// -------------------------------------------------------------------------
// @author Leonardo Florez-Valencia (florez-l@javeriana.edu.co)
// -------------------------------------------------------------------------

#ifndef __CPPLUGINS__EXTENSIONS__ALGORITHMS__ITERATIVEGAUSSIANMODELESTIMATOR__HXX__
#define __CPPLUGINS__EXTENSIONS__ALGORITHMS__ITERATIVEGAUSSIANMODELESTIMATOR__HXX__

#include <cmath>
#include <cstdarg>
#include <vnl/vnl_math.h>
#include <vnl/vnl_inverse.h>

// -------------------------------------------------------------------------
template< class S, unsigned int D >
void
cpPlugins::Extensions::Algorithms::IterativeGaussianModelEstimator< S, D >::
UpdateModel( )
{
  if( this->m_Updated )
    return;

  this->m_Cov.set_size( D, D );
  this->m_Mean.set_size( D, 1 );

  // Compute covariance matrix and mean vector
  unsigned long N = this->m_Samples.size( );
  this->m_Mean = this->m_S1;
  if( N > 0 )
    this->m_Mean /= S( N );
  if( N > 1 )
  {
    this->m_Cov  = this->m_S2;
    this->m_Cov -= ( this->m_S1 * this->m_S1.transpose( ) ) / S( N );
    this->m_Cov /= S( N - 1 );
  }
  else
    this->m_Cov.fill( S( 0 ) );

  // Compute inverse and determinant
  S det = vnl_determinant( this->m_Cov );
  if( !( det > S( 0 ) ) )
  {
    this->m_InvCov.set_size( D, D );
    this->m_InvCov.fill( S( 0 ) );
    this->m_DensityCoeff = S( 0 );
  }
  else
  {
    this->m_InvCov = vnl_inverse( this->m_Cov );
    double _2piD = std::pow( double( 2 ) * double( vnl_math::pi ), D );
    this->m_DensityCoeff = S( 1 ) / S( std::sqrt( _2piD * double( det ) ) );

  } // fi

  // Compute minimum and maximum probabilities from input samples
  static S sample[ D ];
  for( unsigned long i = 0; i < this->m_Samples.size( ); ++i )
  {
    for( unsigned int d = 0; d < D; ++d )
      sample[ d ] = this->m_Samples[ i ][ d ][ 0 ];
    S p = this->Probability( sample );
    if( i == 0 )
    {
      this->m_MinimumProbability = p;
      this->m_MaximumProbability = p;
    }
    else
    {
      if( p < this->m_MinimumProbability ) this->m_MinimumProbability = p;
      if( this->m_MaximumProbability < p ) this->m_MaximumProbability = p;

    } // fi

  } // rof
  this->m_Updated = true;
}

// -------------------------------------------------------------------------
template< class S, unsigned int D >
template< class V >
S cpPlugins::Extensions::Algorithms::IterativeGaussianModelEstimator< S, D >::
Probability( const V& sample ) const
{
  TMatrix c( D, 1 );
  for( unsigned int d = 0; d < D; ++d )
    c[ d ][ 0 ] = S( sample[ d ] ) - this->m_Mean[ d ][ 0 ];
  if( S( 0 ) < this->m_DensityCoeff )
  {
    // Covariance is NOT null
    double v = double( ( c.transpose( ) * ( this->m_InvCov * c ) )[ 0 ][ 0 ] );
    v /= double( 2 );

    return( S( std::exp( -v ) ) );
  }
  else
  {
    // Covariance is null
    bool equal = true;
    for( unsigned int d = 0; d < D && equal; ++d )
      equal &= !( S( 0 ) < S( std::fabs( double( c[ d ][ 0 ] ) ) ) );
    return( ( equal )? S( 1 ): S( 0 ) );

  } // fi
}

// -------------------------------------------------------------------------
template< class S, unsigned int D >
S cpPlugins::Extensions::Algorithms::IterativeGaussianModelEstimator< S, D >::
Probability( const S& s_x, const S& s_y, ... ) const
{
  static S sample[ D ];

  std::va_list args_lst;
  va_start( args_lst, s_y );
  sample[ 0 ] = s_x;
  sample[ 1 ] = s_y;
  for( unsigned int d = 2; d < D; ++d )
    sample[ d ] = S( va_arg( args_lst, double ) );
  va_end( args_lst );
  return( this->Probability( sample ) );
}

// -------------------------------------------------------------------------
template< class S, unsigned int D >
template< class V, class M >
void
cpPlugins::Extensions::Algorithms::IterativeGaussianModelEstimator< S, D >::
GetModel( V& m, M& E ) const
{
  for( unsigned int i = 0; i < D; ++i )
  {
    m[ i ] = double( this->m_Mean[ i ][ 0 ] );
    for( unsigned int j = 0; j < D; ++j )
      E[ i ][ j ] = double( this->m_Cov[ i ][ j ] );

  } // rof
}

// -------------------------------------------------------------------------
template< class S, unsigned int D >
void
cpPlugins::Extensions::Algorithms::IterativeGaussianModelEstimator< S, D >::
Clear( )
{
  this->m_Updated = false;
  this->m_Samples.clear( );
  this->m_S1.set_size( D, 1 );
  this->m_S2.set_size( D, D );
  this->m_S1.fill( S( 0 ) );
  this->m_S2.fill( S( 0 ) );
  this->Modified( );
}

// -------------------------------------------------------------------------
template< class S, unsigned int D >
template< class V >
void
cpPlugins::Extensions::Algorithms::IterativeGaussianModelEstimator< S, D >::
AddSample( const V& sample )
{
  TMatrix s( D, 1 );
  for( unsigned int d = 0; d < D; ++d )
    s[ d ][ 0 ] = S( sample[ d ] );

  this->m_Samples.push_back( s );
  this->m_S1 += s;
  this->m_S2 += s * s.transpose( );

  this->m_Updated = false;
  this->Modified( );
}

// -------------------------------------------------------------------------
template< class S, unsigned int D >
void
cpPlugins::Extensions::Algorithms::IterativeGaussianModelEstimator< S, D >::
AddSample( const S& s_x, const S& s_y, ... )
{
  static S sample[ D ];

  std::va_list args_lst;
  va_start( args_lst, s_y );
  sample[ 0 ] = s_x;
  sample[ 1 ] = s_y;
  for( unsigned int d = 2; d < D; ++d )
    sample[ d ] = S( va_arg( args_lst, double ) );
  va_end( args_lst );
  this->AddSample( sample );
}

// -------------------------------------------------------------------------
template< class S, unsigned int D >
cpPlugins::Extensions::Algorithms::IterativeGaussianModelEstimator< S, D >::
IterativeGaussianModelEstimator( )
  : Superclass( )
{
  this->Clear( );
}

// -------------------------------------------------------------------------
template< class S, unsigned int D >
cpPlugins::Extensions::Algorithms::IterativeGaussianModelEstimator< S, D >::
~IterativeGaussianModelEstimator( )
{
}

#endif // __CPPLUGINS__EXTENSIONS__ALGORITHMS__ITERATIVEGAUSSIANMODELESTIMATOR__HXX__

// eof - $RCSfile$