ChaiBapchya commented on a change in pull request #12749: [MXNET-1029] Feature
request: randint operator
URL: https://github.com/apache/incubator-mxnet/pull/12749#discussion_r223223276
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File path: python/mxnet/ndarray/random.py
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@@ -518,3 +518,47 @@ def shuffle(data, **kwargs):
<NDArray 2x3 @cpu(0)>
"""
return _internal._shuffle(data, **kwargs)
+
+
+def randint(low=0, high=1, shape=_Null, dtype=_Null, ctx=None, out=None,
**kwargs):
+ """Draw random samples from a discrete uniform distribution.
+
+ Samples are uniformly distributed over the half-open interval *[low, high)*
+ (includes *low*, but excludes *high*).
+
+ Parameters
+ ----------
+ low : float or NDArray
+ Lower boundary of the output interval. All values generated will be
+ greater than or equal to low. The default value is 0.
+ high : float or NDArray
+ Upper boundary of the output interval. All values generated will be
+ less than high. The default value is 1.
+ shape : int or tuple of ints
+ The number of samples to draw. If shape is, e.g., `(m, n)` and `low`
and
+ `high` are scalars, output shape will be `(m, n)`. If `low` and `high`
+ are NDArrays with shape, e.g., `(x, y)`, then output will have shape
+ `(x, y, m, n)`, where `m*n` samples are drawn for each `[low, high)`
pair.
Review comment:
I have tried to follow the process of sampling used in other Random
generators in MXNet to maintain consistency.
For instance, you can find similar departure in convention for # of sample
to draw in random.uniform (or others) of MXNet vis-a-vis Numpy
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