# AnomalyDetector¶

An anomaly detector.

## 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.

learn_one

Update the model.

Parameters

• x (dict)

Returns

AnomalyDetector: self

score_one

Return an outlier score.

A high score is indicative of an anomaly. A low score corresponds to a normal observation.

Parameters

• x (dict)

Returns

float: An anomaly score. A high score is indicative of an anomaly. A low score corresponds a