mars.tensor.random.randint¶

mars.tensor.random.
randint
(low, high=None, size=None, dtype='l', density=None, chunk_size=None, gpu=None)[source]¶ Return random integers from low (inclusive) to high (exclusive).
Return random integers from the “discrete uniform” distribution of the specified dtype in the “halfopen” interval [low, high). If high is None (the default), then results are from [0, low).
 Parameters
low (
int
) – Lowest (signed) integer to be drawn from the distribution (unlesshigh=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 ifhigh=None
).size (
int
ortuple
ofints
, optional) – Output shape. If the given shape is, e.g.,(m, n, k)
, thenm * n * k
samples are drawn. Default is None, in which case a single value is returned.dtype (
datatype
, 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 createdchunk_size (
int
ortuple
ofint
ortuple
ofints
, optional) – Desired chunk size on each dimensiongpu (
bool
, optional) – Allocate the tensor on GPU if True, False as defaultdtype – Datatype of the returned tensor.
 Returns
out – sizeshaped tensor of random integers from the appropriate distribution, or a single such random int if size not provided.
 Return type
int
orTensor
ofints
See also
random.random_integers()
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 nonintegers.
Examples
>>> 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]])