import numpy as np
[docs]class Seeder:
"""
A random seed generator.
Given an initial seed,
the seeder can be called continuously to sample a single
or a batch of random seeds.
.. note::
The seeder creates an independent RandomState to generate random
numbers. It does not affect the RandomState in ``np.random``.
Example::
>>> seeder = Seeder(init_seed=0)
>>> seeder(size=5)
[209652396, 398764591, 924231285, 1478610112, 441365315]
"""
[docs] def __init__(self, init_seed: int = 0, max_seed: int = None):
"""
Initialize the seeder.
Args:
init_seed (int, optional):
Initial seed for generating random seeds. Default: ``0``.
"""
assert isinstance(init_seed, int) and init_seed >= 0, \
f"expected non-negative integer, got {init_seed}"
self.rng = np.random.RandomState(seed=init_seed)
# Upper bound for sampling new random seeds
self.max = max_seed or np.iinfo(np.int32).max
def __call__(self, size=1):
"""
Return the sampled random seeds according to the given size.
Args:
size (int or list): The size of random seeds to sample.
Returns
-------
seeds : list
a list of sampled random seeds.
"""
seeds = self.rng.randint(low=0, high=self.max, size=size).tolist()
return seeds