Use rng everywhere #40
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Remark to myself: we drop assertions for now because it seems that it is hard/impossible to create identical random numbers accross machines/versions for numpy. See: numpy/numpy#22975 |
rkern
reviewed
Mar 7, 2023
| @@ -216,13 +220,15 @@ def sample_covariance_matrix(Sigma, N, seed = None): | |||
| return: sample covariance matrix S | |||
| """ | |||
| if seed is not None: | |||
| np.random.seed(seed) | |||
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| rng = np.random.default_rng(seed) | |||
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Do this unconditionally. default_rng(None) will pull random entropy from the OS and will be vastly more likely to give you a distinct PRNG than np.random.randint(low=11111, high=99999) will.
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Ok, thanks for the hint! Will adjust this
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