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.

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

Examples

>>> cluster = RegCluster(radius=5.1)
>>> cluster.fit(data)
cluster_centers

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

centerFrames

list – list of indices of center points in data array

labels_

list – list with number of cluster of each frame

clusterSize_

list – list with number of frames in each cluster

clusterSize
cluster_centers_
fit(data)

performs clustering of data

Parameters:
  • 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.

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

X_new – Transformed array.

Return type:

numpy array of shape [n_samples, n_features_new]

get_params(deep=True)

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_params(**params)

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.

Returns:
Return type:self