| """ 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 |
|
|
