cma

Covariance matrix adaptation (CMA).

CMA

__call__(cov, step, X, J)

Performs the CMA update.

Parameters:

Name	Type	Description	Default
cov	array_like, of shape (d, d)	Current covariance or correlation matrix.	required
step	array_like, of shape (d,)	New step of control vector. Used to update the evolution path.	required
X	array_like, of shape (n, d)	Control ensemble of size n.	required
J	array_like, of shape (n,)	Objective ensemble of size n.	required

Returns:

Name	Type	Description
out	array_like, of shape (d, d)	CMA updated covariance (correlation) matrix.

__init__(ne, dim, alpha_mu=None, n_mu=None, alpha_1=None, alpha_c=None, corr_update=False, equal_weights=True)

This is a rather simple simple CMA class hansen2006.

Parameters:

Name	Type	Description	Default
ne	int	Ensemble size	required
dim	int	Dimensions of control vector	required
alpha_mu	float	Learning rate for rank-mu update. If None, value proposed in [1] is used.	None
n_mu	int, `n_mu < ne`	Number of best samples of ne, to be used for rank-mu update. Default is int(ne/2).	None
alpha_1	float	Learning rate fro rank-one update. If None, value proposed in [1] is used.	None
alpha_c	float	Parameter (inverse if backwards time horizen)for evolution path update in the rank-one update. See [1] for more info. If None, value proposed in [1] is used.	None
corr_update	bool	If True, CMA is used to update a correlation matrix. Default is False.	False
equal_weights	bool	If True, all n_mu members are assign equal weighting, w_i = 1/n_mu. If False, the weighting scheme proposed in [1], where w_i = log(n_mu + 1)-log(i), and normalized such that they sum to one. Defualt is True.	True