source: trunk/yat/utility/kNNI.cc @ 680

Last change on this file since 680 was 680, checked in by Jari Häkkinen, 15 years ago

Addresses #153. Introduced yat namespace. Removed alignment namespace. Clean up of code.

  • Property svn:eol-style set to native
  • Property svn:keywords set to Author Date Id Revision
File size: 2.8 KB
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1// $Id: kNNI.cc 680 2006-10-11 17:49:03Z jari $
2
3/*
4  Copyright (C) 2004 Jari Häkkinen
5  Copyright (C) 2005 Peter Johansson
6  Copyright (C) 2006 Jari Häkkinen
7
8  This file is part of the yat library, http://lev.thep.lu.se/trac/yat
9
10  The yat library is free software; you can redistribute it and/or
11  modify it under the terms of the GNU General Public License as
12  published by the Free Software Foundation; either version 2 of the
13  License, or (at your option) any later version.
14
15  The yat library is distributed in the hope that it will be useful,
16  but WITHOUT ANY WARRANTY; without even the implied warranty of
17  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
18  General Public License for more details.
19
20  You should have received a copy of the GNU General Public License
21  along with this program; if not, write to the Free Software
22  Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA
23  02111-1307, USA.
24*/
25
26#include "kNNI.h"
27#include "stl_utility.h"
28
29#include <algorithm>
30#include <cmath>
31#include <fstream>
32#include <vector>
33
34namespace theplu {
35namespace yat {
36namespace utility {
37
38  kNNI::kNNI(const utility::matrix& matrix,const utility::matrix& flag,
39             const u_int neighbours)
40    : NNI(matrix,flag,neighbours)
41  {
42    for (unsigned int i=0; i<weight_.rows(); i++)
43      for (unsigned int j=0; j<weight_.columns(); j++)
44        if (!weight_(i,j)) {
45          mv_rows_.push_back(i);
46          break;
47        }
48    //estimate();
49  }
50
51
52
53  // \hat{x_{ij}}=\frac{ \sum_{k=1,N} \frac{x_{kj}}{d_{ki}} }
54  //                   { \sum_{k=1,N} \frac{1     }{d_{ki}} },
55  // where N is defined in the paper cited in the NNI class definition
56  // documentation.
57  u_int kNNI::estimate(void)
58  {
59    for (unsigned int i=0; i<mv_rows_.size(); i++) {
60      // Jari, avoid copying in next line
61      std::vector<std::pair<u_int,double> > distance=
62        calculate_distances(mv_rows_[i]);
63      std::sort(distance.begin(),distance.end(),
64                pair_value_compare<u_int,double>());
65      for (unsigned int j=0; j<data_.columns(); j++)
66        if (!weight_(mv_rows_[i],j)) {
67          std::vector<u_int> knn=nearest_neighbours(j,distance);
68          double new_value=0.0;
69          double norm=0.0;
70          for (std::vector<u_int>::const_iterator k=knn.begin(); k!=knn.end();
71               ++k) {
72            // Jari, a small number needed here, use something standardized.
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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