[GitHub] [incubator-mxnet] haojin2 commented on a change in pull request #17824: [Numpy] FFI: max/min/amax/amin

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haojin2 commented on a change in pull request #17824: [Numpy] FFI: max/min/amax/amin
URL: https://github.com/apache/incubator-mxnet/pull/17824#discussion_r392515930
 
 

 ##########
 File path: python/mxnet/numpy/multiarray.py
 ##########
 @@ -6450,6 +6578,132 @@ def argmin(a, axis=None, out=None):
     return _mx_nd_np.argmin(a, axis, out)
 
 
+@set_module('mxnet.numpy')
+def amax(a, axis=None, out=None, keepdims=False):
+    """
+    Return the maximum of an array or maximum along an axis.
+
+    Parameters
+    ----------
+    a : ndarray
+        Input data.
+    axis : int, optional
+        Axis along which to operate.  By default, flattened input is used.
+    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 original `arr`.
+
+    Returns
+    -------
+    max : ndarray
+        Maximum of `a`. If `axis` is None, the result is an array of dimension 1.
+        If `axis` is given, the result is an array of dimension
+        ``a.ndim - 1``.
+
+    See Also
+    --------
+    min :
+        The minimum value of an array along a given axis, ignoring any nan.
+    maximum :
+        Element-wise maximum of two arrays, ignoring any nan.
+    argmax :
+        Return the indices of the maximum values.
+
+    Notes
+    -----
+    NaN in the orginal `numpy` is denoted as nan and will be ignored.
+
+    Don't use `max` for element-wise comparison of 2 arrays; when
+    ``a.shape[0]`` is 2, ``maximum(a[0], a[1])`` is faster than
+    ``max(a, axis=0)``.
+
+    Examples
+    --------
+    >>> a = np.arange(4).reshape((2,2))
+    >>> a
+    array([[0., 1.],
+        [2., 3.]])
+    >>> np.max(a)            # Maximum of the flattened array
+    array(3.)
+    >>> np.max(a, axis=0)    # Maxima along the first axis
+    array([2., 3.])
+    >>> np.max(a, axis=1)    # Maxima along the second axis
+    array([1., 3.])
+
+    >>> b = np.arange(5, dtype=np.float32)
+    >>> b[2] = np.nan
+    >>> np.max(b)
+    array(4.)
+    """
+    return _mx_nd_np.amax(a, axis=axis, out=out, keepdims=keepdims)
+
+
+@set_module('mxnet.numpy')
+def amin(a, axis=None, out=None, keepdims=False):
+    """
+    Return the minimum of an array or minimum along an axis.
+
+    Parameters
+    ----------
+    a : ndarray
+        Input data.
+    axis : int, optional
+        Axis along which to operate.  By default, flattened input is used.
+    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 original `arr`.
+
+    Returns
+    -------
+    min : ndarray
+        Minimum of `a`. If `axis` is None, the result is an array of dimension 1.
+        If `axis` is given, the result is an array of dimension
+        ``a.ndim - 1``.
+
+    See Also
+    --------
+    max :
+        The maximum value of an array along a given axis, ignoring any nan.
+    minimum :
+        Element-wise minimum of two arrays, ignoring any nan.
+
+    Notes
+    -----
+    NaN in the orginal `numpy` is denoted as nan and will be ignored.
+
+    Don't use `min` for element-wise comparison of 2 arrays; when
+    ``a.shape[0]`` is 2, ``minimum(a[0], a[1])`` is faster than
+    ``min(a, axis=0)``.
+
+    Examples
+    --------
+    >>> a = np.arange(4).reshape((2,2))
+    >>> a
+    array([[0., 1.],
+        [2., 3.]])
+    >>> np.min(a)           # Minimum of the flattened array
+    array(0.)
+    >>> np.min(a, axis=0)   # Minima along the first axis
+    array([0., 1.])
+    >>> np.min(a, axis=1)   # Minima along the second axis
+    array([0., 2.])
+    >>> b = np.arange(5, dtype=np.float32)
+    >>> b[2] = np.nan
+    >>> np.min(b)
+    array(0.) # nan will be ignored
+    """
+    return _mx_nd_np.amin(a, axis=axis, out=out, keepdims=keepdims)
+
 
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