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/*=========================================================================
Program: Advanced Normalization Tools
Module: $RCSfile: antsLogEuclideanGaussianListSampleFunction.hxx,v $
Language: C++
Date: $Date: $
Version: $Revision: $
Copyright (c) ConsortiumOfANTS. All rights reserved.
See accompanying COPYING.txt or
http://sourceforge.net/projects/advants/files/ANTS/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 __antsLogEuclideanGaussianListSampleFunction_hxx
#define __antsLogEuclideanGaussianListSampleFunction_hxx
#include "antsLogEuclideanGaussianListSampleFunction.h"
#include "itkDecomposeTensorFunction.h"
#include "vnl/vnl_trace.h"
namespace itk
{
namespace ants
{
namespace Statistics
{
template <class TListSample, class TOutput, class TCoordRep>
LogEuclideanGaussianListSampleFunction<TListSample, TOutput, TCoordRep>
::LogEuclideanGaussianListSampleFunction()
{
}
template <class TListSample, class TOutput, class TCoordRep>
LogEuclideanGaussianListSampleFunction<TListSample, TOutput, TCoordRep>
::~LogEuclideanGaussianListSampleFunction()
{
}
template <class TListSample, class TOutput, class TCoordRep>
void
LogEuclideanGaussianListSampleFunction<TListSample, TOutput, TCoordRep>
::SetInputListSample( const InputListSampleType * ptr )
{
Superclass::SetInputListSample( ptr );
if( !this->GetInputListSample() )
{
return;
}
if( this->GetInputListSample()->Size() > 1 )
{
RealType L = static_cast<RealType>(
this->GetInputListSample()->GetMeasurementVectorSize() );
unsigned int D = static_cast<unsigned int>( 0.5 * ( -1 + vcl_sqrt( 1.0
+ 8.0 * L ) ) );
this->m_MeanTensor.SetSize( D, D );
this->m_MeanTensor.Fill( 0.0 );
unsigned long N = 0;
RealType totalWeight = 0.0;
typename InputListSampleType::ConstIterator It =
this->GetInputListSample()->Begin();
while( It != this->GetInputListSample()->End() )
{
InputMeasurementVectorType measurement = It.GetMeasurementVector();
TensorType T( D, D );
unsigned int index = 0;
for( unsigned int i = 0; i < D; i++ )
{
for( unsigned int j = i; j < D; j++ )
{
T( i, j ) = measurement( index++ );
T( j, i ) = T( i, j );
}
}
T = this->LogTensorTransform( T );
RealType weight = 1.0;
if( this->GetListSampleWeights()->Size() == this->GetInputListSample()->Size() )
{
weight = ( *this->GetListSampleWeights() )[N++];
}
totalWeight += weight;
this->m_MeanTensor += ( T * weight );
++It;
}
if( totalWeight > 0.0 )
{
this->m_MeanTensor /= totalWeight;
}
this->m_MeanTensor = this->ExpTensorTransform( this->m_MeanTensor );
/**
* Now calculate the dispersion (i.e. variance)
*/
this->m_Dispersion = 0.0;
N = 0;
It = this->GetInputListSample()->Begin();
while( It != this->GetInputListSample()->End() )
{
InputMeasurementVectorType measurement = It.GetMeasurementVector();
TensorType T( D, D );
unsigned int index = 0;
for( unsigned int i = 0; i < D; i++ )
{
for( unsigned int j = i; j < D; j++ )
{
T( i, j ) = measurement( index++ );
T( j, i ) = T( i, j );
}
}
RealType distance = this->CalculateTensorDistance( T, this->m_MeanTensor );
RealType weight = 1.0;
if( this->GetListSampleWeights()->Size() == this->GetInputListSample()->Size() )
{
weight = ( *this->GetListSampleWeights() )[N++];
}
this->m_Dispersion += ( weight * vnl_math_sqr( distance ) );
++It;
}
this->m_Dispersion /= static_cast<RealType>( N );
}
else
{
itkWarningMacro( "The input list sample has <= 1 element."
<< "Function evaluations will be equal to 0." );
}
}
template <class TListSample, class TOutput, class TCoordRep>
typename LogEuclideanGaussianListSampleFunction<TListSample, TOutput, TCoordRep>
::TensorType
LogEuclideanGaussianListSampleFunction<TListSample, TOutput, TCoordRep>
::LogTensorTransform( const TensorType & T ) const
{
TensorType V;
TensorType W;
TensorType Tc = T;
typedef DecomposeTensorFunction<TensorType> DecomposerType;
typename DecomposerType::Pointer decomposer = DecomposerType::New();
decomposer->EvaluateSymmetricEigenDecomposition( Tc, W, V );
for( unsigned int i = 0; i < W.Rows(); i++ )
{
if( W( i, i ) > 0.0 )
{
W( i, i ) = vcl_log( W( i, i ) );
}
else
{
W( i, i ) = 0.0;
}
}
W *= V.GetTranspose();
TensorType logT = V * W;
return logT;
}
template <class TListSample, class TOutput, class TCoordRep>
typename LogEuclideanGaussianListSampleFunction<TListSample, TOutput, TCoordRep>
::TensorType
LogEuclideanGaussianListSampleFunction<TListSample, TOutput, TCoordRep>
::ExpTensorTransform( const TensorType & T ) const
{
TensorType V;
TensorType W;
TensorType Tc = T;
typedef DecomposeTensorFunction<TensorType> DecomposerType;
typename DecomposerType::Pointer decomposer = DecomposerType::New();
decomposer->EvaluateSymmetricEigenDecomposition( Tc, W, V );
for( unsigned int i = 0; i < W.Rows(); i++ )
{
W( i, i ) = vcl_exp( W( i, i ) );
}
W *= V.GetTranspose();
TensorType expT = V * W;
return expT;
}
template <class TListSample, class TOutput, class TCoordRep>
typename LogEuclideanGaussianListSampleFunction<TListSample, TOutput, TCoordRep>
::RealType
LogEuclideanGaussianListSampleFunction<TListSample, TOutput, TCoordRep>
::CalculateTensorDistance( const TensorType & S, const TensorType & T ) const
{
TensorType logS = this->LogTensorTransform( S );
TensorType logT = this->LogTensorTransform( T );
TensorType diff = logS - logT;
TensorType diffSq = diff * diff;
RealType distance = vcl_sqrt( vnl_trace( ( diffSq ).GetVnlMatrix() ) );
// RealType distance = ( ( logS - logT ).GetVnlMatrix() ).frobenius_norm();
return distance;
}
template <class TListSample, class TOutput, class TCoordRep>
TOutput
LogEuclideanGaussianListSampleFunction<TListSample, TOutput, TCoordRep>
::Evaluate( const InputMeasurementVectorType & measurement ) const
{
unsigned int D = this->m_MeanTensor.Rows();
TensorType T( D, D );
unsigned int index = 0;
for( unsigned int i = 0; i < D; i++ )
{
for( unsigned int j = i; j < D; j++ )
{
T( i, j ) = measurement( index++ );
T( j, i ) = T( i, j );
}
}
RealType distance = this->CalculateTensorDistance( T, this->m_MeanTensor );
RealType preFactor = 1.0
/ ( vcl_sqrt( 2.0 * vnl_math::pi * this->m_Dispersion ) );
RealType probability = preFactor * vcl_exp( -0.5
* vnl_math_sqr( distance ) / this->m_Dispersion );
return probability;
}
/**
* Standard "PrintSelf" method
*/
template <class TListSample, class TOutput, class TCoordRep>
void
LogEuclideanGaussianListSampleFunction<TListSample, TOutput, TCoordRep>
::PrintSelf(
std::ostream& os,
Indent indent) const
{
os << indent << "Mean tensor = [";
for( unsigned int r = 0; r < this->m_MeanTensor.Rows(); r++ )
{
for( unsigned int c = 0; c < this->m_MeanTensor.Cols() - 1; c++ )
{
os << this->m_MeanTensor( r, c ) << ", ";
}
if( r == this->m_MeanTensor.Rows() - 1 )
{
os << this->m_MeanTensor( r, this->m_MeanTensor.Cols() - 1 ) << "], ";
}
else
{
os << this->m_MeanTensor( r, this->m_MeanTensor.Cols() - 1 ) << "; ";
}
}
os << "Dispersion (variance) = " << this->m_Dispersion << std::endl;
}
} // end of namespace Statistics
} // end of namespace ants
} // end of namespace itk
#endif
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