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Commit 990b4739 authored by Tiago Peixoto's avatar Tiago Peixoto
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Fix "correlated" rewiring mode 

This fixes a bug in the "correlated" mode in random_rewire().
parent bad74126
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src/graph/generation/graph_rewiring.hh
View file @ 990b4739
......@@ -245,13 +245,18 @@ struct graph_rewire
typedef typename graph_traits<Graph>::edge_descriptor edge_t;
vector<edge_t> edges;
typedef random_permutation_iterator<typename vector<edge_t>::iterator,
vector<size_t> edge_pos;
typedef random_permutation_iterator<typename vector<size_t>::iterator,
rng_t>
random_edge_iter;
typename graph_traits<Graph>::edge_iterator e, e_end;
for (tie(e, e_end) = boost::edges(g); e != e_end; ++e)
{
edges.push_back(*e);
edge_pos.push_back(edge_pos.size());
}
RewireStrategy<Graph, EdgeIndexMap, CorrProb, BlockDeg>
rewire(g, edge_index, edges, corr_prob, bd, rng);
......@@ -266,8 +271,8 @@ struct graph_rewire
for (size_t i = 0; i < niter; ++i)
{
random_edge_iter
ei_begin(edges.begin(), edges.end(), rng),
ei_end(edges.end(), edges.end(), rng);
ei_begin(edge_pos.begin(), edge_pos.end(), rng),
ei_end(edge_pos.end(), edge_pos.end(), rng);
// for each edge rewire its source or target
for (random_edge_iter ei = ei_begin; ei != ei_end; ++ei)
......@@ -276,7 +281,7 @@ struct graph_rewire
if (verbose)
print_progress(i, niter, e_pos, no_sweep ? 1 : edges.size(),
str);
bool success = rewire(e_pos, self_loops, parallel_edges);
bool success = rewire(*ei, self_loops, parallel_edges);
if (!success)
++pcount;
......@@ -492,11 +497,9 @@ public:
base_t;
typedef Graph graph_t;
typedef EdgeIndexMap edge_index_t;
typedef typename graph_traits<Graph>::vertex_descriptor vertex_t;
typedef typename graph_traits<Graph>::edge_descriptor edge_t;
typedef typename EdgeIndexMap::value_type index_t;
CorrelatedRewireStrategy(Graph& g, EdgeIndexMap edge_index,
vector<edge_t>& edges, CorrProb, BlockDeg,
......@@ -506,40 +509,42 @@ public:
for (size_t ei = 0; ei < base_t::_edges.size(); ++ei)
{
// For undirected graphs, there is no difference between source and
// target, and each edge will appear _twice_ in the lists below,
// target, and each edge will appear _twice_ in the list below,
// once for each different ordering of source and target.
edge_t& e = base_t::_edges[ei];
_edges_by_target[make_pair(in_degreeS()(target(e, _g), _g),
out_degree(target(e, _g), _g))]
.push_back(make_pair(ei, false));
vertex_t t = target(e, _g);
deg_t tdeg = make_pair(in_degreeS()(t, _g), out_degree(t, _g));
_edges_by_target[tdeg].push_back(make_pair(ei, false));
if (!is_directed::apply<Graph>::type::value)
{
_edges_by_target[make_pair(in_degreeS()(source(e, _g), _g),
out_degree(source(e, _g), _g))]
.push_back(make_pair(ei, true));
t = source(e, _g);
tdeg = make_pair(in_degreeS()(t, _g), out_degree(t, _g));
_edges_by_target[tdeg].push_back(make_pair(ei, true));
}
}
}
pair<size_t,bool> get_target_edge(size_t ei)
{
pair<size_t, size_t> deg =
make_pair(in_degreeS()(target(base_t::_edges[ei], _g), _g),
out_degree(target(base_t::_edges[ei], _g), _g));
edges_by_end_deg_t& edges = _edges_by_target;
typename edges_by_end_deg_t::mapped_type& elist = edges[deg];
edge_t& e = base_t::_edges[ei];
vertex_t t = target(e, _g);
deg_t tdeg = make_pair(in_degreeS()(t, _g), out_degree(t, _g));
typename edges_by_end_deg_t::mapped_type& elist =
_edges_by_target[tdeg];
tr1::uniform_int<> sample(0, elist.size() - 1);
return elist[sample(base_t::_rng)];
}
void update_edge(size_t e, bool insert) {}
private:
typedef tr1::unordered_map<pair<size_t, size_t>,
vector<pair<index_t, bool> >,
hash<pair<size_t, size_t> > >
typedef pair<size_t, size_t> deg_t;
typedef tr1::unordered_map<deg_t,
vector<pair<size_t, bool> >,
hash<deg_t> >
edges_by_end_deg_t;
edges_by_end_deg_t _edges_by_target;
......
src/graph_tool/centrality/__init__.py
View file @ 990b4739
......@@ -132,23 +132,23 @@ def pagerank(g, damping=0.85, pers=None, weight=None, prop=None, epsilon=1e-6,
>>> g = gt.random_graph(100, lambda: (poisson(3), poisson(3)))
>>> pr = gt.pagerank(g)
>>> print pr.a
[ 0.00867754 0.00729246 0.00363279 0.00668265 0.0015 0.00859964
0.00449637 0.00961946 0.01295288 0.00882362 0.00719256 0.00280697
0.00518114 0.01047904 0.00569656 0.00519058 0.00759745 0.00700835
0.00870244 0.00522561 0.00233159 0.00236035 0.0015 0.00255374
0.00872139 0.00227483 0.00686341 0.001755 0.00488567 0.01045994
0.00393206 0.00988283 0.01376133 0.00721883 0.01429166 0.00752748
0.01846797 0.00674401 0.00412138 0.00842639 0.0015 0.00233159
0.00306271 0.01902149 0.0099247 0.00428981 0.00215072 0.01123842
0.00236035 0.00768803 0.00463719 0.01130437 0.00392423 0.00491263
0.00899519 0.00680983 0.0091988 0.00459334 0.00809094 0.00881614
0.01381946 0.00489171 0.00425249 0.01957383 0.00708763 0.0015
0.00418613 0.00607306 0.01287535 0.00639268 0.01578391 0.0015
0.01541987 0.00860721 0.01378758 0.0173314 0.00775072 0.00247939
0.00524088 0.00686587 0.00436895 0.00755964 0.00708251 0.0015
0.00226438 0.00184085 0.00555171 0.01159494 0.01297596 0.00460887
0.00406717 0.00578091 0.00548516 0.01197071 0.00674202 0.01011666
0.01072786 0.00646937 0.01430012 0.01483996]
[ 0.00865316 0.0054067 0.00406312 0.00426668 0.0015 0.00991696
0.00550065 0.00936397 0.00347917 0.00731864 0.00689843 0.00286274
0.00508731 0.01020047 0.00562247 0.00584915 0.02457086 0.00438568
0.0057385 0.00621745 0.001755 0.0045073 0.0015 0.00225167
0.00698342 0.00206302 0.01094466 0.001925 0.00710093 0.00519877
0.00460646 0.00994648 0.01005248 0.00904629 0.00676221 0.00789208
0.00933103 0.00301154 0.00264951 0.00842812 0.0015 0.00191034
0.00594069 0.00884372 0.00453417 0.00388987 0.00317433 0.0086067
0.00385394 0.00672702 0.00258411 0.01468262 0.00454 0.00381159
0.00402607 0.00451133 0.00480966 0.00811557 0.00571949 0.00317433
0.00856838 0.00280517 0.00280563 0.00906324 0.00614421 0.0015
0.00292034 0.00479769 0.00552694 0.00604799 0.0115922 0.0015
0.00676183 0.00695336 0.01023352 0.01737541 0.00451443 0.00197688
0.00553866 0.00486233 0.0078653 0.00867599 0.01248092 0.0015
0.00399605 0.00399605 0.00881571 0.00638008 0.01056944 0.00353724
0.00249869 0.00684919 0.00241374 0.01061397 0.00673569 0.00590937
0.01004638 0.00331612 0.00926359 0.00460809]
Now with a personalization vector, and edge weights:
......@@ -159,23 +159,23 @@ def pagerank(g, damping=0.85, pers=None, weight=None, prop=None, epsilon=1e-6,
>>> p.a /= p.a.sum()
>>> pr = gt.pagerank(g, pers=p, weight=w)
>>> print pr.a
[ 0.00761942 0.00761689 0.00418938 0.00947758 0.00092715 0.00349991
0.00811226 0.00448968 0.01209889 0.01384828 0.00600036 0.00221745
0.00432908 0.01036427 0.00536132 0.00692364 0.00575216 0.00750936
0.00924536 0.00461255 0.00422277 0.00055639 0.00250025 0.00289125
0.00925617 0.003356 0.00642017 0.00298276 0.00571097 0.01115541
0.00452616 0.01670105 0.01788592 0.00580217 0.01350007 0.00837655
0.01535733 0.00497981 0.00436008 0.01324374 0.00148972 0.00287379
0.00408663 0.02785282 0.00790422 0.00491795 0.00070143 0.00789247
0.00033551 0.00777089 0.00278393 0.00801468 0.00452296 0.00378295
0.00642244 0.00698618 0.01069855 0.0019177 0.00742151 0.00872767
0.01868187 0.00442359 0.00593616 0.01517386 0.00712472 0.00238803
0.00468324 0.0024983 0.011788 0.00577489 0.01242015 0.00300867
0.01390361 0.00796192 0.00822753 0.01062897 0.00815637 0.00332914
0.00911336 0.00915715 0.00945334 0.00880299 0.00758402 0.0009367
0.00378413 0.00174124 0.00283594 0.00929262 0.01090867 0.00460206
0.00341061 0.00699703 0.00232131 0.01244958 0.00731098 0.01288061
0.00820259 0.00430521 0.01633379 0.0119308 ]
[ 0.00712999 0.00663336 0.00685722 0.00402663 0.00092715 0.01021926
0.00269502 0.0073301 0.00449892 0.00582793 0.00580542 0.00275149
0.00676363 0.01157972 0.00486918 0.00616345 0.02506695 0.00607967
0.00553375 0.00359075 0.00293808 0.00362247 0.00250025 0.00186946
0.00895516 0.00318147 0.01489786 0.00312436 0.0074751 0.0040342
0.006254 0.00687051 0.0098073 0.01076278 0.00887077 0.00806759
0.00969532 0.00252648 0.00278688 0.00972144 0.00148972 0.00215428
0.00713602 0.00559849 0.00495517 0.00457118 0.00323767 0.01257406
0.00120179 0.00514838 0.00130655 0.01724465 0.00343819 0.00420962
0.00297617 0.00588287 0.00657206 0.00775082 0.00758217 0.00433776
0.00576829 0.00464595 0.00307274 0.00585795 0.00745881 0.00238803
0.00230431 0.00437046 0.00492464 0.00275414 0.01524646 0.00300867
0.00816665 0.00548853 0.00874738 0.01871498 0.00216776 0.00245196
0.00308878 0.00646323 0.01287978 0.00911384 0.01628604 0.0009367
0.00222119 0.00864202 0.01199119 0.01126539 0.01086846 0.00309224
0.0020319 0.00659422 0.00226965 0.0134399 0.01094141 0.00732916
0.00489314 0.0030402 0.00783914 0.00278588]
References
----------
......@@ -259,23 +259,23 @@ def betweenness(g, vprop=None, eprop=None, weight=None, norm=True):
>>> g = gt.random_graph(100, lambda: (poisson(3), poisson(3)))
>>> vb, eb = gt.betweenness(g)
>>> print vb.a
[ 0.02412512 0.08233748 0.01789612 0.03997773 0. 0.03476439
0.03490215 0.02812729 0.05385124 0.01614861 0.01894254 0.03552189
0.01648793 0.02743878 0.02243743 0.0052126 0. 0.02648145
0.05045875 0.01670867 0.00027069 0.00235053 0. 0.00424986
0.02153982 0.01635659 0.03177692 0.00152088 0.02099129 0.05311383
0.00802715 0. 0.06129706 0.03148129 0.10265395 0.02762436
0.05323276 0.028209 0.01641328 0.03547918 0. 0.00998142
0.01043084 0.06810444 0.01435047 0.02884138 0. 0.02336079
0. 0.09098673 0.02911358 0.05909676 0.01314448 0.0304931
0.01315283 0.03795536 0.02756845 0.020655 0.00268182 0.0151298
0.05597887 0.04095171 0.00853146 0.06176456 0.03165429 0. 0.0114488
0.05435076 0.04046437 0.0122299 0.03417369 0. 0.05979601
0.01502672 0.05908044 0.09567821 0.01206665 0.01259951 0.02381255
0. 0.01840359 0.00708731 0. 0. 0.01274922
0.00385357 0. 0.11470908 0.04903743 0.03336991 0.01270614
0.00448173 0.02163365 0.06685948 0.01032924 0.02400925 0. 0.0300603
0.03220004 0.03932736]
[ 0.04889806 0.07181892 0.0256799 0.02885791 0. 0.05060927
0.04490836 0.03763462 0.02033383 0.03163202 0.02641248 0.03171598
0.03771112 0.02194663 0.0374907 0.01072567 0. 0.03079281
0.05409258 0.00163434 0.00051978 0.01045902 0. 0.00796784
0.0494527 0.00647576 0.03708252 0.00304503 0.0663657 0.03903257
0.03305169 0. 0.07787098 0.03938866 0.08577116 0.020183
0.06024004 0.01004935 0.0443127 0.06397736 0. 0.00363548
0.01742486 0.03216543 0.01918144 0.02059159 0. 0.01476213
0. 0.0466751 0.01072612 0.10288046 0.00563973 0.03850413
0.00629595 0.01292137 0.0537963 0.04454985 0.01227018 0.00729488
0.02092959 0.02308238 0.00712703 0.02193975 0.03823342 0.
0.00995364 0.04023839 0.0312708 0.0111312 0.00228516 0.
0.09659583 0.01327402 0.05792071 0.08606828 0.0143541 0.00221604
0.02144698 0. 0.04023879 0.00715758 0. 0.
0.02348452 0.00760922 0.01486521 0.08132792 0.0382674 0.03078318
0.00430209 0.01772787 0.02280666 0.0373011 0.03077511 0.02871265
0. 0.01044655 0.04415432 0.04447525]
References
----------
......@@ -341,7 +341,7 @@ def central_point_dominance(g, betweenness):
>>> g = gt.random_graph(100, lambda: (poisson(3), poisson(3)))
>>> vb, eb = gt.betweenness(g)
>>> print gt.central_point_dominance(g, vb)
0.0888588565184
0.0766473408634
References
----------
......@@ -419,25 +419,25 @@ def eigenvector(g, weight=None, vprop=None, epsilon=1e-6, max_iter=None):
>>> w.a = random(g.num_edges()) * 42
>>> x = gt.eigenvector(g, w)
>>> print x[0]
0.0144966901652
0.0160851991895
>>> print x[1].a
[ 0.06930562 0.15259089 0.02186899 0.16605389 0. 0.03329417
0.15389839 0.05956241 0.02364241 0.14555449 0.12223866 0.04284837
0.02930074 0.12362235 0.08914056 0.06613553 0.04127041 0.06894616
0.11768146 0.10576438 0.03184658 0.01097733 0. 0.00244834
0.12373674 0.04521477 0.06729261 0. 0.0119752 0.09613809
0.05371652 0.0760725 0.26342747 0.06901843 0.12261332 0.07428021
0.16074381 0.04316101 0.01382325 0.07976272 0. 0.02358784
0.04087066 0.22404933 0.05037626 0.07311321 0.00118498 0.10162138
0.00695398 0.15475802 0.03601454 0.12771193 0.00204537 0.00850904
0.06338004 0.11785637 0.171928 0.00577511 0.08163299 0.02358024
0.25112631 0.05618482 0.00868661 0.15998601 0.11513557 0.
0.07307991 0.06984483 0.1043859 0.15786829 0.12612902 0.
0.11247494 0.06210605 0.07547538 0.09774456 0.01932 0.01132741
0.07459586 0.14437367 0.06477981 0.13211211 0.04886609 0.
0.09043424 0.00644994 0.0112379 0.10472704 0.2077468 0.04146317
0.04745921 0.07442587 0.00757032 0.18575827 0.09975438 0.11682436
0.22233397 0.08072367 0.13527766 0.16050936]
[ 0.1376411 0.07207366 0.02727508 0.05805304 0. 0.10690994
0.04315491 0.01040908 0.02300252 0.08874163 0.04968119 0.06718114
0.05526028 0.20449371 0.02337425 0.07581173 0.19993899 0.14718912
0.08464664 0.08474977 0. 0.04843894 0. 0.0089388
0.16831573 0.00138653 0.11741616 0. 0.13455019 0.03642682
0.06729803 0.06229526 0.08937098 0.05693976 0.0793375 0.04076743
0.22176891 0.07717256 0.00518048 0.05722748 0. 0.00055799
0.04541778 0.06420469 0.06189998 0.08011859 0.05377224 0.29979873
0.01211309 0.15503588 0.02804072 0.1692873 0.01420732 0.02507
0.02959899 0.02702304 0.1652933 0.01434992 0.1073001 0.04582697
0.04618913 0.0220902 0.01421926 0.09891276 0.04522928 0.
0.00236599 0.07686829 0.03243909 0.00346715 0.1954776 0.
0.25583217 0.11710921 0.07804282 0.21188464 0.04800656 0.00321866
0.0552824 0.11204116 0.11420818 0.24071304 0.15451676 0.
0.00475456 0.10680434 0.17054333 0.18945499 0.15673649 0.03405238
0.01653319 0.02563015 0.00186129 0.12061027 0.11449362 0.11114196
0.06779788 0.00595725 0.09127559 0.02380386]
References
----------
......@@ -525,23 +525,31 @@ def eigentrust(g, trust_map, vprop=None, norm=False, epsilon=1e-6, max_iter=0,
>>> trust.a = random(g.num_edges())*42
>>> t = gt.eigentrust(g, trust, norm=True)
>>> print t.a
[ 0.01047704 0.00916079 0.00329728 0.02183509 0. 0.00570515
0.01795154 0.00745325 0.01556352 0.02103068 0.00766733 0.0017898
0.00477604 0.02075469 0.00596795 0.00777273 0.00486571 0.00922539
0.00947171 0.00808096 0.00159634 0.00039577 0. 0.00170102
0.01221233 0.00105577 0.00499953 0. 0.00104692 0.02124238
0.00460677 0.02377163 0.03682221 0.00788995 0.01588396 0.01090949
0.01738658 0.00629269 0.00252249 0.01494837 0. 0.00118236
0.00086828 0.04746846 0.01541295 0.00777287 0.00014366 0.01079173
0.00025071 0.0110701 0.0041122 0.00931269 0.0003396 0.00085525
0.00715489 0.01178172 0.01968206 0.00086696 0.01155932 0.01291423
0.03650986 0.00409654 0.00635913 0.02706258 0.01329375 0. 0.004781
0.00393831 0.0159743 0.01175119 0.01924371 0. 0.01624875
0.01213196 0.01327894 0.02122698 0.01120801 0.00261826 0.01197119
0.01071532 0.01065604 0.00891656 0.00394593 0. 0.00519856
0.00013063 0.00206336 0.01471883 0.02029324 0.00793793 0.00560927
0.00944371 0.00205155 0.01610597 0.009506 0.0211548 0.01688137
0.00731456 0.0175582 0.02643281]
[ 1.12095562e-02 3.97280231e-03 1.31675503e-02 9.61282478e-03
0.00000000e+00 1.73295741e-02 3.53395497e-03 1.06203582e-02
1.36906165e-03 8.64587777e-03 1.12049516e-02 3.18891993e-03
9.28265221e-03 2.25294315e-02 3.24795656e-03 9.16555333e-03
5.68412465e-02 6.79686311e-03 6.37474649e-03 6.04696712e-03
0.00000000e+00 8.51131034e-03 0.00000000e+00 1.09336777e-03
1.49885187e-02 1.09327367e-04 3.73928902e-02 0.00000000e+00
1.74638522e-02 8.21101864e-03 5.79876899e-03 1.34905262e-02
1.71525132e-02 2.25425503e-02 1.04184903e-02 1.05537922e-02
1.34096247e-02 2.82760533e-03 4.31713918e-04 7.39114668e-03
0.00000000e+00 2.21328121e-05 8.79050007e-03 7.08148889e-03
5.88651144e-03 7.45401425e-03 5.66098580e-03 2.80738199e-02
2.41472197e-03 1.00673881e-02 2.29910658e-03 3.23790630e-02
3.02136064e-03 2.25030440e-03 3.53325357e-03 6.90672383e-03
1.01692058e-02 1.03783022e-02 1.22476413e-02 4.82453065e-03
1.15878890e-02 3.41943633e-03 1.57958469e-03 6.56648121e-03
1.28152141e-02 0.00000000e+00 1.29192164e-03 9.35867476e-03
3.89329603e-03 1.78002682e-03 2.81987911e-02 0.00000000e+00
1.74943514e-02 6.24079508e-03 1.57572103e-02 3.77119257e-02
4.78552984e-03 3.30463136e-04 5.60118687e-03 5.75656186e-03
2.65412905e-02 1.59663210e-02 2.88844192e-02 0.00000000e+00
7.87754853e-04 1.76957899e-02 3.19907905e-02 1.94650690e-02
1.32052233e-02 3.57577093e-03 7.09968545e-04 8.70787481e-03
1.24901391e-04 2.61215462e-02 2.25923034e-02 1.10928239e-02
9.39210737e-03 5.61073138e-04 1.59987179e-02 3.02799309e-03]
References
----------
......@@ -643,23 +651,31 @@ def trust_transitivity(g, trust_map, source=None, target=None, vprop=None):
>>> trust.a = random(g.num_edges())
>>> t = gt.trust_transitivity(g, trust, source=g.vertex(0))
>>> print t.a
[ 1. 0.05067451 0.0289009 0.08405839 0. 0.03545782
0.07538031 0.22379499 0.05854478 0.07125532 0.05587198 0.02518448
0.02466963 0.15362921 0.06018343 0.05567524 0.27358358 0.05275904
0.03729045 0.09492006 0.05238415 0.00737096 0. 0.00510552
0.08817629 0.03785795 0.02446122 0. 0.00738919 0.05119031
0.34527065 0.03114924 0.07197484 0.47425602 0.03472084 0.04662672
0.05560849 0.02364994 0.01215894 0.31788601 0. 0.03879942
0.01857147 0.06889803 0.04377159 0.08730759 0.00246369 0.39098931
0.0046694 0.04502054 0.02463968 0.02645761 0.00707666 0.00472783
0.02984028 0.1148617 0.0572029 0.00675625 0.02800698 0.12556046
0.08124247 0.05626964 0.06027068 0.04269226 0.07518783 0.
0.02654427 0.013547 0.35126413 0.05338214 0.02683736 0.
0.03967174 0.15764245 0.03434911 0.06712879 0.01081633 0.01438191
0.22453002 0.13426137 0.11622003 0.18696878 0.02270863 0.
0.09096154 0.00622639 0.01807092 0.06891202 0.05429632 0.03123112
0.04239384 0.06347529 0.00980117 0.08103904 0.06022526 0.09774797
0.0442361 0.03511221 0.09291923 0.10395899]
[ 1.00000000e+00 9.59916062e-02 4.27717883e-02 7.70755875e-02
0.00000000e+00 2.04476926e-01 5.55315822e-02 2.82854665e-02
5.08479257e-02 1.68128402e-01 3.28567434e-02 7.39525583e-02
1.34463196e-01 8.83740756e-02 1.79990535e-01 7.08809615e-02
6.37757645e-02 7.24187957e-02 4.83082241e-02 9.90676983e-02
0.00000000e+00 6.50497060e-02 0.00000000e+00 1.77344948e-02
1.08677897e-01 1.00958718e-03 4.49524961e-02 0.00000000e+00
1.64902280e-01 4.31492976e-02 2.19446085e-01 3.00890381e-02
6.86750847e-02 2.72460575e-02 3.57314594e-02 4.87776483e-02
4.11748930e-01 7.91396467e-02 2.54835127e-03 3.01711432e-01
0.00000000e+00 4.14406224e-04 4.24794624e-02 9.14096554e-02
4.17528677e-01 3.79112573e-02 1.16489950e-01 5.18112902e-02
8.49111259e-03 5.26399996e-02 2.45690139e-02 7.51435125e-02
5.62381854e-02 2.90115777e-02 2.72543383e-02 1.46877163e-01
7.81446822e-02 1.24417763e-02 1.01337976e-01 9.92776442e-02
3.14622176e-02 1.20097319e-01 3.30335980e-02 4.61757040e-02
1.01085599e-01 0.00000000e+00 4.44660446e-03 6.31066845e-02
1.94702084e-02 8.45343379e-04 4.82190327e-02 0.00000000e+00
6.60346087e-02 7.44581695e-02 6.19535229e-02 1.82072422e-01
1.45366611e-02 2.59020075e-02 2.52208295e-02 6.80519730e-02
6.74671969e-02 1.14198914e-01 5.12493343e-02 0.00000000e+00
6.33427008e-03 1.42290348e-01 6.90459437e-02 1.00565411e-01
5.88966867e-02 3.28157280e-02 2.80046903e-02 2.41520032e-01
8.45879329e-04 6.76633672e-02 6.05080467e-02 9.12575826e-02
1.97789973e-02 6.40885493e-02 4.80934526e-02 1.28787181e-02]
References
----------
......
src/graph_tool/clustering/__init__.py
View file @ 990b4739
......@@ -115,7 +115,7 @@ def local_clustering(g, prop=None, undirected=True):
>>> g = gt.random_graph(1000, lambda: (5,5))
>>> clust = gt.local_clustering(g)
>>> print gt.vertex_average(g, clust)
(0.008338888888888889, 0.0004126098432127491)
(0.00908888888888889, 0.0004449824521439575)
References
----------
......@@ -172,7 +172,7 @@ def global_clustering(g):
>>> seed(42)
>>> g = gt.random_graph(1000, lambda: (5,5))
>>> print gt.global_clustering(g)
(0.008353381448810478, 0.00041351594365452094)
(0.009114059777509717, 0.0004464454368899158)
References
----------
......@@ -247,11 +247,11 @@ def extended_clustering(g, props=None, max_depth=3, undirected=False):
>>> for i in xrange(0, 5):
... print gt.vertex_average(g, clusts[i])
...
(0.0061200000000000004, 0.0004859481453817887)
(0.024368333333333332, 0.0009455588573842338)
(0.11548333333333334, 0.0020091290954595783)
(0.3999433333333333, 0.0030720255912562527)
(0.43571666666666664, 0.0031127199163718177)
(0.0058850000000000005, 0.0004726257592782405)
(0.026346666666666668, 0.0009562588213100747)
(0.11638833333333333, 0.002086419787711849)
(0.3862533333333333, 0.003020064612995335)
(0.44685499999999995, 0.003124572962377774)
References
----------
......@@ -324,7 +324,7 @@ def motifs(g, k, p=1.0, motif_list=None):
>>> print len(motifs)
11
>>> print counts
[116256, 392719, 335, 380, 2954, 850, 808, 1, 11, 4, 1]
[115104, 389090, 724, 820, 1828, 3208, 791, 4, 12, 12, 3]
References
......@@ -489,7 +489,7 @@ def motif_significance(g, k, n_shuffles=100, p=1.0, motif_list=None,
>>> print len(motifs)
11
>>> print zscores
[0.37033485499317503, 0.2502032768488251, 0.11813557960717248, -0.64062741073137097, -0.6280946022901569, -0.36863102213820809, 0.54809736376108453, 1.89, 0.87, -0.48, -0.19]
[0.014875553792545083, 0.016154998074953769, 0.002455801898331304, -1.9579019397305546, 0.83542298414538518, 0.84715258999068244, -0.93385230436820643, -0.11, -0.1, -0.31, -0.14]
"""
s_ms, counts = motifs(g, k, p, motif_list)
......
src/graph_tool/correlations/__init__.py
View file @ 990b4739
......@@ -109,7 +109,7 @@ def assortativity(g, deg):
... lambda i,k: 1.0 / (1 + abs(i - k)), directed=False,
... mix_time=100)
>>> gt.assortativity(g, "out")
(0.15024063611634234, 0.0051996387349654925)
(0.14145218664992676, 0.005077209994557802)
References
----------
......@@ -179,13 +179,13 @@ def scalar_assortativity(g, deg):
>>> g = gt.random_graph(1000, lambda: sample_k(40), lambda i,k: abs(i-k),
... directed=False, mix_time=100)
>>> gt.scalar_assortativity(g, "out")
(-0.4569707008340512, 0.010227503673605132)
(-0.46972665544654923, 0.010035656615797507)
>>> g = gt.random_graph(1000, lambda: sample_k(40),
... lambda i, k: 1.0 / (1 + abs(i - k)),
... directed=False, mix_time=100)
>>> gt.scalar_assortativity(g, "out")
(0.5921961942149022, 0.011625836226217939)
(0.6120658464996896, 0.011388445161055338)
References
----------
......
src/graph_tool/generation/__init__.py
View file @ 990b4739
......@@ -181,7 +181,7 @@ def random_graph(N, deg_sampler, deg_corr=None, directed=True,
... lambda i, k: 1.0 / (1 + abs(i - k)), directed=False,
... mix_time=100)
>>> gt.scalar_assortativity(g, "out")
(0.6279771609121966, 0.010942827982112517)
(0.6435658697163692, 0.010420519538259333)
The following samples an in,out-degree pair from the joint distribution:
......
src/graph_tool/spectral/__init__.py
View file @ 990b4739
......@@ -84,12 +84,12 @@ def adjacency(g, sparse=True, weight=None):
>>> m = gt.adjacency(g)
>>> print m.todense()
[[ 0. 0. 0. ..., 0. 0. 0.]
[ 1. 0. 1. ..., 0. 0. 0.]
[ 0. 0. 0. ..., 0. 0. 0.]
...,
[ 0. 0. 0. ..., 0. 0. 0.]
[ 0. 0. 0. ..., 1. 0. 0.]
[ 0. 0. 1. ..., 0. 0. 0.]]
...,
[ 0. 0. 0. ..., 0. 0. 1.]
[ 0. 0. 1. ..., 0. 0. 0.]
[ 0. 1. 0. ..., 0. 0. 0.]]
References
----------
......@@ -192,12 +192,12 @@ def laplacian(g, deg="total", normalized=True, sparse=True, weight=None):
>>> m = gt.laplacian(g)
>>> print m.todense()
[[ 1. 0. 0. ..., 0. 0. 0. ]
[ 0.05 1. 0.05 ..., 0. 0. 0. ]
[ 0. 1. 0. ..., 0. 0. 0. ]
[ 0. 0. 1. ..., 0. 0. 0. ]
...,
[ 0. 0. 0. ..., 1. 0. 0. ]
[ 0. 0. 0. ..., 0.05 1. 0. ]
[ 0. 0. 0.05 ..., 0. 0. 1. ]]
[ 0. 0. 0. ..., 1. 0. 0.05]
[ 0. 0. 0.05 ..., 0. 1. 0. ]
[ 0. 0.05 0. ..., 0. 0. 1. ]]
References
----------
......
src/graph_tool/stats/__init__.py
View file @ 990b4739
......@@ -371,12 +371,10 @@ def distance_histogram(g, weight=None, bins=[0, 1], samples=None,
>>> g = gt.random_graph(100, lambda: (3, 3))
>>> hist = gt.distance_histogram(g)
>>> print hist
[array([ 0.00000000e+00, 3.00000000e+02, 8.44000000e+02,
2.12300000e+03, 3.76100000e+03, 2.62500000e+03,
2.46000000e+02, 1.00000000e+00]), array([0, 1, 2, 3, 4, 5, 6, 7, 8], dtype=uint64)]
[array([ 0., 300., 861., 2165., 3801., 2576., 197.]), array([0, 1, 2, 3, 4, 5, 6, 7], dtype=uint64)]
>>> hist = gt.distance_histogram(g, samples=10)
>>> print hist
[array([ 0., 30., 83., 213., 377., 257., 30.]), array([0, 1, 2, 3, 4, 5, 6, 7], dtype=uint64)]
[array([ 0., 30., 87., 221., 395., 234., 23.]), array([0, 1, 2, 3, 4, 5, 6, 7], dtype=uint64)]
"""
if samples != None:
......
src/graph_tool/topology/__init__.py
View file @ 990b4739
......@@ -173,7 +173,7 @@ def subgraph_isomorphism(sub, g, max_n=0, random=True):
>>> sub = gt.random_graph(10, lambda: (poisson(1.8), poisson(1.9)))
>>> vm, em = gt.subgraph_isomorphism(sub, g)
>>> print len(vm)
118
79
>>> for i in xrange(len(vm)):
... g.set_vertex_filter(None)
... g.set_edge_filter(None)
......@@ -386,10 +386,9 @@ def dominator_tree(g, root, dom_map=None):
>>> root = [v for v in g.vertices() if v.in_degree() == 0]
>>> dom = gt.dominator_tree(g, root[0])
>>> print dom.a
[ 0 0 33 81 0 6 0 0 0 0 0 33 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 67 0 0 5 0 0 81 0 0 0 0 87 0 0 0 0 0 0 0 0 0 0 0
0 80 0 0 0 0 0 99 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0
0 0 0 0 0 0 5 11 0 0 0 0 88 30 0 0 0 0 0 0 0 0 0 0 84]
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
References
----------
......@@ -431,8 +430,8 @@ def topological_sort(g):
>>> g.set_edge_filter(tree)
>>> sort = gt.topological_sort(g)
>>> print sort
[ 0 1 2 15 3 4 25 5 6 7 28 14 29 26 12 24 8 9 10 11 13 16 17 18 19
27 20 21 22 23]
[ 3 20 9 29 15 0 10 23 1 2 21 7 4 12 11 5 26 27 6 8 13 14 22 16 17
28 18 19 24 25]
References
----------
......@@ -521,11 +520,11 @@ def label_components(g, vprop=None, directed=None):
>>> g = gt.random_graph(100, lambda: (1, 1))
>>> comp, hist = gt.label_components(g)
>>> print comp.a
[0 1 0 1 1 1 2 1 1 1 0 1 0 1 0 0 1 0 1 0 3 0 1 0 1 0 4 3 0 0 0 0 1 1 0 4 1
1 1 1 3 1 0 2 1 1 0 1 0 0 0 1 1 0 0 1 3 1 1 1 1 1 0 1 2 1 1 1 1 0 1 0 0 1
0 3 1 1 1 1 2 3 1 3 0 1 0 0 1 0 0 0 1 1 0 3 1 4 1 1]
[0 0 0 1 0 2 0 0 0 0 2 0 0 0 2 1 0 2 0 1 2 0 1 0 0 1 0 2 0 2 1 0 2 0 0 0 0
0 0 1 0 0 2 2 2 0 0 0 0 0 0 2 0 0 1 1 0 0 2 0 1 0 0 0 2 0 0 2 2 1 2 1 0 0
2 0 0 1 2 1 2 2 0 0 0 0 0 2 0 0 0 1 1 0 0 0 1 1 2 2]
>>> print hist
[35 50 4 8 3]
[58 18 24]
"""
if vprop is None:
......@@ -573,12 +572,12 @@ def label_largest_component(g, directed=None):
>>> g = gt.random_graph(100, lambda: poisson(1), directed=False)
>>> l = gt.label_largest_component(g)
>>> print l.a
[0 0 0 1 0 1 0 0 1 1 0 1 1 0 0 0 0 1 1 0 0 1 1 0 1 0 0 1 1 0 0 0 1 0 1 0 0
1 1 1 1 0 1 1 0 1 1 0 1 0 1 1 0 0 1 1 0 0 0 1 0 0 1 1 1 1 1 0 0 1 0 0 1 1
0 1 0 0 1 0 1 1 1 1 1 0 0 1 1 0 1 0 1 0 1 0 0 1 0 0]
[1 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 1 0 0 1 1 1 1 0 0 0 1 0 0 0 0 0 0 0 1
1 1 0 0 0 0 1 0 1 1 0 0 0 1 1 0 0 1 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0
0 0 0 1 1 0 1 1 0 0 0 0 0 1 1 0 1 0 1 0 1 0 0 0 0 0]
>>> u = gt.GraphView(g, vfilt=l) # extract the largest component as a graph
>>> print u.num_vertices()
50
31
"""
label = g.new_vertex_property("bool")
......@@ -648,16 +647,15 @@ def label_biconnected_components(g, eprop=None, vprop=None):
>>> g = gt.random_graph(100, lambda: 2, directed=False)
>>> comp, art, hist = gt.label_biconnected_components(g)
>>> print comp.a
[0 1 1 0 0 0 0 0 0 2 2 0 0 0 0 0 1 2 0 0 0 0 0 0 0 0 0 0 2 0 1 0 0 0 0 0 0
1 0 0 0 0 1 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0]
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0
0 0 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 1
0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0]
>>> print art.a
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
>>> print hist
[83 13 4]
[87 13]