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/*=========================================================================
Program: Insight Segmentation & Registration Toolkit
Module: $RCSfile: itkGaussianGoodnessOfFitComponent.h,v $
Language: C++
Date: $Date: 2009-03-04 15:23:48 $
Version: $Revision: 1.10 $
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 __itkGaussianGoodnessOfFitComponent_h
#define __itkGaussianGoodnessOfFitComponent_h
#include "itkGoodnessOfFitComponentBase.h"
#include "itkGaussianDensityFunction.h"
#include "itkFunctionBase.h"
#include "itkWeightedCovarianceCalculator.h"
#include "itkSymmetricEigenAnalysis.h"
namespace itk {
namespace Statistics {
/** \class GaussianGoodnessOfFitComponent
* \brief is a GoodnessOfFitComponent for Gaussian distribution.
*
* Among the GoodnessOfFitComponentBase's methods, this class provides
* implementations for the CalculateProjectionAxess, the
* GetCumulativeProbability (univariate CDF), and the
* GetProbabilityDensity (multivariate PDF)methods.
*
* The CalculateProjectionAxes method creats an array of projection
* axes that are the eigen vectors generated from the weighted
* covariance matrix of the resampled sample using a spherical kernel.
*
* <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. This is now obtained from
* the input sample.
*
* \sa GoodnessOfFitComponentBase, GoodnessOfFitMixtureModelCostFunction
*/
template< class TInputSample >
class GaussianGoodnessOfFitComponent
: public GoodnessOfFitComponentBase< TInputSample >
{
public:
/** Standard class typedefs */
typedef GaussianGoodnessOfFitComponent Self;
typedef GoodnessOfFitComponentBase< TInputSample > Superclass;
typedef SmartPointer< Self > Pointer;
typedef SmartPointer< const Self > ConstPointer;
/** Run-time type information (and related methods). */
itkTypeMacro(GaussianGoodnessOfFitComponent,
GoodnessOfFitComponentBase);
/** Method for creation through the object factory. */
itkNewMacro(Self);
/** Typedefs from input sample */
typedef typename TInputSample::MeasurementType MeasurementType;
typedef typename TInputSample::MeasurementVectorType MeasurementVectorType;
/** Typedefs from Superclass */
typedef typename Superclass::CenterType CenterType;
typedef typename Superclass::RadiusType RadiusType;
typedef typename Superclass::MeanType MeanType;
typedef typename Superclass::StandardDeviationType StandardDeviationType;
typedef typename Superclass::ResampledSampleType ResampledSampleType;
typedef typename Superclass::ProjectionAxisArrayType ProjectionAxisArrayType;
typedef typename Superclass::MeasurementVectorSizeType MeasurementVectorSizeType;
typedef Array< double > ParametersType;
/** Weight function type. The density values are used as weights of
* each instance (measurement vector) for the Covariance calulator */
typedef GaussianDensityFunction< MeasurementVectorType >
ProbabilityDensityFunctionType;
typedef typename ProbabilityDensityFunctionType::CovarianceType CovarianceType;
/** Type of the covariance calculator. the output of this calculator is
* a covariance matrix that is used as the input of the Projection
* calculator */
typedef WeightedCovarianceCalculator< ResampledSampleType >
CovarianceCalculatorType;
/** Default projection axis calculator type. */
typedef Array< double > EigenValuesArrayType;
typedef SymmetricEigenAnalysis< ProjectionAxisArrayType, EigenValuesArrayType >
ProjectionAxisCalculatorType;
/** Gets the size of parameters which consists of mean
* and standard deviation */
unsigned int GetNumberOfParameters() const
{ return (unsigned int)(this->GetMeasurementVectorSize() + 1); }
/** Sets the component distribution parameters */
void SetParameters(const ParametersType ¶meter);
/** Gets the center point for the neighborhood sampling */
CenterType* GetCenter();
/** Gets the radius for the neighborhood sampling */
RadiusType* GetRadius();
/** Gets the mean of the distributon */
MeanType* GetMean();
/** Gets the standard deviation of the distribution */
StandardDeviationType* GetStandardDeviation();
/** Univariate (standard) cumulative probability function */
double GetCumulativeProbability(double x) const;
/** Multivariate probability density function */
double GetProbabilityDensity(MeasurementVectorType &measurements) const;
/** Prints all the parameters. Usually for debugging. */
void PrintParameters(std::ostream &os) const;
/** Gets the full distribution parameters which consists of
* mean vector and covariance matrix in a single array */
ParametersType GetFullParameters() const;
/** Set the input sample */
virtual void SetInputSample( const TInputSample* sample );
protected:
GaussianGoodnessOfFitComponent();
virtual ~GaussianGoodnessOfFitComponent();
virtual void PrintSelf(std::ostream& os, Indent indent) const;
/** Calculates the base axes for projection */
virtual void CalculateProjectionAxes();
private:
typename ProbabilityDensityFunctionType::Pointer
m_ProbabilityDensityFunction;
typename CovarianceCalculatorType::Pointer m_CovarianceCalculator;
ProjectionAxisCalculatorType * m_ProjectionAxisCalculator;
MeanType m_Mean;
CenterType m_Center;
RadiusType m_Radius;
StandardDeviationType m_StandardDeviation;
CovarianceType m_Covariance;
unsigned int m_NumberOfParameters;
int m_LongestAxisIndex;
double m_LargestEigenValue;
}; // end of class
} // end of namespace Statistics
} // end of namespace itk
#ifndef ITK_MANUAL_INSTANTIATION
#include "itkGaussianGoodnessOfFitComponent.txx"
#endif
#endif
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