source: trunk/yat/statistics/Pearson.h @ 1703

#ifndef _theplu_yat_statistics_pearson_
#define _theplu_yat_statistics_pearson_ 

// $Id: Pearson.h 1487 2008-09-10 08:41:36Z jari $

/*
  Copyright (C) 2004, 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 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 yat. If not, see <http://www.gnu.org/licenses/>.
*/

#include "Score.h"

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

  ///
  /// @brief Class for calculating Pearson correlation.
  ///   
  
  class Pearson : public Score
  {
  public:
    ///
    /// @brief The default constructor.
    ///
    Pearson(bool absolute=true);

    ///
    /// @brief The destructor.
    ///
    virtual ~Pearson(void);
         
    
    /**
       \f$ \frac{\vert \sum_i(x_i-\bar{x})(y_i-\bar{y})\vert
       }{\sqrt{\sum_i (x_i-\bar{x})^2\sum_i (x_i-\bar{x})^2}} \f$.
       @return Pearson correlation, if absolute=true absolute value
       of Pearson is used.
    */
    double score(const classifier::Target& target, 
                 const utility::VectorBase& value) const;

    /**
       \f$ \frac{\vert \sum_iw^2_i(x_i-\bar{x})(y_i-\bar{y})\vert }
       {\sqrt{\sum_iw^2_i(x_i-\bar{x})^2\sum_iw^2_i(y_i-\bar{y})^2}}
       \f$, where \f$ m_x = \frac{\sum w_ix_i}{\sum w_i} \f$ and \f$
       m_x = \frac{\sum w_ix_i}{\sum w_i} \f$. This expression is
       chosen to get a correlation equal to unity when \a x and \a y
       are equal. @return absolute value of weighted version of
       Pearson correlation.
    */
    double score(const classifier::Target& target, 
                 const classifier::DataLookupWeighted1D& value) const; 

    /**
       \f$ \frac{\vert \sum_iw^2_i(x_i-\bar{x})(y_i-\bar{y})\vert }
       {\sqrt{\sum_iw^2_i(x_i-\bar{x})^2\sum_iw^2_i(y_i-\bar{y})^2}}
       \f$, where \f$ m_x = \frac{\sum w_ix_i}{\sum w_i} \f$ and \f$
       m_x = \frac{\sum w_ix_i}{\sum w_i} \f$. This expression is
       chosen to get a correlation equal to unity when \a x and \a y
       are equal. @return absolute value of weighted version of
       Pearson correlation.
    */
    double score(const classifier::Target& target, 
                 const utility::VectorBase& value,
                 const utility::VectorBase& weight) const; 

  };

}}} // of namespace statistics, yat, and theplu

#endif