Diagonal

## Usage

torch_diagonal(self, outdim, dim1 = 1L, dim2 = 2L, offset = 0L)

## Arguments

self

(Tensor) the input tensor. Must be at least 2-dimensional.

outdim

dimension name if self is a named tensor.

dim1

(int, optional) first dimension with respect to which to take diagonal. Default: 0.

dim2

(int, optional) second dimension with respect to which to take diagonal. Default: 1.

offset

(int, optional) which diagonal to consider. Default: 0 (main diagonal).

## diagonal(input, offset=0, dim1=0, dim2=1) -> Tensor

Returns a partial view of input with the its diagonal elements with respect to dim1 and dim2 appended as a dimension at the end of the shape.

The argument offset controls which diagonal to consider:

• If offset = 0, it is the main diagonal.

• If offset > 0, it is above the main diagonal.

• If offset < 0, it is below the main diagonal.

Applying torch_diag_embed to the output of this function with the same arguments yields a diagonal matrix with the diagonal entries of the input. However, torch_diag_embed has different default dimensions, so those need to be explicitly specified.

## Examples

if (torch_is_installed()) {

a = torch_randn(c(3, 3))
a
torch_diagonal(a, offset = 0)
torch_diagonal(a, offset = 1)
x = torch_randn(c(2, 5, 4, 2))
torch_diagonal(x, offset=-1, dim1=1, dim2=2)
}
#> torch_tensor
#> (1,.,.) =
#>   0.4168
#>   1.2713
#>
#> (2,.,.) =
#>   0.9342
#>   1.1195
#>
#> (3,.,.) =
#>   0.7601
#>   1.1118
#>
#> (4,.,.) =
#>   0.1644
#>   0.4915
#> [ CPUFloatType{4,2,1} ]