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/*=========================================================================
Program: Insight Segmentation & Registration Toolkit
Module: itkGaussianDensityFunction.h
Language: C++
Date: $Date$
Version: $Revision$
Copyright (c) Insight Software Consortium. All rights reserved.
See ITKCopyright.txt or http://www.itk.org/HTML/Copyright.htm for details.
This software is distributed WITHOUT ANY WARRANTY; without even
the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
PURPOSE. See the above copyright notices for more information.
=========================================================================*/
#ifndef __itkGaussianDensityFunction_h
#define __itkGaussianDensityFunction_h
#include "itkArray.h"
#include "itkVariableSizeMatrix.h"
#include "vnl/algo/vnl_matrix_inverse.h"
#include "vnl/algo/vnl_determinant.h"
#include "vnl/vnl_math.h"
#include "itkMatrix.h"
#include "itkDensityFunction.h"
namespace itk {
namespace Statistics {
/** \class GaussianDensityFunction
* \brief GaussianDensityFunction class represents Gaussian Density Function.
*
* This class keeps parameter to define Gaussian Density Function and has
* method to return the probability density
* of an instance (pattern) .
* If the all element of the covariance matrix is zero the "usual" density
* calculations ignored. if the measurement vector to be evaluated is equal to
* the mean, then the Evaluate method will return maximum value of
* double and return 0 for others
*
* <b>Recent API changes:</b>
* The static const macro to get the length of a measurement vector,
* \c MeasurementVectorSize has been removed to allow the length of a measurement
* vector to be specified at run time. It is now obtained at run time from the
* sample set as input. Please use the function
* GetMeasurementVectorSize() to get the length. The typedef for the Mean has
* changed from FixedArray to Array. The typedef for the covariance matrix
* has changed from Matrix to VariableSizeMatrix.
*
*/
template< class TMeasurementVector >
class ITK_EXPORT GaussianDensityFunction :
public DensityFunction< TMeasurementVector >
{
public:
/** Standard class typedefs */
typedef GaussianDensityFunction Self;
typedef DensityFunction< TMeasurementVector > Superclass;
typedef SmartPointer<Self> Pointer;
typedef SmartPointer<const Self> ConstPointer;
/** Strandard macros */
itkTypeMacro(GaussianDensityFunction, DensityFunction);
itkNewMacro(Self);
/** Typedef alias for the measurement vectors */
typedef TMeasurementVector MeasurementVectorType;
/** Length of each measurement vector */
typedef typename Superclass::MeasurementVectorSizeType MeasurementVectorSizeType;
/** Type of the mean vector */
typedef Array< double > MeanType;
/** Type of the covariance matrix */
typedef VariableSizeMatrix< double > CovarianceType;
/** Sets the mean */
void SetMean( const MeanType * mean )
{
if( this->GetMeasurementVectorSize() )
{
MeasurementVectorTraits::Assert(mean, this->GetMeasurementVectorSize(),
"GaussianDensityFunction::SetMean Size of measurement vectors in the sample must the same as the size of the mean." );
}
else
{
this->SetMeasurementVectorSize( mean->Size() );
}
if ( m_Mean != mean)
{
m_Mean = mean;
this->Modified();
}
}
/** Gets the mean */
const MeanType * GetMean() const
{ return m_Mean; }
/** Sets the covariance matrix.
* Also, this function calculates inverse covariance and pre factor of
* Gaussian Distribution to speed up GetProbability */
void SetCovariance(const CovarianceType* cov);
/** Gets the covariance matrix */
const CovarianceType* GetCovariance() const;
/** Gets the probability density of a measurement vector. */
double Evaluate(const MeasurementVectorType &measurement) const;
protected:
GaussianDensityFunction(void);
virtual ~GaussianDensityFunction(void) {}
void PrintSelf(std::ostream& os, Indent indent) const;
private:
const MeanType * m_Mean; // mean
const CovarianceType * m_Covariance; // covariance matrix
// inverse covariance matrix which is automatically calculated
// when covariace matirx is set. This speed up the GetProbability()
CovarianceType m_InverseCovariance;
// pre_factor which is automatically calculated
// when covariace matirx is set. This speeds up the GetProbability()
double m_PreFactor;
/** if the all element of the given covarinace is zero, then this
* value set to true */
bool m_IsCovarianceZero;
};
} // end of namespace Statistics
} // end namespace itk
#ifndef ITK_MANUAL_INSTANTIATION
#include "itkGaussianDensityFunction.txx"
#endif
#endif
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