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The code would be much more flexible if you passed a numpy random state in and used that for the random draws. Object oriented random number generator would allow the user to create and manage multiple lhs (and other) designs simultaneously.
def lhs(rstate, n, samples=None, criterion=None, iterations=None):
"""
Generate a latin-hypercube design
Parameters
rstate : np.random.RandomState
Random state which controls the seed and random draws
An example use is:
# set the random state once for each independent design
rstate = np.random.RandomState(master_seed)
# in the source code the draw is now controlled by the explicit random state object
u = rstate.rand(samples, n) # instead of np.random.rand(samples, n)
This design philosophy is much more flexible.
The text was updated successfully, but these errors were encountered:
All, please accept my apologies for not updating this repo with the recent pull requests. I haven't had much time to do so, being extremely busy with my previous employment, but I recently changed jobs and hope to have much more time to get things up to date. Thanks for your interest and support!
@RickardSjogren and @tisimst you two should coordinate. Maybe both of you can be repo owners.
Having one really good package with multiple contributors is what we all need. The new changes in pydoe2 are excellent (including the addition of RandomState).
Ideally there could be a single DOE package and it could somehow get included into the Anaconda distribution.
The code would be much more flexible if you passed a numpy random state in and used that for the random draws. Object oriented random number generator would allow the user to create and manage multiple lhs (and other) designs simultaneously.
An example use is:
This design philosophy is much more flexible.
The text was updated successfully, but these errors were encountered: