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 Feb 21, 2008, 3:59:30 PM (15 years ago)
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trunk/doc/concepts.doxygen
r1094 r1112 5 5 // This file is part of the yat library, http://trac.thep.lu.se/yat 6 6 // 7 // The yat library is free software; you can redistribute it and /or7 // The yat library is free software; you can redistribute it and./or 8 8 // modify it under the terms of the GNU General Public License as 9 9 // published by the Free Software Foundation; either version 2 of the … … 22 22 23 23 /** 24 24 \page Concepts Concepts 25 25 26 26 This page lists all the C++ concepts in the yat project. 27 27 28  \subpage concept_distance 28  \subpage concept_distance 29  \subpage concept_neighbor_weighting 29 30 */ 30 31 31 32 32 33 /** 33 \page concept_distance Distance 34 \section Description 35 Distance is a concept ... 36 \section Requirements 37 Classes implementing the concept Distance should fulfill ... 38 Examples include theplu::yat::statistics::PearsonDistance and theplu::yat::statistics::EuclideanDistance. 34 \page concept_distance Distance 35 36 \section Description 37 38 \ref concept_distance is a concept .. 39 40 \section Requirements 41 42 Classes modelling the concept \ref concept_distance should implement ... 43 44 Examples of classes modelling the concept \ref concept_distance 45 include theplu::yat::statistics::PearsonDistance and 46 theplu::yat::statistics::EuclideanDistance. 47 39 48 */ 49 50 /** 51 \page concept_neighbor_weighting Neighbor Weighting Method 52 53 \section Description 54 55 \ref concept_neighbor_weighting is a concept used in connection with 56 theplu::yat::classifier::KNN  classes used as the template argument 57 NeighborWeighting should implement this concept. 58 59 \section Requirements 60 61 Classes modelling the concept \ref concept_neighbor_weighting should 62 implement the following function: 63 64 \verbatim 65 void operator()(const utility::VectorBase& distance, const std::vector<size_t> k_sorted, 66 const Target& target, utility::VectorMutable& prediction) const 67 \endverbatim 68 69 For a test sample, this function should calculate a total vote 70 (i.e. based on all k nearest neighbors) for each class. The vector \a 71 distance contains the distances from a test sample to all training 72 samples. The vector \a k_sorted contains the indices (for both \a 73 distance and \a target) to the k training samples with the smallest 74 distances to the test sample. The class for each training sample is 75 given by \a target, which is sorted in the same sample order as \a 76 distance. For each class the function calculates a total vote based on 77 the the nearest neighbors of the test sample that belong to the 78 class. The total vote for each class is stored in the vector \a prediction. 79 80 Examples of classes modelling the concept \ref 81 concept_neighbor_weighting include 82 theplu::yat::classifier::KNN_Uniform, 83 theplu::yat::classifier::KNN_ReciprocalDistance and 84 theplu::yat::classifier::KNN_ReciprocalRank. 85 86 */
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