Support Vector Machines. More...

#include <opencv2/ml.hpp>

Inheritance diagram for cv::ml::SVM:

Collaboration diagram for cv::ml::SVM:

Classes
class	Kernel

Public Types
enum	Flags { UPDATE_MODEL = 1, RAW_OUTPUT =1, COMPRESSED_INPUT =2, PREPROCESSED_INPUT =4 }
	Predict options. More...

enum	KernelTypes { CUSTOM =-1, LINEAR =0, POLY =1, RBF =2, SIGMOID =3, CHI2 =4, INTER =5 }
	SVM kernel type More...

enum	ParamTypes { C =0, GAMMA =1, P =2, NU =3, COEF =4, DEGREE =5 }
	SVM params type More...

enum	Types { C_SVC =100, NU_SVC =101, ONE_CLASS =102, EPS_SVR =103, NU_SVR =104 }
	SVM type More...

Public Member Functions
virtual float	calcError (const Ptr< TrainData > &data, bool test, OutputArray resp) const
	Computes error on the training or test dataset. More...

virtual void	clear ()
	Clears the algorithm state. More...

virtual bool	empty () const CV_OVERRIDE
	Returns true if the Algorithm is empty (e.g. More...

virtual double	getC () const =0
	Parameter C of a SVM optimization problem. More...

virtual cv::Mat	getClassWeights () const =0
	Optional weights in the SVM::C_SVC problem, assigned to particular classes. More...

virtual double	getCoef0 () const =0
	Parameter coef0 of a kernel function. More...

virtual double	getDecisionFunction (int i, OutputArray alpha, OutputArray svidx) const =0
	Retrieves the decision function. More...

virtual String	getDefaultName () const
	Returns the algorithm string identifier. More...

virtual double	getDegree () const =0
	Parameter degree of a kernel function. More...

virtual double	getGamma () const =0
	Parameter \(\gamma\) of a kernel function. More...

virtual int	getKernelType () const =0
	Type of a SVM kernel. More...

virtual double	getNu () const =0
	Parameter \(\nu\) of a SVM optimization problem. More...

virtual double	getP () const =0
	Parameter \(\epsilon\) of a SVM optimization problem. More...

virtual Mat	getSupportVectors () const =0
	Retrieves all the support vectors. More...

virtual cv::TermCriteria	getTermCriteria () const =0
	Termination criteria of the iterative SVM training procedure which solves a partial case of constrained quadratic optimization problem. More...

virtual int	getType () const =0
	Type of a SVM formulation. More...

virtual Mat	getUncompressedSupportVectors () const =0
	Retrieves all the uncompressed support vectors of a linear SVM. More...

virtual int	getVarCount () const =0
	Returns the number of variables in training samples. More...

virtual bool	isClassifier () const =0
	Returns true if the model is classifier. More...

virtual bool	isTrained () const =0
	Returns true if the model is trained. More...

virtual float	predict (InputArray samples, OutputArray results=noArray(), int flags=0) const =0
	Predicts response(s) for the provided sample(s) More...

virtual void	read (const FileNode &fn)
	Reads algorithm parameters from a file storage. More...

virtual void	save (const String &filename) const
	Saves the algorithm to a file. More...

virtual void	setC (double val)=0
	Parameter C of a SVM optimization problem. More...

virtual void	setClassWeights (const cv::Mat &val)=0
	Optional weights in the SVM::C_SVC problem, assigned to particular classes. More...

virtual void	setCoef0 (double val)=0
	Parameter coef0 of a kernel function. More...

virtual void	setCustomKernel (const Ptr< Kernel > &_kernel)=0
	Initialize with custom kernel. More...

virtual void	setDegree (double val)=0
	Parameter degree of a kernel function. More...

virtual void	setGamma (double val)=0
	Parameter \(\gamma\) of a kernel function. More...

virtual void	setKernel (int kernelType)=0
	Initialize with one of predefined kernels. More...

virtual void	setNu (double val)=0
	Parameter \(\nu\) of a SVM optimization problem. More...

virtual void	setP (double val)=0
	Parameter \(\epsilon\) of a SVM optimization problem. More...

virtual void	setTermCriteria (const cv::TermCriteria &val)=0
	Termination criteria of the iterative SVM training procedure which solves a partial case of constrained quadratic optimization problem. More...

virtual void	setType (int val)=0
	Type of a SVM formulation. More...

virtual bool	train (const Ptr< TrainData > &trainData, int flags=0)
	Trains the statistical model. More...

virtual bool	train (InputArray samples, int layout, InputArray responses)
	Trains the statistical model. More...

virtual bool	trainAuto (const Ptr< TrainData > &data, int kFold=10, ParamGrid Cgrid=getDefaultGrid(C), ParamGrid gammaGrid=getDefaultGrid(GAMMA), ParamGrid pGrid=getDefaultGrid(P), ParamGrid nuGrid=getDefaultGrid(NU), ParamGrid coeffGrid=getDefaultGrid(COEF), ParamGrid degreeGrid=getDefaultGrid(DEGREE), bool balanced=false)=0
	Trains an SVM with optimal parameters. More...

virtual bool	trainAuto (InputArray samples, int layout, InputArray responses, int kFold=10, Ptr< ParamGrid > Cgrid=SVM::getDefaultGridPtr(SVM::C), Ptr< ParamGrid > gammaGrid=SVM::getDefaultGridPtr(SVM::GAMMA), Ptr< ParamGrid > pGrid=SVM::getDefaultGridPtr(SVM::P), Ptr< ParamGrid > nuGrid=SVM::getDefaultGridPtr(SVM::NU), Ptr< ParamGrid > coeffGrid=SVM::getDefaultGridPtr(SVM::COEF), Ptr< ParamGrid > degreeGrid=SVM::getDefaultGridPtr(SVM::DEGREE), bool balanced=false)=0
	Trains an SVM with optimal parameters. More...

virtual void	write (FileStorage &fs) const
	Stores algorithm parameters in a file storage. More...

void	write (const Ptr< FileStorage > &fs, const String &name=String()) const
	simplified API for language bindings This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts. More...

Static Public Member Functions
static Ptr< SVM >	create ()
	Creates empty model. More...

static ParamGrid	getDefaultGrid (int param_id)
	Generates a grid for SVM parameters. More...

static Ptr< ParamGrid >	getDefaultGridPtr (int param_id)
	Generates a grid for SVM parameters. More...

static Ptr< SVM >	load (const String &filepath)
	Loads and creates a serialized svm from a file. More...

template<typename _Tp >
static Ptr< _Tp >	load (const String &filename, const String &objname=String())
	Loads algorithm from the file. More...

template<typename _Tp >
static Ptr< _Tp >	loadFromString (const String &strModel, const String &objname=String())
	Loads algorithm from a String. More...

template<typename _Tp >
static Ptr< _Tp >	read (const FileNode &fn)
	Reads algorithm from the file node. More...

template<typename _Tp >
static Ptr< _Tp >	train (const Ptr< TrainData > &data, int flags=0)
	Create and train model with default parameters. More...

Protected Member Functions
void	writeFormat (FileStorage &fs) const

Detailed Description

Support Vector Machines.

See also: Support Vector Machines

Member Enumeration Documentation

◆ Flags

enum cv::ml::StatModel::Flags

inherited

Predict options.

Enumerator
UPDATE_MODEL
RAW_OUTPUT	makes the method return the raw results (the sum), not the class label
COMPRESSED_INPUT
PREPROCESSED_INPUT

◆ KernelTypes

enum cv::ml::SVM::KernelTypes

SVM kernel type

A comparison of different kernels on the following 2D test case with four classes. Four SVM::C_SVC SVMs have been trained (one against rest) with auto_train. Evaluation on three different kernels (SVM::CHI2, SVM::INTER, SVM::RBF). The color depicts the class with max score. Bright means max-score > 0, dark means max-score < 0.

image

Enumerator
CUSTOM	Returned by SVM::getKernelType in case when custom kernel has been set.
LINEAR	Linear kernel. No mapping is done, linear discrimination (or regression) is done in the original feature space. It is the fastest option. \(K(x_i, x_j) = x_i^T x_j\).
POLY	Polynomial kernel: \(K(x_i, x_j) = (\gamma x_i^T x_j + coef0)^{degree}, \gamma > 0\).
RBF	Radial basis function (RBF), a good choice in most cases. \(K(x_i, x_j) = e^{-\gamma \|\|x_i - x_j\|\|^2}, \gamma > 0\).
SIGMOID	Sigmoid kernel: \(K(x_i, x_j) = \tanh(\gamma x_i^T x_j + coef0)\).
CHI2	Exponential Chi2 kernel, similar to the RBF kernel: \(K(x_i, x_j) = e^{-\gamma \chi^2(x_i,x_j)}, \chi^2(x_i,x_j) = (x_i-x_j)^2/(x_i+x_j), \gamma > 0\).
INTER	Histogram intersection kernel. A fast kernel. \(K(x_i, x_j) = min(x_i,x_j)\).

◆ ParamTypes

enum cv::ml::SVM::ParamTypes

SVM params type

Enumerator
C
GAMMA
P
NU
COEF
DEGREE

◆ Types

enum cv::ml::SVM::Types

SVM type

Enumerator
C_SVC	C-Support Vector Classification. n-class classification (n \(\geq\) 2), allows imperfect separation of classes with penalty multiplier C for outliers.
NU_SVC	\(\nu\)-Support Vector Classification. n-class classification with possible imperfect separation. Parameter \(\nu\) (in the range 0..1, the larger the value, the smoother the decision boundary) is used instead of C.
ONE_CLASS	Distribution Estimation (One-class SVM). All the training data are from the same class, SVM builds a boundary that separates the class from the rest of the feature space.
EPS_SVR	\(\epsilon\)-Support Vector Regression. The distance between feature vectors from the training set and the fitting hyper-plane must be less than p. For outliers the penalty multiplier C is used.
NU_SVR	\(\nu\)-Support Vector Regression. \(\nu\) is used instead of p. See [19] for details.

Member Function Documentation

◆ calcError()

virtual float cv::ml::StatModel::calcError	(	const Ptr< TrainData > &	data,
		bool	test,
		OutputArray	resp
	)		const

virtualinherited

Computes error on the training or test dataset.

Parameters

data	the training data
test	if true, the error is computed over the test subset of the data, otherwise it's computed over the training subset of the data. Please note that if you loaded a completely different dataset to evaluate already trained classifier, you will probably want not to set the test subset at all with TrainData::setTrainTestSplitRatio and specify test=false, so that the error is computed for the whole new set. Yes, this sounds a bit confusing.
resp	the optional output responses.

The method uses StatModel::predict to compute the error. For regression models the error is computed as RMS, for classifiers - as a percent of missclassified samples (0%-100%).

◆ clear()

virtual void cv::Algorithm::clear ( )

inlinevirtualinherited

Clears the algorithm state.

Reimplemented in cv::FlannBasedMatcher, and cv::DescriptorMatcher.

◆ create()

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

static

Creates empty model.

Use StatModel::train to train the model. Since SVM has several parameters, you may want to find the best parameters for your problem, it can be done with SVM::trainAuto.

Examples:: samples/cpp/train_HOG.cpp.

◆ empty()

virtual bool cv::ml::StatModel::empty ( ) const

virtualinherited

Returns true if the Algorithm is empty (e.g.

in the very beginning or after unsuccessful read

Reimplemented from cv::Algorithm.

◆ getC()

virtual double cv::ml::SVM::getC ( ) const

pure virtual

Parameter C of a SVM optimization problem.

For SVM::C_SVC, SVM::EPS_SVR or SVM::NU_SVR. Default value is 0.

See also: setC

◆ getClassWeights()

virtual cv::Mat cv::ml::SVM::getClassWeights ( ) const

pure virtual

Optional weights in the SVM::C_SVC problem, assigned to particular classes.

They are multiplied by C so the parameter C of class i becomes classWeights(i) * C. Thus these weights affect the misclassification penalty for different classes. The larger weight, the larger penalty on misclassification of data from the corresponding class. Default value is empty Mat.

See also: setClassWeights

◆ getCoef0()

virtual double cv::ml::SVM::getCoef0 ( ) const

pure virtual

Parameter coef0 of a kernel function.

For SVM::POLY or SVM::SIGMOID. Default value is 0.

See also: setCoef0

◆ getDecisionFunction()

virtual double cv::ml::SVM::getDecisionFunction	(	int	i,
		OutputArray	alpha,
		OutputArray	svidx
	)		const

pure virtual

Retrieves the decision function.

Parameters

i	the index of the decision function. If the problem solved is regression, 1-class or 2-class classification, then there will be just one decision function and the index should always be 0. Otherwise, in the case of N-class classification, there will be \(N(N-1)/2\) decision functions.
alpha	the optional output vector for weights, corresponding to different support vectors. In the case of linear SVM all the alpha's will be 1's.
svidx	the optional output vector of indices of support vectors within the matrix of support vectors (which can be retrieved by SVM::getSupportVectors). In the case of linear SVM each decision function consists of a single "compressed" support vector.

The method returns rho parameter of the decision function, a scalar subtracted from the weighted sum of kernel responses.

◆ getDefaultGrid()

static ParamGrid cv::ml::SVM::getDefaultGrid ( int param_id )

static

Generates a grid for SVM parameters.

Parameters

param_id SVM parameters IDs that must be one of the SVM::ParamTypes. The grid is generated for the parameter with this ID.

The function generates a grid for the specified parameter of the SVM algorithm. The grid may be passed to the function SVM::trainAuto.

◆ getDefaultGridPtr()

static Ptr<ParamGrid> cv::ml::SVM::getDefaultGridPtr ( int param_id )

static

Generates a grid for SVM parameters.

Parameters

param_id SVM parameters IDs that must be one of the SVM::ParamTypes. The grid is generated for the parameter with this ID.

The function generates a grid pointer for the specified parameter of the SVM algorithm. The grid may be passed to the function SVM::trainAuto.

◆ getDefaultName()

virtual String cv::Algorithm::getDefaultName ( ) const

virtualinherited

Returns the algorithm string identifier.

This string is used as top level xml/yml node tag when the object is saved to a file or string.

Reimplemented in cv::AKAZE, cv::KAZE, cv::SimpleBlobDetector, cv::GFTTDetector, cv::AgastFeatureDetector, cv::FastFeatureDetector, cv::MSER, cv::ORB, cv::BRISK, and cv::Feature2D.

◆ getDegree()

virtual double cv::ml::SVM::getDegree ( ) const

pure virtual

Parameter degree of a kernel function.

For SVM::POLY. Default value is 0.

See also: setDegree

◆ getGamma()

virtual double cv::ml::SVM::getGamma ( ) const

pure virtual

Parameter \(\gamma\) of a kernel function.

For SVM::POLY, SVM::RBF, SVM::SIGMOID or SVM::CHI2. Default value is 1.

See also: setGamma

◆ getKernelType()

virtual int cv::ml::SVM::getKernelType ( ) const

pure virtual

Type of a SVM kernel.

See SVM::KernelTypes. Default value is SVM::RBF.

◆ getNu()

virtual double cv::ml::SVM::getNu ( ) const

pure virtual

Parameter \(\nu\) of a SVM optimization problem.

For SVM::NU_SVC, SVM::ONE_CLASS or SVM::NU_SVR. Default value is 0.

See also: setNu

◆ getP()

virtual double cv::ml::SVM::getP ( ) const

pure virtual

Parameter \(\epsilon\) of a SVM optimization problem.

For SVM::EPS_SVR. Default value is 0.

See also: setP

◆ getSupportVectors()

virtual Mat cv::ml::SVM::getSupportVectors ( ) const

pure virtual

Retrieves all the support vectors.

The method returns all the support vectors as a floating-point matrix, where support vectors are stored as matrix rows.

◆ getTermCriteria()

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

pure virtual

Termination criteria of the iterative SVM training procedure which solves a partial case of constrained quadratic optimization problem.

You can specify tolerance and/or the maximum number of iterations. Default value is TermCriteria( TermCriteria::MAX_ITER + TermCriteria::EPS, 1000, FLT_EPSILON );

See also: setTermCriteria

◆ getType()

virtual int cv::ml::SVM::getType ( ) const

pure virtual

Type of a SVM formulation.

See SVM::Types. Default value is SVM::C_SVC.

See also: setType

◆ getUncompressedSupportVectors()

virtual Mat cv::ml::SVM::getUncompressedSupportVectors ( ) const

pure virtual

Retrieves all the uncompressed support vectors of a linear SVM.

The method returns all the uncompressed support vectors of a linear SVM that the compressed support vector, used for prediction, was derived from. They are returned in a floating-point matrix, where the support vectors are stored as matrix rows.

◆ getVarCount()

virtual int cv::ml::StatModel::getVarCount ( ) const

pure virtualinherited

Returns the number of variables in training samples.

◆ isClassifier()

virtual bool cv::ml::StatModel::isClassifier ( ) const

pure virtualinherited

Returns true if the model is classifier.

◆ isTrained()

virtual bool cv::ml::StatModel::isTrained ( ) const

pure virtualinherited

Returns true if the model is trained.

◆ load() [1/2]

static Ptr<SVM> cv::ml::SVM::load ( const String & filepath )

static

Loads and creates a serialized svm from a file.

Use SVM::save to serialize and store an SVM to disk. Load the SVM from this file again, by calling this function with the path to the file.

Parameters

filepath path to serialized svm

◆ load() [2/2]

template<typename _Tp >

static Ptr<_Tp> cv::Algorithm::load	(	const String &	filename,
		const String &	objname = `String()`
	)

inlinestaticinherited

Loads algorithm from the file.

Parameters

filename	Name of the file to read.
objname	The optional name of the node to read (if empty, the first top-level node will be used)

This is static template method of Algorithm. It's usage is following (in the case of SVM):

Ptr<SVM> svm = Algorithm::load<SVM>("my_svm_model.xml");

In order to make this method work, the derived class must overwrite Algorithm::read(const FileNode& fn).

References CV_Assert, cv::FileNode::empty(), cv::FileStorage::getFirstTopLevelNode(), cv::FileStorage::isOpened(), and cv::FileStorage::READ.

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◆ loadFromString()

template<typename _Tp >

static Ptr<_Tp> cv::Algorithm::loadFromString	(	const String &	strModel,
		const String &	objname = `String()`
	)

inlinestaticinherited

Loads algorithm from a String.

Parameters

strModel	The string variable containing the model you want to load.
objname	The optional name of the node to read (if empty, the first top-level node will be used)

This is static template method of Algorithm. It's usage is following (in the case of SVM):

Ptr<SVM> svm = Algorithm::loadFromString<SVM>(myStringModel);

References CV_WRAP, cv::FileNode::empty(), cv::FileStorage::getFirstTopLevelNode(), cv::FileStorage::MEMORY, and cv::FileStorage::READ.

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◆ predict()

virtual float cv::ml::StatModel::predict	(	InputArray	samples,
		OutputArray	results = `noArray()`,
		int	flags = `0`
	)		const

pure virtualinherited

Predicts response(s) for the provided sample(s)

Parameters

samples	The input samples, floating-point matrix
results	The optional output matrix of results.
flags	The optional flags, model-dependent. See cv::ml::StatModel::Flags.

Implemented in cv::ml::LogisticRegression, and cv::ml::EM.

◆ read() [1/2]

virtual void cv::Algorithm::read ( const FileNode & fn )

inlinevirtualinherited

Reads algorithm parameters from a file storage.

Reimplemented in cv::FlannBasedMatcher, cv::DescriptorMatcher, and cv::Feature2D.

◆ read() [2/2]

template<typename _Tp >

static Ptr<_Tp> cv::Algorithm::read ( const FileNode & fn )

inlinestaticinherited

Reads algorithm from the file node.

This is static template method of Algorithm. It's usage is following (in the case of SVM):

cv::FileStorage fsRead("example.xml", FileStorage::READ);

Ptr<SVM> svm = Algorithm::read<SVM>(fsRead.root());

In order to make this method work, the derived class must overwrite Algorithm::read(const FileNode& fn) and also have static create() method without parameters (or with all the optional parameters)

◆ save()

virtual void cv::Algorithm::save ( const String & filename ) const

virtualinherited

Saves the algorithm to a file.

In order to make this method work, the derived class must implement Algorithm::write(FileStorage& fs).

◆ setC()

virtual void cv::ml::SVM::setC ( double val )

pure virtual

Parameter C of a SVM optimization problem.

See also: getC

◆ setClassWeights()

virtual void cv::ml::SVM::setClassWeights ( const cv::Mat & val )

pure virtual

Optional weights in the SVM::C_SVC problem, assigned to particular classes.

See also: getClassWeights

◆ setCoef0()

virtual void cv::ml::SVM::setCoef0 ( double val )

pure virtual

Parameter coef0 of a kernel function.

See also: getCoef0

◆ setCustomKernel()

virtual void cv::ml::SVM::setCustomKernel ( const Ptr< Kernel > & _kernel )

pure virtual

Initialize with custom kernel.

See SVM::Kernel class for implementation details

◆ setDegree()

virtual void cv::ml::SVM::setDegree ( double val )

pure virtual

Parameter degree of a kernel function.

See also: getDegree

◆ setGamma()

virtual void cv::ml::SVM::setGamma ( double val )

pure virtual

Parameter \(\gamma\) of a kernel function.

See also: getGamma

◆ setKernel()

virtual void cv::ml::SVM::setKernel ( int kernelType )

pure virtual

Initialize with one of predefined kernels.

See SVM::KernelTypes.

◆ setNu()

virtual void cv::ml::SVM::setNu ( double val )

pure virtual

Parameter \(\nu\) of a SVM optimization problem.

See also: getNu

◆ setP()

virtual void cv::ml::SVM::setP ( double val )

pure virtual

Parameter \(\epsilon\) of a SVM optimization problem.

See also: getP

◆ setTermCriteria()

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

pure virtual

Termination criteria of the iterative SVM training procedure which solves a partial case of constrained quadratic optimization problem.

See also: getTermCriteria

◆ setType()

virtual void cv::ml::SVM::setType ( int val )

pure virtual

Type of a SVM formulation.

See also: getType

◆ train() [1/3]

virtual bool cv::ml::StatModel::train	(	const Ptr< TrainData > &	trainData,
		int	flags = `0`
	)

virtualinherited

Trains the statistical model.

Parameters

trainData	training data that can be loaded from file using TrainData::loadFromCSV or created with TrainData::create.
flags	optional flags, depending on the model. Some of the models can be updated with the new training samples, not completely overwritten (such as NormalBayesClassifier or ANN_MLP).

◆ train() [2/3]

virtual bool cv::ml::StatModel::train	(	InputArray	samples,
		int	layout,
		InputArray	responses
	)

virtualinherited

Trains the statistical model.

Parameters

samples	training samples
layout	See ml::SampleTypes.
responses	vector of responses associated with the training samples.

◆ train() [3/3]

template<typename _Tp >

static Ptr<_Tp> cv::ml::StatModel::train	(	const Ptr< TrainData > &	data,
		int	flags = `0`
	)

inlinestaticinherited

Create and train model with default parameters.

The class must implement static create() method with no parameters or with all default parameter values

◆ trainAuto() [1/2]

virtual bool cv::ml::SVM::trainAuto	(	const Ptr< TrainData > &	data,
		int	kFold = `10`,
		ParamGrid	Cgrid = `getDefaultGrid(C)`,
		ParamGrid	gammaGrid = `getDefaultGrid(GAMMA)`,
		ParamGrid	pGrid = `getDefaultGrid(P)`,
		ParamGrid	nuGrid = `getDefaultGrid(NU)`,
		ParamGrid	coeffGrid = `getDefaultGrid(COEF)`,
		ParamGrid	degreeGrid = `getDefaultGrid(DEGREE)`,
		bool	balanced = `false`
	)

pure virtual

Trains an SVM with optimal parameters.

Parameters

data	the training data that can be constructed using TrainData::create or TrainData::loadFromCSV.
kFold	Cross-validation parameter. The training set is divided into kFold subsets. One subset is used to test the model, the others form the train set. So, the SVM algorithm is executed kFold times.
Cgrid	grid for C
gammaGrid	grid for gamma
pGrid	grid for p
nuGrid	grid for nu
coeffGrid	grid for coeff
degreeGrid	grid for degree
balanced	If true and the problem is 2-class classification then the method creates more balanced cross-validation subsets that is proportions between classes in subsets are close to such proportion in the whole train dataset.

The method trains the SVM model automatically by choosing the optimal parameters C, gamma, p, nu, coef0, degree. Parameters are considered optimal when the cross-validation estimate of the test set error is minimal.

If there is no need to optimize a parameter, the corresponding grid step should be set to any value less than or equal to 1. For example, to avoid optimization in gamma, set gammaGrid.step = 0, gammaGrid.minVal, gamma_grid.maxVal as arbitrary numbers. In this case, the value Gamma is taken for gamma.

And, finally, if the optimization in a parameter is required but the corresponding grid is unknown, you may call the function SVM::getDefaultGrid. To generate a grid, for example, for gamma, call SVM::getDefaultGrid(SVM::GAMMA).

This function works for the classification (SVM::C_SVC or SVM::NU_SVC) as well as for the regression (SVM::EPS_SVR or SVM::NU_SVR). If it is SVM::ONE_CLASS, no optimization is made and the usual SVM with parameters specified in params is executed.

◆ trainAuto() [2/2]

virtual bool cv::ml::SVM::trainAuto	(	InputArray	samples,
		int	layout,
		InputArray	responses,
		int	kFold = `10`,
		Ptr< ParamGrid >	Cgrid = `SVM::getDefaultGridPtr(SVM::C)`,
		Ptr< ParamGrid >	gammaGrid = `SVM::getDefaultGridPtr(SVM::GAMMA)`,
		Ptr< ParamGrid >	pGrid = `SVM::getDefaultGridPtr(SVM::P)`,
		Ptr< ParamGrid >	nuGrid = `SVM::getDefaultGridPtr(SVM::NU)`,
		Ptr< ParamGrid >	coeffGrid = `SVM::getDefaultGridPtr(SVM::COEF)`,
		Ptr< ParamGrid >	degreeGrid = `SVM::getDefaultGridPtr(SVM::DEGREE)`,
		bool	balanced = `false`
	)

pure virtual

Trains an SVM with optimal parameters.

Parameters

samples	training samples
layout	See ml::SampleTypes.
responses	vector of responses associated with the training samples.
kFold	Cross-validation parameter. The training set is divided into kFold subsets. One subset is used to test the model, the others form the train set. So, the SVM algorithm is
Cgrid	grid for C
gammaGrid	grid for gamma
pGrid	grid for p
nuGrid	grid for nu
coeffGrid	grid for coeff
degreeGrid	grid for degree
balanced	If true and the problem is 2-class classification then the method creates more balanced cross-validation subsets that is proportions between classes in subsets are close to such proportion in the whole train dataset.

The method trains the SVM model automatically by choosing the optimal parameters C, gamma, p, nu, coef0, degree. Parameters are considered optimal when the cross-validation estimate of the test set error is minimal.

This function only makes use of SVM::getDefaultGrid for parameter optimization and thus only offers rudimentary parameter options.

This function works for the classification (SVM::C_SVC or SVM::NU_SVC) as well as for the regression (SVM::EPS_SVR or SVM::NU_SVR). If it is SVM::ONE_CLASS, no optimization is made and the usual SVM with parameters specified in params is executed.

◆ write() [1/2]

virtual void cv::Algorithm::write ( FileStorage & fs ) const

inlinevirtualinherited

Stores algorithm parameters in a file storage.

Reimplemented in cv::FlannBasedMatcher, cv::DescriptorMatcher, and cv::Feature2D.

References CV_WRAP.

Referenced by cv::Feature2D::write(), and cv::DescriptorMatcher::write().

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◆ write() [2/2]

void cv::Algorithm::write	(	const Ptr< FileStorage > &	fs,
		const String &	name = `String()`
	)		const

inherited

simplified API for language bindings This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

◆ writeFormat()

void cv::Algorithm::writeFormat ( FileStorage & fs ) const

protectedinherited

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

opencv2/ml.hpp

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◆ Flags

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Member Function Documentation

◆ calcError()

◆ clear()

◆ create()

◆ empty()

◆ getC()

◆ getClassWeights()

◆ getCoef0()

◆ getDecisionFunction()

◆ getDefaultGrid()

◆ getDefaultGridPtr()

◆ getDefaultName()

◆ getDegree()

◆ getGamma()

◆ getKernelType()

◆ getNu()

◆ getP()

◆ getSupportVectors()

◆ getTermCriteria()

◆ getType()

◆ getUncompressedSupportVectors()

◆ getVarCount()

◆ isClassifier()

◆ isTrained()

◆ load() [1/2]

◆ load() [2/2]

◆ loadFromString()

◆ predict()

◆ read() [1/2]

◆ read() [2/2]

◆ save()

◆ setC()

◆ setClassWeights()

◆ setCoef0()

◆ setCustomKernel()

◆ setDegree()

◆ setGamma()

◆ setKernel()

◆ setNu()

◆ setP()

◆ setTermCriteria()

◆ setType()

◆ train() [1/3]

◆ train() [2/3]

◆ train() [3/3]

◆ trainAuto() [1/2]

◆ trainAuto() [2/2]

◆ write() [1/2]

◆ write() [2/2]

◆ writeFormat()