def kmeans(points, k, cutoff):
tmp = []
for p in points:
print p
try:
lat = float(p[1])
lon = float(p[2])
tmp.append(Point([lat, lon]))
except:
continue
points = tmp
# Randomly sample k Points from the points list, build Clusters around them
initial = random.sample(points, k)
clusters = []
for p in initial:
clusters.append(Cluster([p]))
print " clusters: %s" % clusters
# Enter the program loop
while True:
# Make a list for each Cluster
lists = []
for c in clusters:
lists.append([])
# For each Point:
for p in points:
# Figure out which Cluster's centroid is the nearest
smallest_distance = dist_on_earth(p.coords,
clusters[0].centroid.coords)
index = 0
for i in range(len(clusters[1:])):
distance = dist_on_earth(p.coords,
clusters[i + 1].centroid.coords)
if distance < smallest_distance:
smallest_distance = distance
index = i + 1
# Add this Point to that Cluster's corresponding list
lists[index].append(p)
# Update each Cluster with the corresponding list
# Record the biggest centroid shift for any Cluster
biggest_shift = 0.0
for i in range(len(clusters)):
shift = clusters[i].update(lists[i])
biggest_shift = max(biggest_shift, shift)
# If the biggest centroid shift is less than the cutoff, stop
if biggest_shift < cutoff: break
tmp = []
for c in clusters:
tmp.append([len(c.points), c.centroid.coords])
return tmp
# Return the list of cluster attributes
return tmp
def kmeans(points, k, cutoff):
tmp = []
for p in points:
print p
try:
lat = float(p[1])
lon = float(p[2])
tmp.append(Point([lat,lon]))
except:
continue
points = tmp
# Randomly sample k Points from the points list, build Clusters around them
initial = random.sample(points, k)
clusters = []
for p in initial: clusters.append(Cluster([p]))
print " clusters: %s" % clusters
# Enter the program loop
while True:
# Make a list for each Cluster
lists = []
for c in clusters: lists.append([])
# For each Point:
for p in points:
# Figure out which Cluster's centroid is the nearest
smallest_distance = dist_on_earth(p.coords, clusters[0].centroid.coords)
index = 0
for i in range(len(clusters[1:])):
distance = dist_on_earth(p.coords, clusters[i+1].centroid.coords)
if distance < smallest_distance:
smallest_distance = distance
index = i+1
# Add this Point to that Cluster's corresponding list
lists[index].append(p)
# Update each Cluster with the corresponding list
# Record the biggest centroid shift for any Cluster
biggest_shift = 0.0
for i in range(len(clusters)):
shift = clusters[i].update(lists[i])
biggest_shift = max(biggest_shift, shift)
# If the biggest centroid shift is less than the cutoff, stop
if biggest_shift < cutoff: break
tmp = []
for c in clusters:
tmp.append([len(c.points),c.centroid.coords])
return tmp
# Return the list of cluster attributes
return tmp
def update(self, points):
old_centroid = self.centroid
self.points = points
self.centroid = self.calculateCentroid()
return dist_on_earth(old_centroid.coords, self.centroid.coords)
def update(self, points):
old_centroid = self.centroid
self.points = points
self.centroid = self.calculateCentroid()
return dist_on_earth(old_centroid.coords, self.centroid.coords)
