def test_minimum_spanning_tree():
# Create a graph with two connected components.
graph = [[0,1,0,0,0],
[1,0,0,0,0],
[0,0,0,8,5],
[0,0,8,0,1],
[0,0,5,1,0]]
graph = np.asarray(graph)
# Create the expected spanning tree.
expected = [[0,1,0,0,0],
[0,0,0,0,0],
[0,0,0,0,5],
[0,0,0,0,1],
[0,0,0,0,0]]
expected = np.asarray(expected)
# Ensure minimum spanning tree code gives this expected output.
csgraph = csr_matrix(graph)
mintree = minimum_spanning_tree(csgraph)
npt.assert_array_equal(mintree.todense(), expected,
'Incorrect spanning tree found.')
# Ensure that the original graph was not modified.
npt.assert_array_equal(csgraph.todense(), graph,
'Original graph was modified.')
# Now let the algorithm modify the csgraph in place.
mintree = minimum_spanning_tree(csgraph, overwrite=True)
npt.assert_array_equal(mintree.todense(), expected,
'Graph was not properly modified to contain MST.')
np.random.seed(1234)
for N in (5, 10, 15, 20):
# Create a random graph.
graph = 3 + np.random.random((N, N))
csgraph = csr_matrix(graph)
# The spanning tree has at most N - 1 edges.
mintree = minimum_spanning_tree(csgraph)
assert_(mintree.nnz < N)
# Set the sub diagonal to 1 to create a known spanning tree.
idx = np.arange(N-1)
graph[idx,idx+1] = 1
csgraph = csr_matrix(graph)
mintree = minimum_spanning_tree(csgraph)
# We expect to see this pattern in the spanning tree and otherwise
# have this zero.
expected = np.zeros((N, N))
expected[idx, idx+1] = 1
npt.assert_array_equal(mintree.todense(), expected,
'Incorrect spanning tree found.')
def test_minimum_spanning_tree():
# Create a graph with two connected components.
graph = [[0, 1, 0, 0, 0],
[1, 0, 0, 0, 0],
[0, 0, 0, 8, 5],
[0, 0, 8, 0, 1],
[0, 0, 5, 1, 0]]
graph = np.asarray(graph)
# Create the expected spanning tree.
expected = [[0, 1, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 5],
[0, 0, 0, 0, 1],
[0, 0, 0, 0, 0]]
expected = np.asarray(expected)
# Ensure minimum spanning tree code gives this expected output.
csgraph = csr_matrix(graph)
mintree = minimum_spanning_tree(csgraph)
npt.assert_array_equal(mintree.todense(), expected,
'Incorrect spanning tree found.')
# Ensure that the original graph was not modified.
npt.assert_array_equal(csgraph.todense(), graph,
'Original graph was modified.')
# Now let the algorithm modify the csgraph in place.
mintree = minimum_spanning_tree(csgraph, overwrite=True)
npt.assert_array_equal(mintree.todense(), expected,
'Graph was not properly modified to contain MST.')
np.random.seed(1234)
for N in (5, 10, 15, 20):
# Create a random graph.
graph = 3 + np.random.random((N, N))
csgraph = csr_matrix(graph)
# The spanning tree has at most N - 1 edges.
mintree = minimum_spanning_tree(csgraph)
assert_(mintree.nnz < N)
# Set the sub diagonal to 1 to create a known spanning tree.
idx = np.arange(N - 1)
graph[idx, idx + 1] = 1
csgraph = csr_matrix(graph)
mintree = minimum_spanning_tree(csgraph)
# We expect to see this pattern in the spanning tree and otherwise
# have this zero.
expected = np.zeros((N, N))
expected[idx, idx + 1] = 1
npt.assert_array_equal(mintree.todense(), expected,
'Incorrect spanning tree found.')
def chow_liu_tree(y_):
# compute mutual information using sklearn
n_labels = y_.shape[1]
mi = np.zeros((n_labels, n_labels))
for i in xrange(n_labels):
for j in xrange(n_labels):
mi[i, j] = mutual_info_score(y_[:, i], y_[:, j])
mst = minimum_spanning_tree(sparse.csr_matrix(-mi))
edges = np.vstack(mst.nonzero()).T
edges.sort(axis=1)
return edges
def chow_liu_tree(y_):
# compute mutual information using sklearn
n_labels = y_.shape[1]
mi = np.zeros((n_labels, n_labels))
for i in xrange(n_labels):
for j in xrange(n_labels):
mi[i, j] = mutual_info_score(y_[:, i], y_[:, j])
mst = minimum_spanning_tree(sparse.csr_matrix(-mi))
edges = np.vstack(mst.nonzero()).T
edges.sort(axis=1)
return edges
