cv::ml::Boost Class Reference

Boosted tree classifier derived from DTrees. More...

#include <opencv2/ml.hpp>

Collaboration diagram for cv::ml::Boost:

Public Types
enum	Types {   DISCRETE =0 ,   REAL =1 ,   LOGIT =2 ,   GENTLE =3 }
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 }

Public Member Functions
virtual int	getBoostType () const =0
virtual int	getWeakCount () const =0
virtual double	getWeightTrimRate () const =0
virtual void	setBoostType (int val)=0
virtual void	setWeakCount (int val)=0
virtual void	setWeightTrimRate (double 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< Boost >	create ()
static Ptr< Boost >	load (const String &filepath, const String &nodeName=String())
	Loads and creates a serialized Boost 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
Protected Member Functions inherited from cv::Algorithm
void	writeFormat (FileStorage &fs) const

Detailed Description

Boosted tree classifier derived from DTrees.

See also

Boosting

Member Enumeration Documentation

Types

enum cv::ml::Boost::Types

Boosting type. Gentle AdaBoost and Real AdaBoost are often the preferable choices.

Enumerator
DISCRETE	Discrete AdaBoost.
REAL	Real AdaBoost. It is a technique that utilizes confidence-rated predictions and works well with categorical data.
LOGIT	LogitBoost. It can produce good regression fits.
GENTLE	Gentle AdaBoost. It puts less weight on outlier data points and for that reason is often good with regression data.

Member Function Documentation

create()

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

static

Python:
	cv.ml.Boost.create(		) ->	retval
	cv.ml.Boost_create(		) ->	retval

Creates the empty model. Use StatModel::train to train the model, Algorithm::load<Boost>(filename) to load the pre-trained model.

getBoostType()

virtual int cv::ml::Boost::getBoostType ( ) const

pure virtual

Python:
	cv.ml.Boost.getBoostType(		) ->	retval

Type of the boosting algorithm. See Boost::Types. Default value is Boost::REAL.

See also

setBoostType

getWeakCount()

virtual int cv::ml::Boost::getWeakCount ( ) const

pure virtual

Python:
	cv.ml.Boost.getWeakCount(		) ->	retval

The number of weak classifiers. Default value is 100.

See also

setWeakCount

getWeightTrimRate()

virtual double cv::ml::Boost::getWeightTrimRate ( ) const

pure virtual

Python:
	cv.ml.Boost.getWeightTrimRate(		) ->	retval

A threshold between 0 and 1 used to save computational time. Samples with summary weight \(\leq 1 - weight_trim_rate\) do not participate in the next iteration of training. Set this parameter to 0 to turn off this functionality. Default value is 0.95.

See also

setWeightTrimRate

load()

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

static

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

Loads and creates a serialized Boost from a file.

Use Boost::save to serialize and store an RTree to disk. Load the Boost 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 Boost
nodeName	name of node containing the classifier

setBoostType()

virtual void cv::ml::Boost::setBoostType ( int val )

pure virtual

Python:
	cv.ml.Boost.setBoostType(	val	) ->	None

See also

getBoostType

setWeakCount()

virtual void cv::ml::Boost::setWeakCount ( int val )

pure virtual

Python:
	cv.ml.Boost.setWeakCount(	val	) ->	None

See also

getWeakCount

setWeightTrimRate()

virtual void cv::ml::Boost::setWeightTrimRate ( double val )

pure virtual

Python:
	cv.ml.Boost.setWeightTrimRate(	val	) ->	None

See also

getWeightTrimRate

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

• opencv2/ml.hpp