torch (version 0.8.1)

torch_sparse_coo_tensor: Sparse_coo_tensor

Description

Sparse_coo_tensor

Usage

torch_sparse_coo_tensor(
  indices,
  values,
  size = NULL,
  dtype = NULL,
  device = NULL,
  requires_grad = FALSE
)

Arguments

indices

(array_like) Initial data for the tensor. Can be a list, tuple, NumPy ndarray, scalar, and other types. Will be cast to a torch_LongTensor internally. The indices are the coordinates of the non-zero values in the matrix, and thus should be two-dimensional where the first dimension is the number of tensor dimensions and the second dimension is the number of non-zero values.

values

(array_like) Initial values for the tensor. Can be a list, tuple, NumPy ndarray, scalar, and other types.

size

(list, tuple, or torch.Size, optional) Size of the sparse tensor. If not provided the size will be inferred as the minimum size big enough to hold all non-zero elements.

dtype

(torch.dtype, optional) the desired data type of returned tensor. Default: if NULL, infers data type from values.

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.

requires_grad

(bool, optional) If autograd should record operations on the returned tensor. Default: FALSE.

sparse_coo_tensor(indices, values, size=NULL, dtype=NULL, device=NULL, requires_grad=False) -> Tensor

Constructs a sparse tensors in COO(rdinate) format with non-zero elements at the given indices with the given values. A sparse tensor can be uncoalesced, in that case, there are duplicate coordinates in the indices, and the value at that index is the sum of all duplicate value entries: torch_sparse_.

Examples

if (torch_is_installed()) {

i = torch_tensor(matrix(c(1, 2, 2, 3, 1, 3), ncol = 3, byrow = TRUE), dtype=torch_int64())
v = torch_tensor(c(3, 4, 5), dtype=torch_float32())
torch_sparse_coo_tensor(i, v)
torch_sparse_coo_tensor(i, v, c(2, 4))

# create empty sparse tensors
S = torch_sparse_coo_tensor(
  torch_empty(c(1, 0), dtype = torch_int64()), 
  torch_tensor(numeric(), dtype = torch_float32()), 
  c(1)
)
S = torch_sparse_coo_tensor(
  torch_empty(c(1, 0), dtype = torch_int64()), 
  torch_empty(c(0, 2)), 
  c(1, 2)
)
}