Mercurial > repos > shellac > sam_consensus_v3
annotate env/lib/python3.9/site-packages/networkx/algorithms/shortest_paths/tests/test_astar.py @ 0:4f3585e2f14b draft default tip
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author | shellac |
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date | Mon, 22 Mar 2021 18:12:50 +0000 |
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1 import pytest |
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2 |
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3 import networkx as nx |
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4 from networkx.utils import pairwise |
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5 |
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6 |
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7 class TestAStar: |
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8 @classmethod |
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9 def setup_class(cls): |
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10 edges = [ |
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11 ("s", "u", 10), |
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12 ("s", "x", 5), |
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13 ("u", "v", 1), |
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14 ("u", "x", 2), |
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15 ("v", "y", 1), |
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16 ("x", "u", 3), |
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17 ("x", "v", 5), |
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18 ("x", "y", 2), |
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19 ("y", "s", 7), |
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20 ("y", "v", 6), |
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21 ] |
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22 cls.XG = nx.DiGraph() |
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23 cls.XG.add_weighted_edges_from(edges) |
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24 |
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25 def test_multiple_optimal_paths(self): |
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26 """Tests that A* algorithm finds any of multiple optimal paths""" |
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27 heuristic_values = {"a": 1.35, "b": 1.18, "c": 0.67, "d": 0} |
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28 |
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29 def h(u, v): |
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30 return heuristic_values[u] |
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31 |
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32 graph = nx.Graph() |
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33 points = ["a", "b", "c", "d"] |
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34 edges = [("a", "b", 0.18), ("a", "c", 0.68), ("b", "c", 0.50), ("c", "d", 0.67)] |
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35 |
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36 graph.add_nodes_from(points) |
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37 graph.add_weighted_edges_from(edges) |
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38 |
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39 path1 = ["a", "c", "d"] |
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40 path2 = ["a", "b", "c", "d"] |
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41 assert nx.astar_path(graph, "a", "d", h) in (path1, path2) |
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42 |
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43 def test_astar_directed(self): |
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44 assert nx.astar_path(self.XG, "s", "v") == ["s", "x", "u", "v"] |
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45 assert nx.astar_path_length(self.XG, "s", "v") == 9 |
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46 |
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47 def test_astar_multigraph(self): |
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48 G = nx.MultiDiGraph(self.XG) |
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49 G.add_weighted_edges_from((u, v, 1000) for (u, v) in list(G.edges())) |
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50 assert nx.astar_path(G, "s", "v") == ["s", "x", "u", "v"] |
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51 assert nx.astar_path_length(G, "s", "v") == 9 |
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52 |
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53 def test_astar_undirected(self): |
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54 GG = self.XG.to_undirected() |
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55 # make sure we get lower weight |
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56 # to_undirected might choose either edge with weight 2 or weight 3 |
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57 GG["u"]["x"]["weight"] = 2 |
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58 GG["y"]["v"]["weight"] = 2 |
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59 assert nx.astar_path(GG, "s", "v") == ["s", "x", "u", "v"] |
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60 assert nx.astar_path_length(GG, "s", "v") == 8 |
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61 |
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62 def test_astar_directed2(self): |
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63 XG2 = nx.DiGraph() |
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64 edges = [ |
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65 (1, 4, 1), |
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66 (4, 5, 1), |
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67 (5, 6, 1), |
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68 (6, 3, 1), |
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69 (1, 3, 50), |
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70 (1, 2, 100), |
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71 (2, 3, 100), |
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72 ] |
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73 XG2.add_weighted_edges_from(edges) |
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74 assert nx.astar_path(XG2, 1, 3) == [1, 4, 5, 6, 3] |
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75 |
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76 def test_astar_undirected2(self): |
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77 XG3 = nx.Graph() |
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78 edges = [(0, 1, 2), (1, 2, 12), (2, 3, 1), (3, 4, 5), (4, 5, 1), (5, 0, 10)] |
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79 XG3.add_weighted_edges_from(edges) |
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80 assert nx.astar_path(XG3, 0, 3) == [0, 1, 2, 3] |
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81 assert nx.astar_path_length(XG3, 0, 3) == 15 |
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82 |
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83 def test_astar_undirected3(self): |
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84 XG4 = nx.Graph() |
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85 edges = [ |
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86 (0, 1, 2), |
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87 (1, 2, 2), |
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88 (2, 3, 1), |
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89 (3, 4, 1), |
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90 (4, 5, 1), |
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91 (5, 6, 1), |
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92 (6, 7, 1), |
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93 (7, 0, 1), |
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94 ] |
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95 XG4.add_weighted_edges_from(edges) |
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96 assert nx.astar_path(XG4, 0, 2) == [0, 1, 2] |
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97 assert nx.astar_path_length(XG4, 0, 2) == 4 |
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98 |
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99 """ Tests that A* finds correct path when multiple paths exist |
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100 and the best one is not expanded first (GH issue #3464) |
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101 """ |
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102 |
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103 def test_astar_directed3(self): |
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104 heuristic_values = {"n5": 36, "n2": 4, "n1": 0, "n0": 0} |
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105 |
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106 def h(u, v): |
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107 return heuristic_values[u] |
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108 |
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109 edges = [("n5", "n1", 11), ("n5", "n2", 9), ("n2", "n1", 1), ("n1", "n0", 32)] |
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110 graph = nx.DiGraph() |
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111 graph.add_weighted_edges_from(edges) |
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112 answer = ["n5", "n2", "n1", "n0"] |
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113 assert nx.astar_path(graph, "n5", "n0", h) == answer |
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114 |
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115 """ Tests that that parent is not wrongly overridden when a |
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116 node is re-explored multiple times. |
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117 """ |
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118 |
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119 def test_astar_directed4(self): |
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120 edges = [ |
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121 ("a", "b", 1), |
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122 ("a", "c", 1), |
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123 ("b", "d", 2), |
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124 ("c", "d", 1), |
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125 ("d", "e", 1), |
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126 ] |
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127 graph = nx.DiGraph() |
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128 graph.add_weighted_edges_from(edges) |
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129 assert nx.astar_path(graph, "a", "e") == ["a", "c", "d", "e"] |
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130 |
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131 # >>> MXG4=NX.MultiGraph(XG4) |
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132 # >>> MXG4.add_edge(0,1,3) |
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133 # >>> NX.dijkstra_path(MXG4,0,2) |
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134 # [0, 1, 2] |
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135 |
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136 def test_astar_w1(self): |
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137 G = nx.DiGraph() |
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138 G.add_edges_from( |
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139 [ |
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140 ("s", "u"), |
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141 ("s", "x"), |
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142 ("u", "v"), |
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143 ("u", "x"), |
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144 ("v", "y"), |
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145 ("x", "u"), |
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146 ("x", "w"), |
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147 ("w", "v"), |
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148 ("x", "y"), |
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149 ("y", "s"), |
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150 ("y", "v"), |
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151 ] |
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152 ) |
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153 assert nx.astar_path(G, "s", "v") == ["s", "u", "v"] |
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154 assert nx.astar_path_length(G, "s", "v") == 2 |
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155 |
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156 def test_astar_nopath(self): |
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157 with pytest.raises(nx.NodeNotFound): |
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158 nx.astar_path(self.XG, "s", "moon") |
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159 |
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160 def test_cycle(self): |
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161 C = nx.cycle_graph(7) |
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162 assert nx.astar_path(C, 0, 3) == [0, 1, 2, 3] |
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163 assert nx.dijkstra_path(C, 0, 4) == [0, 6, 5, 4] |
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164 |
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165 def test_unorderable_nodes(self): |
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166 """Tests that A* accommodates nodes that are not orderable. |
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167 |
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168 For more information, see issue #554. |
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169 |
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170 """ |
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171 # Create the cycle graph on four nodes, with nodes represented |
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172 # as (unorderable) Python objects. |
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173 nodes = [object() for n in range(4)] |
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174 G = nx.Graph() |
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175 G.add_edges_from(pairwise(nodes, cyclic=True)) |
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176 path = nx.astar_path(G, nodes[0], nodes[2]) |
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177 assert len(path) == 3 |