aub_htp.machine_learning.kmeans#

Functions

compute_inertia(X, cluster_centers, labels, ...)

compute_labels(X, cluster_centers)

initialize_cluster_centers(X, n_clusters)

update_cluster_centers(X, cluster_centers, ...)

Classes

AlphaStableKMeans([n_clusters, alpha, ...])

class aub_htp.machine_learning.kmeans.AlphaStableKMeans(n_clusters: int = 8, alpha: float = 1.0, *, max_iter: int = 100, tol: float = 1e-06)#

Bases: ClusterMixin, BaseEstimator

fit(X, y=None)#
predict(X)#
score(X, y=None, sample_weight=None)#
set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → AlphaStableKMeans#

Configure whether metadata should be requested to be passed to the score method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to score.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Parameters#

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for sample_weight parameter in score.

Returns#

selfobject

The updated object.

aub_htp.machine_learning.kmeans.compute_inertia(X: ndarray, cluster_centers: ndarray, labels: ndarray, alpha: float) → float#
aub_htp.machine_learning.kmeans.compute_labels(X: ndarray, cluster_centers: ndarray) → ndarray#
aub_htp.machine_learning.kmeans.initialize_cluster_centers(X: ndarray, n_clusters: int) → ndarray#
aub_htp.machine_learning.kmeans.update_cluster_centers(X: ndarray, cluster_centers: ndarray, labels: ndarray, alpha: float)#