clusterAlg = DivisiveKmeans().__fit__
penguin = PenguinAggregation()
gold_subjects = penguin.__get_gold_subjects__()
gold_sample = gold_subjects[:50]
penguin.__readin_users__()
for count,zooniverse_id in enumerate(gold_sample):
if count == 50:
break
print count, zooniverse_id
penguin.__readin_subject__(zooniverse_id,read_in_gold=True)
blankImage = penguin.__cluster_subject__(zooniverse_id, clusterAlg,fix_distinct_clusters=True,correction_alg=correctionAlg)
penguin.__soy_it__(zooniverse_id)
penguin.__signal_ibcc__()
penguin.__roc__()
# one_overlap = penguin.__off_by_one__(display=True)
# last_id = None
#
# for t in one_overlap:
# if t[0] != last_id:
# print "*****"
# print "====="
# last_id = t[0]
# penguin.__relative_confusion__(t)
