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125 lines
4.1 KiB
C++
125 lines
4.1 KiB
C++
/*=========================================================================
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Program: Visualization Toolkit
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Module: vtkAutoCorrelativeStatistics.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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* @class vtkAutoCorrelativeStatistics
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* @brief A class for univariate auto-correlative 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 sample mean and M2 aggregates for each variable w.r.t. itself
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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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* for each specified time lag.
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* * Derive: calculate unbiased autocovariance matrix estimators and its determinant,
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* linear regressions, and Pearson correlation coefficient, for each specified
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* time lag.
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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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*
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*
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* @par Thanks:
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* This class was written by Philippe Pebay, Kitware SAS 2012
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*/
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#ifndef vtkAutoCorrelativeStatistics_h
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#define vtkAutoCorrelativeStatistics_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 vtkAutoCorrelativeStatistics : public vtkStatisticsAlgorithm
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{
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public:
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vtkTypeMacro(vtkAutoCorrelativeStatistics, vtkStatisticsAlgorithm);
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void PrintSelf(ostream& os, vtkIndent indent) override;
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static vtkAutoCorrelativeStatistics* New();
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//@{
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/**
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* Set/get the cardinality of the data set at given time, i.e., of
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* any given time slice. It cannot be negative.
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* The input data set is assumed to have a cardinality which
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* is a multiple of this value.
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* The default is 0, meaning that the user must specify a value
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* that is consistent with the input data set.
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*/
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vtkSetClampMacro(SliceCardinality, vtkIdType, 0, VTK_ID_MAX);
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vtkGetMacro(SliceCardinality, vtkIdType);
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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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vtkAutoCorrelativeStatistics();
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~vtkAutoCorrelativeStatistics() 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 { return; }
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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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vtkIdType SliceCardinality;
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private:
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vtkAutoCorrelativeStatistics(const vtkAutoCorrelativeStatistics&) = delete;
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void operator=(const vtkAutoCorrelativeStatistics&) = delete;
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};
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#endif
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