mindspore.mint.bincount

mindspore.mint.bincount(input, weights=None, minlength=0)[source]

Count the occurrences of each value in the input.

If minlength is not specified, the length of the output Tensor is the maximum value in the input plus one. If minlength is specified, the length of the output Tensor is the maximum value between minlength or the maximum value in the input plus one.

Each value in the output Tensor represents the number of occurrences of that index value in the input. If weights is specified, the output results are weighted, i.e., \(out[n] += weight[i]\) instead of \(out[n] += 1\).

Warning

This is an experimental API that is subject to change or deletion.

Parameters

  • input (Tensor) – A one-dimensional Tensor.

  • weights (Tensor, optional) – Weights with the same shape as the input. Default: None.

  • minlength (int, optional) – The minimum length of output Tensor. Should be non-negative. Default: 0.

Returns

Tensor, If input is non-empty, the output shape is \((max(max(input)+1, minlength), )\), otherwise the shape is \((0, )\).

Raises

  • TypeError – If input or weights is not a Tensor.

  • ValueError – If input contains negative values.

  • ValueError – If input is not one-dimensional or input and weights do not have the same shape.

Supported Platforms:

Ascend

Examples

>>> from mindspore import mint, Tensor
>>> print(mint.bincount(Tensor(np.arange(5))))
[1 1 1 1 1]
>>> print(mint.bincount(Tensor(np.array([0, 1, 1, 3, 2, 1, 7]))))
[1 3 1 1 0 0 0 1]
>>> w = Tensor(np.array([0.3, 0.5, 0.2, 0.7, 1., -0.6])) # weights
>>> x = Tensor(np.array([0, 1, 1, 2, 2, 2]))
>>> print(mint.bincount(x,  weights=w, minlength=5))
[0.3 0.7 1.1 0.  0. ]