# once we have k clusters,
# examine each cross-clusters edge and pick the minimum
if min_spacing > w:
min_spacing = w
self.spacing = min_spacing
def __sorted_edges(self):
edges = []
for u in range(self.graph.V()):
for e in self.graph.edges(u):
# source vertex, dest vertex, edge weight
edges.append((u, e[0], e[1]))
def compare_edges(e1, e2):
return cmp(e1[2], e2[2])
return sorted(edges, compare_edges)
def max_spacing(self):
return self.spacing
if __name__ == "__main__":
graph = graph_utils.load_weighted_graph('../data/clustering1.txt', False)
slk = SingleLinkedClustering(4, graph)
print slk.max_spacing()
self.w += w
def __sorted_edges(self):
edges = []
for u in range(self.graph.V()):
for e in self.graph.edges(u):
# source vertex, dest vertex, edge weight
edges.append((u, e[0], e[1]))
def compare_edges(e1, e2):
return cmp(e1[2], e2[2])
return sorted(edges, compare_edges)
def weight(self):
return self.w
if __name__ == "__main__":
graph = graph_utils.load_weighted_graph("../data/tinyEWG.txt")
mst = KruskalMST(graph)
assert abs(1.81 - mst.weight()) < 0.0001
graph = graph_utils.load_weighted_graph("../data/mediumEWG.txt")
mst = KruskalMST(graph)
assert abs(10.46351 - mst.weight()) < 0.0001