CovarianceStat
Online covariance and Pearson correlation between two streams.
Derived from UnivariateRegressionStat: the same Chan's parallel algorithm drives accumulation, and CovarianceResult is projected from UnivariateRegressionResult via mapResult.
Use cases: monitoring joint variability and correlation between two scalar streams (input metric vs output, feature drift detection).
Memory: O(1); same as UnivariateRegressionStat.
Update: O(1) per paired observation.
Concurrency: Inherits UnivariateRegressionStat's concurrency model.
Properties
Functions
Spawn a fresh accumulator with the same configuration. Optionally override the Concurrency; useful for materialising a wire spec at a different concurrency level than the source.
Fold another accumulator's snapshot into this one. The unit of merge is the immutable Result; not a live Stat; which is what lets the merge cross a process boundary. Many workers track slices of the same stream, call read periodically, ship snapshots to a coordinator, and the coordinator merges them in.
Materialise the current state as an immutable Result. Reads never mutate, so the caller can read as often as it likes without affecting the stream.
Reset the stat to its prior-seeded baseline. Equivalent to constructing a fresh stat with the same configuration, but in place; keeps the same Concurrency and any per-stat tunables.