libinsighttoolkit4-dev 4.9.0-4ubuntu1 / usr / include / ITK-4.9 / itkMahalanobisDistanceMetric.hxx

/*=========================================================================
 *
 *  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 itkMahalanobisDistanceMetric_hxx
#define itkMahalanobisDistanceMetric_hxx

#include "itkMahalanobisDistanceMetric.h"

namespace itk
{
namespace Statistics
{
template< typename TVector >
MahalanobisDistanceMetric< TVector >
::MahalanobisDistanceMetric():
  m_Epsilon(1e-100),
  m_DoubleMax(1e+20)
{
  MeasurementVectorSizeType size;

  size = this->GetMeasurementVectorSize();

  this->m_Covariance.set_size(size, size);
  this->m_InverseCovariance.set_size(size, size);

  m_Covariance.set_identity();
  m_InverseCovariance.set_identity();
}

template< typename TVector >
void
MahalanobisDistanceMetric< TVector >
::SetMean(const MeanVectorType & mean)
{
  Superclass::SetOrigin(mean);
}

template< typename TVector >
const typename
MahalanobisDistanceMetric< TVector >::MeanVectorType &
MahalanobisDistanceMetric< TVector >
::GetMean() const
{
  return Superclass::GetOrigin();
}

template< typename TVector >
void
MahalanobisDistanceMetric< TVector >
::SetMeasurementVectorSize(MeasurementVectorSizeType size)
{
  this->Superclass::SetMeasurementVectorSize(size);
  this->m_Covariance.set_size(size, size);
  this->m_InverseCovariance.set_size(size, size);

  this->m_Covariance.set_identity();
  this->m_InverseCovariance.set_identity();
  this->Modified();
}

template< typename TVector >
void
MahalanobisDistanceMetric< TVector >
::SetCovariance(const CovarianceMatrixType & cov)
{
  if ( this->GetMeasurementVectorSize() != 0 )
    {
    if ( cov.rows() != this->GetMeasurementVectorSize()
         || cov.cols() != this->GetMeasurementVectorSize() )
      {
      itkExceptionMacro(<< "Size of the covariance matrix must be same as the length of"
                        << " the measurement vector.");
      }
    }

  m_Covariance = cov;
  this->CalculateInverseCovariance();
}

template< typename TVector >
void
MahalanobisDistanceMetric< TVector >
::SetInverseCovariance(const CovarianceMatrixType & invcov)
{
  if ( this->GetMeasurementVectorSize() != 0 )
    {
    if ( invcov.rows() != this->GetMeasurementVectorSize()
         || invcov.cols() != this->GetMeasurementVectorSize() )
      {
      itkExceptionMacro(<< "Size of the covariance matrix xcmust be same as the length of"
                        << " each measurement vector.");
      }
    }

  // use the inverse computation
  m_Covariance = invcov;
  this->CalculateInverseCovariance();
  m_Covariance = m_InverseCovariance;
  m_InverseCovariance = invcov;
}

template< typename TVector >
void
MahalanobisDistanceMetric< TVector >
::CalculateInverseCovariance()
{
  // pack the cov matrix from in_model to tmp_cov_mat
  double cov_sum = 0;

  for ( unsigned int band_x = 0; band_x < m_Covariance.cols(); band_x++ )
    {
    for ( unsigned int band_y = 0; band_y < m_Covariance.rows(); band_y++ )
      {
      cov_sum += vnl_math_abs(m_Covariance[band_x][band_y]);
      }
    }
  // check if it is a zero covariance, if it is, we make its
  // inverse as an identity matrix with diagonal elements as
  // a very large number; otherwise, inverse it
  if ( cov_sum < m_Epsilon )
    {
    m_InverseCovariance.set_size( m_Covariance.rows(), m_Covariance.cols() );
    m_InverseCovariance.set_identity();
    m_InverseCovariance *= m_DoubleMax;
    }
  else
    {
    // check if num_bands == 1, if it is, we just use 1 to divide it
    if ( m_Covariance.rows() < 2 )
      {
      m_InverseCovariance.set_size(1, 1);
      m_InverseCovariance[0][0] = 1.0 / m_Covariance[0][0];
      }
    else
      {
      m_InverseCovariance = vnl_matrix_inverse< double >(m_Covariance);
      }
    } // end inverse calculations
}

template< typename TVector >
double
MahalanobisDistanceMetric< TVector >
::Evaluate(const MeasurementVectorType & measurement) const
{
  vnl_matrix< double > tempVec;
  vnl_matrix< double > tempMat;

  tempVec.set_size( 1, this->GetMeasurementVectorSize() );
  tempMat.set_size( 1, this->GetMeasurementVectorSize() );

  // Compute |y - mean |
  for ( unsigned int i = 0; i < this->GetMeasurementVectorSize(); i++ )
    {
    tempVec[0][i] = measurement[i] - this->GetOrigin()[i];
    }

  // Compute |y - mean | * inverse(cov)
  tempMat = tempVec * m_InverseCovariance;

  // Compute |y - mean | * inverse(cov) * |y - mean|^T
  double temp;
  temp = std::sqrt( dot_product( tempMat.as_ref(), tempVec.as_ref() ) );

  return temp;
}

template< typename TVector >
inline double
MahalanobisDistanceMetric< TVector >
::Evaluate(const MeasurementVectorType & x1, const MeasurementVectorType & x2) const
{
  if ( NumericTraits<MeasurementVectorType>::GetLength(x1) != this->GetMeasurementVectorSize()
       || NumericTraits<MeasurementVectorType>::GetLength(x2) != this->GetMeasurementVectorSize() )
    {
    itkExceptionMacro(<< "Size of the measurement vectors is not the same as the length of"
                      << " the measurement vector set in the distance metric.");
    }

  vnl_matrix< double > tempVec;
  vnl_matrix< double > tempMat;

  tempVec.set_size( 1, this->GetMeasurementVectorSize() );
  tempMat.set_size( 1, this->GetMeasurementVectorSize() );

  // Compute |x1 - x2 |
  for ( unsigned int i = 0; i < this->GetMeasurementVectorSize(); i++ )
    {
    tempVec[0][i] = x1[i] - x2[i];
    }

  // Compute |x1 - x2 | * inverse(cov)
  tempMat = tempVec * m_InverseCovariance;

  // Compute |x1 - x2 | * inverse(cov) * |x1 - x2|^T
  double temp;
  temp = std::sqrt( dot_product( tempMat.as_ref(), tempVec.as_ref() ) );

  return temp;
}

template< typename TVector >
void
MahalanobisDistanceMetric< TVector >
::PrintSelf(std::ostream & os, Indent indent) const
{
  Superclass::PrintSelf(os, indent);

  os << indent << "Covariance:        " << std::endl;
  os << this->GetCovariance() << std::endl;
  os << indent << "Inverse covariance:        " << std::endl;
  os << this->GetInverseCovariance() << std::endl;
  os << indent << "Mean:        " << std::endl;
  os << this->GetMean() << std::endl;
  os << indent << "Epsilon:        " << std::endl;
  os << this->GetEpsilon() << std::endl;
  os << indent << "Double max:        " << std::endl;
  os << this->GetDoubleMax() << std::endl;
}
} // end namespace Statistics
} // end of namespace itk

#endif