torch_searchsorted {torch}

Searchsorted

Description

Searchsorted

Usage

torch_searchsorted(
  sorted_sequence,
  self,
  out_int32 = FALSE,
  right = FALSE,
  side = NULL,
  sorter = list()
)

Arguments

sorted_sequence

(Tensor) N-D or 1-D tensor, containing monotonically increasing sequence on the innermost dimension.

self

(Tensor or Scalar) N-D tensor or a Scalar containing the search value(s).

out_int32

(bool, optional) – indicate the output data type. torch_int32() if True, torch_int64() otherwise. Default value is FALSE, i.e. default output data type is torch_int64().

right

(bool, optional) – if False, return the first suitable location that is found. If True, return the last such index. If no suitable index found, return 0 for non-numerical value (eg. nan, inf) or the size of boundaries (one pass the last index). In other words, if False, gets the lower bound index for each value in input from boundaries. If True, gets the upper bound index instead. Default value is False.

side

the same as right but preferred. “left” corresponds to FALSE for right and “right” corresponds to TRUE for right. It will error if this is set to “left” while right is TRUE.

sorter

if provided, a tensor matching the shape of the unsorted sorted_sequence containing a sequence of indices that sort it in the ascending order on the innermost dimension.

searchsorted(sorted_sequence, values, *, out_int32=FALSE, right=FALSE, out=None) -> Tensor

Find the indices from the innermost dimension of sorted_sequence such that, if the corresponding values in values were inserted before the indices, the order of the corresponding innermost dimension within sorted_sequence would be preserved. Return a new tensor with the same size as values. If right is FALSE (default), then the left boundary of sorted_sequence is closed.

Examples

if (torch_is_installed()) {

sorted_sequence <- torch_tensor(rbind(c(1, 3, 5, 7, 9), c(2, 4, 6, 8, 10)))
sorted_sequence
values <- torch_tensor(rbind(c(3, 6, 9), c(3, 6, 9)))
values
torch_searchsorted(sorted_sequence, values)
torch_searchsorted(sorted_sequence, values, right=TRUE)
sorted_sequence_1d <- torch_tensor(c(1, 3, 5, 7, 9))
sorted_sequence_1d
torch_searchsorted(sorted_sequence_1d, values)
}