| /prebuilts/python/linux-x86/2.7.5/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/bipartite/ |
| centrality.py | 7 import networkx as nx namespace
163 betweenness = nx.betweenness_centrality(G, normalized=False,
240 path_length=nx.single_source_shortest_path_length
|
| /prebuilts/python/linux-x86/2.7.5/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/ |
| closeness.py | 11 import networkx as nx namespace
79 path_length = functools.partial(nx.single_source_dijkstra_path_length,
82 path_length = nx.single_source_shortest_path_length
|
| degree_alg.py | 19 import networkx as nx namespace
85 raise nx.NetworkXError(\
123 raise nx.NetworkXError(\
|
| /prebuilts/python/linux-x86/2.7.5/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/community/ |
| kclique.py | 8 import networkx as nx namespace
35 >>> G = nx.complete_graph(5)
36 >>> K5 = nx.convert_node_labels_to_integers(G,first_label=2)
38 >>> c = list(nx.k_clique_communities(G, 4))
41 >>> list(nx.k_clique_communities(G, 6))
52 raise nx.NetworkXError("k=%d, k must be greater than 1."%k)
54 cliques = nx.find_cliques(G)
64 perc_graph = nx.Graph()
73 for component in nx.connected_components(perc_graph):
|
| /prebuilts/python/linux-x86/2.7.5/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/ |
| distance_regular.py | 11 import networkx as nx namespace
37 >>> G=nx.hypercube_graph(6)
38 >>> nx.is_distance_regular(G)
60 except nx.NetworkXError:
86 >>> G=nx.dodecahedral_graph()
87 >>> b,c=nx.intersection_array(G)
88 >>> list(nx.global_parameters(b,c))
132 >>> G=nx.icosahedral_graph()
133 >>> nx.intersection_array(G)
148 raise nx.NetworkxException('Not implemented for directed '
[all...] |
| mis.py | 20 import networkx as nx namespace
52 >>> G = nx.path_graph(5)
53 >>> nx.maximal_independent_set(G) # doctest: +SKIP
55 >>> nx.maximal_independent_set(G, [1]) # doctest: +SKIP
68 raise nx.NetworkXUnfeasible(
72 raise nx.NetworkXUnfeasible(
|
| simple_paths.py | 6 import networkx as nx namespace
38 >>> G = nx.complete_graph(4)
39 >>> for path in nx.all_simple_paths(G, source=0, target=3):
47 >>> paths = nx.all_simple_paths(G, source=0, target=3, cutoff=2)
68 raise nx.NetworkXError('source node %s not in graph'%source)
70 raise nx.NetworkXError('target node %s not in graph'%target)
|
| vitality.py | 10 import networkx as nx namespace
21 path_length=nx.single_source_shortest_path_length(G,n)
25 path_length=nx.single_source_dijkstra_path_length(G,
52 >>> G=nx.cycle_graph(3)
53 >>> nx.closeness_vitality(G)
|
| swap.py | 12 import networkx as nx namespace
59 raise nx.NetworkXError(\
62 raise nx.NetworkXError("Number of swaps > number of tries allowed.")
64 raise nx.NetworkXError("Graph has less than four nodes.")
71 cdf=nx.utils.cumulative_distribution(degrees) # cdf of degree
76 (ui,xi)=nx.utils.discrete_sequence(2,cdistribution=cdf)
95 raise nx.NetworkXAlgorithmError(e)
138 if not nx.is_connected(G):
139 raise nx.NetworkXError("Graph not connected")
141 raise nx.NetworkXError("Graph has less than four nodes."
[all...] |
| /prebuilts/python/linux-x86/2.7.5/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/flow/tests/ |
| test_maxflow_large_graph.py | 13 import networkx as nx namespace
20 G = nx.DiGraph()
41 G = nx.complete_graph(N)
44 assert_equal(nx.ford_fulkerson(G, 1, 2)[0], 5 * (N - 1))
50 assert_almost_equal(nx.ford_fulkerson(G, (0, 0), 't')[0], 1.)
|
| /prebuilts/python/linux-x86/2.7.5/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/operators/ |
| unary.py | 8 import networkx as nx namespace
66 raise nx.NetworkXError("Cannot reverse an undirected graph.")
|
| /prebuilts/python/linux-x86/2.7.5/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/tests/ |
| test_smetric.py | 4 import networkx as nx namespace
7 g = nx.Graph()
12 sm = nx.s_metric(g,normalized=False)
14 # smNorm = nx.s_metric(g,normalized=True)
17 @raises(nx.NetworkXError)
19 sm = nx.s_metric(nx.Graph(),normalized=True)
|
| test_distance_regular.py | 3 import networkx as nx namespace
8 assert_true(nx.is_distance_regular(nx.icosahedral_graph()))
9 assert_true(nx.is_distance_regular(nx.petersen_graph()))
10 assert_true(nx.is_distance_regular(nx.cubical_graph()))
11 assert_true(nx.is_distance_regular(nx.complete_bipartite_graph(3,3)))
12 assert_true(nx.is_distance_regular(nx.tetrahedral_graph())
[all...] |
| /prebuilts/python/linux-x86/2.7.5/lib/python2.7/site-packages/setoolsgui/networkx/classes/tests/ |
| test_graph_historical.py | 5 import networkx as nx namespace
13 self.G=nx.Graph
|
| /prebuilts/python/linux-x86/2.7.5/lib/python2.7/site-packages/setoolsgui/networkx/generators/ |
| hybrid.py | 16 import networkx as nx namespace
63 path=nx.shortest_path(G2,u,v) # ??? should "Cutoff" be k+1?
64 except nx.NetworkXNoPath:
107 path=nx.shortest_path(G2,u,v) # ??? should "Cutoff" be k+1?
108 except nx.NetworkXNoPath:
|
| random_clustered.py | 11 import networkx as nx namespace
77 >>> G = nx.random_clustered_graph(deg_tri)
81 >>> G=nx.Graph(G)
89 create_using = nx.MultiGraph()
91 raise nx.NetworkXError("Directed Graph not supported")
100 G = nx.empty_graph(N,create_using)
112 raise nx.NetworkXError('Invalid degree sequence')
|
| /prebuilts/python/linux-x86/2.7.5/lib/python2.7/site-packages/setoolsgui/networkx/generators/tests/ |
| test_intersection.py | 3 import networkx as nx namespace
7 G=nx.uniform_random_intersection_graph(10,5,0.5)
11 G=nx.k_random_intersection_graph(10,5,2)
15 G=nx.general_random_intersection_graph(10,5,[0.1,0.2,0.2,0.1,0.1])
17 assert_raises(ValueError, nx.general_random_intersection_graph,10,5,
|
| /prebuilts/python/linux-x86/2.7.5/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/approximation/tests/ |
| test_clique.py | 2 import networkx as nx namespace
6 graph = nx.complete_graph(10)
8 idens = nx.density(graph.subgraph(i))
11 cdens = nx.density(graph.subgraph(clique))
14 graph = nx.trivial_graph(nx.Graph())
16 idens = nx.density(graph.subgraph(i))
20 cdens = nx.density(graph.subgraph(clique))
23 graph = nx.barbell_graph(10, 5, nx.Graph()
[all...] |
| test_dominating_set.py | 3 import networkx as nx namespace
10 graph = nx.Graph()
27 graph = nx.path_graph(5)
41 graph = nx.complete_graph(10)
|
| /prebuilts/python/linux-x86/2.7.5/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/assortativity/ |
| connectivity.py | 8 import networkx as nx namespace
86 >>> G=nx.path_graph(4)
88 >>> nx.k_nearest_neighbors(G)
90 >>> nx.k_nearest_neighbors(G, weight='weight')
|
| /prebuilts/python/linux-x86/2.7.5/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/assortativity/tests/ |
| test_neighbor_degree.py | 3 import networkx as nx namespace
8 G=nx.path_graph(4)
10 nd = nx.average_neighbor_degree(G)
14 nd = nx.average_neighbor_degree(D)
18 nd = nx.average_neighbor_degree(D)
22 nd = nx.average_neighbor_degree(D, source='in', target='in')
26 G=nx.path_graph(4)
29 nd = nx.average_neighbor_degree(G,weight='weight')
33 nd = nx.average_neighbor_degree(D,weight='weight')
37 nd = nx.average_neighbor_degree(D,weight='weight'
[all...] |
| /prebuilts/python/linux-x86/2.7.5/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/community/tests/ |
| test_kclique.py | 3 import networkx as nx namespace
8 G = nx.Graph()
11 c = list(nx.k_clique_communities(G, 4))
13 c= list(nx.k_clique_communities(G, 5))
17 G = nx.Graph()
20 c= list(nx.k_clique_communities(G, 5))
24 z = nx.karate_club_graph()
44 @raises(nx.NetworkXError)
46 c = list(k_clique_communities(nx.Graph(),1))
|
| /prebuilts/python/linux-x86/2.7.5/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/components/ |
| attracting.py | 11 import networkx as nx namespace
48 scc = nx.strongly_connected_components(G)
49 cG = nx.condensation(G, scc)
|
| /prebuilts/python/linux-x86/2.7.5/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/components/tests/ |
| test_attracting.py | 3 import networkx as nx namespace
8 self.G1 = nx.DiGraph()
11 self.G2 = nx.DiGraph()
14 self.G3 = nx.DiGraph()
18 ac = nx.attracting_components(self.G1)
23 ac = nx.attracting_components(self.G2)
27 ac = nx.attracting_components(self.G3)
34 assert_equal(len(nx.attracting_components(self.G1)), 3)
35 assert_equal(len(nx.attracting_components(self.G2)), 1)
36 assert_equal(len(nx.attracting_components(self.G3)), 2
[all...] |
| /prebuilts/python/linux-x86/2.7.5/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/shortest_paths/ |
| generic.py | 17 import networkx as nx namespace
38 sp = nx.shortest_path(G,source, target)
39 except nx.NetworkXNoPath:
85 >>> G=nx.path_graph(5)
86 >>> print(nx.shortest_path(G,source=0,target=4))
88 >>> p=nx.shortest_path(G,source=0) # target not specified
91 >>> p=nx.shortest_path(G,target=4) # source not specified
94 >>> p=nx.shortest_path(G) # source,target not specified
118 paths=nx.all_pairs_shortest_path(G)
120 paths=nx.all_pairs_dijkstra_path(G,weight=weight
[all...] |
