1 | // $Id: concepts.doxygen 1171 2008-02-27 12:55:26Z peter $ |
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2 | // |
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3 | // Copyright (C) 2008 Peter Johansson, Markus Ringnér |
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4 | // |
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5 | // This file is part of the yat library, http://trac.thep.lu.se/yat |
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6 | // |
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7 | // The yat library is free software; you can redistribute it and/or |
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8 | // modify it under the terms of the GNU General Public License as |
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9 | // published by the Free Software Foundation; either version 2 of the |
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10 | // License, or (at your option) any later version. |
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11 | // |
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12 | // The yat library is distributed in the hope that it will be useful, |
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13 | // but WITHOUT ANY WARRANTY; without even the implied warranty of |
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14 | // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU |
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15 | // General Public License for more details. |
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16 | // |
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17 | // You should have received a copy of the GNU General Public License |
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18 | // along with this program; if not, write to the Free Software |
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19 | // Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA |
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20 | // 02111-1307, USA. |
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21 | |
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22 | |
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23 | /** |
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24 | \page Concepts Concepts |
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25 | |
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26 | This page lists all the C++ concepts in the yat project. |
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27 | |
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28 | - \subpage concept_distance |
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29 | - \subpage concept_neighbor_weighting |
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30 | */ |
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31 | |
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32 | |
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33 | /** |
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34 | \page concept_distance Distance |
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35 | |
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36 | \section Description |
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37 | |
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38 | \ref concept_distance is a concept for classes implementing different |
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39 | alternatives to calculate the distance between two points. |
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40 | |
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41 | \section Requirements |
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42 | |
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43 | Classes modelling the concept \ref concept_distance should implement |
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44 | the following public function: |
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45 | |
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46 | \verbatim |
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47 | template<typename Iter1, typename Iter2> |
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48 | double operator() (Iter1 beg1, Iter1 end1, Iter2 beg2) const |
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49 | \endverbatim |
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50 | |
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51 | This function should calculate and return the distance between |
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52 | elements of two ranges. The first range is given by [\a beg1, \a end1) |
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53 | and the second range starts with \a beg2 and has the same length as |
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54 | the first range. The function should support iterators of the category |
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55 | std::forward_iterator. The function should provide both a fast |
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56 | calculation for unweighted iterators and a calculation for weighted |
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57 | iterators. The latter correspond to the case where elements in a range |
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58 | have both a value and a weight. The selection between unweighted and |
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59 | weighted implementations should utilize |
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60 | theplu::yat::utility::unweighted_iterator_tag and |
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61 | theplu::yat::utility::weighted_iterator_tag. Moreover |
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62 | theplu::yat::utility::weighted_if_any2 should be utilized to provide a |
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63 | weighted implementation if any of the two ranges is weighted, and an |
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64 | unweighted implementation when both ranges are unweighted. |
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65 | |
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66 | \section Implementations |
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67 | |
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68 | Examples of classes modelling the concept \ref concept_distance |
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69 | include theplu::yat::statistics::PearsonDistance and |
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70 | theplu::yat::statistics::EuclideanDistance. |
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71 | |
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72 | */ |
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73 | |
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74 | /** |
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75 | \page concept_neighbor_weighting Neighbor Weighting Method |
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76 | |
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77 | \section Description |
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78 | |
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79 | \ref concept_neighbor_weighting is a concept used in connection with |
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80 | theplu::yat::classifier::KNN - classes used as the template argument |
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81 | NeighborWeighting should implement this concept. |
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82 | |
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83 | \section Requirements |
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84 | |
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85 | Classes modelling the concept \ref concept_neighbor_weighting should |
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86 | implement the following public function: |
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87 | |
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88 | \verbatim |
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89 | void operator()(const utility::VectorBase& distance, const std::vector<size_t> k_sorted, |
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90 | const Target& target, utility::VectorMutable& prediction) const |
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91 | \endverbatim |
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92 | |
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93 | For a test sample, this function should calculate a total vote |
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94 | (i.e. based on all k nearest neighbors) for each class. The vector \a |
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95 | distance contains the distances from a test sample to all training |
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96 | samples. The vector \a k_sorted contains the indices (for both \a |
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97 | distance and \a target) to the k training samples with the smallest |
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98 | distances to the test sample. The class for each training sample is |
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99 | given by \a target, which is sorted in the same sample order as \a |
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100 | distance. For each class the function calculates a total vote based on |
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101 | the the nearest neighbors of the test sample that belong to the |
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102 | class. The total vote for each class is stored in the vector \a |
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103 | prediction. The function should be implemented such that nearest |
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104 | neighbors with distance infinity do not vote. |
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105 | |
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106 | \section Implementations |
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107 | |
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108 | Examples of classes modelling the concept \ref |
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109 | concept_neighbor_weighting include |
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110 | theplu::yat::classifier::KNN_Uniform, |
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111 | theplu::yat::classifier::KNN_ReciprocalDistance and |
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112 | theplu::yat::classifier::KNN_ReciprocalRank. |
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113 | |
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114 | */ |
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