35 #ifndef GRT_DECISION_TREE_THRESHOLD_NODE_HEADER
36 #define GRT_DECISION_TREE_THRESHOLD_NODE_HEADER
64 virtual bool predict(
const VectorDouble &x);
80 virtual bool print()
const;
89 virtual bool getModel(ostream &stream)
const;
129 bool set(
const UINT nodeSize,
const UINT featureIndex,
const double threshold,
const VectorDouble &classProbabilities);
133 virtual bool computeBestSpiltBestIterativeSpilt(
const UINT &numSplittingSteps,
const ClassificationData &trainingData,
const vector< UINT > &features,
const vector< UINT > &classLabels, UINT &featureIndex,
double &minError );
135 virtual bool computeBestSpiltBestRandomSpilt(
const UINT &numSplittingSteps,
const ClassificationData &trainingData,
const vector< UINT > &features,
const vector< UINT > &classLabels, UINT &featureIndex,
double &minError );
162 #endif //GRT_DECISION_TREE_THRESHOLD_NODE_HEADER
virtual bool predict(const VectorDouble &x)
virtual bool getModel(ostream &stream) const
DecisionTreeThresholdNode * deepCopy() const
double getThreshold() const
DecisionTreeThresholdNode()
virtual Node * deepCopyNode() const
virtual bool saveParametersToFile(fstream &file) const
virtual ~DecisionTreeThresholdNode()
This file implements a DecisionTreeNode, which is a specific base node used for a DecisionTree...
bool set(const UINT nodeSize, const UINT featureIndex, const double threshold, const VectorDouble &classProbabilities)
virtual bool print() const
virtual bool loadParametersFromFile(fstream &file)
UINT getFeatureIndex() const