def helper(y1,y2):
alphabet = ('A','B','')
toy_alphabet = OrderedDict([('A',0),('B',1)])
profile1= profile(y1,alphabet)
profile2=profile(y2,alphabet)
joint_prof = joint_profile(profile1, profile2)
gamma = decoding.pair_gamma(y1,y2)
self.assertTrue(np.isclose(gamma[0,0], joint_prof.prob_agree))
def helper(y1,y2):
alphabet = ('A','B','')
toy_alphabet = OrderedDict([('A',0),('B',1)])
profile1= profile(y1,alphabet)
profile2=profile(y2,alphabet)
joint_prof = joint_profile(profile1, profile2)
gamma = decoding.decoding_cy.pair_gamma_log(np.log(y1).astype(np.float64),np.log(y2).astype(np.float64))
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 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 helper(y1,y2):
alphabet = ('A','B','')
toy_alphabet = OrderedDict([('A',0),('B',1)])
profile1= profile(y1,alphabet)
profile2=profile(y2,alphabet)
joint_prof = joint_profile(profile1, profile2)
top_label = joint_prof.top_label()
search_top_label = decoding.pair_prefix_search(y1,y2,alphabet=toy_alphabet)
return((top_label[0] == search_top_label[0]) and np.isclose(top_label[1] / joint_prof.prob_agree, search_top_label[1]))
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 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 test_flipflop_same(self):
alphabet_tuple = ('A', 'B', 'a', 'b')
alphabet = "AB"
y = np.array([[0.8, 0.1, 0.05, 0.05], [0.1, 0.3, 0.5, 0.1],
[0.7, 0.2, 0.05, 0.05], [0.1, 0.1, 0.2, 0.6]],
dtype=np.float32)
prof = flipflop_profile(y, alphabet_tuple)
joint_prof = joint_profile(prof, prof)
result_1d = decoding.decoding_cpp.cpp_beam_search(
np.log(y), alphabet_="AB", model_='ctc_flipflop')
result_2d = decoding.decoding_cpp.cpp_beam_search_2d(
np.log(y),
np.log(y),
alphabet_="AB",
method_="row",
model_='ctc_flipflop')
self.assertTrue(result_1d == result_2d)
