1 | #ifndef _theplu_yat_utility_knni_ |
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2 | #define _theplu_yat_utility_knni_ |
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3 | |
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4 | // $Id: kNNI.h 4207 2022-08-26 04:36:28Z peter $ |
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5 | |
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6 | /* |
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7 | Copyright (C) 2004 Jari Häkkinen |
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8 | Copyright (C) 2005 Jari Häkkinen, Peter Johansson |
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9 | Copyright (C) 2006 Jari Häkkinen |
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10 | Copyright (C) 2007, 2008 Jari Häkkinen, Peter Johansson |
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11 | Copyright (C) 2009 Jari Häkkinen |
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12 | Copyright (C) 2022 Peter Johansson |
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13 | |
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14 | This file is part of the yat library, http://dev.thep.lu.se/yat |
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15 | |
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16 | The yat library is free software; you can redistribute it and/or |
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17 | modify it under the terms of the GNU General Public License as |
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18 | published by the Free Software Foundation; either version 3 of the |
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19 | License, or (at your option) any later version. |
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20 | |
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21 | The yat library is distributed in the hope that it will be useful, |
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22 | but WITHOUT ANY WARRANTY; without even the implied warranty of |
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23 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU |
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24 | General Public License for more details. |
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25 | |
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26 | You should have received a copy of the GNU General Public License |
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27 | along with yat. If not, see <http://www.gnu.org/licenses/>. |
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28 | */ |
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29 | |
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30 | #include "NNI.h" |
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31 | |
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32 | #include <vector> |
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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 | /// @brief kNNimpute |
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40 | /// |
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41 | /// kNNI is the binary weight implementation of NNI. This follows |
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42 | /// the work done by Troyanskaya et al. cited in the NNI document |
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43 | /// referred to in the NNI class documentation. |
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44 | /// |
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45 | /// This is a special case of the WeNNI, but is maintained since it |
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46 | /// is faster than the more general WeNNI. |
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47 | /// |
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48 | /// @see NNI and WeNNI |
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49 | /// |
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50 | class kNNI : public NNI |
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51 | { |
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52 | public: |
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53 | /// |
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54 | /// Constructor |
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55 | /// |
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56 | kNNI(const MatrixBase& matrix, const MatrixBase& weight, |
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57 | const unsigned int neighbours); |
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58 | |
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59 | /** |
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60 | \brief Function doing kNNI imputation. |
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61 | |
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62 | Perform kNNI on data in \a matrix with binary uncertainty |
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63 | weights in \a weight using \a neighbours for the new impute |
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64 | value. |
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65 | |
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66 | The return value can be used as an indication of how well the |
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67 | imputation worked. The return value should be zero if proper |
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68 | pre-processing of data is done. An example of bad data is a |
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69 | matrix with a column of zero weights, another is a |
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70 | corresponding situation with a row with all weights zero. |
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71 | |
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72 | \return The number of rows that have at least one value not |
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73 | imputed. |
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74 | */ |
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75 | unsigned int estimate(void); |
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76 | |
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77 | private: |
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78 | std::vector<size_t> mv_rows_; // index to rows that have values to estimate |
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79 | }; |
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80 | |
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81 | }}} // of namespace utility, yat, and theplu |
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82 | |
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83 | #endif |
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