mars.tensor.random.sample#
- mars.tensor.random.sample(size=None, chunk_size=None, gpu=None, dtype=None)#
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
- 参数
size (int or tuple of ints, 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.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.
- 返回
out – Array of random floats of shape size (unless
size=None
, in which case a single float is returned).- 返回类型
float or Tensor of floats
实际案例
>>> 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]])