def test_neg_inf():
expected_costs = np.where(a == 1, np.inf, 0)
expected_path = [(1, 6), (1, 5), (1, 4), (1, 3), (1, 2), (2, 1), (3, 1),
(4, 1), (5, 1), (6, 1)]
test_neg = np.where(a == 1, -1, 0)
test_inf = np.where(a == 1, np.inf, 0)
m = mcp.MCP(test_neg, fully_connected=True)
costs, traceback = m.find_costs([(1, 6)])
return_path = m.traceback((6, 1))
assert_array_equal(costs, expected_costs)
assert_array_equal(return_path, expected_path)
m = mcp.MCP(test_inf, fully_connected=True)
costs, traceback = m.find_costs([(1, 6)])
return_path = m.traceback((6, 1))
assert_array_equal(costs, expected_costs)
assert_array_equal(return_path, expected_path)
def test_no_diagonal():
m = mcp.MCP(a, fully_connected=False)
costs, traceback = m.find_costs([(1, 6)])
return_path = m.traceback((7, 2))
assert_array_equal(costs,
[[2., 1., 1., 1., 1., 1., 1., 2.],
[1., 0., 0., 0., 0., 0., 0., 1.],
[1., 0., 1., 1., 1., 1., 1., 2.],
[1., 0., 1., 2., 2., 2., 2., 3.],
[1., 0., 1., 2., 3., 3., 3., 4.],
[1., 0., 1., 2., 3., 4., 4., 5.],
[1., 0., 1., 2., 3., 4., 5., 6.],
[2., 1., 2., 3., 4., 5., 6., 7.]])
assert_array_equal(return_path,
[(1, 6),
(1, 5),
(1, 4),
(1, 3),
(1, 2),
(1, 1),
(2, 1),
(3, 1),
(4, 1),
(5, 1),
(6, 1),
(7, 1),
(7, 2)])
def test_offsets():
offsets = [(1, i) for i in range(10)] + [(1, -i) for i in range(1, 10)]
m = mcp.MCP(a, offsets=offsets)
costs, traceback = m.find_costs([(1, 6)])
assert_array_equal(
traceback,
[[-2, -2, -2, -2, -2, -2, -2, -2], [-2, -2, -2, -2, -2, -2, -1, -2],
[15, 14, 13, 12, 11, 10, 0, 1], [10, 0, 1, 2, 3, 4, 5, 6],
[10, 0, 1, 2, 3, 4, 5, 6], [10, 0, 1, 2, 3, 4, 5, 6],
[10, 0, 1, 2, 3, 4, 5, 6], [10, 0, 1, 2, 3, 4, 5, 6]])
def test_offsets():
offsets = [(1, i) for i in range(10)] + [(1, -i) for i in range(1, 10)]
with expected_warnings(['Upgrading NumPy' + warning_optional]):
m = mcp.MCP(a, offsets=offsets)
costs, traceback = m.find_costs([(1, 6)])
assert_array_equal(
traceback,
[[-2, -2, -2, -2, -2, -2, -2, -2], [-2, -2, -2, -2, -2, -2, -1, -2],
[15, 14, 13, 12, 11, 10, 0, 1], [10, 0, 1, 2, 3, 4, 5, 6],
[10, 0, 1, 2, 3, 4, 5, 6], [10, 0, 1, 2, 3, 4, 5, 6],
[10, 0, 1, 2, 3, 4, 5, 6], [10, 0, 1, 2, 3, 4, 5, 6]])
def test_basic():
with expected_warnings(['Upgrading NumPy' + warning_optional]):
m = mcp.MCP(a, fully_connected=True)
costs, traceback = m.find_costs([(1, 6)])
return_path = m.traceback((7, 2))
assert_array_equal(
costs,
[[1., 1., 1., 1., 1., 1., 1., 1.], [1., 0., 0., 0., 0., 0., 0., 1.],
[1., 0., 1., 1., 1., 1., 1., 1.], [1., 0., 1., 2., 2., 2., 2., 2.],
[1., 0., 1., 2., 3., 3., 3., 3.], [1., 0., 1., 2., 3., 4., 4., 4.],
[1., 0., 1., 2., 3., 4., 5., 5.], [1., 1., 1., 2., 3., 4., 5., 6.]])
assert_array_equal(return_path, [(1, 6), (1, 5), (1, 4), (1, 3), (1, 2),
(2, 1), (3, 1), (4, 1), (5, 1), (6, 1),
(7, 2)])
def _test_random(shape):
# Just tests for crashing -- not for correctness.
a = np.random.rand(*shape).astype(np.float32)
starts = [[0] * len(shape), [-1] * len(shape),
(np.random.rand(len(shape)) * shape).astype(int)]
ends = [(np.random.rand(len(shape)) * shape).astype(int) for i in range(4)]
m = mcp.MCP(a, fully_connected=True)
costs, offsets = m.find_costs(starts)
for point in [(np.random.rand(len(shape)) * shape).astype(int)
for i in range(4)]:
m.traceback(point)
m._reset()
m.find_costs(starts, ends)
for end in ends:
m.traceback(end)
return a, costs, offsets
