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mindspore.ops.MatrixDiagPartV3

class mindspore.ops.MatrixDiagPartV3(align='RIGHT_LEFT')

Returns the diagonal part of a tensor.

Warning

This is an experimental API that is subject to change or deletion.

Refer to mindspore.ops.matrix_diag_part() for more details.

Parameters

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).

Inputs:

• x (Tensor) - Rank r, where r >= 2.

• k (Tensor) - A Tensor of type int32. Diagonal offset(s). Positive value means superdiagonal, 0 refers to the main diagonal, and negative value means subdiagonals. k can be a single integer (for a single diagonal) or a pair of integers specifying the low and high ends of a matrix band. k[0] must not be larger than k[1]. The value of k has restructions, meaning value of k must be in (-x.shape[-2], x.shape[-1]).

• padding_value (Tensor) - A Tensor. Have the same dtype as x. The number to fill the area outside the specified diagonal band with. There must be only one value.

Outputs:

A Tensor. Has the same type as x. Assume x has r dimensions $(I,J,...,M,N)$ . Let max_diag_len be the maximum length among all diagonals to be extracted, $max\mathrm{\_}diag\mathrm{\_}len=min(M+min(k[1],0),N+min(-k[0],0))$ Let num_diags be the number of diagonals to extract, $num\mathrm{\_}diags=k[1]-k[0]+1$. If $num\mathrm{\_}diags==1$, the output tensor is of rank r - 1 with shape $(I,J,...,L,max\mathrm{\_}diag\mathrm{\_}len)$ Otherwise, the output tensor has rank r with dimensions $(I,J,...,L,num\mathrm{\_}diags,max\mathrm{\_}diag\mathrm{\_}len)$ .

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.array([[1, 2, 3, 4],
...                      [5, 6, 7, 8],
...                      [9, 8, 7, 6]]), mindspore.float32)
>>> k =Tensor(np.array([1, 3]), mindspore.int32)
>>> padding_value = Tensor(np.array(9), mindspore.float32)
>>> matrix_diag_part_v3 = ops.MatrixDiagPartV3(align='RIGHT_LEFT')
>>> output = matrix_diag_part_v3(x, k, padding_value)
>>> print(output)
[[9. 9. 4.]
 [9. 3. 8.]
 [2. 7. 6.]]
>>> print(output.shape)
(3, 3)