def assign_item(self, item, origin):
"""
Assigns an item from a given cluster to the closest located cluster.
:param item: the item to be moved.
:param origin: the originating cluster.
"""
closest_cluster = origin
for cluster in self.__clusters:
if self.distance(item, centroid(cluster)) < self.distance(
item, centroid(closest_cluster)):
closest_cluster = cluster
if id(closest_cluster) != id(origin):
self.move_item(item, origin, closest_cluster)
return True
else:
return False
def assign_item(self, item, origin):
"""
Assigns an item from a given cluster to the closest located cluster.
:param item: the item to be moved.
:param origin: the originating cluster.
"""
closest_cluster = origin
for cluster in self.__clusters:
if self.distance(item, centroid(cluster)) < self.distance(
item, centroid(closest_cluster)):
closest_cluster = cluster
if id(closest_cluster) != id(origin):
self.move_item(item, origin, closest_cluster)
return True
else:
return False
kml_items = [{'label': label, 'coords': '%s,%s' % coords[0]} for (label,
coords) in expanded_coords]
# It would also be helpful to include names of your contacts on the map
for item in kml_items:
item['contacts'] = '\n'.join(['%s %s.' % (c['firstName'], c['lastName'])
for c in connections if c.has_key('location') and
c['location']['name'] == item['label']])
# Step 3 - Cluster locations and extend the KML data structure with centroids
cl = KMeansClustering([coords for (label, coords_list) in expanded_coords
for coords in coords_list])
centroids = [{'label': 'CENTROID', 'coords': '%s,%s' % centroid(c)} for c in
cl.getclusters(K)]
kml_items.extend(centroids)
# Step 4 - Create the final KML output and write it to a file
kml = createKML(kml_items)
f = open(OUT_FILE, 'w')
f.write(kml)
f.close()
print 'Data written to ' + OUT_FILE
def main():
# company_distribution()
# job_role_distribution()
# g = geocoders.Bing(GEO_APP_KEY)
CLUSTER_COUNT = 3
i = 0
for trend in mainTrend:
location_list = list()
for tweet in tweepy.Cursor(api.search,
q=trend['query']).items(TWEET_SAMPLE_SIZE):
if tweet.user.location:
print(i)
i += 1
try:
location = geocoder.geocode(tweet.user.location)
location_list.append({
"lat": location.latitude,
"lon": location.longitude
})
except Exception as e:
print("An exception occurred: ")
print(e)
pass
with open('out.txt', 'w') as f:
print(location_list, file=f)
cluster = KMeansClustering([(l['lat'], l['lon']) for l in location_list])
centroids = [centroid(c) for c in cluster.getclusters(CLUSTER_COUNT)]
kml_clusters = simplekml.Kml()
for i, c in enumerate(centroids):
kml_clusters.newpoint(name='Cluster {}'.format(i), coords=[(c[1], c[0])])
kml_clusters.save('{}.kml'.format(trend['query']))