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

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

/*
  Copyright (C) 2005 Jari Häkkinen
  Copyright (C) 2006, 2007, 2008 Jari Häkkinen, 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 "MultiDimensional.h"
#include "yat/utility/Exception.h"
#include "yat/utility/Matrix.h"
#include "yat/utility/VectorBase.h"
#include "yat/utility/Vector.h"

#include <cassert>

namespace theplu {
namespace yat {
namespace regression {


  MultiDimensional::MultiDimensional(void)
    : chisquare_(0), work_(NULL)
  {
  }


  MultiDimensional::~MultiDimensional(void)
  {
    if (work_)
      gsl_multifit_linear_free(work_);
  }


  const utility::Matrix& MultiDimensional::covariance(void) const
  {
    return covariance_;
  }


  void MultiDimensional::fit(const utility::Matrix& x, 
                             const utility::VectorBase& y)
  {
    assert(x.rows()==y.size());
    covariance_.resize(x.columns(),x.columns());
    fit_parameters_ = utility::Vector(x.columns());
    if (work_)
      gsl_multifit_linear_free(work_);
    if (!(work_=gsl_multifit_linear_alloc(x.rows(),fit_parameters_.size())))
      throw utility::GSL_error("MultiDimensional::fit failed to allocate memory");

    int status = gsl_multifit_linear(x.gsl_matrix_p(), y.gsl_vector_p(),
                                     fit_parameters_.gsl_vector_p(),
                                     covariance_.gsl_matrix_p(), &chisquare_,
                                     work_);
    if (status)
      throw utility::GSL_error(std::string("MultiDimensional::fit",status));

    s2_ = chisquare_/(x.rows()-x.columns());
  }

  const utility::Vector& MultiDimensional::fit_parameters(void) const
  { 
    return fit_parameters_; 
  }


  double MultiDimensional::chisq(void) const
  {
    return chisquare_;
  }


  double MultiDimensional::predict(const utility::VectorBase& x) const 
  {
    assert(x.size()==fit_parameters_.size());
    return fit_parameters_ * x;
  }


  double MultiDimensional::prediction_error2(const utility::VectorBase& x) const
  {
    return standard_error2(x) + s2_;
  }


  double MultiDimensional::standard_error2(const utility::VectorBase& x) const
  {
    double s2 = 0;
    for (size_t i=0; i<x.size(); ++i){
      s2 += covariance_(i,i)*x(i)*x(i);
      for (size_t j=i+1; j<x.size(); ++j)
        s2 += 2*covariance_(i,j)*x(i)*x(j);
    }
    return s2;
  }

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