skopt.sampler
.Halton¶

class
skopt.sampler.
Halton
(min_skip=1, max_skip=1, primes=None)[source][source]¶ Creates
Halton
sequence samples. In statistics, Halton sequences are sequences used to generate points in space for numerical methods such as Monte Carlo simulations. Although these sequences are deterministic, they are of low discrepancy, that is, appear to be random for many purposes. They were first introduced in 1960 and are an example of a quasirandom number sequence. They generalise the onedimensional van der Corput sequences.For
dim == 1
the sequence falls back to Van Der Corput sequence. Parameters
 min_skipint
minimum skipped seed number. When
min_skip != max_skip
a random number is picked. max_skipint
maximum skipped seed number. When
min_skip != max_skip
a random number is picked. primestuple, default=None
The (non)prime base to calculate values along each axis. If empty or None, growing prime values starting from 2 will be used.
Methods
generate
(dimensions, n_samples[, random_state])Creates samples from Halton set.
set_params
(**params)Set the parameters of this initial point generator.

__init__
(min_skip=1, max_skip=1, primes=None)[source][source]¶ Initialize self. See help(type(self)) for accurate signature.

generate
(dimensions, n_samples, random_state=None)[source][source]¶ Creates samples from Halton set.
 Parameters
 dimensionslist, shape (n_dims,)
List of search space dimensions. Each search dimension can be defined either as
a
(lower_bound, upper_bound)
tuple (forReal
orInteger
dimensions),a
(lower_bound, upper_bound, "prior")
tuple (forReal
dimensions),as a list of categories (for
Categorical
dimensions), oran instance of a
Dimension
object (Real
,Integer
orCategorical
).
 n_samplesint
The order of the Halton sequence. Defines the number of samples.
 random_stateint, RandomState instance, or None (default)
Set random state to something other than None for reproducible results.
 Returns
 np.array, shape=(n_dim, n_samples)
Halton set