#########################################################
## 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'
