hzfan commented on a change in pull request #18049: [numpy] add numpy op
fill_diagonal
URL: https://github.com/apache/incubator-mxnet/pull/18049#discussion_r409986072
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File path: python/mxnet/ndarray/numpy/_op.py
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@@ -7477,6 +7477,94 @@ def resize(a, new_shape):
return _npi.resize_fallback(a, new_shape=new_shape)
+@set_module('mxnet.ndarray.numpy')
+def fill_diagonal(a, val, wrap=False):
+ """
+ Fill the main diagonal of the given array of any dimensionality.
+ For an array `a` with ``a.ndim >= 2``, the diagonal is the list of
+ locations with indices ``a[i, ..., i]`` all identical. This function
+ modifies the input array in-place, it does not return a value.
+ Parameters
+ ----------
+ a : array, at least 2-D.
+ Array whose diagonal is to be filled, it gets modified in-place.
+ val : scalar
+ Value to be written on the diagonal, its type must be compatible with
+ that of the array a.
+ wrap : bool
+ For tall matrices in NumPy version up to 1.6.2, the
+ diagonal "wrapped" after N columns. You can have this behavior
+ with this option. This affects only tall matrices.
+
+ Examples
+ --------
+ >>> a = np.zeros((3, 3), int)
+ >>> np.fill_diagonal(a, 5)
+ >>> a
+ array([[5, 0, 0],
+ [0, 5, 0],
+ [0, 0, 5]])
+ The same function can operate on a 4-D array:
+ >>> a = np.zeros((3, 3, 3, 3), int)
+ >>> np.fill_diagonal(a, 4)
+ We only show a few blocks for clarity:
+ >>> a[0, 0]
+ array([[4, 0, 0],
+ [0, 0, 0],
+ [0, 0, 0]])
+ >>> a[1, 1]
+ array([[0, 0, 0],
+ [0, 4, 0],
+ [0, 0, 0]])
+ >>> a[2, 2]
+ array([[0, 0, 0],
+ [0, 0, 0],
+ [0, 0, 4]])
+ The wrap option affects only tall matrices:
+ >>> # tall matrices no wrap
+ >>> a = np.zeros((5, 3), int)
+ >>> np.fill_diagonal(a, 4)
+ >>> a
+ array([[4, 0, 0],
+ [0, 4, 0],
+ [0, 0, 4],
+ [0, 0, 0],
+ [0, 0, 0]])
+ >>> # tall matrices wrap
+ >>> a = np.zeros((5, 3), int)
+ >>> np.fill_diagonal(a, 4, wrap=True)
+ >>> a
+ array([[4, 0, 0],
+ [0, 4, 0],
+ [0, 0, 4],
+ [0, 0, 0],
+ [4, 0, 0]])
+ >>> # wide matrices
+ >>> a = np.zeros((3, 5), int)
+ >>> np.fill_diagonal(a, 4, wrap=True)
+ >>> a
+ array([[4, 0, 0, 0, 0],
+ [0, 4, 0, 0, 0],
+ [0, 0, 4, 0, 0]])
+ The anti-diagonal can be filled by reversing the order of elements
+ using either `numpy.flipud` or `numpy.fliplr`.
+ >>> a = np.zeros((3, 3), int);
+ >>> np.fill_diagonal(np.fliplr(a), [1,2,3]) # Horizontal flip
+ >>> a
+ array([[0, 0, 1],
+ [0, 2, 0],
+ [3, 0, 0]])
+ >>> np.fill_diagonal(np.flipud(a), [1,2,3]) # Vertical flip
+ >>> a
+ array([[0, 0, 3],
+ [0, 2, 0],
+ [1, 0, 0]])
+ Note that the order in which the diagonal is filled varies depending
+ on the flip function.
+ """
+ return _npi.fill_diagonal(a, val=val, wrap=wrap, out=a)
Review comment:
Use `_api_internal`
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