Mercurial > repos > shellac > sam_consensus_v3
annotate env/lib/python3.9/site-packages/networkx/generators/tests/test_random_graphs.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 """Unit tests for the :mod:`networkx.generators.random_graphs` module. |
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2 |
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3 """ |
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4 import pytest |
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5 |
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6 from networkx.exception import NetworkXError |
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7 from networkx.generators.random_graphs import barabasi_albert_graph |
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8 from networkx.generators.random_graphs import dual_barabasi_albert_graph |
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9 from networkx.generators.random_graphs import extended_barabasi_albert_graph |
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10 from networkx.generators.random_graphs import binomial_graph |
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11 from networkx.generators.random_graphs import connected_watts_strogatz_graph |
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12 from networkx.generators.random_graphs import dense_gnm_random_graph |
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13 from networkx.generators.random_graphs import erdos_renyi_graph |
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14 from networkx.generators.random_graphs import fast_gnp_random_graph |
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15 from networkx.generators.random_graphs import gnm_random_graph |
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16 from networkx.generators.random_graphs import gnp_random_graph |
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17 from networkx.generators.random_graphs import newman_watts_strogatz_graph |
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18 from networkx.generators.random_graphs import powerlaw_cluster_graph |
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19 from networkx.generators.random_graphs import random_kernel_graph |
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20 from networkx.generators.random_graphs import random_lobster |
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21 from networkx.generators.random_graphs import random_powerlaw_tree |
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22 from networkx.generators.random_graphs import random_powerlaw_tree_sequence |
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23 from networkx.generators.random_graphs import random_regular_graph |
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24 from networkx.generators.random_graphs import random_shell_graph |
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25 from networkx.generators.random_graphs import watts_strogatz_graph |
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26 |
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27 |
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28 class TestGeneratorsRandom: |
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29 def test_random_graph(self): |
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30 seed = 42 |
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31 G = gnp_random_graph(100, 0.25, seed) |
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32 G = gnp_random_graph(100, 0.25, seed, directed=True) |
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33 G = binomial_graph(100, 0.25, seed) |
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34 G = erdos_renyi_graph(100, 0.25, seed) |
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35 G = fast_gnp_random_graph(100, 0.25, seed) |
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36 G = fast_gnp_random_graph(100, 0.25, seed, directed=True) |
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37 G = gnm_random_graph(100, 20, seed) |
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38 G = gnm_random_graph(100, 20, seed, directed=True) |
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39 G = dense_gnm_random_graph(100, 20, seed) |
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40 |
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41 G = watts_strogatz_graph(10, 2, 0.25, seed) |
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42 assert len(G) == 10 |
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43 assert G.number_of_edges() == 10 |
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44 |
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45 G = connected_watts_strogatz_graph(10, 2, 0.1, tries=10, seed=seed) |
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46 assert len(G) == 10 |
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47 assert G.number_of_edges() == 10 |
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48 pytest.raises( |
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49 NetworkXError, connected_watts_strogatz_graph, 10, 2, 0.1, tries=0 |
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50 ) |
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51 |
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52 G = watts_strogatz_graph(10, 4, 0.25, seed) |
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53 assert len(G) == 10 |
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54 assert G.number_of_edges() == 20 |
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55 |
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56 G = newman_watts_strogatz_graph(10, 2, 0.0, seed) |
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57 assert len(G) == 10 |
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58 assert G.number_of_edges() == 10 |
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59 |
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60 G = newman_watts_strogatz_graph(10, 4, 0.25, seed) |
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61 assert len(G) == 10 |
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62 assert G.number_of_edges() >= 20 |
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63 |
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64 G = barabasi_albert_graph(100, 1, seed) |
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65 G = barabasi_albert_graph(100, 3, seed) |
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66 assert G.number_of_edges() == (97 * 3) |
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67 |
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68 G = extended_barabasi_albert_graph(100, 1, 0, 0, seed) |
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69 assert G.number_of_edges() == 99 |
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70 G = extended_barabasi_albert_graph(100, 3, 0, 0, seed) |
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71 assert G.number_of_edges() == 97 * 3 |
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72 G = extended_barabasi_albert_graph(100, 1, 0, 0.5, seed) |
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73 assert G.number_of_edges() == 99 |
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74 G = extended_barabasi_albert_graph(100, 2, 0.5, 0, seed) |
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75 assert G.number_of_edges() > 100 * 3 |
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76 assert G.number_of_edges() < 100 * 4 |
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77 |
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78 G = extended_barabasi_albert_graph(100, 2, 0.3, 0.3, seed) |
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79 assert G.number_of_edges() > 100 * 2 |
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80 assert G.number_of_edges() < 100 * 4 |
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81 |
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82 G = powerlaw_cluster_graph(100, 1, 1.0, seed) |
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83 G = powerlaw_cluster_graph(100, 3, 0.0, seed) |
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84 assert G.number_of_edges() == (97 * 3) |
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85 |
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86 G = random_regular_graph(10, 20, seed) |
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87 |
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88 pytest.raises(NetworkXError, random_regular_graph, 3, 21) |
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89 pytest.raises(NetworkXError, random_regular_graph, 33, 21) |
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90 |
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91 constructor = [(10, 20, 0.8), (20, 40, 0.8)] |
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92 G = random_shell_graph(constructor, seed) |
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93 |
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94 def is_caterpillar(g): |
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95 """ |
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96 A tree is a caterpillar iff all nodes of degree >=3 are surrounded |
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97 by at most two nodes of degree two or greater. |
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98 ref: http://mathworld.wolfram.com/CaterpillarGraph.html |
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99 """ |
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100 deg_over_3 = [n for n in g if g.degree(n) >= 3] |
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101 for n in deg_over_3: |
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102 nbh_deg_over_2 = [nbh for nbh in g.neighbors(n) if g.degree(nbh) >= 2] |
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103 if not len(nbh_deg_over_2) <= 2: |
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104 return False |
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105 return True |
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106 |
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107 def is_lobster(g): |
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108 """ |
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109 A tree is a lobster if it has the property that the removal of leaf |
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110 nodes leaves a caterpillar graph (Gallian 2007) |
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111 ref: http://mathworld.wolfram.com/LobsterGraph.html |
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112 """ |
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113 non_leafs = [n for n in g if g.degree(n) > 1] |
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114 return is_caterpillar(g.subgraph(non_leafs)) |
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115 |
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116 G = random_lobster(10, 0.1, 0.5, seed) |
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117 assert max([G.degree(n) for n in G.nodes()]) > 3 |
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118 assert is_lobster(G) |
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119 pytest.raises(NetworkXError, random_lobster, 10, 0.1, 1, seed) |
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120 pytest.raises(NetworkXError, random_lobster, 10, 1, 1, seed) |
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121 pytest.raises(NetworkXError, random_lobster, 10, 1, 0.5, seed) |
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122 |
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123 # docstring says this should be a caterpillar |
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124 G = random_lobster(10, 0.1, 0.0, seed) |
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125 assert is_caterpillar(G) |
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126 |
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127 # difficult to find seed that requires few tries |
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128 seq = random_powerlaw_tree_sequence(10, 3, seed=14, tries=1) |
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129 G = random_powerlaw_tree(10, 3, seed=14, tries=1) |
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130 |
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131 def test_dual_barabasi_albert(self, m1=1, m2=4, p=0.5): |
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132 """ |
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133 Tests that the dual BA random graph generated behaves consistently. |
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134 |
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135 Tests the exceptions are raised as expected. |
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136 |
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137 The graphs generation are repeated several times to prevent lucky shots |
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138 |
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139 """ |
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140 seed = 42 |
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141 repeats = 2 |
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142 |
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143 while repeats: |
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144 repeats -= 1 |
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145 |
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146 # This should be BA with m = m1 |
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147 BA1 = barabasi_albert_graph(100, m1, seed) |
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148 DBA1 = dual_barabasi_albert_graph(100, m1, m2, 1, seed) |
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149 assert BA1.size() == DBA1.size() |
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150 |
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151 # This should be BA with m = m2 |
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152 BA2 = barabasi_albert_graph(100, m2, seed) |
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153 DBA2 = dual_barabasi_albert_graph(100, m1, m2, 0, seed) |
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154 assert BA2.size() == DBA2.size() |
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155 |
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156 # Testing exceptions |
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157 dbag = dual_barabasi_albert_graph |
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158 pytest.raises(NetworkXError, dbag, m1, m1, m2, 0) |
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159 pytest.raises(NetworkXError, dbag, m2, m1, m2, 0) |
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160 pytest.raises(NetworkXError, dbag, 100, m1, m2, -0.5) |
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161 pytest.raises(NetworkXError, dbag, 100, m1, m2, 1.5) |
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162 |
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163 def test_extended_barabasi_albert(self, m=2): |
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164 """ |
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165 Tests that the extended BA random graph generated behaves consistently. |
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166 |
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167 Tests the exceptions are raised as expected. |
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168 |
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169 The graphs generation are repeated several times to prevent lucky-shots |
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170 |
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171 """ |
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172 seed = 42 |
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173 repeats = 2 |
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174 BA_model = barabasi_albert_graph(100, m, seed) |
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175 BA_model_edges = BA_model.number_of_edges() |
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176 |
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177 while repeats: |
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178 repeats -= 1 |
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179 |
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180 # This behaves just like BA, the number of edges must be the same |
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181 G1 = extended_barabasi_albert_graph(100, m, 0, 0, seed) |
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182 assert G1.size() == BA_model_edges |
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183 |
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184 # More than twice more edges should have been added |
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185 G1 = extended_barabasi_albert_graph(100, m, 0.8, 0, seed) |
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186 assert G1.size() > BA_model_edges * 2 |
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187 |
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188 # Only edge rewiring, so the number of edges less than original |
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189 G2 = extended_barabasi_albert_graph(100, m, 0, 0.8, seed) |
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190 assert G2.size() == BA_model_edges |
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191 |
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192 # Mixed scenario: less edges than G1 and more edges than G2 |
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193 G3 = extended_barabasi_albert_graph(100, m, 0.3, 0.3, seed) |
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194 assert G3.size() > G2.size() |
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195 assert G3.size() < G1.size() |
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196 |
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197 # Testing exceptions |
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198 ebag = extended_barabasi_albert_graph |
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199 pytest.raises(NetworkXError, ebag, m, m, 0, 0) |
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200 pytest.raises(NetworkXError, ebag, 1, 0.5, 0, 0) |
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201 pytest.raises(NetworkXError, ebag, 100, 2, 0.5, 0.5) |
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202 |
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203 def test_random_zero_regular_graph(self): |
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204 """Tests that a 0-regular graph has the correct number of nodes and |
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205 edges. |
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206 |
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207 """ |
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208 seed = 42 |
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209 G = random_regular_graph(0, 10, seed) |
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210 assert len(G) == 10 |
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211 assert sum(1 for _ in G.edges()) == 0 |
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212 |
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213 def test_gnp(self): |
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214 for generator in [ |
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215 gnp_random_graph, |
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216 binomial_graph, |
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217 erdos_renyi_graph, |
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218 fast_gnp_random_graph, |
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219 ]: |
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220 G = generator(10, -1.1) |
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221 assert len(G) == 10 |
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222 assert sum(1 for _ in G.edges()) == 0 |
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223 |
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224 G = generator(10, 0.1) |
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225 assert len(G) == 10 |
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226 |
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227 G = generator(10, 0.1, seed=42) |
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228 assert len(G) == 10 |
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229 |
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230 G = generator(10, 1.1) |
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231 assert len(G) == 10 |
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232 assert sum(1 for _ in G.edges()) == 45 |
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233 |
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234 G = generator(10, -1.1, directed=True) |
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235 assert G.is_directed() |
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236 assert len(G) == 10 |
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237 assert sum(1 for _ in G.edges()) == 0 |
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238 |
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239 G = generator(10, 0.1, directed=True) |
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240 assert G.is_directed() |
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241 assert len(G) == 10 |
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242 |
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243 G = generator(10, 1.1, directed=True) |
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244 assert G.is_directed() |
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245 assert len(G) == 10 |
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246 assert sum(1 for _ in G.edges()) == 90 |
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247 |
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248 # assert that random graphs generate all edges for p close to 1 |
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249 edges = 0 |
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250 runs = 100 |
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251 for i in range(runs): |
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252 edges += sum(1 for _ in generator(10, 0.99999, directed=True).edges()) |
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253 assert abs(edges / float(runs) - 90) <= runs * 2.0 / 100 |
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254 |
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255 def test_gnm(self): |
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256 G = gnm_random_graph(10, 3) |
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257 assert len(G) == 10 |
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258 assert sum(1 for _ in G.edges()) == 3 |
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259 |
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260 G = gnm_random_graph(10, 3, seed=42) |
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261 assert len(G) == 10 |
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262 assert sum(1 for _ in G.edges()) == 3 |
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263 |
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264 G = gnm_random_graph(10, 100) |
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265 assert len(G) == 10 |
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266 assert sum(1 for _ in G.edges()) == 45 |
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267 |
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268 G = gnm_random_graph(10, 100, directed=True) |
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269 assert len(G) == 10 |
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270 assert sum(1 for _ in G.edges()) == 90 |
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271 |
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272 G = gnm_random_graph(10, -1.1) |
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273 assert len(G) == 10 |
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274 assert sum(1 for _ in G.edges()) == 0 |
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275 |
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276 def test_watts_strogatz_big_k(self): |
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277 # Test to make sure than n <= k |
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278 pytest.raises(NetworkXError, watts_strogatz_graph, 10, 11, 0.25) |
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279 pytest.raises(NetworkXError, newman_watts_strogatz_graph, 10, 11, 0.25) |
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280 |
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281 # could create an infinite loop, now doesn't |
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282 # infinite loop used to occur when a node has degree n-1 and needs to rewire |
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283 watts_strogatz_graph(10, 9, 0.25, seed=0) |
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284 newman_watts_strogatz_graph(10, 9, 0.5, seed=0) |
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285 |
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286 # Test k==n scenario |
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287 watts_strogatz_graph(10, 10, 0.25, seed=0) |
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288 newman_watts_strogatz_graph(10, 10, 0.25, seed=0) |
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289 |
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290 def test_random_kernel_graph(self): |
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291 def integral(u, w, z): |
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292 return c * (z - w) |
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293 |
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294 def root(u, w, r): |
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295 return r / c + w |
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296 |
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297 c = 1 |
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298 graph = random_kernel_graph(1000, integral, root) |
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299 graph = random_kernel_graph(1000, integral, root, seed=42) |
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300 assert len(graph) == 1000 |