# add the paths necessary for clustering algorithm and ibcc - currently only works on Greg's computer
if os.path.exists("/home/ggdhines"):
sys.path.append(
"/home/ggdhines/PycharmProjects/reduction/experimental/clusteringAlg")
else:
sys.path.append("/home/greg/github/reduction/experimental/clusteringAlg")
from agglomerativeClustering import Ward, TooBig
clusterAlg = Ward().__fit__
penguin = PenguinAggregation()
subject_ids = pickle.load(
open(aggregation.base_directory + "/Databases/penguin_gold.pickle", "rb"))
for i, subject in enumerate(random.sample(subject_ids, 50)):
#subject = "APZ000173v"
print i, subject
penguin.__readin_subject__(subject, users_to_skip=["caitlin.black"])
try:
numClusters, time = penguin.__cluster_subject__(subject, clusterAlg)
except TooBig:
print "too big"
continue
if not blankImage:
penguin.__display_raw_markings__(subject)
penguin.__display__markings__(subject)
import cPickle as pickle
import aggregation
# add the paths necessary for clustering algorithm and ibcc - currently only works on Greg's computer
if os.path.exists("/home/ggdhines"):
sys.path.append("/home/ggdhines/PycharmProjects/reduction/experimental/clusteringAlg")
else:
sys.path.append("/home/greg/github/reduction/experimental/clusteringAlg")
from agglomerativeClustering import Ward, TooBig
clusterAlg = Ward().__fit__
penguin = PenguinAggregation()
subject_ids = pickle.load(open(aggregation.base_directory + "/Databases/penguin_gold.pickle", "rb"))
for i, subject in enumerate(random.sample(subject_ids, 50)):
# subject = "APZ000173v"
print i, subject
penguin.__readin_subject__(subject, users_to_skip=["caitlin.black"])
try:
numClusters, time = penguin.__cluster_subject__(subject, clusterAlg)
except TooBig:
print "too big"
continue
if not blankImage:
penguin.__display_raw_markings__(subject)
penguin.__display__markings__(subject)
