######################################################### ## Quantum K-Means ######################################################### print 'Initiating QK-Means...' qk_results=list() qk_rounds_genNum=list() #list with the number of generations of each round for i in range(numRounds): start = datetime.now() qk_centroids,qk_assignment,fitnessEvolution,qk_timings_cg,qk_db_timings=QK_Means.qk_means(mixture,numOracles,numClusters,qubitStringLen,qGenerations,dim,timeDB=True,earlyStop=earlyStop) qk_results.append([qk_centroids,qk_assignment,fitnessEvolution,qk_timings_cg,qk_db_timings]) round=(datetime.now() - start).total_seconds() print float(i+1)*100/numRounds,'%\t','round ', i,':',round,'s - estimated:',(float(numRounds-1)-i)*round,'s / ',(float(numRounds-1)-i)*round/60,'m' qk_rounds_genNum.append(fitnessEvolution.shape[0]) #number of generations of current round ######################################################### print 'Preparing data structures...' qk_rounds=dict() qk_rounds['centroids']=list() qk_rounds['assignment']=list()
del parser ######################################################### ## Quantum K-Means ######################################################### print 'Initiating QK-Means...' qk_results = list() for i in range(numRounds): start = datetime.now() qk_centroids, qk_assignment, fitnessEvolution, qk_timings_cg = QK_Means.qk_means( mixture, numOracles, numClusters, qubitStringLen, qGenerations, dim) qk_results.append( [qk_centroids, qk_assignment, fitnessEvolution, qk_timings_cg]) round = (datetime.now() - start).total_seconds() print float( i + 1 ) * 100 / numRounds, '%\t', 'round ', i, ':', round, 's - estimated:', ( float(numRounds - 1) - i) * round, 's / ', (float(numRounds - 1) - i) * round / 60, 'm' ######################################################### print 'Preparing data structures...' qk_rounds = dict() qk_rounds['centroids'] = list()
del parser ######################################################### ## Quantum K-Means ######################################################### print 'Initiating QK-Means...' qk_results=list() for i in range(numRounds): start = datetime.now() qk_centroids,qk_assignment,fitnessEvolution,qk_timings_cg=QK_Means.qk_means(mixture,numOracles,numClusters,qubitStringLen,qGenerations,dim) qk_results.append([qk_centroids,qk_assignment,fitnessEvolution,qk_timings_cg]) round=(datetime.now() - start).total_seconds() print float(i+1)*100/numRounds,'%\t','round ', i,':',round,'s - estimated:',(float(numRounds-1)-i)*round,'s / ',(float(numRounds-1)-i)*round/60,'m' ######################################################### print 'Preparing data structures...' qk_rounds=dict() qk_rounds['centroids']=list() qk_rounds['assignment']=list() qk_rounds['fitness']=list() qk_rounds['times']=list()
######################################################### print 'Initiating QK-Means...' qk_results = list() qk_rounds_genNum = list() #list with the number of generations of each round for i in range(numRounds): start = datetime.now() qk_centroids, qk_assignment, fitnessEvolution, qk_timings_cg, qk_db_timings = QK_Means.qk_means( mixture, numOracles, numClusters, qubitStringLen, qGenerations, dim, timeDB=True, earlyStop=earlyStop) qk_results.append([ qk_centroids, qk_assignment, fitnessEvolution, qk_timings_cg, qk_db_timings ]) round = (datetime.now() - start).total_seconds() print float( i + 1 ) * 100 / numRounds, '%\t', 'round ', i, ':', round, 's - estimated:', ( float(numRounds - 1) - i) * round, 's / ', (float(numRounds - 1) - i) * round / 60, 'm'