chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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import unittest
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import easygraph as eg
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class test_mst(unittest.TestCase):
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def setUp(self):
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self.g1 = eg.get_graph_karateclub()
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# source graph: https://zh.wikipedia.org/zh-cn/%E6%88%B4%E5%85%8B%E6%96%AF%E7%89%B9%E6%8B%89%E7%AE%97%E6%B3%95#/media/File:Dijkstra_Animation.gif
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edges = [(1, 2), (1, 3), (1, 6), (2, 3), (2, 4), (3, 4), (3, 6), (4, 5), (5, 6)]
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self.g2 = eg.Graph(edges)
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self.g2.add_edges(
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edges,
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edges_attr=[
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{"weight": 7},
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{"weight": 9},
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{"weight": 14},
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{"weight": 10},
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{"weight": 15},
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{"weight": 11},
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{"weight": 2},
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{"weight": 6},
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{"weight": 9},
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],
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)
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# source graph: https://static.javatpoint.com/tutorial/daa/images/dijkstra-algorithm.png
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self.g3 = eg.Graph()
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edges = [
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(0, 1),
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(0, 4),
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(1, 4),
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(1, 2),
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(4, 5),
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(4, 8),
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(2, 3),
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(2, 6),
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(2, 8),
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(5, 6),
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(5, 8),
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(3, 6),
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(3, 7),
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(6, 7),
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]
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self.g3.add_edges(
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edges,
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edges_attr=[
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{"weight": 4},
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{"weight": 1},
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{"weight": 11},
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{"weight": 8},
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{"weight": 1},
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{"weight": 7},
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{"weight": 7},
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{"weight": 4},
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{"weight": 2},
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{"weight": 2},
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{"weight": 6},
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{"weight": 14},
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{"weight": 9},
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{"weight": 10},
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],
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)
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self.g4 = eg.DiGraph()
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edges = [(0, 1), (1, 2), (2, 3), (3, 0), (0, 2), (1, 3)]
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self.g4.add_edges(
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edges,
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edges_attr=[
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{"weight": -1},
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{"weight": -2},
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{"weight": -3},
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{"weight": -4},
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{"weight": -5},
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{"weight": -6},
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],
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)
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self.nan_graph = eg.Graph()
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self.nan_graph.add_edges(
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[(0, 1), (1, 2)], edges_attr=[{"weight": float("nan")}, {"weight": 1}]
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)
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self.no_weight_graph = eg.Graph()
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self.no_weight_graph.add_edges([(0, 1), (1, 2)])
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self.equal_weight_graph = eg.Graph()
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self.equal_weight_graph.add_edges(
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[(0, 1), (1, 2), (2, 0)],
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edges_attr=[{"weight": 1}, {"weight": 1}, {"weight": 1}],
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)
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self.negative_weight_graph = eg.Graph()
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self.negative_weight_graph.add_edges(
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[(0, 1), (1, 2), (2, 3)],
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edges_attr=[{"weight": -1}, {"weight": -2}, {"weight": -3}],
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)
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self.disconnected_graph = eg.Graph()
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self.disconnected_graph.add_edges(
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[(0, 1), (2, 3)], edges_attr=[{"weight": 1}, {"weight": 2}]
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)
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self.G = eg.Graph()
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self.G.add_edges(
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[(0, 1), (1, 2), (2, 3), (3, 0)],
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edges_attr=[{"weight": 1}, {"weight": 2}, {"weight": 3}, {"weight": 4}],
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)
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def helper(self, g: eg.Graph, func):
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result = func(g)
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if isinstance(result, eg.Graph):
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print("nodes: " + str(result.nodes))
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print("edges: " + str(result.edges))
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else:
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for i in result:
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print(i)
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def test_minimum_spanning_edges(self):
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print("test_minimum_spanning_edges")
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self.helper(self.g2, eg.minimum_spanning_edges)
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self.helper(self.g2, eg.minimum_spanning_edges)
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self.helper(self.g4, eg.minimum_spanning_edges)
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def test_maximum_spanning_edges(self):
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print("test_maximum_spanning_edges")
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self.helper(self.g2, eg.maximum_spanning_edges)
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self.helper(self.g2, eg.maximum_spanning_edges)
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self.helper(self.g4, eg.maximum_spanning_edges)
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def test_minimum_spanning_tree(self):
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print("test_minimum_spanning_tree")
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self.helper(self.g2, eg.minimum_spanning_tree)
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self.helper(self.g2, eg.minimum_spanning_tree)
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self.helper(self.g4, eg.minimum_spanning_tree)
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def test_maximum_spanning_tree(self):
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print("test_maximum_spanning_tree")
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self.helper(self.g2, eg.maximum_spanning_tree)
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self.helper(self.g2, eg.maximum_spanning_tree)
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self.helper(self.g4, eg.maximum_spanning_tree)
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def test_nan_handling(self):
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with self.assertRaises(ValueError):
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list(eg.minimum_spanning_edges(self.nan_graph))
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edges = list(eg.minimum_spanning_edges(self.nan_graph, ignore_nan=True))
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self.assertEqual(len(edges), 1)
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def test_missing_weight_defaults_to_one(self):
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edges = list(eg.minimum_spanning_edges(self.no_weight_graph))
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self.assertEqual(len(edges), 2)
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def test_negative_weights(self):
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edges = list(eg.minimum_spanning_edges(self.negative_weight_graph))
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weights = [attr["weight"] for _, _, attr in edges]
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self.assertIn(-3, weights)
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self.assertEqual(len(edges), 3)
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def test_disconnected_graph(self):
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edges = list(eg.minimum_spanning_edges(self.disconnected_graph))
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self.assertEqual(len(edges), 2)
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def test_maximum_vs_minimum_edges(self):
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min_edges = list(eg.minimum_spanning_edges(self.G))
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max_edges = list(eg.maximum_spanning_edges(self.G))
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min_set = {(min(u, v), max(u, v)) for u, v, _ in min_edges}
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max_set = {(min(u, v), max(u, v)) for u, v, _ in max_edges}
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self.assertNotEqual(min_set, max_set)
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def test_invalid_algorithm_name(self):
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with self.assertRaises(ValueError):
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list(eg.minimum_spanning_edges(self.G, algorithm="invalid_algo"))
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if __name__ == "__main__":
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unittest.main()
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