00001 /* 00002 The Evolving Distribution Objects framework (EDO) is a template-based, 00003 ANSI-C++ evolutionary computation library which helps you to write your 00004 own estimation of distribution algorithms. 00005 00006 This library is free software; you can redistribute it and/or 00007 modify it under the terms of the GNU Lesser General Public 00008 License as published by the Free Software Foundation; either 00009 version 2.1 of the License, or (at your option) any later version. 00010 00011 This library is distributed in the hope that it will be useful, 00012 but WITHOUT ANY WARRANTY; without even the implied warranty of 00013 MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU 00014 Lesser General Public License for more details. 00015 00016 You should have received a copy of the GNU Lesser General Public 00017 License along with this library; if not, write to the Free Software 00018 Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA 00019 00020 Copyright (C) 2010 Thales group 00021 */ 00022 /* 00023 Authors: 00024 Johann Dréo <johann.dreo@thalesgroup.com> 00025 Caner Candan <caner.candan@thalesgroup.com> 00026 */ 00027 00028 #ifndef _edoSamplerUniform_h 00029 #define _edoSamplerUniform_h 00030 00031 #include <utils/eoRNG.h> 00032 00033 #include "edoSampler.h" 00034 #include "edoUniform.h" 00035 00048 template < typename EOT, class D = edoUniform<EOT> > 00049 class edoSamplerUniform : public edoSampler< D > 00050 { 00051 public: 00052 typedef D Distrib; 00053 00054 edoSamplerUniform( edoRepairer<EOT> & repairer ) : edoSampler< D >( repairer) {} 00055 00056 EOT sample( edoUniform< EOT >& distrib ) 00057 { 00058 unsigned int size = distrib.size(); 00059 assert(size > 0); 00060 00061 // Point we want to sample to get higher a set of points 00062 // (coordinates in n dimension) 00063 // x = {x1, x2, ..., xn} 00064 EOT solution; 00065 00066 // Sampling all dimensions 00067 for (unsigned int i = 0; i < size; ++i) 00068 { 00069 double min = distrib.min()[i]; 00070 double max = distrib.max()[i]; 00071 double random = rng.uniform(min, max); 00072 00073 assert( ( min == random && random == max ) || ( min <= random && random < max) ); // random in [ min, max [ 00074 00075 solution.push_back(random); 00076 } 00077 00078 return solution; 00079 } 00080 }; 00081 00082 #endif // !_edoSamplerUniform_h