htmd.clustering.kcenters module

class htmd.clustering.kcenters.KCenter(n_clusters)

Bases: sklearn.base.BaseEstimator, sklearn.base.ClusterMixin, sklearn.base.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


>>> cluster = KCenter(n_cluster=200)

list – list with the points, which are the centers of the clusters


list – list of indices of center points in data array


list – list with number of cluster of each frame


list – list with number of frames in each cluster


list – list with the distance of each frame from the nearest center


Compute the centroids of data.

Parameters:data (np.ndarray) – A 2D array of data. Columns are features and rows are data examples.
fit_predict(X, y=None)

Performs clustering on X and returns cluster labels.

Parameters:X (ndarray, shape (n_samples, n_features)) – Input data.
Returns:y – cluster labels
Return type:ndarray, shape (n_samples,)
fit_transform(X, y=None, **fit_params)

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

  • X (numpy array of shape [n_samples, n_features]) – Training set.
  • y (numpy array of shape [n_samples]) – Target values.

X_new – Transformed array.

Return type:

numpy array of shape [n_samples, n_features_new]


Get parameters for this estimator.

Parameters:deep (boolean, optional) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:params – Parameter names mapped to their values.
Return type:mapping of string to any

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Return type:self