Random sampling¶

lsqfitgp.raniter(mean, cov, n=None, eps=None, rng=None)[source]¶

Take random samples from a multivariate Gaussian.

This generator mimics the interface of gvar.raniter, but takes as input the mean and covariance separately instead of a collection of gvars.

Parameters:

meanscalar, array, or dictionary of scalars/arrays: The mean of the Gaussian distribution.
covscalar, array, or dictionary of scalars/arrays: The covariance matrix. If mean is a dictionary, cov must be a dictionary with pair of keys from mean as keys.
nint, optional: The maximum number of iterations. Default unlimited.
epsfloat, optional: Used to correct the eigenvalues of the covariance matrix to handle non-positivity due to roundoff, relative to the largest eigenvalue. Default is number of variables times floating point epsilon.
rngseed or random generator, optional: rng is passed through numpy.random.default_rng to produce a random number generator.

Yields:

sampscalar, array, or dictionary of scalars/arrays: The random sample in the same format of mean.

Examples

>>> mean = {'a': np.arange(3)}
>>> cov = {('a', 'a'): np.eye(3)}
>>> for sample in lgp.raniter(mean, cov, 3):
>>>     print(sample)

lsqfitgp