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: |
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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: |
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fit_predict
(X, y=None)¶Performs clustering on X and returns cluster labels.
Parameters: | X (ndarray, shape (n_samples, n_features)) – Input data. |
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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: |
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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. |
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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: | |
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Return type: | self |