def test_run( self, state_factory: Callable[[], State], state_count: int, limit: int, count: int, iteration: int, accepted_final_states: int, ) -> None: """Test running the annealing.""" beam = Beam() state = state_factory() for _ in range(state_count): cloned_state = state.clone() cloned_state.iteration = state.iteration + 1 beam.add_state(cloned_state) predictor = AdaptiveSimulatedAnnealing() context = flexmock( accepted_final_states_count=accepted_final_states, count=count, iteration=iteration, limit=limit, beam=beam, ) with predictor.assigned_context(context): next_state, package_tuple = predictor.run() assert next_state in beam.iter_states() assert package_tuple is not None assert package_tuple[0] in next_state.unresolved_dependencies assert package_tuple in next_state.unresolved_dependencies[ package_tuple[0]].values()
def test_pre_run(self) -> None: """Test pre-run initialization.""" context = flexmock(limit=100) predictor = AdaptiveSimulatedAnnealing() assert predictor._temperature == 0.0 predictor._temperature_history = [(0.1, False, 0.2, 3), (0.42, True, 0.66, 47)] with predictor.assigned_context(context): predictor.pre_run() assert predictor._temperature == context.limit, "Predictor's limit not initialized correctly" assert predictor._temperature_history == [], "Predictor's temperature history no discarded"
def test_temperature_function( self, t0: float, accepted_final_states_count: int, limit: int, iteration: int, count: int, ) -> None: """Test the temperature function never drops bellow 0.""" context = flexmock( accepted_final_states_count=accepted_final_states_count, limit=limit, iteration=iteration, count=count, beam=flexmock(size=96), ) predictor = AdaptiveSimulatedAnnealing() assert (predictor._temperature_function(t0=t0, context=context) >= 0.0), "Temperature dropped bellow 0 or is NaN"