U àd¨i¾ã@s@dZddlZddlmZddlmZddœdd„Zd d d „ZdS)zy Graph utilities and algorithms Graphs are represented with their adjacency matrices, preferably using sparse matrices. éN)Úsparseé)Úpairwise_distances)ÚcutoffcCs„t |¡r| ¡}n t |¡}i}d}|g}|r€|}tƒ}|D]$}||kr>|||<| |j|¡q>|dk rv||krvq€|d7}q,|S)aAReturn the length of the shortest path from source to all reachable nodes. Parameters ---------- graph : {sparse matrix, ndarray} of shape (n_nodes, n_nodes) Adjacency matrix of the graph. Sparse matrix of format LIL is preferred. source : int Start node for path. cutoff : int, default=None Depth to stop the search - only paths of length <= cutoff are returned. Returns ------- paths : dict Reachable end nodes mapped to length of path from source, i.e. `{end: path_length}`. Examples -------- >>> from sklearn.utils.graph import single_source_shortest_path_length >>> import numpy as np >>> graph = np.array([[ 0, 1, 0, 0], ... [ 1, 0, 1, 0], ... [ 0, 1, 0, 0], ... [ 0, 0, 0, 0]]) >>> single_source_shortest_path_length(graph, 0) {0: 0, 1: 1, 2: 2} >>> graph = np.ones((6, 6)) >>> sorted(single_source_shortest_path_length(graph, 2).items()) [(0, 1), (1, 1), (2, 0), (3, 1), (4, 1), (5, 1)] rNé)rÚissparseZtolilZ lil_matrixÚsetÚupdateZrows)ÚgraphÚsourcerÚseenÚlevelZ next_levelZ this_levelÚv©rú7/tmp/pip-unpacked-wheel-vgbd3m5j/sklearn/utils/graph.pyÚ"single_source_shortest_path_lengths"#    rÚdistanceÚ euclideancKs4|dkrt |¡rtdƒ‚t|ƒD] }t ||k¡}||} t|ƒD]æ} t || k¡} || } |dkrz|t || ¡} nt| | fd|i|—Ž} t | j dd| j ¡\}}|dkrÜd|||| |f<d|| |||f<qF|dkr | ||f|||| |f<| ||f|| |||f<qFt d |ƒ‚qFq"|S) a Add connections to sparse graph to connect unconnected components. For each pair of unconnected components, compute all pairwise distances from one component to the other, and add a connection on the closest pair of samples. This is a hacky way to get a graph with a single connected component, which is necessary for example to compute a shortest path between all pairs of samples in the graph. Parameters ---------- X : array of shape (n_samples, n_features) or (n_samples, n_samples) Features to compute the pairwise distances. If `metric = "precomputed"`, X is the matrix of pairwise distances. graph : sparse matrix of shape (n_samples, n_samples) Graph of connection between samples. n_connected_components : int Number of connected components, as computed by `scipy.sparse.csgraph.connected_components`. component_labels : array of shape (n_samples) Labels of connected components, as computed by `scipy.sparse.csgraph.connected_components`. mode : {'connectivity', 'distance'}, default='distance' Type of graph matrix: 'connectivity' corresponds to the connectivity matrix with ones and zeros, and 'distance' corresponds to the distances between neighbors according to the given metric. metric : str Metric used in `sklearn.metrics.pairwise.pairwise_distances`. kwargs : kwargs Keyword arguments passed to `sklearn.metrics.pairwise.pairwise_distances`. Returns ------- graph : sparse matrix of shape (n_samples, n_samples) Graph of connection between samples, with a single connected component. Z precomputedzŒ_fix_connected_components with metric='precomputed' requires the full distance matrix in X, and does not work with a sparse neighbors graph.ÚmetricN)ZaxisZ connectivityrrz?Unknown mode=%r, should be one of ['connectivity', 'distance'].) rrÚ RuntimeErrorÚrangeÚnpZ flatnonzeroZix_rZ unravel_indexZargminÚshapeÚ ValueError)ÚXr Zn_connected_componentsZcomponent_labelsÚmoderÚkwargsÚiZidx_iÚXiÚjZidx_jZXjÚDÚiiZjjrrrÚ_fix_connected_componentsMs43ÿ  ÿÿr")rr) Ú__doc__ZnumpyrZscipyrZmetrics.pairwiserrr"rrrrÚs   <ú