source: trunk/yat/regression/Naive.h @ 1486

#ifndef _theplu_yat_regression_naive_
#define _theplu_yat_regression_naive_

// $Id: Naive.h 1486 2008-09-09 21:17:19Z jari $

/*
  Copyright (C) 2004 Peter Johansson
  Copyright (C) 2005, 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 3 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 "OneDimensional.h"

#include <iostream>
#include <utility>

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

  /**
     @brief Naive Regression
    
     Data are modeled as \f$ y_i = \alpha + \epsilon_i \f$ 

  */ 
  class Naive : public OneDimensional 
  {
  
  public:
    ///
    /// @brief The default constructor
    ///
    Naive(void);

    ///
    /// @brief The destructor 
    ///
    virtual ~Naive(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{(y_i-m)^2} \f$. 
    ///
    void fit(const utility::VectorBase& x, const utility::VectorBase& y);

    ///
    /// The predicted value is the average \f$ m \f$ 
    ///
    double predict(const double x) const;
  
    /**
       \f$ \frac{\sum \epsilon_i^2}{N-1} \f$

       @return Conditional variance
    */
    double s2(void) const;

    ///
    /// \f$ \frac{s^2}{N} \f$
    ///
    /// @return squared standard error
    ///
    /// @see statistics::Averager
    ///
    double standard_error2(const double x) const;

  private:
    ///
    /// @brief The copy constructor (not implemented).
    ///
    Naive(const Naive&);

    double mse_;
  };

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

#endif