/usr/include/InsightToolkit/Review/Statistics/itkMahalanobisDistanceMetric.h is in libinsighttoolkit3-dev 3.20.1-1.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 | /*=========================================================================
Program: Insight Segmentation & Registration Toolkit
Module: itkMahalanobisDistanceMetric.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 __itkMahalanobisDistanceMetric_h
#define __itkMahalanobisDistanceMetric_h
#include <vnl/vnl_vector.h>
#include <vnl/vnl_vector_ref.h>
#include <vnl/vnl_transpose.h>
#include <vnl/vnl_matrix.h>
#include <vnl/algo/vnl_matrix_inverse.h>
#include <vnl/algo/vnl_determinant.h>
#include "itkArray.h"
#include "itkDistanceMetric.h"
namespace itk {
namespace Statistics{
/** \class MahalanobisDistanceMetric
* \brief MahalanobisDistanceMetric class computes a Mahalanobis
* distance given a mean and covariance.
*
* \sa DistanceMetric
* \sa EuclideanDistanceMetric
* \sa EuclideanSquareDistanceMetric
*/
template< class TVector >
class ITK_EXPORT MahalanobisDistanceMetric :
public DistanceMetric< TVector >
{
public:
/** Standard class typedefs */
typedef MahalanobisDistanceMetric Self;
typedef DistanceMetric< TVector > Superclass;
typedef SmartPointer<Self> Pointer;
typedef SmartPointer<const Self> ConstPointer;
/** Strandard macros */
itkTypeMacro(MahalanobisDistanceMetric, DistanceMetric);
itkNewMacro(Self);
/** Typedef to represent the measurement vector type */
typedef typename Superclass::MeasurementVectorType MeasurementVectorType;
/** Typedef to represent the length of measurement vectors */
typedef typename Superclass::MeasurementVectorSizeType MeasurementVectorSizeType;
/** Type used for representing the mean vector */
typedef typename Superclass::OriginType MeanVectorType;
/** Type used for representing the covariance matrix */
typedef vnl_matrix<double> CovarianceMatrixType;
/** Set the length of each measurement vector. */
virtual void SetMeasurementVectorSize( MeasurementVectorSizeType );
/** Method to set mean */
void SetMean(const MeanVectorType &mean);
/** Method to get mean */
const MeanVectorType & GetMean() const;
/**
* Method to set covariance matrix
* Also, this function calculates inverse covariance and pre factor of
* MahalanobisDistance Distribution to speed up GetProbability */
void SetCovariance(const CovarianceMatrixType &cov);
/** Method to get covariance matrix */
itkGetConstReferenceMacro( Covariance, CovarianceMatrixType );
/**
* Method to set inverse covariance matrix */
void SetInverseCovariance(const CovarianceMatrixType &invcov);
/** Method to get covariance matrix */
itkGetConstReferenceMacro( InverseCovariance, CovarianceMatrixType );
/**
* Method to get probability of an instance. The return value is the
* value of the density function, not probability. */
double Evaluate(const MeasurementVectorType &measurement) const;
/** Gets the distance between x1 and x2. */
double Evaluate(const MeasurementVectorType &x1, const MeasurementVectorType &x2) const;
/** Set/Get tolerance values */
itkSetMacro( Epsilon, double );
itkGetConstMacro( Epsilon, double );
itkSetMacro( DoubleMax, double );
itkGetConstMacro( DoubleMax, double );
protected:
MahalanobisDistanceMetric(void);
virtual ~MahalanobisDistanceMetric(void) {}
void PrintSelf(std::ostream& os, Indent indent) const;
private:
MeanVectorType m_Mean; // mean
CovarianceMatrixType m_Covariance; // covariance matrix
// inverse covariance matrix which is automatically calculated
// when covariace matirx is set. This speed up the GetProbability()
CovarianceMatrixType m_InverseCovariance;
double m_Epsilon;
double m_DoubleMax;
void CalculateInverseCovariance();
};
} // end of namespace Statistics
} // end namespace itk
#ifndef ITK_MANUAL_INSTANTIATION
#include "itkMahalanobisDistanceMetric.txx"
#endif
#endif
|