source: branches/0.4-stable/yat/classifier/Kernel_SEV.h @ 1392

#ifndef _theplu_yat_classifier_kernel_sev_
#define _theplu_yat_classifier_kernel_sev_

// $Id$

/*
  Copyright (C) 2005 Jari Häkkinen, Peter Johansson
  Copyright (C) 2006 Jari Häkkinen, Peter Johansson, Markus Ringnér
  Copyright (C) 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 "Kernel.h"
#include "yat/utility/Matrix.h"

namespace theplu {
namespace yat {
namespace classifier {

  class DataLookup1D;
  class KernelFunction;

  ///
  ///   @brief Speed Efficient Kernel
  ///
  ///   Class taking care of the \f$ NxN \f$ kernel matrix, where
  ///   \f$ N \f$ is number of samples. Type of Kernel is defined by a
  ///   KernelFunction. This Speed Efficient Version (SEV) calculates
  ///   the kernel matrix once by construction and the kernel is stored in
  ///   memory. When \f$ N \f$ is large and the kernel matrix cannot be
  ///   stored in memory, use Kernel_MEV instead.
  ///
  class Kernel_SEV : public Kernel 
  {
    
  public:

    ///
    /// Constructor taking the data matrix and KernelFunction as
    /// input. @note Can not handle NaNs. When dealing with missing values,
    /// use KernelWeighted_SEV instead.
    ///
    Kernel_SEV(const MatrixLookup&, const KernelFunction&,const bool own=false);
    
    ///
    /// Constructor taking the data matrix and KernelFunction as
    /// input. @note Can not handle NaNs. When dealing with missing values,
    /// use KernelWeighted_SEV instead.
    ///
    Kernel_SEV(const MatrixLookupWeighted&, const KernelFunction&,
               const bool own=false);
    
    ///
    /// Constructs a Kernel based on selected features defined by @a index
    ///
    Kernel_SEV(const Kernel_SEV& kernel, const std::vector<size_t>& index);


    ///
    /// An interface for making new classifier objects. This function
    /// allows for specification at run-time of which kernel to
    /// instatiate (see 'Prototype' in Design Patterns).
    ///
    /// @note Returns a dynamically allocated Kernel, which has
    /// to be deleted by the caller to avoid memory leaks.
    ///
    const Kernel_SEV* make_kernel(const MatrixLookup&, 
                                  const bool own=false) const;


    ///
    /// An interface for making new classifier objects. This function
    /// allows for specification at run-time of which kernel to
    /// instatiate (see 'Prototype' in Design Patterns).
    ///
    /// @note Returns a dynamically allocated Kernel, which has
    /// to be deleted by the caller to avoid memory leaks.
    ///
    const Kernel_SEV* make_kernel(const MatrixLookupWeighted&, 
                                  const bool own=false) const;


    ///
    /// @return element at position (\a row, \a column) in the Kernel
    /// matrix
    ///
    double operator()(const size_t row,const size_t column) const;


  private:
    ///
    /// Copy constructor (not implemented)
    ///
    Kernel_SEV(const Kernel_SEV&);
    const Kernel_SEV& operator=(const Kernel_SEV&);

    void build_kernel(void);

    utility::Matrix kernel_matrix_;

  }; // class Kernel_SEV

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

#endif