RandomForests.h

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#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 Tree::TrainingMode trainingMode = Tree::BEST_RANDOM_SPLIT,
                  const bool removeFeaturesAtEachSplit = 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 getRemoveFeaturesAtEachSplit() 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 setRemoveFeaturesAtEachSplit(const bool removeFeaturesAtEachSplit);

    GRT_DEPRECATED_MSG( "setRemoveFeaturesAtEachSpilt(const bool removeFeaturesAtEachSpilt) is deprecated, use setRemoveFeaturesAtEachSplit(const bool removeFeaturesAtEachSplit) instead", bool setRemoveFeaturesAtEachSpilt(const bool removeFeaturesAtEachSpilt) );

    bool setTrainingMode(const Tree::TrainingMode 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;
    Tree::TrainingMode trainingMode;
    bool removeFeaturesAtEachSplit;
    Float bootstrappedDatasetWeight;
    DecisionTreeNode* decisionTreeNode;
    Vector< DecisionTreeNode* > forest;
    
private:
    static RegisterClassifierModule< RandomForests > registerModule;
    static const std::string id;
    
};

GRT_END_NAMESPACE

#endif //GRT_RANDOM_FORESTS_HEADER