torch_tril_indices: Tril_indices

In torch: Tensors and Neural Networks with 'GPU' Acceleration

Tril_indices

Description

Tril_indices

Usage

torch_tril_indices(
  row,
  col,
  offset = 0,
  dtype = NULL,
  device = NULL,
  layout = NULL
)

Arguments

row	(int) number of rows in the 2-D matrix.
col	(int) number of columns in the 2-D matrix.
offset	(int) diagonal offset from the main diagonal. Default: if not provided, 0.
dtype	(torch.dtype, optional) the desired data type of returned tensor. Default: if NULL, torch_long.
device	(torch.device, optional) the desired device of returned tensor. Default: if NULL, uses the current device for the default tensor type (see torch_set_default_tensor_type). device will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types.
layout	(torch.layout, optional) currently only support torch_strided.

tril_indices(row, col, offset=0, dtype=torch.long, device='cpu', layout=torch.strided) -> Tensor

Returns the indices of the lower triangular part of a row-by- col matrix in a 2-by-N Tensor, where the first row contains row coordinates of all indices and the second row contains column coordinates. Indices are ordered based on rows and then columns.

The lower triangular part of the matrix is defined as the elements on and below the diagonal.

The argument offset controls which diagonal to consider. If offset = 0, all elements on and below the main diagonal are retained. A positive value includes just as many diagonals above the main diagonal, and similarly a negative value excludes just as many diagonals below the main diagonal. The main diagonal are the set of indices \lbrace (i, i) \rbrace for i \in [0, \min\{d_{1}, d_{2}\} - 1] where d_{1}, d_{2} are the dimensions of the matrix.

Note

When running on CUDA, `row * col` must be less than \eqn{2^{59}} to
prevent overflow during calculation.

Examples

if (torch_is_installed()) {
## Not run: 
a = torch_tril_indices(3, 3)
a
a = torch_tril_indices(4, 3, -1)
a
a = torch_tril_indices(4, 3, 1)
a

## End(Not run)
}

torch documentation built on June 7, 2023, 6:19 p.m.