/*  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*/#include <iostream.h>#include "conf.h"#include "evaluation.h"#include <time.h>#include "accl.h"using namespace std;double square(double x);void fileoutput(char *, double *, int);void output(clustering * cl, int size, char * name);long idum;int main(int argc, char ** argv) {    idum = 4284509;    conf c(TEST);        databin<float> d(&c, argv[1]);    evaluation e(&c);    accl * antrun;     double * runtime = new double[c.evalnbr];    double * number = new double[c.evalnbr];    double * fmeasure = new double[c.evalnbr];    double * variance = new double[c.evalnbr];    double * dunn_av = new double[c.evalnbr];    double * rand = new double[c.evalnbr];        c.evalctr = 0;    for (c.evalctr=0; c.evalctr<c.evalnbr; c.evalctr++) {	databin<float> d(&c, argv[1]);	d.permutate();		antrun  = new accl(&c, &d, &e);	antrun->init();	cout << "Evaluation No. " << c.evalctr << " with radius "  << c.radius << endl;	time_t start = time(NULL);	antrun->run();	time_t end = time(NULL);	antrun->constructclustering();	cerr << "Evaluation initialization" << endl;	e.init(&d, antrun->getmapping(), antrun->getclustering(), NULL);	cerr << "Evaluation" << endl;        number[c.evalctr] = e.clusternumber(); 	fmeasure[c.evalctr] = e.fmeasure(1);	cout << "F-Measure = " << fmeasure[c.evalctr] << endl;	rand[c.evalctr] = e.randindex();	cout << "Rand Index = " << rand[c.evalctr] << endl;	variance[c.evalctr] = e.variance();	cout << "Variance = " << variance[c.evalctr] << endl;	dunn_av[c.evalctr] = e.dunn_av();	cout << "Dunn Index = " << dunn_av[c.evalctr] << endl;	runtime[c.evalctr] = difftime(end,start);	cout << "Runtime = " << runtime[c.evalctr] << endl;	char name[20];	sprintf(name, "%d.solution",c.evalctr);	output(antrun->getclustering(),c.binsize, name);	delete antrun;    }    double fmeasure_mean = 0.0, fmeasure_sd = 0.0;    double variance_mean = 0.0, variance_sd = 0.0;    double dunn_avmean = 0.0, dunn_avsd = 0.0;    double rand_mean = 0.0, rand_sd = 0.0;    double number_mean = 0.0, number_sd = 0.0;    double time_mean = 0.0, time_sd = 0.0;         int topctr = c.evalnbr;    for (int i=0; i<c.evalnbr; i++) {	fmeasure_mean += fmeasure[i];	variance_mean += variance[i];	dunn_avmean += dunn_av[i];	rand_mean += rand[i];	number_mean += number[i];	time_mean += runtime[i];    }       fmeasure_mean /= double(c.evalnbr);    variance_mean /= double(c.evalnbr);    dunn_avmean /= double(c.evalnbr);    rand_mean /= double(c.evalnbr);    number_mean /= double(c.evalnbr);    time_mean /= double(c.evalnbr);      for (int i=0; i<c.evalnbr; i++) {	fmeasure_sd += square(fmeasure[i]-fmeasure_mean);	variance_sd += square(variance[i]-variance_mean);	dunn_avsd += square(dunn_av[i]-dunn_avmean);	rand_sd += square(rand[i]-rand_mean);	number_sd += square(number[i]-number_mean);	time_sd += square(runtime[i]-time_mean);   }        fmeasure_sd /= double(c.evalnbr);    variance_sd /= double(c.evalnbr);    dunn_avsd /= double(c.evalnbr);    rand_sd /= double(c.evalnbr);    number_sd /= double(c.evalnbr);    time_sd /= double(c.evalnbr);    fmeasure_sd = sqrt(fmeasure_sd);    variance_sd = sqrt(variance_sd);    dunn_avsd = sqrt(dunn_avsd);    rand_sd = sqrt(rand_sd);    number_sd = sqrt(number_sd);    time_sd = sqrt(time_sd);    fileoutput("accl_fmeasure.dat",fmeasure,c.evalnbr);    fileoutput("accl_variance.dat",variance,c.evalnbr);    fileoutput("accl_dunn_av.dat",dunn_av,c.evalnbr);    fileoutput("accl_rand.dat",rand,c.evalnbr);    fileoutput("accl_number.dat",number,c.evalnbr);    fileoutput("accl_time.dat",runtime,c.evalnbr);    ofstream summary("accl_summary.dat");    summary << "Time: Mean = " << time_mean << ", SD = " << time_sd << endl;    summary << "Number: Mean = " << number_mean << ", SD = " << number_sd << endl;    summary << "FMeasure: Mean = " << fmeasure_mean << ", SD = " << fmeasure_sd << endl;    summary << "Rand: Mean = " << rand_mean << ", SD = " << rand_sd << endl;    summary << "Variance: Mean = " << variance_mean << ", SD = " << variance_sd << endl;    summary << "Dunn (Average): Mean = " << dunn_avmean << ", SD = " << dunn_avsd << endl;    summary.close();    delete [] number;    delete [] fmeasure;    delete [] variance;    delete [] dunn_av;    delete [] rand;      delete [] runtime; } double square(double x) {    return x*x;}void fileoutput(char * name, double * values, int nbr) {    ofstream out(name);    for (int i=0; i<nbr; i++) {	out << values[i] << endl;    }}void output(clustering * cl, int size, char * name) {  ofstream out(name);  for (int i=0; i<size; i++) {    out << i << " " << (*cl)[i]+1 << endl;  }  out << endl;}