source: trunk/yat/utility/kNNI.h @ 1726

#ifndef _theplu_yat_utility_knni_
#define _theplu_yat_utility_knni_ 

// $Id: kNNI.h 1726 2009-01-15 21:15:26Z jari $

/*
  Copyright (C) 2004 Jari Häkkinen
  Copyright (C) 2005 Jari Häkkinen, Peter Johansson
  Copyright (C) 2006 Jari Häkkinen
  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 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 "NNI.h"

#include <iostream>
#include <vector>

namespace theplu {
namespace yat {
namespace utility {

  ///
  /// @brief kNNimpute
  ///
  /// kNNI is the binary weight implementation of NNI. This follows
  /// the work done by Troyanskaya et al. cited in the NNI document
  /// referred to in the NNI class documentation.
  ///
  /// This is a special case of the WeNNI, but is maintained since it
  /// is faster than the more general WeNNI.
  ///
  /// @see NNI and WeNNI
  ///
  class kNNI : public NNI
  {
  public:
    ///
    /// Constructor
    ///
    kNNI(const utility::Matrix& matrix,const utility::Matrix& weight,
         const unsigned int neighbours);

    /**
       \brief Function doing kNNI imputation.

       Perform kNNI on data in \a matrix with binary uncertainty
       weights in \a weight using \a neighbours for the new impute
       value.

       The return value can be used as an indication of how well the
       imputation worked. The return value should be zero if proper
       pre-processing of data is done. An example of bad data is a
       matrix with a column of zero weights, another is a
       corresponding situation with a row with all weights zero.

       \return The number of rows that have at least one value not
       imputed.
    */
    unsigned int estimate(void);

  private:
    std::vector<size_t> mv_rows_; // index to rows that have values to estimate
  };

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

#endif