-i#` SrSSKrSSKJrJrJr SSKJrJrJ r J r J r SSK J r SSKJrJrJrJr SS KJr S rS r\ "S S \/\"\SSSS9/\ "SS15/\ "\"\R0"\R2"5655\/\"\ SSSS9S/\S/S\ "S15/\S/S.SS9SSSSSSS.Sj5r\ "S S \/\"\ SSSS9/\ "SS15/\ "\"\R0"\R2"5655\/\"\ SSSS9S/\S/S\ "S15/\S/S.SS9SSSSSSS.Sj5r"SS\\\\5r"S S!\\\\5rg)"z!Nearest Neighbors graph functionsN)ClassNamePrefixFeaturesOutMixinTransformerMixin _fit_context)IntegralIntervalReal StrOptionsvalidate_params)check_is_fitted) VALID_METRICSKNeighborsMixin NeighborsBaseRadiusNeighborsMixin)NearestNeighborsc [/SQXU/5nUR5nUH'upgXuU:wdM[SU<SU<SXV<S35e g)z*Check the validity of the input parameters)metricp metric_paramszGot z for z, while the estimator has z for the same parameter.N)zip get_params ValueError)Xrrrparams est_params param_name func_params K/var/www/html/venv/lib/python3.13/site-packages/sklearn/neighbors/_graph.py _check_paramsr sT 1F}3M NFJ"( J/ /z:+AC #)c.US:XaUS:HnU(dSnU$)z,Return the query based on include_self paramauto connectivityN)r include_selfmodes r_query_include_selfr(!s#v~-    Hr!z array-likez sparse matrixleft)closedr$distancerightbooleanr#)r n_neighborsr'rrrr&n_jobsFprefer_skip_nested_validation minkowski)r'rrrr&r/c[U[5(d[UUUUUS9RU5nO [ XXE5 [ UR Xb5nURXUS9$)a Compute the (weighted) graph of k-Neighbors for points in X. Read more in the :ref:`User Guide `. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Sample data. n_neighbors : int Number of neighbors for each sample. mode : {'connectivity', 'distance'}, default='connectivity' Type of returned matrix: 'connectivity' will return the connectivity matrix with ones and zeros, and 'distance' will return the distances between neighbors according to the given metric. metric : str, default='minkowski' Metric to use for distance computation. Default is "minkowski", which results in the standard Euclidean distance when p = 2. See the documentation of `scipy.spatial.distance `_ and the metrics listed in :class:`~sklearn.metrics.pairwise.distance_metrics` for valid metric values. p : float, default=2 Power parameter for the Minkowski metric. When p = 1, this is equivalent to using manhattan_distance (l1), and euclidean_distance (l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used. This parameter is expected to be positive. metric_params : dict, default=None Additional keyword arguments for the metric function. include_self : bool or 'auto', default=False Whether or not to mark each sample as the first nearest neighbor to itself. If 'auto', then True is used for mode='connectivity' and False for mode='distance'. 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. Returns ------- A : sparse matrix of shape (n_samples, n_samples) Graph where A[i, j] is assigned the weight of edge that connects i to j. The matrix is of CSR format. See Also -------- radius_neighbors_graph: Compute the (weighted) graph of Neighbors for points in X. Examples -------- >>> X = [[0], [3], [1]] >>> from sklearn.neighbors import kneighbors_graph >>> A = kneighbors_graph(X, 2, mode='connectivity', include_self=True) >>> A.toarray() array([[1., 0., 1.], [0., 1., 1.], [1., 0., 1.]]) )r.rrrr/)rr.r') isinstancerrfitr r(_fit_Xkneighbors_graph) rr.r'rrrr&r/querys rr7r7-skt a ) ) #'  #a&  a2 , =E  T  JJr!both)rradiusr'rrrr&r/c[U[5(d[UUUUUS9RU5nO [ XXE5 [ UR Xb5nURXU5$)aE Compute the (weighted) graph of Neighbors for points in X. Neighborhoods are restricted the points at a distance lower than radius. Read more in the :ref:`User Guide `. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Sample data. radius : float Radius of neighborhoods. mode : {'connectivity', 'distance'}, default='connectivity' Type of returned matrix: 'connectivity' will return the connectivity matrix with ones and zeros, and 'distance' will return the distances between neighbors according to the given metric. metric : str, default='minkowski' Metric to use for distance computation. Default is "minkowski", which results in the standard Euclidean distance when p = 2. See the documentation of `scipy.spatial.distance `_ and the metrics listed in :class:`~sklearn.metrics.pairwise.distance_metrics` for valid metric values. p : float, default=2 Power parameter for the Minkowski metric. When p = 1, this is equivalent to using manhattan_distance (l1), and euclidean_distance (l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used. metric_params : dict, default=None Additional keyword arguments for the metric function. include_self : bool or 'auto', default=False Whether or not to mark each sample as the first nearest neighbor to itself. If 'auto', then True is used for mode='connectivity' and False for mode='distance'. 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. Returns ------- A : sparse matrix of shape (n_samples, n_samples) Graph where A[i, j] is assigned the weight of edge that connects i to j. The matrix is of CSR format. See Also -------- kneighbors_graph: Compute the weighted graph of k-neighbors for points in X. Examples -------- >>> X = [[0], [3], [1]] >>> from sklearn.neighbors import radius_neighbors_graph >>> A = radius_neighbors_graph(X, 1.5, mode='connectivity', ... include_self=True) >>> A.toarray() array([[1., 0., 1.], [0., 1., 0.], [1., 0., 1.]]) )r:rrrr/)r4rrr5r r(r6radius_neighbors_graph) rr:r'rrrr&r/r8s rr<r<siz a- . . '  #a&  a2 , =E # #E4 88r!c ^\rSrSr%Sr0\R ES\"SS15/0Er\\ S'\RS5 SSS S S S S S S.U4Sjjr \ "SS9SSj5r SrSSjrSrU=r$)KNeighborsTransformeriaTransform X into a (weighted) graph of k nearest neighbors. The transformed data is a sparse graph as returned by kneighbors_graph. Read more in the :ref:`User Guide `. .. versionadded:: 0.22 Parameters ---------- mode : {'distance', 'connectivity'}, default='distance' Type of returned matrix: 'connectivity' will return the connectivity matrix with ones and zeros, and 'distance' will return the distances between neighbors according to the given metric. n_neighbors : int, default=5 Number of neighbors for each sample in the transformed sparse graph. For compatibility reasons, as each sample is considered as its own neighbor, one extra neighbor will be computed when mode == 'distance'. In this case, the sparse graph contains (n_neighbors + 1) neighbors. algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, default='auto' Algorithm used to compute the nearest neighbors: - 'ball_tree' will use :class:`BallTree` - 'kd_tree' will use :class:`KDTree` - 'brute' will use a brute-force search. - 'auto' will attempt to decide the most appropriate algorithm based on the values passed to :meth:`fit` method. Note: fitting on sparse input will override the setting of this parameter, using brute force. leaf_size : int, default=30 Leaf size passed to BallTree or KDTree. 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. metric : str or callable, default='minkowski' Metric to use for distance computation. Default is "minkowski", which results in the standard Euclidean distance when p = 2. See the documentation of `scipy.spatial.distance `_ and the metrics listed in :class:`~sklearn.metrics.pairwise.distance_metrics` for valid metric values. If metric is a callable function, it takes two arrays representing 1D vectors as inputs and must return one value indicating the distance between those vectors. This works for Scipy's metrics, but is less efficient than passing the metric name as a string. Distance matrices are not supported. p : float, default=2 Parameter for the Minkowski metric from sklearn.metrics.pairwise.pairwise_distances. When p = 1, this is equivalent to using manhattan_distance (l1), and euclidean_distance (l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used. This parameter is expected to be positive. metric_params : dict, default=None Additional keyword arguments for the metric function. n_jobs : int, default=None 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. Attributes ---------- effective_metric_ : str or callable The distance metric used. It will be same as the `metric` parameter or a synonym of it, e.g. 'euclidean' if the `metric` parameter set to 'minkowski' and `p` parameter set to 2. effective_metric_params_ : dict Additional keyword arguments for the metric function. For most metrics will be same with `metric_params` parameter, but may also contain the `p` parameter value if the `effective_metric_` attribute is set to 'minkowski'. 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 n_samples_fit_ : int Number of samples in the fitted data. See Also -------- kneighbors_graph : Compute the weighted graph of k-neighbors for points in X. RadiusNeighborsTransformer : Transform X into a weighted graph of neighbors nearer than a radius. Notes ----- For an example of using :class:`~sklearn.neighbors.KNeighborsTransformer` in combination with :class:`~sklearn.manifold.TSNE` see :ref:`sphx_glr_auto_examples_neighbors_approximate_nearest_neighbors.py`. Examples -------- >>> from sklearn.datasets import load_wine >>> from sklearn.neighbors import KNeighborsTransformer >>> X, _ = load_wine(return_X_y=True) >>> X.shape (178, 13) >>> transformer = KNeighborsTransformer(n_neighbors=5, mode='distance') >>> X_dist_graph = transformer.fit_transform(X) >>> X_dist_graph.shape (178, 178) r'r+r$_parameter_constraintsr:r#r2rN)r'r. algorithm leaf_sizerrrr/c :>[T U]USUUUUUUS9 XlgN)r.r:rBrCrrrr/super__init__r') selfr'r.rBrCrrrr/ __class__s rrHKNeighborsTransformer.__init__s6 #'   r!Fr0cJURU5 URUlU$)aFit the k-nearest neighbors transformer from the training dataset. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) or (n_samples, n_samples) if metric='precomputed' Training data. y : Ignored Not used, present for API consistency by convention. Returns ------- self : KNeighborsTransformer The fitted k-nearest neighbors transformer. _fitn_samples_fit__n_features_outrIrys rr5KNeighborsTransformer.fits"( ! #22 r!c[U5 URS:HnURXRURU-S9$)Compute the (weighted) graph of Neighbors for points in X. Parameters ---------- X : array-like of shape (n_samples_transform, n_features) Sample data. Returns ------- Xt : sparse matrix of shape (n_samples_transform, n_samples_fit) Xt[i, j] is assigned the weight of edge that connects i to j. Only the neighbors have an explicit value. The diagonal is always explicit. The matrix is of CSR format. r+)r'r.)r r'r7r.)rIradd_ones r transformKNeighborsTransformer.transformsF ))z)$$ II4+;+;g+E%  r!cBURU5RU5$a#Fit to data, then transform it. Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X. Parameters ---------- X : array-like of shape (n_samples, n_features) Training set. y : Ignored Not used, present for API consistency by convention. Returns ------- Xt : sparse matrix of shape (n_samples, n_samples) Xt[i, j] is assigned the weight of edge that connects i to j. Only the neighbors have an explicit value. The diagonal is always explicit. The matrix is of CSR format. r5rWrQs r fit_transform#KNeighborsTransformer.fit_transform,xx{$$Q''r!rPr'N__name__ __module__ __qualname____firstlineno____doc__rr?r dict__annotations__poprHrr5rWr\__static_attributes__ __classcell__rJs@rr>r>sxt$  . .$Z89:$Dx(  0&+ ( ,((r!r>c ^\rSrSr%Sr0\R ES\"SS15/0Er\\ S'\RS5 SSS S S S S S S.U4Sjjr \ "SS9SSj5r SrSSjrSrU=r$)RadiusNeighborsTransformeriaTransform X into a (weighted) graph of neighbors nearer than a radius. The transformed data is a sparse graph as returned by `radius_neighbors_graph`. Read more in the :ref:`User Guide `. .. versionadded:: 0.22 Parameters ---------- mode : {'distance', 'connectivity'}, default='distance' Type of returned matrix: 'connectivity' will return the connectivity matrix with ones and zeros, and 'distance' will return the distances between neighbors according to the given metric. radius : float, default=1.0 Radius of neighborhood in the transformed sparse graph. algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, default='auto' Algorithm used to compute the nearest neighbors: - 'ball_tree' will use :class:`BallTree` - 'kd_tree' will use :class:`KDTree` - 'brute' will use a brute-force search. - 'auto' will attempt to decide the most appropriate algorithm based on the values passed to :meth:`fit` method. Note: fitting on sparse input will override the setting of this parameter, using brute force. leaf_size : int, default=30 Leaf size passed to BallTree or KDTree. 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. metric : str or callable, default='minkowski' Metric to use for distance computation. Default is "minkowski", which results in the standard Euclidean distance when p = 2. See the documentation of `scipy.spatial.distance `_ and the metrics listed in :class:`~sklearn.metrics.pairwise.distance_metrics` for valid metric values. If metric is a callable function, it takes two arrays representing 1D vectors as inputs and must return one value indicating the distance between those vectors. This works for Scipy's metrics, but is less efficient than passing the metric name as a string. Distance matrices are not supported. p : float, default=2 Parameter for the Minkowski metric from sklearn.metrics.pairwise.pairwise_distances. When p = 1, this is equivalent to using manhattan_distance (l1), and euclidean_distance (l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used. This parameter is expected to be positive. metric_params : dict, default=None Additional keyword arguments for the metric function. n_jobs : int, default=None 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. Attributes ---------- effective_metric_ : str or callable The distance metric used. It will be same as the `metric` parameter or a synonym of it, e.g. 'euclidean' if the `metric` parameter set to 'minkowski' and `p` parameter set to 2. effective_metric_params_ : dict Additional keyword arguments for the metric function. For most metrics will be same with `metric_params` parameter, but may also contain the `p` parameter value if the `effective_metric_` attribute is set to 'minkowski'. 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 n_samples_fit_ : int Number of samples in the fitted data. See Also -------- kneighbors_graph : Compute the weighted graph of k-neighbors for points in X. KNeighborsTransformer : Transform X into a weighted graph of k nearest neighbors. Examples -------- >>> import numpy as np >>> from sklearn.datasets import load_wine >>> from sklearn.cluster import DBSCAN >>> from sklearn.neighbors import RadiusNeighborsTransformer >>> from sklearn.pipeline import make_pipeline >>> X, _ = load_wine(return_X_y=True) >>> estimator = make_pipeline( ... RadiusNeighborsTransformer(radius=42.0, mode='distance'), ... DBSCAN(eps=25.0, metric='precomputed')) >>> X_clustered = estimator.fit_predict(X) >>> clusters, counts = np.unique(X_clustered, return_counts=True) >>> print(counts) [ 29 15 111 11 12] r'r+r$r?r.g?r#rAr2rN)r'r:rBrCrrrr/c :>[T U]SUUUUUUUS9 XlgrErF) rIr'r:rBrCrrrr/rJs rrH#RadiusNeighborsTransformer.__init__fs6 '   r!Fr0cJURU5 URUlU$)aFit the radius neighbors transformer from the training dataset. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) or (n_samples, n_samples) if metric='precomputed' Training data. y : Ignored Not used, present for API consistency by convention. 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