deephyper.skopt.sampler.Hammersly#

class deephyper.skopt.sampler.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

generate

Creates samples from Hammersly set.

set_params

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)

set_params(**params)#

Set the parameters of this initial point generator.

Parameters:

**params (dict) – Generator parameters.

Returns:

self – Generator instance.

Return type:

object