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/*=========================================================================
Program: Insight Segmentation & Registration Toolkit
Module: itkGaussianDensityFunction.txx
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 __itkGaussianDensityFunction_txx
#define __itkGaussianDensityFunction_txx
#include "itkGaussianDensityFunction.h"
namespace itk {
namespace Statistics {
template < class TMeasurementVector >
GaussianDensityFunction< TMeasurementVector >
::GaussianDensityFunction()
{
m_Mean = 0;
m_Covariance = 0;
m_PreFactor = 0.0;
}
template < class TMeasurementVector >
void
GaussianDensityFunction< TMeasurementVector >
::PrintSelf(std::ostream& os, Indent indent) const
{
Superclass::PrintSelf(os,indent);
os << indent << "Mean: ";
if ( m_Mean != 0 )
{
os << (*m_Mean) << std::endl;
}
else
{
os << " not set." << std::endl;
}
os << indent << "Covariance: " << std::endl;
if ( m_Covariance != 0 )
{
os << m_Covariance->GetVnlMatrix();
os << indent << "InverseCovariance: " << std::endl;
os << indent << m_InverseCovariance.GetVnlMatrix();
os << indent << "Prefactor: " << m_PreFactor << std::endl;
}
os << indent << " not set." << std::endl;
}
template < class TMeasurementVector >
void
GaussianDensityFunction< TMeasurementVector >
::SetCovariance(const CovarianceType* cov)
{
// Sanity check
if( cov->GetVnlMatrix().rows() != cov->GetVnlMatrix().cols() )
{
itkExceptionMacro( << "Covariance matrix must be square" );
}
if( this->GetMeasurementVectorSize() )
{
if( cov->GetVnlMatrix().rows() != this->GetMeasurementVectorSize() )
{
itkExceptionMacro( << "Length of measurement vectors in the sample must be"
<< " the same as the size of the covariance." );
}
}
else
{
this->SetMeasurementVectorSize( cov->GetVnlMatrix().rows() );
}
m_Covariance = cov;
m_IsCovarianceZero = m_Covariance->GetVnlMatrix().is_zero();
if ( !m_IsCovarianceZero )
{
// allocate the memory for m_InverseCovariance matrix
m_InverseCovariance.GetVnlMatrix() =
vnl_matrix_inverse< double >(m_Covariance->GetVnlMatrix());
// the determinant of the covaraince matrix
double det = vnl_determinant(m_Covariance->GetVnlMatrix());
// calculate coefficient C of multivariate gaussian
m_PreFactor = 1.0 / (vcl_sqrt(det) *
vcl_pow(vcl_sqrt(2.0 * vnl_math::pi), double(this->GetMeasurementVectorSize())));
}
}
template < class TMeasurementVector >
const typename GaussianDensityFunction< TMeasurementVector >::CovarianceType*
GaussianDensityFunction< TMeasurementVector >
::GetCovariance() const
{
return m_Covariance;
}
template < class TMeasurementVector >
inline double
GaussianDensityFunction< TMeasurementVector >
::Evaluate(const MeasurementVectorType &measurement) const
{
double temp;
const MeasurementVectorSizeType measurementVectorSize =
this->GetMeasurementVectorSize();
MeanType tempVector;
MeasurementVectorTraits::SetLength( tempVector, measurementVectorSize );
MeanType tempVector2;
MeasurementVectorTraits::SetLength( tempVector2, measurementVectorSize );
if ( !m_IsCovarianceZero )
{
// Compute |y - mean |
for ( unsigned int i = 0; i < measurementVectorSize; i++)
{
tempVector[i] = measurement[i] - (*m_Mean)[i];
}
// Compute |y - mean | * inverse(cov)
for (unsigned int i = 0; i < measurementVectorSize; i++)
{
temp = 0;
for (unsigned int j = 0; j < measurementVectorSize; j++)
{
temp += tempVector[j] * m_InverseCovariance.GetVnlMatrix().get(j, i);
}
tempVector2[i] = temp;
}
// Compute |y - mean | * inverse(cov) * |y - mean|^T
temp = 0;
for (unsigned int i = 0; i < measurementVectorSize; i++)
{
temp += tempVector2[i] * tempVector[i];
}
return m_PreFactor * vcl_exp(-0.5 * temp );
}
else
{
for ( unsigned int i = 0; i < measurementVectorSize; i++)
{
if ( (*m_Mean)[i] != (double) measurement[i] )
{
return 0;
}
}
return NumericTraits< double >::max();
}
}
} // end namespace Statistics
} // end of namespace itk
#endif
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