/usr/include/InsightToolkit/Review/Statistics/itkProbabilityDistribution.h is in libinsighttoolkit3-dev 3.20.1-1.
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Program: Insight Segmentation & Registration Toolkit
Module: itkProbabilityDistribution.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 __itkProbabilityDistribution_h
#define __itkProbabilityDistribution_h
#include "itkObject.h"
#include "itkObjectFactory.h"
#include "itkArray.h"
namespace itk {
namespace Statistics {
/** \class ProbabilityDistribution
* \brief ProbabilityDistribution class defines common interface for
* statistical distributions (pdfs, cdfs, etc.).
*
* ProbabilityDistribution defines a common interface for parameteric
* and non-parametric distributions. ProbabilityDistribution provides
* access to the probability density function (pdf), the cumulative
* distribution function (cdf), and the inverse cumulative
* distribution function.
*
* ProbabilityDistribution also defines an abstract interface for
* setting parameters of distribution (mean/variance for a Gaussian,
* degrees of freedom for Student-t, etc.).
*
* Note that nonparametric subclasses of ProbabilityDistribution are
* possible. For instance, a nonparametric implementation may use a
* histogram or kernel density function to model the distribution.
*
* The EvaluatePDF(), EvaluateCDF, EvaluateInverseCDF() methods are
* all virtual, allowing algorithms to be written with an abstract
* interface to a distribution (with said distribution provided to the
* algorithm at run-time). Static methods, not requiring an instance
* of the distribution, are also allowed. The static methods allow
* for optimized access to distributions when the distribution is
* known a priori to the algorithm.
*
* ProbabilityDistributions are univariate. Multivariate versions may
* be provided under a separate superclass (since the parameters to the
* pdf and cdf would have to be vectors not scalars). Perhaps this
* class will be named MultivariateProbabilityDistribution.
*
* ProbabilityDistributions can be used for standard statistical
* tests: Z-scores, t-tests, chi-squared tests, F-tests, etc.
*
* \note This work is part of the National Alliance for Medical Image
* Computing (NAMIC), funded by the National Institutes of Health
* through the NIH Roadmap for Medical Research, Grant U54 EB005149.
* Information on the National Centers for Biomedical Computing
* can be obtained from http://nihroadmap.nih.gov/bioinformatics.
*/
class ITK_EXPORT ProbabilityDistribution :
public Object
{
public:
/** Standard class typedefs */
typedef ProbabilityDistribution Self;
typedef Object Superclass;
typedef SmartPointer<Self> Pointer;
typedef SmartPointer<const Self> ConstPointer;
/** Standard macros */
itkTypeMacro(ProbabilityDistribution, Object);
/** Type of the parameter vector. */
typedef Array< double > ParametersType;
/** Return the number of parameters that describe the
* distribution. For nonparametric distributions, this will be a
* function of the number of samples. */
virtual unsigned long GetNumberOfParameters() const = 0;
/** Get the parameters of the distribution. See concrete subclasses
* for the order of parameters. Subclasses may provide convenience
* methods for setting parameters, i.e. SetDegreesOfFreedom(), etc. */
itkGetConstReferenceMacro(Parameters, ParametersType);
/** Set the parameters of the distribution. See concrete subclasses
* for the order of the parameters. Subclasses may provide convenience
* methods for setting parameters, i.e. SetDegreesOfFreedom(), etc. */
virtual void SetParameters(const ParametersType& params)
{
if ((params.GetSize() != m_Parameters.GetSize())
|| (params != m_Parameters))
{
m_Parameters = params;
this->Modified();
}
}
/** Evaluate the probability density function (pdf). The parameters
* of the distribution are assigned via SetParameters(). */
virtual double EvaluatePDF(double x) const = 0;
/** Evaluate the probability density function (pdf). The parameters
* for the distribution are passed as a parameters vector. See
* concrete subclasses for the ordering of parameters. */
virtual double EvaluatePDF(double x, const ParametersType&) const = 0;
/** Evaluate the cumulative distribution function (cdf). The parameters
* of the distribution are assigned via SetParameters(). See
* concrete subclasses for the ordering of parameters. */
virtual double EvaluateCDF(double x) const = 0;
/** Evaluate the cumulative distribution function (cdf). The parameters
* for the distribution are passed as a parameters vector. See
* concrete subclasses for the ordering of parameters. */
virtual double EvaluateCDF(double x, const ParametersType&) const = 0;
/** Evaluate the inverse cumulative distribution function (inverse
* cdf). Parameter p must be between 0.0 and 1.0. The parameters
* of the distribution are assigned via SetParameters(). See
* concrete subclasses for the ordering of parameters. */
virtual double EvaluateInverseCDF(double p) const = 0;
/** Evaluate the inverse cumulative distribution function (inverse
* cdf). Parameter p must be between 0.0 and 1.0. The parameters
* for the distribution are passed as a parameters vector. See
* concrete subclasses for the ordering of parameters. */
virtual double EvaluateInverseCDF(double p, const ParametersType&) const = 0;
/** Does this distribution have a mean? */
virtual bool HasMean() const = 0;
/** Does this distribution have a variance? */
virtual bool HasVariance() const = 0;
/** Get the mean of the distribution. If the mean does not exist,
* then quiet_NaN may is returned. */
virtual double GetMean() const = 0;
/** Get the variance of the distribution. If the variance does not
* exist, then quiet_NaN is returned. */
virtual double GetVariance() const = 0;
protected:
ProbabilityDistribution(void) {}
virtual ~ProbabilityDistribution(void) {}
void PrintSelf(std::ostream& os, Indent indent) const
{
Superclass::PrintSelf(os,indent);
os << indent << "Parameters: " << m_Parameters << std::endl;
}
ParametersType m_Parameters;
private:
ProbabilityDistribution(const Self&); //purposely not implemented
void operator=(const Self&); //purposely not implemented
}; // end of class
} // end of namespace Statistics
} // end namespace itk
#endif
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