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