- class htmd.clustering.kcenters.KCenter(n_clusters)#
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.
n_clusters (int) – desired number of clusters
>>> cluster = KCenter(n_cluster=200) >>> cluster.fit(data)
Compute the centroids of data.
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 #
Request metadata passed to the
Note that this method is only relevant if
sklearn.set_config()). Please see User Guide on how the routing mechanism works.
The options for each parameter are:
True: metadata is requested, and passed to
fitif provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to
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.
This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a
Pipeline. Otherwise it has no effect.