/usr/include/vtk-5.8/vtkStatisticsAlgorithm.h is in libvtk5-dev 5.8.0-5.
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Program: Visualization Toolkit
Module: vtkStatisticsAlgorithm.h
Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen
All rights reserved.
See Copyright.txt or http://www.kitware.com/Copyright.htm for details.
This software is distributed WITHOUT ANY WARRANTY; without even
the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
PURPOSE. See the above copyright notice for more information.
=========================================================================*/
/*-------------------------------------------------------------------------
Copyright 2010 Sandia Corporation.
Under the terms of Contract DE-AC04-94AL85000 with Sandia Corporation,
the U.S. Government retains certain rights in this software.
-------------------------------------------------------------------------*/
// .NAME vtkStatisticsAlgorithm - Base class for statistics algorithms
//
// .SECTION Description
// All statistics algorithms can conceptually be operated with several options:
// * 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 vtkTable ports
// * 3 input ports:
// * Data (mandatory)
// * Parameters to the learn phase (optional)
// * Input model (optional)
// * 3 output port (called Output):
// * Data (annotated with assessments when the Assess option is ON).
// * Output model (identical to the the input model when Learn option 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 option is ON.
//
// .SECTION Thanks
// Thanks to Philippe Pebay and David Thompson from Sandia National Laboratories
// for implementing this class.
#ifndef __vtkStatisticsAlgorithm_h
#define __vtkStatisticsAlgorithm_h
#include "vtkTableAlgorithm.h"
class vtkDataObjectCollection;
class vtkMultiBlockDataSet;
class vtkStdString;
class vtkStringArray;
class vtkVariant;
class vtkVariantArray;
class vtkStatisticsAlgorithmPrivate;
class VTK_INFOVIS_EXPORT vtkStatisticsAlgorithm : public vtkTableAlgorithm
{
public:
vtkTypeMacro(vtkStatisticsAlgorithm, vtkTableAlgorithm);
void PrintSelf(ostream& os, vtkIndent indent);
//BTX
// Description:
// 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
};
// Description:
// 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
ASSESSMENT = 2, //!< This is an old, deprecated name for OUTPUT_TEST.
OUTPUT_TEST = 2 //!< Output 2 contains result of statistical test(s)
};
//ETX
// Description:
// 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 ); }
// Description:
// A convenience method for setting learn input parameters (if one is expected or allowed).
// It is equivalent to calling SetInput( 1, params );
virtual void SetLearnOptionParameters( vtkDataObject* params )
{ this->SetInput( vtkStatisticsAlgorithm::LEARN_PARAMETERS, params ); }
// Description:
// 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 ); }
// Description:
// A convenience method for setting the input model (if one is expected or allowed).
// It is equivalent to calling SetInput( 2, model );
virtual void SetInputModel( vtkDataObject* model )
{ this->SetInput( vtkStatisticsAlgorithm::INPUT_MODEL, model ); }
// Description:
// Set/Get the Learn option.
vtkSetMacro( LearnOption, bool );
vtkGetMacro( LearnOption, bool );
// Description:
// Set/Get the Derive option.
vtkSetMacro( DeriveOption, bool );
vtkGetMacro( DeriveOption, bool );
// Description:
// Set/Get the Assess option.
vtkSetMacro( AssessOption, bool );
vtkGetMacro( AssessOption, bool );
// Description:
// Set/Get the Test option.
vtkSetMacro( TestOption, bool );
vtkGetMacro( TestOption, bool );
// Description:
// Set/Get the number of tables in the primary model.
vtkSetMacro( NumberOfPrimaryTables, vtkIdType );
vtkGetMacro( NumberOfPrimaryTables, vtkIdType );
// Description:
// Set/get assessment parameters.
virtual void SetAssessParameters( vtkStringArray* );
vtkGetObjectMacro(AssessParameters,vtkStringArray);
// Description:
// Set/get assessment names.
virtual void SetAssessNames( vtkStringArray* );
vtkGetObjectMacro(AssessNames,vtkStringArray);
// Description:
// Set the name of a parameter of the Assess option
void SetAssessOptionParameter( vtkIdType id, vtkStdString name );
// Description:
// Get the name of a parameter of the Assess option
vtkStdString GetAssessParameter( vtkIdType id );
//BTX
// Description:
// A base class for a functor that assesses data.
class AssessFunctor {
public:
virtual void operator() ( vtkVariantArray*,
vtkIdType ) = 0;
virtual ~AssessFunctor() { }
};
//ETX
// Description:
// 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 );
// Description:
// Set the the status of each and every column in the current request to OFF (0).
virtual void ResetAllColumnStates();
// Description:
// 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();
// Description:
// Empty the list of current requests.
virtual void ResetRequests();
// Description:
// 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();
// Description:
// Return the number of columns for a given request.
virtual vtkIdType GetNumberOfColumnsForRequest( vtkIdType request );
// Description:
// 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 NULL. Otherwise it returns the column name.
// This version is not thread-safe.
virtual const char* GetColumnForRequest( vtkIdType r, vtkIdType c );
//BTX
virtual int GetColumnForRequest( vtkIdType r, vtkIdType c, vtkStdString& columnName );
//ETX
// Description:
// 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 excuted, 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 );
// Description:
// Given a collection of models, calculate aggregate model
virtual void Aggregate( vtkDataObjectCollection*,
vtkMultiBlockDataSet* ) = 0;
protected:
vtkStatisticsAlgorithm();
~vtkStatisticsAlgorithm();
virtual int FillInputPortInformation( int port, vtkInformation* info );
virtual int FillOutputPortInformation( int port, vtkInformation* info );
virtual int RequestData(
vtkInformation*,
vtkInformationVector**,
vtkInformationVector* );
// Description:
// Execute the calculations required by the Learn option, given some input Data
// NB: input parameters are unused.
virtual void Learn( vtkTable*,
vtkTable*,
vtkMultiBlockDataSet* ) = 0;
// Description:
// Execute the calculations required by the Derive option.
virtual void Derive( vtkMultiBlockDataSet* ) = 0;
// Description:
// Execute the calculations required by the Assess option.
virtual void Assess( vtkTable*,
vtkMultiBlockDataSet*,
vtkTable* ) = 0;
// Description:
// Execute the calculations required by the Test option.
virtual void Test( vtkTable*,
vtkMultiBlockDataSet*,
vtkTable* ) = 0;
//BTX
// Description:
// A pure virtual method to select the appropriate assessment functor.
virtual void SelectAssessFunctor( vtkTable* outData,
vtkDataObject* inMeta,
vtkStringArray* rowNames,
AssessFunctor*& dfunc ) = 0;
//ETX
int NumberOfPrimaryTables;
bool LearnOption;
bool DeriveOption;
bool AssessOption;
bool TestOption;
vtkStringArray* AssessParameters;
vtkStringArray* AssessNames;
vtkStatisticsAlgorithmPrivate* Internals;
private:
vtkStatisticsAlgorithm(const vtkStatisticsAlgorithm&); // Not implemented
void operator=(const vtkStatisticsAlgorithm&); // Not implemented
};
#endif
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