construct_dist_matrix(graph, predecessors, directed=True, null_value=np.inf)
The predecessor matrix is of the form 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
The N x N matrix representation of a directed or undirected graph. If dense, then non-edges are indicated by zeros or infinities.
The N x N matrix of predecessors of each node (see Notes below).
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].
value to use for distances between unconnected nodes. Default is np.inf
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.
Construct distance matrix from a predecessor matrix
>>> from scipy.sparse import csr_matrix
... 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_matrix(graph)
... print(graph) (0, 1) 1 (0, 2) 2 (1, 3) 1 (2, 3) 3
>>> 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) array([[0., 1., 2., 5.], [1., 0., 3., 1.], [2., 3., 0., 3.], [2., 1., 3., 0.]])See :
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scipy.sparse.csgraph._tools.construct_dist_matrix
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