class documentation

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

pass dimension of the underlying sample space

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

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, **kwargs):

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):

return list of i.i.d. samples.

Parameters
numberis the number of samples.
updatecontrols a possibly lazy update of the sampler.
def to_linear_transformation(self, reset=False):
def to_linear_transformation_inverse(self, reset=False):

return inverse of associated linear transformation

def transform(self, x):

transform x as implied from the distribution parameters

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

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
correlation_matrix =

Undocumented

@property
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