/usr/include/ITK-4.9/itkMahalanobisDistanceMetric.h is in libinsighttoolkit4-dev 4.9.0-4ubuntu1.
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*
* 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) ITK_OVERRIDE;
/** 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 ITK_OVERRIDE;
/** Gets the distance between x1 and x2. */
double Evaluate(const MeasurementVectorType & x1, const MeasurementVectorType & x2) const ITK_OVERRIDE;
/** Set/Get tolerance values */
itkSetMacro(Epsilon, double);
itkGetConstMacro(Epsilon, double);
itkSetMacro(DoubleMax, double);
itkGetConstMacro(DoubleMax, double);
protected:
MahalanobisDistanceMetric();
virtual ~MahalanobisDistanceMetric(void) {}
void PrintSelf(std::ostream & os, Indent indent) const ITK_OVERRIDE;
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
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