This file is indexed.

/usr/include/ITK-4.5/itkMahalanobisDistanceMetric.h is in libinsighttoolkit4-dev 4.5.0-3.

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
/*=========================================================================
 *
 *  Copyright Insight Software Consortium
 *
 *  Licensed under the Apache License, Version 2.0 (the "License");
 *  you may not use this file except in compliance with the License.
 *  You may obtain a copy of the License at
 *
 *         http://www.apache.org/licenses/LICENSE-2.0.txt
 *
 *  Unless required by applicable law or agreed to in writing, software
 *  distributed under the License is distributed on an "AS IS" BASIS,
 *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 *  See the License for the specific language governing permissions and
 *  limitations under the License.
 *
 *=========================================================================*/
#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
 * \ingroup ITKStatistics
 */

template< typename TVector >
class 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.hxx"
#endif

#endif