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

Examples

>>> cluster = KCenter(n_cluster=200)
>>> cluster.fit(data)
cluster_centers

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

Type

list

centerFrames

list of indices of center points in data array

Type

list

labels_

list with number of cluster of each frame

Type

list

clusterSize_

list with number of frames in each cluster

Type

list

distance

list with the distance of each frame from the nearest center

Type

list

fit(data)

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)

Perform clustering on X and returns cluster labels.

Parameters
  • X (ndarray, shape (n_samples, n_features)) – Input data.

  • y (Ignored) – Not used, present for API consistency by convention.

Returns

labels – 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.

  • **fit_params (dict) – Additional fit parameters.

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 (bool, default=True) – 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.

Parameters

**params (dict) – Estimator parameters.

Returns

self – Estimator instance.

Return type

object