nearest_neighbor_classifier.cpp
Go to the documentation of this file.
00001 /*********************************************************************
00002  *
00003  * Software License Agreement (BSD License)
00004  *
00005  *  Copyright (c) 2012, Scott Niekum
00006  *  All rights reserved.
00007  *
00008  *  Redistribution and use in source and binary forms, with or without
00009  *  modification, are permitted provided that the following conditions
00010  *  are met:
00011  *
00012  *   * Redistributions of source code must retain the above copyright
00013  *     notice, this list of conditions and the following disclaimer.
00014  *   * Redistributions in binary form must reproduce the above
00015  *     copyright notice, this list of conditions and the following
00016  *     disclaimer in the documentation and/or other materials provided
00017  *     with the distribution.
00018  *   * Neither the name of the Willow Garage nor the names of its
00019  *     contributors may be used to endorse or promote products derived
00020  *     from this software without specific prior written permission.
00021  *
00022  *  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
00023  *  "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
00024  *  LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
00025  *  FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
00026  *  COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
00027  *  INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
00028  *  BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
00029  *  LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
00030  *  CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
00031  *  LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
00032  *  ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
00033  *  POSSIBILITY OF SUCH DAMAGE.
00034  *
00035  *********************************************************************/
00036 
00042 #include "ml_classifiers/nearest_neighbor_classifier.h"
00043 #include <pluginlib/class_list_macros.h>
00044 
00045 PLUGINLIB_DECLARE_CLASS(ml_classifiers, NearestNeighborClassifier, ml_classifiers::NearestNeighborClassifier, ml_classifiers::Classifier)
00046 
00047 using namespace std;
00048 
00049 namespace ml_classifiers{
00050 
00051     NearestNeighborClassifier::NearestNeighborClassifier(){}
00052     
00053     NearestNeighborClassifier::~NearestNeighborClassifier(){}
00054     
00055     void NearestNeighborClassifier::save(const std::string filename){}
00056     
00057     bool NearestNeighborClassifier::load(const std::string filename){return false;}
00058     
00059     void NearestNeighborClassifier::addTrainingPoint(string target_class, const std::vector<double> point)
00060     {
00061         class_data[target_class].push_back(point);
00062     }
00063     
00064     void NearestNeighborClassifier::train(){}
00065     
00066     void NearestNeighborClassifier::clear()
00067     {
00068         class_data.clear();
00069     }
00070     
00071     string NearestNeighborClassifier::classifyPoint(const std::vector<double> point)
00072     {
00073         size_t dims = point.size();
00074         double min_diff=0;
00075         string ans;
00076         bool first = true;
00077         
00078         for(ClassMap::iterator iter = class_data.begin(); iter != class_data.end(); iter++){
00079             string cname = iter->first;
00080             CPointList cpl = iter->second;
00081             
00082             for(size_t i=0; i<cpl.size(); i++){
00083                 double diff = 0;
00084                 for(size_t j=0; j<dims; j++){
00085                     diff += fabs(cpl[i][j] - point[j]);
00086                 }
00087                 if(first){
00088                     first = false;
00089                     min_diff = diff;
00090                     ans = cname;
00091                 }
00092                 else if(diff < min_diff){
00093                     min_diff = diff;
00094                     ans = cname;
00095                 }
00096             }
00097         }
00098         return ans;
00099     }
00100 
00101 }
00102 


ml_classifiers
Author(s): Scott Niekum
autogenerated on Fri Jan 3 2014 11:30:23