reconstruct_path — SciPy v1.15.3 Manual (original) (raw)

scipy.sparse.csgraph.

scipy.sparse.csgraph.reconstruct_path(csgraph, predecessors, directed=True)#

Construct a tree from a graph and a predecessor list.

Added in version 0.11.0.

Parameters:

csgrapharray_like or sparse array or matrix

The N x N matrix representing the directed or undirected graph from which the predecessors are drawn.

predecessorsarray_like, one dimension

The length-N array of indices of predecessors for the tree. The index of the parent of node i is given by predecessors[i].

directedbool, 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].

Returns:

cstreecsr matrix

The N x N directed compressed-sparse representation of the tree drawn from csgraph which is encoded by the predecessor list.

Examples

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

graph = [ ... [0, 1, 2, 0], ... [0, 0, 0, 1], ... [0, 0, 0, 3], ... [0, 0, 0, 0] ... ] graph = csr_array(graph) print(graph) <Compressed Sparse Row sparse array of dtype 'int64' with 4 stored elements and shape (4, 4)> Coords Values (0, 1) 1 (0, 2) 2 (1, 3) 1 (2, 3) 3

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

cstree = reconstruct_path(csgraph=graph, predecessors=pred, directed=False) cstree.todense() array([[0., 1., 2., 0.], [0., 0., 0., 1.], [0., 0., 0., 0.], [0., 0., 0., 0.]])