clips argument (scalar or array) from below at lower

abstract method, AKA "get" or "sample_distribution", deliver new candidate solution(s), a list of "vectors"

samples `number` solutions and evaluates them on `func`, where each solution `s` is resampled until ``func(s) not in (numpy.NaN, None)``.

get new candidate solutions in genotyp, sampled from a multi-variate normal distribution. Arguments are `number`

Make output and jobs sub directories.

make sure that solutions fit to sample distribution, this interface will probably change. In particular the frequency of long vectors appea

add all missing options with their default values

prints some infos according to `disp_annotation()`, if ``iteration_counter % modulo == 0``

print annotation for `disp()`

eigendecomposition of a symmetric matrix, much slower than `numpy.linalg.eigh`, return ``(EVals, Basis)``, the eigenvalues and an or

matrix exponential for a symmetric matrix

counts for each coordinate the number of feasible values in ``solutions`` and returns an array of length ``len(solutions[0])`` with

return flattened data ``(x, f)`` such that for the sweep through coordinate ``i`` we have for data point ``j`` that ``f[i][j] == func(x[i][j]