source: trunk/yat/regression/MultiDimensional.h @ 2119

#ifndef _theplu_yat_regression_multidimensional_
#define _theplu_yat_regression_multidimensional_

// $Id: MultiDimensional.h 2119 2009-12-12 23:11:43Z peter $

/*
  Copyright (C) 2005, 2006, 2007, 2008 Jari Häkkinen, 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 "yat/utility/Matrix.h"
#include "yat/utility/VectorBase.h"

#include <gsl/gsl_multifit.h>

namespace theplu {
namespace yat {
namespace regression {

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

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

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

    ///
    /// @brief covariance of parameters
    ///
    const utility::Matrix& covariance(void) const;

    /**
       \brief Function fitting parameters of the linear model by
       miminizing the quadratic deviation between model and data.

       Number of rows in \a X must match size of \a y.

       \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);

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

    /**
       @brief Summed Squared Error
     */
    double chisq(void) const;

    ///
    /// @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;

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

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

  };

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

#endif