cotengra.hyperoptimizers.hyper_random#

Fake hyper optimization using random sampling.

Module Contents#

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(*_, **__)

cotengra.hyperoptimizers.hyper_random.sample_bool(rng)[source]#
cotengra.hyperoptimizers.hyper_random.sample_int(rng, low, high)[source]#
cotengra.hyperoptimizers.hyper_random.sample_option(rng, options)[source]#
cotengra.hyperoptimizers.hyper_random.sample_uniform(rng, low, high)[source]#
cotengra.hyperoptimizers.hyper_random.sample_loguniform(rng, low, high)[source]#
class cotengra.hyperoptimizers.hyper_random.RandomSpace(space, seed=None)[source]#
sample()[source]#
class cotengra.hyperoptimizers.hyper_random.RandomSampler(methods, spaces, seed=None)[source]#
ask()[source]#
cotengra.hyperoptimizers.hyper_random.random_init_optimizers(self, methods, space, seed=None)[source]#

Initialize a completely random sampling optimizer.

Parameters:

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

cotengra.hyperoptimizers.hyper_random.random_get_setting(self)[source]#
cotengra.hyperoptimizers.hyper_random.random_report_result(*_, **__)[source]#