gyshi commented on a change in pull request #15390: [Numpy fix-doc]modify
numpy doc
URL: https://github.com/apache/incubator-mxnet/pull/15390#discussion_r298543064
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File path: python/mxnet/_numpy_op_doc.py
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@@ -173,3 +212,73 @@ def _np_cumsum(a, axis=None, dtype=None, out=None):
`axis` is not None or `a` is a 1-d array.
"""
pass
+
+
+def _np_max(axis=None, keepdims=False, initial=None, out=None):
+ """
+ Return the maximum of an array or maximum along an axis.
+
+ Parameters
+ ----------
+ a : ndarray
+ Input data.
+ axis : None or int or tuple of ints, optional
+ Axis or axes along which to operate. By default, flattened input is
+ used.
+
+ If this is a tuple of ints, the maximum is selected over multiple axes,
+ instead of a single axis or all the axes as before.
+
+ 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 `amax` 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 :
+ Parameter initial is not supported yet, we will support it in the
future.
+ now it must be None.
+
+ out : ndarray, optional
+ Alternative output array in which to place the result. Must
+ be of the same shape and buffer length as the expected output.
+
+ Returns
+ -------
+ amax : ndarray or scalar
+ Maximum of `a`. If `axis` is None, the result is a scalar value.
+ If `axis` is given, the result is an array of dimension
+ ``a.ndim - 1``.
+
+ See Also
+ --------
+ amax equivalent function
+
+ Notes
+ -----
+ - Not support axis < 0.
+
+ Examples not work:
+ >>> np.max(np.array([[-50], [10]]), axis= -1)
+ i >= 0 && i < ndim(): axis = -1 must be in range [0, 2)
+
+ Examples
+ --------
+ >>> a = np.arange(4).reshape((2,2))
+ >>> a
+ array([[0., 1.],
+ [2., 3.]])
+ >>> np.amax(a) # Maximum of the flattened array
Review comment:
they are the same function
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