pykoop.ClusterCenters
- class ClusterCenters(estimator=None)
Bases:
Centers
Centers generated from a clustering algorithm.
Also supports taking centers from the means of a Gaussian mixture model.
Inspired by center generation approach used in [DTK20].
- Parameters:
estimator (BaseEstimator | None)
- centers_
Centers, shape (n_centers, n_features).
- Type:
np.ndarray
- estimator_
Fit clustering estimator or Gaussian mixture model.
Examples
Generate centers using K-means clustering
>>> kmeans = pykoop.ClusterCenters(sklearn.cluster.KMeans(n_clusters=3)) >>> kmeans.fit(X_msd[:, 1:]) # Remove episode feature ClusterCenters(estimator=KMeans(n_clusters=3)) >>> kmeans.centers_ array([...])
- __init__(estimator=None)
Instantiate
ClusterCenters
.- Parameters:
estimator (Optional[sklearn.base.BaseEstimator]) –
Clustering estimator or Gaussian mixture model. Must provide
cluster_centers_
ormeans_
once fit. Possible algorithms includeThe number of centers generated is controlled by the chosen estimator. If a random seed is desired, it must be set in the chosen estimator. Defaults to
sklearn.cluster.KMeans
.- Return type:
None
Methods
__init__
([estimator])Instantiate
ClusterCenters
.fit
(X[, y])Generate centers from data.
Get metadata routing of this object.
get_params
([deep])Get parameters for this estimator.
set_params
(**params)Set the parameters of this estimator.
- fit(X, y=None)
Generate centers from data.
- Parameters:
X (np.ndarray) – Data matrix.
y (Optional[np.ndarray]) – Ignored.
- Returns:
Instance of itself.
- Return type:
- Raises:
ValueError – If any of the constructor parameters are incorrect.
- get_metadata_routing()
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
- Returns:
routing – A
MetadataRequest
encapsulating routing information.- Return type:
MetadataRequest
- get_params(deep=True)
Get parameters for this estimator.
- set_params(**params)
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline
). The latter have parameters of the form<component>__<parameter>
so that it’s possible to update each component of a nested object.- Parameters:
**params (dict) – Estimator parameters.
- Returns:
self – Estimator instance.
- Return type:
estimator instance