ribs.emitters.GaussianEmitter¶
 class
ribs.emitters.
GaussianEmitter
(archive, x0, sigma0, bounds=None, batch_size=64, seed=None)[source]¶ Emits solutions by adding Gaussian noise to existing archive solutions.
If the archive is empty, calls to
ask()
will generate solutions from a userspecified Gaussian distribution with meanx0
and standard deviationsigma0
. Otherwise, this emitter selects solutions from the archive and generates solutions from a Gaussian distribution centered around each solution with standard deviationsigma0
.This is the classic variation operator presented in Mouret 2015.
 Parameters
archive (ribs.archives.ArchiveBase) – An archive to use when creating and inserting solutions. For instance, this can be
ribs.archives.GridArchive
.x0 (arraylike) – Center of the Gaussian distribution from which to sample solutions when the archive is empty.
sigma0 (float or arraylike) – Standard deviation of the Gaussian distribution, both when the archive is empty and afterwards. Note we assume the Gaussian is diagonal, so if this argument is an array, it must be 1D.
bounds (None or arraylike) – Bounds of the solution space. Solutions are clipped to these bounds. Pass None to indicate there are no bounds. Alternatively, pass an arraylike to specify the bounds for each dim. Each element in this arraylike can be None to indicate no bound, or a tuple of
(lower_bound, upper_bound)
, wherelower_bound
orupper_bound
may be None to indicate no bound.batch_size (int) – Number of solutions to return in
ask()
.seed (int) – Value to seed the random number generator. Set to None to avoid a fixed seed.
 Raises
ValueError – There is an error in the bounds configuration.
Methods
ask
()Creates solutions by adding Gaussian noise to elites in the archive.
tell
(solutions, objective_values, …[, …])Inserts entries into the archive.
Attributes
The archive which stores solutions generated by this emitter.
Number of solutions to return in
ask()
.(solution_dim,)
array with lower bounds of solution space.Standard deviation of the (diagonal) Gaussian distribution.
The dimension of solutions produced by this emitter.
(solution_dim,)
array with upper bounds of solution space.Center of the Gaussian distribution from which to sample solutions when the archive is empty.

ask
()[source]¶ Creates solutions by adding Gaussian noise to elites in the archive.
If the archive is empty, solutions are drawn from a (diagonal) Gaussian distribution centered at
self.x0
. Otherwise, each solution is drawn from a distribution centered at a randomly chosen elite. In either case, the standard deviation isself.sigma0
. Returns
(batch_size, solution_dim)
array – containsbatch_size
new solutions to evaluate.

tell
(solutions, objective_values, behavior_values, metadata=None)¶ Inserts entries into the archive.
This base class implementation (in
EmitterBase
) simply inserts entries into the archive by callingadd()
. It is enough for simple emitters likeGaussianEmitter
, but more complex emitters will almost certainly need to override it. Parameters
solutions (numpy.ndarray) – Array of solutions generated by this emitter’s
ask()
method.objective_values (numpy.ndarray) – 1D array containing the objective function value of each solution.
behavior_values (numpy.ndarray) –
(n, <behavior space dimension>)
array with the behavior space coordinates of each solution.metadata (numpy.ndarray) – 1D object array containing a metadata object for each solution.
 property
archive
¶ The archive which stores solutions generated by this emitter.
 property
lower_bounds
¶ (solution_dim,)
array with lower bounds of solution space.For instance,
[1, 1, 1]
indicates that every dimension of the solution space has a lower bound of 1. Type
 property
sigma0
¶ Standard deviation of the (diagonal) Gaussian distribution.
 Type
 property
upper_bounds
¶ (solution_dim,)
array with upper bounds of solution space.For instance,
[1, 1, 1]
indicates that every dimension of the solution space has an upper bound of 1. Type
 property
x0
¶ Center of the Gaussian distribution from which to sample solutions when the archive is empty.
 Type