Update docs and help
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GPG Key ID:
46064262FD9A7ADE
@ -1,4 +1,4 @@
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compilation_database_dir: build_debug
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compilation_database_dir: build_Debug
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output_directory: diagrams
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diagrams:
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BayesNet:
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2
Makefile
2
Makefile
@ -172,7 +172,7 @@ docdir = ""
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doc-install: ## Install documentation
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@echo ">>> Installing documentation..."
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@if [ "$(docdir)" = "" ]; then \
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echo "docdir parameter has to be set when calling doc-install"; \
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echo "docdir parameter has to be set when calling doc-install, i.e. docdir=../bayesnet_help"; \
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exit 1; \
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fi
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@if [ ! -d $(docdir) ]; then \
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@ -8,6 +8,7 @@
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[](https://sonarcloud.io/summary/new_code?id=rmontanana_BayesNet)
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[](html/index.html)
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[](https://doi.org/10.5281/zenodo.14210344)
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Bayesian Network Classifiers library
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@ -1,36 +1,16 @@
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@startuml
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title clang-uml class diagram model
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class "bayesnet::Metrics" as C_0000736965376885623323
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class C_0000736965376885623323 #aliceblue;line:blue;line.dotted;text:blue {
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+Metrics() = default : void
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+Metrics(const torch::Tensor & samples, const std::vector<std::string> & features, const std::string & className, const int classNumStates) : void
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+Metrics(const std::vector<std::vector<int>> & vsamples, const std::vector<int> & labels, const std::vector<std::string> & features, const std::string & className, const int classNumStates) : void
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..
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+SelectKBestWeighted(const torch::Tensor & weights, bool ascending = false, unsigned int k = 0) : std::vector<int>
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+conditionalEdge(const torch::Tensor & weights) : torch::Tensor
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+conditionalEdgeWeights(std::vector<float> & weights) : std::vector<float>
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#doCombinations<T>(const std::vector<T> & source) : std::vector<std::pair<T, T> >
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#entropy(const torch::Tensor & feature, const torch::Tensor & weights) : double
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+getScoresKBest() const : std::vector<double>
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+maximumSpanningTree(const std::vector<std::string> & features, const torch::Tensor & weights, const int root) : std::vector<std::pair<int,int>>
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+mutualInformation(const torch::Tensor & firstFeature, const torch::Tensor & secondFeature, const torch::Tensor & weights) : double
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#pop_first<T>(std::vector<T> & v) : T
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__
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#className : std::string
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#features : std::vector<std::string>
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#samples : torch::Tensor
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}
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class "bayesnet::Node" as C_0001303524929067080934
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class C_0001303524929067080934 #aliceblue;line:blue;line.dotted;text:blue {
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class "bayesnet::Node" as C_0010428199432536647474
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class C_0010428199432536647474 #aliceblue;line:blue;line.dotted;text:blue {
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+Node(const std::string &) : void
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..
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+addChild(Node *) : void
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+addParent(Node *) : void
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+clear() : void
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+computeCPT(const torch::Tensor & dataset, const std::vector<std::string> & features, const double laplaceSmoothing, const torch::Tensor & weights) : void
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+computeCPT(const torch::Tensor & dataset, const std::vector<std::string> & features, const double smoothing, const torch::Tensor & weights) : void
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+getCPT() : torch::Tensor &
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+getChildren() : std::vector<Node *> &
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+getFactorValue(std::map<std::string,int> &) : float
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+getFactorValue(std::map<std::string,int> &) : double
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+getName() const : std::string
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+getNumStates() const : int
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+getParents() : std::vector<Node *> &
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@ -41,24 +21,29 @@ class C_0001303524929067080934 #aliceblue;line:blue;line.dotted;text:blue {
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+setNumStates(int) : void
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__
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}
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class "bayesnet::Network" as C_0001186707649890429575
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class C_0001186707649890429575 #aliceblue;line:blue;line.dotted;text:blue {
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enum "bayesnet::Smoothing_t" as C_0013393078277439680282
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enum C_0013393078277439680282 {
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NONE
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ORIGINAL
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LAPLACE
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CESTNIK
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}
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class "bayesnet::Network" as C_0009493661199123436603
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class C_0009493661199123436603 #aliceblue;line:blue;line.dotted;text:blue {
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+Network() : void
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+Network(float) : void
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+Network(const Network &) : void
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+~Network() = default : void
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..
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+addEdge(const std::string &, const std::string &) : void
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+addNode(const std::string &) : void
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+dump_cpt() const : std::string
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+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) : void
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+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
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+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
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+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, const Smoothing_t smoothing) : void
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+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, const Smoothing_t smoothing) : void
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+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, const Smoothing_t smoothing) : void
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+getClassName() const : std::string
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+getClassNumStates() const : int
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+getEdges() const : std::vector<std::pair<std::string,std::string>>
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+getFeatures() const : std::vector<std::string>
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+getMaxThreads() const : float
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+getNodes() : std::map<std::string,std::unique_ptr<Node>> &
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+getNumEdges() const : int
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+getSamples() : torch::Tensor &
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@ -76,21 +61,21 @@ class C_0001186707649890429575 #aliceblue;line:blue;line.dotted;text:blue {
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+version() : std::string
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__
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}
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enum "bayesnet::status_t" as C_0000738420730783851375
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enum C_0000738420730783851375 {
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enum "bayesnet::status_t" as C_0005907365846270811004
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enum C_0005907365846270811004 {
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NORMAL
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WARNING
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ERROR
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}
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abstract "bayesnet::BaseClassifier" as C_0000327135989451974539
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abstract C_0000327135989451974539 #aliceblue;line:blue;line.dotted;text:blue {
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abstract "bayesnet::BaseClassifier" as C_0002617087915615796317
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abstract C_0002617087915615796317 #aliceblue;line:blue;line.dotted;text:blue {
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+~BaseClassifier() = default : void
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..
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{abstract} +dump_cpt() const = 0 : std::string
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{abstract} +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 : BaseClassifier &
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{abstract} +fit(torch::Tensor & dataset, const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states) = 0 : BaseClassifier &
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{abstract} +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 : BaseClassifier &
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{abstract} +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 : BaseClassifier &
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{abstract} +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, const Smoothing_t smoothing) = 0 : BaseClassifier &
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{abstract} +fit(torch::Tensor & dataset, const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states, const Smoothing_t smoothing) = 0 : BaseClassifier &
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{abstract} +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, const Smoothing_t smoothing) = 0 : BaseClassifier &
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{abstract} +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, const Smoothing_t smoothing) = 0 : BaseClassifier &
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{abstract} +getClassNumStates() const = 0 : int
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{abstract} +getNotes() const = 0 : std::vector<std::string>
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{abstract} +getNumberOfEdges() const = 0 : int
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@ -109,12 +94,35 @@ abstract C_0000327135989451974539 #aliceblue;line:blue;line.dotted;text:blue {
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{abstract} +setHyperparameters(const nlohmann::json & hyperparameters) = 0 : void
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{abstract} +show() const = 0 : std::vector<std::string>
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{abstract} +topological_order() = 0 : std::vector<std::string>
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{abstract} #trainModel(const torch::Tensor & weights) = 0 : void
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{abstract} #trainModel(const torch::Tensor & weights, const Smoothing_t smoothing) = 0 : void
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__
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#validHyperparameters : std::vector<std::string>
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}
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abstract "bayesnet::Classifier" as C_0002043996622900301644
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abstract C_0002043996622900301644 #aliceblue;line:blue;line.dotted;text:blue {
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class "bayesnet::Metrics" as C_0005895723015084986588
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class C_0005895723015084986588 #aliceblue;line:blue;line.dotted;text:blue {
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+Metrics() = default : void
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+Metrics(const torch::Tensor & samples, const std::vector<std::string> & features, const std::string & className, const int classNumStates) : void
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+Metrics(const std::vector<std::vector<int>> & vsamples, const std::vector<int> & labels, const std::vector<std::string> & features, const std::string & className, const int classNumStates) : void
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..
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+SelectKBestWeighted(const torch::Tensor & weights, bool ascending = false, unsigned int k = 0) : std::vector<int>
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+SelectKPairs(const torch::Tensor & weights, std::vector<int> & featuresExcluded, bool ascending = false, unsigned int k = 0) : std::vector<std::pair<int,int>>
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+conditionalEdge(const torch::Tensor & weights) : torch::Tensor
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+conditionalEntropy(const torch::Tensor & firstFeature, const torch::Tensor & secondFeature, const torch::Tensor & labels, const torch::Tensor & weights) : double
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+conditionalMutualInformation(const torch::Tensor & firstFeature, const torch::Tensor & secondFeature, const torch::Tensor & labels, const torch::Tensor & weights) : double
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#doCombinations<T>(const std::vector<T> & source) : std::vector<std::pair<T, T> >
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+entropy(const torch::Tensor & feature, const torch::Tensor & weights) : double
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+getScoresKBest() const : std::vector<double>
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+getScoresKPairs() const : std::vector<std::pair<std::pair<int,int>,double>>
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+maximumSpanningTree(const std::vector<std::string> & features, const torch::Tensor & weights, const int root) : std::vector<std::pair<int,int>>
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+mutualInformation(const torch::Tensor & firstFeature, const torch::Tensor & secondFeature, const torch::Tensor & weights) : double
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#pop_first<T>(std::vector<T> & v) : T
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__
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#className : std::string
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#features : std::vector<std::string>
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#samples : torch::Tensor
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}
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abstract "bayesnet::Classifier" as C_0016351972983202413152
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abstract C_0016351972983202413152 #aliceblue;line:blue;line.dotted;text:blue {
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+Classifier(Network model) : void
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+~Classifier() = default : void
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..
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@ -123,10 +131,10 @@ abstract C_0002043996622900301644 #aliceblue;line:blue;line.dotted;text:blue {
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{abstract} #buildModel(const torch::Tensor & weights) = 0 : void
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#checkFitParameters() : void
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+dump_cpt() const : std::string
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+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) : Classifier &
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+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) : Classifier &
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+fit(torch::Tensor & dataset, const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states) : Classifier &
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+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) : Classifier &
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+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, const Smoothing_t smoothing) : Classifier &
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+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, const Smoothing_t smoothing) : Classifier &
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+fit(torch::Tensor & dataset, const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states, const Smoothing_t smoothing) : Classifier &
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+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, const Smoothing_t smoothing) : Classifier &
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+getClassNumStates() const : int
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+getNotes() const : std::vector<std::string>
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+getNumberOfEdges() const : int
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@ -143,7 +151,7 @@ abstract C_0002043996622900301644 #aliceblue;line:blue;line.dotted;text:blue {
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+setHyperparameters(const nlohmann::json & hyperparameters) : void
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+show() const : std::vector<std::string>
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+topological_order() : std::vector<std::string>
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#trainModel(const torch::Tensor & weights) : void
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#trainModel(const torch::Tensor & weights, const Smoothing_t smoothing) : void
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__
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#className : std::string
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#dataset : torch::Tensor
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@ -157,8 +165,8 @@ __
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#states : std::map<std::string,std::vector<int>>
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#status : status_t
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}
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class "bayesnet::KDB" as C_0001112865019015250005
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class C_0001112865019015250005 #aliceblue;line:blue;line.dotted;text:blue {
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class "bayesnet::KDB" as C_0008902920152122000044
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class C_0008902920152122000044 #aliceblue;line:blue;line.dotted;text:blue {
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+KDB(int k, float theta = 0.03) : void
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+~KDB() = default : void
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..
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@ -167,8 +175,26 @@ class C_0001112865019015250005 #aliceblue;line:blue;line.dotted;text:blue {
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+setHyperparameters(const nlohmann::json & hyperparameters_) : void
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__
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}
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class "bayesnet::TAN" as C_0001760994424884323017
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class C_0001760994424884323017 #aliceblue;line:blue;line.dotted;text:blue {
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class "bayesnet::SPODE" as C_0004096182510460307610
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class C_0004096182510460307610 #aliceblue;line:blue;line.dotted;text:blue {
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+SPODE(int root) : void
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+~SPODE() = default : void
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..
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#buildModel(const torch::Tensor & weights) : void
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+graph(const std::string & name = "SPODE") const : std::vector<std::string>
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__
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}
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class "bayesnet::SPnDE" as C_0016268916386101512883
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class C_0016268916386101512883 #aliceblue;line:blue;line.dotted;text:blue {
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+SPnDE(std::vector<int> parents) : void
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+~SPnDE() = default : void
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..
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#buildModel(const torch::Tensor & weights) : void
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+graph(const std::string & name = "SPnDE") const : std::vector<std::string>
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__
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}
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class "bayesnet::TAN" as C_0014087955399074584137
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class C_0014087955399074584137 #aliceblue;line:blue;line.dotted;text:blue {
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+TAN() : void
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+~TAN() = default : void
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..
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@ -176,8 +202,8 @@ class C_0001760994424884323017 #aliceblue;line:blue;line.dotted;text:blue {
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+graph(const std::string & name = "TAN") const : std::vector<std::string>
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__
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}
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class "bayesnet::Proposal" as C_0002219995589162262979
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class C_0002219995589162262979 #aliceblue;line:blue;line.dotted;text:blue {
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class "bayesnet::Proposal" as C_0017759964713298103839
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class C_0017759964713298103839 #aliceblue;line:blue;line.dotted;text:blue {
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+Proposal(torch::Tensor & pDataset, std::vector<std::string> & features_, std::string & className_) : void
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+~Proposal() : void
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..
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@ -190,74 +216,42 @@ __
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#discretizers : map<std::string,mdlp::CPPFImdlp *>
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#y : torch::Tensor
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}
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class "bayesnet::TANLd" as C_0001668829096702037834
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class C_0001668829096702037834 #aliceblue;line:blue;line.dotted;text:blue {
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+TANLd() : void
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+~TANLd() = default : void
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class "bayesnet::KDBLd" as C_0002756018222998454702
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class C_0002756018222998454702 #aliceblue;line:blue;line.dotted;text:blue {
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+KDBLd(int k) : void
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+~KDBLd() = default : void
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..
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+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) : TANLd &
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+graph(const std::string & name = "TAN") const : std::vector<std::string>
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+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, const Smoothing_t smoothing) : KDBLd &
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+graph(const std::string & name = "KDB") const : std::vector<std::string>
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+predict(torch::Tensor & X) : torch::Tensor
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{static} +version() : std::string
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__
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}
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abstract "bayesnet::FeatureSelect" as C_0001695326193250580823
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abstract C_0001695326193250580823 #aliceblue;line:blue;line.dotted;text:blue {
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+FeatureSelect(const torch::Tensor & samples, const std::vector<std::string> & features, const std::string & className, const int maxFeatures, const int classNumStates, const torch::Tensor & weights) : void
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+~FeatureSelect() : void
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class "bayesnet::SPODELd" as C_0010957245114062042836
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class C_0010957245114062042836 #aliceblue;line:blue;line.dotted;text:blue {
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+SPODELd(int root) : void
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+~SPODELd() = default : void
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..
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#computeMeritCFS() : double
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#computeSuFeatures(const int a, const int b) : double
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#computeSuLabels() : void
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{abstract} +fit() = 0 : void
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+getFeatures() const : std::vector<int>
|
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+getScores() const : std::vector<double>
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#initialize() : void
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#symmetricalUncertainty(int a, int b) : double
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__
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#fitted : bool
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#maxFeatures : int
|
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#selectedFeatures : std::vector<int>
|
||||
#selectedScores : std::vector<double>
|
||||
#suFeatures : std::map<std::pair<int,int>,double>
|
||||
#suLabels : std::vector<double>
|
||||
#weights : const torch::Tensor &
|
||||
}
|
||||
class "bayesnet::CFS" as C_0000011627355691342494
|
||||
class C_0000011627355691342494 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
+CFS(const torch::Tensor & samples, const std::vector<std::string> & features, const std::string & className, const int maxFeatures, const int classNumStates, const torch::Tensor & weights) : void
|
||||
+~CFS() : void
|
||||
..
|
||||
+fit() : void
|
||||
+commonFit(const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states, const Smoothing_t smoothing) : SPODELd &
|
||||
+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, const Smoothing_t smoothing) : SPODELd &
|
||||
+fit(torch::Tensor & dataset, const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states, const Smoothing_t smoothing) : SPODELd &
|
||||
+graph(const std::string & name = "SPODELd") const : std::vector<std::string>
|
||||
+predict(torch::Tensor & X) : torch::Tensor
|
||||
{static} +version() : std::string
|
||||
__
|
||||
}
|
||||
class "bayesnet::FCBF" as C_0000144682015341746929
|
||||
class C_0000144682015341746929 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
+FCBF(const torch::Tensor & samples, const std::vector<std::string> & features, const std::string & className, const int maxFeatures, const int classNumStates, const torch::Tensor & weights, const double threshold) : void
|
||||
+~FCBF() : void
|
||||
class "bayesnet::TANLd" as C_0013350632773616302678
|
||||
class C_0013350632773616302678 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
+TANLd() : void
|
||||
+~TANLd() = default : void
|
||||
..
|
||||
+fit() : void
|
||||
+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, const Smoothing_t smoothing) : TANLd &
|
||||
+graph(const std::string & name = "TANLd") const : std::vector<std::string>
|
||||
+predict(torch::Tensor & X) : torch::Tensor
|
||||
__
|
||||
}
|
||||
class "bayesnet::IWSS" as C_0000008268514674428553
|
||||
class C_0000008268514674428553 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
+IWSS(const torch::Tensor & samples, const std::vector<std::string> & features, const std::string & className, const int maxFeatures, const int classNumStates, const torch::Tensor & weights, const double threshold) : void
|
||||
+~IWSS() : void
|
||||
..
|
||||
+fit() : void
|
||||
__
|
||||
}
|
||||
class "bayesnet::SPODE" as C_0000512022813807538451
|
||||
class C_0000512022813807538451 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
+SPODE(int root) : void
|
||||
+~SPODE() = default : void
|
||||
..
|
||||
#buildModel(const torch::Tensor & weights) : void
|
||||
+graph(const std::string & name = "SPODE") const : std::vector<std::string>
|
||||
__
|
||||
}
|
||||
class "bayesnet::Ensemble" as C_0001985241386355360576
|
||||
class C_0001985241386355360576 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
class "bayesnet::Ensemble" as C_0015881931090842884611
|
||||
class C_0015881931090842884611 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
+Ensemble(bool predict_voting = true) : void
|
||||
+~Ensemble() = default : void
|
||||
..
|
||||
@ -280,7 +274,7 @@ class C_0001985241386355360576 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
+score(torch::Tensor & X, torch::Tensor & y) : float
|
||||
+show() const : std::vector<std::string>
|
||||
+topological_order() : std::vector<std::string>
|
||||
#trainModel(const torch::Tensor & weights) : void
|
||||
#trainModel(const torch::Tensor & weights, const Smoothing_t smoothing) : void
|
||||
#voting(torch::Tensor & votes) : torch::Tensor
|
||||
__
|
||||
#models : std::vector<std::unique_ptr<Classifier>>
|
||||
@ -288,41 +282,223 @@ __
|
||||
#predict_voting : bool
|
||||
#significanceModels : std::vector<double>
|
||||
}
|
||||
class "bayesnet::(anonymous_45089536)" as C_0001186398587753535158
|
||||
class C_0001186398587753535158 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
class "bayesnet::A2DE" as C_0001410789567057647859
|
||||
class C_0001410789567057647859 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
+A2DE(bool predict_voting = false) : void
|
||||
+~A2DE() : void
|
||||
..
|
||||
#buildModel(const torch::Tensor & weights) : void
|
||||
+graph(const std::string & title = "A2DE") const : std::vector<std::string>
|
||||
+setHyperparameters(const nlohmann::json & hyperparameters) : void
|
||||
__
|
||||
}
|
||||
class "bayesnet::AODE" as C_0006288892608974306258
|
||||
class C_0006288892608974306258 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
+AODE(bool predict_voting = false) : void
|
||||
+~AODE() : void
|
||||
..
|
||||
#buildModel(const torch::Tensor & weights) : void
|
||||
+graph(const std::string & title = "AODE") const : std::vector<std::string>
|
||||
+setHyperparameters(const nlohmann::json & hyperparameters) : void
|
||||
__
|
||||
}
|
||||
abstract "bayesnet::FeatureSelect" as C_0013562609546004646591
|
||||
abstract C_0013562609546004646591 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
+FeatureSelect(const torch::Tensor & samples, const std::vector<std::string> & features, const std::string & className, const int maxFeatures, const int classNumStates, const torch::Tensor & weights) : void
|
||||
+~FeatureSelect() : void
|
||||
..
|
||||
#computeMeritCFS() : double
|
||||
#computeSuFeatures(const int a, const int b) : double
|
||||
#computeSuLabels() : void
|
||||
{abstract} +fit() = 0 : void
|
||||
+getFeatures() const : std::vector<int>
|
||||
+getScores() const : std::vector<double>
|
||||
#initialize() : void
|
||||
#symmetricalUncertainty(int a, int b) : double
|
||||
__
|
||||
#fitted : bool
|
||||
#maxFeatures : int
|
||||
#selectedFeatures : std::vector<int>
|
||||
#selectedScores : std::vector<double>
|
||||
#suFeatures : std::map<std::pair<int,int>,double>
|
||||
#suLabels : std::vector<double>
|
||||
#weights : const torch::Tensor &
|
||||
}
|
||||
class "bayesnet::(anonymous_60342586)" as C_0005584545181746538542
|
||||
class C_0005584545181746538542 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
__
|
||||
+CFS : std::string
|
||||
+FCBF : std::string
|
||||
+IWSS : std::string
|
||||
}
|
||||
class "bayesnet::(anonymous_45090163)" as C_0000602764946063116717
|
||||
class C_0000602764946063116717 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
class "bayesnet::(anonymous_60343240)" as C_0016227156982041949444
|
||||
class C_0016227156982041949444 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
__
|
||||
+ASC : std::string
|
||||
+DESC : std::string
|
||||
+RAND : std::string
|
||||
}
|
||||
class "bayesnet::BoostAODE" as C_0000358471592399852382
|
||||
class C_0000358471592399852382 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
class "bayesnet::Boost" as C_0009819322948617116148
|
||||
class C_0009819322948617116148 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
+Boost(bool predict_voting = false) : void
|
||||
+~Boost() = default : void
|
||||
..
|
||||
#buildModel(const torch::Tensor & weights) : void
|
||||
#featureSelection(torch::Tensor & weights_) : std::vector<int>
|
||||
+setHyperparameters(const nlohmann::json & hyperparameters_) : void
|
||||
#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) : std::tuple<torch::Tensor &,double,bool>
|
||||
__
|
||||
#X_test : torch::Tensor
|
||||
#X_train : torch::Tensor
|
||||
#bisection : bool
|
||||
#block_update : bool
|
||||
#convergence : bool
|
||||
#convergence_best : bool
|
||||
#featureSelector : FeatureSelect *
|
||||
#maxTolerance : int
|
||||
#order_algorithm : std::string
|
||||
#selectFeatures : bool
|
||||
#select_features_algorithm : std::string
|
||||
#threshold : double
|
||||
#y_test : torch::Tensor
|
||||
#y_train : torch::Tensor
|
||||
}
|
||||
class "bayesnet::AODELd" as C_0003898187834670349177
|
||||
class C_0003898187834670349177 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
+AODELd(bool predict_voting = true) : void
|
||||
+~AODELd() = default : void
|
||||
..
|
||||
#buildModel(const torch::Tensor & weights) : void
|
||||
+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_, const Smoothing_t smoothing) : AODELd &
|
||||
+graph(const std::string & name = "AODELd") const : std::vector<std::string>
|
||||
#trainModel(const torch::Tensor & weights, const Smoothing_t smoothing) : void
|
||||
__
|
||||
}
|
||||
class "bayesnet::(anonymous_60275628)" as C_0009086919615463763584
|
||||
class C_0009086919615463763584 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
__
|
||||
+CFS : std::string
|
||||
+FCBF : std::string
|
||||
+IWSS : std::string
|
||||
}
|
||||
class "bayesnet::(anonymous_60276282)" as C_0015251985607563196159
|
||||
class C_0015251985607563196159 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
__
|
||||
+ASC : std::string
|
||||
+DESC : std::string
|
||||
+RAND : std::string
|
||||
}
|
||||
class "bayesnet::BoostA2DE" as C_0000272055465257861326
|
||||
class C_0000272055465257861326 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
+BoostA2DE(bool predict_voting = false) : void
|
||||
+~BoostA2DE() = default : void
|
||||
..
|
||||
+graph(const std::string & title = "BoostA2DE") const : std::vector<std::string>
|
||||
#trainModel(const torch::Tensor & weights, const Smoothing_t smoothing) : void
|
||||
__
|
||||
}
|
||||
class "bayesnet::(anonymous_60275502)" as C_0016033655851510053155
|
||||
class C_0016033655851510053155 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
__
|
||||
+CFS : std::string
|
||||
+FCBF : std::string
|
||||
+IWSS : std::string
|
||||
}
|
||||
class "bayesnet::(anonymous_60276156)" as C_0000379522761622473555
|
||||
class C_0000379522761622473555 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
__
|
||||
+ASC : std::string
|
||||
+DESC : std::string
|
||||
+RAND : std::string
|
||||
}
|
||||
class "bayesnet::BoostAODE" as C_0002867772739198819061
|
||||
class C_0002867772739198819061 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
+BoostAODE(bool predict_voting = false) : void
|
||||
+~BoostAODE() = default : void
|
||||
..
|
||||
#buildModel(const torch::Tensor & weights) : void
|
||||
+graph(const std::string & title = "BoostAODE") const : std::vector<std::string>
|
||||
+setHyperparameters(const nlohmann::json & hyperparameters_) : void
|
||||
#trainModel(const torch::Tensor & weights) : void
|
||||
#trainModel(const torch::Tensor & weights, const Smoothing_t smoothing) : void
|
||||
__
|
||||
}
|
||||
class "bayesnet::MST" as C_0000131858426172291700
|
||||
class C_0000131858426172291700 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
class "bayesnet::CFS" as C_0000093018845530739957
|
||||
class C_0000093018845530739957 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
+CFS(const torch::Tensor & samples, const std::vector<std::string> & features, const std::string & className, const int maxFeatures, const int classNumStates, const torch::Tensor & weights) : void
|
||||
+~CFS() : void
|
||||
..
|
||||
+fit() : void
|
||||
__
|
||||
}
|
||||
class "bayesnet::FCBF" as C_0001157456122733975432
|
||||
class C_0001157456122733975432 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
+FCBF(const torch::Tensor & samples, const std::vector<std::string> & features, const std::string & className, const int maxFeatures, const int classNumStates, const torch::Tensor & weights, const double threshold) : void
|
||||
+~FCBF() : void
|
||||
..
|
||||
+fit() : void
|
||||
__
|
||||
}
|
||||
class "bayesnet::IWSS" as C_0000066148117395428429
|
||||
class C_0000066148117395428429 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
+IWSS(const torch::Tensor & samples, const std::vector<std::string> & features, const std::string & className, const int maxFeatures, const int classNumStates, const torch::Tensor & weights, const double threshold) : void
|
||||
+~IWSS() : void
|
||||
..
|
||||
+fit() : void
|
||||
__
|
||||
}
|
||||
class "bayesnet::(anonymous_60730495)" as C_0004857727320042830573
|
||||
class C_0004857727320042830573 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
__
|
||||
+CFS : std::string
|
||||
+FCBF : std::string
|
||||
+IWSS : std::string
|
||||
}
|
||||
class "bayesnet::(anonymous_60731150)" as C_0000076541533312623385
|
||||
class C_0000076541533312623385 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
__
|
||||
+ASC : std::string
|
||||
+DESC : std::string
|
||||
+RAND : std::string
|
||||
}
|
||||
class "bayesnet::(anonymous_60653004)" as C_0001444063444142949758
|
||||
class C_0001444063444142949758 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
__
|
||||
+CFS : std::string
|
||||
+FCBF : std::string
|
||||
+IWSS : std::string
|
||||
}
|
||||
class "bayesnet::(anonymous_60653658)" as C_0007139277546931322856
|
||||
class C_0007139277546931322856 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
__
|
||||
+ASC : std::string
|
||||
+DESC : std::string
|
||||
+RAND : std::string
|
||||
}
|
||||
class "bayesnet::(anonymous_60731375)" as C_0010493853592456211189
|
||||
class C_0010493853592456211189 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
__
|
||||
+CFS : std::string
|
||||
+FCBF : std::string
|
||||
+IWSS : std::string
|
||||
}
|
||||
class "bayesnet::(anonymous_60732030)" as C_0007011438637915849564
|
||||
class C_0007011438637915849564 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
__
|
||||
+ASC : std::string
|
||||
+DESC : std::string
|
||||
+RAND : std::string
|
||||
}
|
||||
class "bayesnet::MST" as C_0001054867409378333602
|
||||
class C_0001054867409378333602 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
+MST() = default : void
|
||||
+MST(const std::vector<std::string> & features, const torch::Tensor & weights, const int root) : void
|
||||
..
|
||||
+insertElement(std::list<int> & variables, int variable) : void
|
||||
+maximumSpanningTree() : std::vector<std::pair<int,int>>
|
||||
+reorder(std::vector<std::pair<float,std::pair<int,int>>> T, int root_original) : std::vector<std::pair<int,int>>
|
||||
__
|
||||
}
|
||||
class "bayesnet::Graph" as C_0001197041682001898467
|
||||
class C_0001197041682001898467 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
class "bayesnet::Graph" as C_0009576333456015187741
|
||||
class C_0009576333456015187741 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
+Graph(int V) : void
|
||||
..
|
||||
+addEdge(int u, int v, float wt) : void
|
||||
@ -332,81 +508,73 @@ class C_0001197041682001898467 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
+union_set(int u, int v) : void
|
||||
__
|
||||
}
|
||||
class "bayesnet::KDBLd" as C_0000344502277874806837
|
||||
class C_0000344502277874806837 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
+KDBLd(int k) : void
|
||||
+~KDBLd() = default : void
|
||||
..
|
||||
+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) : KDBLd &
|
||||
+graph(const std::string & name = "KDB") const : std::vector<std::string>
|
||||
+predict(torch::Tensor & X) : torch::Tensor
|
||||
{static} +version() : std::string
|
||||
__
|
||||
}
|
||||
class "bayesnet::AODE" as C_0000786111576121788282
|
||||
class C_0000786111576121788282 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
+AODE(bool predict_voting = false) : void
|
||||
+~AODE() : void
|
||||
..
|
||||
#buildModel(const torch::Tensor & weights) : void
|
||||
+graph(const std::string & title = "AODE") const : std::vector<std::string>
|
||||
+setHyperparameters(const nlohmann::json & hyperparameters) : void
|
||||
__
|
||||
}
|
||||
class "bayesnet::SPODELd" as C_0001369655639257755354
|
||||
class C_0001369655639257755354 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
+SPODELd(int root) : void
|
||||
+~SPODELd() = default : void
|
||||
..
|
||||
+commonFit(const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states) : SPODELd &
|
||||
+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) : SPODELd &
|
||||
+fit(torch::Tensor & dataset, const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states) : SPODELd &
|
||||
+graph(const std::string & name = "SPODE") const : std::vector<std::string>
|
||||
+predict(torch::Tensor & X) : torch::Tensor
|
||||
{static} +version() : std::string
|
||||
__
|
||||
}
|
||||
class "bayesnet::AODELd" as C_0000487273479333793647
|
||||
class C_0000487273479333793647 #aliceblue;line:blue;line.dotted;text:blue {
|
||||
+AODELd(bool predict_voting = true) : void
|
||||
+~AODELd() = default : void
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Reference in New Issue
Block a user