mars.tensor.average(a, axis=None, weights=None, returned=False)[source]

Compute the weighted average along the specified axis.

  • a (array_like) – Tensor containing data to be averaged. If a is not a tensor, a conversion is attempted.

  • axis (None or int or tuple of ints, optional) –

    Axis or axes along which to average a. The default, axis=None, will average over all of the elements of the input tensor. If axis is negative it counts from the last to the first axis.

    If axis is a tuple of ints, averaging is performed on all of the axes specified in the tuple instead of a single axis or all the axes as before.

  • weights (array_like, optional) – A tensor of weights associated with the values in a. Each value in a contributes to the average according to its associated weight. The weights tensor can either be 1-D (in which case its length must be the size of a along the given axis) or of the same shape as a. If weights=None, then all data in a are assumed to have a weight equal to one.

  • returned (bool, optional) – Default is False. If True, the tuple (average, sum_of_weights) is returned, otherwise only the average is returned. If weights=None, sum_of_weights is equivalent to the number of elements over which the average is taken.


average, [sum_of_weights] – Return the average along the specified axis. When returned is True, return a tuple with the average as the first element and the sum of the weights as the second element. The return type is Float if a is of integer type, otherwise it is of the same type as a. sum_of_weights is of the same type as average.

Return type

tensor_type or double

  • ZeroDivisionError – When all weights along axis are zero. See for a version robust to this type of error.

  • TypeError – When the length of 1D weights is not the same as the shape of a along axis.

See also



>>> import mars.tensor as mt
>>> data = list(range(1,5))
>>> data
[1, 2, 3, 4]
>>> mt.average(data).execute()
>>> mt.average(range(1,11), weights=range(10,0,-1)).execute()
>>> data = mt.arange(6).reshape((3,2))
>>> data.execute()
array([[0, 1],
       [2, 3],
       [4, 5]])
>>> mt.average(data, axis=1, weights=[1./4, 3./4]).execute()
array([ 0.75,  2.75,  4.75])
>>> mt.average(data, weights=[1./4, 3./4]).execute()
Traceback (most recent call last):
TypeError: Axis must be specified when shapes of a and weights differ.