#include <cmath>
#include <cstdarg>
+#include <fstream>
#include <vnl/vnl_math.h>
#include <vnl/vnl_inverse.h>
template< class S, unsigned int D >
void
cpPlugins::Extensions::Algorithms::IterativeGaussianModelEstimator< S, D >::
-UpdateModel( )
+SetNumberOfSamples( unsigned long n )
{
- 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_N = S( n );
+ this->m_Updated = false;
+ this->Modified( );
+}
+
+// -------------------------------------------------------------------------
+template< class S, unsigned int D >
+void
+cpPlugins::Extensions::Algorithms::IterativeGaussianModelEstimator< S, D >::
+SetMu( const TMatrix& m )
+{
+ if( m.rows( ) == D && m.columns( ) == 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 );
+ this->m_M = m;
+ this->m_Updated = false;
+ this->Modified( );
}
else
- this->m_Cov.fill( S( 0 ) );
+ {
+ itkExceptionMacro(
+ << "Input Mu matrix is not a " << D << "x1 matrix"
+ );
- // Compute inverse and determinant
- S det = vnl_determinant( this->m_Cov );
- if( !( det > S( 0 ) ) )
+ } // fi
+}
+
+// -------------------------------------------------------------------------
+template< class S, unsigned int D >
+void
+cpPlugins::Extensions::Algorithms::IterativeGaussianModelEstimator< S, D >::
+SetOmega( const TMatrix& O )
+{
+ if( O.rows( ) == D && O.columns( ) == D )
{
- this->m_InvCov.set_size( D, D );
- this->m_InvCov.fill( S( 0 ) );
- this->m_DensityCoeff = S( 0 );
+ this->m_O = O;
+ this->m_Updated = false;
+ this->Modified( );
}
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 ) ) );
+ itkExceptionMacro(
+ << "Input Omega matrix is not a " << D << "x" << D << " matrix"
+ );
} // fi
+}
- // Compute minimum and maximum probabilities from input samples
- static S sample[ D ];
- for( unsigned long i = 0; i < this->m_Samples.size( ); ++i )
+// -------------------------------------------------------------------------
+template< class S, unsigned int D >
+bool
+cpPlugins::Extensions::Algorithms::IterativeGaussianModelEstimator< S, D >::
+SaveModelToFile( const std::string& filename ) const
+{
+ std::ofstream out( filename.c_str( ) );
+ if( out )
{
- 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
+ out << this->m_N << std::endl;
+ out << this->m_M << std::endl;
+ out << this->m_O << std::endl;
+ out.close( );
+ return( true );
+ }
+ else
+ return( false );
+}
- } // rof
- this->m_Updated = true;
+// -------------------------------------------------------------------------
+template< class S, unsigned int D >
+bool
+cpPlugins::Extensions::Algorithms::IterativeGaussianModelEstimator< S, D >::
+LoadModelFromFile( const std::string& filename )
+{
+ std::ifstream in( filename.c_str( ) );
+ if( in )
+ {
+ this->Clear( );
+ in >> this->m_N;
+ for( unsigned int i = 0; i < D; ++i )
+ in >> this->m_M[ i ][ 0 ];
+ for( unsigned int j = 0; j < D; ++j )
+ for( unsigned int i = 0; i < D; ++i )
+ in >> this->m_O[ i ][ j ];
+ in.close( );
+ return( true );
+ }
+ else
+ return( false );
}
// -------------------------------------------------------------------------
S cpPlugins::Extensions::Algorithms::IterativeGaussianModelEstimator< S, D >::
Probability( const V& sample ) const
{
+ if( !this->m_Updated )
+ this->_UpdateModel( );
+
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 )
+ c[ d ][ 0 ] = S( sample[ d ] ) - this->m_M[ d ][ 0 ];
+ if( S( 0 ) < this->m_Norm )
{
// Covariance is NOT null
- double v = double( ( c.transpose( ) * ( this->m_InvCov * c ) )[ 0 ][ 0 ] );
+ double v = double( ( c.transpose( ) * ( this->m_Inv * 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 ] ) ) ) );
+ S n = S( 0 );
+ for( unsigned int d = 0; d < D; ++d )
+ n += c[ d ][ 0 ] * c[ d ][ 0 ];
+ bool equal = ( double( n ) < double( 1e-10 ) );
return( ( equal )? S( 1 ): S( 0 ) );
} // fi
cpPlugins::Extensions::Algorithms::IterativeGaussianModelEstimator< S, D >::
GetModel( V& m, M& E ) const
{
+ if( !this->m_Updated )
+ this->_UpdateModel( );
for( unsigned int i = 0; i < D; ++i )
{
- m[ i ] = double( this->m_Mean[ i ][ 0 ] );
+ m[ i ] = double( this->m_M[ i ][ 0 ] );
for( unsigned int j = 0; j < D; ++j )
E[ i ][ j ] = double( this->m_Cov[ i ][ j ] );
cpPlugins::Extensions::Algorithms::IterativeGaussianModelEstimator< S, D >::
Clear( )
{
+ this->m_N = S( 0 );
+ this->m_M.set_size( D, 1 );
+ this->m_O.set_size( D, D );
+ this->m_M.fill( S( 0 ) );
+ this->m_O.fill( S( 0 ) );
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( );
}
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( );
+ // Update mean
+ S coeff = this->m_N;
+ this->m_N += S( 1 );
+ coeff /= this->m_N;
+ this->m_M = ( this->m_M * coeff ) + ( s / this->m_N );
+
+ // Update omega operand
+ if( this->m_N == 1 )
+ this->m_O = s * s.transpose( );
+ else if( this->m_N == 2 )
+ this->m_O += s * s.transpose( );
+ else
+ {
+ S inv = S( 1 ) / ( this->m_N - S( 1 ) );
+ this->m_O = this->m_O * ( this->m_N - S( 2 ) ) * inv;
+ this->m_O += ( s * s.transpose( ) ) * inv;
+
+ } // fi
this->m_Updated = false;
this->Modified( );
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 );
+ if( D > 1 )
+ {
+ sample[ 1 ] = s_y;
+ for( unsigned int d = 2; d < D; ++d )
+ sample[ d ] = S( va_arg( args_lst, double ) );
+ va_end( args_lst );
+
+ } // fi
this->AddSample( sample );
}
{
}
+// -------------------------------------------------------------------------
+template< class S, unsigned int D >
+void
+cpPlugins::Extensions::Algorithms::IterativeGaussianModelEstimator< S, D >::
+_UpdateModel( ) const
+{
+ static const double _2piD =
+ std::pow( double( 2 ) * double( vnl_math::pi ), D );
+
+ // Update covariance
+ this->m_Cov =
+ this->m_O -
+ (
+ ( this->m_M * this->m_M.transpose( ) ) *
+ ( this->m_N / ( this->m_N - S( 1 ) ) )
+ );
+
+ // Compute inverse and determinant
+ S det = vnl_determinant( this->m_Cov );
+ if( !( det > S( 0 ) ) )
+ {
+ this->m_Inv.set_size( D, D );
+ this->m_Inv.fill( S( 0 ) );
+ this->m_Norm = S( 0 );
+ }
+ else
+ {
+ this->m_Inv = vnl_inverse( this->m_Cov );
+ this->m_Norm = S( 1 ) / S( std::sqrt( _2piD * double( det ) ) );
+
+ } // fi
+
+ // Object now is updated
+ this->m_Updated = true;
+}
+
#endif // __CPPLUGINS__EXTENSIONS__ALGORITHMS__ITERATIVEGAUSSIANMODELESTIMATOR__HXX__
// eof - $RCSfile$
git://git.creatis.insa-lyon.fr / cpPlugins.git / blobdiff