def max_spacing_k_clustering_big(graph_set):
disjoint_set = DisjointSet.DisjointSet()
# Create sets
for node in list(graph_set):
disjoint_set.makeSet(DisjointSet.Node(node))
# Merge distance 1 nodes
for node in list(graph_set):
candidates = hamming_distance_1_candidates(node)
matches = candidates & graph_set
for match in list(matches):
n1 = disjoint_set.nodes[match]
n2 = disjoint_set.nodes[node]
disjoint_set.union(n1, n2)
# Merge distance 2 nodes
for node in list(graph_set):
candidates = hamming_distance_2_candidates(node)
matches = candidates & graph_set
for match in list(matches):
n1 = disjoint_set.nodes[match]
n2 = disjoint_set.nodes[node]
disjoint_set.union(n1, n2)
return disjoint_set.connected_components
def max_spacing_k_clustering(graph, k):
disjoint_set = DisjointSet.DisjointSet()
# Create sets
for edge in graph:
disjoint_set.makeSet(DisjointSet.Node(edge[1]))
disjoint_set.makeSet(DisjointSet.Node(edge[2]))
# Union until there's k sets
for edge in graph:
distance = edge[0]
n1 = disjoint_set.nodes[edge[1]]
n2 = disjoint_set.nodes[edge[2]]
disjoint_set.union(n1, n2)
if disjoint_set.connected_components == k - 1:
break
return distance
