def test_gradient_large_state(self):
transitions = np.array([[0, 0, 1, 0],
[1, 0, 1, 1],
[0, 1, 1, 2],
[1, 1, 1, 3],
[0, 1, 0, 4],
[1, 1, 0, 5]])
transition_parameters = np.array([1, 0, 2, 1, 3, -2], dtype='float64')
x = np.array([[2, 3, -1],
[1, 4, -2],
[5, 2, -3],
[-2, 5, 3]], dtype='float64')
state_parameters = np.array([[[-1, 2],
[3, -4]],
[[5, -6],
[7, 8]],
[[-3, 6],
[2, 13]]], dtype='float64')
cy = 1
delta = 5.0**-4
K, S, W = state_parameters.shape
for k in range(K):
for s in range(S):
for w in range(W):
spd = np.zeros(state_parameters.shape, dtype='float64')
spd[k, s, w] = delta
ll0, dsp0, _ = log_likelihood(x, cy, state_parameters, transition_parameters, transitions)
ll1, dsp1, _ = log_likelihood(x, cy, state_parameters + spd, transition_parameters, transitions)
expected_der = (ll1 - ll0) / delta
actual_der = dsp0[k, s, w]
print k, s, w, ' ', expected_der, actual_der#, dsp0
self.assertAlmostEqual(expected_der, actual_der, places=2)
def test_gradient_large_transition(self):
transitions = np.array([[0, 0, 1, 0],
[1, 0, 1, 1],
[0, 1, 1, 2],
[1, 1, 1, 3],
[0, 1, 0, 4],
[1, 1, 0, 5]])
transition_parameters = np.array([1, -5, 20, 1, 3, -2], dtype='float64')
x = np.array([[-2, 3, -1],
[1, 4, -2],
[4, -4, 2],
[3, 5, 3]], dtype='float64')
state_parameters = np.array([[[-1, 2],
[3, -4]],
[[5, -6],
[7, 8]],
[[-3, 6],
[2, 13]]], dtype='float64')
cy = 1
delta = 5.0**-5
for trans in range(len(transition_parameters)):
tpd = np.zeros(transition_parameters.shape, dtype='float64')
tpd[trans] = delta
ll0, _, dtp0 = log_likelihood(x, cy, state_parameters, transition_parameters, transitions)
ll1, _, dtp1 = log_likelihood(x, cy, state_parameters, transition_parameters + tpd, transitions)
expected_der = (ll1 - ll0) / delta
actual_der = dtp0[trans]
print trans, ' ', expected_der, actual_der#, dsp0
#print ll1, ll0, delta
#all_ders.append(abs(expected_der - actual_der) < 10.0**-2)
self.assertAlmostEqual(expected_der, actual_der, places=2)
def test_gradient_small_transition(self):
transitions = np.array([[0, 0, 0, 0],
[1, 0, 0, 1]])
transition_parameters = np.array([1, 0], dtype='float64')
x = np.array([[2, 3, -1]], dtype='float64').reshape(1, 3)
state_parameters = np.array([[[-1, 2]],
[[5, -6]],
[[2, 13]]], dtype='float64')
cy = 1
delta = 5.0**-5
for trans in range(len(transition_parameters)):
tpd = np.zeros(transition_parameters.shape, dtype='float64')
tpd[trans] = delta
ll0, _, dtp0 = log_likelihood(x, cy, state_parameters, transition_parameters, transitions)
ll1, _, dtp1 = log_likelihood(x, cy, state_parameters, transition_parameters + tpd, transitions)
expected_der = (ll1 - ll0) / delta
actual_der = dtp0[trans]
print trans, ' ', expected_der, actual_der#, dsp0
#print ll1, ll0, delta
self.assertAlmostEqual(expected_der, actual_der, places=2)
