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# torch.bincount¶

torch.bincount(input, weights=None, 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.

Note

In some circumstances when using the CUDA backend with CuDNN, this operator may select a nondeterministic algorithm to increase performance. If this is undesirable, you can try to make the operation deterministic (potentially at a performance cost) by setting torch.backends.cudnn.deterministic = True. Please see the notes on Reproducibility for background.

Parameters
• input (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.

Returns

a tensor of shape Size([max(input) + 1]) if input is non-empty, else Size(0)

Return type

output (Tensor)

Example:

>>> input = torch.randint(0, 8, (5,), dtype=torch.int64)
>>> weights = torch.linspace(0, 1, steps=5)
>>> input, weights
(tensor([4, 3, 6, 3, 4]),
tensor([ 0.0000,  0.2500,  0.5000,  0.7500,  1.0000])

>>> torch.bincount(input)
tensor([0, 0, 0, 2, 2, 0, 1])

>>> input.bincount(weights)
tensor([0.0000, 0.0000, 0.0000, 1.0000, 1.0000, 0.0000, 0.5000]) ## Docs

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