haojin2 commented on a change in pull request #15390: [Numpy fix-doc]modify
numpy doc
URL: https://github.com/apache/incubator-mxnet/pull/15390#discussion_r298428829
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File path: python/mxnet/_numpy_op_doc.py
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@@ -32,24 +33,70 @@ def _np_reshape(a, newshape, order='C'):
an integer, then the result will be a 1-D array of that length.
One shape dimension can be -1. In this case, the value is
inferred from the length of the array and remaining dimensions.
- order : {'C'}, optional
+ order : {'C', 'F', 'A'}, optional
Read the elements of `a` using this index order, and place the
elements into the reshaped array using this index order. 'C'
means to read / write the elements using C-like index order,
with the last axis index changing fastest, back to the first
- axis index changing slowest. Other order types such as 'F'/'A'
- may be added in the future.
+ axis index changing slowest. 'F' means to read / write the
+ elements using Fortran-like index order, with the first index
+ changing fastest, and the last index changing slowest. Note that
+ the 'C' and 'F' options take no account of the memory layout of
+ the underlying array, and only refer to the order of indexing.
+ 'A' means to read / write the elements in Fortran-like index
+ order if `a` is Fortran *contiguous* in memory, C-like order
+ otherwise.
Returns
-------
reshaped_array : ndarray
- It will be always a copy of the original array. This behavior is
different
- from the official NumPy package where views of the original array may
be
- generated.
+ This will be a new view object if possible; otherwise, it will
+ be a copy. Note there is no guarantee of the *memory layout* (C- or
+ Fortran- contiguous) of the returned array.
- See Also
+
+ Notes
+ -----
+ It is not always possible to change the shape of an array without
+ copying the data. If you want an error to be raised when the data is
copied,
+ you should assign the new shape to the shape attribute of the array::
+
+ >>> a = np.zeros((10, 2))
+ # A transpose makes the array non-contiguous
+ >>> b = a.T
+ # Taking a view makes it possible to modify the shape without modifying
+ # the initial object.
+
+ >>> a = np.arange(6).reshape((3, 2))
+ >>> a
+ array([[0., 1.],
+ [2., 3.],
+ [4., 5.]])
+
+ You can think of reshaping as first raveling the array (using the given
+ index order), then inserting the elements from the raveled array into the
+ new array using the same kind of index ordering as was used for the
+ raveling.
+
+ >>> np.reshape(a, (2, 3)) # C-like index ordering
+ array([[0., 1., 2.],
+ [3., 4., 5.]])
Review comment:
Please make sure the alignment looks good on the rendered website.
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