def test_balanced_tree_maxmin():
""" max-min epistemic closure on a balanced tree with branching factor 3
and depth 2.
"""
G = np.matrix([[0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]])
G = sp.csr_matrix(G, dtype=np.double)
G = weighted(G, undirected=True)
expect = np.matrix(
[[1., 1., 1., 1., 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2],
[1., 1., 0.25, 0.25, 1., 1., 1., 0.2, 0.2, 0.2, 0.2, 0.2, 0.2],
[1., 0.25, 1., 0.25, 0.2, 0.2, 0.2, 1., 1., 1., 0.2, 0.2, 0.2],
[1., 0.25, 0.25, 1., 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 1., 1., 1.0],
[0.2, 1., 0.2, 0.2, 1., 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2],
[0.2, 1., 0.2, 0.2, 0.2, 1., 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2],
[0.2, 1., 0.2, 0.2, 0.2, 0.2, 1., 0.2, 0.2, 0.2, 0.2, 0.2, 0.2],
[0.2, 0.2, 1., 0.2, 0.2, 0.2, 0.2, 1., 0.2, 0.2, 0.2, 0.2, 0.2],
[0.2, 0.2, 1., 0.2, 0.2, 0.2, 0.2, 0.2, 1., 0.2, 0.2, 0.2, 0.2],
[0.2, 0.2, 1., 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 1., 0.2, 0.2, 0.2],
[0.2, 0.2, 0.2, 1., 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 1., 0.2, 0.2],
[0.2, 0.2, 0.2, 1., 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 1., 0.2],
[0.2, 0.2, 0.2, 1., 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 1.0]])
run_test(G, expect)
def test_graph5_maxmin():
""" max-min epistemic closure on an arbitraty graph (ex. #5). """
G = np.matrix([[0, 0, 1, 1, 1], [0, 0, 1, 1, 0], [1, 1, 0, 0, 0],
[1, 1, 0, 0, 1], [1, 0, 0, 1, 0]])
G = sp.csr_matrix(G, dtype=np.double)
G = weighted(G, undirected=True)
expect = np.matrix([[1., 0.33, 1., 1., 1.00], [0.33, 1., 1., 1., 0.25],
[1., 1., 1., 0.33, 0.25], [1., 1., 0.33, 1., 1.00],
[1., 0.25, 0.25, 1., 1.00]])
run_test(G, expect)
def test_cycle_graph_maxmin():
""" max-min epistemic closure on a 4-cycle. """
G = np.matrix([[False, True, False, False, True],
[True, False, True, False, False],
[False, True, False, True, False],
[False, False, True, False, True],
[True, False, False, True, False]])
G = sp.csr_matrix(G, dtype=np.double)
G = weighted(G, undirected=True)
expect = np.matrix([[1., 1., 0.33, 0.33, 1.00], [1., 1., 1., 0.33, 0.33],
[0.33, 1., 1., 1., 0.33], [0.33, 0.33, 1., 1., 1.00],
[1., 0.33, 0.33, 1., 1.00]])
run_test(G, expect)
def test_graph2_maxmin():
""" max-min epistemic closure on an arbitraty graph (ex. #2). """
data = np.ones(12, dtype=np.double)
ptr = np.array([0, 3, 6, 9, 10, 11, 12])
idx = np.array([1, 2, 3, 0, 2, 4, 0, 1, 5, 0, 1, 2])
N = 6
G = sp.csr_matrix((data, idx, ptr), shape=(N, N))
G = weighted(G, undirected=True)
expect = np.matrix([[1., 1., 1., 1., 0.25, 0.25],
[1., 1., 1., 0.25, 1., 0.25],
[1., 1., 1., 0.25, 0.25, 1.00],
[1., 0.25, 0.25, 1., 0.25, 0.25],
[0.25, 1., 0.25, 0.25, 1., 0.25],
[0.25, 0.25, 1., 0.25, 0.25, 1.00]])
run_test(G, expect)
def test_graph1_maxmin():
""" max-min epistemic closure on an arbitraty graph (ex. #1). """
G = np.matrix(
[[0., 1., 0., 0., 0., 1., 0., 0.], [1., 0., 1., 0., 0., 0., 1., 0.],
[0., 1., 0., 1., 0., 0., 0., 0.], [0., 0., 1., 0., 1., 1., 1., 0.],
[0., 0., 0., 1., 0., 0., 0., 0.], [1., 0., 0., 1., 0., 0., 0., 1.],
[0., 1., 0., 1., 0., 0., 0., 0.], [0., 0., 0., 0., 0., 1., 0., 0.]],
dtype=np.double)
G = weighted(G, undirected=True)
expect = np.matrix([[1., 1., 0.25, 0.25, 0.2, 1., 0.25, 0.25],
[1., 1., 1., 0.33, 0.2, 0.33, 1., 0.25],
[0.25, 1., 1., 1., 0.2, 0.25, 0.25, 0.25],
[0.25, 0.33, 1., 1., 1., 1., 1., 0.25],
[0.2, 0.2, 0.2, 1., 1., 0.2, 0.2, 0.20],
[1., 0.33, 0.25, 1., 0.2, 1., 0.25, 1.00],
[0.25, 1., 0.25, 1., 0.2, 0.25, 1., 0.25],
[0.25, 0.25, 0.25, 0.25, 0.2, 1., 0.25, 1.00]])
run_test(G, expect)
