def test_conditional_entropy_2D(self):
xs = np.random.randint(0, 5, 20)
ys = np.random.randint(0, 5, 20)
expect = conditional_entropy(xs, ys)
us = np.copy(np.reshape(xs, (4, 5)))
vs = np.copy(np.reshape(ys, (4, 5)))
got = conditional_entropy(us, vs)
self.assertAlmostEqual(expect, got)
def test_conditional_entropy_2D(self):
xs = np.random.randint(0, 5, 20)
ys = np.random.randint(0, 5, 20)
expect = conditional_entropy(xs, ys, local=True)
self.assertEqual(xs.shape, expect.shape)
us = np.copy(np.reshape(xs, (4, 5)))
vs = np.copy(np.reshape(ys, (4, 5)))
got = conditional_entropy(us, vs, local=True)
self.assertTrue(us.shape, got.shape)
self.assertTrue((expect == np.reshape(got, expect.shape)).all())
def test_conditional_entropy_empty(self):
with self.assertRaises(ValueError):
conditional_entropy([], [], local=True)
with self.assertRaises(ValueError):
conditional_entropy([1, 2, 3], [], local=True)
with self.assertRaises(ValueError):
conditional_entropy([], [1, 2, 3], local=True)
def calculate_CE(signal, savePath):
'''
Calculate conditional entropy between each channels
Parameters
----------
signal : np.ndarray (epoch, channel, time)
Signal data
savePath : str
Path for saving the data
Returns
-------
CE : np.ndarray (epoch, features)
Conditional entropy between each channels
'''
assert isinstance(signal, np.ndarray) and signal.ndim == 3
assert isinstance(savePath, str)
num_channels = signal.shape[1]
channel_perm = list(permutations(range(num_channels), 2))
CE_all = np.zeros((signal.shape[0], len(channel_perm)))
for i_perm, (i, j) in enumerate(channel_perm):
for i_sample, sample in enumerate(signal):
# Begin from 0 and round to integer
signal1 = np.round(signal[i_sample, i, :] -
np.min(signal[i_sample, i, :]))
signal2 = np.round(signal[i_sample, j, :] -
np.min(signal[i_sample, j, :]))
CE = conditional_entropy(signal1, signal2)
CE_all[i_sample, i_perm] = CE
with open(savePath, 'wb') as fp:
pickle.dump(CE_all, fp)
return CE_all
def test_conditional_entropy(self):
self.assertAlmostEqual(0.899985,
conditional_entropy([0, 0, 1, 1, 1, 1, 0, 0, 0], [1, 0, 0, 1, 0, 0, 1, 0, 0]), places=6)
self.assertAlmostEqual(0.972765,
conditional_entropy([1, 0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 1, 1, 1, 1, 0, 0, 0]), places=6)
self.assertAlmostEqual(0.000000,
conditional_entropy([0, 0, 0, 0, 1, 1, 1, 1], [1, 1, 1, 1, 0, 0, 0, 0]), places=6)
self.assertAlmostEqual(0.000000,
conditional_entropy([0, 0, 1, 1, 1, 1, 0, 0, 0], [1, 1, 0, 0, 0, 0, 1, 1, 1]), places=6)
self.assertAlmostEqual(0.918296,
conditional_entropy([1, 1, 0, 1, 0, 1, 1, 1, 0], [1, 1, 0, 0, 0, 1, 0, 1, 1]), places=6)
self.assertAlmostEqual(0.918296,
conditional_entropy([0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 1, 1, 1]), places=6)
self.assertAlmostEqual(0.845516,
conditional_entropy([1, 1, 1, 1, 0, 0, 0, 0, 1], [1, 1, 1, 0, 0, 0, 1, 1, 1]), places=6)
self.assertAlmostEqual(0.899985,
conditional_entropy([1, 1, 0, 0, 1, 1, 0, 0, 1], [1, 1, 1, 0, 0, 0, 1, 1, 1]), places=6)
self.assertAlmostEqual(0.000000,
conditional_entropy([0, 1, 0, 1, 0, 1, 0, 1], [0, 2, 0, 2, 0, 2, 0, 2]), places=6)
self.assertAlmostEqual(0.918296,
conditional_entropy([0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2]), places=6)
self.assertAlmostEqual(0.444444,
conditional_entropy([0, 0, 1, 1, 2, 1, 1, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0]), places=6)
self.assertAlmostEqual(0.666667,
conditional_entropy([0, 1, 0, 0, 1, 0, 0, 1, 0], [1, 0, 0, 1, 0, 0, 1, 0, 0]), places=6)
self.assertAlmostEqual(0.606844,
conditional_entropy([1, 0, 0, 1, 0, 0, 1, 0], [2, 0, 1, 2, 0, 1, 2, 0]), places=6)
def test_conditional_entropy_negative_states(self):
with self.assertRaises(InformError):
conditional_entropy([-1, 0, 0], [0, 0, 1])
with self.assertRaises(InformError):
conditional_entropy([1, 0, 0], [0, 0, -1])
def test_conditional_entropy_size(self):
with self.assertRaises(ValueError):
conditional_entropy([1, 2, 3], [1, 2])
with self.assertRaises(ValueError):
conditional_entropy([1, 2], [1, 2, 3])
def test_conditional_entropy_dimensions(self):
with self.assertRaises(ValueError):
conditional_entropy([[1]], [1])
with self.assertRaises(ValueError):
conditional_entropy([1], [[1]])
