#!/bin/bashalg=${1:-"fuzzy"}cnt=${2:-10}rm -f iris.resif [ $alg == "fuzzy" ]; then  echo "fuzzy clustering"     | tee -a iris.reselse  echo "competitive learning" | tee -a iris.resfidom -sa iris.tab - 2> /dev/null | sed "/iris_type/d" > iris.dncsed 's/Iris-setosa/0/;s/Iris-versicolor/1/;s/Iris-virginica/2/;' \iris.tab > iris.trninit="-icenter -o0.5"fcp=""clp="-acomplrn -k30 -t0.3:0.1"std="-I0.4 -H-4 -Vc3 iris.dnc iris.trn iris.cls"for (( seed=1 ; seed <= cnt ; seed++ )); do  if [ $alg == "fuzzy" ]; then    cli -e30 -S$seed $init $std $fcp 2> /dev/null  else     cli -e30 -S$seed $init $std $clp 2> /dev/null  fi  echo "" | tee -a iris.res  clx -cclx iris.cls iris.trn iris.out 2> /dev/null  xmat -c iris.out 2> /dev/null | tee -a iris.resdonecnt=`wc iris.trn | gawk '{ printf("%d", $1-1); }'`gawk -v k=$cnt 'BEGIN            { n = 0; }($2 == "errors") { e[n++] = $NF; }END {  for (i = 0; i < n; i++) {    sum += e[i]; sqr += e[i] * e[i]; }  printf("\nstatistics:\n");  printf("cnt: %d, ", n);  t = (n > 0) ? sum /n : 0;  printf("avg: %5.1f (%4.1f), ", t, t/k*100);  t = (n > 1) ? sqrt((sqr -sum*sum/n) /(n-1)) : 1;  printf("std: %5.1f (%4.1f)\n", t, t/k*100);}' iris.resrm -f iris.cls iris.dnc iris.out iris.res