source: trunk/yat/regression/PolynomialWeighted.h @ 729

#ifndef _theplu_yat_regression_polynomialweighted_
#define _theplu_yat_regression_polynomialweighted_

// $Id: PolynomialWeighted.h 729 2007-01-05 16:00:15Z 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 "OneDimensionalWeighted.h"
#include "MultiDimensionalWeighted.h"
#include "yat/utility/vector.h"

#include <cassert>

namespace theplu {
namespace yat {
namespace regression {

  ///
  /// @todo document
  ///
  class PolynomialWeighted : public OneDimensionalWeighted
  {
  public:

    ///
    /// @param power degree of polynomial model
    ///
    PolynomialWeighted(size_t power);

    ///
    /// @brief Destructor
    ///
    ~PolynomialWeighted(void);

    ///
    /// This function computes the best-fit given the polynomial model
    /// model by minimizing \f$ \sum{w_i(\hat{y_i}-y_i)^2} \f$, where
    /// \f$ \hat{y} \f$ is the fitted value. The weight \f$ w_i \f$
    /// should be proportional to the inverse of the variance for \f$
    /// y_i \f$
    ///
    void fit(const utility::vector& x, const utility::vector& y,
             const utility::vector& w);

    ///
    /// @return parameters for polynomial model
    ///
    utility::vector fit_parameters(void) { return md_.fit_parameters(); }

    ///
    /// @brief Mean Squared Error
    ///
    double s2(const double) const;

    ///
    /// function predicting in one point. 
    ///
    double predict(const double x) const;

    ///
    /// @return expected prediction error for a new data point in @a x
    /// with weight @a w
    ///
    double prediction_error(const double x, const double w=1) const;

    ///
    /// @return error of model value in @a x
    ///
    double standard_error2(const double x) const;

  private:
    MultiDimensionalWeighted md_;
    double mse_;
    size_t power_;

  };

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

#endif