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 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

property clusterSize¶

property 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)¶

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