BayesNet/diagrams/BayesNet.puml

@startuml
title clang-uml class diagram model
class "bayesnet::Metrics" as C_0000736965376885623323
class C_0000736965376885623323 #aliceblue;line:blue;line.dotted;text:blue {
+Metrics() = default : void
+Metrics(const torch::Tensor & samples, const std::vector<std::string> & features, const std::string & className, const int classNumStates) : void
+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
..
+SelectKBestWeighted(const torch::Tensor & weights, bool ascending = false, unsigned int k = 0) : std::vector<int>
+conditionalEdge(const torch::Tensor & weights) : torch::Tensor
+conditionalEdgeWeights(std::vector<float> & weights) : std::vector<float>
#doCombinations<T>(const std::vector<T> & source) : std::vector<std::pair<T, T> >
#entropy(const torch::Tensor & feature, const torch::Tensor & weights) : double
+getScoresKBest() const : std::vector<double>
+maximumSpanningTree(const std::vector<std::string> & features, const torch::Tensor & weights, const int root) : std::vector<std::pair<int,int>>
+mutualInformation(const torch::Tensor & firstFeature, const torch::Tensor & secondFeature, const torch::Tensor & weights) : double
#pop_first<T>(std::vector<T> & v) : T
__
#className : std::string
#features : std::vector<std::string>
#samples : torch::Tensor
}
class "bayesnet::Node" as C_0001303524929067080934
class C_0001303524929067080934 #aliceblue;line:blue;line.dotted;text:blue {
+Node(const std::string &) : void
..
+addChild(Node *) : void
+addParent(Node *) : void
+clear() : void
+computeCPT(const torch::Tensor & dataset, const std::vector<std::string> & features, const double laplaceSmoothing, const torch::Tensor & weights) : void
+getCPT() : torch::Tensor &
+getChildren() : std::vector<Node *> &
+getFactorValue(std::map<std::string,int> &) : float
+getName() const : std::string
+getNumStates() const : int
+getParents() : std::vector<Node *> &
+graph(const std::string & clasName) : std::vector<std::string>
+minFill() : unsigned int
+removeChild(Node *) : void
+removeParent(Node *) : void
+setNumStates(int) : void
__
}
class "bayesnet::Network" as C_0001186707649890429575
class C_0001186707649890429575 #aliceblue;line:blue;line.dotted;text:blue {
+Network() : void
+Network(float) : void
+Network(const Network &) : void
+~Network() = default : void
..
+addEdge(const std::string &, const std::string &) : void
+addNode(const std::string &) : void
+dump_cpt() const : std::string
+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
+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 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
+getClassName() const : std::string
+getClassNumStates() const : int
+getEdges() const : std::vector<std::pair<std::string,std::string>>
+getFeatures() const : std::vector<std::string>
+getMaxThreads() const : float
+getNodes() : std::map<std::string,std::unique_ptr<Node>> &
+getNumEdges() const : int
+getSamples() : torch::Tensor &
+getStates() const : int
+graph(const std::string & title) const : std::vector<std::string>
+initialize() : void
+predict(const std::vector<std::vector<int>> &) : std::vector<int>
+predict(const torch::Tensor &) : torch::Tensor
+predict_proba(const std::vector<std::vector<int>> &) : std::vector<std::vector<double>>
+predict_proba(const torch::Tensor &) : torch::Tensor
+predict_tensor(const torch::Tensor & samples, const bool proba) : torch::Tensor
+score(const std::vector<std::vector<int>> &, const std::vector<int> &) : double
+show() const : std::vector<std::string>
+topological_sort() : std::vector<std::string>
+version() : std::string
__
}
enum "bayesnet::status_t" as C_0000738420730783851375
enum C_0000738420730783851375 {
NORMAL
WARNING
ERROR
}
abstract "bayesnet::BaseClassifier" as C_0000327135989451974539
abstract C_0000327135989451974539 #aliceblue;line:blue;line.dotted;text:blue {
+~BaseClassifier() = default : void
..
{abstract} +dump_cpt() const = 0 : std::string
{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 &
{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 &
{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 &
{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 &
{abstract} +getClassNumStates() const = 0 : int
{abstract} +getNotes() const = 0 : std::vector<std::string>
{abstract} +getNumberOfEdges() const = 0 : int
{abstract} +getNumberOfNodes() const = 0 : int
{abstract} +getNumberOfStates() const = 0 : int
{abstract} +getStatus() const = 0 : status_t
+getValidHyperparameters() : std::vector<std::string> &
{abstract} +getVersion() = 0 : std::string
{abstract} +graph(const std::string & title = "") const = 0 : std::vector<std::string>
{abstract} +predict(std::vector<std::vector<int>> & X) = 0 : std::vector<int>
{abstract} +predict(torch::Tensor & X) = 0 : torch::Tensor
{abstract} +predict_proba(std::vector<std::vector<int>> & X) = 0 : std::vector<std::vector<double>>
{abstract} +predict_proba(torch::Tensor & X) = 0 : torch::Tensor
{abstract} +score(std::vector<std::vector<int>> & X, std::vector<int> & y) = 0 : float
{abstract} +score(torch::Tensor & X, torch::Tensor & y) = 0 : float
{abstract} +setHyperparameters(const nlohmann::json & hyperparameters) = 0 : void
{abstract} +show() const = 0 : std::vector<std::string>
{abstract} +topological_order() = 0 : std::vector<std::string>
{abstract} #trainModel(const torch::Tensor & weights) = 0 : void
__
#validHyperparameters : std::vector<std::string>
}
abstract "bayesnet::Classifier" as C_0002043996622900301644
abstract C_0002043996622900301644 #aliceblue;line:blue;line.dotted;text:blue {
+Classifier(Network model) : void
+~Classifier() = default : void
..
+addNodes() : void
#buildDataset(torch::Tensor & y) : void
{abstract} #buildModel(const torch::Tensor & weights) = 0 : void
#checkFitParameters() : void
+dump_cpt() const : std::string
+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 &
+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 &
+fit(torch::Tensor & dataset, const std::vector<std::string> & features, const std::string & className, std::map<std::string,std::vector<int>> & states) : 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) : Classifier &
+getClassNumStates() const : int
+getNotes() const : std::vector<std::string>
+getNumberOfEdges() const : int
+getNumberOfNodes() const : int
+getNumberOfStates() const : int
+getStatus() const : status_t
+getVersion() : std::string
+predict(std::vector<std::vector<int>> & X) : std::vector<int>
+predict(torch::Tensor & X) : torch::Tensor
+predict_proba(std::vector<std::vector<int>> & X) : std::vector<std::vector<double>>
+predict_proba(torch::Tensor & X) : torch::Tensor
+score(torch::Tensor & X, torch::Tensor & y) : float
+score(std::vector<std::vector<int>> & X, std::vector<int> & y) : float
+setHyperparameters(const nlohmann::json & hyperparameters) : void
+show() const : std::vector<std::string>
+topological_order() : std::vector<std::string>
#trainModel(const torch::Tensor & weights) : void
__
#className : std::string
#dataset : torch::Tensor
#features : std::vector<std::string>
#fitted : bool
#m : unsigned int
#metrics : Metrics
#model : Network
#n : unsigned int
#notes : std::vector<std::string>
#states : std::map<std::string,std::vector<int>>
#status : status_t
}
class "bayesnet::KDB" as C_0001112865019015250005
class C_0001112865019015250005 #aliceblue;line:blue;line.dotted;text:blue {
+KDB(int k, float theta = 0.03) : void
+~KDB() = default : void
..
#buildModel(const torch::Tensor & weights) : void
+graph(const std::string & name = "KDB") const : std::vector<std::string>
+setHyperparameters(const nlohmann::json & hyperparameters_) : void
__
}
class "bayesnet::TAN" as C_0001760994424884323017
class C_0001760994424884323017 #aliceblue;line:blue;line.dotted;text:blue {
+TAN() : void
+~TAN() = default : void
..
#buildModel(const torch::Tensor & weights) : void
+graph(const std::string & name = "TAN") const : std::vector<std::string>
__
}
class "bayesnet::Proposal" as C_0002219995589162262979
class C_0002219995589162262979 #aliceblue;line:blue;line.dotted;text:blue {
+Proposal(torch::Tensor & pDataset, std::vector<std::string> & features_, std::string & className_) : void
+~Proposal() : void
..
#checkInput(const torch::Tensor & X, const torch::Tensor & y) : void
#fit_local_discretization(const torch::Tensor & y) : std::map<std::string,std::vector<int>>
#localDiscretizationProposal(const std::map<std::string,std::vector<int>> & states, Network & model) : std::map<std::string,std::vector<int>>
#prepareX(torch::Tensor & X) : torch::Tensor
__
#Xf : torch::Tensor
#discretizers : map<std::string,mdlp::CPPFImdlp *>
#y : torch::Tensor
}
class "bayesnet::TANLd" as C_0001668829096702037834
class C_0001668829096702037834 #aliceblue;line:blue;line.dotted;text:blue {
+TANLd() : void
+~TANLd() = 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) : TANLd &
+graph(const std::string & name = "TAN") const : std::vector<std::string>
+predict(torch::Tensor & X) : torch::Tensor
{static} +version() : std::string
__
}
abstract "bayesnet::FeatureSelect" as C_0001695326193250580823
abstract C_0001695326193250580823 #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::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
__
}
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
..
+fit() : void
__
}
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 {
+Ensemble(bool predict_voting = true) : void
+~Ensemble() = default : void
..
#compute_arg_max(std::vector<std::vector<double>> & X) : std::vector<int>
#compute_arg_max(torch::Tensor & X) : torch::Tensor
+dump_cpt() const : std::string
+getNumberOfEdges() const : int
+getNumberOfNodes() const : int
+getNumberOfStates() const : int
+graph(const std::string & title) const : std::vector<std::string>
+predict(std::vector<std::vector<int>> & X) : std::vector<int>
+predict(torch::Tensor & X) : torch::Tensor
#predict_average_proba(torch::Tensor & X) : torch::Tensor
#predict_average_proba(std::vector<std::vector<int>> & X) : std::vector<std::vector<double>>
#predict_average_voting(torch::Tensor & X) : torch::Tensor
#predict_average_voting(std::vector<std::vector<int>> & X) : std::vector<std::vector<double>>
+predict_proba(std::vector<std::vector<int>> & X) : std::vector<std::vector<double>>
+predict_proba(torch::Tensor & X) : torch::Tensor
+score(std::vector<std::vector<int>> & X, std::vector<int> & y) : float
+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
#voting(torch::Tensor & votes) : torch::Tensor
__
#models : std::vector<std::unique_ptr<Classifier>>
#n_models : unsigned int
#predict_voting : bool
#significanceModels : std::vector<double>
}
class "bayesnet::(anonymous_45089536)" as C_0001186398587753535158
class C_0001186398587753535158 #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 {
__
+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 {
+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
__
}
class "bayesnet::MST" as C_0000131858426172291700
class C_0000131858426172291700 #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
..
+maximumSpanningTree() : std::vector<std::pair<int,int>>
__
}
class "bayesnet::Graph" as C_0001197041682001898467
class C_0001197041682001898467 #aliceblue;line:blue;line.dotted;text:blue {
+Graph(int V) : void
..
+addEdge(int u, int v, float wt) : void
+find_set(int i) : int
+get_mst() : std::vector<std::pair<float,std::pair<int,int>>>
+kruskal_algorithm() : void
+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
..
#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_) : AODELd &
+graph(const std::string & name = "AODELd") const : std::vector<std::string>
#trainModel(const torch::Tensor & weights) : void
__
}
C_0001303524929067080934 --> C_0001303524929067080934 : -parents
C_0001303524929067080934 --> C_0001303524929067080934 : -children
C_0001186707649890429575 o-- C_0001303524929067080934 : -nodes
C_0000327135989451974539 ..> C_0000738420730783851375
C_0002043996622900301644 o-- C_0001186707649890429575 : #model
C_0002043996622900301644 o-- C_0000736965376885623323 : #metrics
C_0002043996622900301644 o-- C_0000738420730783851375 : #status
C_0000327135989451974539 <|-- C_0002043996622900301644
C_0002043996622900301644 <|-- C_0001112865019015250005
C_0002043996622900301644 <|-- C_0001760994424884323017
C_0002219995589162262979 ..> C_0001186707649890429575
C_0001760994424884323017 <|-- C_0001668829096702037834
C_0002219995589162262979 <|-- C_0001668829096702037834
C_0000736965376885623323 <|-- C_0001695326193250580823
C_0001695326193250580823 <|-- C_0000011627355691342494
C_0001695326193250580823 <|-- C_0000144682015341746929
C_0001695326193250580823 <|-- C_0000008268514674428553
C_0002043996622900301644 <|-- C_0000512022813807538451
C_0001985241386355360576 o-- C_0002043996622900301644 : #models
C_0002043996622900301644 <|-- C_0001985241386355360576
C_0000358471592399852382 --> C_0001695326193250580823 : -featureSelector
C_0001985241386355360576 <|-- C_0000358471592399852382
C_0001112865019015250005 <|-- C_0000344502277874806837
C_0002219995589162262979 <|-- C_0000344502277874806837
C_0001985241386355360576 <|-- C_0000786111576121788282
C_0000512022813807538451 <|-- C_0001369655639257755354
C_0002219995589162262979 <|-- C_0001369655639257755354
C_0001985241386355360576 <|-- C_0000487273479333793647
C_0002219995589162262979 <|-- C_0000487273479333793647

'Generated with clang-uml, version 0.5.1
'LLVM version clang version 17.0.6 (Fedora 17.0.6-2.fc39)
@enduml