htmd.clustering.kcenters module#
- class htmd.clustering.kcenters.KCenter(n_clusters)#
Bases:
BaseEstimator
,ClusterMixin
,TransformerMixin
Class to perform KCenter clustering of a given data set
KCenter randomly picks one point from the data, which is now the center of the first cluster. All points are put into the first cluster. In general the furthest point from its center is chosen to be the new center. All points, which are closer to the new center than the old one are assigned to the new cluster. This goes on, until K clusters have been created.
- Parameters:
n_clusters (int) – desired number of clusters
Examples
>>> cluster = KCenter(n_cluster=200) >>> cluster.fit(data)
- fit(data)#
Compute the centroids of data.
- Parameters:
data (np.ndarray) – A 2D array of data. Columns are features and rows are data examples.
- set_fit_request(*, data: bool | None | str = '$UNCHANGED$') KCenter #
Configure whether metadata should be requested to be passed to the
fit
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
(seesklearn.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 tofit
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it tofit
.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.New in version 1.3.
- datastr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for
data
parameter infit
.
- selfobject
The updated object.