scipy.sparse.csgraph.construct_dist_matrix

scipy.sparse.csgraph.construct_dist_matrix(graph, predecessors, directed=True, null_value=np.inf)

Construct distance matrix from a predecessor matrix

New in version 0.11.0.

Parameters

grapharray_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.

predecessorsarray_like

The N x N matrix of predecessors of each node (see Notes below).

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].

null_valuebool, optional

value to use for distances between unconnected nodes. Default is np.inf

Returns

dist_matrixndarray

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 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

Examples

>>> 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.]])

scipy.sparse.csgraph.NegativeCycleError scipy.sparse.csgraph.csgraph_from_dense