It can be anything, but “good practice” is to use a number that would have 2 properties:
The binary representation of the one I chose (by mashing numbers) is: '0b10011110000100100101100111001110010001101111111001100111100011000101001111111100100010000' This has 43 0s and 46 1s. Many people just use 0, which is fine in the sense that the stream should have the same properties as if any of 2**N number were chosen. Simple choices so, however, have a slight consequence in the sense that these generate strange dependence across researchers if everyone uses a small number of seeds (e.g., 0 or 1234). Kevin From: Evgeni Burovski Thanks Kevin! A possibly dumb follow-up question: in your example, > entropy = 382193877439745928479635728 is it relevant that `entropy` is a long integer? I.e., what are the constraints on its value, can one use entropy = 1234 or entropy = 0 or entropy = 1 instead? On Mon, Jun 29, 2020 at 5:37 PM Kevin Sheppard <kevin.k.shepp...@gmail.com> wrote: > > The best practice is to use a SeedSequence to spawn child SeedSequences, and then to use these children to initialize your generators or bit generators. > > > > > > from numpy.random import SeedSequence, Generator, PCG64, default_rng > > > > entropy = 382193877439745928479635728 > > > > seed_seq = SeedSequence(entropy) > > NUM_STREAMS = 2**15 > > children = seed_seq.spawn(NUM_STREAMS) > > # if you want the current best bit generator, which may change > > rngs = [default_rng(child) for child in children] > > # If you want the most control across version, set the bit generator > > # this uses PCG64, which is the current default. Each bit generator needs to be wrapped in a generator > > rngs = [Generator(PCG64(child)) for child in children] > > > > Kevin > > > > > > From: Evgeni Burovski > Sent: Monday, June 29, 2020 2:21 PM > To: Discussion of Numerical Python > Subject: Re: [Numpy-discussion] reseed random generator (1.19) > > > > (apologies for jumping into a conversation) > > So what is the recommendation for instantiating a number of generators > > with manually controlled seeds? > > > > The use case is running a series of MC simulations with reproducible > > streams. The runs are independent and are run in parallel in separate > > OS processes, where I do not control the time each process starts > > (jobs are submitted to the batch queue), so default seeding seems > > dubious? > > > > Previously, I would just do roughly > > > > seeds = [1234, 1235, 1236, ...] > > rngs = [np.random.RandomState(seed) for seed in seeds] > > ... > > and each process operates with its own `rng`. > > What would be the recommended way with the new `Generator` framework? > > A human-friendly way would be preferable if possible. > > > > Thanks, > > > > Evgeni > > > > > > On Mon, Jun 29, 2020 at 3:20 PM Kevin Sheppard > > <kevin.k.shepp...@gmail.com> wrote: > > > > > > If you want to use the same entropy-initialized generator for temporarily-reproducible experiments, then you can use > > > > > > > > > > > > gen = np.random.default_rng() > > > > > > state = gen.bit_generator.state > > > > > > gen.standard_normal() > > > > > > # 0.5644742559549797, will vary across runs > > > > > > gen.bit_generator.state = state > > > > > > gen.standard_normal() > > > > > > # Always the same as before 0.5644742559549797 > > > > > > > > > > > > The equivalent to the old way of calling seed to reseed is: > > > > > > > > > > > > SEED = 918273645 > > > > > > gen = np.random.default_rng(SEED) > > > > > > gen.standard_normal() > > > > > > # 0.12345677 > > > > > > gen = np.random.default_rng(SEED) > > > > > > gen.standard_normal() > > > > > > # Identical value > > > > > > > > > > > > Rather than reseeding the same object, you just create a new object. At some point in the development of Generator both methods were timed and there was no performance to reusing the same object by reseeding. > > > > > > > > > > > > Kevin > > > > > > > > > > > > > > > > > > > > > > > > From: Neal Becker > > > Sent: Monday, June 29, 2020 1:01 PM > > > To: Discussion of Numerical Python > > > Subject: Re: [Numpy-discussion] reseed random generator (1.19) > > > > > > > > > > > > I was using this to reset the generator, in order to repeat the same sequence again for testing purposes. > > > > > > > > > > > > On Wed, Jun 24, 2020 at 6:40 PM Robert Kern <robert.k...@gmail.com> wrote: > > > > > > On Wed, Jun 24, 2020 at 3:31 PM Neal Becker <ndbeck...@gmail.com> wrote: > > > > > > Consider the following: > > > > > > > > > > > > from numpy.random import default_rng > > > rs = default_rng() > > > > > > > > > > > > Now how do I re-seed the generator? > > > > > > I thought perhaps rs.bit_generator.seed(), but there is no such attribute. > > > > > > > > > > > > In general, reseeding an existing generator instance is not a good practice. What effect are you trying to accomplish? I assume that you are asking this because you are currently using `RandomState.seed()`. In what circumstances? > > > > > > > > > > > > The raw `bit_generator.state` property *can* be assigned to, in order to support some advanced use cases (mostly involving de/serialization and similar kinds of meta-programming tasks). It's also been helpful for me to construct worst-case scenarios for testing parallel streams. But it quite deliberately bypasses the notion of deriving the state from a human-friendly seed number. > > > > > > > > > > > > -- > > > > > > Robert Kern > > > > > > _______________________________________________ > > > NumPy-Discussion mailing list > > > NumPy-Discussion@python.org > > > https://mail.python.org/mailman/listinfo/numpy-discussion > > > > > > > > > > > > > > > -- > > > > > > Those who don't understand recursion are doomed to repeat it > > > > > > > > > > > > _______________________________________________ > > > NumPy-Discussion mailing list > > > NumPy-Discussion@python.org > > > https://mail.python.org/mailman/listinfo/numpy-discussion > > _______________________________________________ > > NumPy-Discussion mailing list > > NumPy-Discussion@python.org > > https://mail.python.org/mailman/listinfo/numpy-discussion > > > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@python.org > https://mail.python.org/mailman/listinfo/numpy-discussion _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@python.org https://mail.python.org/mailman/listinfo/numpy-discussion |
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