utils – Friendly random numbers

Guide

Since Aesara uses a functional design, producing pseudo-random numbers in a graph is not quite as straightforward as it is in numpy.

The way to think about putting randomness into Aesara’s computations is to put random variables in your graph. Aesara will allocate a numpy RandomState object for each such variable, and draw from it as necessary. We will call this sort of sequence of random numbers a random stream.

For an example of how to use random numbers, see Using Random Numbers.

Reference

class aesara.tensor.random.utils.RandomStream[source]

This is a symbolic stand-in for numpy.random.RandomState.

updates()[source]
Returns:a list of all the (state, new_state) update pairs for the random variables created by this object

This can be a convenient shortcut to enumerating all the random variables in a large graph in the update parameter of function.

seed(meta_seed)[source]

meta_seed will be used to seed a temporary random number generator, that will in turn generate seeds for all random variables created by this object (via gen).

Returns:None
gen(op, *args, **kwargs)[source]

Return the random variable from op(*args, **kwargs), but also install special attributes (.rng and update, see RandomVariable ) into it.

This function also adds the returned variable to an internal list so that it can be seeded later by a call to seed.

uniform, normal, binomial, multinomial, random_integers, ...

See basic.RandomVariable.