mindspore.ops.MatrixDiagV3

class mindspore.ops.MatrixDiagV3(align='RIGHT_LEFT')[source]

Constructs a diagonal matrix or a batch of diagonal matrices from a given input Tensor.

Warning

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

Refer to mindspore.ops.matrix_diag() 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) - The diagonal Tensor.
k (Union[int, Tensor], optional) - Diagonal offsets. A Tensor of type int32. 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 must be in the range of given or derivated num_rows and num_cols, meaning value of k must be in (-num_rows, num_cols). Default: 0 .
num_rows (Union[int, Tensor], optional) - The number of rows of the output Tensor. A Tensor of type int32 with only one value. If num_rows is -1, indicating that the innermost matrix of the output Tensor is a square matrix, and the real number of rows will be derivated by other inputs. That is \(num\_rows = x.shape[-1] - min(k[1], 0)\). Otherwise, the value must be equal or greater than \(x.shape[-1] - min(k[1], 0)\). Default: -1.
num_cols (Union[int, Tensor], optional) - The number of columns of the output Tensor. A Tensor of type int32 with only one value. If num_cols is -1, indicating that the innermost matrix of the output Tensor is a square matrix, and the real number of columns will be derivated by other inputs. That is \(num\_cols = x.shape[-1] + max(k[0], 0)\). Otherwise, the value must be equal or greater than \(x.shape[-1] - min(k[1], 0)\). Default: -1.
padding_value (Union[int, float, Tensor], optional) - The number to fill the area outside the specified diagonal band. A Tensor with only one value. Have the same dtype as x. Default: 0 .

Outputs:

A Tensor. Has the same type as x. Suppose x has r dimensions with shape \((I, J, ..., M, N)\) . The output Tensor has rank r + 1 with shape \((I, J, ..., M, num\_rows, num\_cols)\) when only one diagonal is given (k is an integer or k[0] == k[1]). Otherwise, it has rank r with shape \((I, J, ..., num\_rows, num\_cols)\) .

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.array([[8, 9, 0],
...                      [1, 2, 3],
...                      [0, 4, 5]]), mindspore.float32)
>>> k =Tensor(np.array([-1, 1]), mindspore.int32)
>>> num_rows = Tensor(np.array(3), mindspore.int32)
>>> num_cols = Tensor(np.array(3), mindspore.int32)
>>> padding_value = Tensor(np.array(11), mindspore.float32)
>>> matrix_diag_v3 = ops.MatrixDiagV3(align='LEFT_RIGHT')
>>> output = matrix_diag_v3(x, k, num_rows, num_cols, padding_value)
>>> print(output)
[[ 1.  8. 11.]
 [ 4.  2.  9.]
 [11.  5.  3.]]
>>> print(output.shape)
(3, 3)