source: trunk/yat/classifier/KNN_ReciprocalDistance.cc @ 2881

Last change on this file since 2881 was 2881, checked in by Peter, 9 years ago

Define PP variables in config.h rather than in CPPFLAGS. Include
config.h into all source files. Only ammend CXXFLAGS with '-Wall
-pedantic' when --enable-debug. In default mode we respect CXXFLAGS
value set by user, or set to default value '-O3'.

  • Property svn:eol-style set to native
  • Property svn:keywords set to Id
File size: 1.3 KB
Line 
1// $Id: KNN_ReciprocalDistance.cc 2881 2012-11-18 01:28:05Z peter $
2
3// $Id: KNN_ReciprocalDistance.cc 2881 2012-11-18 01:28:05Z peter $
4
5/*
6  Copyright (C) 2008 Jari Häkkinen, Peter Johansson, Markus Ringnér
7
8  This file is part of the yat library, http://dev.thep.lu.se/yat
9
10  The yat library is free software; you can redistribute it and/or
11  modify it under the terms of the GNU General Public License as
12  published by the Free Software Foundation; either version 3 of the
13  License, or (at your option) any later version.
14
15  The yat library is distributed in the hope that it will be useful,
16  but WITHOUT ANY WARRANTY; without even the implied warranty of
17  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
18  General Public License for more details.
19
20  You should have received a copy of the GNU General Public License
21  along with yat. If not, see <http://www.gnu.org/licenses/>.
22*/
23
24#include <config.h>
25
26#include "KNN_ReciprocalDistance.h"
27#include "Target.h"
28
29#include "yat/utility/VectorBase.h"
30#include "yat/utility/VectorMutable.h"
31
32#include <vector>
33
34namespace theplu {
35namespace yat {
36namespace classifier {
37
38  void KNN_ReciprocalDistance::operator()(const utility::VectorBase& distance,
39                                          const std::vector<size_t>& k_sorted, 
40                                          const Target& target, 
41                                          utility::VectorMutable& prediction) const
42  {
43    for(size_t j=0;j<k_sorted.size();j++) 
44      prediction(target(k_sorted[j]))+=1.0/distance(k_sorted[j]);           
45  }
46
47}}}
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