/usr/include/InsightToolkit/Review/Statistics/itkGaussianMembershipFunction.h is in libinsighttoolkit3-dev 3.20.1-1.
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Program: Insight Segmentation & Registration Toolkit
Module: itkGaussianMembershipFunction.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 __itkGaussianMembershipFunction_h
#define __itkGaussianMembershipFunction_h
#include "itkArray.h"
#include "itkMatrix.h"
#include "itkMembershipFunctionBase.h"
namespace itk {
namespace Statistics {
/** \class GaussianMembershipFunction
* \brief GaussianMembershipFunction class represents Gaussian function.
*
* This class keeps parameter to define Gaussian 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
*
*
*/
template< class TMeasurementVector >
class ITK_EXPORT GaussianMembershipFunction :
public MembershipFunctionBase< TMeasurementVector >
{
public:
/** Standard class typedefs */
typedef GaussianMembershipFunction Self;
typedef MembershipFunctionBase< TMeasurementVector > Superclass;
typedef SmartPointer<Self> Pointer;
typedef SmartPointer<const Self> ConstPointer;
/** Strandard macros */
itkTypeMacro(GaussianMembershipFunction, MembershipFunction);
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;
/** Set/Get the mean */
void SetMean( const MeanType & mean );
itkGetConstMacro( Mean, MeanType );
/** 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);
itkGetConstMacro( Covariance, CovarianceType );
/** Gets the probability density of a measurement vector. */
double Evaluate(const MeasurementVectorType &measurement) const;
/** Return a copy of the current membership function */
Pointer Clone();
protected:
GaussianMembershipFunction(void);
virtual ~GaussianMembershipFunction(void) {}
void PrintSelf(std::ostream& os, Indent indent) const;
private:
MeanType m_Mean; // mean
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 "itkGaussianMembershipFunction.txx"
#endif
#endif
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