STK_IRegressMultivariate.h

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/*--------------------------------------------------------------------*/
/*     Copyright (C) 2004-2010  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::Regress
 * created on: 23 juin 2010
 * Purpose:  partial specialization for regression methods.
 * Author:   iovleff, serge.iovleff@stkpp.org
 **/

#ifndef STK_IREGRESSMULTIVARIATE_H
#define STK_IREGRESSMULTIVARIATE_H

#include "STK_IRegress.h"

#include "../../Algebra/include/STK_Matrix.h"

namespace STK
{

template <class YContainer>
class IRegress<YContainer, Matrix>
{
  public:
    typedef IContainerBase<YContainer> YContainer;

  protected:
    YContainer const* y_;
    Matrix const* x_;
    Vector const* p_weights_;
    YContainer* p_predicted_;
    YContainer* p_residuals_;

  public:
    IRegress( YContainer const* y =0, Matrix const* x=0)
            : y_(y)
            , x_(x)
            , p_weights_(0)
            , p_predicted_(0)
            , p_residuals_(0)
            , decomposition(*x)
    { ;}

    virtual ~IRegress()
    { clean();}

    void run()
    {
      // remove any existing storage
      clean();
      // compute regression
      regression();
      // predictions
      prediction();
      // compute residuals
      computeResiduals();
    }

    void run( Vector const* p_weights)
    {
      // set weights
      p_weights_ = p_weights;
      // remove any existing storage
      clean();
      // compute weighted regression
      wregression();
      // create container of the predicted value and compute predictions
      prediction();
      // create container of the residuals and compute them
      computeResiduals();
    }

    void deleteCol( Integer const& pos);
    void insertCol( Integer const& pos, Vector const& v);

    inline YContainer* p_predicted() const { return p_predicted_;}
    inline YContainer* p_residuals() const { return p_residuals_;}

    void setData( YContainer const* y, Matrix const* x)
    { y_ = y; x_ = x; decomposition.run(*x);}

    void clean()
    {
      if (p_predicted_) delete p_predicted_;
      if (p_residuals_) delete p_residuals_;
    }

  protected:
    inline void computeResiduals()
    {
      if (p_residuals_) delete p_residuals_;
       p_residuals_ = y_->clone();
      *p_residuals_ = y_->asLeaf() - *p_predicted_;
    }

  private:
    virtual void regression() =0;
    virtual void wregression() =0;
    virtual void prediction() =0;
    Qr decomposition;
};

}

#endif /* STK_IREGRESS_H */