# mars.tensor.random.randint¶

mars.tensor.random.randint = <bound method randint of <mars.tensor.random.core.RandomState object>>[source]

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.
dtype : dtype, 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, optional
Data-type of the returned tensor.
out : int or Tensor of ints
size-shaped tensor of random integers from the appropriate distribution, or a single such random int if size not provided.
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 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]])