mindspore.ops.matrix_set_diag

mindspore.ops.matrix_set_diag(x, diagonal, k=0, align='RIGHT_LEFT')[source]

Returns a batched matrix tensor with new batched diagonal values. Given x and diagonal, this operation returns a tensor with the same shape and values as x, except for the specified diagonals of the innermost matrices. These will be overwritten by the values in diagonal. Some diagonals are shorter than max_diag_len and need to be padded. The diagonal \(shape[-2]\) must be equal to num_diags calculated by \(k[1] - k[0] + 1\). The diagonal \(shape[-1]\) must be equal to the longest diagonal value max_diag_len calculated by \(min(x.shape[-2] + min(k[1], 0), x.shape[-1] + min(-k[0], 0))\). Let x have r + 1 dimensions \([I, J, ..., L, M, N]\). The diagonal tensor has rank r with shape \([I, J, ..., L, max\_diag\_len]\) when k is an integer or \(k[0] == k[1]\). Otherwise, it has rank r + 1 with shape \([I, J, ... L, num\_diags, max\_diag\_len]\).

Parameters

  • x (Tensor) – Rank r + 1, where r >= 1.

  • diagonal (Tensor) – A Tensor. Have the same dtype as x. Rank r when k is an integer or k[0] == k[1]. Otherwise, it has rank r + 1.

  • k (Union[int, Tensor], optional) – A int32 Scalar or int32 Tensor. 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 alue of k has restructions, meaning value of k must be in (-x.shape[-2], x.shape[-1]). Input k must be const Tensor when taking Graph mode.

  • align (str, optional) – 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

Tensor, The same type as x. Let x has r+1 dimensions [I, J, …, L, M, N]. The output is a tensor of rank r+1 with dimensions [I, J, …, L, M, N], the same as input x.

Raises

  • TypeError – If input x or diagonal is not Tensor.

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

  • TypeError – If k is not int32 dtype.

  • ValueError – If align is not a string or not in the valid range.

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

  • ValueError – If rank of x is not greater equal to 2.

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

  • ValueError – If k[1] is not greater equal to k[0] in case the size of k is 2.

  • ValueError – If the diagonal rank size don’t match with input x rank size.

  • ValueError – If the diagonal shape value don’t match with input x shape value.

  • ValueError – If the diagonal.shape[-2] is not equal to num_diags calculated by k[1] - k[0] + 1.

  • ValueError – If the value of k is not in (-x.shape[-2], x.shape[-1]).

  • ValueError – If the diagonal.shape[-1] is not equal to the max_diag_len calculated by min(x.shape[-2] + min(k[1], 0), x.shape[-1] + min(-k[0], 0)).

Supported Platforms:

GPU CPU

Examples

>>> x = Tensor(np.array([[7, 7, 7, 7],
...                      [7, 7, 7, 7],
...                      [7, 7, 7, 7]]), mindspore.float32)
>>> diagonal = Tensor(np.array([[0, 9, 1],
...                             [6, 5, 8],
...                             [1, 2, 3],
...                             [4, 5, 0]]), mindspore.float32)
>>> k = Tensor(np.array([-1, 2]), mindspore.int32)
>>> align = 'RIGHT_LEFT'
>>> output = ops.matrix_set_diag(x, diagonal, k, align)
>>> print(output)
[[1. 6. 9. 7.]
 [4. 2. 5. 1.]
 [7. 5. 3. 8.]]
>>> print(output.shape)
(3, 4)