Add class and dependency diagrams
This commit is contained in:
412
diagrams/BayesNet.puml
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412
diagrams/BayesNet.puml
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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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+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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+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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+getName() const : std::string
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+getNumStates() const : int
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+getParents() : std::vector<Node *> &
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+graph(const std::string & clasName) : std::vector<std::string>
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+minFill() : unsigned int
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+removeChild(Node *) : void
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+removeParent(Node *) : void
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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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+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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+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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+getStates() const : int
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+graph(const std::string & title) const : std::vector<std::string>
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+initialize() : void
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+predict(const std::vector<std::vector<int>> &) : std::vector<int>
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+predict(const torch::Tensor &) : torch::Tensor
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+predict_proba(const std::vector<std::vector<int>> &) : std::vector<std::vector<double>>
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+predict_proba(const torch::Tensor &) : torch::Tensor
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+predict_tensor(const torch::Tensor & samples, const bool proba) : torch::Tensor
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+score(const std::vector<std::vector<int>> &, const std::vector<int> &) : double
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+show() const : std::vector<std::string>
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+topological_sort() : std::vector<std::string>
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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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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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+~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} +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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{abstract} +getNumberOfNodes() const = 0 : int
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{abstract} +getNumberOfStates() const = 0 : int
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{abstract} +getStatus() const = 0 : status_t
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+getValidHyperparameters() : std::vector<std::string> &
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{abstract} +getVersion() = 0 : std::string
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{abstract} +graph(const std::string & title = "") const = 0 : std::vector<std::string>
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{abstract} +predict(std::vector<std::vector<int>> & X) = 0 : std::vector<int>
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{abstract} +predict(torch::Tensor & X) = 0 : torch::Tensor
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{abstract} +predict_proba(std::vector<std::vector<int>> & X) = 0 : std::vector<std::vector<double>>
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{abstract} +predict_proba(torch::Tensor & X) = 0 : torch::Tensor
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{abstract} +score(std::vector<std::vector<int>> & X, std::vector<int> & y) = 0 : float
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{abstract} +score(torch::Tensor & X, torch::Tensor & y) = 0 : float
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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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__
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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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+Classifier(Network model) : void
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+~Classifier() = default : void
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..
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+addNodes() : void
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#buildDataset(torch::Tensor & y) : void
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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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+getClassNumStates() const : int
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+getNotes() const : std::vector<std::string>
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+getNumberOfEdges() const : int
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+getNumberOfNodes() const : int
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+getNumberOfStates() const : int
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+getStatus() const : status_t
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+getVersion() : std::string
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+predict(std::vector<std::vector<int>> & X) : std::vector<int>
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+predict(torch::Tensor & X) : torch::Tensor
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+predict_proba(std::vector<std::vector<int>> & X) : std::vector<std::vector<double>>
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+predict_proba(torch::Tensor & X) : torch::Tensor
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+score(torch::Tensor & X, torch::Tensor & y) : float
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+score(std::vector<std::vector<int>> & X, std::vector<int> & y) : float
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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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__
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#className : std::string
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#dataset : torch::Tensor
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#features : std::vector<std::string>
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#fitted : bool
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#m : unsigned int
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#metrics : Metrics
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#model : Network
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#n : unsigned int
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#notes : std::vector<std::string>
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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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+KDB(int k, float theta = 0.03) : void
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+~KDB() = 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 = "KDB") const : std::vector<std::string>
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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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+TAN() : void
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+~TAN() = 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 = "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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+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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#checkInput(const torch::Tensor & X, const torch::Tensor & y) : void
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#fit_local_discretization(const torch::Tensor & y) : std::map<std::string,std::vector<int>>
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#localDiscretizationProposal(const std::map<std::string,std::vector<int>> & states, Network & model) : std::map<std::string,std::vector<int>>
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#prepareX(torch::Tensor & X) : torch::Tensor
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__
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#Xf : torch::Tensor
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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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..
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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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+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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..
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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>
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#selectedScores : std::vector<double>
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#suFeatures : std::map<std::pair<int,int>,double>
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#suLabels : std::vector<double>
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#weights : const torch::Tensor &
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}
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class "bayesnet::CFS" as C_0000011627355691342494
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class C_0000011627355691342494 #aliceblue;line:blue;line.dotted;text:blue {
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+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
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+~CFS() : void
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..
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+fit() : void
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__
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}
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class "bayesnet::FCBF" as C_0000144682015341746929
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class C_0000144682015341746929 #aliceblue;line:blue;line.dotted;text:blue {
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+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
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+~FCBF() : void
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..
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+fit() : void
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__
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}
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class "bayesnet::IWSS" as C_0000008268514674428553
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class C_0000008268514674428553 #aliceblue;line:blue;line.dotted;text:blue {
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+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
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+~IWSS() : void
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..
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+fit() : void
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__
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}
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class "bayesnet::SPODE" as C_0000512022813807538451
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class C_0000512022813807538451 #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::Ensemble" as C_0001985241386355360576
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class C_0001985241386355360576 #aliceblue;line:blue;line.dotted;text:blue {
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+Ensemble(bool predict_voting = true) : void
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+~Ensemble() = default : void
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..
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#compute_arg_max(std::vector<std::vector<double>> & X) : std::vector<int>
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#compute_arg_max(torch::Tensor & X) : torch::Tensor
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+dump_cpt() const : std::string
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+getNumberOfEdges() const : int
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+getNumberOfNodes() const : int
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+getNumberOfStates() const : int
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+graph(const std::string & title) const : std::vector<std::string>
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+predict(std::vector<std::vector<int>> & X) : std::vector<int>
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+predict(torch::Tensor & X) : torch::Tensor
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#predict_average_proba(torch::Tensor & X) : torch::Tensor
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#predict_average_proba(std::vector<std::vector<int>> & X) : std::vector<std::vector<double>>
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#predict_average_voting(torch::Tensor & X) : torch::Tensor
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#predict_average_voting(std::vector<std::vector<int>> & X) : std::vector<std::vector<double>>
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+predict_proba(std::vector<std::vector<int>> & X) : std::vector<std::vector<double>>
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+predict_proba(torch::Tensor & X) : torch::Tensor
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+score(std::vector<std::vector<int>> & X, std::vector<int> & y) : float
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+score(torch::Tensor & X, torch::Tensor & y) : float
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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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#voting(torch::Tensor & votes) : torch::Tensor
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__
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#models : std::vector<std::unique_ptr<Classifier>>
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#n_models : unsigned int
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#predict_voting : bool
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#significanceModels : std::vector<double>
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}
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class "bayesnet::(anonymous_45089536)" as C_0001186398587753535158
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class C_0001186398587753535158 #aliceblue;line:blue;line.dotted;text:blue {
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__
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+CFS : std::string
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+FCBF : std::string
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+IWSS : std::string
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}
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class "bayesnet::(anonymous_45090163)" as C_0000602764946063116717
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class C_0000602764946063116717 #aliceblue;line:blue;line.dotted;text:blue {
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__
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+ASC : std::string
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+DESC : std::string
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+RAND : std::string
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}
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class "bayesnet::BoostAODE" as C_0000358471592399852382
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class C_0000358471592399852382 #aliceblue;line:blue;line.dotted;text:blue {
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+BoostAODE(bool predict_voting = false) : void
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+~BoostAODE() = default : void
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..
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#buildModel(const torch::Tensor & weights) : void
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+graph(const std::string & title = "BoostAODE") const : std::vector<std::string>
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+setHyperparameters(const nlohmann::json & hyperparameters_) : void
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#trainModel(const torch::Tensor & weights) : void
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__
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}
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class "bayesnet::MST" as C_0000131858426172291700
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class C_0000131858426172291700 #aliceblue;line:blue;line.dotted;text:blue {
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+MST() = default : void
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+MST(const std::vector<std::string> & features, const torch::Tensor & weights, const int root) : void
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..
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+maximumSpanningTree() : std::vector<std::pair<int,int>>
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__
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}
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class "bayesnet::Graph" as C_0001197041682001898467
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class C_0001197041682001898467 #aliceblue;line:blue;line.dotted;text:blue {
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+Graph(int V) : void
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..
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+addEdge(int u, int v, float wt) : void
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+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
|
||||
Reference in New Issue
Block a user