source: trunk/yat/utility/kNNI.cc @ 1437

Last change on this file since 1437 was 1437, checked in by Peter, 13 years ago

merge patch release 0.4.2 to trunk. Delta 0.4.2-0.4.1

  • Property svn:eol-style set to native
  • Property svn:keywords set to Author Date Id Revision
File size: 2.7 KB
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1// $Id: kNNI.cc 1437 2008-08-25 17:55:00Z peter $
2
3/*
4  Copyright (C) 2004 Jari Häkkinen
5  Copyright (C) 2005 Peter Johansson
6  Copyright (C) 2006 Jari Häkkinen
7  Copyright (C) 2007 Jari Häkkinen, Peter Johansson
8  Copyright (C) 2008 Peter Johansson
9
10  This file is part of the yat library, http://dev.thep.lu.se/yat
11
12  The yat library is free software; you can redistribute it and/or
13  modify it under the terms of the GNU General Public License as
14  published by the Free Software Foundation; either version 2 of the
15  License, or (at your option) any later version.
16
17  The yat library is distributed in the hope that it will be useful,
18  but WITHOUT ANY WARRANTY; without even the implied warranty of
19  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
20  General Public License for more details.
21
22  You should have received a copy of the GNU General Public License
23  along with this program; if not, write to the Free Software
24  Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA
25  02111-1307, USA.
26*/
27
28#include "kNNI.h"
29#include "stl_utility.h"
30
31#include <algorithm>
32#include <cmath>
33#include <fstream>
34#include <vector>
35
36namespace theplu {
37namespace yat {
38namespace utility {
39
40  kNNI::kNNI(const utility::Matrix& matrix,const utility::Matrix& flag,
41             const unsigned int neighbours)
42    : NNI(matrix,flag,neighbours)
43  {
44    for (unsigned int i=0; i<weight_.rows(); i++)
45      for (unsigned int j=0; j<weight_.columns(); j++)
46        if (!weight_(i,j)) {
47          mv_rows_.push_back(i);
48          break;
49        }
50    //estimate();
51  }
52
53
54
55  // \hat{x_{ij}}=\frac{ \sum_{k=1,N} \frac{x_{kj}}{d_{ki}} }
56  //                   { \sum_{k=1,N} \frac{1     }{d_{ki}} },
57  // where N is defined in the paper cited in the NNI class definition
58  // documentation.
59  unsigned int kNNI::estimate(void)
60  {
61    for (size_t i=0; i<mv_rows_.size(); i++) {
62      std::vector<std::pair<size_t,double> >
63        distance(calculate_distances(mv_rows_[i]));
64      std::sort(distance.begin(),distance.end(),
65                pair_value_compare<size_t,double>());
66      for (size_t j=0; j<data_.columns(); j++)
67        if (!weight_(mv_rows_[i],j)) {
68          std::vector<size_t> knn=nearest_neighbours(j,distance);
69          double new_value=0.0;
70          double norm=0.0;
71          for (std::vector<size_t>::const_iterator k=knn.begin(); k!=knn.end();
72               ++k) {
73            // Avoid division with zero (perfect match vectors)
74            double d=(distance[*k].second ? distance[*k].second : 1e-10);
75            new_value+=data_(distance[*k].first,j)/d;
76            norm+=1.0/d;
77          }
78          // No impute if no contributions from neighbours.
79          if (norm)
80            imputed_data_(mv_rows_[i],j)=new_value/norm;
81          else {
82            not_imputed_.push_back(i);
83            // if norm is zero for one column it is zero for all columns
84            // having zero weight
85            break;
86          }
87        }
88    }
89    return not_imputed_.size();
90  }
91
92
93}}} // of namespace utility, yat, and theplu
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