org.bytedeco.opencv.opencv_ml

Class SVM

java.lang.Object

org.bytedeco.javacpp.Pointer

org.bytedeco.opencv.opencv_core.Algorithm

org.bytedeco.opencv.opencv_ml.AbstractStatModel

org.bytedeco.opencv.opencv_ml.StatModel

org.bytedeco.opencv.opencv_ml.SVM

All Implemented Interfaces:

AutoCloseable

@Namespace(value="cv::ml")
 @Properties(inherit=opencv_ml.class)
public class SVM
extends StatModel

\brief Support Vector Machines.

See Also:

ml_intro_svm

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	SVM.Kernel

Nested classes/interfaces inherited from class org.bytedeco.javacpp.Pointer

Pointer.CustomDeallocator, Pointer.Deallocator, Pointer.NativeDeallocator, Pointer.ReferenceCounter

Field Summary

Fields

Modifier and Type	Field and Description
static int	C enum cv::ml::SVM::ParamTypes
static int	C_SVC enum cv::ml::SVM::Types
static int	CHI2 enum cv::ml::SVM::KernelTypes
static int	COEF enum cv::ml::SVM::ParamTypes
static int	CUSTOM enum cv::ml::SVM::KernelTypes
static int	DEGREE enum cv::ml::SVM::ParamTypes
static int	EPS_SVR enum cv::ml::SVM::Types
static int	GAMMA enum cv::ml::SVM::ParamTypes
static int	INTER enum cv::ml::SVM::KernelTypes
static int	LINEAR enum cv::ml::SVM::KernelTypes
static int	NU enum cv::ml::SVM::ParamTypes
static int	NU_SVC enum cv::ml::SVM::Types
static int	NU_SVR enum cv::ml::SVM::Types
static int	ONE_CLASS enum cv::ml::SVM::Types
static int	P enum cv::ml::SVM::ParamTypes
static int	POLY enum cv::ml::SVM::KernelTypes
static int	RBF enum cv::ml::SVM::KernelTypes
static int	SIGMOID enum cv::ml::SVM::KernelTypes

Fields inherited from class org.bytedeco.opencv.opencv_ml.StatModel

COMPRESSED_INPUT, PREPROCESSED_INPUT, RAW_OUTPUT, UPDATE_MODEL

Fields inherited from class org.bytedeco.javacpp.Pointer

address, capacity, limit, position

Constructor Summary

Constructors

Constructor and Description
SVM(Pointer p) Pointer cast constructor.

Method Summary

Modifier and Type	Method and Description
static SVM	create() Creates empty model.
double	getC()
Mat	getClassWeights()
double	getCoef0()
double	getDecisionFunction(int i, GpuMat alpha, GpuMat svidx)
double	getDecisionFunction(int i, Mat alpha, Mat svidx) \brief Retrieves the decision function
double	getDecisionFunction(int i, UMat alpha, UMat svidx)
static ParamGrid	getDefaultGrid(int param_id) \brief Generates a grid for %SVM parameters.
static ParamGrid	getDefaultGridPtr(int param_id) \brief Generates a grid for %SVM parameters.
double	getDegree()
double	getGamma()
int	getKernelType() Type of a %SVM kernel.
double	getNu()
double	getP()
Mat	getSupportVectors() \brief Retrieves all the support vectors
TermCriteria	getTermCriteria()
int	getType()
Mat	getUncompressedSupportVectors() \brief Retrieves all the uncompressed support vectors of a linear %SVM
static SVM	load(BytePointer filepath) \brief Loads and creates a serialized svm from a file Use SVM::save to serialize and store an SVM to disk.
static SVM	load(String filepath)
void	setC(double val) \copybrief getC @see getC
void	setClassWeights(Mat val) \copybrief getClassWeights @see getClassWeights
void	setCoef0(double val) \copybrief getCoef0 @see getCoef0
void	setCustomKernel(SVM.Kernel _kernel) Initialize with custom kernel.
void	setDegree(double val) \copybrief getDegree @see getDegree
void	setGamma(double val) \copybrief getGamma @see getGamma
void	setKernel(int kernelType) Initialize with one of predefined kernels.
void	setNu(double val) \copybrief getNu @see getNu
void	setP(double val) \copybrief getP @see getP
void	setTermCriteria(TermCriteria val) \copybrief getTermCriteria @see getTermCriteria
void	setType(int val) \copybrief getType @see getType
boolean	trainAuto(GpuMat samples, int layout, GpuMat responses)
boolean	trainAuto(GpuMat samples, int layout, GpuMat responses, int kFold, ParamGrid Cgrid, ParamGrid gammaGrid, ParamGrid pGrid, ParamGrid nuGrid, ParamGrid coeffGrid, ParamGrid degreeGrid, boolean balanced)
boolean	trainAuto(Mat samples, int layout, Mat responses)
boolean	trainAuto(Mat samples, int layout, Mat responses, int kFold, ParamGrid Cgrid, ParamGrid gammaGrid, ParamGrid pGrid, ParamGrid nuGrid, ParamGrid coeffGrid, ParamGrid degreeGrid, boolean balanced) \brief Trains an %SVM with optimal parameters
boolean	trainAuto(TrainData data)
boolean	trainAuto(TrainData data, int kFold, ParamGrid Cgrid, ParamGrid gammaGrid, ParamGrid pGrid, ParamGrid nuGrid, ParamGrid coeffGrid, ParamGrid degreeGrid, boolean balanced) \brief Trains an %SVM with optimal parameters.
boolean	trainAuto(UMat samples, int layout, UMat responses)
boolean	trainAuto(UMat samples, int layout, UMat responses, int kFold, ParamGrid Cgrid, ParamGrid gammaGrid, ParamGrid pGrid, ParamGrid nuGrid, ParamGrid coeffGrid, ParamGrid degreeGrid, boolean balanced)

Methods inherited from class org.bytedeco.opencv.opencv_ml.StatModel

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

Methods inherited from class org.bytedeco.opencv.opencv_ml.AbstractStatModel

loadANN_MLP, loadANN_MLP, loadBoost, loadBoost, loadDTrees, loadDTrees, loadEM, loadEM, loadKNearest, loadKNearest, loadLogisticRegression, loadLogisticRegression, loadNormalBayesClassifier, loadNormalBayesClassifier, loadRTrees, loadRTrees, loadSVM, loadSVM

Methods inherited from class org.bytedeco.opencv.opencv_core.Algorithm

clear, getDefaultName, position, read, save, save, write, write, write

Methods inherited from class org.bytedeco.javacpp.Pointer

address, asBuffer, asByteBuffer, availablePhysicalBytes, calloc, capacity, capacity, close, deallocate, deallocate, deallocateReferences, deallocator, deallocator, equals, fill, formatBytes, free, hashCode, isNull, limit, limit, malloc, maxBytes, maxPhysicalBytes, memchr, memcmp, memcpy, memmove, memset, offsetof, parseBytes, physicalBytes, position, put, realloc, referenceCount, releaseReference, retainReference, setNull, sizeof, toString, totalBytes, totalPhysicalBytes, withDeallocator, zero

Methods inherited from class java.lang.Object

clone, finalize, getClass, notify, notifyAll, wait, wait, wait

Field Detail

C_SVC

public static final int C_SVC

enum cv::ml::SVM::Types

See Also:

Constant Field Values

NU_SVC

public static final int NU_SVC

enum cv::ml::SVM::Types

See Also:

Constant Field Values

ONE_CLASS

public static final int ONE_CLASS

enum cv::ml::SVM::Types

See Also:

Constant Field Values

EPS_SVR

public static final int EPS_SVR

enum cv::ml::SVM::Types

See Also:

Constant Field Values

NU_SVR

public static final int NU_SVR

enum cv::ml::SVM::Types

See Also:

Constant Field Values

CUSTOM

public static final int CUSTOM

enum cv::ml::SVM::KernelTypes

See Also:

Constant Field Values

LINEAR

public static final int LINEAR

enum cv::ml::SVM::KernelTypes

See Also:

Constant Field Values

POLY

public static final int POLY

enum cv::ml::SVM::KernelTypes

See Also:

Constant Field Values

RBF

public static final int RBF

enum cv::ml::SVM::KernelTypes

See Also:

Constant Field Values

SIGMOID

public static final int SIGMOID

enum cv::ml::SVM::KernelTypes

See Also:

Constant Field Values

CHI2

public static final int CHI2

enum cv::ml::SVM::KernelTypes

See Also:

Constant Field Values

INTER

public static final int INTER

enum cv::ml::SVM::KernelTypes

See Also:

Constant Field Values

C

public static final int C

enum cv::ml::SVM::ParamTypes

See Also:

Constant Field Values

GAMMA

public static final int GAMMA

enum cv::ml::SVM::ParamTypes

See Also:

Constant Field Values

P

public static final int P

enum cv::ml::SVM::ParamTypes

See Also:

Constant Field Values

NU

public static final int NU

enum cv::ml::SVM::ParamTypes

See Also:

Constant Field Values

COEF

public static final int COEF

enum cv::ml::SVM::ParamTypes

See Also:

Constant Field Values

DEGREE

public static final int DEGREE

enum cv::ml::SVM::ParamTypes

See Also:

Constant Field Values

Constructor Detail

SVM

public SVM(Pointer p)

Pointer cast constructor. Invokes Pointer.Pointer(Pointer).

Method Detail

getType

public int getType()

See Also:

setType

setType

public void setType(int val)

\copybrief getType @see getType

getGamma

public double getGamma()

See Also:

setGamma

setGamma

public void setGamma(double val)

\copybrief getGamma @see getGamma

getCoef0

public double getCoef0()

See Also:

setCoef0

setCoef0

public void setCoef0(double val)

\copybrief getCoef0 @see getCoef0

getDegree

public double getDegree()

See Also:

setDegree

setDegree

public void setDegree(double val)

\copybrief getDegree @see getDegree

getC

public double getC()

See Also:

setC

setC

public void setC(double val)

\copybrief getC @see getC

getNu

public double getNu()

See Also:

setNu

setNu

public void setNu(double val)

\copybrief getNu @see getNu

getP

public double getP()

See Also:

setP

setP

public void setP(double val)

\copybrief getP @see getP

getClassWeights

@ByVal
public Mat getClassWeights()

See Also:

setClassWeights

setClassWeights

public void setClassWeights(@Const @ByRef
                            Mat val)

\copybrief getClassWeights @see getClassWeights

getTermCriteria

@ByVal
public TermCriteria getTermCriteria()

See Also:

setTermCriteria

setTermCriteria

public void setTermCriteria(@Const @ByRef
                            TermCriteria val)

\copybrief getTermCriteria @see getTermCriteria

getKernelType

public int getKernelType()

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

setKernel

public void setKernel(int kernelType)

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

setCustomKernel

public void setCustomKernel(@opencv_core.Ptr
                            SVM.Kernel _kernel)

Initialize with custom kernel. See SVM::Kernel class for implementation details

trainAuto

@Cast(value="bool")
public boolean trainAuto(@opencv_core.Ptr
                                             TrainData data,
                                             int kFold,
                                             @ByVal(nullValue="cv::ml::ParamGrid(cv::ml::SVM::getDefaultGrid(cv::ml::SVM::C))")
                                             ParamGrid Cgrid,
                                             @ByVal(nullValue="cv::ml::ParamGrid(cv::ml::SVM::getDefaultGrid(cv::ml::SVM::GAMMA))")
                                             ParamGrid gammaGrid,
                                             @ByVal(nullValue="cv::ml::ParamGrid(cv::ml::SVM::getDefaultGrid(cv::ml::SVM::P))")
                                             ParamGrid pGrid,
                                             @ByVal(nullValue="cv::ml::ParamGrid(cv::ml::SVM::getDefaultGrid(cv::ml::SVM::NU))")
                                             ParamGrid nuGrid,
                                             @ByVal(nullValue="cv::ml::ParamGrid(cv::ml::SVM::getDefaultGrid(cv::ml::SVM::COEF))")
                                             ParamGrid coeffGrid,
                                             @ByVal(nullValue="cv::ml::ParamGrid(cv::ml::SVM::getDefaultGrid(cv::ml::SVM::DEGREE))")
                                             ParamGrid degreeGrid,
                                             @Cast(value="bool")
                                             boolean balanced)

\brief 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

@Cast(value="bool")
public boolean trainAuto(@opencv_core.Ptr
                                             TrainData data)

trainAuto

@Cast(value="bool")
public boolean trainAuto(@ByVal
                                             Mat samples,
                                             int layout,
                                             @ByVal
                                             Mat responses,
                                             int kFold,
                                             @opencv_core.Ptr
                                             ParamGrid Cgrid,
                                             @opencv_core.Ptr
                                             ParamGrid gammaGrid,
                                             @opencv_core.Ptr
                                             ParamGrid pGrid,
                                             @opencv_core.Ptr
                                             ParamGrid nuGrid,
                                             @opencv_core.Ptr
                                             ParamGrid coeffGrid,
                                             @opencv_core.Ptr
                                             ParamGrid degreeGrid,
                                             @Cast(value="bool")
                                             boolean balanced)

\brief 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.

trainAuto

@Cast(value="bool")
public boolean trainAuto(@ByVal
                                             Mat samples,
                                             int layout,
                                             @ByVal
                                             Mat responses)

trainAuto

@Cast(value="bool")
public boolean trainAuto(@ByVal
                                             UMat samples,
                                             int layout,
                                             @ByVal
                                             UMat responses,
                                             int kFold,
                                             @opencv_core.Ptr
                                             ParamGrid Cgrid,
                                             @opencv_core.Ptr
                                             ParamGrid gammaGrid,
                                             @opencv_core.Ptr
                                             ParamGrid pGrid,
                                             @opencv_core.Ptr
                                             ParamGrid nuGrid,
                                             @opencv_core.Ptr
                                             ParamGrid coeffGrid,
                                             @opencv_core.Ptr
                                             ParamGrid degreeGrid,
                                             @Cast(value="bool")
                                             boolean balanced)

trainAuto

@Cast(value="bool")
public boolean trainAuto(@ByVal
                                             UMat samples,
                                             int layout,
                                             @ByVal
                                             UMat responses)

trainAuto

@Cast(value="bool")
public boolean trainAuto(@ByVal
                                             GpuMat samples,
                                             int layout,
                                             @ByVal
                                             GpuMat responses,
                                             int kFold,
                                             @opencv_core.Ptr
                                             ParamGrid Cgrid,
                                             @opencv_core.Ptr
                                             ParamGrid gammaGrid,
                                             @opencv_core.Ptr
                                             ParamGrid pGrid,
                                             @opencv_core.Ptr
                                             ParamGrid nuGrid,
                                             @opencv_core.Ptr
                                             ParamGrid coeffGrid,
                                             @opencv_core.Ptr
                                             ParamGrid degreeGrid,
                                             @Cast(value="bool")
                                             boolean balanced)

trainAuto

@Cast(value="bool")
public boolean trainAuto(@ByVal
                                             GpuMat samples,
                                             int layout,
                                             @ByVal
                                             GpuMat responses)

getSupportVectors

@ByVal
public Mat getSupportVectors()

\brief 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.

getUncompressedSupportVectors

@ByVal
public Mat getUncompressedSupportVectors()

\brief 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.

getDecisionFunction

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

\brief 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.

getDecisionFunction

public double getDecisionFunction(int i,
                                  @ByVal
                                  UMat alpha,
                                  @ByVal
                                  UMat svidx)

getDecisionFunction

public double getDecisionFunction(int i,
                                  @ByVal
                                  GpuMat alpha,
                                  @ByVal
                                  GpuMat svidx)

getDefaultGrid

@ByVal
public static ParamGrid getDefaultGrid(int param_id)

\brief 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

@opencv_core.Ptr
public static ParamGrid getDefaultGridPtr(int param_id)

\brief 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.

create

@opencv_core.Ptr
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.

load

@opencv_core.Ptr
public static SVM load(@opencv_core.Str
                                        BytePointer filepath)

\brief 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

@opencv_core.Ptr
public static SVM load(@opencv_core.Str
                                        String filepath)