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/*=========================================================================
*
* Copyright NumFOCUS
*
* 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
*
* https://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 itkCovarianceSampleFilter_h
#define itkCovarianceSampleFilter_h
#include "itkProcessObject.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 component of the \f$\vec\mu\f$,
* and the \f$\sigma_{ij}\f$ is the \f$ij\f$th component \f$\Sigma\f$,
* \f$\sigma_{ij} = (x_{i} - \mu_{i})(x_{j} - \mu_{j})\f$
*
* This estimator is an unbiased one, because it divisor in the covariance
* computation takes into account that one degree of freedom has been taken
* for computing the mean.
*
* Without the plugged in mean vector, this calculator will perform
* the single pass mean and covariance calculation algorithm.
*
* \ingroup ITKStatistics
*/
template <typename TSample>
class ITK_TEMPLATE_EXPORT CovarianceSampleFilter : public ProcessObject
{
public:
ITK_DISALLOW_COPY_AND_MOVE(CovarianceSampleFilter);
/** Standard class type aliases. */
using Self = CovarianceSampleFilter;
using Superclass = ProcessObject;
using Pointer = SmartPointer<Self>;
using ConstPointer = SmartPointer<const Self>;
using SampleType = TSample;
/** \see LightObject::GetNameOfClass() */
itkOverrideGetNameOfClassMacro(CovarianceSampleFilter);
itkNewMacro(Self);
/** Type of each measurement vector in sample */
using MeasurementVectorType = typename SampleType::MeasurementVectorType;
/** Type of the length of each measurement vector */
using MeasurementVectorSizeType = typename SampleType::MeasurementVectorSizeType;
/** Type of measurement vector component value */
using MeasurementType = typename SampleType::MeasurementType;
/** Type of a measurement vector, holding floating point values */
using MeasurementVectorRealType = typename NumericTraits<MeasurementVectorType>::RealType;
/** Type of a floating point measurement component value */
using MeasurementRealType = typename NumericTraits<MeasurementType>::RealType;
/** Method to set the sample */
using Superclass::SetInput;
void
SetInput(const SampleType * sample);
/** Method to get the sample */
const SampleType *
GetInput() const;
/** Type of covariance matrix output */
using MatrixType = VariableSizeMatrix<MeasurementRealType>;
/** Return the covariance matrix */
const MatrixType
GetCovarianceMatrix() const;
/** VariableSizeMatrix is not a DataObject, we need to decorate it to push it down
* a ProcessObject's pipeline */
using MatrixDecoratedType = SimpleDataObjectDecorator<MatrixType>;
const MatrixDecoratedType *
GetCovarianceMatrixOutput() const;
/** Return the mean vector */
const MeasurementVectorRealType
GetMean() const;
/** MeasurementVector is not a DataObject, we need to decorate it to push it down
* a ProcessObject's pipeline */
using MeasurementVectorDecoratedType = SimpleDataObjectDecorator<MeasurementVectorRealType>;
const MeasurementVectorDecoratedType *
GetMeanOutput() const;
using OutputType = MeasurementVectorDecoratedType;
MeasurementVectorSizeType
GetMeasurementVectorSize() const;
protected:
CovarianceSampleFilter();
~CovarianceSampleFilter() override = default;
void
PrintSelf(std::ostream & os, Indent indent) const override;
/** DataObject pointer */
using DataObjectPointer = DataObject::Pointer;
using DataObjectPointerArraySizeType = ProcessObject::DataObjectPointerArraySizeType;
using Superclass::MakeOutput;
DataObjectPointer
MakeOutput(DataObjectPointerArraySizeType index) override;
void
GenerateData() override;
}; // end of class
} // end of namespace Statistics
} // end of namespace itk
#ifndef ITK_MANUAL_INSTANTIATION
# include "itkCovarianceSampleFilter.hxx"
#endif
#endif
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