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/*=========================================================================
Program: Insight Segmentation & Registration Toolkit
Module: itkCovarianceSampleFilter.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 __itkCovarianceSampleFilter_h
#define __itkCovarianceSampleFilter_h
#include "itkProcessObject.h"
#include "itkArray.h"
#include "itkVariableSizeMatrix.h"
#include "itkSimpleDataObjectDecorator.h"
namespace itk {
namespace Statistics {
/** \class CovarianceSampleFilter
* \brief Calculates the covariance matrix of the target sample data.
*
* The filter calculates first the sample mean and use it in the covariance
* calculation. The covariance is computed as follows
* Let \f$\Sigma\f$ denotes covariance matrix for the sample, then:
* When \f$x_{i}\f$ is \f$i\f$th component of a measurement vector
* \f$\vec x\f$, \f$\mu_{i}\f$ is the \f$i\f$th componet of the \f$\vec\mu\f$,
* and the \f$\sigma_{ij}\f$ is the \f$ij\f$th componet \f$\Sigma\f$,
* \f$\sigma_{ij} = (x_{i} - \mu_{i})(x_{j} - \mu_{j})\f$
*
* Without the plugged in mean vector, this calculator will perform
* the single pass mean and covariance calculation algorithm.
*
*/
template< class TSample >
class ITK_EXPORT CovarianceSampleFilter :
public ProcessObject
{
public:
/** Standard class typedefs. */
typedef CovarianceSampleFilter Self;
typedef ProcessObject Superclass;
typedef SmartPointer<Self> Pointer;
typedef SmartPointer<const Self> ConstPointer;
typedef TSample SampleType;
/** Standard Macros */
itkTypeMacro(CovarianceSampleFilter, ProcessObject);
itkNewMacro(Self);
/** Length of a measurement vector */
typedef typename TSample::MeasurementVectorSizeType MeasurementVectorSizeType;
/** Measurement vector type */
typedef typename TSample::MeasurementVectorType MeasurementVectorType;
/** Typedef for Covariance output */
typedef VariableSizeMatrix< double > MatrixType;
/** Method to set/get the sample */
void SetInput( const SampleType * sample );
const SampleType * GetInput() const;
/** VariableSizeMatrix is not a DataObject, we need to decorate it to push it down
* a ProcessObject's pipeline */
typedef SimpleDataObjectDecorator< MatrixType > MatrixDecoratedType;
/** MeasurementVector is not a DataObject, we need to decorate it to push it down
* a ProcessObject's pipeline */
typedef SimpleDataObjectDecorator< MeasurementVectorType > MeasurementVectorDecoratedType;
typedef MeasurementVectorDecoratedType OutputType;
/** Return the covariance matrix */
const MatrixType GetCovarianceMatrix() const;
const MatrixDecoratedType* GetCovarianceMatrixOutput() const;
/** Return the mean vector */
const MeasurementVectorType GetMean() const;
const MeasurementVectorDecoratedType* GetMeanOutput() const;
protected:
CovarianceSampleFilter(const Self&); //purposely not implemented
void operator=(const Self&); //purposely not implemented
CovarianceSampleFilter();
virtual ~CovarianceSampleFilter();
void PrintSelf(std::ostream& os, Indent indent) const;
/** DataObject pointer */
typedef DataObject::Pointer DataObjectPointer;
virtual DataObjectPointer MakeOutput(unsigned int idx);
void GenerateData();
MeasurementVectorSizeType GetMeasurementVectorSize() const;
private:
}; // end of class
} // end of namespace Statistics
} // end of namespace itk
#ifndef ITK_MANUAL_INSTANTIATION
#include "itkCovarianceSampleFilter.txx"
#endif
#endif
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