SVM (OpenPnP OpenCV 4.5.4-0 API)

java.lang.Object
- org.opencv.core.Algorithm
- - org.opencv.ml.StatModel
  - - org.opencv.ml.SVM

```
public class SVM
extends StatModel
```
Support Vector Machines. SEE: REF: ml_intro_svm

Field Summary

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

Fields inherited from class org.opencv.ml.StatModel
COMPRESSED_INPUT, PREPROCESSED_INPUT, RAW_OUTPUT, UPDATE_MODEL

Fields inherited from class org.opencv.core.Algorithm
nativeObj

Constructor Summary

Constructors
Modifier Constructor and Description

protected SVM(long addr)

Constructors
Modifier	Constructor and Description
`protected`	`SVM(long addr)`

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`static SVM`	`__fromPtr__(long addr)`
`static SVM`	`create()` Creates empty model.
`protected void`	`finalize()`
`double`	`getC()` SEE: setC
`Mat`	`getClassWeights()` SEE: setClassWeights
`double`	`getCoef0()` SEE: setCoef0
`double`	`getDecisionFunction(int i, Mat alpha, Mat svidx)` Retrieves the decision function
`static ParamGrid`	`getDefaultGridPtr(int param_id)` Generates a grid for %SVM parameters.
`double`	`getDegree()` SEE: setDegree
`double`	`getGamma()` SEE: setGamma
`int`	`getKernelType()` Type of a %SVM kernel.
`double`	`getNu()` SEE: setNu
`double`	`getP()` SEE: setP
`Mat`	`getSupportVectors()` Retrieves all the support vectors The method returns all the support vectors as a floating-point matrix, where support vectors are stored as matrix rows.
`TermCriteria`	`getTermCriteria()` SEE: setTermCriteria
`int`	`getType()` SEE: setType
`Mat`	`getUncompressedSupportVectors()` Retrieves all the uncompressed support vectors of a linear %SVM The method returns all the uncompressed support vectors of a linear %SVM that the compressed support vector, used for prediction, was derived from.
`static SVM`	`load(String filepath)` Loads and creates a serialized svm from a file Use SVM::save to serialize and store an SVM to disk.
`void`	`setC(double val)` getC SEE: getC
`void`	`setClassWeights(Mat val)` getClassWeights SEE: getClassWeights
`void`	`setCoef0(double val)` getCoef0 SEE: getCoef0
`void`	`setDegree(double val)` getDegree SEE: getDegree
`void`	`setGamma(double val)` getGamma SEE: getGamma
`void`	`setKernel(int kernelType)` Initialize with one of predefined kernels.
`void`	`setNu(double val)` getNu SEE: getNu
`void`	`setP(double val)` getP SEE: getP
`void`	`setTermCriteria(TermCriteria val)` getTermCriteria SEE: getTermCriteria
`void`	`setType(int val)` getType SEE: getType
`boolean`	`trainAuto(Mat samples, int layout, Mat responses)` Trains an %SVM with optimal parameters
`boolean`	`trainAuto(Mat samples, int layout, Mat responses, int kFold)` Trains an %SVM with optimal parameters
`boolean`	`trainAuto(Mat samples, int layout, Mat responses, int kFold, ParamGrid Cgrid)` Trains an %SVM with optimal parameters
`boolean`	`trainAuto(Mat samples, int layout, Mat responses, int kFold, ParamGrid Cgrid, ParamGrid gammaGrid)` Trains an %SVM with optimal parameters
`boolean`	`trainAuto(Mat samples, int layout, Mat responses, int kFold, ParamGrid Cgrid, ParamGrid gammaGrid, ParamGrid pGrid)` Trains an %SVM with optimal parameters
`boolean`	`trainAuto(Mat samples, int layout, Mat responses, int kFold, ParamGrid Cgrid, ParamGrid gammaGrid, ParamGrid pGrid, ParamGrid nuGrid)` Trains an %SVM with optimal parameters
`boolean`	`trainAuto(Mat samples, int layout, Mat responses, int kFold, ParamGrid Cgrid, ParamGrid gammaGrid, ParamGrid pGrid, ParamGrid nuGrid, ParamGrid coeffGrid)` Trains an %SVM with optimal parameters
`boolean`	`trainAuto(Mat samples, int layout, Mat responses, int kFold, ParamGrid Cgrid, ParamGrid gammaGrid, ParamGrid pGrid, ParamGrid nuGrid, ParamGrid coeffGrid, ParamGrid degreeGrid)` Trains an %SVM with optimal parameters
`boolean`	`trainAuto(Mat samples, int layout, Mat responses, int kFold, ParamGrid Cgrid, ParamGrid gammaGrid, ParamGrid pGrid, ParamGrid nuGrid, ParamGrid coeffGrid, ParamGrid degreeGrid, boolean balanced)` Trains an %SVM with optimal parameters

Methods inherited from class org.opencv.ml.StatModel
calcError, empty, getVarCount, isClassifier, isTrained, predict, predict, predict, train, train, train

Methods inherited from class org.opencv.core.Algorithm
clear, getDefaultName, getNativeObjAddr, save

Methods inherited from class java.lang.Object
clone, equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Field Detail
  - CUSTOM
```
public static final int CUSTOM
```
    See Also:
    
    Constant Field Values
  - LINEAR
```
public static final int LINEAR
```
    See Also:
    
    Constant Field Values
  - POLY
```
public static final int POLY
```
    See Also:
    
    Constant Field Values
  - RBF
```
public static final int RBF
```
    See Also:
    
    Constant Field Values
  - SIGMOID
```
public static final int SIGMOID
```
    See Also:
    
    Constant Field Values
  - CHI2
```
public static final int CHI2
```
    See Also:
    
    Constant Field Values
  - INTER
```
public static final int INTER
```
    See Also:
    
    Constant Field Values
  - C
```
public static final int C
```
    See Also:
    
    Constant Field Values
  - GAMMA
```
public static final int GAMMA
```
    See Also:
    
    Constant Field Values
  - P
```
public static final int P
```
    See Also:
    
    Constant Field Values
  - NU
```
public static final int NU
```
    See Also:
    
    Constant Field Values
  - COEF
```
public static final int COEF
```
    See Also:
    
    Constant Field Values
  - DEGREE
```
public static final int DEGREE
```
    See Also:
    
    Constant Field Values
  - C_SVC
```
public static final int C_SVC
```
    See Also:
    
    Constant Field Values
  - NU_SVC
```
public static final int NU_SVC
```
    See Also:
    
    Constant Field Values
  - ONE_CLASS
```
public static final int ONE_CLASS
```
    See Also:
    
    Constant Field Values
  - EPS_SVR
```
public static final int EPS_SVR
```
    See Also:
    
    Constant Field Values
  - NU_SVR
```
public static final int NU_SVR
```
    See Also:
    
    Constant Field Values
- Constructor Detail
  - SVM
```
protected SVM(long addr)
```
- Method Detail
  - __fromPtr__
```
public static SVM __fromPtr__(long addr)
```
  - getType
```
public int getType()
```
    SEE: setType
    
    Returns:
    
    automatically generated
  - setType
```
public void setType(int val)
```
    getType SEE: getType
    
    Parameters:
    
    val - automatically generated
  - getGamma
```
public double getGamma()
```
    SEE: setGamma
    
    Returns:
    
    automatically generated
  - setGamma
```
public void setGamma(double val)
```
    getGamma SEE: getGamma
    
    Parameters:
    
    val - automatically generated
  - getCoef0
```
public double getCoef0()
```
    SEE: setCoef0
    
    Returns:
    
    automatically generated
  - setCoef0
```
public void setCoef0(double val)
```
    getCoef0 SEE: getCoef0
    
    Parameters:
    
    val - automatically generated
  - getDegree
```
public double getDegree()
```
    SEE: setDegree
    
    Returns:
    
    automatically generated
  - setDegree
```
public void setDegree(double val)
```
    getDegree SEE: getDegree
    
    Parameters:
    
    val - automatically generated
  - getC
```
public double getC()
```
    SEE: setC
    
    Returns:
    
    automatically generated
  - setC
```
public void setC(double val)
```
    getC SEE: getC
    
    Parameters:
    
    val - automatically generated
  - getNu
```
public double getNu()
```
    SEE: setNu
    
    Returns:
    
    automatically generated
  - setNu
```
public void setNu(double val)
```
    getNu SEE: getNu
    
    Parameters:
    
    val - automatically generated
  - getP
```
public double getP()
```
    SEE: setP
    
    Returns:
    
    automatically generated
  - setP
```
public void setP(double val)
```
    getP SEE: getP
    
    Parameters:
    
    val - automatically generated
  - getClassWeights
```
public Mat getClassWeights()
```
    SEE: setClassWeights
    
    Returns:
    
    automatically generated
  - setClassWeights
```
public void setClassWeights(Mat val)
```
    getClassWeights SEE: getClassWeights
    
    Parameters:
    
    val - automatically generated
  - getTermCriteria
```
public TermCriteria getTermCriteria()
```
    SEE: setTermCriteria
    
    Returns:
    
    automatically generated
  - setTermCriteria
```
public void setTermCriteria(TermCriteria val)
```
    getTermCriteria SEE: getTermCriteria
    
    Parameters:
    
    val - automatically generated
  - getKernelType
```
public int getKernelType()
```
    Type of a %SVM kernel. See SVM::KernelTypes. Default value is SVM::RBF.
    
    Returns:
    
    automatically generated
  - setKernel
```
public void setKernel(int kernelType)
```
    Initialize with one of predefined kernels. See SVM::KernelTypes.
    
    Parameters:
    
    kernelType - automatically generated
  - trainAuto
```
public boolean trainAuto(Mat samples,
                         int layout,
                         Mat responses,
                         int kFold,
                         ParamGrid Cgrid,
                         ParamGrid gammaGrid,
                         ParamGrid pGrid,
                         ParamGrid nuGrid,
                         ParamGrid coeffGrid,
                         ParamGrid degreeGrid,
                         boolean balanced)
```
    Trains an %SVM with optimal parameters
    
    Parameters:
    
    samples - training samples
    
    layout - See ml::SampleTypes.
    
    responses - vector of responses associated with the training samples.
    
    kFold - Cross-validation parameter. The training set is divided into kFold subsets. One subset is used to test the model, the others form the train set. So, the %SVM algorithm is
    
    Cgrid - grid for C
    
    gammaGrid - grid for gamma
    
    pGrid - grid for p
    
    nuGrid - grid for nu
    
    coeffGrid - grid for coeff
    
    degreeGrid - grid for degree
    
    balanced - If true and the problem is 2-class classification then the method creates more balanced cross-validation subsets that is proportions between classes in subsets are close to such proportion in the whole train dataset. The method trains the %SVM model automatically by choosing the optimal parameters C, gamma, p, nu, coef0, degree. Parameters are considered optimal when the cross-validation estimate of the test set error is minimal. This function only makes use of SVM::getDefaultGrid for parameter optimization and thus only offers rudimentary parameter options. This function works for the classification (SVM::C_SVC or SVM::NU_SVC) as well as for the regression (SVM::EPS_SVR or SVM::NU_SVR). If it is SVM::ONE_CLASS, no optimization is made and the usual %SVM with parameters specified in params is executed.
    
    Returns:
    
    automatically generated
  - trainAuto
```
public boolean trainAuto(Mat samples,
                         int layout,
                         Mat responses,
                         int kFold,
                         ParamGrid Cgrid,
                         ParamGrid gammaGrid,
                         ParamGrid pGrid,
                         ParamGrid nuGrid,
                         ParamGrid coeffGrid,
                         ParamGrid degreeGrid)
```
    Trains an %SVM with optimal parameters
    
    Parameters:
    
    samples - training samples
    
    layout - See ml::SampleTypes.
    
    responses - vector of responses associated with the training samples.
    
    kFold - Cross-validation parameter. The training set is divided into kFold subsets. One subset is used to test the model, the others form the train set. So, the %SVM algorithm is
    
    Cgrid - grid for C
    
    gammaGrid - grid for gamma
    
    pGrid - grid for p
    
    nuGrid - grid for nu
    
    coeffGrid - grid for coeff
    
    degreeGrid - grid for degree balanced cross-validation subsets that is proportions between classes in subsets are close to such proportion in the whole train dataset. The method trains the %SVM model automatically by choosing the optimal parameters C, gamma, p, nu, coef0, degree. Parameters are considered optimal when the cross-validation estimate of the test set error is minimal. This function only makes use of SVM::getDefaultGrid for parameter optimization and thus only offers rudimentary parameter options. This function works for the classification (SVM::C_SVC or SVM::NU_SVC) as well as for the regression (SVM::EPS_SVR or SVM::NU_SVR). If it is SVM::ONE_CLASS, no optimization is made and the usual %SVM with parameters specified in params is executed.
    
    Returns:
    
    automatically generated
  - trainAuto
```
public boolean trainAuto(Mat samples,
                         int layout,
                         Mat responses,
                         int kFold,
                         ParamGrid Cgrid,
                         ParamGrid gammaGrid,
                         ParamGrid pGrid,
                         ParamGrid nuGrid,
                         ParamGrid coeffGrid)
```
    Trains an %SVM with optimal parameters
    
    Parameters:
    
    samples - training samples
    
    layout - See ml::SampleTypes.
    
    responses - vector of responses associated with the training samples.
    
    kFold - Cross-validation parameter. The training set is divided into kFold subsets. One subset is used to test the model, the others form the train set. So, the %SVM algorithm is
    
    Cgrid - grid for C
    
    gammaGrid - grid for gamma
    
    pGrid - grid for p
    
    nuGrid - grid for nu
    
    coeffGrid - grid for coeff balanced cross-validation subsets that is proportions between classes in subsets are close to such proportion in the whole train dataset. The method trains the %SVM model automatically by choosing the optimal parameters C, gamma, p, nu, coef0, degree. Parameters are considered optimal when the cross-validation estimate of the test set error is minimal. This function only makes use of SVM::getDefaultGrid for parameter optimization and thus only offers rudimentary parameter options. This function works for the classification (SVM::C_SVC or SVM::NU_SVC) as well as for the regression (SVM::EPS_SVR or SVM::NU_SVR). If it is SVM::ONE_CLASS, no optimization is made and the usual %SVM with parameters specified in params is executed.
    
    Returns:
    
    automatically generated
  - trainAuto
```
public boolean trainAuto(Mat samples,
                         int layout,
                         Mat responses,
                         int kFold,
                         ParamGrid Cgrid,
                         ParamGrid gammaGrid,
                         ParamGrid pGrid,
                         ParamGrid nuGrid)
```
    Trains an %SVM with optimal parameters
    
    Parameters:
    
    samples - training samples
    
    layout - See ml::SampleTypes.
    
    responses - vector of responses associated with the training samples.
    
    kFold - Cross-validation parameter. The training set is divided into kFold subsets. One subset is used to test the model, the others form the train set. So, the %SVM algorithm is
    
    Cgrid - grid for C
    
    gammaGrid - grid for gamma
    
    pGrid - grid for p
    
    nuGrid - grid for nu balanced cross-validation subsets that is proportions between classes in subsets are close to such proportion in the whole train dataset. The method trains the %SVM model automatically by choosing the optimal parameters C, gamma, p, nu, coef0, degree. Parameters are considered optimal when the cross-validation estimate of the test set error is minimal. This function only makes use of SVM::getDefaultGrid for parameter optimization and thus only offers rudimentary parameter options. This function works for the classification (SVM::C_SVC or SVM::NU_SVC) as well as for the regression (SVM::EPS_SVR or SVM::NU_SVR). If it is SVM::ONE_CLASS, no optimization is made and the usual %SVM with parameters specified in params is executed.
    
    Returns:
    
    automatically generated
  - trainAuto
```
public boolean trainAuto(Mat samples,
                         int layout,
                         Mat responses,
                         int kFold,
                         ParamGrid Cgrid,
                         ParamGrid gammaGrid,
                         ParamGrid pGrid)
```
    Trains an %SVM with optimal parameters
    
    Parameters:
    
    samples - training samples
    
    layout - See ml::SampleTypes.
    
    responses - vector of responses associated with the training samples.
    
    kFold - Cross-validation parameter. The training set is divided into kFold subsets. One subset is used to test the model, the others form the train set. So, the %SVM algorithm is
    
    Cgrid - grid for C
    
    gammaGrid - grid for gamma
    
    pGrid - grid for p balanced cross-validation subsets that is proportions between classes in subsets are close to such proportion in the whole train dataset. The method trains the %SVM model automatically by choosing the optimal parameters C, gamma, p, nu, coef0, degree. Parameters are considered optimal when the cross-validation estimate of the test set error is minimal. This function only makes use of SVM::getDefaultGrid for parameter optimization and thus only offers rudimentary parameter options. This function works for the classification (SVM::C_SVC or SVM::NU_SVC) as well as for the regression (SVM::EPS_SVR or SVM::NU_SVR). If it is SVM::ONE_CLASS, no optimization is made and the usual %SVM with parameters specified in params is executed.
    
    Returns:
    
    automatically generated
  - trainAuto
```
public boolean trainAuto(Mat samples,
                         int layout,
                         Mat responses,
                         int kFold,
                         ParamGrid Cgrid,
                         ParamGrid gammaGrid)
```
    Trains an %SVM with optimal parameters
    
    Parameters:
    
    samples - training samples
    
    layout - See ml::SampleTypes.
    
    responses - vector of responses associated with the training samples.
    
    kFold - Cross-validation parameter. The training set is divided into kFold subsets. One subset is used to test the model, the others form the train set. So, the %SVM algorithm is
    
    Cgrid - grid for C
    
    gammaGrid - grid for gamma balanced cross-validation subsets that is proportions between classes in subsets are close to such proportion in the whole train dataset. The method trains the %SVM model automatically by choosing the optimal parameters C, gamma, p, nu, coef0, degree. Parameters are considered optimal when the cross-validation estimate of the test set error is minimal. This function only makes use of SVM::getDefaultGrid for parameter optimization and thus only offers rudimentary parameter options. This function works for the classification (SVM::C_SVC or SVM::NU_SVC) as well as for the regression (SVM::EPS_SVR or SVM::NU_SVR). If it is SVM::ONE_CLASS, no optimization is made and the usual %SVM with parameters specified in params is executed.
    
    Returns:
    
    automatically generated
  - trainAuto
```
public boolean trainAuto(Mat samples,
                         int layout,
                         Mat responses,
                         int kFold,
                         ParamGrid Cgrid)
```
    Trains an %SVM with optimal parameters
    
    Parameters:
    
    samples - training samples
    
    layout - See ml::SampleTypes.
    
    responses - vector of responses associated with the training samples.
    
    kFold - Cross-validation parameter. The training set is divided into kFold subsets. One subset is used to test the model, the others form the train set. So, the %SVM algorithm is
    
    Cgrid - grid for C balanced cross-validation subsets that is proportions between classes in subsets are close to such proportion in the whole train dataset. The method trains the %SVM model automatically by choosing the optimal parameters C, gamma, p, nu, coef0, degree. Parameters are considered optimal when the cross-validation estimate of the test set error is minimal. This function only makes use of SVM::getDefaultGrid for parameter optimization and thus only offers rudimentary parameter options. This function works for the classification (SVM::C_SVC or SVM::NU_SVC) as well as for the regression (SVM::EPS_SVR or SVM::NU_SVR). If it is SVM::ONE_CLASS, no optimization is made and the usual %SVM with parameters specified in params is executed.
    
    Returns:
    
    automatically generated
  - trainAuto
```
public boolean trainAuto(Mat samples,
                         int layout,
                         Mat responses,
                         int kFold)
```
    Trains an %SVM with optimal parameters
    
    Parameters:
    
    samples - training samples
    
    layout - See ml::SampleTypes.
    
    responses - vector of responses associated with the training samples.
    
    kFold - Cross-validation parameter. The training set is divided into kFold subsets. One subset is used to test the model, the others form the train set. So, the %SVM algorithm is balanced cross-validation subsets that is proportions between classes in subsets are close to such proportion in the whole train dataset. The method trains the %SVM model automatically by choosing the optimal parameters C, gamma, p, nu, coef0, degree. Parameters are considered optimal when the cross-validation estimate of the test set error is minimal. This function only makes use of SVM::getDefaultGrid for parameter optimization and thus only offers rudimentary parameter options. This function works for the classification (SVM::C_SVC or SVM::NU_SVC) as well as for the regression (SVM::EPS_SVR or SVM::NU_SVR). If it is SVM::ONE_CLASS, no optimization is made and the usual %SVM with parameters specified in params is executed.
    
    Returns:
    
    automatically generated
  - trainAuto
```
public boolean trainAuto(Mat samples,
                         int layout,
                         Mat responses)
```
    Trains an %SVM with optimal parameters
    
    Parameters:
    
    samples - training samples
    
    layout - See ml::SampleTypes.
    
    responses - vector of responses associated with the training samples. subset is used to test the model, the others form the train set. So, the %SVM algorithm is balanced cross-validation subsets that is proportions between classes in subsets are close to such proportion in the whole train dataset. The method trains the %SVM model automatically by choosing the optimal parameters C, gamma, p, nu, coef0, degree. Parameters are considered optimal when the cross-validation estimate of the test set error is minimal. This function only makes use of SVM::getDefaultGrid for parameter optimization and thus only offers rudimentary parameter options. This function works for the classification (SVM::C_SVC or SVM::NU_SVC) as well as for the regression (SVM::EPS_SVR or SVM::NU_SVR). If it is SVM::ONE_CLASS, no optimization is made and the usual %SVM with parameters specified in params is executed.
    
    Returns:
    
    automatically generated
  - getSupportVectors
```
public Mat getSupportVectors()
```
    Retrieves all the support vectors The method returns all the support vectors as a floating-point matrix, where support vectors are stored as matrix rows.
    
    Returns:
    
    automatically generated
  - getUncompressedSupportVectors
```
public Mat getUncompressedSupportVectors()
```
    Retrieves all the uncompressed support vectors of a linear %SVM The method returns all the uncompressed support vectors of a linear %SVM that the compressed support vector, used for prediction, was derived from. They are returned in a floating-point matrix, where the support vectors are stored as matrix rows.
    
    Returns:
    
    automatically generated
  - getDecisionFunction
```
public double getDecisionFunction(int i,
                                  Mat alpha,
                                  Mat svidx)
```
    Retrieves the decision function
    
    Parameters:
    
    i - the index of the decision function. If the problem solved is regression, 1-class or 2-class classification, then there will be just one decision function and the index should always be 0. Otherwise, in the case of N-class classification, there will be \(N(N-1)/2\) decision functions.
    
    alpha - the optional output vector for weights, corresponding to different support vectors. In the case of linear %SVM all the alpha's will be 1's.
    
    svidx - the optional output vector of indices of support vectors within the matrix of support vectors (which can be retrieved by SVM::getSupportVectors). In the case of linear %SVM each decision function consists of a single "compressed" support vector. The method returns rho parameter of the decision function, a scalar subtracted from the weighted sum of kernel responses.
    
    Returns:
    
    automatically generated
  - getDefaultGridPtr
```
public static ParamGrid getDefaultGridPtr(int param_id)
```
    Generates a grid for %SVM parameters.
    
    Parameters:
    
    param_id - %SVM parameters IDs that must be one of the SVM::ParamTypes. The grid is generated for the parameter with this ID. The function generates a grid pointer for the specified parameter of the %SVM algorithm. The grid may be passed to the function SVM::trainAuto.
    
    Returns:
    
    automatically generated
  - create
```
public static SVM create()
```
    Creates empty model. Use StatModel::train to train the model. Since %SVM has several parameters, you may want to find the best parameters for your problem, it can be done with SVM::trainAuto.
    
    Returns:
    
    automatically generated
  - load
```
public static SVM load(String filepath)
```
    Loads and creates a serialized svm from a file Use SVM::save to serialize and store an SVM to disk. Load the SVM from this file again, by calling this function with the path to the file.
    
    Parameters:
    
    filepath - path to serialized svm
    
    Returns:
    
    automatically generated
  - finalize
```
protected void finalize()
                 throws Throwable
```
    Overrides:
    
    finalize in class StatModel
    
    Throws:
    
    Throwable

Class SVM

Field Summary

Fields inherited from class org.opencv.ml.StatModel

Fields inherited from class org.opencv.core.Algorithm

Constructor Summary

Method Summary

Methods inherited from class org.opencv.ml.StatModel

Methods inherited from class org.opencv.core.Algorithm

Methods inherited from class java.lang.Object

Field Detail

CUSTOM

LINEAR

POLY

RBF

SIGMOID

CHI2

INTER

C

GAMMA

P

NU

COEF

DEGREE

C_SVC

NU_SVC

ONE_CLASS

EPS_SVR

NU_SVR

Constructor Detail

SVM

Method Detail

__fromPtr__

getType

setType

getGamma

setGamma

getCoef0

setCoef0

getDegree

setDegree

getC

setC

getNu

setNu

getP

setP

getClassWeights

setClassWeights

getTermCriteria

setTermCriteria

getKernelType

setKernel

trainAuto

trainAuto

trainAuto

trainAuto

trainAuto

trainAuto

trainAuto

trainAuto

trainAuto

getSupportVectors

getUncompressedSupportVectors

getDecisionFunction

getDefaultGridPtr

create

load

finalize

fromPtr