def __init__(self, dims, ranges, seed=None, dtype=np.float64):
self._dims = np.array(dims)
if len(self._dims) != len(ranges):
raise ValueError(f"dims (length {len(self._dims)}) and ranges "
f"(length {len(ranges)}) must be the same length")
ArchiveBase.__init__(
self,
storage_dims=tuple(self._dims),
behavior_dim=len(self._dims),
seed=seed,
dtype=dtype,
)
ranges = list(zip(*ranges))
self._lower_bounds = np.array(ranges[0], dtype=self.dtype)
self._upper_bounds = np.array(ranges[1], dtype=self.dtype)
self._interval_size = self._upper_bounds - self._lower_bounds
self._boundaries = []
for dim, lower_bound, upper_bound in zip(self._dims,
self._lower_bounds,
self._upper_bounds):
self._boundaries.append(
np.linspace(lower_bound, upper_bound, dim + 1))
def __init__(self,
dims,
ranges,
seed=None,
dtype=np.float64,
remap_frequency=100,
buffer_capacity=1000):
self._dims = np.array(dims)
if len(self._dims) != len(ranges):
raise ValueError(f"dims (length {len(self._dims)}) and ranges "
f"(length {len(ranges)}) must be the same length")
ArchiveBase.__init__(
self,
storage_dims=tuple(self._dims),
behavior_dim=len(self._dims),
seed=seed,
dtype=dtype,
)
ranges = list(zip(*ranges))
self._lower_bounds = np.array(ranges[0], dtype=self.dtype)
self._upper_bounds = np.array(ranges[1], dtype=self.dtype)
self._interval_size = self._upper_bounds - self._lower_bounds
# Specifics for sliding boundaries.
self._remap_frequency = remap_frequency
# Allocate an extra entry in each row so we can put the upper bound at
# the end.
self._boundaries = np.full(
(self._behavior_dim, np.max(self._dims) + 1),
np.nan,
dtype=self.dtype)
# Initialize the boundaries.
for i, (dim, lower_bound, upper_bound) in enumerate(
zip(self._dims, self._lower_bounds, self._upper_bounds)):
self._boundaries[i, :dim + 1] = np.linspace(
lower_bound, upper_bound, dim + 1)
# Create buffer.
self._buffer = SolutionBuffer(buffer_capacity, self._behavior_dim)
# Total number of solutions encountered.
self._total_num_sol = 0
the wrong shape.
"""
def __init__(self,
bins,
ranges,
seed=None,
dtype=np.float64,
samples=100_000,
custom_centroids=None,
k_means_kwargs=None,
use_kd_tree=False,
ckdtree_kwargs=None):
ArchiveBase.__init__(
self,
storage_dims=(bins, ),
behavior_dim=len(ranges),
seed=seed,
dtype=dtype,
)
ranges = list(zip(*ranges))
self._lower_bounds = np.array(ranges[0], dtype=self.dtype)
self._upper_bounds = np.array(ranges[1], dtype=self.dtype)
self._bins = bins
# Apply default args for k-means. Users can easily override these,
# particularly if they want higher quality clusters.
self._k_means_kwargs = ({} if k_means_kwargs is None else
k_means_kwargs.copy())
if "n_init" not in self._k_means_kwargs: