def main():
print("--------------------------------------")
print("STARTTIME:", (datetime.datetime.now()))
print("--------------------------------------")
# getting data
#locations = ctag.open_location_tags(USER_TAGS_FILE)
#corpus = ctag.get_corpus([x.tags for x in locations.values()])
# getting data - way 2
locations = clocation.open_locations()
users = cuser.open_users_posts_afile(USER_POSTS_FILE)
print("Sampling users posts...")
for key, a_user in users.items():
posts = [x for x in a_user.posts if (x.time > FILTER_TIME_S) and (x.time < FILTER_TIME_E)]
users[key].posts = posts
locations = clocation.fit_users_to_location(locations, users, "tags")
corpus = ctag.get_location_posts_corpus(locations)
vector, tag_name = clda.get_tag_vector(corpus)
topic_word, doc_topic = clda.fit_lda(vector, tag_name, TOPIC_NUM)
ccluster.output_topics(topic_word, doc_topic, tag_name, [x.lid for x in locations.values()], OUTPUT_TAG_TOPIC, OUTPUT_LOCATION_TOPIC)
print("--------------------------------------")
print("ENDTIME:", (datetime.datetime.now()))
print("--------------------------------------")
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 main(*argv):
print("--------------------------------------")
print("STARTTIME:", (datetime.datetime.now()))
print("--------------------------------------")
# set UNIXTIME
global FILTER_TIME_S
global FILTER_TIME_E
if len(argv) > 0:
FILTER_TIME_S = argv[0]
FILTER_TIME_E = argv[1]
# getting data
locations = clocation.open_locations()
users = cuser.open_users_posts_afile(USER_POSTS_FILE)
# preporcessing. remove unqualified users
removes = filter_users_timeperiod(users, FILTER_TIME_S, FILTER_TIME_E)
sequences = ctrajectory.split_trajectory_byday([a_user.posts for a_user in users.values() if len(a_user.posts) != 0])
sequences = ctrajectory.remove_adjacent_location(sequences)
sequences = ctrajectory.remove_short(sequences)
removes = list(set(removes) | (set(users.keys()) - set([x[0].uid for x in sequences])))
for key in removes:
del users[key]
print(" remain users #:", len(users.keys()))
locations = clocation.fit_users_to_location(locations, users, "uid")
set_location_user_count(locations)
coordinate = numpy.array([(float(x.lat), float(x.lng)) for x in locations.values()])
location_frequency = numpy.array([x.usercount for x in locations.values()])
# clustering locations
cntr, u, u0, d, jm, p, fpc, membership = cfuzzy.cmeans_location(coordinate.T, CLUSTER_NUM, MAX_KTH, location_frequency, Y, e=ERROR, algorithm="kthCluster_LocationFrequency")
locations = ccluster.fit_locations_membership(locations, u, locations.keys())
locations = ccluster.fit_locations_cluster(locations, membership, locations.keys())
# output result
"""
cpygmaps.output_clusters(\
[(float(x.lat), float(x.lng), str(x.cluster) + " >> " + x.lname + "(" + x.lid + ")>>u:" + str(u[x.cluster, i])) \
for i, x in enumerate(locations.values())], membership, CLUSTER_NUM, OUTPUT_MAP)
output_representatives(coordinate, u, MAX_KTH)
"""
#ccluster.output_location_cluster(locations.values(), "cluster", OUTPUT_CLUSTER)
print("--------------------------------------")
print("ENDTIME:", (datetime.datetime.now()))
print("--------------------------------------")
return users, locations
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("--------------------------------------")
