def test_small_graph_prim():
g = create_graph(directed=False)
generators.gnp_random_graph(g, n=500, p=0.1, seed=17)
mst_w, mst_edges = spanning.prim(g)
assert mst_w == 499.0
def test_prim():
g = build_graph()
mst_w, mst_edges = spanning.prim(g)
assert mst_w == 9.0
expected = set([0, 1, 2, 3, 4, 5, 6, 7, 8])
solution = set(mst_edges)
assert expected == solution
def test_result_with_difference_symmetric():
g = build_graph()
mst_weight, mst_tree = spanning.prim(g)
non_mst_edges = g.edges - set(mst_tree)
# test that our intermediate set results, property implement
# method _from_iterable
assert non_mst_edges == {9, 10, 11, 12, 13, 14, 15, 16, 17}
def test_anyhashableg_prim():
g = create_graph(
directed=False,
allowing_self_loops=False,
allowing_multiple_edges=False,
weighted=True,
any_hashable=True,
)
g.add_vertex("0")
g.add_vertex("1")
g.add_vertex("2")
e1 = g.add_edge("0", "1")
g.set_edge_weight(e1, 1.0)
e2 = g.add_edge("1", "2")
g.set_edge_weight(e2, 2.0)
e3 = g.add_edge("2", "0")
g.set_edge_weight(e3, 3.0)
mst_w, mst_edges = spanning.prim(g)
assert mst_w == 3.0
assert set(mst_edges) == {e1, e2}
# %%
# We also assign some random weights from [0, 1) to the edges.
rng = random.Random(17)
for e in g.edges:
g.set_edge_weight(e, rng.random())
# %%
# Let us print the graph
print(g)
# %%
# Then, we execute Prim's algorithm.
mst_weight, mst_edges = spanning.prim(g)
# %%
# The result is a tuple which contains the weight and the minimum spanning tree.
#
print('mst weight: {}'.format(mst_weight))
print('mst tree: {}'.format(mst_edges))
# %%
# Ploting the graph with highlighted the MST edges
#
positions = drawing.layout(g, name="fruchterman_reingold", seed=17)
drawing.draw_jgrapht_vertices(g, positions=positions)