-iaH SrSSKrSSKJrJr SSKrSSKJr SSK J r J r J r SSK Jr SSKJr SS KJrJrJr SS KJrJr S S KJr \"S S/S S/S.SS9SSSSSSSSSS.Sjj5r"SS\ \ 5rg)zE DBSCAN: Density-Based Spatial Clustering of Applications with Noise N)IntegralReal)sparse) BaseEstimator ClusterMixin _fit_context)_VALID_METRICS)NearestNeighbors)Interval StrOptionsvalidate_params)_check_sample_weight validate_data) dbscan_innerz array-likez sparse matrix)X sample_weightFprefer_skip_nested_validation minkowskiauto) min_samplesmetric metric_params algorithm leaf_sizeprn_jobsc p[UUUUUUUU S9n U RXS9 U RU R4$)aSPerform DBSCAN clustering from vector array or distance matrix. Read more in the :ref:`User Guide `. Parameters ---------- X : {array-like, sparse (CSR) matrix} of shape (n_samples, n_features) or (n_samples, n_samples) A feature array, or array of distances between samples if ``metric='precomputed'``. eps : float, default=0.5 The maximum distance between two samples for one to be considered as in the neighborhood of the other. This is not a maximum bound on the distances of points within a cluster. This is the most important DBSCAN parameter to choose appropriately for your data set and distance function. min_samples : int, default=5 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 : str or callable, default='minkowski' 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 :func:`sklearn.metrics.pairwise_distances` for its metric parameter. If metric is "precomputed", X is assumed to be a distance matrix and must be square during fit. X may be a :term:`sparse graph `, in which case only "nonzero" elements may be considered neighbors. metric_params : dict, default=None Additional keyword arguments for the metric function. .. versionadded:: 0.19 algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, default='auto' 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, 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, default=2 The power of the Minkowski metric to be used to calculate distance between points. sample_weight : array-like of shape (n_samples,), default=None 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, default=None The number of parallel jobs to run for neighbors search. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary ` for more details. If precomputed distance are used, parallel execution is not available and thus n_jobs will have no effect. Returns ------- core_samples : ndarray of shape (n_core_samples,) Indices of core samples. labels : ndarray of shape (n_samples,) Cluster labels for each point. Noisy samples are given the label -1. See Also -------- DBSCAN : An estimator interface for this clustering algorithm. OPTICS : A similar estimator interface clustering at multiple values of eps. Our implementation is optimized for memory usage. Notes ----- For an example, see :ref:`sphx_glr_auto_examples_cluster_plot_dbscan.py`. 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). It may attract a higher memory complexity when querying these nearest neighborhoods, depending on the ``algorithm``. One way to avoid the query complexity is to pre-compute sparse neighborhoods in chunks using :func:`NearestNeighbors.radius_neighbors_graph ` with ``mode='distance'``, then using ``metric='precomputed'`` here. Another way to reduce memory and computation time is to remove (near-)duplicate points and use ``sample_weight`` instead. :class:`~sklearn.cluster.OPTICS` provides a similar clustering with lower memory usage. 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 Schubert, E., Sander, J., Ester, M., Kriegel, H. P., & Xu, X. (2017). :doi:`"DBSCAN revisited, revisited: why and how you should (still) use DBSCAN." <10.1145/3068335>` ACM Transactions on Database Systems (TODS), 42(3), 19. Examples -------- >>> from sklearn.cluster import dbscan >>> X = [[1, 2], [2, 2], [2, 3], [8, 7], [8, 8], [25, 80]] >>> core_samples, labels = dbscan(X, eps=3, min_samples=2) >>> core_samples array([0, 1, 2, 3, 4]) >>> labels array([ 0, 0, 0, 1, 1, -1]) epsrrrrrr r!r)DBSCANfitcore_sample_indices_labels_) rr$rrrrrr rr!ests J/var/www/html/venv/lib/python3.13/site-packages/sklearn/cluster/_dbscan.pydbscanr,sLb  #  CGGAG+  # #S[[ 00c ^\rSrSr%Sr\"\SSSS9/\"\SSSS9/\"\ "\ 5S 1-5\ /\ S/\"1S k5/\"\SSSS9/\"\SSSS9S/\S/S .r \ \S 'SS SSSSSSS.Sjjr\"SS9SSj5rSSjrU4SjrSrU=r$)r&aPerform DBSCAN clustering from vector array or distance matrix. DBSCAN - Density-Based Spatial Clustering of Applications with Noise. Finds core samples of high density and expands clusters from them. Good for data which contains clusters of similar density. This implementation has a worst case memory complexity of :math:`O({n}^2)`, which can occur when the `eps` param is large and `min_samples` is low, while the original DBSCAN only uses linear memory. For further details, see the Notes below. Read more in the :ref:`User Guide `. Parameters ---------- eps : float, default=0.5 The maximum distance between two samples for one to be considered as in the neighborhood of the other. This is not a maximum bound on the distances of points within a cluster. This is the most important DBSCAN parameter to choose appropriately for your data set and distance function. min_samples : int, default=5 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. If `min_samples` is set to a higher value, DBSCAN will find denser clusters, whereas if it is set to a lower value, the found clusters will be more sparse. metric : str, or callable, default='euclidean' 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 :func:`sklearn.metrics.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 :term:`sparse graph`, in which case only "nonzero" elements may be considered neighbors for DBSCAN. .. versionadded:: 0.17 metric *precomputed* to accept precomputed sparse matrix. metric_params : dict, default=None Additional keyword arguments for the metric function. .. versionadded:: 0.19 algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, default='auto' 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, 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, default=None The power of the Minkowski metric to be used to calculate distance between points. If None, then ``p=2`` (equivalent to the Euclidean distance). n_jobs : int, default=None The number of parallel jobs to run. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary ` for more details. Attributes ---------- core_sample_indices_ : ndarray of shape (n_core_samples,) Indices of core samples. components_ : ndarray of shape (n_core_samples, n_features) Copy of each core sample found by training. labels_ : ndarray of shape (n_samples) Cluster labels for each point in the dataset given to fit(). Noisy samples are given the label -1. 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 -------- OPTICS : A similar clustering at multiple values of eps. Our implementation is optimized for memory usage. Notes ----- 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). It may attract a higher memory complexity when querying these nearest neighborhoods, depending on the ``algorithm``. One way to avoid the query complexity is to pre-compute sparse neighborhoods in chunks using :func:`NearestNeighbors.radius_neighbors_graph ` with ``mode='distance'``, then using ``metric='precomputed'`` here. Another way to reduce memory and computation time is to remove (near-)duplicate points and use ``sample_weight`` instead. :class:`~sklearn.cluster.OPTICS` provides a similar clustering with lower memory usage. 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 Schubert, E., Sander, J., Ester, M., Kriegel, H. P., & Xu, X. (2017). :doi:`"DBSCAN revisited, revisited: why and how you should (still) use DBSCAN." <10.1145/3068335>` ACM Transactions on Database Systems (TODS), 42(3), 19. Examples -------- >>> from sklearn.cluster import DBSCAN >>> import numpy as np >>> X = np.array([[1, 2], [2, 2], [2, 3], ... [8, 7], [8, 8], [25, 80]]) >>> clustering = DBSCAN(eps=3, min_samples=2).fit(X) >>> clustering.labels_ array([ 0, 0, 0, 1, 1, -1]) >>> clustering DBSCAN(eps=3, min_samples=2) For an example, see :ref:`sphx_glr_auto_examples_cluster_plot_dbscan.py`. For a comparison of DBSCAN with other clustering algorithms, see :ref:`sphx_glr_auto_examples_cluster_plot_cluster_comparison.py` gNneither)closedrleft precomputed>rbrutekd_tree ball_treer#_parameter_constraintsr euclideanrr)rrrrrr r!cdXlX lX0lX@lXPlX`lXplXlg)Nr#) selfr$rrrrrr r!s r+__init__DBSCAN.__init__Xs.& *"" r-Frc d[XSS9nUb [X15nURS:Xa[R"U5(arUR 5n[ R"5 [ R"S[R5 URUR55 SSS5 [URURURURUR UR"UR$S9nUR'U5 UR)USS9nUc1[*R,"UVs/sHn[/U5PM sn5nO=[*R,"UVs/sHn[*R0"X65PM sn5n[*R2"UR4S S [*R6S 9n[*R8"XpR::[*R<S 9n [?XU5 [*R@"U 5S Ul!Xl"[/URB5(a#XRBR 5Ul#U$[*RH"S UR4S 45Ul#U$!,(df  GN=fs snfs snf) aGPerform DBSCAN clustering from features, or distance matrix. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features), or (n_samples, n_samples) Training instances to cluster, or distances between instances if ``metric='precomputed'``. If a sparse matrix is provided, it will be converted into a sparse ``csr_matrix``. y : Ignored Not used, present here for API consistency by convention. sample_weight : array-like of shape (n_samples,), default=None Weight of each sample, such that a sample with a weight of at least ``min_samples`` is by itself a core sample; a sample with a negative weight may inhibit its eps-neighbor from being core. Note that weights are absolute, and default to 1. Returns ------- self : object Returns a fitted instance of self. csr) accept_sparseNr3ignore)radiusrrrrr r!F)return_distancer)dtyper)%rrrrissparsecopywarningscatch_warnings simplefilterSparseEfficiencyWarningsetdiagdiagonalr r$rrrr r!r'radius_neighborsnparraylensumfullshapeintpasarrayruint8rwherer(r) components_empty) r:ryrneighbors_model neighborhoods neighbors n_neighborslabels core_sampless r+r' DBSCAN.fitms: $ 7  $0BM ;;- 'FOOA,>,>A((*%%h0N0NO !**,'++88nnnn;;,,ff;;  A'88E8R  ((M#RMyC NM#RSK((CPQ=i 01=QK Rrww7zz+1A1A"AR \&9$&HH\$:1$=! t(( ) ) !:!:;@@BD   "xxAGGAJ8D  Q+*$$SRs)AJJ(4"J- J%c8URXS9 UR$)azCompute clusters from a data or distance matrix and predict labels. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features), or (n_samples, n_samples) Training instances to cluster, or distances between instances if ``metric='precomputed'``. If a sparse matrix is provided, it will be converted into a sparse ``csr_matrix``. y : Ignored Not used, present here for API consistency by convention. sample_weight : array-like of shape (n_samples,), default=None Weight of each sample, such that a sample with a weight of at least ``min_samples`` is by itself a core sample; a sample with a negative weight may inhibit its eps-neighbor from being core. Note that weights are absolute, and default to 1. Returns ------- labels : ndarray of shape (n_samples,) Cluster labels. Noisy samples are given the label -1. r%)r'r))r:rrZrs r+ fit_predictDBSCAN.fit_predicts2 0||r-c>[TU]5nURS:HURlSURlU$)Nr3T)super__sklearn_tags__r input_tagspairwiser)r:tags __class__s r+rgDBSCAN.__sklearn_tags__s6w')#';;-#? !% r-) rrXr(r$r)rrrrr!r g?)NN)__name__ __module__ __qualname____firstlineno____doc__r rrr setr callabledictr7__annotations__r;r r'rcrg__static_attributes__ __classcell__)rks@r+r&r&sRjsD;< 1d6BC s>*m_< =   !JKLxD@AtS$v6 =T" $D   *&+M M^8r-r&rm)rrrGnumbersrrnumpyrNscipyrbaserrr metrics.pairwiser r]r utils._param_validationr r rutils.validationrr _dbscan_innerrr,r&r-r+rs"<<-(KKB'O ,&-#(  U1  U1U1pk\=kr-