/*=========================================================================

  Program:   ParaView
  Module:    vtkPSciVizMultiCorrelativeStats.h

  Copyright (c) Kitware, Inc.
  All rights reserved.
  See Copyright.txt or http://www.paraview.org/HTML/Copyright.html 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.

=========================================================================*/
// .NAME vtkPSciVizMultiCorrelativeStats - Fit a multivariate Gaussian to data and/or assess Mahalanobis distance of each datum from the mean.
// .SECTION Description
// 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 computes the covariance matrix for all
// the arrays you select plus the mean of each array.
// The model is thus a multivariate Gaussian distribution
// with the mean vector and variances provided.
// Data is assessed using this model by computing the
// Mahalanobis distance for each input point.
// This distance will always be positive.
//
// The learned model output format is rather dense and can be confusing,
// so it is discussed here.
// The first filter output is a multiblock dataset consisting of 2 tables:
// <ol>
// <li> Raw covariance data.
// <li> Covariance matrix and its Cholesky decomposition.
// <ol>
// The raw covariance table has 3 meaningful columns: 2 titled "Column1"
// and "Column2" whose entries generally refer to the N arrays you selected
// when preparing the filter and 1 column titled "Entries" that contains
// numeric values.
// The first row will always contain the number of observations in the
// statistical analysis.
// The next N rows contain the mean for each of the N arrays you selected.
// The remaining rows contain covariances of pairs of arrays.
//
// The second table (covariance matrix and Cholesky decomposition) contains
// information derived from the raw covariance data of the first table.
// The first N rows of the first column contain the name of one array you
// selected for analysis.
// These rows are followed by a single entry
// labeled "Cholesky" for a total of N+1 rows.
// The second column, Mean contains the mean of each variable in the first N
// entries and the number of observations processed in the final (N+1) row.
//
// The remaining columns (there are N, one for each array)
// contain 2 matrices in triangular format.
// The upper right triangle contains the covariance matrix
// (which is symmetric, so its lower triangle may be inferred).
// The lower left triangle contains the Cholesky decomposition of the
// covariance matrix (which is triangular, so its upper triangle is zero).
// Because the diagonal must be stored for both matrices, an additional
// row is required - hence the N+1 rows and the final entry of the column
// named "Column".

#ifndef __vtkPSciVizMultiCorrelativeStats_h
#define __vtkPSciVizMultiCorrelativeStats_h

#include "vtkPVVTKExtensionsDefaultModule.h" //needed for exports
#include "vtkSciVizStatistics.h"

class VTKPVVTKEXTENSIONSDEFAULT_EXPORT vtkPSciVizMultiCorrelativeStats : public vtkSciVizStatistics
{
public:
  static vtkPSciVizMultiCorrelativeStats* New();
  vtkTypeMacro(vtkPSciVizMultiCorrelativeStats,vtkSciVizStatistics);
  virtual void PrintSelf( ostream& os, vtkIndent indent );

protected:
  vtkPSciVizMultiCorrelativeStats();
  virtual ~vtkPSciVizMultiCorrelativeStats();

  virtual int LearnAndDerive( vtkMultiBlockDataSet* model, vtkTable* inData );
  virtual int AssessData( vtkTable* observations, vtkDataObject* dataset, vtkMultiBlockDataSet* model );

private:
  vtkPSciVizMultiCorrelativeStats( const vtkPSciVizMultiCorrelativeStats& ); // Not implemented.
  void operator = ( const vtkPSciVizMultiCorrelativeStats& ); // Not implemented.
};

#endif // __vtkPSciVizMultiCorrelativeStats_h
