Class cv::ml::SVM#

Support Vector Machines. View details

#include <opencv2/ml.hpp>

Collaboration diagram for cv::ml::SVM:

Public Types#

SVM kernel type

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

SVM params type

enum ParamTypes {
    C =0,
    GAMMA =1,
    P =2,
    NU =3,
    COEF =4,
    DEGREE =5
}

SVM type

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

Public Member Functions#

Public Member Functions inherited from cv::ml::StatModel

Return	Name	Description
`float`	`calcError(` `const Ptr< TrainData > & data,` `bool test,` `OutputArray resp )`	Computes error on the training or test dataset.
`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.
`float`	`predict(` `InputArray samples,` `OutputArray results = noArray(),` `int flags = 0 )`	Predicts response(s) for the provided sample(s)
`bool`	`train(` `const Ptr< TrainData > & trainData,` `int flags = 0 )`	Trains the statistical model.
`bool`	`train(` `InputArray samples,` `int layout,` `InputArray responses )`	Trains the statistical model.

Public Member Functions inherited from cv::Algorithm

Return	Name	Description
	`Algorithm()`
	`~Algorithm()`
`void`	`clear()`	Clears the algorithm state.
`bool`	`empty()`	Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read.
`String`	`getDefaultName()`
`void`	`read(const FileNode & fn)`	Reads algorithm parameters from a file storage.
`void`	`save(const String & filename)`
`void`	`write(` `const Ptr< FileStorage > & fs,` `const String & name = String() )`
`void`	`write(FileStorage & fs)`	Stores algorithm parameters in a file storage.
`void`	`write(` `FileStorage & fs,` `const String & name )`

Static Public Member Functions#

Static Public Member Functions inherited from cv::ml::StatModel

Return	Name	Description
`static Ptr< _Tp >`	`train(` `const Ptr< TrainData > & data,` `int flags = 0 )`	Create and train model with default parameters.

Static Public Member Functions inherited from cv::Algorithm

Return	Name	Description
`static Ptr< _Tp >`	`load(` `const String & filename,` `const String & objname = String() )`	Loads algorithm from the file.
`static Ptr< _Tp >`	`loadFromString(` `const String & strModel,` `const String & objname = String() )`	Loads algorithm from a String.
`static Ptr< _Tp >`	`read(const FileNode & fn)`	Reads algorithm from the file node.

Additional Inherited Members#

Detailed Description#

Support Vector Machines.

See also

ml_intro_svm

Member Enumeration Documentation#

enum KernelTypes

SVM kernel type

`CUSTOM` Python: `cv.ml.SVM_CUSTOM`	Returned by SVM::getKernelType in case when custom kernel has been set
`LINEAR` Python: `cv.ml.SVM_LINEAR`	Linear kernel. No mapping is done, linear discrimination (or regression) is done in the original feature space. It is the fastest option. \(K(x_i, x_j) = x_i^T x_j\).
`POLY` Python: `cv.ml.SVM_POLY`	Polynomial kernel: \(K(x_i, x_j) = (\gamma x_i^T x_j + coef0)^{degree}, \gamma > 0\).
`RBF` Python: `cv.ml.SVM_RBF`	Radial basis function (RBF), a good choice in most cases. \(K(x_i, x_j) = e^{-\gamma \|\|x_i - x_j\|\|^2}, \gamma > 0\).
`SIGMOID` Python: `cv.ml.SVM_SIGMOID`	Sigmoid kernel: \(K(x_i, x_j) = \tanh(\gamma x_i^T x_j + coef0)\).
`CHI2` Python: `cv.ml.SVM_CHI2`	Exponential Chi2 kernel, similar to the RBF kernel: \(K(x_i, x_j) = e^{-\gamma \chi^2(x_i,x_j)}, \chi^2(x_i,x_j) = (x_i-x_j)^2/(x_i+x_j), \gamma > 0\).
`INTER` Python: `cv.ml.SVM_INTER`	Histogram intersection kernel. A fast kernel. \(K(x_i, x_j) = min(x_i,x_j)\).

enum ParamTypes

SVM params type

C
Python: cv.ml.SVM_C

GAMMA
Python: cv.ml.SVM_GAMMA

P
Python: cv.ml.SVM_P

NU
Python: cv.ml.SVM_NU

COEF
Python: cv.ml.SVM_COEF

DEGREE
Python: cv.ml.SVM_DEGREE

enum Types

SVM type

`C_SVC` Python: `cv.ml.SVM_C_SVC`	C-Support Vector Classification. n-class classification (n \(\geq\) 2), allows imperfect separation of classes with penalty multiplier C for outliers.
`NU_SVC` Python: `cv.ml.SVM_NU_SVC`	\(\nu\)-Support Vector Classification. n-class classification with possible imperfect separation. Parameter \(\nu\) (in the range 0..1, the larger the value, the smoother the decision boundary) is used instead of C.
`ONE_CLASS` Python: `cv.ml.SVM_ONE_CLASS`	Distribution Estimation (One-class SVM). All the training data are from the same class, SVM builds a boundary that separates the class from the rest of the feature space.
`EPS_SVR` Python: `cv.ml.SVM_EPS_SVR`	\(\epsilon\)-Support Vector Regression. The distance between feature vectors from the training set and the fitting hyper-plane must be less than p. For outliers the penalty multiplier C is used.
`NU_SVR` Python: `cv.ml.SVM_NU_SVR`	\(\nu\)-Support Vector Regression. \(\nu\) is used instead of p. See [56] for details.

Member Function Documentation#

getC()#

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

Python:

cv.ml.SVM.getC() -> retval

Parameter C of a SVM optimization problem. For SVM::C_SVC, SVM::EPS_SVR or SVM::NU_SVR. Default value is 0.

See also

setC

getClassWeights()#

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

Python:

cv.ml.SVM.getClassWeights() -> retval

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

See also

setClassWeights

getCoef0()#

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

Python:

cv.ml.SVM.getCoef0() -> retval

Parameter coef0 of a kernel function. For SVM::POLY or SVM::SIGMOID. Default value is 0.

See also

setCoef0

getDecisionFunction()#

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

Python:

cv.ml.SVM.getDecisionFunction(i[, alpha[, svidx]]) -> retval, alpha, svidx

Retrieves the decision function.

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

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.

getDegree()#

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

Python:

cv.ml.SVM.getDegree() -> retval

Parameter degree of a kernel function. For SVM::POLY. Default value is 0.

See also

setDegree

getGamma()#

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

Python:

cv.ml.SVM.getGamma() -> retval

Parameter \(\gamma\) of a kernel function. For SVM::POLY, SVM::RBF, SVM::SIGMOID or SVM::CHI2. Default value is 1.

See also

setGamma

getKernelType()#

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

Python:

cv.ml.SVM.getKernelType() -> retval

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

getNu()#

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

Python:

cv.ml.SVM.getNu() -> retval

Parameter \(\nu\) of a SVM optimization problem. For SVM::NU_SVC, SVM::ONE_CLASS or SVM::NU_SVR. Default value is 0.

See also

setNu

getP()#

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

Python:

cv.ml.SVM.getP() -> retval

Parameter \(\epsilon\) of a SVM optimization problem. For SVM::EPS_SVR. Default value is 0.

See also

setP

getSupportVectors()#

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

Python:

cv.ml.SVM.getSupportVectors() -> retval

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()#

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

Python:

cv.ml.SVM.getTermCriteria() -> retval

Termination criteria of the iterative SVM training procedure which solves a partial case of constrained quadratic optimization problem. You can specify tolerance and/or the maximum number of iterations. Default value is TermCriteria( TermCriteria::MAX_ITER + TermCriteria::EPS, 1000, FLT_EPSILON );

See also

setTermCriteria

getType()#

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

Python:

cv.ml.SVM.getType() -> retval

Type of a SVM formulation. See SVM::Types. Default value is SVM::C_SVC.

See also

setType

getUncompressedSupportVectors()#

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

Python:

cv.ml.SVM.getUncompressedSupportVectors() -> retval

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.

setC()#

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

Python:

cv.ml.SVM.setC(val)

See also

getC

setClassWeights()#

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

Python:

cv.ml.SVM.setClassWeights(val)

See also

getClassWeights

setCoef0()#

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

Python:

cv.ml.SVM.setCoef0(val)

See also

getCoef0

setCustomKernel()#

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

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

setDegree()#

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

Python:

cv.ml.SVM.setDegree(val)

See also

getDegree

setGamma()#

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

Python:

cv.ml.SVM.setGamma(val)

See also

getGamma

setKernel()#

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

Python:

cv.ml.SVM.setKernel(kernelType)

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

setNu()#

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

Python:

cv.ml.SVM.setNu(val)

See also

getNu

setP()#

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

Python:

cv.ml.SVM.setP(val)

See also

getP

setTermCriteria()#

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

Python:

cv.ml.SVM.setTermCriteria(val)

See also

getTermCriteria

setType()#

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

Python:

cv.ml.SVM.setType(val)

See also

getType

trainAuto()#

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

Python:

cv.ml.SVM.trainAuto(samples, layout, responses[, kFold[, Cgrid[, gammaGrid[, pGrid[, nuGrid[, coeffGrid[, degreeGrid[, balanced]]]]]]]]) -> retval

Trains an SVM with optimal parameters.

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.

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.

trainAuto()#

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

Python:

cv.ml.SVM.trainAuto(samples, layout, responses[, kFold[, Cgrid[, gammaGrid[, pGrid[, nuGrid[, coeffGrid[, degreeGrid[, balanced]]]]]]]]) -> retval

Trains an SVM with optimal parameters.

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.

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.

create()#

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

Python:

cv.ml.SVM.create() -> retval

cv.ml.SVM_create() -> retval

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.

getDefaultGrid()#

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

Generates a grid for SVM parameters.

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

Parameters

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

getDefaultGridPtr()#

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

Python:

cv.ml.SVM.getDefaultGridPtr(param_id) -> retval

cv.ml.SVM_getDefaultGridPtr(param_id) -> retval

Generates a grid for SVM parameters.

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

Parameters

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

load()#

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

Python:

cv.ml.SVM.load(filepath) -> retval

cv.ml.SVM_load(filepath) -> retval

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

Source file#

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

opencv2/ml.hpp