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/*=========================================================================
Program: Advanced Normalization Tools
Copyright (c) ConsortiumOfANTS. All rights reserved.
See accompanying COPYING.txt or
https://github.com/stnava/ANTs/blob/master/ANTSCopyright.txt
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 __antsPartialVolumeGaussianListSampleFunction_hxx
#define __antsPartialVolumeGaussianListSampleFunction_hxx
#include "itkMeanSampleFilter.h"
#include "itkWeightedMeanSampleFilter.h"
namespace itk
{
namespace ants
{
namespace Statistics
{
template <typename TListSample, typename TOutput, typename TCoordRep>
PartialVolumeGaussianListSampleFunction<TListSample, TOutput, TCoordRep>::PartialVolumeGaussianListSampleFunction()
{
this->m_Gaussian = GaussianType::New();
this->m_IsCalculated[0] = false;
this->m_IsCalculated[1] = false;
}
template <typename TListSample, typename TOutput, typename TCoordRep>
PartialVolumeGaussianListSampleFunction<TListSample, TOutput, TCoordRep>::~PartialVolumeGaussianListSampleFunction() =
default;
template <typename TListSample, typename TOutput, typename TCoordRep>
void
PartialVolumeGaussianListSampleFunction<TListSample, TOutput, TCoordRep>::SetIndexedInputListSample(
const unsigned int d,
const InputListSampleType * ptr)
{
Superclass::SetIndexedInputListSample(d, ptr);
if (d > 1)
{
itkExceptionMacro("This class only requires two input list samples.");
}
if (!this->GetInputListSample(d))
{
return;
}
else
{
this->CalculateGaussianParametersFromListSample(
this->GetInputListSample(d), this->GetListSampleWeights(d), this->m_Mean[d]);
this->m_IsCalculated[d] = true;
}
if (this->m_IsCalculated[0] && this->m_IsCalculated[1])
{
this->CalculateGaussianParameters();
}
}
template <typename TListSample, typename TOutput, typename TCoordRep>
void
PartialVolumeGaussianListSampleFunction<TListSample, TOutput, TCoordRep>::CalculateGaussianParametersFromListSample(
const InputListSampleType * listSample,
const ListSampleWeightArrayType * weights,
MeanType & mean)
{
if (!listSample)
{
return;
}
if (listSample->Size() > 1)
{
if (weights->Size() == listSample->Size())
{
typedef typename itk::Statistics::WeightedMeanSampleFilter<InputListSampleType> MeanCalculatorType;
typename MeanCalculatorType::Pointer meanCalculator = MeanCalculatorType::New();
meanCalculator->SetWeights(*weights);
meanCalculator->SetInput(listSample);
meanCalculator->Update();
NumericTraits<MeanType>::SetLength(mean, listSample->GetMeasurementVectorSize());
for (unsigned int d = 0; d < listSample->GetMeasurementVectorSize(); d++)
{
mean[d] = meanCalculator->GetMean()[d];
}
}
else
{
typedef itk::Statistics::MeanSampleFilter<InputListSampleType> MeanCalculatorType;
typename MeanCalculatorType::Pointer meanCalculator = MeanCalculatorType::New();
meanCalculator->SetInput(listSample);
meanCalculator->Update();
NumericTraits<MeanType>::SetLength(mean, listSample->GetMeasurementVectorSize());
for (unsigned int d = 0; d < listSample->GetMeasurementVectorSize(); d++)
{
mean[d] = meanCalculator->GetMean()[d];
}
}
}
else
{
itkWarningMacro("The input list sample has <= 1 element.");
}
}
template <typename TListSample, typename TOutput, typename TCoordRep>
void
PartialVolumeGaussianListSampleFunction<TListSample, TOutput, TCoordRep>::CalculateGaussianParameters()
{
if (this->m_Mean[0].Size() != this->m_Mean[1].Size())
{
itkExceptionMacro("Mean sizes are unequal.");
}
MeanType mean;
NumericTraits<MeanType>::SetLength(mean, this->m_Mean[0].Size());
CovarianceType covariance;
covariance.SetSize(mean.Size(), mean.Size());
covariance.SetIdentity();
for (unsigned int d = 0; d < mean.Size(); d++)
{
mean[d] = 0.5 * (this->m_Mean[0][d] + this->m_Mean[1][d]);
covariance(d, d) = 1.0 / 12.0 * itk::Math::sqr(this->m_Mean[0][d]) +
-1.0 / 6.0 * this->m_Mean[0][d] * this->m_Mean[1][d] +
1.0 / 12.0 * itk::Math::sqr(this->m_Mean[1][d]);
}
this->m_Gaussian->SetMean(mean);
this->m_Gaussian->SetCovariance(covariance);
}
template <typename TListSample, typename TOutput, typename TCoordRep>
TOutput
PartialVolumeGaussianListSampleFunction<TListSample, TOutput, TCoordRep>::Evaluate(
const InputMeasurementVectorType & measurement) const
{
if (this->m_IsCalculated[0] && this->m_IsCalculated[1])
{
try
{
return this->m_Gaussian->Evaluate(measurement);
}
catch (...)
{
return 0.0;
}
}
else
{
return 0.0;
}
}
/**
* Standard "PrintSelf" method
*/
template <typename TListSample, typename TOutput, typename TCoordRep>
void
PartialVolumeGaussianListSampleFunction<TListSample, TOutput, TCoordRep>::PrintSelf(std::ostream & os,
Indent indent) const
{
os << indent << "mean = " << this->m_Gaussian->GetMean() << ", ";
CovarianceType covariance = this->m_Gaussian->GetCovariance();
os << "covariance = [";
for (unsigned int r = 0; r < covariance.Rows(); r++)
{
for (unsigned int c = 0; c < covariance.Cols() - 1; c++)
{
os << covariance(r, c) << ", ";
}
if (r == covariance.Rows() - 1)
{
os << covariance(r, covariance.Cols() - 1) << "]" << std::endl;
}
else
{
os << covariance(r, covariance.Cols() - 1) << "; ";
}
}
}
} // end of namespace Statistics
} // end of namespace ants
} // end of namespace itk
#endif
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