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Clusterer

A clustering model.

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 with a set of features x.

Parameters

  • x (dict)
  • sample_weight (int)

Returns

Clusterer: self

predict_one

Predicts the cluster number for a set of features x.

Parameters

  • x (dict)

Returns

int: A cluster number.