Nonzero

Usage

torch_nonzero(self, as_list = FALSE)

Arguments

self

(Tensor) the input tensor.

as_list

If FALSE, the output tensor containing indices. If TRUE, one 1-D tensor for each dimension, containing the indices of each nonzero element along that dimension.

When as_list is FALSE (default):

Returns a tensor containing the indices of all non-zero elements of input. Each row in the result contains the indices of a non-zero element in input. The result is sorted lexicographically, with the last index changing the fastest (C-style).

If input has $n$ dimensions, then the resulting indices tensor out is of size $(z\times n)$, where $z$ is the total number of non-zero elements in the input tensor.

When as_list is TRUE:

Returns a tuple of 1-D tensors, one for each dimension in input, each containing the indices (in that dimension) of all non-zero elements of input .

If input has $n$ dimensions, then the resulting tuple contains $n$ tensors of size $z$, where $z$ is the total number of non-zero elements in the input tensor.

As a special case, when input has zero dimensions and a nonzero scalar value, it is treated as a one-dimensional tensor with one element.

Examples

if (torch_is_installed()) {

torch_nonzero(torch_tensor(c(1, 1, 1, 0, 1)))
}
#> torch_tensor
#>  1
#>  2
#>  3
#>  5
#> [ CPULongType{4,1} ]