/usr/include/vtk-5.8/vtkKMeansStatistics.h is in libvtk5-dev 5.8.0-5.
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Program: Visualization Toolkit
Module: vtkKMeansStatistics.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 vtkKMeansStatistics - A class for KMeans clustering
//
// .SECTION Description
// This class takes as input an optional vtkTable on port LEARN_PARAMETERS
// specifying initial set(s) of cluster values of the following form:
// <pre>
// K | Col1 | ... | ColN
// -----------+-----------------+---------+---------------
// M |clustCoord(1, 1) | ... | clustCoord(1, N)
// M |clustCoord(2, 1) | ... | clustCoord(2, N)
// . | . | . | .
// . | . | . | .
// . | . | . | .
// M |clustCoord(M, 1) | ... | clustCoord(M, N)
// L |clustCoord(1, 1) | ... | clustCoord(1, N)
// L |clustCoord(2, 1) | ... | clustCoord(2, N)
// . | . | . | .
// . | . | . | .
// . | . | . | .
// L |clustCoord(L, 1) | ... | clustCoord(L, N)
// </pre>
//
// Because the desired value of K is often not known in advance and the
// results of the algorithm are dependent on the initial cluster centers,
// we provide a mechanism for the user to test multiple runs or sets of cluster centers
// within a single call to the Learn phase. The first column of the table identifies
// the number of clusters K in the particular run (the entries in this column should be
// of type vtkIdType), while the remaining columns are a
// subset of the columns contained in the table on port INPUT_DATA. We require that
// all user specified clusters be of the same dimension N and consequently, that the
// LEARN_PARAMETERS table have N+1 columns. Due to this restriction, only one request
// can be processed for each call to the Learn phase and subsequent requests are
// silently ignored. Note that, if the first column of the LEARN_PARAMETERS table is not
// of type vtkIdType, then the table will be ignored and a single run will be performed using
// the first DefaultNumberOfClusters input data observations as initial cluster centers.
//
// When the user does not supply an initial set of clusters, then the first
// DefaultNumberOfClusters input data observations are used as initial cluster
// centers and a single run is performed.
//
//
// This class provides the following functionalities, depending on the
// mode it is executed in:
// * Learn: calculates new cluster centers for each run. The output metadata on
// port OUTPUT_MODEL is a multiblock dataset containing at a minimum
// one vtkTable with columns specifying the following for each run:
// the run ID, number of clusters, number of iterations required for convergence,
// total error associated with the cluster (sum of squared Euclidean distance from each observation
// to its nearest cluster center), the cardinality of the cluster, and the new
// cluster coordinates.
//
// *Derive: An additional vtkTable is stored in the multiblock dataset output on port OUTPUT_MODEL.
// This table contains columns that store for each run: the runID, number of clusters,
// total error for all clusters in the run, local rank, and global rank.
// The local rank is computed by comparing squared Euclidean errors of all runs with
// the same number of clusters. The global rank is computed analagously across all runs.
//
// * Assess: This requires a multiblock dataset (as computed from Learn and Derive) on input port INPUT_MODEL
// and tabular data on input port INPUT_DATA that contains column names matching those
// of the tables on input port INPUT_MODEL. The assess mode reports the closest cluster center
// and associated squared Euclidean distance of each observation in port INPUT_DATA's table to the cluster centers for
// each run in the multiblock dataset provided on port INPUT_MODEL.
//
// The code can handle a wide variety of data types as it operates on vtkAbstractArrays
// and is not limited to vtkDataArrays. A default distance functor that
// computes the sum of the squares of the Euclidean distance between two objects is provided
// (vtkKMeansDistanceFunctor). The default distance functor can be overridden to use alternative distance metrics.
//
// .SECTION Thanks
// Thanks to Janine Bennett, David Thompson, and Philippe Pebay of
// Sandia National Laboratories for implementing this class.
#ifndef __vtkKMeansStatistics_h
#define __vtkKMeansStatistics_h
#include "vtkStatisticsAlgorithm.h"
class vtkIdTypeArray;
class vtkIntArray;
class vtkDoubleArray;
class vtkKMeansDistanceFunctor;
class vtkMultiBlockDataSet;
class VTK_INFOVIS_EXPORT vtkKMeansStatistics : public vtkStatisticsAlgorithm
{
public:
vtkTypeMacro(vtkKMeansStatistics, vtkStatisticsAlgorithm);
virtual void PrintSelf( ostream& os, vtkIndent indent );
static vtkKMeansStatistics* New();
// Description:
// Set the DistanceFunctor.
virtual void SetDistanceFunctor( vtkKMeansDistanceFunctor* );
vtkGetObjectMacro(DistanceFunctor,vtkKMeansDistanceFunctor);
// Description:
// Set/get the \a DefaultNumberOfClusters, used when no initial cluster coordinates are specified.
vtkSetMacro(DefaultNumberOfClusters, int);
vtkGetMacro(DefaultNumberOfClusters, int);
// Description:
// Set/get the KValuesArrayName.
vtkSetStringMacro(KValuesArrayName);
vtkGetStringMacro(KValuesArrayName);
// Description:
// Set/get the MaxNumIterations used to terminate iterations on
// cluster center coordinates when the relative tolerance can not be met.
vtkSetMacro( MaxNumIterations, int );
vtkGetMacro( MaxNumIterations, int );
// Description:
// Set/get the relative \a Tolerance used to terminate iterations on
// cluster center coordinates.
vtkSetMacro( Tolerance, double );
vtkGetMacro( Tolerance, double );
// Description:
// Given a collection of models, calculate aggregate model
// NB: not implemented
virtual void Aggregate( vtkDataObjectCollection*,
vtkMultiBlockDataSet* ) { return; };
//BTX
// Description:
// A convenience method for setting properties by name.
virtual bool SetParameter(
const char* parameter, int index, vtkVariant value );
//ETX
protected:
vtkKMeansStatistics();
~vtkKMeansStatistics();
// Description:
// Execute the calculations required by the Learn option.
virtual void Learn( vtkTable* inData,
vtkTable* inParameters,
vtkMultiBlockDataSet* outMeta );
// Description:
// Execute the calculations required by the Derive option.
virtual void Derive( vtkMultiBlockDataSet* );
// Description:
// Execute the calculations required by the Assess option.
virtual void Assess( vtkTable*,
vtkMultiBlockDataSet*,
vtkTable* );
// Description:
// Execute the calculations required by the Test option.
virtual void Test( vtkTable*,
vtkMultiBlockDataSet*,
vtkTable* ) { return; };
//BTX
// Description:
// Provide the appropriate assessment functor.
virtual void SelectAssessFunctor( vtkTable* inData,
vtkDataObject* inMeta,
vtkStringArray* rowNames,
AssessFunctor*& dfunc );
//ETX
// Description:
// Subroutine to update new cluster centers from the old centers.
// Called from within Learn (and will be overridden by vtkPKMeansStatistics
// to handle distributed datasets).
virtual void UpdateClusterCenters( vtkTable* newClusterElements,
vtkTable* curClusterElements,
vtkIdTypeArray* numMembershipChanges,
vtkIdTypeArray* numElementsInCluster,
vtkDoubleArray* error,
vtkIdTypeArray* startRunID,
vtkIdTypeArray* endRunID,
vtkIntArray *computeRun );
// Description:
// Subroutine to get the total number of observations.
// Called from within Learn (and will be overridden by vtkPKMeansStatistics
// to handle distributed datasets).
virtual vtkIdType GetTotalNumberOfObservations( vtkIdType numObservations );
// Description:
// Subroutine to initalize the cluster centers using those provided by the user
// in input port LEARN_PARAMETERS. If no cluster centers are provided, the subroutine uses the
// first DefaultNumberOfClusters input data points as initial cluster centers.
// Called from within Learn.
int InitializeDataAndClusterCenters(vtkTable* inParameters,
vtkTable* inData,
vtkTable* dataElements,
vtkIdTypeArray* numberOfClusters,
vtkTable* curClusterElements,
vtkTable* newClusterElements,
vtkIdTypeArray* startRunID,
vtkIdTypeArray* endRunID);
// Description:
// Subroutine to initialize cluster centerss if not provided by the user.
// Called from within Learn (and will be overridden by vtkPKMeansStatistics
// to handle distributed datasets).
virtual void CreateInitialClusterCenters(vtkIdType numToAllocate,
vtkIdTypeArray* numberOfClusters,
vtkTable* inData,
vtkTable* curClusterElements,
vtkTable* newClusterElements);
// Description:
// This is the default number of clusters used when the user does not provide initial cluster centers.
int DefaultNumberOfClusters;
// Description:
// This is the name of the column that specifies the number of clusters in each run.
// This is only used if the user has not specified initial clusters.
char* KValuesArrayName;
// Description:
// This is the maximum number of iterations allowed if the new cluster centers have not yet converged.
int MaxNumIterations;
// Description:
// This is the percentage of data elements that swap cluster IDs
double Tolerance;
// Description:
// This is the Distance functor. The default is Euclidean distance, however this can be overridden.
vtkKMeansDistanceFunctor* DistanceFunctor;
private:
vtkKMeansStatistics( const vtkKMeansStatistics& ); // Not implemented
void operator=( const vtkKMeansStatistics& ); // Not implemented
};
#endif
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