cma.sampler.GaussStandardConstant

class documentation

class GaussStandardConstant(GaussSampler):

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

quadratic = ¶

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