X-Git-Url: https://git.creatis.insa-lyon.fr/pubgit/?a=blobdiff_plain;f=lib%2FcpExtensions%2FAlgorithms%2FIterativeGaussianModelEstimator.hxx;fp=lib%2FcpExtensions%2FAlgorithms%2FIterativeGaussianModelEstimator.hxx;h=0000000000000000000000000000000000000000;hb=2e142df11d6f312a2a2b5097b8da73571ed523e8;hp=acd1da76f82f380381eac997b8aed548012c4013;hpb=61b3659afe961ed248f30e26f9ca8f28fcfafddc;p=cpPlugins.git

diff --git a/lib/cpExtensions/Algorithms/IterativeGaussianModelEstimator.hxx b/lib/cpExtensions/Algorithms/IterativeGaussianModelEstimator.hxx
deleted file mode 100644
index acd1da7..0000000
--- a/lib/cpExtensions/Algorithms/IterativeGaussianModelEstimator.hxx
+++ /dev/null
@@ -1,365 +0,0 @@
-// -------------------------------------------------------------------------
-// @author Leonardo Florez-Valencia (florez-l@javeriana.edu.co)
-// -------------------------------------------------------------------------
-
-#ifndef __CPEXTENSIONS__ALGORITHMS__ITERATIVEGAUSSIANMODELESTIMATOR__HXX__
-#define __CPEXTENSIONS__ALGORITHMS__ITERATIVEGAUSSIANMODELESTIMATOR__HXX__
-
-#include <cstdarg>
-
-// -------------------------------------------------------------------------
-template< class _TScalar, unsigned int _VDimension >
-void cpExtensions::Algorithms::
-IterativeGaussianModelEstimator< _TScalar, _VDimension >::
-Clear( )
-{
-  this->m_NumberOfSamples = 0;
-  this->m_Mean.Fill( TScalar( 0 ) );
-  this->m_Covariance.Fill( TScalar( 0 ) );
-  this->m_InverseUpdated = false;
-  this->m_InverseUnbiasedCovariance.Fill( TScalar( 0 ) );
-}
-
-// -------------------------------------------------------------------------
-template< class _TScalar, unsigned int _VDimension >
-template< class _TOtherScalar >
-void cpExtensions::Algorithms::
-IterativeGaussianModelEstimator< _TScalar, _VDimension >::
-AddSample( const _TOtherScalar& x1, ... )
-{
-  TVector s;
-  std::va_list lst;
-  va_start( lst, x1 );
-  s[ 0 ] = TScalar( x1 );
-  for( unsigned int d = 1; d < _VDimension; ++d )
-    s[ d ] = TScalar( va_arg( lst, double ) );
-  va_end( lst );
-  this->_AddSample( s );
-}
-
-// -------------------------------------------------------------------------
-template< class _TScalar, unsigned int _VDimension >
-template< class _TOtherScalar >
-void cpExtensions::Algorithms::
-IterativeGaussianModelEstimator< _TScalar, _VDimension >::
-AddSample( const _TOtherScalar* array )
-{
-  TVector s;
-  for( unsigned d = 0; d < _VDimension; ++d )
-    s[ d ] = TScalar( array[ d ] );
-  this->_AddSample( s );
-}
-
-// -------------------------------------------------------------------------
-template< class _TScalar, unsigned int _VDimension >
-template< class _TOtherScalar >
-void cpExtensions::Algorithms::
-IterativeGaussianModelEstimator< _TScalar, _VDimension >::
-AddSample( const vnl_vector< _TOtherScalar >& v )
-{
-  unsigned int len = ( v.size( ) < _VDimension )? v.size: _VDimension;
-  TVector s;
-  for( unsigned d = 0; d < len; ++d )
-    s[ d ] = TScalar( v[ d ] );
-  this->_AddSample( s );
-}
-
-// -------------------------------------------------------------------------
-template< class _TScalar, unsigned int _VDimension >
-template< class _TOtherScalar >
-void cpExtensions::Algorithms::
-IterativeGaussianModelEstimator< _TScalar, _VDimension >::
-AddSample( const itk::CovariantVector< _TOtherScalar, _VDimension >& v )
-{
-  TVector s;
-  for( unsigned d = 0; d < _VDimension; ++d )
-    s[ d ] = TScalar( v[ d ] );
-  this->_AddSample( s );
-}
-
-// -------------------------------------------------------------------------
-template< class _TScalar, unsigned int _VDimension >
-template< class _TOtherScalar >
-void cpExtensions::Algorithms::
-IterativeGaussianModelEstimator< _TScalar, _VDimension >::
-AddSample( const itk::Point< _TOtherScalar, _VDimension >& p )
-{
-  TVector s;
-  for( unsigned d = 0; d < _VDimension; ++d )
-    s[ d ] = TScalar( p[ d ] );
-  this->_AddSample( s );
-}
-
-// -------------------------------------------------------------------------
-template< class _TScalar, unsigned int _VDimension >
-template< class _TOtherScalar >
-void cpExtensions::Algorithms::
-IterativeGaussianModelEstimator< _TScalar, _VDimension >::
-AddSample( const itk::Vector< _TOtherScalar, _VDimension >& v )
-{
-  TVector s;
-  for( unsigned d = 0; d < _VDimension; ++d )
-    s[ d ] = TScalar( v[ d ] );
-  this->_AddSample( s );
-}
-
-// -------------------------------------------------------------------------
-template< class _TScalar, unsigned int _VDimension >
-template< class _TOtherScalar >
-_TScalar cpExtensions::Algorithms::
-IterativeGaussianModelEstimator< _TScalar, _VDimension >::
-SquaredMahalanobis( const _TOtherScalar& x1, ... ) const
-{
-  TVector s;
-  std::va_list lst;
-  va_start( lst, x1 );
-  s[ 0 ] = TScalar( x1 );
-  for( unsigned int d = 1; d < _VDimension; ++d )
-    s[ d ] = TScalar( va_arg( lst, double ) );
-  va_end( lst );
-  return( this->_SquaredMahalanobis( s ) );
-}
-
-// -------------------------------------------------------------------------
-template< class _TScalar, unsigned int _VDimension >
-template< class _TOtherScalar >
-_TScalar cpExtensions::Algorithms::
-IterativeGaussianModelEstimator< _TScalar, _VDimension >::
-SquaredMahalanobis( const _TOtherScalar* array ) const
-{
-  TVector s;
-  for( unsigned d = 0; d < _VDimension; ++d )
-    s[ d ] = TScalar( array[ d ] );
-  return( this->_SquaredMahalanobis( s ) );
-}
-
-// -------------------------------------------------------------------------
-template< class _TScalar, unsigned int _VDimension >
-template< class _TOtherScalar >
-_TScalar cpExtensions::Algorithms::
-IterativeGaussianModelEstimator< _TScalar, _VDimension >::
-SquaredMahalanobis( const vnl_vector< _TOtherScalar >& v ) const
-{
-  unsigned int len = ( v.size( ) < _VDimension )? v.size: _VDimension;
-  TVector s;
-  for( unsigned d = 0; d < len; ++d )
-    s[ d ] = TScalar( v[ d ] );
-  return( this->_SquaredMahalanobis( s ) );
-}
-
-// -------------------------------------------------------------------------
-template< class _TScalar, unsigned int _VDimension >
-template< class _TOtherScalar >
-_TScalar cpExtensions::Algorithms::
-IterativeGaussianModelEstimator< _TScalar, _VDimension >::
-SquaredMahalanobis(
-  const itk::CovariantVector< _TOtherScalar, _VDimension >& v
-  ) const
-{
-  TVector s;
-  for( unsigned d = 0; d < _VDimension; ++d )
-    s[ d ] = TScalar( v[ d ] );
-  return( this->_SquaredMahalanobis( s ) );
-}
-
-// -------------------------------------------------------------------------
-template< class _TScalar, unsigned int _VDimension >
-template< class _TOtherScalar >
-_TScalar cpExtensions::Algorithms::
-IterativeGaussianModelEstimator< _TScalar, _VDimension >::
-SquaredMahalanobis( const itk::Point< _TOtherScalar, _VDimension >& p ) const
-{
-  TVector s;
-  for( unsigned d = 0; d < _VDimension; ++d )
-    s[ d ] = TScalar( p[ d ] );
-  return( this->_SquaredMahalanobis( s ) );
-}
-
-// -------------------------------------------------------------------------
-template< class _TScalar, unsigned int _VDimension >
-template< class _TOtherScalar >
-_TScalar cpExtensions::Algorithms::
-IterativeGaussianModelEstimator< _TScalar, _VDimension >::
-SquaredMahalanobis( const itk::Vector< _TOtherScalar, _VDimension >& v ) const
-{
-  TVector s;
-  for( unsigned d = 0; d < _VDimension; ++d )
-    s[ d ] = TScalar( v[ d ] );
-  return( this->_SquaredMahalanobis( s ) );
-}
-
-// -------------------------------------------------------------------------
-template< class _TScalar, unsigned int _VDimension >
-template< class _TOtherScalar >
-_TScalar cpExtensions::Algorithms::
-IterativeGaussianModelEstimator< _TScalar, _VDimension >::
-Density( const _TOtherScalar& x1, ... ) const
-{
-  TVector s;
-  std::va_list lst;
-  va_start( lst, x1 );
-  s[ 0 ] = TScalar( x1 );
-  for( unsigned int d = 1; d < _VDimension; ++d )
-    s[ d ] = TScalar( va_arg( lst, double ) );
-  va_end( lst );
-  return( this->_Density( s ) );
-}
-
-// -------------------------------------------------------------------------
-template< class _TScalar, unsigned int _VDimension >
-template< class _TOtherScalar >
-_TScalar cpExtensions::Algorithms::
-IterativeGaussianModelEstimator< _TScalar, _VDimension >::
-Density( const _TOtherScalar* array ) const
-{
-  TVector s;
-  for( unsigned d = 0; d < _VDimension; ++d )
-    s[ d ] = TScalar( array[ d ] );
-  return( this->_Density( s ) );
-}
-
-// -------------------------------------------------------------------------
-template< class _TScalar, unsigned int _VDimension >
-template< class _TOtherScalar >
-_TScalar cpExtensions::Algorithms::
-IterativeGaussianModelEstimator< _TScalar, _VDimension >::
-Density( const vnl_vector< _TOtherScalar >& v ) const
-{
-  unsigned int len = ( v.size( ) < _VDimension )? v.size: _VDimension;
-  TVector s;
-  for( unsigned d = 0; d < len; ++d )
-    s[ d ] = TScalar( v[ d ] );
-  return( this->_Density( s ) );
-}
-
-// -------------------------------------------------------------------------
-template< class _TScalar, unsigned int _VDimension >
-template< class _TOtherScalar >
-_TScalar cpExtensions::Algorithms::
-IterativeGaussianModelEstimator< _TScalar, _VDimension >::
-Density(
-  const itk::CovariantVector< _TOtherScalar, _VDimension >& v
-  ) const
-{
-  TVector s;
-  for( unsigned d = 0; d < _VDimension; ++d )
-    s[ d ] = TScalar( v[ d ] );
-  return( this->_Density( s ) );
-}
-
-// -------------------------------------------------------------------------
-template< class _TScalar, unsigned int _VDimension >
-template< class _TOtherScalar >
-_TScalar cpExtensions::Algorithms::
-IterativeGaussianModelEstimator< _TScalar, _VDimension >::
-Density( const itk::Point< _TOtherScalar, _VDimension >& p ) const
-{
-  TVector s;
-  for( unsigned d = 0; d < _VDimension; ++d )
-    s[ d ] = TScalar( p[ d ] );
-  return( this->_Density( s ) );
-}
-
-// -------------------------------------------------------------------------
-template< class _TScalar, unsigned int _VDimension >
-template< class _TOtherScalar >
-_TScalar cpExtensions::Algorithms::
-IterativeGaussianModelEstimator< _TScalar, _VDimension >::
-Density( const itk::Vector< _TOtherScalar, _VDimension >& v ) const
-{
-  TVector s;
-  for( unsigned d = 0; d < _VDimension; ++d )
-    s[ d ] = TScalar( v[ d ] );
-  return( this->_Density( s ) );
-}
-
-// -------------------------------------------------------------------------
-template< class _TScalar, unsigned int _VDimension >
-cpExtensions::Algorithms::
-IterativeGaussianModelEstimator< _TScalar, _VDimension >::
-IterativeGaussianModelEstimator( )
-  : Superclass( )
-{
-  this->Clear( );
-}
-
-// -------------------------------------------------------------------------
-template< class _TScalar, unsigned int _VDimension >
-cpExtensions::Algorithms::
-IterativeGaussianModelEstimator< _TScalar, _VDimension >::
-~IterativeGaussianModelEstimator( )
-{
-}
-
-// -------------------------------------------------------------------------
-template< class _TScalar, unsigned int _VDimension >
-void cpExtensions::Algorithms::
-IterativeGaussianModelEstimator< _TScalar, _VDimension >::
-_AddSample( const TVector& v ) const
-{
-  this->m_NumberOfSamples += 1;
-  TScalar a = TScalar( 1 ) / TScalar( this->m_NumberOfSamples );
-  TScalar b = TScalar( 1 ) - a;
-  TVector c = v - this->m_Mean;
-  TMatrix m;
-  for( unsigned int i = 0; i < _VDimension; ++i )
-  {
-    m[ i ][ i ] = c[ i ] * c[ i ];
-    for( unsigned int j = i + 1; j < _VDimension; ++j )
-      m[ i ][ j ] = m[ j ][ i ] = c[ i ] * c[ j ];
-
-  } // rof
-  this->m_Covariance = ( this->m_Covariance + ( m * a ) ) * b;
-  this->m_Mean = ( this->m_Mean * b ) + ( v * a );
-
-  TScalar u =
-    TScalar( this->m_NumberOfSamples ) /
-    TScalar( this->m_NumberOfSamples - 1 );
-  this->m_UnbiasedCovariance = this->m_Covariance * u;
-  this->m_InverseUpdated = false;
-}
-
-// -------------------------------------------------------------------------
-template< class _TScalar, unsigned int _VDimension >
-_TScalar cpExtensions::Algorithms::
-IterativeGaussianModelEstimator< _TScalar, _VDimension >::
-_SquaredMahalanobis( const TVector& v ) const
-{
-  if( !this->m_InverseUpdated )
-  {
-    double d =
-      double(
-        vnl_determinant( this->m_UnbiasedCovariance.GetVnlMatrix( ) )
-        );
-    if( std::fabs( d ) > double( 0 ) )
-      this->m_InverseUnbiasedCovariance =
-        this->m_UnbiasedCovariance.GetInverse( );
-    else
-      this->m_InverseUnbiasedCovariance.SetIdentity( );
-    this->m_InverseUpdated = true;
-
-  } // fi
-  TVector x = v - this->m_Mean;
-  return( x * ( this->m_InverseUnbiasedCovariance * x ) );
-}
-
-// -------------------------------------------------------------------------
-template< class _TScalar, unsigned int _VDimension >
-_TScalar cpExtensions::Algorithms::
-IterativeGaussianModelEstimator< _TScalar, _VDimension >::
-_Density( const TVector& v ) const
-{
-  static const double _2pik =
-    std::pow( double( 2 ) * double( vnl_math::pi ), _VDimension );
-
-  double s = double( this->_SquaredMahalanobis( v ) ) / double( 2 );
-  double d =
-    double(
-      vnl_determinant( this->m_UnbiasedCovariance.GetVnlMatrix( ) )
-      );
-  return( _TScalar( std::exp( -s ) / std::sqrt( _2pik * d ) ) );
-}
-
-#endif // __CPEXTENSIONS__ALGORITHMS__ITERATIVEGAUSSIANMODELESTIMATOR__HXX__
-
-// eof - $RCSfile$