mindspore.ops.matrix_diag

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

Returns 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 specify the dimension of the innermost matrix of the output. If both are not specified, the op assumes the innermost matrix of output Tensor is square and infers its size from k and the innermost dimension of x. If the num_rows and num_cols specify only one of them, the operator will derive the smallest legal value as the dimension of output. Moreover, when only one diagonal is given (k is an integer or k[0] == k[1]), the first to the second innermost dimension of x is the batch size. Otherwise, the second innermost dimension is not a part of batch size.

Parameters

  • x (Tensor) – The diagonal Tensor.

  • k (Union[int, Tensor], optional) – A Tensor of type int32. Diagonal offsets. 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) – A Tensor of type int32 with only one value. The number of rows of the output Tensor. 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) – A Tensor of type int32 with only one value. The number of columns of the output Tensor. 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) – A Tensor with only one value. Have the same dtype as x. The number to fill the area outside the specified diagonal band. Default: 0.

  • align (str) – An optional string from: “RIGHT_LEFT”(default), “LEFT_RIGHT”, “LEFT_LEFT”, “RIGHT_RIGHT”. Align is a string specifying how superdiagonals and subdiagonals should be aligned, respectively. “RIGHT_LEFT” aligns superdiagonals to the right (left-pads the row) and subdiagonals to the left (right-pads the row).

Returns

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).

Raises

  • TypeError – If x is not Tensor.

  • TypeError – If input x and padding_value are not the same dtype.

  • TypeError – If k, num_rows or num_cols is not int32 dtype.

  • ValueError – If rank of k is not equal to 0 or 1.

  • ValueError – If rank of num_rows, num_cols or padding_value is not equal to 0.

  • ValueError – If size of k is not equal to 1 or 2.

  • ValueError – If the value of k is not in (-num_rows, num_cols).

  • ValueError – If k[1] is not greater equal to k[0] when k[0] != k[1].

  • ValueError – If rank of x is not greater than or is equal to 1 when k is an integer or k[0] == k[1].

  • ValueError – If rank of x is not greater than or is equal to 2 when k[0] != k[1].

  • ValueError – If x.shape[-2] is not equal to k[1] - k[0] + 1 when k[0] != k[1].

  • ValueError – If num_rows and num_cols do not match the dimensions of x and the values of k.

  • ValueError – If align is not a string or not in the valid set of values.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor
>>> from mindspore import 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)
>>> output = 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)