Support Vector Machines. More...

Inheritance diagram for OpenCVForUnity.MlModule.SVM:

Public Member Functions
int	getType ()

void	setType (int val)

double	getGamma ()

void	setGamma (double val)

double	getCoef0 ()

void	setCoef0 (double val)

double	getDegree ()

void	setDegree (double val)

double	getC ()

void	setC (double val)

double	getNu ()

void	setNu (double val)

double	getP ()

void	setP (double val)

Mat	getClassWeights ()

void	setClassWeights (Mat val)

TermCriteria	getTermCriteria ()

void	setTermCriteria (TermCriteria val)

int	getKernelType ()

void	setKernel (int kernelType)

bool	trainAuto (Mat samples, int layout, Mat responses, int kFold, ParamGrid Cgrid, ParamGrid gammaGrid, ParamGrid pGrid, ParamGrid nuGrid, ParamGrid coeffGrid, ParamGrid degreeGrid, bool balanced)
	Trains an SVM with optimal parameters. More...

bool	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. More...

bool	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. More...

bool	trainAuto (Mat samples, int layout, Mat responses, int kFold, ParamGrid Cgrid, ParamGrid gammaGrid, ParamGrid pGrid, ParamGrid nuGrid)
	Trains an SVM with optimal parameters. More...

bool	trainAuto (Mat samples, int layout, Mat responses, int kFold, ParamGrid Cgrid, ParamGrid gammaGrid, ParamGrid pGrid)
	Trains an SVM with optimal parameters. More...

bool	trainAuto (Mat samples, int layout, Mat responses, int kFold, ParamGrid Cgrid, ParamGrid gammaGrid)
	Trains an SVM with optimal parameters. More...

bool	trainAuto (Mat samples, int layout, Mat responses, int kFold, ParamGrid Cgrid)
	Trains an SVM with optimal parameters. More...

bool	trainAuto (Mat samples, int layout, Mat responses, int kFold)
	Trains an SVM with optimal parameters. More...

bool	trainAuto (Mat samples, int layout, Mat responses)
	Trains an SVM with optimal parameters. More...

Mat	getSupportVectors ()
	Retrieves all the support vectors. More...

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

double	getDecisionFunction (int i, Mat alpha, Mat svidx)
	Retrieves the decision function. More...

Public Member Functions inherited from OpenCVForUnity.MlModule.StatModel
int	getVarCount ()
	Returns the number of variables in training samples. More...

override bool	empty ()
	Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read. More...

bool	isTrained ()
	Returns true if the model is trained. More...

bool	isClassifier ()
	Returns true if the model is classifier. More...

bool	train (TrainData trainData, int flags)
	Trains the statistical model. More...

bool	train (TrainData trainData)
	Trains the statistical model. More...

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

float	calcError (TrainData data, bool test, Mat resp)
	Computes error on the training or test dataset. More...

virtual float	predict (Mat samples, Mat results, int flags)
	Predicts response(s) for the provided sample(s) More...

virtual float	predict (Mat samples, Mat results)
	Predicts response(s) for the provided sample(s) More...

virtual float	predict (Mat samples)
	Predicts response(s) for the provided sample(s) More...

Public Member Functions inherited from OpenCVForUnity.CoreModule.Algorithm
IntPtr	getNativeObjAddr ()

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

void	save (string filename)

virtual string	getDefaultName ()

Public Member Functions inherited from OpenCVForUnity.DisposableObject
void	Dispose ()

void	ThrowIfDisposed ()

Static Public Member Functions
static new SVM	__fromPtr__ (IntPtr addr)

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

static SVM	create ()

static SVM	load (string filepath)
	Loads and creates a serialized svm from a file. More...

Static Public Member Functions inherited from OpenCVForUnity.MlModule.StatModel
static new StatModel	__fromPtr__ (IntPtr addr)

Static Public Member Functions inherited from OpenCVForUnity.CoreModule.Algorithm
static Algorithm	__fromPtr__ (IntPtr addr)

Static Public Member Functions inherited from OpenCVForUnity.DisposableObject
static IntPtr	ThrowIfNullIntPtr (IntPtr ptr)

Public Attributes
const int	CUSTOM = -1

const int	LINEAR = 0

const int	POLY = 1

const int	RBF = 2

const int	SIGMOID = 3

const int	CHI2 = 4

const int	INTER = 5

const int	C = 0

const int	GAMMA = 1

const int	P = 2

const int	NU = 3

const int	COEF = 4

const int	DEGREE = 5

const int	C_SVC = 100

const int	NU_SVC = 101

const int	ONE_CLASS = 102

const int	EPS_SVR = 103

const int	NU_SVR = 104

Public Attributes inherited from OpenCVForUnity.MlModule.StatModel
const int	UPDATE_MODEL = 1

const int	RAW_OUTPUT = 1

const int	COMPRESSED_INPUT = 2

const int	PREPROCESSED_INPUT = 4

Protected Member Functions
override void	Dispose (bool disposing)

Protected Member Functions inherited from OpenCVForUnity.DisposableOpenCVObject
	DisposableOpenCVObject ()

	DisposableOpenCVObject (IntPtr ptr)

	DisposableOpenCVObject (bool isEnabledDispose)

	DisposableOpenCVObject (IntPtr ptr, bool isEnabledDispose)

Protected Member Functions inherited from OpenCVForUnity.DisposableObject
	DisposableObject ()

	DisposableObject (bool isEnabledDispose)

Additional Inherited Members
Properties inherited from OpenCVForUnity.DisposableObject
bool	IsDisposed `[get, protected set]`

bool	IsEnabledDispose `[get, set]`

Detailed Description

Support Vector Machines.

See also: ml_intro_svm

Member Function Documentation

◆ fromPtr()

static new SVM OpenCVForUnity.MlModule.SVM.__fromPtr__ ( IntPtr addr )

static

◆ create()

static SVM OpenCVForUnity.MlModule.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.

◆ Dispose()

override void OpenCVForUnity.MlModule.SVM.Dispose ( bool disposing )

protectedvirtual

Reimplemented from OpenCVForUnity.MlModule.StatModel.

◆ getC()

double OpenCVForUnity.MlModule.SVM.getC ( )

See also: setC

◆ getClassWeights()

Mat OpenCVForUnity.MlModule.SVM.getClassWeights ( )

See also: setClassWeights

◆ getCoef0()

double OpenCVForUnity.MlModule.SVM.getCoef0 ( )

See also: setCoef0

◆ getDecisionFunction()

double OpenCVForUnity.MlModule.SVM.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.

◆ getDefaultGridPtr()

static ParamGrid OpenCVForUnity.MlModule.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.

◆ getDegree()

double OpenCVForUnity.MlModule.SVM.getDegree ( )

See also: setDegree

◆ getGamma()

double OpenCVForUnity.MlModule.SVM.getGamma ( )

See also: setGamma

◆ getKernelType()

int OpenCVForUnity.MlModule.SVM.getKernelType ( )

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

◆ getNu()

double OpenCVForUnity.MlModule.SVM.getNu ( )

See also: setNu

◆ getP()

double OpenCVForUnity.MlModule.SVM.getP ( )

See also: setP

◆ getSupportVectors()

Mat OpenCVForUnity.MlModule.SVM.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.

◆ getTermCriteria()

TermCriteria OpenCVForUnity.MlModule.SVM.getTermCriteria ( )

See also: setTermCriteria

◆ getType()

int OpenCVForUnity.MlModule.SVM.getType ( )

See also: setType

◆ getUncompressedSupportVectors()

Mat OpenCVForUnity.MlModule.SVM.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.

◆ load()

static SVM OpenCVForUnity.MlModule.SVM.load ( 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

◆ setC()

void OpenCVForUnity.MlModule.SVM.setC ( double val )

See also: getC

◆ setClassWeights()

void OpenCVForUnity.MlModule.SVM.setClassWeights ( Mat val )

See also: getClassWeights

◆ setCoef0()

void OpenCVForUnity.MlModule.SVM.setCoef0 ( double val )

See also: getCoef0

◆ setDegree()

void OpenCVForUnity.MlModule.SVM.setDegree ( double val )

See also: getDegree

◆ setGamma()

void OpenCVForUnity.MlModule.SVM.setGamma ( double val )

See also: getGamma

◆ setKernel()

void OpenCVForUnity.MlModule.SVM.setKernel ( int kernelType )

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

◆ setNu()

void OpenCVForUnity.MlModule.SVM.setNu ( double val )

See also: getNu

◆ setP()

void OpenCVForUnity.MlModule.SVM.setP ( double val )

See also: getP

◆ setTermCriteria()

void OpenCVForUnity.MlModule.SVM.setTermCriteria ( TermCriteria val )

See also: getTermCriteria

◆ setType()

void OpenCVForUnity.MlModule.SVM.setType ( int val )

See also: getType

◆ trainAuto() [1/9]

bool OpenCVForUnity.MlModule.SVM.trainAuto	(	Mat	samples,
		int	layout,
		Mat	responses,
		int	kFold,
		ParamGrid	Cgrid,
		ParamGrid	gammaGrid,
		ParamGrid	pGrid,
		ParamGrid	nuGrid,
		ParamGrid	coeffGrid,
		ParamGrid	degreeGrid,
		bool	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.

◆ trainAuto() [2/9]

bool OpenCVForUnity.MlModule.SVM.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	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() [3/9]

bool OpenCVForUnity.MlModule.SVM.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
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() [4/9]

bool OpenCVForUnity.MlModule.SVM.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
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() [5/9]

bool OpenCVForUnity.MlModule.SVM.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
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() [6/9]

bool OpenCVForUnity.MlModule.SVM.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
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() [7/9]

bool OpenCVForUnity.MlModule.SVM.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
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() [8/9]

bool OpenCVForUnity.MlModule.SVM.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
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() [9/9]

bool OpenCVForUnity.MlModule.SVM.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.
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.

Member Data Documentation

◆ C

const int OpenCVForUnity.MlModule.SVM.C = 0

◆ C_SVC

const int OpenCVForUnity.MlModule.SVM.C_SVC = 100

◆ CHI2

const int OpenCVForUnity.MlModule.SVM.CHI2 = 4

◆ COEF

const int OpenCVForUnity.MlModule.SVM.COEF = 4

◆ CUSTOM

const int OpenCVForUnity.MlModule.SVM.CUSTOM = -1

◆ DEGREE

const int OpenCVForUnity.MlModule.SVM.DEGREE = 5

◆ EPS_SVR

const int OpenCVForUnity.MlModule.SVM.EPS_SVR = 103

◆ GAMMA

const int OpenCVForUnity.MlModule.SVM.GAMMA = 1

◆ INTER

const int OpenCVForUnity.MlModule.SVM.INTER = 5

◆ LINEAR

const int OpenCVForUnity.MlModule.SVM.LINEAR = 0

◆ NU

const int OpenCVForUnity.MlModule.SVM.NU = 3

◆ NU_SVC

const int OpenCVForUnity.MlModule.SVM.NU_SVC = 101

◆ NU_SVR

const int OpenCVForUnity.MlModule.SVM.NU_SVR = 104

◆ ONE_CLASS

const int OpenCVForUnity.MlModule.SVM.ONE_CLASS = 102

◆ P

const int OpenCVForUnity.MlModule.SVM.P = 2

◆ POLY

const int OpenCVForUnity.MlModule.SVM.POLY = 1

◆ RBF

const int OpenCVForUnity.MlModule.SVM.RBF = 2

◆ SIGMOID

const int OpenCVForUnity.MlModule.SVM.SIGMOID = 3

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

OpenCVForUnity/Assets/OpenCVForUnity/org/opencv/ml/SVM.cs

Public Member Functions

Static Public Member Functions

Public Attributes

Protected Member Functions

Additional Inherited Members

Detailed Description

Member Function Documentation

◆ __fromPtr__()

◆ create()

◆ Dispose()

◆ getC()

◆ getClassWeights()

◆ getCoef0()

◆ getDecisionFunction()

◆ getDefaultGridPtr()

◆ getDegree()

◆ getGamma()

◆ getKernelType()

◆ getNu()

◆ getP()

◆ getSupportVectors()

◆ getTermCriteria()

◆ getType()

◆ getUncompressedSupportVectors()

◆ load()

◆ setC()

◆ setClassWeights()

◆ setCoef0()

◆ setDegree()

◆ setGamma()

◆ setKernel()

◆ setNu()

◆ setP()

◆ setTermCriteria()

◆ setType()

◆ trainAuto() [1/9]

◆ trainAuto() [2/9]

◆ trainAuto() [3/9]

◆ trainAuto() [4/9]

◆ trainAuto() [5/9]

◆ trainAuto() [6/9]

◆ trainAuto() [7/9]

◆ trainAuto() [8/9]

◆ trainAuto() [9/9]

Member Data Documentation

◆ C

◆ C_SVC

◆ CHI2

◆ COEF

◆ CUSTOM

◆ DEGREE

◆ EPS_SVR

◆ GAMMA

◆ INTER

◆ LINEAR

◆ NU

◆ NU_SVC

◆ NU_SVR

◆ ONE_CLASS

◆ P

◆ POLY

◆ RBF

◆ SIGMOID

◆ fromPtr()