`scipy.sparse.csgraph._tools:construct_dist_matrix`

Summary

Construct distance matrix from a predecessor matrix

Extended Summary

Parameters

graph : array_like or sparse: The N x N matrix representation of a directed or undirected graph. If dense, then non-edges are indicated by zeros or infinities.
predecessors : array_like: The N x N matrix of predecessors of each node (see Notes below).
directed : bool, optional: If True (default), then operate on a directed graph: only move from point i to point j along paths csgraph[i, j]. If False, then operate on an undirected graph: the algorithm can progress from point i to j along csgraph[i, j] or csgraph[j, i].
null_value : bool, optional: value to use for distances between unconnected nodes. Default is np.inf

Returns

dist_matrix : ndarray: The N x N matrix of distances between nodes along the path specified by the predecessor matrix. If no path exists, the distance is zero.

Notes

The predecessor matrix is of the form optionally returned by shortest_path. Row i of the predecessor matrix contains information on the shortest paths from point i: each entry predecessors[i, j] gives the index of the previous node in the path from point i to point j. If no path exists between point i and j, then predecessors[i, j] = -9999

It should be noted that shortest_path only returns distance matrix by default. With return_predecessors=True, it returns a tuple with distance matrix as its first element and predecessors array as second element.

Examples

import numpy as np
from scipy.sparse import csr_array
from scipy.sparse.csgraph import construct_dist_matrix

✓

graph = [
[0, 1, 2, 0],
[0, 0, 0, 1],
[0, 0, 0, 3],
[0, 0, 0, 0]
]
graph = csr_array(graph)

✓

print(graph)

✗

pred = np.array([[-9999, 0, 0, 2],
                 [1, -9999, 0, 1],
                 [2, 0, -9999, 2],
                 [1, 3, 3, -9999]], dtype=np.int32)

✓

construct_dist_matrix(graph=graph, predecessors=pred, directed=False)

✓

Aliases

scipy.sparse.csgraph.construct_dist_matrix