haojin2 commented on a change in pull request #15385: added doc for numpy
operators
URL: https://github.com/apache/incubator-mxnet/pull/15385#discussion_r298424996
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File path: python/mxnet/_numpy_op_doc.py
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@@ -176,3 +176,229 @@ def _np_cumsum(a, axis=None, dtype=None, out=None):
`axis` is not None or `a` is a 1-d array.
"""
pass
+
+
+def _np_amax(a, axis=None, out=None):
+ r"""
+ amax(a, axis=None, out=None, keepdims=_Null, initial=_Null)
+
+ Return the maximum of an array or maximum along an axis.
+
+ Parameters
+ ----------
+ a : ndarray
+ Input data.
+ Python native iterables not supported.
+
+ axis : None or int or tuple of ints, optional
+ Axis or axes along which to operate. By default, flattened input is
+ used.
+
+ Negative indices not supported.
+
+ 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.
+ See `doc.ufuncs` (Section "Output arguments") for more details.
+
+ 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 : scalar, optional
+ The minimum value of an output element. Must be present to allow
+ computation on empty slice.
+
+ Not supported yet.
+
+ 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``.
+
+ Examples
+ --------
+ >>> a = np.arange(4).reshape((2,2))
+ >>> a
+ array([[0, 1],
+ [2, 3]])
+ >>> np.amax(a) # Maximum of the flattened array
+ array(3)
+ >>> np.amax(a, axis=0) # Maxima along the first axis
+ array([2, 3])
+ >>> np.amax(a, axis=1) # Maxima along the second axis
+ array([1, 3])
+ >>> np.amax(a, axis=1, keepdims=True)
+ array([[1],
+ [3]])
Review comment:
Please make sure the alignment of the print-outs look correct on the
rendered webpage.
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