#!/bin/bash

n_proc=1	# Number of processes to execute in parallel, -1 unlimited

pre_cluster=0 	# Use COP-KMeans to initialise the clustering


trap "trap - SIGTERM && kill -- -$$ && exit" SIGINT SIGTERM EXIT

function limitjobs {
   	if [ "${n_proc}" != "-1" ]
	then
		while [ `jobs -rp | wc -l` -ge ${n_proc} ]
		do
			sleep 5
		done
	fi
}

subdir=Babaki14
data_path=./datasets
test_type=test

COPKMeansBaseDir=`pwd`/methods/Babaki14/bin/COP-KMeans
COPKMeansDir=${COPKMeansBaseDir}/copkmeans

outdir=./methods/${subdir}/clusterings/unconstrained
resdir=./results/${subdir}/unconstrained

mkdir -p ${resdir}
mkdir -p ${outdir}

for d in ${data_path}/*/
do
	dataname=`basename ${d}`
	echo ${dataname}

	distancemetricfilename=${data_path}/${dataname}/${test_type}/${dataname}.metric
	distance_metric=`cat ${distancemetricfilename}`
	nfeaturesfilename=${data_path}/${dataname}/${test_type}/${dataname}.nfeatures
	nfeatures=`cat ${nfeaturesfilename}`

	distancefilename=${data_path}/${dataname}/${test_type}/${dataname}.distances
	datafilename=${data_path}/${dataname}/${test_type}/${dataname}.data
	kfilename=${data_path}/${dataname}/${test_type}/${dataname}.k
	labelsfilename=${data_path}/${dataname}/${test_type}/${dataname}.labels

	kvalue=`cat ${kfilename}`

	resultdir=./methods/${subdir}/results/${test_type}/unconstrained
	mkdir -p ${resultdir}

	for (( i=1; i<=10; i++ ))
	do

		limitjobs

		{
			cnstrnt_frac=0
			cnstrnt_iter=${i}

			filenamepattern=${dataname}_${test_type}_${cnstrnt_frac}_${cnstrnt_iter}
			outfilename=${outdir}/${filenamepattern}.clustering
			resultfilename=${resultdir}/${filenamepattern}.results
			initialclusterfilename=${COPKMeansBaseDir}/clusterings/unconstrained/${filenamepattern}.clustering

			if [ ! -f ${resultfilename} ]
			then
				echo ${resultfilename}

				rm -f ${outfilename}

				if [ "${pre_cluster}" == "1" ]
				then
					python methods/${subdir}/bin/cccg-seed.py -d ${datafilename} -k ${kvalue} -c 1 -t 9999999 -copdir ${COPKMeansDir} -f ${nfeatures} -p ${distance_metric} --ofile=${outfilename} --distfile=${distancefilename} --l=1 #--init=${initialclusterfilename}
				else
					python methods/${subdir}/bin/cccg-seed.py -d ${datafilename} -k ${kvalue} --consfile=${constraintsfilename} -c 1 -t 9999999 -copdir ${COPKMeansDir} -f ${nfeatures} -p ${distance_metric} --ofile=${outfilename} --distfile=${distancefilename} --l=1 --init=-1
				fi

				if [ -f ${outfilename} ]
				then
					Rscript --vanilla ./utils/cluster_index.R ${labelsfilename} ${outfilename} ${resultfilename}
				fi
			fi
		} &
	done

	wait

	for cnstrnt_frac in 0
	do
		outfilename=${resdir}/${dataname}_${test_type}_${cnstrnt_frac}.results

		rm -f ${outfilename}

                Rscript --vanilla ./utils/summarise_results.R ${resultdir} ${dataname} ${test_type} ${cnstrnt_frac} ${outfilename}
	done
done