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/*=========================================================================
Program: Insight Segmentation & Registration Toolkit
Module: itkTrainingFunctionBase.h
Language: C++
Date: $Date$
Version: $Revision$
Copyright (c) Insight Software Consortium. All rights reserved.
See ITKCopyright.txt or http://www.itk.org/HTML/Copyright.htm for details.
This software is distributed WITHOUT ANY WARRANTY; without even
the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
PURPOSE. See the above copyright notices for more information.
=========================================================================*/
#ifndef __itkTrainingFunctionBase_h
#define __itkTrainingFunctionBase_h
#include <iostream>
#include "itkLightProcessObject.h"
#include "itkNeuralNetworkObject.h"
#include "itkSquaredDifferenceErrorFunction.h"
#include "itkMeanSquaredErrorFunction.h"
namespace itk
{
namespace Statistics
{
template<class TSample, class TTargetVector, class ScalarType>
class 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, long);
itkGetConstReferenceMacro(Iterations, long);
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(){};
/** Method to print the object. */
virtual void PrintSelf( std::ostream& os, Indent indent ) const;
TSample* m_TrainingSamples;// original samples
TTargetVector* m_SampleTargets; // original samples
InputSampleVectorType m_InputSamples; // itk::vectors
OutputSampleVectorType m_Targets; // itk::vectors
long m_Iterations;
ValueType m_LearningRate;
typename PerformanceFunctionType::Pointer m_PerformanceFunction;
};
} // end namespace Statistics
} // end namespace itk
#ifndef ITK_MANUAL_INSTANTIATION
#include "itkTrainingFunctionBase.txx"
#endif
#endif
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