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)
)
}