libinsighttoolkit4-dev 4.5.0-3 / usr / include / ITK-4.5 / itkProbabilityDistribution.h

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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 __itkProbabilityDistribution_h
#define __itkProbabilityDistribution_h

#include "itkIntTypes.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://commonfund.nih.gov/bioinformatics.
 * \ingroup ITKStatistics
 */
class 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 SizeValueType 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