def test_init(self):
"""
Test the creation of CategoricalMarkovChain object
"""
#
# Plates
#
p0 = np.random.dirichlet([1, 1])
P = np.random.dirichlet([1, 1], size=(3, 2))
Z = CategoricalMarkovChain(p0, P)
self.assertEqual((), Z.plates, msg="Incorrect plates")
self.assertEqual(((2, ), (3, 2, 2)),
Z.dims,
msg="Incorrect dimensions")
p0 = np.random.dirichlet([1, 1], size=(4, ))
P = np.random.dirichlet([1, 1], size=(3, 2))
Z = CategoricalMarkovChain(p0, P)
self.assertEqual((4, ), Z.plates, msg="Incorrect plates")
self.assertEqual(((2, ), (3, 2, 2)),
Z.dims,
msg="Incorrect dimensions")
p0 = np.random.dirichlet([1, 1])
P = np.random.dirichlet([1, 1], size=(4, 3, 2))
Z = CategoricalMarkovChain(p0, P)
self.assertEqual((4, ), Z.plates, msg="Incorrect plates")
self.assertEqual(((2, ), (3, 2, 2)),
Z.dims,
msg="Incorrect dimensions")
# Test some overflow bugs
p0 = np.array([0.5, 0.5])
P = Dirichlet(1e-3 * np.ones(2), plates=(2, ))
Z = CategoricalMarkovChain(p0, P, states=2000)
u = Z._message_to_child()
self.assertTrue(np.all(~np.isnan(u[0])), msg="Nans in moments")
self.assertTrue(np.all(~np.isnan(u[1])), msg="Nans in moments")
pass
def test_message_to_child(self):
"""
Test the message of CategoricalMarkovChain to child
"""
with warnings.catch_warnings():
warnings.simplefilter("ignore", RuntimeWarning)
# Deterministic oscillator
p0 = np.array([1.0, 0.0])
P = np.array(3 * [[[0.0, 1.0], [1.0, 0.0]]])
Z = CategoricalMarkovChain(p0, P)
u = Z._message_to_child()
self.assertAllClose(u[0], [1.0, 0])
self.assertAllClose(
u[1], [[[0.0, 1.0], [0.0, 0.0]], [[0.0, 0.0], [1.0, 0.0]],
[[0.0, 1.0], [0.0, 0.0]]])
# Maximum randomness
p0 = np.array([0.5, 0.5])
P = np.array(3 * [[[0.5, 0.5], [0.5, 0.5]]])
Z = CategoricalMarkovChain(p0, P)
u = Z._message_to_child()
self.assertAllClose(u[0], [0.5, 0.5])
self.assertAllClose(
u[1],
[[[0.25, 0.25], [0.25, 0.25]], [[0.25, 0.25], [0.25, 0.25]],
[[0.25, 0.25], [0.25, 0.25]]])
# Random init, deterministic dynamics
p0 = np.array([0.5, 0.5])
P = np.array(3 * [[[0, 1], [1, 0]]])
Z = CategoricalMarkovChain(p0, P)
u = Z._message_to_child()
self.assertAllClose(u[0], [0.5, 0.5])
self.assertAllClose(
u[1], [[[0.0, 0.5], [0.5, 0.0]], [[0.0, 0.5], [0.5, 0.0]],
[[0.0, 0.5], [0.5, 0.0]]])
# Test plates
p0 = np.array([[1.0, 0.0], [0.5, 0.5]])
P = np.array([[[[0.0, 1.0], [1.0, 0.0]]], [[[0.5, 0.5], [0.5,
0.5]]]])
Z = CategoricalMarkovChain(p0, P)
u = Z._message_to_child()
self.assertAllClose(u[0], [[1.0, 0.0], [0.5, 0.5]])
self.assertAllClose(
u[1],
[[[[0.0, 1.0], [0.0, 0.0]]], [[[0.25, 0.25], [0.25, 0.25]]]])
# Test broadcasted state transition probabilities
p0 = np.array([1.0, 0.0])
P = Dirichlet([1e-10, 1e10], plates=(3, 2))
Z = CategoricalMarkovChain(p0, P)
u = Z._message_to_child()
self.assertAllClose(u[0], [1.0, 0])
self.assertAllClose(
u[1], [[[0.0, 1.0], [0.0, 0.0]], [[0.0, 0.0], [0.0, 1.0]],
[[0.0, 0.0], [0.0, 1.0]]])
pass
def test_message_to_child(self):
"""
Test the message of CategoricalMarkovChain to child
"""
np.seterr(divide='ignore')
# Deterministic oscillator
p0 = np.array([1.0, 0.0])
P = np.array(3 * [[[0.0, 1.0], [1.0, 0.0]]])
Z = CategoricalMarkovChain(p0, P)
u = Z._message_to_child()
self.assertAllClose(u[0], [1.0, 0])
self.assertAllClose(
u[1], [[[0.0, 1.0], [0.0, 0.0]], [[0.0, 0.0], [1.0, 0.0]],
[[0.0, 1.0], [0.0, 0.0]]])
# Maximum randomness
p0 = np.array([0.5, 0.5])
P = np.array(3 * [[[0.5, 0.5], [0.5, 0.5]]])
Z = CategoricalMarkovChain(p0, P)
u = Z._message_to_child()
self.assertAllClose(u[0], [0.5, 0.5])
self.assertAllClose(
u[1], [[[0.25, 0.25], [0.25, 0.25]], [[0.25, 0.25], [0.25, 0.25]],
[[0.25, 0.25], [0.25, 0.25]]])
# Random init, deterministic dynamics
p0 = np.array([0.5, 0.5])
P = np.array(3 * [[[0, 1], [1, 0]]])
Z = CategoricalMarkovChain(p0, P)
u = Z._message_to_child()
self.assertAllClose(u[0], [0.5, 0.5])
self.assertAllClose(
u[1], [[[0.0, 0.5], [0.5, 0.0]], [[0.0, 0.5], [0.5, 0.0]],
[[0.0, 0.5], [0.5, 0.0]]])
# Unnormalized probabilities
p0 = np.array([2, 2])
P = np.array([[[4, 4], [4, 4]], [[8, 8], [8, 8]], [[20, 20], [20,
20]]])
Z = CategoricalMarkovChain(p0, P)
u = Z._message_to_child()
self.assertAllClose(u[0], [0.5, 0.5])
self.assertAllClose(
u[1], [[[0.25, 0.25], [0.25, 0.25]], [[0.25, 0.25], [0.25, 0.25]],
[[0.25, 0.25], [0.25, 0.25]]])
# Test plates
p0 = np.array([[1.0, 0.0], [0.5, 0.5]])
P = np.array([[[[0.0, 1.0], [1.0, 0.0]]], [[[0.5, 0.5], [0.5, 0.5]]]])
Z = CategoricalMarkovChain(p0, P)
u = Z._message_to_child()
self.assertAllClose(u[0], [[1.0, 0.0], [0.5, 0.5]])
self.assertAllClose(
u[1], [[[[0.0, 1.0], [0.0, 0.0]]], [[[0.25, 0.25], [0.25, 0.25]]]])
# Test broadcasted state transition probabilities
p0 = np.array([1.0, 0.0])
P = Dirichlet([1e-10, 1e10], plates=(3, 2))
Z = CategoricalMarkovChain(p0, P)
u = Z._message_to_child()
self.assertAllClose(u[0], [1.0, 0])
self.assertAllClose(
u[1], [[[0.0, 1.0], [0.0, 0.0]], [[0.0, 0.0], [0.0, 1.0]],
[[0.0, 0.0], [0.0, 1.0]]])
pass