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torch_bucketize(self, boundaries, out_int32 = FALSE, right = FALSE)



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


(Tensor) 1-D tensor, must contain a monotonically increasing sequence.


(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().


(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.

bucketize(input, boundaries, *, out_int32=FALSE, right=FALSE, out=None) -> Tensor

Returns the indices of the buckets to which each value in the input belongs, where the boundaries of the buckets are set by boundaries. Return a new tensor with the same size as input. If right is FALSE (default), then the left boundary is closed.


if (torch_is_installed()) {

boundaries <- torch_tensor(c(1, 3, 5, 7, 9))
v <- torch_tensor(rbind(c(3, 6, 9), c(3, 6, 9)))
torch_bucketize(v, boundaries)
torch_bucketize(v, boundaries, right=TRUE)
#> torch_tensor
#>  2  3  5
#>  2  3  5
#> [ CPULongType{2,3} ]