/*  Ant-based Clustering    Copyright (C) 2004 Julia Handl    Email: Julia.Handl@gmx.de    This program is free software; you can redistribute it and/or modify    it under the terms of the GNU General Public License as published by    the Free Software Foundation; either version 2 of the License, or    (at your option) any later version.    This program is distributed in the hope that it will be useful,    but WITHOUT ANY WARRANTY; without even the implied warranty of    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the    GNU General Public License for more details.    You should have received a copy of the GNU General Public License    along with this program; if not, write to the Free Software    Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA*//***************************************************date: 7.4.2003author: Julia Handl (julia.Handl@gmx.de)description: - wrapper class for all parameter settings***************************************************/#ifndef CONF_JH_2003#define CONF_JH_2003#include <iostream.h>#include <fstream.h> #define USED_DATA_TYPE float#define FALSE 0#define TRUE 1/* define the distance measure to be used */#define EUCLIDEAN/* define the types of artificial test data */#define A1 1#define A2 2#define A3 3#define A4 4#define TEST 0class conf {     public:    /* Experimental setup */    const static int evalnbr = 1;    int evalctr;    const static int clustering = TRUE;    const static int newprob = TRUE;    const static int increase = TRUE;    const static int weighting = FALSE;    const static int adaptalpha = TRUE;    const static int lookahead = TRUE;   const static int initradius = 1;             /* Parameters used by ACCL */        // ant parameters     int clusterphase;     const static int colonysize = 10;     int maxspeed;     const static int memorysize = 10;     double alpha;     double alphasigma;     const static double kd = 0.3;     const static double kp = 0.3;     int generations;     const static int generation_length = 40000;     int radius;     const static int maxfailure = 100;     // grid parameters     int imax;     int jmax;     // databin parameters     int bintype;     int binsize;     int bindim;     USED_DATA_TYPE mu;     USED_DATA_TYPE max;     int type;     int num_cluster;     USED_DATA_TYPE ** mu_cluster;     USED_DATA_TYPE ** sigma_cluster;     int * size_cluster;     /* Parameters for other algorithms .... */     /* Kmeans */     /* Number of intended clusters */     int kclusters;     /* SOM (Kohonen Networks) */     int imax_som;     int jmax_som;     double lr0_som;     double ns0_som;     double ns1_som;     int gen_som;     /* MDS */     const static double lr = 0.05;     const static int T_MDS = 10;     conf(int type) {	 if (adaptalpha == FALSE) {	     alpha = 0.5;	 }	 #ifdef COSINE	 cout << "Using Cosine similarity" << endl;#endif#ifdef EUCLIDEAN	 cout << "Using Euclidean distance" << endl;#endif#ifdef CORRELATION	 cout << "Using Correlation-based similarity" << endl;#endif	      }};#endif