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

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

/*
  Copyright (C) 2005 Peter Johansson
  Copyright (C) 2006 Jari Häkkinen, Peter Johansson, Markus Ringnér
  Copyright (C) 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 "LinearWeighted.h"
#include "yat/statistics/AveragerPairWeighted.h"
#include "yat/utility/Vector.h"

#include <cassert>

namespace theplu {
namespace yat {
namespace regression {

  LinearWeighted::LinearWeighted(void)
    : OneDimensionalWeighted(), alpha_(0), alpha_var_(0), beta_(0),
      beta_var_(0)
  {
  }

  LinearWeighted::~LinearWeighted(void)
  {
  }

  double LinearWeighted::alpha(void) const
  {
    return alpha_;
  }

  double LinearWeighted::alpha_var(void) const
  {
    return alpha_var_;
  }

  double LinearWeighted::beta(void) const
  {
    return beta_;
  }

  double LinearWeighted::beta_var(void) const
  {
    return beta_var_;
  }

  void LinearWeighted::fit(const utility::VectorBase& x,
                           const utility::VectorBase& y,
                           const utility::VectorBase& w)
  {
    assert(x.size()==y.size());
    assert(x.size()==w.size());

    // AveragerPairWeighted requires 2 weights but works only on the
    // product wx*wy, so we can send in w and a dummie to get what we
    // want.
    ap_.reset();
    yat::utility::Vector dummy(x.size(), 1.0);
    add(ap_, x.begin(), x.end(), y.begin(),dummy.begin(),w.begin());

    alpha_ = m_y();
    beta_ = sxy()/sxx();

    chisq_=0;
    for (size_t i=0; i<x.size(); ++i){
      double res = predict(x(i))-y(i);
      chisq_ += w(i)*res*res;
    }

    alpha_var_ = s2()/ap_.y_averager().sum_w();
    beta_var_ = s2()/sxx(); 
  }

  double LinearWeighted::m_x(void) const
  {
    return ap_.x_averager().mean();
  }

  double LinearWeighted::m_y(void) const
  {
    return ap_.y_averager().mean();
  }

  double LinearWeighted::predict(const double x) const
  { 
    return alpha_ + beta_ * (x-m_x()); 
  }

  
  double LinearWeighted::s2(double w) const
  {
    return chisq_/(w*(ap_.y_averager().n()-2));
  }

  double LinearWeighted::standard_error2(const double x) const
  {
    return alpha_var_ + beta_var_*(x-m_x())*(x-m_x());
  }


  double LinearWeighted::sxx(void) const
  {
    return ap_.x_averager().sum_xx_centered();
  }


  double LinearWeighted::sxy(void) const
  {
    return ap_.sum_xy_centered();
  }


  double LinearWeighted::syy(void) const
  {
    return ap_.y_averager().sum_xx_centered();
  }

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