/usr/include/ITK-4.5/itkTDistribution.h is in libinsighttoolkit4-dev 4.5.0-3.
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*
* Copyright Insight Software Consortium
*
* 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
*
* http://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 __itkTDistribution_h
#define __itkTDistribution_h
#include "itkProbabilityDistribution.h"
#include "itkNumericTraits.h"
namespace itk
{
namespace Statistics
{
/** \class TDistribution
* \brief TDistribution class defines the interface for a univariate
* Student-t distribution (pdfs, cdfs, etc.).
*
* TDistribution provides access to the probability density
* function (pdf), the cumulative distribution function (cdf), and the
* inverse cumulative distribution function for a Student-t 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 provided. The static methods allow
* for optimized access to distributions when the distribution is
* known a priori to the algorithm.
*
* TDistributions 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).
*
* TDistributions can be used for t tests.
*
* \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://commonfund.nih.gov/bioinformatics.
* \ingroup ITKStatistics
*/
class TDistribution:
public ProbabilityDistribution
{
public:
/** Standard class typedefs */
typedef TDistribution Self;
typedef ProbabilityDistribution Superclass;
typedef SmartPointer< Self > Pointer;
typedef SmartPointer< const Self > ConstPointer;
/** Strandard macros */
itkTypeMacro(TDistribution, ProbabilityDistribution);
/** Method for creation through the object factory. */
itkNewMacro(Self);
/** Return the number of parameters. For a univariate Student-t
* distribution, the number of parameters is 1 (degrees of freedom) */
virtual SizeValueType GetNumberOfParameters() const { return 1; }
/** Evaluate the probability density function (pdf). The parameters
* of the distribution are assigned via SetParameters(). */
virtual double EvaluatePDF(double x) const;
/** Evaluate the probability density function (pdf). The parameters
* for the distribution are passed as a parameters vector. The
* ordering of the parameters is (degrees of freedom). */
virtual double EvaluatePDF(double x, const ParametersType &) const;
/** Evaluate the probability density function (pdf). The parameters
* of the distribution are passed as separate parameters. */
virtual double EvaluatePDF(double x, SizeValueType degreesOfFreedom) const;
/** Evaluate the cumulative distribution function (cdf). The parameters
* of the distribution are assigned via SetParameters(). */
virtual double EvaluateCDF(double x) const;
/** Evaluate the cumulative distribution function (cdf). The parameters
* for the distribution are passed as a parameters vector. The
* ordering of the parameters is (degreesOfFreedom). */
virtual double EvaluateCDF(double x, const ParametersType &) const;
/** Evaluate the cumulative distribution function (cdf). The parameters
* of the distribution are passed as separate parameters. */
virtual double EvaluateCDF(double x, SizeValueType degreesOfFreedom) const;
/** 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(). */
virtual double EvaluateInverseCDF(double p) const;
/** 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. The
* ordering of the parameters is (degrees of freedom). */
virtual double EvaluateInverseCDF(double p, const ParametersType &) const;
/** Evaluate the inverse cumulative distribution function (inverse
* cdf). Parameter p must be between 0.0 and 1.0. The parameters
* of the distribution are passed as separate parameters. */
virtual double EvaluateInverseCDF(double p, SizeValueType degreesOfFreedom) const;
/** Set the number of degrees of freedom in the Student-t distribution.
* Defaults to 1 */
virtual void SetDegreesOfFreedom(SizeValueType);
/** Get the number of degrees of freedom in the t
* distribution. Defaults to 1 */
virtual SizeValueType GetDegreesOfFreedom() const;
/** Does the Student-t distribution have a mean? */
virtual bool HasMean() const { return true; }
/** Get the mean of the distribution. */
virtual double GetMean() const;
/** Does the Student-t distribution have a variance? Variance is
* only defined for degrees of freedom greater than 2 */
virtual bool HasVariance() const;
/** Get the variance of the distribution. If the variance does not exist,
* then quiet_NaN is returned. */
virtual double GetVariance() const;
/** Static method to evaluate the probability density function (pdf)
* of a Student-t with a specified number of degrees of freedom. The
* static method provides optimized access without requiring an
* instance of the class. The degrees of freedom for the
* distribution are passed in a parameters vector. */
static double PDF(double x, const ParametersType &);
/** Static method to evaluate the probability density function (pdf)
* of a Student-t with a specified number of degrees of freedom. The
* static method provides optimized access without requiring an
* instance of the class. */
static double PDF(double x, SizeValueType degreesOfFreedom);
/** Static method to evaluate the cumulative distribution function
* (cdf) of a Student-t with a specified number of degrees of
* freedom. The static method provides optimized access without
* requiring an instance of the class. The degrees of freedom are
* passed as a parameters vector.
*
* This is based on Abramowitz and Stegun 26.7.1. Accuracy is
* approximately 10^-14.
*/
static double CDF(double x, const ParametersType &);
/** Static method to evaluate the cumulative distribution function
* (cdf) of a Student-t with a specified number of degrees of
* freedom. The static method provides optimized access without
* requiring an instance of the class.
*
* This is based on Abramowitz and Stegun 26.7.1. Accuracy is
* approximately 10^-14.
*/
static double CDF(double x, SizeValueType degreesOfFreedom);
/** Static method to evaluate the inverse cumulative distribution
* function of a Student-t with a specified number of degrees of
* freedom. The static method provides optimized access without
* requiring an instance of the class. Parameter p must be between
* 0.0 and 1.0. The degrees of freedom are passed as a parameters vector.
*
* This is based on Abramowitz and Stegun 26.7.5 followed by a few
* Newton iterations to improve the precision at low degrees of
* freedom. Accuracy is approximately 10^-10.
**/
static double InverseCDF(double p, const ParametersType &);
/** Static method to evaluate the inverse cumulative distribution
* function of a Student-t with a specified number of degrees of
* freedom. The static method provides optimized access without
* requiring an instance of the class. Parameter p must be between
* 0.0 and 1.0.
*
* This is based on Abramowitz and Stegun 26.7.5 followed by a few
* Newton iterations to improve the precision at low degrees of
* freedom. Accuracy is approximately 10^-10.
**/
static double InverseCDF(double p, SizeValueType degreesOfFreedom);
protected:
TDistribution(void);
virtual ~TDistribution(void) {}
void PrintSelf(std::ostream & os, Indent indent) const;
private:
TDistribution(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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