[GitHub] [incubator-mxnet] reminisce commented on a change in pull request #15878: numpy multinomial op

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

reminisce commented on a change in pull request #15878: numpy multinomial op
URL: https://github.com/apache/incubator-mxnet/pull/15878#discussion_r320088809
 
 

 ##########
 File path: python/mxnet/ndarray/numpy/random.py
 ##########
 @@ -189,3 +190,55 @@ def normal(loc=0.0, scale=1.0, size=None, **kwargs):
         raise NotImplementedError('np.random.normal only supports loc and scale of '
                                   'numeric types for now')
     return _npi.random_normal(loc, scale, shape=size, dtype=dtype, ctx=ctx, out=out, **kwargs)
+
+
+def multinomial(n, pvals, size=None):
+    """multinomial(n, pvals, size=None)
+
+    Draw samples from a multinomial distribution.
+
+    The multinomial distribution is a multivariate generalisation of the binomial distribution.
+    Take an experiment with one of ``p`` possible outcomes. An example of such an experiment is throwing a dice,
+    where the outcome can be 1 through 6. Each sample drawn from the distribution represents n such experiments.
+    Its values, ``X_i = [X_0, X_1, ..., X_p]``, represent the number of times the outcome was ``i``.
+
+    Parameters
+    ----------
+    n : int
+        Number of experiments.
+    pvals : sequence of floats, length p
+        Probabilities of each of the p different outcomes. These should sum 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. Default is None, in which case a single value is returned.
+
+    Returns
+    -------
+    out : ndarray
+        The drawn samples, of shape size, if that was provided. If not, the shape is ``(N,)``.
+        In other words, each entry ``out[i,j,...,:]`` is an N-dimensional value drawn from the distribution.
+
+    Examples
+    --------
+    Throw a dice 1000 times, and 1000 times again:
+
+    >>> np.random.multinomial(1000, [1/6.]*6, size=2)
+    array([[164, 161, 179, 158, 150, 188],
+           [178, 162, 177, 143, 163, 177]])
+
+    A loaded die is more likely to land on number 6:
+
+    >>> np.random.multinomial(100, [1/7.]*5 + [2/7.])
+    array([19, 14, 12, 11, 21, 23])
+
+    >>> np.random.multinomial(100, [1.0 / 3, 2.0 / 3])
+    array([32, 68])
+    """
+    if isinstance(pvals, NDArray):
 
 Review comment:
   This condition is basically the same as the first one in `else` block. What is the consideration here?

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