from penguinAggregation import PenguinAggregation
import random
import os
import sys
# 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 divisiveKmeans import DivisiveKmeans
clusterAlg = DivisiveKmeans().__fit__
penguin = PenguinAggregation()
zooniverse_id_list = random.sample(penguin.__get_subjects_per_site__("APZ0001x3p"),40)
for i,zooniverse_id in enumerate(zooniverse_id_list):
print i
penguin.__readin_subject__(zooniverse_id)
blankImage = penguin.__cluster_subject__(zooniverse_id, clusterAlg)
if not blankImage:
print "+--"
penguin.__find_closest_neighbour__(zooniverse_id)
#penguin.__plot_cluster_size__(zooniverse_id_list)
penguin.__find_one__(zooniverse_id_list)
import random
import os
import sys
# 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 divisiveKmeans import DivisiveKmeans
clusterAlg = DivisiveKmeans().__fit__
penguin = PenguinAggregation()
zooniverse_id_list = random.sample(
penguin.__get_subjects_per_site__("APZ0001x3p"), 40)
for i, zooniverse_id in enumerate(zooniverse_id_list):
print i
penguin.__readin_subject__(zooniverse_id)
blankImage = penguin.__cluster_subject__(zooniverse_id, clusterAlg)
if not blankImage:
print "+--"
penguin.__find_closest_neighbour__(zooniverse_id)
#penguin.__plot_cluster_size__(zooniverse_id_list)
penguin.__find_one__(zooniverse_id_list)
