Cov¶
Covariance.
Parameters¶
-
ddof – defaults to
1
Delta Degrees of Freedom.
Examples¶
>>> from river import stats
>>> x = [-2.1, -1, 4.3]
>>> y = [ 3, 1.1, 0.12]
>>> cov = stats.Cov()
>>> for xi, yi in zip(x, y):
... print(cov.update(xi, yi).get())
0.0
-1.044999
-4.286
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.
update
Update and return the called instance.
Parameters
- x
- y
- w – defaults to
1.0
update_many
Notes¶
The outcomes of the incremental and parallel updates are consistent with numpy's batch processing when \(\text{ddof} \le 1\).
References¶
-
Schubert, E. and Gertz, M., 2018, July. Numerically stable parallel computation of (co-) variance. In Proceedings of the 30th International Conference on Scientific and Statistical Database Management (pp. 1-12). ↩