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vtkStatisticsAlgorithm.h
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vtkStatisticsAlgorithm.h
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// SPDX-FileCopyrightText: Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen
// SPDX-FileCopyrightText: Copyright 2011 Sandia Corporation
// SPDX-License-Identifier: LicenseRef-BSD-3-Clause-Sandia-USGov
/**
* @class vtkStatisticsAlgorithm
* @brief Base class for statistics algorithms
*
*
* All statistics algorithms can conceptually be operated with several operations:
* * Learn: given an input data set, calculate a minimal statistical model (e.g.,
* sums, raw moments, joint probabilities).
* * Derive: given an input minimal statistical model, derive the full model
* (e.g., descriptive statistics, quantiles, correlations, conditional
* probabilities).
* NB: It may be, or not be, a problem that a full model was not derived. For
* instance, when doing parallel calculations, one only wants to derive the full
* model after all partial calculations have completed. On the other hand, one
* can also directly provide a full model, that was previously calculated or
* guessed, and not derive a new one.
* * Assess: given an input data set, input statistics, and some form of
* threshold, assess a subset of the data set.
* * Test: perform at least one statistical test.
* Therefore, a vtkStatisticsAlgorithm has the following ports
* * 3 optional input ports:
* * Data (vtkTable)
* * Parameters to the learn operation (vtkTable)
* * Input model (vtkMultiBlockDataSet)
* * 3 output ports:
* * Data (input annotated with assessments when the Assess operation is ON).
* * Output model (identical to the input model when Learn operation is OFF).
* * Output of statistical tests. Some engines do not offer such tests yet, in
* which case this output will always be empty even when the Test operation is ON.
*
* @par Thanks:
* Thanks to Philippe Pebay and David Thompson from Sandia National Laboratories
* for implementing this class.
* Updated by Philippe Pebay, Kitware SAS 2012
*/
#ifndef vtkStatisticsAlgorithm_h
#define vtkStatisticsAlgorithm_h
#include "vtkFiltersStatisticsModule.h" // For export macro
#include "vtkTableAlgorithm.h"
VTK_ABI_NAMESPACE_BEGIN
class vtkDataObjectCollection;
class vtkMultiBlockDataSet;
class vtkStdString;
class vtkStringArray;
class vtkVariant;
class vtkVariantArray;
class vtkDoubleArray;
class vtkStatisticsAlgorithmPrivate;
class VTKFILTERSSTATISTICS_EXPORT vtkStatisticsAlgorithm : public vtkTableAlgorithm
{
public:
vtkTypeMacro(vtkStatisticsAlgorithm, vtkTableAlgorithm);
void PrintSelf(ostream& os, vtkIndent indent) override;
/**
* enumeration values to specify input port types
*/
enum InputPorts
{
INPUT_DATA = 0, //!< Port 0 is for learn data
LEARN_PARAMETERS = 1, //!< Port 1 is for learn parameters (initial guesses, etc.)
INPUT_MODEL = 2 //!< Port 2 is for a priori models
};
/**
* enumeration values to specify output port types
*/
enum OutputIndices
{
OUTPUT_DATA = 0, //!< Output 0 mirrors the input data, plus optional assessment columns
OUTPUT_MODEL = 1, //!< Output 1 contains any generated model
OUTPUT_TEST = 2 //!< Output 2 contains result of statistical test(s)
};
/**
* A convenience method for setting learn input parameters (if one is expected or allowed).
* It is equivalent to calling SetInputConnection( 1, params );
*/
virtual void SetLearnOptionParameterConnection(vtkAlgorithmOutput* params)
{
this->SetInputConnection(vtkStatisticsAlgorithm::LEARN_PARAMETERS, params);
}
/**
* A convenience method for setting learn input parameters (if one is expected or allowed).
* It is equivalent to calling SetInputData( 1, params );
*/
virtual void SetLearnOptionParameters(vtkDataObject* params)
{
this->SetInputData(vtkStatisticsAlgorithm::LEARN_PARAMETERS, params);
}
/**
* A convenience method for setting the input model connection (if one is expected or allowed).
* It is equivalent to calling SetInputConnection( 2, model );
*/
virtual void SetInputModelConnection(vtkAlgorithmOutput* model)
{
this->SetInputConnection(vtkStatisticsAlgorithm::INPUT_MODEL, model);
}
/**
* A convenience method for setting the input model (if one is expected or allowed).
* It is equivalent to calling SetInputData( 2, model );
*/
virtual void SetInputModel(vtkDataObject* model)
{
this->SetInputData(vtkStatisticsAlgorithm::INPUT_MODEL, model);
}
///@{
/**
* Set/Get the Learn operation.
*/
vtkSetMacro(LearnOption, bool);
vtkGetMacro(LearnOption, bool);
///@}
///@{
/**
* Set/Get the Derive operation.
*/
vtkSetMacro(DeriveOption, bool);
vtkGetMacro(DeriveOption, bool);
///@}
///@{
/**
* Set/Get the Assess operation.
*/
vtkSetMacro(AssessOption, bool);
vtkGetMacro(AssessOption, bool);
///@}
///@{
/**
* Set/Get the Test operation.
*/
vtkSetMacro(TestOption, bool);
vtkGetMacro(TestOption, bool);
///@}
///@{
/**
* Set/Get the number of tables in the primary model.
*/
vtkSetMacro(NumberOfPrimaryTables, vtkIdType);
vtkGetMacro(NumberOfPrimaryTables, vtkIdType);
///@}
///@{
/**
* Set/get assessment names.
*/
virtual void SetAssessNames(vtkStringArray*);
vtkGetObjectMacro(AssessNames, vtkStringArray);
///@}
///@{
/**
* A base class for a functor that assesses data.
*/
class AssessFunctor
{
public:
virtual void operator()(vtkDoubleArray*, vtkIdType) = 0;
virtual ~AssessFunctor() = default;
};
///@}
/**
* Add or remove a column from the current analysis request.
* Once all the column status values are set, call RequestSelectedColumns()
* before selecting another set of columns for a different analysis request.
* The way that columns selections are used varies from algorithm to algorithm.
* Note: the set of selected columns is maintained in vtkStatisticsAlgorithmPrivate::Buffer
* until RequestSelectedColumns() is called, at which point the set is appended
* to vtkStatisticsAlgorithmPrivate::Requests.
* If there are any columns in vtkStatisticsAlgorithmPrivate::Buffer at the time
* RequestData() is called, RequestSelectedColumns() will be called and the
* selection added to the list of requests.
*/
virtual void SetColumnStatus(const char* namCol, int status);
/**
* Set the status of each and every column in the current request to OFF (0).
*/
virtual void ResetAllColumnStates();
/**
* Use the current column status values to produce a new request for statistics
* to be produced when RequestData() is called. See SetColumnStatus() for more information.
*/
virtual int RequestSelectedColumns();
/**
* Empty the list of current requests.
*/
virtual void ResetRequests();
/**
* Return the number of requests.
* This does not include any request that is in the column-status buffer
* but for which RequestSelectedColumns() has not yet been called (even though
* it is possible this request will be honored when the filter is run -- see SetColumnStatus()
* for more information).
*/
virtual vtkIdType GetNumberOfRequests();
/**
* Return the number of columns for a given request.
*/
virtual vtkIdType GetNumberOfColumnsForRequest(vtkIdType request);
/**
* Provide the name of the \a c-th column for the \a r-th request.
* For the version of this routine that returns an integer,
* if the request or column does not exist because \a r or \a c is out of bounds,
* this routine returns 0 and the value of \a columnName is unspecified.
* Otherwise, it returns 1 and the value of \a columnName is set.
* For the version of this routine that returns const char*,
* if the request or column does not exist because \a r or \a c is out of bounds,
* the routine returns nullptr. Otherwise it returns the column name.
* This version is not thread-safe.
*/
virtual const char* GetColumnForRequest(vtkIdType r, vtkIdType c);
virtual int GetColumnForRequest(vtkIdType r, vtkIdType c, vtkStdString& columnName);
/**
* Convenience method to create a request with a single column name \p namCol in a single
* call; this is the preferred method to select columns, ensuring selection consistency
* (a single column per request).
* Warning: no name checking is performed on \p namCol; it is the user's
* responsibility to use valid column names.
*/
void AddColumn(const char* namCol);
/**
* Convenience method to create a request with a single column name pair
* (\p namColX, \p namColY) in a single call; this is the preferred method to select
* columns pairs, ensuring selection consistency (a pair of columns per request).
* Unlike SetColumnStatus(), you need not call RequestSelectedColumns() after AddColumnPair().
* Warning: \p namColX and \p namColY are only checked for their validity as strings;
* no check is made that either are valid column names.
*/
void AddColumnPair(const char* namColX, const char* namColY);
/**
* A convenience method (in particular for access from other applications) to
* set parameter values of Learn mode.
* Return true if setting of requested parameter name was executed, false otherwise.
* NB: default method (which is sufficient for most statistics algorithms) does not
* have any Learn parameters to set and always returns false.
*/
virtual bool SetParameter(const char* parameter, int index, vtkVariant value);
/**
* Given a collection of models, calculate aggregate model
*/
virtual void Aggregate(vtkDataObjectCollection*, vtkMultiBlockDataSet*) = 0;
protected:
vtkStatisticsAlgorithm();
~vtkStatisticsAlgorithm() override;
int FillInputPortInformation(int port, vtkInformation* info) override;
int FillOutputPortInformation(int port, vtkInformation* info) override;
int RequestData(vtkInformation*, vtkInformationVector**, vtkInformationVector*) override;
/**
* Execute the calculations required by the Learn option, given some input Data
*/
virtual void Learn(vtkTable*, vtkTable*, vtkMultiBlockDataSet*) = 0;
/**
* Execute the calculations required by the Derive option.
*/
virtual void Derive(vtkMultiBlockDataSet*) = 0;
/**
* Execute the calculations required by the Assess option.
*/
virtual void Assess(vtkTable*, vtkMultiBlockDataSet*, vtkTable*) = 0;
/**
* A convenience implementation for generic assessment with variable number of variables.
*/
void Assess(vtkTable*, vtkMultiBlockDataSet*, vtkTable*, int);
/**
* Execute the calculations required by the Test option.
*/
virtual void Test(vtkTable*, vtkMultiBlockDataSet*, vtkTable*) = 0;
/**
* A pure virtual method to select the appropriate assessment functor.
*/
virtual void SelectAssessFunctor(
vtkTable* outData, vtkDataObject* inMeta, vtkStringArray* rowNames, AssessFunctor*& dfunc) = 0;
vtkIdType NumberOfPrimaryTables;
bool LearnOption;
bool DeriveOption;
bool AssessOption;
bool TestOption;
vtkStringArray* AssessNames;
vtkStatisticsAlgorithmPrivate* Internals;
private:
vtkStatisticsAlgorithm(const vtkStatisticsAlgorithm&) = delete;
void operator=(const vtkStatisticsAlgorithm&) = delete;
};
VTK_ABI_NAMESPACE_END
#endif