source: branches/0.4-stable/yat/regression/NaiveWeighted.h @ 1392

#ifndef _theplu_yat_regression_naiveweighted_
#define _theplu_yat_regression_naiveweighted_

// $Id: NaiveWeighted.h 1392 2008-07-28 19:35:30Z peter $

/*
  Copyright (C) 2005 Peter Johansson
  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 "OneDimensionalWeighted.h"

#include <cmath>
#include <iostream>
#include <utility>

namespace theplu {
namespace yat {
  namespace utility {
    class VectorBase;
  }
namespace regression {

  ///
  /// @brief naive fitting.
  ///
  /// @todo document
  ///
  class NaiveWeighted : public OneDimensionalWeighted 
  {
  
  public:
    ///
    /// @brief The default constructor
    ///
    NaiveWeighted(void);

    ///
    /// @brief The destructor
    ///
    virtual ~NaiveWeighted(void);
         
    /**
       This function computes the best-fit for the naive model \f$ y
       = m \f$ from vectors \a x and \a y, by minimizing \f$ \sum
       w_i(y_i-m)^2 \f$. The weight \f$ w_i \f$ is proportional to
       the inverse of the variance for \f$ y_i \f$
    */
    void fit(const utility::VectorBase& x,
             const utility::VectorBase& y,
             const utility::VectorBase& w);

    ///
    /// Function predicting value using the naive model, i.e. a
    /// weighted average. 
    ///
    double predict(const double x) const;

    /**
       \f$ \frac{\sum w_i\epsilon_i^2}{ w \left(\frac{\left(\sum
       w_i\right)^2}{\sum w_i^2}-1\right)} \f$

       Rescaling all weights, both in fit and the passed @a w, results
       in the same returned value.

       @return Conditional variance of Y with weight @a w.
    */
    double s2(const double w=1) const;

    /**
       \f$ \frac{\sum w_i\epsilon_i^2}{ \left(\frac{\left(\sum
       w_i\right)^2}{\sum w_i^2}-1\right)\sum w_i} \f$

       @return estimated squared error of model value in @a x
    */
    double standard_error2(const double x) const;

  private:
    ///
    /// Copy Constructor. (not implemented)
    ///
    NaiveWeighted(const NaiveWeighted&);

  };

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

#endif