STK_Stat_Multivariate.h

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/*--------------------------------------------------------------------*/
/*     Copyright (C) 2004-2007  Serge Iovleff

    This program is free software; you can redistribute it and/or modify
    it under the terms of the GNU Lesser General Public License as
    published by the Free Software Foundation; either version 2 of the
    License, or (at your option) any later version.

    This program is distributed in the hope that it will be useful,
    but WITHOUT ANY WARRANTY; without even the implied warranty of
    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
    GNU Lesser General Public License for more details.

    You should have received a copy of the GNU Lesser General Public
    License along with this program; if not, write to the
    Free Software Foundation, Inc.,
    59 Temple Place,
    Suite 330,
    Boston, MA 02111-1307
    USA

    Contact : Serge.Iovleff@stkpp.org
*/

/*
 * Project:  stkpp::StatistiK::StatDesc
 * Purpose:  Compute multivariate elementary statistics for a 2D container.
 * Author:   Serge Iovleff, serge.iovleff@stkpp.org
 **/

#ifndef STK_STAT_MULTIVARIATE_H
#define STK_STAT_MULTIVARIATE_H

#include "../../Arrays/include/STK_Vector.h"
#include "../../Sdk/include/STK_IRunnerPtr2D.h"

namespace STK
{
namespace Stat
{
template < class TYPE, class TContainer2D >
class Multivariate : public IRunnerPtr2D< TYPE, TContainer2D>
{
  typedef IRunnerPtr2D< Real, TContainer2D> Runner2D;
  public:
    Multivariate( TContainer2D const* p_data)
                : Runner2D(p_data)
                , p_weights_(0)
                , nbSamples_(p_data->sizeVe())
                , nbVar_(p_data->sizeHo())
                , nMiss_(p_data->rangeHo(), 0)
//                , nbSamples_(p_data->rangeHo(), nbSamples_)
    { }

    virtual ~Multivariate() { ;}
  
    inline Integer const& nbSamples() const {return nbSamples_;}

    inline Integer const& nbVar() const {return nbVar_;}

    inline Array1D<Integer> const& nbMissingSamples() const {return nMiss_;}

    inline Array1D<Integer> const& nbAvailableSamples() const {return nbAvailable_;}

    virtual bool run()
    {
      nbSamples_ = this->p_data_->sizeVe();
      nbVar_ = this->p_data_->sizeHo();
      nMiss_.resize(this->p_data_->rangeHo());
      nMiss_ = 0;
      nbAvailable_.resize(this->p_data_->rangeHo());
      // get dimensions
      const Integer first_ind = this->p_data_->firstRow();
      const Integer last_ind  = this->p_data_->lastRow();
      const Integer first_var = this->p_data_->firstCol();
      const Integer last_var  = this->p_data_->lastCol();
      // for each variables
      for (Integer j= first_var; j<= last_var; j++)
      {
        // number of not missing observations
        Integer nobs = nbSamples_;
        // compute the mean
        for (Integer i= first_ind; i<= last_ind; i++)
          if (!Arithmetic<TYPE>::isFinite((*this->p_data_)(i,j))) nobs--;
        nbAvailable_[j] = nobs;
        nMiss_[j] = nbSamples_ - nobs;
      }
      return true;
    }

    virtual void run( Vector const& weights)
    {
      p_weights_ = &weights;
      run();
    }

  protected:
    Vector const* p_weights_;
    Integer nbSamples_;
    Integer nbVar_;
    Array1D<Integer> nMiss_;
    Array1D<Integer> nbAvailable_;
};

}  // namespace Stat

}  // namespace STK

#endif /*STK_STAT_MULTIVARIATE_H*/