def gumbel_like(x, loc=0.0, scale=1.0, seed=auto_select, name=''):
"""gumbel_like(x, mean=0.0, scale=1.0, seed=auto_select, name='')
Generates samples from the Gumbel distribution with location `loc` and scale `scale`.
Args:
x: cntk variable (input, output, parameter, or constant) from which to copy the shape, data type, and dynamic axes.
loc (float): location of the distribution
scale (float): scale of the distribution
seed (int): pseudo random number generator seed (default: automatically select a unique seed)
name (str, optional): the name of the Function instance in the network
Returns:
:class:`~cntk.ops.functions.Function`
Examples:
>>> x = C.constant(np.zeros((2,3,4), dtype=np.float32))
>>> g = C.random.gumbel_like(x, seed=98052)
>>> s = g.eval(device=C.cpu()) # explicitly setting cpu because this is tested on multiple platforms; leave it unspecified in your code
>>> np.round(s, 4)
array([[[-0.9877, -0.5223, 0.4259, -1.0196],
[ 5.4352, 1.5861, 5.0608, 2.0668],
[-0.2135, 1.0139, 3.1217, -1.4834]],
<BLANKLINE>
[[ 0.4507, 0.6325, 2.1682, 0.4463],
[-0.6583, 0.1147, -0.3144, -0.7925],
[ 1.9773, -0.3627, -0.4566, -0.2368]]], dtype=float32)
See also:
`The Gumbel-Max Trick
<https://hips.seas.harvard.edu/blog/2013/04/06/the-gumbel-max-trick-for-discrete-distributions/>`_.
"""
from cntk.cntk_py import gumbel_random_like
x = sanitize_input(x)
return gumbel_random_like(x, loc, scale, seed, name)
def gumbel_like(x, loc=0.0, scale=1.0, seed=auto_select, name=''):
"""gumbel_like(x, mean=0.0, scale=1.0, seed=auto_select, name='')
Generates samples from the Gumbel distribution with location `loc` and scale `scale`.
Args:
x: cntk variable (input, output, parameter, or constant) from which to copy the shape, data type, and dynamic axes.
loc (float): location of the distribution
scale (float): scale of the distribution
seed (int): pseudo random number generator seed (default: automatically select a unique seed)
name (str, optional): the name of the Function instance in the network
Returns:
:class:`~cntk.ops.functions.Function`
Examples:
>>> x = C.constant(np.zeros((2,3,4), dtype=np.float32))
>>> g = C.random.gumbel_like(x, seed=98052)
>>> s = g.eval(device=C.cpu()) # explicitly setting cpu because this is tested on multiple platforms; leave it unspecified in your code
>>> np.round(s, 4)
array([[[-0.9877, -0.5223, 0.4259, -1.0196],
[ 5.4352, 1.5861, 5.0608, 2.0668],
[-0.2135, 1.0139, 3.1217, -1.4834]],
<BLANKLINE>
[[ 0.4507, 0.6325, 2.1682, 0.4463],
[-0.6583, 0.1147, -0.3144, -0.7925],
[ 1.9773, -0.3627, -0.4566, -0.2368]]], dtype=float32)
See also:
`The Gumbel-Max Trick
<https://hips.seas.harvard.edu/blog/2013/04/06/the-gumbel-max-trick-for-discrete-distributions/>`_.
"""
from cntk.cntk_py import gumbel_random_like
x = sanitize_input(x)
return gumbel_random_like(x, loc, scale, seed, name)