class_docstr: Mean 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 flat 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. The following tasks benefit
      from the parallelization:
  
      - The search of nearest neighbors for bandwidth estimation and label
        assignments. See the details in the docstring of the
        ``NearestNeighbors`` class.
      - Hill-climbing optimization for all seeds.
  
      See :term:`Glossary <n_jobs>` for more details.
  
      ``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_ : ndarray of shape (n_clusters, n_features)
      Coordinates of cluster centers.
  
  labels_ : ndarray of shape (n_samples,)
      Labels of each point.
  
  n_iter_ : int
      Maximum number of iterations performed on each seed.
  
      .. versionadded:: 0.22
  
  n_features_in_ : int
      Number of features seen during :term:`fit`.
  
      .. versionadded:: 0.24
  
  feature_names_in_ : ndarray of shape (`n_features_in_`,)
      Names of features seen during :term:`fit`. Defined only when `X`
      has feature names that are all strings.
  
      .. versionadded:: 1.0
  
  See Also
  --------
  KMeans : K-Means clustering.
  
  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.
  
  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)
code: def __init__(
      self,
      *,
      bandwidth=None,
      seeds=None,
      bin_seeding=False,
      min_bin_freq=1,
      cluster_all=True,
      n_jobs=None,
      max_iter=300,
  ):
      self.bandwidth = bandwidth
      self.seeds = seeds
      self.bin_seeding = bin_seeding
      self.cluster_all = cluster_all
      self.min_bin_freq = min_bin_freq
      self.n_jobs = n_jobs
      self.max_iter = max_iter
  
imports: import warnings
  from collections import defaultdict
  from numbers import Integral, Real
  import numpy
  from .._config import config_context
  from ..base import BaseEstimator, ClusterMixin, _fit_context
  from ..metrics.pairwise import pairwise_distances_argmin
  from ..neighbors import NearestNeighbors
  from ..utils import check_array, check_random_state, gen_batches
  from ..utils._param_validation import Interval, validate_params
  from ..utils.parallel import Parallel, delayed
  from ..utils.validation import check_is_fitted
label_desc: Use image node_id 1 to create a new MeanShift object from inherited base classes: ClusterMixin, BaseEstimator with example: obj = MeanShift()