def test_without_specified_starting_character(self):
mc = MarkovChain(('a', 'b'), [('a', 'a'), ('a', 'b'), ('b', 'a')])
result = mc.generate_sequence(5)
assert len(result) == 5
assert all((e == 'a') or (e == 'b') for e in result)
def test_update_from_edge(self):
expected_matrix = np.array([[0, 1], [0, 0]])
mc = MarkovChain(('a', 'b'))
mc.update_from_edge('a', 'b')
assert np.all(mc._prob_mat._core_mat == expected_matrix)
def test_generate_probability_vector_from_probability_vector(self):
mc1 = MarkovChain(('a', 'b'), [('a', 'a')])
mc2 = MarkovChain(('a', 'b'), [('b', 'b')])
a_vector = np.array([1, 0])
b_vector = np.array([0, 1])
assert np.all(mc1._prob_mat._core_mat == np.array([[1, 0], [0, 0]]))
assert np.all(mc2._prob_mat._core_mat == np.array([[0, 0], [0, 1]]))
assert np.all(
mc1._prob_mat._generate_prob_vect_from_prob_vect(a_vector) ==
np.array([1, 0]))
assert np.all(
mc1._prob_mat._generate_prob_vect_from_prob_vect(b_vector) ==
np.array([0, 0]))
assert np.all(
mc2._prob_mat._generate_prob_vect_from_prob_vect(a_vector) ==
np.array([0, 0]))
assert np.all(
mc2._prob_mat._generate_prob_vect_from_prob_vect(b_vector) ==
np.array([0, 1]))
def test_increment_prob_mat_cell(self):
A_INDEX = 0
B_INDEX = 1
initial_prob_matrix = np.array([[0, 0], [0, 0]])
final_prob_matrix = np.array([[0, 1], [0, 0]])
mc = MarkovChain(('a', 'b'))
assert np.all(mc._prob_mat._core_mat == initial_prob_matrix)
mc._prob_mat._increment_prob_mat_cell(A_INDEX, B_INDEX)
assert np.all(mc._prob_mat._core_mat == final_prob_matrix)
def test_with_edge_list_argument(self):
mc = MarkovChain(('a', 'b'), [('a', 'a')])
assert np.all(mc._gen_alphabet == np.array(['a', 'b']))
assert np.all(mc._prob_mat._core_mat == np.array([[1, 0], [0, 0]]))
assert np.all(mc._prob_mat._mat_normalizer == np.array([[1], [1]]))
def test_get_mat_prob_index_for(self):
mc = MarkovChain(('a', 'b'))
assert mc._get_prob_mat_index_for_('a') == 0
assert mc._get_prob_mat_index_for_('b') == 1
def test_get_single_result_prob_vector(self):
expected_vector = np.array([1, 0])
mc = MarkovChain(('a', 'b'))
assert np.all(
mc._get_single_result_prob_vector('a') == expected_vector)
def test_get_equally_likely_prob_vect(self):
expected_vector = np.array([0.5, 0.5])
mc = MarkovChain(('a', 'b'))
assert np.all(
mc._get_equally_likely_prob_vect() == expected_vector)