def test_fit_shapes():
K = 5
V = 3
T = 10
es = EventSegment(K, n_iter=2)
sample_data = np.random.rand(V, T)
es.fit(sample_data.T)
assert es.segments_[0].shape == (T, K), "Segmentation from fit " \
"has incorrect shape"
assert np.isclose(np.sum(es.segments_[0], axis=1), np.ones(T)).all(), \
"Segmentation from learn_events not correctly normalized"
T2 = 15
sample_data2 = np.random.rand(V, T2)
test_segments, test_ll = es.find_events(sample_data2.T)
assert test_segments.shape == (T2, K), "Segmentation from find_events " \
"has incorrect shape"
assert np.isclose(np.sum(test_segments, axis=1), np.ones(T2)).all(), \
"Segmentation from find_events not correctly normalized"
es_invalid = EventSegment(K)
with pytest.raises(ValueError, message="T < K should cause error"):
es_invalid.model_prior(K - 1)
with pytest.raises(ValueError, message="#Events < K should cause error"):
es_invalid.set_event_patterns(np.zeros((V, K - 1)))
def test_fit_shapes():
K = 5
V = 3
T = 10
es = EventSegment(K, n_iter=2)
sample_data = np.random.rand(V, T)
es.fit(sample_data.T)
assert es.segments_[0].shape == (T, K), "Segmentation from fit " \
"has incorrect shape"
assert np.isclose(np.sum(es.segments_[0], axis=1), np.ones(T)).all(), \
"Segmentation from learn_events not correctly normalized"
T2 = 15
sample_data2 = np.random.rand(V, T2)
test_segments, test_ll = es.find_events(sample_data2.T)
assert test_segments.shape == (T2, K), "Segmentation from find_events " \
"has incorrect shape"
assert np.isclose(np.sum(test_segments, axis=1), np.ones(T2)).all(), \
"Segmentation from find_events not correctly normalized"
es_invalid = EventSegment(K)
with pytest.raises(ValueError, message="T < K should cause error"):
es_invalid.model_prior(K-1)
with pytest.raises(ValueError, message="#Events < K should cause error"):
es_invalid.set_event_patterns(np.zeros((V, K-1)))
def test_prior():
K = 10
T = 100
es = EventSegment(K)
mp = es.model_prior(T)[0]
p_bound = np.zeros((T, K - 1))
norm = comb(T - 1, K - 1)
for t in range(T - 1):
for k in range(K - 1):
# See supplementary material of Neuron paper
# https://doi.org/10.1016/j.neuron.2017.06.041
p_bound[t + 1, k] = comb(t, k) * comb(T - t - 2, K - k - 2) / norm
p_bound = np.cumsum(p_bound, axis=0)
mp_gt = np.zeros((T, K))
for k in range(K):
if k == 0:
mp_gt[:, k] = 1 - p_bound[:, 0]
elif k == K - 1:
mp_gt[:, k] = p_bound[:, k - 1]
else:
mp_gt[:, k] = p_bound[:, k - 1] - p_bound[:, k]
assert np.all(np.isclose(mp, mp_gt)),\
"Prior does not match analytic solution"
def test_prior():
K = 10
T = 100
es = EventSegment(K)
mp = es.model_prior(T)[0]
p_bound = np.zeros((T, K-1))
norm = comb(T-1, K-1)
for t in range(T-1):
for k in range(K-1):
# See supplementary material of Neuron paper
# https://doi.org/10.1016/j.neuron.2017.06.041
p_bound[t+1, k] = comb(t, k) * comb(T-t-2, K-k-2) / norm
p_bound = np.cumsum(p_bound, axis=0)
mp_gt = np.zeros((T, K))
for k in range(K):
if k == 0:
mp_gt[:, k] = 1 - p_bound[:, 0]
elif k == K - 1:
mp_gt[:, k] = p_bound[:, k-1]
else:
mp_gt[:, k] = p_bound[:, k-1] - p_bound[:, k]
assert np.all(np.isclose(mp, mp_gt)),\
"Prior does not match analytic solution"