Var¶

Running variance using Welford's algorithm.

Parameters¶

ddof – defaults to 1

Delta Degrees of Freedom. The divisor used in calculations is n - ddof, where n represents the number of seen elements.

Attributes¶

mean

It is necessary to calculate the mean of the data in order to calculate its variance.

Examples¶

>>> import river.stats

>>> X = [3, 5, 4, 7, 10, 12]

>>> var = river.stats.Var()
>>> for x in X:
...     print(var.update(x).get())
0.0
2.0
1.0
2.916666
7.7
12.56666

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
w – defaults to 1.0

update_many

Notes¶

The outcomes of the incremental and parallel updates are consistent with numpy's batch processing when \(\text{ddof} \le 1\).