source: trunk/doc/concepts.doxygen @ 1113

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typo - right?

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1// $Id: concepts.doxygen 1113 2008-02-21 16:49:45Z peter $
2//
3// Copyright (C) 2008 Peter Johansson, Markus Ringnér
4//
5// This file is part of the yat library, http://trac.thep.lu.se/yat
6//
7// The yat library is free software; you can redistribute it and/or
8// modify it under the terms of the GNU General Public License as
9// published by the Free Software Foundation; either version 2 of the
10// License, or (at your option) any later version.
11//
12// The yat library is distributed in the hope that it will be useful,
13// but WITHOUT ANY WARRANTY; without even the implied warranty of
14// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
15// General Public License for more details.
16//
17// You should have received a copy of the GNU General Public License
18// along with this program; if not, write to the Free Software
19// Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA
20// 02111-1307, USA.
21
22
23/**
24\page Concepts Concepts
25
26This page lists all the C++ concepts in the yat project.
27
28- \subpage concept_distance
29- \subpage concept_neighbor_weighting
30*/
31
32
33/**
34\page concept_distance Distance
35
36\section Description
37
38\ref concept_distance is a concept ..
39
40\section Requirements
41
42Classes modelling the concept \ref concept_distance should implement ... 
43
44Examples of classes modelling the concept \ref concept_distance
45include theplu::yat::statistics::PearsonDistance and
46theplu::yat::statistics::EuclideanDistance.
47
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
56theplu::yat::classifier::KNN - classes used as the template argument
57NeighborWeighting should implement this concept.
58
59\section Requirements
60
61Classes modelling the concept \ref concept_neighbor_weighting should
62implement the following function:
63 
64\verbatim   
65void operator()(const utility::VectorBase& distance, const std::vector<size_t> k_sorted,
66                const Target& target, utility::VectorMutable& prediction) const
67\endverbatim
68
69For 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
71distance contains the distances from a test sample to all training
72samples. The vector \a k_sorted contains the indices (for both \a
73distance and \a target) to the k training samples with the smallest
74distances to the test sample. The class for each training sample is
75given by \a target, which is sorted in the same sample order as \a
76distance. For each class the function calculates a total vote based on
77the the nearest neighbors of the test sample that belong to the
78class. The total vote for each class is stored in the vector \a prediction.
79
80Examples of classes modelling the concept \ref
81concept_neighbor_weighting include
82theplu::yat::classifier::KNN_Uniform,
83theplu::yat::classifier::KNN_ReciprocalDistance and
84theplu::yat::classifier::KNN_ReciprocalRank.
85
86*/
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