def __init__(self,
archive,
x0,
sigma0,
bounds=None,
batch_size=64,
seed=None):
self._rng = np.random.default_rng(seed)
self._batch_size = batch_size
self._x0 = np.array(x0, dtype=archive.dtype)
self._sigma0 = archive.dtype(sigma0) if isinstance(
sigma0, (float, np.floating)) else np.array(sigma0)
EmitterBase.__init__(
self,
archive,
len(self._x0),
bounds,
)
def __init__(self,
archive,
x0,
iso_sigma=0.01,
line_sigma=0.2,
bounds=None,
batch_size=64,
seed=None):
self._x0 = np.array(x0, dtype=archive.dtype)
self._iso_sigma = archive.dtype(iso_sigma)
self._line_sigma = archive.dtype(line_sigma)
EmitterBase.__init__(
self,
archive,
len(self._x0),
bounds,
batch_size,
seed,
)
def __init__(self,
archive,
x0,
sigma0,
selection_rule="filter",
restart_rule="no_improvement",
weight_rule="truncation",
bounds=None,
batch_size=None,
seed=None):
self._rng = np.random.default_rng(seed)
self._batch_size = batch_size
self._x0 = np.array(x0, dtype=archive.dtype)
self._sigma0 = sigma0
EmitterBase.__init__(
self,
archive,
len(self._x0),
bounds,
)
if selection_rule not in ["mu", "filter"]:
raise ValueError(f"Invalid selection_rule {selection_rule}")
self._selection_rule = selection_rule
if restart_rule not in ["basic", "no_improvement"]:
raise ValueError(f"Invalid restart_rule {restart_rule}")
self._restart_rule = restart_rule
opt_seed = None if seed is None else self._rng.integers(10_000)
self.opt = CMAEvolutionStrategy(sigma0, batch_size, self._solution_dim,
weight_rule, opt_seed,
self.archive.dtype)
self.opt.reset(self._x0)
self._num_parents = (self.opt.batch_size //
2 if selection_rule == "mu" else None)
self._target_behavior_dir = self._generate_random_direction()
self._batch_size = self.opt.batch_size
self._restarts = 0 # Currently not exposed publicly.