def main():
"""end for intersection clustering"""
print("--------------------------------------")
print("STARTTIME:", (datetime.datetime.now()))
print("--------------------------------------")
# getting data
locations = clocation.get_locations_list()
users = cuser.get_users_posts_afile(USER_POSTS_FILE)
locations = clocation.fit_users_to_location(locations, users, "tags",
"uid")
del users
set_location_tags(locations)
set_location_user_count(locations)
coordinate = numpy.array([(float(x.lat), float(x.lng))
for x in locations.values()])
intersection = get_tag_intersection(locations.values())
location_frequency = numpy.array([x.usercount for x in locations.values()])
print("avg location_frequency:",
sum(location_frequency) / len(location_frequency), " max:",
max(location_frequency), " min:", min(location_frequency))
print("location 1:",
list(locations.values())[0].lname,
list(locations.values())[0].lid)
print("intersection.sum:",
intersection.sum(axis=0)[0:6],
intersection.sum(axis=1)[0:6])
print("location_frequency:", location_frequency.shape)
# original intersect clustering
#cntr1, u, u0, d1, d2, d, jm, p, fpc, membership = cfuzzy.cmeans_intersect(coordinate.T, intersection, CLUSTER_NUM, w=WEIGHT, e=ERROR)
# intersect clustering with the kth locations in each cluster
#cntr1, u, u0, d1, d2, d, jm, p, fpc, membership = cfuzzy.cmeans_intersect(coordinate.T, intersection, CLUSTER_NUM, MAX_KTH, w=WEIGHT, e=ERROR, algorithm="kthCluster")
# intersect clustering with the kth locations in each cluster & location frequency as weight
cntr1, u, u0, d1, d2, d, jm, p, fpc, membership = cfuzzy.cmeans_intersect(
coordinate.T,
intersection,
CLUSTER_NUM,
MAX_KTH,
location_frequency,
w=WEIGHT,
e=ERROR,
algorithm="kthCluster_LocationFrequency")
for i, key in enumerate(locations.keys()):
setattr(locations[key], "cluster", membership[i])
cpygmaps.output_clusters([(float(x.lat), float(x.lng), str(x.cluster) + " >> " + x.lname) for x in locations.values()], \
membership, CLUSTER_NUM, OUTPUT_MAP)
cfuzzy.output_location_cluster(locations.values(), "cluster",
OUTPUT_CLUSTER)
print("--------------------------------------")
print("ENDTIME:", (datetime.datetime.now()))
print("--------------------------------------")
def output_representatives(data, u, k):
"""
cpygmaps.output_clusters(\
[(float(x[0]), float(x[1]), "") \
for x in cntr], range(30), 30, "./data/Result/center" + str(R) + ".html")
"""
representatives = []
membership = []
for i in range(u.shape[0]):
indices = numpy.where(u[i, :] >= sorted(u[i, :], reverse=True)[k - 1])[0]
representatives.extend(data[indices, :])
membership.extend([i] * len(indices))
cpygmaps.output_clusters([(float(x[0]), float(x[1]), str(mem)) \
for x, mem in zip(representatives, membership)], membership, u.shape[0], OUTPUT_REPRESENTATIVES)
def main():
"""end for intersection clustering"""
print("--------------------------------------")
print("STARTTIME:", (datetime.datetime.now()))
print("--------------------------------------")
# getting data
locations = clocation.get_locations_list()
users = cuser.get_users_posts_afile(USER_POSTS_FILE)
locations = clocation.fit_users_to_location(locations, users, "tags", "uid")
del users
set_location_tags(locations)
set_location_user_count(locations)
coordinate = numpy.array([(float(x.lat), float(x.lng)) for x in locations.values()])
intersection = get_tag_intersection(locations.values())
location_frequency = numpy.array([x.usercount for x in locations.values()])
print("avg location_frequency:", sum(location_frequency) / len(location_frequency), " max:", max(location_frequency), " min:", min(location_frequency))
print("location 1:", list(locations.values())[0].lname, list(locations.values())[0].lid)
print("intersection.sum:", intersection.sum(axis=0)[0:6], intersection.sum(axis=1)[0:6])
print("location_frequency:", location_frequency.shape)
# original intersect clustering
#cntr1, u, u0, d1, d2, d, jm, p, fpc, membership = cfuzzy.cmeans_intersect(coordinate.T, intersection, CLUSTER_NUM, w=WEIGHT, e=ERROR)
# intersect clustering with the kth locations in each cluster
#cntr1, u, u0, d1, d2, d, jm, p, fpc, membership = cfuzzy.cmeans_intersect(coordinate.T, intersection, CLUSTER_NUM, MAX_KTH, w=WEIGHT, e=ERROR, algorithm="kthCluster")
# intersect clustering with the kth locations in each cluster & location frequency as weight
cntr1, u, u0, d1, d2, d, jm, p, fpc, membership = cfuzzy.cmeans_intersect(coordinate.T, intersection, CLUSTER_NUM, MAX_KTH, location_frequency, w=WEIGHT, e=ERROR, algorithm="kthCluster_LocationFrequency")
for i, key in enumerate(locations.keys()):
setattr(locations[key], "cluster", membership[i])
cpygmaps.output_clusters([(float(x.lat), float(x.lng), str(x.cluster) + " >> " + x.lname) for x in locations.values()], \
membership, CLUSTER_NUM, OUTPUT_MAP)
cfuzzy.output_location_cluster(locations.values(), "cluster", OUTPUT_CLUSTER)
print("--------------------------------------")
print("ENDTIME:", (datetime.datetime.now()))
print("--------------------------------------")
