haojin2 commented on a change in pull request #15699: Numpy take operator
implementation & bug fix in ndarray.take
URL: https://github.com/apache/incubator-mxnet/pull/15699#discussion_r310810460
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File path: python/mxnet/numpy/multiarray.py
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@@ -1879,6 +1879,85 @@ def concatenate(seq, axis=0, out=None):
return _mx_nd_np.concatenate(seq, axis=axis, out=out)
+@set_module('mxnet.numpy')
+def take(a, indices, axis=None, mode='clip', out=None):
+ r"""
+ Take elements from an array along an axis.
+
+ When axis is not None, this function does the same thing as "fancy"
+ indexing (indexing arrays using arrays); however, it can be easier to use
+ if you need elements along a given axis. A call such as
+ ``np.take(arr, indices, axis=3)`` is equivalent to
+ ``arr[:,:,:,indices,...]``.
+
+ Explained without fancy indexing, this is equivalent to the following use
+ of `ndindex`, which sets each of ``ii``, ``jj``, and ``kk`` to a tuple of
+ indices::
+
+ Ni, Nk = a.shape[:axis], a.shape[axis+1:]
+ Nj = indices.shape
+ for ii in ndindex(Ni):
+ for jj in ndindex(Nj):
+ for kk in ndindex(Nk):
+ out[ii + jj + kk] = a[ii + (indices[jj],) + kk]
+
+ Parameters
+ ----------
+ a : ndarray
+ The source array.
+ indices : ndarray
+ The indices of the values to extract. Also allow scalars for indices.
+ axis : int, optional
+ The axis over which to select values. By default, the flattened
+ input array is used.
+ out : ndarray, optional
+ If provided, the result will be placed in this array. It should
+ be of the appropriate shape and dtype.
+ mode : {'clip', 'wrap'}, optional
+ Specifies how out-of-bounds indices will behave.
+
+ * 'clip' -- clip to the range (default)
+ * 'wrap' -- wrap around
+
+ 'clip' mode means that all indices that are too large are replaced
+ by the index that addresses the last element along that axis. Note
+ that this disables indexing with negative numbers.
+
+ Returns
+ -------
+ out : ndarray
+ The returned array has the same type as `a`.
+
+ Notes
+ -----
+
+ This function differs from the original `numpy.take
+ <https://docs.scipy.org/doc/numpy/reference/generated/numpy.take.html>`_ in
+ the following way(s):
+
+ - Only ndarray or scalar ndarray is accepted as valid input.
+ - 'raise' mode is not supported.
+
+ Examples
+ --------
+ >>> a = np.array([4, 3, 5, 7, 6, 8])
+ >>> indices = np.array([0, 1, 4])
+ >>> np.take(a, indices)
+ array([4., 3., 6.])
+
+ In this example for `a` is an ndarray, "fancy" indexing can be used.
+
+ >>> a[indices]
+ array([4., 3., 6.])
+
+ If `indices` is not one dimensional, the output also has these dimensions.
+
+ >>> np.take(a, np.array([[0, 1], [2, 3]]))
+ array([[4., 3.],
+ [5., 7.]])
+ """
+ return _mx_nd_np.take(a, indices, axis, mode, out)
+
Review comment:
Same here and for each and every new Python functions.
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