Return random integers from low (inclusive) to high (exclusive).
Return random integers from the “discrete uniform” distribution of
the specified dtype in the “half-open” interval [low, high). If
high is None (the default), then results are from [0, low).
low (int) – Lowest (signed) integer to be drawn from the distribution (unless
high=None, in which case this parameter is one above the
highest such integer).
high (int, optional) – If provided, one above the largest (signed) integer to be drawn
from the distribution (see above for behavior if high=None).
size (int or tuple of ints, optional) – Output shape. If the given shape is, e.g., (m, n, k), then
m * n * k samples are drawn. Default is None, in which case a
single value is returned.
(m, n, k)
m * n * k
dtype (data-type, optional) – Desired dtype of the result. All dtypes are determined by their
name, i.e., ‘int64’, ‘int’, etc, so byteorder is not available
and a specific precision may have different C types depending
on the platform. The default value is ‘np.int’.
density (float, optional) – if density specified, a sparse tensor will be created
chunk_size (int or tuple of int or tuple of ints, optional) – Desired chunk size on each dimension
gpu (bool, optional) – Allocate the tensor on GPU if True, False as default
dtype – Data-type of the returned tensor.
out – size-shaped tensor of random integers from the appropriate
distribution, or a single such random int if size not provided.
int or Tensor of ints
similar to randint, only for the closed interval [low, high], and 1 is the lowest value if high is omitted. In particular, this other one is the one to use to generate uniformly distributed discrete non-integers.
>>> import mars.tensor as mt
>>> mt.random.randint(2, size=10).execute()
array([1, 0, 0, 0, 1, 1, 0, 0, 1, 0])
>>> mt.random.randint(1, size=10).execute()
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
Generate a 2 x 4 tensor of ints between 0 and 4, inclusive:
>>> mt.random.randint(5, size=(2, 4)).execute()
array([[4, 0, 2, 1],
[3, 2, 2, 0]])