cotengra.hyperoptimizers.hyper_random

Fake hyper optimization using random sampling.

Classes

RandomSpace
RandomSampler

Functions

sample_bool(rng)
sample_int(rng, low, high)
sample_option(rng, options)
sample_uniform(rng, low, high)
sample_loguniform(rng, low, high)
random_init_optimizers(self, methods, space[, seed])	Initialize a completely random sampling optimizer.
random_get_setting(self)
random_report_result(*_, **__)

Module Contents

cotengra.hyperoptimizers.hyper_random.sample_bool(rng)

cotengra.hyperoptimizers.hyper_random.sample_int(rng, low, high)

cotengra.hyperoptimizers.hyper_random.sample_option(rng, options)

cotengra.hyperoptimizers.hyper_random.sample_uniform(rng, low, high)

cotengra.hyperoptimizers.hyper_random.sample_loguniform(rng, low, high)

class cotengra.hyperoptimizers.hyper_random.RandomSpace(space, seed=None)

rng

_samplers

sample()

class cotengra.hyperoptimizers.hyper_random.RandomSampler(methods, spaces, seed=None)

rng

_rmethods

_rspaces

ask()

cotengra.hyperoptimizers.hyper_random.random_init_optimizers(self, methods, space, seed=None)

Initialize a completely random sampling optimizer.

Parameters:

space (dict[str, dict[str, dict]]) – The search space.

cotengra.hyperoptimizers.hyper_random.random_get_setting(self)

cotengra.hyperoptimizers.hyper_random.random_report_result(*_, **__)