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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_hxx
#define itkIterativeSupervisedTrainingFunction_hxx
#include "itkIterativeSupervisedTrainingFunction.h"
#include <fstream>
namespace itk
{
namespace Statistics
{
template<typename TSample, typename TTargetVector, typename ScalarType>
IterativeSupervisedTrainingFunction<TSample,TTargetVector,ScalarType>
::IterativeSupervisedTrainingFunction()
{
this->m_LearningRate = 0.5;
m_Threshold = 0;
m_Stop = false;
}
template<typename TSample, typename TTargetVector, typename ScalarType>
void IterativeSupervisedTrainingFunction<TSample,TTargetVector,ScalarType>
::SetNumOfIterations(SizeValueType i)
{
this->SetIterations(i);
this->Modified();
}
template<typename TSample, typename TTargetVector, typename ScalarType>
void IterativeSupervisedTrainingFunction<TSample,TTargetVector,ScalarType>
::Train(typename IterativeSupervisedTrainingFunction<TSample, TTargetVector, ScalarType>::NetworkType* Net,
TSample* samples, TTargetVector* targets)
{
this->SetTrainingSamples(samples);
this->SetTargetValues(targets);
InternalVectorType outputvector;
InternalVectorType errorvector;
outputvector.SetSize(targets->GetMeasurementVectorSize());
errorvector.SetSize(targets->GetMeasurementVectorSize());
//typename Superclass::OutputVectorType outputvector;
typename Superclass::VectorType inputvector;
typename Superclass::OutputVectorType targetvector;
//typename Superclass::OutputVectorType errorvector;
#ifdef __OLD_CODE__
std::ofstream outfile;
outfile.open("output.txt");
#endif
const SizeValueType num_iterations = this->GetIterations();
m_Stop = false;
SizeValueType i = 0;
while (!m_Stop)
{
int temp = rand() % (this->m_InputSamples.size());
inputvector = this->m_InputSamples[temp];
targetvector = this->m_Targets[temp];
outputvector = Net->GenerateOutput(inputvector);
for(unsigned int k=0; k<targetvector.Size(); k++)
{
errorvector[k] = targetvector[k] - outputvector[k];
}
#ifdef __OLD_CODE__
outfile <<errorvector[0] << std::endl;
#endif
Net->BackwardPropagate(this->m_PerformanceFunction->EvaluateDerivative(errorvector));
Net->UpdateWeights(this->m_LearningRate);
i++;
if (i > num_iterations)
{
m_Stop = true;
}
}
#ifdef __OLD_CODE__
if (this->m_PerformanceFunction->Evaluate(errorvector) < m_Threshold
&& i < num_iterations)
{
std::cout << "Goal Met " << std::endl;
}
else
{
std::cout << "Goal Not Met Max Iterations Reached " << std::endl;
}
#endif
}
/** Print the object */
template<typename TSample, typename TTargetVector, typename ScalarType>
void
IterativeSupervisedTrainingFunction<TSample,TTargetVector,ScalarType>
::PrintSelf( std::ostream& os, Indent indent ) const
{
os << indent << "IterativeSupervisedTrainingFunction(" << this << ")" << std::endl;
os << indent << "m_Threshold = " << m_Threshold << std::endl;
os << indent << "m_Stop = " << m_Stop << std::endl;
Superclass::PrintSelf( os, indent );
}
} // end namespace Statistics
} // end namespace itk
#endif
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