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Date: Fri, 27 May 2011 09:17:00 -0000
From: sherry mikheal <mikheal@inue.uni-stuttgart.de>
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I am a student who is trying to use the histogram methods that were
published , but my problem is that i have tried to get the logarithmic
values of these histograms using these 2 methods :

first method for normalization :=20

=C2=A0Make continuous double probability density functions by appropriate
normalization
=C2=A0*/
template <typename T, typename V>
gsl_histogram** CHistogram<T,
V>::getContinuousDoubleProbabilityDensities()
{
=C2=A0=C2=A0=C2=A0int i;

=C2=A0=C2=A0=C2=A0if ( this->doubleHistograms !=3D NULL ) {
=C2=A0=C2=A0=C2=A0// Clone histograms and normalize
=C2=A0=C2=A0=C2=A0if ( this->continuousDoubleProbabilityDensities =3D=3D NU=
LL ) {
=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0this->continuousDoubleProbabilityDensit=
ies =3D new gsl_histogram*[
this->numberOfHistograms ];

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0for ( i =3D 0; i < this->numberOfHistog=
rams; i++ ) {
=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0this->continuousDoubleProbabilityDensit=
ies[ i ] =3D
gsl_histogram_clone( this->doubleHistograms[ i ] );
=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0gsl_histogram_scale( this->continuousDo=
ubleProbabilityDensities[ i
],
=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=
=A0=C2=A0=C2=A0=C2=A01.0 / ( gsl_histogram_sum(
this->continuousDoubleProbabilityDensities[ i ] )
=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=
=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0* this->binWidth ) );
=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0}
=C2=A0=C2=A0=C2=A0}

=C2=A0=C2=A0=C2=A0return this->continuousDoubleProbabilityDensities;
=C2=A0=C2=A0=C2=A0}
=C2=A0=C2=A0=C2=A0else {
=C2=A0=C2=A0=C2=A0this->printError( "getContinuousDoubleProbablilityDensiti=
es(),"
=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0"No continuous =
double probability densities available!" );

=C2=A0=C2=A0=C2=A0return NULL;
=C2=A0=C2=A0=C2=A0}
}

second method to get log :=20

/*
=C2=A0* Get logarithmized continuous double probability density functions
=C2=A0*/
template <typename T, typename V>
gsl_histogram** CHistogram<T,
V>::getLogContinuousDoubleProbabilityDensities()
{
=C2=A0=C2=A0=C2=A0int i, j;

=C2=A0=C2=A0=C2=A0if ( this->doubleHistograms !=3D NULL ) {
=C2=A0=C2=A0=C2=A0// Make continuous double probability density functions
=C2=A0=C2=A0=C2=A0if ( this->continuousDoubleProbabilityDensities =3D=3D NU=
LL ) {
=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0getContinuousDoubleProbabilityDensities=
();
=C2=A0=C2=A0=C2=A0}

=C2=A0=C2=A0=C2=A0// Create histograms and logarithmize content
=C2=A0=C2=A0=C2=A0if ( this->logContinuousDoubleProbabilityDensities =3D=3D=
 NULL ) {
=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0this->logContinuousDoubleProbabilityDen=
sities =3D new
gsl_histogram*[ this->numberOfHistograms ];

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0for ( i =3D 0; i < this->numberOfHistog=
rams; i++ ) {
=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0this->logContinuousDoubleProbabilityDen=
sities[ i ] =3D
gsl_histogram_alloc( this->numberOfBins );
=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0gsl_histogram_set_ranges_uniform(
this->logContinuousDoubleProbabilityDensities[ i ],
=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=
=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0this->minValue,
=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=
=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0this->maxValue );

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0for ( j =3D 0; j < this->numberOfBins; =
j++ ) {
=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0gsl_histogram_accumul=
ate(
this->logContinuousDoubleProbabilityDensities[ i ],
=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=
=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0this->minValue + ( j + 0.5 ) *=
 this->binWidth,
=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=
=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0log10( gsl_histogram_get(
this->continuousDoubleProbabilityDensities[ i ],
=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=
=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=
=C2=A0=C2=A0j ) ) );
=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=
=A0//std::cout << "result new=C2=A0=C2=A0 "
<<logContinuousDoubleProbabilityDensities<<std::endl;



=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=
=A0=C2=A0=C2=A0}
=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0}
=C2=A0=C2=A0=C2=A0}

=C2=A0=C2=A0=C2=A0return this->logContinuousDoubleProbabilityDensities;


=C2=A0=C2=A0=C2=A0}
=C2=A0=C2=A0=C2=A0else {
=C2=A0=C2=A0=C2=A0this->printError( "getLogContinuousDoubleProbabilityDensi=
ties()",
=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0"No logarithmiz=
ed continuous double probability densities
available!" );
=C2=A0=C2=A0=C2=A0return NULL;
=C2=A0=C2=A0=C2=A0}
}

/*
=C2=A0* Get logarithmized continuous complex probability density functions
=C2=A0*/
template <typename T, typename V>
gsl_histogram2d** CHistogram<T,
V>::getLogContinuousComplexProbabilityDensities()
{
=C2=A0=C2=A0=C2=A0int i, j, k;

=C2=A0=C2=A0=C2=A0if ( this->complexHistograms !=3D NULL ) {
=C2=A0=C2=A0=C2=A0// Make discrete complex probability density functions
=C2=A0=C2=A0=C2=A0if ( this->discreteComplexProbabilityDensities =3D=3D NUL=
L ) {
=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0getDiscreteComplexProbabilityDensities(=
);
=C2=A0=C2=A0=C2=A0}

=C2=A0=C2=A0=C2=A0// Create histograms and logarithmize content
=C2=A0=C2=A0=C2=A0if ( this->logDiscreteComplexProbabilityDensities =3D=3D =
NULL ) {
=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0this->logDiscreteComplexProbabilityDens=
ities =3D new
gsl_histogram2d*[ this->numberOfHistograms ];

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0for ( i =3D 0; i < this->numberOfHistog=
rams; i++ ) {
=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0this->logDiscreteComplexProbabilityDens=
ities[ i ] =3D
gsl_histogram2d_alloc( this->numberOfBins,
=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=
=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=
=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0this->numberOfB=
ins );
=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0gsl_histogram2d_set_ranges_uniform(
this->logDiscreteComplexProbabilityDensities[ i ],
=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=
=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0this->minValue,
=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=
=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0this->maxValue,
=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=
=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0this->minValue,
=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=
=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0this->maxValue );

=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0for ( j =3D 0; j < this->numberOfBins; =
j++ ) {
=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0for ( k =3D 0; k < th=
is->numberOfHistograms; k++ ) {
=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0gsl_histogram2d_accum=
ulate(
this->logDiscreteComplexProbabilityDensities[ i ],
=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=
=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0this->minValue + ( j + 0=
.5 ) * this->binWidth,
=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=
=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0this->minValue + ( k + 0=
.5 ) * this->binWidth,
=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=
=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0log10( gsl_histogram2d_g=
et(
this->discreteComplexProbabilityDensities[ i ],
=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=
=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=
=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0j,
=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=
=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=
=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0k ) ) );
=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0}
=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0}
=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0}
=C2=A0=C2=A0=C2=A0}

=C2=A0=C2=A0=C2=A0return this->logDiscreteComplexProbabilityDensities;
=C2=A0=C2=A0=C2=A0}
=C2=A0=C2=A0=C2=A0else {
=C2=A0=C2=A0=C2=A0this->printError( "getLogDiscreteComplexProbabilityDensit=
ies()",
=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0"No logarithmiz=
ed discrete complex probability densities
available!" );
=C2=A0=C2=A0=C2=A0return NULL;
=C2=A0=C2=A0=C2=A0}
}


but then he results of the histogram are either negative infinty (which=20
is ok ) or positive values which is not ok as my probabilities should=20
range between negative infinity and zero after getting log ....so any=20
idea about this ,please help=20

Best Regards ,=20

Sherry=20