class cv::ml::RTrees
Overview
The class implements the random forest predictor. More…
#include <ml.hpp> class RTrees: public cv::ml::DTrees { public: // methods virtual int getActiveVarCount() const = 0; virtual bool getCalculateVarImportance() const = 0; virtual TermCriteria getTermCriteria() const = 0; virtual Mat getVarImportance() const = 0; void getVotes( InputArray samples, OutputArray results, int flags ) const; virtual void setActiveVarCount(int val) = 0; virtual void setCalculateVarImportance(bool val) = 0; virtual void setTermCriteria(const TermCriteria& val) = 0; static Ptr<RTrees> create(); static Ptr<RTrees> load( const String& filepath, const String& nodeName = String() ); };
Inherited Members
public: // enums enum Flags; enum Flags; // classes class Node; class Split; // methods virtual void clear(); virtual bool empty() const; virtual String getDefaultName() const; virtual void read(const FileNode& fn); virtual void save(const String& filename) const; virtual void write(FileStorage& fs) const; template <typename _Tp> static Ptr<_Tp> load( const String& filename, const String& objname = String() ); template <typename _Tp> static Ptr<_Tp> loadFromString( const String& strModel, const String& objname = String() ); template <typename _Tp> static Ptr<_Tp> read(const FileNode& fn); virtual float calcError( const Ptr<TrainData>& data, bool test, OutputArray resp ) const; virtual bool empty() const; virtual int getVarCount() const = 0; virtual bool isClassifier() const = 0; virtual bool isTrained() const = 0; virtual float predict( InputArray samples, OutputArray results = noArray(), int flags = 0 ) const = 0; virtual bool train( const Ptr<TrainData>& trainData, int flags = 0 ); virtual bool train( InputArray samples, int layout, InputArray responses ); template <typename _Tp> static Ptr<_Tp> train( const Ptr<TrainData>& data, int flags = 0 ); virtual int getCVFolds() const = 0; virtual int getMaxCategories() const = 0; virtual int getMaxDepth() const = 0; virtual int getMinSampleCount() const = 0; virtual const std::vector<Node>& getNodes() const = 0; virtual cv::Mat getPriors() const = 0; virtual float getRegressionAccuracy() const = 0; virtual const std::vector<int>& getRoots() const = 0; virtual const std::vector<Split>& getSplits() const = 0; virtual const std::vector<int>& getSubsets() const = 0; virtual bool getTruncatePrunedTree() const = 0; virtual bool getUse1SERule() const = 0; virtual bool getUseSurrogates() const = 0; virtual void setCVFolds(int val) = 0; virtual void setMaxCategories(int val) = 0; virtual void setMaxDepth(int val) = 0; virtual void setMinSampleCount(int val) = 0; virtual void setPriors(const cv::Mat& val) = 0; virtual void setRegressionAccuracy(float val) = 0; virtual void setTruncatePrunedTree(bool val) = 0; virtual void setUse1SERule(bool val) = 0; virtual void setUseSurrogates(bool val) = 0; static Ptr<DTrees> create(); static Ptr<DTrees> load( const String& filepath, const String& nodeName = String() ); protected: // methods void writeFormat(FileStorage& fs) const;
Detailed Documentation
The class implements the random forest predictor.
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Methods
virtual int getActiveVarCount() const = 0
The size of the randomly selected subset of features at each tree node and that are used to find the best split(s). If you set it to 0 then the size will be set to the square root of the total number of features. Default value is 0.
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virtual bool getCalculateVarImportance() const = 0
If true then variable importance will be calculated and then it can be retrieved by RTrees::getVarImportance. Default value is false.
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virtual TermCriteria getTermCriteria() const = 0
The termination criteria that specifies when the training algorithm stops. Either when the specified number of trees is trained and added to the ensemble or when sufficient accuracy (measured as OOB error) is achieved. Typically the more trees you have the better the accuracy. However, the improvement in accuracy generally diminishes and asymptotes pass a certain number of trees. Also to keep in mind, the number of tree increases the prediction time linearly. Default value is TermCriteria (TermCriteria::MAX_ITERS + TermCriteria::EPS, 50, 0.1)
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virtual Mat getVarImportance() const = 0
Returns the variable importance array. The method returns the variable importance vector, computed at the training stage when CalculateVarImportance is set to true. If this flag was set to false, the empty matrix is returned.
void getVotes( InputArray samples, OutputArray results, int flags ) const
Returns the result of each individual tree in the forest. In case the model is a regression problem, the method will return each of the trees’ results for each of the sample cases. If the model is a classifier, it will return a Mat with samples + 1 rows, where the first row gives the class number and the following rows return the votes each class had for each sample.
Parameters:
| samples | Array containg the samples for which votes will be calculated. |
| results | Array where the result of the calculation will be written. |
| flags | Flags for defining the type of RTrees. |
virtual void setActiveVarCount(int val) = 0
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virtual void setCalculateVarImportance(bool val) = 0
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virtual void setTermCriteria(const TermCriteria& val) = 0
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static Ptr<RTrees> create()
Creates the empty model. Use StatModel::train to train the model, StatModel::train to create and train the model, Algorithm::load to load the pre-trained model.
static Ptr<RTrees> load( const String& filepath, const String& nodeName = String() )
Loads and creates a serialized RTree from a file.
Use RTree::save to serialize and store an RTree to disk. Load the RTree from this file again, by calling this function with the path to the file. Optionally specify the node for the file containing the classifier
Parameters:
| filepath | path to serialized RTree |
| nodeName | name of node containing the classifier |