OpenCVForUnity.MlModule.SVM Class Reference

Support Vector Machines. More...

Public Member Functions
double	getC ()
Mat	getClassWeights ()
double	getCoef0 ()
double	getDecisionFunction (int i, Mat alpha, Mat svidx)
	Retrieves the decision function.
double	getDegree ()
double	getGamma ()
int	getKernelType ()
double	getNu ()
double	getP ()
Mat	getSupportVectors ()
	Retrieves all the support vectors.
TermCriteria	getTermCriteria ()
double double double epsilon	getTermCriteriaAsValueTuple ()
Vec3d	getTermCriteriaAsVec3d ()
int	getType ()
Mat	getUncompressedSupportVectors ()
	Retrieves all the uncompressed support vectors of a linear SVM.
void	setC (double val)
void	setClassWeights (Mat val)
void	setCoef0 (double val)
void	setDegree (double val)
void	setGamma (double val)
void	setKernel (int kernelType)
void	setNu (double val)
void	setP (double val)
void	setTermCriteria (in Vec3d val)
void	setTermCriteria (in(double type, double maxCount, double epsilon) val)
void	setTermCriteria (TermCriteria val)
void	setType (int val)
bool	trainAuto (Mat samples, int layout, Mat responses)
	Trains an SVM with optimal parameters.
bool	trainAuto (Mat samples, int layout, Mat responses, int kFold)
	Trains an SVM with optimal parameters.
bool	trainAuto (Mat samples, int layout, Mat responses, int kFold, ParamGrid Cgrid)
	Trains an SVM with optimal parameters.
bool	trainAuto (Mat samples, int layout, Mat responses, int kFold, ParamGrid Cgrid, ParamGrid gammaGrid)
	Trains an SVM with optimal parameters.
bool	trainAuto (Mat samples, int layout, Mat responses, int kFold, ParamGrid Cgrid, ParamGrid gammaGrid, ParamGrid pGrid)
	Trains an SVM with optimal parameters.
bool	trainAuto (Mat samples, int layout, Mat responses, int kFold, ParamGrid Cgrid, ParamGrid gammaGrid, ParamGrid pGrid, ParamGrid nuGrid)
	Trains an SVM with optimal parameters.
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.
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.
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.
Public Member Functions inherited from OpenCVForUnity.MlModule.StatModel
float	calcError (TrainData data, bool test, Mat resp)
	Computes error on the training or test dataset.
override bool	empty ()
	Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read.
int	getVarCount ()
	Returns the number of variables in training samples.
bool	isClassifier ()
	Returns true if the model is classifier.
bool	isTrained ()
	Returns true if the model is trained.
virtual float	predict (Mat samples)
	Predicts response(s) for the provided sample(s)
virtual float	predict (Mat samples, Mat results)
	Predicts response(s) for the provided sample(s)
virtual float	predict (Mat samples, Mat results, int flags)
	Predicts response(s) for the provided sample(s)
bool	train (Mat samples, int layout, Mat responses)
	Trains the statistical model.
bool	train (TrainData trainData)
	Trains the statistical model.
bool	train (TrainData trainData, int flags)
	Trains the statistical model.
Public Member Functions inherited from OpenCVForUnity.CoreModule.Algorithm
virtual void	clear ()
	Clears the algorithm state.
virtual string	getDefaultName ()
IntPtr	getNativeObjAddr ()
void	save (string filename)
Public Member Functions inherited from OpenCVForUnity.DisposableObject
void	Dispose ()
void	ThrowIfDisposed ()

Static Public Member Functions
static new SVM	__fromPtr__ (IntPtr addr)
static SVM	create ()
static ParamGrid	getDefaultGridPtr (int param_id)
	Generates a grid for SVM parameters.
static SVM	load (string filepath)
	Loads and creates a serialized svm from a file.
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
double double	maxCount
double	type

Static Public Attributes
const int	C = 0
const int	C_SVC = 100
const int	CHI2 = 4
const int	COEF = 4
const int	CUSTOM = -1
const int	DEGREE = 5
const int	EPS_SVR = 103
const int	GAMMA = 1
const int	INTER = 5
const int	LINEAR = 0
const int	NU = 3
const int	NU_SVC = 101
const int	NU_SVR = 104
const int	ONE_CLASS = 102
const int	P = 2
const int	POLY = 1
const int	RBF = 2
const int	SIGMOID = 3
Static Public Attributes inherited from OpenCVForUnity.MlModule.StatModel
const int	COMPRESSED_INPUT = 2
const int	PREPROCESSED_INPUT = 4
const int	RAW_OUTPUT = 1
const int	UPDATE_MODEL = 1

Protected Member Functions
override void	Dispose (bool disposing)
Protected Member Functions inherited from OpenCVForUnity.MlModule.StatModel
Protected Member Functions inherited from OpenCVForUnity.CoreModule.Algorithm
Protected Member Functions inherited from OpenCVForUnity.DisposableOpenCVObject
	DisposableOpenCVObject ()
	DisposableOpenCVObject (bool isEnabledDispose)
	DisposableOpenCVObject (IntPtr ptr)
	DisposableOpenCVObject (IntPtr ptr, bool isEnabledDispose)
Protected Member Functions inherited from OpenCVForUnity.DisposableObject
	DisposableObject ()
	DisposableObject (bool isEnabledDispose)

Additional Inherited Members
Package Functions inherited from OpenCVForUnity.MlModule.StatModel
Package Functions inherited from OpenCVForUnity.CoreModule.Algorithm
Package Attributes inherited from OpenCVForUnity.DisposableOpenCVObject
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

getTermCriteriaAsValueTuple()

double double double epsilon OpenCVForUnity.MlModule.SVM.getTermCriteriaAsValueTuple

(

)

getTermCriteriaAsVec3d()

Vec3d OpenCVForUnity.MlModule.SVM.getTermCriteriaAsVec3d

(

)

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() [1/3]

void OpenCVForUnity.MlModule.SVM.setTermCriteria

(

in Vec3d

val

)

See also

getTermCriteria

setTermCriteria() [2/3]

void OpenCVForUnity.MlModule.SVM.setTermCriteria

(

in(double type, double maxCount, double epsilon)

val

)

See also

getTermCriteria

setTermCriteria() [3/3]

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 )

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 )

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 )

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 )

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,
		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() [7/9]

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() [8/9]

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() [9/9]

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.

Member Data Documentation

C

const int OpenCVForUnity.MlModule.SVM.C = 0

static

C_SVC

const int OpenCVForUnity.MlModule.SVM.C_SVC = 100

static

CHI2

const int OpenCVForUnity.MlModule.SVM.CHI2 = 4

static

COEF

const int OpenCVForUnity.MlModule.SVM.COEF = 4

static

CUSTOM

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

static

DEGREE

const int OpenCVForUnity.MlModule.SVM.DEGREE = 5

static

EPS_SVR

const int OpenCVForUnity.MlModule.SVM.EPS_SVR = 103

static

GAMMA

const int OpenCVForUnity.MlModule.SVM.GAMMA = 1

static

INTER

const int OpenCVForUnity.MlModule.SVM.INTER = 5

static

LINEAR

const int OpenCVForUnity.MlModule.SVM.LINEAR = 0

static

maxCount

double double OpenCVForUnity.MlModule.SVM.maxCount

NU

const int OpenCVForUnity.MlModule.SVM.NU = 3

static

NU_SVC

const int OpenCVForUnity.MlModule.SVM.NU_SVC = 101

static

NU_SVR

const int OpenCVForUnity.MlModule.SVM.NU_SVR = 104

static

ONE_CLASS

const int OpenCVForUnity.MlModule.SVM.ONE_CLASS = 102

static

P

const int OpenCVForUnity.MlModule.SVM.P = 2

static

POLY

const int OpenCVForUnity.MlModule.SVM.POLY = 1

static

RBF

const int OpenCVForUnity.MlModule.SVM.RBF = 2

static

SIGMOID

const int OpenCVForUnity.MlModule.SVM.SIGMOID = 3

static

type

double OpenCVForUnity.MlModule.SVM.type

See also

setTermCriteria

---

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

• OpenCVForUnity/Assets/OpenCVForUnity/org/opencv/ml/SVM.cs
• OpenCVForUnity/Assets/OpenCVForUnity/org/opencv/ml/SVM_Struct.cs
• OpenCVForUnity/Assets/OpenCVForUnity/org/opencv/ml/SVM_ValueTuple.cs