65 lines
1.8 KiB
Python
65 lines
1.8 KiB
Python
from collections import defaultdict
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import numpy as np
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from numpy.testing import assert_array_almost_equal
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from sklearn.utils.graph import single_source_shortest_path_length
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def floyd_warshall_slow(graph, directed=False):
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N = graph.shape[0]
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# set nonzero entries to infinity
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graph[np.where(graph == 0)] = np.inf
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# set diagonal to zero
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graph.flat[:: N + 1] = 0
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if not directed:
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graph = np.minimum(graph, graph.T)
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for k in range(N):
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for i in range(N):
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for j in range(N):
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graph[i, j] = min(graph[i, j], graph[i, k] + graph[k, j])
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graph[np.where(np.isinf(graph))] = 0
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return graph
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def generate_graph(N=20):
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# sparse grid of distances
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rng = np.random.RandomState(0)
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dist_matrix = rng.random_sample((N, N))
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# make symmetric: distances are not direction-dependent
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dist_matrix = dist_matrix + dist_matrix.T
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# make graph sparse
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i = (rng.randint(N, size=N * N // 2), rng.randint(N, size=N * N // 2))
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dist_matrix[i] = 0
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# set diagonal to zero
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dist_matrix.flat[:: N + 1] = 0
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return dist_matrix
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def test_shortest_path():
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dist_matrix = generate_graph(20)
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# We compare path length and not costs (-> set distances to 0 or 1)
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dist_matrix[dist_matrix != 0] = 1
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for directed in (True, False):
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if not directed:
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dist_matrix = np.minimum(dist_matrix, dist_matrix.T)
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graph_py = floyd_warshall_slow(dist_matrix.copy(), directed)
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for i in range(dist_matrix.shape[0]):
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# Non-reachable nodes have distance 0 in graph_py
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dist_dict = defaultdict(int)
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dist_dict.update(single_source_shortest_path_length(dist_matrix, i))
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for j in range(graph_py[i].shape[0]):
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assert_array_almost_equal(dist_dict[j], graph_py[i, j])
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