source: trunk/yat/statistics/Naive.h @ 676

#ifndef _theplu_statistics_regression_naive_
#define _theplu_statistics_regression_naive_

// $Id: Naive.h 675 2006-10-10 12:08:45Z jari $

/*
  Copyright (C) The authors contributing to this file.

  This file is part of the yat library, http://lev.thep.lu.se/trac/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 "yat/statistics/OneDimensional.h"

#include <iostream>
#include <utility>

namespace theplu {
  namespace utility {
    class vector;
  }
namespace statistics {
namespace regression {

  ///
  /// @bief naive fitting.
  ///
  /// @todo document
  ///
  class Naive : public OneDimensional 
  {
  
  public:
    ///
    /// Default Constructor.
    ///
    inline Naive(void) : OneDimensional(), m_(0.0), m_err_(0.0) {}

    ///
    /// Copy Constructor. (not implemented)
    ///
    Naive(const Naive&);

    ///
    /// 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$. This function is the same as using the
    /// weighted version with unity weights.
    ///
    void fit(const utility::vector& x, const utility::vector& y);

    ///
    /// Function predicting value using the naive model. 
    ///
    double predict(const double x) const;
  
    /// 
    /// The expected deviation from the line for a new data point. The
    /// error has two components: the variance of point and error in
    /// estimation of the mean. 
    ///
    double prediction_error(const double x) const;

    ///
    /// @return standard error
    ///
    double standard_error(const double x) const;

  private:
    double m_;
    double m_err_; // error of estimation of mean m_

  };


}}} // of namespaces regression, statisitcs and thep

#endif