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""" Inspired by https://github.com/jonathf/chaospy/blob/master/chaospy/
distributions/sampler/sequences/hammersley.py
"""
import numpy as np
from sklearn.utils import check_random_state
from ..space import Space
from .base import InitialPointGenerator
from .halton import Halton
class Hammersly(InitialPointGenerator):
"""Creates `Hammersley` sequence samples.
The Hammersley set is equivalent to the Halton sequence, except for one
dimension is replaced with a regular grid. It is not recommended to
generate a Hammersley sequence with more than 10 dimension.
For ``dim == 1`` the sequence falls back to Van Der Corput sequence.
References
----------
T-T. Wong, W-S. Luk, and P-A. Heng, "Sampling with Hammersley and Halton
Points," Journal of Graphics Tools, vol. 2, no. 2, 1997, pp. 9 - 24.
Parameters
----------
min_skip : int, default=-1
Minimum skipped seed number. When `min_skip != max_skip` and
both are > -1, a random number is picked.
max_skip : int, default=-1
Maximum skipped seed number. When `min_skip != max_skip` and
both are > -1, a random number is picked.
primes : tuple, default=None
The (non-)prime base to calculate values along each axis. If
empty, growing prime values starting from 2 will be used.
"""
def __init__(self, min_skip=0, max_skip=0, primes=None):
self.primes = primes
self.min_skip = min_skip
self.max_skip = max_skip
def generate(self, dimensions, n_samples, random_state=None):
"""Creates samples from Hammersly set.
Parameters
----------
dimensions : list, shape (n_dims,)
List of search space dimensions.
Each search dimension can be defined either as
- a `(lower_bound, upper_bound)` tuple (for `Real` or `Integer`
dimensions),
- a `(lower_bound, upper_bound, "prior")` tuple (for `Real`
dimensions),
- as a list of categories (for `Categorical` dimensions), or
- an instance of a `Dimension` object (`Real`, `Integer` or
`Categorical`).
n_samples : int
The order of the Hammersley sequence.
Defines the number of samples.
random_state : int, RandomState instance, or None (default)
Set random state to something other than None for reproducible
results.
Returns
-------
np.array, shape=(n_dim, n_samples)
Hammersley set.
"""
rng = check_random_state(random_state)
halton = Halton(
min_skip=self.min_skip, max_skip=self.max_skip, primes=self.primes
)
space = Space(dimensions)
n_dim = space.n_dims
transformer = space.get_transformer()
space.set_transformer("normalize")
if n_dim == 1:
out = halton.generate(dimensions, n_samples, random_state=rng)
else:
out = np.empty((n_dim, n_samples), dtype=float)
out[: n_dim - 1] = np.array(
halton.generate(
[
(0.0, 1.0),
]
* (n_dim - 1),
n_samples,
random_state=rng,
)
).T
out[n_dim - 1] = np.linspace(0, 1, n_samples + 1)[:-1]
out = space.inverse_transform(out.T)
space.set_transformer(transformer)
return out
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