def test_message_to_child(self):
"""
Test the message of CategoricalMarkovChain to child
"""
with warnings.catch_warnings(record=True) as w:
# 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_init(self):
"""
Test the creation of CategoricalMarkovChain object
"""
#
# Plates
#
p0 = np.random.rand(2)
P = np.random.rand(3, 2, 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.rand(4, 2)
P = np.random.rand(3, 2, 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.rand(2)
P = np.random.rand(4, 3, 2, 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_random(self):
"""
Test random sampling of categorical Markov chain
"""
with warnings.catch_warnings():
warnings.simplefilter("ignore", RuntimeWarning)
# Simple random sample
Z = CategoricalMarkovChain([1, 0], [[0, 1],
[1, 0]],
states=3)
z = Z.random()
self.assertAllClose(z, [0, 1, 0])
# Draw sample with plates
p0 = [ [1,0], [0,1] ]
P = [ [ [[0,1],
[1,0]] ],
[ [[1,0],
[0,1]] ] ]
Z = CategoricalMarkovChain(p0, P,
states=3)
z = Z.random()
self.assertAllClose(z, [[0, 1, 0], [1, 1, 1]])
# Draw sample with plates, parameters broadcasted
Z = CategoricalMarkovChain([1, 0], [[0, 1],
[1, 0]],
states=3,
plates=(3,4))
z = Z.random()
self.assertAllClose(z, np.ones((3,4,1))*[0, 1, 0])
# Draw sample with time-varying transition matrix
p0 = [1, 0]
P = [ [[0,1],
[1,0]],
[[1,0],
[0,1]],
[[1, 0],
[1, 0]] ]
Z = CategoricalMarkovChain(p0, P,
states=4)
z = Z.random()
self.assertAllClose(z, [0, 1, 1, 0])
pass
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_random(self):
"""
Test random sampling of categorical Markov chain
"""
with warnings.catch_warnings():
warnings.simplefilter("ignore", RuntimeWarning)
# Simple random sample
Z = CategoricalMarkovChain([1, 0], [[0, 1], [1, 0]], states=3)
z = Z.random()
self.assertAllClose(z, [0, 1, 0])
# Draw sample with plates
p0 = [[1, 0], [0, 1]]
P = [[[[0, 1], [1, 0]]], [[[1, 0], [0, 1]]]]
Z = CategoricalMarkovChain(p0, P, states=3)
z = Z.random()
self.assertAllClose(z, [[0, 1, 0], [1, 1, 1]])
# Draw sample with plates, parameters broadcasted
Z = CategoricalMarkovChain([1, 0], [[0, 1], [1, 0]],
states=3,
plates=(3, 4))
z = Z.random()
self.assertAllClose(z, np.ones((3, 4, 1)) * [0, 1, 0])
# Draw sample with time-varying transition matrix
p0 = [1, 0]
P = [[[0, 1], [1, 0]], [[1, 0], [0, 1]], [[1, 0], [1, 0]]]
Z = CategoricalMarkovChain(p0, P, states=4)
z = Z.random()
self.assertAllClose(z, [0, 1, 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
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
