source: branches/0.4-stable/yat/regression/Polynomial.cc @ 1392

// $Id: Polynomial.cc 1392 2008-07-28 19:35:30Z peter $

/*
  Copyright (C) 2005 Jari Häkkinen
  Copyright (C) 2006, 2007 Jari Häkkinen, Peter Johansson
  Copyright (C) 2008 Peter Johansson

  This file is part of the yat library, http://dev.thep.lu.se/yat

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

  The yat library 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
  General Public License for more details.

  You should have received a copy of the GNU 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.
*/

#include "Polynomial.h"
#include "yat/utility/Matrix.h"
#include "yat/utility/VectorBase.h"

namespace theplu {
namespace yat {
namespace regression {

  Polynomial::Polynomial(size_t power)
    : OneDimensional(), power_(power)
  {
  }


  Polynomial::~Polynomial(void)
  {
  }


  const utility::Matrix& Polynomial::covariance(void) const
  {
    return md_.covariance();
  }


  void Polynomial::fit(const utility::VectorBase& x, 
                       const utility::VectorBase& y)
  {
    add(ap_, x.begin(), x.end(), y.begin());
    utility::Matrix X=utility::Matrix(x.size(),power_+1,1);
    for (size_t i=0; i<X.rows(); ++i)
      for (size_t j=1; j<X.columns(); ++j)
        X(i,j)=X(i,j-1)*x(i);
    md_.fit(X,y);
    chisq_ = md_.chisq();
  }


  const utility::Vector& Polynomial::fit_parameters(void) const
  {
    return md_.fit_parameters(); 
  }


  double Polynomial::predict(const double x) const
  {
    utility::Vector vec(power_+1,1);
    for (size_t i=1; i<=power_; ++i)
      vec(i) = vec(i-1)*x;
    return md_.predict(vec);
  }


  double Polynomial::s2(void) const
  {
    return chisq()/(ap_.n()-power_-1);
  }


  double Polynomial::standard_error2(const double x) const
  {
    utility::Vector vec(power_+1,1);
    for (size_t i=1; i<=power_; ++i)
      vec(i) = vec(i-1)*x;
    return md_.standard_error2(vec);
  }

}}} // of namespaces regression, yat, and theplu