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:
mean.update(x)
print(mean.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:
rmean.update(x)
print(rmean.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 the called instance.
Parameters
- x — 'numbers.Number'
- w — defaults to
1.0
update_many
-
West, D. H. D. (1979). Updating mean and variance estimates: An improved method. Communications of the ACM, 22(9), 532-535. ↩
-
Finch, T., 2009. Incremental calculation of weighted mean and variance. University of Cambridge, 4(11-5), pp.41-42. ↩
-
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. ↩