source: trunk/c++_tools/classifier/Kernel_SEV.h @ 675

#ifndef _theplu_classifier_kernel_sev_
#define _theplu_classifier_kernel_sev_

// $Id$

/*
  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 "yat/classifier/Kernel.h"
#include "yat/utility/matrix.h"

namespace theplu {
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) calculated
  ///   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;


    ///
    /// @todo doc
    ///
    const Kernel* selected(const std::vector<size_t>& index) 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 and namespace theplu

#endif