diff --git a/CMakeLists.txt b/CMakeLists.txt index 152447e..ffaabd6 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -88,4 +88,18 @@ install(TARGETS BayesNet LIBRARY DESTINATION lib CONFIGURATIONS Release) install(DIRECTORY bayesnet/ DESTINATION include/bayesnet FILES_MATCHING CONFIGURATIONS Release PATTERN "*.h") -install(FILES ${CMAKE_BINARY_DIR}/configured_files/include/bayesnet/config.h DESTINATION include/bayesnet CONFIGURATIONS Release) \ No newline at end of file +install(FILES ${CMAKE_BINARY_DIR}/configured_files/include/bayesnet/config.h DESTINATION include/bayesnet CONFIGURATIONS Release) + +# Documentation +# ------------- +set(doxyfile_in ${CMAKE_CURRENT_SOURCE_DIR}/doxygen/Doxyfile.in) +set(doxyfile ${CMAKE_CURRENT_BINARY_DIR}/Doxyfile) +configure_file(${doxyfile_in} ${doxyfile} @ONLY) + +# doc build only target, target is not in default build, so it must be +# triggered explicitly +add_custom_target(doc + COMMAND ${DOXYGEN_EXECUTABLE} ${doxyfile} + WORKING_DIRECTORY ${PROJECT_SOURCE_DIR} + COMMENT "Generating API documentation with Doxygen" + VERBATIM) \ No newline at end of file diff --git a/docs/Doxyfile b/docs/Doxyfile.in similarity index 100% rename from docs/Doxyfile rename to docs/Doxyfile.in diff --git a/docs/man3/bayesnet_A2DE.3 b/docs/man3/bayesnet_A2DE.3 deleted file mode 100644 index 6a1593f..0000000 --- a/docs/man3/bayesnet_A2DE.3 +++ /dev/null @@ -1,257 +0,0 @@ -.TH "bayesnet::A2DE" 3 "Version 1.0.5" "BayesNet" \" -*- nroff -*- -.ad l -.nh -.SH NAME -bayesnet::A2DE -.SH SYNOPSIS -.br -.PP -.PP -Inherits \fBbayesnet::Ensemble\fP\&. -.SS "Public Member Functions" - -.in +1c -.ti -1c -.RI "\fBA2DE\fP (bool predict_voting=false)" -.br -.ti -1c -.RI "void \fBsetHyperparameters\fP (const nlohmann::json &hyperparameters) override" -.br -.ti -1c -.RI "std::vector< std::string > \fBgraph\fP (const std::string &title='A2DE') const override" -.br -.in -1c - -Public Member Functions inherited from \fBbayesnet::Ensemble\fP -.in +1c -.ti -1c -.RI "\fBEnsemble\fP (bool predict_voting=true)" -.br -.ti -1c -.RI "torch::Tensor \fBpredict\fP (torch::Tensor &X) override" -.br -.ti -1c -.RI "std::vector< int > \fBpredict\fP (std::vector< std::vector< int > > &X) override" -.br -.ti -1c -.RI "torch::Tensor \fBpredict_proba\fP (torch::Tensor &X) override" -.br -.ti -1c -.RI "std::vector< std::vector< double > > \fBpredict_proba\fP (std::vector< std::vector< int > > &X) override" -.br -.ti -1c -.RI "float \fBscore\fP (torch::Tensor &X, torch::Tensor &y) override" -.br -.ti -1c -.RI "float \fBscore\fP (std::vector< std::vector< int > > &X, std::vector< int > &y) override" -.br -.ti -1c -.RI "int \fBgetNumberOfNodes\fP () const override" -.br -.ti -1c -.RI "int \fBgetNumberOfEdges\fP () const override" -.br -.ti -1c -.RI "int \fBgetNumberOfStates\fP () const override" -.br -.ti -1c -.RI "std::vector< std::string > \fBshow\fP () const override" -.br -.ti -1c -.RI "std::vector< std::string > \fBgraph\fP (const std::string &title) const override" -.br -.ti -1c -.RI "std::vector< std::string > \fBtopological_order\fP () override" -.br -.ti -1c -.RI "std::string \fBdump_cpt\fP () const override" -.br -.in -1c - -Public Member Functions inherited from \fBbayesnet::Classifier\fP -.in +1c -.ti -1c -.RI "\fBClassifier\fP (\fBNetwork\fP model)" -.br -.ti -1c -.RI "\fBClassifier\fP & \fBfit\fP (std::vector< std::vector< int > > &X, std::vector< int > &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override" -.br -.ti -1c -.RI "\fBClassifier\fP & \fBfit\fP (torch::Tensor &X, torch::Tensor &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override" -.br -.ti -1c -.RI "\fBClassifier\fP & \fBfit\fP (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override" -.br -.ti -1c -.RI "\fBClassifier\fP & \fBfit\fP (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states, const torch::Tensor &weights) override" -.br -.ti -1c -.RI "void \fBaddNodes\fP ()" -.br -.ti -1c -.RI "int \fBgetClassNumStates\fP () const override" -.br -.ti -1c -.RI "status_t \fBgetStatus\fP () const override" -.br -.ti -1c -.RI "std::string \fBgetVersion\fP () override" -.br -.ti -1c -.RI "std::vector< std::string > \fBgetNotes\fP () const override" -.br -.ti -1c -.RI "void \fBsetHyperparameters\fP (const nlohmann::json &hyperparameters) override" -.br -.in -1c - -Public Member Functions inherited from \fBbayesnet::BaseClassifier\fP -.in +1c -.ti -1c -.RI "std::vector< std::string > & \fBgetValidHyperparameters\fP ()" -.br -.in -1c -.SS "Protected Member Functions" - -.in +1c -.ti -1c -.RI "void \fBbuildModel\fP (const torch::Tensor &weights) override" -.br -.in -1c - -Protected Member Functions inherited from \fBbayesnet::Ensemble\fP -.in +1c -.ti -1c -.RI "torch::Tensor \fBpredict_average_voting\fP (torch::Tensor &X)" -.br -.ti -1c -.RI "std::vector< std::vector< double > > \fBpredict_average_voting\fP (std::vector< std::vector< int > > &X)" -.br -.ti -1c -.RI "torch::Tensor \fBpredict_average_proba\fP (torch::Tensor &X)" -.br -.ti -1c -.RI "std::vector< std::vector< double > > \fBpredict_average_proba\fP (std::vector< std::vector< int > > &X)" -.br -.ti -1c -.RI "torch::Tensor \fBcompute_arg_max\fP (torch::Tensor &X)" -.br -.ti -1c -.RI "std::vector< int > \fBcompute_arg_max\fP (std::vector< std::vector< double > > &X)" -.br -.ti -1c -.RI "torch::Tensor \fBvoting\fP (torch::Tensor &votes)" -.br -.ti -1c -.RI "void \fBtrainModel\fP (const torch::Tensor &weights) override" -.br -.in -1c - -Protected Member Functions inherited from \fBbayesnet::Classifier\fP -.in +1c -.ti -1c -.RI "void \fBcheckFitParameters\fP ()" -.br -.ti -1c -.RI "void \fBbuildDataset\fP (torch::Tensor &y)" -.br -.in -1c -.SS "Additional Inherited Members" - - -Protected Attributes inherited from \fBbayesnet::Ensemble\fP -.in +1c -.ti -1c -.RI "unsigned \fBn_models\fP" -.br -.ti -1c -.RI "std::vector< std::unique_ptr< \fBClassifier\fP > > \fBmodels\fP" -.br -.ti -1c -.RI "std::vector< double > \fBsignificanceModels\fP" -.br -.ti -1c -.RI "bool \fBpredict_voting\fP" -.br -.in -1c - -Protected Attributes inherited from \fBbayesnet::Classifier\fP -.in +1c -.ti -1c -.RI "bool \fBfitted\fP" -.br -.ti -1c -.RI "unsigned int \fBm\fP" -.br -.ti -1c -.RI "unsigned int \fBn\fP" -.br -.ti -1c -.RI "\fBNetwork\fP \fBmodel\fP" -.br -.ti -1c -.RI "Metrics \fBmetrics\fP" -.br -.ti -1c -.RI "std::vector< std::string > \fBfeatures\fP" -.br -.ti -1c -.RI "std::string \fBclassName\fP" -.br -.ti -1c -.RI "std::map< std::string, std::vector< int > > \fBstates\fP" -.br -.ti -1c -.RI "torch::Tensor \fBdataset\fP" -.br -.ti -1c -.RI "status_t \fBstatus\fP = NORMAL" -.br -.ti -1c -.RI "std::vector< std::string > \fBnotes\fP" -.br -.in -1c - -Protected Attributes inherited from \fBbayesnet::BaseClassifier\fP -.in +1c -.ti -1c -.RI "std::vector< std::string > \fBvalidHyperparameters\fP" -.br -.in -1c -.SH "Detailed Description" -.PP -Definition at line \fB12\fP of file \fBA2DE\&.h\fP\&. -.SH "Constructor & Destructor Documentation" -.PP -.SS "bayesnet::A2DE::A2DE (bool predict_voting = \fRfalse\fP)" - -.PP -Definition at line \fB10\fP of file \fBA2DE\&.cc\fP\&. -.SS "virtual bayesnet::A2DE::~A2DE ()\fR [inline]\fP, \fR [virtual]\fP" - -.PP -Definition at line \fB15\fP of file \fBA2DE\&.h\fP\&. -.SH "Member Function Documentation" -.PP -.SS "void bayesnet::A2DE::buildModel (const torch::Tensor & weights)\fR [override]\fP, \fR [protected]\fP, \fR [virtual]\fP" - -.PP -Implements \fBbayesnet::Classifier\fP\&. -.PP -Definition at line \fB23\fP of file \fBA2DE\&.cc\fP\&. -.SS "std::vector< std::string > bayesnet::A2DE::graph (const std::string & title = \fR'A2DE'\fP) const\fR [override]\fP, \fR [virtual]\fP" - -.PP -Implements \fBbayesnet::BaseClassifier\fP\&. -.PP -Definition at line \fB36\fP of file \fBA2DE\&.cc\fP\&. -.SS "void bayesnet::A2DE::setHyperparameters (const nlohmann::json & hyperparameters)\fR [override]\fP, \fR [virtual]\fP" - -.PP -Implements \fBbayesnet::BaseClassifier\fP\&. -.PP -Definition at line \fB14\fP of file \fBA2DE\&.cc\fP\&. - -.SH "Author" -.PP -Generated automatically by Doxygen for BayesNet from the source code\&. diff --git a/docs/man3/bayesnet_AODE.3 b/docs/man3/bayesnet_AODE.3 deleted file mode 100644 index 6894802..0000000 --- a/docs/man3/bayesnet_AODE.3 +++ /dev/null @@ -1,257 +0,0 @@ -.TH "bayesnet::AODE" 3 "Version 1.0.5" "BayesNet" \" -*- nroff -*- -.ad l -.nh -.SH NAME -bayesnet::AODE -.SH SYNOPSIS -.br -.PP -.PP -Inherits \fBbayesnet::Ensemble\fP\&. -.SS "Public Member Functions" - -.in +1c -.ti -1c -.RI "\fBAODE\fP (bool predict_voting=false)" -.br -.ti -1c -.RI "void \fBsetHyperparameters\fP (const nlohmann::json &hyperparameters) override" -.br -.ti -1c -.RI "std::vector< std::string > \fBgraph\fP (const std::string &title='AODE') const override" -.br -.in -1c - -Public Member Functions inherited from \fBbayesnet::Ensemble\fP -.in +1c -.ti -1c -.RI "\fBEnsemble\fP (bool predict_voting=true)" -.br -.ti -1c -.RI "torch::Tensor \fBpredict\fP (torch::Tensor &X) override" -.br -.ti -1c -.RI "std::vector< int > \fBpredict\fP (std::vector< std::vector< int > > &X) override" -.br -.ti -1c -.RI "torch::Tensor \fBpredict_proba\fP (torch::Tensor &X) override" -.br -.ti -1c -.RI "std::vector< std::vector< double > > \fBpredict_proba\fP (std::vector< std::vector< int > > &X) override" -.br -.ti -1c -.RI "float \fBscore\fP (torch::Tensor &X, torch::Tensor &y) override" -.br -.ti -1c -.RI "float \fBscore\fP (std::vector< std::vector< int > > &X, std::vector< int > &y) override" -.br -.ti -1c -.RI "int \fBgetNumberOfNodes\fP () const override" -.br -.ti -1c -.RI "int \fBgetNumberOfEdges\fP () const override" -.br -.ti -1c -.RI "int \fBgetNumberOfStates\fP () const override" -.br -.ti -1c -.RI "std::vector< std::string > \fBshow\fP () const override" -.br -.ti -1c -.RI "std::vector< std::string > \fBgraph\fP (const std::string &title) const override" -.br -.ti -1c -.RI "std::vector< std::string > \fBtopological_order\fP () override" -.br -.ti -1c -.RI "std::string \fBdump_cpt\fP () const override" -.br -.in -1c - -Public Member Functions inherited from \fBbayesnet::Classifier\fP -.in +1c -.ti -1c -.RI "\fBClassifier\fP (\fBNetwork\fP model)" -.br -.ti -1c -.RI "\fBClassifier\fP & \fBfit\fP (std::vector< std::vector< int > > &X, std::vector< int > &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override" -.br -.ti -1c -.RI "\fBClassifier\fP & \fBfit\fP (torch::Tensor &X, torch::Tensor &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override" -.br -.ti -1c -.RI "\fBClassifier\fP & \fBfit\fP (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override" -.br -.ti -1c -.RI "\fBClassifier\fP & \fBfit\fP (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states, const torch::Tensor &weights) override" -.br -.ti -1c -.RI "void \fBaddNodes\fP ()" -.br -.ti -1c -.RI "int \fBgetClassNumStates\fP () const override" -.br -.ti -1c -.RI "status_t \fBgetStatus\fP () const override" -.br -.ti -1c -.RI "std::string \fBgetVersion\fP () override" -.br -.ti -1c -.RI "std::vector< std::string > \fBgetNotes\fP () const override" -.br -.ti -1c -.RI "void \fBsetHyperparameters\fP (const nlohmann::json &hyperparameters) override" -.br -.in -1c - -Public Member Functions inherited from \fBbayesnet::BaseClassifier\fP -.in +1c -.ti -1c -.RI "std::vector< std::string > & \fBgetValidHyperparameters\fP ()" -.br -.in -1c -.SS "Protected Member Functions" - -.in +1c -.ti -1c -.RI "void \fBbuildModel\fP (const torch::Tensor &weights) override" -.br -.in -1c - -Protected Member Functions inherited from \fBbayesnet::Ensemble\fP -.in +1c -.ti -1c -.RI "torch::Tensor \fBpredict_average_voting\fP (torch::Tensor &X)" -.br -.ti -1c -.RI "std::vector< std::vector< double > > \fBpredict_average_voting\fP (std::vector< std::vector< int > > &X)" -.br -.ti -1c -.RI "torch::Tensor \fBpredict_average_proba\fP (torch::Tensor &X)" -.br -.ti -1c -.RI "std::vector< std::vector< double > > \fBpredict_average_proba\fP (std::vector< std::vector< int > > &X)" -.br -.ti -1c -.RI "torch::Tensor \fBcompute_arg_max\fP (torch::Tensor &X)" -.br -.ti -1c -.RI "std::vector< int > \fBcompute_arg_max\fP (std::vector< std::vector< double > > &X)" -.br -.ti -1c -.RI "torch::Tensor \fBvoting\fP (torch::Tensor &votes)" -.br -.ti -1c -.RI "void \fBtrainModel\fP (const torch::Tensor &weights) override" -.br -.in -1c - -Protected Member Functions inherited from \fBbayesnet::Classifier\fP -.in +1c -.ti -1c -.RI "void \fBcheckFitParameters\fP ()" -.br -.ti -1c -.RI "void \fBbuildDataset\fP (torch::Tensor &y)" -.br -.in -1c -.SS "Additional Inherited Members" - - -Protected Attributes inherited from \fBbayesnet::Ensemble\fP -.in +1c -.ti -1c -.RI "unsigned \fBn_models\fP" -.br -.ti -1c -.RI "std::vector< std::unique_ptr< \fBClassifier\fP > > \fBmodels\fP" -.br -.ti -1c -.RI "std::vector< double > \fBsignificanceModels\fP" -.br -.ti -1c -.RI "bool \fBpredict_voting\fP" -.br -.in -1c - -Protected Attributes inherited from \fBbayesnet::Classifier\fP -.in +1c -.ti -1c -.RI "bool \fBfitted\fP" -.br -.ti -1c -.RI "unsigned int \fBm\fP" -.br -.ti -1c -.RI "unsigned int \fBn\fP" -.br -.ti -1c -.RI "\fBNetwork\fP \fBmodel\fP" -.br -.ti -1c -.RI "Metrics \fBmetrics\fP" -.br -.ti -1c -.RI "std::vector< std::string > \fBfeatures\fP" -.br -.ti -1c -.RI "std::string \fBclassName\fP" -.br -.ti -1c -.RI "std::map< std::string, std::vector< int > > \fBstates\fP" -.br -.ti -1c -.RI "torch::Tensor \fBdataset\fP" -.br -.ti -1c -.RI "status_t \fBstatus\fP = NORMAL" -.br -.ti -1c -.RI "std::vector< std::string > \fBnotes\fP" -.br -.in -1c - -Protected Attributes inherited from \fBbayesnet::BaseClassifier\fP -.in +1c -.ti -1c -.RI "std::vector< std::string > \fBvalidHyperparameters\fP" -.br -.in -1c -.SH "Detailed Description" -.PP -Definition at line \fB12\fP of file \fBAODE\&.h\fP\&. -.SH "Constructor & Destructor Documentation" -.PP -.SS "bayesnet::AODE::AODE (bool predict_voting = \fRfalse\fP)" - -.PP -Definition at line \fB10\fP of file \fBAODE\&.cc\fP\&. -.SS "virtual bayesnet::AODE::~AODE ()\fR [inline]\fP, \fR [virtual]\fP" - -.PP -Definition at line \fB15\fP of file \fBAODE\&.h\fP\&. -.SH "Member Function Documentation" -.PP -.SS "void bayesnet::AODE::buildModel (const torch::Tensor & weights)\fR [override]\fP, \fR [protected]\fP, \fR [virtual]\fP" - -.PP -Implements \fBbayesnet::Classifier\fP\&. -.PP -Definition at line \fB24\fP of file \fBAODE\&.cc\fP\&. -.SS "std::vector< std::string > bayesnet::AODE::graph (const std::string & title = \fR'AODE'\fP) const\fR [override]\fP, \fR [virtual]\fP" - -.PP -Implements \fBbayesnet::BaseClassifier\fP\&. -.PP -Definition at line \fB34\fP of file \fBAODE\&.cc\fP\&. -.SS "void bayesnet::AODE::setHyperparameters (const nlohmann::json & hyperparameters)\fR [override]\fP, \fR [virtual]\fP" - -.PP -Implements \fBbayesnet::BaseClassifier\fP\&. -.PP -Definition at line \fB15\fP of file \fBAODE\&.cc\fP\&. - -.SH "Author" -.PP -Generated automatically by Doxygen for BayesNet from the source code\&. diff --git a/docs/man3/bayesnet_AODELd.3 b/docs/man3/bayesnet_AODELd.3 deleted file mode 100644 index a684913..0000000 --- a/docs/man3/bayesnet_AODELd.3 +++ /dev/null @@ -1,296 +0,0 @@ -.TH "bayesnet::AODELd" 3 "Version 1.0.5" "BayesNet" \" -*- nroff -*- -.ad l -.nh -.SH NAME -bayesnet::AODELd -.SH SYNOPSIS -.br -.PP -.PP -Inherits \fBbayesnet::Ensemble\fP, and \fBbayesnet::Proposal\fP\&. -.SS "Public Member Functions" - -.in +1c -.ti -1c -.RI "\fBAODELd\fP (bool predict_voting=true)" -.br -.ti -1c -.RI "\fBAODELd\fP & \fBfit\fP (torch::Tensor &X_, torch::Tensor &y_, const std::vector< std::string > &features_, const std::string &className_, map< std::string, std::vector< int > > &states_) override" -.br -.ti -1c -.RI "std::vector< std::string > \fBgraph\fP (const std::string &name='AODELd') const override" -.br -.in -1c - -Public Member Functions inherited from \fBbayesnet::Ensemble\fP -.in +1c -.ti -1c -.RI "\fBEnsemble\fP (bool predict_voting=true)" -.br -.ti -1c -.RI "torch::Tensor \fBpredict\fP (torch::Tensor &X) override" -.br -.ti -1c -.RI "std::vector< int > \fBpredict\fP (std::vector< std::vector< int > > &X) override" -.br -.ti -1c -.RI "torch::Tensor \fBpredict_proba\fP (torch::Tensor &X) override" -.br -.ti -1c -.RI "std::vector< std::vector< double > > \fBpredict_proba\fP (std::vector< std::vector< int > > &X) override" -.br -.ti -1c -.RI "float \fBscore\fP (torch::Tensor &X, torch::Tensor &y) override" -.br -.ti -1c -.RI "float \fBscore\fP (std::vector< std::vector< int > > &X, std::vector< int > &y) override" -.br -.ti -1c -.RI "int \fBgetNumberOfNodes\fP () const override" -.br -.ti -1c -.RI "int \fBgetNumberOfEdges\fP () const override" -.br -.ti -1c -.RI "int \fBgetNumberOfStates\fP () const override" -.br -.ti -1c -.RI "std::vector< std::string > \fBshow\fP () const override" -.br -.ti -1c -.RI "std::vector< std::string > \fBgraph\fP (const std::string &title) const override" -.br -.ti -1c -.RI "std::vector< std::string > \fBtopological_order\fP () override" -.br -.ti -1c -.RI "std::string \fBdump_cpt\fP () const override" -.br -.in -1c - -Public Member Functions inherited from \fBbayesnet::Classifier\fP -.in +1c -.ti -1c -.RI "\fBClassifier\fP (\fBNetwork\fP model)" -.br -.ti -1c -.RI "\fBClassifier\fP & \fBfit\fP (std::vector< std::vector< int > > &X, std::vector< int > &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override" -.br -.ti -1c -.RI "\fBClassifier\fP & \fBfit\fP (torch::Tensor &X, torch::Tensor &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override" -.br -.ti -1c -.RI "\fBClassifier\fP & \fBfit\fP (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override" -.br -.ti -1c -.RI "\fBClassifier\fP & \fBfit\fP (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states, const torch::Tensor &weights) override" -.br -.ti -1c -.RI "void \fBaddNodes\fP ()" -.br -.ti -1c -.RI "int \fBgetClassNumStates\fP () const override" -.br -.ti -1c -.RI "status_t \fBgetStatus\fP () const override" -.br -.ti -1c -.RI "std::string \fBgetVersion\fP () override" -.br -.ti -1c -.RI "std::vector< std::string > \fBgetNotes\fP () const override" -.br -.ti -1c -.RI "void \fBsetHyperparameters\fP (const nlohmann::json &hyperparameters) override" -.br -.in -1c - -Public Member Functions inherited from \fBbayesnet::BaseClassifier\fP -.in +1c -.ti -1c -.RI "std::vector< std::string > & \fBgetValidHyperparameters\fP ()" -.br -.in -1c - -Public Member Functions inherited from \fBbayesnet::Proposal\fP -.in +1c -.ti -1c -.RI "\fBProposal\fP (torch::Tensor &pDataset, std::vector< std::string > &features_, std::string &className_)" -.br -.in -1c -.SS "Protected Member Functions" - -.in +1c -.ti -1c -.RI "void \fBtrainModel\fP (const torch::Tensor &weights) override" -.br -.ti -1c -.RI "void \fBbuildModel\fP (const torch::Tensor &weights) override" -.br -.in -1c - -Protected Member Functions inherited from \fBbayesnet::Ensemble\fP -.in +1c -.ti -1c -.RI "torch::Tensor \fBpredict_average_voting\fP (torch::Tensor &X)" -.br -.ti -1c -.RI "std::vector< std::vector< double > > \fBpredict_average_voting\fP (std::vector< std::vector< int > > &X)" -.br -.ti -1c -.RI "torch::Tensor \fBpredict_average_proba\fP (torch::Tensor &X)" -.br -.ti -1c -.RI "std::vector< std::vector< double > > \fBpredict_average_proba\fP (std::vector< std::vector< int > > &X)" -.br -.ti -1c -.RI "torch::Tensor \fBcompute_arg_max\fP (torch::Tensor &X)" -.br -.ti -1c -.RI "std::vector< int > \fBcompute_arg_max\fP (std::vector< std::vector< double > > &X)" -.br -.ti -1c -.RI "torch::Tensor \fBvoting\fP (torch::Tensor &votes)" -.br -.ti -1c -.RI "void \fBtrainModel\fP (const torch::Tensor &weights) override" -.br -.in -1c - -Protected Member Functions inherited from \fBbayesnet::Classifier\fP -.in +1c -.ti -1c -.RI "void \fBcheckFitParameters\fP ()" -.br -.ti -1c -.RI "void \fBbuildDataset\fP (torch::Tensor &y)" -.br -.in -1c - -Protected Member Functions inherited from \fBbayesnet::Proposal\fP -.in +1c -.ti -1c -.RI "void \fBcheckInput\fP (const torch::Tensor &X, const torch::Tensor &y)" -.br -.ti -1c -.RI "torch::Tensor \fBprepareX\fP (torch::Tensor &X)" -.br -.ti -1c -.RI "map< std::string, std::vector< int > > \fBlocalDiscretizationProposal\fP (const map< std::string, std::vector< int > > &states, \fBNetwork\fP &model)" -.br -.ti -1c -.RI "map< std::string, std::vector< int > > \fBfit_local_discretization\fP (const torch::Tensor &y)" -.br -.in -1c -.SS "Additional Inherited Members" - - -Protected Attributes inherited from \fBbayesnet::Ensemble\fP -.in +1c -.ti -1c -.RI "unsigned \fBn_models\fP" -.br -.ti -1c -.RI "std::vector< std::unique_ptr< \fBClassifier\fP > > \fBmodels\fP" -.br -.ti -1c -.RI "std::vector< double > \fBsignificanceModels\fP" -.br -.ti -1c -.RI "bool \fBpredict_voting\fP" -.br -.in -1c - -Protected Attributes inherited from \fBbayesnet::Classifier\fP -.in +1c -.ti -1c -.RI "bool \fBfitted\fP" -.br -.ti -1c -.RI "unsigned int \fBm\fP" -.br -.ti -1c -.RI "unsigned int \fBn\fP" -.br -.ti -1c -.RI "\fBNetwork\fP \fBmodel\fP" -.br -.ti -1c -.RI "Metrics \fBmetrics\fP" -.br -.ti -1c -.RI "std::vector< std::string > \fBfeatures\fP" -.br -.ti -1c -.RI "std::string \fBclassName\fP" -.br -.ti -1c -.RI "std::map< std::string, std::vector< int > > \fBstates\fP" -.br -.ti -1c -.RI "torch::Tensor \fBdataset\fP" -.br -.ti -1c -.RI "status_t \fBstatus\fP = NORMAL" -.br -.ti -1c -.RI "std::vector< std::string > \fBnotes\fP" -.br -.in -1c - -Protected Attributes inherited from \fBbayesnet::BaseClassifier\fP -.in +1c -.ti -1c -.RI "std::vector< std::string > \fBvalidHyperparameters\fP" -.br -.in -1c - -Protected Attributes inherited from \fBbayesnet::Proposal\fP -.in +1c -.ti -1c -.RI "torch::Tensor \fBXf\fP" -.br -.ti -1c -.RI "torch::Tensor \fBy\fP" -.br -.ti -1c -.RI "map< std::string, mdlp::CPPFImdlp * > \fBdiscretizers\fP" -.br -.in -1c -.SH "Detailed Description" -.PP -Definition at line \fB14\fP of file \fBAODELd\&.h\fP\&. -.SH "Constructor & Destructor Documentation" -.PP -.SS "bayesnet::AODELd::AODELd (bool predict_voting = \fRtrue\fP)" - -.PP -Definition at line \fB10\fP of file \fBAODELd\&.cc\fP\&. -.SH "Member Function Documentation" -.PP -.SS "void bayesnet::AODELd::buildModel (const torch::Tensor & weights)\fR [override]\fP, \fR [protected]\fP, \fR [virtual]\fP" - -.PP -Implements \fBbayesnet::Classifier\fP\&. -.PP -Definition at line \fB28\fP of file \fBAODELd\&.cc\fP\&. -.SS "\fBAODELd\fP & bayesnet::AODELd::fit (torch::Tensor & X_, torch::Tensor & y_, const std::vector< std::string > & features_, const std::string & className_, map< std::string, std::vector< int > > & states_)\fR [override]\fP" - -.PP -Definition at line \fB13\fP of file \fBAODELd\&.cc\fP\&. -.SS "std::vector< std::string > bayesnet::AODELd::graph (const std::string & name = \fR'AODELd'\fP) const\fR [override]\fP, \fR [virtual]\fP" - -.PP -Implements \fBbayesnet::BaseClassifier\fP\&. -.PP -Definition at line \fB43\fP of file \fBAODELd\&.cc\fP\&. -.SS "void bayesnet::AODELd::trainModel (const torch::Tensor & weights)\fR [override]\fP, \fR [protected]\fP, \fR [virtual]\fP" - -.PP -Implements \fBbayesnet::BaseClassifier\fP\&. -.PP -Definition at line \fB37\fP of file \fBAODELd\&.cc\fP\&. - -.SH "Author" -.PP -Generated automatically by Doxygen for BayesNet from the source code\&. diff --git a/docs/man3/bayesnet_BaseClassifier.3 b/docs/man3/bayesnet_BaseClassifier.3 deleted file mode 100644 index 44f3411..0000000 --- a/docs/man3/bayesnet_BaseClassifier.3 +++ /dev/null @@ -1,116 +0,0 @@ -.TH "bayesnet::BaseClassifier" 3 "Version 1.0.5" "BayesNet" \" -*- nroff -*- -.ad l -.nh -.SH NAME -bayesnet::BaseClassifier -.SH SYNOPSIS -.br -.PP -.PP -Inherited by \fBbayesnet::Classifier\fP\&. -.SS "Public Member Functions" - -.in +1c -.ti -1c -.RI "virtual \fBBaseClassifier\fP & \fBfit\fP (std::vector< std::vector< int > > &X, std::vector< int > &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states)=0" -.br -.ti -1c -.RI "virtual \fBBaseClassifier\fP & \fBfit\fP (torch::Tensor &X, torch::Tensor &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states)=0" -.br -.ti -1c -.RI "virtual \fBBaseClassifier\fP & \fBfit\fP (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states)=0" -.br -.ti -1c -.RI "virtual \fBBaseClassifier\fP & \fBfit\fP (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states, const torch::Tensor &weights)=0" -.br -.ti -1c -.RI "virtual torch::Tensor \fBpredict\fP (torch::Tensor &X)=0" -.br -.ti -1c -.RI "virtual std::vector< int > \fBpredict\fP (std::vector< std::vector< int > > &X)=0" -.br -.ti -1c -.RI "virtual torch::Tensor \fBpredict_proba\fP (torch::Tensor &X)=0" -.br -.ti -1c -.RI "virtual std::vector< std::vector< double > > \fBpredict_proba\fP (std::vector< std::vector< int > > &X)=0" -.br -.ti -1c -.RI "virtual status_t \fBgetStatus\fP () const =0" -.br -.ti -1c -.RI "virtual float \fBscore\fP (std::vector< std::vector< int > > &X, std::vector< int > &y)=0" -.br -.ti -1c -.RI "virtual float \fBscore\fP (torch::Tensor &X, torch::Tensor &y)=0" -.br -.ti -1c -.RI "virtual int \fBgetNumberOfNodes\fP () const =0" -.br -.ti -1c -.RI "virtual int \fBgetNumberOfEdges\fP () const =0" -.br -.ti -1c -.RI "virtual int \fBgetNumberOfStates\fP () const =0" -.br -.ti -1c -.RI "virtual int \fBgetClassNumStates\fP () const =0" -.br -.ti -1c -.RI "virtual std::vector< std::string > \fBshow\fP () const =0" -.br -.ti -1c -.RI "virtual std::vector< std::string > \fBgraph\fP (const std::string &title='') const =0" -.br -.ti -1c -.RI "virtual std::string \fBgetVersion\fP ()=0" -.br -.ti -1c -.RI "virtual std::vector< std::string > \fBtopological_order\fP ()=0" -.br -.ti -1c -.RI "virtual std::vector< std::string > \fBgetNotes\fP () const =0" -.br -.ti -1c -.RI "virtual std::string \fBdump_cpt\fP () const =0" -.br -.ti -1c -.RI "virtual void \fBsetHyperparameters\fP (const nlohmann::json &hyperparameters)=0" -.br -.ti -1c -.RI "std::vector< std::string > & \fBgetValidHyperparameters\fP ()" -.br -.in -1c -.SS "Protected Member Functions" - -.in +1c -.ti -1c -.RI "virtual void \fBtrainModel\fP (const torch::Tensor &weights)=0" -.br -.in -1c -.SS "Protected Attributes" - -.in +1c -.ti -1c -.RI "std::vector< std::string > \fBvalidHyperparameters\fP" -.br -.in -1c -.SH "Detailed Description" -.PP -Definition at line \fB13\fP of file \fBBaseClassifier\&.h\fP\&. -.SH "Member Function Documentation" -.PP -.SS "std::vector< std::string > & bayesnet::BaseClassifier::getValidHyperparameters ()\fR [inline]\fP" - -.PP -Definition at line \fB40\fP of file \fBBaseClassifier\&.h\fP\&. -.SH "Member Data Documentation" -.PP -.SS "std::vector bayesnet::BaseClassifier::validHyperparameters\fR [protected]\fP" - -.PP -Definition at line \fB43\fP of file \fBBaseClassifier\&.h\fP\&. - -.SH "Author" -.PP -Generated automatically by Doxygen for BayesNet from the source code\&. diff --git a/docs/man3/bayesnet_Boost.3 b/docs/man3/bayesnet_Boost.3 deleted file mode 100644 index 04ec0cc..0000000 --- a/docs/man3/bayesnet_Boost.3 +++ /dev/null @@ -1,369 +0,0 @@ -.TH "bayesnet::Boost" 3 "Version 1.0.5" "BayesNet" \" -*- nroff -*- -.ad l -.nh -.SH NAME -bayesnet::Boost -.SH SYNOPSIS -.br -.PP -.PP -Inherits \fBbayesnet::Ensemble\fP\&. -.PP -Inherited by \fBbayesnet::BoostA2DE\fP, and \fBbayesnet::BoostAODE\fP\&. -.SS "Public Member Functions" - -.in +1c -.ti -1c -.RI "\fBBoost\fP (bool predict_voting=false)" -.br -.ti -1c -.RI "void \fBsetHyperparameters\fP (const nlohmann::json &hyperparameters_) override" -.br -.in -1c - -Public Member Functions inherited from \fBbayesnet::Ensemble\fP -.in +1c -.ti -1c -.RI "\fBEnsemble\fP (bool predict_voting=true)" -.br -.ti -1c -.RI "torch::Tensor \fBpredict\fP (torch::Tensor &X) override" -.br -.ti -1c -.RI "std::vector< int > \fBpredict\fP (std::vector< std::vector< int > > &X) override" -.br -.ti -1c -.RI "torch::Tensor \fBpredict_proba\fP (torch::Tensor &X) override" -.br -.ti -1c -.RI "std::vector< std::vector< double > > \fBpredict_proba\fP (std::vector< std::vector< int > > &X) override" -.br -.ti -1c -.RI "float \fBscore\fP (torch::Tensor &X, torch::Tensor &y) override" -.br -.ti -1c -.RI "float \fBscore\fP (std::vector< std::vector< int > > &X, std::vector< int > &y) override" -.br -.ti -1c -.RI "int \fBgetNumberOfNodes\fP () const override" -.br -.ti -1c -.RI "int \fBgetNumberOfEdges\fP () const override" -.br -.ti -1c -.RI "int \fBgetNumberOfStates\fP () const override" -.br -.ti -1c -.RI "std::vector< std::string > \fBshow\fP () const override" -.br -.ti -1c -.RI "std::vector< std::string > \fBgraph\fP (const std::string &title) const override" -.br -.ti -1c -.RI "std::vector< std::string > \fBtopological_order\fP () override" -.br -.ti -1c -.RI "std::string \fBdump_cpt\fP () const override" -.br -.in -1c - -Public Member Functions inherited from \fBbayesnet::Classifier\fP -.in +1c -.ti -1c -.RI "\fBClassifier\fP (\fBNetwork\fP model)" -.br -.ti -1c -.RI "\fBClassifier\fP & \fBfit\fP (std::vector< std::vector< int > > &X, std::vector< int > &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override" -.br -.ti -1c -.RI "\fBClassifier\fP & \fBfit\fP (torch::Tensor &X, torch::Tensor &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override" -.br -.ti -1c -.RI "\fBClassifier\fP & \fBfit\fP (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override" -.br -.ti -1c -.RI "\fBClassifier\fP & \fBfit\fP (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states, const torch::Tensor &weights) override" -.br -.ti -1c -.RI "void \fBaddNodes\fP ()" -.br -.ti -1c -.RI "int \fBgetClassNumStates\fP () const override" -.br -.ti -1c -.RI "status_t \fBgetStatus\fP () const override" -.br -.ti -1c -.RI "std::string \fBgetVersion\fP () override" -.br -.ti -1c -.RI "std::vector< std::string > \fBgetNotes\fP () const override" -.br -.ti -1c -.RI "void \fBsetHyperparameters\fP (const nlohmann::json &hyperparameters) override" -.br -.in -1c - -Public Member Functions inherited from \fBbayesnet::BaseClassifier\fP -.in +1c -.ti -1c -.RI "std::vector< std::string > & \fBgetValidHyperparameters\fP ()" -.br -.in -1c -.SS "Protected Member Functions" - -.in +1c -.ti -1c -.RI "std::vector< int > \fBfeatureSelection\fP (torch::Tensor &weights_)" -.br -.ti -1c -.RI "void \fBbuildModel\fP (const torch::Tensor &weights) override" -.br -.ti -1c -.RI "std::tuple< torch::Tensor &, double, bool > \fBupdate_weights\fP (torch::Tensor &ytrain, torch::Tensor &ypred, torch::Tensor &weights)" -.br -.ti -1c -.RI "std::tuple< torch::Tensor &, double, bool > \fBupdate_weights_block\fP (int k, torch::Tensor &ytrain, torch::Tensor &weights)" -.br -.in -1c - -Protected Member Functions inherited from \fBbayesnet::Ensemble\fP -.in +1c -.ti -1c -.RI "torch::Tensor \fBpredict_average_voting\fP (torch::Tensor &X)" -.br -.ti -1c -.RI "std::vector< std::vector< double > > \fBpredict_average_voting\fP (std::vector< std::vector< int > > &X)" -.br -.ti -1c -.RI "torch::Tensor \fBpredict_average_proba\fP (torch::Tensor &X)" -.br -.ti -1c -.RI "std::vector< std::vector< double > > \fBpredict_average_proba\fP (std::vector< std::vector< int > > &X)" -.br -.ti -1c -.RI "torch::Tensor \fBcompute_arg_max\fP (torch::Tensor &X)" -.br -.ti -1c -.RI "std::vector< int > \fBcompute_arg_max\fP (std::vector< std::vector< double > > &X)" -.br -.ti -1c -.RI "torch::Tensor \fBvoting\fP (torch::Tensor &votes)" -.br -.ti -1c -.RI "void \fBtrainModel\fP (const torch::Tensor &weights) override" -.br -.in -1c - -Protected Member Functions inherited from \fBbayesnet::Classifier\fP -.in +1c -.ti -1c -.RI "void \fBcheckFitParameters\fP ()" -.br -.ti -1c -.RI "void \fBbuildDataset\fP (torch::Tensor &y)" -.br -.in -1c -.SS "Protected Attributes" - -.in +1c -.ti -1c -.RI "torch::Tensor \fBX_train\fP" -.br -.ti -1c -.RI "torch::Tensor \fBy_train\fP" -.br -.ti -1c -.RI "torch::Tensor \fBX_test\fP" -.br -.ti -1c -.RI "torch::Tensor \fBy_test\fP" -.br -.ti -1c -.RI "bool \fBbisection\fP = true" -.br -.ti -1c -.RI "int \fBmaxTolerance\fP = 3" -.br -.ti -1c -.RI "std::string \fBorder_algorithm\fP" -.br -.ti -1c -.RI "bool \fBconvergence\fP = true" -.br -.ti -1c -.RI "bool \fBconvergence_best\fP = false" -.br -.ti -1c -.RI "bool \fBselectFeatures\fP = false" -.br -.ti -1c -.RI "std::string \fBselect_features_algorithm\fP = Orders\&.DESC" -.br -.ti -1c -.RI "FeatureSelect * \fBfeatureSelector\fP = nullptr" -.br -.ti -1c -.RI "double \fBthreshold\fP = \-1" -.br -.ti -1c -.RI "bool \fBblock_update\fP = false" -.br -.in -1c - -Protected Attributes inherited from \fBbayesnet::Ensemble\fP -.in +1c -.ti -1c -.RI "unsigned \fBn_models\fP" -.br -.ti -1c -.RI "std::vector< std::unique_ptr< \fBClassifier\fP > > \fBmodels\fP" -.br -.ti -1c -.RI "std::vector< double > \fBsignificanceModels\fP" -.br -.ti -1c -.RI "bool \fBpredict_voting\fP" -.br -.in -1c - -Protected Attributes inherited from \fBbayesnet::Classifier\fP -.in +1c -.ti -1c -.RI "bool \fBfitted\fP" -.br -.ti -1c -.RI "unsigned int \fBm\fP" -.br -.ti -1c -.RI "unsigned int \fBn\fP" -.br -.ti -1c -.RI "\fBNetwork\fP \fBmodel\fP" -.br -.ti -1c -.RI "Metrics \fBmetrics\fP" -.br -.ti -1c -.RI "std::vector< std::string > \fBfeatures\fP" -.br -.ti -1c -.RI "std::string \fBclassName\fP" -.br -.ti -1c -.RI "std::map< std::string, std::vector< int > > \fBstates\fP" -.br -.ti -1c -.RI "torch::Tensor \fBdataset\fP" -.br -.ti -1c -.RI "status_t \fBstatus\fP = NORMAL" -.br -.ti -1c -.RI "std::vector< std::string > \fBnotes\fP" -.br -.in -1c - -Protected Attributes inherited from \fBbayesnet::BaseClassifier\fP -.in +1c -.ti -1c -.RI "std::vector< std::string > \fBvalidHyperparameters\fP" -.br -.in -1c -.SH "Detailed Description" -.PP -Definition at line \fB27\fP of file \fBBoost\&.h\fP\&. -.SH "Constructor & Destructor Documentation" -.PP -.SS "bayesnet::Boost::Boost (bool predict_voting = \fRfalse\fP)\fR [explicit]\fP" - -.PP -Definition at line \fB13\fP of file \fBBoost\&.cc\fP\&. -.SH "Member Function Documentation" -.PP -.SS "void bayesnet::Boost::buildModel (const torch::Tensor & weights)\fR [override]\fP, \fR [protected]\fP, \fR [virtual]\fP" - -.PP -Implements \fBbayesnet::Classifier\fP\&. -.PP -Definition at line \fB71\fP of file \fBBoost\&.cc\fP\&. -.SS "std::vector< int > bayesnet::Boost::featureSelection (torch::Tensor & weights_)\fR [protected]\fP" - -.PP -Definition at line \fB102\fP of file \fBBoost\&.cc\fP\&. -.SS "void bayesnet::Boost::setHyperparameters (const nlohmann::json & hyperparameters_)\fR [override]\fP, \fR [virtual]\fP" - -.PP -Implements \fBbayesnet::BaseClassifier\fP\&. -.PP -Definition at line \fB18\fP of file \fBBoost\&.cc\fP\&. -.SS "std::tuple< torch::Tensor &, double, bool > bayesnet::Boost::update_weights (torch::Tensor & ytrain, torch::Tensor & ypred, torch::Tensor & weights)\fR [protected]\fP" - -.PP -Definition at line \fB123\fP of file \fBBoost\&.cc\fP\&. -.SS "std::tuple< torch::Tensor &, double, bool > bayesnet::Boost::update_weights_block (int k, torch::Tensor & ytrain, torch::Tensor & weights)\fR [protected]\fP" - -.PP -Definition at line \fB150\fP of file \fBBoost\&.cc\fP\&. -.SH "Member Data Documentation" -.PP -.SS "bool bayesnet::Boost::bisection = true\fR [protected]\fP" - -.PP -Definition at line \fB39\fP of file \fBBoost\&.h\fP\&. -.SS "bool bayesnet::Boost::block_update = false\fR [protected]\fP" - -.PP -Definition at line \fB48\fP of file \fBBoost\&.h\fP\&. -.SS "bool bayesnet::Boost::convergence = true\fR [protected]\fP" - -.PP -Definition at line \fB42\fP of file \fBBoost\&.h\fP\&. -.SS "bool bayesnet::Boost::convergence_best = false\fR [protected]\fP" - -.PP -Definition at line \fB43\fP of file \fBBoost\&.h\fP\&. -.SS "FeatureSelect* bayesnet::Boost::featureSelector = nullptr\fR [protected]\fP" - -.PP -Definition at line \fB46\fP of file \fBBoost\&.h\fP\&. -.SS "int bayesnet::Boost::maxTolerance = 3\fR [protected]\fP" - -.PP -Definition at line \fB40\fP of file \fBBoost\&.h\fP\&. -.SS "std::string bayesnet::Boost::order_algorithm\fR [protected]\fP" - -.PP -Definition at line \fB41\fP of file \fBBoost\&.h\fP\&. -.SS "std::string bayesnet::Boost::select_features_algorithm = Orders\&.DESC\fR [protected]\fP" - -.PP -Definition at line \fB45\fP of file \fBBoost\&.h\fP\&. -.SS "bool bayesnet::Boost::selectFeatures = false\fR [protected]\fP" - -.PP -Definition at line \fB44\fP of file \fBBoost\&.h\fP\&. -.SS "double bayesnet::Boost::threshold = \-1\fR [protected]\fP" - -.PP -Definition at line \fB47\fP of file \fBBoost\&.h\fP\&. -.SS "torch::Tensor bayesnet::Boost::X_test\fR [protected]\fP" - -.PP -Definition at line \fB37\fP of file \fBBoost\&.h\fP\&. -.SS "torch::Tensor bayesnet::Boost::X_train\fR [protected]\fP" - -.PP -Definition at line \fB37\fP of file \fBBoost\&.h\fP\&. -.SS "torch::Tensor bayesnet::Boost::y_test\fR [protected]\fP" - -.PP -Definition at line \fB37\fP of file \fBBoost\&.h\fP\&. -.SS "torch::Tensor bayesnet::Boost::y_train\fR [protected]\fP" - -.PP -Definition at line \fB37\fP of file \fBBoost\&.h\fP\&. - -.SH "Author" -.PP -Generated automatically by Doxygen for BayesNet from the source code\&. diff --git a/docs/man3/bayesnet_BoostA2DE.3 b/docs/man3/bayesnet_BoostA2DE.3 deleted file mode 100644 index b3a9dfe..0000000 --- a/docs/man3/bayesnet_BoostA2DE.3 +++ /dev/null @@ -1,316 +0,0 @@ -.TH "bayesnet::BoostA2DE" 3 "Version 1.0.5" "BayesNet" \" -*- nroff -*- -.ad l -.nh -.SH NAME -bayesnet::BoostA2DE -.SH SYNOPSIS -.br -.PP -.PP -Inherits \fBbayesnet::Boost\fP\&. -.SS "Public Member Functions" - -.in +1c -.ti -1c -.RI "\fBBoostA2DE\fP (bool predict_voting=false)" -.br -.ti -1c -.RI "std::vector< std::string > \fBgraph\fP (const std::string &title='BoostA2DE') const override" -.br -.in -1c - -Public Member Functions inherited from \fBbayesnet::Boost\fP -.in +1c -.ti -1c -.RI "\fBBoost\fP (bool predict_voting=false)" -.br -.ti -1c -.RI "void \fBsetHyperparameters\fP (const nlohmann::json &hyperparameters_) override" -.br -.in -1c - -Public Member Functions inherited from \fBbayesnet::Ensemble\fP -.in +1c -.ti -1c -.RI "\fBEnsemble\fP (bool predict_voting=true)" -.br -.ti -1c -.RI "torch::Tensor \fBpredict\fP (torch::Tensor &X) override" -.br -.ti -1c -.RI "std::vector< int > \fBpredict\fP (std::vector< std::vector< int > > &X) override" -.br -.ti -1c -.RI "torch::Tensor \fBpredict_proba\fP (torch::Tensor &X) override" -.br -.ti -1c -.RI "std::vector< std::vector< double > > \fBpredict_proba\fP (std::vector< std::vector< int > > &X) override" -.br -.ti -1c -.RI "float \fBscore\fP (torch::Tensor &X, torch::Tensor &y) override" -.br -.ti -1c -.RI "float \fBscore\fP (std::vector< std::vector< int > > &X, std::vector< int > &y) override" -.br -.ti -1c -.RI "int \fBgetNumberOfNodes\fP () const override" -.br -.ti -1c -.RI "int \fBgetNumberOfEdges\fP () const override" -.br -.ti -1c -.RI "int \fBgetNumberOfStates\fP () const override" -.br -.ti -1c -.RI "std::vector< std::string > \fBshow\fP () const override" -.br -.ti -1c -.RI "std::vector< std::string > \fBgraph\fP (const std::string &title) const override" -.br -.ti -1c -.RI "std::vector< std::string > \fBtopological_order\fP () override" -.br -.ti -1c -.RI "std::string \fBdump_cpt\fP () const override" -.br -.in -1c - -Public Member Functions inherited from \fBbayesnet::Classifier\fP -.in +1c -.ti -1c -.RI "\fBClassifier\fP (\fBNetwork\fP model)" -.br -.ti -1c -.RI "\fBClassifier\fP & \fBfit\fP (std::vector< std::vector< int > > &X, std::vector< int > &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override" -.br -.ti -1c -.RI "\fBClassifier\fP & \fBfit\fP (torch::Tensor &X, torch::Tensor &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override" -.br -.ti -1c -.RI "\fBClassifier\fP & \fBfit\fP (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override" -.br -.ti -1c -.RI "\fBClassifier\fP & \fBfit\fP (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states, const torch::Tensor &weights) override" -.br -.ti -1c -.RI "void \fBaddNodes\fP ()" -.br -.ti -1c -.RI "int \fBgetClassNumStates\fP () const override" -.br -.ti -1c -.RI "status_t \fBgetStatus\fP () const override" -.br -.ti -1c -.RI "std::string \fBgetVersion\fP () override" -.br -.ti -1c -.RI "std::vector< std::string > \fBgetNotes\fP () const override" -.br -.ti -1c -.RI "void \fBsetHyperparameters\fP (const nlohmann::json &hyperparameters) override" -.br -.in -1c - -Public Member Functions inherited from \fBbayesnet::BaseClassifier\fP -.in +1c -.ti -1c -.RI "std::vector< std::string > & \fBgetValidHyperparameters\fP ()" -.br -.in -1c -.SS "Protected Member Functions" - -.in +1c -.ti -1c -.RI "void \fBtrainModel\fP (const torch::Tensor &weights) override" -.br -.in -1c - -Protected Member Functions inherited from \fBbayesnet::Boost\fP -.in +1c -.ti -1c -.RI "std::vector< int > \fBfeatureSelection\fP (torch::Tensor &weights_)" -.br -.ti -1c -.RI "void \fBbuildModel\fP (const torch::Tensor &weights) override" -.br -.ti -1c -.RI "std::tuple< torch::Tensor &, double, bool > \fBupdate_weights\fP (torch::Tensor &ytrain, torch::Tensor &ypred, torch::Tensor &weights)" -.br -.ti -1c -.RI "std::tuple< torch::Tensor &, double, bool > \fBupdate_weights_block\fP (int k, torch::Tensor &ytrain, torch::Tensor &weights)" -.br -.in -1c - -Protected Member Functions inherited from \fBbayesnet::Ensemble\fP -.in +1c -.ti -1c -.RI "torch::Tensor \fBpredict_average_voting\fP (torch::Tensor &X)" -.br -.ti -1c -.RI "std::vector< std::vector< double > > \fBpredict_average_voting\fP (std::vector< std::vector< int > > &X)" -.br -.ti -1c -.RI "torch::Tensor \fBpredict_average_proba\fP (torch::Tensor &X)" -.br -.ti -1c -.RI "std::vector< std::vector< double > > \fBpredict_average_proba\fP (std::vector< std::vector< int > > &X)" -.br -.ti -1c -.RI "torch::Tensor \fBcompute_arg_max\fP (torch::Tensor &X)" -.br -.ti -1c -.RI "std::vector< int > \fBcompute_arg_max\fP (std::vector< std::vector< double > > &X)" -.br -.ti -1c -.RI "torch::Tensor \fBvoting\fP (torch::Tensor &votes)" -.br -.ti -1c -.RI "void \fBtrainModel\fP (const torch::Tensor &weights) override" -.br -.in -1c - -Protected Member Functions inherited from \fBbayesnet::Classifier\fP -.in +1c -.ti -1c -.RI "void \fBcheckFitParameters\fP ()" -.br -.ti -1c -.RI "void \fBbuildDataset\fP (torch::Tensor &y)" -.br -.in -1c -.SS "Additional Inherited Members" - - -Protected Attributes inherited from \fBbayesnet::Boost\fP -.in +1c -.ti -1c -.RI "torch::Tensor \fBX_train\fP" -.br -.ti -1c -.RI "torch::Tensor \fBy_train\fP" -.br -.ti -1c -.RI "torch::Tensor \fBX_test\fP" -.br -.ti -1c -.RI "torch::Tensor \fBy_test\fP" -.br -.ti -1c -.RI "bool \fBbisection\fP = true" -.br -.ti -1c -.RI "int \fBmaxTolerance\fP = 3" -.br -.ti -1c -.RI "std::string \fBorder_algorithm\fP" -.br -.ti -1c -.RI "bool \fBconvergence\fP = true" -.br -.ti -1c -.RI "bool \fBconvergence_best\fP = false" -.br -.ti -1c -.RI "bool \fBselectFeatures\fP = false" -.br -.ti -1c -.RI "std::string \fBselect_features_algorithm\fP = Orders\&.DESC" -.br -.ti -1c -.RI "FeatureSelect * \fBfeatureSelector\fP = nullptr" -.br -.ti -1c -.RI "double \fBthreshold\fP = \-1" -.br -.ti -1c -.RI "bool \fBblock_update\fP = false" -.br -.in -1c - -Protected Attributes inherited from \fBbayesnet::Ensemble\fP -.in +1c -.ti -1c -.RI "unsigned \fBn_models\fP" -.br -.ti -1c -.RI "std::vector< std::unique_ptr< \fBClassifier\fP > > \fBmodels\fP" -.br -.ti -1c -.RI "std::vector< double > \fBsignificanceModels\fP" -.br -.ti -1c -.RI "bool \fBpredict_voting\fP" -.br -.in -1c - -Protected Attributes inherited from \fBbayesnet::Classifier\fP -.in +1c -.ti -1c -.RI "bool \fBfitted\fP" -.br -.ti -1c -.RI "unsigned int \fBm\fP" -.br -.ti -1c -.RI "unsigned int \fBn\fP" -.br -.ti -1c -.RI "\fBNetwork\fP \fBmodel\fP" -.br -.ti -1c -.RI "Metrics \fBmetrics\fP" -.br -.ti -1c -.RI "std::vector< std::string > \fBfeatures\fP" -.br -.ti -1c -.RI "std::string \fBclassName\fP" -.br -.ti -1c -.RI "std::map< std::string, std::vector< int > > \fBstates\fP" -.br -.ti -1c -.RI "torch::Tensor \fBdataset\fP" -.br -.ti -1c -.RI "status_t \fBstatus\fP = NORMAL" -.br -.ti -1c -.RI "std::vector< std::string > \fBnotes\fP" -.br -.in -1c - -Protected Attributes inherited from \fBbayesnet::BaseClassifier\fP -.in +1c -.ti -1c -.RI "std::vector< std::string > \fBvalidHyperparameters\fP" -.br -.in -1c -.SH "Detailed Description" -.PP -Definition at line \fB14\fP of file \fBBoostA2DE\&.h\fP\&. -.SH "Constructor & Destructor Documentation" -.PP -.SS "bayesnet::BoostA2DE::BoostA2DE (bool predict_voting = \fRfalse\fP)\fR [explicit]\fP" - -.PP -Definition at line \fB19\fP of file \fBBoostA2DE\&.cc\fP\&. -.SH "Member Function Documentation" -.PP -.SS "std::vector< std::string > bayesnet::BoostA2DE::graph (const std::string & title = \fR'BoostA2DE'\fP) const\fR [override]\fP, \fR [virtual]\fP" - -.PP -Implements \fBbayesnet::BaseClassifier\fP\&. -.PP -Definition at line \fB163\fP of file \fBBoostA2DE\&.cc\fP\&. -.SS "void bayesnet::BoostA2DE::trainModel (const torch::Tensor & weights)\fR [override]\fP, \fR [protected]\fP, \fR [virtual]\fP" - -.PP -Implements \fBbayesnet::BaseClassifier\fP\&. -.PP -Definition at line \fB44\fP of file \fBBoostA2DE\&.cc\fP\&. - -.SH "Author" -.PP -Generated automatically by Doxygen for BayesNet from the source code\&. diff --git a/docs/man3/bayesnet_BoostAODE.3 b/docs/man3/bayesnet_BoostAODE.3 deleted file mode 100644 index d5b74c1..0000000 --- a/docs/man3/bayesnet_BoostAODE.3 +++ /dev/null @@ -1,316 +0,0 @@ -.TH "bayesnet::BoostAODE" 3 "Version 1.0.5" "BayesNet" \" -*- nroff -*- -.ad l -.nh -.SH NAME -bayesnet::BoostAODE -.SH SYNOPSIS -.br -.PP -.PP -Inherits \fBbayesnet::Boost\fP\&. -.SS "Public Member Functions" - -.in +1c -.ti -1c -.RI "\fBBoostAODE\fP (bool predict_voting=false)" -.br -.ti -1c -.RI "std::vector< std::string > \fBgraph\fP (const std::string &title='BoostAODE') const override" -.br -.in -1c - -Public Member Functions inherited from \fBbayesnet::Boost\fP -.in +1c -.ti -1c -.RI "\fBBoost\fP (bool predict_voting=false)" -.br -.ti -1c -.RI "void \fBsetHyperparameters\fP (const nlohmann::json &hyperparameters_) override" -.br -.in -1c - -Public Member Functions inherited from \fBbayesnet::Ensemble\fP -.in +1c -.ti -1c -.RI "\fBEnsemble\fP (bool predict_voting=true)" -.br -.ti -1c -.RI "torch::Tensor \fBpredict\fP (torch::Tensor &X) override" -.br -.ti -1c -.RI "std::vector< int > \fBpredict\fP (std::vector< std::vector< int > > &X) override" -.br -.ti -1c -.RI "torch::Tensor \fBpredict_proba\fP (torch::Tensor &X) override" -.br -.ti -1c -.RI "std::vector< std::vector< double > > \fBpredict_proba\fP (std::vector< std::vector< int > > &X) override" -.br -.ti -1c -.RI "float \fBscore\fP (torch::Tensor &X, torch::Tensor &y) override" -.br -.ti -1c -.RI "float \fBscore\fP (std::vector< std::vector< int > > &X, std::vector< int > &y) override" -.br -.ti -1c -.RI "int \fBgetNumberOfNodes\fP () const override" -.br -.ti -1c -.RI "int \fBgetNumberOfEdges\fP () const override" -.br -.ti -1c -.RI "int \fBgetNumberOfStates\fP () const override" -.br -.ti -1c -.RI "std::vector< std::string > \fBshow\fP () const override" -.br -.ti -1c -.RI "std::vector< std::string > \fBgraph\fP (const std::string &title) const override" -.br -.ti -1c -.RI "std::vector< std::string > \fBtopological_order\fP () override" -.br -.ti -1c -.RI "std::string \fBdump_cpt\fP () const override" -.br -.in -1c - -Public Member Functions inherited from \fBbayesnet::Classifier\fP -.in +1c -.ti -1c -.RI "\fBClassifier\fP (\fBNetwork\fP model)" -.br -.ti -1c -.RI "\fBClassifier\fP & \fBfit\fP (std::vector< std::vector< int > > &X, std::vector< int > &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override" -.br -.ti -1c -.RI "\fBClassifier\fP & \fBfit\fP (torch::Tensor &X, torch::Tensor &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override" -.br -.ti -1c -.RI "\fBClassifier\fP & \fBfit\fP (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override" -.br -.ti -1c -.RI "\fBClassifier\fP & \fBfit\fP (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states, const torch::Tensor &weights) override" -.br -.ti -1c -.RI "void \fBaddNodes\fP ()" -.br -.ti -1c -.RI "int \fBgetClassNumStates\fP () const override" -.br -.ti -1c -.RI "status_t \fBgetStatus\fP () const override" -.br -.ti -1c -.RI "std::string \fBgetVersion\fP () override" -.br -.ti -1c -.RI "std::vector< std::string > \fBgetNotes\fP () const override" -.br -.ti -1c -.RI "void \fBsetHyperparameters\fP (const nlohmann::json &hyperparameters) override" -.br -.in -1c - -Public Member Functions inherited from \fBbayesnet::BaseClassifier\fP -.in +1c -.ti -1c -.RI "std::vector< std::string > & \fBgetValidHyperparameters\fP ()" -.br -.in -1c -.SS "Protected Member Functions" - -.in +1c -.ti -1c -.RI "void \fBtrainModel\fP (const torch::Tensor &weights) override" -.br -.in -1c - -Protected Member Functions inherited from \fBbayesnet::Boost\fP -.in +1c -.ti -1c -.RI "std::vector< int > \fBfeatureSelection\fP (torch::Tensor &weights_)" -.br -.ti -1c -.RI "void \fBbuildModel\fP (const torch::Tensor &weights) override" -.br -.ti -1c -.RI "std::tuple< torch::Tensor &, double, bool > \fBupdate_weights\fP (torch::Tensor &ytrain, torch::Tensor &ypred, torch::Tensor &weights)" -.br -.ti -1c -.RI "std::tuple< torch::Tensor &, double, bool > \fBupdate_weights_block\fP (int k, torch::Tensor &ytrain, torch::Tensor &weights)" -.br -.in -1c - -Protected Member Functions inherited from \fBbayesnet::Ensemble\fP -.in +1c -.ti -1c -.RI "torch::Tensor \fBpredict_average_voting\fP (torch::Tensor &X)" -.br -.ti -1c -.RI "std::vector< std::vector< double > > \fBpredict_average_voting\fP (std::vector< std::vector< int > > &X)" -.br -.ti -1c -.RI "torch::Tensor \fBpredict_average_proba\fP (torch::Tensor &X)" -.br -.ti -1c -.RI "std::vector< std::vector< double > > \fBpredict_average_proba\fP (std::vector< std::vector< int > > &X)" -.br -.ti -1c -.RI "torch::Tensor \fBcompute_arg_max\fP (torch::Tensor &X)" -.br -.ti -1c -.RI "std::vector< int > \fBcompute_arg_max\fP (std::vector< std::vector< double > > &X)" -.br -.ti -1c -.RI "torch::Tensor \fBvoting\fP (torch::Tensor &votes)" -.br -.ti -1c -.RI "void \fBtrainModel\fP (const torch::Tensor &weights) override" -.br -.in -1c - -Protected Member Functions inherited from \fBbayesnet::Classifier\fP -.in +1c -.ti -1c -.RI "void \fBcheckFitParameters\fP ()" -.br -.ti -1c -.RI "void \fBbuildDataset\fP (torch::Tensor &y)" -.br -.in -1c -.SS "Additional Inherited Members" - - -Protected Attributes inherited from \fBbayesnet::Boost\fP -.in +1c -.ti -1c -.RI "torch::Tensor \fBX_train\fP" -.br -.ti -1c -.RI "torch::Tensor \fBy_train\fP" -.br -.ti -1c -.RI "torch::Tensor \fBX_test\fP" -.br -.ti -1c -.RI "torch::Tensor \fBy_test\fP" -.br -.ti -1c -.RI "bool \fBbisection\fP = true" -.br -.ti -1c -.RI "int \fBmaxTolerance\fP = 3" -.br -.ti -1c -.RI "std::string \fBorder_algorithm\fP" -.br -.ti -1c -.RI "bool \fBconvergence\fP = true" -.br -.ti -1c -.RI "bool \fBconvergence_best\fP = false" -.br -.ti -1c -.RI "bool \fBselectFeatures\fP = false" -.br -.ti -1c -.RI "std::string \fBselect_features_algorithm\fP = Orders\&.DESC" -.br -.ti -1c -.RI "FeatureSelect * \fBfeatureSelector\fP = nullptr" -.br -.ti -1c -.RI "double \fBthreshold\fP = \-1" -.br -.ti -1c -.RI "bool \fBblock_update\fP = false" -.br -.in -1c - -Protected Attributes inherited from \fBbayesnet::Ensemble\fP -.in +1c -.ti -1c -.RI "unsigned \fBn_models\fP" -.br -.ti -1c -.RI "std::vector< std::unique_ptr< \fBClassifier\fP > > \fBmodels\fP" -.br -.ti -1c -.RI "std::vector< double > \fBsignificanceModels\fP" -.br -.ti -1c -.RI "bool \fBpredict_voting\fP" -.br -.in -1c - -Protected Attributes inherited from \fBbayesnet::Classifier\fP -.in +1c -.ti -1c -.RI "bool \fBfitted\fP" -.br -.ti -1c -.RI "unsigned int \fBm\fP" -.br -.ti -1c -.RI "unsigned int \fBn\fP" -.br -.ti -1c -.RI "\fBNetwork\fP \fBmodel\fP" -.br -.ti -1c -.RI "Metrics \fBmetrics\fP" -.br -.ti -1c -.RI "std::vector< std::string > \fBfeatures\fP" -.br -.ti -1c -.RI "std::string \fBclassName\fP" -.br -.ti -1c -.RI "std::map< std::string, std::vector< int > > \fBstates\fP" -.br -.ti -1c -.RI "torch::Tensor \fBdataset\fP" -.br -.ti -1c -.RI "status_t \fBstatus\fP = NORMAL" -.br -.ti -1c -.RI "std::vector< std::string > \fBnotes\fP" -.br -.in -1c - -Protected Attributes inherited from \fBbayesnet::BaseClassifier\fP -.in +1c -.ti -1c -.RI "std::vector< std::string > \fBvalidHyperparameters\fP" -.br -.in -1c -.SH "Detailed Description" -.PP -Definition at line \fB15\fP of file \fBBoostAODE\&.h\fP\&. -.SH "Constructor & Destructor Documentation" -.PP -.SS "bayesnet::BoostAODE::BoostAODE (bool predict_voting = \fRfalse\fP)\fR [explicit]\fP" - -.PP -Definition at line \fB16\fP of file \fBBoostAODE\&.cc\fP\&. -.SH "Member Function Documentation" -.PP -.SS "std::vector< std::string > bayesnet::BoostAODE::graph (const std::string & title = \fR'BoostAODE'\fP) const\fR [override]\fP, \fR [virtual]\fP" - -.PP -Implements \fBbayesnet::BaseClassifier\fP\&. -.PP -Definition at line \fB157\fP of file \fBBoostAODE\&.cc\fP\&. -.SS "void bayesnet::BoostAODE::trainModel (const torch::Tensor & weights)\fR [override]\fP, \fR [protected]\fP, \fR [virtual]\fP" - -.PP -Implements \fBbayesnet::BaseClassifier\fP\&. -.PP -Definition at line \fB33\fP of file \fBBoostAODE\&.cc\fP\&. - -.SH "Author" -.PP -Generated automatically by Doxygen for BayesNet from the source code\&. diff --git a/docs/man3/bayesnet_Classifier.3 b/docs/man3/bayesnet_Classifier.3 deleted file mode 100644 index 02549d5..0000000 --- a/docs/man3/bayesnet_Classifier.3 +++ /dev/null @@ -1,360 +0,0 @@ -.TH "bayesnet::Classifier" 3 "Version 1.0.5" "BayesNet" \" -*- nroff -*- -.ad l -.nh -.SH NAME -bayesnet::Classifier -.SH SYNOPSIS -.br -.PP -.PP -Inherits \fBbayesnet::BaseClassifier\fP\&. -.PP -Inherited by \fBbayesnet::Ensemble\fP, \fBbayesnet::KDB\fP, \fBbayesnet::SPODE\fP, \fBbayesnet::SPnDE\fP, and \fBbayesnet::TAN\fP\&. -.SS "Public Member Functions" - -.in +1c -.ti -1c -.RI "\fBClassifier\fP (\fBNetwork\fP model)" -.br -.ti -1c -.RI "\fBClassifier\fP & \fBfit\fP (std::vector< std::vector< int > > &X, std::vector< int > &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override" -.br -.ti -1c -.RI "\fBClassifier\fP & \fBfit\fP (torch::Tensor &X, torch::Tensor &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override" -.br -.ti -1c -.RI "\fBClassifier\fP & \fBfit\fP (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override" -.br -.ti -1c -.RI "\fBClassifier\fP & \fBfit\fP (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states, const torch::Tensor &weights) override" -.br -.ti -1c -.RI "void \fBaddNodes\fP ()" -.br -.ti -1c -.RI "int \fBgetNumberOfNodes\fP () const override" -.br -.ti -1c -.RI "int \fBgetNumberOfEdges\fP () const override" -.br -.ti -1c -.RI "int \fBgetNumberOfStates\fP () const override" -.br -.ti -1c -.RI "int \fBgetClassNumStates\fP () const override" -.br -.ti -1c -.RI "torch::Tensor \fBpredict\fP (torch::Tensor &X) override" -.br -.ti -1c -.RI "std::vector< int > \fBpredict\fP (std::vector< std::vector< int > > &X) override" -.br -.ti -1c -.RI "torch::Tensor \fBpredict_proba\fP (torch::Tensor &X) override" -.br -.ti -1c -.RI "std::vector< std::vector< double > > \fBpredict_proba\fP (std::vector< std::vector< int > > &X) override" -.br -.ti -1c -.RI "status_t \fBgetStatus\fP () const override" -.br -.ti -1c -.RI "std::string \fBgetVersion\fP () override" -.br -.ti -1c -.RI "float \fBscore\fP (torch::Tensor &X, torch::Tensor &y) override" -.br -.ti -1c -.RI "float \fBscore\fP (std::vector< std::vector< int > > &X, std::vector< int > &y) override" -.br -.ti -1c -.RI "std::vector< std::string > \fBshow\fP () const override" -.br -.ti -1c -.RI "std::vector< std::string > \fBtopological_order\fP () override" -.br -.ti -1c -.RI "std::vector< std::string > \fBgetNotes\fP () const override" -.br -.ti -1c -.RI "std::string \fBdump_cpt\fP () const override" -.br -.ti -1c -.RI "void \fBsetHyperparameters\fP (const nlohmann::json &hyperparameters) override" -.br -.in -1c - -Public Member Functions inherited from \fBbayesnet::BaseClassifier\fP -.in +1c -.ti -1c -.RI "virtual std::vector< std::string > \fBgraph\fP (const std::string &title='') const =0" -.br -.ti -1c -.RI "std::vector< std::string > & \fBgetValidHyperparameters\fP ()" -.br -.in -1c -.SS "Protected Member Functions" - -.in +1c -.ti -1c -.RI "void \fBcheckFitParameters\fP ()" -.br -.ti -1c -.RI "virtual void \fBbuildModel\fP (const torch::Tensor &weights)=0" -.br -.ti -1c -.RI "void \fBtrainModel\fP (const torch::Tensor &weights) override" -.br -.ti -1c -.RI "void \fBbuildDataset\fP (torch::Tensor &y)" -.br -.in -1c -.SS "Protected Attributes" - -.in +1c -.ti -1c -.RI "bool \fBfitted\fP" -.br -.ti -1c -.RI "unsigned int \fBm\fP" -.br -.ti -1c -.RI "unsigned int \fBn\fP" -.br -.ti -1c -.RI "\fBNetwork\fP \fBmodel\fP" -.br -.ti -1c -.RI "Metrics \fBmetrics\fP" -.br -.ti -1c -.RI "std::vector< std::string > \fBfeatures\fP" -.br -.ti -1c -.RI "std::string \fBclassName\fP" -.br -.ti -1c -.RI "std::map< std::string, std::vector< int > > \fBstates\fP" -.br -.ti -1c -.RI "torch::Tensor \fBdataset\fP" -.br -.ti -1c -.RI "status_t \fBstatus\fP = NORMAL" -.br -.ti -1c -.RI "std::vector< std::string > \fBnotes\fP" -.br -.in -1c - -Protected Attributes inherited from \fBbayesnet::BaseClassifier\fP -.in +1c -.ti -1c -.RI "std::vector< std::string > \fBvalidHyperparameters\fP" -.br -.in -1c -.SH "Detailed Description" -.PP -Definition at line \fB15\fP of file \fBClassifier\&.h\fP\&. -.SH "Constructor & Destructor Documentation" -.PP -.SS "bayesnet::Classifier::Classifier (\fBNetwork\fP model)" - -.PP -Definition at line \fB12\fP of file \fBClassifier\&.cc\fP\&. -.SH "Member Function Documentation" -.PP -.SS "void bayesnet::Classifier::addNodes ()" - -.PP -Definition at line \fB155\fP of file \fBClassifier\&.cc\fP\&. -.SS "void bayesnet::Classifier::buildDataset (torch::Tensor & y)\fR [protected]\fP" - -.PP -Definition at line \fB30\fP of file \fBClassifier\&.cc\fP\&. -.SS "void bayesnet::Classifier::checkFitParameters ()\fR [protected]\fP" - -.PP -Definition at line \fB79\fP of file \fBClassifier\&.cc\fP\&. -.SS "std::string bayesnet::Classifier::dump_cpt () const\fR [override]\fP, \fR [virtual]\fP" - -.PP -Implements \fBbayesnet::BaseClassifier\fP\&. -.PP -Definition at line \fB184\fP of file \fBClassifier\&.cc\fP\&. -.SS "\fBClassifier\fP & bayesnet::Classifier::fit (std::vector< std::vector< int > > & X, std::vector< int > & y, const std::vector< std::string > & features, const std::string & className, std::map< std::string, std::vector< int > > & states)\fR [override]\fP, \fR [virtual]\fP" - -.PP -Implements \fBbayesnet::BaseClassifier\fP\&. -.PP -Definition at line \fB57\fP of file \fBClassifier\&.cc\fP\&. -.SS "\fBClassifier\fP & bayesnet::Classifier::fit (torch::Tensor & dataset, const std::vector< std::string > & features, const std::string & className, std::map< std::string, std::vector< int > > & states)\fR [override]\fP, \fR [virtual]\fP" - -.PP -Implements \fBbayesnet::BaseClassifier\fP\&. -.PP -Definition at line \fB68\fP of file \fBClassifier\&.cc\fP\&. -.SS "\fBClassifier\fP & bayesnet::Classifier::fit (torch::Tensor & dataset, const std::vector< std::string > & features, const std::string & className, std::map< std::string, std::vector< int > > & states, const torch::Tensor & weights)\fR [override]\fP, \fR [virtual]\fP" - -.PP -Implements \fBbayesnet::BaseClassifier\fP\&. -.PP -Definition at line \fB74\fP of file \fBClassifier\&.cc\fP\&. -.SS "\fBClassifier\fP & bayesnet::Classifier::fit (torch::Tensor & X, torch::Tensor & y, const std::vector< std::string > & features, const std::string & className, std::map< std::string, std::vector< int > > & states)\fR [override]\fP, \fR [virtual]\fP" - -.PP -Implements \fBbayesnet::BaseClassifier\fP\&. -.PP -Definition at line \fB49\fP of file \fBClassifier\&.cc\fP\&. -.SS "int bayesnet::Classifier::getClassNumStates () const\fR [override]\fP, \fR [virtual]\fP" - -.PP -Implements \fBbayesnet::BaseClassifier\fP\&. -.PP -Definition at line \fB176\fP of file \fBClassifier\&.cc\fP\&. -.SS "std::vector< std::string > bayesnet::Classifier::getNotes () const\fR [inline]\fP, \fR [override]\fP, \fR [virtual]\fP" - -.PP -Implements \fBbayesnet::BaseClassifier\fP\&. -.PP -Definition at line \fB38\fP of file \fBClassifier\&.h\fP\&. -.SS "int bayesnet::Classifier::getNumberOfEdges () const\fR [override]\fP, \fR [virtual]\fP" - -.PP -Implements \fBbayesnet::BaseClassifier\fP\&. -.PP -Definition at line \fB168\fP of file \fBClassifier\&.cc\fP\&. -.SS "int bayesnet::Classifier::getNumberOfNodes () const\fR [override]\fP, \fR [virtual]\fP" - -.PP -Implements \fBbayesnet::BaseClassifier\fP\&. -.PP -Definition at line \fB163\fP of file \fBClassifier\&.cc\fP\&. -.SS "int bayesnet::Classifier::getNumberOfStates () const\fR [override]\fP, \fR [virtual]\fP" - -.PP -Implements \fBbayesnet::BaseClassifier\fP\&. -.PP -Definition at line \fB172\fP of file \fBClassifier\&.cc\fP\&. -.SS "status_t bayesnet::Classifier::getStatus () const\fR [inline]\fP, \fR [override]\fP, \fR [virtual]\fP" - -.PP -Implements \fBbayesnet::BaseClassifier\fP\&. -.PP -Definition at line \fB32\fP of file \fBClassifier\&.h\fP\&. -.SS "std::string bayesnet::Classifier::getVersion ()\fR [inline]\fP, \fR [override]\fP, \fR [virtual]\fP" - -.PP -Implements \fBbayesnet::BaseClassifier\fP\&. -.PP -Definition at line \fB33\fP of file \fBClassifier\&.h\fP\&. -.SS "std::vector< int > bayesnet::Classifier::predict (std::vector< std::vector< int > > & X)\fR [override]\fP, \fR [virtual]\fP" - -.PP -Implements \fBbayesnet::BaseClassifier\fP\&. -.PP -Definition at line \fB103\fP of file \fBClassifier\&.cc\fP\&. -.SS "torch::Tensor bayesnet::Classifier::predict (torch::Tensor & X)\fR [override]\fP, \fR [virtual]\fP" - -.PP -Implements \fBbayesnet::BaseClassifier\fP\&. -.PP -Definition at line \fB96\fP of file \fBClassifier\&.cc\fP\&. -.SS "std::vector< std::vector< double > > bayesnet::Classifier::predict_proba (std::vector< std::vector< int > > & X)\fR [override]\fP, \fR [virtual]\fP" - -.PP -Implements \fBbayesnet::BaseClassifier\fP\&. -.PP -Definition at line \fB124\fP of file \fBClassifier\&.cc\fP\&. -.SS "torch::Tensor bayesnet::Classifier::predict_proba (torch::Tensor & X)\fR [override]\fP, \fR [virtual]\fP" - -.PP -Implements \fBbayesnet::BaseClassifier\fP\&. -.PP -Definition at line \fB117\fP of file \fBClassifier\&.cc\fP\&. -.SS "float bayesnet::Classifier::score (std::vector< std::vector< int > > & X, std::vector< int > & y)\fR [override]\fP, \fR [virtual]\fP" - -.PP -Implements \fBbayesnet::BaseClassifier\fP\&. -.PP -Definition at line \fB144\fP of file \fBClassifier\&.cc\fP\&. -.SS "float bayesnet::Classifier::score (torch::Tensor & X, torch::Tensor & y)\fR [override]\fP, \fR [virtual]\fP" - -.PP -Implements \fBbayesnet::BaseClassifier\fP\&. -.PP -Definition at line \fB139\fP of file \fBClassifier\&.cc\fP\&. -.SS "void bayesnet::Classifier::setHyperparameters (const nlohmann::json & hyperparameters)\fR [override]\fP, \fR [virtual]\fP" - -.PP -Implements \fBbayesnet::BaseClassifier\fP\&. -.PP -Definition at line \fB188\fP of file \fBClassifier\&.cc\fP\&. -.SS "std::vector< std::string > bayesnet::Classifier::show () const\fR [override]\fP, \fR [virtual]\fP" - -.PP -Implements \fBbayesnet::BaseClassifier\fP\&. -.PP -Definition at line \fB151\fP of file \fBClassifier\&.cc\fP\&. -.SS "std::vector< std::string > bayesnet::Classifier::topological_order ()\fR [override]\fP, \fR [virtual]\fP" - -.PP -Implements \fBbayesnet::BaseClassifier\fP\&. -.PP -Definition at line \fB180\fP of file \fBClassifier\&.cc\fP\&. -.SS "void bayesnet::Classifier::trainModel (const torch::Tensor & weights)\fR [override]\fP, \fR [protected]\fP, \fR [virtual]\fP" - -.PP -Implements \fBbayesnet::BaseClassifier\fP\&. -.PP -Definition at line \fB44\fP of file \fBClassifier\&.cc\fP\&. -.SH "Member Data Documentation" -.PP -.SS "std::string bayesnet::Classifier::className\fR [protected]\fP" - -.PP -Definition at line \fB47\fP of file \fBClassifier\&.h\fP\&. -.SS "torch::Tensor bayesnet::Classifier::dataset\fR [protected]\fP" - -.PP -Definition at line \fB49\fP of file \fBClassifier\&.h\fP\&. -.SS "std::vector bayesnet::Classifier::features\fR [protected]\fP" - -.PP -Definition at line \fB46\fP of file \fBClassifier\&.h\fP\&. -.SS "bool bayesnet::Classifier::fitted\fR [protected]\fP" - -.PP -Definition at line \fB42\fP of file \fBClassifier\&.h\fP\&. -.SS "unsigned int bayesnet::Classifier::m\fR [protected]\fP" - -.PP -Definition at line \fB43\fP of file \fBClassifier\&.h\fP\&. -.SS "Metrics bayesnet::Classifier::metrics\fR [protected]\fP" - -.PP -Definition at line \fB45\fP of file \fBClassifier\&.h\fP\&. -.SS "\fBNetwork\fP bayesnet::Classifier::model\fR [protected]\fP" - -.PP -Definition at line \fB44\fP of file \fBClassifier\&.h\fP\&. -.SS "unsigned int bayesnet::Classifier::n\fR [protected]\fP" - -.PP -Definition at line \fB43\fP of file \fBClassifier\&.h\fP\&. -.SS "std::vector bayesnet::Classifier::notes\fR [protected]\fP" - -.PP -Definition at line \fB51\fP of file \fBClassifier\&.h\fP\&. -.SS "std::map > bayesnet::Classifier::states\fR [protected]\fP" - -.PP -Definition at line \fB48\fP of file \fBClassifier\&.h\fP\&. -.SS "status_t bayesnet::Classifier::status = NORMAL\fR [protected]\fP" - -.PP -Definition at line \fB50\fP of file \fBClassifier\&.h\fP\&. - -.SH "Author" -.PP -Generated automatically by Doxygen for BayesNet from the source code\&. diff --git a/docs/man3/bayesnet_Ensemble.3 b/docs/man3/bayesnet_Ensemble.3 deleted file mode 100644 index f4c6664..0000000 --- a/docs/man3/bayesnet_Ensemble.3 +++ /dev/null @@ -1,348 +0,0 @@ -.TH "bayesnet::Ensemble" 3 "Version 1.0.5" "BayesNet" \" -*- nroff -*- -.ad l -.nh -.SH NAME -bayesnet::Ensemble -.SH SYNOPSIS -.br -.PP -.PP -Inherits \fBbayesnet::Classifier\fP\&. -.PP -Inherited by \fBbayesnet::A2DE\fP, \fBbayesnet::AODE\fP, \fBbayesnet::AODELd\fP, and \fBbayesnet::Boost\fP\&. -.SS "Public Member Functions" - -.in +1c -.ti -1c -.RI "\fBEnsemble\fP (bool predict_voting=true)" -.br -.ti -1c -.RI "torch::Tensor \fBpredict\fP (torch::Tensor &X) override" -.br -.ti -1c -.RI "std::vector< int > \fBpredict\fP (std::vector< std::vector< int > > &X) override" -.br -.ti -1c -.RI "torch::Tensor \fBpredict_proba\fP (torch::Tensor &X) override" -.br -.ti -1c -.RI "std::vector< std::vector< double > > \fBpredict_proba\fP (std::vector< std::vector< int > > &X) override" -.br -.ti -1c -.RI "float \fBscore\fP (torch::Tensor &X, torch::Tensor &y) override" -.br -.ti -1c -.RI "float \fBscore\fP (std::vector< std::vector< int > > &X, std::vector< int > &y) override" -.br -.ti -1c -.RI "int \fBgetNumberOfNodes\fP () const override" -.br -.ti -1c -.RI "int \fBgetNumberOfEdges\fP () const override" -.br -.ti -1c -.RI "int \fBgetNumberOfStates\fP () const override" -.br -.ti -1c -.RI "std::vector< std::string > \fBshow\fP () const override" -.br -.ti -1c -.RI "std::vector< std::string > \fBgraph\fP (const std::string &title) const override" -.br -.ti -1c -.RI "std::vector< std::string > \fBtopological_order\fP () override" -.br -.ti -1c -.RI "std::string \fBdump_cpt\fP () const override" -.br -.in -1c - -Public Member Functions inherited from \fBbayesnet::Classifier\fP -.in +1c -.ti -1c -.RI "\fBClassifier\fP (\fBNetwork\fP model)" -.br -.ti -1c -.RI "\fBClassifier\fP & \fBfit\fP (std::vector< std::vector< int > > &X, std::vector< int > &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override" -.br -.ti -1c -.RI "\fBClassifier\fP & \fBfit\fP (torch::Tensor &X, torch::Tensor &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override" -.br -.ti -1c -.RI "\fBClassifier\fP & \fBfit\fP (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override" -.br -.ti -1c -.RI "\fBClassifier\fP & \fBfit\fP (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states, const torch::Tensor &weights) override" -.br -.ti -1c -.RI "void \fBaddNodes\fP ()" -.br -.ti -1c -.RI "int \fBgetClassNumStates\fP () const override" -.br -.ti -1c -.RI "status_t \fBgetStatus\fP () const override" -.br -.ti -1c -.RI "std::string \fBgetVersion\fP () override" -.br -.ti -1c -.RI "std::vector< std::string > \fBgetNotes\fP () const override" -.br -.ti -1c -.RI "void \fBsetHyperparameters\fP (const nlohmann::json &hyperparameters) override" -.br -.in -1c - -Public Member Functions inherited from \fBbayesnet::BaseClassifier\fP -.in +1c -.ti -1c -.RI "std::vector< std::string > & \fBgetValidHyperparameters\fP ()" -.br -.in -1c -.SS "Protected Member Functions" - -.in +1c -.ti -1c -.RI "torch::Tensor \fBpredict_average_voting\fP (torch::Tensor &X)" -.br -.ti -1c -.RI "std::vector< std::vector< double > > \fBpredict_average_voting\fP (std::vector< std::vector< int > > &X)" -.br -.ti -1c -.RI "torch::Tensor \fBpredict_average_proba\fP (torch::Tensor &X)" -.br -.ti -1c -.RI "std::vector< std::vector< double > > \fBpredict_average_proba\fP (std::vector< std::vector< int > > &X)" -.br -.ti -1c -.RI "torch::Tensor \fBcompute_arg_max\fP (torch::Tensor &X)" -.br -.ti -1c -.RI "std::vector< int > \fBcompute_arg_max\fP (std::vector< std::vector< double > > &X)" -.br -.ti -1c -.RI "torch::Tensor \fBvoting\fP (torch::Tensor &votes)" -.br -.ti -1c -.RI "void \fBtrainModel\fP (const torch::Tensor &weights) override" -.br -.in -1c - -Protected Member Functions inherited from \fBbayesnet::Classifier\fP -.in +1c -.ti -1c -.RI "void \fBcheckFitParameters\fP ()" -.br -.ti -1c -.RI "virtual void \fBbuildModel\fP (const torch::Tensor &weights)=0" -.br -.ti -1c -.RI "void \fBbuildDataset\fP (torch::Tensor &y)" -.br -.in -1c -.SS "Protected Attributes" - -.in +1c -.ti -1c -.RI "unsigned \fBn_models\fP" -.br -.ti -1c -.RI "std::vector< std::unique_ptr< \fBClassifier\fP > > \fBmodels\fP" -.br -.ti -1c -.RI "std::vector< double > \fBsignificanceModels\fP" -.br -.ti -1c -.RI "bool \fBpredict_voting\fP" -.br -.in -1c - -Protected Attributes inherited from \fBbayesnet::Classifier\fP -.in +1c -.ti -1c -.RI "bool \fBfitted\fP" -.br -.ti -1c -.RI "unsigned int \fBm\fP" -.br -.ti -1c -.RI "unsigned int \fBn\fP" -.br -.ti -1c -.RI "\fBNetwork\fP \fBmodel\fP" -.br -.ti -1c -.RI "Metrics \fBmetrics\fP" -.br -.ti -1c -.RI "std::vector< std::string > \fBfeatures\fP" -.br -.ti -1c -.RI "std::string \fBclassName\fP" -.br -.ti -1c -.RI "std::map< std::string, std::vector< int > > \fBstates\fP" -.br -.ti -1c -.RI "torch::Tensor \fBdataset\fP" -.br -.ti -1c -.RI "status_t \fBstatus\fP = NORMAL" -.br -.ti -1c -.RI "std::vector< std::string > \fBnotes\fP" -.br -.in -1c - -Protected Attributes inherited from \fBbayesnet::BaseClassifier\fP -.in +1c -.ti -1c -.RI "std::vector< std::string > \fBvalidHyperparameters\fP" -.br -.in -1c -.SH "Detailed Description" -.PP -Definition at line \fB15\fP of file \fBEnsemble\&.h\fP\&. -.SH "Constructor & Destructor Documentation" -.PP -.SS "bayesnet::Ensemble::Ensemble (bool predict_voting = \fRtrue\fP)" - -.PP -Definition at line \fB11\fP of file \fBEnsemble\&.cc\fP\&. -.SH "Member Function Documentation" -.PP -.SS "std::vector< int > bayesnet::Ensemble::compute_arg_max (std::vector< std::vector< double > > & X)\fR [protected]\fP" - -.PP -Definition at line \fB24\fP of file \fBEnsemble\&.cc\fP\&. -.SS "torch::Tensor bayesnet::Ensemble::compute_arg_max (torch::Tensor & X)\fR [protected]\fP" - -.PP -Definition at line \fB33\fP of file \fBEnsemble\&.cc\fP\&. -.SS "std::string bayesnet::Ensemble::dump_cpt () const\fR [inline]\fP, \fR [override]\fP, \fR [virtual]\fP" - -.PP -Reimplemented from \fBbayesnet::Classifier\fP\&. -.PP -Definition at line \fB34\fP of file \fBEnsemble\&.h\fP\&. -.SS "int bayesnet::Ensemble::getNumberOfEdges () const\fR [override]\fP, \fR [virtual]\fP" - -.PP -Reimplemented from \fBbayesnet::Classifier\fP\&. -.PP -Definition at line \fB206\fP of file \fBEnsemble\&.cc\fP\&. -.SS "int bayesnet::Ensemble::getNumberOfNodes () const\fR [override]\fP, \fR [virtual]\fP" - -.PP -Reimplemented from \fBbayesnet::Classifier\fP\&. -.PP -Definition at line \fB198\fP of file \fBEnsemble\&.cc\fP\&. -.SS "int bayesnet::Ensemble::getNumberOfStates () const\fR [override]\fP, \fR [virtual]\fP" - -.PP -Reimplemented from \fBbayesnet::Classifier\fP\&. -.PP -Definition at line \fB214\fP of file \fBEnsemble\&.cc\fP\&. -.SS "std::vector< std::string > bayesnet::Ensemble::graph (const std::string & title) const\fR [override]\fP, \fR [virtual]\fP" - -.PP -Implements \fBbayesnet::BaseClassifier\fP\&. -.PP -Definition at line \fB189\fP of file \fBEnsemble\&.cc\fP\&. -.SS "std::vector< int > bayesnet::Ensemble::predict (std::vector< std::vector< int > > & X)\fR [override]\fP, \fR [virtual]\fP" - -.PP -Reimplemented from \fBbayesnet::Classifier\fP\&. -.PP -Definition at line \fB74\fP of file \fBEnsemble\&.cc\fP\&. -.SS "torch::Tensor bayesnet::Ensemble::predict (torch::Tensor & X)\fR [override]\fP, \fR [virtual]\fP" - -.PP -Reimplemented from \fBbayesnet::Classifier\fP\&. -.PP -Definition at line \fB79\fP of file \fBEnsemble\&.cc\fP\&. -.SS "std::vector< std::vector< double > > bayesnet::Ensemble::predict_average_proba (std::vector< std::vector< int > > & X)\fR [protected]\fP" - -.PP -Definition at line \fB104\fP of file \fBEnsemble\&.cc\fP\&. -.SS "torch::Tensor bayesnet::Ensemble::predict_average_proba (torch::Tensor & X)\fR [protected]\fP" - -.PP -Definition at line \fB84\fP of file \fBEnsemble\&.cc\fP\&. -.SS "std::vector< std::vector< double > > bayesnet::Ensemble::predict_average_voting (std::vector< std::vector< int > > & X)\fR [protected]\fP" - -.PP -Definition at line \fB133\fP of file \fBEnsemble\&.cc\fP\&. -.SS "torch::Tensor bayesnet::Ensemble::predict_average_voting (torch::Tensor & X)\fR [protected]\fP" - -.PP -Definition at line \fB140\fP of file \fBEnsemble\&.cc\fP\&. -.SS "std::vector< std::vector< double > > bayesnet::Ensemble::predict_proba (std::vector< std::vector< int > > & X)\fR [override]\fP, \fR [virtual]\fP" - -.PP -Reimplemented from \fBbayesnet::Classifier\fP\&. -.PP -Definition at line \fB60\fP of file \fBEnsemble\&.cc\fP\&. -.SS "torch::Tensor bayesnet::Ensemble::predict_proba (torch::Tensor & X)\fR [override]\fP, \fR [virtual]\fP" - -.PP -Reimplemented from \fBbayesnet::Classifier\fP\&. -.PP -Definition at line \fB67\fP of file \fBEnsemble\&.cc\fP\&. -.SS "float bayesnet::Ensemble::score (std::vector< std::vector< int > > & X, std::vector< int > & y)\fR [override]\fP, \fR [virtual]\fP" - -.PP -Reimplemented from \fBbayesnet::Classifier\fP\&. -.PP -Definition at line \fB169\fP of file \fBEnsemble\&.cc\fP\&. -.SS "float bayesnet::Ensemble::score (torch::Tensor & X, torch::Tensor & y)\fR [override]\fP, \fR [virtual]\fP" - -.PP -Reimplemented from \fBbayesnet::Classifier\fP\&. -.PP -Definition at line \fB158\fP of file \fBEnsemble\&.cc\fP\&. -.SS "std::vector< std::string > bayesnet::Ensemble::show () const\fR [override]\fP, \fR [virtual]\fP" - -.PP -Reimplemented from \fBbayesnet::Classifier\fP\&. -.PP -Definition at line \fB180\fP of file \fBEnsemble\&.cc\fP\&. -.SS "std::vector< std::string > bayesnet::Ensemble::topological_order ()\fR [inline]\fP, \fR [override]\fP, \fR [virtual]\fP" - -.PP -Reimplemented from \fBbayesnet::Classifier\fP\&. -.PP -Definition at line \fB30\fP of file \fBEnsemble\&.h\fP\&. -.SS "void bayesnet::Ensemble::trainModel (const torch::Tensor & weights)\fR [override]\fP, \fR [protected]\fP, \fR [virtual]\fP" - -.PP -Reimplemented from \fBbayesnet::Classifier\fP\&. -.PP -Definition at line \fB16\fP of file \fBEnsemble\&.cc\fP\&. -.SS "torch::Tensor bayesnet::Ensemble::voting (torch::Tensor & votes)\fR [protected]\fP" - -.PP -Definition at line \fB38\fP of file \fBEnsemble\&.cc\fP\&. -.SH "Member Data Documentation" -.PP -.SS "std::vector > bayesnet::Ensemble::models\fR [protected]\fP" - -.PP -Definition at line \fB47\fP of file \fBEnsemble\&.h\fP\&. -.SS "unsigned bayesnet::Ensemble::n_models\fR [protected]\fP" - -.PP -Definition at line \fB46\fP of file \fBEnsemble\&.h\fP\&. -.SS "bool bayesnet::Ensemble::predict_voting\fR [protected]\fP" - -.PP -Definition at line \fB50\fP of file \fBEnsemble\&.h\fP\&. -.SS "std::vector bayesnet::Ensemble::significanceModels\fR [protected]\fP" - -.PP -Definition at line \fB48\fP of file \fBEnsemble\&.h\fP\&. - -.SH "Author" -.PP -Generated automatically by Doxygen for BayesNet from the source code\&. diff --git a/docs/man3/bayesnet_KDB.3 b/docs/man3/bayesnet_KDB.3 deleted file mode 100644 index 0f9615b..0000000 --- a/docs/man3/bayesnet_KDB.3 +++ /dev/null @@ -1,201 +0,0 @@ -.TH "bayesnet::KDB" 3 "Version 1.0.5" "BayesNet" \" -*- nroff -*- -.ad l -.nh -.SH NAME -bayesnet::KDB -.SH SYNOPSIS -.br -.PP -.PP -Inherits \fBbayesnet::Classifier\fP\&. -.PP -Inherited by \fBbayesnet::KDBLd\fP\&. -.SS "Public Member Functions" - -.in +1c -.ti -1c -.RI "\fBKDB\fP (int k, float theta=0\&.03)" -.br -.ti -1c -.RI "void \fBsetHyperparameters\fP (const nlohmann::json &hyperparameters_) override" -.br -.ti -1c -.RI "std::vector< std::string > \fBgraph\fP (const std::string &name='KDB') const override" -.br -.in -1c - -Public Member Functions inherited from \fBbayesnet::Classifier\fP -.in +1c -.ti -1c -.RI "\fBClassifier\fP (\fBNetwork\fP model)" -.br -.ti -1c -.RI "\fBClassifier\fP & \fBfit\fP (std::vector< std::vector< int > > &X, std::vector< int > &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override" -.br -.ti -1c -.RI "\fBClassifier\fP & \fBfit\fP (torch::Tensor &X, torch::Tensor &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override" -.br -.ti -1c -.RI "\fBClassifier\fP & \fBfit\fP (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override" -.br -.ti -1c -.RI "\fBClassifier\fP & \fBfit\fP (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states, const torch::Tensor &weights) override" -.br -.ti -1c -.RI "void \fBaddNodes\fP ()" -.br -.ti -1c -.RI "int \fBgetNumberOfNodes\fP () const override" -.br -.ti -1c -.RI "int \fBgetNumberOfEdges\fP () const override" -.br -.ti -1c -.RI "int \fBgetNumberOfStates\fP () const override" -.br -.ti -1c -.RI "int \fBgetClassNumStates\fP () const override" -.br -.ti -1c -.RI "torch::Tensor \fBpredict\fP (torch::Tensor &X) override" -.br -.ti -1c -.RI "std::vector< int > \fBpredict\fP (std::vector< std::vector< int > > &X) override" -.br -.ti -1c -.RI "torch::Tensor \fBpredict_proba\fP (torch::Tensor &X) override" -.br -.ti -1c -.RI "std::vector< std::vector< double > > \fBpredict_proba\fP (std::vector< std::vector< int > > &X) override" -.br -.ti -1c -.RI "status_t \fBgetStatus\fP () const override" -.br -.ti -1c -.RI "std::string \fBgetVersion\fP () override" -.br -.ti -1c -.RI "float \fBscore\fP (torch::Tensor &X, torch::Tensor &y) override" -.br -.ti -1c -.RI "float \fBscore\fP (std::vector< std::vector< int > > &X, std::vector< int > &y) override" -.br -.ti -1c -.RI "std::vector< std::string > \fBshow\fP () const override" -.br -.ti -1c -.RI "std::vector< std::string > \fBtopological_order\fP () override" -.br -.ti -1c -.RI "std::vector< std::string > \fBgetNotes\fP () const override" -.br -.ti -1c -.RI "std::string \fBdump_cpt\fP () const override" -.br -.in -1c - -Public Member Functions inherited from \fBbayesnet::BaseClassifier\fP -.in +1c -.ti -1c -.RI "std::vector< std::string > & \fBgetValidHyperparameters\fP ()" -.br -.in -1c -.SS "Protected Member Functions" - -.in +1c -.ti -1c -.RI "void \fBbuildModel\fP (const torch::Tensor &weights) override" -.br -.in -1c - -Protected Member Functions inherited from \fBbayesnet::Classifier\fP -.in +1c -.ti -1c -.RI "void \fBcheckFitParameters\fP ()" -.br -.ti -1c -.RI "void \fBtrainModel\fP (const torch::Tensor &weights) override" -.br -.ti -1c -.RI "void \fBbuildDataset\fP (torch::Tensor &y)" -.br -.in -1c -.SS "Additional Inherited Members" - - -Protected Attributes inherited from \fBbayesnet::Classifier\fP -.in +1c -.ti -1c -.RI "bool \fBfitted\fP" -.br -.ti -1c -.RI "unsigned int \fBm\fP" -.br -.ti -1c -.RI "unsigned int \fBn\fP" -.br -.ti -1c -.RI "\fBNetwork\fP \fBmodel\fP" -.br -.ti -1c -.RI "Metrics \fBmetrics\fP" -.br -.ti -1c -.RI "std::vector< std::string > \fBfeatures\fP" -.br -.ti -1c -.RI "std::string \fBclassName\fP" -.br -.ti -1c -.RI "std::map< std::string, std::vector< int > > \fBstates\fP" -.br -.ti -1c -.RI "torch::Tensor \fBdataset\fP" -.br -.ti -1c -.RI "status_t \fBstatus\fP = NORMAL" -.br -.ti -1c -.RI "std::vector< std::string > \fBnotes\fP" -.br -.in -1c - -Protected Attributes inherited from \fBbayesnet::BaseClassifier\fP -.in +1c -.ti -1c -.RI "std::vector< std::string > \fBvalidHyperparameters\fP" -.br -.in -1c -.SH "Detailed Description" -.PP -Definition at line \fB13\fP of file \fBKDB\&.h\fP\&. -.SH "Constructor & Destructor Documentation" -.PP -.SS "bayesnet::KDB::KDB (int k, float theta = \fR0\&.03\fP)\fR [explicit]\fP" - -.PP -Definition at line \fB10\fP of file \fBKDB\&.cc\fP\&. -.SH "Member Function Documentation" -.PP -.SS "void bayesnet::KDB::buildModel (const torch::Tensor & weights)\fR [override]\fP, \fR [protected]\fP, \fR [virtual]\fP" - -.PP -Implements \fBbayesnet::Classifier\fP\&. -.PP -Definition at line \fB28\fP of file \fBKDB\&.cc\fP\&. -.SS "std::vector< std::string > bayesnet::KDB::graph (const std::string & name = \fR'KDB'\fP) const\fR [override]\fP, \fR [virtual]\fP" - -.PP -Implements \fBbayesnet::BaseClassifier\fP\&. -.PP -Definition at line \fB103\fP of file \fBKDB\&.cc\fP\&. -.SS "void bayesnet::KDB::setHyperparameters (const nlohmann::json & hyperparameters_)\fR [override]\fP, \fR [virtual]\fP" - -.PP -Reimplemented from \fBbayesnet::Classifier\fP\&. -.PP -Definition at line \fB15\fP of file \fBKDB\&.cc\fP\&. - -.SH "Author" -.PP -Generated automatically by Doxygen for BayesNet from the source code\&. diff --git a/docs/man3/bayesnet_KDBLd.3 b/docs/man3/bayesnet_KDBLd.3 deleted file mode 100644 index 4ed0bb5..0000000 --- a/docs/man3/bayesnet_KDBLd.3 +++ /dev/null @@ -1,254 +0,0 @@ -.TH "bayesnet::KDBLd" 3 "Version 1.0.5" "BayesNet" \" -*- nroff -*- -.ad l -.nh -.SH NAME -bayesnet::KDBLd -.SH SYNOPSIS -.br -.PP -.PP -Inherits \fBbayesnet::KDB\fP, and \fBbayesnet::Proposal\fP\&. -.SS "Public Member Functions" - -.in +1c -.ti -1c -.RI "\fBKDBLd\fP (int k)" -.br -.ti -1c -.RI "\fBKDBLd\fP & \fBfit\fP (torch::Tensor &X, torch::Tensor &y, const std::vector< std::string > &features, const std::string &className, map< std::string, std::vector< int > > &states) override" -.br -.ti -1c -.RI "std::vector< std::string > \fBgraph\fP (const std::string &name='KDB') const override" -.br -.ti -1c -.RI "torch::Tensor \fBpredict\fP (torch::Tensor &X) override" -.br -.in -1c - -Public Member Functions inherited from \fBbayesnet::KDB\fP -.in +1c -.ti -1c -.RI "\fBKDB\fP (int k, float theta=0\&.03)" -.br -.ti -1c -.RI "void \fBsetHyperparameters\fP (const nlohmann::json &hyperparameters_) override" -.br -.in -1c - -Public Member Functions inherited from \fBbayesnet::Classifier\fP -.in +1c -.ti -1c -.RI "\fBClassifier\fP (\fBNetwork\fP model)" -.br -.ti -1c -.RI "\fBClassifier\fP & \fBfit\fP (std::vector< std::vector< int > > &X, std::vector< int > &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override" -.br -.ti -1c -.RI "\fBClassifier\fP & \fBfit\fP (torch::Tensor &X, torch::Tensor &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override" -.br -.ti -1c -.RI "\fBClassifier\fP & \fBfit\fP (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override" -.br -.ti -1c -.RI "\fBClassifier\fP & \fBfit\fP (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states, const torch::Tensor &weights) override" -.br -.ti -1c -.RI "void \fBaddNodes\fP ()" -.br -.ti -1c -.RI "int \fBgetNumberOfNodes\fP () const override" -.br -.ti -1c -.RI "int \fBgetNumberOfEdges\fP () const override" -.br -.ti -1c -.RI "int \fBgetNumberOfStates\fP () const override" -.br -.ti -1c -.RI "int \fBgetClassNumStates\fP () const override" -.br -.ti -1c -.RI "std::vector< int > \fBpredict\fP (std::vector< std::vector< int > > &X) override" -.br -.ti -1c -.RI "torch::Tensor \fBpredict_proba\fP (torch::Tensor &X) override" -.br -.ti -1c -.RI "std::vector< std::vector< double > > \fBpredict_proba\fP (std::vector< std::vector< int > > &X) override" -.br -.ti -1c -.RI "status_t \fBgetStatus\fP () const override" -.br -.ti -1c -.RI "std::string \fBgetVersion\fP () override" -.br -.ti -1c -.RI "float \fBscore\fP (torch::Tensor &X, torch::Tensor &y) override" -.br -.ti -1c -.RI "float \fBscore\fP (std::vector< std::vector< int > > &X, std::vector< int > &y) override" -.br -.ti -1c -.RI "std::vector< std::string > \fBshow\fP () const override" -.br -.ti -1c -.RI "std::vector< std::string > \fBtopological_order\fP () override" -.br -.ti -1c -.RI "std::vector< std::string > \fBgetNotes\fP () const override" -.br -.ti -1c -.RI "std::string \fBdump_cpt\fP () const override" -.br -.in -1c - -Public Member Functions inherited from \fBbayesnet::BaseClassifier\fP -.in +1c -.ti -1c -.RI "std::vector< std::string > & \fBgetValidHyperparameters\fP ()" -.br -.in -1c - -Public Member Functions inherited from \fBbayesnet::Proposal\fP -.in +1c -.ti -1c -.RI "\fBProposal\fP (torch::Tensor &pDataset, std::vector< std::string > &features_, std::string &className_)" -.br -.in -1c -.SS "Static Public Member Functions" - -.in +1c -.ti -1c -.RI "static std::string \fBversion\fP ()" -.br -.in -1c -.SS "Additional Inherited Members" - - -Protected Member Functions inherited from \fBbayesnet::KDB\fP -.in +1c -.ti -1c -.RI "void \fBbuildModel\fP (const torch::Tensor &weights) override" -.br -.in -1c - -Protected Member Functions inherited from \fBbayesnet::Classifier\fP -.in +1c -.ti -1c -.RI "void \fBcheckFitParameters\fP ()" -.br -.ti -1c -.RI "void \fBtrainModel\fP (const torch::Tensor &weights) override" -.br -.ti -1c -.RI "void \fBbuildDataset\fP (torch::Tensor &y)" -.br -.in -1c - -Protected Member Functions inherited from \fBbayesnet::Proposal\fP -.in +1c -.ti -1c -.RI "void \fBcheckInput\fP (const torch::Tensor &X, const torch::Tensor &y)" -.br -.ti -1c -.RI "torch::Tensor \fBprepareX\fP (torch::Tensor &X)" -.br -.ti -1c -.RI "map< std::string, std::vector< int > > \fBlocalDiscretizationProposal\fP (const map< std::string, std::vector< int > > &states, \fBNetwork\fP &model)" -.br -.ti -1c -.RI "map< std::string, std::vector< int > > \fBfit_local_discretization\fP (const torch::Tensor &y)" -.br -.in -1c - -Protected Attributes inherited from \fBbayesnet::Classifier\fP -.in +1c -.ti -1c -.RI "bool \fBfitted\fP" -.br -.ti -1c -.RI "unsigned int \fBm\fP" -.br -.ti -1c -.RI "unsigned int \fBn\fP" -.br -.ti -1c -.RI "\fBNetwork\fP \fBmodel\fP" -.br -.ti -1c -.RI "Metrics \fBmetrics\fP" -.br -.ti -1c -.RI "std::vector< std::string > \fBfeatures\fP" -.br -.ti -1c -.RI "std::string \fBclassName\fP" -.br -.ti -1c -.RI "std::map< std::string, std::vector< int > > \fBstates\fP" -.br -.ti -1c -.RI "torch::Tensor \fBdataset\fP" -.br -.ti -1c -.RI "status_t \fBstatus\fP = NORMAL" -.br -.ti -1c -.RI "std::vector< std::string > \fBnotes\fP" -.br -.in -1c - -Protected Attributes inherited from \fBbayesnet::BaseClassifier\fP -.in +1c -.ti -1c -.RI "std::vector< std::string > \fBvalidHyperparameters\fP" -.br -.in -1c - -Protected Attributes inherited from \fBbayesnet::Proposal\fP -.in +1c -.ti -1c -.RI "torch::Tensor \fBXf\fP" -.br -.ti -1c -.RI "torch::Tensor \fBy\fP" -.br -.ti -1c -.RI "map< std::string, mdlp::CPPFImdlp * > \fBdiscretizers\fP" -.br -.in -1c -.SH "Detailed Description" -.PP -Definition at line \fB13\fP of file \fBKDBLd\&.h\fP\&. -.SH "Constructor & Destructor Documentation" -.PP -.SS "bayesnet::KDBLd::KDBLd (int k)\fR [explicit]\fP" - -.PP -Definition at line \fB10\fP of file \fBKDBLd\&.cc\fP\&. -.SH "Member Function Documentation" -.PP -.SS "\fBKDBLd\fP & bayesnet::KDBLd::fit (torch::Tensor & X, torch::Tensor & y, const std::vector< std::string > & features, const std::string & className, map< std::string, std::vector< int > > & states)\fR [override]\fP" - -.PP -Definition at line \fB11\fP of file \fBKDBLd\&.cc\fP\&. -.SS "std::vector< std::string > bayesnet::KDBLd::graph (const std::string & name = \fR'KDB'\fP) const\fR [override]\fP, \fR [virtual]\fP" - -.PP -Reimplemented from \fBbayesnet::KDB\fP\&. -.PP -Definition at line \fB31\fP of file \fBKDBLd\&.cc\fP\&. -.SS "torch::Tensor bayesnet::KDBLd::predict (torch::Tensor & X)\fR [override]\fP, \fR [virtual]\fP" - -.PP -Reimplemented from \fBbayesnet::Classifier\fP\&. -.PP -Definition at line \fB26\fP of file \fBKDBLd\&.cc\fP\&. -.SS "static std::string bayesnet::KDBLd::version ()\fR [inline]\fP, \fR [static]\fP" - -.PP -Definition at line \fB21\fP of file \fBKDBLd\&.h\fP\&. - -.SH "Author" -.PP -Generated automatically by Doxygen for BayesNet from the source code\&. diff --git a/docs/man3/bayesnet_Network.3 b/docs/man3/bayesnet_Network.3 deleted file mode 100644 index e5b40a9..0000000 --- a/docs/man3/bayesnet_Network.3 +++ /dev/null @@ -1,223 +0,0 @@ -.TH "bayesnet::Network" 3 "Version 1.0.5" "BayesNet" \" -*- nroff -*- -.ad l -.nh -.SH NAME -bayesnet::Network -.SH SYNOPSIS -.br -.PP -.SS "Public Member Functions" - -.in +1c -.ti -1c -.RI "\fBNetwork\fP (float)" -.br -.ti -1c -.RI "\fBNetwork\fP (const \fBNetwork\fP &)" -.br -.ti -1c -.RI "torch::Tensor & \fBgetSamples\fP ()" -.br -.ti -1c -.RI "float \fBgetMaxThreads\fP () const" -.br -.ti -1c -.RI "void \fBaddNode\fP (const std::string &)" -.br -.ti -1c -.RI "void \fBaddEdge\fP (const std::string &, const std::string &)" -.br -.ti -1c -.RI "std::map< std::string, std::unique_ptr< \fBNode\fP > > & \fBgetNodes\fP ()" -.br -.ti -1c -.RI "std::vector< std::string > \fBgetFeatures\fP () const" -.br -.ti -1c -.RI "int \fBgetStates\fP () const" -.br -.ti -1c -.RI "std::vector< std::pair< std::string, std::string > > \fBgetEdges\fP () const" -.br -.ti -1c -.RI "int \fBgetNumEdges\fP () const" -.br -.ti -1c -.RI "int \fBgetClassNumStates\fP () const" -.br -.ti -1c -.RI "std::string \fBgetClassName\fP () const" -.br -.ti -1c -.RI "void \fBfit\fP (const std::vector< std::vector< int > > &input_data, const std::vector< int > &labels, const std::vector< double > &weights, const std::vector< std::string > &featureNames, const std::string &className, const std::map< std::string, std::vector< int > > &states)" -.br -.ti -1c -.RI "void \fBfit\fP (const torch::Tensor &X, const torch::Tensor &y, const torch::Tensor &weights, const std::vector< std::string > &featureNames, const std::string &className, const std::map< std::string, std::vector< int > > &states)" -.br -.ti -1c -.RI "void \fBfit\fP (const torch::Tensor &samples, const torch::Tensor &weights, const std::vector< std::string > &featureNames, const std::string &className, const std::map< std::string, std::vector< int > > &states)" -.br -.ti -1c -.RI "std::vector< int > \fBpredict\fP (const std::vector< std::vector< int > > &)" -.br -.ti -1c -.RI "torch::Tensor \fBpredict\fP (const torch::Tensor &)" -.br -.ti -1c -.RI "torch::Tensor \fBpredict_tensor\fP (const torch::Tensor &samples, const bool proba)" -.br -.ti -1c -.RI "std::vector< std::vector< double > > \fBpredict_proba\fP (const std::vector< std::vector< int > > &)" -.br -.ti -1c -.RI "torch::Tensor \fBpredict_proba\fP (const torch::Tensor &)" -.br -.ti -1c -.RI "double \fBscore\fP (const std::vector< std::vector< int > > &, const std::vector< int > &)" -.br -.ti -1c -.RI "std::vector< std::string > \fBtopological_sort\fP ()" -.br -.ti -1c -.RI "std::vector< std::string > \fBshow\fP () const" -.br -.ti -1c -.RI "std::vector< std::string > \fBgraph\fP (const std::string &title) const" -.br -.ti -1c -.RI "void \fBinitialize\fP ()" -.br -.ti -1c -.RI "std::string \fBdump_cpt\fP () const" -.br -.ti -1c -.RI "std::string \fBversion\fP ()" -.br -.in -1c -.SH "Detailed Description" -.PP -Definition at line \fB15\fP of file \fBNetwork\&.h\fP\&. -.SH "Constructor & Destructor Documentation" -.PP -.SS "bayesnet::Network::Network ()" - -.PP -Definition at line \fB13\fP of file \fBNetwork\&.cc\fP\&. -.SS "bayesnet::Network::Network (float maxT)\fR [explicit]\fP" - -.PP -Definition at line \fB16\fP of file \fBNetwork\&.cc\fP\&. -.SS "bayesnet::Network::Network (const \fBNetwork\fP & other)\fR [explicit]\fP" - -.PP -Definition at line \fB20\fP of file \fBNetwork\&.cc\fP\&. -.SH "Member Function Documentation" -.PP -.SS "void bayesnet::Network::addEdge (const std::string & parent, const std::string & child)" - -.PP -Definition at line \fB95\fP of file \fBNetwork\&.cc\fP\&. -.SS "void bayesnet::Network::addNode (const std::string & name)" - -.PP -Definition at line \fB46\fP of file \fBNetwork\&.cc\fP\&. -.SS "std::string bayesnet::Network::dump_cpt () const" - -.PP -Definition at line \fB420\fP of file \fBNetwork\&.cc\fP\&. -.SS "void bayesnet::Network::fit (const std::vector< std::vector< int > > & input_data, const std::vector< int > & labels, const std::vector< double > & weights, const std::vector< std::string > & featureNames, const std::string & className, const std::map< std::string, std::vector< int > > & states)" - -.PP -Definition at line \fB177\fP of file \fBNetwork\&.cc\fP\&. -.SS "void bayesnet::Network::fit (const torch::Tensor & samples, const torch::Tensor & weights, const std::vector< std::string > & featureNames, const std::string & className, const std::map< std::string, std::vector< int > > & states)" - -.PP -Definition at line \fB169\fP of file \fBNetwork\&.cc\fP\&. -.SS "void bayesnet::Network::fit (const torch::Tensor & X, const torch::Tensor & y, const torch::Tensor & weights, const std::vector< std::string > & featureNames, const std::string & className, const std::map< std::string, std::vector< int > > & states)" - -.PP -Definition at line \fB158\fP of file \fBNetwork\&.cc\fP\&. -.SS "std::string bayesnet::Network::getClassName () const" - -.PP -Definition at line \fB75\fP of file \fBNetwork\&.cc\fP\&. -.SS "int bayesnet::Network::getClassNumStates () const" - -.PP -Definition at line \fB63\fP of file \fBNetwork\&.cc\fP\&. -.SS "std::vector< std::pair< std::string, std::string > > bayesnet::Network::getEdges () const" - -.PP -Definition at line \fB371\fP of file \fBNetwork\&.cc\fP\&. -.SS "std::vector< std::string > bayesnet::Network::getFeatures () const" - -.PP -Definition at line \fB59\fP of file \fBNetwork\&.cc\fP\&. -.SS "float bayesnet::Network::getMaxThreads () const" - -.PP -Definition at line \fB38\fP of file \fBNetwork\&.cc\fP\&. -.SS "std::map< std::string, std::unique_ptr< \fBNode\fP > > & bayesnet::Network::getNodes ()" - -.PP -Definition at line \fB116\fP of file \fBNetwork\&.cc\fP\&. -.SS "int bayesnet::Network::getNumEdges () const" - -.PP -Definition at line \fB383\fP of file \fBNetwork\&.cc\fP\&. -.SS "torch::Tensor & bayesnet::Network::getSamples ()" - -.PP -Definition at line \fB42\fP of file \fBNetwork\&.cc\fP\&. -.SS "int bayesnet::Network::getStates () const" - -.PP -Definition at line \fB67\fP of file \fBNetwork\&.cc\fP\&. -.SS "std::vector< std::string > bayesnet::Network::graph (const std::string & title) const" - -.PP -Definition at line \fB357\fP of file \fBNetwork\&.cc\fP\&. -.SS "void bayesnet::Network::initialize ()" - -.PP -Definition at line \fB29\fP of file \fBNetwork\&.cc\fP\&. -.SS "std::vector< int > bayesnet::Network::predict (const std::vector< std::vector< int > > & tsamples)" - -.PP -Definition at line \fB237\fP of file \fBNetwork\&.cc\fP\&. -.SS "torch::Tensor bayesnet::Network::predict (const torch::Tensor & samples)" - -.PP -Definition at line \fB230\fP of file \fBNetwork\&.cc\fP\&. -.SS "std::vector< std::vector< double > > bayesnet::Network::predict_proba (const std::vector< std::vector< int > > & tsamples)" - -.PP -Definition at line \fB259\fP of file \fBNetwork\&.cc\fP\&. -.SS "torch::Tensor bayesnet::Network::predict_proba (const torch::Tensor & samples)" - -.PP -Definition at line \fB224\fP of file \fBNetwork\&.cc\fP\&. -.SS "torch::Tensor bayesnet::Network::predict_tensor (const torch::Tensor & samples, const bool proba)" - -.PP -Definition at line \fB205\fP of file \fBNetwork\&.cc\fP\&. -.SS "double bayesnet::Network::score (const std::vector< std::vector< int > > & tsamples, const std::vector< int > & labels)" - -.PP -Definition at line \fB275\fP of file \fBNetwork\&.cc\fP\&. -.SS "std::vector< std::string > bayesnet::Network::show () const" - -.PP -Definition at line \fB344\fP of file \fBNetwork\&.cc\fP\&. -.SS "std::vector< std::string > bayesnet::Network::topological_sort ()" - -.PP -Definition at line \fB387\fP of file \fBNetwork\&.cc\fP\&. -.SS "std::string bayesnet::Network::version ()\fR [inline]\fP" - -.PP -Definition at line \fB49\fP of file \fBNetwork\&.h\fP\&. - -.SH "Author" -.PP -Generated automatically by Doxygen for BayesNet from the source code\&. diff --git a/docs/man3/bayesnet_Node.3 b/docs/man3/bayesnet_Node.3 deleted file mode 100644 index d3279e4..0000000 --- a/docs/man3/bayesnet_Node.3 +++ /dev/null @@ -1,135 +0,0 @@ -.TH "bayesnet::Node" 3 "Version 1.0.5" "BayesNet" \" -*- nroff -*- -.ad l -.nh -.SH NAME -bayesnet::Node -.SH SYNOPSIS -.br -.PP -.SS "Public Member Functions" - -.in +1c -.ti -1c -.RI "\fBNode\fP (const std::string &)" -.br -.ti -1c -.RI "void \fBclear\fP ()" -.br -.ti -1c -.RI "void \fBaddParent\fP (\fBNode\fP *)" -.br -.ti -1c -.RI "void \fBaddChild\fP (\fBNode\fP *)" -.br -.ti -1c -.RI "void \fBremoveParent\fP (\fBNode\fP *)" -.br -.ti -1c -.RI "void \fBremoveChild\fP (\fBNode\fP *)" -.br -.ti -1c -.RI "std::string \fBgetName\fP () const" -.br -.ti -1c -.RI "std::vector< \fBNode\fP * > & \fBgetParents\fP ()" -.br -.ti -1c -.RI "std::vector< \fBNode\fP * > & \fBgetChildren\fP ()" -.br -.ti -1c -.RI "torch::Tensor & \fBgetCPT\fP ()" -.br -.ti -1c -.RI "void \fBcomputeCPT\fP (const torch::Tensor &dataset, const std::vector< std::string > &features, const double laplaceSmoothing, const torch::Tensor &weights)" -.br -.ti -1c -.RI "int \fBgetNumStates\fP () const" -.br -.ti -1c -.RI "void \fBsetNumStates\fP (int)" -.br -.ti -1c -.RI "unsigned \fBminFill\fP ()" -.br -.ti -1c -.RI "std::vector< std::string > \fBgraph\fP (const std::string &clasName)" -.br -.ti -1c -.RI "float \fBgetFactorValue\fP (std::map< std::string, int > &)" -.br -.in -1c -.SH "Detailed Description" -.PP -Definition at line \fB14\fP of file \fBNode\&.h\fP\&. -.SH "Constructor & Destructor Documentation" -.PP -.SS "bayesnet::Node::Node (const std::string & name)\fR [explicit]\fP" - -.PP -Definition at line \fB11\fP of file \fBNode\&.cc\fP\&. -.SH "Member Function Documentation" -.PP -.SS "void bayesnet::Node::addChild (\fBNode\fP * child)" - -.PP -Definition at line \fB39\fP of file \fBNode\&.cc\fP\&. -.SS "void bayesnet::Node::addParent (\fBNode\fP * parent)" - -.PP -Definition at line \fB27\fP of file \fBNode\&.cc\fP\&. -.SS "void bayesnet::Node::clear ()" - -.PP -Definition at line \fB15\fP of file \fBNode\&.cc\fP\&. -.SS "void bayesnet::Node::computeCPT (const torch::Tensor & dataset, const std::vector< std::string > & features, const double laplaceSmoothing, const torch::Tensor & weights)" - -.PP -Definition at line \fB93\fP of file \fBNode\&.cc\fP\&. -.SS "std::vector< \fBNode\fP * > & bayesnet::Node::getChildren ()" - -.PP -Definition at line \fB47\fP of file \fBNode\&.cc\fP\&. -.SS "torch::Tensor & bayesnet::Node::getCPT ()" - -.PP -Definition at line \fB59\fP of file \fBNode\&.cc\fP\&. -.SS "float bayesnet::Node::getFactorValue (std::map< std::string, int > & evidence)" - -.PP -Definition at line \fB124\fP of file \fBNode\&.cc\fP\&. -.SS "std::string bayesnet::Node::getName () const" - -.PP -Definition at line \fB23\fP of file \fBNode\&.cc\fP\&. -.SS "int bayesnet::Node::getNumStates () const" - -.PP -Definition at line \fB51\fP of file \fBNode\&.cc\fP\&. -.SS "std::vector< \fBNode\fP * > & bayesnet::Node::getParents ()" - -.PP -Definition at line \fB43\fP of file \fBNode\&.cc\fP\&. -.SS "std::vector< std::string > bayesnet::Node::graph (const std::string & clasName)" - -.PP -Definition at line \fB132\fP of file \fBNode\&.cc\fP\&. -.SS "unsigned bayesnet::Node::minFill ()" - -.PP -Definition at line \fB70\fP of file \fBNode\&.cc\fP\&. -.SS "void bayesnet::Node::removeChild (\fBNode\fP * child)" - -.PP -Definition at line \fB35\fP of file \fBNode\&.cc\fP\&. -.SS "void bayesnet::Node::removeParent (\fBNode\fP * parent)" - -.PP -Definition at line \fB31\fP of file \fBNode\&.cc\fP\&. -.SS "void bayesnet::Node::setNumStates (int numStates)" - -.PP -Definition at line \fB55\fP of file \fBNode\&.cc\fP\&. - -.SH "Author" -.PP -Generated automatically by Doxygen for BayesNet from the source code\&. diff --git a/docs/man3/bayesnet_Proposal.3 b/docs/man3/bayesnet_Proposal.3 deleted file mode 100644 index ad37186..0000000 --- a/docs/man3/bayesnet_Proposal.3 +++ /dev/null @@ -1,95 +0,0 @@ -.TH "bayesnet::Proposal" 3 "Version 1.0.5" "BayesNet" \" -*- nroff -*- -.ad l -.nh -.SH NAME -bayesnet::Proposal -.SH SYNOPSIS -.br -.PP -.PP -Inherited by \fBbayesnet::AODELd\fP, \fBbayesnet::KDBLd\fP, \fBbayesnet::SPODELd\fP, and \fBbayesnet::TANLd\fP\&. -.SS "Public Member Functions" - -.in +1c -.ti -1c -.RI "\fBProposal\fP (torch::Tensor &pDataset, std::vector< std::string > &features_, std::string &className_)" -.br -.in -1c -.SS "Protected Member Functions" - -.in +1c -.ti -1c -.RI "void \fBcheckInput\fP (const torch::Tensor &X, const torch::Tensor &y)" -.br -.ti -1c -.RI "torch::Tensor \fBprepareX\fP (torch::Tensor &X)" -.br -.ti -1c -.RI "map< std::string, std::vector< int > > \fBlocalDiscretizationProposal\fP (const map< std::string, std::vector< int > > &states, \fBNetwork\fP &model)" -.br -.ti -1c -.RI "map< std::string, std::vector< int > > \fBfit_local_discretization\fP (const torch::Tensor &y)" -.br -.in -1c -.SS "Protected Attributes" - -.in +1c -.ti -1c -.RI "torch::Tensor \fBXf\fP" -.br -.ti -1c -.RI "torch::Tensor \fBy\fP" -.br -.ti -1c -.RI "map< std::string, mdlp::CPPFImdlp * > \fBdiscretizers\fP" -.br -.in -1c -.SH "Detailed Description" -.PP -Definition at line \fB17\fP of file \fBProposal\&.h\fP\&. -.SH "Constructor & Destructor Documentation" -.PP -.SS "bayesnet::Proposal::Proposal (torch::Tensor & pDataset, std::vector< std::string > & features_, std::string & className_)" - -.PP -Definition at line \fB10\fP of file \fBProposal\&.cc\fP\&. -.SS "bayesnet::Proposal::~Proposal ()\fR [virtual]\fP" - -.PP -Definition at line \fB11\fP of file \fBProposal\&.cc\fP\&. -.SH "Member Function Documentation" -.PP -.SS "void bayesnet::Proposal::checkInput (const torch::Tensor & X, const torch::Tensor & y)\fR [protected]\fP" - -.PP -Definition at line \fB17\fP of file \fBProposal\&.cc\fP\&. -.SS "map< std::string, std::vector< int > > bayesnet::Proposal::fit_local_discretization (const torch::Tensor & y)\fR [protected]\fP" - -.PP -Definition at line \fB77\fP of file \fBProposal\&.cc\fP\&. -.SS "map< std::string, std::vector< int > > bayesnet::Proposal::localDiscretizationProposal (const map< std::string, std::vector< int > > & states, \fBNetwork\fP & model)\fR [protected]\fP" - -.PP -Definition at line \fB26\fP of file \fBProposal\&.cc\fP\&. -.SS "torch::Tensor bayesnet::Proposal::prepareX (torch::Tensor & X)\fR [protected]\fP" - -.PP -Definition at line \fB104\fP of file \fBProposal\&.cc\fP\&. -.SH "Member Data Documentation" -.PP -.SS "map bayesnet::Proposal::discretizers\fR [protected]\fP" - -.PP -Definition at line \fB28\fP of file \fBProposal\&.h\fP\&. -.SS "torch::Tensor bayesnet::Proposal::Xf\fR [protected]\fP" - -.PP -Definition at line \fB26\fP of file \fBProposal\&.h\fP\&. -.SS "torch::Tensor bayesnet::Proposal::y\fR [protected]\fP" - -.PP -Definition at line \fB27\fP of file \fBProposal\&.h\fP\&. - -.SH "Author" -.PP -Generated automatically by Doxygen for BayesNet from the source code\&. diff --git a/docs/man3/bayesnet_SPODE.3 b/docs/man3/bayesnet_SPODE.3 deleted file mode 100644 index d75d468..0000000 --- a/docs/man3/bayesnet_SPODE.3 +++ /dev/null @@ -1,195 +0,0 @@ -.TH "bayesnet::SPODE" 3 "Version 1.0.5" "BayesNet" \" -*- nroff -*- -.ad l -.nh -.SH NAME -bayesnet::SPODE -.SH SYNOPSIS -.br -.PP -.PP -Inherits \fBbayesnet::Classifier\fP\&. -.PP -Inherited by \fBbayesnet::SPODELd\fP\&. -.SS "Public Member Functions" - -.in +1c -.ti -1c -.RI "\fBSPODE\fP (int root)" -.br -.ti -1c -.RI "std::vector< std::string > \fBgraph\fP (const std::string &name='SPODE') const override" -.br -.in -1c - -Public Member Functions inherited from \fBbayesnet::Classifier\fP -.in +1c -.ti -1c -.RI "\fBClassifier\fP (\fBNetwork\fP model)" -.br -.ti -1c -.RI "\fBClassifier\fP & \fBfit\fP (std::vector< std::vector< int > > &X, std::vector< int > &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override" -.br -.ti -1c -.RI "\fBClassifier\fP & \fBfit\fP (torch::Tensor &X, torch::Tensor &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override" -.br -.ti -1c -.RI "\fBClassifier\fP & \fBfit\fP (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override" -.br -.ti -1c -.RI "\fBClassifier\fP & \fBfit\fP (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states, const torch::Tensor &weights) override" -.br -.ti -1c -.RI "void \fBaddNodes\fP ()" -.br -.ti -1c -.RI "int \fBgetNumberOfNodes\fP () const override" -.br -.ti -1c -.RI "int \fBgetNumberOfEdges\fP () const override" -.br -.ti -1c -.RI "int \fBgetNumberOfStates\fP () const override" -.br -.ti -1c -.RI "int \fBgetClassNumStates\fP () const override" -.br -.ti -1c -.RI "torch::Tensor \fBpredict\fP (torch::Tensor &X) override" -.br -.ti -1c -.RI "std::vector< int > \fBpredict\fP (std::vector< std::vector< int > > &X) override" -.br -.ti -1c -.RI "torch::Tensor \fBpredict_proba\fP (torch::Tensor &X) override" -.br -.ti -1c -.RI "std::vector< std::vector< double > > \fBpredict_proba\fP (std::vector< std::vector< int > > &X) override" -.br -.ti -1c -.RI "status_t \fBgetStatus\fP () const override" -.br -.ti -1c -.RI "std::string \fBgetVersion\fP () override" -.br -.ti -1c -.RI "float \fBscore\fP (torch::Tensor &X, torch::Tensor &y) override" -.br -.ti -1c -.RI "float \fBscore\fP (std::vector< std::vector< int > > &X, std::vector< int > &y) override" -.br -.ti -1c -.RI "std::vector< std::string > \fBshow\fP () const override" -.br -.ti -1c -.RI "std::vector< std::string > \fBtopological_order\fP () override" -.br -.ti -1c -.RI "std::vector< std::string > \fBgetNotes\fP () const override" -.br -.ti -1c -.RI "std::string \fBdump_cpt\fP () const override" -.br -.ti -1c -.RI "void \fBsetHyperparameters\fP (const nlohmann::json &hyperparameters) override" -.br -.in -1c - -Public Member Functions inherited from \fBbayesnet::BaseClassifier\fP -.in +1c -.ti -1c -.RI "std::vector< std::string > & \fBgetValidHyperparameters\fP ()" -.br -.in -1c -.SS "Protected Member Functions" - -.in +1c -.ti -1c -.RI "void \fBbuildModel\fP (const torch::Tensor &weights) override" -.br -.in -1c - -Protected Member Functions inherited from \fBbayesnet::Classifier\fP -.in +1c -.ti -1c -.RI "void \fBcheckFitParameters\fP ()" -.br -.ti -1c -.RI "void \fBtrainModel\fP (const torch::Tensor &weights) override" -.br -.ti -1c -.RI "void \fBbuildDataset\fP (torch::Tensor &y)" -.br -.in -1c -.SS "Additional Inherited Members" - - -Protected Attributes inherited from \fBbayesnet::Classifier\fP -.in +1c -.ti -1c -.RI "bool \fBfitted\fP" -.br -.ti -1c -.RI "unsigned int \fBm\fP" -.br -.ti -1c -.RI "unsigned int \fBn\fP" -.br -.ti -1c -.RI "\fBNetwork\fP \fBmodel\fP" -.br -.ti -1c -.RI "Metrics \fBmetrics\fP" -.br -.ti -1c -.RI "std::vector< std::string > \fBfeatures\fP" -.br -.ti -1c -.RI "std::string \fBclassName\fP" -.br -.ti -1c -.RI "std::map< std::string, std::vector< int > > \fBstates\fP" -.br -.ti -1c -.RI "torch::Tensor \fBdataset\fP" -.br -.ti -1c -.RI "status_t \fBstatus\fP = NORMAL" -.br -.ti -1c -.RI "std::vector< std::string > \fBnotes\fP" -.br -.in -1c - -Protected Attributes inherited from \fBbayesnet::BaseClassifier\fP -.in +1c -.ti -1c -.RI "std::vector< std::string > \fBvalidHyperparameters\fP" -.br -.in -1c -.SH "Detailed Description" -.PP -Definition at line \fB12\fP of file \fBSPODE\&.h\fP\&. -.SH "Constructor & Destructor Documentation" -.PP -.SS "bayesnet::SPODE::SPODE (int root)\fR [explicit]\fP" - -.PP -Definition at line \fB11\fP of file \fBSPODE\&.cc\fP\&. -.SH "Member Function Documentation" -.PP -.SS "void bayesnet::SPODE::buildModel (const torch::Tensor & weights)\fR [override]\fP, \fR [protected]\fP, \fR [virtual]\fP" - -.PP -Implements \fBbayesnet::Classifier\fP\&. -.PP -Definition at line \fB13\fP of file \fBSPODE\&.cc\fP\&. -.SS "std::vector< std::string > bayesnet::SPODE::graph (const std::string & name = \fR'SPODE'\fP) const\fR [override]\fP, \fR [virtual]\fP" - -.PP -Implements \fBbayesnet::BaseClassifier\fP\&. -.PP -Definition at line \fB26\fP of file \fBSPODE\&.cc\fP\&. - -.SH "Author" -.PP -Generated automatically by Doxygen for BayesNet from the source code\&. diff --git a/docs/man3/bayesnet_SPODELd.3 b/docs/man3/bayesnet_SPODELd.3 deleted file mode 100644 index 43e1bdc..0000000 --- a/docs/man3/bayesnet_SPODELd.3 +++ /dev/null @@ -1,268 +0,0 @@ -.TH "bayesnet::SPODELd" 3 "Version 1.0.5" "BayesNet" \" -*- nroff -*- -.ad l -.nh -.SH NAME -bayesnet::SPODELd -.SH SYNOPSIS -.br -.PP -.PP -Inherits \fBbayesnet::SPODE\fP, and \fBbayesnet::Proposal\fP\&. -.SS "Public Member Functions" - -.in +1c -.ti -1c -.RI "\fBSPODELd\fP (int root)" -.br -.ti -1c -.RI "\fBSPODELd\fP & \fBfit\fP (torch::Tensor &X, torch::Tensor &y, const std::vector< std::string > &features, const std::string &className, map< std::string, std::vector< int > > &states) override" -.br -.ti -1c -.RI "\fBSPODELd\fP & \fBfit\fP (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, map< std::string, std::vector< int > > &states) override" -.br -.ti -1c -.RI "\fBSPODELd\fP & \fBcommonFit\fP (const std::vector< std::string > &features, const std::string &className, map< std::string, std::vector< int > > &states)" -.br -.ti -1c -.RI "std::vector< std::string > \fBgraph\fP (const std::string &name='SPODE') const override" -.br -.ti -1c -.RI "torch::Tensor \fBpredict\fP (torch::Tensor &X) override" -.br -.in -1c - -Public Member Functions inherited from \fBbayesnet::SPODE\fP -.in +1c -.ti -1c -.RI "\fBSPODE\fP (int root)" -.br -.in -1c - -Public Member Functions inherited from \fBbayesnet::Classifier\fP -.in +1c -.ti -1c -.RI "\fBClassifier\fP (\fBNetwork\fP model)" -.br -.ti -1c -.RI "\fBClassifier\fP & \fBfit\fP (std::vector< std::vector< int > > &X, std::vector< int > &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override" -.br -.ti -1c -.RI "\fBClassifier\fP & \fBfit\fP (torch::Tensor &X, torch::Tensor &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override" -.br -.ti -1c -.RI "\fBClassifier\fP & \fBfit\fP (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override" -.br -.ti -1c -.RI "\fBClassifier\fP & \fBfit\fP (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states, const torch::Tensor &weights) override" -.br -.ti -1c -.RI "void \fBaddNodes\fP ()" -.br -.ti -1c -.RI "int \fBgetNumberOfNodes\fP () const override" -.br -.ti -1c -.RI "int \fBgetNumberOfEdges\fP () const override" -.br -.ti -1c -.RI "int \fBgetNumberOfStates\fP () const override" -.br -.ti -1c -.RI "int \fBgetClassNumStates\fP () const override" -.br -.ti -1c -.RI "std::vector< int > \fBpredict\fP (std::vector< std::vector< int > > &X) override" -.br -.ti -1c -.RI "torch::Tensor \fBpredict_proba\fP (torch::Tensor &X) override" -.br -.ti -1c -.RI "std::vector< std::vector< double > > \fBpredict_proba\fP (std::vector< std::vector< int > > &X) override" -.br -.ti -1c -.RI "status_t \fBgetStatus\fP () const override" -.br -.ti -1c -.RI "std::string \fBgetVersion\fP () override" -.br -.ti -1c -.RI "float \fBscore\fP (torch::Tensor &X, torch::Tensor &y) override" -.br -.ti -1c -.RI "float \fBscore\fP (std::vector< std::vector< int > > &X, std::vector< int > &y) override" -.br -.ti -1c -.RI "std::vector< std::string > \fBshow\fP () const override" -.br -.ti -1c -.RI "std::vector< std::string > \fBtopological_order\fP () override" -.br -.ti -1c -.RI "std::vector< std::string > \fBgetNotes\fP () const override" -.br -.ti -1c -.RI "std::string \fBdump_cpt\fP () const override" -.br -.ti -1c -.RI "void \fBsetHyperparameters\fP (const nlohmann::json &hyperparameters) override" -.br -.in -1c - -Public Member Functions inherited from \fBbayesnet::BaseClassifier\fP -.in +1c -.ti -1c -.RI "std::vector< std::string > & \fBgetValidHyperparameters\fP ()" -.br -.in -1c - -Public Member Functions inherited from \fBbayesnet::Proposal\fP -.in +1c -.ti -1c -.RI "\fBProposal\fP (torch::Tensor &pDataset, std::vector< std::string > &features_, std::string &className_)" -.br -.in -1c -.SS "Static Public Member Functions" - -.in +1c -.ti -1c -.RI "static std::string \fBversion\fP ()" -.br -.in -1c -.SS "Additional Inherited Members" - - -Protected Member Functions inherited from \fBbayesnet::SPODE\fP -.in +1c -.ti -1c -.RI "void \fBbuildModel\fP (const torch::Tensor &weights) override" -.br -.in -1c - -Protected Member Functions inherited from \fBbayesnet::Classifier\fP -.in +1c -.ti -1c -.RI "void \fBcheckFitParameters\fP ()" -.br -.ti -1c -.RI "void \fBtrainModel\fP (const torch::Tensor &weights) override" -.br -.ti -1c -.RI "void \fBbuildDataset\fP (torch::Tensor &y)" -.br -.in -1c - -Protected Member Functions inherited from \fBbayesnet::Proposal\fP -.in +1c -.ti -1c -.RI "void \fBcheckInput\fP (const torch::Tensor &X, const torch::Tensor &y)" -.br -.ti -1c -.RI "torch::Tensor \fBprepareX\fP (torch::Tensor &X)" -.br -.ti -1c -.RI "map< std::string, std::vector< int > > \fBlocalDiscretizationProposal\fP (const map< std::string, std::vector< int > > &states, \fBNetwork\fP &model)" -.br -.ti -1c -.RI "map< std::string, std::vector< int > > \fBfit_local_discretization\fP (const torch::Tensor &y)" -.br -.in -1c - -Protected Attributes inherited from \fBbayesnet::Classifier\fP -.in +1c -.ti -1c -.RI "bool \fBfitted\fP" -.br -.ti -1c -.RI "unsigned int \fBm\fP" -.br -.ti -1c -.RI "unsigned int \fBn\fP" -.br -.ti -1c -.RI "\fBNetwork\fP \fBmodel\fP" -.br -.ti -1c -.RI "Metrics \fBmetrics\fP" -.br -.ti -1c -.RI "std::vector< std::string > \fBfeatures\fP" -.br -.ti -1c -.RI "std::string \fBclassName\fP" -.br -.ti -1c -.RI "std::map< std::string, std::vector< int > > \fBstates\fP" -.br -.ti -1c -.RI "torch::Tensor \fBdataset\fP" -.br -.ti -1c -.RI "status_t \fBstatus\fP = NORMAL" -.br -.ti -1c -.RI "std::vector< std::string > \fBnotes\fP" -.br -.in -1c - -Protected Attributes inherited from \fBbayesnet::BaseClassifier\fP -.in +1c -.ti -1c -.RI "std::vector< std::string > \fBvalidHyperparameters\fP" -.br -.in -1c - -Protected Attributes inherited from \fBbayesnet::Proposal\fP -.in +1c -.ti -1c -.RI "torch::Tensor \fBXf\fP" -.br -.ti -1c -.RI "torch::Tensor \fBy\fP" -.br -.ti -1c -.RI "map< std::string, mdlp::CPPFImdlp * > \fBdiscretizers\fP" -.br -.in -1c -.SH "Detailed Description" -.PP -Definition at line \fB13\fP of file \fBSPODELd\&.h\fP\&. -.SH "Constructor & Destructor Documentation" -.PP -.SS "bayesnet::SPODELd::SPODELd (int root)\fR [explicit]\fP" - -.PP -Definition at line \fB10\fP of file \fBSPODELd\&.cc\fP\&. -.SH "Member Function Documentation" -.PP -.SS "\fBSPODELd\fP & bayesnet::SPODELd::commonFit (const std::vector< std::string > & features, const std::string & className, map< std::string, std::vector< int > > & states)" - -.PP -Definition at line \fB29\fP of file \fBSPODELd\&.cc\fP\&. -.SS "\fBSPODELd\fP & bayesnet::SPODELd::fit (torch::Tensor & dataset, const std::vector< std::string > & features, const std::string & className, map< std::string, std::vector< int > > & states)\fR [override]\fP" - -.PP -Definition at line \fB19\fP of file \fBSPODELd\&.cc\fP\&. -.SS "\fBSPODELd\fP & bayesnet::SPODELd::fit (torch::Tensor & X, torch::Tensor & y, const std::vector< std::string > & features, const std::string & className, map< std::string, std::vector< int > > & states)\fR [override]\fP" - -.PP -Definition at line \fB11\fP of file \fBSPODELd\&.cc\fP\&. -.SS "std::vector< std::string > bayesnet::SPODELd::graph (const std::string & name = \fR'SPODE'\fP) const\fR [override]\fP, \fR [virtual]\fP" - -.PP -Reimplemented from \fBbayesnet::SPODE\fP\&. -.PP -Definition at line \fB46\fP of file \fBSPODELd\&.cc\fP\&. -.SS "torch::Tensor bayesnet::SPODELd::predict (torch::Tensor & X)\fR [override]\fP, \fR [virtual]\fP" - -.PP -Reimplemented from \fBbayesnet::Classifier\fP\&. -.PP -Definition at line \fB41\fP of file \fBSPODELd\&.cc\fP\&. -.SS "static std::string bayesnet::SPODELd::version ()\fR [inline]\fP, \fR [static]\fP" - -.PP -Definition at line \fB22\fP of file \fBSPODELd\&.h\fP\&. - -.SH "Author" -.PP -Generated automatically by Doxygen for BayesNet from the source code\&. diff --git a/docs/man3/bayesnet_SPnDE.3 b/docs/man3/bayesnet_SPnDE.3 deleted file mode 100644 index afb6e61..0000000 --- a/docs/man3/bayesnet_SPnDE.3 +++ /dev/null @@ -1,193 +0,0 @@ -.TH "bayesnet::SPnDE" 3 "Version 1.0.5" "BayesNet" \" -*- nroff -*- -.ad l -.nh -.SH NAME -bayesnet::SPnDE -.SH SYNOPSIS -.br -.PP -.PP -Inherits \fBbayesnet::Classifier\fP\&. -.SS "Public Member Functions" - -.in +1c -.ti -1c -.RI "\fBSPnDE\fP (std::vector< int > parents)" -.br -.ti -1c -.RI "std::vector< std::string > \fBgraph\fP (const std::string &name='SPnDE') const override" -.br -.in -1c - -Public Member Functions inherited from \fBbayesnet::Classifier\fP -.in +1c -.ti -1c -.RI "\fBClassifier\fP (\fBNetwork\fP model)" -.br -.ti -1c -.RI "\fBClassifier\fP & \fBfit\fP (std::vector< std::vector< int > > &X, std::vector< int > &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override" -.br -.ti -1c -.RI "\fBClassifier\fP & \fBfit\fP (torch::Tensor &X, torch::Tensor &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override" -.br -.ti -1c -.RI "\fBClassifier\fP & \fBfit\fP (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override" -.br -.ti -1c -.RI "\fBClassifier\fP & \fBfit\fP (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states, const torch::Tensor &weights) override" -.br -.ti -1c -.RI "void \fBaddNodes\fP ()" -.br -.ti -1c -.RI "int \fBgetNumberOfNodes\fP () const override" -.br -.ti -1c -.RI "int \fBgetNumberOfEdges\fP () const override" -.br -.ti -1c -.RI "int \fBgetNumberOfStates\fP () const override" -.br -.ti -1c -.RI "int \fBgetClassNumStates\fP () const override" -.br -.ti -1c -.RI "torch::Tensor \fBpredict\fP (torch::Tensor &X) override" -.br -.ti -1c -.RI "std::vector< int > \fBpredict\fP (std::vector< std::vector< int > > &X) override" -.br -.ti -1c -.RI "torch::Tensor \fBpredict_proba\fP (torch::Tensor &X) override" -.br -.ti -1c -.RI "std::vector< std::vector< double > > \fBpredict_proba\fP (std::vector< std::vector< int > > &X) override" -.br -.ti -1c -.RI "status_t \fBgetStatus\fP () const override" -.br -.ti -1c -.RI "std::string \fBgetVersion\fP () override" -.br -.ti -1c -.RI "float \fBscore\fP (torch::Tensor &X, torch::Tensor &y) override" -.br -.ti -1c -.RI "float \fBscore\fP (std::vector< std::vector< int > > &X, std::vector< int > &y) override" -.br -.ti -1c -.RI "std::vector< std::string > \fBshow\fP () const override" -.br -.ti -1c -.RI "std::vector< std::string > \fBtopological_order\fP () override" -.br -.ti -1c -.RI "std::vector< std::string > \fBgetNotes\fP () const override" -.br -.ti -1c -.RI "std::string \fBdump_cpt\fP () const override" -.br -.ti -1c -.RI "void \fBsetHyperparameters\fP (const nlohmann::json &hyperparameters) override" -.br -.in -1c - -Public Member Functions inherited from \fBbayesnet::BaseClassifier\fP -.in +1c -.ti -1c -.RI "std::vector< std::string > & \fBgetValidHyperparameters\fP ()" -.br -.in -1c -.SS "Protected Member Functions" - -.in +1c -.ti -1c -.RI "void \fBbuildModel\fP (const torch::Tensor &weights) override" -.br -.in -1c - -Protected Member Functions inherited from \fBbayesnet::Classifier\fP -.in +1c -.ti -1c -.RI "void \fBcheckFitParameters\fP ()" -.br -.ti -1c -.RI "void \fBtrainModel\fP (const torch::Tensor &weights) override" -.br -.ti -1c -.RI "void \fBbuildDataset\fP (torch::Tensor &y)" -.br -.in -1c -.SS "Additional Inherited Members" - - -Protected Attributes inherited from \fBbayesnet::Classifier\fP -.in +1c -.ti -1c -.RI "bool \fBfitted\fP" -.br -.ti -1c -.RI "unsigned int \fBm\fP" -.br -.ti -1c -.RI "unsigned int \fBn\fP" -.br -.ti -1c -.RI "\fBNetwork\fP \fBmodel\fP" -.br -.ti -1c -.RI "Metrics \fBmetrics\fP" -.br -.ti -1c -.RI "std::vector< std::string > \fBfeatures\fP" -.br -.ti -1c -.RI "std::string \fBclassName\fP" -.br -.ti -1c -.RI "std::map< std::string, std::vector< int > > \fBstates\fP" -.br -.ti -1c -.RI "torch::Tensor \fBdataset\fP" -.br -.ti -1c -.RI "status_t \fBstatus\fP = NORMAL" -.br -.ti -1c -.RI "std::vector< std::string > \fBnotes\fP" -.br -.in -1c - -Protected Attributes inherited from \fBbayesnet::BaseClassifier\fP -.in +1c -.ti -1c -.RI "std::vector< std::string > \fBvalidHyperparameters\fP" -.br -.in -1c -.SH "Detailed Description" -.PP -Definition at line \fB13\fP of file \fBSPnDE\&.h\fP\&. -.SH "Constructor & Destructor Documentation" -.PP -.SS "bayesnet::SPnDE::SPnDE (std::vector< int > parents)\fR [explicit]\fP" - -.PP -Definition at line \fB11\fP of file \fBSPnDE\&.cc\fP\&. -.SH "Member Function Documentation" -.PP -.SS "void bayesnet::SPnDE::buildModel (const torch::Tensor & weights)\fR [override]\fP, \fR [protected]\fP, \fR [virtual]\fP" - -.PP -Implements \fBbayesnet::Classifier\fP\&. -.PP -Definition at line \fB13\fP of file \fBSPnDE\&.cc\fP\&. -.SS "std::vector< std::string > bayesnet::SPnDE::graph (const std::string & name = \fR'SPnDE'\fP) const\fR [override]\fP, \fR [virtual]\fP" - -.PP -Implements \fBbayesnet::BaseClassifier\fP\&. -.PP -Definition at line \fB33\fP of file \fBSPnDE\&.cc\fP\&. - -.SH "Author" -.PP -Generated automatically by Doxygen for BayesNet from the source code\&. diff --git a/docs/man3/bayesnet_TAN.3 b/docs/man3/bayesnet_TAN.3 deleted file mode 100644 index 1aa8b71..0000000 --- a/docs/man3/bayesnet_TAN.3 +++ /dev/null @@ -1,192 +0,0 @@ -.TH "bayesnet::TAN" 3 "Version 1.0.5" "BayesNet" \" -*- nroff -*- -.ad l -.nh -.SH NAME -bayesnet::TAN -.SH SYNOPSIS -.br -.PP -.PP -Inherits \fBbayesnet::Classifier\fP\&. -.PP -Inherited by \fBbayesnet::TANLd\fP\&. -.SS "Public Member Functions" - -.in +1c -.ti -1c -.RI "std::vector< std::string > \fBgraph\fP (const std::string &name='TAN') const override" -.br -.in -1c - -Public Member Functions inherited from \fBbayesnet::Classifier\fP -.in +1c -.ti -1c -.RI "\fBClassifier\fP (\fBNetwork\fP model)" -.br -.ti -1c -.RI "\fBClassifier\fP & \fBfit\fP (std::vector< std::vector< int > > &X, std::vector< int > &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override" -.br -.ti -1c -.RI "\fBClassifier\fP & \fBfit\fP (torch::Tensor &X, torch::Tensor &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override" -.br -.ti -1c -.RI "\fBClassifier\fP & \fBfit\fP (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override" -.br -.ti -1c -.RI "\fBClassifier\fP & \fBfit\fP (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states, const torch::Tensor &weights) override" -.br -.ti -1c -.RI "void \fBaddNodes\fP ()" -.br -.ti -1c -.RI "int \fBgetNumberOfNodes\fP () const override" -.br -.ti -1c -.RI "int \fBgetNumberOfEdges\fP () const override" -.br -.ti -1c -.RI "int \fBgetNumberOfStates\fP () const override" -.br -.ti -1c -.RI "int \fBgetClassNumStates\fP () const override" -.br -.ti -1c -.RI "torch::Tensor \fBpredict\fP (torch::Tensor &X) override" -.br -.ti -1c -.RI "std::vector< int > \fBpredict\fP (std::vector< std::vector< int > > &X) override" -.br -.ti -1c -.RI "torch::Tensor \fBpredict_proba\fP (torch::Tensor &X) override" -.br -.ti -1c -.RI "std::vector< std::vector< double > > \fBpredict_proba\fP (std::vector< std::vector< int > > &X) override" -.br -.ti -1c -.RI "status_t \fBgetStatus\fP () const override" -.br -.ti -1c -.RI "std::string \fBgetVersion\fP () override" -.br -.ti -1c -.RI "float \fBscore\fP (torch::Tensor &X, torch::Tensor &y) override" -.br -.ti -1c -.RI "float \fBscore\fP (std::vector< std::vector< int > > &X, std::vector< int > &y) override" -.br -.ti -1c -.RI "std::vector< std::string > \fBshow\fP () const override" -.br -.ti -1c -.RI "std::vector< std::string > \fBtopological_order\fP () override" -.br -.ti -1c -.RI "std::vector< std::string > \fBgetNotes\fP () const override" -.br -.ti -1c -.RI "std::string \fBdump_cpt\fP () const override" -.br -.ti -1c -.RI "void \fBsetHyperparameters\fP (const nlohmann::json &hyperparameters) override" -.br -.in -1c - -Public Member Functions inherited from \fBbayesnet::BaseClassifier\fP -.in +1c -.ti -1c -.RI "std::vector< std::string > & \fBgetValidHyperparameters\fP ()" -.br -.in -1c -.SS "Protected Member Functions" - -.in +1c -.ti -1c -.RI "void \fBbuildModel\fP (const torch::Tensor &weights) override" -.br -.in -1c - -Protected Member Functions inherited from \fBbayesnet::Classifier\fP -.in +1c -.ti -1c -.RI "void \fBcheckFitParameters\fP ()" -.br -.ti -1c -.RI "void \fBtrainModel\fP (const torch::Tensor &weights) override" -.br -.ti -1c -.RI "void \fBbuildDataset\fP (torch::Tensor &y)" -.br -.in -1c -.SS "Additional Inherited Members" - - -Protected Attributes inherited from \fBbayesnet::Classifier\fP -.in +1c -.ti -1c -.RI "bool \fBfitted\fP" -.br -.ti -1c -.RI "unsigned int \fBm\fP" -.br -.ti -1c -.RI "unsigned int \fBn\fP" -.br -.ti -1c -.RI "\fBNetwork\fP \fBmodel\fP" -.br -.ti -1c -.RI "Metrics \fBmetrics\fP" -.br -.ti -1c -.RI "std::vector< std::string > \fBfeatures\fP" -.br -.ti -1c -.RI "std::string \fBclassName\fP" -.br -.ti -1c -.RI "std::map< std::string, std::vector< int > > \fBstates\fP" -.br -.ti -1c -.RI "torch::Tensor \fBdataset\fP" -.br -.ti -1c -.RI "status_t \fBstatus\fP = NORMAL" -.br -.ti -1c -.RI "std::vector< std::string > \fBnotes\fP" -.br -.in -1c - -Protected Attributes inherited from \fBbayesnet::BaseClassifier\fP -.in +1c -.ti -1c -.RI "std::vector< std::string > \fBvalidHyperparameters\fP" -.br -.in -1c -.SH "Detailed Description" -.PP -Definition at line \fB11\fP of file \fBTAN\&.h\fP\&. -.SH "Constructor & Destructor Documentation" -.PP -.SS "bayesnet::TAN::TAN ()" - -.PP -Definition at line \fB10\fP of file \fBTAN\&.cc\fP\&. -.SH "Member Function Documentation" -.PP -.SS "void bayesnet::TAN::buildModel (const torch::Tensor & weights)\fR [override]\fP, \fR [protected]\fP, \fR [virtual]\fP" - -.PP -Implements \fBbayesnet::Classifier\fP\&. -.PP -Definition at line \fB12\fP of file \fBTAN\&.cc\fP\&. -.SS "std::vector< std::string > bayesnet::TAN::graph (const std::string & name = \fR'TAN'\fP) const\fR [override]\fP, \fR [virtual]\fP" - -.PP -Implements \fBbayesnet::BaseClassifier\fP\&. -.PP -Definition at line \fB41\fP of file \fBTAN\&.cc\fP\&. - -.SH "Author" -.PP -Generated automatically by Doxygen for BayesNet from the source code\&. diff --git a/docs/man3/bayesnet_TANLd.3 b/docs/man3/bayesnet_TANLd.3 deleted file mode 100644 index 45b05d3..0000000 --- a/docs/man3/bayesnet_TANLd.3 +++ /dev/null @@ -1,244 +0,0 @@ -.TH "bayesnet::TANLd" 3 "Version 1.0.5" "BayesNet" \" -*- nroff -*- -.ad l -.nh -.SH NAME -bayesnet::TANLd -.SH SYNOPSIS -.br -.PP -.PP -Inherits \fBbayesnet::TAN\fP, and \fBbayesnet::Proposal\fP\&. -.SS "Public Member Functions" - -.in +1c -.ti -1c -.RI "\fBTANLd\fP & \fBfit\fP (torch::Tensor &X, torch::Tensor &y, const std::vector< std::string > &features, const std::string &className, map< std::string, std::vector< int > > &states) override" -.br -.ti -1c -.RI "std::vector< std::string > \fBgraph\fP (const std::string &name='TAN') const override" -.br -.ti -1c -.RI "torch::Tensor \fBpredict\fP (torch::Tensor &X) override" -.br -.in -1c - -Public Member Functions inherited from \fBbayesnet::Classifier\fP -.in +1c -.ti -1c -.RI "\fBClassifier\fP (\fBNetwork\fP model)" -.br -.ti -1c -.RI "\fBClassifier\fP & \fBfit\fP (std::vector< std::vector< int > > &X, std::vector< int > &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override" -.br -.ti -1c -.RI "\fBClassifier\fP & \fBfit\fP (torch::Tensor &X, torch::Tensor &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override" -.br -.ti -1c -.RI "\fBClassifier\fP & \fBfit\fP (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override" -.br -.ti -1c -.RI "\fBClassifier\fP & \fBfit\fP (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states, const torch::Tensor &weights) override" -.br -.ti -1c -.RI "void \fBaddNodes\fP ()" -.br -.ti -1c -.RI "int \fBgetNumberOfNodes\fP () const override" -.br -.ti -1c -.RI "int \fBgetNumberOfEdges\fP () const override" -.br -.ti -1c -.RI "int \fBgetNumberOfStates\fP () const override" -.br -.ti -1c -.RI "int \fBgetClassNumStates\fP () const override" -.br -.ti -1c -.RI "std::vector< int > \fBpredict\fP (std::vector< std::vector< int > > &X) override" -.br -.ti -1c -.RI "torch::Tensor \fBpredict_proba\fP (torch::Tensor &X) override" -.br -.ti -1c -.RI "std::vector< std::vector< double > > \fBpredict_proba\fP (std::vector< std::vector< int > > &X) override" -.br -.ti -1c -.RI "status_t \fBgetStatus\fP () const override" -.br -.ti -1c -.RI "std::string \fBgetVersion\fP () override" -.br -.ti -1c -.RI "float \fBscore\fP (torch::Tensor &X, torch::Tensor &y) override" -.br -.ti -1c -.RI "float \fBscore\fP (std::vector< std::vector< int > > &X, std::vector< int > &y) override" -.br -.ti -1c -.RI "std::vector< std::string > \fBshow\fP () const override" -.br -.ti -1c -.RI "std::vector< std::string > \fBtopological_order\fP () override" -.br -.ti -1c -.RI "std::vector< std::string > \fBgetNotes\fP () const override" -.br -.ti -1c -.RI "std::string \fBdump_cpt\fP () const override" -.br -.ti -1c -.RI "void \fBsetHyperparameters\fP (const nlohmann::json &hyperparameters) override" -.br -.in -1c - -Public Member Functions inherited from \fBbayesnet::BaseClassifier\fP -.in +1c -.ti -1c -.RI "std::vector< std::string > & \fBgetValidHyperparameters\fP ()" -.br -.in -1c - -Public Member Functions inherited from \fBbayesnet::Proposal\fP -.in +1c -.ti -1c -.RI "\fBProposal\fP (torch::Tensor &pDataset, std::vector< std::string > &features_, std::string &className_)" -.br -.in -1c -.SS "Static Public Member Functions" - -.in +1c -.ti -1c -.RI "static std::string \fBversion\fP ()" -.br -.in -1c -.SS "Additional Inherited Members" - - -Protected Member Functions inherited from \fBbayesnet::TAN\fP -.in +1c -.ti -1c -.RI "void \fBbuildModel\fP (const torch::Tensor &weights) override" -.br -.in -1c - -Protected Member Functions inherited from \fBbayesnet::Classifier\fP -.in +1c -.ti -1c -.RI "void \fBcheckFitParameters\fP ()" -.br -.ti -1c -.RI "void \fBtrainModel\fP (const torch::Tensor &weights) override" -.br -.ti -1c -.RI "void \fBbuildDataset\fP (torch::Tensor &y)" -.br -.in -1c - -Protected Member Functions inherited from \fBbayesnet::Proposal\fP -.in +1c -.ti -1c -.RI "void \fBcheckInput\fP (const torch::Tensor &X, const torch::Tensor &y)" -.br -.ti -1c -.RI "torch::Tensor \fBprepareX\fP (torch::Tensor &X)" -.br -.ti -1c -.RI "map< std::string, std::vector< int > > \fBlocalDiscretizationProposal\fP (const map< std::string, std::vector< int > > &states, \fBNetwork\fP &model)" -.br -.ti -1c -.RI "map< std::string, std::vector< int > > \fBfit_local_discretization\fP (const torch::Tensor &y)" -.br -.in -1c - -Protected Attributes inherited from \fBbayesnet::Classifier\fP -.in +1c -.ti -1c -.RI "bool \fBfitted\fP" -.br -.ti -1c -.RI "unsigned int \fBm\fP" -.br -.ti -1c -.RI "unsigned int \fBn\fP" -.br -.ti -1c -.RI "\fBNetwork\fP \fBmodel\fP" -.br -.ti -1c -.RI "Metrics \fBmetrics\fP" -.br -.ti -1c -.RI "std::vector< std::string > \fBfeatures\fP" -.br -.ti -1c -.RI "std::string \fBclassName\fP" -.br -.ti -1c -.RI "std::map< std::string, std::vector< int > > \fBstates\fP" -.br -.ti -1c -.RI "torch::Tensor \fBdataset\fP" -.br -.ti -1c -.RI "status_t \fBstatus\fP = NORMAL" -.br -.ti -1c -.RI "std::vector< std::string > \fBnotes\fP" -.br -.in -1c - -Protected Attributes inherited from \fBbayesnet::BaseClassifier\fP -.in +1c -.ti -1c -.RI "std::vector< std::string > \fBvalidHyperparameters\fP" -.br -.in -1c - -Protected Attributes inherited from \fBbayesnet::Proposal\fP -.in +1c -.ti -1c -.RI "torch::Tensor \fBXf\fP" -.br -.ti -1c -.RI "torch::Tensor \fBy\fP" -.br -.ti -1c -.RI "map< std::string, mdlp::CPPFImdlp * > \fBdiscretizers\fP" -.br -.in -1c -.SH "Detailed Description" -.PP -Definition at line \fB13\fP of file \fBTANLd\&.h\fP\&. -.SH "Constructor & Destructor Documentation" -.PP -.SS "bayesnet::TANLd::TANLd ()" - -.PP -Definition at line \fB10\fP of file \fBTANLd\&.cc\fP\&. -.SH "Member Function Documentation" -.PP -.SS "\fBTANLd\fP & bayesnet::TANLd::fit (torch::Tensor & X, torch::Tensor & y, const std::vector< std::string > & features, const std::string & className, map< std::string, std::vector< int > > & states)\fR [override]\fP" - -.PP -Definition at line \fB11\fP of file \fBTANLd\&.cc\fP\&. -.SS "std::vector< std::string > bayesnet::TANLd::graph (const std::string & name = \fR'TAN'\fP) const\fR [override]\fP, \fR [virtual]\fP" - -.PP -Reimplemented from \fBbayesnet::TAN\fP\&. -.PP -Definition at line \fB32\fP of file \fBTANLd\&.cc\fP\&. -.SS "torch::Tensor bayesnet::TANLd::predict (torch::Tensor & X)\fR [override]\fP, \fR [virtual]\fP" - -.PP -Reimplemented from \fBbayesnet::Classifier\fP\&. -.PP -Definition at line \fB27\fP of file \fBTANLd\&.cc\fP\&. -.SS "static std::string bayesnet::TANLd::version ()\fR [inline]\fP, \fR [static]\fP" - -.PP -Definition at line \fB21\fP of file \fBTANLd\&.h\fP\&. - -.SH "Author" -.PP -Generated automatically by Doxygen for BayesNet from the source code\&. diff --git a/docs/manual/_a2_d_e_8cc_source.html b/docs/manual/_a2_d_e_8cc_source.html deleted file mode 100644 index c60a9ee..0000000 --- a/docs/manual/_a2_d_e_8cc_source.html +++ /dev/null @@ -1,154 +0,0 @@ - - - - - - - -BayesNet: /Users/rmontanana/Code/BayesNet/bayesnet/ensembles/A2DE.cc Source File - - - - - - - - - - - - - - - -
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A2DE.cc
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1// ***************************************************************
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2// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
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3// SPDX-FileType: SOURCE
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4// SPDX-License-Identifier: MIT
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5// ***************************************************************
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6
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7#include "A2DE.h"
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8
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9namespace bayesnet {
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10 A2DE::A2DE(bool predict_voting) : Ensemble(predict_voting)
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11 {
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12 validHyperparameters = { "predict_voting" };
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13 }
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14 void A2DE::setHyperparameters(const nlohmann::json& hyperparameters_)
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15 {
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16 auto hyperparameters = hyperparameters_;
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17 if (hyperparameters.contains("predict_voting")) {
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18 predict_voting = hyperparameters["predict_voting"];
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19 hyperparameters.erase("predict_voting");
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20 }
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21 Classifier::setHyperparameters(hyperparameters);
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22 }
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23 void A2DE::buildModel(const torch::Tensor& weights)
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24 {
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25 models.clear();
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26 significanceModels.clear();
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27 for (int i = 0; i < features.size() - 1; ++i) {
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28 for (int j = i + 1; j < features.size(); ++j) {
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29 auto model = std::make_unique<SPnDE>(std::vector<int>({ i, j }));
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30 models.push_back(std::move(model));
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31 }
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32 }
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33 n_models = static_cast<unsigned>(models.size());
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34 significanceModels = std::vector<double>(n_models, 1.0);
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35 }
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36 std::vector<std::string> A2DE::graph(const std::string& title) const
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37 {
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38 return Ensemble::graph(title);
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39 }
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40}
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- - - - diff --git a/docs/manual/_a2_d_e_8h_source.html b/docs/manual/_a2_d_e_8h_source.html deleted file mode 100644 index 9264c69..0000000 --- a/docs/manual/_a2_d_e_8h_source.html +++ /dev/null @@ -1,140 +0,0 @@ - - - - - - - -BayesNet: /Users/rmontanana/Code/BayesNet/bayesnet/ensembles/A2DE.h Source File - - - - - - - - - - - - - - - -
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Bayesian Network Classifiers using libtorch from scratch
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A2DE.h
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1// ***************************************************************
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2// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
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3// SPDX-FileType: SOURCE
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4// SPDX-License-Identifier: MIT
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5// ***************************************************************
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6
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7#ifndef A2DE_H
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8#define A2DE_H
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9#include "bayesnet/classifiers/SPnDE.h"
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10#include "Ensemble.h"
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11namespace bayesnet {
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12 class A2DE : public Ensemble {
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13 public:
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14 A2DE(bool predict_voting = false);
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15 virtual ~A2DE() {};
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16 void setHyperparameters(const nlohmann::json& hyperparameters) override;
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17 std::vector<std::string> graph(const std::string& title = "A2DE") const override;
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18 protected:
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19 void buildModel(const torch::Tensor& weights) override;
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20 };
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21}
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22#endif
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- - - - diff --git a/docs/manual/_a_o_d_e_8cc_source.html b/docs/manual/_a_o_d_e_8cc_source.html deleted file mode 100644 index 01ec16d..0000000 --- a/docs/manual/_a_o_d_e_8cc_source.html +++ /dev/null @@ -1,152 +0,0 @@ - - - - - - - -BayesNet: /Users/rmontanana/Code/BayesNet/bayesnet/ensembles/AODE.cc Source File - - - - - - - - - - - - - - - -
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AODE.cc
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1// ***************************************************************
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2// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
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3// SPDX-FileType: SOURCE
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4// SPDX-License-Identifier: MIT
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5// ***************************************************************
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6
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7#include "AODE.h"
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8
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9namespace bayesnet {
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10 AODE::AODE(bool predict_voting) : Ensemble(predict_voting)
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11 {
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12 validHyperparameters = { "predict_voting" };
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13
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14 }
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15 void AODE::setHyperparameters(const nlohmann::json& hyperparameters_)
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16 {
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17 auto hyperparameters = hyperparameters_;
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18 if (hyperparameters.contains("predict_voting")) {
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19 predict_voting = hyperparameters["predict_voting"];
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20 hyperparameters.erase("predict_voting");
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21 }
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22 Classifier::setHyperparameters(hyperparameters);
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23 }
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24 void AODE::buildModel(const torch::Tensor& weights)
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25 {
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26 models.clear();
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27 significanceModels.clear();
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28 for (int i = 0; i < features.size(); ++i) {
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29 models.push_back(std::make_unique<SPODE>(i));
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30 }
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31 n_models = models.size();
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32 significanceModels = std::vector<double>(n_models, 1.0);
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33 }
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34 std::vector<std::string> AODE::graph(const std::string& title) const
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35 {
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36 return Ensemble::graph(title);
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37 }
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38}
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-
- - - - diff --git a/docs/manual/_a_o_d_e_8h_source.html b/docs/manual/_a_o_d_e_8h_source.html deleted file mode 100644 index dbd20c0..0000000 --- a/docs/manual/_a_o_d_e_8h_source.html +++ /dev/null @@ -1,140 +0,0 @@ - - - - - - - -BayesNet: /Users/rmontanana/Code/BayesNet/bayesnet/ensembles/AODE.h Source File - - - - - - - - - - - - - - - -
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AODE.h
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1// ***************************************************************
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2// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
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3// SPDX-FileType: SOURCE
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4// SPDX-License-Identifier: MIT
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5// ***************************************************************
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6
-
7#ifndef AODE_H
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8#define AODE_H
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9#include "bayesnet/classifiers/SPODE.h"
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10#include "Ensemble.h"
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11namespace bayesnet {
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12 class AODE : public Ensemble {
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13 public:
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14 AODE(bool predict_voting = false);
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15 virtual ~AODE() {};
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16 void setHyperparameters(const nlohmann::json& hyperparameters) override;
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17 std::vector<std::string> graph(const std::string& title = "AODE") const override;
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18 protected:
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19 void buildModel(const torch::Tensor& weights) override;
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20 };
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21}
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22#endif
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- - - - diff --git a/docs/manual/_a_o_d_e_ld_8cc_source.html b/docs/manual/_a_o_d_e_ld_8cc_source.html deleted file mode 100644 index bcc508c..0000000 --- a/docs/manual/_a_o_d_e_ld_8cc_source.html +++ /dev/null @@ -1,161 +0,0 @@ - - - - - - - -BayesNet: /Users/rmontanana/Code/BayesNet/bayesnet/ensembles/AODELd.cc Source File - - - - - - - - - - - - - - - -
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AODELd.cc
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1// ***************************************************************
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2// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
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3// SPDX-FileType: SOURCE
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4// SPDX-License-Identifier: MIT
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5// ***************************************************************
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6
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7#include "AODELd.h"
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8
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9namespace bayesnet {
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10 AODELd::AODELd(bool predict_voting) : Ensemble(predict_voting), Proposal(dataset, features, className)
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11 {
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12 }
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13 AODELd& AODELd::fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_)
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14 {
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15 checkInput(X_, y_);
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16 features = features_;
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17 className = className_;
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18 Xf = X_;
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19 y = y_;
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20 // Fills std::vectors Xv & yv with the data from tensors X_ (discretized) & y
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21 states = fit_local_discretization(y);
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22 // We have discretized the input data
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23 // 1st we need to fit the model to build the normal TAN structure, TAN::fit initializes the base Bayesian network
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24 Ensemble::fit(dataset, features, className, states);
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25 return *this;
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26
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27 }
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28 void AODELd::buildModel(const torch::Tensor& weights)
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29 {
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30 models.clear();
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31 for (int i = 0; i < features.size(); ++i) {
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32 models.push_back(std::make_unique<SPODELd>(i));
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33 }
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34 n_models = models.size();
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35 significanceModels = std::vector<double>(n_models, 1.0);
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36 }
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37 void AODELd::trainModel(const torch::Tensor& weights)
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38 {
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39 for (const auto& model : models) {
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40 model->fit(Xf, y, features, className, states);
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41 }
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42 }
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43 std::vector<std::string> AODELd::graph(const std::string& name) const
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44 {
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45 return Ensemble::graph(name);
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46 }
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47}
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-
- - - - diff --git a/docs/manual/_a_o_d_e_ld_8h_source.html b/docs/manual/_a_o_d_e_ld_8h_source.html deleted file mode 100644 index 1420501..0000000 --- a/docs/manual/_a_o_d_e_ld_8h_source.html +++ /dev/null @@ -1,144 +0,0 @@ - - - - - - - -BayesNet: /Users/rmontanana/Code/BayesNet/bayesnet/ensembles/AODELd.h Source File - - - - - - - - - - - - - - - -
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AODELd.h
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1// ***************************************************************
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2// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
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3// SPDX-FileType: SOURCE
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4// SPDX-License-Identifier: MIT
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5// ***************************************************************
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6
-
7#ifndef AODELD_H
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8#define AODELD_H
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9#include "bayesnet/classifiers/Proposal.h"
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10#include "bayesnet/classifiers/SPODELd.h"
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11#include "Ensemble.h"
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12
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13namespace bayesnet {
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14 class AODELd : public Ensemble, public Proposal {
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15 public:
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16 AODELd(bool predict_voting = true);
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17 virtual ~AODELd() = default;
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18 AODELd& fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_) override;
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19 std::vector<std::string> graph(const std::string& name = "AODELd") const override;
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20 protected:
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21 void trainModel(const torch::Tensor& weights) override;
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22 void buildModel(const torch::Tensor& weights) override;
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23 };
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-
24}
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25#endif // !AODELD_H
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- - - - diff --git a/docs/manual/_base_classifier_8h_source.html b/docs/manual/_base_classifier_8h_source.html deleted file mode 100644 index e27c0b8..0000000 --- a/docs/manual/_base_classifier_8h_source.html +++ /dev/null @@ -1,162 +0,0 @@ - - - - - - - -BayesNet: /Users/rmontanana/Code/BayesNet/bayesnet/BaseClassifier.h Source File - - - - - - - - - - - - - - - -
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- - - - - - - -
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BaseClassifier.h
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1// ***************************************************************
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2// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
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3// SPDX-FileType: SOURCE
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4// SPDX-License-Identifier: MIT
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5// ***************************************************************
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6
-
7#pragma once
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8#include <vector>
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9#include <torch/torch.h>
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10#include <nlohmann/json.hpp>
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11namespace bayesnet {
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12 enum status_t { NORMAL, WARNING, ERROR };
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13 class BaseClassifier {
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14 public:
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15 // X is nxm std::vector, y is nx1 std::vector
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16 virtual BaseClassifier& fit(std::vector<std::vector<int>>& X, std::vector<int>& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states) = 0;
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17 // X is nxm tensor, y is nx1 tensor
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18 virtual BaseClassifier& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states) = 0;
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19 virtual BaseClassifier& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states) = 0;
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20 virtual BaseClassifier& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights) = 0;
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21 virtual ~BaseClassifier() = default;
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22 torch::Tensor virtual predict(torch::Tensor& X) = 0;
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23 std::vector<int> virtual predict(std::vector<std::vector<int >>& X) = 0;
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24 torch::Tensor virtual predict_proba(torch::Tensor& X) = 0;
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25 std::vector<std::vector<double>> virtual predict_proba(std::vector<std::vector<int >>& X) = 0;
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26 status_t virtual getStatus() const = 0;
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27 float virtual score(std::vector<std::vector<int>>& X, std::vector<int>& y) = 0;
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28 float virtual score(torch::Tensor& X, torch::Tensor& y) = 0;
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29 int virtual getNumberOfNodes()const = 0;
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30 int virtual getNumberOfEdges()const = 0;
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31 int virtual getNumberOfStates() const = 0;
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32 int virtual getClassNumStates() const = 0;
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33 std::vector<std::string> virtual show() const = 0;
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34 std::vector<std::string> virtual graph(const std::string& title = "") const = 0;
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35 virtual std::string getVersion() = 0;
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36 std::vector<std::string> virtual topological_order() = 0;
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37 std::vector<std::string> virtual getNotes() const = 0;
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38 std::string virtual dump_cpt()const = 0;
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39 virtual void setHyperparameters(const nlohmann::json& hyperparameters) = 0;
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40 std::vector<std::string>& getValidHyperparameters() { return validHyperparameters; }
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41 protected:
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42 virtual void trainModel(const torch::Tensor& weights) = 0;
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43 std::vector<std::string> validHyperparameters;
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44 };
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45}
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- - - - diff --git a/docs/manual/_boost_8cc_source.html b/docs/manual/_boost_8cc_source.html deleted file mode 100644 index a6fda15..0000000 --- a/docs/manual/_boost_8cc_source.html +++ /dev/null @@ -1,360 +0,0 @@ - - - - - - - -BayesNet: /Users/rmontanana/Code/BayesNet/bayesnet/ensembles/Boost.cc Source File - - - - - - - - - - - - - - - -
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Boost.cc
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-
-
1// ***************************************************************
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2// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
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3// SPDX-FileType: SOURCE
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4// SPDX-License-Identifier: MIT
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5// ***************************************************************
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6#include <folding.hpp>
-
7#include "bayesnet/feature_selection/CFS.h"
-
8#include "bayesnet/feature_selection/FCBF.h"
-
9#include "bayesnet/feature_selection/IWSS.h"
-
10#include "Boost.h"
-
11
-
12namespace bayesnet {
-
13 Boost::Boost(bool predict_voting) : Ensemble(predict_voting)
-
14 {
-
15 validHyperparameters = { "order", "convergence", "convergence_best", "bisection", "threshold", "maxTolerance",
-
16 "predict_voting", "select_features", "block_update" };
-
17 }
-
18 void Boost::setHyperparameters(const nlohmann::json& hyperparameters_)
-
19 {
-
20 auto hyperparameters = hyperparameters_;
-
21 if (hyperparameters.contains("order")) {
-
22 std::vector<std::string> algos = { Orders.ASC, Orders.DESC, Orders.RAND };
-
23 order_algorithm = hyperparameters["order"];
-
24 if (std::find(algos.begin(), algos.end(), order_algorithm) == algos.end()) {
-
25 throw std::invalid_argument("Invalid order algorithm, valid values [" + Orders.ASC + ", " + Orders.DESC + ", " + Orders.RAND + "]");
-
26 }
-
27 hyperparameters.erase("order");
-
28 }
-
29 if (hyperparameters.contains("convergence")) {
-
30 convergence = hyperparameters["convergence"];
-
31 hyperparameters.erase("convergence");
-
32 }
-
33 if (hyperparameters.contains("convergence_best")) {
-
34 convergence_best = hyperparameters["convergence_best"];
-
35 hyperparameters.erase("convergence_best");
-
36 }
-
37 if (hyperparameters.contains("bisection")) {
-
38 bisection = hyperparameters["bisection"];
-
39 hyperparameters.erase("bisection");
-
40 }
-
41 if (hyperparameters.contains("threshold")) {
-
42 threshold = hyperparameters["threshold"];
-
43 hyperparameters.erase("threshold");
-
44 }
-
45 if (hyperparameters.contains("maxTolerance")) {
-
46 maxTolerance = hyperparameters["maxTolerance"];
-
47 if (maxTolerance < 1 || maxTolerance > 4)
-
48 throw std::invalid_argument("Invalid maxTolerance value, must be greater in [1, 4]");
-
49 hyperparameters.erase("maxTolerance");
-
50 }
-
51 if (hyperparameters.contains("predict_voting")) {
-
52 predict_voting = hyperparameters["predict_voting"];
-
53 hyperparameters.erase("predict_voting");
-
54 }
-
55 if (hyperparameters.contains("select_features")) {
-
56 auto selectedAlgorithm = hyperparameters["select_features"];
-
57 std::vector<std::string> algos = { SelectFeatures.IWSS, SelectFeatures.CFS, SelectFeatures.FCBF };
-
58 selectFeatures = true;
-
59 select_features_algorithm = selectedAlgorithm;
-
60 if (std::find(algos.begin(), algos.end(), selectedAlgorithm) == algos.end()) {
-
61 throw std::invalid_argument("Invalid selectFeatures value, valid values [" + SelectFeatures.IWSS + ", " + SelectFeatures.CFS + ", " + SelectFeatures.FCBF + "]");
-
62 }
-
63 hyperparameters.erase("select_features");
-
64 }
-
65 if (hyperparameters.contains("block_update")) {
-
66 block_update = hyperparameters["block_update"];
-
67 hyperparameters.erase("block_update");
-
68 }
-
69 Classifier::setHyperparameters(hyperparameters);
-
70 }
-
71 void Boost::buildModel(const torch::Tensor& weights)
-
72 {
-
73 // Models shall be built in trainModel
-
74 models.clear();
-
75 significanceModels.clear();
-
76 n_models = 0;
-
77 // Prepare the validation dataset
-
78 auto y_ = dataset.index({ -1, "..." });
-
79 if (convergence) {
-
80 // Prepare train & validation sets from train data
-
81 auto fold = folding::StratifiedKFold(5, y_, 271);
-
82 auto [train, test] = fold.getFold(0);
-
83 auto train_t = torch::tensor(train);
-
84 auto test_t = torch::tensor(test);
-
85 // Get train and validation sets
-
86 X_train = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), train_t });
-
87 y_train = dataset.index({ -1, train_t });
-
88 X_test = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), test_t });
-
89 y_test = dataset.index({ -1, test_t });
-
90 dataset = X_train;
-
91 m = X_train.size(1);
-
92 auto n_classes = states.at(className).size();
-
93 // Build dataset with train data
-
94 buildDataset(y_train);
-
95 metrics = Metrics(dataset, features, className, n_classes);
-
96 } else {
-
97 // Use all data to train
-
98 X_train = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), "..." });
-
99 y_train = y_;
-
100 }
-
101 }
-
102 std::vector<int> Boost::featureSelection(torch::Tensor& weights_)
-
103 {
-
104 int maxFeatures = 0;
-
105 if (select_features_algorithm == SelectFeatures.CFS) {
-
106 featureSelector = new CFS(dataset, features, className, maxFeatures, states.at(className).size(), weights_);
-
107 } else if (select_features_algorithm == SelectFeatures.IWSS) {
-
108 if (threshold < 0 || threshold >0.5) {
-
109 throw std::invalid_argument("Invalid threshold value for " + SelectFeatures.IWSS + " [0, 0.5]");
-
110 }
-
111 featureSelector = new IWSS(dataset, features, className, maxFeatures, states.at(className).size(), weights_, threshold);
-
112 } else if (select_features_algorithm == SelectFeatures.FCBF) {
-
113 if (threshold < 1e-7 || threshold > 1) {
-
114 throw std::invalid_argument("Invalid threshold value for " + SelectFeatures.FCBF + " [1e-7, 1]");
-
115 }
-
116 featureSelector = new FCBF(dataset, features, className, maxFeatures, states.at(className).size(), weights_, threshold);
-
117 }
-
118 featureSelector->fit();
-
119 auto featuresUsed = featureSelector->getFeatures();
-
120 delete featureSelector;
-
121 return featuresUsed;
-
122 }
-
123 std::tuple<torch::Tensor&, double, bool> Boost::update_weights(torch::Tensor& ytrain, torch::Tensor& ypred, torch::Tensor& weights)
-
124 {
-
125 bool terminate = false;
-
126 double alpha_t = 0;
-
127 auto mask_wrong = ypred != ytrain;
-
128 auto mask_right = ypred == ytrain;
-
129 auto masked_weights = weights * mask_wrong.to(weights.dtype());
-
130 double epsilon_t = masked_weights.sum().item<double>();
-
131 if (epsilon_t > 0.5) {
-
132 // Inverse the weights policy (plot ln(wt))
-
133 // "In each round of AdaBoost, there is a sanity check to ensure that the current base
-
134 // learner is better than random guess" (Zhi-Hua Zhou, 2012)
-
135 terminate = true;
-
136 } else {
-
137 double wt = (1 - epsilon_t) / epsilon_t;
-
138 alpha_t = epsilon_t == 0 ? 1 : 0.5 * log(wt);
-
139 // Step 3.2: Update weights for next classifier
-
140 // Step 3.2.1: Update weights of wrong samples
-
141 weights += mask_wrong.to(weights.dtype()) * exp(alpha_t) * weights;
-
142 // Step 3.2.2: Update weights of right samples
-
143 weights += mask_right.to(weights.dtype()) * exp(-alpha_t) * weights;
-
144 // Step 3.3: Normalise the weights
-
145 double totalWeights = torch::sum(weights).item<double>();
-
146 weights = weights / totalWeights;
-
147 }
-
148 return { weights, alpha_t, terminate };
-
149 }
-
150 std::tuple<torch::Tensor&, double, bool> Boost::update_weights_block(int k, torch::Tensor& ytrain, torch::Tensor& weights)
-
151 {
-
152 /* Update Block algorithm
-
153 k = # of models in block
-
154 n_models = # of models in ensemble to make predictions
-
155 n_models_bak = # models saved
-
156 models = vector of models to make predictions
-
157 models_bak = models not used to make predictions
-
158 significances_bak = backup of significances vector
-
159
-
160 Case list
-
161 A) k = 1, n_models = 1 => n = 0 , n_models = n + k
-
162 B) k = 1, n_models = n + 1 => n_models = n + k
-
163 C) k > 1, n_models = k + 1 => n= 1, n_models = n + k
-
164 D) k > 1, n_models = k => n = 0, n_models = n + k
-
165 E) k > 1, n_models = k + n => n_models = n + k
-
166
-
167 A, D) n=0, k > 0, n_models == k
-
168 1. n_models_bak <- n_models
-
169 2. significances_bak <- significances
-
170 3. significances = vector(k, 1)
-
171 4. Don’t move any classifiers out of models
-
172 5. n_models <- k
-
173 6. Make prediction, compute alpha, update weights
-
174 7. Don’t restore any classifiers to models
-
175 8. significances <- significances_bak
-
176 9. Update last k significances
-
177 10. n_models <- n_models_bak
-
178
-
179 B, C, E) n > 0, k > 0, n_models == n + k
-
180 1. n_models_bak <- n_models
-
181 2. significances_bak <- significances
-
182 3. significances = vector(k, 1)
-
183 4. Move first n classifiers to models_bak
-
184 5. n_models <- k
-
185 6. Make prediction, compute alpha, update weights
-
186 7. Insert classifiers in models_bak to be the first n models
-
187 8. significances <- significances_bak
-
188 9. Update last k significances
-
189 10. n_models <- n_models_bak
-
190 */
-
191 //
-
192 // Make predict with only the last k models
-
193 //
-
194 std::unique_ptr<Classifier> model;
-
195 std::vector<std::unique_ptr<Classifier>> models_bak;
-
196 // 1. n_models_bak <- n_models 2. significances_bak <- significances
-
197 auto significance_bak = significanceModels;
-
198 auto n_models_bak = n_models;
-
199 // 3. significances = vector(k, 1)
-
200 significanceModels = std::vector<double>(k, 1.0);
-
201 // 4. Move first n classifiers to models_bak
-
202 // backup the first n_models - k models (if n_models == k, don't backup any)
-
203 for (int i = 0; i < n_models - k; ++i) {
-
204 model = std::move(models[0]);
-
205 models.erase(models.begin());
-
206 models_bak.push_back(std::move(model));
-
207 }
-
208 assert(models.size() == k);
-
209 // 5. n_models <- k
-
210 n_models = k;
-
211 // 6. Make prediction, compute alpha, update weights
-
212 auto ypred = predict(X_train);
-
213 //
-
214 // Update weights
-
215 //
-
216 double alpha_t;
-
217 bool terminate;
-
218 std::tie(weights, alpha_t, terminate) = update_weights(y_train, ypred, weights);
-
219 //
-
220 // Restore the models if needed
-
221 //
-
222 // 7. Insert classifiers in models_bak to be the first n models
-
223 // if n_models_bak == k, don't restore any, because none of them were moved
-
224 if (k != n_models_bak) {
-
225 // Insert in the same order as they were extracted
-
226 int bak_size = models_bak.size();
-
227 for (int i = 0; i < bak_size; ++i) {
-
228 model = std::move(models_bak[bak_size - 1 - i]);
-
229 models_bak.erase(models_bak.end() - 1);
-
230 models.insert(models.begin(), std::move(model));
-
231 }
-
232 }
-
233 // 8. significances <- significances_bak
-
234 significanceModels = significance_bak;
-
235 //
-
236 // Update the significance of the last k models
-
237 //
-
238 // 9. Update last k significances
-
239 for (int i = 0; i < k; ++i) {
-
240 significanceModels[n_models_bak - k + i] = alpha_t;
-
241 }
-
242 // 10. n_models <- n_models_bak
-
243 n_models = n_models_bak;
-
244 return { weights, alpha_t, terminate };
-
245 }
-
246}
-
-
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Boost.h
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1// ***************************************************************
-
2// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
-
3// SPDX-FileType: SOURCE
-
4// SPDX-License-Identifier: MIT
-
5// ***************************************************************
-
6
-
7#ifndef BOOST_H
-
8#define BOOST_H
-
9#include <string>
-
10#include <tuple>
-
11#include <vector>
-
12#include <nlohmann/json.hpp>
-
13#include <torch/torch.h>
-
14#include "Ensemble.h"
-
15#include "bayesnet/feature_selection/FeatureSelect.h"
-
16namespace bayesnet {
-
17 const struct {
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18 std::string CFS = "CFS";
-
19 std::string FCBF = "FCBF";
-
20 std::string IWSS = "IWSS";
-
21 }SelectFeatures;
-
22 const struct {
-
23 std::string ASC = "asc";
-
24 std::string DESC = "desc";
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25 std::string RAND = "rand";
-
26 }Orders;
-
-
27 class Boost : public Ensemble {
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28 public:
-
29 explicit Boost(bool predict_voting = false);
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30 virtual ~Boost() = default;
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31 void setHyperparameters(const nlohmann::json& hyperparameters_) override;
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32 protected:
-
33 std::vector<int> featureSelection(torch::Tensor& weights_);
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34 void buildModel(const torch::Tensor& weights) override;
-
35 std::tuple<torch::Tensor&, double, bool> update_weights(torch::Tensor& ytrain, torch::Tensor& ypred, torch::Tensor& weights);
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36 std::tuple<torch::Tensor&, double, bool> update_weights_block(int k, torch::Tensor& ytrain, torch::Tensor& weights);
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37 torch::Tensor X_train, y_train, X_test, y_test;
-
38 // Hyperparameters
-
39 bool bisection = true; // if true, use bisection stratety to add k models at once to the ensemble
-
40 int maxTolerance = 3;
-
41 std::string order_algorithm; // order to process the KBest features asc, desc, rand
-
42 bool convergence = true; //if true, stop when the model does not improve
-
43 bool convergence_best = false; // wether to keep the best accuracy to the moment or the last accuracy as prior accuracy
-
44 bool selectFeatures = false; // if true, use feature selection
-
45 std::string select_features_algorithm = Orders.DESC; // Selected feature selection algorithm
-
46 FeatureSelect* featureSelector = nullptr;
-
47 double threshold = -1;
-
48 bool block_update = false;
-
49
-
50 };
-
-
51}
-
52#endif
- - -
-
- - - - diff --git a/docs/manual/_boost_a2_d_e_8cc_source.html b/docs/manual/_boost_a2_d_e_8cc_source.html deleted file mode 100644 index 70825be..0000000 --- a/docs/manual/_boost_a2_d_e_8cc_source.html +++ /dev/null @@ -1,281 +0,0 @@ - - - - - - - -BayesNet: /Users/rmontanana/Code/BayesNet/bayesnet/ensembles/BoostA2DE.cc Source File - - - - - - - - - - - - - - - -
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BoostA2DE.cc
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1// ***************************************************************
-
2// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
-
3// SPDX-FileType: SOURCE
-
4// SPDX-License-Identifier: MIT
-
5// ***************************************************************
-
6
-
7#include <set>
-
8#include <functional>
-
9#include <limits.h>
-
10#include <tuple>
-
11#include <folding.hpp>
-
12#include "bayesnet/feature_selection/CFS.h"
-
13#include "bayesnet/feature_selection/FCBF.h"
-
14#include "bayesnet/feature_selection/IWSS.h"
-
15#include "BoostA2DE.h"
-
16
-
17namespace bayesnet {
-
18
-
19 BoostA2DE::BoostA2DE(bool predict_voting) : Boost(predict_voting)
-
20 {
-
21 }
-
22 std::vector<int> BoostA2DE::initializeModels()
-
23 {
-
24 torch::Tensor weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64);
-
25 std::vector<int> featuresSelected = featureSelection(weights_);
-
26 if (featuresSelected.size() < 2) {
-
27 notes.push_back("No features selected in initialization");
-
28 status = ERROR;
-
29 return std::vector<int>();
-
30 }
-
31 for (int i = 0; i < featuresSelected.size() - 1; i++) {
-
32 for (int j = i + 1; j < featuresSelected.size(); j++) {
-
33 auto parents = { featuresSelected[i], featuresSelected[j] };
-
34 std::unique_ptr<Classifier> model = std::make_unique<SPnDE>(parents);
-
35 model->fit(dataset, features, className, states, weights_);
-
36 models.push_back(std::move(model));
-
37 significanceModels.push_back(1.0); // They will be updated later in trainModel
-
38 n_models++;
-
39 }
-
40 }
-
41 notes.push_back("Used features in initialization: " + std::to_string(featuresSelected.size()) + " of " + std::to_string(features.size()) + " with " + select_features_algorithm);
-
42 return featuresSelected;
-
43 }
-
44 void BoostA2DE::trainModel(const torch::Tensor& weights)
-
45 {
-
46 //
-
47 // Logging setup
-
48 //
-
49 // loguru::set_thread_name("BoostA2DE");
-
50 // loguru::g_stderr_verbosity = loguru::Verbosity_OFF;
-
51 // loguru::add_file("boostA2DE.log", loguru::Truncate, loguru::Verbosity_MAX);
-
52
-
53 // Algorithm based on the adaboost algorithm for classification
-
54 // as explained in Ensemble methods (Zhi-Hua Zhou, 2012)
-
55 fitted = true;
-
56 double alpha_t = 0;
-
57 torch::Tensor weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64);
-
58 bool finished = false;
-
59 std::vector<int> featuresUsed;
-
60 if (selectFeatures) {
-
61 featuresUsed = initializeModels();
-
62 auto ypred = predict(X_train);
-
63 std::tie(weights_, alpha_t, finished) = update_weights(y_train, ypred, weights_);
-
64 // Update significance of the models
-
65 for (int i = 0; i < n_models; ++i) {
-
66 significanceModels[i] = alpha_t;
-
67 }
-
68 if (finished) {
-
69 return;
-
70 }
-
71 }
-
72 int numItemsPack = 0; // The counter of the models inserted in the current pack
-
73 // Variables to control the accuracy finish condition
-
74 double priorAccuracy = 0.0;
-
75 double improvement = 1.0;
-
76 double convergence_threshold = 1e-4;
-
77 int tolerance = 0; // number of times the accuracy is lower than the convergence_threshold
-
78 // Step 0: Set the finish condition
-
79 // epsilon sub t > 0.5 => inverse the weights policy
-
80 // validation error is not decreasing
-
81 // run out of features
-
82 bool ascending = order_algorithm == Orders.ASC;
-
83 std::mt19937 g{ 173 };
-
84 std::vector<std::pair<int, int>> pairSelection;
-
85 while (!finished) {
-
86 // Step 1: Build ranking with mutual information
-
87 pairSelection = metrics.SelectKPairs(weights_, featuresUsed, ascending, 0); // Get all the pairs sorted
-
88 if (order_algorithm == Orders.RAND) {
-
89 std::shuffle(pairSelection.begin(), pairSelection.end(), g);
-
90 }
-
91 int k = bisection ? pow(2, tolerance) : 1;
-
92 int counter = 0; // The model counter of the current pack
-
93 // VLOG_SCOPE_F(1, "counter=%d k=%d featureSelection.size: %zu", counter, k, featureSelection.size());
-
94 while (counter++ < k && pairSelection.size() > 0) {
-
95 auto feature_pair = pairSelection[0];
-
96 pairSelection.erase(pairSelection.begin());
-
97 std::unique_ptr<Classifier> model;
-
98 model = std::make_unique<SPnDE>(std::vector<int>({ feature_pair.first, feature_pair.second }));
-
99 model->fit(dataset, features, className, states, weights_);
-
100 alpha_t = 0.0;
-
101 if (!block_update) {
-
102 auto ypred = model->predict(X_train);
-
103 // Step 3.1: Compute the classifier amout of say
-
104 std::tie(weights_, alpha_t, finished) = update_weights(y_train, ypred, weights_);
-
105 }
-
106 // Step 3.4: Store classifier and its accuracy to weigh its future vote
-
107 numItemsPack++;
-
108 models.push_back(std::move(model));
-
109 significanceModels.push_back(alpha_t);
-
110 n_models++;
-
111 // VLOG_SCOPE_F(2, "numItemsPack: %d n_models: %d featuresUsed: %zu", numItemsPack, n_models, featuresUsed.size());
-
112 }
-
113 if (block_update) {
-
114 std::tie(weights_, alpha_t, finished) = update_weights_block(k, y_train, weights_);
-
115 }
-
116 if (convergence && !finished) {
-
117 auto y_val_predict = predict(X_test);
-
118 double accuracy = (y_val_predict == y_test).sum().item<double>() / (double)y_test.size(0);
-
119 if (priorAccuracy == 0) {
-
120 priorAccuracy = accuracy;
-
121 } else {
-
122 improvement = accuracy - priorAccuracy;
-
123 }
-
124 if (improvement < convergence_threshold) {
-
125 // VLOG_SCOPE_F(3, " (improvement<threshold) tolerance: %d numItemsPack: %d improvement: %f prior: %f current: %f", tolerance, numItemsPack, improvement, priorAccuracy, accuracy);
-
126 tolerance++;
-
127 } else {
-
128 // VLOG_SCOPE_F(3, "* (improvement>=threshold) Reset. tolerance: %d numItemsPack: %d improvement: %f prior: %f current: %f", tolerance, numItemsPack, improvement, priorAccuracy, accuracy);
-
129 tolerance = 0; // Reset the counter if the model performs better
-
130 numItemsPack = 0;
-
131 }
-
132 if (convergence_best) {
-
133 // Keep the best accuracy until now as the prior accuracy
-
134 priorAccuracy = std::max(accuracy, priorAccuracy);
-
135 } else {
-
136 // Keep the last accuray obtained as the prior accuracy
-
137 priorAccuracy = accuracy;
-
138 }
-
139 }
-
140 // VLOG_SCOPE_F(1, "tolerance: %d featuresUsed.size: %zu features.size: %zu", tolerance, featuresUsed.size(), features.size());
-
141 finished = finished || tolerance > maxTolerance || pairSelection.size() == 0;
-
142 }
-
143 if (tolerance > maxTolerance) {
-
144 if (numItemsPack < n_models) {
-
145 notes.push_back("Convergence threshold reached & " + std::to_string(numItemsPack) + " models eliminated");
-
146 // VLOG_SCOPE_F(4, "Convergence threshold reached & %d models eliminated of %d", numItemsPack, n_models);
-
147 for (int i = 0; i < numItemsPack; ++i) {
-
148 significanceModels.pop_back();
-
149 models.pop_back();
-
150 n_models--;
-
151 }
-
152 } else {
-
153 notes.push_back("Convergence threshold reached & 0 models eliminated");
-
154 // VLOG_SCOPE_F(4, "Convergence threshold reached & 0 models eliminated n_models=%d numItemsPack=%d", n_models, numItemsPack);
-
155 }
-
156 }
-
157 if (pairSelection.size() > 0) {
-
158 notes.push_back("Pairs not used in train: " + std::to_string(pairSelection.size()));
-
159 status = WARNING;
-
160 }
-
161 notes.push_back("Number of models: " + std::to_string(n_models));
-
162 }
-
163 std::vector<std::string> BoostA2DE::graph(const std::string& title) const
-
164 {
-
165 return Ensemble::graph(title);
-
166 }
-
167}
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-
- - - - diff --git a/docs/manual/_boost_a2_d_e_8h_source.html b/docs/manual/_boost_a2_d_e_8h_source.html deleted file mode 100644 index cf8b626..0000000 --- a/docs/manual/_boost_a2_d_e_8h_source.html +++ /dev/null @@ -1,143 +0,0 @@ - - - - - - - -BayesNet: /Users/rmontanana/Code/BayesNet/bayesnet/ensembles/BoostA2DE.h Source File - - - - - - - - - - - - - - - -
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BoostA2DE.h
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1// ***************************************************************
-
2// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
-
3// SPDX-FileType: SOURCE
-
4// SPDX-License-Identifier: MIT
-
5// ***************************************************************
-
6
-
7#ifndef BOOSTA2DE_H
-
8#define BOOSTA2DE_H
-
9#include <string>
-
10#include <vector>
-
11#include "bayesnet/classifiers/SPnDE.h"
-
12#include "Boost.h"
-
13namespace bayesnet {
-
-
14 class BoostA2DE : public Boost {
-
15 public:
-
16 explicit BoostA2DE(bool predict_voting = false);
-
17 virtual ~BoostA2DE() = default;
-
18 std::vector<std::string> graph(const std::string& title = "BoostA2DE") const override;
-
19 protected:
-
20 void trainModel(const torch::Tensor& weights) override;
-
21 private:
-
22 std::vector<int> initializeModels();
-
23 };
-
-
24}
-
25#endif
- - -
-
- - - - diff --git a/docs/manual/_boost_a_o_d_e_8cc_source.html b/docs/manual/_boost_a_o_d_e_8cc_source.html deleted file mode 100644 index c22d8e9..0000000 --- a/docs/manual/_boost_a_o_d_e_8cc_source.html +++ /dev/null @@ -1,275 +0,0 @@ - - - - - - - -BayesNet: /Users/rmontanana/Code/BayesNet/bayesnet/ensembles/BoostAODE.cc Source File - - - - - - - - - - - - - - - -
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- - - - - - - -
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BayesNet 1.0.5 -
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Bayesian Network Classifiers using libtorch from scratch
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BoostAODE.cc
-
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1// ***************************************************************
-
2// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
-
3// SPDX-FileType: SOURCE
-
4// SPDX-License-Identifier: MIT
-
5// ***************************************************************
-
6
-
7#include <random>
-
8#include <set>
-
9#include <functional>
-
10#include <limits.h>
-
11#include <tuple>
-
12#include "BoostAODE.h"
-
13
-
14namespace bayesnet {
-
15
-
16 BoostAODE::BoostAODE(bool predict_voting) : Boost(predict_voting)
-
17 {
-
18 }
-
19 std::vector<int> BoostAODE::initializeModels()
-
20 {
-
21 torch::Tensor weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64);
-
22 std::vector<int> featuresSelected = featureSelection(weights_);
-
23 for (const int& feature : featuresSelected) {
-
24 std::unique_ptr<Classifier> model = std::make_unique<SPODE>(feature);
-
25 model->fit(dataset, features, className, states, weights_);
-
26 models.push_back(std::move(model));
-
27 significanceModels.push_back(1.0); // They will be updated later in trainModel
-
28 n_models++;
-
29 }
-
30 notes.push_back("Used features in initialization: " + std::to_string(featuresSelected.size()) + " of " + std::to_string(features.size()) + " with " + select_features_algorithm);
-
31 return featuresSelected;
-
32 }
-
33 void BoostAODE::trainModel(const torch::Tensor& weights)
-
34 {
-
35 //
-
36 // Logging setup
-
37 //
-
38 // loguru::set_thread_name("BoostAODE");
-
39 // loguru::g_stderr_verbosity = loguru::Verbosity_OFF;
-
40 // loguru::add_file("boostAODE.log", loguru::Truncate, loguru::Verbosity_MAX);
-
41
-
42 // Algorithm based on the adaboost algorithm for classification
-
43 // as explained in Ensemble methods (Zhi-Hua Zhou, 2012)
-
44 fitted = true;
-
45 double alpha_t = 0;
-
46 torch::Tensor weights_ = torch::full({ m }, 1.0 / m, torch::kFloat64);
-
47 bool finished = false;
-
48 std::vector<int> featuresUsed;
-
49 if (selectFeatures) {
-
50 featuresUsed = initializeModels();
-
51 auto ypred = predict(X_train);
-
52 std::tie(weights_, alpha_t, finished) = update_weights(y_train, ypred, weights_);
-
53 // Update significance of the models
-
54 for (int i = 0; i < n_models; ++i) {
-
55 significanceModels[i] = alpha_t;
-
56 }
-
57 if (finished) {
-
58 return;
-
59 }
-
60 }
-
61 int numItemsPack = 0; // The counter of the models inserted in the current pack
-
62 // Variables to control the accuracy finish condition
-
63 double priorAccuracy = 0.0;
-
64 double improvement = 1.0;
-
65 double convergence_threshold = 1e-4;
-
66 int tolerance = 0; // number of times the accuracy is lower than the convergence_threshold
-
67 // Step 0: Set the finish condition
-
68 // epsilon sub t > 0.5 => inverse the weights policy
-
69 // validation error is not decreasing
-
70 // run out of features
-
71 bool ascending = order_algorithm == Orders.ASC;
-
72 std::mt19937 g{ 173 };
-
73 while (!finished) {
-
74 // Step 1: Build ranking with mutual information
-
75 auto featureSelection = metrics.SelectKBestWeighted(weights_, ascending, n); // Get all the features sorted
-
76 if (order_algorithm == Orders.RAND) {
-
77 std::shuffle(featureSelection.begin(), featureSelection.end(), g);
-
78 }
-
79 // Remove used features
-
80 featureSelection.erase(remove_if(begin(featureSelection), end(featureSelection), [&](auto x)
-
81 { return std::find(begin(featuresUsed), end(featuresUsed), x) != end(featuresUsed);}),
-
82 end(featureSelection)
-
83 );
-
84 int k = bisection ? pow(2, tolerance) : 1;
-
85 int counter = 0; // The model counter of the current pack
-
86 // VLOG_SCOPE_F(1, "counter=%d k=%d featureSelection.size: %zu", counter, k, featureSelection.size());
-
87 while (counter++ < k && featureSelection.size() > 0) {
-
88 auto feature = featureSelection[0];
-
89 featureSelection.erase(featureSelection.begin());
-
90 std::unique_ptr<Classifier> model;
-
91 model = std::make_unique<SPODE>(feature);
-
92 model->fit(dataset, features, className, states, weights_);
-
93 alpha_t = 0.0;
-
94 if (!block_update) {
-
95 auto ypred = model->predict(X_train);
-
96 // Step 3.1: Compute the classifier amout of say
-
97 std::tie(weights_, alpha_t, finished) = update_weights(y_train, ypred, weights_);
-
98 }
-
99 // Step 3.4: Store classifier and its accuracy to weigh its future vote
-
100 numItemsPack++;
-
101 featuresUsed.push_back(feature);
-
102 models.push_back(std::move(model));
-
103 significanceModels.push_back(alpha_t);
-
104 n_models++;
-
105 // VLOG_SCOPE_F(2, "numItemsPack: %d n_models: %d featuresUsed: %zu", numItemsPack, n_models, featuresUsed.size());
-
106 }
-
107 if (block_update) {
-
108 std::tie(weights_, alpha_t, finished) = update_weights_block(k, y_train, weights_);
-
109 }
-
110 if (convergence && !finished) {
-
111 auto y_val_predict = predict(X_test);
-
112 double accuracy = (y_val_predict == y_test).sum().item<double>() / (double)y_test.size(0);
-
113 if (priorAccuracy == 0) {
-
114 priorAccuracy = accuracy;
-
115 } else {
-
116 improvement = accuracy - priorAccuracy;
-
117 }
-
118 if (improvement < convergence_threshold) {
-
119 // VLOG_SCOPE_F(3, " (improvement<threshold) tolerance: %d numItemsPack: %d improvement: %f prior: %f current: %f", tolerance, numItemsPack, improvement, priorAccuracy, accuracy);
-
120 tolerance++;
-
121 } else {
-
122 // VLOG_SCOPE_F(3, "* (improvement>=threshold) Reset. tolerance: %d numItemsPack: %d improvement: %f prior: %f current: %f", tolerance, numItemsPack, improvement, priorAccuracy, accuracy);
-
123 tolerance = 0; // Reset the counter if the model performs better
-
124 numItemsPack = 0;
-
125 }
-
126 if (convergence_best) {
-
127 // Keep the best accuracy until now as the prior accuracy
-
128 priorAccuracy = std::max(accuracy, priorAccuracy);
-
129 } else {
-
130 // Keep the last accuray obtained as the prior accuracy
-
131 priorAccuracy = accuracy;
-
132 }
-
133 }
-
134 // VLOG_SCOPE_F(1, "tolerance: %d featuresUsed.size: %zu features.size: %zu", tolerance, featuresUsed.size(), features.size());
-
135 finished = finished || tolerance > maxTolerance || featuresUsed.size() == features.size();
-
136 }
-
137 if (tolerance > maxTolerance) {
-
138 if (numItemsPack < n_models) {
-
139 notes.push_back("Convergence threshold reached & " + std::to_string(numItemsPack) + " models eliminated");
-
140 // VLOG_SCOPE_F(4, "Convergence threshold reached & %d models eliminated of %d", numItemsPack, n_models);
-
141 for (int i = 0; i < numItemsPack; ++i) {
-
142 significanceModels.pop_back();
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143 models.pop_back();
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144 n_models--;
-
145 }
-
146 } else {
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147 notes.push_back("Convergence threshold reached & 0 models eliminated");
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148 // VLOG_SCOPE_F(4, "Convergence threshold reached & 0 models eliminated n_models=%d numItemsPack=%d", n_models, numItemsPack);
-
149 }
-
150 }
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151 if (featuresUsed.size() != features.size()) {
-
152 notes.push_back("Used features in train: " + std::to_string(featuresUsed.size()) + " of " + std::to_string(features.size()));
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153 status = WARNING;
-
154 }
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155 notes.push_back("Number of models: " + std::to_string(n_models));
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156 }
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157 std::vector<std::string> BoostAODE::graph(const std::string& title) const
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158 {
-
159 return Ensemble::graph(title);
-
160 }
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161}
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-
- - - - diff --git a/docs/manual/_boost_a_o_d_e_8h_source.html b/docs/manual/_boost_a_o_d_e_8h_source.html deleted file mode 100644 index 7b04ec2..0000000 --- a/docs/manual/_boost_a_o_d_e_8h_source.html +++ /dev/null @@ -1,144 +0,0 @@ - - - - - - - -BayesNet: /Users/rmontanana/Code/BayesNet/bayesnet/ensembles/BoostAODE.h Source File - - - - - - - - - - - - - - - -
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BoostAODE.h
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1// ***************************************************************
-
2// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
-
3// SPDX-FileType: SOURCE
-
4// SPDX-License-Identifier: MIT
-
5// ***************************************************************
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6
-
7#ifndef BOOSTAODE_H
-
8#define BOOSTAODE_H
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9#include <string>
-
10#include <vector>
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11#include "bayesnet/classifiers/SPODE.h"
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12#include "Boost.h"
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13
-
14namespace bayesnet {
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-
15 class BoostAODE : public Boost {
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16 public:
-
17 explicit BoostAODE(bool predict_voting = false);
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18 virtual ~BoostAODE() = default;
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19 std::vector<std::string> graph(const std::string& title = "BoostAODE") const override;
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20 protected:
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21 void trainModel(const torch::Tensor& weights) override;
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22 private:
-
23 std::vector<int> initializeModels();
-
24 };
-
-
25}
-
26#endif
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- - - - diff --git a/docs/manual/_classifier_8cc_source.html b/docs/manual/_classifier_8cc_source.html deleted file mode 100644 index 7d8cd8c..0000000 --- a/docs/manual/_classifier_8cc_source.html +++ /dev/null @@ -1,308 +0,0 @@ - - - - - - - -BayesNet: /Users/rmontanana/Code/BayesNet/bayesnet/classifiers/Classifier.cc Source File - - - - - - - - - - - - - - - -
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- - - - - - - -
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BayesNet 1.0.5 -
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Bayesian Network Classifiers using libtorch from scratch
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Classifier.cc
-
-
-
1// ***************************************************************
-
2// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
-
3// SPDX-FileType: SOURCE
-
4// SPDX-License-Identifier: MIT
-
5// ***************************************************************
-
6
-
7#include <sstream>
-
8#include "bayesnet/utils/bayesnetUtils.h"
-
9#include "Classifier.h"
-
10
-
11namespace bayesnet {
-
12 Classifier::Classifier(Network model) : model(model), m(0), n(0), metrics(Metrics()), fitted(false) {}
-
13 const std::string CLASSIFIER_NOT_FITTED = "Classifier has not been fitted";
-
14 Classifier& Classifier::build(const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights)
-
15 {
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16 this->features = features;
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17 this->className = className;
-
18 this->states = states;
-
19 m = dataset.size(1);
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20 n = features.size();
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21 checkFitParameters();
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22 auto n_classes = states.at(className).size();
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23 metrics = Metrics(dataset, features, className, n_classes);
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24 model.initialize();
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25 buildModel(weights);
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26 trainModel(weights);
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27 fitted = true;
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28 return *this;
-
29 }
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30 void Classifier::buildDataset(torch::Tensor& ytmp)
-
31 {
-
32 try {
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33 auto yresized = torch::transpose(ytmp.view({ ytmp.size(0), 1 }), 0, 1);
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34 dataset = torch::cat({ dataset, yresized }, 0);
-
35 }
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36 catch (const std::exception& e) {
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37 std::stringstream oss;
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38 oss << "* Error in X and y dimensions *\n";
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39 oss << "X dimensions: " << dataset.sizes() << "\n";
-
40 oss << "y dimensions: " << ytmp.sizes();
-
41 throw std::runtime_error(oss.str());
-
42 }
-
43 }
-
44 void Classifier::trainModel(const torch::Tensor& weights)
-
45 {
-
46 model.fit(dataset, weights, features, className, states);
-
47 }
-
48 // X is nxm where n is the number of features and m the number of samples
-
49 Classifier& Classifier::fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states)
-
50 {
-
51 dataset = X;
-
52 buildDataset(y);
-
53 const torch::Tensor weights = torch::full({ dataset.size(1) }, 1.0 / dataset.size(1), torch::kDouble);
-
54 return build(features, className, states, weights);
-
55 }
-
56 // X is nxm where n is the number of features and m the number of samples
-
57 Classifier& Classifier::fit(std::vector<std::vector<int>>& X, std::vector<int>& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states)
-
58 {
-
59 dataset = torch::zeros({ static_cast<int>(X.size()), static_cast<int>(X[0].size()) }, torch::kInt32);
-
60 for (int i = 0; i < X.size(); ++i) {
-
61 dataset.index_put_({ i, "..." }, torch::tensor(X[i], torch::kInt32));
-
62 }
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63 auto ytmp = torch::tensor(y, torch::kInt32);
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64 buildDataset(ytmp);
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65 const torch::Tensor weights = torch::full({ dataset.size(1) }, 1.0 / dataset.size(1), torch::kDouble);
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66 return build(features, className, states, weights);
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67 }
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68 Classifier& Classifier::fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states)
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69 {
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70 this->dataset = dataset;
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71 const torch::Tensor weights = torch::full({ dataset.size(1) }, 1.0 / dataset.size(1), torch::kDouble);
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72 return build(features, className, states, weights);
-
73 }
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74 Classifier& Classifier::fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights)
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75 {
-
76 this->dataset = dataset;
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77 return build(features, className, states, weights);
-
78 }
-
79 void Classifier::checkFitParameters()
-
80 {
-
81 if (torch::is_floating_point(dataset)) {
-
82 throw std::invalid_argument("dataset (X, y) must be of type Integer");
-
83 }
-
84 if (dataset.size(0) - 1 != features.size()) {
-
85 throw std::invalid_argument("Classifier: X " + std::to_string(dataset.size(0) - 1) + " and features " + std::to_string(features.size()) + " must have the same number of features");
-
86 }
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87 if (states.find(className) == states.end()) {
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88 throw std::invalid_argument("class name not found in states");
-
89 }
-
90 for (auto feature : features) {
-
91 if (states.find(feature) == states.end()) {
-
92 throw std::invalid_argument("feature [" + feature + "] not found in states");
-
93 }
-
94 }
-
95 }
-
96 torch::Tensor Classifier::predict(torch::Tensor& X)
-
97 {
-
98 if (!fitted) {
-
99 throw std::logic_error(CLASSIFIER_NOT_FITTED);
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100 }
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101 return model.predict(X);
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102 }
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103 std::vector<int> Classifier::predict(std::vector<std::vector<int>>& X)
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104 {
-
105 if (!fitted) {
-
106 throw std::logic_error(CLASSIFIER_NOT_FITTED);
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107 }
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108 auto m_ = X[0].size();
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109 auto n_ = X.size();
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110 std::vector<std::vector<int>> Xd(n_, std::vector<int>(m_, 0));
-
111 for (auto i = 0; i < n_; i++) {
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112 Xd[i] = std::vector<int>(X[i].begin(), X[i].end());
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113 }
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114 auto yp = model.predict(Xd);
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115 return yp;
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116 }
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117 torch::Tensor Classifier::predict_proba(torch::Tensor& X)
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118 {
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119 if (!fitted) {
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120 throw std::logic_error(CLASSIFIER_NOT_FITTED);
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121 }
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122 return model.predict_proba(X);
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123 }
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124 std::vector<std::vector<double>> Classifier::predict_proba(std::vector<std::vector<int>>& X)
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125 {
-
126 if (!fitted) {
-
127 throw std::logic_error(CLASSIFIER_NOT_FITTED);
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128 }
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129 auto m_ = X[0].size();
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130 auto n_ = X.size();
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131 std::vector<std::vector<int>> Xd(n_, std::vector<int>(m_, 0));
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132 // Convert to nxm vector
-
133 for (auto i = 0; i < n_; i++) {
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134 Xd[i] = std::vector<int>(X[i].begin(), X[i].end());
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135 }
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136 auto yp = model.predict_proba(Xd);
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137 return yp;
-
138 }
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139 float Classifier::score(torch::Tensor& X, torch::Tensor& y)
-
140 {
-
141 torch::Tensor y_pred = predict(X);
-
142 return (y_pred == y).sum().item<float>() / y.size(0);
-
143 }
-
144 float Classifier::score(std::vector<std::vector<int>>& X, std::vector<int>& y)
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145 {
-
146 if (!fitted) {
-
147 throw std::logic_error(CLASSIFIER_NOT_FITTED);
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148 }
-
149 return model.score(X, y);
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150 }
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151 std::vector<std::string> Classifier::show() const
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152 {
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153 return model.show();
-
154 }
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155 void Classifier::addNodes()
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156 {
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157 // Add all nodes to the network
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158 for (const auto& feature : features) {
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159 model.addNode(feature);
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160 }
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161 model.addNode(className);
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162 }
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163 int Classifier::getNumberOfNodes() const
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164 {
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165 // Features does not include class
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166 return fitted ? model.getFeatures().size() : 0;
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167 }
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168 int Classifier::getNumberOfEdges() const
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169 {
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170 return fitted ? model.getNumEdges() : 0;
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171 }
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172 int Classifier::getNumberOfStates() const
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173 {
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174 return fitted ? model.getStates() : 0;
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175 }
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176 int Classifier::getClassNumStates() const
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177 {
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178 return fitted ? model.getClassNumStates() : 0;
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179 }
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180 std::vector<std::string> Classifier::topological_order()
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181 {
-
182 return model.topological_sort();
-
183 }
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184 std::string Classifier::dump_cpt() const
-
185 {
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186 return model.dump_cpt();
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187 }
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188 void Classifier::setHyperparameters(const nlohmann::json& hyperparameters)
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189 {
-
190 if (!hyperparameters.empty()) {
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191 throw std::invalid_argument("Invalid hyperparameters" + hyperparameters.dump());
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192 }
-
193 }
-
194}
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-
- - - - diff --git a/docs/manual/_classifier_8h_source.html b/docs/manual/_classifier_8h_source.html deleted file mode 100644 index 5b39713..0000000 --- a/docs/manual/_classifier_8h_source.html +++ /dev/null @@ -1,184 +0,0 @@ - - - - - - - -BayesNet: /Users/rmontanana/Code/BayesNet/bayesnet/classifiers/Classifier.h Source File - - - - - - - - - - - - - - - -
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BayesNet 1.0.5 -
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Bayesian Network Classifiers using libtorch from scratch
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Classifier.h
-
-
-
1// ***************************************************************
-
2// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
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3// SPDX-FileType: SOURCE
-
4// SPDX-License-Identifier: MIT
-
5// ***************************************************************
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6
-
7#ifndef CLASSIFIER_H
-
8#define CLASSIFIER_H
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9#include <torch/torch.h>
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10#include "bayesnet/utils/BayesMetrics.h"
-
11#include "bayesnet/network/Network.h"
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12#include "bayesnet/BaseClassifier.h"
-
13
-
14namespace bayesnet {
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-
15 class Classifier : public BaseClassifier {
-
16 public:
-
17 Classifier(Network model);
-
18 virtual ~Classifier() = default;
-
19 Classifier& fit(std::vector<std::vector<int>>& X, std::vector<int>& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states) override;
-
20 Classifier& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states) override;
-
21 Classifier& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states) override;
-
22 Classifier& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights) override;
-
23 void addNodes();
-
24 int getNumberOfNodes() const override;
-
25 int getNumberOfEdges() const override;
-
26 int getNumberOfStates() const override;
-
27 int getClassNumStates() const override;
-
28 torch::Tensor predict(torch::Tensor& X) override;
-
29 std::vector<int> predict(std::vector<std::vector<int>>& X) override;
-
30 torch::Tensor predict_proba(torch::Tensor& X) override;
-
31 std::vector<std::vector<double>> predict_proba(std::vector<std::vector<int>>& X) override;
-
32 status_t getStatus() const override { return status; }
-
33 std::string getVersion() override { return { project_version.begin(), project_version.end() }; };
-
34 float score(torch::Tensor& X, torch::Tensor& y) override;
-
35 float score(std::vector<std::vector<int>>& X, std::vector<int>& y) override;
-
36 std::vector<std::string> show() const override;
-
37 std::vector<std::string> topological_order() override;
-
38 std::vector<std::string> getNotes() const override { return notes; }
-
39 std::string dump_cpt() const override;
-
40 void setHyperparameters(const nlohmann::json& hyperparameters) override; //For classifiers that don't have hyperparameters
-
41 protected:
-
42 bool fitted;
-
43 unsigned int m, n; // m: number of samples, n: number of features
-
44 Network model;
-
45 Metrics metrics;
-
46 std::vector<std::string> features;
-
47 std::string className;
-
48 std::map<std::string, std::vector<int>> states;
-
49 torch::Tensor dataset; // (n+1)xm tensor
-
50 status_t status = NORMAL;
-
51 std::vector<std::string> notes; // Used to store messages occurred during the fit process
-
52 void checkFitParameters();
-
53 virtual void buildModel(const torch::Tensor& weights) = 0;
-
54 void trainModel(const torch::Tensor& weights) override;
-
55 void buildDataset(torch::Tensor& y);
-
56 private:
-
57 Classifier& build(const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights);
-
58 };
-
-
59}
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60#endif
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61
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62
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63
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64
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65
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- - - - diff --git a/docs/manual/_ensemble_8cc_source.html b/docs/manual/_ensemble_8cc_source.html deleted file mode 100644 index 7cffac3..0000000 --- a/docs/manual/_ensemble_8cc_source.html +++ /dev/null @@ -1,336 +0,0 @@ - - - - - - - -BayesNet: /Users/rmontanana/Code/BayesNet/bayesnet/ensembles/Ensemble.cc Source File - - - - - - - - - - - - - - - -
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- - - - - - - -
-
BayesNet 1.0.5 -
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Bayesian Network Classifiers using libtorch from scratch
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Ensemble.cc
-
-
-
1// ***************************************************************
-
2// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
-
3// SPDX-FileType: SOURCE
-
4// SPDX-License-Identifier: MIT
-
5// ***************************************************************
-
6
-
7#include "Ensemble.h"
-
8
-
9namespace bayesnet {
-
10
-
11 Ensemble::Ensemble(bool predict_voting) : Classifier(Network()), n_models(0), predict_voting(predict_voting)
-
12 {
-
13
-
14 };
-
15 const std::string ENSEMBLE_NOT_FITTED = "Ensemble has not been fitted";
-
16 void Ensemble::trainModel(const torch::Tensor& weights)
-
17 {
-
18 n_models = models.size();
-
19 for (auto i = 0; i < n_models; ++i) {
-
20 // fit with std::vectors
-
21 models[i]->fit(dataset, features, className, states);
-
22 }
-
23 }
-
24 std::vector<int> Ensemble::compute_arg_max(std::vector<std::vector<double>>& X)
-
25 {
-
26 std::vector<int> y_pred;
-
27 for (auto i = 0; i < X.size(); ++i) {
-
28 auto max = std::max_element(X[i].begin(), X[i].end());
-
29 y_pred.push_back(std::distance(X[i].begin(), max));
-
30 }
-
31 return y_pred;
-
32 }
-
33 torch::Tensor Ensemble::compute_arg_max(torch::Tensor& X)
-
34 {
-
35 auto y_pred = torch::argmax(X, 1);
-
36 return y_pred;
-
37 }
-
38 torch::Tensor Ensemble::voting(torch::Tensor& votes)
-
39 {
-
40 // Convert m x n_models tensor to a m x n_class_states with voting probabilities
-
41 auto y_pred_ = votes.accessor<int, 2>();
-
42 std::vector<int> y_pred_final;
-
43 int numClasses = states.at(className).size();
-
44 // votes is m x n_models with the prediction of every model for each sample
-
45 auto result = torch::zeros({ votes.size(0), numClasses }, torch::kFloat32);
-
46 auto sum = std::reduce(significanceModels.begin(), significanceModels.end());
-
47 for (int i = 0; i < votes.size(0); ++i) {
-
48 // n_votes store in each index (value of class) the significance added by each model
-
49 // i.e. n_votes[0] contains how much value has the value 0 of class. That value is generated by the models predictions
-
50 std::vector<double> n_votes(numClasses, 0.0);
-
51 for (int j = 0; j < n_models; ++j) {
-
52 n_votes[y_pred_[i][j]] += significanceModels.at(j);
-
53 }
-
54 result[i] = torch::tensor(n_votes);
-
55 }
-
56 // To only do one division and gain precision
-
57 result /= sum;
-
58 return result;
-
59 }
-
60 std::vector<std::vector<double>> Ensemble::predict_proba(std::vector<std::vector<int>>& X)
-
61 {
-
62 if (!fitted) {
-
63 throw std::logic_error(ENSEMBLE_NOT_FITTED);
-
64 }
-
65 return predict_voting ? predict_average_voting(X) : predict_average_proba(X);
-
66 }
-
67 torch::Tensor Ensemble::predict_proba(torch::Tensor& X)
-
68 {
-
69 if (!fitted) {
-
70 throw std::logic_error(ENSEMBLE_NOT_FITTED);
-
71 }
-
72 return predict_voting ? predict_average_voting(X) : predict_average_proba(X);
-
73 }
-
74 std::vector<int> Ensemble::predict(std::vector<std::vector<int>>& X)
-
75 {
-
76 auto res = predict_proba(X);
-
77 return compute_arg_max(res);
-
78 }
-
79 torch::Tensor Ensemble::predict(torch::Tensor& X)
-
80 {
-
81 auto res = predict_proba(X);
-
82 return compute_arg_max(res);
-
83 }
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84 torch::Tensor Ensemble::predict_average_proba(torch::Tensor& X)
-
85 {
-
86 auto n_states = models[0]->getClassNumStates();
-
87 torch::Tensor y_pred = torch::zeros({ X.size(1), n_states }, torch::kFloat32);
-
88 auto threads{ std::vector<std::thread>() };
-
89 std::mutex mtx;
-
90 for (auto i = 0; i < n_models; ++i) {
-
91 threads.push_back(std::thread([&, i]() {
-
92 auto ypredict = models[i]->predict_proba(X);
-
93 std::lock_guard<std::mutex> lock(mtx);
-
94 y_pred += ypredict * significanceModels[i];
-
95 }));
-
96 }
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97 for (auto& thread : threads) {
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98 thread.join();
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99 }
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100 auto sum = std::reduce(significanceModels.begin(), significanceModels.end());
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101 y_pred /= sum;
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102 return y_pred;
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103 }
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104 std::vector<std::vector<double>> Ensemble::predict_average_proba(std::vector<std::vector<int>>& X)
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105 {
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106 auto n_states = models[0]->getClassNumStates();
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107 std::vector<std::vector<double>> y_pred(X[0].size(), std::vector<double>(n_states, 0.0));
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108 auto threads{ std::vector<std::thread>() };
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109 std::mutex mtx;
-
110 for (auto i = 0; i < n_models; ++i) {
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111 threads.push_back(std::thread([&, i]() {
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112 auto ypredict = models[i]->predict_proba(X);
-
113 assert(ypredict.size() == y_pred.size());
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114 assert(ypredict[0].size() == y_pred[0].size());
-
115 std::lock_guard<std::mutex> lock(mtx);
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116 // Multiply each prediction by the significance of the model and then add it to the final prediction
-
117 for (auto j = 0; j < ypredict.size(); ++j) {
-
118 std::transform(y_pred[j].begin(), y_pred[j].end(), ypredict[j].begin(), y_pred[j].begin(),
-
119 [significanceModels = significanceModels[i]](double x, double y) { return x + y * significanceModels; });
-
120 }
-
121 }));
-
122 }
-
123 for (auto& thread : threads) {
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124 thread.join();
-
125 }
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126 auto sum = std::reduce(significanceModels.begin(), significanceModels.end());
-
127 //Divide each element of the prediction by the sum of the significances
-
128 for (auto j = 0; j < y_pred.size(); ++j) {
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129 std::transform(y_pred[j].begin(), y_pred[j].end(), y_pred[j].begin(), [sum](double x) { return x / sum; });
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130 }
-
131 return y_pred;
-
132 }
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133 std::vector<std::vector<double>> Ensemble::predict_average_voting(std::vector<std::vector<int>>& X)
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134 {
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135 torch::Tensor Xt = bayesnet::vectorToTensor(X, false);
-
136 auto y_pred = predict_average_voting(Xt);
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137 std::vector<std::vector<double>> result = tensorToVectorDouble(y_pred);
-
138 return result;
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139 }
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140 torch::Tensor Ensemble::predict_average_voting(torch::Tensor& X)
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141 {
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142 // Build a m x n_models tensor with the predictions of each model
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143 torch::Tensor y_pred = torch::zeros({ X.size(1), n_models }, torch::kInt32);
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144 auto threads{ std::vector<std::thread>() };
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145 std::mutex mtx;
-
146 for (auto i = 0; i < n_models; ++i) {
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147 threads.push_back(std::thread([&, i]() {
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148 auto ypredict = models[i]->predict(X);
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149 std::lock_guard<std::mutex> lock(mtx);
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150 y_pred.index_put_({ "...", i }, ypredict);
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151 }));
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152 }
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153 for (auto& thread : threads) {
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154 thread.join();
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155 }
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156 return voting(y_pred);
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157 }
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158 float Ensemble::score(torch::Tensor& X, torch::Tensor& y)
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159 {
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160 auto y_pred = predict(X);
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161 int correct = 0;
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162 for (int i = 0; i < y_pred.size(0); ++i) {
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163 if (y_pred[i].item<int>() == y[i].item<int>()) {
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164 correct++;
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165 }
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166 }
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167 return (double)correct / y_pred.size(0);
-
168 }
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169 float Ensemble::score(std::vector<std::vector<int>>& X, std::vector<int>& y)
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170 {
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171 auto y_pred = predict(X);
-
172 int correct = 0;
-
173 for (int i = 0; i < y_pred.size(); ++i) {
-
174 if (y_pred[i] == y[i]) {
-
175 correct++;
-
176 }
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177 }
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178 return (double)correct / y_pred.size();
-
179 }
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180 std::vector<std::string> Ensemble::show() const
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181 {
-
182 auto result = std::vector<std::string>();
-
183 for (auto i = 0; i < n_models; ++i) {
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184 auto res = models[i]->show();
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185 result.insert(result.end(), res.begin(), res.end());
-
186 }
-
187 return result;
-
188 }
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189 std::vector<std::string> Ensemble::graph(const std::string& title) const
-
190 {
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191 auto result = std::vector<std::string>();
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192 for (auto i = 0; i < n_models; ++i) {
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193 auto res = models[i]->graph(title + "_" + std::to_string(i));
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194 result.insert(result.end(), res.begin(), res.end());
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195 }
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196 return result;
-
197 }
-
198 int Ensemble::getNumberOfNodes() const
-
199 {
-
200 int nodes = 0;
-
201 for (auto i = 0; i < n_models; ++i) {
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202 nodes += models[i]->getNumberOfNodes();
-
203 }
-
204 return nodes;
-
205 }
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206 int Ensemble::getNumberOfEdges() const
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207 {
-
208 int edges = 0;
-
209 for (auto i = 0; i < n_models; ++i) {
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210 edges += models[i]->getNumberOfEdges();
-
211 }
-
212 return edges;
-
213 }
-
214 int Ensemble::getNumberOfStates() const
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215 {
-
216 int nstates = 0;
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217 for (auto i = 0; i < n_models; ++i) {
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218 nstates += models[i]->getNumberOfStates();
-
219 }
-
220 return nstates;
-
221 }
-
222}
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-
- - - - diff --git a/docs/manual/_ensemble_8h_source.html b/docs/manual/_ensemble_8h_source.html deleted file mode 100644 index 716f7f8..0000000 --- a/docs/manual/_ensemble_8h_source.html +++ /dev/null @@ -1,171 +0,0 @@ - - - - - - - -BayesNet: /Users/rmontanana/Code/BayesNet/bayesnet/ensembles/Ensemble.h Source File - - - - - - - - - - - - - - - -
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- - - - - - - -
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Ensemble.h
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-
-
1// ***************************************************************
-
2// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
-
3// SPDX-FileType: SOURCE
-
4// SPDX-License-Identifier: MIT
-
5// ***************************************************************
-
6
-
7#ifndef ENSEMBLE_H
-
8#define ENSEMBLE_H
-
9#include <torch/torch.h>
-
10#include "bayesnet/utils/BayesMetrics.h"
-
11#include "bayesnet/utils/bayesnetUtils.h"
-
12#include "bayesnet/classifiers/Classifier.h"
-
13
-
14namespace bayesnet {
-
-
15 class Ensemble : public Classifier {
-
16 public:
-
17 Ensemble(bool predict_voting = true);
-
18 virtual ~Ensemble() = default;
-
19 torch::Tensor predict(torch::Tensor& X) override;
-
20 std::vector<int> predict(std::vector<std::vector<int>>& X) override;
-
21 torch::Tensor predict_proba(torch::Tensor& X) override;
-
22 std::vector<std::vector<double>> predict_proba(std::vector<std::vector<int>>& X) override;
-
23 float score(torch::Tensor& X, torch::Tensor& y) override;
-
24 float score(std::vector<std::vector<int>>& X, std::vector<int>& y) override;
-
25 int getNumberOfNodes() const override;
-
26 int getNumberOfEdges() const override;
-
27 int getNumberOfStates() const override;
-
28 std::vector<std::string> show() const override;
-
29 std::vector<std::string> graph(const std::string& title) const override;
-
30 std::vector<std::string> topological_order() override
-
31 {
-
32 return std::vector<std::string>();
-
33 }
-
34 std::string dump_cpt() const override
-
35 {
-
36 return "";
-
37 }
-
38 protected:
-
39 torch::Tensor predict_average_voting(torch::Tensor& X);
-
40 std::vector<std::vector<double>> predict_average_voting(std::vector<std::vector<int>>& X);
-
41 torch::Tensor predict_average_proba(torch::Tensor& X);
-
42 std::vector<std::vector<double>> predict_average_proba(std::vector<std::vector<int>>& X);
-
43 torch::Tensor compute_arg_max(torch::Tensor& X);
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44 std::vector<int> compute_arg_max(std::vector<std::vector<double>>& X);
-
45 torch::Tensor voting(torch::Tensor& votes);
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46 unsigned n_models;
-
47 std::vector<std::unique_ptr<Classifier>> models;
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48 std::vector<double> significanceModels;
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49 void trainModel(const torch::Tensor& weights) override;
-
50 bool predict_voting;
-
51 };
-
-
52}
-
53#endif
- - -
-
- - - - diff --git a/docs/manual/_k_d_b_8cc_source.html b/docs/manual/_k_d_b_8cc_source.html deleted file mode 100644 index 0bf9c42..0000000 --- a/docs/manual/_k_d_b_8cc_source.html +++ /dev/null @@ -1,225 +0,0 @@ - - - - - - - -BayesNet: /Users/rmontanana/Code/BayesNet/bayesnet/classifiers/KDB.cc Source File - - - - - - - - - - - - - - - -
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KDB.cc
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1// ***************************************************************
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2// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
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3// SPDX-FileType: SOURCE
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4// SPDX-License-Identifier: MIT
-
5// ***************************************************************
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6
-
7#include "KDB.h"
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8
-
9namespace bayesnet {
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10 KDB::KDB(int k, float theta) : Classifier(Network()), k(k), theta(theta)
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11 {
-
12 validHyperparameters = { "k", "theta" };
-
13
-
14 }
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15 void KDB::setHyperparameters(const nlohmann::json& hyperparameters_)
-
16 {
-
17 auto hyperparameters = hyperparameters_;
-
18 if (hyperparameters.contains("k")) {
-
19 k = hyperparameters["k"];
-
20 hyperparameters.erase("k");
-
21 }
-
22 if (hyperparameters.contains("theta")) {
-
23 theta = hyperparameters["theta"];
-
24 hyperparameters.erase("theta");
-
25 }
-
26 Classifier::setHyperparameters(hyperparameters);
-
27 }
-
28 void KDB::buildModel(const torch::Tensor& weights)
-
29 {
-
30 /*
-
31 1. For each feature Xi, compute mutual information, I(X;C),
-
32 where C is the class.
-
33 2. Compute class conditional mutual information I(Xi;XjIC), f or each
-
34 pair of features Xi and Xj, where i#j.
-
35 3. Let the used variable list, S, be empty.
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36 4. Let the DAG network being constructed, BN, begin with a single
-
37 class node, C.
-
38 5. Repeat until S includes all domain features
-
39 5.1. Select feature Xmax which is not in S and has the largest value
-
40 I(Xmax;C).
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41 5.2. Add a node to BN representing Xmax.
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42 5.3. Add an arc from C to Xmax in BN.
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43 5.4. Add m = min(lSl,/c) arcs from m distinct features Xj in S with
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44 the highest value for I(Xmax;X,jC).
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45 5.5. Add Xmax to S.
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46 Compute the conditional probabilility infered by the structure of BN by
-
47 using counts from DB, and output BN.
-
48 */
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49 // 1. For each feature Xi, compute mutual information, I(X;C),
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50 // where C is the class.
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51 addNodes();
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52 const torch::Tensor& y = dataset.index({ -1, "..." });
-
53 std::vector<double> mi;
-
54 for (auto i = 0; i < features.size(); i++) {
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55 torch::Tensor firstFeature = dataset.index({ i, "..." });
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56 mi.push_back(metrics.mutualInformation(firstFeature, y, weights));
-
57 }
-
58 // 2. Compute class conditional mutual information I(Xi;XjIC), f or each
-
59 auto conditionalEdgeWeights = metrics.conditionalEdge(weights);
-
60 // 3. Let the used variable list, S, be empty.
-
61 std::vector<int> S;
-
62 // 4. Let the DAG network being constructed, BN, begin with a single
-
63 // class node, C.
-
64 // 5. Repeat until S includes all domain features
-
65 // 5.1. Select feature Xmax which is not in S and has the largest value
-
66 // I(Xmax;C).
-
67 auto order = argsort(mi);
-
68 for (auto idx : order) {
-
69 // 5.2. Add a node to BN representing Xmax.
-
70 // 5.3. Add an arc from C to Xmax in BN.
-
71 model.addEdge(className, features[idx]);
-
72 // 5.4. Add m = min(lSl,/c) arcs from m distinct features Xj in S with
-
73 // the highest value for I(Xmax;X,jC).
-
74 add_m_edges(idx, S, conditionalEdgeWeights);
-
75 // 5.5. Add Xmax to S.
-
76 S.push_back(idx);
-
77 }
-
78 }
-
79 void KDB::add_m_edges(int idx, std::vector<int>& S, torch::Tensor& weights)
-
80 {
-
81 auto n_edges = std::min(k, static_cast<int>(S.size()));
-
82 auto cond_w = clone(weights);
-
83 bool exit_cond = k == 0;
-
84 int num = 0;
-
85 while (!exit_cond) {
-
86 auto max_minfo = argmax(cond_w.index({ idx, "..." })).item<int>();
-
87 auto belongs = find(S.begin(), S.end(), max_minfo) != S.end();
-
88 if (belongs && cond_w.index({ idx, max_minfo }).item<float>() > theta) {
-
89 try {
-
90 model.addEdge(features[max_minfo], features[idx]);
-
91 num++;
-
92 }
-
93 catch (const std::invalid_argument& e) {
-
94 // Loops are not allowed
-
95 }
-
96 }
-
97 cond_w.index_put_({ idx, max_minfo }, -1);
-
98 auto candidates_mask = cond_w.index({ idx, "..." }).gt(theta);
-
99 auto candidates = candidates_mask.nonzero();
-
100 exit_cond = num == n_edges || candidates.size(0) == 0;
-
101 }
-
102 }
-
103 std::vector<std::string> KDB::graph(const std::string& title) const
-
104 {
-
105 std::string header{ title };
-
106 if (title == "KDB") {
-
107 header += " (k=" + std::to_string(k) + ", theta=" + std::to_string(theta) + ")";
-
108 }
-
109 return model.graph(header);
-
110 }
-
111}
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-
- - - - diff --git a/docs/manual/_k_d_b_8h_source.html b/docs/manual/_k_d_b_8h_source.html deleted file mode 100644 index 57a4623..0000000 --- a/docs/manual/_k_d_b_8h_source.html +++ /dev/null @@ -1,145 +0,0 @@ - - - - - - - -BayesNet: /Users/rmontanana/Code/BayesNet/bayesnet/classifiers/KDB.h Source File - - - - - - - - - - - - - - - -
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BayesNet 1.0.5 -
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KDB.h
-
-
-
1// ***************************************************************
-
2// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
-
3// SPDX-FileType: SOURCE
-
4// SPDX-License-Identifier: MIT
-
5// ***************************************************************
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6
-
7#ifndef KDB_H
-
8#define KDB_H
-
9#include <torch/torch.h>
-
10#include "bayesnet/utils/bayesnetUtils.h"
-
11#include "Classifier.h"
-
12namespace bayesnet {
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-
13 class KDB : public Classifier {
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14 private:
-
15 int k;
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16 float theta;
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17 void add_m_edges(int idx, std::vector<int>& S, torch::Tensor& weights);
-
18 protected:
-
19 void buildModel(const torch::Tensor& weights) override;
-
20 public:
-
21 explicit KDB(int k, float theta = 0.03);
-
22 virtual ~KDB() = default;
-
23 void setHyperparameters(const nlohmann::json& hyperparameters_) override;
-
24 std::vector<std::string> graph(const std::string& name = "KDB") const override;
-
25 };
-
-
26}
-
27#endif
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- - - - diff --git a/docs/manual/_k_d_b_ld_8cc_source.html b/docs/manual/_k_d_b_ld_8cc_source.html deleted file mode 100644 index 4ee98ea..0000000 --- a/docs/manual/_k_d_b_ld_8cc_source.html +++ /dev/null @@ -1,149 +0,0 @@ - - - - - - - -BayesNet: /Users/rmontanana/Code/BayesNet/bayesnet/classifiers/KDBLd.cc Source File - - - - - - - - - - - - - - - -
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KDBLd.cc
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1// ***************************************************************
-
2// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
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3// SPDX-FileType: SOURCE
-
4// SPDX-License-Identifier: MIT
-
5// ***************************************************************
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6
-
7#include "KDBLd.h"
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8
-
9namespace bayesnet {
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10 KDBLd::KDBLd(int k) : KDB(k), Proposal(dataset, features, className) {}
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11 KDBLd& KDBLd::fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_)
-
12 {
-
13 checkInput(X_, y_);
-
14 features = features_;
-
15 className = className_;
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16 Xf = X_;
-
17 y = y_;
-
18 // Fills std::vectors Xv & yv with the data from tensors X_ (discretized) & y
-
19 states = fit_local_discretization(y);
-
20 // We have discretized the input data
-
21 // 1st we need to fit the model to build the normal KDB structure, KDB::fit initializes the base Bayesian network
-
22 KDB::fit(dataset, features, className, states);
-
23 states = localDiscretizationProposal(states, model);
-
24 return *this;
-
25 }
-
26 torch::Tensor KDBLd::predict(torch::Tensor& X)
-
27 {
-
28 auto Xt = prepareX(X);
-
29 return KDB::predict(Xt);
-
30 }
-
31 std::vector<std::string> KDBLd::graph(const std::string& name) const
-
32 {
-
33 return KDB::graph(name);
-
34 }
-
35}
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-
- - - - diff --git a/docs/manual/_k_d_b_ld_8h_source.html b/docs/manual/_k_d_b_ld_8h_source.html deleted file mode 100644 index 3c26e26..0000000 --- a/docs/manual/_k_d_b_ld_8h_source.html +++ /dev/null @@ -1,143 +0,0 @@ - - - - - - - -BayesNet: /Users/rmontanana/Code/BayesNet/bayesnet/classifiers/KDBLd.h Source File - - - - - - - - - - - - - - - -
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- - - - - - - -
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BayesNet 1.0.5 -
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Bayesian Network Classifiers using libtorch from scratch
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KDBLd.h
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-
-
1// ***************************************************************
-
2// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
-
3// SPDX-FileType: SOURCE
-
4// SPDX-License-Identifier: MIT
-
5// ***************************************************************
-
6
-
7#ifndef KDBLD_H
-
8#define KDBLD_H
-
9#include "Proposal.h"
-
10#include "KDB.h"
-
11
-
12namespace bayesnet {
-
-
13 class KDBLd : public KDB, public Proposal {
-
14 private:
-
15 public:
-
16 explicit KDBLd(int k);
-
17 virtual ~KDBLd() = default;
-
18 KDBLd& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, map<std::string, std::vector<int>>& states) override;
-
19 std::vector<std::string> graph(const std::string& name = "KDB") const override;
-
20 torch::Tensor predict(torch::Tensor& X) override;
-
21 static inline std::string version() { return "0.0.1"; };
-
22 };
-
-
23}
-
24#endif // !KDBLD_H
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- - - - diff --git a/docs/manual/_network_8cc_source.html b/docs/manual/_network_8cc_source.html deleted file mode 100644 index 2f277c8..0000000 --- a/docs/manual/_network_8cc_source.html +++ /dev/null @@ -1,543 +0,0 @@ - - - - - - - -BayesNet: /Users/rmontanana/Code/BayesNet/bayesnet/network/Network.cc Source File - - - - - - - - - - - - - - - -
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Network.cc
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1// ***************************************************************
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2// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
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3// SPDX-FileType: SOURCE
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4// SPDX-License-Identifier: MIT
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5// ***************************************************************
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6
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7#include <thread>
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8#include <mutex>
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9#include <sstream>
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10#include "Network.h"
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11#include "bayesnet/utils/bayesnetUtils.h"
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12namespace bayesnet {
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13 Network::Network() : fitted{ false }, maxThreads{ 0.95 }, classNumStates{ 0 }, laplaceSmoothing{ 0 }
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14 {
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15 }
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16 Network::Network(float maxT) : fitted{ false }, maxThreads{ maxT }, classNumStates{ 0 }, laplaceSmoothing{ 0 }
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17 {
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18
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19 }
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20 Network::Network(const Network& other) : laplaceSmoothing(other.laplaceSmoothing), features(other.features), className(other.className), classNumStates(other.getClassNumStates()),
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21 maxThreads(other.getMaxThreads()), fitted(other.fitted), samples(other.samples)
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22 {
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23 if (samples.defined())
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24 samples = samples.clone();
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25 for (const auto& node : other.nodes) {
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26 nodes[node.first] = std::make_unique<Node>(*node.second);
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27 }
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28 }
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29 void Network::initialize()
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30 {
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31 features.clear();
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32 className = "";
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33 classNumStates = 0;
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34 fitted = false;
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35 nodes.clear();
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36 samples = torch::Tensor();
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37 }
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38 float Network::getMaxThreads() const
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39 {
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40 return maxThreads;
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41 }
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42 torch::Tensor& Network::getSamples()
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43 {
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44 return samples;
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45 }
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46 void Network::addNode(const std::string& name)
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47 {
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48 if (name == "") {
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49 throw std::invalid_argument("Node name cannot be empty");
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50 }
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51 if (nodes.find(name) != nodes.end()) {
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52 return;
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53 }
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54 if (find(features.begin(), features.end(), name) == features.end()) {
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55 features.push_back(name);
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56 }
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57 nodes[name] = std::make_unique<Node>(name);
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58 }
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59 std::vector<std::string> Network::getFeatures() const
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60 {
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61 return features;
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62 }
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63 int Network::getClassNumStates() const
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64 {
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65 return classNumStates;
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66 }
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67 int Network::getStates() const
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68 {
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69 int result = 0;
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70 for (auto& node : nodes) {
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71 result += node.second->getNumStates();
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72 }
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73 return result;
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74 }
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75 std::string Network::getClassName() const
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76 {
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77 return className;
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78 }
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79 bool Network::isCyclic(const std::string& nodeId, std::unordered_set<std::string>& visited, std::unordered_set<std::string>& recStack)
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80 {
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81 if (visited.find(nodeId) == visited.end()) // if node hasn't been visited yet
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82 {
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83 visited.insert(nodeId);
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84 recStack.insert(nodeId);
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85 for (Node* child : nodes[nodeId]->getChildren()) {
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86 if (visited.find(child->getName()) == visited.end() && isCyclic(child->getName(), visited, recStack))
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87 return true;
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88 if (recStack.find(child->getName()) != recStack.end())
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89 return true;
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90 }
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91 }
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92 recStack.erase(nodeId); // remove node from recursion stack before function ends
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93 return false;
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94 }
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95 void Network::addEdge(const std::string& parent, const std::string& child)
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96 {
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97 if (nodes.find(parent) == nodes.end()) {
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98 throw std::invalid_argument("Parent node " + parent + " does not exist");
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99 }
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100 if (nodes.find(child) == nodes.end()) {
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101 throw std::invalid_argument("Child node " + child + " does not exist");
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102 }
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103 // Temporarily add edge to check for cycles
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104 nodes[parent]->addChild(nodes[child].get());
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105 nodes[child]->addParent(nodes[parent].get());
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106 std::unordered_set<std::string> visited;
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107 std::unordered_set<std::string> recStack;
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108 if (isCyclic(nodes[child]->getName(), visited, recStack)) // if adding this edge forms a cycle
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109 {
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110 // remove problematic edge
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111 nodes[parent]->removeChild(nodes[child].get());
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112 nodes[child]->removeParent(nodes[parent].get());
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113 throw std::invalid_argument("Adding this edge forms a cycle in the graph.");
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114 }
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115 }
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116 std::map<std::string, std::unique_ptr<Node>>& Network::getNodes()
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117 {
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118 return nodes;
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119 }
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120 void Network::checkFitData(int n_samples, int n_features, int n_samples_y, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights)
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121 {
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122 if (weights.size(0) != n_samples) {
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123 throw std::invalid_argument("Weights (" + std::to_string(weights.size(0)) + ") must have the same number of elements as samples (" + std::to_string(n_samples) + ") in Network::fit");
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124 }
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125 if (n_samples != n_samples_y) {
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126 throw std::invalid_argument("X and y must have the same number of samples in Network::fit (" + std::to_string(n_samples) + " != " + std::to_string(n_samples_y) + ")");
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127 }
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128 if (n_features != featureNames.size()) {
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129 throw std::invalid_argument("X and features must have the same number of features in Network::fit (" + std::to_string(n_features) + " != " + std::to_string(featureNames.size()) + ")");
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130 }
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131 if (features.size() == 0) {
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132 throw std::invalid_argument("The network has not been initialized. You must call addNode() before calling fit()");
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133 }
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134 if (n_features != features.size() - 1) {
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135 throw std::invalid_argument("X and local features must have the same number of features in Network::fit (" + std::to_string(n_features) + " != " + std::to_string(features.size() - 1) + ")");
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136 }
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137 if (find(features.begin(), features.end(), className) == features.end()) {
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138 throw std::invalid_argument("Class Name not found in Network::features");
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139 }
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140 for (auto& feature : featureNames) {
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141 if (find(features.begin(), features.end(), feature) == features.end()) {
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142 throw std::invalid_argument("Feature " + feature + " not found in Network::features");
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143 }
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144 if (states.find(feature) == states.end()) {
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145 throw std::invalid_argument("Feature " + feature + " not found in states");
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146 }
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147 }
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148 }
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149 void Network::setStates(const std::map<std::string, std::vector<int>>& states)
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150 {
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151 // Set states to every Node in the network
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152 for_each(features.begin(), features.end(), [this, &states](const std::string& feature) {
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153 nodes.at(feature)->setNumStates(states.at(feature).size());
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154 });
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155 classNumStates = nodes.at(className)->getNumStates();
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156 }
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157 // X comes in nxm, where n is the number of features and m the number of samples
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158 void Network::fit(const torch::Tensor& X, const torch::Tensor& y, const torch::Tensor& weights, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states)
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159 {
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160 checkFitData(X.size(1), X.size(0), y.size(0), featureNames, className, states, weights);
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161 this->className = className;
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162 torch::Tensor ytmp = torch::transpose(y.view({ y.size(0), 1 }), 0, 1);
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163 samples = torch::cat({ X , ytmp }, 0);
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164 for (int i = 0; i < featureNames.size(); ++i) {
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165 auto row_feature = X.index({ i, "..." });
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166 }
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167 completeFit(states, weights);
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168 }
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169 void Network::fit(const torch::Tensor& samples, const torch::Tensor& weights, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states)
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170 {
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171 checkFitData(samples.size(1), samples.size(0) - 1, samples.size(1), featureNames, className, states, weights);
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172 this->className = className;
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173 this->samples = samples;
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174 completeFit(states, weights);
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175 }
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176 // input_data comes in nxm, where n is the number of features and m the number of samples
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177 void Network::fit(const std::vector<std::vector<int>>& input_data, const std::vector<int>& labels, const std::vector<double>& weights_, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states)
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178 {
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179 const torch::Tensor weights = torch::tensor(weights_, torch::kFloat64);
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180 checkFitData(input_data[0].size(), input_data.size(), labels.size(), featureNames, className, states, weights);
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181 this->className = className;
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182 // Build tensor of samples (nxm) (n+1 because of the class)
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183 samples = torch::zeros({ static_cast<int>(input_data.size() + 1), static_cast<int>(input_data[0].size()) }, torch::kInt32);
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184 for (int i = 0; i < featureNames.size(); ++i) {
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185 samples.index_put_({ i, "..." }, torch::tensor(input_data[i], torch::kInt32));
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186 }
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187 samples.index_put_({ -1, "..." }, torch::tensor(labels, torch::kInt32));
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188 completeFit(states, weights);
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189 }
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190 void Network::completeFit(const std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights)
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191 {
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192 setStates(states);
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193 laplaceSmoothing = 1.0 / samples.size(1); // To use in CPT computation
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194 std::vector<std::thread> threads;
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195 for (auto& node : nodes) {
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196 threads.emplace_back([this, &node, &weights]() {
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197 node.second->computeCPT(samples, features, laplaceSmoothing, weights);
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198 });
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199 }
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200 for (auto& thread : threads) {
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201 thread.join();
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202 }
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203 fitted = true;
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204 }
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205 torch::Tensor Network::predict_tensor(const torch::Tensor& samples, const bool proba)
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206 {
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207 if (!fitted) {
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208 throw std::logic_error("You must call fit() before calling predict()");
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209 }
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210 torch::Tensor result;
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211 result = torch::zeros({ samples.size(1), classNumStates }, torch::kFloat64);
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212 for (int i = 0; i < samples.size(1); ++i) {
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213 const torch::Tensor sample = samples.index({ "...", i });
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214 auto psample = predict_sample(sample);
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215 auto temp = torch::tensor(psample, torch::kFloat64);
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216 // result.index_put_({ i, "..." }, torch::tensor(predict_sample(sample), torch::kFloat64));
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217 result.index_put_({ i, "..." }, temp);
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218 }
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219 if (proba)
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220 return result;
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221 return result.argmax(1);
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222 }
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223 // Return mxn tensor of probabilities
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224 torch::Tensor Network::predict_proba(const torch::Tensor& samples)
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225 {
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226 return predict_tensor(samples, true);
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227 }
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228
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229 // Return mxn tensor of probabilities
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230 torch::Tensor Network::predict(const torch::Tensor& samples)
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231 {
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232 return predict_tensor(samples, false);
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233 }
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234
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235 // Return mx1 std::vector of predictions
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236 // tsamples is nxm std::vector of samples
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237 std::vector<int> Network::predict(const std::vector<std::vector<int>>& tsamples)
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238 {
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239 if (!fitted) {
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240 throw std::logic_error("You must call fit() before calling predict()");
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241 }
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242 std::vector<int> predictions;
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243 std::vector<int> sample;
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244 for (int row = 0; row < tsamples[0].size(); ++row) {
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245 sample.clear();
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246 for (int col = 0; col < tsamples.size(); ++col) {
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247 sample.push_back(tsamples[col][row]);
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248 }
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249 std::vector<double> classProbabilities = predict_sample(sample);
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250 // Find the class with the maximum posterior probability
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251 auto maxElem = max_element(classProbabilities.begin(), classProbabilities.end());
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252 int predictedClass = distance(classProbabilities.begin(), maxElem);
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253 predictions.push_back(predictedClass);
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254 }
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255 return predictions;
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256 }
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257 // Return mxn std::vector of probabilities
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258 // tsamples is nxm std::vector of samples
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259 std::vector<std::vector<double>> Network::predict_proba(const std::vector<std::vector<int>>& tsamples)
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260 {
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261 if (!fitted) {
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262 throw std::logic_error("You must call fit() before calling predict_proba()");
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263 }
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264 std::vector<std::vector<double>> predictions;
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265 std::vector<int> sample;
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266 for (int row = 0; row < tsamples[0].size(); ++row) {
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267 sample.clear();
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268 for (int col = 0; col < tsamples.size(); ++col) {
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269 sample.push_back(tsamples[col][row]);
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270 }
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271 predictions.push_back(predict_sample(sample));
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272 }
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273 return predictions;
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274 }
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275 double Network::score(const std::vector<std::vector<int>>& tsamples, const std::vector<int>& labels)
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276 {
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277 std::vector<int> y_pred = predict(tsamples);
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278 int correct = 0;
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279 for (int i = 0; i < y_pred.size(); ++i) {
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280 if (y_pred[i] == labels[i]) {
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281 correct++;
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282 }
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283 }
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284 return (double)correct / y_pred.size();
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285 }
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286 // Return 1xn std::vector of probabilities
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287 std::vector<double> Network::predict_sample(const std::vector<int>& sample)
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288 {
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289 // Ensure the sample size is equal to the number of features
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290 if (sample.size() != features.size() - 1) {
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291 throw std::invalid_argument("Sample size (" + std::to_string(sample.size()) +
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292 ") does not match the number of features (" + std::to_string(features.size() - 1) + ")");
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293 }
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294 std::map<std::string, int> evidence;
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295 for (int i = 0; i < sample.size(); ++i) {
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296 evidence[features[i]] = sample[i];
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297 }
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298 return exactInference(evidence);
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299 }
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300 // Return 1xn std::vector of probabilities
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301 std::vector<double> Network::predict_sample(const torch::Tensor& sample)
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302 {
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303 // Ensure the sample size is equal to the number of features
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304 if (sample.size(0) != features.size() - 1) {
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305 throw std::invalid_argument("Sample size (" + std::to_string(sample.size(0)) +
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306 ") does not match the number of features (" + std::to_string(features.size() - 1) + ")");
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307 }
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308 std::map<std::string, int> evidence;
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309 for (int i = 0; i < sample.size(0); ++i) {
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310 evidence[features[i]] = sample[i].item<int>();
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311 }
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312 return exactInference(evidence);
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313 }
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314 double Network::computeFactor(std::map<std::string, int>& completeEvidence)
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315 {
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316 double result = 1.0;
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317 for (auto& node : getNodes()) {
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318 result *= node.second->getFactorValue(completeEvidence);
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319 }
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320 return result;
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321 }
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322 std::vector<double> Network::exactInference(std::map<std::string, int>& evidence)
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323 {
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324 std::vector<double> result(classNumStates, 0.0);
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325 std::vector<std::thread> threads;
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326 std::mutex mtx;
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327 for (int i = 0; i < classNumStates; ++i) {
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328 threads.emplace_back([this, &result, &evidence, i, &mtx]() {
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329 auto completeEvidence = std::map<std::string, int>(evidence);
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330 completeEvidence[getClassName()] = i;
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331 double factor = computeFactor(completeEvidence);
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332 std::lock_guard<std::mutex> lock(mtx);
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333 result[i] = factor;
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334 });
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335 }
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336 for (auto& thread : threads) {
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337 thread.join();
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338 }
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339 // Normalize result
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340 double sum = accumulate(result.begin(), result.end(), 0.0);
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341 transform(result.begin(), result.end(), result.begin(), [sum](const double& value) { return value / sum; });
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342 return result;
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343 }
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344 std::vector<std::string> Network::show() const
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345 {
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346 std::vector<std::string> result;
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347 // Draw the network
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348 for (auto& node : nodes) {
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349 std::string line = node.first + " -> ";
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350 for (auto child : node.second->getChildren()) {
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351 line += child->getName() + ", ";
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352 }
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353 result.push_back(line);
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354 }
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355 return result;
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356 }
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357 std::vector<std::string> Network::graph(const std::string& title) const
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358 {
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359 auto output = std::vector<std::string>();
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360 auto prefix = "digraph BayesNet {\nlabel=<BayesNet ";
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361 auto suffix = ">\nfontsize=30\nfontcolor=blue\nlabelloc=t\nlayout=circo\n";
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362 std::string header = prefix + title + suffix;
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363 output.push_back(header);
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364 for (auto& node : nodes) {
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365 auto result = node.second->graph(className);
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366 output.insert(output.end(), result.begin(), result.end());
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367 }
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368 output.push_back("}\n");
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369 return output;
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370 }
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371 std::vector<std::pair<std::string, std::string>> Network::getEdges() const
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372 {
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373 auto edges = std::vector<std::pair<std::string, std::string>>();
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374 for (const auto& node : nodes) {
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375 auto head = node.first;
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376 for (const auto& child : node.second->getChildren()) {
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377 auto tail = child->getName();
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378 edges.push_back({ head, tail });
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379 }
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380 }
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381 return edges;
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382 }
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383 int Network::getNumEdges() const
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384 {
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385 return getEdges().size();
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386 }
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387 std::vector<std::string> Network::topological_sort()
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388 {
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389 /* Check if al the fathers of every node are before the node */
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390 auto result = features;
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391 result.erase(remove(result.begin(), result.end(), className), result.end());
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392 bool ending{ false };
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393 while (!ending) {
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394 ending = true;
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395 for (auto feature : features) {
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396 auto fathers = nodes[feature]->getParents();
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397 for (const auto& father : fathers) {
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398 auto fatherName = father->getName();
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399 if (fatherName == className) {
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400 continue;
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401 }
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402 // Check if father is placed before the actual feature
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403 auto it = find(result.begin(), result.end(), fatherName);
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404 if (it != result.end()) {
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405 auto it2 = find(result.begin(), result.end(), feature);
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406 if (it2 != result.end()) {
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407 if (distance(it, it2) < 0) {
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408 // if it is not, insert it before the feature
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409 result.erase(remove(result.begin(), result.end(), fatherName), result.end());
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410 result.insert(it2, fatherName);
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411 ending = false;
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412 }
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413 }
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414 }
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415 }
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416 }
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417 }
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418 return result;
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419 }
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420 std::string Network::dump_cpt() const
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421 {
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422 std::stringstream oss;
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423 for (auto& node : nodes) {
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424 oss << "* " << node.first << ": (" << node.second->getNumStates() << ") : " << node.second->getCPT().sizes() << std::endl;
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425 oss << node.second->getCPT() << std::endl;
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426 }
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427 return oss.str();
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428 }
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429}
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Bayesian Network Classifiers using libtorch from scratch
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Network.h
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1// ***************************************************************
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2// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
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3// SPDX-FileType: SOURCE
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4// SPDX-License-Identifier: MIT
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5// ***************************************************************
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6
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7#ifndef NETWORK_H
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8#define NETWORK_H
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9#include <map>
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10#include <vector>
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11#include "bayesnet/config.h"
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12#include "Node.h"
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13
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14namespace bayesnet {
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15 class Network {
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16 public:
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17 Network();
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18 explicit Network(float);
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19 explicit Network(const Network&);
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20 ~Network() = default;
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21 torch::Tensor& getSamples();
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22 float getMaxThreads() const;
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23 void addNode(const std::string&);
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24 void addEdge(const std::string&, const std::string&);
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25 std::map<std::string, std::unique_ptr<Node>>& getNodes();
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26 std::vector<std::string> getFeatures() const;
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27 int getStates() const;
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28 std::vector<std::pair<std::string, std::string>> getEdges() const;
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29 int getNumEdges() const;
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30 int getClassNumStates() const;
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31 std::string getClassName() const;
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32 /*
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33 Notice: Nodes have to be inserted in the same order as they are in the dataset, i.e., first node is first column and so on.
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34 */
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35 void fit(const std::vector<std::vector<int>>& input_data, const std::vector<int>& labels, const std::vector<double>& weights, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states);
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36 void fit(const torch::Tensor& X, const torch::Tensor& y, const torch::Tensor& weights, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states);
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37 void fit(const torch::Tensor& samples, const torch::Tensor& weights, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states);
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38 std::vector<int> predict(const std::vector<std::vector<int>>&); // Return mx1 std::vector of predictions
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39 torch::Tensor predict(const torch::Tensor&); // Return mx1 tensor of predictions
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40 torch::Tensor predict_tensor(const torch::Tensor& samples, const bool proba);
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41 std::vector<std::vector<double>> predict_proba(const std::vector<std::vector<int>>&); // Return mxn std::vector of probabilities
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42 torch::Tensor predict_proba(const torch::Tensor&); // Return mxn tensor of probabilities
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43 double score(const std::vector<std::vector<int>>&, const std::vector<int>&);
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44 std::vector<std::string> topological_sort();
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45 std::vector<std::string> show() const;
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46 std::vector<std::string> graph(const std::string& title) const; // Returns a std::vector of std::strings representing the graph in graphviz format
-
47 void initialize();
-
48 std::string dump_cpt() const;
-
49 inline std::string version() { return { project_version.begin(), project_version.end() }; }
-
50 private:
-
51 std::map<std::string, std::unique_ptr<Node>> nodes;
-
52 bool fitted;
-
53 float maxThreads = 0.95;
-
54 int classNumStates;
-
55 std::vector<std::string> features; // Including classname
-
56 std::string className;
-
57 double laplaceSmoothing;
-
58 torch::Tensor samples; // n+1xm tensor used to fit the model
-
59 bool isCyclic(const std::string&, std::unordered_set<std::string>&, std::unordered_set<std::string>&);
-
60 std::vector<double> predict_sample(const std::vector<int>&);
-
61 std::vector<double> predict_sample(const torch::Tensor&);
-
62 std::vector<double> exactInference(std::map<std::string, int>&);
-
63 double computeFactor(std::map<std::string, int>&);
-
64 void completeFit(const std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights);
-
65 void checkFitData(int n_features, int n_samples, int n_samples_y, const std::vector<std::string>& featureNames, const std::string& className, const std::map<std::string, std::vector<int>>& states, const torch::Tensor& weights);
-
66 void setStates(const std::map<std::string, std::vector<int>>&);
-
67 };
-
-
68}
-
69#endif
- -
-
- - - - diff --git a/docs/manual/_node_8cc_source.html b/docs/manual/_node_8cc_source.html deleted file mode 100644 index 2046da7..0000000 --- a/docs/manual/_node_8cc_source.html +++ /dev/null @@ -1,254 +0,0 @@ - - - - - - - -BayesNet: /Users/rmontanana/Code/BayesNet/bayesnet/network/Node.cc Source File - - - - - - - - - - - - - - - -
-
- - - - - - - -
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BayesNet 1.0.5 -
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Bayesian Network Classifiers using libtorch from scratch
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Node.cc
-
-
-
1// ***************************************************************
-
2// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
-
3// SPDX-FileType: SOURCE
-
4// SPDX-License-Identifier: MIT
-
5// ***************************************************************
-
6
-
7#include "Node.h"
-
8
-
9namespace bayesnet {
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10
-
11 Node::Node(const std::string& name)
-
12 : name(name)
-
13 {
-
14 }
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15 void Node::clear()
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16 {
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17 parents.clear();
-
18 children.clear();
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19 cpTable = torch::Tensor();
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20 dimensions.clear();
-
21 numStates = 0;
-
22 }
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23 std::string Node::getName() const
-
24 {
-
25 return name;
-
26 }
-
27 void Node::addParent(Node* parent)
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28 {
-
29 parents.push_back(parent);
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30 }
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31 void Node::removeParent(Node* parent)
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32 {
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33 parents.erase(std::remove(parents.begin(), parents.end(), parent), parents.end());
-
34 }
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35 void Node::removeChild(Node* child)
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36 {
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37 children.erase(std::remove(children.begin(), children.end(), child), children.end());
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38 }
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39 void Node::addChild(Node* child)
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40 {
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41 children.push_back(child);
-
42 }
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43 std::vector<Node*>& Node::getParents()
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44 {
-
45 return parents;
-
46 }
-
47 std::vector<Node*>& Node::getChildren()
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48 {
-
49 return children;
-
50 }
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51 int Node::getNumStates() const
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52 {
-
53 return numStates;
-
54 }
-
55 void Node::setNumStates(int numStates)
-
56 {
-
57 this->numStates = numStates;
-
58 }
-
59 torch::Tensor& Node::getCPT()
-
60 {
-
61 return cpTable;
-
62 }
-
63 /*
-
64 The MinFill criterion is a heuristic for variable elimination.
-
65 The variable that minimizes the number of edges that need to be added to the graph to make it triangulated.
-
66 This is done by counting the number of edges that need to be added to the graph if the variable is eliminated.
-
67 The variable with the minimum number of edges is chosen.
-
68 Here this is done computing the length of the combinations of the node neighbors taken 2 by 2.
-
69 */
-
70 unsigned Node::minFill()
-
71 {
-
72 std::unordered_set<std::string> neighbors;
-
73 for (auto child : children) {
-
74 neighbors.emplace(child->getName());
-
75 }
-
76 for (auto parent : parents) {
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77 neighbors.emplace(parent->getName());
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78 }
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79 auto source = std::vector<std::string>(neighbors.begin(), neighbors.end());
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80 return combinations(source).size();
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81 }
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82 std::vector<std::pair<std::string, std::string>> Node::combinations(const std::vector<std::string>& source)
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83 {
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84 std::vector<std::pair<std::string, std::string>> result;
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85 for (int i = 0; i < source.size(); ++i) {
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86 std::string temp = source[i];
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87 for (int j = i + 1; j < source.size(); ++j) {
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88 result.push_back({ temp, source[j] });
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89 }
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90 }
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91 return result;
-
92 }
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93 void Node::computeCPT(const torch::Tensor& dataset, const std::vector<std::string>& features, const double laplaceSmoothing, const torch::Tensor& weights)
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94 {
-
95 dimensions.clear();
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96 // Get dimensions of the CPT
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97 dimensions.push_back(numStates);
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98 transform(parents.begin(), parents.end(), back_inserter(dimensions), [](const auto& parent) { return parent->getNumStates(); });
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99 // Create a tensor of zeros with the dimensions of the CPT
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100 cpTable = torch::zeros(dimensions, torch::kFloat) + laplaceSmoothing;
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101 // Fill table with counts
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102 auto pos = find(features.begin(), features.end(), name);
-
103 if (pos == features.end()) {
-
104 throw std::logic_error("Feature " + name + " not found in dataset");
-
105 }
-
106 int name_index = pos - features.begin();
-
107 for (int n_sample = 0; n_sample < dataset.size(1); ++n_sample) {
-
108 c10::List<c10::optional<at::Tensor>> coordinates;
-
109 coordinates.push_back(dataset.index({ name_index, n_sample }));
-
110 for (auto parent : parents) {
-
111 pos = find(features.begin(), features.end(), parent->getName());
-
112 if (pos == features.end()) {
-
113 throw std::logic_error("Feature parent " + parent->getName() + " not found in dataset");
-
114 }
-
115 int parent_index = pos - features.begin();
-
116 coordinates.push_back(dataset.index({ parent_index, n_sample }));
-
117 }
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118 // Increment the count of the corresponding coordinate
-
119 cpTable.index_put_({ coordinates }, cpTable.index({ coordinates }) + weights.index({ n_sample }).item<double>());
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120 }
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121 // Normalize the counts
-
122 cpTable = cpTable / cpTable.sum(0);
-
123 }
-
124 float Node::getFactorValue(std::map<std::string, int>& evidence)
-
125 {
-
126 c10::List<c10::optional<at::Tensor>> coordinates;
-
127 // following predetermined order of indices in the cpTable (see Node.h)
-
128 coordinates.push_back(at::tensor(evidence[name]));
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129 transform(parents.begin(), parents.end(), std::back_inserter(coordinates), [&evidence](const auto& parent) { return at::tensor(evidence[parent->getName()]); });
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130 return cpTable.index({ coordinates }).item<float>();
-
131 }
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132 std::vector<std::string> Node::graph(const std::string& className)
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133 {
-
134 auto output = std::vector<std::string>();
-
135 auto suffix = name == className ? ", fontcolor=red, fillcolor=lightblue, style=filled " : "";
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136 output.push_back(name + " [shape=circle" + suffix + "] \n");
-
137 transform(children.begin(), children.end(), back_inserter(output), [this](const auto& child) { return name + " -> " + child->getName(); });
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138 return output;
-
139 }
-
140}
-
-
- - - - diff --git a/docs/manual/_node_8h_source.html b/docs/manual/_node_8h_source.html deleted file mode 100644 index 5121b18..0000000 --- a/docs/manual/_node_8h_source.html +++ /dev/null @@ -1,159 +0,0 @@ - - - - - - - -BayesNet: /Users/rmontanana/Code/BayesNet/bayesnet/network/Node.h Source File - - - - - - - - - - - - - - - -
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- - - - - - - -
-
BayesNet 1.0.5 -
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Bayesian Network Classifiers using libtorch from scratch
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Node.h
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1// ***************************************************************
-
2// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
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3// SPDX-FileType: SOURCE
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4// SPDX-License-Identifier: MIT
-
5// ***************************************************************
-
6
-
7#ifndef NODE_H
-
8#define NODE_H
-
9#include <unordered_set>
-
10#include <vector>
-
11#include <string>
-
12#include <torch/torch.h>
-
13namespace bayesnet {
-
-
14 class Node {
-
15 public:
-
16 explicit Node(const std::string&);
-
17 void clear();
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18 void addParent(Node*);
-
19 void addChild(Node*);
-
20 void removeParent(Node*);
-
21 void removeChild(Node*);
-
22 std::string getName() const;
-
23 std::vector<Node*>& getParents();
-
24 std::vector<Node*>& getChildren();
-
25 torch::Tensor& getCPT();
-
26 void computeCPT(const torch::Tensor& dataset, const std::vector<std::string>& features, const double laplaceSmoothing, const torch::Tensor& weights);
-
27 int getNumStates() const;
-
28 void setNumStates(int);
-
29 unsigned minFill();
-
30 std::vector<std::string> graph(const std::string& clasName); // Returns a std::vector of std::strings representing the graph in graphviz format
-
31 float getFactorValue(std::map<std::string, int>&);
-
32 private:
-
33 std::string name;
-
34 std::vector<Node*> parents;
-
35 std::vector<Node*> children;
-
36 int numStates = 0; // number of states of the variable
-
37 torch::Tensor cpTable; // Order of indices is 0-> node variable, 1-> 1st parent, 2-> 2nd parent, ...
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38 std::vector<int64_t> dimensions; // dimensions of the cpTable
-
39 std::vector<std::pair<std::string, std::string>> combinations(const std::vector<std::string>&);
-
40 };
-
-
41}
-
42#endif
- -
-
- - - - diff --git a/docs/manual/_proposal_8cc_source.html b/docs/manual/_proposal_8cc_source.html deleted file mode 100644 index 89130d5..0000000 --- a/docs/manual/_proposal_8cc_source.html +++ /dev/null @@ -1,243 +0,0 @@ - - - - - - - -BayesNet: /Users/rmontanana/Code/BayesNet/bayesnet/classifiers/Proposal.cc Source File - - - - - - - - - - - - - - - -
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BayesNet 1.0.5 -
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Proposal.cc
-
-
-
1// ***************************************************************
-
2// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
-
3// SPDX-FileType: SOURCE
-
4// SPDX-License-Identifier: MIT
-
5// ***************************************************************
-
6
-
7#include "Proposal.h"
-
8
-
9namespace bayesnet {
-
10 Proposal::Proposal(torch::Tensor& dataset_, std::vector<std::string>& features_, std::string& className_) : pDataset(dataset_), pFeatures(features_), pClassName(className_) {}
-
11 Proposal::~Proposal()
-
12 {
-
13 for (auto& [key, value] : discretizers) {
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14 delete value;
-
15 }
-
16 }
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17 void Proposal::checkInput(const torch::Tensor& X, const torch::Tensor& y)
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18 {
-
19 if (!torch::is_floating_point(X)) {
-
20 throw std::invalid_argument("X must be a floating point tensor");
-
21 }
-
22 if (torch::is_floating_point(y)) {
-
23 throw std::invalid_argument("y must be an integer tensor");
-
24 }
-
25 }
-
26 map<std::string, std::vector<int>> Proposal::localDiscretizationProposal(const map<std::string, std::vector<int>>& oldStates, Network& model)
-
27 {
-
28 // order of local discretization is important. no good 0, 1, 2...
-
29 // although we rediscretize features after the local discretization of every feature
-
30 auto order = model.topological_sort();
-
31 auto& nodes = model.getNodes();
-
32 map<std::string, std::vector<int>> states = oldStates;
-
33 std::vector<int> indicesToReDiscretize;
-
34 bool upgrade = false; // Flag to check if we need to upgrade the model
-
35 for (auto feature : order) {
-
36 auto nodeParents = nodes[feature]->getParents();
-
37 if (nodeParents.size() < 2) continue; // Only has class as parent
-
38 upgrade = true;
-
39 int index = find(pFeatures.begin(), pFeatures.end(), feature) - pFeatures.begin();
-
40 indicesToReDiscretize.push_back(index); // We need to re-discretize this feature
-
41 std::vector<std::string> parents;
-
42 transform(nodeParents.begin(), nodeParents.end(), back_inserter(parents), [](const auto& p) { return p->getName(); });
-
43 // Remove class as parent as it will be added later
-
44 parents.erase(remove(parents.begin(), parents.end(), pClassName), parents.end());
-
45 // Get the indices of the parents
-
46 std::vector<int> indices;
-
47 indices.push_back(-1); // Add class index
-
48 transform(parents.begin(), parents.end(), back_inserter(indices), [&](const auto& p) {return find(pFeatures.begin(), pFeatures.end(), p) - pFeatures.begin(); });
-
49 // Now we fit the discretizer of the feature, conditioned on its parents and the class i.e. discretizer.fit(X[index], X[indices] + y)
-
50 std::vector<std::string> yJoinParents(Xf.size(1));
-
51 for (auto idx : indices) {
-
52 for (int i = 0; i < Xf.size(1); ++i) {
-
53 yJoinParents[i] += to_string(pDataset.index({ idx, i }).item<int>());
-
54 }
-
55 }
-
56 auto yxv = factorize(yJoinParents);
-
57 auto xvf_ptr = Xf.index({ index }).data_ptr<float>();
-
58 auto xvf = std::vector<mdlp::precision_t>(xvf_ptr, xvf_ptr + Xf.size(1));
-
59 discretizers[feature]->fit(xvf, yxv);
-
60 }
-
61 if (upgrade) {
-
62 // Discretize again X (only the affected indices) with the new fitted discretizers
-
63 for (auto index : indicesToReDiscretize) {
-
64 auto Xt_ptr = Xf.index({ index }).data_ptr<float>();
-
65 auto Xt = std::vector<float>(Xt_ptr, Xt_ptr + Xf.size(1));
-
66 pDataset.index_put_({ index, "..." }, torch::tensor(discretizers[pFeatures[index]]->transform(Xt)));
-
67 auto xStates = std::vector<int>(discretizers[pFeatures[index]]->getCutPoints().size() + 1);
-
68 iota(xStates.begin(), xStates.end(), 0);
-
69 //Update new states of the feature/node
-
70 states[pFeatures[index]] = xStates;
-
71 }
-
72 const torch::Tensor weights = torch::full({ pDataset.size(1) }, 1.0 / pDataset.size(1), torch::kDouble);
-
73 model.fit(pDataset, weights, pFeatures, pClassName, states);
-
74 }
-
75 return states;
-
76 }
-
77 map<std::string, std::vector<int>> Proposal::fit_local_discretization(const torch::Tensor& y)
-
78 {
-
79 // Discretize the continuous input data and build pDataset (Classifier::dataset)
-
80 int m = Xf.size(1);
-
81 int n = Xf.size(0);
-
82 map<std::string, std::vector<int>> states;
-
83 pDataset = torch::zeros({ n + 1, m }, torch::kInt32);
-
84 auto yv = std::vector<int>(y.data_ptr<int>(), y.data_ptr<int>() + y.size(0));
-
85 // discretize input data by feature(row)
-
86 for (auto i = 0; i < pFeatures.size(); ++i) {
-
87 auto* discretizer = new mdlp::CPPFImdlp();
-
88 auto Xt_ptr = Xf.index({ i }).data_ptr<float>();
-
89 auto Xt = std::vector<float>(Xt_ptr, Xt_ptr + Xf.size(1));
-
90 discretizer->fit(Xt, yv);
-
91 pDataset.index_put_({ i, "..." }, torch::tensor(discretizer->transform(Xt)));
-
92 auto xStates = std::vector<int>(discretizer->getCutPoints().size() + 1);
-
93 iota(xStates.begin(), xStates.end(), 0);
-
94 states[pFeatures[i]] = xStates;
-
95 discretizers[pFeatures[i]] = discretizer;
-
96 }
-
97 int n_classes = torch::max(y).item<int>() + 1;
-
98 auto yStates = std::vector<int>(n_classes);
-
99 iota(yStates.begin(), yStates.end(), 0);
-
100 states[pClassName] = yStates;
-
101 pDataset.index_put_({ n, "..." }, y);
-
102 return states;
-
103 }
-
104 torch::Tensor Proposal::prepareX(torch::Tensor& X)
-
105 {
-
106 auto Xtd = torch::zeros_like(X, torch::kInt32);
-
107 for (int i = 0; i < X.size(0); ++i) {
-
108 auto Xt = std::vector<float>(X[i].data_ptr<float>(), X[i].data_ptr<float>() + X.size(1));
-
109 auto Xd = discretizers[pFeatures[i]]->transform(Xt);
-
110 Xtd.index_put_({ i }, torch::tensor(Xd, torch::kInt32));
-
111 }
-
112 return Xtd;
-
113 }
-
114 std::vector<int> Proposal::factorize(const std::vector<std::string>& labels_t)
-
115 {
-
116 std::vector<int> yy;
-
117 yy.reserve(labels_t.size());
-
118 std::map<std::string, int> labelMap;
-
119 int i = 0;
-
120 for (const std::string& label : labels_t) {
-
121 if (labelMap.find(label) == labelMap.end()) {
-
122 labelMap[label] = i++;
-
123 bool allDigits = std::all_of(label.begin(), label.end(), ::isdigit);
-
124 }
-
125 yy.push_back(labelMap[label]);
-
126 }
-
127 return yy;
-
128 }
-
129}
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-
- - - - diff --git a/docs/manual/_proposal_8h_source.html b/docs/manual/_proposal_8h_source.html deleted file mode 100644 index dcce17b..0000000 --- a/docs/manual/_proposal_8h_source.html +++ /dev/null @@ -1,155 +0,0 @@ - - - - - - - -BayesNet: /Users/rmontanana/Code/BayesNet/bayesnet/classifiers/Proposal.h Source File - - - - - - - - - - - - - - - -
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Proposal.h
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-
-
1// ***************************************************************
-
2// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
-
3// SPDX-FileType: SOURCE
-
4// SPDX-License-Identifier: MIT
-
5// ***************************************************************
-
6
-
7#ifndef PROPOSAL_H
-
8#define PROPOSAL_H
-
9#include <string>
-
10#include <map>
-
11#include <torch/torch.h>
-
12#include <CPPFImdlp.h>
-
13#include "bayesnet/network/Network.h"
-
14#include "Classifier.h"
-
15
-
16namespace bayesnet {
-
-
17 class Proposal {
-
18 public:
-
19 Proposal(torch::Tensor& pDataset, std::vector<std::string>& features_, std::string& className_);
-
20 virtual ~Proposal();
-
21 protected:
-
22 void checkInput(const torch::Tensor& X, const torch::Tensor& y);
-
23 torch::Tensor prepareX(torch::Tensor& X);
-
24 map<std::string, std::vector<int>> localDiscretizationProposal(const map<std::string, std::vector<int>>& states, Network& model);
-
25 map<std::string, std::vector<int>> fit_local_discretization(const torch::Tensor& y);
-
26 torch::Tensor Xf; // X continuous nxm tensor
-
27 torch::Tensor y; // y discrete nx1 tensor
-
28 map<std::string, mdlp::CPPFImdlp*> discretizers;
-
29 private:
-
30 std::vector<int> factorize(const std::vector<std::string>& labels_t);
-
31 torch::Tensor& pDataset; // (n+1)xm tensor
-
32 std::vector<std::string>& pFeatures;
-
33 std::string& pClassName;
-
34 };
-
-
35}
-
36
-
37#endif
- - -
-
- - - - diff --git a/docs/manual/_s_p_o_d_e_8cc_source.html b/docs/manual/_s_p_o_d_e_8cc_source.html deleted file mode 100644 index 2e5c363..0000000 --- a/docs/manual/_s_p_o_d_e_8cc_source.html +++ /dev/null @@ -1,145 +0,0 @@ - - - - - - - -BayesNet: /Users/rmontanana/Code/BayesNet/bayesnet/classifiers/SPODE.cc Source File - - - - - - - - - - - - - - - -
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SPODE.cc
-
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-
1// ***************************************************************
-
2// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
-
3// SPDX-FileType: SOURCE
-
4// SPDX-License-Identifier: MIT
-
5// ***************************************************************
-
6
-
7#include "SPODE.h"
-
8
-
9namespace bayesnet {
-
10
-
11 SPODE::SPODE(int root) : Classifier(Network()), root(root) {}
-
12
-
13 void SPODE::buildModel(const torch::Tensor& weights)
-
14 {
-
15 // 0. Add all nodes to the model
-
16 addNodes();
-
17 // 1. Add edges from the class node to all other nodes
-
18 // 2. Add edges from the root node to all other nodes
-
19 for (int i = 0; i < static_cast<int>(features.size()); ++i) {
-
20 model.addEdge(className, features[i]);
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21 if (i != root) {
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22 model.addEdge(features[root], features[i]);
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23 }
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24 }
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25 }
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26 std::vector<std::string> SPODE::graph(const std::string& name) const
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27 {
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28 return model.graph(name);
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29 }
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30
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31}
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SPODE.h
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1// ***************************************************************
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2// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
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3// SPDX-FileType: SOURCE
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4// SPDX-License-Identifier: MIT
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5// ***************************************************************
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6
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7#ifndef SPODE_H
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8#define SPODE_H
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9#include "Classifier.h"
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10
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11namespace bayesnet {
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12 class SPODE : public Classifier {
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13 private:
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14 int root;
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15 protected:
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16 void buildModel(const torch::Tensor& weights) override;
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17 public:
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18 explicit SPODE(int root);
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19 virtual ~SPODE() = default;
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20 std::vector<std::string> graph(const std::string& name = "SPODE") const override;
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21 };
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22}
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23#endif
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SPODELd.cc
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1// ***************************************************************
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2// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
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3// SPDX-FileType: SOURCE
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4// SPDX-License-Identifier: MIT
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5// ***************************************************************
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6
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7#include "SPODELd.h"
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8
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9namespace bayesnet {
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10 SPODELd::SPODELd(int root) : SPODE(root), Proposal(dataset, features, className) {}
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11 SPODELd& SPODELd::fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_)
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12 {
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13 checkInput(X_, y_);
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14 Xf = X_;
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15 y = y_;
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16 return commonFit(features_, className_, states_);
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17 }
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18
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19 SPODELd& SPODELd::fit(torch::Tensor& dataset, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_)
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20 {
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21 if (!torch::is_floating_point(dataset)) {
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22 throw std::runtime_error("Dataset must be a floating point tensor");
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23 }
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24 Xf = dataset.index({ torch::indexing::Slice(0, dataset.size(0) - 1), "..." }).clone();
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25 y = dataset.index({ -1, "..." }).clone().to(torch::kInt32);
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26 return commonFit(features_, className_, states_);
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27 }
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28
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29 SPODELd& SPODELd::commonFit(const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_)
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30 {
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31 features = features_;
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32 className = className_;
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33 // Fills std::vectors Xv & yv with the data from tensors X_ (discretized) & y
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34 states = fit_local_discretization(y);
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35 // We have discretized the input data
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36 // 1st we need to fit the model to build the normal SPODE structure, SPODE::fit initializes the base Bayesian network
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37 SPODE::fit(dataset, features, className, states);
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38 states = localDiscretizationProposal(states, model);
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39 return *this;
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40 }
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41 torch::Tensor SPODELd::predict(torch::Tensor& X)
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42 {
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43 auto Xt = prepareX(X);
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44 return SPODE::predict(Xt);
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45 }
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46 std::vector<std::string> SPODELd::graph(const std::string& name) const
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47 {
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48 return SPODE::graph(name);
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49 }
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50}
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SPODELd.h
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1// ***************************************************************
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2// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
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3// SPDX-FileType: SOURCE
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4// SPDX-License-Identifier: MIT
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5// ***************************************************************
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6
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7#ifndef SPODELD_H
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8#define SPODELD_H
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9#include "SPODE.h"
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10#include "Proposal.h"
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11
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12namespace bayesnet {
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13 class SPODELd : public SPODE, public Proposal {
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14 public:
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15 explicit SPODELd(int root);
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16 virtual ~SPODELd() = default;
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17 SPODELd& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, map<std::string, std::vector<int>>& states) override;
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18 SPODELd& fit(torch::Tensor& dataset, const std::vector<std::string>& features, const std::string& className, map<std::string, std::vector<int>>& states) override;
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19 SPODELd& commonFit(const std::vector<std::string>& features, const std::string& className, map<std::string, std::vector<int>>& states);
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20 std::vector<std::string> graph(const std::string& name = "SPODE") const override;
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21 torch::Tensor predict(torch::Tensor& X) override;
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22 static inline std::string version() { return "0.0.1"; };
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23 };
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24}
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25#endif // !SPODELD_H
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SPnDE.cc
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1// ***************************************************************
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2// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
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3// SPDX-FileType: SOURCE
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4// SPDX-License-Identifier: MIT
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5// ***************************************************************
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6
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7#include "SPnDE.h"
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8
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9namespace bayesnet {
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10
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11 SPnDE::SPnDE(std::vector<int> parents) : Classifier(Network()), parents(parents) {}
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12
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13 void SPnDE::buildModel(const torch::Tensor& weights)
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14 {
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15 // 0. Add all nodes to the model
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16 addNodes();
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17 std::vector<int> attributes;
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18 for (int i = 0; i < static_cast<int>(features.size()); ++i) {
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19 if (std::find(parents.begin(), parents.end(), i) == parents.end()) {
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20 attributes.push_back(i);
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21 }
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22 }
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23 // 1. Add edges from the class node to all other nodes
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24 // 2. Add edges from the parents nodes to all other nodes
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25 for (const auto& attribute : attributes) {
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26 model.addEdge(className, features[attribute]);
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27 for (const auto& root : parents) {
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28
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29 model.addEdge(features[root], features[attribute]);
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30 }
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31 }
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32 }
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33 std::vector<std::string> SPnDE::graph(const std::string& name) const
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34 {
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35 return model.graph(name);
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36 }
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37
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38}
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SPnDE.h
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1// ***************************************************************
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2// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
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3// SPDX-FileType: SOURCE
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4// SPDX-License-Identifier: MIT
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5// ***************************************************************
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6
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7#ifndef SPnDE_H
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8#define SPnDE_H
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9#include <vector>
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10#include "Classifier.h"
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11
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12namespace bayesnet {
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13 class SPnDE : public Classifier {
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14 public:
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15 explicit SPnDE(std::vector<int> parents);
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16 virtual ~SPnDE() = default;
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17 std::vector<std::string> graph(const std::string& name = "SPnDE") const override;
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18 protected:
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19 void buildModel(const torch::Tensor& weights) override;
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20 private:
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21 std::vector<int> parents;
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22
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23
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24 };
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25}
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26#endif
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TAN.cc
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1// ***************************************************************
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2// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
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3// SPDX-FileType: SOURCE
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4// SPDX-License-Identifier: MIT
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5// ***************************************************************
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6
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7#include "TAN.h"
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8
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9namespace bayesnet {
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10 TAN::TAN() : Classifier(Network()) {}
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11
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12 void TAN::buildModel(const torch::Tensor& weights)
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13 {
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14 // 0. Add all nodes to the model
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15 addNodes();
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16 // 1. Compute mutual information between each feature and the class and set the root node
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17 // as the highest mutual information with the class
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18 auto mi = std::vector <std::pair<int, float >>();
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19 torch::Tensor class_dataset = dataset.index({ -1, "..." });
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20 for (int i = 0; i < static_cast<int>(features.size()); ++i) {
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21 torch::Tensor feature_dataset = dataset.index({ i, "..." });
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22 auto mi_value = metrics.mutualInformation(class_dataset, feature_dataset, weights);
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23 mi.push_back({ i, mi_value });
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24 }
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25 sort(mi.begin(), mi.end(), [](const auto& left, const auto& right) {return left.second < right.second;});
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26 auto root = mi[mi.size() - 1].first;
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27 // 2. Compute mutual information between each feature and the class
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28 auto weights_matrix = metrics.conditionalEdge(weights);
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29 // 3. Compute the maximum spanning tree
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30 auto mst = metrics.maximumSpanningTree(features, weights_matrix, root);
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31 // 4. Add edges from the maximum spanning tree to the model
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32 for (auto i = 0; i < mst.size(); ++i) {
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33 auto [from, to] = mst[i];
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34 model.addEdge(features[from], features[to]);
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35 }
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36 // 5. Add edges from the class to all features
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37 for (auto feature : features) {
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38 model.addEdge(className, feature);
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39 }
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40 }
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41 std::vector<std::string> TAN::graph(const std::string& title) const
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42 {
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43 return model.graph(title);
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44 }
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45}
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TAN.h
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1// ***************************************************************
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2// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
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3// SPDX-FileType: SOURCE
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4// SPDX-License-Identifier: MIT
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5// ***************************************************************
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6
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7#ifndef TAN_H
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8#define TAN_H
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9#include "Classifier.h"
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10namespace bayesnet {
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11 class TAN : public Classifier {
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12 private:
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13 protected:
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14 void buildModel(const torch::Tensor& weights) override;
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15 public:
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16 TAN();
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17 virtual ~TAN() = default;
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18 std::vector<std::string> graph(const std::string& name = "TAN") const override;
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19 };
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20}
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21#endif
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TANLd.cc
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1// ***************************************************************
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2// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
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3// SPDX-FileType: SOURCE
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4// SPDX-License-Identifier: MIT
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5// ***************************************************************
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6
-
7#include "TANLd.h"
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8
-
9namespace bayesnet {
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10 TANLd::TANLd() : TAN(), Proposal(dataset, features, className) {}
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11 TANLd& TANLd::fit(torch::Tensor& X_, torch::Tensor& y_, const std::vector<std::string>& features_, const std::string& className_, map<std::string, std::vector<int>>& states_)
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12 {
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13 checkInput(X_, y_);
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14 features = features_;
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15 className = className_;
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16 Xf = X_;
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17 y = y_;
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18 // Fills std::vectors Xv & yv with the data from tensors X_ (discretized) & y
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19 states = fit_local_discretization(y);
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20 // We have discretized the input data
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21 // 1st we need to fit the model to build the normal TAN structure, TAN::fit initializes the base Bayesian network
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22 TAN::fit(dataset, features, className, states);
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23 states = localDiscretizationProposal(states, model);
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24 return *this;
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25
-
26 }
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27 torch::Tensor TANLd::predict(torch::Tensor& X)
-
28 {
-
29 auto Xt = prepareX(X);
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30 return TAN::predict(Xt);
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31 }
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32 std::vector<std::string> TANLd::graph(const std::string& name) const
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33 {
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34 return TAN::graph(name);
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35 }
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36}
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TANLd.h
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1// ***************************************************************
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2// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
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3// SPDX-FileType: SOURCE
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4// SPDX-License-Identifier: MIT
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5// ***************************************************************
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6
-
7#ifndef TANLD_H
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8#define TANLD_H
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9#include "TAN.h"
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10#include "Proposal.h"
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11
-
12namespace bayesnet {
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13 class TANLd : public TAN, public Proposal {
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14 private:
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15 public:
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16 TANLd();
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17 virtual ~TANLd() = default;
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18 TANLd& fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, map<std::string, std::vector<int>>& states) override;
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19 std::vector<std::string> graph(const std::string& name = "TAN") const override;
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20 torch::Tensor predict(torch::Tensor& X) override;
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21 static inline std::string version() { return "0.0.1"; };
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22 };
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-
23}
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24#endif // !TANLD_H
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Class List
-
-
-
Here are the classes, structs, unions and interfaces with brief descriptions:
-
[detail level 12]
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 ▼Nbayesnet
 CA2DE
 CAODE
 CAODELd
 CBaseClassifier
 CBoost
 CBoostA2DE
 CBoostAODE
 CClassifier
 CEnsemble
 CKDB
 CKDBLd
 CNetwork
 CNode
 CProposal
 CSPnDE
 CSPODE
 CSPODELd
 CTAN
 CTANLd
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bayesnet::A2DE Member List
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This is the complete list of members for bayesnet::A2DE, including all inherited members.

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A2DE(bool predict_voting=false) (defined in bayesnet::A2DE)bayesnet::A2DE
addNodes() (defined in bayesnet::Classifier)bayesnet::Classifier
buildDataset(torch::Tensor &y) (defined in bayesnet::Classifier)bayesnet::Classifierprotected
buildModel(const torch::Tensor &weights) override (defined in bayesnet::A2DE)bayesnet::A2DEprotectedvirtual
checkFitParameters() (defined in bayesnet::Classifier)bayesnet::Classifierprotected
Classifier(Network model) (defined in bayesnet::Classifier)bayesnet::Classifier
className (defined in bayesnet::Classifier)bayesnet::Classifierprotected
compute_arg_max(torch::Tensor &X) (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
compute_arg_max(std::vector< std::vector< double > > &X) (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
dataset (defined in bayesnet::Classifier)bayesnet::Classifierprotected
dump_cpt() const override (defined in bayesnet::Ensemble)bayesnet::Ensembleinlinevirtual
Ensemble(bool predict_voting=true) (defined in bayesnet::Ensemble)bayesnet::Ensemble
features (defined in bayesnet::Classifier)bayesnet::Classifierprotected
fit(std::vector< std::vector< int > > &X, std::vector< int > &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
fit(torch::Tensor &X, torch::Tensor &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
fit(torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
fit(torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states, const torch::Tensor &weights) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
fitted (defined in bayesnet::Classifier)bayesnet::Classifierprotected
getClassNumStates() const override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
getNotes() const override (defined in bayesnet::Classifier)bayesnet::Classifierinlinevirtual
getNumberOfEdges() const override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
getNumberOfNodes() const override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
getNumberOfStates() const override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
getStatus() const override (defined in bayesnet::Classifier)bayesnet::Classifierinlinevirtual
getValidHyperparameters() (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifierinline
getVersion() override (defined in bayesnet::Classifier)bayesnet::Classifierinlinevirtual
graph(const std::string &title="A2DE") const override (defined in bayesnet::A2DE)bayesnet::A2DEvirtual
m (defined in bayesnet::Classifier)bayesnet::Classifierprotected
metrics (defined in bayesnet::Classifier)bayesnet::Classifierprotected
model (defined in bayesnet::Classifier)bayesnet::Classifierprotected
models (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
n (defined in bayesnet::Classifier)bayesnet::Classifierprotected
n_models (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
notes (defined in bayesnet::Classifier)bayesnet::Classifierprotected
predict(torch::Tensor &X) override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
predict(std::vector< std::vector< int > > &X) override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
predict_average_proba(torch::Tensor &X) (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
predict_average_proba(std::vector< std::vector< int > > &X) (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
predict_average_voting(torch::Tensor &X) (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
predict_average_voting(std::vector< std::vector< int > > &X) (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
predict_proba(torch::Tensor &X) override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
predict_proba(std::vector< std::vector< int > > &X) override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
predict_voting (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
score(torch::Tensor &X, torch::Tensor &y) override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
score(std::vector< std::vector< int > > &X, std::vector< int > &y) override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
setHyperparameters(const nlohmann::json &hyperparameters) override (defined in bayesnet::A2DE)bayesnet::A2DEvirtual
show() const override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
significanceModels (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
states (defined in bayesnet::Classifier)bayesnet::Classifierprotected
status (defined in bayesnet::Classifier)bayesnet::Classifierprotected
topological_order() override (defined in bayesnet::Ensemble)bayesnet::Ensembleinlinevirtual
trainModel(const torch::Tensor &weights) override (defined in bayesnet::Ensemble)bayesnet::Ensembleprotectedvirtual
validHyperparameters (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifierprotected
voting(torch::Tensor &votes) (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
~A2DE() (defined in bayesnet::A2DE)bayesnet::A2DEinlinevirtual
~BaseClassifier()=default (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifiervirtual
~Classifier()=default (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
~Ensemble()=default (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
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bayesnet::A2DE Class Reference
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-Inheritance diagram for bayesnet::A2DE:
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-Public Member Functions

 A2DE (bool predict_voting=false)
 
void setHyperparameters (const nlohmann::json &hyperparameters) override
 
std::vector< std::string > graph (const std::string &title="A2DE") const override
 
- Public Member Functions inherited from bayesnet::Ensemble
 Ensemble (bool predict_voting=true)
 
torch::Tensor predict (torch::Tensor &X) override
 
std::vector< int > predict (std::vector< std::vector< int > > &X) override
 
torch::Tensor predict_proba (torch::Tensor &X) override
 
std::vector< std::vector< double > > predict_proba (std::vector< std::vector< int > > &X) override
 
float score (torch::Tensor &X, torch::Tensor &y) override
 
float score (std::vector< std::vector< int > > &X, std::vector< int > &y) override
 
int getNumberOfNodes () const override
 
int getNumberOfEdges () const override
 
int getNumberOfStates () const override
 
std::vector< std::string > show () const override
 
std::vector< std::string > graph (const std::string &title) const override
 
std::vector< std::string > topological_order () override
 
std::string dump_cpt () const override
 
- Public Member Functions inherited from bayesnet::Classifier
 Classifier (Network model)
 
Classifier & fit (std::vector< std::vector< int > > &X, std::vector< int > &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override
 
Classifier & fit (torch::Tensor &X, torch::Tensor &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override
 
Classifier & fit (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override
 
Classifier & fit (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states, const torch::Tensor &weights) override
 
void addNodes ()
 
int getClassNumStates () const override
 
status_t getStatus () const override
 
std::string getVersion () override
 
std::vector< std::string > getNotes () const override
 
void setHyperparameters (const nlohmann::json &hyperparameters) override
 
- Public Member Functions inherited from bayesnet::BaseClassifier
std::vector< std::string > & getValidHyperparameters ()
 
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-Protected Member Functions

void buildModel (const torch::Tensor &weights) override
 
- Protected Member Functions inherited from bayesnet::Ensemble
torch::Tensor predict_average_voting (torch::Tensor &X)
 
std::vector< std::vector< double > > predict_average_voting (std::vector< std::vector< int > > &X)
 
torch::Tensor predict_average_proba (torch::Tensor &X)
 
std::vector< std::vector< double > > predict_average_proba (std::vector< std::vector< int > > &X)
 
torch::Tensor compute_arg_max (torch::Tensor &X)
 
std::vector< int > compute_arg_max (std::vector< std::vector< double > > &X)
 
torch::Tensor voting (torch::Tensor &votes)
 
void trainModel (const torch::Tensor &weights) override
 
- Protected Member Functions inherited from bayesnet::Classifier
void checkFitParameters ()
 
void buildDataset (torch::Tensor &y)
 
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-Additional Inherited Members

- Protected Attributes inherited from bayesnet::Ensemble
unsigned n_models
 
std::vector< std::unique_ptr< Classifier > > models
 
std::vector< double > significanceModels
 
bool predict_voting
 
- Protected Attributes inherited from bayesnet::Classifier
bool fitted
 
unsigned int m
 
unsigned int n
 
Network model
 
Metrics metrics
 
std::vector< std::string > features
 
std::string className
 
std::map< std::string, std::vector< int > > states
 
torch::Tensor dataset
 
status_t status = NORMAL
 
std::vector< std::string > notes
 
- Protected Attributes inherited from bayesnet::BaseClassifier
std::vector< std::string > validHyperparameters
 
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Detailed Description

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Definition at line 12 of file A2DE.h.

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Constructor & Destructor Documentation

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◆ A2DE()

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bayesnet::A2DE::A2DE (bool predict_voting = false)
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Definition at line 10 of file A2DE.cc.

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◆ ~A2DE()

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virtual bayesnet::A2DE::~A2DE ()
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Definition at line 15 of file A2DE.h.

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Member Function Documentation

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◆ buildModel()

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void bayesnet::A2DE::buildModel (const torch::Tensor & weights)
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Implements bayesnet::Classifier.

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Definition at line 23 of file A2DE.cc.

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◆ graph()

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std::vector< std::string > bayesnet::A2DE::graph (const std::string & title = "A2DE") const
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Implements bayesnet::BaseClassifier.

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Definition at line 36 of file A2DE.cc.

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◆ setHyperparameters()

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void bayesnet::A2DE::setHyperparameters (const nlohmann::json & hyperparameters)
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Implements bayesnet::BaseClassifier.

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Definition at line 14 of file A2DE.cc.

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The documentation for this class was generated from the following files:
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  • /Users/rmontanana/Code/BayesNet/bayesnet/ensembles/A2DE.h
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bayesnet::AODE Member List
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This is the complete list of members for bayesnet::AODE, including all inherited members.

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addNodes() (defined in bayesnet::Classifier)bayesnet::Classifier
AODE(bool predict_voting=false) (defined in bayesnet::AODE)bayesnet::AODE
buildDataset(torch::Tensor &y) (defined in bayesnet::Classifier)bayesnet::Classifierprotected
buildModel(const torch::Tensor &weights) override (defined in bayesnet::AODE)bayesnet::AODEprotectedvirtual
checkFitParameters() (defined in bayesnet::Classifier)bayesnet::Classifierprotected
Classifier(Network model) (defined in bayesnet::Classifier)bayesnet::Classifier
className (defined in bayesnet::Classifier)bayesnet::Classifierprotected
compute_arg_max(torch::Tensor &X) (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
compute_arg_max(std::vector< std::vector< double > > &X) (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
dataset (defined in bayesnet::Classifier)bayesnet::Classifierprotected
dump_cpt() const override (defined in bayesnet::Ensemble)bayesnet::Ensembleinlinevirtual
Ensemble(bool predict_voting=true) (defined in bayesnet::Ensemble)bayesnet::Ensemble
features (defined in bayesnet::Classifier)bayesnet::Classifierprotected
fit(std::vector< std::vector< int > > &X, std::vector< int > &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
fit(torch::Tensor &X, torch::Tensor &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
fit(torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
fit(torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states, const torch::Tensor &weights) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
fitted (defined in bayesnet::Classifier)bayesnet::Classifierprotected
getClassNumStates() const override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
getNotes() const override (defined in bayesnet::Classifier)bayesnet::Classifierinlinevirtual
getNumberOfEdges() const override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
getNumberOfNodes() const override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
getNumberOfStates() const override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
getStatus() const override (defined in bayesnet::Classifier)bayesnet::Classifierinlinevirtual
getValidHyperparameters() (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifierinline
getVersion() override (defined in bayesnet::Classifier)bayesnet::Classifierinlinevirtual
graph(const std::string &title="AODE") const override (defined in bayesnet::AODE)bayesnet::AODEvirtual
m (defined in bayesnet::Classifier)bayesnet::Classifierprotected
metrics (defined in bayesnet::Classifier)bayesnet::Classifierprotected
model (defined in bayesnet::Classifier)bayesnet::Classifierprotected
models (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
n (defined in bayesnet::Classifier)bayesnet::Classifierprotected
n_models (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
notes (defined in bayesnet::Classifier)bayesnet::Classifierprotected
predict(torch::Tensor &X) override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
predict(std::vector< std::vector< int > > &X) override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
predict_average_proba(torch::Tensor &X) (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
predict_average_proba(std::vector< std::vector< int > > &X) (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
predict_average_voting(torch::Tensor &X) (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
predict_average_voting(std::vector< std::vector< int > > &X) (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
predict_proba(torch::Tensor &X) override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
predict_proba(std::vector< std::vector< int > > &X) override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
predict_voting (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
score(torch::Tensor &X, torch::Tensor &y) override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
score(std::vector< std::vector< int > > &X, std::vector< int > &y) override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
setHyperparameters(const nlohmann::json &hyperparameters) override (defined in bayesnet::AODE)bayesnet::AODEvirtual
show() const override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
significanceModels (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
states (defined in bayesnet::Classifier)bayesnet::Classifierprotected
status (defined in bayesnet::Classifier)bayesnet::Classifierprotected
topological_order() override (defined in bayesnet::Ensemble)bayesnet::Ensembleinlinevirtual
trainModel(const torch::Tensor &weights) override (defined in bayesnet::Ensemble)bayesnet::Ensembleprotectedvirtual
validHyperparameters (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifierprotected
voting(torch::Tensor &votes) (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
~AODE() (defined in bayesnet::AODE)bayesnet::AODEinlinevirtual
~BaseClassifier()=default (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifiervirtual
~Classifier()=default (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
~Ensemble()=default (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
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 AODE (bool predict_voting=false)
 
void setHyperparameters (const nlohmann::json &hyperparameters) override
 
std::vector< std::string > graph (const std::string &title="AODE") const override
 
- Public Member Functions inherited from bayesnet::Ensemble
 Ensemble (bool predict_voting=true)
 
torch::Tensor predict (torch::Tensor &X) override
 
std::vector< int > predict (std::vector< std::vector< int > > &X) override
 
torch::Tensor predict_proba (torch::Tensor &X) override
 
std::vector< std::vector< double > > predict_proba (std::vector< std::vector< int > > &X) override
 
float score (torch::Tensor &X, torch::Tensor &y) override
 
float score (std::vector< std::vector< int > > &X, std::vector< int > &y) override
 
int getNumberOfNodes () const override
 
int getNumberOfEdges () const override
 
int getNumberOfStates () const override
 
std::vector< std::string > show () const override
 
std::vector< std::string > graph (const std::string &title) const override
 
std::vector< std::string > topological_order () override
 
std::string dump_cpt () const override
 
- Public Member Functions inherited from bayesnet::Classifier
 Classifier (Network model)
 
Classifier & fit (std::vector< std::vector< int > > &X, std::vector< int > &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override
 
Classifier & fit (torch::Tensor &X, torch::Tensor &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override
 
Classifier & fit (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override
 
Classifier & fit (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states, const torch::Tensor &weights) override
 
void addNodes ()
 
int getClassNumStates () const override
 
status_t getStatus () const override
 
std::string getVersion () override
 
std::vector< std::string > getNotes () const override
 
void setHyperparameters (const nlohmann::json &hyperparameters) override
 
- Public Member Functions inherited from bayesnet::BaseClassifier
std::vector< std::string > & getValidHyperparameters ()
 
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void buildModel (const torch::Tensor &weights) override
 
- Protected Member Functions inherited from bayesnet::Ensemble
torch::Tensor predict_average_voting (torch::Tensor &X)
 
std::vector< std::vector< double > > predict_average_voting (std::vector< std::vector< int > > &X)
 
torch::Tensor predict_average_proba (torch::Tensor &X)
 
std::vector< std::vector< double > > predict_average_proba (std::vector< std::vector< int > > &X)
 
torch::Tensor compute_arg_max (torch::Tensor &X)
 
std::vector< int > compute_arg_max (std::vector< std::vector< double > > &X)
 
torch::Tensor voting (torch::Tensor &votes)
 
void trainModel (const torch::Tensor &weights) override
 
- Protected Member Functions inherited from bayesnet::Classifier
void checkFitParameters ()
 
void buildDataset (torch::Tensor &y)
 
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unsigned n_models
 
std::vector< std::unique_ptr< Classifier > > models
 
std::vector< double > significanceModels
 
bool predict_voting
 
- Protected Attributes inherited from bayesnet::Classifier
bool fitted
 
unsigned int m
 
unsigned int n
 
Network model
 
Metrics metrics
 
std::vector< std::string > features
 
std::string className
 
std::map< std::string, std::vector< int > > states
 
torch::Tensor dataset
 
status_t status = NORMAL
 
std::vector< std::string > notes
 
- Protected Attributes inherited from bayesnet::BaseClassifier
std::vector< std::string > validHyperparameters
 
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Detailed Description

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Definition at line 12 of file AODE.h.

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Constructor & Destructor Documentation

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◆ AODE()

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bayesnet::AODE::AODE (bool predict_voting = false)
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Definition at line 10 of file AODE.cc.

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Definition at line 15 of file AODE.h.

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◆ buildModel()

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void bayesnet::AODE::buildModel (const torch::Tensor & weights)
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Implements bayesnet::Classifier.

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Definition at line 24 of file AODE.cc.

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std::vector< std::string > bayesnet::AODE::graph (const std::string & title = "AODE") const
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Definition at line 34 of file AODE.cc.

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Definition at line 15 of file AODE.cc.

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bayesnet::AODELd Member List
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This is the complete list of members for bayesnet::AODELd, including all inherited members.

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addNodes() (defined in bayesnet::Classifier)bayesnet::Classifier
AODELd(bool predict_voting=true) (defined in bayesnet::AODELd)bayesnet::AODELd
buildDataset(torch::Tensor &y) (defined in bayesnet::Classifier)bayesnet::Classifierprotected
buildModel(const torch::Tensor &weights) override (defined in bayesnet::AODELd)bayesnet::AODELdprotectedvirtual
checkFitParameters() (defined in bayesnet::Classifier)bayesnet::Classifierprotected
checkInput(const torch::Tensor &X, const torch::Tensor &y) (defined in bayesnet::Proposal)bayesnet::Proposalprotected
Classifier(Network model) (defined in bayesnet::Classifier)bayesnet::Classifier
className (defined in bayesnet::Classifier)bayesnet::Classifierprotected
compute_arg_max(torch::Tensor &X) (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
compute_arg_max(std::vector< std::vector< double > > &X) (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
dataset (defined in bayesnet::Classifier)bayesnet::Classifierprotected
discretizers (defined in bayesnet::Proposal)bayesnet::Proposalprotected
dump_cpt() const override (defined in bayesnet::Ensemble)bayesnet::Ensembleinlinevirtual
Ensemble(bool predict_voting=true) (defined in bayesnet::Ensemble)bayesnet::Ensemble
features (defined in bayesnet::Classifier)bayesnet::Classifierprotected
fit(torch::Tensor &X_, torch::Tensor &y_, const std::vector< std::string > &features_, const std::string &className_, map< std::string, std::vector< int > > &states_) override (defined in bayesnet::AODELd)bayesnet::AODELd
fit(std::vector< std::vector< int > > &X, std::vector< int > &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
fit(torch::Tensor &X, torch::Tensor &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
fit(torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
fit(torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states, const torch::Tensor &weights) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
fit_local_discretization(const torch::Tensor &y) (defined in bayesnet::Proposal)bayesnet::Proposalprotected
fitted (defined in bayesnet::Classifier)bayesnet::Classifierprotected
getClassNumStates() const override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
getNotes() const override (defined in bayesnet::Classifier)bayesnet::Classifierinlinevirtual
getNumberOfEdges() const override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
getNumberOfNodes() const override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
getNumberOfStates() const override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
getStatus() const override (defined in bayesnet::Classifier)bayesnet::Classifierinlinevirtual
getValidHyperparameters() (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifierinline
getVersion() override (defined in bayesnet::Classifier)bayesnet::Classifierinlinevirtual
graph(const std::string &name="AODELd") const override (defined in bayesnet::AODELd)bayesnet::AODELdvirtual
localDiscretizationProposal(const map< std::string, std::vector< int > > &states, Network &model) (defined in bayesnet::Proposal)bayesnet::Proposalprotected
m (defined in bayesnet::Classifier)bayesnet::Classifierprotected
metrics (defined in bayesnet::Classifier)bayesnet::Classifierprotected
model (defined in bayesnet::Classifier)bayesnet::Classifierprotected
models (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
n (defined in bayesnet::Classifier)bayesnet::Classifierprotected
n_models (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
notes (defined in bayesnet::Classifier)bayesnet::Classifierprotected
predict(torch::Tensor &X) override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
predict(std::vector< std::vector< int > > &X) override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
predict_average_proba(torch::Tensor &X) (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
predict_average_proba(std::vector< std::vector< int > > &X) (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
predict_average_voting(torch::Tensor &X) (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
predict_average_voting(std::vector< std::vector< int > > &X) (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
predict_proba(torch::Tensor &X) override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
predict_proba(std::vector< std::vector< int > > &X) override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
predict_voting (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
prepareX(torch::Tensor &X) (defined in bayesnet::Proposal)bayesnet::Proposalprotected
Proposal(torch::Tensor &pDataset, std::vector< std::string > &features_, std::string &className_) (defined in bayesnet::Proposal)bayesnet::Proposal
score(torch::Tensor &X, torch::Tensor &y) override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
score(std::vector< std::vector< int > > &X, std::vector< int > &y) override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
setHyperparameters(const nlohmann::json &hyperparameters) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
show() const override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
significanceModels (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
states (defined in bayesnet::Classifier)bayesnet::Classifierprotected
status (defined in bayesnet::Classifier)bayesnet::Classifierprotected
topological_order() override (defined in bayesnet::Ensemble)bayesnet::Ensembleinlinevirtual
trainModel(const torch::Tensor &weights) override (defined in bayesnet::AODELd)bayesnet::AODELdprotectedvirtual
validHyperparameters (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifierprotected
voting(torch::Tensor &votes) (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
Xf (defined in bayesnet::Proposal)bayesnet::Proposalprotected
y (defined in bayesnet::Proposal)bayesnet::Proposalprotected
~AODELd()=default (defined in bayesnet::AODELd)bayesnet::AODELdvirtual
~BaseClassifier()=default (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifiervirtual
~Classifier()=default (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
~Ensemble()=default (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
~Proposal() (defined in bayesnet::Proposal)bayesnet::Proposalvirtual
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-Public Member Functions

 AODELd (bool predict_voting=true)
 
AODELd & fit (torch::Tensor &X_, torch::Tensor &y_, const std::vector< std::string > &features_, const std::string &className_, map< std::string, std::vector< int > > &states_) override
 
std::vector< std::string > graph (const std::string &name="AODELd") const override
 
- Public Member Functions inherited from bayesnet::Ensemble
 Ensemble (bool predict_voting=true)
 
torch::Tensor predict (torch::Tensor &X) override
 
std::vector< int > predict (std::vector< std::vector< int > > &X) override
 
torch::Tensor predict_proba (torch::Tensor &X) override
 
std::vector< std::vector< double > > predict_proba (std::vector< std::vector< int > > &X) override
 
float score (torch::Tensor &X, torch::Tensor &y) override
 
float score (std::vector< std::vector< int > > &X, std::vector< int > &y) override
 
int getNumberOfNodes () const override
 
int getNumberOfEdges () const override
 
int getNumberOfStates () const override
 
std::vector< std::string > show () const override
 
std::vector< std::string > graph (const std::string &title) const override
 
std::vector< std::string > topological_order () override
 
std::string dump_cpt () const override
 
- Public Member Functions inherited from bayesnet::Classifier
 Classifier (Network model)
 
Classifier & fit (std::vector< std::vector< int > > &X, std::vector< int > &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override
 
Classifier & fit (torch::Tensor &X, torch::Tensor &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override
 
Classifier & fit (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override
 
Classifier & fit (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states, const torch::Tensor &weights) override
 
void addNodes ()
 
int getClassNumStates () const override
 
status_t getStatus () const override
 
std::string getVersion () override
 
std::vector< std::string > getNotes () const override
 
void setHyperparameters (const nlohmann::json &hyperparameters) override
 
- Public Member Functions inherited from bayesnet::BaseClassifier
std::vector< std::string > & getValidHyperparameters ()
 
- Public Member Functions inherited from bayesnet::Proposal
 Proposal (torch::Tensor &pDataset, std::vector< std::string > &features_, std::string &className_)
 
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void trainModel (const torch::Tensor &weights) override
 
void buildModel (const torch::Tensor &weights) override
 
- Protected Member Functions inherited from bayesnet::Ensemble
torch::Tensor predict_average_voting (torch::Tensor &X)
 
std::vector< std::vector< double > > predict_average_voting (std::vector< std::vector< int > > &X)
 
torch::Tensor predict_average_proba (torch::Tensor &X)
 
std::vector< std::vector< double > > predict_average_proba (std::vector< std::vector< int > > &X)
 
torch::Tensor compute_arg_max (torch::Tensor &X)
 
std::vector< int > compute_arg_max (std::vector< std::vector< double > > &X)
 
torch::Tensor voting (torch::Tensor &votes)
 
void trainModel (const torch::Tensor &weights) override
 
- Protected Member Functions inherited from bayesnet::Classifier
void checkFitParameters ()
 
void buildDataset (torch::Tensor &y)
 
- Protected Member Functions inherited from bayesnet::Proposal
void checkInput (const torch::Tensor &X, const torch::Tensor &y)
 
torch::Tensor prepareX (torch::Tensor &X)
 
map< std::string, std::vector< int > > localDiscretizationProposal (const map< std::string, std::vector< int > > &states, Network &model)
 
map< std::string, std::vector< int > > fit_local_discretization (const torch::Tensor &y)
 
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unsigned n_models
 
std::vector< std::unique_ptr< Classifier > > models
 
std::vector< double > significanceModels
 
bool predict_voting
 
- Protected Attributes inherited from bayesnet::Classifier
bool fitted
 
unsigned int m
 
unsigned int n
 
Network model
 
Metrics metrics
 
std::vector< std::string > features
 
std::string className
 
std::map< std::string, std::vector< int > > states
 
torch::Tensor dataset
 
status_t status = NORMAL
 
std::vector< std::string > notes
 
- Protected Attributes inherited from bayesnet::BaseClassifier
std::vector< std::string > validHyperparameters
 
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torch::Tensor Xf
 
torch::Tensor y
 
map< std::string, mdlp::CPPFImdlp * > discretizers
 
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Definition at line 14 of file AODELd.h.

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Constructor & Destructor Documentation

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◆ AODELd()

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Definition at line 10 of file AODELd.cc.

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◆ buildModel()

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void bayesnet::AODELd::buildModel (const torch::Tensor & weights)
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Implements bayesnet::Classifier.

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Definition at line 28 of file AODELd.cc.

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◆ fit()

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AODELd & bayesnet::AODELd::fit (torch::Tensor & X_,
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const std::vector< std::string > & features_,
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Definition at line 13 of file AODELd.cc.

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◆ graph()

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std::vector< std::string > bayesnet::AODELd::graph (const std::string & name = "AODELd") const
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Implements bayesnet::BaseClassifier.

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Definition at line 43 of file AODELd.cc.

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◆ trainModel()

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void bayesnet::AODELd::trainModel (const torch::Tensor & weights)
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Implements bayesnet::BaseClassifier.

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Definition at line 37 of file AODELd.cc.

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This is the complete list of members for bayesnet::BaseClassifier, including all inherited members.

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dump_cpt() const =0 (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifierpure virtual
fit(std::vector< std::vector< int > > &X, std::vector< int > &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states)=0 (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifierpure virtual
fit(torch::Tensor &X, torch::Tensor &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states)=0 (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifierpure virtual
fit(torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states)=0 (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifierpure virtual
fit(torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states, const torch::Tensor &weights)=0 (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifierpure virtual
getClassNumStates() const =0 (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifierpure virtual
getNotes() const =0 (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifierpure virtual
getNumberOfEdges() const =0 (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifierpure virtual
getNumberOfNodes() const =0 (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifierpure virtual
getNumberOfStates() const =0 (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifierpure virtual
getStatus() const =0 (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifierpure virtual
getValidHyperparameters() (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifierinline
getVersion()=0 (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifierpure virtual
graph(const std::string &title="") const =0 (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifierpure virtual
predict(torch::Tensor &X)=0 (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifierpure virtual
predict(std::vector< std::vector< int > > &X)=0 (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifierpure virtual
predict_proba(torch::Tensor &X)=0 (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifierpure virtual
predict_proba(std::vector< std::vector< int > > &X)=0 (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifierpure virtual
score(std::vector< std::vector< int > > &X, std::vector< int > &y)=0 (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifierpure virtual
score(torch::Tensor &X, torch::Tensor &y)=0 (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifierpure virtual
setHyperparameters(const nlohmann::json &hyperparameters)=0 (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifierpure virtual
show() const =0 (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifierpure virtual
topological_order()=0 (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifierpure virtual
trainModel(const torch::Tensor &weights)=0 (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifierprotectedpure virtual
validHyperparameters (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifierprotected
~BaseClassifier()=default (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifiervirtual
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-Public Member Functions

-virtual BaseClassifier & fit (std::vector< std::vector< int > > &X, std::vector< int > &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states)=0
 
-virtual BaseClassifier & fit (torch::Tensor &X, torch::Tensor &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states)=0
 
-virtual BaseClassifier & fit (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states)=0
 
-virtual BaseClassifier & fit (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states, const torch::Tensor &weights)=0
 
-virtual torch::Tensor predict (torch::Tensor &X)=0
 
-virtual std::vector< int > predict (std::vector< std::vector< int > > &X)=0
 
-virtual torch::Tensor predict_proba (torch::Tensor &X)=0
 
-virtual std::vector< std::vector< double > > predict_proba (std::vector< std::vector< int > > &X)=0
 
-virtual status_t getStatus () const =0
 
-virtual float score (std::vector< std::vector< int > > &X, std::vector< int > &y)=0
 
-virtual float score (torch::Tensor &X, torch::Tensor &y)=0
 
-virtual int getNumberOfNodes () const =0
 
-virtual int getNumberOfEdges () const =0
 
-virtual int getNumberOfStates () const =0
 
-virtual int getClassNumStates () const =0
 
-virtual std::vector< std::string > show () const =0
 
-virtual std::vector< std::string > graph (const std::string &title="") const =0
 
-virtual std::string getVersion ()=0
 
-virtual std::vector< std::string > topological_order ()=0
 
-virtual std::vector< std::string > getNotes () const =0
 
-virtual std::string dump_cpt () const =0
 
-virtual void setHyperparameters (const nlohmann::json &hyperparameters)=0
 
std::vector< std::string > & getValidHyperparameters ()
 
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-virtual void trainModel (const torch::Tensor &weights)=0
 
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std::vector< std::string > validHyperparameters
 
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Detailed Description

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Definition at line 13 of file BaseClassifier.h.

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Member Function Documentation

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◆ getValidHyperparameters()

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std::vector< std::string > & bayesnet::BaseClassifier::getValidHyperparameters ()
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Definition at line 40 of file BaseClassifier.h.

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◆ validHyperparameters

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std::vector<std::string> bayesnet::BaseClassifier::validHyperparameters
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Definition at line 43 of file BaseClassifier.h.

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bayesnet::Boost Member List
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This is the complete list of members for bayesnet::Boost, including all inherited members.

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addNodes() (defined in bayesnet::Classifier)bayesnet::Classifier
bisection (defined in bayesnet::Boost)bayesnet::Boostprotected
block_update (defined in bayesnet::Boost)bayesnet::Boostprotected
Boost(bool predict_voting=false) (defined in bayesnet::Boost)bayesnet::Boostexplicit
buildDataset(torch::Tensor &y) (defined in bayesnet::Classifier)bayesnet::Classifierprotected
buildModel(const torch::Tensor &weights) override (defined in bayesnet::Boost)bayesnet::Boostprotectedvirtual
checkFitParameters() (defined in bayesnet::Classifier)bayesnet::Classifierprotected
Classifier(Network model) (defined in bayesnet::Classifier)bayesnet::Classifier
className (defined in bayesnet::Classifier)bayesnet::Classifierprotected
compute_arg_max(torch::Tensor &X) (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
compute_arg_max(std::vector< std::vector< double > > &X) (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
convergence (defined in bayesnet::Boost)bayesnet::Boostprotected
convergence_best (defined in bayesnet::Boost)bayesnet::Boostprotected
dataset (defined in bayesnet::Classifier)bayesnet::Classifierprotected
dump_cpt() const override (defined in bayesnet::Ensemble)bayesnet::Ensembleinlinevirtual
Ensemble(bool predict_voting=true) (defined in bayesnet::Ensemble)bayesnet::Ensemble
features (defined in bayesnet::Classifier)bayesnet::Classifierprotected
featureSelection(torch::Tensor &weights_) (defined in bayesnet::Boost)bayesnet::Boostprotected
featureSelector (defined in bayesnet::Boost)bayesnet::Boostprotected
fit(std::vector< std::vector< int > > &X, std::vector< int > &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
fit(torch::Tensor &X, torch::Tensor &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
fit(torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
fit(torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states, const torch::Tensor &weights) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
fitted (defined in bayesnet::Classifier)bayesnet::Classifierprotected
getClassNumStates() const override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
getNotes() const override (defined in bayesnet::Classifier)bayesnet::Classifierinlinevirtual
getNumberOfEdges() const override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
getNumberOfNodes() const override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
getNumberOfStates() const override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
getStatus() const override (defined in bayesnet::Classifier)bayesnet::Classifierinlinevirtual
getValidHyperparameters() (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifierinline
getVersion() override (defined in bayesnet::Classifier)bayesnet::Classifierinlinevirtual
graph(const std::string &title) const override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
m (defined in bayesnet::Classifier)bayesnet::Classifierprotected
maxTolerance (defined in bayesnet::Boost)bayesnet::Boostprotected
metrics (defined in bayesnet::Classifier)bayesnet::Classifierprotected
model (defined in bayesnet::Classifier)bayesnet::Classifierprotected
models (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
n (defined in bayesnet::Classifier)bayesnet::Classifierprotected
n_models (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
notes (defined in bayesnet::Classifier)bayesnet::Classifierprotected
order_algorithm (defined in bayesnet::Boost)bayesnet::Boostprotected
predict(torch::Tensor &X) override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
predict(std::vector< std::vector< int > > &X) override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
predict_average_proba(torch::Tensor &X) (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
predict_average_proba(std::vector< std::vector< int > > &X) (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
predict_average_voting(torch::Tensor &X) (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
predict_average_voting(std::vector< std::vector< int > > &X) (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
predict_proba(torch::Tensor &X) override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
predict_proba(std::vector< std::vector< int > > &X) override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
predict_voting (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
score(torch::Tensor &X, torch::Tensor &y) override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
score(std::vector< std::vector< int > > &X, std::vector< int > &y) override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
select_features_algorithm (defined in bayesnet::Boost)bayesnet::Boostprotected
selectFeatures (defined in bayesnet::Boost)bayesnet::Boostprotected
setHyperparameters(const nlohmann::json &hyperparameters_) override (defined in bayesnet::Boost)bayesnet::Boostvirtual
show() const override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
significanceModels (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
states (defined in bayesnet::Classifier)bayesnet::Classifierprotected
status (defined in bayesnet::Classifier)bayesnet::Classifierprotected
threshold (defined in bayesnet::Boost)bayesnet::Boostprotected
topological_order() override (defined in bayesnet::Ensemble)bayesnet::Ensembleinlinevirtual
trainModel(const torch::Tensor &weights) override (defined in bayesnet::Ensemble)bayesnet::Ensembleprotectedvirtual
update_weights(torch::Tensor &ytrain, torch::Tensor &ypred, torch::Tensor &weights) (defined in bayesnet::Boost)bayesnet::Boostprotected
update_weights_block(int k, torch::Tensor &ytrain, torch::Tensor &weights) (defined in bayesnet::Boost)bayesnet::Boostprotected
validHyperparameters (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifierprotected
voting(torch::Tensor &votes) (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
X_test (defined in bayesnet::Boost)bayesnet::Boostprotected
X_train (defined in bayesnet::Boost)bayesnet::Boostprotected
y_test (defined in bayesnet::Boost)bayesnet::Boostprotected
y_train (defined in bayesnet::Boost)bayesnet::Boostprotected
~BaseClassifier()=default (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifiervirtual
~Boost()=default (defined in bayesnet::Boost)bayesnet::Boostvirtual
~Classifier()=default (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
~Ensemble()=default (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
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-Public Member Functions

 Boost (bool predict_voting=false)
 
void setHyperparameters (const nlohmann::json &hyperparameters_) override
 
- Public Member Functions inherited from bayesnet::Ensemble
 Ensemble (bool predict_voting=true)
 
torch::Tensor predict (torch::Tensor &X) override
 
std::vector< int > predict (std::vector< std::vector< int > > &X) override
 
torch::Tensor predict_proba (torch::Tensor &X) override
 
std::vector< std::vector< double > > predict_proba (std::vector< std::vector< int > > &X) override
 
float score (torch::Tensor &X, torch::Tensor &y) override
 
float score (std::vector< std::vector< int > > &X, std::vector< int > &y) override
 
int getNumberOfNodes () const override
 
int getNumberOfEdges () const override
 
int getNumberOfStates () const override
 
std::vector< std::string > show () const override
 
std::vector< std::string > graph (const std::string &title) const override
 
std::vector< std::string > topological_order () override
 
std::string dump_cpt () const override
 
- Public Member Functions inherited from bayesnet::Classifier
 Classifier (Network model)
 
Classifier & fit (std::vector< std::vector< int > > &X, std::vector< int > &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override
 
Classifier & fit (torch::Tensor &X, torch::Tensor &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override
 
Classifier & fit (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override
 
Classifier & fit (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states, const torch::Tensor &weights) override
 
void addNodes ()
 
int getClassNumStates () const override
 
status_t getStatus () const override
 
std::string getVersion () override
 
std::vector< std::string > getNotes () const override
 
void setHyperparameters (const nlohmann::json &hyperparameters) override
 
- Public Member Functions inherited from bayesnet::BaseClassifier
std::vector< std::string > & getValidHyperparameters ()
 
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std::vector< int > featureSelection (torch::Tensor &weights_)
 
void buildModel (const torch::Tensor &weights) override
 
std::tuple< torch::Tensor &, double, bool > update_weights (torch::Tensor &ytrain, torch::Tensor &ypred, torch::Tensor &weights)
 
std::tuple< torch::Tensor &, double, bool > update_weights_block (int k, torch::Tensor &ytrain, torch::Tensor &weights)
 
- Protected Member Functions inherited from bayesnet::Ensemble
torch::Tensor predict_average_voting (torch::Tensor &X)
 
std::vector< std::vector< double > > predict_average_voting (std::vector< std::vector< int > > &X)
 
torch::Tensor predict_average_proba (torch::Tensor &X)
 
std::vector< std::vector< double > > predict_average_proba (std::vector< std::vector< int > > &X)
 
torch::Tensor compute_arg_max (torch::Tensor &X)
 
std::vector< int > compute_arg_max (std::vector< std::vector< double > > &X)
 
torch::Tensor voting (torch::Tensor &votes)
 
void trainModel (const torch::Tensor &weights) override
 
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void checkFitParameters ()
 
void buildDataset (torch::Tensor &y)
 
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torch::Tensor X_train
 
torch::Tensor y_train
 
torch::Tensor X_test
 
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bool bisection = true
 
int maxTolerance = 3
 
std::string order_algorithm
 
bool convergence = true
 
bool convergence_best = false
 
bool selectFeatures = false
 
std::string select_features_algorithm = Orders.DESC
 
FeatureSelect * featureSelector = nullptr
 
double threshold = -1
 
bool block_update = false
 
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unsigned n_models
 
std::vector< std::unique_ptr< Classifier > > models
 
std::vector< double > significanceModels
 
bool predict_voting
 
- Protected Attributes inherited from bayesnet::Classifier
bool fitted
 
unsigned int m
 
unsigned int n
 
Network model
 
Metrics metrics
 
std::vector< std::string > features
 
std::string className
 
std::map< std::string, std::vector< int > > states
 
torch::Tensor dataset
 
status_t status = NORMAL
 
std::vector< std::string > notes
 
- Protected Attributes inherited from bayesnet::BaseClassifier
std::vector< std::string > validHyperparameters
 
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Definition at line 27 of file Boost.h.

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bayesnet::BoostA2DE Member List
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This is the complete list of members for bayesnet::BoostA2DE, including all inherited members.

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addNodes() (defined in bayesnet::Classifier)bayesnet::Classifier
bisection (defined in bayesnet::Boost)bayesnet::Boostprotected
block_update (defined in bayesnet::Boost)bayesnet::Boostprotected
Boost(bool predict_voting=false) (defined in bayesnet::Boost)bayesnet::Boostexplicit
BoostA2DE(bool predict_voting=false) (defined in bayesnet::BoostA2DE)bayesnet::BoostA2DEexplicit
buildDataset(torch::Tensor &y) (defined in bayesnet::Classifier)bayesnet::Classifierprotected
buildModel(const torch::Tensor &weights) override (defined in bayesnet::Boost)bayesnet::Boostprotectedvirtual
checkFitParameters() (defined in bayesnet::Classifier)bayesnet::Classifierprotected
Classifier(Network model) (defined in bayesnet::Classifier)bayesnet::Classifier
className (defined in bayesnet::Classifier)bayesnet::Classifierprotected
compute_arg_max(torch::Tensor &X) (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
compute_arg_max(std::vector< std::vector< double > > &X) (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
convergence (defined in bayesnet::Boost)bayesnet::Boostprotected
convergence_best (defined in bayesnet::Boost)bayesnet::Boostprotected
dataset (defined in bayesnet::Classifier)bayesnet::Classifierprotected
dump_cpt() const override (defined in bayesnet::Ensemble)bayesnet::Ensembleinlinevirtual
Ensemble(bool predict_voting=true) (defined in bayesnet::Ensemble)bayesnet::Ensemble
features (defined in bayesnet::Classifier)bayesnet::Classifierprotected
featureSelection(torch::Tensor &weights_) (defined in bayesnet::Boost)bayesnet::Boostprotected
featureSelector (defined in bayesnet::Boost)bayesnet::Boostprotected
fit(std::vector< std::vector< int > > &X, std::vector< int > &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
fit(torch::Tensor &X, torch::Tensor &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
fit(torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
fit(torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states, const torch::Tensor &weights) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
fitted (defined in bayesnet::Classifier)bayesnet::Classifierprotected
getClassNumStates() const override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
getNotes() const override (defined in bayesnet::Classifier)bayesnet::Classifierinlinevirtual
getNumberOfEdges() const override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
getNumberOfNodes() const override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
getNumberOfStates() const override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
getStatus() const override (defined in bayesnet::Classifier)bayesnet::Classifierinlinevirtual
getValidHyperparameters() (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifierinline
getVersion() override (defined in bayesnet::Classifier)bayesnet::Classifierinlinevirtual
graph(const std::string &title="BoostA2DE") const override (defined in bayesnet::BoostA2DE)bayesnet::BoostA2DEvirtual
m (defined in bayesnet::Classifier)bayesnet::Classifierprotected
maxTolerance (defined in bayesnet::Boost)bayesnet::Boostprotected
metrics (defined in bayesnet::Classifier)bayesnet::Classifierprotected
model (defined in bayesnet::Classifier)bayesnet::Classifierprotected
models (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
n (defined in bayesnet::Classifier)bayesnet::Classifierprotected
n_models (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
notes (defined in bayesnet::Classifier)bayesnet::Classifierprotected
order_algorithm (defined in bayesnet::Boost)bayesnet::Boostprotected
predict(torch::Tensor &X) override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
predict(std::vector< std::vector< int > > &X) override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
predict_average_proba(torch::Tensor &X) (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
predict_average_proba(std::vector< std::vector< int > > &X) (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
predict_average_voting(torch::Tensor &X) (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
predict_average_voting(std::vector< std::vector< int > > &X) (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
predict_proba(torch::Tensor &X) override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
predict_proba(std::vector< std::vector< int > > &X) override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
predict_voting (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
score(torch::Tensor &X, torch::Tensor &y) override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
score(std::vector< std::vector< int > > &X, std::vector< int > &y) override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
select_features_algorithm (defined in bayesnet::Boost)bayesnet::Boostprotected
selectFeatures (defined in bayesnet::Boost)bayesnet::Boostprotected
setHyperparameters(const nlohmann::json &hyperparameters_) override (defined in bayesnet::Boost)bayesnet::Boostvirtual
show() const override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
significanceModels (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
states (defined in bayesnet::Classifier)bayesnet::Classifierprotected
status (defined in bayesnet::Classifier)bayesnet::Classifierprotected
threshold (defined in bayesnet::Boost)bayesnet::Boostprotected
topological_order() override (defined in bayesnet::Ensemble)bayesnet::Ensembleinlinevirtual
trainModel(const torch::Tensor &weights) override (defined in bayesnet::BoostA2DE)bayesnet::BoostA2DEprotectedvirtual
update_weights(torch::Tensor &ytrain, torch::Tensor &ypred, torch::Tensor &weights) (defined in bayesnet::Boost)bayesnet::Boostprotected
update_weights_block(int k, torch::Tensor &ytrain, torch::Tensor &weights) (defined in bayesnet::Boost)bayesnet::Boostprotected
validHyperparameters (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifierprotected
voting(torch::Tensor &votes) (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
X_test (defined in bayesnet::Boost)bayesnet::Boostprotected
X_train (defined in bayesnet::Boost)bayesnet::Boostprotected
y_test (defined in bayesnet::Boost)bayesnet::Boostprotected
y_train (defined in bayesnet::Boost)bayesnet::Boostprotected
~BaseClassifier()=default (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifiervirtual
~Boost()=default (defined in bayesnet::Boost)bayesnet::Boostvirtual
~BoostA2DE()=default (defined in bayesnet::BoostA2DE)bayesnet::BoostA2DEvirtual
~Classifier()=default (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
~Ensemble()=default (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
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bayesnet::BoostA2DE Class Reference
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-Public Member Functions

 BoostA2DE (bool predict_voting=false)
 
std::vector< std::string > graph (const std::string &title="BoostA2DE") const override
 
- Public Member Functions inherited from bayesnet::Boost
 Boost (bool predict_voting=false)
 
void setHyperparameters (const nlohmann::json &hyperparameters_) override
 
- Public Member Functions inherited from bayesnet::Ensemble
 Ensemble (bool predict_voting=true)
 
torch::Tensor predict (torch::Tensor &X) override
 
std::vector< int > predict (std::vector< std::vector< int > > &X) override
 
torch::Tensor predict_proba (torch::Tensor &X) override
 
std::vector< std::vector< double > > predict_proba (std::vector< std::vector< int > > &X) override
 
float score (torch::Tensor &X, torch::Tensor &y) override
 
float score (std::vector< std::vector< int > > &X, std::vector< int > &y) override
 
int getNumberOfNodes () const override
 
int getNumberOfEdges () const override
 
int getNumberOfStates () const override
 
std::vector< std::string > show () const override
 
std::vector< std::string > graph (const std::string &title) const override
 
std::vector< std::string > topological_order () override
 
std::string dump_cpt () const override
 
- Public Member Functions inherited from bayesnet::Classifier
 Classifier (Network model)
 
Classifier & fit (std::vector< std::vector< int > > &X, std::vector< int > &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override
 
Classifier & fit (torch::Tensor &X, torch::Tensor &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override
 
Classifier & fit (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override
 
Classifier & fit (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states, const torch::Tensor &weights) override
 
void addNodes ()
 
int getClassNumStates () const override
 
status_t getStatus () const override
 
std::string getVersion () override
 
std::vector< std::string > getNotes () const override
 
void setHyperparameters (const nlohmann::json &hyperparameters) override
 
- Public Member Functions inherited from bayesnet::BaseClassifier
std::vector< std::string > & getValidHyperparameters ()
 
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void trainModel (const torch::Tensor &weights) override
 
- Protected Member Functions inherited from bayesnet::Boost
std::vector< int > featureSelection (torch::Tensor &weights_)
 
void buildModel (const torch::Tensor &weights) override
 
std::tuple< torch::Tensor &, double, bool > update_weights (torch::Tensor &ytrain, torch::Tensor &ypred, torch::Tensor &weights)
 
std::tuple< torch::Tensor &, double, bool > update_weights_block (int k, torch::Tensor &ytrain, torch::Tensor &weights)
 
- Protected Member Functions inherited from bayesnet::Ensemble
torch::Tensor predict_average_voting (torch::Tensor &X)
 
std::vector< std::vector< double > > predict_average_voting (std::vector< std::vector< int > > &X)
 
torch::Tensor predict_average_proba (torch::Tensor &X)
 
std::vector< std::vector< double > > predict_average_proba (std::vector< std::vector< int > > &X)
 
torch::Tensor compute_arg_max (torch::Tensor &X)
 
std::vector< int > compute_arg_max (std::vector< std::vector< double > > &X)
 
torch::Tensor voting (torch::Tensor &votes)
 
void trainModel (const torch::Tensor &weights) override
 
- Protected Member Functions inherited from bayesnet::Classifier
void checkFitParameters ()
 
void buildDataset (torch::Tensor &y)
 
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- Protected Attributes inherited from bayesnet::Boost
torch::Tensor X_train
 
torch::Tensor y_train
 
torch::Tensor X_test
 
torch::Tensor y_test
 
bool bisection = true
 
int maxTolerance = 3
 
std::string order_algorithm
 
bool convergence = true
 
bool convergence_best = false
 
bool selectFeatures = false
 
std::string select_features_algorithm = Orders.DESC
 
FeatureSelect * featureSelector = nullptr
 
double threshold = -1
 
bool block_update = false
 
- Protected Attributes inherited from bayesnet::Ensemble
unsigned n_models
 
std::vector< std::unique_ptr< Classifier > > models
 
std::vector< double > significanceModels
 
bool predict_voting
 
- Protected Attributes inherited from bayesnet::Classifier
bool fitted
 
unsigned int m
 
unsigned int n
 
Network model
 
Metrics metrics
 
std::vector< std::string > features
 
std::string className
 
std::map< std::string, std::vector< int > > states
 
torch::Tensor dataset
 
status_t status = NORMAL
 
std::vector< std::string > notes
 
- Protected Attributes inherited from bayesnet::BaseClassifier
std::vector< std::string > validHyperparameters
 
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Detailed Description

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Definition at line 14 of file BoostA2DE.h.

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Constructor & Destructor Documentation

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◆ BoostA2DE()

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Definition at line 19 of file BoostA2DE.cc.

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◆ graph()

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std::vector< std::string > bayesnet::BoostA2DE::graph (const std::string & title = "BoostA2DE") const
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Implements bayesnet::BaseClassifier.

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Definition at line 163 of file BoostA2DE.cc.

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Definition at line 44 of file BoostA2DE.cc.

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bayesnet::BoostAODE Member List
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This is the complete list of members for bayesnet::BoostAODE, including all inherited members.

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addNodes() (defined in bayesnet::Classifier)bayesnet::Classifier
bisection (defined in bayesnet::Boost)bayesnet::Boostprotected
block_update (defined in bayesnet::Boost)bayesnet::Boostprotected
Boost(bool predict_voting=false) (defined in bayesnet::Boost)bayesnet::Boostexplicit
BoostAODE(bool predict_voting=false) (defined in bayesnet::BoostAODE)bayesnet::BoostAODEexplicit
buildDataset(torch::Tensor &y) (defined in bayesnet::Classifier)bayesnet::Classifierprotected
buildModel(const torch::Tensor &weights) override (defined in bayesnet::Boost)bayesnet::Boostprotectedvirtual
checkFitParameters() (defined in bayesnet::Classifier)bayesnet::Classifierprotected
Classifier(Network model) (defined in bayesnet::Classifier)bayesnet::Classifier
className (defined in bayesnet::Classifier)bayesnet::Classifierprotected
compute_arg_max(torch::Tensor &X) (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
compute_arg_max(std::vector< std::vector< double > > &X) (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
convergence (defined in bayesnet::Boost)bayesnet::Boostprotected
convergence_best (defined in bayesnet::Boost)bayesnet::Boostprotected
dataset (defined in bayesnet::Classifier)bayesnet::Classifierprotected
dump_cpt() const override (defined in bayesnet::Ensemble)bayesnet::Ensembleinlinevirtual
Ensemble(bool predict_voting=true) (defined in bayesnet::Ensemble)bayesnet::Ensemble
features (defined in bayesnet::Classifier)bayesnet::Classifierprotected
featureSelection(torch::Tensor &weights_) (defined in bayesnet::Boost)bayesnet::Boostprotected
featureSelector (defined in bayesnet::Boost)bayesnet::Boostprotected
fit(std::vector< std::vector< int > > &X, std::vector< int > &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
fit(torch::Tensor &X, torch::Tensor &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
fit(torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
fit(torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states, const torch::Tensor &weights) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
fitted (defined in bayesnet::Classifier)bayesnet::Classifierprotected
getClassNumStates() const override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
getNotes() const override (defined in bayesnet::Classifier)bayesnet::Classifierinlinevirtual
getNumberOfEdges() const override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
getNumberOfNodes() const override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
getNumberOfStates() const override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
getStatus() const override (defined in bayesnet::Classifier)bayesnet::Classifierinlinevirtual
getValidHyperparameters() (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifierinline
getVersion() override (defined in bayesnet::Classifier)bayesnet::Classifierinlinevirtual
graph(const std::string &title="BoostAODE") const override (defined in bayesnet::BoostAODE)bayesnet::BoostAODEvirtual
m (defined in bayesnet::Classifier)bayesnet::Classifierprotected
maxTolerance (defined in bayesnet::Boost)bayesnet::Boostprotected
metrics (defined in bayesnet::Classifier)bayesnet::Classifierprotected
model (defined in bayesnet::Classifier)bayesnet::Classifierprotected
models (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
n (defined in bayesnet::Classifier)bayesnet::Classifierprotected
n_models (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
notes (defined in bayesnet::Classifier)bayesnet::Classifierprotected
order_algorithm (defined in bayesnet::Boost)bayesnet::Boostprotected
predict(torch::Tensor &X) override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
predict(std::vector< std::vector< int > > &X) override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
predict_average_proba(torch::Tensor &X) (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
predict_average_proba(std::vector< std::vector< int > > &X) (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
predict_average_voting(torch::Tensor &X) (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
predict_average_voting(std::vector< std::vector< int > > &X) (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
predict_proba(torch::Tensor &X) override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
predict_proba(std::vector< std::vector< int > > &X) override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
predict_voting (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
score(torch::Tensor &X, torch::Tensor &y) override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
score(std::vector< std::vector< int > > &X, std::vector< int > &y) override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
select_features_algorithm (defined in bayesnet::Boost)bayesnet::Boostprotected
selectFeatures (defined in bayesnet::Boost)bayesnet::Boostprotected
setHyperparameters(const nlohmann::json &hyperparameters_) override (defined in bayesnet::Boost)bayesnet::Boostvirtual
show() const override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
significanceModels (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
states (defined in bayesnet::Classifier)bayesnet::Classifierprotected
status (defined in bayesnet::Classifier)bayesnet::Classifierprotected
threshold (defined in bayesnet::Boost)bayesnet::Boostprotected
topological_order() override (defined in bayesnet::Ensemble)bayesnet::Ensembleinlinevirtual
trainModel(const torch::Tensor &weights) override (defined in bayesnet::BoostAODE)bayesnet::BoostAODEprotectedvirtual
update_weights(torch::Tensor &ytrain, torch::Tensor &ypred, torch::Tensor &weights) (defined in bayesnet::Boost)bayesnet::Boostprotected
update_weights_block(int k, torch::Tensor &ytrain, torch::Tensor &weights) (defined in bayesnet::Boost)bayesnet::Boostprotected
validHyperparameters (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifierprotected
voting(torch::Tensor &votes) (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
X_test (defined in bayesnet::Boost)bayesnet::Boostprotected
X_train (defined in bayesnet::Boost)bayesnet::Boostprotected
y_test (defined in bayesnet::Boost)bayesnet::Boostprotected
y_train (defined in bayesnet::Boost)bayesnet::Boostprotected
~BaseClassifier()=default (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifiervirtual
~Boost()=default (defined in bayesnet::Boost)bayesnet::Boostvirtual
~BoostAODE()=default (defined in bayesnet::BoostAODE)bayesnet::BoostAODEvirtual
~Classifier()=default (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
~Ensemble()=default (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
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bayesnet::BoostAODE Class Reference
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-Public Member Functions

 BoostAODE (bool predict_voting=false)
 
std::vector< std::string > graph (const std::string &title="BoostAODE") const override
 
- Public Member Functions inherited from bayesnet::Boost
 Boost (bool predict_voting=false)
 
void setHyperparameters (const nlohmann::json &hyperparameters_) override
 
- Public Member Functions inherited from bayesnet::Ensemble
 Ensemble (bool predict_voting=true)
 
torch::Tensor predict (torch::Tensor &X) override
 
std::vector< int > predict (std::vector< std::vector< int > > &X) override
 
torch::Tensor predict_proba (torch::Tensor &X) override
 
std::vector< std::vector< double > > predict_proba (std::vector< std::vector< int > > &X) override
 
float score (torch::Tensor &X, torch::Tensor &y) override
 
float score (std::vector< std::vector< int > > &X, std::vector< int > &y) override
 
int getNumberOfNodes () const override
 
int getNumberOfEdges () const override
 
int getNumberOfStates () const override
 
std::vector< std::string > show () const override
 
std::vector< std::string > graph (const std::string &title) const override
 
std::vector< std::string > topological_order () override
 
std::string dump_cpt () const override
 
- Public Member Functions inherited from bayesnet::Classifier
 Classifier (Network model)
 
Classifier & fit (std::vector< std::vector< int > > &X, std::vector< int > &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override
 
Classifier & fit (torch::Tensor &X, torch::Tensor &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override
 
Classifier & fit (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override
 
Classifier & fit (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states, const torch::Tensor &weights) override
 
void addNodes ()
 
int getClassNumStates () const override
 
status_t getStatus () const override
 
std::string getVersion () override
 
std::vector< std::string > getNotes () const override
 
void setHyperparameters (const nlohmann::json &hyperparameters) override
 
- Public Member Functions inherited from bayesnet::BaseClassifier
std::vector< std::string > & getValidHyperparameters ()
 
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void trainModel (const torch::Tensor &weights) override
 
- Protected Member Functions inherited from bayesnet::Boost
std::vector< int > featureSelection (torch::Tensor &weights_)
 
void buildModel (const torch::Tensor &weights) override
 
std::tuple< torch::Tensor &, double, bool > update_weights (torch::Tensor &ytrain, torch::Tensor &ypred, torch::Tensor &weights)
 
std::tuple< torch::Tensor &, double, bool > update_weights_block (int k, torch::Tensor &ytrain, torch::Tensor &weights)
 
- Protected Member Functions inherited from bayesnet::Ensemble
torch::Tensor predict_average_voting (torch::Tensor &X)
 
std::vector< std::vector< double > > predict_average_voting (std::vector< std::vector< int > > &X)
 
torch::Tensor predict_average_proba (torch::Tensor &X)
 
std::vector< std::vector< double > > predict_average_proba (std::vector< std::vector< int > > &X)
 
torch::Tensor compute_arg_max (torch::Tensor &X)
 
std::vector< int > compute_arg_max (std::vector< std::vector< double > > &X)
 
torch::Tensor voting (torch::Tensor &votes)
 
void trainModel (const torch::Tensor &weights) override
 
- Protected Member Functions inherited from bayesnet::Classifier
void checkFitParameters ()
 
void buildDataset (torch::Tensor &y)
 
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- Protected Attributes inherited from bayesnet::Boost
torch::Tensor X_train
 
torch::Tensor y_train
 
torch::Tensor X_test
 
torch::Tensor y_test
 
bool bisection = true
 
int maxTolerance = 3
 
std::string order_algorithm
 
bool convergence = true
 
bool convergence_best = false
 
bool selectFeatures = false
 
std::string select_features_algorithm = Orders.DESC
 
FeatureSelect * featureSelector = nullptr
 
double threshold = -1
 
bool block_update = false
 
- Protected Attributes inherited from bayesnet::Ensemble
unsigned n_models
 
std::vector< std::unique_ptr< Classifier > > models
 
std::vector< double > significanceModels
 
bool predict_voting
 
- Protected Attributes inherited from bayesnet::Classifier
bool fitted
 
unsigned int m
 
unsigned int n
 
Network model
 
Metrics metrics
 
std::vector< std::string > features
 
std::string className
 
std::map< std::string, std::vector< int > > states
 
torch::Tensor dataset
 
status_t status = NORMAL
 
std::vector< std::string > notes
 
- Protected Attributes inherited from bayesnet::BaseClassifier
std::vector< std::string > validHyperparameters
 
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Detailed Description

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Definition at line 15 of file BoostAODE.h.

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Constructor & Destructor Documentation

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◆ BoostAODE()

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Definition at line 16 of file BoostAODE.cc.

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◆ graph()

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std::vector< std::string > bayesnet::BoostAODE::graph (const std::string & title = "BoostAODE") const
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Implements bayesnet::BaseClassifier.

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Definition at line 157 of file BoostAODE.cc.

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◆ trainModel()

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void bayesnet::BoostAODE::trainModel (const torch::Tensor & weights)
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Definition at line 33 of file BoostAODE.cc.

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bayesnet::Classifier Member List
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This is the complete list of members for bayesnet::Classifier, including all inherited members.

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addNodes() (defined in bayesnet::Classifier)bayesnet::Classifier
buildDataset(torch::Tensor &y) (defined in bayesnet::Classifier)bayesnet::Classifierprotected
buildModel(const torch::Tensor &weights)=0 (defined in bayesnet::Classifier)bayesnet::Classifierprotectedpure virtual
checkFitParameters() (defined in bayesnet::Classifier)bayesnet::Classifierprotected
Classifier(Network model) (defined in bayesnet::Classifier)bayesnet::Classifier
className (defined in bayesnet::Classifier)bayesnet::Classifierprotected
dataset (defined in bayesnet::Classifier)bayesnet::Classifierprotected
dump_cpt() const override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
features (defined in bayesnet::Classifier)bayesnet::Classifierprotected
fit(std::vector< std::vector< int > > &X, std::vector< int > &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
fit(torch::Tensor &X, torch::Tensor &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
fit(torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
fit(torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states, const torch::Tensor &weights) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
fitted (defined in bayesnet::Classifier)bayesnet::Classifierprotected
getClassNumStates() const override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
getNotes() const override (defined in bayesnet::Classifier)bayesnet::Classifierinlinevirtual
getNumberOfEdges() const override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
getNumberOfNodes() const override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
getNumberOfStates() const override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
getStatus() const override (defined in bayesnet::Classifier)bayesnet::Classifierinlinevirtual
getValidHyperparameters() (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifierinline
getVersion() override (defined in bayesnet::Classifier)bayesnet::Classifierinlinevirtual
graph(const std::string &title="") const =0 (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifierpure virtual
m (defined in bayesnet::Classifier)bayesnet::Classifierprotected
metrics (defined in bayesnet::Classifier)bayesnet::Classifierprotected
model (defined in bayesnet::Classifier)bayesnet::Classifierprotected
n (defined in bayesnet::Classifier)bayesnet::Classifierprotected
notes (defined in bayesnet::Classifier)bayesnet::Classifierprotected
predict(torch::Tensor &X) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
predict(std::vector< std::vector< int > > &X) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
predict_proba(torch::Tensor &X) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
predict_proba(std::vector< std::vector< int > > &X) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
score(torch::Tensor &X, torch::Tensor &y) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
score(std::vector< std::vector< int > > &X, std::vector< int > &y) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
setHyperparameters(const nlohmann::json &hyperparameters) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
show() const override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
states (defined in bayesnet::Classifier)bayesnet::Classifierprotected
status (defined in bayesnet::Classifier)bayesnet::Classifierprotected
topological_order() override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
trainModel(const torch::Tensor &weights) override (defined in bayesnet::Classifier)bayesnet::Classifierprotectedvirtual
validHyperparameters (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifierprotected
~BaseClassifier()=default (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifiervirtual
~Classifier()=default (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
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-Public Member Functions | -Protected Member Functions | -Protected Attributes | -List of all members
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bayesnet::Classifier Class Referenceabstract
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-Public Member Functions

 Classifier (Network model)
 
Classifier & fit (std::vector< std::vector< int > > &X, std::vector< int > &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override
 
Classifier & fit (torch::Tensor &X, torch::Tensor &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override
 
Classifier & fit (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override
 
Classifier & fit (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states, const torch::Tensor &weights) override
 
void addNodes ()
 
int getNumberOfNodes () const override
 
int getNumberOfEdges () const override
 
int getNumberOfStates () const override
 
int getClassNumStates () const override
 
torch::Tensor predict (torch::Tensor &X) override
 
std::vector< int > predict (std::vector< std::vector< int > > &X) override
 
torch::Tensor predict_proba (torch::Tensor &X) override
 
std::vector< std::vector< double > > predict_proba (std::vector< std::vector< int > > &X) override
 
status_t getStatus () const override
 
std::string getVersion () override
 
float score (torch::Tensor &X, torch::Tensor &y) override
 
float score (std::vector< std::vector< int > > &X, std::vector< int > &y) override
 
std::vector< std::string > show () const override
 
std::vector< std::string > topological_order () override
 
std::vector< std::string > getNotes () const override
 
std::string dump_cpt () const override
 
void setHyperparameters (const nlohmann::json &hyperparameters) override
 
- Public Member Functions inherited from bayesnet::BaseClassifier
-virtual std::vector< std::string > graph (const std::string &title="") const =0
 
std::vector< std::string > & getValidHyperparameters ()
 
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void checkFitParameters ()
 
-virtual void buildModel (const torch::Tensor &weights)=0
 
void trainModel (const torch::Tensor &weights) override
 
void buildDataset (torch::Tensor &y)
 
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bool fitted
 
unsigned int m
 
unsigned int n
 
Network model
 
Metrics metrics
 
std::vector< std::string > features
 
std::string className
 
std::map< std::string, std::vector< int > > states
 
torch::Tensor dataset
 
status_t status = NORMAL
 
std::vector< std::string > notes
 
- Protected Attributes inherited from bayesnet::BaseClassifier
std::vector< std::string > validHyperparameters
 
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Detailed Description

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Definition at line 15 of file Classifier.h.

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◆ Classifier()

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Definition at line 12 of file Classifier.cc.

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◆ addNodes()

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Definition at line 155 of file Classifier.cc.

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◆ buildDataset()

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void bayesnet::Classifier::buildDataset (torch::Tensor & y)
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Definition at line 30 of file Classifier.cc.

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Definition at line 79 of file Classifier.cc.

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Definition at line 184 of file Classifier.cc.

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Definition at line 57 of file Classifier.cc.

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Definition at line 68 of file Classifier.cc.

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Definition at line 74 of file Classifier.cc.

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Definition at line 49 of file Classifier.cc.

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Definition at line 176 of file Classifier.cc.

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Definition at line 38 of file Classifier.h.

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Definition at line 168 of file Classifier.cc.

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Definition at line 163 of file Classifier.cc.

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Definition at line 33 of file Classifier.h.

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Definition at line 144 of file Classifier.cc.

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Definition at line 139 of file Classifier.cc.

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Definition at line 188 of file Classifier.cc.

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Definition at line 47 of file Classifier.h.

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Definition at line 50 of file Classifier.h.

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bayesnet::Ensemble Member List
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This is the complete list of members for bayesnet::Ensemble, including all inherited members.

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addNodes() (defined in bayesnet::Classifier)bayesnet::Classifier
buildDataset(torch::Tensor &y) (defined in bayesnet::Classifier)bayesnet::Classifierprotected
buildModel(const torch::Tensor &weights)=0 (defined in bayesnet::Classifier)bayesnet::Classifierprotectedpure virtual
checkFitParameters() (defined in bayesnet::Classifier)bayesnet::Classifierprotected
Classifier(Network model) (defined in bayesnet::Classifier)bayesnet::Classifier
className (defined in bayesnet::Classifier)bayesnet::Classifierprotected
compute_arg_max(torch::Tensor &X) (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
compute_arg_max(std::vector< std::vector< double > > &X) (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
dataset (defined in bayesnet::Classifier)bayesnet::Classifierprotected
dump_cpt() const override (defined in bayesnet::Ensemble)bayesnet::Ensembleinlinevirtual
Ensemble(bool predict_voting=true) (defined in bayesnet::Ensemble)bayesnet::Ensemble
features (defined in bayesnet::Classifier)bayesnet::Classifierprotected
fit(std::vector< std::vector< int > > &X, std::vector< int > &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
fit(torch::Tensor &X, torch::Tensor &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
fit(torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
fit(torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states, const torch::Tensor &weights) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
fitted (defined in bayesnet::Classifier)bayesnet::Classifierprotected
getClassNumStates() const override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
getNotes() const override (defined in bayesnet::Classifier)bayesnet::Classifierinlinevirtual
getNumberOfEdges() const override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
getNumberOfNodes() const override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
getNumberOfStates() const override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
getStatus() const override (defined in bayesnet::Classifier)bayesnet::Classifierinlinevirtual
getValidHyperparameters() (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifierinline
getVersion() override (defined in bayesnet::Classifier)bayesnet::Classifierinlinevirtual
graph(const std::string &title) const override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
m (defined in bayesnet::Classifier)bayesnet::Classifierprotected
metrics (defined in bayesnet::Classifier)bayesnet::Classifierprotected
model (defined in bayesnet::Classifier)bayesnet::Classifierprotected
models (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
n (defined in bayesnet::Classifier)bayesnet::Classifierprotected
n_models (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
notes (defined in bayesnet::Classifier)bayesnet::Classifierprotected
predict(torch::Tensor &X) override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
predict(std::vector< std::vector< int > > &X) override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
predict_average_proba(torch::Tensor &X) (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
predict_average_proba(std::vector< std::vector< int > > &X) (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
predict_average_voting(torch::Tensor &X) (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
predict_average_voting(std::vector< std::vector< int > > &X) (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
predict_proba(torch::Tensor &X) override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
predict_proba(std::vector< std::vector< int > > &X) override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
predict_voting (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
score(torch::Tensor &X, torch::Tensor &y) override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
score(std::vector< std::vector< int > > &X, std::vector< int > &y) override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
setHyperparameters(const nlohmann::json &hyperparameters) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
show() const override (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
significanceModels (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
states (defined in bayesnet::Classifier)bayesnet::Classifierprotected
status (defined in bayesnet::Classifier)bayesnet::Classifierprotected
topological_order() override (defined in bayesnet::Ensemble)bayesnet::Ensembleinlinevirtual
trainModel(const torch::Tensor &weights) override (defined in bayesnet::Ensemble)bayesnet::Ensembleprotectedvirtual
validHyperparameters (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifierprotected
voting(torch::Tensor &votes) (defined in bayesnet::Ensemble)bayesnet::Ensembleprotected
~BaseClassifier()=default (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifiervirtual
~Classifier()=default (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
~Ensemble()=default (defined in bayesnet::Ensemble)bayesnet::Ensemblevirtual
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-Public Member Functions | -Protected Member Functions | -Protected Attributes | -List of all members
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bayesnet::Ensemble Class Reference
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-Public Member Functions

 Ensemble (bool predict_voting=true)
 
torch::Tensor predict (torch::Tensor &X) override
 
std::vector< int > predict (std::vector< std::vector< int > > &X) override
 
torch::Tensor predict_proba (torch::Tensor &X) override
 
std::vector< std::vector< double > > predict_proba (std::vector< std::vector< int > > &X) override
 
float score (torch::Tensor &X, torch::Tensor &y) override
 
float score (std::vector< std::vector< int > > &X, std::vector< int > &y) override
 
int getNumberOfNodes () const override
 
int getNumberOfEdges () const override
 
int getNumberOfStates () const override
 
std::vector< std::string > show () const override
 
std::vector< std::string > graph (const std::string &title) const override
 
std::vector< std::string > topological_order () override
 
std::string dump_cpt () const override
 
- Public Member Functions inherited from bayesnet::Classifier
 Classifier (Network model)
 
Classifier & fit (std::vector< std::vector< int > > &X, std::vector< int > &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override
 
Classifier & fit (torch::Tensor &X, torch::Tensor &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override
 
Classifier & fit (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override
 
Classifier & fit (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states, const torch::Tensor &weights) override
 
void addNodes ()
 
int getClassNumStates () const override
 
status_t getStatus () const override
 
std::string getVersion () override
 
std::vector< std::string > getNotes () const override
 
void setHyperparameters (const nlohmann::json &hyperparameters) override
 
- Public Member Functions inherited from bayesnet::BaseClassifier
std::vector< std::string > & getValidHyperparameters ()
 
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torch::Tensor predict_average_voting (torch::Tensor &X)
 
std::vector< std::vector< double > > predict_average_voting (std::vector< std::vector< int > > &X)
 
torch::Tensor predict_average_proba (torch::Tensor &X)
 
std::vector< std::vector< double > > predict_average_proba (std::vector< std::vector< int > > &X)
 
torch::Tensor compute_arg_max (torch::Tensor &X)
 
std::vector< int > compute_arg_max (std::vector< std::vector< double > > &X)
 
torch::Tensor voting (torch::Tensor &votes)
 
void trainModel (const torch::Tensor &weights) override
 
- Protected Member Functions inherited from bayesnet::Classifier
void checkFitParameters ()
 
-virtual void buildModel (const torch::Tensor &weights)=0
 
void buildDataset (torch::Tensor &y)
 
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unsigned n_models
 
std::vector< std::unique_ptr< Classifier > > models
 
std::vector< double > significanceModels
 
bool predict_voting
 
- Protected Attributes inherited from bayesnet::Classifier
bool fitted
 
unsigned int m
 
unsigned int n
 
Network model
 
Metrics metrics
 
std::vector< std::string > features
 
std::string className
 
std::map< std::string, std::vector< int > > states
 
torch::Tensor dataset
 
status_t status = NORMAL
 
std::vector< std::string > notes
 
- Protected Attributes inherited from bayesnet::BaseClassifier
std::vector< std::string > validHyperparameters
 
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Detailed Description

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Definition at line 15 of file Ensemble.h.

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Constructor & Destructor Documentation

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◆ Ensemble()

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Definition at line 11 of file Ensemble.cc.

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◆ compute_arg_max() [1/2]

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std::vector< int > bayesnet::Ensemble::compute_arg_max (std::vector< std::vector< double > > & X)
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Definition at line 24 of file Ensemble.cc.

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Definition at line 33 of file Ensemble.cc.

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◆ dump_cpt()

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Definition at line 34 of file Ensemble.h.

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Definition at line 206 of file Ensemble.cc.

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Definition at line 198 of file Ensemble.cc.

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Definition at line 214 of file Ensemble.cc.

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◆ graph()

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Definition at line 189 of file Ensemble.cc.

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Definition at line 74 of file Ensemble.cc.

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Definition at line 133 of file Ensemble.cc.

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Definition at line 38 of file Ensemble.cc.

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Definition at line 47 of file Ensemble.h.

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Definition at line 48 of file Ensemble.h.

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bayesnet::KDB Member List
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This is the complete list of members for bayesnet::KDB, including all inherited members.

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addNodes() (defined in bayesnet::Classifier)bayesnet::Classifier
buildDataset(torch::Tensor &y) (defined in bayesnet::Classifier)bayesnet::Classifierprotected
buildModel(const torch::Tensor &weights) override (defined in bayesnet::KDB)bayesnet::KDBprotectedvirtual
checkFitParameters() (defined in bayesnet::Classifier)bayesnet::Classifierprotected
Classifier(Network model) (defined in bayesnet::Classifier)bayesnet::Classifier
className (defined in bayesnet::Classifier)bayesnet::Classifierprotected
dataset (defined in bayesnet::Classifier)bayesnet::Classifierprotected
dump_cpt() const override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
features (defined in bayesnet::Classifier)bayesnet::Classifierprotected
fit(std::vector< std::vector< int > > &X, std::vector< int > &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
fit(torch::Tensor &X, torch::Tensor &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
fit(torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
fit(torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states, const torch::Tensor &weights) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
fitted (defined in bayesnet::Classifier)bayesnet::Classifierprotected
getClassNumStates() const override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
getNotes() const override (defined in bayesnet::Classifier)bayesnet::Classifierinlinevirtual
getNumberOfEdges() const override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
getNumberOfNodes() const override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
getNumberOfStates() const override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
getStatus() const override (defined in bayesnet::Classifier)bayesnet::Classifierinlinevirtual
getValidHyperparameters() (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifierinline
getVersion() override (defined in bayesnet::Classifier)bayesnet::Classifierinlinevirtual
graph(const std::string &name="KDB") const override (defined in bayesnet::KDB)bayesnet::KDBvirtual
KDB(int k, float theta=0.03) (defined in bayesnet::KDB)bayesnet::KDBexplicit
m (defined in bayesnet::Classifier)bayesnet::Classifierprotected
metrics (defined in bayesnet::Classifier)bayesnet::Classifierprotected
model (defined in bayesnet::Classifier)bayesnet::Classifierprotected
n (defined in bayesnet::Classifier)bayesnet::Classifierprotected
notes (defined in bayesnet::Classifier)bayesnet::Classifierprotected
predict(torch::Tensor &X) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
predict(std::vector< std::vector< int > > &X) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
predict_proba(torch::Tensor &X) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
predict_proba(std::vector< std::vector< int > > &X) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
score(torch::Tensor &X, torch::Tensor &y) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
score(std::vector< std::vector< int > > &X, std::vector< int > &y) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
setHyperparameters(const nlohmann::json &hyperparameters_) override (defined in bayesnet::KDB)bayesnet::KDBvirtual
show() const override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
states (defined in bayesnet::Classifier)bayesnet::Classifierprotected
status (defined in bayesnet::Classifier)bayesnet::Classifierprotected
topological_order() override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
trainModel(const torch::Tensor &weights) override (defined in bayesnet::Classifier)bayesnet::Classifierprotectedvirtual
validHyperparameters (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifierprotected
~BaseClassifier()=default (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifiervirtual
~Classifier()=default (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
~KDB()=default (defined in bayesnet::KDB)bayesnet::KDBvirtual
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bayesnet::KDB Class Reference
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-Public Member Functions

 KDB (int k, float theta=0.03)
 
void setHyperparameters (const nlohmann::json &hyperparameters_) override
 
std::vector< std::string > graph (const std::string &name="KDB") const override
 
- Public Member Functions inherited from bayesnet::Classifier
 Classifier (Network model)
 
Classifier & fit (std::vector< std::vector< int > > &X, std::vector< int > &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override
 
Classifier & fit (torch::Tensor &X, torch::Tensor &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override
 
Classifier & fit (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override
 
Classifier & fit (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states, const torch::Tensor &weights) override
 
void addNodes ()
 
int getNumberOfNodes () const override
 
int getNumberOfEdges () const override
 
int getNumberOfStates () const override
 
int getClassNumStates () const override
 
torch::Tensor predict (torch::Tensor &X) override
 
std::vector< int > predict (std::vector< std::vector< int > > &X) override
 
torch::Tensor predict_proba (torch::Tensor &X) override
 
std::vector< std::vector< double > > predict_proba (std::vector< std::vector< int > > &X) override
 
status_t getStatus () const override
 
std::string getVersion () override
 
float score (torch::Tensor &X, torch::Tensor &y) override
 
float score (std::vector< std::vector< int > > &X, std::vector< int > &y) override
 
std::vector< std::string > show () const override
 
std::vector< std::string > topological_order () override
 
std::vector< std::string > getNotes () const override
 
std::string dump_cpt () const override
 
- Public Member Functions inherited from bayesnet::BaseClassifier
std::vector< std::string > & getValidHyperparameters ()
 
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-Protected Member Functions

void buildModel (const torch::Tensor &weights) override
 
- Protected Member Functions inherited from bayesnet::Classifier
void checkFitParameters ()
 
void trainModel (const torch::Tensor &weights) override
 
void buildDataset (torch::Tensor &y)
 
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-Additional Inherited Members

- Protected Attributes inherited from bayesnet::Classifier
bool fitted
 
unsigned int m
 
unsigned int n
 
Network model
 
Metrics metrics
 
std::vector< std::string > features
 
std::string className
 
std::map< std::string, std::vector< int > > states
 
torch::Tensor dataset
 
status_t status = NORMAL
 
std::vector< std::string > notes
 
- Protected Attributes inherited from bayesnet::BaseClassifier
std::vector< std::string > validHyperparameters
 
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Detailed Description

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Definition at line 13 of file KDB.h.

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Constructor & Destructor Documentation

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◆ KDB()

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bayesnet::KDB::KDB (int k,
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Definition at line 10 of file KDB.cc.

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Member Function Documentation

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◆ buildModel()

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void bayesnet::KDB::buildModel (const torch::Tensor & weights)
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Implements bayesnet::Classifier.

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Definition at line 28 of file KDB.cc.

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◆ graph()

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std::vector< std::string > bayesnet::KDB::graph (const std::string & name = "KDB") const
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Implements bayesnet::BaseClassifier.

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Definition at line 103 of file KDB.cc.

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◆ setHyperparameters()

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void bayesnet::KDB::setHyperparameters (const nlohmann::json & hyperparameters_)
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Reimplemented from bayesnet::Classifier.

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Definition at line 15 of file KDB.cc.

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bayesnet::KDBLd Member List
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addNodes() (defined in bayesnet::Classifier)bayesnet::Classifier
buildDataset(torch::Tensor &y) (defined in bayesnet::Classifier)bayesnet::Classifierprotected
buildModel(const torch::Tensor &weights) override (defined in bayesnet::KDB)bayesnet::KDBprotectedvirtual
checkFitParameters() (defined in bayesnet::Classifier)bayesnet::Classifierprotected
checkInput(const torch::Tensor &X, const torch::Tensor &y) (defined in bayesnet::Proposal)bayesnet::Proposalprotected
Classifier(Network model) (defined in bayesnet::Classifier)bayesnet::Classifier
className (defined in bayesnet::Classifier)bayesnet::Classifierprotected
dataset (defined in bayesnet::Classifier)bayesnet::Classifierprotected
discretizers (defined in bayesnet::Proposal)bayesnet::Proposalprotected
dump_cpt() const override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
features (defined in bayesnet::Classifier)bayesnet::Classifierprotected
fit(torch::Tensor &X, torch::Tensor &y, const std::vector< std::string > &features, const std::string &className, map< std::string, std::vector< int > > &states) override (defined in bayesnet::KDBLd)bayesnet::KDBLd
fit(std::vector< std::vector< int > > &X, std::vector< int > &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
fit(torch::Tensor &X, torch::Tensor &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
fit(torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
fit(torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states, const torch::Tensor &weights) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
fit_local_discretization(const torch::Tensor &y) (defined in bayesnet::Proposal)bayesnet::Proposalprotected
fitted (defined in bayesnet::Classifier)bayesnet::Classifierprotected
getClassNumStates() const override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
getNotes() const override (defined in bayesnet::Classifier)bayesnet::Classifierinlinevirtual
getNumberOfEdges() const override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
getNumberOfNodes() const override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
getNumberOfStates() const override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
getStatus() const override (defined in bayesnet::Classifier)bayesnet::Classifierinlinevirtual
getValidHyperparameters() (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifierinline
getVersion() override (defined in bayesnet::Classifier)bayesnet::Classifierinlinevirtual
graph(const std::string &name="KDB") const override (defined in bayesnet::KDBLd)bayesnet::KDBLdvirtual
KDB(int k, float theta=0.03) (defined in bayesnet::KDB)bayesnet::KDBexplicit
KDBLd(int k) (defined in bayesnet::KDBLd)bayesnet::KDBLdexplicit
localDiscretizationProposal(const map< std::string, std::vector< int > > &states, Network &model) (defined in bayesnet::Proposal)bayesnet::Proposalprotected
m (defined in bayesnet::Classifier)bayesnet::Classifierprotected
metrics (defined in bayesnet::Classifier)bayesnet::Classifierprotected
model (defined in bayesnet::Classifier)bayesnet::Classifierprotected
n (defined in bayesnet::Classifier)bayesnet::Classifierprotected
notes (defined in bayesnet::Classifier)bayesnet::Classifierprotected
predict(torch::Tensor &X) override (defined in bayesnet::KDBLd)bayesnet::KDBLdvirtual
predict(std::vector< std::vector< int > > &X) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
predict_proba(torch::Tensor &X) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
predict_proba(std::vector< std::vector< int > > &X) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
prepareX(torch::Tensor &X) (defined in bayesnet::Proposal)bayesnet::Proposalprotected
Proposal(torch::Tensor &pDataset, std::vector< std::string > &features_, std::string &className_) (defined in bayesnet::Proposal)bayesnet::Proposal
score(torch::Tensor &X, torch::Tensor &y) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
score(std::vector< std::vector< int > > &X, std::vector< int > &y) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
setHyperparameters(const nlohmann::json &hyperparameters_) override (defined in bayesnet::KDB)bayesnet::KDBvirtual
show() const override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
states (defined in bayesnet::Classifier)bayesnet::Classifierprotected
status (defined in bayesnet::Classifier)bayesnet::Classifierprotected
topological_order() override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
trainModel(const torch::Tensor &weights) override (defined in bayesnet::Classifier)bayesnet::Classifierprotectedvirtual
validHyperparameters (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifierprotected
version() (defined in bayesnet::KDBLd)bayesnet::KDBLdinlinestatic
Xf (defined in bayesnet::Proposal)bayesnet::Proposalprotected
y (defined in bayesnet::Proposal)bayesnet::Proposalprotected
~BaseClassifier()=default (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifiervirtual
~Classifier()=default (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
~KDB()=default (defined in bayesnet::KDB)bayesnet::KDBvirtual
~KDBLd()=default (defined in bayesnet::KDBLd)bayesnet::KDBLdvirtual
~Proposal() (defined in bayesnet::Proposal)bayesnet::Proposalvirtual
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-Public Member Functions

 KDBLd (int k)
 
KDBLd & fit (torch::Tensor &X, torch::Tensor &y, const std::vector< std::string > &features, const std::string &className, map< std::string, std::vector< int > > &states) override
 
std::vector< std::string > graph (const std::string &name="KDB") const override
 
torch::Tensor predict (torch::Tensor &X) override
 
- Public Member Functions inherited from bayesnet::KDB
 KDB (int k, float theta=0.03)
 
void setHyperparameters (const nlohmann::json &hyperparameters_) override
 
- Public Member Functions inherited from bayesnet::Classifier
 Classifier (Network model)
 
Classifier & fit (std::vector< std::vector< int > > &X, std::vector< int > &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override
 
Classifier & fit (torch::Tensor &X, torch::Tensor &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override
 
Classifier & fit (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override
 
Classifier & fit (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states, const torch::Tensor &weights) override
 
void addNodes ()
 
int getNumberOfNodes () const override
 
int getNumberOfEdges () const override
 
int getNumberOfStates () const override
 
int getClassNumStates () const override
 
std::vector< int > predict (std::vector< std::vector< int > > &X) override
 
torch::Tensor predict_proba (torch::Tensor &X) override
 
std::vector< std::vector< double > > predict_proba (std::vector< std::vector< int > > &X) override
 
status_t getStatus () const override
 
std::string getVersion () override
 
float score (torch::Tensor &X, torch::Tensor &y) override
 
float score (std::vector< std::vector< int > > &X, std::vector< int > &y) override
 
std::vector< std::string > show () const override
 
std::vector< std::string > topological_order () override
 
std::vector< std::string > getNotes () const override
 
std::string dump_cpt () const override
 
- Public Member Functions inherited from bayesnet::BaseClassifier
std::vector< std::string > & getValidHyperparameters ()
 
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 Proposal (torch::Tensor &pDataset, std::vector< std::string > &features_, std::string &className_)
 
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- Protected Member Functions inherited from bayesnet::KDB
void buildModel (const torch::Tensor &weights) override
 
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void checkFitParameters ()
 
void trainModel (const torch::Tensor &weights) override
 
void buildDataset (torch::Tensor &y)
 
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void checkInput (const torch::Tensor &X, const torch::Tensor &y)
 
torch::Tensor prepareX (torch::Tensor &X)
 
map< std::string, std::vector< int > > localDiscretizationProposal (const map< std::string, std::vector< int > > &states, Network &model)
 
map< std::string, std::vector< int > > fit_local_discretization (const torch::Tensor &y)
 
- Protected Attributes inherited from bayesnet::Classifier
bool fitted
 
unsigned int m
 
unsigned int n
 
Network model
 
Metrics metrics
 
std::vector< std::string > features
 
std::string className
 
std::map< std::string, std::vector< int > > states
 
torch::Tensor dataset
 
status_t status = NORMAL
 
std::vector< std::string > notes
 
- Protected Attributes inherited from bayesnet::BaseClassifier
std::vector< std::string > validHyperparameters
 
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torch::Tensor Xf
 
torch::Tensor y
 
map< std::string, mdlp::CPPFImdlp * > discretizers
 
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addEdge(const std::string &, const std::string &) (defined in bayesnet::Network)bayesnet::Network
addNode(const std::string &) (defined in bayesnet::Network)bayesnet::Network
dump_cpt() const (defined in bayesnet::Network)bayesnet::Network
fit(const std::vector< std::vector< int > > &input_data, const std::vector< int > &labels, const std::vector< double > &weights, const std::vector< std::string > &featureNames, const std::string &className, const std::map< std::string, std::vector< int > > &states) (defined in bayesnet::Network)bayesnet::Network
fit(const torch::Tensor &X, const torch::Tensor &y, const torch::Tensor &weights, const std::vector< std::string > &featureNames, const std::string &className, const std::map< std::string, std::vector< int > > &states) (defined in bayesnet::Network)bayesnet::Network
fit(const torch::Tensor &samples, const torch::Tensor &weights, const std::vector< std::string > &featureNames, const std::string &className, const std::map< std::string, std::vector< int > > &states) (defined in bayesnet::Network)bayesnet::Network
getClassName() const (defined in bayesnet::Network)bayesnet::Network
getClassNumStates() const (defined in bayesnet::Network)bayesnet::Network
getEdges() const (defined in bayesnet::Network)bayesnet::Network
getFeatures() const (defined in bayesnet::Network)bayesnet::Network
getMaxThreads() const (defined in bayesnet::Network)bayesnet::Network
getNodes() (defined in bayesnet::Network)bayesnet::Network
getNumEdges() const (defined in bayesnet::Network)bayesnet::Network
getSamples() (defined in bayesnet::Network)bayesnet::Network
getStates() const (defined in bayesnet::Network)bayesnet::Network
graph(const std::string &title) const (defined in bayesnet::Network)bayesnet::Network
initialize() (defined in bayesnet::Network)bayesnet::Network
Network() (defined in bayesnet::Network)bayesnet::Network
Network(float) (defined in bayesnet::Network)bayesnet::Networkexplicit
Network(const Network &) (defined in bayesnet::Network)bayesnet::Networkexplicit
predict(const std::vector< std::vector< int > > &) (defined in bayesnet::Network)bayesnet::Network
predict(const torch::Tensor &) (defined in bayesnet::Network)bayesnet::Network
predict_proba(const std::vector< std::vector< int > > &) (defined in bayesnet::Network)bayesnet::Network
predict_proba(const torch::Tensor &) (defined in bayesnet::Network)bayesnet::Network
predict_tensor(const torch::Tensor &samples, const bool proba) (defined in bayesnet::Network)bayesnet::Network
score(const std::vector< std::vector< int > > &, const std::vector< int > &) (defined in bayesnet::Network)bayesnet::Network
show() const (defined in bayesnet::Network)bayesnet::Network
topological_sort() (defined in bayesnet::Network)bayesnet::Network
version() (defined in bayesnet::Network)bayesnet::Networkinline
~Network()=default (defined in bayesnet::Network)bayesnet::Network
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 Network (float)
 
 Network (const Network &)
 
torch::Tensor & getSamples ()
 
float getMaxThreads () const
 
void addNode (const std::string &)
 
void addEdge (const std::string &, const std::string &)
 
std::map< std::string, std::unique_ptr< Node > > & getNodes ()
 
std::vector< std::string > getFeatures () const
 
int getStates () const
 
std::vector< std::pair< std::string, std::string > > getEdges () const
 
int getNumEdges () const
 
int getClassNumStates () const
 
std::string getClassName () const
 
void fit (const std::vector< std::vector< int > > &input_data, const std::vector< int > &labels, const std::vector< double > &weights, const std::vector< std::string > &featureNames, const std::string &className, const std::map< std::string, std::vector< int > > &states)
 
void fit (const torch::Tensor &X, const torch::Tensor &y, const torch::Tensor &weights, const std::vector< std::string > &featureNames, const std::string &className, const std::map< std::string, std::vector< int > > &states)
 
void fit (const torch::Tensor &samples, const torch::Tensor &weights, const std::vector< std::string > &featureNames, const std::string &className, const std::map< std::string, std::vector< int > > &states)
 
std::vector< int > predict (const std::vector< std::vector< int > > &)
 
torch::Tensor predict (const torch::Tensor &)
 
torch::Tensor predict_tensor (const torch::Tensor &samples, const bool proba)
 
std::vector< std::vector< double > > predict_proba (const std::vector< std::vector< int > > &)
 
torch::Tensor predict_proba (const torch::Tensor &)
 
double score (const std::vector< std::vector< int > > &, const std::vector< int > &)
 
std::vector< std::string > topological_sort ()
 
std::vector< std::string > show () const
 
std::vector< std::string > graph (const std::string &title) const
 
void initialize ()
 
std::string dump_cpt () const
 
std::string version ()
 
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Definition at line 15 of file Network.h.

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Definition at line 13 of file Network.cc.

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Definition at line 20 of file Network.cc.

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void bayesnet::Network::addEdge (const std::string & parent,
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Definition at line 95 of file Network.cc.

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Definition at line 420 of file Network.cc.

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Definition at line 49 of file Network.h.

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bayesnet::Node Member List
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This is the complete list of members for bayesnet::Node, including all inherited members.

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addChild(Node *) (defined in bayesnet::Node)bayesnet::Node
addParent(Node *) (defined in bayesnet::Node)bayesnet::Node
clear() (defined in bayesnet::Node)bayesnet::Node
computeCPT(const torch::Tensor &dataset, const std::vector< std::string > &features, const double laplaceSmoothing, const torch::Tensor &weights) (defined in bayesnet::Node)bayesnet::Node
getChildren() (defined in bayesnet::Node)bayesnet::Node
getCPT() (defined in bayesnet::Node)bayesnet::Node
getFactorValue(std::map< std::string, int > &) (defined in bayesnet::Node)bayesnet::Node
getName() const (defined in bayesnet::Node)bayesnet::Node
getNumStates() const (defined in bayesnet::Node)bayesnet::Node
getParents() (defined in bayesnet::Node)bayesnet::Node
graph(const std::string &clasName) (defined in bayesnet::Node)bayesnet::Node
minFill() (defined in bayesnet::Node)bayesnet::Node
Node(const std::string &) (defined in bayesnet::Node)bayesnet::Nodeexplicit
removeChild(Node *) (defined in bayesnet::Node)bayesnet::Node
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setNumStates(int) (defined in bayesnet::Node)bayesnet::Node
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 Node (const std::string &)
 
void clear ()
 
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void removeChild (Node *)
 
std::string getName () const
 
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torch::Tensor & getCPT ()
 
void computeCPT (const torch::Tensor &dataset, const std::vector< std::string > &features, const double laplaceSmoothing, const torch::Tensor &weights)
 
int getNumStates () const
 
void setNumStates (int)
 
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float getFactorValue (std::map< std::string, int > &)
 
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This is the complete list of members for bayesnet::Proposal, including all inherited members.

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checkInput(const torch::Tensor &X, const torch::Tensor &y) (defined in bayesnet::Proposal)bayesnet::Proposalprotected
discretizers (defined in bayesnet::Proposal)bayesnet::Proposalprotected
fit_local_discretization(const torch::Tensor &y) (defined in bayesnet::Proposal)bayesnet::Proposalprotected
localDiscretizationProposal(const map< std::string, std::vector< int > > &states, Network &model) (defined in bayesnet::Proposal)bayesnet::Proposalprotected
prepareX(torch::Tensor &X) (defined in bayesnet::Proposal)bayesnet::Proposalprotected
Proposal(torch::Tensor &pDataset, std::vector< std::string > &features_, std::string &className_) (defined in bayesnet::Proposal)bayesnet::Proposal
Xf (defined in bayesnet::Proposal)bayesnet::Proposalprotected
y (defined in bayesnet::Proposal)bayesnet::Proposalprotected
~Proposal() (defined in bayesnet::Proposal)bayesnet::Proposalvirtual
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 Proposal (torch::Tensor &pDataset, std::vector< std::string > &features_, std::string &className_)
 
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void checkInput (const torch::Tensor &X, const torch::Tensor &y)
 
torch::Tensor prepareX (torch::Tensor &X)
 
map< std::string, std::vector< int > > localDiscretizationProposal (const map< std::string, std::vector< int > > &states, Network &model)
 
map< std::string, std::vector< int > > fit_local_discretization (const torch::Tensor &y)
 
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torch::Tensor Xf
 
torch::Tensor y
 
map< std::string, mdlp::CPPFImdlp * > discretizers
 
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Definition at line 17 of file Proposal.h.

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Definition at line 10 of file Proposal.cc.

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Definition at line 11 of file Proposal.cc.

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Definition at line 17 of file Proposal.cc.

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Definition at line 77 of file Proposal.cc.

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Definition at line 26 of file Proposal.cc.

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Definition at line 104 of file Proposal.cc.

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Definition at line 28 of file Proposal.h.

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Definition at line 26 of file Proposal.h.

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Definition at line 27 of file Proposal.h.

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addNodes() (defined in bayesnet::Classifier)bayesnet::Classifier
buildDataset(torch::Tensor &y) (defined in bayesnet::Classifier)bayesnet::Classifierprotected
buildModel(const torch::Tensor &weights) override (defined in bayesnet::SPODE)bayesnet::SPODEprotectedvirtual
checkFitParameters() (defined in bayesnet::Classifier)bayesnet::Classifierprotected
Classifier(Network model) (defined in bayesnet::Classifier)bayesnet::Classifier
className (defined in bayesnet::Classifier)bayesnet::Classifierprotected
dataset (defined in bayesnet::Classifier)bayesnet::Classifierprotected
dump_cpt() const override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
features (defined in bayesnet::Classifier)bayesnet::Classifierprotected
fit(std::vector< std::vector< int > > &X, std::vector< int > &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
fit(torch::Tensor &X, torch::Tensor &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
fit(torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
fit(torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states, const torch::Tensor &weights) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
fitted (defined in bayesnet::Classifier)bayesnet::Classifierprotected
getClassNumStates() const override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
getNotes() const override (defined in bayesnet::Classifier)bayesnet::Classifierinlinevirtual
getNumberOfEdges() const override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
getNumberOfNodes() const override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
getNumberOfStates() const override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
getStatus() const override (defined in bayesnet::Classifier)bayesnet::Classifierinlinevirtual
getValidHyperparameters() (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifierinline
getVersion() override (defined in bayesnet::Classifier)bayesnet::Classifierinlinevirtual
graph(const std::string &name="SPODE") const override (defined in bayesnet::SPODE)bayesnet::SPODEvirtual
m (defined in bayesnet::Classifier)bayesnet::Classifierprotected
metrics (defined in bayesnet::Classifier)bayesnet::Classifierprotected
model (defined in bayesnet::Classifier)bayesnet::Classifierprotected
n (defined in bayesnet::Classifier)bayesnet::Classifierprotected
notes (defined in bayesnet::Classifier)bayesnet::Classifierprotected
predict(torch::Tensor &X) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
predict(std::vector< std::vector< int > > &X) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
predict_proba(torch::Tensor &X) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
predict_proba(std::vector< std::vector< int > > &X) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
score(torch::Tensor &X, torch::Tensor &y) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
score(std::vector< std::vector< int > > &X, std::vector< int > &y) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
setHyperparameters(const nlohmann::json &hyperparameters) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
show() const override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
SPODE(int root) (defined in bayesnet::SPODE)bayesnet::SPODEexplicit
states (defined in bayesnet::Classifier)bayesnet::Classifierprotected
status (defined in bayesnet::Classifier)bayesnet::Classifierprotected
topological_order() override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
trainModel(const torch::Tensor &weights) override (defined in bayesnet::Classifier)bayesnet::Classifierprotectedvirtual
validHyperparameters (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifierprotected
~BaseClassifier()=default (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifiervirtual
~Classifier()=default (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
~SPODE()=default (defined in bayesnet::SPODE)bayesnet::SPODEvirtual
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 SPODE (int root)
 
std::vector< std::string > graph (const std::string &name="SPODE") const override
 
- Public Member Functions inherited from bayesnet::Classifier
 Classifier (Network model)
 
Classifier & fit (std::vector< std::vector< int > > &X, std::vector< int > &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override
 
Classifier & fit (torch::Tensor &X, torch::Tensor &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override
 
Classifier & fit (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override
 
Classifier & fit (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states, const torch::Tensor &weights) override
 
void addNodes ()
 
int getNumberOfNodes () const override
 
int getNumberOfEdges () const override
 
int getNumberOfStates () const override
 
int getClassNumStates () const override
 
torch::Tensor predict (torch::Tensor &X) override
 
std::vector< int > predict (std::vector< std::vector< int > > &X) override
 
torch::Tensor predict_proba (torch::Tensor &X) override
 
std::vector< std::vector< double > > predict_proba (std::vector< std::vector< int > > &X) override
 
status_t getStatus () const override
 
std::string getVersion () override
 
float score (torch::Tensor &X, torch::Tensor &y) override
 
float score (std::vector< std::vector< int > > &X, std::vector< int > &y) override
 
std::vector< std::string > show () const override
 
std::vector< std::string > topological_order () override
 
std::vector< std::string > getNotes () const override
 
std::string dump_cpt () const override
 
void setHyperparameters (const nlohmann::json &hyperparameters) override
 
- Public Member Functions inherited from bayesnet::BaseClassifier
std::vector< std::string > & getValidHyperparameters ()
 
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-Protected Member Functions

void buildModel (const torch::Tensor &weights) override
 
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void checkFitParameters ()
 
void trainModel (const torch::Tensor &weights) override
 
void buildDataset (torch::Tensor &y)
 
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bool fitted
 
unsigned int m
 
unsigned int n
 
Network model
 
Metrics metrics
 
std::vector< std::string > features
 
std::string className
 
std::map< std::string, std::vector< int > > states
 
torch::Tensor dataset
 
status_t status = NORMAL
 
std::vector< std::string > notes
 
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std::vector< std::string > validHyperparameters
 
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Definition at line 12 of file SPODE.h.

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Definition at line 11 of file SPODE.cc.

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Implements bayesnet::Classifier.

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Definition at line 13 of file SPODE.cc.

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◆ graph()

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std::vector< std::string > bayesnet::SPODE::graph (const std::string & name = "SPODE") const
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Implements bayesnet::BaseClassifier.

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Definition at line 26 of file SPODE.cc.

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bayesnet::SPODELd Member List
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This is the complete list of members for bayesnet::SPODELd, including all inherited members.

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addNodes() (defined in bayesnet::Classifier)bayesnet::Classifier
buildDataset(torch::Tensor &y) (defined in bayesnet::Classifier)bayesnet::Classifierprotected
buildModel(const torch::Tensor &weights) override (defined in bayesnet::SPODE)bayesnet::SPODEprotectedvirtual
checkFitParameters() (defined in bayesnet::Classifier)bayesnet::Classifierprotected
checkInput(const torch::Tensor &X, const torch::Tensor &y) (defined in bayesnet::Proposal)bayesnet::Proposalprotected
Classifier(Network model) (defined in bayesnet::Classifier)bayesnet::Classifier
className (defined in bayesnet::Classifier)bayesnet::Classifierprotected
commonFit(const std::vector< std::string > &features, const std::string &className, map< std::string, std::vector< int > > &states) (defined in bayesnet::SPODELd)bayesnet::SPODELd
dataset (defined in bayesnet::Classifier)bayesnet::Classifierprotected
discretizers (defined in bayesnet::Proposal)bayesnet::Proposalprotected
dump_cpt() const override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
features (defined in bayesnet::Classifier)bayesnet::Classifierprotected
fit(torch::Tensor &X, torch::Tensor &y, const std::vector< std::string > &features, const std::string &className, map< std::string, std::vector< int > > &states) override (defined in bayesnet::SPODELd)bayesnet::SPODELd
fit(torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, map< std::string, std::vector< int > > &states) override (defined in bayesnet::SPODELd)bayesnet::SPODELd
fit(std::vector< std::vector< int > > &X, std::vector< int > &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
fit(torch::Tensor &X, torch::Tensor &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
fit(torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
fit(torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states, const torch::Tensor &weights) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
fit_local_discretization(const torch::Tensor &y) (defined in bayesnet::Proposal)bayesnet::Proposalprotected
fitted (defined in bayesnet::Classifier)bayesnet::Classifierprotected
getClassNumStates() const override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
getNotes() const override (defined in bayesnet::Classifier)bayesnet::Classifierinlinevirtual
getNumberOfEdges() const override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
getNumberOfNodes() const override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
getNumberOfStates() const override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
getStatus() const override (defined in bayesnet::Classifier)bayesnet::Classifierinlinevirtual
getValidHyperparameters() (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifierinline
getVersion() override (defined in bayesnet::Classifier)bayesnet::Classifierinlinevirtual
graph(const std::string &name="SPODE") const override (defined in bayesnet::SPODELd)bayesnet::SPODELdvirtual
localDiscretizationProposal(const map< std::string, std::vector< int > > &states, Network &model) (defined in bayesnet::Proposal)bayesnet::Proposalprotected
m (defined in bayesnet::Classifier)bayesnet::Classifierprotected
metrics (defined in bayesnet::Classifier)bayesnet::Classifierprotected
model (defined in bayesnet::Classifier)bayesnet::Classifierprotected
n (defined in bayesnet::Classifier)bayesnet::Classifierprotected
notes (defined in bayesnet::Classifier)bayesnet::Classifierprotected
predict(torch::Tensor &X) override (defined in bayesnet::SPODELd)bayesnet::SPODELdvirtual
predict(std::vector< std::vector< int > > &X) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
predict_proba(torch::Tensor &X) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
predict_proba(std::vector< std::vector< int > > &X) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
prepareX(torch::Tensor &X) (defined in bayesnet::Proposal)bayesnet::Proposalprotected
Proposal(torch::Tensor &pDataset, std::vector< std::string > &features_, std::string &className_) (defined in bayesnet::Proposal)bayesnet::Proposal
score(torch::Tensor &X, torch::Tensor &y) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
score(std::vector< std::vector< int > > &X, std::vector< int > &y) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
setHyperparameters(const nlohmann::json &hyperparameters) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
show() const override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
SPODE(int root) (defined in bayesnet::SPODE)bayesnet::SPODEexplicit
SPODELd(int root) (defined in bayesnet::SPODELd)bayesnet::SPODELdexplicit
states (defined in bayesnet::Classifier)bayesnet::Classifierprotected
status (defined in bayesnet::Classifier)bayesnet::Classifierprotected
topological_order() override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
trainModel(const torch::Tensor &weights) override (defined in bayesnet::Classifier)bayesnet::Classifierprotectedvirtual
validHyperparameters (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifierprotected
version() (defined in bayesnet::SPODELd)bayesnet::SPODELdinlinestatic
Xf (defined in bayesnet::Proposal)bayesnet::Proposalprotected
y (defined in bayesnet::Proposal)bayesnet::Proposalprotected
~BaseClassifier()=default (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifiervirtual
~Classifier()=default (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
~Proposal() (defined in bayesnet::Proposal)bayesnet::Proposalvirtual
~SPODE()=default (defined in bayesnet::SPODE)bayesnet::SPODEvirtual
~SPODELd()=default (defined in bayesnet::SPODELd)bayesnet::SPODELdvirtual
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bayesnet::SPODELd Class Reference
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-Public Member Functions

 SPODELd (int root)
 
SPODELd & fit (torch::Tensor &X, torch::Tensor &y, const std::vector< std::string > &features, const std::string &className, map< std::string, std::vector< int > > &states) override
 
SPODELd & fit (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, map< std::string, std::vector< int > > &states) override
 
SPODELd & commonFit (const std::vector< std::string > &features, const std::string &className, map< std::string, std::vector< int > > &states)
 
std::vector< std::string > graph (const std::string &name="SPODE") const override
 
torch::Tensor predict (torch::Tensor &X) override
 
- Public Member Functions inherited from bayesnet::SPODE
 SPODE (int root)
 
- Public Member Functions inherited from bayesnet::Classifier
 Classifier (Network model)
 
Classifier & fit (std::vector< std::vector< int > > &X, std::vector< int > &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override
 
Classifier & fit (torch::Tensor &X, torch::Tensor &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override
 
Classifier & fit (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override
 
Classifier & fit (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states, const torch::Tensor &weights) override
 
void addNodes ()
 
int getNumberOfNodes () const override
 
int getNumberOfEdges () const override
 
int getNumberOfStates () const override
 
int getClassNumStates () const override
 
std::vector< int > predict (std::vector< std::vector< int > > &X) override
 
torch::Tensor predict_proba (torch::Tensor &X) override
 
std::vector< std::vector< double > > predict_proba (std::vector< std::vector< int > > &X) override
 
status_t getStatus () const override
 
std::string getVersion () override
 
float score (torch::Tensor &X, torch::Tensor &y) override
 
float score (std::vector< std::vector< int > > &X, std::vector< int > &y) override
 
std::vector< std::string > show () const override
 
std::vector< std::string > topological_order () override
 
std::vector< std::string > getNotes () const override
 
std::string dump_cpt () const override
 
void setHyperparameters (const nlohmann::json &hyperparameters) override
 
- Public Member Functions inherited from bayesnet::BaseClassifier
std::vector< std::string > & getValidHyperparameters ()
 
- Public Member Functions inherited from bayesnet::Proposal
 Proposal (torch::Tensor &pDataset, std::vector< std::string > &features_, std::string &className_)
 
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static std::string version ()
 
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-Additional Inherited Members

- Protected Member Functions inherited from bayesnet::SPODE
void buildModel (const torch::Tensor &weights) override
 
- Protected Member Functions inherited from bayesnet::Classifier
void checkFitParameters ()
 
void trainModel (const torch::Tensor &weights) override
 
void buildDataset (torch::Tensor &y)
 
- Protected Member Functions inherited from bayesnet::Proposal
void checkInput (const torch::Tensor &X, const torch::Tensor &y)
 
torch::Tensor prepareX (torch::Tensor &X)
 
map< std::string, std::vector< int > > localDiscretizationProposal (const map< std::string, std::vector< int > > &states, Network &model)
 
map< std::string, std::vector< int > > fit_local_discretization (const torch::Tensor &y)
 
- Protected Attributes inherited from bayesnet::Classifier
bool fitted
 
unsigned int m
 
unsigned int n
 
Network model
 
Metrics metrics
 
std::vector< std::string > features
 
std::string className
 
std::map< std::string, std::vector< int > > states
 
torch::Tensor dataset
 
status_t status = NORMAL
 
std::vector< std::string > notes
 
- Protected Attributes inherited from bayesnet::BaseClassifier
std::vector< std::string > validHyperparameters
 
- Protected Attributes inherited from bayesnet::Proposal
torch::Tensor Xf
 
torch::Tensor y
 
map< std::string, mdlp::CPPFImdlp * > discretizers
 
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Detailed Description

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Definition at line 13 of file SPODELd.h.

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Constructor & Destructor Documentation

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◆ SPODELd()

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Definition at line 10 of file SPODELd.cc.

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Member Function Documentation

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◆ commonFit()

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SPODELd & bayesnet::SPODELd::commonFit (const std::vector< std::string > & features,
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map< std::string, std::vector< int > > & states )
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Definition at line 29 of file SPODELd.cc.

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◆ fit() [1/2]

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SPODELd & bayesnet::SPODELd::fit (torch::Tensor & dataset,
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map< std::string, std::vector< int > > & states )
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Definition at line 19 of file SPODELd.cc.

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◆ fit() [2/2]

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SPODELd & bayesnet::SPODELd::fit (torch::Tensor & X,
torch::Tensor & y,
const std::vector< std::string > & features,
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map< std::string, std::vector< int > > & states )
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Definition at line 11 of file SPODELd.cc.

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◆ graph()

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std::vector< std::string > bayesnet::SPODELd::graph (const std::string & name = "SPODE") const
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Reimplemented from bayesnet::SPODE.

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Definition at line 46 of file SPODELd.cc.

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◆ predict()

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torch::Tensor bayesnet::SPODELd::predict (torch::Tensor & X)
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Reimplemented from bayesnet::Classifier.

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Definition at line 41 of file SPODELd.cc.

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◆ version()

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static std::string bayesnet::SPODELd::version ()
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Definition at line 22 of file SPODELd.h.

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bayesnet::SPnDE Member List
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This is the complete list of members for bayesnet::SPnDE, including all inherited members.

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addNodes() (defined in bayesnet::Classifier)bayesnet::Classifier
buildDataset(torch::Tensor &y) (defined in bayesnet::Classifier)bayesnet::Classifierprotected
buildModel(const torch::Tensor &weights) override (defined in bayesnet::SPnDE)bayesnet::SPnDEprotectedvirtual
checkFitParameters() (defined in bayesnet::Classifier)bayesnet::Classifierprotected
Classifier(Network model) (defined in bayesnet::Classifier)bayesnet::Classifier
className (defined in bayesnet::Classifier)bayesnet::Classifierprotected
dataset (defined in bayesnet::Classifier)bayesnet::Classifierprotected
dump_cpt() const override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
features (defined in bayesnet::Classifier)bayesnet::Classifierprotected
fit(std::vector< std::vector< int > > &X, std::vector< int > &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
fit(torch::Tensor &X, torch::Tensor &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
fit(torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
fit(torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states, const torch::Tensor &weights) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
fitted (defined in bayesnet::Classifier)bayesnet::Classifierprotected
getClassNumStates() const override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
getNotes() const override (defined in bayesnet::Classifier)bayesnet::Classifierinlinevirtual
getNumberOfEdges() const override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
getNumberOfNodes() const override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
getNumberOfStates() const override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
getStatus() const override (defined in bayesnet::Classifier)bayesnet::Classifierinlinevirtual
getValidHyperparameters() (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifierinline
getVersion() override (defined in bayesnet::Classifier)bayesnet::Classifierinlinevirtual
graph(const std::string &name="SPnDE") const override (defined in bayesnet::SPnDE)bayesnet::SPnDEvirtual
m (defined in bayesnet::Classifier)bayesnet::Classifierprotected
metrics (defined in bayesnet::Classifier)bayesnet::Classifierprotected
model (defined in bayesnet::Classifier)bayesnet::Classifierprotected
n (defined in bayesnet::Classifier)bayesnet::Classifierprotected
notes (defined in bayesnet::Classifier)bayesnet::Classifierprotected
predict(torch::Tensor &X) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
predict(std::vector< std::vector< int > > &X) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
predict_proba(torch::Tensor &X) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
predict_proba(std::vector< std::vector< int > > &X) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
score(torch::Tensor &X, torch::Tensor &y) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
score(std::vector< std::vector< int > > &X, std::vector< int > &y) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
setHyperparameters(const nlohmann::json &hyperparameters) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
show() const override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
SPnDE(std::vector< int > parents) (defined in bayesnet::SPnDE)bayesnet::SPnDEexplicit
states (defined in bayesnet::Classifier)bayesnet::Classifierprotected
status (defined in bayesnet::Classifier)bayesnet::Classifierprotected
topological_order() override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
trainModel(const torch::Tensor &weights) override (defined in bayesnet::Classifier)bayesnet::Classifierprotectedvirtual
validHyperparameters (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifierprotected
~BaseClassifier()=default (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifiervirtual
~Classifier()=default (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
~SPnDE()=default (defined in bayesnet::SPnDE)bayesnet::SPnDEvirtual
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 SPnDE (std::vector< int > parents)
 
std::vector< std::string > graph (const std::string &name="SPnDE") const override
 
- Public Member Functions inherited from bayesnet::Classifier
 Classifier (Network model)
 
Classifier & fit (std::vector< std::vector< int > > &X, std::vector< int > &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override
 
Classifier & fit (torch::Tensor &X, torch::Tensor &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override
 
Classifier & fit (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override
 
Classifier & fit (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states, const torch::Tensor &weights) override
 
void addNodes ()
 
int getNumberOfNodes () const override
 
int getNumberOfEdges () const override
 
int getNumberOfStates () const override
 
int getClassNumStates () const override
 
torch::Tensor predict (torch::Tensor &X) override
 
std::vector< int > predict (std::vector< std::vector< int > > &X) override
 
torch::Tensor predict_proba (torch::Tensor &X) override
 
std::vector< std::vector< double > > predict_proba (std::vector< std::vector< int > > &X) override
 
status_t getStatus () const override
 
std::string getVersion () override
 
float score (torch::Tensor &X, torch::Tensor &y) override
 
float score (std::vector< std::vector< int > > &X, std::vector< int > &y) override
 
std::vector< std::string > show () const override
 
std::vector< std::string > topological_order () override
 
std::vector< std::string > getNotes () const override
 
std::string dump_cpt () const override
 
void setHyperparameters (const nlohmann::json &hyperparameters) override
 
- Public Member Functions inherited from bayesnet::BaseClassifier
std::vector< std::string > & getValidHyperparameters ()
 
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-Protected Member Functions

void buildModel (const torch::Tensor &weights) override
 
- Protected Member Functions inherited from bayesnet::Classifier
void checkFitParameters ()
 
void trainModel (const torch::Tensor &weights) override
 
void buildDataset (torch::Tensor &y)
 
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- Protected Attributes inherited from bayesnet::Classifier
bool fitted
 
unsigned int m
 
unsigned int n
 
Network model
 
Metrics metrics
 
std::vector< std::string > features
 
std::string className
 
std::map< std::string, std::vector< int > > states
 
torch::Tensor dataset
 
status_t status = NORMAL
 
std::vector< std::string > notes
 
- Protected Attributes inherited from bayesnet::BaseClassifier
std::vector< std::string > validHyperparameters
 
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Definition at line 13 of file SPnDE.h.

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std::vector< std::string > bayesnet::SPnDE::graph (const std::string & name = "SPnDE") const
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Definition at line 33 of file SPnDE.cc.

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bayesnet::TAN Member List
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This is the complete list of members for bayesnet::TAN, including all inherited members.

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addNodes() (defined in bayesnet::Classifier)bayesnet::Classifier
buildDataset(torch::Tensor &y) (defined in bayesnet::Classifier)bayesnet::Classifierprotected
buildModel(const torch::Tensor &weights) override (defined in bayesnet::TAN)bayesnet::TANprotectedvirtual
checkFitParameters() (defined in bayesnet::Classifier)bayesnet::Classifierprotected
Classifier(Network model) (defined in bayesnet::Classifier)bayesnet::Classifier
className (defined in bayesnet::Classifier)bayesnet::Classifierprotected
dataset (defined in bayesnet::Classifier)bayesnet::Classifierprotected
dump_cpt() const override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
features (defined in bayesnet::Classifier)bayesnet::Classifierprotected
fit(std::vector< std::vector< int > > &X, std::vector< int > &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
fit(torch::Tensor &X, torch::Tensor &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
fit(torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
fit(torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states, const torch::Tensor &weights) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
fitted (defined in bayesnet::Classifier)bayesnet::Classifierprotected
getClassNumStates() const override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
getNotes() const override (defined in bayesnet::Classifier)bayesnet::Classifierinlinevirtual
getNumberOfEdges() const override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
getNumberOfNodes() const override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
getNumberOfStates() const override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
getStatus() const override (defined in bayesnet::Classifier)bayesnet::Classifierinlinevirtual
getValidHyperparameters() (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifierinline
getVersion() override (defined in bayesnet::Classifier)bayesnet::Classifierinlinevirtual
graph(const std::string &name="TAN") const override (defined in bayesnet::TAN)bayesnet::TANvirtual
m (defined in bayesnet::Classifier)bayesnet::Classifierprotected
metrics (defined in bayesnet::Classifier)bayesnet::Classifierprotected
model (defined in bayesnet::Classifier)bayesnet::Classifierprotected
n (defined in bayesnet::Classifier)bayesnet::Classifierprotected
notes (defined in bayesnet::Classifier)bayesnet::Classifierprotected
predict(torch::Tensor &X) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
predict(std::vector< std::vector< int > > &X) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
predict_proba(torch::Tensor &X) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
predict_proba(std::vector< std::vector< int > > &X) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
score(torch::Tensor &X, torch::Tensor &y) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
score(std::vector< std::vector< int > > &X, std::vector< int > &y) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
setHyperparameters(const nlohmann::json &hyperparameters) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
show() const override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
states (defined in bayesnet::Classifier)bayesnet::Classifierprotected
status (defined in bayesnet::Classifier)bayesnet::Classifierprotected
TAN() (defined in bayesnet::TAN)bayesnet::TAN
topological_order() override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
trainModel(const torch::Tensor &weights) override (defined in bayesnet::Classifier)bayesnet::Classifierprotectedvirtual
validHyperparameters (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifierprotected
~BaseClassifier()=default (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifiervirtual
~Classifier()=default (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
~TAN()=default (defined in bayesnet::TAN)bayesnet::TANvirtual
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-Public Member Functions

std::vector< std::string > graph (const std::string &name="TAN") const override
 
- Public Member Functions inherited from bayesnet::Classifier
 Classifier (Network model)
 
Classifier & fit (std::vector< std::vector< int > > &X, std::vector< int > &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override
 
Classifier & fit (torch::Tensor &X, torch::Tensor &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override
 
Classifier & fit (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override
 
Classifier & fit (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states, const torch::Tensor &weights) override
 
void addNodes ()
 
int getNumberOfNodes () const override
 
int getNumberOfEdges () const override
 
int getNumberOfStates () const override
 
int getClassNumStates () const override
 
torch::Tensor predict (torch::Tensor &X) override
 
std::vector< int > predict (std::vector< std::vector< int > > &X) override
 
torch::Tensor predict_proba (torch::Tensor &X) override
 
std::vector< std::vector< double > > predict_proba (std::vector< std::vector< int > > &X) override
 
status_t getStatus () const override
 
std::string getVersion () override
 
float score (torch::Tensor &X, torch::Tensor &y) override
 
float score (std::vector< std::vector< int > > &X, std::vector< int > &y) override
 
std::vector< std::string > show () const override
 
std::vector< std::string > topological_order () override
 
std::vector< std::string > getNotes () const override
 
std::string dump_cpt () const override
 
void setHyperparameters (const nlohmann::json &hyperparameters) override
 
- Public Member Functions inherited from bayesnet::BaseClassifier
std::vector< std::string > & getValidHyperparameters ()
 
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-Protected Member Functions

void buildModel (const torch::Tensor &weights) override
 
- Protected Member Functions inherited from bayesnet::Classifier
void checkFitParameters ()
 
void trainModel (const torch::Tensor &weights) override
 
void buildDataset (torch::Tensor &y)
 
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-Additional Inherited Members

- Protected Attributes inherited from bayesnet::Classifier
bool fitted
 
unsigned int m
 
unsigned int n
 
Network model
 
Metrics metrics
 
std::vector< std::string > features
 
std::string className
 
std::map< std::string, std::vector< int > > states
 
torch::Tensor dataset
 
status_t status = NORMAL
 
std::vector< std::string > notes
 
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Detailed Description

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Definition at line 11 of file TAN.h.

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Constructor & Destructor Documentation

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◆ TAN()

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Definition at line 10 of file TAN.cc.

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◆ buildModel()

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Definition at line 12 of file TAN.cc.

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◆ graph()

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std::vector< std::string > bayesnet::TAN::graph (const std::string & name = "TAN") const
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Definition at line 41 of file TAN.cc.

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This is the complete list of members for bayesnet::TANLd, including all inherited members.

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addNodes() (defined in bayesnet::Classifier)bayesnet::Classifier
buildDataset(torch::Tensor &y) (defined in bayesnet::Classifier)bayesnet::Classifierprotected
buildModel(const torch::Tensor &weights) override (defined in bayesnet::TAN)bayesnet::TANprotectedvirtual
checkFitParameters() (defined in bayesnet::Classifier)bayesnet::Classifierprotected
checkInput(const torch::Tensor &X, const torch::Tensor &y) (defined in bayesnet::Proposal)bayesnet::Proposalprotected
Classifier(Network model) (defined in bayesnet::Classifier)bayesnet::Classifier
className (defined in bayesnet::Classifier)bayesnet::Classifierprotected
dataset (defined in bayesnet::Classifier)bayesnet::Classifierprotected
discretizers (defined in bayesnet::Proposal)bayesnet::Proposalprotected
dump_cpt() const override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
features (defined in bayesnet::Classifier)bayesnet::Classifierprotected
fit(torch::Tensor &X, torch::Tensor &y, const std::vector< std::string > &features, const std::string &className, map< std::string, std::vector< int > > &states) override (defined in bayesnet::TANLd)bayesnet::TANLd
fit(std::vector< std::vector< int > > &X, std::vector< int > &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
fit(torch::Tensor &X, torch::Tensor &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
fit(torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
fit(torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states, const torch::Tensor &weights) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
fit_local_discretization(const torch::Tensor &y) (defined in bayesnet::Proposal)bayesnet::Proposalprotected
fitted (defined in bayesnet::Classifier)bayesnet::Classifierprotected
getClassNumStates() const override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
getNotes() const override (defined in bayesnet::Classifier)bayesnet::Classifierinlinevirtual
getNumberOfEdges() const override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
getNumberOfNodes() const override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
getNumberOfStates() const override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
getStatus() const override (defined in bayesnet::Classifier)bayesnet::Classifierinlinevirtual
getValidHyperparameters() (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifierinline
getVersion() override (defined in bayesnet::Classifier)bayesnet::Classifierinlinevirtual
graph(const std::string &name="TAN") const override (defined in bayesnet::TANLd)bayesnet::TANLdvirtual
localDiscretizationProposal(const map< std::string, std::vector< int > > &states, Network &model) (defined in bayesnet::Proposal)bayesnet::Proposalprotected
m (defined in bayesnet::Classifier)bayesnet::Classifierprotected
metrics (defined in bayesnet::Classifier)bayesnet::Classifierprotected
model (defined in bayesnet::Classifier)bayesnet::Classifierprotected
n (defined in bayesnet::Classifier)bayesnet::Classifierprotected
notes (defined in bayesnet::Classifier)bayesnet::Classifierprotected
predict(torch::Tensor &X) override (defined in bayesnet::TANLd)bayesnet::TANLdvirtual
predict(std::vector< std::vector< int > > &X) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
predict_proba(torch::Tensor &X) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
predict_proba(std::vector< std::vector< int > > &X) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
prepareX(torch::Tensor &X) (defined in bayesnet::Proposal)bayesnet::Proposalprotected
Proposal(torch::Tensor &pDataset, std::vector< std::string > &features_, std::string &className_) (defined in bayesnet::Proposal)bayesnet::Proposal
score(torch::Tensor &X, torch::Tensor &y) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
score(std::vector< std::vector< int > > &X, std::vector< int > &y) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
setHyperparameters(const nlohmann::json &hyperparameters) override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
show() const override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
states (defined in bayesnet::Classifier)bayesnet::Classifierprotected
status (defined in bayesnet::Classifier)bayesnet::Classifierprotected
TAN() (defined in bayesnet::TAN)bayesnet::TAN
TANLd() (defined in bayesnet::TANLd)bayesnet::TANLd
topological_order() override (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
trainModel(const torch::Tensor &weights) override (defined in bayesnet::Classifier)bayesnet::Classifierprotectedvirtual
validHyperparameters (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifierprotected
version() (defined in bayesnet::TANLd)bayesnet::TANLdinlinestatic
Xf (defined in bayesnet::Proposal)bayesnet::Proposalprotected
y (defined in bayesnet::Proposal)bayesnet::Proposalprotected
~BaseClassifier()=default (defined in bayesnet::BaseClassifier)bayesnet::BaseClassifiervirtual
~Classifier()=default (defined in bayesnet::Classifier)bayesnet::Classifiervirtual
~Proposal() (defined in bayesnet::Proposal)bayesnet::Proposalvirtual
~TAN()=default (defined in bayesnet::TAN)bayesnet::TANvirtual
~TANLd()=default (defined in bayesnet::TANLd)bayesnet::TANLdvirtual
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-Public Member Functions | -Static Public Member Functions | -List of all members
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bayesnet::TANLd Class Reference
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-Inheritance diagram for bayesnet::TANLd:
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-Public Member Functions

TANLd & fit (torch::Tensor &X, torch::Tensor &y, const std::vector< std::string > &features, const std::string &className, map< std::string, std::vector< int > > &states) override
 
std::vector< std::string > graph (const std::string &name="TAN") const override
 
torch::Tensor predict (torch::Tensor &X) override
 
- Public Member Functions inherited from bayesnet::Classifier
 Classifier (Network model)
 
Classifier & fit (std::vector< std::vector< int > > &X, std::vector< int > &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override
 
Classifier & fit (torch::Tensor &X, torch::Tensor &y, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override
 
Classifier & fit (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states) override
 
Classifier & fit (torch::Tensor &dataset, const std::vector< std::string > &features, const std::string &className, std::map< std::string, std::vector< int > > &states, const torch::Tensor &weights) override
 
void addNodes ()
 
int getNumberOfNodes () const override
 
int getNumberOfEdges () const override
 
int getNumberOfStates () const override
 
int getClassNumStates () const override
 
std::vector< int > predict (std::vector< std::vector< int > > &X) override
 
torch::Tensor predict_proba (torch::Tensor &X) override
 
std::vector< std::vector< double > > predict_proba (std::vector< std::vector< int > > &X) override
 
status_t getStatus () const override
 
std::string getVersion () override
 
float score (torch::Tensor &X, torch::Tensor &y) override
 
float score (std::vector< std::vector< int > > &X, std::vector< int > &y) override
 
std::vector< std::string > show () const override
 
std::vector< std::string > topological_order () override
 
std::vector< std::string > getNotes () const override
 
std::string dump_cpt () const override
 
void setHyperparameters (const nlohmann::json &hyperparameters) override
 
- Public Member Functions inherited from bayesnet::BaseClassifier
std::vector< std::string > & getValidHyperparameters ()
 
- Public Member Functions inherited from bayesnet::Proposal
 Proposal (torch::Tensor &pDataset, std::vector< std::string > &features_, std::string &className_)
 
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static std::string version ()
 
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-Additional Inherited Members

- Protected Member Functions inherited from bayesnet::TAN
void buildModel (const torch::Tensor &weights) override
 
- Protected Member Functions inherited from bayesnet::Classifier
void checkFitParameters ()
 
void trainModel (const torch::Tensor &weights) override
 
void buildDataset (torch::Tensor &y)
 
- Protected Member Functions inherited from bayesnet::Proposal
void checkInput (const torch::Tensor &X, const torch::Tensor &y)
 
torch::Tensor prepareX (torch::Tensor &X)
 
map< std::string, std::vector< int > > localDiscretizationProposal (const map< std::string, std::vector< int > > &states, Network &model)
 
map< std::string, std::vector< int > > fit_local_discretization (const torch::Tensor &y)
 
- Protected Attributes inherited from bayesnet::Classifier
bool fitted
 
unsigned int m
 
unsigned int n
 
Network model
 
Metrics metrics
 
std::vector< std::string > features
 
std::string className
 
std::map< std::string, std::vector< int > > states
 
torch::Tensor dataset
 
status_t status = NORMAL
 
std::vector< std::string > notes
 
- Protected Attributes inherited from bayesnet::BaseClassifier
std::vector< std::string > validHyperparameters
 
- Protected Attributes inherited from bayesnet::Proposal
torch::Tensor Xf
 
torch::Tensor y
 
map< std::string, mdlp::CPPFImdlp * > discretizers
 
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Detailed Description

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Definition at line 13 of file TANLd.h.

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Constructor & Destructor Documentation

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◆ TANLd()

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bayesnet::TANLd::TANLd ()
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Definition at line 10 of file TANLd.cc.

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Member Function Documentation

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◆ fit()

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TANLd & bayesnet::TANLd::fit (torch::Tensor & X,
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const std::vector< std::string > & features,
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Definition at line 11 of file TANLd.cc.

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◆ graph()

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std::vector< std::string > bayesnet::TANLd::graph (const std::string & name = "TAN") const
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Reimplemented from bayesnet::TAN.

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Definition at line 32 of file TANLd.cc.

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◆ predict()

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torch::Tensor bayesnet::TANLd::predict (torch::Tensor & X)
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Reimplemented from bayesnet::Classifier.

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Definition at line 27 of file TANLd.cc.

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◆ version()

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static std::string bayesnet::TANLd::version ()
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Definition at line 21 of file TANLd.h.

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The documentation for this class was generated from the following files:
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  • /Users/rmontanana/Code/BayesNet/bayesnet/classifiers/TANLd.h
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TANLd (bayesnet)
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---nav-font-size-level1: 13px; ---nav-font-size-level2: 10px; ---nav-font-size-level3: 9px; ---nav-text-normal-color: #B6C4DF; ---nav-text-hover-color: #DCE2EF; ---nav-text-active-color: #DCE2EF; ---nav-text-normal-shadow: 0px 1px 1px black; ---nav-text-hover-shadow: 0px 1px 1px rgba(0, 0, 0, 1.0); ---nav-text-active-shadow: 0px 1px 1px rgba(0, 0, 0, 1.0); ---nav-menu-button-color: #B6C4DF; ---nav-menu-background-color: #05070C; ---nav-menu-foreground-color: #BBBBBB; ---nav-menu-toggle-color: rgba(255, 255, 255, 0.2); ---nav-arrow-color: #334975; ---nav-arrow-selected-color: #90A5CE; - -/* table of contents */ ---toc-background-color: #151E30; ---toc-border-color: #202E4A; ---toc-header-color: #A3B4D7; ---toc-down-arrow-image: url("data:image/svg+xml;utf8,&%238595;"); - -/** search field */ ---search-background-color: black; ---search-foreground-color: #C5C5C5; ---search-magnification-image: url('mag_d.svg'); ---search-magnification-select-image: url('mag_seld.svg'); ---search-active-color: #C5C5C5; ---search-filter-background-color: #101826; ---search-filter-foreground-color: #90A5CE; ---search-filter-border-color: #7C95C6; ---search-filter-highlight-text-color: #BCC9E2; ---search-filter-highlight-bg-color: #283A5D; ---search-results-background-color: #101826; ---search-results-foreground-color: #90A5CE; ---search-results-border-color: #7C95C6; ---search-box-shadow: inset 0.5px 0.5px 3px 0px #2F436C; - -/** code fragments */ ---code-keyword-color: #CC99CD; ---code-type-keyword-color: #AB99CD; ---code-flow-keyword-color: #E08000; ---code-comment-color: #717790; ---code-preprocessor-color: #65CABE; ---code-string-literal-color: #7EC699; ---code-char-literal-color: #00E0F0; ---code-xml-cdata-color: #C9D1D9; ---code-vhdl-digit-color: #FF00FF; ---code-vhdl-char-color: #C0C0C0; ---code-vhdl-keyword-color: #CF53C9; ---code-vhdl-logic-color: #FF0000; ---code-link-color: #79C0FF; ---code-external-link-color: #79C0FF; ---fragment-foreground-color: #C9D1D9; ---fragment-background-color: #090D16; ---fragment-border-color: #30363D; ---fragment-lineno-border-color: #30363D; ---fragment-lineno-background-color: black; ---fragment-lineno-foreground-color: #6E7681; ---fragment-lineno-link-fg-color: #6E7681; ---fragment-lineno-link-bg-color: #303030; ---fragment-lineno-link-hover-fg-color: #8E96A1; ---fragment-lineno-link-hover-bg-color: #505050; ---fragment-copy-ok-color: #0EA80E; ---tooltip-foreground-color: #C9D1D9; ---tooltip-background-color: #202020; ---tooltip-border-color: #C9D1D9; ---tooltip-doc-color: #D9E1E9; ---tooltip-declaration-color: #20C348; ---tooltip-link-color: #79C0FF; ---tooltip-shadow: none; ---fold-line-color: #808080; ---fold-minus-image: url('minusd.svg'); ---fold-plus-image: url('plusd.svg'); ---fold-minus-image-relpath: url('../../minusd.svg'); ---fold-plus-image-relpath: url('../../plusd.svg'); - -/** font-family */ ---font-family-normal: Roboto,sans-serif; ---font-family-monospace: 'JetBrains Mono',Consolas,Monaco,'Andale Mono','Ubuntu Mono',monospace,fixed; ---font-family-nav: 'Lucida Grande',Geneva,Helvetica,Arial,sans-serif; ---font-family-title: Tahoma,Arial,sans-serif; ---font-family-toc: Verdana,'DejaVu Sans',Geneva,sans-serif; ---font-family-search: Arial,Verdana,sans-serif; ---font-family-icon: Arial,Helvetica; ---font-family-tooltip: Roboto,sans-serif; - -/** special sections */ ---warning-color-bg: #2e1917; ---warning-color-hl: #ad2617; ---warning-color-text: #f5b1aa; ---note-color-bg: #3b2e04; ---note-color-hl: #f1b602; ---note-color-text: #ceb670; ---todo-color-bg: #163750; ---todo-color-hl: #1982D2; ---todo-color-text: #dcf0fa; ---test-color-bg: #121258; ---test-color-hl: #4242cf; ---test-color-text: #c0c0da; ---deprecated-color-bg: #2e323b; ---deprecated-color-hl: #738396; ---deprecated-color-text: #abb0bd; ---bug-color-bg: #2a2536; ---bug-color-hl: #7661b3; ---bug-color-text: #ae9ed6; ---invariant-color-bg: #303a35; ---invariant-color-hl: #76ce96; ---invariant-color-text: #cceed5; -}} -body { - background-color: var(--page-background-color); - color: var(--page-foreground-color); -} - -body, table, div, p, dl { - font-weight: 400; - font-size: 14px; - font-family: var(--font-family-normal); - line-height: 22px; -} - -/* @group Heading Levels */ - -.title { - font-family: var(--font-family-normal); - line-height: 28px; - font-size: 150%; - font-weight: bold; - margin: 10px 2px; -} - -h1.groupheader { - font-size: 150%; -} - -h2.groupheader { - border-bottom: 1px solid var(--group-header-separator-color); - color: var(--group-header-color); - font-size: 150%; - font-weight: normal; - margin-top: 1.75em; - padding-top: 8px; - padding-bottom: 4px; - width: 100%; -} - -h3.groupheader { - font-size: 100%; -} - -h1, h2, h3, h4, h5, h6 { - -webkit-transition: text-shadow 0.5s linear; - -moz-transition: text-shadow 0.5s linear; - -ms-transition: text-shadow 0.5s linear; - -o-transition: text-shadow 0.5s linear; - transition: text-shadow 0.5s linear; - margin-right: 15px; -} - -h1.glow, h2.glow, h3.glow, h4.glow, h5.glow, h6.glow { - text-shadow: 0 0 15px var(--glow-color); -} - -dt { - font-weight: bold; -} - -p.startli, p.startdd { - margin-top: 2px; -} - -th p.starttd, th p.intertd, th p.endtd { - font-size: 100%; - font-weight: 700; -} - -p.starttd { - margin-top: 0px; -} - -p.endli { - margin-bottom: 0px; -} - -p.enddd { - margin-bottom: 4px; -} - -p.endtd { - margin-bottom: 2px; -} - -p.interli { -} - -p.interdd { -} - -p.intertd { -} - -/* @end */ - -caption { - font-weight: bold; -} - -span.legend { - font-size: 70%; - text-align: center; -} - -h3.version { - font-size: 90%; - text-align: center; -} - -div.navtab { - padding-right: 15px; - text-align: right; - line-height: 110%; -} - -div.navtab table { - border-spacing: 0; -} - -td.navtab { - padding-right: 6px; - padding-left: 6px; -} - -td.navtabHL { - background-image: var(--nav-gradient-active-image); - background-repeat:repeat-x; - padding-right: 6px; - padding-left: 6px; -} - -td.navtabHL a, td.navtabHL a:visited { - color: var(--nav-text-hover-color); - text-shadow: var(--nav-text-hover-shadow); -} - -a.navtab { - font-weight: bold; -} - -div.qindex{ - text-align: center; - width: 100%; - line-height: 140%; - font-size: 130%; - color: var(--index-separator-color); -} - -#main-menu a:focus { - outline: auto; - z-index: 10; - position: relative; -} - -dt.alphachar{ - font-size: 180%; - font-weight: bold; -} - -.alphachar a{ - color: var(--index-header-color); -} - -.alphachar a:hover, .alphachar a:visited{ - text-decoration: none; -} - -.classindex dl { - padding: 25px; - column-count:1 -} - -.classindex dd { - display:inline-block; - margin-left: 50px; - width: 90%; - line-height: 1.15em; -} - -.classindex dl.even { - background-color: var(--index-even-item-bg-color); -} - -.classindex dl.odd { - background-color: var(--index-odd-item-bg-color); -} - -@media(min-width: 1120px) { - .classindex dl { - column-count:2 - } -} - -@media(min-width: 1320px) { - .classindex dl { - column-count:3 - } -} - - -/* @group Link Styling */ - -a { - color: var(--page-link-color); - font-weight: normal; - text-decoration: none; -} - -.contents a:visited { - color: var(--page-visited-link-color); -} - -a:hover { - text-decoration: none; - background: linear-gradient(to bottom, transparent 0,transparent calc(100% - 1px), currentColor 100%); -} - -a:hover > span.arrow { - text-decoration: none; - background : var(--nav-background-color); -} - -a.el { - font-weight: bold; -} - -a.elRef { -} - -a.code, a.code:visited, a.line, a.line:visited { - color: var(--code-link-color); -} - -a.codeRef, a.codeRef:visited, a.lineRef, a.lineRef:visited { - color: var(--code-external-link-color); -} - -a.code.hl_class { /* style for links to class names in code snippets */ } -a.code.hl_struct { /* style for links to struct names in code snippets */ } -a.code.hl_union { /* style for links to union names in code snippets */ } -a.code.hl_interface { /* style for links to interface names in code snippets */ } -a.code.hl_protocol { /* style for links to protocol names in code snippets */ } -a.code.hl_category { /* style for links to category names in code snippets */ } -a.code.hl_exception { /* style for links to exception names in code snippets */ } -a.code.hl_service { /* style for links to service names in code snippets */ } -a.code.hl_singleton { /* style for links to singleton names in code snippets */ } -a.code.hl_concept { /* style for links to concept names in code snippets */ } -a.code.hl_namespace { /* style for links to namespace names in code snippets */ } -a.code.hl_package { /* style for links to package names in code snippets */ } -a.code.hl_define { /* style for links to macro names in code snippets */ } -a.code.hl_function { /* style for links to function names in code snippets */ } -a.code.hl_variable { /* style for links to variable names in code snippets */ } -a.code.hl_typedef { /* style for links to typedef names in code snippets */ } -a.code.hl_enumvalue { /* style for links to enum value names in code snippets */ } -a.code.hl_enumeration { /* style for links to enumeration names in code snippets */ } -a.code.hl_signal { /* style for links to Qt signal names in code snippets */ } -a.code.hl_slot { /* style for links to Qt slot names in code snippets */ } -a.code.hl_friend { /* style for links to friend names in code snippets */ } -a.code.hl_dcop { /* style for links to KDE3 DCOP names in code snippets */ } -a.code.hl_property { /* style for links to property names in code snippets */ } -a.code.hl_event { /* style for links to event names in code snippets */ } -a.code.hl_sequence { /* style for links to sequence names in code snippets */ } -a.code.hl_dictionary { /* style for links to dictionary names in code snippets */ } - -/* @end */ - -dl.el { - margin-left: -1cm; -} - -ul.check { - list-style:none; - text-indent: -16px; - padding-left: 38px; -} -li.unchecked:before { - content: "\2610\A0"; -} -li.checked:before { - content: "\2611\A0"; -} - -ol { - text-indent: 0px; -} - -ul { - text-indent: 0px; - overflow: visible; -} - -ul.multicol { - -moz-column-gap: 1em; - -webkit-column-gap: 1em; - column-gap: 1em; - -moz-column-count: 3; - -webkit-column-count: 3; - column-count: 3; - list-style-type: none; -} - -#side-nav ul { - overflow: visible; /* reset ul rule for scroll bar in GENERATE_TREEVIEW window */ -} - -#main-nav ul { - overflow: visible; /* reset ul rule for the navigation bar drop down lists */ -} - -.fragment { - text-align: left; - direction: ltr; - overflow-x: auto; - overflow-y: hidden; - position: relative; - min-height: 12px; - margin: 10px 0px; - padding: 10px 10px; - border: 1px solid var(--fragment-border-color); - border-radius: 4px; - background-color: var(--fragment-background-color); - color: var(--fragment-foreground-color); -} - -pre.fragment { - word-wrap: break-word; - font-size: 10pt; - line-height: 125%; - font-family: var(--font-family-monospace); -} - -.clipboard { - width: 24px; - height: 24px; - right: 5px; - top: 5px; - opacity: 0; - position: absolute; - display: inline; - overflow: auto; - fill: var(--fragment-foreground-color); - justify-content: center; - align-items: center; - cursor: pointer; -} - -.clipboard.success { - border: 1px solid var(--fragment-foreground-color); - border-radius: 4px; -} - -.fragment:hover .clipboard, .clipboard.success { - opacity: .28; -} - -.clipboard:hover, .clipboard.success { - opacity: 1 !important; -} - -.clipboard:active:not([class~=success]) svg { - transform: scale(.91); -} - -.clipboard.success svg { - fill: var(--fragment-copy-ok-color); -} - -.clipboard.success { - border-color: var(--fragment-copy-ok-color); -} - -div.line { - font-family: var(--font-family-monospace); - font-size: 13px; - min-height: 13px; - line-height: 1.2; - text-wrap: unrestricted; - white-space: -moz-pre-wrap; /* Moz */ - white-space: -pre-wrap; /* Opera 4-6 */ - white-space: -o-pre-wrap; /* Opera 7 */ - white-space: pre-wrap; /* CSS3 */ - word-wrap: break-word; /* IE 5.5+ */ - text-indent: -53px; - padding-left: 53px; - padding-bottom: 0px; - margin: 0px; - -webkit-transition-property: background-color, box-shadow; - -webkit-transition-duration: 0.5s; - -moz-transition-property: background-color, box-shadow; - -moz-transition-duration: 0.5s; - -ms-transition-property: background-color, box-shadow; - -ms-transition-duration: 0.5s; - -o-transition-property: background-color, box-shadow; - -o-transition-duration: 0.5s; - transition-property: background-color, box-shadow; - transition-duration: 0.5s; -} - -div.line:after { - content:"\000A"; - white-space: pre; -} - -div.line.glow { - background-color: var(--glow-color); - box-shadow: 0 0 10px var(--glow-color); -} - -span.fold { - margin-left: 5px; - margin-right: 1px; - margin-top: 0px; - margin-bottom: 0px; - padding: 0px; - display: inline-block; - width: 12px; - height: 12px; - background-repeat:no-repeat; - background-position:center; -} - -span.lineno { - padding-right: 4px; - margin-right: 9px; - text-align: right; - border-right: 2px solid var(--fragment-lineno-border-color); - color: var(--fragment-lineno-foreground-color); - background-color: var(--fragment-lineno-background-color); - white-space: pre; -} -span.lineno a, span.lineno a:visited { - color: var(--fragment-lineno-link-fg-color); - background-color: var(--fragment-lineno-link-bg-color); -} - -span.lineno a:hover { - color: var(--fragment-lineno-link-hover-fg-color); - background-color: var(--fragment-lineno-link-hover-bg-color); -} - -.lineno { - -webkit-touch-callout: none; - -webkit-user-select: none; - -khtml-user-select: none; - -moz-user-select: none; - -ms-user-select: none; - user-select: none; -} - -div.classindex ul { - list-style: none; - padding-left: 0; -} - -div.classindex span.ai { - display: inline-block; -} - -div.groupHeader { - margin-left: 16px; - margin-top: 12px; - font-weight: bold; -} - -div.groupText { - margin-left: 16px; - font-style: italic; -} - -body { - color: var(--page-foreground-color); - margin: 0; -} - -div.contents { - margin-top: 10px; - margin-left: 12px; - margin-right: 8px; -} - -p.formulaDsp { - text-align: center; -} - -img.dark-mode-visible { - display: none; -} -img.light-mode-visible { - display: none; -} - -img.formulaInl, img.inline { - vertical-align: middle; -} - -div.center { - text-align: center; - margin-top: 0px; - margin-bottom: 0px; - padding: 0px; -} - -div.center img { - border: 0px; -} - -address.footer { - text-align: right; - padding-right: 12px; -} - -img.footer { - border: 0px; - vertical-align: middle; - width: var(--footer-logo-width); -} - -.compoundTemplParams { - color: var(--memdecl-template-color); - font-size: 80%; - line-height: 120%; -} - -/* @group Code Colorization */ - -span.keyword { - color: var(--code-keyword-color); -} - -span.keywordtype { - color: var(--code-type-keyword-color); -} - -span.keywordflow { - color: var(--code-flow-keyword-color); -} - -span.comment { - color: var(--code-comment-color); -} - -span.preprocessor { - color: var(--code-preprocessor-color); -} - -span.stringliteral { - color: var(--code-string-literal-color); -} - -span.charliteral { - color: var(--code-char-literal-color); -} - -span.xmlcdata { - color: var(--code-xml-cdata-color); -} - -span.vhdldigit { - color: var(--code-vhdl-digit-color); -} - -span.vhdlchar { - color: var(--code-vhdl-char-color); -} - -span.vhdlkeyword { - color: var(--code-vhdl-keyword-color); -} - -span.vhdllogic { - color: var(--code-vhdl-logic-color); -} - -blockquote { - background-color: var(--blockquote-background-color); - border-left: 2px solid var(--blockquote-border-color); - margin: 0 24px 0 4px; - padding: 0 12px 0 16px; -} - -/* @end */ - -td.tiny { - font-size: 75%; -} - -.dirtab { - padding: 4px; - border-collapse: collapse; - border: 1px solid var(--table-cell-border-color); -} - -th.dirtab { - background-color: var(--table-header-background-color); - color: var(--table-header-foreground-color); - font-weight: bold; -} - -hr { - height: 0px; - border: none; - border-top: 1px solid var(--separator-color); -} - -hr.footer { - height: 1px; -} - -/* @group Member Descriptions */ - -table.memberdecls { - border-spacing: 0px; - padding: 0px; -} - -.memberdecls td, .fieldtable tr { - -webkit-transition-property: background-color, box-shadow; - -webkit-transition-duration: 0.5s; - -moz-transition-property: background-color, box-shadow; - -moz-transition-duration: 0.5s; - -ms-transition-property: background-color, box-shadow; - -ms-transition-duration: 0.5s; - -o-transition-property: background-color, box-shadow; - -o-transition-duration: 0.5s; - transition-property: background-color, box-shadow; - transition-duration: 0.5s; -} - -.memberdecls td.glow, .fieldtable tr.glow { - background-color: var(--glow-color); - box-shadow: 0 0 15px var(--glow-color); -} - -.mdescLeft, .mdescRight, -.memItemLeft, .memItemRight, -.memTemplItemLeft, .memTemplItemRight, .memTemplParams { - background-color: var(--memdecl-background-color); - border: none; - margin: 4px; - padding: 1px 0 0 8px; -} - -.mdescLeft, .mdescRight { - padding: 0px 8px 4px 8px; - color: var(--memdecl-foreground-color); -} - -.memSeparator { - border-bottom: 1px solid var(--memdecl-separator-color); - line-height: 1px; - margin: 0px; - padding: 0px; -} - -.memItemLeft, .memTemplItemLeft { - white-space: nowrap; -} - -.memItemRight, .memTemplItemRight { - width: 100%; -} - -.memTemplParams { - color: var(--memdecl-template-color); - white-space: nowrap; - font-size: 80%; -} - -/* @end */ - -/* @group Member Details */ - -/* Styles for detailed member documentation */ - -.memtitle { - padding: 8px; - border-top: 1px solid var(--memdef-border-color); - border-left: 1px solid var(--memdef-border-color); - border-right: 1px solid var(--memdef-border-color); - border-top-right-radius: 4px; - border-top-left-radius: 4px; - margin-bottom: -1px; - background-image: var(--memdef-title-gradient-image); - background-repeat: repeat-x; - background-color: var(--memdef-title-background-color); 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-
- - - - - - - -
-
BayesNet 1.0.5 -
-
Bayesian Network Classifiers using libtorch from scratch
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Here is a list of all documented files with brief descriptions:
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[detail level 123]
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
 ▼ bayesnet
 ▼ classifiers
 Classifier.cc
 Classifier.h
 KDB.cc
 KDB.h
 KDBLd.cc
 KDBLd.h
 Proposal.cc
 Proposal.h
 SPnDE.cc
 SPnDE.h
 SPODE.cc
 SPODE.h
 SPODELd.cc
 SPODELd.h
 TAN.cc
 TAN.h
 TANLd.cc
 TANLd.h
 ▼ ensembles
 A2DE.cc
 A2DE.h
 AODE.cc
 AODE.h
 AODELd.cc
 AODELd.h
 Boost.cc
 Boost.h
 BoostA2DE.cc
 BoostA2DE.h
 BoostAODE.cc
 BoostAODE.h
 Ensemble.cc
 Ensemble.h
 ▼ network
 Network.cc
 Network.h
 Node.cc
 Node.h
 BaseClassifier.h
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- - - - diff --git a/docs/manual/files_dup.js b/docs/manual/files_dup.js deleted file mode 100644 index 3a9b985..0000000 --- a/docs/manual/files_dup.js +++ /dev/null @@ -1,4 +0,0 @@ -var files_dup = -[ - [ "bayesnet", "dir_40070fdff85d618b4d1d3ab4ac4f79bb.html", "dir_40070fdff85d618b4d1d3ab4ac4f79bb" ] -]; \ No newline at end of file diff --git a/docs/manual/folderclosed.svg b/docs/manual/folderclosed.svg deleted file mode 100644 index b04bed2..0000000 --- a/docs/manual/folderclosed.svg +++ /dev/null @@ -1,11 +0,0 @@ - - - - - - - - - - diff --git a/docs/manual/folderclosedd.svg b/docs/manual/folderclosedd.svg deleted file mode 100644 index 52f0166..0000000 --- a/docs/manual/folderclosedd.svg +++ /dev/null @@ -1,11 +0,0 @@ - - - - - - - - - - diff --git a/docs/manual/folderopen.svg b/docs/manual/folderopen.svg deleted file mode 100644 index f6896dd..0000000 --- a/docs/manual/folderopen.svg +++ /dev/null @@ -1,17 +0,0 @@ - - - - - - - - - - diff --git a/docs/manual/folderopend.svg b/docs/manual/folderopend.svg deleted file mode 100644 index 2d1f06e..0000000 --- a/docs/manual/folderopend.svg +++ /dev/null @@ -1,12 +0,0 @@ - - - - - - - - - - - diff --git a/docs/manual/graph_legend.html b/docs/manual/graph_legend.html deleted file mode 100644 index 46efc2c..0000000 --- a/docs/manual/graph_legend.html +++ /dev/null @@ -1,173 +0,0 @@ - - - - - - - -BayesNet: Graph Legend - - - - - - - - - - - - - - - -
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- - - - - - - -
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BayesNet 1.0.5 -
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Bayesian Network Classifiers using libtorch from scratch
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Graph Legend
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This page explains how to interpret the graphs that are generated by doxygen.

-

Consider the following example:

/*! Invisible class because of truncation */
-
class Invisible { };
-
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/*! Truncated class, inheritance relation is hidden */
-
class Truncated : public Invisible { };
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/* Class not documented with doxygen comments */
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class Undocumented { };
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/*! Class that is inherited using public inheritance */
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class PublicBase : public Truncated { };
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/*! A template class */
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template<class T> class Templ { };
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/*! Class that is inherited using protected inheritance */
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class ProtectedBase { };
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/*! Class that is inherited using private inheritance */
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class PrivateBase { };
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/*! Class that is used by the Inherited class */
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class Used { };
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/*! Super class that inherits a number of other classes */
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class Inherited : public PublicBase,
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protected ProtectedBase,
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private PrivateBase,
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public Undocumented,
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public Templ<int>
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{
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private:
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Used *m_usedClass;
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};
-

This will result in the following graph:

-

The boxes in the above graph have the following meaning:

-
    -
  • -A filled gray box represents the struct or class for which the graph is generated.
    • -
  • -A box with a black border denotes a documented struct or class.
    • -
  • -A box with a gray border denotes an undocumented struct or class.
    • -
  • -A box with a red border denotes a documented struct or class forwhich not all inheritance/containment relations are shown. A graph is truncated if it does not fit within the specified boundaries.
    • -
-

The arrows have the following meaning:

-
    -
  • -A blue arrow is used to visualize a public inheritance relation between two classes.
    • -
  • -A dark green arrow is used for protected inheritance.
    • -
  • -A dark red arrow is used for private inheritance.
    • -
  • -A purple dashed arrow is used if a class is contained or used by another class. The arrow is labelled with the variable(s) through which the pointed class or struct is accessible.
    • -
  • -A yellow dashed arrow denotes a relation between a template instance and the template class it was instantiated from. The arrow is labelled with the template parameters of the instance.
    • -
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- - - - diff --git a/docs/manual/graph_legend.md5 b/docs/manual/graph_legend.md5 deleted file mode 100644 index da515da..0000000 --- a/docs/manual/graph_legend.md5 +++ /dev/null @@ -1 +0,0 @@ -f74606a252eb303675caf37987d0b7af \ No newline at end of file diff --git a/docs/manual/graph_legend.png b/docs/manual/graph_legend.png deleted file mode 100644 index 9f80d23..0000000 Binary files a/docs/manual/graph_legend.png and /dev/null differ diff --git a/docs/manual/hierarchy.html b/docs/manual/hierarchy.html deleted file mode 100644 index 5b7dbca..0000000 --- a/docs/manual/hierarchy.html +++ /dev/null @@ -1,142 +0,0 @@ - - - - - - - -BayesNet: Class Hierarchy - - - - - - - - - - - - - - - -
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- - - - - - - -
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BayesNet 1.0.5 -
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Bayesian Network Classifiers using libtorch from scratch
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Class Hierarchy
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Go to the graphical class hierarchy

-This inheritance list is sorted roughly, but not completely, alphabetically:
-
[detail level 12345]
- - - - - - - - - - - - - - - - - - - - - - - -
 ▼Cbayesnet::BaseClassifier
 ▼Cbayesnet::Classifier
 ▼Cbayesnet::Ensemble
 Cbayesnet::A2DE
 Cbayesnet::AODE
 Cbayesnet::AODELd
 ▼Cbayesnet::Boost
 Cbayesnet::BoostA2DE
 Cbayesnet::BoostAODE
 ▼Cbayesnet::KDB
 Cbayesnet::KDBLd
 ▼Cbayesnet::SPODE
 Cbayesnet::SPODELd
 Cbayesnet::SPnDE
 ▼Cbayesnet::TAN
 Cbayesnet::TANLd
 Cbayesnet::Network
 Cbayesnet::Node
 ▼Cbayesnet::Proposal
 Cbayesnet::AODELd
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- - - - diff --git a/docs/manual/hierarchy.js b/docs/manual/hierarchy.js deleted file mode 100644 index 5202a5c..0000000 --- a/docs/manual/hierarchy.js +++ /dev/null @@ -1,34 +0,0 @@ -var hierarchy = -[ - [ "bayesnet::BaseClassifier", "classbayesnet_1_1_base_classifier.html", [ - [ "bayesnet::Classifier", "classbayesnet_1_1_classifier.html", [ - [ "bayesnet::Ensemble", "classbayesnet_1_1_ensemble.html", [ - [ "bayesnet::A2DE", "classbayesnet_1_1_a2_d_e.html", null ], - [ "bayesnet::AODE", "classbayesnet_1_1_a_o_d_e.html", null ], - [ "bayesnet::AODELd", "classbayesnet_1_1_a_o_d_e_ld.html", null ], - [ "bayesnet::Boost", "classbayesnet_1_1_boost.html", [ - [ "bayesnet::BoostA2DE", "classbayesnet_1_1_boost_a2_d_e.html", null ], - [ "bayesnet::BoostAODE", "classbayesnet_1_1_boost_a_o_d_e.html", null ] - ] ] - ] ], - [ "bayesnet::KDB", "classbayesnet_1_1_k_d_b.html", [ - [ "bayesnet::KDBLd", "classbayesnet_1_1_k_d_b_ld.html", null ] - ] ], - [ "bayesnet::SPODE", "classbayesnet_1_1_s_p_o_d_e.html", [ - [ "bayesnet::SPODELd", "classbayesnet_1_1_s_p_o_d_e_ld.html", null ] - ] ], - [ "bayesnet::SPnDE", "classbayesnet_1_1_s_pn_d_e.html", null ], - [ "bayesnet::TAN", "classbayesnet_1_1_t_a_n.html", [ - [ "bayesnet::TANLd", "classbayesnet_1_1_t_a_n_ld.html", null ] - ] ] - ] ] - ] ], - [ "bayesnet::Network", "classbayesnet_1_1_network.html", null ], - [ "bayesnet::Node", "classbayesnet_1_1_node.html", null ], - [ "bayesnet::Proposal", "classbayesnet_1_1_proposal.html", [ - [ "bayesnet::AODELd", "classbayesnet_1_1_a_o_d_e_ld.html", null ], - [ "bayesnet::KDBLd", "classbayesnet_1_1_k_d_b_ld.html", null ], - [ "bayesnet::SPODELd", "classbayesnet_1_1_s_p_o_d_e_ld.html", null ], - [ "bayesnet::TANLd", "classbayesnet_1_1_t_a_n_ld.html", null ] - ] ] -]; \ No newline at end of file diff --git a/docs/manual/index.html b/docs/manual/index.html deleted file mode 100644 index fe22924..0000000 --- a/docs/manual/index.html +++ /dev/null @@ -1,114 +0,0 @@ - - - - - - - -BayesNet: Main Page - - - - - - - - - - - - - - - -
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BayesNet 1.0.5 -
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e=function(){t.css("z-index","")};this.isCollapsible()?canAnimate&&this.opts.collapsibleHideFunction?this.opts.collapsibleHideFunction.call(this,t,e):t.hide(this.opts.collapsibleHideDuration,e):canAnimate&&this.opts.hideFunction?this.opts.hideFunction.call(this,t,e):t.hide(this.opts.hideDuration,e),t.dataSM("scroll")&&(this.menuScrollStop(t),t.css({"touch-action":"","-ms-touch-action":"","-webkit-transform":"",transform:""}).off(".smartmenus_scroll").removeDataSM("scroll").dataSM("scroll-arrows").hide()),t.dataSM("parent-a").removeClass("highlighted").attr("aria-expanded","false"),t.attr({"aria-expanded":"false","aria-hidden":"true"});var i=t.dataSM("level");this.activatedItems.splice(i-1,1),this.visibleSubMenus.splice($.inArray(t,this.visibleSubMenus),1),this.$root.triggerHandler("hide.smapi",t[0])}},menuHideAll:function(){this.showTimeout&&(clearTimeout(this.showTimeout),this.showTimeout=0);for(var 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x=this;t.dataSM("scroll-arrows")||t.dataSM("scroll-arrows",$([$('')[0],$('')[0]]).on({mouseenter:function(){t.dataSM("scroll").up=$(this).hasClass("scroll-up"),x.menuScroll(t)},mouseleave:function(e){x.menuScrollStop(t),x.menuScrollOut(t,e)},"mousewheel DOMMouseScroll":function(t){t.preventDefault()}}).insertAfter(t));var A=".smartmenus_scroll";if(t.dataSM("scroll",{y:this.cssTransforms3d?0:i-m,step:1,itemH:m,subH:h,arrowDownH:this.getHeight(t.dataSM("scroll-arrows").eq(1))}).on(getEventsNS({mouseover:function(e){x.menuScrollOver(t,e)},mouseout:function(e){x.menuScrollOut(t,e)},"mousewheel DOMMouseScroll":function(e){x.menuScrollMousewheel(t,e)}},A)).dataSM("scroll-arrows").css({top:"auto",left:"0",marginLeft:e+(parseInt(t.css("border-left-width"))||0),width:r-(parseInt(t.css("border-left-width"))||0)-(parseInt(t.css("border-right-width"))||0),zIndex:t.css("z-index")}).eq(g&&this.opts.bottomToTopSubMenus?0:1).show(),this.isFixed()){var C={};C[touchEvents?"touchstart touchmove touchend":"pointerdown pointermove pointerup MSPointerDown MSPointerMove MSPointerUp"]=function(e){x.menuScrollTouch(t,e)},t.css({"touch-action":"none","-ms-touch-action":"none"}).on(getEventsNS(C,A))}}}t.css({top:"auto",left:"0",marginLeft:e,marginTop:i-m})},menuScroll:function(t,e,i){var s,o=t.dataSM("scroll"),a=t.dataSM("scroll-arrows"),n=o.up?o.upEnd:o.downEnd;if(!e&&o.momentum){if(o.momentum*=.92,s=o.momentum,.5>s)return this.menuScrollStop(t),void 0}else s=i||(e||!this.opts.scrollAccelerate?this.opts.scrollStep:Math.floor(o.step));var r=t.dataSM("level");if(this.activatedItems[r-1]&&this.activatedItems[r-1].dataSM("sub")&&this.activatedItems[r-1].dataSM("sub").is(":visible")&&this.menuHideSubMenus(r-1),o.y=o.up&&o.y>=n||!o.up&&n>=o.y?o.y:Math.abs(n-o.y)>s?o.y+(o.up?s:-s):n,t.css(this.cssTransforms3d?{"-webkit-transform":"translate3d(0, "+o.y+"px, 0)",transform:"translate3d(0, "+o.y+"px, 0)"}:{marginTop:o.y}),mouse&&(o.up&&o.y>o.downEnd||!o.up&&o.y0;t.dataSM("scroll-arrows").eq(i?0:1).is(":visible")&&(t.dataSM("scroll").up=i,this.menuScroll(t,!0))}e.preventDefault()},menuScrollOut:function(t,e){mouse&&(/^scroll-(up|down)/.test((e.relatedTarget||"").className)||(t[0]==e.relatedTarget||$.contains(t[0],e.relatedTarget))&&this.getClosestMenu(e.relatedTarget)==t[0]||t.dataSM("scroll-arrows").css("visibility","hidden"))},menuScrollOver:function(t,e){if(mouse&&!/^scroll-(up|down)/.test(e.target.className)&&this.getClosestMenu(e.target)==t[0]){this.menuScrollRefreshData(t);var i=t.dataSM("scroll"),s=$(window).scrollTop()-t.dataSM("parent-a").offset().top-i.itemH;t.dataSM("scroll-arrows").eq(0).css("margin-top",s).end().eq(1).css("margin-top",s+this.getViewportHeight()-i.arrowDownH).end().css("visibility","visible")}},menuScrollRefreshData:function(t){var e=t.dataSM("scroll"),i=$(window).scrollTop()-t.dataSM("parent-a").offset().top-e.itemH;this.cssTransforms3d&&(i=-(parseFloat(t.css("margin-top"))-i)),$.extend(e,{upEnd:i,downEnd:i+this.getViewportHeight()-e.subH})},menuScrollStop:function(t){return this.scrollTimeout?(cancelAnimationFrame(this.scrollTimeout),this.scrollTimeout=0,t.dataSM("scroll").step=1,!0):void 0},menuScrollTouch:function(t,e){if(e=e.originalEvent,isTouchEvent(e)){var i=this.getTouchPoint(e);if(this.getClosestMenu(i.target)==t[0]){var s=t.dataSM("scroll");if(/(start|down)$/i.test(e.type))this.menuScrollStop(t)?(e.preventDefault(),this.$touchScrollingSub=t):this.$touchScrollingSub=null,this.menuScrollRefreshData(t),$.extend(s,{touchStartY:i.pageY,touchStartTime:e.timeStamp});else if(/move$/i.test(e.type)){var o=void 0!==s.touchY?s.touchY:s.touchStartY;if(void 0!==o&&o!=i.pageY){this.$touchScrollingSub=t;var a=i.pageY>o;void 0!==s.up&&s.up!=a&&$.extend(s,{touchStartY:i.pageY,touchStartTime:e.timeStamp}),$.extend(s,{up:a,touchY:i.pageY}),this.menuScroll(t,!0,Math.abs(i.pageY-o))}e.preventDefault()}else void 0!==s.touchY&&((s.momentum=15*Math.pow(Math.abs(i.pageY-s.touchStartY)/(e.timeStamp-s.touchStartTime),2))&&(this.menuScrollStop(t),this.menuScroll(t),e.preventDefault()),delete s.touchY)}}},menuShow:function(t){if((t.dataSM("beforefirstshowfired")||(t.dataSM("beforefirstshowfired",!0),this.$root.triggerHandler("beforefirstshow.smapi",t[0])!==!1))&&this.$root.triggerHandler("beforeshow.smapi",t[0])!==!1&&(t.dataSM("shown-before",!0),canAnimate&&t.stop(!0,!0),!t.is(":visible"))){var e=t.dataSM("parent-a"),i=this.isCollapsible();if((this.opts.keepHighlighted||i)&&e.addClass("highlighted"),i)t.removeClass("sm-nowrap").css({zIndex:"",width:"auto",minWidth:"",maxWidth:"",top:"",left:"",marginLeft:"",marginTop:""});else{if(t.css("z-index",this.zIndexInc=(this.zIndexInc||this.getStartZIndex())+1),(this.opts.subMenusMinWidth||this.opts.subMenusMaxWidth)&&(t.css({width:"auto",minWidth:"",maxWidth:""}).addClass("sm-nowrap"),this.opts.subMenusMinWidth&&t.css("min-width",this.opts.subMenusMinWidth),this.opts.subMenusMaxWidth)){var s=this.getWidth(t);t.css("max-width",this.opts.subMenusMaxWidth),s>this.getWidth(t)&&t.removeClass("sm-nowrap").css("width",this.opts.subMenusMaxWidth)}this.menuPosition(t)}var o=function(){t.css("overflow","")};i?canAnimate&&this.opts.collapsibleShowFunction?this.opts.collapsibleShowFunction.call(this,t,o):t.show(this.opts.collapsibleShowDuration,o):canAnimate&&this.opts.showFunction?this.opts.showFunction.call(this,t,o):t.show(this.opts.showDuration,o),e.attr("aria-expanded","true"),t.attr({"aria-expanded":"true","aria-hidden":"false"}),this.visibleSubMenus.push(t),this.$root.triggerHandler("show.smapi",t[0])}},popupHide:function(t){this.hideTimeout&&(clearTimeout(this.hideTimeout),this.hideTimeout=0);var e=this;this.hideTimeout=setTimeout(function(){e.menuHideAll()},t?1:this.opts.hideTimeout)},popupShow:function(t,e){if(!this.opts.isPopup)return alert('SmartMenus jQuery Error:\n\nIf you want to show this menu via the "popupShow" method, set the isPopup:true option.'),void 0;if(this.hideTimeout&&(clearTimeout(this.hideTimeout),this.hideTimeout=0),this.$root.dataSM("shown-before",!0),canAnimate&&this.$root.stop(!0,!0),!this.$root.is(":visible")){this.$root.css({left:t,top:e});var i=this,s=function(){i.$root.css("overflow","")};canAnimate&&this.opts.showFunction?this.opts.showFunction.call(this,this.$root,s):this.$root.show(this.opts.showDuration,s),this.visibleSubMenus[0]=this.$root}},refresh:function(){this.destroy(!0),this.init(!0)},rootKeyDown:function(t){if(this.handleEvents())switch(t.keyCode){case 27:var e=this.activatedItems[0];if(e){this.menuHideAll(),e[0].focus();var i=e.dataSM("sub");i&&this.menuHide(i)}break;case 32:var s=$(t.target);if(s.is("a")&&this.handleItemEvents(s)){var i=s.dataSM("sub");i&&!i.is(":visible")&&(this.itemClick({currentTarget:t.target}),t.preventDefault())}}},rootOut:function(t){if(this.handleEvents()&&!this.isTouchMode()&&t.target!=this.$root[0]&&(this.hideTimeout&&(clearTimeout(this.hideTimeout),this.hideTimeout=0),!this.opts.showOnClick||!this.opts.hideOnClick)){var e=this;this.hideTimeout=setTimeout(function(){e.menuHideAll()},this.opts.hideTimeout)}},rootOver:function(t){this.handleEvents()&&!this.isTouchMode()&&t.target!=this.$root[0]&&this.hideTimeout&&(clearTimeout(this.hideTimeout),this.hideTimeout=0)},winResize:function(t){if(this.handleEvents()){if(!("onorientationchange"in window)||"orientationchange"==t.type){var e=this.isCollapsible();this.wasCollapsible&&e||(this.activatedItems.length&&this.activatedItems[this.activatedItems.length-1][0].blur(),this.menuHideAll()),this.wasCollapsible=e}}else if(this.$disableOverlay){var i=this.$root.offset();this.$disableOverlay.css({top:i.top,left:i.left,width:this.$root.outerWidth(),height:this.$root.outerHeight()})}}}}),$.fn.dataSM=function(t,e){return e?this.data(t+"_smartmenus",e):this.data(t+"_smartmenus")},$.fn.removeDataSM=function(t){return this.removeData(t+"_smartmenus")},$.fn.smartmenus=function(options){if("string"==typeof options){var args=arguments,method=options;return Array.prototype.shift.call(args),this.each(function(){var t=$(this).data("smartmenus");t&&t[method]&&t[method].apply(t,args)})}return this.each(function(){var dataOpts=$(this).data("sm-options")||null;if(dataOpts)try{dataOpts=eval("("+dataOpts+")")}catch(e){dataOpts=null,alert('ERROR\n\nSmartMenus jQuery init:\nInvalid "data-sm-options" attribute value syntax.')}new $.SmartMenus(this,$.extend({},$.fn.smartmenus.defaults,options,dataOpts))})},$.fn.smartmenus.defaults={isPopup:!1,mainMenuSubOffsetX:0,mainMenuSubOffsetY:0,subMenusSubOffsetX:0,subMenusSubOffsetY:0,subMenusMinWidth:"10em",subMenusMaxWidth:"20em",subIndicators:!0,subIndicatorsPos:"append",subIndicatorsText:"",scrollStep:30,scrollAccelerate:!0,showTimeout:250,hideTimeout:500,showDuration:0,showFunction:null,hideDuration:0,hideFunction:function(t,e){t.fadeOut(200,e)},collapsibleShowDuration:0,collapsibleShowFunction:function(t,e){t.slideDown(200,e)},collapsibleHideDuration:0,collapsibleHideFunction:function(t,e){t.slideUp(200,e)},showOnClick:!1,hideOnClick:!0,noMouseOver:!1,keepInViewport:!0,keepHighlighted:!0,markCurrentItem:!1,markCurrentTree:!0,rightToLeftSubMenus:!1,bottomToTopSubMenus:!1,collapsibleBehavior:"default"},$}); \ No newline at end of file diff --git a/docs/manual/logo_small.png b/docs/manual/logo_small.png deleted file mode 100644 index 33030fb..0000000 Binary files a/docs/manual/logo_small.png and /dev/null differ diff --git a/docs/manual/menu.js b/docs/manual/menu.js deleted file mode 100644 index 0fd1e99..0000000 --- a/docs/manual/menu.js +++ /dev/null @@ -1,134 +0,0 @@ -/* - @licstart The following is the entire license notice for the JavaScript code in this file. - - The MIT License (MIT) - - Copyright (C) 1997-2020 by Dimitri van Heesch - - Permission is hereby granted, free of charge, to any person obtaining a copy of this software - and associated documentation files (the "Software"), to deal in the Software without restriction, - including without limitation the rights to use, copy, modify, merge, publish, distribute, - sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is - furnished to do so, subject to the following conditions: - - The above copyright notice and this permission notice shall be included in all copies or - substantial portions of the Software. - - THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING - BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND - NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, - DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, - OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. - - @licend The above is the entire license notice for the JavaScript code in this file - */ -function initMenu(relPath,searchEnabled,serverSide,searchPage,search,treeview) { - function makeTree(data,relPath) { - let result=''; - if ('children' in data) { - result+='
    '; - for (let i in data.children) { - let url; - const link = data.children[i].url; - if (link.substring(0,1)=='^') { - url = link.substring(1); - } else { - url = relPath+link; - } - result+='
  • '+ - data.children[i].text+''+ - makeTree(data.children[i],relPath)+'
    • '; - } - result+='
'; - } - return result; - } - let searchBoxHtml; - if (searchEnabled) { - if (serverSide) { - searchBoxHtml='
'+ - '
'+ - '
 '+ - ''+ - '
'+ - '
'+ - '
'+ - '
'; - } else { - searchBoxHtml='
'+ - ''+ - ' '+ - ''+ - ''+ - ''+ - ''+ - ''+ - '
'; - } - } - - $('#main-nav').before('
'+ - ''+ - ''+ - '
'); - $('#main-nav').append(makeTree(menudata,relPath)); - $('#main-nav').children(':first').addClass('sm sm-dox').attr('id','main-menu'); - if (searchBoxHtml) { - $('#main-menu').append('
  • '); - } - const $mainMenuState = $('#main-menu-state'); - let prevWidth = 0; - if ($mainMenuState.length) { - const initResizableIfExists = function() { - if (typeof initResizable==='function') initResizable(treeview); - } - // animate mobile menu - $mainMenuState.change(function() { - const $menu = $('#main-menu'); - let options = { duration: 250, step: initResizableIfExists }; - if (this.checked) { - options['complete'] = () => $menu.css('display', 'block'); - $menu.hide().slideDown(options); - } else { - options['complete'] = () => $menu.css('display', 'none'); - $menu.show().slideUp(options); - } - }); - // set default menu visibility - const resetState = function() { - const $menu = $('#main-menu'); - const newWidth = $(window).outerWidth(); - if (newWidth!=prevWidth) { - if ($(window).outerWidth()<768) { - $mainMenuState.prop('checked',false); $menu.hide(); - $('#searchBoxPos1').html(searchBoxHtml); - $('#searchBoxPos2').hide(); - } else { - $menu.show(); - $('#searchBoxPos1').empty(); - $('#searchBoxPos2').html(searchBoxHtml); - $('#searchBoxPos2').show(); - } - if (typeof searchBox!=='undefined') { - searchBox.CloseResultsWindow(); - } - prevWidth = newWidth; - } - } - $(window).ready(function() { resetState(); initResizableIfExists(); }); - $(window).resize(resetState); - } - $('#main-menu').smartmenus(); -} -/* @license-end */ diff --git a/docs/manual/menudata.js b/docs/manual/menudata.js deleted file mode 100644 index 1cdd58f..0000000 --- a/docs/manual/menudata.js +++ /dev/null @@ -1,32 +0,0 @@ -/* - @licstart The following is the entire license notice for the JavaScript code in this file. - - The MIT License (MIT) - - Copyright (C) 1997-2020 by Dimitri van Heesch - - Permission is hereby granted, free of charge, to any person obtaining a copy of this software - and associated documentation files (the "Software"), to deal in the Software without restriction, - including without limitation the rights to use, copy, modify, merge, publish, distribute, - sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is - furnished to do so, subject to the following conditions: - - The above copyright notice and this permission notice shall be included in all copies or - substantial portions of the Software. - - THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING - BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND - NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, - DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, - OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. - - @licend The above is the entire license notice for the JavaScript code in this file -*/ -var menudata={children:[ -{text:"Main Page",url:"index.html"}, -{text:"Classes",url:"annotated.html",children:[ -{text:"Class List",url:"annotated.html"}, -{text:"Class Index",url:"classes.html"}, -{text:"Class Hierarchy",url:"inherits.html"}]}, -{text:"Files",url:"files.html",children:[ -{text:"File List",url:"files.html"}]}]} diff --git a/docs/manual/minus.svg b/docs/manual/minus.svg deleted file mode 100644 index f70d0c1..0000000 --- a/docs/manual/minus.svg +++ /dev/null @@ -1,8 +0,0 @@ - - - - - - - - diff --git a/docs/manual/minusd.svg b/docs/manual/minusd.svg deleted file mode 100644 index 5f8e879..0000000 --- a/docs/manual/minusd.svg +++ /dev/null @@ -1,8 +0,0 @@ - - - - - - - - diff --git a/docs/manual/nav_f.png b/docs/manual/nav_f.png deleted file mode 100644 index 72a58a5..0000000 Binary files a/docs/manual/nav_f.png and /dev/null differ diff --git a/docs/manual/nav_fd.png b/docs/manual/nav_fd.png deleted file mode 100644 index 032fbdd..0000000 Binary files a/docs/manual/nav_fd.png and /dev/null differ diff --git a/docs/manual/nav_g.png b/docs/manual/nav_g.png deleted file mode 100644 index 2093a23..0000000 Binary files a/docs/manual/nav_g.png and /dev/null differ diff --git a/docs/manual/nav_h.png b/docs/manual/nav_h.png deleted file mode 100644 index 33389b1..0000000 Binary files a/docs/manual/nav_h.png and /dev/null differ diff --git a/docs/manual/nav_hd.png b/docs/manual/nav_hd.png deleted file mode 100644 index de80f18..0000000 Binary files a/docs/manual/nav_hd.png and /dev/null differ diff --git a/docs/manual/navtree.css b/docs/manual/navtree.css deleted file mode 100644 index 69211d4..0000000 --- a/docs/manual/navtree.css +++ /dev/null @@ -1,149 +0,0 @@ -#nav-tree .children_ul { - margin:0; - padding:4px; -} - -#nav-tree ul { - list-style:none outside none; - margin:0px; - padding:0px; -} - -#nav-tree li { - white-space:nowrap; - margin:0px; - padding:0px; -} - -#nav-tree .plus { - margin:0px; -} - -#nav-tree .selected { - background-image: url('tab_a.png'); - background-repeat:repeat-x; - color: var(--nav-text-active-color); - text-shadow: var(--nav-text-active-shadow); -} - -#nav-tree .selected .arrow { - color: var(--nav-arrow-selected-color); - text-shadow: none; -} - -#nav-tree img { - margin:0px; - padding:0px; - border:0px; - vertical-align: middle; -} - -#nav-tree a { - text-decoration:none; - padding:0px; - margin:0px; -} - -#nav-tree .label { - margin:0px; - padding:0px; - font: 12px var(--font-family-nav); -} - -#nav-tree .label a { - padding:2px; -} - -#nav-tree .selected a { - text-decoration:none; - color:var(--nav-text-active-color); -} - -#nav-tree .children_ul { - margin:0px; - padding:0px; -} - -#nav-tree .item { - margin:0px; - padding:0px; -} - -#nav-tree { - padding: 0px 0px; - font-size:14px; - overflow:auto; -} - -#doc-content { - overflow:auto; - display:block; - padding:0px; - margin:0px; - -webkit-overflow-scrolling : touch; /* iOS 5+ */ -} - -#side-nav { - padding:0 6px 0 0; - margin: 0px; - display:block; - position: absolute; - left: 0px; - width: $width; - overflow : hidden; -} - -.ui-resizable .ui-resizable-handle { - display:block; -} - -.ui-resizable-e { - background-image:var(--nav-splitbar-image); - background-size:100%; - background-repeat:repeat-y; - background-attachment: scroll; - cursor:ew-resize; - height:100%; - right:0; - top:0; - width:6px; -} - -.ui-resizable-handle { - display:none; - font-size:0.1px; - position:absolute; - z-index:1; -} - -#nav-tree-contents { - margin: 6px 0px 0px 0px; -} - -#nav-tree { - background-repeat:repeat-x; - background-color: var(--nav-background-color); - -webkit-overflow-scrolling : touch; /* iOS 5+ */ -} - -#nav-sync { - position:absolute; - top:5px; - right:24px; - z-index:0; -} - -#nav-sync img { - opacity:0.3; -} - -#nav-sync img:hover { - opacity:0.9; -} - -@media print -{ - #nav-tree { display: none; } - div.ui-resizable-handle { display: none; position: relative; } -} - diff --git a/docs/manual/navtree.js b/docs/manual/navtree.js deleted file mode 100644 index 9027ce6..0000000 --- a/docs/manual/navtree.js +++ /dev/null @@ -1,483 +0,0 @@ -/* - @licstart The following is the entire license notice for the JavaScript code in this file. - - The MIT License (MIT) - - Copyright (C) 1997-2020 by Dimitri van Heesch - - Permission is hereby granted, free of charge, to any person obtaining a copy of this software - and associated documentation files (the "Software"), to deal in the Software without restriction, - including without limitation the rights to use, copy, modify, merge, publish, distribute, - sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is - furnished to do so, subject to the following conditions: - - The above copyright notice and this permission notice shall be included in all copies or - substantial portions of the Software. - - THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING - BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND - NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, - DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, - OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. - - @licend The above is the entire license notice for the JavaScript code in this file - */ - -function initNavTree(toroot,relpath) { - let navTreeSubIndices = []; - const ARROW_DOWN = '▼'; - const ARROW_RIGHT = '►'; - const NAVPATH_COOKIE_NAME = ''+'navpath'; - - const getData = function(varName) { - const i = varName.lastIndexOf('/'); - const n = i>=0 ? varName.substring(i+1) : varName; - return eval(n.replace(/-/g,'_')); - } - - const stripPath = function(uri) { - return uri.substring(uri.lastIndexOf('/')+1); - } - - const stripPath2 = function(uri) { - const i = uri.lastIndexOf('/'); - const s = uri.substring(i+1); - const m = uri.substring(0,i+1).match(/\/d\w\/d\w\w\/$/); - return m ? uri.substring(i-6) : s; - } - - const hashValue = function() { - return $(location).attr('hash').substring(1).replace(/[^\w-]/g,''); - } - - const hashUrl = function() { - return '#'+hashValue(); - } - - const pathName = function() { - return $(location).attr('pathname').replace(/[^-A-Za-z0-9+&@#/%?=~_|!:,.;()]/g, ''); - } - - const storeLink = function(link) { - if (!$("#nav-sync").hasClass('sync')) { - Cookie.writeSetting(NAVPATH_COOKIE_NAME,link,0); - } - } - - const deleteLink = function() { - Cookie.eraseSetting(NAVPATH_COOKIE_NAME); - } - - const cachedLink = function() { - return Cookie.readSetting(NAVPATH_COOKIE_NAME,''); - } - - const getScript = function(scriptName,func) { - const head = document.getElementsByTagName("head")[0]; - const script = document.createElement('script'); - script.id = scriptName; - script.type = 'text/javascript'; - script.onload = func; - script.src = scriptName+'.js'; - head.appendChild(script); - } - - const createIndent = function(o,domNode,node) { - let level=-1; - let n = node; - while (n.parentNode) { level++; n=n.parentNode; } - if (node.childrenData) { - const imgNode = document.createElement("span"); - imgNode.className = 'arrow'; - imgNode.style.paddingLeft=(16*level).toString()+'px'; - imgNode.innerHTML=ARROW_RIGHT; - node.plus_img = imgNode; - node.expandToggle = document.createElement("a"); - node.expandToggle.href = "javascript:void(0)"; - node.expandToggle.onclick = function() { - if (node.expanded) { - $(node.getChildrenUL()).slideUp("fast"); - node.plus_img.innerHTML=ARROW_RIGHT; - node.expanded = false; - } else { - expandNode(o, node, false, true); - } - } - node.expandToggle.appendChild(imgNode); - domNode.appendChild(node.expandToggle); - } else { - let span = document.createElement("span"); - span.className = 'arrow'; - span.style.width = 16*(level+1)+'px'; - span.innerHTML = ' '; - domNode.appendChild(span); - } - } - - let animationInProgress = false; - - const gotoAnchor = function(anchor,aname) { - let pos, docContent = $('#doc-content'); - let ancParent = $(anchor.parent()); - if (ancParent.hasClass('memItemLeft') || ancParent.hasClass('memtitle') || - ancParent.hasClass('fieldname') || ancParent.hasClass('fieldtype') || - ancParent.is(':header')) { - pos = ancParent.position().top; - } else if (anchor.position()) { - pos = anchor.position().top; - } - if (pos) { - const dcOffset = docContent.offset().top; - const dcHeight = docContent.height(); - const dcScrHeight = docContent[0].scrollHeight - const dcScrTop = docContent.scrollTop(); - let dist = Math.abs(Math.min(pos-dcOffset,dcScrHeight-dcHeight-dcScrTop)); - animationInProgress = true; - docContent.animate({ - scrollTop: pos + dcScrTop - dcOffset - },Math.max(50,Math.min(500,dist)),function() { - animationInProgress=false; - if (anchor.parent().attr('class')=='memItemLeft') { - let rows = $('.memberdecls tr[class$="'+hashValue()+'"]'); - glowEffect(rows.children(),300); // member without details - } else if (anchor.parent().attr('class')=='fieldname') { - glowEffect(anchor.parent().parent(),1000); // enum value - } else if (anchor.parent().attr('class')=='fieldtype') { - glowEffect(anchor.parent().parent(),1000); // struct field - } else if (anchor.parent().is(":header")) { - glowEffect(anchor.parent(),1000); // section header - } else { - glowEffect(anchor.next(),1000); // normal member - } - }); - } - } - - const newNode = function(o, po, text, link, childrenData, lastNode) { - const node = { - children : [], - childrenData : childrenData, - depth : po.depth + 1, - relpath : po.relpath, - isLast : lastNode, - li : document.createElement("li"), - parentNode : po, - itemDiv : document.createElement("div"), - labelSpan : document.createElement("span"), - label : document.createTextNode(text), - expanded : false, - childrenUL : null, - getChildrenUL : function() { - if (!this.childrenUL) { - this.childrenUL = document.createElement("ul"); - this.childrenUL.className = "children_ul"; - this.childrenUL.style.display = "none"; - this.li.appendChild(node.childrenUL); - } - return node.childrenUL; - }, - }; - - node.itemDiv.className = "item"; - node.labelSpan.className = "label"; - createIndent(o,node.itemDiv,node); - node.itemDiv.appendChild(node.labelSpan); - node.li.appendChild(node.itemDiv); - - const a = document.createElement("a"); - node.labelSpan.appendChild(a); - po.getChildrenUL().appendChild(node.li); - a.appendChild(node.label); - if (link) { - let url; - if (link.substring(0,1)=='^') { - url = link.substring(1); - link = url; - } else { - url = node.relpath+link; - } - a.className = stripPath(link.replace('#',':')); - if (link.indexOf('#')!=-1) { - const aname = '#'+link.split('#')[1]; - const srcPage = stripPath(pathName()); - const targetPage = stripPath(link.split('#')[0]); - a.href = srcPage!=targetPage ? url : aname; - a.onclick = function() { - storeLink(link); - aPPar = $(a).parent().parent(); - if (!aPPar.hasClass('selected')) { - $('.item').removeClass('selected'); - $('.item').removeAttr('id'); - aPPar.addClass('selected'); - aPPar.attr('id','selected'); - } - const anchor = $(aname); - gotoAnchor(anchor,aname); - }; - } else { - a.href = url; - a.onclick = () => storeLink(link); - } - } else if (childrenData != null) { - a.className = "nolink"; - a.href = "javascript:void(0)"; - a.onclick = node.expandToggle.onclick; - } - return node; - } - - const showRoot = function() { - const headerHeight = $("#top").height(); - const footerHeight = $("#nav-path").height(); - const windowHeight = $(window).height() - headerHeight - footerHeight; - (function() { // retry until we can scroll to the selected item - try { - const navtree=$('#nav-tree'); - navtree.scrollTo('#selected',100,{offset:-windowHeight/2}); - } catch (err) { - setTimeout(arguments.callee, 0); - } - })(); - } - - const expandNode = function(o, node, imm, setFocus) { - if (node.childrenData && !node.expanded) { - if (typeof(node.childrenData)==='string') { - const varName = node.childrenData; - getScript(node.relpath+varName,function() { - node.childrenData = getData(varName); - expandNode(o, node, imm, setFocus); - }); - } else { - if (!node.childrenVisited) { - getNode(o, node); - } - $(node.getChildrenUL()).slideDown("fast"); - node.plus_img.innerHTML = ARROW_DOWN; - node.expanded = true; - if (setFocus) { - $(node.expandToggle).focus(); - } - } - } - } - - const glowEffect = function(n,duration) { - n.addClass('glow').delay(duration).queue(function(next) { - $(this).removeClass('glow');next(); - }); - } - - const highlightAnchor = function() { - const aname = hashUrl(); - const anchor = $(aname); - gotoAnchor(anchor,aname); - } - - const selectAndHighlight = function(hash,n) { - let a; - if (hash) { - const link=stripPath(pathName())+':'+hash.substring(1); - a=$('.item a[class$="'+link+'"]'); - } - if (a && a.length) { - a.parent().parent().addClass('selected'); - a.parent().parent().attr('id','selected'); - highlightAnchor(); - } else if (n) { - $(n.itemDiv).addClass('selected'); - $(n.itemDiv).attr('id','selected'); - } - let topOffset=5; - if ($('#nav-tree-contents .item:first').hasClass('selected')) { - topOffset+=25; - } - $('#nav-sync').css('top',topOffset+'px'); - showRoot(); - } - - const showNode = function(o, node, index, hash) { - if (node && node.childrenData) { - if (typeof(node.childrenData)==='string') { - const varName = node.childrenData; - getScript(node.relpath+varName,function() { - node.childrenData = getData(varName); - showNode(o,node,index,hash); - }); - } else { - if (!node.childrenVisited) { - getNode(o, node); - } - $(node.getChildrenUL()).css({'display':'block'}); - node.plus_img.innerHTML = ARROW_DOWN; - node.expanded = true; - const n = node.children[o.breadcrumbs[index]]; - if (index+11 ? '#'+parts[1].replace(/[^\w-]/g,'') : ''; - } - if (hash.match(/^#l\d+$/)) { - const anchor=$('a[name='+hash.substring(1)+']'); - glowEffect(anchor.parent(),1000); // line number - hash=''; // strip line number anchors - } - const url=root+hash; - let i=-1; - while (NAVTREEINDEX[i+1]<=url) i++; - if (i==-1) { i=0; root=NAVTREE[0][1]; } // fallback: show index - if (navTreeSubIndices[i]) { - gotoNode(o,i,root,hash,relpath) - } else { - getScript(relpath+'navtreeindex'+i,function() { - navTreeSubIndices[i] = eval('NAVTREEINDEX'+i); - if (navTreeSubIndices[i]) { - gotoNode(o,i,root,hash,relpath); - } - }); - } - } - - const showSyncOff = function(n,relpath) { - n.html(''); - } - - const showSyncOn = function(n,relpath) { - n.html(''); - } - - const o = { - toroot : toroot, - node : { - childrenData : NAVTREE, - children : [], - childrenUL : document.createElement("ul"), - getChildrenUL : function() { return this.childrenUL }, - li : document.getElementById("nav-tree-contents"), - depth : 0, - relpath : relpath, - expanded : false, - isLast : true, - plus_img : document.createElement("span"), - }, - }; - o.node.li.appendChild(o.node.childrenUL); - o.node.plus_img.className = 'arrow'; - o.node.plus_img.innerHTML = ARROW_RIGHT; - - const navSync = $('#nav-sync'); - if (cachedLink()) { - showSyncOff(navSync,relpath); - navSync.removeClass('sync'); - } else { - showSyncOn(navSync,relpath); - } - - navSync.click(() => { - const navSync = $('#nav-sync'); - if (navSync.hasClass('sync')) { - navSync.removeClass('sync'); - showSyncOff(navSync,relpath); - storeLink(stripPath2(pathName())+hashUrl()); - } else { - navSync.addClass('sync'); - showSyncOn(navSync,relpath); - deleteLink(); - } - }); - - navTo(o,toroot,hashUrl(),relpath); - showRoot(); - - $(window).bind('hashchange', () => { - if (!animationInProgress) { - if (window.location.hash && window.location.hash.length>1) { - let a; - if ($(location).attr('hash')) { - const clslink=stripPath(pathName())+':'+hashValue(); - a=$('.item a[class$="'+clslink.replace(/ - - - - - - - - diff --git a/docs/manual/plusd.svg b/docs/manual/plusd.svg deleted file mode 100644 index 0c65bfe..0000000 --- a/docs/manual/plusd.svg +++ /dev/null @@ -1,9 +0,0 @@ - - - - - - - - - diff --git a/docs/manual/resize.js b/docs/manual/resize.js deleted file mode 100644 index 7d8cdc7..0000000 --- a/docs/manual/resize.js +++ /dev/null @@ -1,145 +0,0 @@ -/* - @licstart The following is the entire license notice for the JavaScript code in this file. - - The MIT License (MIT) - - Copyright (C) 1997-2020 by Dimitri van Heesch - - Permission is hereby granted, free of charge, to any person obtaining a copy of this software - and associated documentation files (the "Software"), to deal in the Software without restriction, - including without limitation the rights to use, copy, modify, merge, publish, distribute, - sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is - furnished to do so, subject to the following conditions: - - The above copyright notice and this permission notice shall be included in all copies or - substantial portions of the Software. - - THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING - BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND - NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, - DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, - OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. - - @licend The above is the entire license notice for the JavaScript code in this file - */ - -function initResizable(treeview) { - let sidenav,navtree,content,header,footer,barWidth=6; - const RESIZE_COOKIE_NAME = ''+'width'; - - function resizeWidth() { - const sidenavWidth = $(sidenav).outerWidth(); - content.css({marginLeft:parseInt(sidenavWidth)+"px"}); - if (typeof page_layout!=='undefined' && page_layout==1) { - footer.css({marginLeft:parseInt(sidenavWidth)+"px"}); - } - Cookie.writeSetting(RESIZE_COOKIE_NAME,sidenavWidth-barWidth); - } - - function restoreWidth(navWidth) { - content.css({marginLeft:parseInt(navWidth)+barWidth+"px"}); - if (typeof page_layout!=='undefined' && page_layout==1) { - footer.css({marginLeft:parseInt(navWidth)+barWidth+"px"}); - } - sidenav.css({width:navWidth + "px"}); - } - - function resizeHeight(treeview) { - const headerHeight = header.outerHeight(); - const windowHeight = $(window).height(); - let contentHeight; - if (treeview) - { - const footerHeight = footer.outerHeight(); - let navtreeHeight,sideNavHeight; - if (typeof page_layout==='undefined' || page_layout==0) { /* DISABLE_INDEX=NO */ - contentHeight = windowHeight - headerHeight - footerHeight; - navtreeHeight = contentHeight; - sideNavHeight = contentHeight; - } else if (page_layout==1) { /* DISABLE_INDEX=YES */ - contentHeight = windowHeight - footerHeight; - navtreeHeight = windowHeight - headerHeight; - sideNavHeight = windowHeight; - } - navtree.css({height:navtreeHeight + "px"}); - sidenav.css({height:sideNavHeight + "px"}); - } - else - { - contentHeight = windowHeight - headerHeight; - } - content.css({height:contentHeight + "px"}); - if (location.hash.slice(1)) { - (document.getElementById(location.hash.slice(1))||document.body).scrollIntoView(); - } - } - - function collapseExpand() { - let newWidth; - if (sidenav.width()>0) { - newWidth=0; - } else { - const width = Cookie.readSetting(RESIZE_COOKIE_NAME,250); - newWidth = (width>250 && width<$(window).width()) ? width : 250; - } - restoreWidth(newWidth); - const sidenavWidth = $(sidenav).outerWidth(); - Cookie.writeSetting(RESIZE_COOKIE_NAME,sidenavWidth-barWidth); - } - - header = $("#top"); - content = $("#doc-content"); - footer = $("#nav-path"); - sidenav = $("#side-nav"); - if (!treeview) { -// title = $("#titlearea"); -// titleH = $(title).height(); -// let animating = false; -// content.on("scroll", function() { -// slideOpts = { duration: 200, -// step: function() { -// contentHeight = $(window).height() - header.outerHeight(); -// content.css({ height : contentHeight + "px" }); -// }, -// done: function() { animating=false; } -// }; -// if (content.scrollTop()>titleH && title.css('display')!='none' && !animating) { -// title.slideUp(slideOpts); -// animating=true; -// } else if (content.scrollTop()<=titleH && title.css('display')=='none' && !animating) { -// title.slideDown(slideOpts); -// animating=true; -// } -// }); - } else { - navtree = $("#nav-tree"); - $(".side-nav-resizable").resizable({resize: function(e, ui) { resizeWidth(); } }); - $(sidenav).resizable({ minWidth: 0 }); - } - $(window).resize(function() { resizeHeight(treeview); }); - if (treeview) - { - const device = navigator.userAgent.toLowerCase(); - const touch_device = device.match(/(iphone|ipod|ipad|android)/); - if (touch_device) { /* wider split bar for touch only devices */ - $(sidenav).css({ paddingRight:'20px' }); - $('.ui-resizable-e').css({ width:'20px' }); - $('#nav-sync').css({ right:'34px' }); - barWidth=20; - } - const width = Cookie.readSetting(RESIZE_COOKIE_NAME,250); - if (width) { restoreWidth(width); } else { resizeWidth(); } - } - resizeHeight(treeview); - const url = location.href; - const i=url.indexOf("#"); - if (i>=0) window.location.hash=url.substr(i); - const _preventDefault = function(evt) { evt.preventDefault(); }; - if (treeview) - { - $("#splitbar").bind("dragstart", _preventDefault).bind("selectstart", _preventDefault); - $(".ui-resizable-handle").dblclick(collapseExpand); - } - $(window).on('load',resizeHeight); -} -/* @license-end */ diff --git a/docs/manual/search/all_0.js b/docs/manual/search/all_0.js deleted file mode 100644 index dc433d2..0000000 --- a/docs/manual/search/all_0.js +++ /dev/null @@ -1,6 +0,0 @@ -var searchData= -[ - ['a2de_0',['A2DE',['../classbayesnet_1_1_a2_d_e.html',1,'bayesnet']]], - ['aode_1',['AODE',['../classbayesnet_1_1_a_o_d_e.html',1,'bayesnet']]], - ['aodeld_2',['AODELd',['../classbayesnet_1_1_a_o_d_e_ld.html',1,'bayesnet']]] -]; diff --git a/docs/manual/search/all_1.js b/docs/manual/search/all_1.js deleted file mode 100644 index 52180aa..0000000 --- a/docs/manual/search/all_1.js +++ /dev/null @@ -1,7 +0,0 @@ -var searchData= -[ - ['baseclassifier_0',['BaseClassifier',['../classbayesnet_1_1_base_classifier.html',1,'bayesnet']]], - ['boost_1',['Boost',['../classbayesnet_1_1_boost.html',1,'bayesnet']]], - ['boosta2de_2',['BoostA2DE',['../classbayesnet_1_1_boost_a2_d_e.html',1,'bayesnet']]], - ['boostaode_3',['BoostAODE',['../classbayesnet_1_1_boost_a_o_d_e.html',1,'bayesnet']]] -]; diff --git a/docs/manual/search/all_2.js b/docs/manual/search/all_2.js deleted file mode 100644 index 6aed91c..0000000 --- a/docs/manual/search/all_2.js +++ /dev/null @@ -1,4 +0,0 @@ -var searchData= -[ - ['classifier_0',['Classifier',['../classbayesnet_1_1_classifier.html',1,'bayesnet']]] -]; diff --git a/docs/manual/search/all_3.js b/docs/manual/search/all_3.js deleted file mode 100644 index f513172..0000000 --- a/docs/manual/search/all_3.js +++ /dev/null @@ -1,4 +0,0 @@ -var searchData= -[ - ['ensemble_0',['Ensemble',['../classbayesnet_1_1_ensemble.html',1,'bayesnet']]] -]; diff --git a/docs/manual/search/all_4.js b/docs/manual/search/all_4.js deleted file mode 100644 index 721cf82..0000000 --- a/docs/manual/search/all_4.js +++ /dev/null @@ -1,5 +0,0 @@ -var searchData= -[ - ['kdb_0',['KDB',['../classbayesnet_1_1_k_d_b.html',1,'bayesnet']]], - ['kdbld_1',['KDBLd',['../classbayesnet_1_1_k_d_b_ld.html',1,'bayesnet']]] -]; diff --git a/docs/manual/search/all_5.js b/docs/manual/search/all_5.js deleted file mode 100644 index 5efa2bd..0000000 --- a/docs/manual/search/all_5.js +++ /dev/null @@ -1,5 +0,0 @@ -var searchData= -[ - ['network_0',['Network',['../classbayesnet_1_1_network.html',1,'bayesnet']]], - ['node_1',['Node',['../classbayesnet_1_1_node.html',1,'bayesnet']]] -]; diff --git a/docs/manual/search/all_6.js b/docs/manual/search/all_6.js deleted file mode 100644 index 4e9c1ce..0000000 --- a/docs/manual/search/all_6.js +++ /dev/null @@ -1,4 +0,0 @@ -var searchData= -[ - ['proposal_0',['Proposal',['../classbayesnet_1_1_proposal.html',1,'bayesnet']]] -]; diff --git a/docs/manual/search/all_7.js b/docs/manual/search/all_7.js deleted file mode 100644 index 3c7da60..0000000 --- a/docs/manual/search/all_7.js +++ /dev/null @@ -1,6 +0,0 @@ -var searchData= -[ - ['spnde_0',['SPnDE',['../classbayesnet_1_1_s_pn_d_e.html',1,'bayesnet']]], - ['spode_1',['SPODE',['../classbayesnet_1_1_s_p_o_d_e.html',1,'bayesnet']]], - ['spodeld_2',['SPODELd',['../classbayesnet_1_1_s_p_o_d_e_ld.html',1,'bayesnet']]] -]; diff --git a/docs/manual/search/all_8.js b/docs/manual/search/all_8.js deleted file mode 100644 index 59e0076..0000000 --- a/docs/manual/search/all_8.js +++ /dev/null @@ -1,5 +0,0 @@ -var searchData= -[ - ['tan_0',['TAN',['../classbayesnet_1_1_t_a_n.html',1,'bayesnet']]], - ['tanld_1',['TANLd',['../classbayesnet_1_1_t_a_n_ld.html',1,'bayesnet']]] -]; diff --git a/docs/manual/search/classes_0.js b/docs/manual/search/classes_0.js deleted file mode 100644 index dc433d2..0000000 --- a/docs/manual/search/classes_0.js +++ /dev/null @@ -1,6 +0,0 @@ -var searchData= -[ - ['a2de_0',['A2DE',['../classbayesnet_1_1_a2_d_e.html',1,'bayesnet']]], - ['aode_1',['AODE',['../classbayesnet_1_1_a_o_d_e.html',1,'bayesnet']]], - ['aodeld_2',['AODELd',['../classbayesnet_1_1_a_o_d_e_ld.html',1,'bayesnet']]] -]; diff --git a/docs/manual/search/classes_1.js b/docs/manual/search/classes_1.js deleted file mode 100644 index 52180aa..0000000 --- a/docs/manual/search/classes_1.js +++ /dev/null @@ -1,7 +0,0 @@ -var searchData= -[ - ['baseclassifier_0',['BaseClassifier',['../classbayesnet_1_1_base_classifier.html',1,'bayesnet']]], - ['boost_1',['Boost',['../classbayesnet_1_1_boost.html',1,'bayesnet']]], - ['boosta2de_2',['BoostA2DE',['../classbayesnet_1_1_boost_a2_d_e.html',1,'bayesnet']]], - ['boostaode_3',['BoostAODE',['../classbayesnet_1_1_boost_a_o_d_e.html',1,'bayesnet']]] -]; diff --git a/docs/manual/search/classes_2.js b/docs/manual/search/classes_2.js deleted file mode 100644 index 6aed91c..0000000 --- a/docs/manual/search/classes_2.js +++ /dev/null @@ -1,4 +0,0 @@ -var searchData= -[ - ['classifier_0',['Classifier',['../classbayesnet_1_1_classifier.html',1,'bayesnet']]] -]; diff --git a/docs/manual/search/classes_3.js b/docs/manual/search/classes_3.js deleted file mode 100644 index f513172..0000000 --- a/docs/manual/search/classes_3.js +++ /dev/null @@ -1,4 +0,0 @@ -var searchData= -[ - ['ensemble_0',['Ensemble',['../classbayesnet_1_1_ensemble.html',1,'bayesnet']]] -]; diff --git a/docs/manual/search/classes_4.js b/docs/manual/search/classes_4.js deleted file mode 100644 index 721cf82..0000000 --- a/docs/manual/search/classes_4.js +++ /dev/null @@ -1,5 +0,0 @@ -var searchData= -[ - ['kdb_0',['KDB',['../classbayesnet_1_1_k_d_b.html',1,'bayesnet']]], - ['kdbld_1',['KDBLd',['../classbayesnet_1_1_k_d_b_ld.html',1,'bayesnet']]] -]; diff --git a/docs/manual/search/classes_5.js b/docs/manual/search/classes_5.js deleted file mode 100644 index 5efa2bd..0000000 --- a/docs/manual/search/classes_5.js +++ /dev/null @@ -1,5 +0,0 @@ -var searchData= -[ - ['network_0',['Network',['../classbayesnet_1_1_network.html',1,'bayesnet']]], - ['node_1',['Node',['../classbayesnet_1_1_node.html',1,'bayesnet']]] -]; diff --git a/docs/manual/search/classes_6.js b/docs/manual/search/classes_6.js deleted file mode 100644 index 4e9c1ce..0000000 --- a/docs/manual/search/classes_6.js +++ /dev/null @@ -1,4 +0,0 @@ -var searchData= -[ - ['proposal_0',['Proposal',['../classbayesnet_1_1_proposal.html',1,'bayesnet']]] -]; diff --git a/docs/manual/search/classes_7.js b/docs/manual/search/classes_7.js deleted file mode 100644 index 3c7da60..0000000 --- a/docs/manual/search/classes_7.js +++ /dev/null @@ -1,6 +0,0 @@ -var searchData= -[ - ['spnde_0',['SPnDE',['../classbayesnet_1_1_s_pn_d_e.html',1,'bayesnet']]], - ['spode_1',['SPODE',['../classbayesnet_1_1_s_p_o_d_e.html',1,'bayesnet']]], - ['spodeld_2',['SPODELd',['../classbayesnet_1_1_s_p_o_d_e_ld.html',1,'bayesnet']]] -]; diff --git a/docs/manual/search/classes_8.js b/docs/manual/search/classes_8.js deleted file mode 100644 index 59e0076..0000000 --- a/docs/manual/search/classes_8.js +++ /dev/null @@ -1,5 +0,0 @@ -var searchData= -[ - ['tan_0',['TAN',['../classbayesnet_1_1_t_a_n.html',1,'bayesnet']]], - ['tanld_1',['TANLd',['../classbayesnet_1_1_t_a_n_ld.html',1,'bayesnet']]] -]; diff --git a/docs/manual/search/close.svg b/docs/manual/search/close.svg deleted file mode 100644 index 337d6cc..0000000 --- a/docs/manual/search/close.svg +++ /dev/null @@ -1,18 +0,0 @@ - - - - - - diff --git a/docs/manual/search/mag.svg b/docs/manual/search/mag.svg deleted file mode 100644 index ffb6cf0..0000000 --- a/docs/manual/search/mag.svg +++ /dev/null @@ -1,24 +0,0 @@ - - - - - - - diff --git a/docs/manual/search/mag_d.svg b/docs/manual/search/mag_d.svg deleted file mode 100644 index 4122773..0000000 --- a/docs/manual/search/mag_d.svg +++ /dev/null @@ -1,24 +0,0 @@ - - - - - - - diff --git a/docs/manual/search/mag_sel.svg b/docs/manual/search/mag_sel.svg deleted file mode 100644 index 553dba8..0000000 --- a/docs/manual/search/mag_sel.svg +++ /dev/null @@ -1,31 +0,0 @@ - - - - - - - - - diff --git a/docs/manual/search/mag_seld.svg b/docs/manual/search/mag_seld.svg deleted file mode 100644 index c906f84..0000000 --- a/docs/manual/search/mag_seld.svg +++ /dev/null @@ -1,31 +0,0 @@ - - - - - - - - - diff --git a/docs/manual/search/search.css b/docs/manual/search/search.css deleted file mode 100644 index 19f76f9..0000000 --- a/docs/manual/search/search.css +++ /dev/null @@ -1,291 +0,0 @@ -/*---------------- Search Box positioning */ - -#main-menu > li:last-child { - /* This
  • object is the parent of the search bar */ - display: flex; - justify-content: center; - align-items: center; - height: 36px; - margin-right: 1em; -} - -/*---------------- Search box styling */ - -.SRPage * { - font-weight: normal; - line-height: normal; -} - -dark-mode-toggle { - margin-left: 5px; - display: flex; - float: right; -} - -#MSearchBox { - display: inline-block; - white-space : nowrap; - background: var(--search-background-color); - border-radius: 0.65em; - box-shadow: var(--search-box-shadow); - z-index: 102; -} - -#MSearchBox .left { - display: inline-block; - vertical-align: middle; - height: 1.4em; -} - -#MSearchSelect { - display: inline-block; - vertical-align: middle; - width: 20px; - height: 19px; - background-image: var(--search-magnification-select-image); - margin: 0 0 0 0.3em; - padding: 0; -} - -#MSearchSelectExt { - display: inline-block; - vertical-align: middle; - width: 10px; - height: 19px; - background-image: var(--search-magnification-image); - margin: 0 0 0 0.5em; - padding: 0; -} - - -#MSearchField { - display: inline-block; - vertical-align: middle; - width: 7.5em; - height: 19px; - margin: 0 0.15em; - padding: 0; - line-height: 1em; - border:none; - color: var(--search-foreground-color); - outline: none; - font-family: var(--font-family-search); - -webkit-border-radius: 0px; - border-radius: 0px; - background: none; -} - -@media(hover: none) { - /* to avoid zooming on iOS */ - #MSearchField { - font-size: 16px; - } -} - -#MSearchBox .right { - display: inline-block; - vertical-align: middle; - width: 1.4em; - height: 1.4em; -} - -#MSearchClose { - display: none; - font-size: inherit; - background : none; - border: none; - margin: 0; - padding: 0; - outline: none; - -} - -#MSearchCloseImg { - padding: 0.3em; - margin: 0; -} - -.MSearchBoxActive #MSearchField { - color: var(--search-active-color); -} - - - -/*---------------- Search filter selection */ - -#MSearchSelectWindow { - display: none; - position: absolute; - left: 0; top: 0; - border: 1px solid var(--search-filter-border-color); - background-color: var(--search-filter-background-color); - z-index: 10001; - padding-top: 4px; - padding-bottom: 4px; - -moz-border-radius: 4px; - -webkit-border-top-left-radius: 4px; - -webkit-border-top-right-radius: 4px; - -webkit-border-bottom-left-radius: 4px; - -webkit-border-bottom-right-radius: 4px; - -webkit-box-shadow: 5px 5px 5px rgba(0, 0, 0, 0.15); -} - -.SelectItem { - font: 8pt var(--font-family-search); - padding-left: 2px; - padding-right: 12px; - border: 0px; -} - -span.SelectionMark { - margin-right: 4px; - font-family: var(--font-family-monospace); - outline-style: none; - text-decoration: none; -} - -a.SelectItem { - display: block; - outline-style: none; - color: var(--search-filter-foreground-color); - text-decoration: none; - padding-left: 6px; - padding-right: 12px; -} - -a.SelectItem:focus, -a.SelectItem:active { - color: var(--search-filter-foreground-color); - outline-style: none; - text-decoration: none; -} - -a.SelectItem:hover { - color: var(--search-filter-highlight-text-color); - background-color: var(--search-filter-highlight-bg-color); - outline-style: none; - text-decoration: none; - cursor: pointer; - display: block; -} - -/*---------------- Search results window */ - -iframe#MSearchResults { - /*width: 60ex;*/ - height: 15em; -} - -#MSearchResultsWindow { - display: none; - position: absolute; - left: 0; top: 0; - border: 1px solid var(--search-results-border-color); - background-color: var(--search-results-background-color); - z-index:10000; - width: 300px; - height: 400px; - overflow: auto; -} - -/* ----------------------------------- */ - - -#SRIndex { - clear:both; -} - -.SREntry { - font-size: 10pt; - padding-left: 1ex; -} - -.SRPage .SREntry { - font-size: 8pt; - padding: 1px 5px; -} - -div.SRPage { - margin: 5px 2px; - background-color: var(--search-results-background-color); -} - -.SRChildren { - padding-left: 3ex; padding-bottom: .5em -} - -.SRPage .SRChildren { - display: none; -} - -.SRSymbol { - font-weight: bold; - color: var(--search-results-foreground-color); - font-family: var(--font-family-search); - text-decoration: none; - outline: none; -} - -a.SRScope { - display: block; - color: var(--search-results-foreground-color); - font-family: var(--font-family-search); - font-size: 8pt; - text-decoration: none; - outline: none; -} - -a.SRSymbol:focus, a.SRSymbol:active, -a.SRScope:focus, a.SRScope:active { - text-decoration: underline; -} - -span.SRScope { - padding-left: 4px; - font-family: var(--font-family-search); -} - -.SRPage .SRStatus { - padding: 2px 5px; - font-size: 8pt; - font-style: italic; - font-family: var(--font-family-search); -} - -.SRResult { - display: none; -} - -div.searchresults { - margin-left: 10px; - margin-right: 10px; -} - -/*---------------- External search page results */ - -.pages b { - color: white; - padding: 5px 5px 3px 5px; - background-image: var(--nav-gradient-active-image-parent); - background-repeat: repeat-x; - text-shadow: 0 1px 1px #000000; -} - -.pages { - line-height: 17px; - margin-left: 4px; - text-decoration: none; -} - -.hl { - font-weight: bold; -} - -#searchresults { - margin-bottom: 20px; -} - -.searchpages { - margin-top: 10px; -} - diff --git a/docs/manual/search/search.js b/docs/manual/search/search.js deleted file mode 100644 index 666af01..0000000 --- a/docs/manual/search/search.js +++ /dev/null @@ -1,694 +0,0 @@ -/* - @licstart The following is the entire license notice for the JavaScript code in this file. - - The MIT License (MIT) - - Copyright (C) 1997-2020 by Dimitri van Heesch - - Permission is hereby granted, free of charge, to any person obtaining a copy of this software - and associated documentation files (the "Software"), to deal in the Software without restriction, - including without limitation the rights to use, copy, modify, merge, publish, distribute, - sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is - furnished to do so, subject to the following conditions: - - The above copyright notice and this permission notice shall be included in all copies or - substantial portions of the Software. - - THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING - BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND - NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, - DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, - OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. - - @licend The above is the entire license notice for the JavaScript code in this file - */ -const SEARCH_COOKIE_NAME = ''+'search_grp'; - -const searchResults = new SearchResults(); - -/* A class handling everything associated with the search panel. - - Parameters: - name - The name of the global variable that will be - storing this instance. Is needed to be able to set timeouts. - resultPath - path to use for external files -*/ -function SearchBox(name, resultsPath, extension) { - if (!name || !resultsPath) { alert("Missing parameters to SearchBox."); } - if (!extension || extension == "") { extension = ".html"; } - - function getXPos(item) { - let x = 0; - if (item.offsetWidth) { - while (item && item!=document.body) { - x += item.offsetLeft; - item = item.offsetParent; - } - } - return x; - } - - function getYPos(item) { - let y = 0; - if (item.offsetWidth) { - while (item && item!=document.body) { - y += item.offsetTop; - item = item.offsetParent; - } - } - return y; - } - - // ---------- Instance variables - this.name = name; - this.resultsPath = resultsPath; - this.keyTimeout = 0; - this.keyTimeoutLength = 500; - this.closeSelectionTimeout = 300; - this.lastSearchValue = ""; - this.lastResultsPage = ""; - this.hideTimeout = 0; - this.searchIndex = 0; - this.searchActive = false; - this.extension = extension; - - // ----------- DOM Elements - - this.DOMSearchField = () => document.getElementById("MSearchField"); - this.DOMSearchSelect = () => document.getElementById("MSearchSelect"); - this.DOMSearchSelectWindow = () => document.getElementById("MSearchSelectWindow"); - this.DOMPopupSearchResults = () => document.getElementById("MSearchResults"); - this.DOMPopupSearchResultsWindow = () => document.getElementById("MSearchResultsWindow"); - this.DOMSearchClose = () => document.getElementById("MSearchClose"); - this.DOMSearchBox = () => document.getElementById("MSearchBox"); - - // ------------ Event Handlers - - // Called when focus is added or removed from the search field. - this.OnSearchFieldFocus = function(isActive) { - this.Activate(isActive); - } - - this.OnSearchSelectShow = function() { - const searchSelectWindow = this.DOMSearchSelectWindow(); - const searchField = this.DOMSearchSelect(); - - const left = getXPos(searchField); - const top = getYPos(searchField) + searchField.offsetHeight; - - // show search selection popup - searchSelectWindow.style.display='block'; - searchSelectWindow.style.left = left + 'px'; - searchSelectWindow.style.top = top + 'px'; - - // stop selection hide timer - if (this.hideTimeout) { - clearTimeout(this.hideTimeout); - this.hideTimeout=0; - } - return false; // to avoid "image drag" default event - } - - this.OnSearchSelectHide = function() { - this.hideTimeout = setTimeout(this.CloseSelectionWindow.bind(this), - this.closeSelectionTimeout); - } - - // Called when the content of the search field is changed. - this.OnSearchFieldChange = function(evt) { - if (this.keyTimeout) { // kill running timer - clearTimeout(this.keyTimeout); - this.keyTimeout = 0; - } - - const e = evt ? evt : window.event; // for IE - if (e.keyCode==40 || e.keyCode==13) { - if (e.shiftKey==1) { - this.OnSearchSelectShow(); - const win=this.DOMSearchSelectWindow(); - for (let i=0;i do a search - this.Search(); - } - } - - this.OnSearchSelectKey = function(evt) { - const e = (evt) ? evt : window.event; // for IE - if (e.keyCode==40 && this.searchIndex0) { // Up - this.searchIndex--; - this.OnSelectItem(this.searchIndex); - } else if (e.keyCode==13 || e.keyCode==27) { - e.stopPropagation(); - this.OnSelectItem(this.searchIndex); - this.CloseSelectionWindow(); - this.DOMSearchField().focus(); - } - return false; - } - - // --------- Actions - - // Closes the results window. - this.CloseResultsWindow = function() { - this.DOMPopupSearchResultsWindow().style.display = 'none'; - this.DOMSearchClose().style.display = 'none'; - this.Activate(false); - } - - this.CloseSelectionWindow = function() { - this.DOMSearchSelectWindow().style.display = 'none'; - } - - // Performs a search. - this.Search = function() { - this.keyTimeout = 0; - - // strip leading whitespace - const searchValue = this.DOMSearchField().value.replace(/^ +/, ""); - - const code = searchValue.toLowerCase().charCodeAt(0); - let idxChar = searchValue.substr(0, 1).toLowerCase(); - if ( 0xD800 <= code && code <= 0xDBFF && searchValue > 1) { // surrogate pair - idxChar = searchValue.substr(0, 2); - } - - let jsFile; - let idx = indexSectionsWithContent[this.searchIndex].indexOf(idxChar); - if (idx!=-1) { - const hexCode=idx.toString(16); - jsFile = this.resultsPath + indexSectionNames[this.searchIndex] + '_' + hexCode + '.js'; - } - - const loadJS = function(url, impl, loc) { - const scriptTag = document.createElement('script'); - scriptTag.src = url; - scriptTag.onload = impl; - scriptTag.onreadystatechange = impl; - loc.appendChild(scriptTag); - } - - const domPopupSearchResultsWindow = this.DOMPopupSearchResultsWindow(); - const domSearchBox = this.DOMSearchBox(); - const domPopupSearchResults = this.DOMPopupSearchResults(); - const domSearchClose = this.DOMSearchClose(); - const resultsPath = this.resultsPath; - - const handleResults = function() { - document.getElementById("Loading").style.display="none"; - if (typeof searchData !== 'undefined') { - createResults(resultsPath); - document.getElementById("NoMatches").style.display="none"; - } - - if (idx!=-1) { - searchResults.Search(searchValue); - } else { // no file with search results => force empty search results - searchResults.Search('===='); - } - - if (domPopupSearchResultsWindow.style.display!='block') { - domSearchClose.style.display = 'inline-block'; - let left = getXPos(domSearchBox) + 150; - let top = getYPos(domSearchBox) + 20; - domPopupSearchResultsWindow.style.display = 'block'; - left -= domPopupSearchResults.offsetWidth; - const maxWidth = document.body.clientWidth; - const maxHeight = document.body.clientHeight; - let width = 300; - if (left<10) left=10; - if (width+left+8>maxWidth) width=maxWidth-left-8; - let height = 400; - if (height+top+8>maxHeight) height=maxHeight-top-8; - domPopupSearchResultsWindow.style.top = top + 'px'; - domPopupSearchResultsWindow.style.left = left + 'px'; - domPopupSearchResultsWindow.style.width = width + 'px'; - domPopupSearchResultsWindow.style.height = height + 'px'; - } - } - - if (jsFile) { - loadJS(jsFile, handleResults, this.DOMPopupSearchResultsWindow()); - } else { - handleResults(); - } - - this.lastSearchValue = searchValue; - } - - // -------- Activation Functions - - // Activates or deactivates the search panel, resetting things to - // their default values if necessary. - this.Activate = function(isActive) { - if (isActive || // open it - this.DOMPopupSearchResultsWindow().style.display == 'block' - ) { - this.DOMSearchBox().className = 'MSearchBoxActive'; - this.searchActive = true; - } else if (!isActive) { // directly remove the panel - this.DOMSearchBox().className = 'MSearchBoxInactive'; - this.searchActive = false; - this.lastSearchValue = '' - this.lastResultsPage = ''; - this.DOMSearchField().value = ''; - } - } -} - -// ----------------------------------------------------------------------- - -// The class that handles everything on the search results page. -function SearchResults() { - - function convertToId(search) { - let result = ''; - for (let i=0;i. - this.lastMatchCount = 0; - this.lastKey = 0; - this.repeatOn = false; - - // Toggles the visibility of the passed element ID. - this.FindChildElement = function(id) { - const parentElement = document.getElementById(id); - let element = parentElement.firstChild; - - while (element && element!=parentElement) { - if (element.nodeName.toLowerCase() == 'div' && element.className == 'SRChildren') { - return element; - } - - if (element.nodeName.toLowerCase() == 'div' && element.hasChildNodes()) { - element = element.firstChild; - } else if (element.nextSibling) { - element = element.nextSibling; - } else { - do { - element = element.parentNode; - } - while (element && element!=parentElement && !element.nextSibling); - - if (element && element!=parentElement) { - element = element.nextSibling; - } - } - } - } - - this.Toggle = function(id) { - const element = this.FindChildElement(id); - if (element) { - if (element.style.display == 'block') { - element.style.display = 'none'; - } else { - element.style.display = 'block'; - } - } - } - - // Searches for the passed string. If there is no parameter, - // it takes it from the URL query. - // - // Always returns true, since other documents may try to call it - // and that may or may not be possible. - this.Search = function(search) { - if (!search) { // get search word from URL - search = window.location.search; - search = search.substring(1); // Remove the leading '?' - search = unescape(search); - } - - search = search.replace(/^ +/, ""); // strip leading spaces - search = search.replace(/ +$/, ""); // strip trailing spaces - search = search.toLowerCase(); - search = convertToId(search); - - const resultRows = document.getElementsByTagName("div"); - let matches = 0; - - let i = 0; - while (i < resultRows.length) { - const row = resultRows.item(i); - if (row.className == "SRResult") { - let rowMatchName = row.id.toLowerCase(); - rowMatchName = rowMatchName.replace(/^sr\d*_/, ''); // strip 'sr123_' - - if (search.length<=rowMatchName.length && - rowMatchName.substr(0, search.length)==search) { - row.style.display = 'block'; - matches++; - } else { - row.style.display = 'none'; - } - } - i++; - } - document.getElementById("Searching").style.display='none'; - if (matches == 0) { // no results - document.getElementById("NoMatches").style.display='block'; - } else { // at least one result - document.getElementById("NoMatches").style.display='none'; - } - this.lastMatchCount = matches; - return true; - } - - // return the first item with index index or higher that is visible - this.NavNext = function(index) { - let focusItem; - for (;;) { - const focusName = 'Item'+index; - focusItem = document.getElementById(focusName); - if (focusItem && focusItem.parentNode.parentNode.style.display=='block') { - break; - } else if (!focusItem) { // last element - break; - } - focusItem=null; - index++; - } - return focusItem; - } - - this.NavPrev = function(index) { - let focusItem; - for (;;) { - const focusName = 'Item'+index; - focusItem = document.getElementById(focusName); - if (focusItem && focusItem.parentNode.parentNode.style.display=='block') { - break; - } else if (!focusItem) { // last element - break; - } - focusItem=null; - index--; - } - return focusItem; - } - - this.ProcessKeys = function(e) { - if (e.type == "keydown") { - this.repeatOn = false; - this.lastKey = e.keyCode; - } else if (e.type == "keypress") { - if (!this.repeatOn) { - if (this.lastKey) this.repeatOn = true; - return false; // ignore first keypress after keydown - } - } else if (e.type == "keyup") { - this.lastKey = 0; - this.repeatOn = false; - } - return this.lastKey!=0; - } - - this.Nav = function(evt,itemIndex) { - const e = (evt) ? evt : window.event; // for IE - if (e.keyCode==13) return true; - if (!this.ProcessKeys(e)) return false; - - if (this.lastKey==38) { // Up - const newIndex = itemIndex-1; - let focusItem = this.NavPrev(newIndex); - if (focusItem) { - let child = this.FindChildElement(focusItem.parentNode.parentNode.id); - if (child && child.style.display == 'block') { // children visible - let n=0; - let tmpElem; - for (;;) { // search for last child - tmpElem = document.getElementById('Item'+newIndex+'_c'+n); - if (tmpElem) { - focusItem = tmpElem; - } else { // found it! - break; - } - n++; - } - } - } - if (focusItem) { - focusItem.focus(); - } else { // return focus to search field - document.getElementById("MSearchField").focus(); - } - } else if (this.lastKey==40) { // Down - const newIndex = itemIndex+1; - let focusItem; - const item = document.getElementById('Item'+itemIndex); - const elem = this.FindChildElement(item.parentNode.parentNode.id); - if (elem && elem.style.display == 'block') { // children visible - focusItem = document.getElementById('Item'+itemIndex+'_c0'); - } - if (!focusItem) focusItem = this.NavNext(newIndex); - if (focusItem) focusItem.focus(); - } else if (this.lastKey==39) { // Right - const item = document.getElementById('Item'+itemIndex); - const elem = this.FindChildElement(item.parentNode.parentNode.id); - if (elem) elem.style.display = 'block'; - } else if (this.lastKey==37) { // Left - const item = document.getElementById('Item'+itemIndex); - const elem = this.FindChildElement(item.parentNode.parentNode.id); - if (elem) elem.style.display = 'none'; - } else if (this.lastKey==27) { // Escape - e.stopPropagation(); - searchBox.CloseResultsWindow(); - document.getElementById("MSearchField").focus(); - } else if (this.lastKey==13) { // Enter - return true; - } - return false; - } - - this.NavChild = function(evt,itemIndex,childIndex) { - const e = (evt) ? evt : window.event; // for IE - if (e.keyCode==13) return true; - if (!this.ProcessKeys(e)) return false; - - if (this.lastKey==38) { // Up - if (childIndex>0) { - const newIndex = childIndex-1; - document.getElementById('Item'+itemIndex+'_c'+newIndex).focus(); - } else { // already at first child, jump to parent - document.getElementById('Item'+itemIndex).focus(); - } - } else if (this.lastKey==40) { // Down - const newIndex = childIndex+1; - let elem = document.getElementById('Item'+itemIndex+'_c'+newIndex); - if (!elem) { // last child, jump to parent next parent - elem = this.NavNext(itemIndex+1); - } - if (elem) { - elem.focus(); - } - } else if (this.lastKey==27) { // Escape - e.stopPropagation(); - searchBox.CloseResultsWindow(); - document.getElementById("MSearchField").focus(); - } else if (this.lastKey==13) { // Enter - return true; - } - return false; - } -} - -function createResults(resultsPath) { - - function setKeyActions(elem,action) { - elem.setAttribute('onkeydown',action); - elem.setAttribute('onkeypress',action); - elem.setAttribute('onkeyup',action); - } - - function setClassAttr(elem,attr) { - elem.setAttribute('class',attr); - elem.setAttribute('className',attr); - } - - const results = document.getElementById("SRResults"); - results.innerHTML = ''; - searchData.forEach((elem,index) => { - const id = elem[0]; - const srResult = document.createElement('div'); - srResult.setAttribute('id','SR_'+id); - setClassAttr(srResult,'SRResult'); - const srEntry = document.createElement('div'); - setClassAttr(srEntry,'SREntry'); - const srLink = document.createElement('a'); - srLink.setAttribute('id','Item'+index); - setKeyActions(srLink,'return searchResults.Nav(event,'+index+')'); - setClassAttr(srLink,'SRSymbol'); - srLink.innerHTML = elem[1][0]; - srEntry.appendChild(srLink); - if (elem[1].length==2) { // single result - srLink.setAttribute('href',resultsPath+elem[1][1][0]); - srLink.setAttribute('onclick','searchBox.CloseResultsWindow()'); - if (elem[1][1][1]) { - srLink.setAttribute('target','_parent'); - } else { - srLink.setAttribute('target','_blank'); - } - const srScope = document.createElement('span'); - setClassAttr(srScope,'SRScope'); - srScope.innerHTML = elem[1][1][2]; - srEntry.appendChild(srScope); - } else { // multiple results - srLink.setAttribute('href','javascript:searchResults.Toggle("SR_'+id+'")'); - const srChildren = document.createElement('div'); - 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