class documentation

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):
def __init__(self, dimension, randn=np.random.randn, quadratic=False, **kwargs):

declarative init, doesn't need to be executed

def norm(self, x):

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

def sample(self, number, same_length=False):

return list of i.i.d. samples.

Parameters
numberis the number of samples.
same_lengthUndocumented
updatecontrols a possibly lazy update of the sampler.
def transform(self, x):

transform x as implied from the distribution parameters

def update(self, vectors, weights):
dimension =

Undocumented

quadratic =

Undocumented

randn =

Undocumented

standard_deviations =

Undocumented

@property
correlation_matrix =

Undocumented

@property
variances =

vector of coordinate-wise (marginal) variances