torch (version 0.1.0)

torch_bincount: Bincount

Description

Bincount

Usage

torch_bincount(self, weights = list(), minlength = 0L)

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

# NOT RUN {
if (torch_is_installed()) {

input = torch_randint(0, 8, list(5), dtype=torch_int64())
weights = torch_linspace(0, 1, steps=5)
input
weights
torch_bincount(input, weights)
input$bincount(weights)
}
# }