source: trunk/yat/statistics/Smoother.h @ 1486

#ifndef _theplu_yat_statistics_smoother_
#define _theplu_yat_statistics_smoother_

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

/*
  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 "yat/utility/iterator_traits.h"

#include <vector>

namespace theplu {
namespace yat {
  namespace regression { 
    class Kernel; 
  }
namespace statistics {

  /**
     @brief Estimating a distribution in a smooth fashion

     \since New in yat 0.5
  */
  class Smoother
  {
  public:
    /**
       Constructor taking vector describing for which values
       distribution should be estimated.

       \note if \a values are not sorted the behavior is undefined
     */
    Smoother(const regression::Kernel&, double width, 
             const std::vector<double>& values);

    /**
       Constructor creating observation points equally distributed
       between \a xmin and \a xmax.

       \param kernel doing the smoothing
       \param width
       \param xmin smallest observation point
       \param xmax largest observation point
       \param n number of observation points
     */
    Smoother(const regression::Kernel& kernel, double width, 
             double xmin, double xmax, size_t n);

    /**
       \brief Add a data point.
    */
    void add(double x, double weight=1.0);

    /**
       \brief estimated values
     */
    const std::vector<double>& density(void) const;

    /**
       \brief reset density to zero
     */
    void reset(void);

    /**
       \brief values in which distribution is estimated
     */
    const std::vector<double>& value(void) const;

  private:
    std::vector<double> density_;
    const regression::Kernel& kernel_;
    double width_;
    std::vector<double> x_;
  };

  /**
     Add a range [first, last) of values to Smoother.
   */
  template<typename ForwardIterator>
  void add(Smoother& h, 
           ForwardIterator first, ForwardIterator last)
  {
    while (first!=last) {
      h.add(utility::iterator_traits<ForwardIterator>().data(),
            utility::iterator_traits<ForwardIterator>().weight());
      ++first;
    }
  }

  /**
     The Smoother output operator
  */
  std::ostream& operator<<(std::ostream& s,const Smoother&);

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

#endif