@@ -183,7 +183,7 @@ class DBSCAN(ClusterMixin, BaseEstimator):
183183 The number of samples (or total weight) in a neighborhood for a point
184184 to be considered as a core point. This includes the point itself.
185185
186- metric : string , or callable, default='euclidean'
186+ metric : str , or callable, default='euclidean'
187187 The metric to use when calculating distance between instances in a
188188 feature array. If metric is a string or callable, it must be one of
189189 the options allowed by :func:`sklearn.metrics.pairwise_distances` for
@@ -239,18 +239,6 @@ class DBSCAN(ClusterMixin, BaseEstimator):
239239
240240 .. versionadded:: 0.24
241241
242- Examples
243- --------
244- >>> from sklearn.cluster import DBSCAN
245- >>> import numpy as np
246- >>> X = np.array([[1, 2], [2, 2], [2, 3],
247- ... [8, 7], [8, 8], [25, 80]])
248- >>> clustering = DBSCAN(eps=3, min_samples=2).fit(X)
249- >>> clustering.labels_
250- array([ 0, 0, 0, 1, 1, -1])
251- >>> clustering
252- DBSCAN(eps=3, min_samples=2)
253-
254242 See Also
255243 --------
256244 OPTICS : A similar clustering at multiple values of eps. Our implementation
@@ -289,6 +277,18 @@ class DBSCAN(ClusterMixin, BaseEstimator):
289277 Schubert, E., Sander, J., Ester, M., Kriegel, H. P., & Xu, X. (2017).
290278 DBSCAN revisited, revisited: why and how you should (still) use DBSCAN.
291279 ACM Transactions on Database Systems (TODS), 42(3), 19.
280+
281+ Examples
282+ --------
283+ >>> from sklearn.cluster import DBSCAN
284+ >>> import numpy as np
285+ >>> X = np.array([[1, 2], [2, 2], [2, 3],
286+ ... [8, 7], [8, 8], [25, 80]])
287+ >>> clustering = DBSCAN(eps=3, min_samples=2).fit(X)
288+ >>> clustering.labels_
289+ array([ 0, 0, 0, 1, 1, -1])
290+ >>> clustering
291+ DBSCAN(eps=3, min_samples=2)
292292 """
293293
294294 def __init__ (
@@ -323,19 +323,19 @@ def fit(self, X, y=None, sample_weight=None):
323323 ``metric='precomputed'``. If a sparse matrix is provided, it will
324324 be converted into a sparse ``csr_matrix``.
325325
326+ y : Ignored
327+ Not used, present here for API consistency by convention.
328+
326329 sample_weight : array-like of shape (n_samples,), default=None
327330 Weight of each sample, such that a sample with a weight of at least
328331 ``min_samples`` is by itself a core sample; a sample with a
329332 negative weight may inhibit its eps-neighbor from being core.
330333 Note that weights are absolute, and default to 1.
331334
332- y : Ignored
333- Not used, present here for API consistency by convention.
334-
335335 Returns
336336 -------
337- self
338-
337+ self : object
338+ Returns a fitted instance of self.
339339 """
340340 X = self ._validate_data (X , accept_sparse = "csr" )
341341
@@ -394,8 +394,7 @@ def fit(self, X, y=None, sample_weight=None):
394394 return self
395395
396396 def fit_predict (self , X , y = None , sample_weight = None ):
397- """Perform DBSCAN clustering from features or distance matrix,
398- and return cluster labels.
397+ """Compute clusters from a data or distance matrix and predict labels.
399398
400399 Parameters
401400 ----------
@@ -405,15 +404,15 @@ def fit_predict(self, X, y=None, sample_weight=None):
405404 ``metric='precomputed'``. If a sparse matrix is provided, it will
406405 be converted into a sparse ``csr_matrix``.
407406
407+ y : Ignored
408+ Not used, present here for API consistency by convention.
409+
408410 sample_weight : array-like of shape (n_samples,), default=None
409411 Weight of each sample, such that a sample with a weight of at least
410412 ``min_samples`` is by itself a core sample; a sample with a
411413 negative weight may inhibit its eps-neighbor from being core.
412414 Note that weights are absolute, and default to 1.
413415
414- y : Ignored
415- Not used, present here for API consistency by convention.
416-
417416 Returns
418417 -------
419418 labels : ndarray of shape (n_samples,)
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