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/*=========================================================================
Program: Insight Segmentation & Registration Toolkit
Module: $RCSfile: itkGoodnessOfFitMixtureModelCostFunction.h,v $
Language: C++
Date: $Date: 2009-03-04 15:23:49 $
Version: $Revision: 1.7 $
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 __itkGoodnessOfFitMixtureModelCostFunction_h
#define __itkGoodnessOfFitMixtureModelCostFunction_h
#include "itkSingleValuedCostFunction.h"
#include "itkHistogram.h"
#include "itkGoodnessOfFitComponentBase.h"
#include "itkGoodnessOfFitFunctionBase.h"
#include "itkFunctionBase.h"
namespace itk {
namespace Statistics {
/** \class GoodnessOfFitMixtureModelCostFunction
* \brief calculates the goodness-of-fit statstics for multivarate
* mixture model
*
* The goodness-of-fit statistics for a single model is discrepancy
* between the observed frequency and the expected frequency.
* To reduce computational load of multivariate case, this class
* uses projective method.
*
* The projective multivariate goodness-of-fit statistics calculation follows
* the following steps:
*
* 1) creates a subsample that includes the measurement vectors that fall
* in a spherical kernel.
* 2) finds the base axes determined by the eigen vectors of the covariance
* matrix.
* 3) project the subsample on to one of the base axes (from step 2)
* 4) calculates the observed frequencies (in an 1D Histogram object) after
* projection (step 3) and the expected frequencies (in an 1D Histogram
* object)
* 5) calculates the discrepancy between the observed histogram and
* the expected histogram using a goodness-of-fit statistics
* 6) repeat step 3) - 5) and sum the goodness-of-fit values
*
* For a mixture model, the above procedure is applied independently for each
* model (module). The sum of the goodness-of-fit values of models is the
* goodness-of-fit statistics for the mixture model.
*
* The step 1) - 4) is done by the subclasses of GoodnessOfFitComponentBase, and
* the step 5) is done by the subclasses of GoodnessOfFitFunctionBase.
*
* To see how this class interacts GoodnessOfFitComponentBase objects and
* GoodnessOfFitFunctionBase objects, please look at the implementation of
* the GetValue method of this class.
*
* Better fit means smaller goodness-of-fit value in this implementation.
* This class is following the SingleValuedCostFunction interfaces so that
* users can uses this function with any subclasses of
* SingleValuedNonLinearOptimizer class as long as they do not use
* GetDerivative and GetValueAndDerivative methods.
*
* <b>Recent API changes:</b>
* The static const macro to get the length of a measurement vector,
* 'MeasurementVectorSize' has been removed to allow the length of a measurement
* vector to be specified at run time.
*
* \sa GoodnessOfFitFunctionBase, GoodnessOfFitComponentBase,
* SingleValuedCostFunction, SingleValuedNonLinearOptimizer
*/
template< class TInputSample >
class ITK_EXPORT GoodnessOfFitMixtureModelCostFunction
: public SingleValuedCostFunction
{
public:
/** Standard class typedefs */
typedef GoodnessOfFitMixtureModelCostFunction Self;
typedef SingleValuedCostFunction Superclass;
typedef SmartPointer< Self > Pointer;
typedef SmartPointer< const Self > ConstPointer;
/** Run-time type information (and related methods). */
itkTypeMacro(GoodnessOfFitMixtureModelCostFunction, SingleValuedCostFunction);
/** Method for creation through the object factory. */
itkNewMacro(Self);
typedef TInputSample InputSampleType;
typedef typename TInputSample::MeasurementType MeasurementType;
typedef typename TInputSample::MeasurementVectorType MeasurementVectorType;
typedef typename TInputSample::MeasurementVectorSizeType MeasurementVectorSizeType;
/** ParametersType typedef.
* It defines a position in the optimization search space. */
typedef SingleValuedCostFunction::ParametersType ParamtersType;
/** MeasureType typedef.
* It defines a type used to return the cost function value. */
typedef SingleValuedCostFunction::MeasureType MeasureType;
typedef GoodnessOfFitComponentBase< TInputSample > ComponentType;
typedef std::vector< ComponentType* > ComponentVectorType;
typedef GoodnessOfFitFunctionBase< typename ComponentType::HistogramType >
FunctionType;
/** aceesing methods for the sample manipulator */
void AddComponent(ComponentType* component);
/** aceesing methods for the expected probability histogram */
void SetFunction(FunctionType* core);
FunctionType* GetFunction()
{ return m_Function; }
virtual unsigned int GetNumberOfParameters() const;
/** This method returns the value of the cost function corresponding
* to the specified parameters. */
virtual MeasureType GetValue( const ParametersType & parameters ) const;
/** This method returns the derivative of the cost function corresponding
* to the specified parameters. */
virtual void GetDerivative( const ParametersType &,
DerivativeType & ) const {}
protected:
GoodnessOfFitMixtureModelCostFunction();
virtual ~GoodnessOfFitMixtureModelCostFunction();
virtual void PrintSelf(std::ostream& os, Indent indent) const;
private:
/** helper classes */
ComponentVectorType m_Components;
FunctionType* m_Function;
}; // end of class
} // end of namespace Statistics
} // end of namespace itk
#ifndef ITK_MANUAL_INSTANTIATION
#include "itkGoodnessOfFitMixtureModelCostFunction.txx"
#endif
#endif
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