def optimize(
self,
black_box: BlackBox,
initial_guess: Optional[numpy.ndarray] = None,
initial_guess_array: Optional[numpy.ndarray] = None
) -> OptimizationResult:
if initial_guess is None:
# coverage: ignore
initial_guess = numpy.zeros(black_box.dimension)
opt = black_box.evaluate(initial_guess)
return OptimizationResult(optimal_value=opt,
optimal_parameters=initial_guess,
num_evaluations=1,
cost_spent=0.0,
status=0,
message='success')
def optimize(self,
black_box: BlackBox,
initial_guess: Optional[numpy.ndarray]=None,
initial_guess_array: Optional[numpy.ndarray]=None
) -> OptimizationResult:
opt = numpy.inf
opt_params = None
for _ in range(5):
guess = numpy.random.randn(black_box.dimension)
val = black_box.evaluate(guess)
if val < opt:
opt = val
opt_params = guess
return OptimizationResult(
optimal_value=opt,
optimal_parameters=cast(numpy.ndarray, opt_params),
num_evaluations=1,
cost_spent=0.0,
status=0,
message='success')
def test_black_box_is_abstract_cant_instantiate():
with pytest.raises(TypeError):
_ = BlackBox()
