276 lines
11 KiB
C++
276 lines
11 KiB
C++
/*=========================================================================
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Program: Visualization Toolkit
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Module: vtkDescriptiveStatistics.h
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Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen
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All rights reserved.
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See Copyright.txt or http://www.kitware.com/Copyright.htm for details.
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This software is distributed WITHOUT ANY WARRANTY; without even
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the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
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PURPOSE. See the above copyright notice for more information.
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=========================================================================*/
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/*-------------------------------------------------------------------------
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Copyright 2010 Sandia Corporation.
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Under the terms of Contract DE-AC04-94AL85000 with Sandia Corporation,
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the U.S. Government retains certain rights in this software.
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-------------------------------------------------------------------------*/
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/**
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* @class vtkDescriptiveStatistics
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* @brief A class for univariate descriptive statistics
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*
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*
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* Given a selection of columns of interest in an input data table, this
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* class provides the following functionalities, depending on the chosen
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* execution options:
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* * Learn: calculate extremal values, sample mean, and M2, M3, and M4 aggregates
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* (cf. P. Pebay, Formulas for robust, one-pass parallel computation of covariances
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* and Arbitrary-Order Statistical Moments, Sandia Report SAND2008-6212, Sep 2008,
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* http://infoserve.sandia.gov/sand_doc/2008/086212.pdf for details)
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* * Derive: calculate unbiased variance estimator, standard deviation estimator,
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* two skewness estimators, and two kurtosis excess estimators.
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* * Assess: given an input data set, a reference value and a non-negative deviation,
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* mark each datum with corresponding relative deviation (1-dimensional Mahlanobis
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* distance). If the deviation is zero, then mark each datum which are equal to the
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* reference value with 0, and all others with 1. By default, the reference value
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* and the deviation are, respectively, the mean and the standard deviation of the
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* input model.
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* * Test: calculate Jarque-Bera statistic and, if VTK to R interface is available,
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* retrieve corresponding p-value for normality testing.
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*
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* Among the derived statistics, the variance, the standard deviation, the skewness
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* and the kurtosis can be estimated in two ways: using the sample version of those
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* statistics, or the population version. Specify whether a sample estimate or population
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* estimate is done by setting `SampleEstimate`. By default, `SampleEstimate == true`, hence
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* the sample version of the statistics is estimated,
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* which produces unbiased estimators (except for the sample standard deviation).
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* The sample estimate should be used for input that represent a subset of the whole
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* population of study. On the other hand, when `SampleEstimate == false`, the population
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* version of the statistics is estimated. If the input doesn't contain all the samples
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* from the population of study, then a bias is induced (the variance is slightly bigger than it
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* should be). One can read about Bessel's correction to understand better where this comes from.
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* That being said, on very large data, the difference between the 2 estimation formulas
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* becomes very low, so in those instances,
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* either state of `SampleEstimate` should yield very similar results
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* (see explicit formulas below).
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*
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* \verbatim
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*
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* The formulas used are as follows, writing \f( \bar{X} \f) the mean of \f( X \f) and \f( N \f)
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* the number of samples:
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* - Sample estimate:
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* \f[
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* Var{X} = s^2 = \frac{\sum_{k=1}^N \left(x_k - \bar{x}\right)^2 }{N - 1}
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* \f]
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* \f[
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* Skew{X} = \frac{n}{(n - 1)(n - 2)}
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* \frac{\sum_{k=1}^N \left(x_k - \bar{x}\right)^3 }{s^3}
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* \f]
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* \f[
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* Kurt{X} = \frac{n(n + 1)}{(n - 1)(n - 2)(n - 3)}
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* \frac{\sum_{k=1}^N \left(x_k - \bar{x}\right)^3 }{s^4}
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* - 3 \frac{(n - 1)^2}{(n - 2)(n - 3)}
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* \f]
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* - Population estimate:
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* \f[
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* Var{X} = \sigma^2 = \frac{\sum_{k=1}^N \left(x_k - \bar{x}\right)^2 }{N}
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* \f]
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* \f[
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* Skew{X} = \frac{1}{N}\frac{\sum_{k=1}^N \left(x_k - \bar{x}\right)^3 }{\sigma^3}
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* \f]
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* \f[
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* Kurt{X} = \frac{1}{N}\frac{\sum_{k=1}^N \left(x_k - \bar{x}\right)^3 }{\sigma^4} - 3
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* \f]
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*
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* \f(\sigma\f) is the population standard deviation, and \f(s\f) is the sample standard deviation.
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* Note that the kurtosis is corrected so the kurtosis of a gaussian distribution should yield 0.
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*
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* In the instance where \f(\sigma = 0\f) or \f(s = 0\f), the skewness and kurtosis are undefined.
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* Thus they output a `NaN`. Similarly, if there are no samples, then all derived statistics
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* yield a `NaN`.
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*
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* \endverbatim
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*
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* @par Thanks:
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* Thanks to Philippe Pebay and David Thompson from Sandia National Laboratories
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* for implementing this class.
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* Updated by Philippe Pebay, Kitware SAS 2012
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*/
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#ifndef vtkDescriptiveStatistics_h
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#define vtkDescriptiveStatistics_h
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#include "vtkDeprecation.h" // For VTK_DEPRECATED_IN_9_2_0
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#include "vtkFiltersStatisticsModule.h" // For export macro
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#include "vtkStatisticsAlgorithm.h"
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class vtkMultiBlockDataSet;
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class vtkStringArray;
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class vtkTable;
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class vtkVariant;
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class vtkDoubleArray;
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class VTKFILTERSSTATISTICS_EXPORT vtkDescriptiveStatistics : public vtkStatisticsAlgorithm
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{
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public:
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vtkTypeMacro(vtkDescriptiveStatistics, vtkStatisticsAlgorithm);
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void PrintSelf(ostream& os, vtkIndent indent) override;
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static vtkDescriptiveStatistics* New();
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///@{
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/**
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* @warning THIS METHOD DOES NOTHING AND IS DEPRECATED.
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*
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* To compute an unbiased variance, please set `SampleEstimate` instead. When set to true,
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* the sample variance is computed, which is unbiased.
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*/
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VTK_DEPRECATED_IN_9_2_0("Please use SetSampleEstimate instead")
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virtual void SetUnbiasedVariance(vtkTypeBool);
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VTK_DEPRECATED_IN_9_2_0("Please use GetSampleEstimate instead")
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virtual vtkTypeBool GetUnbiasedVariance();
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VTK_DEPRECATED_IN_9_2_0("Please use SetSampleEstimate instead")
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virtual void UnbiasedVarianceOn();
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VTK_DEPRECATED_IN_9_2_0("Please use SetSampleEstimate instead")
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virtual void UnbiasedVarianceOff();
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///@}
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///@{
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/**
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* @warning THIS METHOD DOES NOTHING AND IS DEPRECATED.
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*
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* Skewness estimator is picked depending on the state of `SampleEstimate`.
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*/
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VTK_DEPRECATED_IN_9_2_0("Please use SetSampleEstimate instead")
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virtual void SetG1Skewness(vtkTypeBool);
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VTK_DEPRECATED_IN_9_2_0("Please use GetSampleEstimate instead")
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virtual vtkTypeBool GetG1Skewness();
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VTK_DEPRECATED_IN_9_2_0("Please use SetSampleEstimate instead")
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virtual void G1SkewnessOn();
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VTK_DEPRECATED_IN_9_2_0("Please use SetSampleEstimate instead")
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virtual void G1SkewnessOff();
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///@}
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///@{
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/**
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* @warning THIS METHOD DOES NOTHING AND IS DEPRECATED.
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*
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* Kurtosis estimator is picked depending on the state of `SampleEstimate`.
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*/
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VTK_DEPRECATED_IN_9_2_0("Please use SetSampleEstimate instead")
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virtual void SetG2Kurtosis(vtkTypeBool);
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VTK_DEPRECATED_IN_9_2_0("Please use GetSampleEstimate instead")
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virtual vtkTypeBool GetG2Kurtosis();
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VTK_DEPRECATED_IN_9_2_0("Please use SetSampleEstimate instead")
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virtual void G2KurtosisOn();
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VTK_DEPRECATED_IN_9_2_0("Please use SetSampleEstimate instead")
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virtual void G2KurtosisOff();
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///@}
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///@{
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/**
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* Getter / Setter on `SampleEstimate`. When turned on, descriptive statistics
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* computed by this filter assume that the input data only holds a sample of the whole
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* population of study. In effect, the sample variance, the sample standard deviation,
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* the sample skewness and the sample kurtosis are estimated. When turned off, the population
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* variance, the population standard deviation, the population skewness and the population
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* kurtosis are estimated instead.
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*
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* In short, if the input data is a full description of the population being studied,
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* `SampleEstimate` should be turned off. If the input data is a sample of the population being
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* studied, then `SampleEstimate` should be turned on. By default, `SampleEstimate` is turned
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* on, as it is the most likely case.
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*
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* Please see class description for a full description of the formulas.
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*
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* @note For large data, the difference between the population estimate and the sample
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* estimate becomes thin, so this parameter becomes of less worry.
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*/
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vtkSetMacro(SampleEstimate, bool);
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vtkGetMacro(SampleEstimate, bool);
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vtkBooleanMacro(SampleEstimate, bool);
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///@}
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///@{
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/**
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* Set/get whether the deviations returned should be signed, or should
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* only have their magnitude reported.
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* The default is that signed deviations will be computed.
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*/
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vtkSetMacro(SignedDeviations, vtkTypeBool);
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vtkGetMacro(SignedDeviations, vtkTypeBool);
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vtkBooleanMacro(SignedDeviations, vtkTypeBool);
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///@}
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///@{
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/**
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* If there is a ghost array in the input, then ghosts matching `GhostsToSkip` mask
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* will be skipped. It is set to 0xff by default (every ghosts types are skipped).
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*
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* @sa
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* vtkDataSetAttributes
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* vtkFieldData
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* vtkPointData
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* vtkCellData
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*/
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vtkSetMacro(GhostsToSkip, unsigned char);
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vtkGetMacro(GhostsToSkip, unsigned char);
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///@}
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/**
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* Given a collection of models, calculate aggregate model
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*/
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void Aggregate(vtkDataObjectCollection*, vtkMultiBlockDataSet*) override;
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protected:
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vtkDescriptiveStatistics();
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~vtkDescriptiveStatistics() override;
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/**
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* Execute the calculations required by the Learn option, given some input Data
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* NB: input parameters are unused.
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*/
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void Learn(vtkTable*, vtkTable*, vtkMultiBlockDataSet*) override;
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/**
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* Execute the calculations required by the Derive option.
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*/
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void Derive(vtkMultiBlockDataSet*) override;
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/**
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* Execute the calculations required by the Test option.
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*/
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void Test(vtkTable*, vtkMultiBlockDataSet*, vtkTable*) override;
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/**
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* Execute the calculations required by the Assess option.
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*/
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void Assess(vtkTable* inData, vtkMultiBlockDataSet* inMeta, vtkTable* outData) override
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{
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this->Superclass::Assess(inData, inMeta, outData, 1);
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}
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/**
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* Calculate p-value. This will be overridden using the object factory with an
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* R implementation if R is present.
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*/
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virtual vtkDoubleArray* CalculatePValues(vtkDoubleArray*);
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/**
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* Provide the appropriate assessment functor.
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*/
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void SelectAssessFunctor(vtkTable* outData, vtkDataObject* inMeta, vtkStringArray* rowNames,
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AssessFunctor*& dfunc) override;
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bool SampleEstimate;
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vtkTypeBool SignedDeviations;
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unsigned char GhostsToSkip;
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private:
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vtkDescriptiveStatistics(const vtkDescriptiveStatistics&) = delete;
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void operator=(const vtkDescriptiveStatistics&) = delete;
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};
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#endif
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