mars.tensor.random.
random
Return random floats in the half-open interval [0.0, 1.0).
Results are from the “continuous uniform” distribution over the stated interval. To sample \(Unif[a, b), b > a\) multiply the output of random_sample by (b-a) and add a:
(b - a) * random_sample() + a
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
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 – Array of random floats of shape size (unless size=None, in which case a single float is returned).
size=None
float or Tensor of floats
Examples
>>> import mars.tensor as mt
>>> mt.random.random_sample().execute() 0.47108547995356098 >>> type(mt.random.random_sample().execute()) <type 'float'> >>> mt.random.random_sample((5,)).execute() array([ 0.30220482, 0.86820401, 0.1654503 , 0.11659149, 0.54323428])
Three-by-two array of random numbers from [-5, 0):
>>> (5 * mt.random.random_sample((3, 2)) - 5).execute() array([[-3.99149989, -0.52338984], [-2.99091858, -0.79479508], [-1.23204345, -1.75224494]])