//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
}
void DoProcess()
{
if (!(owner.Value is OpenCVForUnityPlayMakerActions.SVM))
{
LogError("owner is not initialized. Add Action \"newSVM\".");
return;
}
OpenCVForUnity.MlModule.SVM wrapped_owner = OpenCVForUnityPlayMakerActionsUtils.GetWrappedObject <OpenCVForUnityPlayMakerActions.SVM, OpenCVForUnity.MlModule.SVM>(owner);
if (!(samples.Value is OpenCVForUnityPlayMakerActions.Mat))
{
LogError("samples is not initialized. Add Action \"newMat\".");
return;
}
OpenCVForUnity.CoreModule.Mat wrapped_samples = OpenCVForUnityPlayMakerActionsUtils.GetWrappedObject <OpenCVForUnityPlayMakerActions.Mat, OpenCVForUnity.CoreModule.Mat>(samples);
if (!(responses.Value is OpenCVForUnityPlayMakerActions.Mat))
{
LogError("responses is not initialized. Add Action \"newMat\".");
return;
}
OpenCVForUnity.CoreModule.Mat wrapped_responses = OpenCVForUnityPlayMakerActionsUtils.GetWrappedObject <OpenCVForUnityPlayMakerActions.Mat, OpenCVForUnity.CoreModule.Mat>(responses);
if (!(Cgrid.Value is OpenCVForUnityPlayMakerActions.ParamGrid))
{
LogError("Cgrid is not initialized. Add Action \"newParamGrid\".");
return;
}
OpenCVForUnity.MlModule.ParamGrid wrapped_Cgrid = OpenCVForUnityPlayMakerActionsUtils.GetWrappedObject <OpenCVForUnityPlayMakerActions.ParamGrid, OpenCVForUnity.MlModule.ParamGrid>(Cgrid);
storeResult.Value = wrapped_owner.trainAuto(wrapped_samples, layout.Value, wrapped_responses, kFold.Value, wrapped_Cgrid);
Fsm.Event(storeResult.Value ? trueEvent : falseEvent);
}
/**
* 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()));
}
//
// C++: static Ptr_ParamGrid cv::ml::SVM::getDefaultGridPtr(int param_id)
//
//javadoc: SVM::getDefaultGridPtr(param_id)
public static ParamGrid getDefaultGridPtr(int param_id)
{
#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
ParamGrid retVal = ParamGrid.__fromPtr__(ml_SVM_getDefaultGridPtr_10(param_id));
return(retVal);
#else
return(null);
#endif
}
//javadoc: ParamGrid::create()
public static ParamGrid create()
{
#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
ParamGrid retVal = ParamGrid.__fromPtr__(ml_ParamGrid_create_13());
return(retVal);
#else
return(null);
#endif
}
//
// C++: static Ptr_ParamGrid cv::ml::ParamGrid::create(double minVal = 0., double maxVal = 0., double logstep = 1.)
//
//javadoc: ParamGrid::create(minVal, maxVal, logstep)
public static ParamGrid create(double minVal, double maxVal, double logstep)
{
#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
ParamGrid retVal = ParamGrid.__fromPtr__(ml_ParamGrid_create_10(minVal, maxVal, logstep));
return(retVal);
#else
return(null);
#endif
}
void DoProcess()
{
if (!(owner.Value is OpenCVForUnityPlayMakerActions.ParamGrid))
{
LogError("owner is not initialized. Add Action \"newParamGrid\".");
return;
}
OpenCVForUnity.MlModule.ParamGrid wrapped_owner = OpenCVForUnityPlayMakerActionsUtils.GetWrappedObject <OpenCVForUnityPlayMakerActions.ParamGrid, OpenCVForUnity.MlModule.ParamGrid>(owner);
wrapped_owner.set_logStep((float)logStep.Value);
}
void DoProcess()
{
if (!(owner.Value is OpenCVForUnityPlayMakerActions.ParamGrid))
{
LogError("owner is not initialized. Add Action \"newParamGrid\".");
return;
}
OpenCVForUnity.MlModule.ParamGrid wrapped_owner = OpenCVForUnityPlayMakerActionsUtils.GetWrappedObject <OpenCVForUnityPlayMakerActions.ParamGrid, OpenCVForUnity.MlModule.ParamGrid>(owner);
storeResult.Value = (float)wrapped_owner.get_maxVal();
}
void DoProcess()
{
if (!(owner.Value is OpenCVForUnityPlayMakerActions.ParamGrid))
{
LogError("owner is not initialized. Add Action \"newParamGrid\".");
return;
}
OpenCVForUnity.MlModule.ParamGrid wrapped_owner = OpenCVForUnityPlayMakerActionsUtils.GetWrappedObject <OpenCVForUnityPlayMakerActions.ParamGrid, OpenCVForUnity.MlModule.ParamGrid>(owner);
if (!(storeResult.Value is OpenCVForUnityPlayMakerActions.Double))
{
storeResult.Value = new OpenCVForUnityPlayMakerActions.Double();
}
((OpenCVForUnityPlayMakerActions.Double)storeResult.Value).wrappedObject = wrapped_owner.get_minVal();
}
void DoProcess()
{
if (!(owner.Value is OpenCVForUnityPlayMakerActions.ParamGrid))
{
LogError("owner is not initialized. Add Action \"newParamGrid\".");
return;
}
OpenCVForUnity.MlModule.ParamGrid wrapped_owner = OpenCVForUnityPlayMakerActionsUtils.GetWrappedObject <OpenCVForUnityPlayMakerActions.ParamGrid, OpenCVForUnity.MlModule.ParamGrid>(owner);
if (!(minVal.Value is OpenCVForUnityPlayMakerActions.Double))
{
LogError("minVal is not initialized. Add Action \"newDouble\".");
return;
}
System.Double wrapped_minVal = OpenCVForUnityPlayMakerActionsUtils.GetWrappedObject <OpenCVForUnityPlayMakerActions.Double, System.Double>(minVal);
wrapped_owner.set_minVal(wrapped_minVal);
}
//
// 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));
}
public ParamGrid(OpenCVForUnity.MlModule.ParamGrid nativeObj) : base(nativeObj)
{
}
/**
* Creates a ParamGrid Ptr that can be given to the %SVM::trainAuto method
*
* param minVal minimum value of the parameter grid
* return automatically generated
*/
public static ParamGrid create(double minVal)
{
return(ParamGrid.__fromPtr__(ml_ParamGrid_create_12(minVal)));
}
//
// C++: static Ptr_ParamGrid cv::ml::ParamGrid::create(double minVal = 0., double maxVal = 0., double logstep = 1.)
//
/**
* Creates a ParamGrid Ptr that can be given to the %SVM::trainAuto method
*
* param minVal minimum value of the parameter grid
* param maxVal maximum value of the parameter grid
* param logstep Logarithmic step for iterating the statmodel parameter
* return automatically generated
*/
public static ParamGrid create(double minVal, double maxVal, double logstep)
{
return(ParamGrid.__fromPtr__(ml_ParamGrid_create_10(minVal, maxVal, logstep)));
}
/**
* Creates a ParamGrid Ptr that can be given to the %SVM::trainAuto method
*
* return automatically generated
*/
public static ParamGrid create()
{
return(ParamGrid.__fromPtr__(ml_ParamGrid_create_13()));
}
//
// 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__(ml_SVM_getDefaultGridPtr_10(param_id)));
}
