[GitHub] [incubator-mxnet] haojin2 commented on a change in pull request #16152: [Numpy] Random.gamma() implemented

[GitBox <gi...@apache.org>]

haojin2 commented on a change in pull request #16152: [Numpy] Random.gamma() implemented
URL: https://github.com/apache/incubator-mxnet/pull/16152#discussion_r364375435
 
 

 ##########
 File path: python/mxnet/ndarray/numpy/random.py
 ##########
 @@ -319,6 +319,63 @@ def choice(a, size=None, replace=True, p=None, ctx=None, out=None):
             return _npi.choice(p, a=a, size=size, replace=replace, ctx=ctx, weighted=True, out=out)
 
 
+def gamma(shape, scale=1.0, size=None, dtype=None, ctx=None, out=None):
+    """Draw samples from a Gamma distribution.
+
+    Samples are drawn from a Gamma distribution with specified parameters,
+    `shape` (sometimes designated "k") and `scale` (sometimes designated
+    "theta"), where both parameters are > 0.
+
+    Parameters
+    ----------
+    shape : float or array_like of floats
+        The shape of the gamma distribution. Should be greater than zero.
+    scale : float or array_like of floats, optional
+        The scale of the gamma distribution. Should be greater than zero.
+        Default is equal to 1.
+    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.  If size is ``None`` (default),
+        a single value is returned if ``shape`` and ``scale`` are both scalars.
+        Otherwise, ``np.broadcast(shape, scale).size`` samples are drawn.
+    ctx : Context, optional
+        Device context of output. Default is current context.
+
+    Returns
+    -------
+    out : ndarray or scalar
+        Drawn samples from the parameterized gamma distribution.
+
+    The Gamma distribution is often used to model the times to failure of
+    electronic components, and arises naturally in processes for which the
+    waiting times between Poisson distributed events are relevant.
+    """
+    from ...numpy import ndarray as np_ndarray
+    input_type = (isinstance(shape, np_ndarray), isinstance(scale, np_ndarray))
+    if dtype is None:
+        dtype = 'float32'
 
 Review comment:
   `dtype = np.float32`

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