def helper(y1, y2):
alphabet = ('A', 'B', '')
toy_alphabet = OrderedDict([('A', 0), ('B', 1)])
profile1 = poreover_profile(y1, alphabet)
profile2 = poreover_profile(y2, alphabet)
joint_prof = joint_profile(profile1, profile2)
gamma = prefix_search.pair_gamma_log(np.log(y1), np.log(y2))
print('log(Z):', gamma[0, 0], np.log(joint_prof.prob_agree))
self.assertTrue(
np.isclose(gamma[0, 0], np.log(joint_prof.prob_agree)))
def test_full_envelope(self):
y1 = np.array([[0.8, 0.1, 0.1], [0.1, 0.3, 0.6], [0.7, 0.2, 0.1],
[0.1, 0.1, 0.8]])
y2 = np.array([[0.7, 0.2, 0.1], [0.2, 0.3, 0.5], [0.7, 0.2, 0.1],
[0.05, 0.05, 0.9]])
full_seq = decoding.decoding_cpp.cpp_beam_search_2d(np.log(y1),
np.log(y2),
alphabet_="AB")
prof1 = poreover_profile(y1, ('A', 'B', ''))
prof2 = poreover_profile(y2, ('A', 'B', ''))
joint_prof = joint_profile(prof1, prof2)
self.assertTrue(full_seq == joint_prof.top_label()[0])
def test2(self):
y = np.array([[0.4, 0.5, 0.1], [0.4, 0.2, 0.4], [0.3, 0.5, 0.2]])
prof = poreover_profile(y, ('A', 'B', ''))
result = decoding.decoding_cpp.cpp_beam_search(np.log(y),
alphabet_="AB")
print(prof.top_label()[0], "------", result)
self.assertTrue(result == prof.top_label()[0])
def test(self):
y = np.array([[0.8, 0.1, 0.1], [0.1, 0.3, 0.6], [0.7, 0.2, 0.1],
[0.1, 0.1, 0.8]])
prof = poreover_profile(y, ('A', 'B', ''))
result = decoding.decoding_cpp.cpp_beam_search(np.log(y),
alphabet_="AB")
self.assertTrue(result == prof.top_label()[0])
def helper(y1, y2):
alphabet = ('A', 'B', '')
toy_alphabet = OrderedDict([('A', 0), ('B', 1)])
profile1 = poreover_profile(y1, alphabet)
profile2 = poreover_profile(y2, alphabet)
joint_prof = joint_profile(profile1, profile2)
top_label = joint_prof.top_label()
search_top_label = prefix_search.pair_prefix_search_log_cy(
np.log(y1), np.log(y2), alphabet=toy_alphabet)
print(top_label[0], np.log(top_label[1] / joint_prof.prob_agree),
search_top_label[0], search_top_label[1])
return ((top_label[0] == search_top_label[0]) and np.isclose(
np.log(top_label[1] / joint_prof.prob_agree),
search_top_label[1]))
def helper(y, l):
label_int = [alphabet_dict[i] for i in label]
prof = poreover_profile(y, alphabet)
alpha = prefix_search.forward(label_int, np.log(y))
prefix_prob = logsumexp(
prefix_search.forward_vec_no_gap_log(label_int, np.log(y),
alpha[-2]))
print(prefix_prob, prof.prefix_prob(label))
self.assertTrue(
np.isclose(prefix_prob, np.log(prof.prefix_prob(label))))
def helper(y):
alphabet = ('A', 'B', '')
toy_alphabet = OrderedDict([('A', 0), ('B', 1)])
prof = poreover_profile(y, alphabet)
top_label = prof.top_label()
search_top_label = prefix_search.prefix_search_log(
np.log(y), alphabet=toy_alphabet)
print(top_label[0], np.log(top_label[1]), search_top_label[0],
search_top_label[1])
return ((top_label[0] == search_top_label[0])
and np.isclose(np.log(top_label[1]), search_top_label[1]))
def test_fw_poreover(self):
alphabet_tuple = ('A','B','')
alphabet = "AB"
y = np.array([[0.8,0.1,0.1],[0.1,0.3,0.6],[0.7,0.2,0.1],[0.1,0.1,0.8]], dtype=np.float32)
examples = ['AAAA','ABBA','ABA','AAA','BBB', 'AA','BB','A','B']
prof=poreover_profile(y, alphabet_tuple)
for label in examples:
fw_prob_actual = np.log(prof.label_prob(label))
fw_prob = decoding.decoding_cpp.cpp_forward(np.log(y), label, alphabet)
print(label, fw_prob_actual, fw_prob)
self.assertTrue(np.isclose(fw_prob_actual, fw_prob))
def test_label_prob_log(self):
alphabet = ('A', 'B', '')
alphabet_dict = {'A': 0, 'B': 1, '': 2}
y = np.array([[0.8, 0.1, 0.1], [0.1, 0.3, 0.6], [0.7, 0.2, 0.1],
[0.1, 0.1, 0.8]])
examples = ['AAAA', 'ABBA', 'ABA', 'AA', 'BB', 'A', 'B']
prof = poreover_profile(y, alphabet)
for label in examples:
label_int = [alphabet_dict[i] for i in label]
alpha = prefix_search.forward(label_int, np.log(y))
self.assertTrue(
np.isclose(alpha[-1, -1], np.log(prof.label_prob(label))))
