htmd.clustering.regular module

class htmd.clustering.regular.RegCluster(radius=None, n_clusters=None)

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

Class to perform regular clustering of a given data set

RegCluster can be passed a radius or an approximate number of clusters. If a number of clusters is passed, KCenter clustering is used to estimate the necessary radius. RegCluster randomly chooses a point and assigns all points within the radius of this point to the same cluster. Then it proceeds with the nearest point, which is not yet assigned to a cluster and puts all unassigned points within the radius of this point in the next cluster and so on.

  • radius (float) – radius of clusters
  • n_clusters (int) – desired number of clusters


>>> cluster = RegCluster(radius=5.1)

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


performs clustering of data

  • data (np.ndarray) – array of data points to cluster
  • merge (int) – minimal number of frames within each cluster. Smaller clusters are merged into next big one
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