pybop.optimisers._random_search#

Classes#

RandomSearchImpl

Random Search (RS) optimisation algorithm.

Module Contents#

class pybop.optimisers._random_search.RandomSearchImpl(x0: numpy.ndarray, sigma0: list[float] | None, boundaries: pints.Boundaries | None)[source]#

Bases: pints.PopulationBasedOptimiser

Random Search (RS) optimisation algorithm. This algorithm explores the parameter space by randomly sampling points.

The algorithm does the following: 1. Initialise a population of solutions. 2. At each iteration, generate n number of random positions within boundaries. 3. Evaluate the quality/fitness of the positions. 4. Replace the best position with improved position if found.

Parameters:

population_size – Number of solutions to evaluate per iteration.
optional – Number of solutions to evaluate per iteration.

References: The Random Search algorithm implemented in this work is based on principles outlined in “Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control” by Spall, J. C. (2003).

The implementation inherits from the PINTS PopulationOptimiser.

_suggested_population_size()[source]#: Returns a suggested population size based on the dimension of the parameter space.

ask()[source]#: Returns a list of positions to evaluate in the optimiser-space.

clip_candidates(x)[source]#: Clip the input array to the boundaries if available.

f_best()[source]#: Returns the best score found so far.

name()[source]#: Returns the name of the optimiser.

running()[source]#: Returns True if the optimisation is in progress.

tell(replies)[source]#: Receives a list of cost function values from points previously specified by self.ask(), and updates the optimiser state accordingly.

x_best()[source]#: Returns the best parameter values found so far.

_dim[source]#

_f_best[source]#

_lower = None[source]#

_ready_for_tell = False[source]#

_running = False[source]#

_upper = None[source]#

_x_best[source]#