STK_IRegression.h

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/*--------------------------------------------------------------------*/
/*     Copyright (C) 2004-2016  Serge Iovleff, Université Lille 1, Inria
 
 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 : S..._Dot_I..._At_stkpp_Dot_org (see copyright for ...)
 */
 
/*
 * Project:  stkpp::Regress
 * created on: 23 juin 2010
 * Purpose:  Interface base class for regression methods.
 * Author:   iovleff, S..._Dot_I..._At_stkpp_Dot_org (see copyright for ...)
 **/
 
#ifndef STK_IREGRESSION_H
#define STK_IREGRESSION_H
 
#include <STKernel.h>
 
namespace STK
{
 
template <class YArray, class XArray, class Weights>
class IRegression: public IRunnerSupervised<YArray, XArray, Weights>
{
  protected:
    typedef IRunnerSupervised<YArray, XArray, Weights> Base;
    using Base::p_x_;
    using Base::p_y_;
    IRegression(): Base(), predicted_(), residuals_(), nbFreeParameter_(0) {}
    IRegression( ArrayBase<YArray> const* p_y, ArrayBase<XArray> const* p_x)
              : Base((p_y == 0) ? 0 : p_y->asPtrDerived(), (p_x == 0) ? 0 : p_x->asPtrDerived())
               , predicted_()
               , residuals_()
               , nbFreeParameter_(0)
     {}
    IRegression( ArrayBase<YArray> const& y, ArrayBase<XArray> const& x)
              : Base(y.asDerived(), x.asDerived())
               , predicted_()
               , residuals_()
               , nbFreeParameter_(0)
     {}
 
  public:
    virtual ~IRegression() {}
 
    inline YArray const& predicted() const { return predicted_;}
    inline YArray const& residuals() const {  return residuals_;}
 
    inline YArray* p_predicted() { return &predicted_;}
    inline YArray* p_residuals() { return &residuals_;}
 
    inline int nbFreeParameter() const { return nbFreeParameter_;}
 
    virtual bool run();
    virtual bool run( Weights const& weights);
    virtual YArray extrapolate(XArray const& x) const =0;
 
  protected:
    virtual bool initializeStep() { return true;}
    virtual bool finalizeStep() { return true;}
    inline bool residualsStep()
    {
      residuals_ = *p_y_ - predicted_;
      return true;
    }
 
    YArray predicted_;
    YArray residuals_;
 
  private:
    int nbFreeParameter_;
    virtual bool regressionStep() =0;
    virtual bool regressionStep(Weights const& weights) =0;
    virtual bool predictionStep() =0;
    virtual int computeNbFreeParameter() const =0;
};
 
template <class YArray, class XArray, class Weights>
bool IRegression<YArray,XArray,Weights>::run()
{
  // perform any initialization step needed before the regression step
  if (!initializeStep()) { return false;}
  // compute the regression
  if (!regressionStep()) { return false;}
  // Compute the number of parameter of the regression function.
  nbFreeParameter_ = computeNbFreeParameter();
  // compute predictions
  predictionStep();
  // compute residuals
  residualsStep();
  // perform any post-operation needed before the regression step
  finalizeStep();
  // return the result of the computations
  this->hasRun_ = true;
  return true;
}
 
template <class YArray, class XArray, class Weights>
bool IRegression<YArray,XArray,Weights>::run( Weights const& weights)
{
  // perform any pre-operation needed before the regression step
  if (!initializeStep()) { return false;}
  // compute weighted regression
  if (!regressionStep(weights)) { return false;}
  // Compute the number of parameter of the regression function.
  nbFreeParameter_ = computeNbFreeParameter();
  // create array of the predicted value and compute prediction
  predictionStep();
  // create array of the residuals and compute them
  residualsStep();
  // perform any post-operation needed before the regression step
  finalizeStep();
  // return the result of the computations
  this->hasRun_ = true;
  return true;
}
 
} // namespace STK
 
#endif /* STK_IREGRESSION_H */