l, const vector &lneg) const { int s = Size(); vector::const_iterator negi = lneg.begin(),negend = lneg.end(); int i = 0; while(negi!=negend && i::const_iterator i=c.card.begin();i!=c.card.end();++i) if (card.find(i->first)==card.end()) return false; return true; } bool Context::IsOverlap(const Context &c) const { for(map::const_iterator i=c.card.begin();i!=c.card.end();++i) if (card.find(i->first)!=card.end()) return true; return false; } // ---------------------------------------------------- Instantiation::Instantiation() : Context() { index = 0; numunknown = 0; } Instantiation::Instantiation(const Context &c, int defaultval) : Context(c) { index = 0; numunknown = defaultval == -1 ? c.NumVars() : 0; int accum = 1; for(map::iterator i=card.begin();i!=card.end();++i) { vals.insert(make_pair(i->first,defaultval)); indexinc.insert(make_pair(i->first,accum)); if (defaultval != -1) index += accum*defaultval; accum *= i->second; } } Instantiation::Instantiation(const Context &c, const Instantiation &v) : Context(c) { index = 0; numunknown = 0; int accum = 1; for(map::iterator i=card.begin();i!=card.end();++i) { int vv = v.Value(i->first); vals.insert(make_pair(i->first,vv)); indexinc.insert(make_pair(i->first,accum)); if (vv!=-1) index += accum*vv; else numunknown++; accum *= i->second; } } Instantiation::~Instantiation() { } bool Instantiation::Inc(int varid) { map::iterator i=vals.find(varid); map::iterator j=card.find(varid); map::iterator k=indexinc.find(varid); if (i!=vals.end() && j!=card.end() && k!=indexinc.end() && i->second != -1) { i->second++; index += k->second; if (i->second==j->second) { index -= k->second*j->second; i->second = 0; return false; } else { return true; } } return false; // I guess... } bool Instantiation::Inc() { index++; for(map::iterator i=vals.begin(),j=card.begin(); i!=vals.end();++i,++j) { i->second++; if (i->second == j->second) i->second = 0; else return true; } index = 0; return false; } bool Instantiation::Inc(const Context &c) { for(map::iterator i=vals.begin(),j=card.begin(),k=indexinc.begin(); i!=vals.end();++i,++j,++k) { if (!c.HasId(i->first)) continue; i->second++; index += k->second; if (i->second == j->second) { index -= k->second*j->second; i->second = 0; } else return true; } return false; } void Instantiation::AddVar(int varid, int cardinality, int val) { if (HasId(varid)) SetVal(varid,val); else { map::iterator ii = indexinc.lower_bound(varid); if (ii==indexinc.end()) { // if no other varids after me... indexinc.insert(make_pair(varid,size)); index += size*(val==-1 ? 0 : val); } else { // otherwise we need to adjust the index values int iinc = ii->second; if (val!=-1) index += iinc*val; // for all (lexographically) later indexs, // adjust the index accordinly and adjust the indexinc map::iterator vi = vals.find(ii->first); for(;ii != indexinc.end(); ++ii,++vi) { index += (cardinality-1)*ii->second*vi->second; ii->second *= cardinality; } indexinc.insert(make_pair(varid,iinc)); } Context::AddVar(varid,cardinality); vals.insert(make_pair(varid,val)); } } ostream& Instantiation::Save(ostream &os) const { return Context::Save(os) << os.fill() << vals; } istream& Instantiation::Load(istream &is) { Context::Load(is) >> vals; Recalc(); return is; } void Instantiation::SetIndex(int i) { numunknown = 0; index = i; for(map::iterator ci=card.begin(); ci!=card.end();++ci) { vals[ci->first] = i%(ci->second); i /= ci->second; } } void Instantiation::Recalc() { indexinc.clear(); index = 0; numunknown = 0; int acc = 1; for(map::iterator i=card.begin(),j=vals.begin(); i!=card.end();++i,++j) { indexinc.insert(make_pair(i->first,acc)); if (j->second != -1) index += acc*j->second; else numunknown++; acc *= i->second; } } Context Instantiation::MissingVars() const { Context c; for(map::const_iterator i=vals.begin(),j=card.begin(); i!=vals.end();++i,++j) if (i->second==-1) c.AddVar(j->first,j->second); return c; } Context Instantiation::KnownVars() const { Context c; for(map::const_iterator i=vals.begin(),j=card.begin(); i!=vals.end();++i,++j) if (i->second!=-1) c.AddVar(j->first,j->second); return c; } void Instantiation::ConsistentIndexes(vector &l) const { l.clear(); vector maxind,incind,v; int n = 0; for(map::const_iterator i=vals.begin(),j=card.begin(),k=indexinc.begin(); i!=vals.end();++i,++j,++k) if (i->second == -1) { v.push_back(0); maxind.push_back(j->second); incind.push_back(k->second); n++; } CConsistentIndexes(l,maxind,incind,index); } void Instantiation::InconsistentIndexes(vector &l) const { l.clear(); vector lneg; CInconsistentIndexes(l,lneg); } void Instantiation::SetAllVal(int val) { for(map::iterator i=vals.begin(),j=indexinc.begin(); i!=vals.end();++i,++j) { index += ((val==-1?0:val)-(i->second==-1?0:i->second))*j->second; i->second = val; } numunknown = val==-1 ? vals.size() : 0; } void Instantiation::PrintVal(ostream &os) const { for(map::const_iterator i=vals.begin();i!=vals.end();++i) os << i->second << ' '; os << endl; } } // end of ctbn namespace ctbnrle.1.0.2/src/impbase.cc0000644000464200007770000001656711344302770015775 0ustar csheltoncsprofs/* 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 "impbase.h" namespace ctbn { using namespace std; void ImpBase::SetProcess(const Process *pr) { p = pr; const CTBNDyn *ctbndyn = dynamic_cast(dynamic_cast(pr)->GetDynamics()); ctbndyn->GetStructure(str); int n = ctbndyn->NumofNodes(); nodes.clear(); for (int i=0; i(ctbndyn->Node(i))); } double ImpBase::SampleTime(const Instantiation &curri, int id, int parindex, int nodeval, int evidencetype, Trajectory::Index &nextindex, int e, double t, Random &rand) { double ret = 0.0; matrix Q = (*nodes[id])(parindex)->Intensity(); if (evidencetype==0) { //no upcoming evidence ret = t + method->SampleTime(Q, id, nodeval, curri, t, evid, -1, -1, rand); } else if (evidencetype==1 && e!=-1) { //there is an upcoming evidence double te = nextindex.Time(); ret = t + method->SampleTime(Q, id, nodeval, curri, t, evid, te, e, rand); if (ret>te&&nodeval==e) { ret = te; changeindex[id] = 1; } } else { //evidence change value ret = nextindex.Time(); changeindex[id] = 1; } return ret; } double ImpBase::SampleTimeWeight(const Instantiation &curri, int nodeid, int transitionid, int parindex, int nodeval, int evidencetype, Trajectory::Index &nextindex, int e, double currt, double nextt) { double retweight = 0.0; matrix Q = (*nodes[nodeid])(parindex)->Intensity(); if (evidencetype==2 || evidencetype==1 && e==-1) { //node is forced to agree the evidence, //calculate the likelihood retweight = Q[nodeval][nodeval] * (nextt-currt); } else if (evidencetype==1 && e!=-1) { //there is an upcoming evidence double te = nextindex.Time(); retweight = method->TimeWeight(Q, curri, nodeid, transitionid, nodeval, currt, nextt-currt, evid, te, e); } else { //double te = nextindex.Time(); retweight = method->TimeWeight(Q, curri, nodeid, transitionid, nodeval, currt, nextt-currt, evid, -1, -1); } return retweight; } double ImpBase::SampleInitial(Instantiation &initval, Random &rand) { const BN* bn = dynamic_cast(dynamic_cast(p)->GetStartDist()); return bn->ImportanceSample(initval, true,rand); } int ImpBase::SampleTransition(Instantiation &inst, int id, int parindex, int nodeval, int evidencetype, Trajectory::Index &nextindex, int e, double t, double &weight, Random &rand) { forceflip = 0; int retval; int oldval = nodeval; double retweight = 0.0; matrix Q = (*nodes[id])(parindex)->Intensity(); if (evidencetype==2) { //force a transition according to the evidence retval = e; //matrix Q = (*nodes[id])(parindex)->Intensity(); retweight = log(Q[nodeval][retval]) - log(-(Q[nodeval][nodeval])); } else if (evidencetype==1) { if (e!=-1) { //there is an upcoming evidence double te = nextindex.Time(); if (te>t) { //matrix Q = (*nodes[id])(parindex)->Intensity(); if (te-t>1e-6 || nodeval==e) { //sample next transition according to the evidence retval = method->SampleTransition(Q, id, nodeval, inst, t, evid, te, e, retweight, rand); } else { //too close to the upcoming evidence, //force the variable transition to the //value of the upcoming evidence and //update the correponding weight retval = e; forceflip = 1; int val = oldval; int nextval = nodeval; //matrix Q = (*nodes[transitionid])(parindex)->Intensity(); retweight = log(Q[nodeval][e]) - log(-(Q[nodeval][nodeval])); forceflip = 0; } } else { //we are at the time where evidence is a transition that //the value changes from unknown to known. At this time, //the current value of the variable should agree with the //evidence. So we do nothing. retval = nodeval; } } else { //the evidence changes from known value to unknow value retval = nodeval; } } else { //there is no upcoming evidence //matrix Q = (*nodes[id])(parindex)->Intensity(); retval = method->SampleTransition(Q, id, nodeval, inst, t, evid, -1, -1, retweight, rand); } //update the instantiation if (oldval != retval) { inst.SetVal(id, retval); } weight += retweight; return retval; } /* double ImpBase::SampleTransitionWeight(const Instantiation &nextinst, int transitionid, int oldval, int parindex, int nodeval, int evidencetype, Trajectory::Index &nextindex, int e, double currt, double nextt) { double retweight = 0.0; if (evidencetype==2) {//force a transition according to the evidence int val = oldval; int nextval = nodeval; matrix Q = (*nodes[transitionid])(parindex)->Intensity(); retweight = log(Q[val][nextval]) - log(-(Q[val][val])); } else if (evidencetype==1&&e==-1) //evidence change to known value to unknown retweight = 0; else if (evidencetype==1&&e!=-1) { //retweight = method->TransitionWeight(); if (forceflip==1) { int val = oldval; int nextval = nodeval; matrix Q = (*nodes[transitionid])(parindex)->Intensity(); retweight = log(Q[oldval][e]) - log(-(Q[val][val])); forceflip = 0; //cout << "force flip weight: " << retweight << endl; } else { double te = nextindex.Time(); matrix Q = (*nodes[transitionid])(parindex)->Intensity(); retweight = method->TransitionWeight(Q, transitionid, oldval, nextinst, nextinst, currt, nextt-currt, evid, te, e); } } else { retweight = 0; } return retweight; } */ void ImpBase::UndefineTime(int id, vector &undefinelist) { undefinelist.clear(); if (id!=-1) { changeindex[id] = 0; undefinelist = str.GetChildren(id); undefinelist.push_back(id); for (int i=0; i(p)->GetDynamics()->CondDomain(); vector varlist = conddomain.VarList(); vector clist(nodes.size(), false); for (int i=0; i c = str.GetChildren(str.Var2Node(varlist[i])); for (int j=0; jDomain(); Trajectory::Index it = evid->Begin(nodecontext); evidindex.push_back(it); } //Add index of conddomain of the whole process if has one Context conddomain = dynamic_cast(p)->GetDynamics()->CondDomain(); if (conddomain.NumVars()!=0) { Trajectory::Index it = evid->Begin(conddomain); evidindex.push_back(it); } } } // end of ctbn namespace ctbnrle.1.0.2/src/dyncomp.cc0000644000464200007770000002760311344302770016017 0ustar csheltoncsprofs/* 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 "dyncomp.h" #include "streamextra.h" #include "markovsimple.h" namespace ctbn { using namespace std; SOBJCLASSDEF(DynCompSS) DynCompSS::DynCompSS(int n) : SS(), ss(n) { } DynCompSS::DynCompSS(istream &is) : SS(is) { Load(is); } DynCompSS::DynCompSS(const DynCompSS &dcss) : SS(dcss) { int n = dcss.ss.size(); for(int i=0;iClone()); } DynCompSS &DynCompSS::operator=(const DynCompSS &dcss) { if (&dcss==this) return *this; SS::operator=(dcss); int n = ss.size(); for(int i=0;iClone(); return *this; } DynCompSS::~DynCompSS() { int n = ss.size(); for(int i=0;i> n; for(int i=0;i(StreamObj::LoadPtr(is)); ss.push_back(ssptr); } } void DynCompSS::Save(ostream &os) const { int n = ss.size(); os << n; for(int i=0;iSavePtr(os); } } void DynCompSS::Scale(double w) { int n = ss.size(); for(int i=0; iScale(w); } void DynCompSS::AddSS(const SS *nss, double w) { int n = ss.size(); const DynCompSS *dynss = dynamic_cast(nss); for(int i=0; iAddSS(dynss->ss[i], w); } double DynCompSS::Element(std::vector index, int s1, int s2) const { double ret = 0.0; int n = ss.size(); for(int i=0; i(ss[i])-> Element(s1, s2); return ret; } SOBJCLASSDEF(DynComp) DynComp::DynComp(const Context &var, const Context &cvar, const DynSimple *base) : Dynamics(var,cvar), impl(cvar.Size()) { for(int i=0;iMakeNew(var.Size()); } DynComp::DynComp(istream &is) : Dynamics(is), impl(CondDomain().Size()) { int n = impl.size(); for(int i=0;i(StreamObj::LoadPtr(is)); } DynComp::DynComp(const DynComp &dc) : Dynamics(dc), impl(dc.impl.size()) { for(int i=0;iClone(); } DynComp::~DynComp() { for(int i=0;i(StreamObj::LoadPtr(is)); } void DynComp::Save(ostream &os) const { Dynamics::Save(os); int n = impl.size(); for(int i=0;iSavePtr(os); } } DynComp &DynComp::operator=(const DynComp &dc) { if (&dc != this) { for(int i=0;iClone(); } return *this; } DynComp *DynComp::Clone() const { return new DynComp(*this); } void DynComp::Normalize() { int n = impl.size(); for(int i=0;iNormalize(); } void DynComp::Restrict(const Instantiation &x) { if (impl.size()==1) { vector ids; Domain().ConsistentIndexes(ids,x); impl[0]->Restrict(ids); } else { vector condi; CondDomain().ConsistentIndexes(condi,x); int ci=0; int n = impl.size(); for(int i=0;i ids; Domain().ConsistentIndexes(ids,x); impl[i]->Restrict(ids); ++ci; } else { vector ids; impl[i]->Restrict(ids); } } } } void DynComp::Mult(const Dynamics *x) { const DynComp *xx = dynamic_cast(x); assert(xx != NULL); // otherwise, I'm not sure what to do // update domains (and save old ones): Context oldv(Domain()); Context oldcv(CondDomain()); Dynamics::Mult(x); vector newimpl(CondDomain().Size()); for(int i=0;iMakeNew(Domain().Size()); Instantiation v(Domain()+CondDomain(),0); Context anycond(oldcv,xx->CondDomain(),Context::UNION); Context otherc = CondDomain() - oldcv; Context notc = v - oldcv; do { int myci = oldcv.Index(v); vector > myind(oldv.Size()); do { int myi = oldv.Index(v); int i = Domain().Index(v); myind[myi].push_back(i); } while(v.Inc(notc)); impl[myci]->Expand(myind,Domain().Size()); do { int ci = CondDomain().Index(v); newimpl[ci]->Mult(impl[myci]); } while(v.Inc(otherc)); } while(v.Inc(oldcv)); otherc = CondDomain() - xx->CondDomain(); notc = v - xx->CondDomain(); do { int xci = xx->CondDomain().Index(v); vector > xind(xx->Domain().Size()); do { int xi = xx->Domain().Index(v); int i = Domain().Index(v); xind[xi].push_back(i); } while(v.Inc(notc)); DynSimple *ds = xx->impl[xci]->Clone(); ds->Expand(xind,Domain().Size()); do { int ci = CondDomain().Index(v); newimpl[ci]->Mult(ds); } while(v.Inc(otherc)); delete ds; } while(v.Inc(xx->CondDomain())); for(int i=0;i i; Domain().ConsistentIndexes(i,x); return impl[condi]->CondRestrict(t0,t1,i); } RVCondSimple *DynComp::Cond(double t, const Instantiation &from, const Instantiation &to, bool transition) const { int condi = CondIndex(from); vector fromi,toi; Domain().ConsistentIndexes(fromi,from); Domain().ConsistentIndexes(toi,to); return impl[condi]->CondRestrict(t,fromi,toi,transition); } DynCompSS *DynComp::BlankSS() const { int nc = impl.size(); DynCompSS *ret = new DynCompSS(nc); for(int i=0;iss[i] = impl[i]->BlankSS(); return ret; } void DynComp::AddSS(const Instantiation &x, double t0, double deltat, SS *ss, double w) const { DynCompSS *dcss = dynamic_cast(ss); int condi = CondIndex(x); int i = Domain().Index(x); impl[condi]->AddSS(i,t0,deltat,dcss->ss[condi],w); } void DynComp::AddTransSS(const Instantiation &x1, const Instantiation &x2, double t, SS *ss, double w) const { DynCompSS *dcss = dynamic_cast(ss); // assume conditioning variables are the same... int condi = CondIndex(x1); int i1 = Domain().Index(x1), i2 = Domain().Index(x2); if(i1!=i2) //add by Yu impl[condi]->AddTransSS(i1,i2,t,dcss->ss[condi],w); } void DynComp::AddExpSS(const RV *alpha, const RV *beta, double t0, double deltat, SS *ss, double w) const { DynCompSS *dcss = dynamic_cast(ss); RV *aa = alpha->Clone(); aa->Project(Domain(),CondDomain()); RV *bb = beta->Clone(); bb->Project(Domain(),CondDomain()); Instantiation ci(CondDomain(),0); do { // assuming that a and b have the same distribution over // the conditioning variables... RVSimple *a = aa->MakeSimple(ci,false); double ww = w*a->Sum(); if (ww<=0.0) continue; a->Normalize(); RVSimple *b = bb->MakeSimple(ci,true); int cind = ci.Index(); impl[cind]->AddExpSS(a,b,t0,deltat,dcss->ss[cind],ww); delete a; delete b; } while(ci.Inc()); delete aa; delete bb; } void DynComp::AddExpTransSS(const RV *x1, const RV *x2, const Context &changevar, double t, SS *ss, double w) const { if (!changevar.IsOverlap(Domain())) return; DynCompSS *dcss = dynamic_cast(ss); RV *aa = x1->Clone(); aa->Project(Domain(),CondDomain()); RV *bb = x2->Clone(); bb->Project(Domain(),CondDomain()); RV *acond = x1->Clone(); acond->Project(CondDomain(),Context()); RV *bcond = x2->Clone(); bcond->Project(CondDomain(),Context()); acond->MultBy(bcond); acond->Normalize(); Instantiation ci(CondDomain(),0); do { RVSimple *a = aa->MakeSimple(ci,false); double ww = w*acond->Prob(ci); if (ww<=0.0) continue; a->Normalize(); RVSimple *b = bb->MakeSimple(ci,true); int cind = ci.Index(); impl[cind]->AddExpTransSS(a,b,t,dcss->ss[cind],ww); delete a; delete b; } while(ci.Inc()); delete acond; delete bcond; delete aa; delete bb; } void DynComp::AddExpSS(const Instantiation &condi, const RVSimple *alpha, const RVSimple *beta, double t0, double deltat, SS *ss, double w) const { AddExpSS(CondDomain().Index(condi),alpha,beta,t0,deltat,ss,w); } void DynComp::AddExpTransSS(const Instantiation &condi, const RVSimple *x1, const RVSimple *x2, const Context &changevar, double t, SS *ss, double w) const { AddExpTransSS(CondDomain().Index(condi),x1,x2,changevar,t,ss,w); } void DynComp::AddExpSS(int condi, const RVSimple *alpha, const RVSimple *beta, double t0, double deltat, SS *ss, double w) const { DynCompSS *dcss = dynamic_cast(ss); impl[condi]->AddExpSS(alpha,beta,t0,deltat,dcss->ss[condi],w); } void DynComp::AddExpTransSS(int condi, const RVSimple *x1, const RVSimple *x2, const Context &changevar, double t, SS *ss, double w) const { if (!changevar.IsOverlap(Domain())) return; DynCompSS *dcss = dynamic_cast(ss); impl[condi]->AddExpTransSS(x1,x2,t,dcss->ss[condi],w); } void DynComp::AddSS(const SS *toadd, const Dynamics *dyn, SS *ss, double w) const { const DynComp *d = dynamic_cast(dyn); assert(d!=NULL); assert(CondDomain().IsSubset(d->CondDomain())); DynCompSS *dcss = dynamic_cast(ss); const DynCompSS *toadddcss = dynamic_cast(toadd); Instantiation i(Domain()+CondDomain(),0); do { int mycondi = CondDomain().Index(i); int dcondi = d->CondDomain().Index(i); vector > mapping(Domain().Size()); do { d->Domain().ConsistentIndexes(mapping[Domain().Index(i)],i); } while(i.Inc(Domain())); impl[mycondi]->AddSS(toadddcss->ss[dcondi],d->impl[dcondi], mapping,dcss->ss[mycondi]); } while(i.Inc(CondDomain())); } void DynComp::SampleNextEvent(const Instantiation &i, double t, double &nextt, Instantiation &nexti, Random &rand) const { int condi = CondIndex(i); int ii = Domain().Index(i); int nextii; impl[condi]->SampleNextEvent(ii,t,nextt,nextii,rand); Instantiation newi(Domain()); newi.SetIndex(nextii); nexti.SetVal(newi,true); } void DynComp::Maximize(const SS *ss) { const DynCompSS *dcss = dynamic_cast(ss); int nc = impl.size(); for(int i=0;iMaximize(dcss->ss[i]); } void DynComp::Scramble(double a, double b, double alpha, double degree, Random &rand) { //by Yu int nc = impl.size(); for(int i=0;iScramble(a, b, alpha, degree, rand); } double DynComp::LLH(const SS *ss) const { const DynCompSS *dynss = dynamic_cast(ss); double llh = 0.0; int nc = impl.size(); for(int i=0; iLLH(dynss->ss[i]); return llh; } double DynComp::GetScore(double numTrans, double amtTime, const SS* ss) const { const DynCompSS *dynss = dynamic_cast (ss); double localScore(0); int nc = impl.size(); // Sum over all possible parent instantiations for(int i = 0; i < nc; i++) localScore += impl[i]->GetScore(nc, numTrans, amtTime, dynss->ss[i]); return localScore; } } // end of ctbn namespace ctbnrle.1.0.2/src/markovsimple.cc0000644000464200007770000004705111344302770017056 0ustar csheltoncsprofs/* 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 "markovsimple.h" #include "params.h" #include "multisimple.h" #include "rk.h" #include "extramath.h" #include "rvsimple.h" namespace ctbn { using namespace std; //----------- template CondTransQ::CondTransQ(const matrix &QQ, double deltat) : RVCondSimple(), Q(QQ), t(deltat) { } template CondTransQ::~CondTransQ() { } template CondTransQ *CondTransQ::Clone() const { return new CondTransQ(*this); } template double CondTransQ::Prob(int ind, int cind, bool log) const { vectr a(Q.getm(),0.0); a[cind] = 1.0; double zlog = vexpmt(a,Q,t); if (log) return ::log(a[cind])+zlog; else return a[cind]*exp(zlog); } template RVSimple *CondTransQ::Condition(int ind) const { vectr a(Q.getm(),0.0); a[ind] = 1.0; double logz = vexpmt(a,Q,t); return new MultiZSimple(a,logz); } template int CondTransQ::Sample(int cx, Random &rand) const { vectr a(Q.getm(),0.0); a[cx] = 1.0; vexpmt(a,Q,t); return rand.SampleMultinomial(a,a.getm(),a.sum()); } template void CondTransQ::Mult(RVSimple *&x) const { vectr a(Q.getm(),0.0); double logz; x->GetDist(a,logz); logz += vexpmt(a,Q,t); x->SetDist(a,logz); } template void CondTransQ::RMult(RVSimple *&x) const { vectr a(Q.getm(),0.0); double logz; x->GetDist(a,logz); logz += expmtv(a,Q,t); x->SetDist(a,logz); } template RVSimple *CondTransQ::Convert(const RVSimple *rvs) const { // any will work... return rvs->Clone(); } //----------- template SparseCondTransQ::SparseCondTransQ(const matrix &QQ, double deltat, const std::vector &ind) : Q(QQ), t(deltat),ix(ind) { } template SparseCondTransQ::~SparseCondTransQ() { } template SparseCondTransQ *SparseCondTransQ::Clone() const { return new SparseCondTransQ(*this); } template double SparseCondTransQ::Prob(int ind, int cind, bool log) const { vector::const_iterator cindpos = lower_bound(ix.begin(),ix.end(),cind); if (cindpos==ix.end() || *cindpos!=cind) return log?-INFINITY:0.0; vectr a(Q.getm(),0.0); int tmp = cindpos-ix.begin(); a[tmp] = 1.0; double zlog = vexpmt(a,Q,t); cindpos = lower_bound(ix.begin(),ix.end(),ind); if (cindpos==ix.end() || *cindpos!=ind) return log?-INFINITY:0.0; else { tmp = cindpos-ix.begin(); return log ? ::log(a[tmp]) + zlog : a[tmp]*exp(zlog); } } template RVSimple *SparseCondTransQ::Condition(int ind) const { vector::const_iterator cindpos = lower_bound(ix.begin(),ix.end(),ind); if (cindpos==ix.end() || *cindpos!=ind) return NULL; // probably should do something else vectr a(Q.getm(),0.0); int tmp = cindpos-ix.begin(); a[tmp] = 1.0; double zlog = vexpmt(a,Q,t); return new SparseMultiZSimple(a,ix,zlog); } template void SparseCondTransQ::Mult(RVSimple *&x) const { // at the moment this assumes that x is a SparseMultiZSimple // with indexes that match up SparseMultiZSimple *xx = dynamic_cast(x); vectr a; double logz; xx->GetSparseDist(a,logz); logz += vexpmt(a,Q,t); xx->SetSparseDist(a,logz); } template void SparseCondTransQ::RMult(RVSimple *&x) const { // at the moment this assumes that x is a SparseMultiZSimple // with indexes that match up SparseMultiZSimple *xx = dynamic_cast(x); vectr a; double logz; xx->GetSparseDist(a,logz); logz += expmtv(a,Q,t); xx->SetSparseDist(a,logz); } template RVSimple *SparseCondTransQ::Convert(const RVSimple *rvs) const { if (dynamic_cast(rvs)!=NULL) return rvs->Clone(); vectr a; double logz; rvs->GetDist(a,logz); return new SparseMultiZSimple(vectr(a,ix),ix,logz); } //----------- template CondTransQ2::CondTransQ2(const matrix &QQ) : Q(QQ) { } template CondTransQ2::~CondTransQ2() { } template CondTransQ2 *CondTransQ2::Clone() const { return new CondTransQ2(*this); } template double CondTransQ2::Prob(int ind, int cind, bool log) const { return log ? ::log(Q[cind][ind]) : Q[cind][ind]; } template RVSimple *CondTransQ2::Condition(int ind) const { vectr a(Q.getm(),0.0); a[ind] = 1.0; a = a*Q; return new MultiZSimple(a); } template void CondTransQ2::Mult(RVSimple *&x) const { // at the moment this assumes that x is a MultiZSimple MultiZSimple *xx = dynamic_cast(x); vectr a; double logz; xx->GetDist(a,logz); a = a*Q; xx->SetDist(a,logz); } template void CondTransQ2::RMult(RVSimple *&x) const { // at the moment this assumes that x is a MultiZSimple // with indexes that match up MultiZSimple *xx = dynamic_cast(x); vectr a; double logz; xx->GetDist(a,logz); a = Q*a; xx->SetDist(a,logz); } template RVSimple *CondTransQ2::Convert(const RVSimple *rvs) const { // any will work... return rvs->Clone(); } //----------- template SparseCondTransQ2::SparseCondTransQ2(const matrix &QQ, const vector &cind, const vector &ind) : Q(QQ), cix(cind), ix(ind) { } template SparseCondTransQ2::~SparseCondTransQ2() { } template SparseCondTransQ2 *SparseCondTransQ2::Clone() const { return new SparseCondTransQ2(*this); } template double SparseCondTransQ2::Prob(int ind, int cind, bool log) const { vector::const_iterator cindpos = lower_bound(cix.begin(),cix.end(),cind); if (cindpos==cix.end() || *cindpos!=cind) return log?-INFINITY:0.0; vector::const_iterator indpos = lower_bound(ix.begin(), ix.end(),ind); if (indpos==ix.end() || *indpos!=ind) return log?-INFINITY:0.0; return log ? ::log(Q[cindpos-cix.begin()][indpos-ix.begin()]) : Q[cindpos-cix.begin()][indpos-ix.begin()]; } template RVSimple *SparseCondTransQ2::Condition(int ind) const { vector::const_iterator cindpos = lower_bound(cix.begin(),cix.end(),ind); if (cindpos==cix.end() || *cindpos!=ind) return NULL; vectr a(Q.getm(),0.0); int tmp = cindpos-ix.begin(); a[tmp] = 1.0; a = a*Q; return new SparseMultiZSimple(a,ix); } template void SparseCondTransQ2::Mult(RVSimple *&x) const { // at the moment this assumes that x is a SparseMultiZSimple // with indexes that match up SparseMultiZSimple *xx = dynamic_cast(x); vectr a; double logz; xx->GetSparseDist(a,logz); //if (Q.sum() > 1e-10) //comment out by Yu a = a*Q; xx->SetSparseDist(a,logz); xx->SetSparseIndexes(ix); } template void SparseCondTransQ2::RMult(RVSimple *&x) const { // at the moment this assumes that x is a SparseMultiZSimple // with indexes that match up SparseMultiZSimple *xx = dynamic_cast(x); vectr a; double logz; xx->GetSparseDist(a,logz); //if (Q.sum() > 1e-10) //by Yu a = Q*a; xx->SetSparseDist(a,logz); xx->SetSparseIndexes(cix); } // this assumes that we will do a "Mult" (and not a "RMult") with this // one template RVSimple *SparseCondTransQ2::Convert(const RVSimple *rvs) const { if (dynamic_cast(rvs)!=NULL) return rvs->Clone(); vectr a; double logz; rvs->GetDist(a,logz); return new SparseMultiZSimple(vectr(a,cix),cix,logz); } //----------- SOBJCLASSDEF(MarkovSimple) MarkovSimple::MarkovSimple(int n) : DynSimple(), Q(n,n,0.0) { } MarkovSimple::MarkovSimple(istream &is) : DynSimple(is) { is >> Q; } MarkovSimple::~MarkovSimple() { } void MarkovSimple::Load(istream &is) { DynSimple::Load(is); is >> Q; } void MarkovSimple::Save(ostream &os) const { DynSimple::Save(os); os << os.fill() << Q; } MarkovSimple *MarkovSimple::Clone() const { return new MarkovSimple(*this); } MarkovSimple *MarkovSimple::MakeNew(int nstates) const { return new MarkovSimple(nstates); } void MarkovSimple::Normalize() { int n = Q.getm(); for(int i=0;i &ind) { int n = ind.size(); int m = Q.getm(); int ii = 0; for(int i=0;i > &ind, int newn) { matrix newQ(newn,newn,0.0); int n = Q.getm(); for(int i=0;i *MarkovSimple::Cond(double t0, double t1) const { return new CondTransQ(Q,t1-t0); } SparseCondTransQ *MarkovSimple::CondRestrict(double t0, double t1, const vector &ind) const { return new SparseCondTransQ(matrix(Q,ind),t1-t0,ind); } CondTransQ2 *MarkovSimple::Cond(double t) const { matrix newQ(Q); int n = Q.getm(); for(int i=0;i(newQ); } SparseCondTransQ2 *MarkovSimple::CondRestrict(double t, const vector &fromind, const vector &toind, bool transition) const { if (transition) return new SparseCondTransQ2(matrix(Q,fromind,toind), fromind,toind); else { matrix m(fromind.size(),toind.size(),0.0); int i=0,j=0; while(i(m,fromind,toind); } } MarkovSimpleSS *MarkovSimple::BlankSS() const { return new MarkovSimpleSS(Q.getm()); } void MarkovSimple::Mult(const DynSimple *x) { const MarkovSimple *xx = dynamic_cast(x); assert(xx!=NULL); // otherwise, not sure what to do... if (Q.getm()!=xx->Q.getm()) Q = xx->Q; else Q += xx->Q; } void MarkovSimple::AddSS(int x, double t0, double deltat, SS *ss, double w) const { MarkovSimpleSS *msss = dynamic_cast(ss); msss->c[x][x] += deltat*w; } void MarkovSimple::AddTransSS(int x1,int x2, double t, SS *ss,double w) const { MarkovSimpleSS *msss = dynamic_cast(ss); if (x1 != x2) msss->c[x1][x2] += w; } void MarkovSimple::AddExpSS(const RVSimple *alpha, const RVSimple *beta, double t0, double deltat, SS *ss, double w) const { MarkovSimpleSS *msss = dynamic_cast(ss); const SparseMultiZSimple *a = dynamic_cast(alpha); assert(a!=NULL); // otherwise, need to implement more code const SparseMultiZSimple *b = dynamic_cast(beta); assert(b!=NULL); // ditto // a->Indexes() should be the same as b->Indexes()! const vector &ind = a->Indexes(); int n = ind.size(); matrix c; double z = suffstat(a->Dist(),b->Dist(),c,matrix(Q,ind),deltat); double sum = 0.0; for(int i=0;ic[ind[j]][ind[i]] += c[i][j]; } void MarkovSimple::AddExpTransSS(const RVSimple *x1, const RVSimple *x2, double t, SS *ss, double w) const { MarkovSimpleSS *msss = dynamic_cast(ss); const SparseMultiZSimple *a = dynamic_cast(x1); assert(a!=NULL); // otherwise, need to implement more code const SparseMultiZSimple *b = dynamic_cast(x2); assert(b!=NULL); // ditto const vector &aind = a->Indexes(); const vector &bind = b->Indexes(); matrix c(Q,aind,bind); c.multbycol(a->Dist()); c.multbyrow(b->Dist()); int mm = c.getm(); int nn = c.getn(); double csum = 0.0; for(int i=0;ic[aind[i]][bind[j]] += c[i][j]; } void MarkovSimple::AddSS(const SS *toadd, const DynSimple *dyn, const std::vector > &mapping, SS *ss, double w) const { const MarkovSimple *m = dynamic_cast(dyn); assert(m!=NULL); // otherwise, need double-dispatch and more code MarkovSimpleSS *mss = dynamic_cast(ss); const MarkovSimpleSS *addmss = dynamic_cast(toadd); int n = Q.getm(); for(int i=0;ic[i][i] += addmss->c[mapping[i][ki]][mapping[i][ki]]; for(int j=0;jc[i][j] += addmss-> c[mapping[i][ki]] [mapping[j][kj]]; } } } } void MarkovSimple::SampleNextEvent(int ind, double t, double &newt, int &newind, Random &rand) const { newt = t + rand.SampleExp(-Q[ind][ind]); double sum = 0.0; int n = Q.getn(); for(int i=0;i(ss); int m = Q.getm(), n = Q.getn(); for(int i=0;ic[i][j]; double tottime = msss->c[i][i]; if (tottime<=0) { for(int j=0;jc[i][j]/tottime; } } for (int i = 0; i < m; ++i) { if (fabs(Q[i][i]) < divzeroq) { for (int j = 0; j < n; ++j) Q[i][j] = divzeroq; Q[i][i] = -divzeroq*(n-1); } else{ for (int j = 0; j < n; ++j) if(i!=j && fabs(Q[i][j]) < divzeroq) { Q[i][j] = divzeroq; Q[i][i] -= divzeroq; } } } } //by Yu void MarkovSimple::Scramble(double a, double b, double alpha, double degree, Random &rand) { int fix = ParamInt("FixScramble", -1); if(fix!=-1) { int n = Q.getn(); int m = Q.getm(); for (int i = 0; i < m; ++i) for (int j = 0; j < n; ++j) if(i==j) Q[i][j] = -a; else Q[i][j] = a/(n-1); return; } int n = Q.getn(); int m = Q.getm(); double *in = new double[n-1]; matrix original(Q); for(int i=0;i(ss); double llh = 0.0; matrix c = mss->c; int n = c.getn(); int m = c.getm(); for(int i=0; i0.0) if(i!=j) llh += c[i][j] * log(Q[i][j]); else llh += c[i][j] * Q[i][j]; return llh; } // Score over a single matrix. (sum over states) double MarkovSimple::GetScore(int parentCard, double numTrans, double amtTime, const SS* ss) const { double localScore(0); const MarkovSimpleSS *mss = dynamic_cast(ss); const matrix& c = mss->c; int size = c.getm(); double alpha_xx_u(numTrans / ((size-1)*size*parentCard)); double alpha_x_u((size-1)*alpha_xx_u); double tau_x_u(amtTime / size*parentCard); for(int i = 0; i < size; i++) { // Sum over each state of the variable double m_x_u(0); double t_x_u = c[i][i]; // Theta score (transitions) for(int j = 0; j < size; j++) { if(i == j) continue; m_x_u += c[i][j]; localScore += lngamma(alpha_xx_u + c[i][j]); } localScore += lngamma(alpha_x_u) - lngamma(alpha_x_u + m_x_u) - (size-1)*lngamma(alpha_xx_u); // q score (time) localScore += lngamma(alpha_x_u + m_x_u + 1) + (alpha_x_u + 1)*log(tau_x_u) - lngamma(alpha_x_u + 1) - (alpha_x_u + m_x_u + 1) * log(tau_x_u + t_x_u); } return localScore; } //----- SOBJCLASSDEF(MarkovSimpleSS) MarkovSimpleSS::MarkovSimpleSS(int n) : c(n,n,0.0) { } MarkovSimpleSS::MarkovSimpleSS(istream &is) { Load(is); } MarkovSimpleSS::~MarkovSimpleSS() { } MarkovSimpleSS *MarkovSimpleSS::Clone() const { return new MarkovSimpleSS(*this); } void MarkovSimpleSS::Load(istream &is) { is >> c; } void MarkovSimpleSS::Save(ostream &os) const { os << c; } void MarkovSimpleSS::Scale(double w) { for(int i=0; i(nss); for(int i=0; ic[i][j] * w; } MarkovSimpleToggle::MarkovSimpleToggle(int n) : MarkovSimple(n) { } MarkovSimpleToggle::MarkovSimpleToggle(istream &is) : MarkovSimple(is) { } MarkovSimpleToggle::~MarkovSimpleToggle() { } MarkovSimpleToggle *MarkovSimpleToggle::Clone() const { return new MarkovSimpleToggle(*this); } MarkovSimpleToggle *MarkovSimpleToggle::MakeNew(int nstates) const { return new MarkovSimpleToggle(nstates); } void MarkovSimpleToggle::Maximize(const SS *ss) { static const double divzeroq = ParamDouble("DivZeroQ",1e-6); const MarkovSimpleSS *msss = dynamic_cast(ss); matrix &Q = Intensity(); int m = Q.getm(), n = Q.getn(); double tottr = 0.0, tottime = 0.0; for (int i = 0; i < m; ++i) for (int j = 0; j < n; ++j) if (j == i) tottime += msss->c[i][i]; else tottr += msss->c[i][j]; double toggleq = divzeroq; if (tottime > 0) toggleq = tottr/tottime; for (int i = 0; i < m; ++i) for (int j = 0; j < n; ++j) { if (j == i) Q[i][i] = -(n-1)*toggleq; else Q[i][j] = toggleq; } for (int i = 0; i < m; ++i) { if (fabs(Q[i][i]) < divzeroq) { for (int j = 0; j < n; ++j) Q[i][j] = divzeroq; Q[i][i] = -divzeroq*(n-1); } else{ for (int j = 0; j < n; ++j) if(i!=j && fabs(Q[i][j]) < divzeroq) { Q[i][j] = divzeroq; Q[i][i] -= divzeroq; } } } } } // end of ctbn namespace ctbnrle.1.0.2/src/utils.cc0000644000464200007770000002115511344302770015502 0ustar csheltoncsprofs/* 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 "utils.h" #include "trajectory.h" #include "context.h" #include "random.h" #if defined __linux__ || __CYGWIN__ #include #include #endif #include #include "params.h" namespace ctbn { using namespace std; bool IsTrajSame(const Trajectory &tr1, const Trajectory &tr2, const Context &c) { Trajectory::Index i1 = tr1.Begin(c); Trajectory::Index i2 = tr2.Begin(c); Instantiation inst1, inst2; for (;;) { if (i1.Done() || i2.Done()) break; inst1 = i1.Values(); inst2 = i2.Values(); Context diff(inst1, inst2); if (diff.VarList().size() > 0) return false; i1++; i2++; } return true; } bool IsTrajValid(const Trajectory &tr, const Context &c) { Trajectory::Index i = tr.Begin(c); Instantiation oldinst, newinst; oldinst = i.Values(); for (;;) { ++i; if (i.Done()) break; newinst = i.Values(); Context diff(oldinst, newinst); oldinst = newinst; if (diff.VarList().size() != 1) return false; } return true; } void RemoveInformation(Trajectory &tr, const Context &c, int var, double start, double end) { Trajectory newtr; newtr.SetBeginTime(tr.TimeBegin()); newtr.SetEndTime(tr.TimeEnd()); for(Trajectory::Index i = tr.Begin(c);!i.Done();++i) { Instantiation inst(i.Values()); if (i.Time()>=start && i.Time()start) { inst.SetVal(var,-1); newtr.AddTransition(inst,start); } if (i.Time()>start && i.Time()=end) { inst.SetVal(var,i.Values().Value(var)); newtr.AddTransition(inst,end); } } tr = newtr; } void RemoveInformation(Trajectory &tr, const Context &c, int nvar, int nit, double frac) { double len = tr.TimeEnd()-tr.TimeBegin(); for(int i=0;i select; double sum = SelectTime(select, frac, begint, endt, thresh); double res = sum - (endt-begint)*thresh; double seg = (endt-begint)*frac; while(!select.empty()) { double st = -select.top(); double nextt = st + seg; select.pop(); if(select.empty()) nextt -= res; RemoveInformation(tr, c, var, st, nextt); } } } double SelectTime(priority_queue &select, double ratio, double begint, double endt, double thresh) { priority_queue Q; double sum=0; double len = endt-begint; double seg = ratio*len; while(sum tmpq(Q); double top = -tmpq.top(); tmpq.pop(); while(!tmpq.empty()) { double next = -tmpq.top(); if(next-top namespace ctbn { using namespace std; Structure::Structure() { empty = vector(0); } Structure::~Structure() { } const vector &Structure::GetChildren(int parent) const { map >::const_iterator ci = children.find(node2var[parent]); if (ci != children.end()) { return ci->second; } else { return empty; } } const vector &Structure::GetParent(int child) const { if(child>=parentlist.size()) { cerr << "incorrect node index" << endl; cerr << "child: " << child << endl; exit(1); } return parentlist[child]; } const vector > &Structure::GetAdjLists() const { return parentlist; } int Structure::HammingDist(const Structure &other) { const vector > &listA = this->GetAdjLists(); const vector > &listB = other.GetAdjLists(); vector vlA = this->GetVarList(); vector vlB = other.GetVarList(); set allvarid; for(int i=0;i::const_iterator it; for(it = allvarid.begin(); it != allvarid.end(); ++it) { int cur = *it; if(this->children.find(cur) == this->children.end()) { ret += listB[other.var2node.find(cur)->second].size(); continue; } if(other.children.find(cur) == other.children.end()) { ret += listA[this->var2node.find(cur)->second].size(); continue; } ret += listDiff(listA[this->var2node.find(cur)->second], listB[other.var2node.find(cur)->second]); } return ret; } vector Structure::GetVarList() const { vector ret; for(int i=0;i zeroInDeg; vector > pl(parentlist); map > cl(children); for(int i = 0; i < pl.size(); i++) if(pl[i].size() == 0) zeroInDeg.push_back(i); while(zeroInDeg.size() != 0) { int rem = zeroInDeg.back(); rem = node2var[rem]; zeroInDeg.pop_back(); map >::iterator ci = cl.find(rem); if(ci == cl.end()) continue; for(int i = 0; i < ci->second.size(); i++) { int rem2 = ci->second.back(); ci->second.pop_back(); vector::iterator it = find(pl[rem2].begin(),pl[rem2].end(), rem); pl[rem2].erase(it); if(pl[rem2].size() == 0) zeroInDeg.push_back(rem2); } } int totalsize = 0; for(int i = 0; i < pl.size(); i++) totalsize += pl[i].size(); return totalsize != 0; } int Structure::Node2Var(int index) const { return node2var[index]; } int Structure::Var2Node(int varid) const { return var2node.find(varid)->second; } void Structure::Print(ostream &os) const { os << "Parent list: " << endl; for(int i=0; i. */ #include "ctbn.h" namespace ctbn { using namespace std; SOBJCLASSDEF(CTBN) CTBN::CTBN(RV *startdist, Dynamics *dyn) : Markov(startdist, dyn) { } CTBN::CTBN(std::istream &is) : Markov(is) { } CTBN::CTBN(const Markov &m) : Markov(m) { } CTBN *CTBN::Clone() const { return new CTBN(*this); } CTBN &CTBN::operator=(const CTBN &ctbn) { if(&ctbn==this) return *this; if(p0!=NULL) delete p0; if(d!=NULL) delete d; p0 = ctbn.p0->Clone(); d = ctbn.d->Clone(); return *this; } const MarkovDyn* CTBN::Node(int i) const { return dynamic_cast(CTBNDynamic()->Node(i)); } const CTBNDyn* CTBN::CTBNDynamic() const { return dynamic_cast(d); } } // end of ctbn namespace ctbnrle.1.0.2/src/extramath.cc0000644000464200007770000000557511344302770016347 0ustar csheltoncsprofs/* 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 "extramath.h" #include #include #include namespace ctbn { using namespace std; double lngamma(double x) { double y,tmp,ser; static double cof[6]={76.18009172947146,-86.50532032941677, 24.01409824083091,-1.231739572450155, 0.1208650973866179e-2,-0.5395239384953e-5}; y=x; tmp=x+5.5; tmp -= (x+0.5)*log(tmp); ser=1.000000000190015; for (int j=0;j<6;j++) ser += cof[j]/++y; return -tmp+log(2.5066282746310005*ser/x); } vector > combo(int nItems, int nChoose) { vector > ret(0); ret.push_back(vector (0)); vector vec; for(int i=0;i= 1) { for(int i=0; i(1,vec[i])); vector > temp = ret; for(int i=1; i > current(0); for(int j=0; j element = temp[j]; int end = element.size()-1; for(int k=element[end]+1;k temp2 = element; temp2.push_back(k); current.push_back(temp2); } } temp = current; for(int j=0; j &listA, const vector &listB) { int ret = 0; for( vector::const_iterator it = listB.begin(); it != listB.end(); it++) { if(find(listA.begin(),listA.end(),*it) != listA.end()) ret++; } return listA.size() + listB.size() - (2*ret); } } // end of ctbn namespace ctbnrle.1.0.2/src/process.cc0000644000464200007770000000170111344302770016013 0ustar csheltoncsprofs/* 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 "process.h" namespace ctbn { Process::~Process() { } } // end of ctbn namespace ctbnrle.1.0.2/src/clique.cc0000644000464200007770000003116711344302770015630 0ustar csheltoncsprofs/* 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 "clique.h" #include namespace ctbn { using namespace std; CliqueTree::CliqueTree() : Head(NULL), numrandrestarts(100), Head_compare(NULL), unit_test(false) { } CliqueTree::CliqueNode::CliqueNode() { prev = NULL; } CliqueTree::CliqueNode::~CliqueNode() { } void CliqueTree::CliqueNode::AddVar(const int &varid) { connected.insert(varid); } void CliqueTree::CliqueNode:: print(ostream &os) { set ::iterator it; for(it = connected.begin(); it != connected.end(); it ++) { os << *it << " "; } os << endl; } //destructor CliqueTree::~CliqueTree() { Destroy(Head); Destroy(Head_compare); } void CliqueTree::BuildClique(const Markov &process, Random &rand) { context_size.clear(); data.clear(); Destroy(Head_compare); Head_compare = NULL; AddFromBN(dynamic_cast(process.GetStartDist())); AddFromCTBNDyn(dynamic_cast(process.GetDynamics())); BuildIt(rand); } void CliqueTree::BuildClique(const BN *bn, Random &rand) { context_size.clear(); data.clear(); Destroy(Head_compare); Head_compare = NULL; AddFromBN(bn); BuildIt(rand); } void CliqueTree::BuildClique(const CTBNDyn *dyn, Random &rand) { context_size.clear(); data.clear(); Destroy(Head_compare); Head_compare = NULL; AddFromCTBNDyn(dyn); BuildIt(rand); } void CliqueTree::AddFromBN(const BN *bn) { int n = bn->NumofNodes(); for(int i = 0; i < n; i++) { CliqueNode newitem; Context allvar = bn->Node(i)->Domain() + bn->Node(i)->CondDomain(); vector varids = allvar.VarList(); for(vector::iterator i=varids.begin(); i!=varids.end();++i) { newitem.AddVar(*i); AddVarSize(*i,allvar.Cardinality(*i)); } data.push_back(newitem); } } void CliqueTree::AddFromCTBNDyn(const CTBNDyn *dyn) { int n = dyn->NumofNodes(); for(int i = 0; i < n; i++) { CliqueNode newitem; Context allvar = dyn->Node(i)->Domain() + dyn->Node(i)->CondDomain(); vector varids = allvar.VarList(); for(vector::iterator i=varids.begin(); i!=varids.end();++i) { newitem.AddVar(*i); AddVarSize(*i,allvar.Cardinality(*i)); } data.push_back(newitem); } } int CliqueTree::VarSize(int varid) const { map::const_iterator loc = context_size.find(varid); if (loc==context_size.end()) return 0; return loc->second; } void CliqueTree::AddVarSize(int varid, int size) { map::iterator loc = context_size.lower_bound(varid); if (loc != context_size.end() && loc->first==varid) { if (loc->second==size) return; cerr << "inconsistent model input to CliqueTree: variable sizes do not agree" << endl; exit(1); } context_size.insert(loc,make_pair(varid,size)); } void CliqueTree::BuildIt(Random &rand) { save_data = data; double max_compare = 0; double maximum; //run the clique tree build on random arrangement as it set for(int i = 0; i < numrandrestarts; i++) { CliqueTrees(rand); maximum = MaxSize(); //get the better clique tree if(maximum < max_compare || i == 0) { Destroy(Head_compare); Head_compare = Head; Head = NULL; max_compare = maximum; } else { Destroy(Head); Head = NULL; } } } //remove all the database; void CliqueTree::Destroy(CliqueNode * place) { if(place) { for(unsigned int i = 0; i < place->next.size(); i++) { Destroy(place->next[i]); } delete place; } } //remove the item from the vector while generating void CliqueTree::RemoveElem(vector & random_clique, int remove) { if((random_clique.size()-1) != (unsigned)remove ) { random_clique[remove] = random_clique[random_clique.size()-1]; } random_clique.pop_back(); } //building a clique_tree base on the node with same //context connected and combine into the tree //eliminate the used node from the list //run it til all the node in the data vector is gone or //all the number has been called void CliqueTree::CliqueTrees(Random &rand) { data = save_data; clique_builded.clear(); int i = 0; vector random_clique; for(map::iterator i=context_size.begin(); i!=context_size.end();++i) { random_clique.push_back(i->first); } while (data.size() > 0) { i = unit_test ? 0 : rand.RandInt(random_clique.size()); CliqueTrees(random_clique[i]); RemoveElem(random_clique, i); } Eliminate(); } void CliqueTree::CliqueTrees(int search) { vector node_exist; set extend; set extend_out; extend.insert(search); set ::iterator it; for(unsigned int j = 0; j < data.size(); j++ ) { if(data[j].connected.find(search) != data[j].connected.end()) { node_exist.push_back(j); } } if(node_exist.size() > 0) { for(unsigned int j = 0; j < node_exist.size(); j ++) { set ::iterator it; for(it = data[node_exist[j]].connected.begin(); it != data[node_exist[j]].connected.end(); it ++) { if(extend.find(*it) == extend.end()) { extend.insert(*it); extend_out.insert(*it); } } } if(extend_out.size() != 0) { CliqueNode * newitem = new CliqueNode(); newitem->connected = extend; Dequeue(node_exist, newitem); if(data.size() > 0) { CliqueNode newitem2; newitem2.connected = extend_out; newitem2.prev = newitem; data.push_back(newitem2); } clique_builded.push_back(newitem); } else { CliqueNode * newitem = new CliqueNode(); newitem->connected = extend; Dequeue(node_exist, newitem); } } } //set up the head and remove all the duplicate node within the tree void CliqueTree::Eliminate() { if(clique_builded.size() > 0) { CliqueNode * find_head = clique_builded[0]; while(find_head->prev != NULL) { find_head = find_head->prev; } Head = find_head; EliminateDups(Head); clique_builded.clear(); data = save_data; } } //eliminate the duplicate with in clique tree // returns whether the previous node was eliminated // (because if it was, then the calling function needs to know!) bool CliqueTree::EliminateDups(CliqueNode * checking) { bool ret = false; if(checking != Head) { if(checking->connected.size() >= checking->prev->connected.size()) { if(includes(checking->connected.begin(), checking->connected.end(), checking->prev->connected.begin(), checking->prev->connected.end())) { EliminatePrev(checking); ret = true; } } } for(unsigned int i = 0; i < checking->next.size(); i++) { // if checking was removed, then I cannot continue // (after removal, other "nexts" will have been checked // by recursive call, so it is okay) if (EliminateDups(checking->next[i])) return ret; } return ret; } void CliqueTree::EliminatePrev(CliqueNode * current) { CliqueNode * delet = current->prev; if(delet == Head) { Head = current; for(unsigned int i = 0;i < delet->next.size(); i++) { if(delet->next[i] != current) { delet->next[i]->prev = current; current->next.push_back(delet->next[i]); } } current->prev = NULL; delete delet; } else { for(unsigned int i = 0;i < delet->next.size(); i++) { if(delet->next[i] != current) { current->next.push_back(delet->next[i]); delet->next[i]->prev = current; } } current->prev = delet->prev; //current->prev->next.push_back(current); for(unsigned int i = 0;i < current->prev->next.size(); i++) { if(current->prev->next[i] == delet) { current->prev->next[i] = current; } } delete delet; } } //remove the unnecessary node from the clique tree void CliqueTree::Dequeue(vector remove, CliqueNode * newitem) { for(int i = remove.size()-1; i > -1; i--) { if(data[remove[i]].prev != NULL) { newitem->next.push_back(data[remove[i]].prev); data[remove[i]].prev->prev = newitem; } if(&data[remove[i]].connected != &data[data.size()-1].connected) { data[remove[i]] = data[data.size()-1]; } data.pop_back(); } } //return the largest value to get the best tree double CliqueTree::MaxSize() const { return MaxSize(Head); } //return the maximum size in the tree by looking into the each node double CliqueTree::MaxSize(CliqueNode * place) const { double ret = 0; if(place) { int compare_size = 1; for(set ::iterator i = place->connected.begin(); i != place->connected.end(); ++i) { compare_size = compare_size * VarSize(*i); } ret = compare_size; for(unsigned int i = 0; i < place->next.size(); ++i) { double temp = MaxSize(place->next[i]); if (temp>ret) ret = temp; } } return ret; } //print the clique tree to stream os void CliqueTree::CliquePrint(ostream &os) { CliqueNodePrint(Head_compare, 0, os); } void CliqueTree::CliqueNodePrint(CliqueNode * print, int size, ostream &os) { int a = size; if(print) { size ++; for(unsigned int i = 0; i < print->next.size(); ++i) { CliqueNodePrint(print->next[i], size, os); } while(a > 0) { os << " "; a--; } if(print->prev != NULL) { prints(print->connected,os); os << "("; prints(print->prev->connected,os); os << ")" << endl << endl; } else { prints(print->connected,os); os << endl << endl; } } } void CliqueTree::prints(set print_out, ostream &os) const { for(set ::iterator i = print_out.begin(); i != print_out.end(); ++i) { os << *i << " "; } } //return the CliqueTree tree by CliqueTree::cliques; vector CliqueTree::ReturnCliques() const { vector ret; ReturnClique(Head_compare, 0, ret); return ret; } void CliqueTree::ReturnClique(CliqueNode * place, int parent, vector &ret) const { if(place) { InsertCliqueNode(place, parent, ret); int myspot = ret.size(); for(unsigned int i = 0; i < place->next.size(); ++i) { ReturnClique(place->next[i], myspot, ret); } } } void CliqueTree::InsertCliqueNode(CliqueNode * place, int parent, vector &ret) const { Node new_node; new_node.vars = Clique2Context(place); if(parent>0) { new_node.adj.push_back(parent-1); ret[parent-1].adj.push_back(ret.size()); } ret.push_back(new_node); } Context CliqueTree::Clique2Context(CliqueNode *node) const { Context ret; set::const_iterator e = node->connected.end(); for(set::const_iterator i=node->connected.begin();i!=e;++i) ret.AddVar(*i,VarSize(*i)); return ret; } void CliqueTree::Save(std::ostream &os) const { set::const_iterator it; for(unsigned int i = 0; i < data.size(); ++i) { for(it = data[i].connected.begin(); it != data[i].connected.end(); it++) { os << *it << os.fill(); } os << -1 <::iterator it; os << size << os.fill(); for(it = print->connected.begin(); it != print->connected.end(); it++) { os << *it << os.fill(); } os << -1 << os.fill(); size ++; for(unsigned int i = 0; i < print->next.size(); ++i) { CliqueSave(os, print->next[i], size); } } } void CliqueTree::Load(std::istream &is) { int get_out; int get_before; data.clear(); is >> get_out; while (get_out != -100) { CliqueNode newitem; while(get_out != -1 && get_out != -100) { newitem.AddVar(get_out); is >> get_out; } if(get_out != -100) { data.push_back(newitem); is >> get_out; } } CliqueNode * curr_pos; Destroy(Head_compare); int move_back; while(is >> get_out) { if(get_out != -1) { CliqueNode * newitem = new CliqueNode(); if(get_out == 0) { get_before = get_out; while(is >> get_out) { if(get_out == -1) break; newitem->AddVar(get_out); } Head_compare = newitem; curr_pos = Head_compare; } else { if(get_before >= get_out) { move_back = get_before - get_out + 1; for(int i = 0; i < move_back; i++) { curr_pos = curr_pos ->prev; } } get_before = get_out; while(is >> get_out) { if(get_out == -1) break; newitem->AddVar(get_out); } curr_pos->next.push_back(newitem); newitem ->prev = curr_pos; curr_pos = newitem; } } } } } // end of ctbn namespace ctbnrle.1.0.2/COPYING0000644000464200007770000010451311344302767014305 0ustar csheltoncsprofs GNU GENERAL PUBLIC LICENSE Version 3, 29 June 2007 Copyright (C) 2007 Free Software Foundation, Inc. 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Notwithstanding any other provision of this License, for material you add to a covered work, you may (if authorized by the copyright holders of that material) supplement the terms of this License with terms: a) Disclaiming warranty or limiting liability differently from the terms of sections 15 and 16 of this License; or b) Requiring preservation of specified reasonable legal notices or author attributions in that material or in the Appropriate Legal Notices displayed by works containing it; or c) Prohibiting misrepresentation of the origin of that material, or requiring that modified versions of such material be marked in reasonable ways as different from the original version; or d) Limiting the use for publicity purposes of names of licensors or authors of the material; or e) Declining to grant rights under trademark law for use of some trade names, trademarks, or service marks; or f) Requiring indemnification of licensors and authors of that material by anyone who conveys the material (or modified versions of it) with contractual assumptions of liability to the recipient, for any liability that these contractual assumptions directly impose on those licensors and authors. All other non-permissive additional terms are considered "further restrictions" within the meaning of section 10. If the Program as you received it, or any part of it, contains a notice stating that it is governed by this License along with a term that is a further restriction, you may remove that term. If a license document contains a further restriction but permits relicensing or conveying under this License, you may add to a covered work material governed by the terms of that license document, provided that the further restriction does not survive such relicensing or conveying. If you add terms to a covered work in accord with this section, you must place, in the relevant source files, a statement of the additional terms that apply to those files, or a notice indicating where to find the applicable terms. Additional terms, permissive or non-permissive, may be stated in the form of a separately written license, or stated as exceptions; the above requirements apply either way. 8. Termination. You may not propagate or modify a covered work except as expressly provided under this License. Any attempt otherwise to propagate or modify it is void, and will automatically terminate your rights under this License (including any patent licenses granted under the third paragraph of section 11). However, if you cease all violation of this License, then your license from a particular copyright holder is reinstated (a) provisionally, unless and until the copyright holder explicitly and finally terminates your license, and (b) permanently, if the copyright holder fails to notify you of the violation by some reasonable means prior to 60 days after the cessation. Moreover, your license from a particular copyright holder is reinstated permanently if the copyright holder notifies you of the violation by some reasonable means, this is the first time you have received notice of violation of this License (for any work) from that copyright holder, and you cure the violation prior to 30 days after your receipt of the notice. Termination of your rights under this section does not terminate the licenses of parties who have received copies or rights from you under this License. If your rights have been terminated and not permanently reinstated, you do not qualify to receive new licenses for the same material under section 10. 9. Acceptance Not Required for Having Copies. You are not required to accept this License in order to receive or run a copy of the Program. Ancillary propagation of a covered work occurring solely as a consequence of using peer-to-peer transmission to receive a copy likewise does not require acceptance. However, nothing other than this License grants you permission to propagate or modify any covered work. These actions infringe copyright if you do not accept this License. Therefore, by modifying or propagating a covered work, you indicate your acceptance of this License to do so. 10. Automatic Licensing of Downstream Recipients. Each time you convey a covered work, the recipient automatically receives a license from the original licensors, to run, modify and propagate that work, subject to this License. You are not responsible for enforcing compliance by third parties with this License. An "entity transaction" is a transaction transferring control of an organization, or substantially all assets of one, or subdividing an organization, or merging organizations. If propagation of a covered work results from an entity transaction, each party to that transaction who receives a copy of the work also receives whatever licenses to the work the party's predecessor in interest had or could give under the previous paragraph, plus a right to possession of the Corresponding Source of the work from the predecessor in interest, if the predecessor has it or can get it with reasonable efforts. You may not impose any further restrictions on the exercise of the rights granted or affirmed under this License. For example, you may not impose a license fee, royalty, or other charge for exercise of rights granted under this License, and you may not initiate litigation (including a cross-claim or counterclaim in a lawsuit) alleging that any patent claim is infringed by making, using, selling, offering for sale, or importing the Program or any portion of it. 11. Patents. A "contributor" is a copyright holder who authorizes use under this License of the Program or a work on which the Program is based. The work thus licensed is called the contributor's "contributor version". A contributor's "essential patent claims" are all patent claims owned or controlled by the contributor, whether already acquired or hereafter acquired, that would be infringed by some manner, permitted by this License, of making, using, or selling its contributor version, but do not include claims that would be infringed only as a consequence of further modification of the contributor version. For purposes of this definition, "control" includes the right to grant patent sublicenses in a manner consistent with the requirements of this License. Each contributor grants you a non-exclusive, worldwide, royalty-free patent license under the contributor's essential patent claims, to make, use, sell, offer for sale, import and otherwise run, modify and propagate the contents of its contributor version. In the following three paragraphs, a "patent license" is any express agreement or commitment, however denominated, not to enforce a patent (such as an express permission to practice a patent or covenant not to sue for patent infringement). To "grant" such a patent license to a party means to make such an agreement or commitment not to enforce a patent against the party. If you convey a covered work, knowingly relying on a patent license, and the Corresponding Source of the work is not available for anyone to copy, free of charge and under the terms of this License, through a publicly available network server or other readily accessible means, then you must either (1) cause the Corresponding Source to be so available, or (2) arrange to deprive yourself of the benefit of the patent license for this particular work, or (3) arrange, in a manner consistent with the requirements of this License, to extend the patent license to downstream recipients. "Knowingly relying" means you have actual knowledge that, but for the patent license, your conveying the covered work in a country, or your recipient's use of the covered work in a country, would infringe one or more identifiable patents in that country that you have reason to believe are valid. If, pursuant to or in connection with a single transaction or arrangement, you convey, or propagate by procuring conveyance of, a covered work, and grant a patent license to some of the parties receiving the covered work authorizing them to use, propagate, modify or convey a specific copy of the covered work, then the patent license you grant is automatically extended to all recipients of the covered work and works based on it. A patent license is "discriminatory" if it does not include within the scope of its coverage, prohibits the exercise of, or is conditioned on the non-exercise of one or more of the rights that are specifically granted under this License. You may not convey a covered work if you are a party to an arrangement with a third party that is in the business of distributing software, under which you make payment to the third party based on the extent of your activity of conveying the work, and under which the third party grants, to any of the parties who would receive the covered work from you, a discriminatory patent license (a) in connection with copies of the covered work conveyed by you (or copies made from those copies), or (b) primarily for and in connection with specific products or compilations that contain the covered work, unless you entered into that arrangement, or that patent license was granted, prior to 28 March 2007. Nothing in this License shall be construed as excluding or limiting any implied license or other defenses to infringement that may otherwise be available to you under applicable patent law. 12. No Surrender of Others' Freedom. If conditions are imposed on you (whether by court order, agreement or otherwise) that contradict the conditions of this License, they do not excuse you from the conditions of this License. If you cannot convey a covered work so as to satisfy simultaneously your obligations under this License and any other pertinent obligations, then as a consequence you may not convey it at all. For example, if you agree to terms that obligate you to collect a royalty for further conveying from those to whom you convey the Program, the only way you could satisfy both those terms and this License would be to refrain entirely from conveying the Program. 13. Use with the GNU Affero General Public License. Notwithstanding any other provision of this License, you have permission to link or combine any covered work with a work licensed under version 3 of the GNU Affero General Public License into a single combined work, and to convey the resulting work. The terms of this License will continue to apply to the part which is the covered work, but the special requirements of the GNU Affero General Public License, section 13, concerning interaction through a network will apply to the combination as such. 14. Revised Versions of this License. The Free Software Foundation may publish revised and/or new versions of the GNU General Public License from time to time. Such new versions will be similar in spirit to the present version, but may differ in detail to address new problems or concerns. Each version is given a distinguishing version number. If the Program specifies that a certain numbered version of the GNU General Public License "or any later version" applies to it, you have the option of following the terms and conditions either of that numbered version or of any later version published by the Free Software Foundation. If the Program does not specify a version number of the GNU General Public License, you may choose any version ever published by the Free Software Foundation. If the Program specifies that a proxy can decide which future versions of the GNU General Public License can be used, that proxy's public statement of acceptance of a version permanently authorizes you to choose that version for the Program. Later license versions may give you additional or different permissions. However, no additional obligations are imposed on any author or copyright holder as a result of your choosing to follow a later version. 15. Disclaimer of Warranty. THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING, REPAIR OR CORRECTION. 16. Limitation of Liability. IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. 17. Interpretation of Sections 15 and 16. If the disclaimer of warranty and limitation of liability provided above cannot be given local legal effect according to their terms, reviewing courts shall apply local law that most closely approximates an absolute waiver of all civil liability in connection with the Program, unless a warranty or assumption of liability accompanies a copy of the Program in return for a fee. END OF TERMS AND CONDITIONS How to Apply These Terms to Your New Programs If you develop a new program, and you want it to be of the greatest possible use to the public, the best way to achieve this is to make it free software which everyone can redistribute and change under these terms. To do so, attach the following notices to the program. It is safest to attach them to the start of each source file to most effectively state the exclusion of warranty; and each file should have at least the "copyright" line and a pointer to where the full notice is found. Copyright (C) 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 . Also add information on how to contact you by electronic and paper mail. If the program does terminal interaction, make it output a short notice like this when it starts in an interactive mode: Copyright (C) This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. This is free software, and you are welcome to redistribute it under certain conditions; type `show c' for details. The hypothetical commands `show w' and `show c' should show the appropriate parts of the General Public License. Of course, your program's commands might be different; for a GUI interface, you would use an "about box". You should also get your employer (if you work as a programmer) or school, if any, to sign a "copyright disclaimer" for the program, if necessary. For more information on this, and how to apply and follow the GNU GPL, see . The GNU General Public License does not permit incorporating your program into proprietary programs. If your program is a subroutine library, you may consider it more useful to permit linking proprietary applications with the library. If this is what you want to do, use the GNU Lesser General Public License instead of this License. But first, please read .