1 | // $Id: WeNNI.cc 1000 2007-12-23 20:09:15Z jari $ |
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2 | |
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3 | /* |
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4 | Copyright (C) 2004 Jari Häkkinen |
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5 | Copyright (C) 2005 Peter Johansson |
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6 | Copyright (C) 2006, 2007 Jari Häkkinen |
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7 | |
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8 | This file is part of the yat library, http://trac.thep.lu.se/yat |
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9 | |
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10 | The yat library is free software; you can redistribute it and/or |
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11 | modify it under the terms of the GNU General Public License as |
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12 | published by the Free Software Foundation; either version 2 of the |
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13 | License, or (at your option) any later version. |
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14 | |
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15 | The yat library is distributed in the hope that it will be useful, |
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16 | but WITHOUT ANY WARRANTY; without even the implied warranty of |
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17 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU |
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18 | General Public License for more details. |
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19 | |
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20 | You should have received a copy of the GNU General Public License |
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21 | along with this program; if not, write to the Free Software |
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22 | Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA |
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23 | 02111-1307, USA. |
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24 | */ |
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25 | |
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26 | #include "WeNNI.h" |
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27 | #include "matrix.h" |
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28 | #include "stl_utility.h" |
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29 | |
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30 | #include <algorithm> |
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31 | #include <cmath> |
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32 | #include <fstream> |
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33 | |
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34 | namespace theplu { |
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35 | namespace yat { |
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36 | namespace utility { |
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37 | |
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38 | |
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39 | WeNNI::WeNNI(const utility::matrix& matrix,const utility::matrix& flag, |
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40 | const u_int neighbours) |
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41 | : NNI(matrix,flag,neighbours), imputed_data_raw_(matrix) |
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42 | { |
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43 | //estimate(); |
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44 | } |
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45 | |
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46 | |
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47 | |
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48 | // \hat{x_{ij}}=\frac{ \sum_{k=1,N} \frac{w_{kj}*x_{kj}}{d_{ki}} } |
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49 | // { \sum_{k=1,N} \frac{w_{kj} }{d_{ki}} } |
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50 | // where N is defined in the paper cited in the NNI class definition |
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51 | // documentation. |
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52 | u_int WeNNI::estimate(void) |
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53 | { |
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54 | for (unsigned int i=0; i<data_.rows(); i++) { |
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55 | std::vector<std::pair<u_int,double> > distance(calculate_distances(i)); |
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56 | std::sort(distance.begin(),distance.end(), |
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57 | pair_value_compare<u_int,double>()); |
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58 | bool row_imputed=true; |
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59 | for (unsigned int j=0; j<data_.columns(); j++) { |
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60 | std::vector<u_int> knn=nearest_neighbours(j,distance); |
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61 | double new_value=0.0; |
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62 | double norm=0.0; |
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63 | for (std::vector<u_int>::const_iterator k=knn.begin(); k!=knn.end(); |
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64 | ++k) { |
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65 | // Avoid division with zero (perfect match vectors) |
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66 | double d=(distance[*k].second ? distance[*k].second : 1e-10); |
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67 | new_value+=(weight_(distance[*k].first,j) * |
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68 | data_(distance[*k].first,j)/d); |
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69 | norm+=weight_(distance[*k].first,j)/d; |
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70 | } |
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71 | // No impute if no contributions from neighbours. |
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72 | if (norm){ |
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73 | imputed_data_raw_(i,j) = new_value/norm; |
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74 | imputed_data_(i,j)= |
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75 | weight_(i,j)*data_(i,j) + (1-weight_(i,j))* imputed_data_raw_(i,j); |
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76 | } |
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77 | else |
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78 | row_imputed=false; |
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79 | } |
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80 | if (!row_imputed) |
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81 | not_imputed_.push_back(i); |
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82 | } |
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83 | return not_imputed_.size(); |
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84 | } |
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85 | |
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86 | |
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87 | }}} // of namespace utility, yat, and theplu |
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