deephyper.skopt.sampler.hammersly.Hammersly#
- class deephyper.skopt.sampler.hammersly.Hammersly(min_skip=0, max_skip=0, primes=None)[source]#
Bases:
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.
Methods
Creates samples from Hammersly set.
Set the parameters of this initial point generator.
- generate(dimensions, n_samples, random_state=None)[source]#
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:
Hammersley set.
- Return type:
np.array, shape=(n_dim, n_samples)