code: def dbscan(
      X,
      eps=0.5,
      min_samples=5,
      metric="minkowski",
      algorithm="auto",
      leaf_size=30,
      p=2,
      sample_weight=None,
      n_jobs=1,
  ):
      """Perform DBSCAN clustering from vector array or distance matrix.
  
      Read more in the :ref:`User Guide <dbscan>`.
  
      Parameters
      ----------
      X : array or sparse (CSR) matrix of shape (n_samples, n_features), or \
              array of shape (n_samples, n_samples)
          A feature array, or array of distances between samples if
          ``metric='precomputed'``.
  
      eps : float, optional
          The maximum distance between two samples for them to be considered
          as in the same neighborhood.
  
      min_samples : int, optional
          The number of samples (or total weight) in a neighborhood for a point
          to be considered as a core point. This includes the point itself.
  
      metric : string, or callable
          The metric to use when calculating distance between instances in a
          feature array. If metric is a string or callable, it must be one of
          the options allowed by metrics.pairwise.pairwise_distances for its
          metric parameter.
          If metric is "precomputed", X is assumed to be a distance matrix and
          must be square. X may be a sparse matrix, in which case only "nonzero"
          elements may be considered neighbors for DBSCAN.
  
      algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, optional
          The algorithm to be used by the NearestNeighbors module
          to compute pointwise distances and find nearest neighbors.
          See NearestNeighbors module documentation for details.
  
      leaf_size : int, optional (default = 30)
          Leaf size passed to BallTree or cKDTree. This can affect the speed
          of the construction and query, as well as the memory required
          to store the tree. The optimal value depends
          on the nature of the problem.
  
      p : float, optional
          The power of the Minkowski metric to be used to calculate distance
          between points.
  
      sample_weight : array, shape (n_samples,), optional
          Weight of each sample, such that a sample with a weight of at least
          ``min_samples`` is by itself a core sample; a sample with negative
          weight may inhibit its eps-neighbor from being core.
          Note that weights are absolute, and default to 1.
  
      n_jobs : int, optional (default = 1)
          The number of parallel jobs to run for neighbors search.
          If ``-1``, then the number of jobs is set to the number of CPU cores.
  
      Returns
      -------
      core_samples : array [n_core_samples]
          Indices of core samples.
  
      labels : array [n_samples]
          Cluster labels for each point.  Noisy samples are given the label -1.
  
      Notes
      -----
      See examples/cluster/plot_dbscan.py for an example.
  
      This implementation bulk-computes all neighborhood queries, which increases
      the memory complexity to O(n.d) where d is the average number of neighbors,
      while original DBSCAN had memory complexity O(n).
  
      Sparse neighborhoods can be precomputed using
      :func:`NearestNeighbors.radius_neighbors_graph
      <sklearn.neighbors.NearestNeighbors.radius_neighbors_graph>`
      with ``mode='distance'``.
  
      References
      ----------
      Ester, M., H. P. Kriegel, J. Sander, and X. Xu, "A Density-Based
      Algorithm for Discovering Clusters in Large Spatial Databases with Noise".
      In: Proceedings of the 2nd International Conference on Knowledge Discovery
      and Data Mining, Portland, OR, AAAI Press, pp. 226-231. 1996
      """
      if not eps > 0.0:
          raise ValueError("eps must be positive.")
  
      X = check_array(X, accept_sparse="csr")
      if sample_weight is not None:
          sample_weight = np.asarray(sample_weight)
          check_consistent_length(X, sample_weight)
  
      # Calculate neighborhood for all samples. This leaves the original point
      # in, which needs to be considered later (i.e. point i is in the
      # neighborhood of point i. While True, its useless information)
      if metric == "precomputed" and sparse.issparse(X):
          neighborhoods = np.empty(X.shape[0], dtype=object)
          X.sum_duplicates()  # XXX: modifies X's internals in-place
          X_mask = X.data <= eps
          masked_indices = astype(X.indices, np.intp, copy=False)[
              X_mask
          ]
          masked_indptr = np.cumsum(X_mask)[X.indptr[1:] - 1]
          # insert the diagonal: a point is its own neighbor, but 0 distance
          # means absence from sparse matrix data
          masked_indices = np.insert(
              masked_indices, masked_indptr, np.arange(X.shape[0])
          )
          masked_indptr = masked_indptr[:-1] + np.arange(1, X.shape[0])
          # split into rows
          neighborhoods[:] = np.split(masked_indices, masked_indptr)
      else:
          neighbors_model = NearestNeighbors(
              radius=eps,
              algorithm=algorithm,
              leaf_size=leaf_size,
              metric=metric,
              p=p,
              n_jobs=n_jobs,
          )
          neighbors_model.fit(X)
          # This has worst case O(n^2) memory complexity
          neighborhoods = neighbors_model.radius_neighbors(
              X, eps, return_distance=False
          )
  
      if sample_weight is None:
          n_neighbors = np.array(
              [len(neighbors) for neighbors in neighborhoods]
          )
      else:
          n_neighbors = np.array(
              [
                  np.sum(sample_weight[neighbors])
                  for neighbors in neighborhoods
              ]
          )
  
      # Initially, all samples are noise.
      labels = -np.ones(X.shape[0], dtype=np.intp)
  
      # A list of all core samples found.
      core_samples = np.asarray(
          n_neighbors >= min_samples, dtype=np.uint8
      )
      dbscan_inner(core_samples, neighborhoods, labels)
      return np.where(core_samples)[0], labels
  
file_path: catboost/contrib/python/scikit-learn/py2/sklearn/cluster/dbscan_.py
imports: import numpy
  from scipy import sparse
  from ..base import BaseEstimator, ClusterMixin
  from ..utils import check_array, check_consistent_length
  from ..utils.fixes import astype
  from ..neighbors import NearestNeighbors
  from ._dbscan_inner import dbscan_inner
label_desc: Use image node_id 1 for calling a global function with example usage: dbscan(X, eps, min_samples, metric, algorithm, leaf_size, p, sample_weight, n_jobs) and returns: np,  labels