utils – Friendly random numbers

Guide

PyTensor assigns NumPy RNG states (e.g. Generator or RandomState objects) to each RandomVariable. The combination of an RNG state, a specific RandomVariable type (e.g. NormalRV), and a set of distribution parameters uniquely defines the RandomVariable instances in a graph.

This means that a “stream” of distinct RNG states is required in order to produce distinct random variables of the same kind. RandomStream provides a means of generating distinct random variables in a fully reproducible way.

RandomStream is also designed to produce simpler graphs and work with more sophisticated Ops like Scan, which makes it the de facto random variable interface in PyTensor.

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

Reference

class pytensor.tensor.random.utils.RandomStream

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

updates()

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 argument to pytensor.function.

seed(meta_seed)

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)

Return the random variable from op(*args, **kwargs).

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.