[GitHub] [incubator-mxnet] hzfan commented on a change in pull request #15388: Doc

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

hzfan commented on a change in pull request #15388: Doc
URL: https://github.com/apache/incubator-mxnet/pull/15388#discussion_r298879802
 
 

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 File path: python/mxnet/_numpy_op_doc.py
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 @@ -176,3 +176,122 @@ def _np_cumsum(a, axis=None, dtype=None, out=None):
         `axis` is not None or `a` is a 1-d array.
     """
     pass
+
+def _np_prod(a, axis=None, dtype=None, out=None, keepdims=None):
+    r"""
+    prod(a, axis=None, dtype=None, keepdims=_Null, initial=None, out=None)
+
+    Return the product of array elements over a given axis.
+    
+    Parameters
+    ----------
+    a : ndarray
+        Input data.
+    axis : None or int or tuple of ints, optional
+        Axis or axes along which a product is performed.  The default,
+        axis=None, will calculate the product of all the elements in the
+        input array. If axis is negative it counts from the last to the
+        first axis.
+    
+        If axis is a tuple of ints, a product is performed on all of the
+        axes specified in the tuple instead of a single axis or all the
+        axes as before.
+    dtype : dtype, optional
+        The type of the returned array, as well as of the accumulator in
+        which the elements are multiplied.  Default is numpy.float32.
+    keepdims : bool, optional
+        If this is set to True, the axes which are reduced are left in the
+        result as dimensions with size one. With this option, the result
+        will broadcast correctly against the input array.
+    
+        If the default value is passed, then `keepdims` will not be
+        passed through to the `prod` method of sub-classes of
+        `ndarray`, however any non-default value will be.  If the
+        sub-class' method does not implement `keepdims` any
+        exceptions will be raised.
+    initial : None, optional
+        Must be None.
+    out : ndarray, optional
+        Alternative output array in which to place the result. It must have
+        the same shape as the expected output and the same type.
+    
+    Returns
+    -------
+    product_along_axis : ndarray, see `dtype` parameter above.
+
+    Notes
+    -----
+    Arithmetic is modular when using integer types, and no error is
+    raised on overflow.  That means that, on a 32-bit platform:
+    
+    >>> x = np.array([536870910, 536870910, 536870910, 536870910]).astype('int')
+    >>> np.prod(x) 
+    array(0)
+ 
+    This function differs to the original numpy.prod in the following aspects:
+        
+        - Do not support empty ndarray or scalar as input.
+        - Keepdims must be 0 or 1. Otherwise, an error will raise.
+        - Can't cast type automatically.
+
+    Examples
+    --------
+    By default, calculate the product of all elements:
+    
+    >>> x = np.array([1.,2.])
+    >>> np.prod(x)
+    array(2.)
+    
+    Even when the input array is two-dimensional:
+    
+    >>> x = np.array([[1.,2.],[3.,4.]])
+    >>> np.prod(x)
+    array(24.)
+    
+    But we can also specify the axis over which to multiply:
+
+    >>> x = np.array([[1.,2.],[3.,4.]])
 
 Review comment:
   same as before, space after comma is recommended

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