org.opencv.ml

Class SVM

java.lang.Object

org.opencv.core.Algorithm

org.opencv.ml.StatModel

org.opencv.ml.SVM

public class SVM
extends StatModel

Support Vector Machines. SEE: REF: ml_intro_svm

Field Summary

Fields

Modifier and Type	Field and Description
static int	C
static int	C_SVC
static int	CHI2
static int	COEF
static int	CUSTOM
static int	DEGREE
static int	EPS_SVR
static int	GAMMA
static int	INTER
static int	LINEAR
static int	NU
static int	NU_SVC
static int	NU_SVR
static int	ONE_CLASS
static int	P
static int	POLY
static int	RBF
static int	SIGMOID

Fields inherited from class org.opencv.ml.StatModel

COMPRESSED_INPUT, PREPROCESSED_INPUT, RAW_OUTPUT, UPDATE_MODEL

Fields inherited from class org.opencv.core.Algorithm

nativeObj

Constructor Summary

Constructors

Modifier	Constructor and Description
protected	SVM(long addr)

Method Summary

Modifier and Type	Method and Description
static SVM	__fromPtr__(long addr)
static SVM	create() Creates empty model.
protected void	finalize()
double	getC() SEE: setC
Mat	getClassWeights() SEE: setClassWeights
double	getCoef0() SEE: setCoef0
double	getDecisionFunction(int i, Mat alpha, Mat svidx) Retrieves the decision function
static ParamGrid	getDefaultGridPtr(int param_id) Generates a grid for %SVM parameters.
double	getDegree() SEE: setDegree
double	getGamma() SEE: setGamma
int	getKernelType() Type of a %SVM kernel.
double	getNu() SEE: setNu
double	getP() SEE: setP
Mat	getSupportVectors() 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.
TermCriteria	getTermCriteria() SEE: setTermCriteria
int	getType() SEE: setType
Mat	getUncompressedSupportVectors() 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.
static SVM	load(String filepath) Loads and creates a serialized svm from a file Use SVM::save to serialize and store an SVM to disk.
void	setC(double val) getC SEE: getC
void	setClassWeights(Mat val) getClassWeights SEE: getClassWeights
void	setCoef0(double val) getCoef0 SEE: getCoef0
void	setDegree(double val) getDegree SEE: getDegree
void	setGamma(double val) getGamma SEE: getGamma
void	setKernel(int kernelType) Initialize with one of predefined kernels.
void	setNu(double val) getNu SEE: getNu
void	setP(double val) getP SEE: getP
void	setTermCriteria(TermCriteria val) getTermCriteria SEE: getTermCriteria
void	setType(int val) getType SEE: getType
boolean	trainAuto(Mat samples, int layout, Mat responses) Trains an %SVM with optimal parameters
boolean	trainAuto(Mat samples, int layout, Mat responses, int kFold) Trains an %SVM with optimal parameters
boolean	trainAuto(Mat samples, int layout, Mat responses, int kFold, ParamGrid Cgrid) Trains an %SVM with optimal parameters
boolean	trainAuto(Mat samples, int layout, Mat responses, int kFold, ParamGrid Cgrid, ParamGrid gammaGrid) Trains an %SVM with optimal parameters
boolean	trainAuto(Mat samples, int layout, Mat responses, int kFold, ParamGrid Cgrid, ParamGrid gammaGrid, ParamGrid pGrid) Trains an %SVM with optimal parameters
boolean	trainAuto(Mat samples, int layout, Mat responses, int kFold, ParamGrid Cgrid, ParamGrid gammaGrid, ParamGrid pGrid, ParamGrid nuGrid) Trains an %SVM with optimal parameters
boolean	trainAuto(Mat samples, int layout, Mat responses, int kFold, ParamGrid Cgrid, ParamGrid gammaGrid, ParamGrid pGrid, ParamGrid nuGrid, ParamGrid coeffGrid) Trains an %SVM with optimal parameters
boolean	trainAuto(Mat samples, int layout, Mat responses, int kFold, ParamGrid Cgrid, ParamGrid gammaGrid, ParamGrid pGrid, ParamGrid nuGrid, ParamGrid coeffGrid, ParamGrid degreeGrid) Trains an %SVM with optimal parameters
boolean	trainAuto(Mat samples, int layout, Mat responses, int kFold, ParamGrid Cgrid, ParamGrid gammaGrid, ParamGrid pGrid, ParamGrid nuGrid, ParamGrid coeffGrid, ParamGrid degreeGrid, boolean balanced) Trains an %SVM with optimal parameters

Methods inherited from class org.opencv.ml.StatModel

calcError, empty, getVarCount, isClassifier, isTrained, predict, predict, predict, train, train, train

Methods inherited from class org.opencv.core.Algorithm

clear, getDefaultName, getNativeObjAddr, save

Methods inherited from class java.lang.Object

clone, equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

CUSTOM

public static final int CUSTOM

See Also:

Constant Field Values

LINEAR

public static final int LINEAR

See Also:

Constant Field Values

POLY

public static final int POLY

See Also:

Constant Field Values

RBF

public static final int RBF

See Also:

Constant Field Values

SIGMOID

public static final int SIGMOID

See Also:

Constant Field Values

CHI2

public static final int CHI2

See Also:

Constant Field Values

INTER

public static final int INTER

See Also:

Constant Field Values

C_SVC

public static final int C_SVC

See Also:

Constant Field Values

NU_SVC

public static final int NU_SVC

See Also:

Constant Field Values

ONE_CLASS

public static final int ONE_CLASS

See Also:

Constant Field Values

EPS_SVR

public static final int EPS_SVR

See Also:

Constant Field Values

NU_SVR

public static final int NU_SVR

See Also:

Constant Field Values

C

public static final int C

See Also:

Constant Field Values

GAMMA

public static final int GAMMA

See Also:

Constant Field Values

P

public static final int P

See Also:

Constant Field Values

NU

public static final int NU

See Also:

Constant Field Values

COEF

public static final int COEF

See Also:

Constant Field Values

DEGREE

public static final int DEGREE

See Also:

Constant Field Values

Constructor Detail

SVM

protected SVM(long addr)

Method Detail

__fromPtr__

public static SVM __fromPtr__(long addr)

getClassWeights

public Mat getClassWeights()

SEE: setClassWeights

Returns:

automatically generated

getSupportVectors

public Mat getSupportVectors()

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.

Returns:

automatically generated

getUncompressedSupportVectors

public Mat getUncompressedSupportVectors()

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.

Returns:

automatically generated

getDefaultGridPtr

public static ParamGrid getDefaultGridPtr(int param_id)

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.

Returns:

automatically generated

create

public static SVM create()

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.

Returns:

automatically generated

load

public static SVM load(String filepath)

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

Returns:

automatically generated

getTermCriteria

public TermCriteria getTermCriteria()

SEE: setTermCriteria

Returns:

automatically generated

trainAuto

public boolean trainAuto(Mat samples,
                         int layout,
                         Mat responses,
                         int kFold,
                         ParamGrid Cgrid,
                         ParamGrid gammaGrid,
                         ParamGrid pGrid,
                         ParamGrid nuGrid,
                         ParamGrid coeffGrid,
                         ParamGrid degreeGrid,
                         boolean balanced)

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.

Returns:

automatically generated

trainAuto

public boolean trainAuto(Mat samples,
                         int layout,
                         Mat responses,
                         int kFold,
                         ParamGrid Cgrid,
                         ParamGrid gammaGrid,
                         ParamGrid pGrid,
                         ParamGrid nuGrid,
                         ParamGrid coeffGrid,
                         ParamGrid degreeGrid)

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 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.

Returns:

automatically generated

trainAuto

public boolean trainAuto(Mat samples,
                         int layout,
                         Mat responses,
                         int kFold,
                         ParamGrid Cgrid,
                         ParamGrid gammaGrid,
                         ParamGrid pGrid,
                         ParamGrid nuGrid,
                         ParamGrid coeffGrid)

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 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.

Returns:

automatically generated

trainAuto

public boolean trainAuto(Mat samples,
                         int layout,
                         Mat responses,
                         int kFold,
                         ParamGrid Cgrid,
                         ParamGrid gammaGrid,
                         ParamGrid pGrid,
                         ParamGrid nuGrid)

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 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.

Returns:

automatically generated

trainAuto

public boolean trainAuto(Mat samples,
                         int layout,
                         Mat responses,
                         int kFold,
                         ParamGrid Cgrid,
                         ParamGrid gammaGrid,
                         ParamGrid pGrid)

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 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.

Returns:

automatically generated

trainAuto

public boolean trainAuto(Mat samples,
                         int layout,
                         Mat responses,
                         int kFold,
                         ParamGrid Cgrid,
                         ParamGrid gammaGrid)

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 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.

Returns:

automatically generated

trainAuto

public boolean trainAuto(Mat samples,
                         int layout,
                         Mat responses,
                         int kFold,
                         ParamGrid Cgrid)

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 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.

Returns:

automatically generated

trainAuto

public boolean trainAuto(Mat samples,
                         int layout,
                         Mat responses,
                         int kFold)

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 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.

Returns:

automatically generated

trainAuto

public boolean trainAuto(Mat samples,
                         int layout,
                         Mat responses)

Trains an %SVM with optimal parameters

Parameters:

samples - training samples

layout - See ml::SampleTypes.

responses - vector of responses associated with the training samples. subset is used to test the model, the others form the train set. So, the %SVM algorithm is 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.

Returns:

automatically generated

getC

public double getC()

SEE: setC

Returns:

automatically generated

getCoef0

public double getCoef0()

SEE: setCoef0

Returns:

automatically generated

getDecisionFunction

public double getDecisionFunction(int i,
                                  Mat alpha,
                                  Mat svidx)

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.

Returns:

automatically generated

getDegree

public double getDegree()

SEE: setDegree

Returns:

automatically generated

getGamma

public double getGamma()

SEE: setGamma

Returns:

automatically generated

getNu

public double getNu()

SEE: setNu

Returns:

automatically generated

getP

public double getP()

SEE: setP

Returns:

automatically generated

getKernelType

public int getKernelType()

Type of a %SVM kernel. See SVM::KernelTypes. Default value is SVM::RBF.

Returns:

automatically generated

getType

public int getType()

SEE: setType

Returns:

automatically generated

setC

public void setC(double val)

getC SEE: getC

Parameters:

val - automatically generated

setClassWeights

public void setClassWeights(Mat val)

getClassWeights SEE: getClassWeights

Parameters:

val - automatically generated

setCoef0

public void setCoef0(double val)

getCoef0 SEE: getCoef0

Parameters:

val - automatically generated

setDegree

public void setDegree(double val)

getDegree SEE: getDegree

Parameters:

val - automatically generated

setGamma

public void setGamma(double val)

getGamma SEE: getGamma

Parameters:

val - automatically generated

setKernel

public void setKernel(int kernelType)

Initialize with one of predefined kernels. See SVM::KernelTypes.

Parameters:

kernelType - automatically generated

setNu

public void setNu(double val)

getNu SEE: getNu

Parameters:

val - automatically generated

setP

public void setP(double val)

getP SEE: getP

Parameters:

val - automatically generated

setTermCriteria

public void setTermCriteria(TermCriteria val)

getTermCriteria SEE: getTermCriteria

Parameters:

val - automatically generated

setType

public void setType(int val)

getType SEE: getType

Parameters:

val - automatically generated

finalize

protected void finalize()
                 throws Throwable

Overrides:

finalize in class StatModel

Throws:

Throwable