def k_means_clustering(k, num_nodes, edges):
print edges
U = UnionFind(num_nodes)
X = []
cluster_count = num_nodes
for everyedge in edges:
p = everyedge[1] - 1
q = everyedge[2] - 1
if cluster_count == k:
break
if U.root(p) != U.root(q):
X.append(everyedge)
U.union(p, q)
cluster_count = cluster_count - 1
print U
i = 0
k_min_spacing = float('inf')
while (i < len(edges)):
w, p, q = edges[i]
if U.root(p - 1) != U.root(q - 1):
print p, q, w
k_min_spacing = w if w < k_min_spacing else k_min_spacing
i = i + 1
return k_min_spacing
def k_means_cluster(edges, num_nodes, dist_bits):
# print pprint.pformat(edges)
U = UnionFind(num_nodes)
cluster_count = num_nodes
# cluster with hamming distance 0
for everyedge in edges:
hamming_0 = [everyedge]
for e in hamming_0:
try:
for p in edges[everyedge]:
for q in edges[e]:
if U.root(p) != U.root(q):
U.union(p, q)
cluster_count = cluster_count - 1
except:
continue
print "Hamming 0 done"
hamming_1 = create_hamming_1(everyedge)
for e in hamming_1:
try:
for p in edges[everyedge]:
for q in edges[e]:
# print p, q
if U.root(p) != U.root(q):
U.union(p, q)
cluster_count = cluster_count - 1
except:
continue
print "Hamming 1 done"
hamming_2 = create_hamming_2(everyedge)
for e in hamming_2:
try:
for p in edges[everyedge]:
for q in edges[e]:
# print p, q
if U.root(p) != U.root(q):
U.union(p, q)
cluster_count = cluster_count - 1
except:
continue
print "Hamming 2 done"
return cluster_count
