source: trunk/yat/utility/WeNNI.h @ 1797

#ifndef _theplu_yat_utility_wenni_
#define _theplu_yat_utility_wenni_ 

// $Id: WeNNI.h 1797 2009-02-12 18:07:10Z peter $

/*
  Copyright (C) 2004 Jari Häkkinen
  Copyright (C) 2005 Peter Johansson
  Copyright (C) 2006 Jari Häkkinen
  Copyright (C) 2007, 2008 Jari Häkkinen, Peter Johansson
  Copyright (C) 2009 Jari Häkkinen

  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 "Matrix.h"

#include <iostream>

namespace theplu {
namespace yat {
namespace utility {

  /**
     \brief Weighted Nearest Neighbour Imputation

     WeNNI is a continuous weights generalization of the (binary
     weights) kNNI algorithm presented by Troyanskaya et al. A
     reference to this paper is found in the NNI document referred to
     in the NNI class documentation. The NNI document also describes
     WeNNI in depth.

     \note Missing values should be represented with a zero
     weight. WeNNI will treat the corresponding data values as zero,
     i.e., this implies that NaNs and Infs with zero weight will not
     have any impact on calculations.a

     \see NNI and kNNI
  */
  class WeNNI : public NNI
  {
  public:
    ///
    /// Constructor
    ///
    WeNNI(const utility::Matrix& matrix,const utility::Matrix& weight,
          const unsigned int neighbours);

    /**
       \brief Function doing WeNNI imputation.

       Perform WeNNI on data in \a matrix with continuous 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);

    ///
    /// @return A const reference to imputed_data_raw.
    ///
    const utility::Matrix& imputed_data_raw(void) const 
    { return imputed_data_raw_; }


  private:

    utility::Matrix imputed_data_raw_;
  };

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

#endif