/**
* 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 bool trainAuto(Mat samples, int layout, Mat responses, int kFold, ParamGrid Cgrid, ParamGrid gammaGrid, ParamGrid pGrid)
{
ThrowIfDisposed();
if (samples != null)
{
samples.ThrowIfDisposed();
}
if (responses != null)
{
responses.ThrowIfDisposed();
}
if (Cgrid != null)
{
Cgrid.ThrowIfDisposed();
}
if (gammaGrid != null)
{
gammaGrid.ThrowIfDisposed();
}
if (pGrid != null)
{
pGrid.ThrowIfDisposed();
}
return(ml_SVM_trainAuto_14(nativeObj, samples.nativeObj, layout, responses.nativeObj, kFold, Cgrid.getNativeObjAddr(), gammaGrid.getNativeObjAddr(), pGrid.getNativeObjAddr()));
}
//javadoc: SVM::trainAuto(samples, layout, responses, kFold, Cgrid, gammaGrid)
public bool trainAuto(Mat samples, int layout, Mat responses, int kFold, ParamGrid Cgrid, ParamGrid gammaGrid)
{
ThrowIfDisposed();
if (samples != null)
{
samples.ThrowIfDisposed();
}
if (responses != null)
{
responses.ThrowIfDisposed();
}
if (Cgrid != null)
{
Cgrid.ThrowIfDisposed();
}
if (gammaGrid != null)
{
gammaGrid.ThrowIfDisposed();
}
#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
bool retVal = ml_SVM_trainAuto_15(nativeObj, samples.nativeObj, layout, responses.nativeObj, kFold, Cgrid.getNativeObjAddr(), gammaGrid.getNativeObjAddr());
return(retVal);
#else
return(false);
#endif
}
//
// 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)
//
//javadoc: SVM::trainAuto(samples, layout, responses, kFold, Cgrid, gammaGrid, pGrid, nuGrid, coeffGrid, degreeGrid, balanced)
public bool trainAuto(Mat samples, int layout, Mat responses, int kFold, ParamGrid Cgrid, ParamGrid gammaGrid, ParamGrid pGrid, ParamGrid nuGrid, ParamGrid coeffGrid, ParamGrid degreeGrid, bool balanced)
{
ThrowIfDisposed();
if (samples != null)
{
samples.ThrowIfDisposed();
}
if (responses != null)
{
responses.ThrowIfDisposed();
}
if (Cgrid != null)
{
Cgrid.ThrowIfDisposed();
}
if (gammaGrid != null)
{
gammaGrid.ThrowIfDisposed();
}
if (pGrid != null)
{
pGrid.ThrowIfDisposed();
}
if (nuGrid != null)
{
nuGrid.ThrowIfDisposed();
}
if (coeffGrid != null)
{
coeffGrid.ThrowIfDisposed();
}
if (degreeGrid != null)
{
degreeGrid.ThrowIfDisposed();
}
#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
bool retVal = ml_SVM_trainAuto_10(nativeObj, samples.nativeObj, layout, responses.nativeObj, kFold, Cgrid.getNativeObjAddr(), gammaGrid.getNativeObjAddr(), pGrid.getNativeObjAddr(), nuGrid.getNativeObjAddr(), coeffGrid.getNativeObjAddr(), degreeGrid.getNativeObjAddr(), balanced);
return(retVal);
#else
return(false);
#endif
}
//
// 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 bool trainAuto(Mat samples, int layout, Mat responses, int kFold, ParamGrid Cgrid, ParamGrid gammaGrid, ParamGrid pGrid, ParamGrid nuGrid, ParamGrid coeffGrid, ParamGrid degreeGrid, bool balanced)
{
ThrowIfDisposed();
if (samples != null)
{
samples.ThrowIfDisposed();
}
if (responses != null)
{
responses.ThrowIfDisposed();
}
if (Cgrid != null)
{
Cgrid.ThrowIfDisposed();
}
if (gammaGrid != null)
{
gammaGrid.ThrowIfDisposed();
}
if (pGrid != null)
{
pGrid.ThrowIfDisposed();
}
if (nuGrid != null)
{
nuGrid.ThrowIfDisposed();
}
if (coeffGrid != null)
{
coeffGrid.ThrowIfDisposed();
}
if (degreeGrid != null)
{
degreeGrid.ThrowIfDisposed();
}
return(ml_SVM_trainAuto_10(nativeObj, samples.nativeObj, layout, responses.nativeObj, kFold, Cgrid.getNativeObjAddr(), gammaGrid.getNativeObjAddr(), pGrid.getNativeObjAddr(), nuGrid.getNativeObjAddr(), coeffGrid.getNativeObjAddr(), degreeGrid.getNativeObjAddr(), balanced));
}