Perform PCA on data and/or project data into a subspace defined by the PCA. More...

#include <vtkPSciVizPCAStats.h>

Inheritance diagram for vtkPSciVizPCAStats:

Collaboration diagram for vtkPSciVizPCAStats:

Public Member Functions
virtual const char *	GetClassName ()
virtual int	IsA (const char *type)
virtual void	PrintSelf (ostream &os, vtkIndent indent)
virtual void	SetNormalizationScheme (int)
virtual int	GetNormalizationScheme ()
virtual void	SetBasisScheme (int)
virtual int	GetBasisScheme ()
virtual void	SetFixedBasisSize (int)
virtual int	GetFixedBasisSize ()
virtual void	SetFixedBasisEnergy (double)
virtual double	GetFixedBasisEnergy ()
Static Public Member Functions
static vtkPSciVizPCAStats *	New ()
static int	IsTypeOf (const char *type)
static vtkPSciVizPCAStats *	SafeDownCast (vtkObject *o)
Protected Member Functions
	vtkPSciVizPCAStats ()
virtual	~vtkPSciVizPCAStats ()
virtual int	LearnAndDerive (vtkMultiBlockDataSet model, vtkTable inData)
virtual int	AssessData (vtkTable observations, vtkDataObject dataset, vtkMultiBlockDataSet *model)
Protected Attributes
int	NormalizationScheme
int	BasisScheme
int	FixedBasisSize
double	FixedBasisEnergy

Detailed Description

Perform PCA on data and/or project data into a subspace defined by the PCA.

This filter either computes a statistical model of a dataset or takes such a model as its second input. Then, the model (however it is obtained) may optionally be used to assess the input dataset.

This filter performs additional analysis above and beyond the vtkPSciVizMultiCorrelativeStats filter. It computes the eigenvalues and eigenvectors of the covariance matrix from the multicorrelative filter. Data is then assessed by projecting the original tuples into a possibly lower-dimensional space.

Since the PCA filter uses the multicorrelative filter's analysis, it shares the same raw covariance table specified in the multicorrelative documentation. The second table in the multiblock dataset comprising the model output is an expanded version of the multicorrelative version.

As with the multicorrlative filter, the second model table contains the mean values, the upper-triangular portion of the symmetric covariance matrix, and the non-zero lower-triangular portion of the Cholesky decomposition of the covariance matrix. Below these entries are the eigenvalues of the covariance matrix (in the column labeled "Mean") and the eigenvectors (as row vectors) in an additional NxN matrix.

Definition at line 50 of file vtkPSciVizPCAStats.h.

Constructor & Destructor Documentation

vtkPSciVizPCAStats::vtkPSciVizPCAStats ( ) [protected]

virtual vtkPSciVizPCAStats::~vtkPSciVizPCAStats ( ) [protected, virtual]

Member Function Documentation

static vtkPSciVizPCAStats* vtkPSciVizPCAStats::New ( ) [static]

Reimplemented from vtkTableAlgorithm.

virtual const char* vtkPSciVizPCAStats::GetClassName ( ) [virtual]

Reimplemented from vtkSciVizStatistics.

static int vtkPSciVizPCAStats::IsTypeOf ( const char * type ) [static]

Reimplemented from vtkSciVizStatistics.

virtual int vtkPSciVizPCAStats::IsA ( const char * type ) [virtual]

Reimplemented from vtkSciVizStatistics.

static vtkPSciVizPCAStats* vtkPSciVizPCAStats::SafeDownCast ( vtkObject * o ) [static]

Reimplemented from vtkSciVizStatistics.

virtual void vtkPSciVizPCAStats::PrintSelf	(	ostream &	os,
		vtkIndent	indent
	)		`[virtual]`

Reimplemented from vtkSciVizStatistics.

virtual void vtkPSciVizPCAStats::SetNormalizationScheme ( int ) [virtual]

virtual int vtkPSciVizPCAStats::GetNormalizationScheme ( ) [virtual]

virtual void vtkPSciVizPCAStats::SetBasisScheme ( int ) [virtual]

virtual int vtkPSciVizPCAStats::GetBasisScheme ( ) [virtual]

virtual void vtkPSciVizPCAStats::SetFixedBasisSize ( int ) [virtual]

virtual int vtkPSciVizPCAStats::GetFixedBasisSize ( ) [virtual]

virtual void vtkPSciVizPCAStats::SetFixedBasisEnergy ( double ) [virtual]

virtual double vtkPSciVizPCAStats::GetFixedBasisEnergy ( ) [virtual]

virtual int vtkPSciVizPCAStats::LearnAndDerive	(	vtkMultiBlockDataSet *	model,
		vtkTable *	inData
	)		`[protected, virtual]`

Method subclasses must override to calculate a full model from the given input data. The model should be placed on the first output port of the passed vtkInformationVector as well as returned in the model parameter.

Implements vtkSciVizStatistics.

virtual int vtkPSciVizPCAStats::AssessData	(	vtkTable *	observations,
		vtkDataObject *	dataset,
		vtkMultiBlockDataSet *	model
	)		`[protected, virtual]`

Method subclasses must override to assess an input table given a model of the proper type. The dataset parameter contains a shallow copy of input port 0 and should be modified to include the assessment. Adding new arrays to point/cell/vertex/edge data should not pose a problem, but any alterations to the dataset itself will probably require that you create a deep copy before modification.

Parameters:

observations	- a table containing the field data of the dataset converted to a table
dataset	- a shallow copy of the input dataset that should be altered to include an assessment of the output.
model	- the statistical model with which to assess the observations.

Implements vtkSciVizStatistics.