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reconstruct_path(csgraph, predecessors, directed=True)
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Parameters

csgraph : array_like or sparse matrix

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

predecessors : array_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].

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].

Returns

cstree : csr matrix

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

Construct a tree from a graph and a predecessor list.

Examples

>>> from scipy.sparse import csr_matrix
... 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_matrix(graph)
... print(graph) (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() matrix([[0., 1., 2., 0.], [0., 0., 0., 1.], [0., 0., 0., 0.], [0., 0., 0., 0.]])
See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

scipy.sparse.csgraph._tools.reconstruct_path

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