`scipy.stats._qmc:QMCEngine.integers`

source: /scipy/stats/_qmc.py :1003

Signature

   def     integers (    self    ,    l_bounds  :  npt.ArrayLike    ,  * ,     u_bounds  :  npt.ArrayLike | None    =  None   ,    n  :  IntNumber    =  1   ,    endpoint  :  bool    =  False   ,    workers  :  IntNumber    =  1     )   →  np.ndarray

Summary

Draw n integers from l_bounds (inclusive) to u_bounds (exclusive), or if endpoint=True, l_bounds (inclusive) to u_bounds (inclusive).

Parameters

l_bounds : int or array-like of ints: Lowest (signed) integers to be drawn (unless u_bounds=None, in which case this parameter is 0 and this value is used for u_bounds).
u_bounds : int or array-like of ints, optional: If provided, one above the largest (signed) integer to be drawn (see above for behavior if u_bounds=None). If array-like, must contain integer values.
n : int, optional: Number of samples to generate in the parameter space. Default is 1.
endpoint : bool, optional: If true, sample from the interval [l_bounds, u_bounds] instead of the default [l_bounds, u_bounds). Defaults is False.
workers : int, optional: Number of workers to use for parallel processing. If -1 is given all CPU threads are used. Only supported when using Halton Default is 1.

Returns

sample : array_like (n, d): QMC sample.

Notes

It is safe to just use the same [0, 1) to integer mapping with QMC that you would use with MC. You still get unbiasedness, a strong law of large numbers, an asymptotically infinite variance reduction and a finite sample variance bound.

To convert a sample from $[0, 1)$ to $[a, b), b > a$ , with $a$ the lower bounds and $b$ the upper bounds, the following transformation is used:

floor ((b - a) \cdot sample + a)

Aliases

scipy.stats._qmc.QMCEngine.integers