#running_stat
Implementation of running variance/standard deviation (Welford 1962). This follows "Accurately computing running variance", derived from work by Brendan O'Connor.
The same interface borrowed from arma is implemented in several languages.
| function | comment |
|---|---|
| X(scalar) | update the statistics so far using the given scalar |
| X.min() | get the minimum value so far |
| X.max() | get the maximum value so far |
| X.mean() | get the mean or average value so far |
| X.var() and X.var(norm_type) | get the variance so far |
| X.stddev() and X.stddev(norm_type) | get the standard deviation so far |
| X.reset() | reset all statistics and set the number of samples to zero |
| X.count() | get the number of samples so far |
Other languages implement update as a separate method: X.update()
Use arma
Pure python implementation.
pip install runstat
from __future__ import print_function
import numpy as np
from runstat import RunStat
rs = RunStat()
X = np.random.rand(10)
for x in X:
rs(x)
print(rs.mean)
print(rs.var)
print(rs.std)Pure matlab implementation.