cv::ml::RTrees Class Reference

The class implements the random forest predictor. More...

#include <opencv2/ml.hpp>

Collaboration diagram for cv::ml::RTrees:

Public Member Functions
virtual int	getActiveVarCount () const =0
virtual bool	getCalculateVarImportance () const =0
virtual double	getOOBError () const =0
virtual TermCriteria	getTermCriteria () const =0
virtual Mat	getVarImportance () const =0
virtual void	getVotes (InputArray samples, OutputArray results, int flags) const =0
virtual void	setActiveVarCount (int val)=0
virtual void	setCalculateVarImportance (bool val)=0
virtual void	setTermCriteria (const TermCriteria &val)=0
Public Member Functions inherited from cv::ml::DTrees
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
	Returns all the nodes.
virtual cv::Mat	getPriors () const =0
	The array of a priori class probabilities, sorted by the class label value.
virtual float	getRegressionAccuracy () const =0
virtual const std::vector< int > &	getRoots () const =0
	Returns indices of root nodes.
virtual const std::vector< Split > &	getSplits () const =0
	Returns all the splits.
virtual const std::vector< int > &	getSubsets () const =0
	Returns all the bitsets for categorical splits.
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
	The array of a priori class probabilities, sorted by the class label value.
virtual void	setRegressionAccuracy (float val)=0
virtual void	setTruncatePrunedTree (bool val)=0
virtual void	setUse1SERule (bool val)=0
virtual void	setUseSurrogates (bool val)=0
Public Member Functions inherited from cv::ml::StatModel
virtual float	calcError (const Ptr< TrainData > &data, bool test, OutputArray resp) const
	Computes error on the training or test dataset.
virtual bool	empty () const CV_OVERRIDE
	Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read.
virtual int	getVarCount () const =0
	Returns the number of variables in training samples.
virtual bool	isClassifier () const =0
	Returns true if the model is classifier.
virtual bool	isTrained () const =0
	Returns true if the model is trained.
virtual float	predict (InputArray samples, OutputArray results=noArray(), int flags=0) const =0
	Predicts response(s) for the provided sample(s)
virtual bool	train (const Ptr< TrainData > &trainData, int flags=0)
	Trains the statistical model.
virtual bool	train (InputArray samples, int layout, InputArray responses)
	Trains the statistical model.
Public Member Functions inherited from cv::Algorithm
	Algorithm ()
virtual	~Algorithm ()
virtual void	clear ()
	Clears the algorithm state.
virtual String	getDefaultName () const
virtual void	read (const FileNode &fn)
	Reads algorithm parameters from a file storage.
virtual void	save (const String &filename) const
virtual void	write (FileStorage &fs) const
	Stores algorithm parameters in a file storage.
void	write (FileStorage &fs, const String &name) const

Static Public Member Functions
static Ptr< RTrees >	create ()
static Ptr< RTrees >	load (const String &filepath, const String &nodeName=String())
	Loads and creates a serialized RTree from a file.
Static Public Member Functions inherited from cv::ml::DTrees
static Ptr< DTrees >	create ()
	Creates the empty model.
static Ptr< DTrees >	load (const String &filepath, const String &nodeName=String())
	Loads and creates a serialized DTrees from a file.
Static Public Member Functions inherited from cv::ml::StatModel
template<typename _Tp >
static Ptr< _Tp >	train (const Ptr< TrainData > &data, int flags=0)
	Create and train model with default parameters.
Static Public Member Functions inherited from cv::Algorithm
template<typename _Tp >
static Ptr< _Tp >	load (const String &filename, const String &objname=String())
	Loads algorithm from the file.
template<typename _Tp >
static Ptr< _Tp >	loadFromString (const String &strModel, const String &objname=String())
	Loads algorithm from a String.
template<typename _Tp >
static Ptr< _Tp >	read (const FileNode &fn)
	Reads algorithm from the file node.

Additional Inherited Members
Public Types inherited from cv::ml::DTrees
enum	Flags {   PREDICT_AUTO =0 ,   PREDICT_SUM =(1<<8) ,   PREDICT_MAX_VOTE =(2<<8) ,   PREDICT_MASK =(3<<8) }
Public Types inherited from cv::ml::StatModel
enum	Flags {   UPDATE_MODEL = 1 ,   RAW_OUTPUT =1 ,   COMPRESSED_INPUT =2 ,   PREPROCESSED_INPUT =4 }
Protected Member Functions inherited from cv::Algorithm
void	writeFormat (FileStorage &fs) const

Detailed Description

The class implements the random forest predictor.

See also

Random Trees

Member Function Documentation

create()

static Ptr< RTrees > cv::ml::RTrees::create ( )

static

Python:
	cv.ml.RTrees.create(		) ->	retval
	cv.ml.RTrees_create(		) ->	retval

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.

getActiveVarCount()

virtual int cv::ml::RTrees::getActiveVarCount ( ) const

pure virtual

Python:
	cv.ml.RTrees.getActiveVarCount(		) ->	retval

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.

See also

setActiveVarCount

getCalculateVarImportance()

virtual bool cv::ml::RTrees::getCalculateVarImportance ( ) const

pure virtual

Python:
	cv.ml.RTrees.getCalculateVarImportance(		) ->	retval

If true then variable importance will be calculated and then it can be retrieved by RTrees::getVarImportance. Default value is false.

See also

setCalculateVarImportance

getOOBError()

virtual double cv::ml::RTrees::getOOBError ( ) const

pure virtual

Python:
	cv.ml.RTrees.getOOBError(		) ->	retval

Returns the OOB error value, computed at the training stage when calcOOBError is set to true. If this flag was set to false, 0 is returned. The OOB error is also scaled by sample weighting.

getTermCriteria()

virtual TermCriteria cv::ml::RTrees::getTermCriteria ( ) const

pure virtual

Python:
	cv.ml.RTrees.getTermCriteria(		) ->	retval

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)

See also

setTermCriteria

getVarImportance()

virtual Mat cv::ml::RTrees::getVarImportance ( ) const

pure virtual

Python:
	cv.ml.RTrees.getVarImportance(		) ->	retval

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.

getVotes()

virtual void cv::ml::RTrees::getVotes ( InputArray samples, OutputArray results, int flags ) const

pure virtual

Python:
	cv.ml.RTrees.getVotes(	samples, flags[, results]	) ->	results

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 containing 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.

load()

static Ptr< RTrees > cv::ml::RTrees::load ( const String & filepath, const String & nodeName = String() )

static

Python:
	cv.ml.RTrees.load(	filepath[, nodeName]	) ->	retval
	cv.ml.RTrees_load(	filepath[, nodeName]	) ->	retval

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

setActiveVarCount()

virtual void cv::ml::RTrees::setActiveVarCount ( int val )

pure virtual

Python:
	cv.ml.RTrees.setActiveVarCount(	val	) ->	None

See also

getActiveVarCount

setCalculateVarImportance()

virtual void cv::ml::RTrees::setCalculateVarImportance ( bool val )

pure virtual

Python:
	cv.ml.RTrees.setCalculateVarImportance(	val	) ->	None

See also

getCalculateVarImportance

setTermCriteria()

virtual void cv::ml::RTrees::setTermCriteria ( const TermCriteria & val )

pure virtual

Python:
	cv.ml.RTrees.setTermCriteria(	val	) ->	None

See also

getTermCriteria

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The documentation for this class was generated from the following file:

• opencv2/ml.hpp