RandomForests.h

#ifndef GRT_RANDOM_FORESTS_HEADER
#define GRT_RANDOM_FORESTS_HEADER

#include "../DecisionTree/DecisionTree.h"

GRT_BEGIN_NAMESPACE

class GRT_API RandomForests : public Classifier
{
 public:
 RandomForests(const DecisionTreeNode &decisionTreeNode = DecisionTreeClusterNode(),
 const UINT forestSize=10,
 const UINT numRandomSplits=100,
 const UINT minNumSamplesPerNode=5,
 const UINT maxDepth=10,
 const UINT trainingMode = DecisionTree::BEST_RANDOM_SPLIT,
 const bool removeFeaturesAtEachSpilt = true,
 const bool useScaling=false,
 const Float bootstrappedDatasetWeight = 0.8);
 
 RandomForests(const RandomForests &rhs);
 
 virtual ~RandomForests(void);
 
 RandomForests &operator=(const RandomForests &rhs);
 
 virtual bool deepCopyFrom(const Classifier *classifier);
 
 virtual bool train_(ClassificationData &trainingData);
 
 virtual bool predict_(VectorDouble &inputVector);
 
 virtual bool clear();
 
 virtual bool print() const;
 
 virtual bool save( std::fstream &file ) const;
 
 virtual bool load( std::fstream &file );
 
 bool combineModels( const RandomForests &forest );
 
 UINT getForestSize() const;
 
 UINT getNumRandomSplits() const;
 
 UINT getMinNumSamplesPerNode() const;
 
 UINT getMaxDepth() const;
 
 UINT getTrainingMode() const;
 
 const Vector< DecisionTreeNode* > &getForest() const;
 
 bool getRemoveFeaturesAtEachSpilt() const;
 
 Float getBootstrappedDatasetWeight() const;
 
 DecisionTreeNode* getTree( const UINT index ) const;
 
 DecisionTreeNode* deepCopyDecisionTreeNode() const;
 
 VectorDouble getFeatureWeights( const bool normWeights = true ) const;
 
 MatrixDouble getLeafNodeFeatureWeights( const bool normWeights = true ) const;
 
 bool setForestSize(const UINT forestSize);
 
 bool setNumRandomSplits(const UINT numSplittingSteps);
 
 bool setMinNumSamplesPerNode(const UINT minNumSamplesPerNode);
 
 bool setMaxDepth(const UINT maxDepth);
 
 bool setRemoveFeaturesAtEachSpilt(const bool removeFeaturesAtEachSpilt);
 
 bool setTrainingMode(const UINT trainingMode);
 
 bool setDecisionTreeNode( const DecisionTreeNode &node );
 
 bool setBootstrappedDatasetWeight( const Float bootstrappedDatasetWeight );

 static std::string getId();
 
 //Tell the compiler we are using the base class train method to stop hidden virtual function warnings
 using MLBase::save;
 using MLBase::load;
 
protected:
 
 UINT forestSize;
 UINT numRandomSplits;
 UINT minNumSamplesPerNode;
 UINT maxDepth;
 UINT trainingMode;
 bool removeFeaturesAtEachSpilt;
 Float bootstrappedDatasetWeight;
 DecisionTreeNode* decisionTreeNode;
 Vector< DecisionTreeNode* > forest;
 
private:
 static RegisterClassifierModule< RandomForests > registerModule;
 static std::string id;
 
};

GRT_END_NAMESPACE

#endif //GRT_RANDOM_FORESTS_HEADER