def test_6():
# test score_ll with novel entries
model = MarkovStateModel(reversible_type='mle')
sequence = ['a', 'a', 'b', 'b', 'a', 'a', 'b', 'b']
model.fit([sequence])
assert not np.isfinite(model.score_ll([['c']]))
assert not np.isfinite(model.score_ll([['c', 'c']]))
assert not np.isfinite(model.score_ll([['a', 'c']]))
def test_6():
# test score_ll with novel entries
model = MarkovStateModel(reversible_type='mle')
sequence = ['a', 'a', 'b', 'b', 'a', 'a', 'b', 'b']
model.fit([sequence])
assert not np.isfinite(model.score_ll([['c']]))
assert not np.isfinite(model.score_ll([['c', 'c']]))
assert not np.isfinite(model.score_ll([['a', 'c']]))
def test_51():
# test score_ll
model = MarkovStateModel(reversible_type='mle')
sequence = ['a', 'a', 'b', 'b', 'a', 'a', 'b', 'b', 'c', 'c', 'c', 'a', 'a']
model.fit([sequence])
assert model.mapping_ == {'a': 0, 'b': 1, 'c': 2}
score_ac = model.score_ll([['a', 'c']])
assert score_ac == np.log(model.transmat_[0, 2])
def test_score_ll_1():
model = MarkovStateModel(reversible_type='mle')
sequence = ['a', 'a', 'b', 'b', 'a', 'a', 'b', 'b']
model.fit([sequence])
assert model.mapping_ == {'a': 0, 'b': 1}
score_aa = model.score_ll([['a', 'a']])
assert score_aa == np.log(model.transmat_[0, 0])
score_bb = model.score_ll([['b', 'b']])
assert score_bb == np.log(model.transmat_[1, 1])
score_ab = model.score_ll([['a', 'b']])
assert score_ab == np.log(model.transmat_[0, 1])
score_abb = model.score_ll([['a', 'b', 'b']])
assert score_abb == (np.log(model.transmat_[0, 1]) +
np.log(model.transmat_[1, 1]))
assert model.state_labels_ == ['a', 'b']
assert np.sum(model.populations_) == 1.0
def test_51():
# test score_ll
model = MarkovStateModel(reversible_type='mle')
sequence = ['a', 'a', 'b', 'b', 'a', 'a', 'b', 'b', 'c', 'c', 'c', 'a', 'a']
model.fit([sequence])
assert model.mapping_ == {'a': 0, 'b': 1, 'c': 2}
score_ac = model.score_ll([['a', 'c']])
assert score_ac == np.log(model.transmat_[0, 2])
def test_score_ll_1():
model = MarkovStateModel(reversible_type='mle')
sequence = ['a', 'a', 'b', 'b', 'a', 'a', 'b', 'b']
model.fit([sequence])
assert model.mapping_ == {'a': 0, 'b': 1}
score_aa = model.score_ll([['a', 'a']])
assert score_aa == np.log(model.transmat_[0, 0])
score_bb = model.score_ll([['b', 'b']])
assert score_bb == np.log(model.transmat_[1, 1])
score_ab = model.score_ll([['a', 'b']])
assert score_ab == np.log(model.transmat_[0, 1])
score_abb = model.score_ll([['a', 'b', 'b']])
assert score_abb == (np.log(model.transmat_[0, 1]) +
np.log(model.transmat_[1, 1]))
assert model.state_labels_ == ['a', 'b']
assert np.sum(model.populations_) == 1.0
