Exponentially weighted variance.
To calculate the variance we use the fact that Var(X) = Mean(x^2) - Mean(x)^2 and internally we use the exponentially weighted mean of x/x^2 to calculate this.
alpha – defaults to
alphais to 1 the more the statistic will adapt to recent values.
The running exponentially weighted variance.
>>> from river import stats >>> X = [1, 3, 5, 4, 6, 8, 7, 9, 11] >>> ewv = stats.EWVar(alpha=0.5) >>> for x in X: ... print(ewv.update(x).get()) 0 1.0 2.75 1.4375 1.984375 3.43359375 1.7958984375 2.198974609375 3.56536865234375
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.
Return the current value of the statistic.
Revert and return the called instance.
Update and return the called instance.