def function_env():
bounds = Bounds(shape=(2, ), high=1, low=1, dtype=int)
env = Function(function=lambda x: np.ones(N_WALKERS), bounds=bounds)
params = {
"actions": {
"dtype": np.int64,
"size": (2, )
},
"dt": {
"dtype": np.float32
}
}
states = States(state_dict=params, batch_size=N_WALKERS)
return env, states
def from_function(cls, function: Callable, bounds: Bounds, *args,
**kwargs) -> "FunctionMapper":
"""
Initialize a :class:`FunctionMapper` using a python callable and a \
:class:`Bounds` instance.
Args:
function: Callable representing an arbitrary function to be optimized.
bounds: Represents the domain of the function to be optimized.
*args: Passed to :class:`FunctionMapper` __init__.
**kwargs: Passed to :class:`FunctionMapper` __init__.
Returns:
Instance of :class:`FunctionMapper` that optimizes the target function.
"""
env = Function(function=function, bounds=bounds)
return FunctionMapper(env=lambda: env, *args, **kwargs)
def test_init_error(self):
with pytest.raises(TypeError):
Function(function=sphere, bounds=(True, False))
def custom_domain_function():
bounds = Bounds(shape=(2,), high=10, low=-5, dtype=float)
env = Function(
function=sphere, bounds=bounds, custom_domain_check=lambda x: numpy.linalg.norm(x) < 5.0
)
return env
def local_minimizer():
bounds = Bounds(shape=(2,), high=10, low=-5, dtype=float)
env = Function(function=sphere, bounds=bounds)
return MinimizerWrapper(env)
def test_minimizer_getattr(self):
bounds = Bounds(shape=(2,), high=10, low=-5, dtype=float)
env = Function(function=sphere, bounds=bounds)
minim = MinimizerWrapper(env)
assert minim.shape == env.shape
def custom_domain_function():
bounds = Bounds(shape=(2, ), high=10, low=-5, dtype=judo.float)
env = Function(function=sphere,
bounds=bounds,
custom_domain_check=lambda x, *args: judo.norm(x, 1) < 5.0)
return env