haojin2 commented on a change in pull request #15385: added doc for numpy
operators
URL: https://github.com/apache/incubator-mxnet/pull/15385#discussion_r298425349
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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]])
+
+ Notes
+ -----
+ This function differs to the original `numpy.amax
+ <https://docs.scipy.org/doc/numpy/reference/generated/numpy.amax.html>`_ in
+ the following aspects:
+
+ - The default value type is `float32` instead of `float64` in numpy.
+ - `a` only supports ndarray.
+ - `axis` doe snot support negative value.
+ - `initial` is not supported.
+ """
+ pass
+
+
+def _np_squeeze(a, axis=None):
+ r"""
+ Remove single-dimensional entries from the shape of an array.
+
+ Parameters
+ ----------
+ a : ndarray
+ Input data.
+
+ axis : None or int or tuple of ints, optional
+
+ Returns
+ -------
+ squeezed : ndarray
+ The input array, but with all or a subset of the
+ dimensions of length 1 removed. This is always `a` itself
+ or a view into `a`.
+
+ Examples
+ --------
+ >>> x = np.array([[[0], [1], [2]]])
+ >>> x.shape
+ (1, 3, 1)
+ >>> np.squeeze(x).shape
+ (3,)
+ >>> np.squeeze(x, axis=(2,)).shape
+ (1, 3)
+
+ Notes
+ -----
+ This function differs to the original `numpy.squeeze
+
<https://docs.scipy.org/doc/numpy/reference/generated/numpy.squeeze.html>`_ in
+ the following aspects:
+
+ - The default value type is `float32` instead of `float64` in numpy.
+ - `a` only supports ndarray.
+ """
+ pass
+
+
+def _npi_ones(shape, dtype=None, order='C'):
+ r"""
+ ones(shape, dtype=None, order='C')
+ Return a new array of given shape and type, filled with ones.
+
+ Parameters
+ ----------
+ shape : int or sequence of ints
+ Shape of the new array, e.g., ``(2, 3)`` or ``2``.
+
+ dtype : data-type, optional
+ The desired data-type for the array, e.g., `int`. Default is `float32`.
+
+ order : {'C', 'F'}, optional, default: C
+ Whether to store multi-dimensional data in row-major
+ (C-style) or column-major (Fortran-style) order in
+ memory.
+
+ Not Supported yet.
+
+ Returns
+ -------
+ out : ndarray
+ Array of ones with the given shape, dtype, and order.
+
+ Examples
+ --------
+ >>> np.ones(5)
+ array([ 1., 1., 1., 1., 1.])
+ >>> np.ones((5,), dtype=int)
+ array([1, 1, 1, 1, 1])
+ >>> np.ones((2, 1))
+ array([[ 1.],
+ [ 1.]])
+ >>> s = (2,2)
+ >>> np.ones(s)
+ array([[ 1., 1.],
+ [ 1., 1.]])
+
+ Notes
+ -----
+ This function differs to the original `numpy.ones
+ <https://docs.scipy.org/doc/numpy/reference/generated/numpy.ones.html>`_ in
+ the following aspects:
+
+ - The default value type is `float32` instead of `float64` in numpy.
+ - `order` is not supported.
+ """
+
+
+def _npi_random_uniform(low=0.0, high=1.0, size=None):
Review comment:
This seems to be the same case as `_npi_ones`.
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