Bincount
Arguments
- self
(Tensor) 1-d int tensor
- weights
(Tensor) optional, weight for each value in the input tensor. Should be of same size as input tensor.
- minlength
(int) optional, minimum number of bins. Should be non-negative.
bincount(input, weights=NULL, minlength=0) -> Tensor
Count the frequency of each value in an array of non-negative ints.
The number of bins (size 1) is one larger than the largest value in
input unless input is empty, in which case the result is a
tensor of size 0. If minlength is specified, the number of bins is at least
minlength and if input is empty, then the result is tensor of size
minlength filled with zeros. If n is the value at position i,
out[n] += weights[i] if weights is specified else
out[n] += 1.
.. include:: cuda_deterministic.rst
Examples
if (torch_is_installed()) {
input = torch_randint(1, 8, list(5), dtype=torch_int64())
weights = torch_linspace(0, 1, steps=5)
input
weights
torch_bincount(input, weights)
input$bincount(weights)
}
#> torch_tensor
#> 0.0000
#> 0.5000
#> 0.0000
#> 0.0000
#> 1.0000
#> 1.0000
#> [ CPUFloatType{6} ]