average(x, axis=None, weights=None, returned=False)¶
Computes the weighted average along the specified axis.
x (Tensor) – A Tensor to be averaged.
axis (Union[None, int, tuple(int)]) – Axis along which to average x. Default: None. If the axis is None, it will average over all of the elements of the tensor x. If the axis is negative, it counts from the last to the first axis.
weights (Union[None, Tensor]) – Weights associated with the values in x. Default: None. If weights is None, all the data in x are assumed to have a weight equal to one. If weights is 1-D tensor, the length must be the same as the given axis. Otherwise, weights should have the same shape as x.
returned (bool) – Default: False. If True, the tuple (average, sum_of_weights) is returned. If False, only the average is returned.
Averaged Tensor. If returned is True, return tuple.
- Supported Platforms:
>>> import mindspore.numpy as np >>> input_x = np.array([[1., 2.], [3., 4.]]) >>> output = np.average(input_x, axis=0, weights=input_x, returned=True) >>> print(output) (Tensor(shape=, dtype=Float32, value= [ 2.50000000e+00, 3.33333325e+00]), Tensor(shape=, dtype=Float32, value= [ 4.00000000e+00, 6.00000000e+00]))