def test_beta():
P = np.array([[0.7, 0.3], [0.5, 0.5], [0.4, 0.6], [0.1, 0.9], [0.7, 0.3],
[0.6, 0.4], [0.6, 0.4], [0.5, 0.5], [0.4, 0.6], [0.4, 0.6]])
A = np.array([[0.35, 0.65], [0.45, 0.55]])
D = np.array([[0.3, 0.4, 0.3], [0.2, 0.5, 0.3]])
num_obs, _ = P.shape
num_states, _ = A.shape
beta = vdhmms.beta(A, P, D)
beta_true = np.array([[0.1538, 0.1422], [0.12, 0.12], [1, 1]])
np.testing.assert_almost_equal(beta[-3:], beta_true, decimal=3)
def test_beta():
P = np.array([[0.7, 0.3],
[0.5, 0.5],
[0.4, 0.6],
[0.1, 0.9],
[0.7, 0.3],
[0.6, 0.4],
[0.6, 0.4],
[0.5, 0.5],
[0.4, 0.6],
[0.4, 0.6]])
A = np.array([[0.35, 0.65],
[0.45, 0.55]])
D = np.array([[0.3, 0.4, 0.3],
[0.2, 0.5, 0.3]])
num_obs, _ = P.shape
num_states, _ = A.shape
beta = vdhmms.beta(A, P, D)
beta_true = np.array([[0.1538, 0.1422],
[0.12, 0.12],
[1, 1]])
np.testing.assert_almost_equal(beta[-3:], beta_true, decimal=3)
patch = imresize(patch, (h, w))
patch = normalise(patch)
descs[i] = patch.flatten()
# OK, these are the labels of our observation !
labels = euclidean_distances(descs, voc).argmin(axis=1)
# Now, we can compute the emission probability matrices
emission_obs = emission[:, labels].T
alphas = vdhmms.alpha(transition,
emission_obs,
occurances,
p_init=first_letter.reshape((len(first_letter), )))
betas = vdhmms.beta(transition,
emission_obs,
occurances,
p_init=last_letter.reshape((len(first_letter), )))
show_segments(im, segments)
g = alphas * betas
chain = g.argmax(axis=1)
prob = g.max(axis=1)
# careful ! Some labels of the chain "just" correspond to null probabilities
letters = 'abcdefghijklmnopqrstuvwxyz'
labels = []
for p, l in zip(prob, chain):
if p == 0:
labels.append('_')
else:
labels.append(letters[l])
descs = np.zeros((len(patches), voc.shape[1]))
for i, patch in enumerate(patches):
patch = imresize(patch, (h, w))
patch = normalise(patch)
descs[i] = patch.flatten()
# OK, these are the labels of our observation !
labels = euclidean_distances(descs, voc).argmin(axis=1)
# Now, we can compute the emission probability matrices
emission_obs = emission[:, labels].T
alphas = vdhmms.alpha(transition, emission_obs, occurances,
p_init=first_letter.reshape((len(first_letter), )))
betas = vdhmms.beta(transition, emission_obs, occurances,
p_init=last_letter.reshape((len(first_letter), )))
show_segments(im, segments)
g = alphas * betas
chain = g.argmax(axis=1)
prob = g.max(axis=1)
# careful ! Some labels of the chain "just" correspond to null probabilities
letters = 'abcdefghijklmnopqrstuvwxyz'
labels = []
for p, l in zip(prob, chain):
if p == 0:
labels.append('_')
else:
labels.append(letters[l])
