source: trunk/doc/concepts.doxygen @ 1115

Last change on this file since 1115 was 1115, checked in by Markus Ringnér, 14 years ago

Fixes #254 and #295

  • Property svn:eol-style set to native
  • Property svn:keywords set to Id
File size: 3.9 KB
Line 
1// $Id: concepts.doxygen 1115 2008-02-21 19:20:59Z markus $
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 for classes implementing different
39alternatives to calculate the distance between two points.
40
41\section Requirements
42
43Classes modelling the concept \ref concept_distance should implement
44the following public function:
45
46\verbatim
47template<typename Iter1, typename Iter2>
48double  operator() (Iter1 beg1, Iter1 end1, Iter2 beg2) const
49\endverbatim
50
51This function should calculate and return the distance between
52elements of two ranges. The first range is given by [\a beg1, \a end1)
53and the second range starts with \a beg2 and has the same length as
54the first range. The function should support iterators of the category
55std::forward_iterator. The function should provide both a fast
56calculation for unweighted iterators and a calculation for weighted
57iterators. The latter correspond to the case where elements in a range
58have both a value and a weight. The selection between unweighted and
59weighted implementations should utilize
60theplu::yat::utility::weighted_iterator_tag. Moreover
61theplu::yat::utility::weighted_if_any2 should be utilized to provide a
62weighted implementation if any of the two ranges is weighted, and an
63unweighted implementation when both ranges are unweighted.
64
65\section Implementations
66
67Examples of classes modelling the concept \ref concept_distance
68include theplu::yat::statistics::PearsonDistance and
69theplu::yat::statistics::EuclideanDistance.
70
71*/
72
73/**
74\page concept_neighbor_weighting Neighbor Weighting Method
75
76\section Description
77
78\ref concept_neighbor_weighting is a concept used in connection with
79theplu::yat::classifier::KNN - classes used as the template argument
80NeighborWeighting should implement this concept.
81
82\section Requirements
83
84Classes modelling the concept \ref concept_neighbor_weighting should
85implement the following public function:
86 
87\verbatim   
88void operator()(const utility::VectorBase& distance, const std::vector<size_t> k_sorted,
89                const Target& target, utility::VectorMutable& prediction) const
90\endverbatim
91
92For a test sample, this function should calculate a total vote
93(i.e. based on all k nearest neighbors) for each class. The vector \a
94distance contains the distances from a test sample to all training
95samples. The vector \a k_sorted contains the indices (for both \a
96distance and \a target) to the k training samples with the smallest
97distances to the test sample. The class for each training sample is
98given by \a target, which is sorted in the same sample order as \a
99distance. For each class the function calculates a total vote based on
100the the nearest neighbors of the test sample that belong to the
101class. The total vote for each class is stored in the vector \a prediction.
102
103\section Implementations
104
105Examples of classes modelling the concept \ref
106concept_neighbor_weighting include
107theplu::yat::classifier::KNN_Uniform,
108theplu::yat::classifier::KNN_ReciprocalDistance and
109theplu::yat::classifier::KNN_ReciprocalRank.
110
111*/
Note: See TracBrowser for help on using the repository browser.