cma.restricted_gaussian_sampler.GaussVDSampler

class documentation

class GaussVDSampler(StatisticalModelSamplerWithZeroMeanBaseClass):

Constructor: GaussVDSampler(dimension, randn, debug)

Restricted Gaussian Sampler for VD-CMA VD-CMA: Linear Time/Space Comparison-based Natural Gradient Optimization The covariance matrix is limited as C = D * (I + v*v^t) * D, where D is a diagonal, v is a vector.

Reference

Youhei Akimoto, Anne Auger, and Nikolaus Hansen. Comparison-Based Natural Gradient Optimization in High Dimension. In Proc. of GECCO 2014, pp. 373 -- 380 (2014)

Static Method	`extend_cma_options`	return correct options to run `cma.fmin` or initialize `cma.CMAEvolutionStrategy` using the `GaussVDSampler` AKA VD-CMA-ES
Method	`__imul__`	Undocumented
Method	`__init__`	pass 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	`parameters_old`	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
Instance Variable	`dvec`	Undocumented
Instance Variable	`N`	Undocumented
Instance Variable	`norm_v`	Undocumented
Instance Variable	`norm_v2`	Undocumented
Instance Variable	`pc`	Undocumented
Instance Variable	`randn`	Undocumented
Instance Variable	`vn`	Undocumented
Instance Variable	`vnn`	Undocumented
Instance Variable	`vvec`	Undocumented
Instance Variable	`weights`	Undocumented
Property	`condition_number`	Undocumented
Property	`correlation_matrix`	Undocumented
Property	`covariance_matrix`	Undocumented
Property	`variances`	vector of coordinate-wise (marginal) variances
Static Method	`_alpha_avec_bsca_invavnn`	Undocumented
Static Method	`_ngv_ngd`	Undocumented
Static Method	`_pvec_and_qvec`	Undocumented
Method	`_get_params`	Undocumented
Method	`_get_params2`	Undocumented
Instance Variable	`_debug`	Undocumented
Instance Variable	`_mueff`	Undocumented
Instance Variable	`_parameters`	Undocumented

Inherited from StatisticalModelSamplerWithZeroMeanBaseClass:

Instance Variable _lam Undocumented

@staticmethod
def extend_cma_options(opts=None): ¶

return correct options to run cma.fmin or initialize cma.CMAEvolutionStrategy using the GaussVDSampler AKA VD-CMA-ES

def __imul__(self, factor): ¶

overrides cma.interfaces.StatisticalModelSamplerWithZeroMeanBaseClass.__imul__

Undocumented

def __init__(self, dimension, randn=np.random.randn, debug=False): ¶

overrides cma.interfaces.StatisticalModelSamplerWithZeroMeanBaseClass.__init__

pass dimension of the underlying sample space

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

overrides cma.interfaces.StatisticalModelSamplerWithZeroMeanBaseClass.inverse_hessian_scalar_correction

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

def norm(self, x): ¶

overrides cma.interfaces.StatisticalModelSamplerWithZeroMeanBaseClass.norm

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

def parameters(self, mueff=None, **kwargs): ¶

overrides cma.interfaces.StatisticalModelSamplerWithZeroMeanBaseClass.parameters

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

See Also
`RecombinationWeights`

def parameters_old(self, weights): ¶

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

See Also
`RecombinationWeights`

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

overrides cma.interfaces.StatisticalModelSamplerWithZeroMeanBaseClass.sample

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): ¶

overrides cma.interfaces.StatisticalModelSamplerWithZeroMeanBaseClass.to_linear_transformation

return associated linear transformation

def to_linear_transformation_inverse(self, reset=False): ¶

overrides cma.interfaces.StatisticalModelSamplerWithZeroMeanBaseClass.to_linear_transformation_inverse

return inverse of associated linear transformation

def transform(self, x): ¶

overrides cma.interfaces.StatisticalModelSamplerWithZeroMeanBaseClass.transform

transform x as implied from the distribution parameters

def transform_inverse(self, x): ¶

overrides cma.interfaces.StatisticalModelSamplerWithZeroMeanBaseClass.transform_inverse

Undocumented

def update(self, vectors, weights, hsig=True): ¶

overrides cma.interfaces.StatisticalModelSamplerWithZeroMeanBaseClass.update

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

dvec = ¶

Undocumented

N = ¶

Undocumented

norm_v = ¶

Undocumented

norm_v2 = ¶

Undocumented

pc = ¶

Undocumented

randn = ¶

Undocumented

vn = ¶

Undocumented

vnn = ¶

Undocumented

vvec = ¶

Undocumented

weights = ¶

Undocumented

@property
condition_number = ¶

overrides cma.interfaces.StatisticalModelSamplerWithZeroMeanBaseClass.condition_number

Undocumented

@property
correlation_matrix = ¶

Undocumented

@property
covariance_matrix = ¶

overrides cma.interfaces.StatisticalModelSamplerWithZeroMeanBaseClass.covariance_matrix

Undocumented

@property
variances = ¶

overrides cma.interfaces.StatisticalModelSamplerWithZeroMeanBaseClass.variances

vector of coordinate-wise (marginal) variances

@staticmethod
def _alpha_avec_bsca_invavnn(vnn, norm_v2): ¶

Undocumented

@staticmethod
def _ngv_ngd(dvec, vn, vnn, norm_v, norm_v2, alpha, avec, bsca, invavnn, pvec, qvec): ¶

Undocumented

@staticmethod
def _pvec_and_qvec(vn, norm_v2, y, weights=0): ¶

Undocumented

def _get_params(self, weights, **kwargs): ¶

Undocumented

def _get_params2(self, mueff, **kwargs): ¶

Undocumented

_debug = ¶

Undocumented

_mueff = ¶

overrides cma.interfaces.StatisticalModelSamplerWithZeroMeanBaseClass._mueff

Undocumented

_parameters = ¶

overrides cma.interfaces.StatisticalModelSamplerWithZeroMeanBaseClass._parameters

Undocumented