"""Python interfaces to DGL random number generators."""
import numpy as np
from ._ffi.function import _init_api
from . import backend as F
from . import ndarray as nd
__all__ = ['seed']
[docs]def seed(val):
"""Set the random seed of DGL.
Parameters
----------
val : int
The seed.
"""
_CAPI_SetSeed(val)
def choice(a, size, replace=True, prob=None): # pylint: disable=invalid-name
"""An equivalent to :func:`numpy.random.choice`.
Use this function if you:
* Perform a non-uniform sampling (probability tensor is given).
* Sample a small set from a very large population (ratio <5%) uniformly
*without* replacement.
* Have a backend tensor on hand and does not want to convert it to numpy
back and forth.
Compared to :func:`numpy.random.choice`, it is slower when replace is True
and is comparable when replace is False. It wins when the population is
very large and the number of draws are quite small (e.g., draw <5%). The
reasons are two folds:
* When ``a`` is a large integer, it avoids creating a large range array as
numpy does.
* When draw ratio is small, it switches to a hashmap based implementation.
It out-performs numpy for non-uniform sampling in general cases.
Parameters
----------
a : 1-D tensor or int
If an ndarray, a random sample is generated from its elements. If an int,
the random sample is generated as if a were F.arange(a)
size : int or tuple of ints
Output shape. E.g., for size ``(m, n, k)``, then ``m * n * k`` samples are drawn.
replace : bool, optional
If true, sample with replacement.
prob : 1-D tensor, optional
The probabilities associated with each entry in a.
If not given the sample assumes a uniform distribution over all entries in a.
Returns
-------
samples : 1-D tensor
The generated random samples
"""
#TODO(minjie): support RNG as one of the arguments.
if isinstance(size, tuple):
num = np.prod(size)
else:
num = size
if F.is_tensor(a):
population = F.shape(a)[0]
else:
population = a
if prob is None:
prob = nd.NULL["int64"]
else:
prob = F.zerocopy_to_dgl_ndarray(prob)
bits = 64 # index array is in 64-bit
chosen_idx = _CAPI_Choice(int(num), int(population), prob, bool(replace), bits)
chosen_idx = F.zerocopy_from_dgl_ndarray(chosen_idx)
if F.is_tensor(a):
chosen = F.gather_row(a, chosen_idx)
else:
chosen = chosen_idx
if isinstance(size, tuple):
return F.reshape(chosen, size)
else:
return chosen
_init_api('dgl.rng', __name__)