hanke580 commented on a change in pull request #16977: [Numpy]add row_stack (=
vstack)
URL: https://github.com/apache/incubator-mxnet/pull/16977#discussion_r361060639
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File path: python/mxnet/ndarray/numpy/_op.py
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@@ -3131,6 +3131,55 @@ def get_list(arrays):
arrays = get_list(arrays)
return _npi.vstack(*arrays)
+@set_module('mxnet.ndarray.numpy')
+def row_stack(arrays, out=None):
+ r"""Stack arrays in sequence vertically (row wise).
+
+ This is equivalent to concatenation along the first axis after 1-D arrays
+ of shape `(N,)` have been reshaped to `(1,N)`. Rebuilds arrays divided by
+ `vsplit`.
+
+ This function makes most sense for arrays with up to 3 dimensions. For
+ instance, for pixel-data with a height (first axis), width (second axis),
+ and r/g/b channels (third axis). The functions `concatenate` and `stack`
+ provide more general stacking and concatenation operations.
+
+ Parameters
+ ----------
+ tup : sequence of ndarrays
+ The arrays must have the same shape along all but the first axis.
+ 1-D arrays must have the same length.
+
+ Returns
+ -------
+ stacked : ndarray
+ The array formed by stacking the given arrays, will be at least 2-D.
+
+ Examples
+ --------
+ >>> a = np.array([1, 2, 3])
+ >>> b = np.array([2, 3, 4])
+ >>> np.vstack((a, b))
+ array([[1., 2., 3.],
+ [2., 3., 4.]])
+
+ >>> a = np.array([[1], [2], [3]])
+ >>> b = np.array([[2], [3], [4]])
+ >>> np.vstack((a, b))
+ array([[1.],
+ [2.],
+ [3.],
+ [2.],
+ [3.],
+ [4.]])
+ """
+ def get_list(arrays):
+ if not hasattr(arrays, '__getitem__') and hasattr(arrays, '__iter__'):
+ raise ValueError("expected iterable for arrays but got
{}".format(type(arrays)))
+ return [arr for arr in arrays]
+
+ arrays = get_list(arrays)
+ return _npi.vstack(*arrays)
Review comment:
Fixed, thanks
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