| /prebuilts/python/linux-x86/2.7.5/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/ |
| smetric.py | 1 import networkx as nx namespace
32 raise nx.NetworkXError("Normalization not implemented")
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| block.py | 12 import networkx as nx namespace
48 >>> G=nx.path_graph(6)
50 >>> M=nx.blockmodel(G,partition)
66 raise nx.NetworkXException("Overlapping node partitions.")
71 M=nx.MultiDiGraph()
73 M=nx.MultiGraph()
76 M=nx.DiGraph()
78 M=nx.Graph()
90 M.node[i]['density']=nx.density(SG)
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| euler.py | 5 import networkx as nx namespace
30 >>> nx.is_eulerian(nx.DiGraph({0:[3], 1:[2], 2:[3], 3:[0, 1]}))
32 >>> nx.is_eulerian(nx.complete_graph(5))
34 >>> nx.is_eulerian(nx.petersen_graph())
48 if not nx.is_strongly_connected(G):
56 if not nx.is_connected(G):
100 >>> G=nx.complete_graph(3
[all...] |
| hierarchy.py | 11 import networkx as nx namespace
51 raise nx.NetworkXError("G must be a digraph in flow_heirarchy")
52 scc = nx.strongly_connected_components(G)
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| isolate.py | 11 import networkx as nx namespace
33 >>> G=nx.Graph()
36 >>> nx.is_isolate(G,2)
38 >>> nx.is_isolate(G,3)
60 >>> G = nx.Graph()
63 >>> nx.isolates(G)
67 >>> G.remove_nodes_from(nx.isolates(G))
72 >>> G = nx.DiGraph([(0,1),(1,2)])
74 >>> nx.isolates(G)
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| richclub.py | 2 import networkx as nx namespace
39 >>> G = nx.Graph([(0,1),(0,2),(1,2),(1,3),(1,4),(4,5)])
40 >>> rc = nx.rich_club_coefficient(G,normalized=False)
74 nx.double_edge_swap(R,Q*E,max_tries=Q*E*10)
84 deghist = nx.degree_histogram(G)
87 nks = [total-cs for cs in nx.utils.cumulative_sum(deghist) if total-cs > 1]
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| /prebuilts/python/linux-x86/2.7.5/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/approximation/ |
| matching.py | 16 import networkx as nx namespace
46 return nx.maximal_matching(G)
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| ramsey.py | 9 import networkx as nx namespace
30 nbrs = nx.all_neighbors(G, node)
31 nnbrs = nx.non_neighbors(G, node)
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| clique.py | 9 import networkx as nx namespace
57 cgraph = nx.complement(G)
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| dominating_set.py | 24 import networkx as nx namespace
114 return nx.maximal_matching(G)
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| /prebuilts/python/linux-x86/2.7.5/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/approximation/tests/ |
| test_vertex_cover.py | 3 import networkx as nx namespace
11 sg = nx.star_graph(size)
17 wg = nx.Graph()
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| test_independent_set.py | 2 import networkx as nx namespace
7 G = nx.Graph()
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| test_matching.py | 2 import networkx as nx namespace
7 G = nx.Graph()
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| test_ramsey.py | 2 import networkx as nx namespace
7 graph = nx.complete_graph(10)
9 cdens = nx.density(graph.subgraph(c))
11 idens = nx.density(graph.subgraph(i))
15 graph = nx.trivial_graph(nx.Graph())
17 cdens = nx.density(graph.subgraph(c))
19 idens = nx.density(graph.subgraph(i))
22 graph = nx.barbell_graph(10, 5, nx.Graph()
[all...] |
| /prebuilts/python/linux-x86/2.7.5/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/assortativity/ |
| neighbor_degree.py | 7 import networkx as nx namespace
79 >>> G=nx.path_graph(4)
83 >>> nx.average_neighbor_degree(G)
85 >>> nx.average_neighbor_degree(G, weight='weight')
88 >>> G=nx.DiGraph()
90 >>> nx.average_neighbor_degree(G, source='in', target='in')
93 >>> nx.average_neighbor_degree(G, source='out', target='out')
124 # raise nx.NetworkXError("Not defined for undirected graphs.")
130 # raise nx.NetworkXError("Not defined for undirected graphs.")
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| /prebuilts/python/linux-x86/2.7.5/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/assortativity/tests/ |
| base_test.py | 1 import networkx as nx namespace
6 G=nx.Graph()
14 D=nx.DiGraph()
22 M=nx.MultiGraph()
30 S=nx.Graph()
42 self.P4=nx.path_graph(4)
43 self.D=nx.DiGraph()
45 self.M=nx.MultiGraph()
48 self.S=nx.Graph()
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| /prebuilts/python/linux-x86/2.7.5/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/bipartite/ |
| redundancy.py | 9 import networkx as nx namespace
47 >>> G = nx.cycle_graph(4)
|
| /prebuilts/python/linux-x86/2.7.5/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/operators/tests/ |
| test_unary.py | 2 import networkx as nx namespace
33 G1=nx.DiGraph()
45 G1=nx.Graph()
46 assert_raises(nx.NetworkXError, nx.reverse, G1)
|
| /prebuilts/python/linux-x86/2.7.5/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/tests/ |
| test_richclub.py | 1 import networkx as nx namespace
6 G = nx.Graph([(0,1),(0,2),(1,2),(1,3),(1,4),(4,5)])
7 rc = nx.richclub.rich_club_coefficient(G,normalized=False)
11 rc0 = nx.richclub.rich_club_coefficient(G,normalized=False)[0]
15 G = nx.Graph([(0,1),(0,2),(1,2),(1,3),(1,4),(4,5)])
16 rcNorm = nx.richclub.rich_club_coefficient(G,Q=2)
21 T = nx.balanced_tree(2,10)
22 rc = nx.richclub.rich_club_coefficient(T,normalized=False)
28 # T = nx.balanced_tree(2,10)
29 # rcNorm = nx.richclub.rich_club_coefficient(T,Q=2
[all...] |
| /prebuilts/python/linux-x86/2.7.5/lib/python2.7/site-packages/setoolsgui/networkx/generators/ |
| ego.py | 14 import networkx as nx namespace
53 sp,_=nx.single_source_dijkstra(G.to_undirected(),
57 sp=nx.single_source_shortest_path_length(G.to_undirected(),
61 sp,_=nx.single_source_dijkstra(G,
65 sp=nx.single_source_shortest_path_length(G,n,cutoff=radius)
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| line.py | 15 import networkx as nx namespace
36 >>> G=nx.star_graph(3)
37 >>> L=nx.line_graph(G)
48 if type(G) == nx.MultiGraph or type(G) == nx.MultiDiGraph:
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| stochastic.py | 8 import networkx as nx namespace
30 if type(G) == nx.MultiGraph or type(G) == nx.MultiDiGraph:
31 raise nx.NetworkXError('stochastic_graph not implemented '
35 raise nx.NetworkXError('stochastic_graph not implemented '
39 W = nx.DiGraph(G)
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| /prebuilts/python/linux-x86/2.7.5/lib/python2.7/site-packages/setoolsgui/networkx/generators/tests/ |
| test_hybrid.py | 2 import networkx as nx namespace
7 G=nx.grid_2d_graph(8,8,periodic=True)
8 assert_true(nx.is_kl_connected(G,3,3))
9 assert_false(nx.is_kl_connected(G,5,9))
10 (H,graphOK)=nx.kl_connected_subgraph(G,5,9,same_as_graph=True)
14 G=nx.Graph()
18 assert_true(nx.is_kl_connected(G,2,2))
19 H=nx.kl_connected_subgraph(G,2,2)
20 (H,graphOK)=nx.kl_connected_subgraph(G,2,2,
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| test_line.py | 7 import networkx as nx namespace
13 G=nx.star_graph(5)
14 L=nx.line_graph(G)
15 assert_true(nx.is_isomorphic(L,nx.complete_graph(5)))
16 G=nx.path_graph(5)
17 L=nx.line_graph(G)
18 assert_true(nx.is_isomorphic(L,nx.path_graph(4)))
19 G=nx.cycle_graph(5
[all...] |
| /prebuilts/python/linux-x86/2.7.5/lib/python2.7/site-packages/setoolsgui/networkx/utils/tests/ |
| test_rcm.py | 3 import networkx as nx namespace
8 G = nx.Graph([(0,3),(0,5),(1,2),(1,4),(1,6),(1,9),(2,3),
|
