def helper(homog):
n, p, T = npr.randint(1, 5), npr.randint(1, 5), npr.randint(10, 50)
lds = rand_lds(n, p, None if homog else T)
states, data = generate_data(T, *lds)
natparam = lds_standard_to_natparam(*lds)
E_stats = natural_lds_Estep(natparam, data)
assert allclose(sub(add(natparam, E_stats), E_stats), natparam)
def helper(homog):
n, p, T = npr.randint(1, 5), npr.randint(1, 5), npr.randint(10,50)
lds = rand_lds(n, p, None if homog else T)
states, data = generate_data(T, *lds)
natparam = lds_standard_to_natparam(*lds)
E_stats = natural_lds_Estep(natparam, data)
assert allclose(sub(add(natparam, E_stats), E_stats), natparam)
def test_E_step_inhomog():
def compare_E_step(lds, data):
natparam = lds_standard_to_natparam(*lds)
E_init_stats, E_pairwise_stats, E_node_stats = natural_lds_Estep(natparam, data)
E_init_stats2, E_pairwise_stats2, E_node_stats2 = pylds_E_step_inhomog(lds, data)
assert all(map(np.allclose, E_init_stats, E_init_stats2))
assert all(map(np.allclose, E_pairwise_stats, E_pairwise_stats2))
assert all(map(np.allclose, E_node_stats, E_node_stats2))
for _ in xrange(10):
n, p, T = npr.randint(1, 5), npr.randint(1, 5), npr.randint(10,50)
lds = rand_lds(n, p, T)
states, data = generate_data(T, *lds)
yield compare_E_step, lds, data
def test_filter():
def check_filter(data, lds):
(filtered_mus, filtered_sigmas), loglike = filter_forward(data, *lds)
filtered_mus2, filtered_sigmas2 = dense_filter(data, lds)
loglike2 = dense_loglike(data, lds)
assert all(map(np.allclose, filtered_mus, filtered_mus2))
assert all(map(np.allclose, filtered_sigmas, filtered_sigmas2))
assert np.isclose(loglike, loglike2)
for _ in xrange(10):
n, p, T = npr.randint(1, 5), npr.randint(1, 5), npr.randint(10, 20)
lds = rand_lds(n, p)
_, data = generate_data(T, *lds)
yield check_filter, data, lds
def test_E_step_inhomog():
def compare_E_step(lds, data):
natparam = lds_standard_to_natparam(*lds)
E_init_stats, E_pairwise_stats, E_node_stats = natural_lds_Estep(
natparam, data)
E_init_stats2, E_pairwise_stats2, E_node_stats2 = pylds_E_step_inhomog(
lds, data)
assert all(map(np.allclose, E_init_stats, E_init_stats2))
assert all(map(np.allclose, E_pairwise_stats, E_pairwise_stats2))
assert all(map(np.allclose, E_node_stats, E_node_stats2))
for _ in xrange(10):
n, p, T = npr.randint(1, 5), npr.randint(1, 5), npr.randint(10, 50)
lds = rand_lds(n, p, T)
states, data = generate_data(T, *lds)
yield compare_E_step, lds, data
def test_filters():
def compare_filters(lds, data):
(filtered_mus, filtered_sigmas), loglike = filter_forward(data, *lds)
messages, lognorm = natural_filter_forward(lds_standard_to_natparam(*lds), data)
prediction_messages, filter_messages = uninterleave(messages)
natural_filtered_mus, natural_filtered_sigmas = zip(*map(natural_to_mean, filter_messages))
assert all(map(np.allclose, filtered_mus, natural_filtered_mus))
assert all(map(np.allclose, filtered_sigmas, natural_filtered_sigmas))
assert np.isclose(loglike, lognorm)
for _ in xrange(10):
n, p, T = npr.randint(1, 5), npr.randint(1, 5), npr.randint(10,50)
lds = rand_lds(n, p)
states, data = generate_data(T, *lds)
yield compare_filters, lds, data
def test_filters():
def compare_filters(lds, data):
(filtered_mus, filtered_sigmas), loglike = filter_forward(data, *lds)
messages, lognorm = natural_filter_forward(
lds_standard_to_natparam(*lds), data)
prediction_messages, filter_messages = uninterleave(messages)
natural_filtered_mus, natural_filtered_sigmas = zip(
*map(natural_to_mean, filter_messages))
assert all(map(np.allclose, filtered_mus, natural_filtered_mus))
assert all(map(np.allclose, filtered_sigmas, natural_filtered_sigmas))
assert np.isclose(loglike, lognorm)
for _ in xrange(10):
n, p, T = npr.randint(1, 5), npr.randint(1, 5), npr.randint(10, 50)
lds = rand_lds(n, p)
states, data = generate_data(T, *lds)
yield compare_filters, lds, data
def test_general_inference():
def get_general_node_params(x, lds):
T, p = x.shape
C, sigma_obs = lds[-2:]
J, Jzx, Jxx, logZ = pair_mean_to_natural(C, sigma_obs)
h = np.einsum('tzx,tx->tz', Jzx, x)
logZ += np.einsum('ti,tij,tj->t', x, Jxx,
x) - p / 2. * np.log(2 * np.pi)
return J, h, logZ
def compare_E_step(lds, data):
natparam = init_params, pair_params, node_params = lds_standard_to_natparam(
*lds)
general_node_params = get_general_node_params(data, lds)
C, sigma_obs = lds[-2:]
sample, E_stats, lognorm = natural_lds_inference(natparam, data)
sample2, E_stats2, lognorm2 = natural_lds_inference_general(
(init_params, pair_params), general_node_params)
sample3, E_stats3, lognorm3 = natural_lds_inference_general_nosaving(
(init_params, pair_params), general_node_params)
sample4, E_stats4, lognorm4 = natural_lds_inference_general_autograd(
(init_params, pair_params), general_node_params)
assert allclose(E_stats[:-1], E_stats2[:-1])
assert allclose(E_stats2, E_stats3)
assert allclose(E_stats2, E_stats4)
assert np.isclose(lognorm, lognorm2)
assert np.isclose(lognorm, lognorm3)
assert np.isclose(lognorm, lognorm4)
for _ in xrange(10):
n, p, T = npr.randint(1, 5), npr.randint(1, 5), npr.randint(10, 50)
lds = rand_lds(n, p, T)
states, data = generate_data(T, *lds)
yield compare_E_step, lds, data
def test_general_inference():
def get_general_node_params(x, lds):
T, p = x.shape
C, sigma_obs = lds[-2:]
J, Jzx, Jxx, logZ = pair_mean_to_natural(C, sigma_obs)
h = np.einsum('tzx,tx->tz', Jzx, x)
logZ += np.einsum('ti,tij,tj->t', x, Jxx, x) - p/2.*np.log(2*np.pi)
return J, h, logZ
def compare_E_step(lds, data):
natparam = init_params, pair_params, node_params = lds_standard_to_natparam(*lds)
general_node_params = get_general_node_params(data, lds)
C, sigma_obs = lds[-2:]
sample, E_stats, lognorm = natural_lds_inference(natparam, data)
sample2, E_stats2, lognorm2 = natural_lds_inference_general(
(init_params, pair_params), general_node_params)
sample3, E_stats3, lognorm3 = natural_lds_inference_general_nosaving(
(init_params, pair_params), general_node_params)
sample4, E_stats4, lognorm4 = natural_lds_inference_general_autograd(
(init_params, pair_params), general_node_params)
assert allclose(E_stats[:-1], E_stats2[:-1])
assert allclose(E_stats2, E_stats3)
assert allclose(E_stats2, E_stats4)
assert np.isclose(lognorm, lognorm2)
assert np.isclose(lognorm, lognorm3)
assert np.isclose(lognorm, lognorm4)
for _ in xrange(10):
n, p, T = npr.randint(1, 5), npr.randint(1, 5), npr.randint(10,50)
lds = rand_lds(n, p, T)
states, data = generate_data(T, *lds)
yield compare_E_step, lds, data
def generate_test_model_and_data(n, p, T):
lds = rand_lds(n, p)
_, data = generate_data(T, *lds)
return lds, data
def generate_test_model_and_data(n, p, T):
lds = rand_lds(n, p)
_, data = generate_data(T, *lds)
return lds, data