35 #ifndef GRT_DECISION_TREE_CLUSTER_NODE_HEADER
36 #define GRT_DECISION_TREE_CLUSTER_NODE_HEADER
39 #include "../../ClusteringModules/KMeans/KMeans.h"
81 virtual bool print()
const;
106 virtual bool getModel( std::ostream &stream )
const;
129 UINT getFeatureIndex()
const;
136 Float getThreshold()
const;
147 bool set(
const UINT nodeSize,
const UINT featureIndex,
const Float threshold,
const VectorFloat &classProbabilities);
184 #endif //GRT_DECISION_TREE_CLUSTER_NODE_HEADER
virtual bool computeBestSpilt(const UINT &trainingMode, const UINT &numSplittingSteps, const ClassificationData &trainingData, const Vector< UINT > &features, const Vector< UINT > &classLabels, UINT &featureIndex, Float &minError)
virtual bool print() const
This file implements a DecisionTreeNode, which is a specific base node used for a DecisionTree...
virtual bool computeFeatureWeights(VectorFloat &weights) const
virtual bool saveParametersToFile(std::fstream &file) const
virtual bool predict(const VectorFloat &x, VectorFloat &classLikelihoods)
DecisionTreeNode * deepCopy() const
virtual Node * deepCopyNode() const
virtual bool computeLeafNodeWeights(MatrixFloat &weights) const
virtual bool loadParametersFromFile(std::fstream &file)
virtual bool getModel(std::ostream &stream) const