cma.interfaces.StatisticalModelSamplerWithZeroMeanBaseClass

class documentation

class StatisticalModelSamplerWithZeroMeanBaseClass(object):

Known subclasses: cma.restricted_gaussian_sampler.GaussVDSampler, cma.restricted_gaussian_sampler.GaussVkDSampler, cma.sampler.GaussSampler

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yet versatile base class to replace a sampler namely in CMAEvolutionStrategy

Method	`__imul__`	Undocumented
Method	`__init__`	pass the vector of initial standard deviations or dimension of the underlying sample space.
Method	`inverse_hessian_scalar_correction`	return scalar correction `alpha` such that `X` and `f` fit to `f(x) = (x-mean) (alpha * C)**-1 (x-mean)`
Method	`norm`	return Mahalanobis norm of `x` w.r.t. the statistical model
Method	`parameters`	return `dict` with (default) parameters, e.g., `c1` and `cmu`.
Method	`sample`	return list of i.i.d. samples.
Method	`to_linear_transformation`	return associated linear transformation
Method	`to_linear_transformation_inverse`	return inverse of associated linear transformation
Method	`transform`	transform `x` as implied from the distribution parameters
Method	`transform_inverse`	Undocumented
Method	`update`	`vectors` is a list of samples, `weights` a corrsponding list of learning rates
Property	`condition_number`	Undocumented
Property	`covariance_matrix`	Undocumented
Property	`variances`	vector of coordinate-wise (marginal) variances
Instance Variable	`_lam`	Undocumented
Instance Variable	`_mueff`	Undocumented
Instance Variable	`_parameters`	Undocumented

def __imul__(self, factor): ¶

overridden in cma.restricted_gaussian_sampler.GaussVDSampler, cma.restricted_gaussian_sampler.GaussVkDSampler, cma.sampler.GaussDiagonalSampler, cma.sampler.GaussFullSampler, cma.sampler.GaussStandardConstant

Undocumented

def __init__(self, std_vec, **kwargs): ¶

overridden in cma.restricted_gaussian_sampler.GaussVDSampler, cma.restricted_gaussian_sampler.GaussVkDSampler, cma.sampler.GaussSampler

pass the vector of initial standard deviations or dimension of the underlying sample space.

Ideally catch the case when std_vec is a scalar and then interpreted as dimension.

def inverse_hessian_scalar_correction(self, mean, X, f): ¶

overridden in cma.restricted_gaussian_sampler.GaussVDSampler, cma.restricted_gaussian_sampler.GaussVkDSampler, cma.sampler.GaussFullSampler

return scalar correction alpha such that X and f fit to f(x) = (x-mean) (alpha * C)**-1 (x-mean)

def norm(self, x): ¶

return Mahalanobis norm of x w.r.t. the statistical model

def parameters(self, mueff=None, lam=None): ¶

overridden in cma.restricted_gaussian_sampler.GaussVDSampler, cma.restricted_gaussian_sampler.GaussVkDSampler

return dict with (default) parameters, e.g., c1 and cmu.

See Also
`RecombinationWeights`

def sample(self, number, update=None): ¶

return list of i.i.d. samples.

Parameters
number	is the number of samples.
update	controls a possibly lazy update of the sampler.

def to_linear_transformation(self, reset=False): ¶

overridden in cma.restricted_gaussian_sampler.GaussVDSampler, cma.restricted_gaussian_sampler.GaussVkDSampler, cma.sampler.GaussDiagonalSampler, cma.sampler.GaussFullSampler

return associated linear transformation

def to_linear_transformation_inverse(self, reset=False): ¶

overridden in cma.restricted_gaussian_sampler.GaussVDSampler, cma.restricted_gaussian_sampler.GaussVkDSampler, cma.sampler.GaussDiagonalSampler, cma.sampler.GaussFullSampler

return inverse of associated linear transformation

def transform(self, x): ¶

transform x as implied from the distribution parameters

def transform_inverse(self, x): ¶

Undocumented

def update(self, vectors, weights): ¶

vectors is a list of samples, weights a corrsponding list of learning rates

@property
condition_number = ¶

Undocumented

@property
covariance_matrix = ¶

Undocumented

@property
variances = ¶

vector of coordinate-wise (marginal) variances

_lam = ¶

Undocumented

_mueff = ¶

overridden in cma.restricted_gaussian_sampler.GaussVDSampler, cma.restricted_gaussian_sampler.GaussVkDSampler

Undocumented

_parameters = ¶

overridden in cma.restricted_gaussian_sampler.GaussVDSampler, cma.restricted_gaussian_sampler.GaussVkDSampler

Undocumented