Skew¶
Running skew using Welford's algorithm.
Parameters¶
-
bias – defaults to
FalseIf
False, then the calculations are corrected for statistical bias.
Attributes¶
- name
Examples¶
>>> import river.stats
>>> import scipy.stats
>>> import numpy as np
>>> np.random.seed(42)
>>> X = np.random.normal(loc=0, scale=1, size=10)
>>> skew = river.stats.Skew(bias=False)
>>> for x in X:
... print(skew.update(x).get())
0
0.0
-1.4802398132849872
0.5127437186677888
0.7803466510704751
1.056115628922055
0.5057840774320389
0.3478402420400934
0.4536710660918704
0.4123070197493227
>>> for i in range(1, len(X)+1):
... print(scipy.stats.skew(X[:i], bias=False))
0.0
0.0
-1.4802398132849874
0.5127437186677893
0.7803466510704746
1.056115628922055
0.5057840774320389
0.3478402420400927
0.4536710660918703
0.4123070197493223
>>> skew = river.stats.Skew(bias=True)
>>> for x in X:
... print(skew.update(x).get())
0
0.0
-0.6043053732501439
0.2960327239981376
0.5234724473423674
0.7712778043924866
0.39022088752624845
0.278892645224261
0.37425953513864063
0.3476878073823696
>>> for i in range(1, len(X)+1):
... print(scipy.stats.skew(X[:i], bias=True))
0.0
0.0
-0.604305373250144
0.29603272399813796
0.5234724473423671
0.7712778043924865
0.39022088752624845
0.2788926452242604
0.3742595351386406
0.34768780738236926
Methods¶
clone
Return a fresh estimator with the same parameters.
The clone has the same parameters but has not been updated with any data. This works by looking at the parameters from the class signature. Each parameter is either - recursively cloned if it's a River classes. - deep-copied via copy.deepcopy if not. If the calling object is stochastic (i.e. it accepts a seed parameter) and has not been seeded, then the clone will not be idempotent. Indeed, this method's purpose if simply to return a new instance with the same input parameters.
get
Return the current value of the statistic.
revert
Revert and return the called instance.
Parameters
- x
update
Update and return the called instance.
Parameters
- x