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119 lines
4.1 KiB
C++
119 lines
4.1 KiB
C++
/*=========================================================================
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Program: Visualization Toolkit
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Module: vtkContingencyStatistics.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 vtkContingencyStatistics
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* @brief A class for bivariate correlation contigency
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* tables, conditional probabilities, and information entropy
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*
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*
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* Given a pair of columns of interest, this class provides the
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* following functionalities, depending on the operation in which it is executed:
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* * Learn: calculate contigency tables and corresponding discrete joint
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* probability distribution.
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* * Derive: calculate conditional probabilities, information entropies, and
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* pointwise mutual information.
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* * Assess: given two columns of interest with the same number of entries as
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* input in port INPUT_DATA, and a corresponding bivariate probability distribution,
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* * Test: calculate Chi-square independence statistic and, if VTK to R interface is available,
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* retrieve corresponding p-value for independence testing.
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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 vtkContingencyStatistics_h
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#define vtkContingencyStatistics_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 vtkIdTypeArray;
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class vtkDoubleArray;
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class VTKFILTERSSTATISTICS_EXPORT vtkContingencyStatistics : public vtkStatisticsAlgorithm
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{
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public:
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vtkTypeMacro(vtkContingencyStatistics, vtkStatisticsAlgorithm);
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void PrintSelf(ostream& os, vtkIndent indent) override;
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static vtkContingencyStatistics* New();
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/**
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* Given a collection of models, calculate aggregate model
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* NB: not implemented
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*/
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void Aggregate(vtkDataObjectCollection*, vtkMultiBlockDataSet*) override { return; }
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protected:
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vtkContingencyStatistics();
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~vtkContingencyStatistics() 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*, vtkMultiBlockDataSet*, vtkTable*) override;
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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 void CalculatePValues(vtkTable*);
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/**
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* Provide the appropriate assessment functor.
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* This one does nothing because the API is not sufficient for tables indexed
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* by a separate summary table.
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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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/**
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* Provide the appropriate assessment functor.
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* This one is the one that is actually used.
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*/
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virtual void SelectAssessFunctor(vtkTable* outData, vtkMultiBlockDataSet* inMeta,
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vtkIdType pairKey, vtkStringArray* rowNames, AssessFunctor*& dfunc);
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private:
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vtkContingencyStatistics(const vtkContingencyStatistics&) = delete;
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void operator=(const vtkContingencyStatistics&) = delete;
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};
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#endif
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