//
// This file is auto-generated. Please don't modify it!
//
package org.opencv.ml;
import org.opencv.core.Mat;
import org.opencv.core.TermCriteria;
import org.opencv.ml.ParamGrid;
import org.opencv.ml.SVM;
import org.opencv.ml.StatModel;
// C++: class SVM
/**
* Support Vector Machines.
*
* SEE: REF: ml_intro_svm
*/
public class SVM extends StatModel {
protected SVM(long addr) { super(addr); }
// internal usage only
public static SVM __fromPtr__(long addr) { return new SVM(addr); }
// C++: enum KernelTypes (cv.ml.SVM.KernelTypes)
public static final int
CUSTOM = -1,
LINEAR = 0,
POLY = 1,
RBF = 2,
SIGMOID = 3,
CHI2 = 4,
INTER = 5;
// C++: enum ParamTypes (cv.ml.SVM.ParamTypes)
public static final int
C = 0,
GAMMA = 1,
P = 2,
NU = 3,
COEF = 4,
DEGREE = 5;
// C++: enum Types (cv.ml.SVM.Types)
public static final int
C_SVC = 100,
NU_SVC = 101,
ONE_CLASS = 102,
EPS_SVR = 103,
NU_SVR = 104;
//
// C++: int cv::ml::SVM::getType()
//
/**
* SEE: setType
* @return automatically generated
*/
public int getType() {
return getType_0(nativeObj);
}
//
// C++: void cv::ml::SVM::setType(int val)
//
/**
* getType SEE: getType
* @param val automatically generated
*/
public void setType(int val) {
setType_0(nativeObj, val);
}
//
// C++: double cv::ml::SVM::getGamma()
//
/**
* SEE: setGamma
* @return automatically generated
*/
public double getGamma() {
return getGamma_0(nativeObj);
}
//
// C++: void cv::ml::SVM::setGamma(double val)
//
/**
* getGamma SEE: getGamma
* @param val automatically generated
*/
public void setGamma(double val) {
setGamma_0(nativeObj, val);
}
//
// C++: double cv::ml::SVM::getCoef0()
//
/**
* SEE: setCoef0
* @return automatically generated
*/
public double getCoef0() {
return getCoef0_0(nativeObj);
}
//
// C++: void cv::ml::SVM::setCoef0(double val)
//
/**
* getCoef0 SEE: getCoef0
* @param val automatically generated
*/
public void setCoef0(double val) {
setCoef0_0(nativeObj, val);
}
//
// C++: double cv::ml::SVM::getDegree()
//
/**
* SEE: setDegree
* @return automatically generated
*/
public double getDegree() {
return getDegree_0(nativeObj);
}
//
// C++: void cv::ml::SVM::setDegree(double val)
//
/**
* getDegree SEE: getDegree
* @param val automatically generated
*/
public void setDegree(double val) {
setDegree_0(nativeObj, val);
}
//
// C++: double cv::ml::SVM::getC()
//
/**
* SEE: setC
* @return automatically generated
*/
public double getC() {
return getC_0(nativeObj);
}
//
// C++: void cv::ml::SVM::setC(double val)
//
/**
* getC SEE: getC
* @param val automatically generated
*/
public void setC(double val) {
setC_0(nativeObj, val);
}
//
// C++: double cv::ml::SVM::getNu()
//
/**
* SEE: setNu
* @return automatically generated
*/
public double getNu() {
return getNu_0(nativeObj);
}
//
// C++: void cv::ml::SVM::setNu(double val)
//
/**
* getNu SEE: getNu
* @param val automatically generated
*/
public void setNu(double val) {
setNu_0(nativeObj, val);
}
//
// C++: double cv::ml::SVM::getP()
//
/**
* SEE: setP
* @return automatically generated
*/
public double getP() {
return getP_0(nativeObj);
}
//
// C++: void cv::ml::SVM::setP(double val)
//
/**
* getP SEE: getP
* @param val automatically generated
*/
public void setP(double val) {
setP_0(nativeObj, val);
}
//
// C++: Mat cv::ml::SVM::getClassWeights()
//
/**
* SEE: setClassWeights
* @return automatically generated
*/
public Mat getClassWeights() {
return new Mat(getClassWeights_0(nativeObj));
}
//
// C++: void cv::ml::SVM::setClassWeights(Mat val)
//
/**
* getClassWeights SEE: getClassWeights
* @param val automatically generated
*/
public void setClassWeights(Mat val) {
setClassWeights_0(nativeObj, val.nativeObj);
}
//
// C++: TermCriteria cv::ml::SVM::getTermCriteria()
//
/**
* SEE: setTermCriteria
* @return automatically generated
*/
public TermCriteria getTermCriteria() {
return new TermCriteria(getTermCriteria_0(nativeObj));
}
//
// C++: void cv::ml::SVM::setTermCriteria(TermCriteria val)
//
/**
* getTermCriteria SEE: getTermCriteria
* @param val automatically generated
*/
public void setTermCriteria(TermCriteria val) {
setTermCriteria_0(nativeObj, val.type, val.maxCount, val.epsilon);
}
//
// C++: int cv::ml::SVM::getKernelType()
//
/**
* Type of a %SVM kernel.
* See SVM::KernelTypes. Default value is SVM::RBF.
* @return automatically generated
*/
public int getKernelType() {
return getKernelType_0(nativeObj);
}
//
// C++: void cv::ml::SVM::setKernel(int kernelType)
//
/**
* Initialize with one of predefined kernels.
* See SVM::KernelTypes.
* @param kernelType automatically generated
*/
public void setKernel(int kernelType) {
setKernel_0(nativeObj, kernelType);
}
//
// C++: bool cv::ml::SVM::trainAuto(Mat samples, int layout, Mat 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)
//
/**
* Trains an %SVM with optimal parameters
*
* @param samples training samples
* @param layout See ml::SampleTypes.
* @param responses vector of responses associated with the training samples.
* @param 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
* @param Cgrid grid for C
* @param gammaGrid grid for gamma
* @param pGrid grid for p
* @param nuGrid grid for nu
* @param coeffGrid grid for coeff
* @param degreeGrid grid for degree
* @param 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.
* @return automatically generated
*/
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) {
return trainAuto_0(nativeObj, samples.nativeObj, layout, responses.nativeObj, kFold, Cgrid.getNativeObjAddr(), gammaGrid.getNativeObjAddr(), pGrid.getNativeObjAddr(), nuGrid.getNativeObjAddr(), coeffGrid.getNativeObjAddr(), degreeGrid.getNativeObjAddr(), balanced);
}
/**
* Trains an %SVM with optimal parameters
*
* @param samples training samples
* @param layout See ml::SampleTypes.
* @param responses vector of responses associated with the training samples.
* @param 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
* @param Cgrid grid for C
* @param gammaGrid grid for gamma
* @param pGrid grid for p
* @param nuGrid grid for nu
* @param coeffGrid grid for coeff
* @param 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.
* @return automatically generated
*/
public boolean trainAuto(Mat samples, int layout, Mat responses, int kFold, ParamGrid Cgrid, ParamGrid gammaGrid, ParamGrid pGrid, ParamGrid nuGrid, ParamGrid coeffGrid, ParamGrid degreeGrid) {
return trainAuto_1(nativeObj, samples.nativeObj, layout, responses.nativeObj, kFold, Cgrid.getNativeObjAddr(), gammaGrid.getNativeObjAddr(), pGrid.getNativeObjAddr(), nuGrid.getNativeObjAddr(), coeffGrid.getNativeObjAddr(), degreeGrid.getNativeObjAddr());
}
/**
* Trains an %SVM with optimal parameters
*
* @param samples training samples
* @param layout See ml::SampleTypes.
* @param responses vector of responses associated with the training samples.
* @param 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
* @param Cgrid grid for C
* @param gammaGrid grid for gamma
* @param pGrid grid for p
* @param nuGrid grid for nu
* @param 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.
* @return automatically generated
*/
public boolean trainAuto(Mat samples, int layout, Mat responses, int kFold, ParamGrid Cgrid, ParamGrid gammaGrid, ParamGrid pGrid, ParamGrid nuGrid, ParamGrid coeffGrid) {
return trainAuto_2(nativeObj, samples.nativeObj, layout, responses.nativeObj, kFold, Cgrid.getNativeObjAddr(), gammaGrid.getNativeObjAddr(), pGrid.getNativeObjAddr(), nuGrid.getNativeObjAddr(), coeffGrid.getNativeObjAddr());
}
/**
* Trains an %SVM with optimal parameters
*
* @param samples training samples
* @param layout See ml::SampleTypes.
* @param responses vector of responses associated with the training samples.
* @param 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
* @param Cgrid grid for C
* @param gammaGrid grid for gamma
* @param pGrid grid for p
* @param 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.
* @return automatically generated
*/
public boolean trainAuto(Mat samples, int layout, Mat responses, int kFold, ParamGrid Cgrid, ParamGrid gammaGrid, ParamGrid pGrid, ParamGrid nuGrid) {
return trainAuto_3(nativeObj, samples.nativeObj, layout, responses.nativeObj, kFold, Cgrid.getNativeObjAddr(), gammaGrid.getNativeObjAddr(), pGrid.getNativeObjAddr(), nuGrid.getNativeObjAddr());
}
/**
* Trains an %SVM with optimal parameters
*
* @param samples training samples
* @param layout See ml::SampleTypes.
* @param responses vector of responses associated with the training samples.
* @param 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
* @param Cgrid grid for C
* @param gammaGrid grid for gamma
* @param 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.
* @return automatically generated
*/
public boolean trainAuto(Mat samples, int layout, Mat responses, int kFold, ParamGrid Cgrid, ParamGrid gammaGrid, ParamGrid pGrid) {
return trainAuto_4(nativeObj, samples.nativeObj, layout, responses.nativeObj, kFold, Cgrid.getNativeObjAddr(), gammaGrid.getNativeObjAddr(), pGrid.getNativeObjAddr());
}
/**
* Trains an %SVM with optimal parameters
*
* @param samples training samples
* @param layout See ml::SampleTypes.
* @param responses vector of responses associated with the training samples.
* @param 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
* @param Cgrid grid for C
* @param 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.
* @return automatically generated
*/
public boolean trainAuto(Mat samples, int layout, Mat responses, int kFold, ParamGrid Cgrid, ParamGrid gammaGrid) {
return trainAuto_5(nativeObj, samples.nativeObj, layout, responses.nativeObj, kFold, Cgrid.getNativeObjAddr(), gammaGrid.getNativeObjAddr());
}
/**
* Trains an %SVM with optimal parameters
*
* @param samples training samples
* @param layout See ml::SampleTypes.
* @param responses vector of responses associated with the training samples.
* @param 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
* @param 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.
* @return automatically generated
*/
public boolean trainAuto(Mat samples, int layout, Mat responses, int kFold, ParamGrid Cgrid) {
return trainAuto_6(nativeObj, samples.nativeObj, layout, responses.nativeObj, kFold, Cgrid.getNativeObjAddr());
}
/**
* Trains an %SVM with optimal parameters
*
* @param samples training samples
* @param layout See ml::SampleTypes.
* @param responses vector of responses associated with the training samples.
* @param 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.
* @return automatically generated
*/
public boolean trainAuto(Mat samples, int layout, Mat responses, int kFold) {
return trainAuto_7(nativeObj, samples.nativeObj, layout, responses.nativeObj, kFold);
}
/**
* Trains an %SVM with optimal parameters
*
* @param samples training samples
* @param layout See ml::SampleTypes.
* @param 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.
* @return automatically generated
*/
public boolean trainAuto(Mat samples, int layout, Mat responses) {
return trainAuto_8(nativeObj, samples.nativeObj, layout, responses.nativeObj);
}
//
// C++: Mat cv::ml::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.
* @return automatically generated
*/
public Mat getSupportVectors() {
return new Mat(getSupportVectors_0(nativeObj));
}
//
// C++: Mat cv::ml::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.
* @return automatically generated
*/
public Mat getUncompressedSupportVectors() {
return new Mat(getUncompressedSupportVectors_0(nativeObj));
}
//
// C++: double cv::ml::SVM::getDecisionFunction(int i, Mat& alpha, Mat& svidx)
//
/**
* Retrieves the decision function
*
* @param 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.
* @param 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.
* @param 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.
* @return automatically generated
*/
public double getDecisionFunction(int i, Mat alpha, Mat svidx) {
return getDecisionFunction_0(nativeObj, i, alpha.nativeObj, svidx.nativeObj);
}
//
// C++: static Ptr_ParamGrid cv::ml::SVM::getDefaultGridPtr(int param_id)
//
/**
* Generates a grid for %SVM parameters.
*
* @param 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.
* @return automatically generated
*/
public static ParamGrid getDefaultGridPtr(int param_id) {
return ParamGrid.__fromPtr__(getDefaultGridPtr_0(param_id));
}
//
// C++: static Ptr_SVM cv::ml::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.
* @return automatically generated
*/
public static SVM create() {
return SVM.__fromPtr__(create_0());
}
//
// C++: static Ptr_SVM cv::ml::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.
*
* @param filepath path to serialized svm
* @return automatically generated
*/
public static SVM load(String filepath) {
return SVM.__fromPtr__(load_0(filepath));
}
@Override
protected void finalize() throws Throwable {
delete(nativeObj);
}
// C++: int cv::ml::SVM::getType()
private static native int getType_0(long nativeObj);
// C++: void cv::ml::SVM::setType(int val)
private static native void setType_0(long nativeObj, int val);
// C++: double cv::ml::SVM::getGamma()
private static native double getGamma_0(long nativeObj);
// C++: void cv::ml::SVM::setGamma(double val)
private static native void setGamma_0(long nativeObj, double val);
// C++: double cv::ml::SVM::getCoef0()
private static native double getCoef0_0(long nativeObj);
// C++: void cv::ml::SVM::setCoef0(double val)
private static native void setCoef0_0(long nativeObj, double val);
// C++: double cv::ml::SVM::getDegree()
private static native double getDegree_0(long nativeObj);
// C++: void cv::ml::SVM::setDegree(double val)
private static native void setDegree_0(long nativeObj, double val);
// C++: double cv::ml::SVM::getC()
private static native double getC_0(long nativeObj);
// C++: void cv::ml::SVM::setC(double val)
private static native void setC_0(long nativeObj, double val);
// C++: double cv::ml::SVM::getNu()
private static native double getNu_0(long nativeObj);
// C++: void cv::ml::SVM::setNu(double val)
private static native void setNu_0(long nativeObj, double val);
// C++: double cv::ml::SVM::getP()
private static native double getP_0(long nativeObj);
// C++: void cv::ml::SVM::setP(double val)
private static native void setP_0(long nativeObj, double val);
// C++: Mat cv::ml::SVM::getClassWeights()
private static native long getClassWeights_0(long nativeObj);
// C++: void cv::ml::SVM::setClassWeights(Mat val)
private static native void setClassWeights_0(long nativeObj, long val_nativeObj);
// C++: TermCriteria cv::ml::SVM::getTermCriteria()
private static native double[] getTermCriteria_0(long nativeObj);
// C++: void cv::ml::SVM::setTermCriteria(TermCriteria val)
private static native void setTermCriteria_0(long nativeObj, int val_type, int val_maxCount, double val_epsilon);
// C++: int cv::ml::SVM::getKernelType()
private static native int getKernelType_0(long nativeObj);
// C++: void cv::ml::SVM::setKernel(int kernelType)
private static native void setKernel_0(long nativeObj, int kernelType);
// C++: bool cv::ml::SVM::trainAuto(Mat samples, int layout, Mat 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)
private static native boolean trainAuto_0(long nativeObj, long samples_nativeObj, int layout, long responses_nativeObj, int kFold, long Cgrid_nativeObj, long gammaGrid_nativeObj, long pGrid_nativeObj, long nuGrid_nativeObj, long coeffGrid_nativeObj, long degreeGrid_nativeObj, boolean balanced);
private static native boolean trainAuto_1(long nativeObj, long samples_nativeObj, int layout, long responses_nativeObj, int kFold, long Cgrid_nativeObj, long gammaGrid_nativeObj, long pGrid_nativeObj, long nuGrid_nativeObj, long coeffGrid_nativeObj, long degreeGrid_nativeObj);
private static native boolean trainAuto_2(long nativeObj, long samples_nativeObj, int layout, long responses_nativeObj, int kFold, long Cgrid_nativeObj, long gammaGrid_nativeObj, long pGrid_nativeObj, long nuGrid_nativeObj, long coeffGrid_nativeObj);
private static native boolean trainAuto_3(long nativeObj, long samples_nativeObj, int layout, long responses_nativeObj, int kFold, long Cgrid_nativeObj, long gammaGrid_nativeObj, long pGrid_nativeObj, long nuGrid_nativeObj);
private static native boolean trainAuto_4(long nativeObj, long samples_nativeObj, int layout, long responses_nativeObj, int kFold, long Cgrid_nativeObj, long gammaGrid_nativeObj, long pGrid_nativeObj);
private static native boolean trainAuto_5(long nativeObj, long samples_nativeObj, int layout, long responses_nativeObj, int kFold, long Cgrid_nativeObj, long gammaGrid_nativeObj);
private static native boolean trainAuto_6(long nativeObj, long samples_nativeObj, int layout, long responses_nativeObj, int kFold, long Cgrid_nativeObj);
private static native boolean trainAuto_7(long nativeObj, long samples_nativeObj, int layout, long responses_nativeObj, int kFold);
private static native boolean trainAuto_8(long nativeObj, long samples_nativeObj, int layout, long responses_nativeObj);
// C++: Mat cv::ml::SVM::getSupportVectors()
private static native long getSupportVectors_0(long nativeObj);
// C++: Mat cv::ml::SVM::getUncompressedSupportVectors()
private static native long getUncompressedSupportVectors_0(long nativeObj);
// C++: double cv::ml::SVM::getDecisionFunction(int i, Mat& alpha, Mat& svidx)
private static native double getDecisionFunction_0(long nativeObj, int i, long alpha_nativeObj, long svidx_nativeObj);
// C++: static Ptr_ParamGrid cv::ml::SVM::getDefaultGridPtr(int param_id)
private static native long getDefaultGridPtr_0(int param_id);
// C++: static Ptr_SVM cv::ml::SVM::create()
private static native long create_0();
// C++: static Ptr_SVM cv::ml::SVM::load(String filepath)
private static native long load_0(String filepath);
// native support for java finalize()
private static native void delete(long nativeObj);
}
