def test_mutual_information_not_fixed_size(self):
"""
Raise a ``ValueError`` if the provided network is not fixed sized, and
the ``size`` argument is ``None``
"""
with self.assertRaises(ValueError):
mutual_information(ECA(30), timesteps=10, local=False)
mutual_information(ECA(30), timesteps=10, size=5, local=False)
def test_mutual_information_not_network(self):
"""
Raise a ``TypeError`` if the provided network is not actually a network
"""
with self.assertRaises(TypeError):
mutual_information(5, timesteps=10, local=False)
with self.assertRaises(TypeError):
mutual_information(5, timesteps=10, local=True)
def test_architecture_mi(self):
"""
The architecture correctly computes the mutual information
"""
k, timesteps = 5, 20
arch = Architecture(s_pombe, k=k, timesteps=timesteps)
expected_mi = mutual_information(s_pombe, timesteps=timesteps)
got_mi = arch.mutual_information()
self.assertEqual(got_mi.shape, expected_mi.shape)
for got, expected in zip(got_mi.flatten(), expected_mi.flatten()):
self.assertAlmostEqual(expected, got, places=6)
expected_mi = mutual_information(s_pombe,
timesteps=timesteps,
local=True)
got_mi = arch.mutual_information(local=True)
self.assertEqual(got_mi.shape, expected_mi.shape)
for got, expected in zip(got_mi.flatten(), expected_mi.flatten()):
self.assertAlmostEqual(expected, got, places=6)
def test_local_mutual_information_s_pombe(self):
"""
local ``mutual_information`` averages to the correct values for
``s_pombe``
"""
known_mi = np.asarray(
[[
0.162326, 0.013747, 0.004285, 0.004285, 0.013409, 0.015862,
0.005170, 0.005170, 0.011028
],
[
0.013747, 0.566610, 0.007457, 0.007457, 0.006391, 0.327908,
0.006761, 0.006761, 0.004683
],
[
0.004285, 0.007457, 0.838373, 0.475582, 0.211577, 0.004329,
0.459025, 0.459025, 0.127557
],
[
0.004285, 0.007457, 0.475582, 0.838373, 0.211577, 0.004329,
0.459025, 0.459025, 0.127557
],
[
0.013409, 0.006391, 0.211577, 0.211577, 0.574591, 0.007031,
0.175608, 0.175608, 0.012334
],
[
0.015862, 0.327908, 0.004329, 0.004329, 0.007031, 0.519051,
0.006211, 0.006211, 0.002607
],
[
0.005170, 0.006761, 0.459025, 0.459025, 0.175608, 0.006211,
0.808317, 0.493495, 0.103905
],
[
0.005170, 0.006761, 0.459025, 0.459025, 0.175608, 0.006211,
0.493495, 0.808317, 0.103905
],
[
0.011028, 0.004683, 0.127557, 0.127557, 0.012334, 0.002607,
0.103905, 0.103905, 0.634238
]])
computed_mi = mutual_information(s_pombe, timesteps=20, local=True)
self.assertEqual((9, 9, 512, 21), computed_mi.shape)
for i in range(9):
for j in range(9):
self.assertAlmostEqual(known_mi[i, j],
np.mean(computed_mi[i, j]),
places=6)
def test_mutual_information_s_pombe(self):
"""
``mutual_information`` computes the correct values for ``s_pombe``
"""
known_mi = np.asarray(
[[
0.162326, 0.013747, 0.004285, 0.004285, 0.013409, 0.015862,
0.005170, 0.005170, 0.011028
],
[
0.013747, 0.566610, 0.007457, 0.007457, 0.006391, 0.327908,
0.006761, 0.006761, 0.004683
],
[
0.004285, 0.007457, 0.838373, 0.475582, 0.211577, 0.004329,
0.459025, 0.459025, 0.127557
],
[
0.004285, 0.007457, 0.475582, 0.838373, 0.211577, 0.004329,
0.459025, 0.459025, 0.127557
],
[
0.013409, 0.006391, 0.211577, 0.211577, 0.574591, 0.007031,
0.175608, 0.175608, 0.012334
],
[
0.015862, 0.327908, 0.004329, 0.004329, 0.007031, 0.519051,
0.006211, 0.006211, 0.002607
],
[
0.005170, 0.006761, 0.459025, 0.459025, 0.175608, 0.006211,
0.808317, 0.493495, 0.103905
],
[
0.005170, 0.006761, 0.459025, 0.459025, 0.175608, 0.006211,
0.493495, 0.808317, 0.103905
],
[
0.011028, 0.004683, 0.127557, 0.127557, 0.012334, 0.002607,
0.103905, 0.103905, 0.634238
]])
computed_mi = mutual_information(s_pombe, timesteps=20)
self.assertEqual(known_mi.shape, computed_mi.shape)
for got, expected in zip(computed_mi.flatten(), known_mi.flatten()):
self.assertAlmostEqual(expected, got, places=6)