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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

  This operation may produce nondeterministic gradients when given tensors on a CUDA device. See Reproducibility for more information.

  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])

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