/usr/include/ITK-4.5/itkChiSquareDistribution.h is in libinsighttoolkit4-dev 4.5.0-3.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 | /*=========================================================================
*
* 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 __itkChiSquareDistribution_h
#define __itkChiSquareDistribution_h
#include "itkProbabilityDistribution.h"
#include "itkNumericTraits.h"
namespace itk
{
namespace Statistics
{
/** \class ChiSquareDistribution
* \brief ChiSquareDistribution class defines the interface for a
* univariate Chi-Square distribution (pdfs, cdfs, etc.).
*
* ChiSquareDistribution provides access to the probability density
* function (pdf), the cumulative distribution function (cdf), and the
* inverse cumulative distribution function for a Chi-Square 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.
*
* ChiSquareDistributions 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).
*
* ChiSquareDistributions can be used for Chi-Square 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 ChiSquareDistribution:
public ProbabilityDistribution
{
public:
/** Standard class typedefs */
typedef ChiSquareDistribution Self;
typedef ProbabilityDistribution Superclass;
typedef SmartPointer< Self > Pointer;
typedef SmartPointer< const Self > ConstPointer;
/** Strandard macros */
itkTypeMacro(ChiSquareDistribution, ProbabilityDistribution);
/** Method for creation through the object factory. */
itkNewMacro(Self);
/** Return the number of parameters. For a Chi-Square
* 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 Chi-Square 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 Chi-Square distribution have a mean? */
virtual bool HasMean() const { return true; }
/** Get the mean of the distribution. */
virtual double GetMean() const;
/** Does the Chi-Square distribution have a variance? */
virtual bool HasVariance() const { return true; }
/** Get the variance of the distribution. */
virtual double GetVariance() const;
/** Static method to evaluate the probability density function (pdf)
* of a Chi-Square 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 Chi-Square 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 Chi-Square 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 Chi-Square 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 Chi-Square 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 Chi-Square 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:
ChiSquareDistribution(void);
virtual ~ChiSquareDistribution(void) {}
void PrintSelf(std::ostream & os, Indent indent) const;
private:
ChiSquareDistribution(const Self &); //purposely not implemented
void operator=(const Self &); //purposely not implemented
}; // end of class
} // end of namespace Statistics
} // end namespace itk
#endif
|