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.
>>> cluster = RegCluster(radius=5.1) >>> cluster.fit(data)
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
Perform clustering on X and returns cluster labels.
X (ndarray, shape (n_samples, n_features)) – Input data.
y (Ignored) – Not used, present for API consistency by convention.
labels – Cluster labels.
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.
**fit_params (dict) – Additional fit parameters.
X_new – Transformed array.
numpy array of shape [n_samples, n_features_new]
Get parameters for this estimator.
deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
params – Parameter names mapped to their values.
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.