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aGrUM
0.20.3
a C++ library for (probabilistic) graphical models
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aGrUM
0.20.3
a C++ library for (probabilistic) graphical models
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This is the complete list of members for gum::BayesNet< GUM_SCALAR >, including all inherited members.
| _clearPotentials_() | gum::BayesNet< GUM_SCALAR > | private |
| _copyPotentials_(const BayesNet< GUM_SCALAR > &source) | gum::BayesNet< GUM_SCALAR > | private |
| _probaMap_ | gum::BayesNet< GUM_SCALAR > | private |
| _unsafeChangePotential_(NodeId id, Potential< GUM_SCALAR > *newPot) | gum::BayesNet< GUM_SCALAR > | private |
| _varMap_ | gum::BayesNet< GUM_SCALAR > | private |
| add(const DiscreteVariable &var) | gum::BayesNet< GUM_SCALAR > | |
| add(const std::string &name, unsigned int nbrmod) | gum::BayesNet< GUM_SCALAR > | |
| add(const DiscreteVariable &var, MultiDimImplementation< GUM_SCALAR > *aContent) | gum::BayesNet< GUM_SCALAR > | |
| add(const DiscreteVariable &var, NodeId id) | gum::BayesNet< GUM_SCALAR > | |
| add(const DiscreteVariable &var, MultiDimImplementation< GUM_SCALAR > *aContent, NodeId id) | gum::BayesNet< GUM_SCALAR > | |
| addAMPLITUDE(const DiscreteVariable &var) | gum::BayesNet< GUM_SCALAR > | |
| addAND(const DiscreteVariable &var) | gum::BayesNet< GUM_SCALAR > | |
| addArc(NodeId tail, NodeId head) | gum::BayesNet< GUM_SCALAR > | |
| addArc(const std::string &tail, const std::string &head) | gum::BayesNet< GUM_SCALAR > | |
| addCOUNT(const DiscreteVariable &var, Idx value=1) | gum::BayesNet< GUM_SCALAR > | |
| addEXISTS(const DiscreteVariable &var, Idx value=1) | gum::BayesNet< GUM_SCALAR > | |
| addFORALL(const DiscreteVariable &var, Idx value=1) | gum::BayesNet< GUM_SCALAR > | |
| addLogit(const DiscreteVariable &var, GUM_SCALAR external_weight, NodeId id) | gum::BayesNet< GUM_SCALAR > | |
| addLogit(const DiscreteVariable &var, GUM_SCALAR external_weight) | gum::BayesNet< GUM_SCALAR > | |
| addMAX(const DiscreteVariable &var) | gum::BayesNet< GUM_SCALAR > | |
| addMEDIAN(const DiscreteVariable &var) | gum::BayesNet< GUM_SCALAR > | |
| addMIN(const DiscreteVariable &var) | gum::BayesNet< GUM_SCALAR > | |
| addNoisyAND(const DiscreteVariable &var, GUM_SCALAR external_weight, NodeId id) | gum::BayesNet< GUM_SCALAR > | |
| addNoisyAND(const DiscreteVariable &var, GUM_SCALAR external_weight) | gum::BayesNet< GUM_SCALAR > | |
| addNoisyOR(const DiscreteVariable &var, GUM_SCALAR external_weight) | gum::BayesNet< GUM_SCALAR > | |
| addNoisyOR(const DiscreteVariable &var, GUM_SCALAR external_weight, NodeId id) | gum::BayesNet< GUM_SCALAR > | |
| addNoisyORCompound(const DiscreteVariable &var, GUM_SCALAR external_weight) | gum::BayesNet< GUM_SCALAR > | |
| addNoisyORCompound(const DiscreteVariable &var, GUM_SCALAR external_weight, NodeId id) | gum::BayesNet< GUM_SCALAR > | |
| addNoisyORNet(const DiscreteVariable &var, GUM_SCALAR external_weight) | gum::BayesNet< GUM_SCALAR > | |
| addNoisyORNet(const DiscreteVariable &var, GUM_SCALAR external_weight, NodeId id) | gum::BayesNet< GUM_SCALAR > | |
| addOR(const DiscreteVariable &var) | gum::BayesNet< GUM_SCALAR > | |
| addSUM(const DiscreteVariable &var) | gum::BayesNet< GUM_SCALAR > | |
| addWeightedArc(NodeId tail, NodeId head, GUM_SCALAR causalWeight) | gum::BayesNet< GUM_SCALAR > | |
| addWeightedArc(const std::string &tail, const std::string &head, GUM_SCALAR causalWeight) | gum::BayesNet< GUM_SCALAR > | inline |
| ancestors(const NodeId id) const | gum::DAGmodel | |
| ancestors(const std::string &name) const | gum::DAGmodel | |
| arcs() const | gum::DAGmodel | |
| BayesNet() | gum::BayesNet< GUM_SCALAR > | |
| BayesNet(std::string name) | gum::BayesNet< GUM_SCALAR > | explicit |
| BayesNet(const BayesNet< GUM_SCALAR > &source) | gum::BayesNet< GUM_SCALAR > | |
| BayesNetFactory< GUM_SCALAR > class | gum::BayesNet< GUM_SCALAR > | friend |
| beginTopologyTransformation() | gum::BayesNet< GUM_SCALAR > | |
| changePotential(NodeId id, Potential< GUM_SCALAR > *newPot) | gum::BayesNet< GUM_SCALAR > | |
| changePotential(const std::string &name, Potential< GUM_SCALAR > *newPot) | gum::BayesNet< GUM_SCALAR > | |
| changeVariableLabel(NodeId id, const std::string &old_label, const std::string &new_label) | gum::BayesNet< GUM_SCALAR > | |
| changeVariableLabel(const std::string &name, const std::string &old_label, const std::string &new_label) | gum::BayesNet< GUM_SCALAR > | inline |
| changeVariableName(NodeId id, const std::string &new_name) | gum::BayesNet< GUM_SCALAR > | |
| changeVariableName(const std::string &name, const std::string &new_name) | gum::BayesNet< GUM_SCALAR > | inline |
| children(const NodeId id) const | gum::DAGmodel | |
| children(const std::string &name) const | gum::DAGmodel | |
| children(const NodeSet &ids) const | gum::DAGmodel | |
| children(const std::vector< std::string > &names) const | gum::DAGmodel | |
| clear() | gum::BayesNet< GUM_SCALAR > | |
| completeInstantiation() const | gum::GraphicalModel | |
| cpt(NodeId varId) const final | gum::BayesNet< GUM_SCALAR > | virtual |
| cpt(const std::string &name) const | gum::BayesNet< GUM_SCALAR > | inline |
| dag() const | gum::DAGmodel | |
| dag_ | gum::DAGmodel | protected |
| DAGmodel() | gum::DAGmodel | |
| DAGmodel(const DAGmodel &source) | gum::DAGmodel | |
| descendants(const NodeId id) const | gum::DAGmodel | |
| descendants(const std::string &name) const | gum::DAGmodel | |
| dim() const | gum::IBayesNet< GUM_SCALAR > | |
| empty() const | gum::GraphicalModel | virtual |
| endTopologyTransformation() | gum::BayesNet< GUM_SCALAR > | |
| erase(NodeId varId) | gum::BayesNet< GUM_SCALAR > | |
| erase(const std::string &name) | gum::BayesNet< GUM_SCALAR > | inline |
| erase(const DiscreteVariable &var) | gum::BayesNet< GUM_SCALAR > | |
| eraseArc(const Arc &arc) | gum::BayesNet< GUM_SCALAR > | |
| eraseArc(NodeId tail, NodeId head) | gum::BayesNet< GUM_SCALAR > | |
| eraseArc(const std::string &tail, const std::string &head) | gum::BayesNet< GUM_SCALAR > | inline |
| exists(NodeId node) const final | gum::DAGmodel | virtual |
| gum::GraphicalModel::exists(const std::string &name) const | gum::GraphicalModel | inline |
| existsArc(const NodeId tail, const NodeId head) const | gum::DAGmodel | |
| existsArc(const std::string &nametail, const std::string &namehead) const | gum::DAGmodel | |
| family(const NodeId id) const | gum::DAGmodel | |
| family(const std::string &name) const | gum::DAGmodel | |
| fastPrototype(const std::string &dotlike, Size domainSize=2) | gum::BayesNet< GUM_SCALAR > | static |
| generateCPT(NodeId node) const | gum::BayesNet< GUM_SCALAR > | |
| generateCPT(const std::string &name) const | gum::BayesNet< GUM_SCALAR > | inline |
| generateCPTs() const | gum::BayesNet< GUM_SCALAR > | |
| GraphicalModel() | gum::GraphicalModel | |
| GraphicalModel(const GraphicalModel &source) | gum::GraphicalModel | |
| hasSameStructure(const DAGmodel &other) | gum::DAGmodel | |
| IBayesNet() | gum::IBayesNet< GUM_SCALAR > | |
| IBayesNet(std::string name) | gum::IBayesNet< GUM_SCALAR > | explicit |
| IBayesNet(const IBayesNet< GUM_SCALAR > &source) | gum::IBayesNet< GUM_SCALAR > | |
| idFromName(const std::string &name) const final | gum::BayesNet< GUM_SCALAR > | virtual |
| ids(const std::vector< std::string > &names) const | gum::GraphicalModel | |
| isIndependent(NodeId X, NodeId Y, const NodeSet &Z) const final | gum::DAGmodel | virtual |
| isIndependent(const NodeSet &X, const NodeSet &Y, const NodeSet &Z) const final | gum::DAGmodel | virtual |
| isIndependent(const std::string &Xname, const std::string &Yname, const std::vector< std::string > &Znames) const | gum::DAGmodel | inline |
| isIndependent(const std::vector< std::string > &Xnames, const std::vector< std::string > &Ynames, const std::vector< std::string > &Znames) const | gum::DAGmodel | inline |
| jointProbability(const Instantiation &i) const | gum::IBayesNet< GUM_SCALAR > | |
| log10DomainSize() const | gum::GraphicalModel | |
| log2JointProbability(const Instantiation &i) const | gum::IBayesNet< GUM_SCALAR > | |
| maxNonOneParam() const | gum::IBayesNet< GUM_SCALAR > | |
| maxParam() const | gum::IBayesNet< GUM_SCALAR > | |
| maxVarDomainSize() const | gum::IBayesNet< GUM_SCALAR > | |
| minimalCondSet(NodeId target, const NodeSet &soids) const | gum::IBayesNet< GUM_SCALAR > | |
| minimalCondSet(const NodeSet &targets, const NodeSet &soids) const | gum::IBayesNet< GUM_SCALAR > | |
| minNonZeroParam() const | gum::IBayesNet< GUM_SCALAR > | |
| minParam() const | gum::IBayesNet< GUM_SCALAR > | |
| moralGraph(bool clear=true) const | gum::DAGmodel | |
| moralizedAncestralGraph(const NodeSet &nodes) const | gum::DAGmodel | |
| moralizedAncestralGraph(const std::vector< std::string > &nodenames) const | gum::DAGmodel | |
| names(const std::vector< NodeId > &ids) const | gum::GraphicalModel | |
| names(const NodeSet &ids) const | gum::GraphicalModel | |
| nodeId(const DiscreteVariable &var) const final | gum::BayesNet< GUM_SCALAR > | virtual |
| nodes() const final | gum::DAGmodel | virtual |
| nodeset(const std::vector< std::string > &names) const | gum::GraphicalModel | |
| operator!=(const IBayesNet< GUM_SCALAR > &from) const | gum::IBayesNet< GUM_SCALAR > | |
| operator=(const BayesNet< GUM_SCALAR > &source) | gum::BayesNet< GUM_SCALAR > | |
| gum::IBayesNet::operator=(const IBayesNet< GUM_SCALAR > &source) | gum::IBayesNet< GUM_SCALAR > | |
| gum::DAGmodel::operator=(const DAGmodel &source) | gum::DAGmodel | protected |
| gum::GraphicalModel::operator=(const GraphicalModel &source) | gum::GraphicalModel | protected |
| operator==(const IBayesNet< GUM_SCALAR > &from) const | gum::IBayesNet< GUM_SCALAR > | |
| parents(const NodeId id) const | gum::DAGmodel | |
| parents(const std::string &name) const | gum::DAGmodel | |
| parents(const NodeSet &ids) const | gum::DAGmodel | |
| parents(const std::vector< std::string > &names) const | gum::DAGmodel | |
| property(const std::string &name) const | gum::GraphicalModel | |
| propertyWithDefault(const std::string &name, const std::string &byDefault) const | gum::GraphicalModel | |
| reverseArc(NodeId tail, NodeId head) | gum::BayesNet< GUM_SCALAR > | |
| reverseArc(const std::string &tail, const std::string &head) | gum::BayesNet< GUM_SCALAR > | inline |
| reverseArc(const Arc &arc) | gum::BayesNet< GUM_SCALAR > | |
| setProperty(const std::string &name, const std::string &value) | gum::GraphicalModel | |
| size() const final | gum::DAGmodel | virtual |
| sizeArcs() const | gum::DAGmodel | |
| toDot() const | gum::IBayesNet< GUM_SCALAR > | virtual |
| topologicalOrder(bool clear=true) const | gum::DAGmodel | |
| toString() const | gum::IBayesNet< GUM_SCALAR > | |
| variable(NodeId id) const final | gum::BayesNet< GUM_SCALAR > | virtual |
| variable(const std::string &name) const | gum::BayesNet< GUM_SCALAR > | inline |
| variableFromName(const std::string &name) const final | gum::BayesNet< GUM_SCALAR > | virtual |
| variableNodeMap() const final | gum::BayesNet< GUM_SCALAR > | virtual |
| ~BayesNet() final | gum::BayesNet< GUM_SCALAR > | virtual |
| ~DAGmodel() | gum::DAGmodel | virtual |
| ~GraphicalModel() | gum::GraphicalModel | virtual |
| ~IBayesNet() | gum::IBayesNet< GUM_SCALAR > | virtual |