xNES

class pints.XNES(x0, sigma0=None, boundaries=None)

Finds the best parameters using the xNES method described in [1], [2].

xNES stands for Exponential Natural Evolution Strategy, and is designed for non-linear derivative-free optimization problems [1].

Extends PopulationBasedOptimiser.

References

[1]	(1, 2) Glasmachers, Schaul, Schmidhuber et al. (2010) “Exponential natural evolution strategies”. Proceedings of the 12th annual conference on Genetic and evolutionary computation. https://doi.org/10.1145/1830483.1830557

[2]	PyBrain: The Python machine learning library http://pybrain.org

ask()

See Optimiser.ask().

fbest()

See Optimiser.fbest().

n_hyper_parameters()

See TunableMethod.n_hyper_parameters().

name()

See Optimiser.name().

needs_sensitivities()

Returns True if this methods needs sensitivities to be passed in to tell along with the evaluated error.

population_size()

Returns this optimiser’s population size.

If no explicit population size has been set, None may be returned. Once running, the correct value will always be returned.

running()

See Optimiser.running().

set_hyper_parameters(x)

The hyper-parameter vector is [population_size].

See TunableMethod.set_hyper_parameters().

set_population_size(population_size=None)

Sets a population size to use in this optimisation.

If population_size is set to None, the population size will be set using the heuristic suggested_population_size().

stop()

Checks if this method has run into trouble and should terminate. Returns False if everything’s fine, or a short message (e.g. “Ill-conditioned matrix.”) if the method should terminate.

suggested_population_size(round_up_to_multiple_of=None)

Returns a suggested population size for this method, based on the dimension of the search space (e.g. the parameter space).

If the optional argument round_up_to_multiple_of is set to an integer greater than 1, the method will round up the estimate to a multiple of that number. This can be useful to obtain a population size based on e.g. the number of worker processes used to perform objective function evaluations.

tell(fx)

See Optimiser.tell().

xbest()

See Optimiser.xbest().