def test_token_on_nested_graph(self):
g1 = Graph([self.states[0]])
g2 = Graph([self.states[1]])
g3_state = State(34, [0.2])
g3 = Graph([g3_state])
top_level = Graph([g1, g2, g3])
token = Token(3, [23])
expected_score = 3 + g3.local_cost()
g3_state.pass_token(token)
g3_state.commit()
self.assertEqual(expected_score, top_level.token.score)
def test_best_path_initially(self):
graph = Graph(self.states)
self.states[0].add_transition(
Transition.free(self.states[0], self.states[1])
)
self.states[1].add_transition(
Transition.free(self.states[1], self.states[1])
)
self.assertEqual([], graph.optimal_path().history)
def test_pass_token(self):
graph = Graph(self.states)
score = 30
expected_score = score + graph.local_cost()
expected_history = [20, 30, self.state1]
graph.pass_token(Token(score, expected_history[:-1]))
graph.commit()
first_state = self.states[0]
self.assertEqual(expected_score, first_state.token.score)
self.assertEqual(expected_history, first_state.token.history)
def test_word_transition(self):
graph_a = Graph([self.states[0]])
graph_b = Graph([self.states[1]])
a_to_b = Transition(graph_a, graph_b, 0.99)
graph_a.add_transition(a_to_b)
top_level = Graph([graph_a, graph_b])
top_level.evolve()
top_level.commit()
self.assertEqual(graph_a.token, top_level.optimal_path())
transit_cost = a_to_b.full_cost()
top_level.evolve()
top_level.commit()
token = top_level.optimal_path()
self.assertEqual(- np.log(0.5) + transit_cost, token.score)
self.assertEqual([self.state1, self.state2], token.history)
def test_transitions_with_self_loops(self):
first_state = State(20, [0.2, 0.4])
graph = Graph([first_state])
graph.add_transition(Transition.free(first_state, first_state))
graph.evolve()
graph.commit()
graph.evolve()
graph.commit()
score = graph._nodes[0].token.score
self.assertEqual(- np.log(0.2) - np.log(0.4), score)
def test_multiple_states_model(self):
graph_id = 7
graph = Graph(self.states, graph_id=graph_id)
graph.add_transition(
Transition.free(self.states[0], self.states[1])
)
graph.add_transition(
Transition.free(self.states[1], self.states[1])
)
graph.evolve()
graph.commit()
self.assertEqual(Token(- np.log(self.p1[0]), [self.state1], [graph_id]),
graph.optimal_path())
graph.evolve()
graph.commit()
expected = Token(- np.log(self.p1[0]) - np.log(self.p2[1]),
[self.state1, self.state2], [graph_id])
self.assertEqual(expected, graph.optimal_path())
def test_one_state_model(self):
states = [self.states[0]]
graph = Graph(states)
graph.add_transition(
Transition.free(self.states[0], self.states[0])
)
graph.evolve()
graph.commit()
t1_prob = self.p1[0]
t2_prob = self.p1[1]
self.assertEqual(Token(- np.log(t1_prob), [self.state1]),
graph.optimal_path())
graph.evolve()
graph.commit()
expected = Token(- np.log(t1_prob) - np.log(t2_prob),
[self.state1, self.state1])
self.assertEqual(expected, graph.optimal_path())
def test_token(self):
graph = Graph(self.states)
self.states[1].pass_token(Token(34, [20, 30]))
self.states[1].commit()
self.assertEqual(self.states[1].token, graph.token)
def test_local_cost(self):
graph = Graph(self.states)
self.assertEqual(- np.log(self.p1[0]), graph.local_cost())
def test_passing_token_to_graph_adds_word_id_to_history(self):
graph = Graph(self.states, graph_id=7)
token = Token(25, [3], words=[3])
graph.pass_token(token)
graph.commit()
self.assertEqual([3, 7], self.states[0].token.words)