mindspore.ops.matrix_diag

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mindspore.ops.matrix_diag(x, k=0, num_rows=- 1, num_cols=- 1, padding_value=0, align='RIGHT_LEFT')[source]

Return a tensor with the contents in x as k[0]-th to k[1]-th diagonals of a matrix, with everything else padded with padding_value .

num_rows and num_cols are tensors type of int32 with only one value, which is -1, indicating that the innermost matrix of the output tensor is a square.

Parameters

  • x (Tensor) – The input tensor.

  • k (Union[int, Tensor], optional) – Diagonal offsets. Positive value means superdiagonal, and negative value means subdiagonals. When k is a pair of integers specifying the low and high ends of a matrix band. Default 0 .

  • num_rows (Union[int, Tensor], optional) – The number of rows of the output tensor. Default -1 .

  • num_cols (Union[int, Tensor], optional) – The number of columns of the output tensor. Default -1 .

  • padding_value (Union[int, float, Tensor], optional) – The number to fill the area outside the specified diagonal band. Default 0 .

  • align (str, optional) –

    specifies how superdiagonals and subdiagonals should be aligned. Supported values "RIGHT_LEFT" , "LEFT_RIGHT" , "LEFT_LEFT" , "RIGHT_RIGHT" . Default "RIGHT_LEFT" .

    • When set to "RIGHT_LEFT", the alignment of superdiagonals will be towards the right side (padding the row on the left), while subdiagonals will be towards the left side (padding the row on the right)

    • When set to "LEFT_RIGHT", the alignment of superdiagonals will be towards the left side (padding the row on the right), while subdiagonals will be towards the right side (padding the row on the left)

    • When set to "LEFT_LEFT", the alignment of both superdiagonals and subdiagonals will be towards the left side(padding the row on the right).

    • When set to "RIGHT_RIGHT", the alignment of both superdiagonals and subdiagonals will be towards the right side(padding the row on the left).

Returns

Tensor

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> x = mindspore.tensor([[8., 9., 0.],
...                      [1., 2., 3.],
...                      [0., 4., 5.]])
>>> k = mindspore.tensor([-1, 1], mindspore.int32)
>>> padding_value = mindspore.tensor(11.)
>>> num_rows = mindspore.tensor(3, mindspore.int32)
>>> num_cols = mindspore.tensor(3, mindspore.int32)
>>> output = mindspore.ops.matrix_diag(x, k, num_rows, num_cols, padding_value, align='LEFT_RIGHT')
>>> print(output)
[[ 1.  8. 11.]
 [ 4.  2.  9.]
 [11.  5.  3.]]
>>> print(output.shape)
(3, 3)