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113 lines
3.9 KiB
C++
113 lines
3.9 KiB
C++
/*=========================================================================
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Program: Visualization Toolkit
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Module: vtkCorrelativeStatistics.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 2011 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 vtkCorrelativeStatistics
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* @brief A class for bivariate linear correlation
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*
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*
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* Given a selection of pairs of columns of interest, this class provides the
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* following functionalities, depending on the chosen execution options:
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* * Learn: calculate sample mean and M2 aggregates for each pair of variables
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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 covariance matrix estimators and its determinant,
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* linear regressions, and Pearson correlation coefficient.
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* * Assess: given an input data set, two means and a 2x2 covariance matrix,
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* mark each datum with corresponding relative deviation (2-dimensional Mahlanobis
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* distance).
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* * Test: Perform Jarque-Bera-Srivastava test of 2-d normality
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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 vtkCorrelativeStatistics_h
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#define vtkCorrelativeStatistics_h
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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 vtkCorrelativeStatistics : public vtkStatisticsAlgorithm
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{
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public:
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vtkTypeMacro(vtkCorrelativeStatistics, vtkStatisticsAlgorithm);
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void PrintSelf(ostream& os, vtkIndent indent) override;
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static vtkCorrelativeStatistics* New();
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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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vtkCorrelativeStatistics();
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~vtkCorrelativeStatistics() override;
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/**
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* Execute the calculations required by the Learn option.
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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, 2);
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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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private:
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vtkCorrelativeStatistics(const vtkCorrelativeStatistics&) = delete;
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void operator=(const vtkCorrelativeStatistics&) = delete;
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};
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#endif
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