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/*=========================================================================
*
* Copyright Insight Software Consortium
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0.txt
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
*=========================================================================*/
#ifndef itkIterativeSupervisedTrainingFunction_h
#define itkIterativeSupervisedTrainingFunction_h
#include "itkTrainingFunctionBase.h"
namespace itk
{
namespace Statistics
{
/** \class IterativeSupervisedTrainingFunction
* \brief This is the itkIterativeSupervisedTrainingFunction class.
*
* \ingroup ITKNeuralNetworks
*/
template<typename TSample, typename TTargetVector, typename ScalarType>
class ITK_TEMPLATE_EXPORT IterativeSupervisedTrainingFunction : public TrainingFunctionBase<TSample, TTargetVector, ScalarType>
{
public:
typedef IterativeSupervisedTrainingFunction Self;
typedef TrainingFunctionBase<TSample, TTargetVector, ScalarType> Superclass;
typedef SmartPointer<Self> Pointer;
typedef SmartPointer<const Self> ConstPointer;
/** Method for creation through the object factory. */
itkTypeMacro(IterativeSupervisedTrainingFunction, TrainingFunctionBase);
/** Method for creation through the object factory. */
itkNewMacro(Self);
typedef typename Superclass::NetworkType NetworkType;
typedef typename Superclass::InternalVectorType InternalVectorType;
void SetNumOfIterations(SizeValueType i);
virtual void Train(NetworkType* net, TSample* samples, TTargetVector* targets) ITK_OVERRIDE;
itkSetMacro(Threshold, ScalarType);
protected:
IterativeSupervisedTrainingFunction();
virtual ~IterativeSupervisedTrainingFunction() ITK_OVERRIDE{};
/** Method to print the object. */
virtual void PrintSelf( std::ostream& os, Indent indent ) const ITK_OVERRIDE;
ScalarType m_Threshold;
bool m_Stop; //stop condition
};
} // end namespace Statistics
} // end namespace itk
#ifndef ITK_MANUAL_INSTANTIATION
#include "itkIterativeSupervisedTrainingFunction.hxx"
#endif
#endif
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