class TransitionTests(TestCase):
def setUp(self):
self.p = [0.5, 0.2, 0.1]
self.state = State(25, self.p)
self.p_transit = 0.25
self.transition = Transition(self.state, self.state, self.p_transit)
def test_cost(self):
transition = Transition(self.state, self.state, 1)
self.assertEqual(0, transition.cost())
self.assertEqual(- np.log(self.p_transit), self.transition.cost())
transition = Transition.free(self.state, self.state)
self.assertEqual(0, transition.cost())
def test_full_cost(self):
self.assertEqual(- np.log(0.5) - np.log(self.p_transit),
self.transition.full_cost())
def test_pass_token(self):
expected_score = self.transition.full_cost()
self.state.initialize_token()
self.transition.pass_token()
self.state.commit()
self.assertEqual(Token(expected_score, [self.state.state]),
self.state.token)
def test_cost(self):
transition = Transition(self.state, self.state, 1)
self.assertEqual(0, transition.cost())
self.assertEqual(- np.log(self.p_transit), self.transition.cost())
transition = Transition.free(self.state, self.state)
self.assertEqual(0, transition.cost())
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_add_transition(self):
state = State(self.state_value, self.p)
dest = State(2, self.p)
transition = Transition(state, dest, 0.2)
state.add_transition(transition)
self.assertEqual(1, len(state.transitions))
destination_state = state.transitions[0].destination
self.assertEqual(2, destination_state.state)
self.assertEqual(self.p, destination_state.p)
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_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_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_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 setUp(self):
self.p = [0.5, 0.2, 0.1]
self.state = State(25, self.p)
self.p_transit = 0.25
self.transition = Transition(self.state, self.state, self.p_transit)