## Mercurial > repos > shellac > sam_consensus_v3

### view env/lib/python3.9/site-packages/networkx/algorithms/shortest_paths/dense.py @ 0:4f3585e2f14b draft default tip

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"planemo upload commit 60cee0fc7c0cda8592644e1aad72851dec82c959"

author | shellac |
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date | Mon, 22 Mar 2021 18:12:50 +0000 |

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"""Floyd-Warshall algorithm for shortest paths. """ import networkx as nx __all__ = [ "floyd_warshall", "floyd_warshall_predecessor_and_distance", "reconstruct_path", "floyd_warshall_numpy", ] def floyd_warshall_numpy(G, nodelist=None, weight="weight"): """Find all-pairs shortest path lengths using Floyd's algorithm. Parameters ---------- G : NetworkX graph nodelist : list, optional The rows and columns are ordered by the nodes in nodelist. If nodelist is None then the ordering is produced by G.nodes(). weight: string, optional (default= 'weight') Edge data key corresponding to the edge weight. Returns ------- distance : NumPy matrix A matrix of shortest path distances between nodes. If there is no path between to nodes the corresponding matrix entry will be Inf. Notes ------ Floyd's algorithm is appropriate for finding shortest paths in dense graphs or graphs with negative weights when Dijkstra's algorithm fails. This algorithm can still fail if there are negative cycles. It has running time $O(n^3)$ with running space of $O(n^2)$. """ try: import numpy as np except ImportError as e: raise ImportError("to_numpy_array() requires numpy: http://numpy.org/ ") from e # To handle cases when an edge has weight=0, we must make sure that # nonedges are not given the value 0 as well. A = nx.to_numpy_array( G, nodelist=nodelist, multigraph_weight=min, weight=weight, nonedge=np.inf ) n, m = A.shape np.fill_diagonal(A, 0) # diagonal elements should be zero for i in range(n): # The second term has the same shape as A due to broadcasting A = np.minimum(A, A[i, :][np.newaxis, :] + A[:, i][:, np.newaxis]) return A def floyd_warshall_predecessor_and_distance(G, weight="weight"): """Find all-pairs shortest path lengths using Floyd's algorithm. Parameters ---------- G : NetworkX graph weight: string, optional (default= 'weight') Edge data key corresponding to the edge weight. Returns ------- predecessor,distance : dictionaries Dictionaries, keyed by source and target, of predecessors and distances in the shortest path. Examples -------- >>> G = nx.DiGraph() >>> G.add_weighted_edges_from( ... [ ... ("s", "u", 10), ... ("s", "x", 5), ... ("u", "v", 1), ... ("u", "x", 2), ... ("v", "y", 1), ... ("x", "u", 3), ... ("x", "v", 5), ... ("x", "y", 2), ... ("y", "s", 7), ... ("y", "v", 6), ... ] ... ) >>> predecessors, _ = nx.floyd_warshall_predecessor_and_distance(G) >>> print(nx.reconstruct_path("s", "v", predecessors)) ['s', 'x', 'u', 'v'] Notes ------ Floyd's algorithm is appropriate for finding shortest paths in dense graphs or graphs with negative weights when Dijkstra's algorithm fails. This algorithm can still fail if there are negative cycles. It has running time $O(n^3)$ with running space of $O(n^2)$. See Also -------- floyd_warshall floyd_warshall_numpy all_pairs_shortest_path all_pairs_shortest_path_length """ from collections import defaultdict # dictionary-of-dictionaries representation for dist and pred # use some defaultdict magick here # for dist the default is the floating point inf value dist = defaultdict(lambda: defaultdict(lambda: float("inf"))) for u in G: dist[u][u] = 0 pred = defaultdict(dict) # initialize path distance dictionary to be the adjacency matrix # also set the distance to self to 0 (zero diagonal) undirected = not G.is_directed() for u, v, d in G.edges(data=True): e_weight = d.get(weight, 1.0) dist[u][v] = min(e_weight, dist[u][v]) pred[u][v] = u if undirected: dist[v][u] = min(e_weight, dist[v][u]) pred[v][u] = v for w in G: dist_w = dist[w] # save recomputation for u in G: dist_u = dist[u] # save recomputation for v in G: d = dist_u[w] + dist_w[v] if dist_u[v] > d: dist_u[v] = d pred[u][v] = pred[w][v] return dict(pred), dict(dist) def reconstruct_path(source, target, predecessors): """Reconstruct a path from source to target using the predecessors dict as returned by floyd_warshall_predecessor_and_distance Parameters ---------- source : node Starting node for path target : node Ending node for path predecessors: dictionary Dictionary, keyed by source and target, of predecessors in the shortest path, as returned by floyd_warshall_predecessor_and_distance Returns ------- path : list A list of nodes containing the shortest path from source to target If source and target are the same, an empty list is returned Notes ------ This function is meant to give more applicability to the floyd_warshall_predecessor_and_distance function See Also -------- floyd_warshall_predecessor_and_distance """ if source == target: return [] prev = predecessors[source] curr = prev[target] path = [target, curr] while curr != source: curr = prev[curr] path.append(curr) return list(reversed(path)) def floyd_warshall(G, weight="weight"): """Find all-pairs shortest path lengths using Floyd's algorithm. Parameters ---------- G : NetworkX graph weight: string, optional (default= 'weight') Edge data key corresponding to the edge weight. Returns ------- distance : dict A dictionary, keyed by source and target, of shortest paths distances between nodes. Notes ------ Floyd's algorithm is appropriate for finding shortest paths in dense graphs or graphs with negative weights when Dijkstra's algorithm fails. This algorithm can still fail if there are negative cycles. It has running time $O(n^3)$ with running space of $O(n^2)$. See Also -------- floyd_warshall_predecessor_and_distance floyd_warshall_numpy all_pairs_shortest_path all_pairs_shortest_path_length """ # could make this its own function to reduce memory costs return floyd_warshall_predecessor_and_distance(G, weight=weight)[1]