source: trunk/yat/regression/Local.h @ 726

#ifndef _theplu_yat_regression_local_
#define _theplu_yat_regression_local_

// $Id: Local.h 726 2007-01-04 14:38:56Z peter $

/*
  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 "Kernel.h"
#include "OneDimensionalWeighted.h"
#include "yat/utility/vector.h"

#include <iostream>

namespace theplu {
namespace yat {
namespace regression {

  ///
  /// Class for Locally weighted regression. 
  ///
  /// Locally weighted regression is an algorithm for learning
  /// continuous non-linear mappings in a non-parametric manner.  In
  /// locally weighted regression, points are weighted by proximity to
  /// the current x in question using a Kernel. A weighted regression
  /// is then computed using the weighted points and a specific
  /// Regression method. This procedure is repeated, which results in
  /// a pointwise approximation of the underlying (unknown) function.
  ///
  class Local
  {
  
  public:
    ///
    /// @brief Constructor taking type of \a regressor,
    /// type of \a kernel.
    ///
    Local(OneDimensionalWeighted& r, Kernel& k);

    ///
    /// @brief The destructor 
    ///
    virtual ~Local(void);

    ///
    /// adding a data point
    ///
    void add(const double x, const double y);

    ///
    /// @param nof_points Number of points used in each fit
    /// @param step_size Size of step between each fit
    ///
    void fit(const size_t step_size, const size_t nof_points);

    ///
    /// @return x-values where fitting was performed.
    ///
    const utility::vector& x(void) const;

    ///
    /// Function returning predicted values
    ///
    const utility::vector& y_predicted(void) const;

    ///
    /// Function returning error of predictions
    ///
    const utility::vector& y_err(void) const;

  private:
    ///
    /// Copy Constructor. (Not implemented)
    ///
    Local(const Local&);

    std::vector<std::pair<double, double> > data_;
    Kernel* kernel_;
    OneDimensionalWeighted* regressor_;
    utility::vector x_;
    utility::vector y_predicted_; 
    utility::vector y_err_; 
  };

  ///
  /// The output operator for the Regression::Local class.
  ///
  std::ostream& operator<<(std::ostream&, const Local& );

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

#endif