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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 itkTrainingFunctionBase_h
#define itkTrainingFunctionBase_h
#include <iostream>
#include "itkLightProcessObject.h"
#include "itkNeuralNetworkObject.h"
#include "itkSquaredDifferenceErrorFunction.h"
#include "itkMeanSquaredErrorFunction.h"
namespace itk
{
namespace Statistics
{
/** \class TrainingFunctionBase
* \brief This is the itkTrainingFunctionBase class.
*
* \ingroup ITKNeuralNetworks
*/
template<typename TSample, typename TTargetVector, typename ScalarType>
class ITK_TEMPLATE_EXPORT TrainingFunctionBase : public LightProcessObject
{
public:
typedef TrainingFunctionBase Self;
typedef LightProcessObject Superclass;
typedef SmartPointer<Self> Pointer;
typedef SmartPointer<const Self> ConstPointer;
/** Method for creation through the object factory. */
itkTypeMacro(TrainingFunctionBase, LightProcessObject);
/** Method for creation through the object factory. */
itkNewMacro(Self);
typedef ScalarType ValueType;
typedef typename TSample::MeasurementVectorType VectorType;
typedef typename TTargetVector::MeasurementVectorType OutputVectorType;
typedef Array<ValueType> InternalVectorType;
typedef std::vector<VectorType> InputSampleVectorType;
typedef std::vector<OutputVectorType> OutputSampleVectorType;
typedef NeuralNetworkObject<VectorType, OutputVectorType> NetworkType;
typedef ErrorFunctionBase<InternalVectorType, ScalarType> PerformanceFunctionType;
typedef SquaredDifferenceErrorFunction<InternalVectorType, ScalarType>
DefaultPerformanceType;
void SetTrainingSamples(TSample* samples);
void SetTargetValues(TTargetVector* targets);
void SetLearningRate(ValueType);
ValueType GetLearningRate();
itkSetMacro(Iterations, SizeValueType);
itkGetConstReferenceMacro(Iterations, SizeValueType);
void SetPerformanceFunction(PerformanceFunctionType* f);
virtual void Train(NetworkType* itkNotUsed(net), TSample* itkNotUsed(samples), TTargetVector* itkNotUsed(targets))
{
// not implemented
};
inline VectorType
defaultconverter(typename TSample::MeasurementVectorType v)
{
VectorType temp;
for (unsigned int i = 0; i < v.Size(); i++)
{
temp[i] = static_cast<ScalarType>(v[i]);
}
return temp;
}
inline OutputVectorType
targetconverter(typename TTargetVector::MeasurementVectorType v)
{
OutputVectorType temp;
for (unsigned int i = 0; i < v.Size(); i++)
{
temp[i] = static_cast<ScalarType>(v[i]);
}
return temp;
}
protected:
TrainingFunctionBase();
~TrainingFunctionBase() ITK_OVERRIDE {}
/** Method to print the object. */
virtual void PrintSelf( std::ostream& os, Indent indent ) const ITK_OVERRIDE;
TSample* m_TrainingSamples;// original samples
TTargetVector* m_SampleTargets; // original samples
InputSampleVectorType m_InputSamples; // itk::vectors
OutputSampleVectorType m_Targets; // itk::vectors
SizeValueType m_Iterations;
ValueType m_LearningRate;
typename PerformanceFunctionType::Pointer m_PerformanceFunction;
};
} // end namespace Statistics
} // end namespace itk
#ifndef ITK_MANUAL_INSTANTIATION
#include "itkTrainingFunctionBase.hxx"
#endif
#endif
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