Cyclops Tensor Framework
parallel arithmetic on multidimensional arrays
ctf.random Namespace Reference


def seed (seed)
def all_seed (seed)
def random (shape, sp=None, p=None, dtype=None)

Function Documentation

def ctf.random.all_seed (   seed)
Seed the random tensor generator with the same seed in all processes.

seed: int
    Seed for random.

Definition at line 28 of file random.pyx.

References CTF_int.init_rng().

def ctf.random.random (   shape,
  sp = None,
  p = None,
  dtype = None 
random(shape, sp=None, p=None, dtype=None)
Return random float (in half-open interval [0.0, 1.0)) tensor with specified parameters. Result tensor is from the continuous uniform distribution over the interval.

shape: tensor_like
    Input tensor with 1-D or 2-D dimensions. If A is 1-D tensor, return a 2-D tensor with A on diagonal.

sp: bool, optional
    When sp is specified True, the output tensor will be sparse.

p: float, optional
    When sp is True, p specifies the fraction of sparsity for the sparse tensor.

dtype: data-type, optional
    Not supportted in current CTF Python.

output: tensor
    Random float tensor.

>>> import ctf
>>> import ctf.random as random
>>> random.random([2, 2])
array([[0.95027513, 0.79755613],
      [0.27834548, 0.55310684]])

Definition at line 40 of file random.pyx.

def ctf.random.seed (   seed)
Seed the random tensor generator.

seed: int
    Seed for random. Each process has the seed with `seed + get_universe().rank`.

Definition at line 15 of file random.pyx.

References CTF.get_universe(), and CTF_int.init_rng().