source: branches/0.4-stable/yat/regression/MultiDimensionalWeighted.h @ 1392

#ifndef _theplu_yat_regression_multidimensionalweighted_
#define _theplu_yat_regression_multidimensionalweighted_

// $Id: MultiDimensionalWeighted.h 1392 2008-07-28 19:35:30Z peter $

/*
  Copyright (C) 2006, 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 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/utility/Matrix.h"
#include "yat/utility/Vector.h"

#include <gsl/gsl_multifit.h>

namespace theplu {
namespace yat {
namespace regression {

  ///
  /// @brief MultiDimesional fitting.
  ///
  class MultiDimensionalWeighted
  {
  public:

    ///
    /// @brief Default Constructor
    ///
    MultiDimensionalWeighted(void);

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

    ///
    /// @return sum of squared residuals
    ///
    double chisq(void) const;

    /**
       \see gsl_multifit_wlinear

       \throw A GSL_error exception is thrown if memory allocation
       fails or the underlying GSL calls fails (usually matrix
       dimension errors).
    */
    void fit(const utility::Matrix& X, const utility::VectorBase& y, 
             const utility::VectorBase& w);

    ///
    /// @return value in @a x according to fitted model
    ///
    double predict(const utility::VectorBase& x) const;

    ///
    /// @return expected squared prediction error for a new data point
    /// in @a x
    ///
    double prediction_error2(const utility::VectorBase& x, const double w=1) const;

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

    ///
    /// @return parameters of fitted model
    ///
    const utility::Vector& fit_parameters(void) const;

    ///
    /// @return variance of residuals
    ///
    double s2(const double w=1.0) const;

  private:
    double chisquare_;
    utility::Matrix covariance_;
    utility::Vector fit_parameters_;
    double s2_;
    gsl_multifit_linear_workspace* work_;

  };

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

#endif