def assignMembership(points, centroids):
k = len(centroids)
membership = []
for point in points:
minDistance = float("inf")
for c in xrange(k):
d = PS.distance(point, centroids[c])
if (d < minDistance):
minDistance = d
minCluster = c
#format (clusterNum, (x,y))
membership.append((minCluster, point))
return membership
def kMeans(k, e, i, o):
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
size = comm.Get_size()
#rank = 0 divides the points and scatters to each node
if rank == 0:
f = open(i, "r")
points = []
for line in f.readlines():
pointStr = line.strip().split(' ')
point = (float(pointStr[0]), float(pointStr[1]))
points.append(point)
f.close()
chunkSize = len(points)/size
chunk = [points[i*chunkSize: min((i+1)*chunkSize, len(points))] for i in range(size)]
#pick centroids and then broadcast
newCen = getInitialCentroids(points, k)
else:
newCen = None
chunk = None
# every other node gets the broadcasted newCen from root
newCen = comm.bcast(newCen, root=0)
# every node gets its chunk of data
chunk = comm.scatter(chunk, root=0)
oldCen = [(0,0)]*k
membership = [-1]*len(chunk)
allMembership = []
while(PS.diffCentroids(oldCen, newCen) > e):
oldCen = newCen[:]
# each node then computes membership based on the current centroids
membership = assignMembership(chunk, newCen)
allMembers = comm.gather(membership, root=0)
#flatten the list of lists
if (rank == 0):
allMembership = []
#flatten the list of lists
for member in allMembers:
# based on newmembership calculate new centroids
allMembership.extend(member)
newCen = updateCentroids(allMembership, k)
#broadcast newCentroids to every node
newCen = comm.bcast(newCen, root=0)
#write output file
if (rank == 0):
fo = open(o, "w+")
for c in newCen:
fo.write("%f %f\n" % (c[0], c[1]))
fo.close()
return 42
