def main(*argv):
start_time = datetime.datetime.now()
print("--------------------------------------")
print("STARTTIME:", (datetime.datetime.now()))
print("--------------------------------------")
# set parameters
global CLUSTER_NUM
global MAX_KTH
global GPS_WEIGHT
global FILTER_TIME_S
global FILTER_TIME_E
global OUTPUT_MAP
global OUTPUT_PATTERN
if len(argv) > 0:
CLUSTER_NUM = argv[0]
MAX_KTH = argv[1]
GPS_WEIGHT = argv[2]
FILTER_TIME_S = argv[3]
FILTER_TIME_E = argv[4]
LOCATION_TOPIC = "./data/LocationTopic/LocationTopic_OCT_c35.txt"
else:
LOCATION_TOPIC = "./data/LocationTopic/LocationTopic_NOVDEC_c35.txt"
OUTPUT_MAP = OUTPUT_MAP + str(CLUSTER_NUM) + "k" + str(MAX_KTH) + "w" + str(GPS_WEIGHT)
OUTPUT_PATTERN = OUTPUT_PATTERN + str(CLUSTER_NUM) + "k" + str(MAX_KTH)
# Getting data
users, locations = locationclustering.main(FILTER_TIME_S, FILTER_TIME_E)
location_id, doc_topic = ccluster.open_doc_topic(LOCATION_TOPIC)
locations = ccluster.fit_locations_membership(locations, numpy.transpose(doc_topic), location_id, "semantic_mem")
print(" users # :", len(users))
# Getting sequences of posts & locations
#sequences = ctrajectory.split_trajectory([a_user.posts for a_user in users.values() if len(a_user.posts) != 0], SPLIT_DAY)
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 = get_specific(sequences)
sequences = ctrajectory.remove_short(sequences)
print(" remain users #:", len(set([x[0].uid for x in sequences])))
location_sequences, longest_len = ctrajectory.convertto_location_sequences(sequences, locations)
spatial_array = ctrajectory.get_vector_array(location_sequences, longest_len)
semantic_array = ctrajectory.get_vector_array(location_sequences, longest_len, "semantic_mem")
u, u0, d, jm, p, fpc, center, membership = cfuzzy.sequences_clustering_i("Location", spatial_array, CLUSTER_NUM, MAX_KTH, semantic_array, GPS_WEIGHT, e = ERROR, algorithm="2WeightedDistance")
#sequences, location_sequences, membership = filter_sequence(sequences, location_sequences, u, membership)
#ouput_pattern(sequences, location_sequences, u, membership)
#output_each_pattern(sequences, location_sequences, u, membership, 30)
#ctrajectory.output_clusters(sequences, membership, u, OUTPUT_PATTERN)
#output_cluster_detail(sequences, location_sequences, u, membership, 39, file = OUTPUT_ANALYSIS)
print("--------------------------------------")
print("ENDTIME:", (datetime.datetime.now()), ", SPEND:", datetime.datetime.now() - start_time)
print("--------------------------------------")
return location_sequences, spatial_array, semantic_array, u
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(*argv):
start_time = datetime.datetime.now()
print("--------------------------------------")
print("STARTTIME:", (datetime.datetime.now()))
print("--------------------------------------")
# set parameters
global CLUSTER_NUM
global MAX_KTH
global FILTER_TIME_S
global FILTER_TIME_E
global OUTPUT_MAP
global OUTPUT_PATTERN
if len(argv) > 0:
CLUSTER_NUM = argv[0]
MAX_KTH = argv[1]
FILTER_TIME_S = argv[3]
FILTER_TIME_E = argv[4]
LOCATION_TOPIC = "./data/LocationTopic/LocationTopic_OCT_c35.txt"
else:
LOCATION_TOPIC = "./data/LocationTopic/LocationTopic_NOVDEC_c35.txt"
OUTPUT_MAP = OUTPUT_MAP + str(CLUSTER_NUM) + "k" + str(MAX_KTH)
OUTPUT_PATTERN = OUTPUT_PATTERN + str(CLUSTER_NUM) + "k" + str(MAX_KTH)
# Getting data
users, locations = locationclustering.main(FILTER_TIME_S, FILTER_TIME_E)
location_id, doc_topic = ccluster.open_doc_topic(LOCATION_TOPIC)
semantic_cluster = numpy.argmax(doc_topic, axis = 1)
locations = ccluster.fit_locations_cluster(locations, semantic_cluster, location_id, "semantic_cluster")
print(" users # :", len(users))
# Getting sequences cluster
#sequences = ctrajectory.split_trajectory([a_user.posts for a_user in users.values() if len(a_user.posts) != 0], SPLIT_DAY)
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 = get_specific(sequences)
sequences = ctrajectory.remove_short(sequences)
print(" remain users #:", len(set([x[0].uid for x in sequences])))
location_sequences, longest_len = ctrajectory.convertto_location_sequences(sequences, locations)
spatial_sequences, semantic_sequences = ctrajectory.get_cluster_array(location_sequences, longest_len, "cluster", "semantic_cluster")
u, u0, d, jm, p, fpc, center, membership = cfuzzy.sequences_clustering_i("Cluster", spatial_sequences, CLUSTER_NUM, MAX_KTH, semantic_sequences, GPS_WEIGHT, e = ERROR, algorithm="2WeightedDistance")
#sequences, location_sequences, membership = filter_sequence(sequences, location_sequences, u, membership)
#ouput_pattern(sequences, location_sequences, u, membership)
output_each_pattern(sequences, location_sequences, u, membership, 30)
ctrajectory.output_clusters(sequences, membership, u, OUTPUT_PATTERN)
#output_cluster_detail(sequences, location_sequences, u, membership, 26, file = OUTPUT_ANALYSIS)
"""
ll = count(sequences, location_sequences, u, membership, 40)
tag = from_to(location_sequences, membership, 40, [ll[0], ll[1], ll[2]])
tag.extend(from_to(location_sequences, membership, 40, [ll[1], ll[0], ll[2]]))
tag.extend(from_to(location_sequences, membership, 40, [ll[0], ll[0]]))
tag.extend(from_to(location_sequences, membership, 40, [ll[1], ll[1]]))
tag.extend(from_to(location_sequences, membership, 40, [ll[1], ll[2], ll[0]]))
tag.extend(from_to(location_sequences, membership, 40, [ll[2], ll[1]]))
f = open("./data/Result/Ana_40.txt", "a")
f.write("\naboves:" + str(len(set(tag))))
f.close()
"""
print("--------------------------------------")
print("ENDTIME:", (datetime.datetime.now()), ", SPEND:", datetime.datetime.now() - start_time)
print("--------------------------------------")
return location_sequences, spatial_sequences, semantic_sequences, u