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Mean

Running mean.

Attributes

  • n (float)

    The current sum of weights. If each passed weight was 1, then this is equal to the number of seen observations.

Examples

>>> from river import stats

>>> X = [-5, -3, -1, 1, 3, 5]
>>> mean = stats.Mean()
>>> for x in X:
...     print(mean.update(x).get())
-5.0
-4.0
-3.0
-2.0
-1.0
0.0

You can calculate a rolling average by wrapping a utils.Rolling around:

>>> from river import utils

>>> X = [1, 2, 3, 4, 5, 6]
>>> rmean = utils.Rolling(stats.Mean(), window_size=2)

>>> for x in X:
...     print(rmean.update(x).get())
1.0
1.5
2.5
3.5
4.5
5.5

Methods

get

Return the current value of the statistic.

revert
update

Update and return the called instance.

Parameters

  • x (numbers.Number)
  • w – defaults to 1.0
update_many

References

  1. West, D. H. D. (1979). Updating mean and variance estimates: An improved method. Communications of the ACM, 22(9), 532-535. ↩

  2. Finch, T., 2009. Incremental calculation of weighted mean and variance. University of Cambridge, 4(11-5), pp.41-42. ↩

  3. Chan, T.F., Golub, G.H. and LeVeque, R.J., 1983. Algorithms for computing the sample variance: Analysis and recommendations. The American Statistician, 37(3), pp.242-247. ↩