type t = [ `BaseEstimator | `ClusterMixin | `MeanShift | `Object ] Obj.tval of_pyobject : Py.Object.t -> tval to_pyobject : [> tag ] Obj.t -> Py.Object.tval create :
?bandwidth:float ->
?seeds:[> `ArrayLike ] Np.Obj.t ->
?bin_seeding:bool ->
?min_bin_freq:int ->
?cluster_all:bool ->
?n_jobs:int ->
?max_iter:int ->
unit ->
tMean shift clustering using a flat kernel.
Mean shift clustering aims to discover 'blobs' in a smooth density of samples. It is a centroid-based algorithm, which works by updating candidates for centroids to be the mean of the points within a given region. These candidates are then filtered in a post-processing stage to eliminate near-duplicates to form the final set of centroids.
Seeding is performed using a binning technique for scalability.
Read more in the :ref:`User Guide <mean_shift>`.
Parameters ---------- bandwidth : float, default=None Bandwidth used in the RBF kernel.
If not given, the bandwidth is estimated using sklearn.cluster.estimate_bandwidth; see the documentation for that function for hints on scalability (see also the Notes, below).
seeds : array-like of shape (n_samples, n_features), default=None Seeds used to initialize kernels. If not set, the seeds are calculated by clustering.get_bin_seeds with bandwidth as the grid size and default values for other parameters.
bin_seeding : bool, default=False If true, initial kernel locations are not locations of all points, but rather the location of the discretized version of points, where points are binned onto a grid whose coarseness corresponds to the bandwidth. Setting this option to True will speed up the algorithm because fewer seeds will be initialized. The default value is False. Ignored if seeds argument is not None.
min_bin_freq : int, default=1 To speed up the algorithm, accept only those bins with at least min_bin_freq points as seeds.
cluster_all : bool, default=True If true, then all points are clustered, even those orphans that are not within any kernel. Orphans are assigned to the nearest kernel. If false, then orphans are given cluster label -1.
n_jobs : int, default=None The number of jobs to use for the computation. This works by computing each of the n_init runs in parallel.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary <n_jobs>` for more details.
max_iter : int, default=300 Maximum number of iterations, per seed point before the clustering operation terminates (for that seed point), if has not converged yet.
.. versionadded:: 0.22
Attributes ---------- cluster_centers_ : array, n_clusters, n_features Coordinates of cluster centers.
labels_ : array of shape (n_samples,) Labels of each point.
n_iter_ : int Maximum number of iterations performed on each seed.
.. versionadded:: 0.22
Examples -------- >>> from sklearn.cluster import MeanShift >>> import numpy as np >>> X = np.array([1, 1], [2, 1], [1, 0],
... [4, 7], [3, 5], [3, 6]) >>> clustering = MeanShift(bandwidth=2).fit(X) >>> clustering.labels_ array(1, 1, 1, 0, 0, 0) >>> clustering.predict([0, 0], [5, 5]) array(1, 0) >>> clustering MeanShift(bandwidth=2)
Notes -----
Scalability:
Because this implementation uses a flat kernel and a Ball Tree to look up members of each kernel, the complexity will tend towards O(T*n*log(n)) in lower dimensions, with n the number of samples and T the number of points. In higher dimensions the complexity will tend towards O(T*n^2).
Scalability can be boosted by using fewer seeds, for example by using a higher value of min_bin_freq in the get_bin_seeds function.
Note that the estimate_bandwidth function is much less scalable than the mean shift algorithm and will be the bottleneck if it is used.
References ----------
Dorin Comaniciu and Peter Meer, 'Mean Shift: A robust approach toward feature space analysis'. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2002. pp. 603-619.
val fit : ?y:Py.Object.t -> x:[> `ArrayLike ] Np.Obj.t -> [> tag ] Obj.t -> tPerform clustering.
Parameters ---------- X : array-like of shape (n_samples, n_features) Samples to cluster.
y : Ignored
val fit_predict :
?y:Py.Object.t ->
x:[> `ArrayLike ] Np.Obj.t ->
[> tag ] Obj.t ->
[> `ArrayLike ] Np.Obj.tPerform clustering on X and returns cluster labels.
Parameters ---------- X : array-like of shape (n_samples, n_features) Input data.
y : Ignored Not used, present for API consistency by convention.
Returns ------- labels : ndarray of shape (n_samples,) Cluster labels.
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 : mapping of string to any Parameter names mapped to their values.
Predict the closest cluster each sample in X belongs to.
Parameters ---------- X : array-like, sparse matrix, shape=n_samples, n_features New data to predict.
Returns ------- labels : array, shape n_samples, Index of the cluster each sample belongs to.
val set_params : ?params:(string * Py.Object.t) list -> [> tag ] Obj.t -> tSet 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 : object Estimator instance.
Attribute cluster_centers_: get value or raise Not_found if None.
Attribute cluster_centers_: get value as an option.
val n_iter_ : t -> intAttribute n_iter_: get value or raise Not_found if None.
val n_iter_opt : t -> int optionAttribute n_iter_: get value as an option.
val to_string : t -> stringPrint the object to a human-readable representation.
val show : t -> stringPrint the object to a human-readable representation.
val pp : Stdlib.Format.formatter -> t -> unitPretty-print the object to a formatter.