def test_derivate_large(self):
classes = ['a', 'b', 'c']
y = 'b'
x = random.randn(8, 3, 10) * 5 + 3
state_machine = DefaultStateMachine(classes)
parameters = Hacrf._initialize_parameters(state_machine, x.shape[2])
parameters = random.randn(*parameters.shape) * 10 - 2
test_model = _Model(state_machine, x, y)
print(test_model._lattice)
expected_dll = np.zeros(parameters.shape)
# Finite difference gradient approximation
delta = 10.0**-7
S, D = expected_dll.shape
for s in range(S):
for d in range(D):
dg = np.zeros(parameters.shape)
dg[s, d] = delta
y0, _ = test_model.forward_backward(parameters)
y1, _ = test_model.forward_backward(parameters + dg)
print(s, d, y0, y1)
expected_dll[s, d] = (y1 - y0) / delta
actual_ll, actual_dll = test_model.forward_backward(parameters)
print(expected_dll)
print(actual_dll)
self.assertEqual((np.isnan(actual_dll)).any(), False)
assert_array_almost_equal(actual_dll, expected_dll, decimal=TEST_PRECISION)
def test_derivate_large():
classes = ['a', 'b', 'c']
y = 'b'
x = random.randn(20, 3, 10) * 5 + 3
state_machine, states_to_classes = Hacrf._default_state_machine(classes)
parameters = Hacrf._initialize_parameters(state_machine, x.shape[2])
parameters = random.randn(*parameters.shape) * 10 - 2
test_model = _Model(state_machine, states_to_classes, x, y)
expected_dll = np.zeros(parameters.shape)
# Finite difference gradient approximation
delta = 10.0**-7
S, D = expected_dll.shape
for s in xrange(S):
for d in xrange(D):
dg = np.zeros(parameters.shape)
dg[s, d] = delta
y0, _ = test_model.forward_backward(parameters)
y1, _ = test_model.forward_backward(parameters + dg)
expected_dll[s, d] = (y1 - y0) / delta
actual_ll, actual_dll = test_model.forward_backward(parameters)
print (abs(actual_dll) - abs(expected_dll)).sum()
assert_array_almost_equal(actual_dll, expected_dll, decimal=4)
def test_derivate_medium(self):
classes = ['a', 'b']
parameters = np.array(range(-8, 8), dtype='float64').reshape((8, 2))
x = np.array([[[0, 1],
[2, 1]],
[[0, 1],
[1, 0.0]]])
y = 'a'
state_machine = DefaultStateMachine(classes)
test_model = _Model(state_machine, x, y)
print(test_model._lattice)
expected_dll = np.zeros(parameters.shape)
# Finite difference gradient approximation
delta = 10.0**-7
S, D = expected_dll.shape
for s in range(S):
for d in range(D):
dg = np.zeros(parameters.shape)
dg[s, d] = delta
y0, _ = test_model.forward_backward(parameters)
y1, _ = test_model.forward_backward(parameters + dg)
print(s, d, y0, y1)
expected_dll[s, d] = (y1 - y0) / delta
actual_ll, actual_dll = test_model.forward_backward(parameters)
print(expected_dll)
print(actual_dll)
assert_array_almost_equal(actual_dll, expected_dll, decimal=TEST_PRECISION)
def test_derivate_large():
classes = ['a', 'b', 'c']
y = 'b'
x = random.randn(20, 3, 10) * 5 + 3
state_machine, states_to_classes = Hacrf._default_state_machine(classes)
parameters = Hacrf._initialize_parameters(state_machine, x.shape[2])
parameters = random.randn(*parameters.shape) * 10 - 2
test_model = _Model(state_machine, states_to_classes, x, y)
expected_dll = np.zeros(parameters.shape)
# Finite difference gradient approximation
delta = 10.0**-7
S, D = expected_dll.shape
for s in xrange(S):
for d in xrange(D):
dg = np.zeros(parameters.shape)
dg[s, d] = delta
y0, _ = test_model.forward_backward(parameters)
y1, _ = test_model.forward_backward(parameters + dg)
expected_dll[s, d] = (y1 - y0) / delta
actual_ll, actual_dll = test_model.forward_backward(parameters)
print(abs(actual_dll) - abs(expected_dll)).sum()
assert_array_almost_equal(actual_dll, expected_dll, decimal=4)
def test_derivate_medium(self):
classes = ['a', 'b']
parameters = np.array(range(-8, 8), dtype='float64').reshape((8, 2))
x = np.array([[[0, 1], [2, 1]], [[0, 1], [1, 0.0]]])
y = 'a'
state_machine = DefaultStateMachine(classes)
test_model = _Model(state_machine, x, y)
print(test_model._lattice)
expected_dll = np.zeros(parameters.shape)
# Finite difference gradient approximation
delta = 10.0**-7
S, D = expected_dll.shape
for s in range(S):
for d in range(D):
dg = np.zeros(parameters.shape)
dg[s, d] = delta
y0, _ = test_model.forward_backward(parameters)
y1, _ = test_model.forward_backward(parameters + dg)
print(s, d, y0, y1)
expected_dll[s, d] = (y1 - y0) / delta
actual_ll, actual_dll = test_model.forward_backward(parameters)
print(expected_dll)
print(actual_dll)
assert_array_almost_equal(actual_dll,
expected_dll,
decimal=TEST_PRECISION)
def test_derivate_large(self):
classes = ['a', 'b', 'c']
y = 'b'
x = random.randn(8, 3, 10) * 5 + 3
state_machine = DefaultStateMachine(classes)
parameters = Hacrf._initialize_parameters(state_machine, x.shape[2])
parameters = random.randn(*parameters.shape) * 10 - 2
test_model = _Model(state_machine, x, y)
print(test_model._lattice)
expected_dll = np.zeros(parameters.shape)
# Finite difference gradient approximation
delta = 10.0**-7
S, D = expected_dll.shape
for s in range(S):
for d in range(D):
dg = np.zeros(parameters.shape)
dg[s, d] = delta
y0, _ = test_model.forward_backward(parameters)
y1, _ = test_model.forward_backward(parameters + dg)
print(s, d, y0, y1)
expected_dll[s, d] = (y1 - y0) / delta
actual_ll, actual_dll = test_model.forward_backward(parameters)
print(expected_dll)
print(actual_dll)
self.assertEqual((np.isnan(actual_dll)).any(), False)
assert_array_almost_equal(actual_dll,
expected_dll,
decimal=TEST_PRECISION)
def test_forward_single(self):
start_states = [0, 1]
transitions = [(0, 0, (1, 1)),
(0, 1, (0, 1)),
(0, 0, (1, 0)),
(0, 2, lambda i, j, k: (0, 2))]
states_to_classes = {0: 'a', 1: 'a', 2: 'b'} # Dummy
state_machine = GeneralStateMachine(start_states, transitions, states_to_classes)
parameters = np.array(range(-7, 7), dtype='float64').reshape((7, 2))
# parameters =
# 0([[-7, -6],
# 1 [-5, -4],
# 2 [-3, -2],
# 3 [-1, 0],
# 4 [ 1, 2],
# 5 [ 3, 4],
# 6 [ 5, 6]])
x = np.array([[[0, 1],
[1, 0],
[2, 1]],
[[0, 1],
[1, 0],
[1, 0]]])
y = 'a'
# Expected lattice:
# # ________
# 1. . . # 1 0 __0 - 21
# # | /
# 0. . . # 0 0
# 0 1 2 # 0 1 2
expected_alpha = {
(0, 0, 0): np.exp(-6),
(0, 0, 0, 1, 0, 0, 5): np.exp(-6) * np.exp(4),
(0, 0, 0, 1, 1, 0, 3): np.exp(-6) * np.exp(-1),
(1, 0, 0): np.exp(-6) * np.exp(4) * np.exp(-6),
(1, 0, 0, 1, 2, 2, 6): np.exp(-6) * np.exp(4) * np.exp(-6) * np.exp(5),
(1, 1, 0): np.exp(-6) * np.exp(-1) * np.exp(-7),
(1, 1, 0, 1, 2, 1, 4): np.exp(-6) * np.exp(-1) * np.exp(-7) * np.exp(1),
(1, 2, 1): np.exp(-6) * np.exp(-1) * np.exp(-7) * np.exp(1) * np.exp(-5),
(1, 2, 2): np.exp(-6) * np.exp(4) * np.exp(-6) * np.exp(5) * np.exp(-3)
}
expected_alpha = {k: np.emath.log(v) for k, v in expected_alpha.items()}
test_model = _Model(state_machine, x, y)
x_dot_parameters = np.dot(x, parameters.T) # Pre-compute the dot product
actual_alpha = test_model._forward(x_dot_parameters)
actual_alpha = {k: v for k, v in actual_alpha.items()
if not np.isneginf(v)}
print(actual_alpha)
self.assertEqual(len(actual_alpha), len(expected_alpha))
print
for key in sorted(expected_alpha.keys()):
print(key, (expected_alpha[key]), (actual_alpha[key]))
self.assertEqual(actual_alpha[key], expected_alpha[key])
def test_forward_connected(self):
classes = ['a', 'b']
parameters = np.array(range(-8, 8), dtype='float64').reshape((8, 2))
# parameters =
#0([[-8, -7],
#1 [-6, -5],
#2 [-4, -3],
#3 [-2, -1],
#4 [ 0, 1],
#5 [ 2, 3],
#6 [ 4, 5],
#7 [ 6, 7]])
x = np.array([[[0, 1], [2, 1]], [[0, 1], [1, 0]]])
y = 'a'
expected_alpha = {
(0, 0, 0): np.exp(-7),
(0, 0, 0, 0, 1, 0, 4): np.exp(-7) * np.exp(1),
(0, 0, 0, 1, 0, 0, 6): np.exp(-7) * np.exp(5),
(0, 0, 0, 1, 1, 0, 2): np.exp(-7) * np.exp(-4),
(0, 0, 1): np.exp(-5),
(0, 0, 1, 0, 1, 1, 5): np.exp(-5) * np.exp(7),
(0, 0, 1, 1, 0, 1, 7): np.exp(-5) * np.exp(7),
(0, 0, 1, 1, 1, 1, 3): np.exp(-5) * np.exp(-2),
(0, 1, 0): np.exp(-7) * np.exp(1) * np.exp(-23),
(0, 1, 0, 1, 1, 0, 6):
np.exp(-7) * np.exp(1) * np.exp(-23) * np.exp(4),
(0, 1, 1): np.exp(-5) * np.exp(7) * np.exp(-17),
(0, 1, 1, 1, 1, 1, 7):
np.exp(-5) * np.exp(7) * np.exp(-17) * np.exp(6),
(1, 0, 0): np.exp(-7) * np.exp(5) * np.exp(-7),
(1, 0, 0, 1, 1, 0, 4):
np.exp(-7) * np.exp(5) * np.exp(-7) * np.exp(0),
(1, 0, 1): np.exp(-5) * np.exp(7) * np.exp(-5),
(1, 0, 1, 1, 1, 1, 5):
np.exp(-5) * np.exp(7) * np.exp(-5) * np.exp(2),
(1, 1, 0): (np.exp(-11) + np.exp(-25) + np.exp(-9)) * np.exp(-8),
(1, 1, 1): (np.exp(-1) + np.exp(-9) + np.exp(-7)) * np.exp(-6)
}
expected_alpha = {
k: np.emath.log(v)
for k, v in expected_alpha.items()
}
state_machine = DefaultStateMachine(classes)
print
test_model = _Model(state_machine, x, y)
for s in test_model._lattice:
print(s)
x_dot_parameters = np.dot(x,
parameters.T) # Pre-compute the dot product
actual_alpha = test_model._forward(x_dot_parameters)
self.assertEqual(len(actual_alpha), len(expected_alpha))
for key in sorted(expected_alpha.keys()):
print(key, expected_alpha[key], actual_alpha[key])
self.assertAlmostEqual(actual_alpha[key], expected_alpha[key])
def test_derivate_chain(self):
classes = ['a', 'b']
parameters = np.array(range(-8, 8), dtype='float64').reshape((8, 2))
# parameters =
#0([[-8, -7],
#1 [-6, -5],
#2 [-4, -3],
#3 [-2, -1],
#4 [ 0, 1],
#5 [ 2, 3],
#6 [ 4, 5],
#7 [ 6, 7]])
x = np.array([[[0, 1], [1, 2]]], dtype='float64')
y = 'a'
state_machine = DefaultStateMachine(classes)
test_model = _Model(state_machine, x, y)
print(test_model._lattice)
#
# 0 01 --- 01
# 0 1
# states_to_classes = {0: 'a', 1: 'b'}
# (0, 0, 0) : exp(-7)
# (0, 0, 0, 0, 1, 0, 4) : exp(-7) * exp(2)
# (0, 0, 1) : exp(-5)
# (0, 0, 1, 0, 1, 1, 5) : exp(-5) * exp(8)
# (0, 1, 0) : exp(-7) * exp(2) * exp(-8 - 14) = exp(-27)
# (0, 1, 1) : exp(-5) * exp(8) * exp(-6 - 10) = exp(-13)
# p(y|G,X) = f0(g00,g01,x00,x01,y) f1(g40,g41,x10,x11,y) f2(g00,g01,x00,x01,y) +
# f0(g10,g11,x00,x01,y) f1(g50,g51,x10,x11,y) f2(g10,g11,x00,x01,y)
# = exp(-27) / (exp(-27) + exp(-13))
expected_ll = np.emath.log(np.exp(-27) / (np.exp(-27) + np.exp(-13)))
expected_dll = np.zeros(parameters.shape)
# Finite difference gradient approximation
delta = 10.0**-7
S, D = expected_dll.shape
for s in range(S):
for d in range(D):
dg = np.zeros(parameters.shape)
dg[s, d] = delta
y0, _ = test_model.forward_backward(parameters)
y1, _ = test_model.forward_backward(parameters + dg)
print(s, d, y0, y1)
expected_dll[s, d] = (y1 - y0) / delta
actual_ll, actual_dll = test_model.forward_backward(parameters)
print(expected_ll, actual_ll)
print(expected_dll)
print(actual_dll)
self.assertAlmostEqual(actual_ll, expected_ll)
assert_array_almost_equal(actual_dll,
expected_dll,
decimal=TEST_PRECISION)
def test_derivate_chain(self):
classes = ['a', 'b']
parameters = np.array(range(-8, 8), dtype='float64').reshape((8, 2))
# parameters =
#0([[-8, -7],
#1 [-6, -5],
#2 [-4, -3],
#3 [-2, -1],
#4 [ 0, 1],
#5 [ 2, 3],
#6 [ 4, 5],
#7 [ 6, 7]])
x = np.array([[[0, 1],
[1, 2]]], dtype='float64')
y = 'a'
state_machine = DefaultStateMachine(classes)
test_model = _Model(state_machine, x, y)
print(test_model._lattice)
#
# 0 01 --- 01
# 0 1
# states_to_classes = {0: 'a', 1: 'b'}
# (0, 0, 0) : exp(-7)
# (0, 0, 0, 0, 1, 0, 4) : exp(-7) * exp(2)
# (0, 0, 1) : exp(-5)
# (0, 0, 1, 0, 1, 1, 5) : exp(-5) * exp(8)
# (0, 1, 0) : exp(-7) * exp(2) * exp(-8 - 14) = exp(-27)
# (0, 1, 1) : exp(-5) * exp(8) * exp(-6 - 10) = exp(-13)
# p(y|G,X) = f0(g00,g01,x00,x01,y) f1(g40,g41,x10,x11,y) f2(g00,g01,x00,x01,y) +
# f0(g10,g11,x00,x01,y) f1(g50,g51,x10,x11,y) f2(g10,g11,x00,x01,y)
# = exp(-27) / (exp(-27) + exp(-13))
expected_ll = np.emath.log(np.exp(-27) / (np.exp(-27) + np.exp(-13)))
expected_dll = np.zeros(parameters.shape)
# Finite difference gradient approximation
delta = 10.0**-7
S, D = expected_dll.shape
for s in range(S):
for d in range(D):
dg = np.zeros(parameters.shape)
dg[s, d] = delta
y0, _ = test_model.forward_backward(parameters)
y1, _ = test_model.forward_backward(parameters + dg)
print(s, d, y0, y1)
expected_dll[s, d] = (y1 - y0) / delta
actual_ll, actual_dll = test_model.forward_backward(parameters)
print(expected_ll, actual_ll)
print(expected_dll)
print(actual_dll)
self.assertAlmostEqual(actual_ll, expected_ll)
assert_array_almost_equal(actual_dll, expected_dll, decimal=TEST_PRECISION)
def test_forward_connected(self):
classes = ['a', 'b']
parameters = np.array(range(-8, 8), dtype='float64').reshape((8, 2))
# parameters =
#0([[-8, -7],
#1 [-6, -5],
#2 [-4, -3],
#3 [-2, -1],
#4 [ 0, 1],
#5 [ 2, 3],
#6 [ 4, 5],
#7 [ 6, 7]])
x = np.array([[[0, 1],
[2, 1]],
[[0, 1],
[1, 0]]])
y = 'a'
expected_alpha = {
(0, 0, 0): np.exp(-7),
(0, 0, 0, 0, 1, 0, 4): np.exp(-7) * np.exp(1),
(0, 0, 0, 1, 0, 0, 6): np.exp(-7) * np.exp(5),
(0, 0, 0, 1, 1, 0, 2): np.exp(-7) * np.exp(-4),
(0, 0, 1): np.exp(-5),
(0, 0, 1, 0, 1, 1, 5): np.exp(-5) * np.exp(7),
(0, 0, 1, 1, 0, 1, 7): np.exp(-5) * np.exp(7),
(0, 0, 1, 1, 1, 1, 3): np.exp(-5) * np.exp(-2),
(0, 1, 0): np.exp(-7) * np.exp(1) * np.exp(-23),
(0, 1, 0, 1, 1, 0, 6): np.exp(-7) * np.exp(1) * np.exp(-23) * np.exp(4),
(0, 1, 1): np.exp(-5) * np.exp(7) * np.exp(-17),
(0, 1, 1, 1, 1, 1, 7): np.exp(-5) * np.exp(7) * np.exp(-17) * np.exp(6),
(1, 0, 0): np.exp(-7) * np.exp(5) * np.exp(-7),
(1, 0, 0, 1, 1, 0, 4): np.exp(-7) * np.exp(5) * np.exp(-7) * np.exp(0),
(1, 0, 1): np.exp(-5) * np.exp(7) * np.exp(-5),
(1, 0, 1, 1, 1, 1, 5): np.exp(-5) * np.exp(7) * np.exp(-5) * np.exp(2),
(1, 1, 0): (np.exp(-11) + np.exp(-25) + np.exp(-9)) * np.exp(-8),
(1, 1, 1): (np.exp(-1) + np.exp(-9) + np.exp(-7)) * np.exp(-6)
}
expected_alpha = {k: np.emath.log(v) for k, v in expected_alpha.items()}
state_machine = DefaultStateMachine(classes)
print
test_model = _Model(state_machine, x, y)
for s in test_model._lattice:
print(s)
x_dot_parameters = np.dot(x, parameters.T) # Pre-compute the dot product
actual_alpha = test_model._forward(x_dot_parameters)
self.assertEqual(len(actual_alpha), len(expected_alpha))
for key in sorted(expected_alpha.keys()):
print(key, expected_alpha[key], actual_alpha[key])
self.assertAlmostEqual(actual_alpha[key], expected_alpha[key])
def test_forward_backward_same_partition_value(self):
classes = ['a', 'b']
parameters = np.array(range(-8, 8), dtype='float64').reshape((8, 2))
x = np.array([[[0, 1],
[2, 1]],
[[0, 1],
[1, 0]]])
y = 'a'
state_machine = DefaultStateMachine(classes)
test_model = _Model(state_machine, x, y)
x_dot_parameters = np.dot(x, parameters.T) # Pre-compute the dot product
actual_alpha = test_model._forward(x_dot_parameters)
actual_beta = test_model._backward(x_dot_parameters)
print(actual_alpha[(1, 1, 0)], actual_beta[(0, 0, 0)])
print(actual_alpha[(1, 1, 1)], actual_beta[(0, 0, 1)])
self.assertAlmostEqual(actual_alpha[(1, 1, 0)], actual_beta[(0, 0, 0)] + (np.dot(x[0, 0, :], parameters[0, :])))
self.assertAlmostEqual(actual_alpha[(1, 1, 1)], actual_beta[(0, 0, 1)] + (np.dot(x[0, 0, :], parameters[1, :])))
def test_backward_connected(self):
parameters = np.array(range(-3, 3), dtype='float64').reshape((3, 2))
# parameters =
#0 ([[-3, -2],
#1 [-1, 0],
#2 [ 1, 2]])
x = np.array([[[0, 1],
[2, 1]],
[[0, 1],
[1, 0]]])
y = 'a'
expected_beta = {
(0, 0, 0): (np.exp(-4) + np.exp(-12)), # * np.exp(-2),
(0, 0, 0, 0, 1, 0, 1): np.exp(-3) * np.exp(1) * np.exp(-8), # * np.exp(-2),
(0, 0, 0, 1, 0, 0, 2): np.exp(-3) * np.exp(-1) * np.exp(-2), # * np.exp(2),
(0, 1, 0): np.exp(-3) * np.exp(1), # * np.exp(-8),
(0, 1, 0, 1, 1, 0, 2): np.exp(-3), # * np.exp(1),
(1, 0, 0): np.exp(-3) * np.exp(-1), # * np.exp(-2),
(1, 0, 0, 1, 1, 0, 1): np.exp(-3), # * np.exp(-1),
(1, 1, 0): 1.0 # np.exp(-3)
}
expected_beta = {k: np.emath.log(v) for k, v in expected_beta.items()}
start_states = [0]
transitions = [(0, 0, (0, 1)),
(0, 0, (1, 0))]
states_to_classes = {0: 'a'}
n_states = 1
state_machine = GeneralStateMachine(start_states, transitions, states_to_classes)
test_model = _Model(state_machine, x, y)
for s in test_model._lattice:
print(s)
x_dot_parameters = np.dot(x, parameters.T) # Pre-compute the dot product
actual_beta = test_model._backward(x_dot_parameters)
print(actual_beta)
print
self.assertEqual(len(actual_beta), len(expected_beta))
for key in sorted(expected_beta.keys(), reverse=True):
print(key, expected_beta[key], actual_beta[key])
self.assertAlmostEqual(actual_beta[key], expected_beta[key])
def test_backward_connected(self):
parameters = np.array(range(-3, 3), dtype='float64').reshape((3, 2))
# parameters =
#0 ([[-3, -2],
#1 [-1, 0],
#2 [ 1, 2]])
x = np.array([[[0, 1], [2, 1]], [[0, 1], [1, 0]]])
y = 'a'
expected_beta = {
(0, 0, 0): (np.exp(-4) + np.exp(-12)), # * np.exp(-2),
(0, 0, 0, 0, 1, 0, 1):
np.exp(-3) * np.exp(1) * np.exp(-8), # * np.exp(-2),
(0, 0, 0, 1, 0, 0, 2):
np.exp(-3) * np.exp(-1) * np.exp(-2), # * np.exp(2),
(0, 1, 0): np.exp(-3) * np.exp(1), # * np.exp(-8),
(0, 1, 0, 1, 1, 0, 2): np.exp(-3), # * np.exp(1),
(1, 0, 0): np.exp(-3) * np.exp(-1), # * np.exp(-2),
(1, 0, 0, 1, 1, 0, 1): np.exp(-3), # * np.exp(-1),
(1, 1, 0): 1.0 # np.exp(-3)
}
expected_beta = {k: np.emath.log(v) for k, v in expected_beta.items()}
start_states = [0]
transitions = [(0, 0, (0, 1)), (0, 0, (1, 0))]
states_to_classes = {0: 'a'}
n_states = 1
state_machine = GeneralStateMachine(start_states, transitions,
states_to_classes)
test_model = _Model(state_machine, x, y)
for s in test_model._lattice:
print(s)
x_dot_parameters = np.dot(x,
parameters.T) # Pre-compute the dot product
actual_beta = test_model._backward(x_dot_parameters)
print(actual_beta)
print
self.assertEqual(len(actual_beta), len(expected_beta))
for key in sorted(expected_beta.keys(), reverse=True):
print(key, expected_beta[key], actual_beta[key])
self.assertAlmostEqual(actual_beta[key], expected_beta[key])
def test_forward_backward_same_partition_value(self):
classes = ['a', 'b']
parameters = np.array(range(-8, 8), dtype='float64').reshape((8, 2))
x = np.array([[[0, 1], [2, 1]], [[0, 1], [1, 0]]])
y = 'a'
state_machine = DefaultStateMachine(classes)
test_model = _Model(state_machine, x, y)
x_dot_parameters = np.dot(x,
parameters.T) # Pre-compute the dot product
actual_alpha = test_model._forward(x_dot_parameters)
actual_beta = test_model._backward(x_dot_parameters)
print(actual_alpha[(1, 1, 0)], actual_beta[(0, 0, 0)])
print(actual_alpha[(1, 1, 1)], actual_beta[(0, 0, 1)])
self.assertAlmostEqual(
actual_alpha[(1, 1, 0)],
actual_beta[(0, 0, 0)] + (np.dot(x[0, 0, :], parameters[0, :])))
self.assertAlmostEqual(
actual_alpha[(1, 1, 1)],
actual_beta[(0, 0, 1)] + (np.dot(x[0, 0, :], parameters[1, :])))
def test_forward_single(self):
start_states = [0, 1]
transitions = [(0, 0, (1, 1)), (0, 1, (0, 1)), (0, 0, (1, 0)),
(0, 2, lambda i, j, k: (0, 2))]
states_to_classes = {0: 'a', 1: 'a', 2: 'b'} # Dummy
state_machine = GeneralStateMachine(start_states, transitions,
states_to_classes)
parameters = np.array(range(-7, 7), dtype='float64').reshape((7, 2))
# parameters =
# 0([[-7, -6],
# 1 [-5, -4],
# 2 [-3, -2],
# 3 [-1, 0],
# 4 [ 1, 2],
# 5 [ 3, 4],
# 6 [ 5, 6]])
x = np.array([[[0, 1], [1, 0], [2, 1]], [[0, 1], [1, 0], [1, 0]]])
y = 'a'
# Expected lattice:
# # ________
# 1. . . # 1 0 __0 - 21
# # | /
# 0. . . # 0 0
# 0 1 2 # 0 1 2
expected_alpha = {
(0, 0, 0):
np.exp(-6),
(0, 0, 0, 1, 0, 0, 5):
np.exp(-6) * np.exp(4),
(0, 0, 0, 1, 1, 0, 3):
np.exp(-6) * np.exp(-1),
(1, 0, 0):
np.exp(-6) * np.exp(4) * np.exp(-6),
(1, 0, 0, 1, 2, 2, 6):
np.exp(-6) * np.exp(4) * np.exp(-6) * np.exp(5),
(1, 1, 0):
np.exp(-6) * np.exp(-1) * np.exp(-7),
(1, 1, 0, 1, 2, 1, 4):
np.exp(-6) * np.exp(-1) * np.exp(-7) * np.exp(1),
(1, 2, 1):
np.exp(-6) * np.exp(-1) * np.exp(-7) * np.exp(1) * np.exp(-5),
(1, 2, 2):
np.exp(-6) * np.exp(4) * np.exp(-6) * np.exp(5) * np.exp(-3)
}
expected_alpha = {
k: np.emath.log(v)
for k, v in expected_alpha.items()
}
test_model = _Model(state_machine, x, y)
x_dot_parameters = np.dot(x,
parameters.T) # Pre-compute the dot product
actual_alpha = test_model._forward(x_dot_parameters)
actual_alpha = {
k: v
for k, v in actual_alpha.items() if not np.isneginf(v)
}
print(actual_alpha)
self.assertEqual(len(actual_alpha), len(expected_alpha))
print
for key in sorted(expected_alpha.keys()):
print(key, (expected_alpha[key]), (actual_alpha[key]))
self.assertEqual(actual_alpha[key], expected_alpha[key])
