class documentation

`class GaussStandardConstant(GaussSampler):`

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Standard Multi-variate normal distribution with zero mean.

No update/change of distribution parameters.

 Method `__imul__` variance multiplier Method `__init__` declarative init, doesn't need to be executed Method `norm` return Mahalanobis norm of `x` w.r.t. the statistical model Method `sample` return list of i.i.d. samples. Method `transform` transform x as implied from the distribution parameters Method `transform_inverse` Undocumented Method `update` do nothing Instance Variable `dimension` Undocumented Instance Variable `quadratic` Undocumented Instance Variable `randn` Undocumented Instance Variable `standard_deviations` Undocumented Property `condition_number` Undocumented Property `correlation_matrix` Undocumented Property `covariance_matrix` Undocumented Property `variances` vector of coordinate-wise (marginal) variances

Inherited from `GaussSampler`:

 Method `set_H` set Hessian w.r.t. which to compute the eigen spectrum. Method `set_H_by_f` set Hessian from f at x0. Property `chin` approximation of the expected length when isotropic with variance 1. Property `corr_condition` condition number of the correlation matrix Property `eigenspectrum` return eigen spectrum w.r.t. H like sqrt(H) C sqrt(H) Instance Variable `_left` Undocumented Instance Variable `_right` Undocumented

Inherited from `StatisticalModelSamplerWithZeroMeanBaseClass` (via `GaussSampler`):

 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 `parameters` return `dict` with (default) parameters, e.g., `c1` and `cmu`. Method `to_linear_transformation` return associated linear transformation Method `to_linear_transformation_inverse` return inverse of associated linear transformation Instance Variable `_lam` Undocumented Instance Variable `_mueff` Undocumented Instance Variable `_parameters` Undocumented
def __imul__(self, factor):
overrides `cma.interfaces.StatisticalModelSamplerWithZeroMeanBaseClass.__imul__`

variance multiplier

def __init__(self, dimension, randn=np.random.randn, quadratic=False, **kwargs):
overrides `cma.sampler.GaussSampler.__init__`

declarative init, doesn't need to be executed

def norm(self, x):
overrides `cma.interfaces.StatisticalModelSamplerWithZeroMeanBaseClass.norm`

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

def sample(self, number, same_length=False):
overrides `cma.interfaces.StatisticalModelSamplerWithZeroMeanBaseClass.sample`

return list of i.i.d. samples.

 Parameters number is the number of samples. same_length Undocumented update controls a possibly lazy update of the sampler.
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):
overrides `cma.interfaces.StatisticalModelSamplerWithZeroMeanBaseClass.update`

do nothing

dimension =
overrides `cma.sampler.GaussSampler.dimension`

Undocumented

Undocumented

randn =

Undocumented

standard_deviations =

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