def iterativeAlgorithmPrima(self):
T = nx.Graph()
for n in T:
T.node[n]=self.node[n].copy()
T.graph=self.graph.copy()
for u, v, d in nx.prim_mst_edges(self, data=True):
T.add_edge(u,v,d)
yield T
def map_mst_bipartite(subset, frame_instances):
logging.debug('Calculating MST in bipartite graph')
graph = nx.Graph()
for id_frame1 in subset['left']:
for id_frame2 in subset['right']:
distance = calculate_distance_measure(frame_instances[id_frame1],
frame_instances[id_frame2])
graph.add_edge(id_frame1, id_frame2, weight=distance)
return list(nx.prim_mst_edges(graph, data=True))
def map_mst_complete(subset, frame_instances):
logging.debug('Calculating MST in completed graph')
graph = nx.Graph()
size = len(subset)
for i in xrange(size):
id_frame1 = subset[i]
for j in xrange(i + 1, size):
id_frame2 = subset[j]
distance = calculate_distance_measure(frame_instances[id_frame1],
frame_instances[id_frame2])
graph.add_edge(id_frame1, id_frame2, weight=distance)
return list(nx.prim_mst_edges(graph, data=True))
def prim_mst(G, weight="weight"):
"""Return a minimum spanning tree or forest of an undirected
weighted graph.
A minimum spanning tree is a subgraph of the graph (a tree) with
the minimum sum of edge weights.
If the graph is not connected a spanning forest is constructed. A
spanning forest is a union of the spanning trees for each
connected component of the graph.
Parameters
----------
G : NetworkX Graph
weight : string
Edge data key to use for weight (default 'weight').
Returns
-------
G : NetworkX Graph
A minimum spanning tree or forest.
Examples
--------
>>> G=nx.cycle_graph(4)
>>> G.add_edge(0,3,weight=2) # assign weight 2 to edge 0-3
>>> T=nx.prim_mst(G)
>>> print(sorted(T.edges(data=True)))
[(0, 1, {}), (1, 2, {}), (2, 3, {})]
Notes
-----
Uses Prim's algorithm.
If the graph edges do not have a weight attribute a default weight of 1
will be used.
"""
T = nx.Graph(nx.prim_mst_edges(G, weight=weight, data=True))
# Add isolated nodes
if len(T) != len(G):
T.add_nodes_from([n for n, d in G.degree().items() if d == 0])
# Add node and graph attributes as shallow copy
for n in T:
T.node[n] = G.node[n].copy()
T.graph = G.graph.copy()
return T
def prim_mst(G, weight='weight'):
"""Return a minimum spanning tree or forest of an undirected
weighted graph.
A minimum spanning tree is a subgraph of the graph (a tree) with
the minimum sum of edge weights.
If the graph is not connected a spanning forest is constructed. A
spanning forest is a union of the spanning trees for each
connected component of the graph.
Parameters
----------
G : NetworkX Graph
weight : string
Edge data key to use for weight (default 'weight').
Returns
-------
G : NetworkX Graph
A minimum spanning tree or forest.
Examples
--------
>>> G=nx.cycle_graph(4)
>>> G.add_edge(0,3,weight=2) # assign weight 2 to edge 0-3
>>> T=nx.prim_mst(G)
>>> print(sorted(T.edges(data=True)))
[(0, 1, {}), (1, 2, {}), (2, 3, {})]
Notes
-----
Uses Prim's algorithm.
If the graph edges do not have a weight attribute a default weight of 1
will be used.
"""
T = nx.Graph(nx.prim_mst_edges(G, weight=weight, data=True))
# Add isolated nodes
if len(T) != len(G):
T.add_nodes_from([n for n, d in G.degree().items() if d == 0])
# Add node and graph attributes as shallow copy
for n in T:
T.node[n] = G.node[n].copy()
T.graph = G.graph.copy()
return T
def test_prim_mst_edges(self):
edgelist=sorted(nx.prim_mst_edges(self.G))
edgelist=sorted((sorted((u, v))[0], sorted((u, v))[1], d)
for u,v,d in edgelist)
assert_equal(edgelist,self.tree_edgelist)
def test_prim_mst_edges(self):
edgelist = sorted(nx.prim_mst_edges(self.G))
edgelist = sorted(
(sorted((u, v))[0], sorted((u, v))[1], d) for u, v, d in edgelist)
assert_equal(edgelist, self.tree_edgelist)
for new_dst in g[dst]:
# skip edges already queued
if not g[dst][new_dst].get('queued'):
g[dst][new_dst]['queued'] = True
g[new_dst][dst]['queued'] = True
heapq.heappush(h, (g[dst][new_dst]['weight'], dst,
new_dst))
return mst
if __name__ == '__main__':
g = utils.read_graph()
# edges = run(g)
# print '-'*10, 'Your answer', '-'*10
# print 'Edges:', edges
# print 'Costs:', sum([e[2]['weight'] for e in edges])
# print
# mst = list(nx.prim_mst_edges(g, data=True))
# print '-'*10, 'Model answer', '-'*10
# print 'Edges:', mst
# print 'Costs:', sum([e[2]['weight'] for e in mst])
for _ in range(N_TESTS):
h = g.copy()
edges = run(h)
mst = list(nx.prim_mst_edges(h, data=True))
assert(sum([e[2]['weight'] for e in edges]) ==
sum([e[2]['weight'] for e in mst]))
