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Tiago Peixoto
graph-tool
Commits
33f7f0bb
Commit
33f7f0bb
authored
Feb 20, 2010
by
Tiago Peixoto
Browse files
Fix doctests
This fixes the expected values for several doctests, after random_graph() re-factoring.
parent
1f28345e
Changes
8
Hide whitespace changes
Inline
Side-by-side
src/graph_tool/centrality/__init__.py
View file @
33f7f0bb
...
...
@@ -105,23 +105,23 @@ def pagerank(g, damping=0.8, prop=None, epslon=1e-6, max_iter=None,
>>> g = gt.random_graph(100, lambda: (poisson(3), poisson(3)))
>>> pr = gt.pagerank(g)
>>> print pr.a
[ 0.
89482844 1.37847566 0.24 1.30716676
0.2 0.7
0397009
0.4
0205781 0.747837
25
1.3
71
6
70
15
0.
66836587 0.5868133 0.47968714
1.52225854 1.07388611 0.76316432 0.39214247 0.9302883 0.8645576
2
0.7
7
54
6264 1.87740317 0.25482139 0.29902553
0.2 0.2
4756383
0.
97205301 0.29727392 1.34742309 0.30905457 0.55032542 0.56654712
0.
40895463 0.77928729 0.73227413 0.59911926 1.39946277 0.72793699
2.27008393 0.88929335 0.48636962 0.73070609
0.2 0.
232
0.
96857512 2.97683022 0.58581032 0.80217847 0.37896569
0.938
66821
0.
27337672 0.98201842 0.48551839 1.22651796 0.73263045 0.43013
22
8
1.00971133 0.72075953
0.
6
671
5456 0.58705749 0.74286661 0.3778586
7
1.
8475279 0.26432925 0.33994628 0.97319326
0.
7
81
04447
0.2
0.3
3333761 0.51756267 0.47811583 0.85905246 1.46428623
0.2
1
.7
0687671 1.0107342
0.
945
047
37
1.
29858046 2.19707395 0.55931282
0.8
5129509
1.0
9
49
3368 1.22168331 0.64108136 0.70690188
0.2
0.3
1736266 0.42372513 0.79429328 1.44749664 1.20741669 0.65763236
0.
40895463 0.62628812 0.32671006 0.85626447 0.59925496 0.3399879
0.81215
04
6
0.
71506902 2.25678844 1.04882679
]
[ 0.
63876901 1.13528868 0.31465963 0.55855277
0.2 0.7
5605741
0.4
2628689 0.53066
25
4
0.55004112 0.91
7170
76
0.
71164749 0.32015438
0.67275227 1.08207389 1.14412231 0.9049167 1.32002 1.469214
2
0.7
6
54
9771 0.71510277 0.23732927 0.40844911
0.2 0.2
7912876
0.
71309781 0.32015438 1.3376236 0.31352887 0.59346569 0.33381039
0.
67300081 0.73318264 0.65812653 0.73409673 0.93051993 0.83241145
1.59816568 0.43979363 0.2512247 1.15663357
0.2 0.
35977148
0.
72182022 1.01267711 0.76304859 0.49247376
0.
4
938
4283 1.8436647
0.
64312224 1.00778243 0.62287633 1.15215387 0.56176895 0.7166
22
7
0.56506109
0.671
04337 0.95570565 0.27996953 0.79975983 0.3363149
7
1.
09471419 0.33631497 0.2512247 2.09126732
0.
6
81
57485
0.2
0.3
7140185 0.65619459 1.27370737 0.48383225 1.36125161
0.2
0
.7
8300573 1.03427279
0.
569
047
55
1.
66077917 1.73302035 0.28749261
0.8
3143045
1.049
69728 0.70090048 0.55991433 0.68440994
0.2
0.3
4018009 0.45485484 0.28 1.2015438 2.11850885 1.24990775
0.
59914308 0.59989185 0.73535564 0.78168417 0.55390281 0.38627667
1.422747
04 0.
51105348 0.92550979 1.27968065
]
References
----------
...
...
@@ -202,23 +202,23 @@ 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
[ 0.03
047981 0.07396685
0.00
2
70
882
0.0
44637
0. 0.0
3259048
0.0
24354
7
0.0
4265909 0.06274696 0.01778475 0.03502657 0.0269227
3
0.0
5170277 0.05522454 0.02303023 0.0038858
0. 0.0
4852871
0.0
2398655 0.00232365 0. 0.01064643
0. 0.0
1105872
0.0
3564021 0.0222059 0.05170383
0.00
14044
7 0.0
3935299 0.02644813
0.0
1831885
0. 0.0
453981 0.04552396 0.1242787
0.049
83878
0.0
7248363 0.04676976 0.03481327 0.04473583
0. 0.0
027
417
0.010
61048 0.0470108 0.01059109 0.05290495
0. 0.02
541583
0. 0.0
4012033 0.02616307 0.09056515 0.01640322
0.0
159
90
0
7
0.0
2784563 0.05008998
0.037
88222 0.03028745 0.01097982
0.001
78571
0.0
5804645
0.010
15181
0.00
61582 0.0255485
0.05
504439
0.
0.00
179516 0.03367643 0.00304982
0.02
333
25
4
0.0
0843039
0.
0.05
947385 0.01936996 0.0521946 0.04928937
0.0
3955121 0.01360865
0.0
2942447
0. 0.0
5149102
0.0
1
054
765
0. 0.
0.005
37915
0.01
251828 0.01097982 0.06667564 0.04090169 0.02161779
0.0
2941671 0.01793679 0.02360528 0.02638257 0.0062989
0.00
94612
3
0. 0.0
2255701 0.05081734 0.04846652
]
[ 0.03
395047 0.07911989
0.0070
2948
0.0
2337119
0. 0.0
2930099
0.0
168437
7 0.0
2558675 0.03440095 0.02886187 0.03124262 0.0097595
3
0.0
1307953 0.03938858 0.07266505 0.01313647
0. 0.0
6450598
0.0
575418 0.00525468 0.00466089 0.01803829
0. 0.0
0050161
0.0
085034 0.02362432 0.05620574
0.00
09715
7 0.0
4006816 0.01301474
0.0
2154916
0. 0.0
6009194 0.02780363 0.08963522
0.
04
049
657
0.0
6993559 0.02082698 0.00288318 0.03264322
0. 0.0
36
417
59
0.010
83859 0.03750864 0.04079359 0.02092599
0. 0.02
153655
0. 0.0
5674631 0.03861911 0.05473282 0.00904367
0.0
324
90
9
7
0.0
0894043 0.0192741
0.03
3
7
9204 0.02125998 0.0018321
0.001
3495
0.0
336502
0.0
2
10
088
0.00
125318 0.0489189
0.05
254974
0.
0.00
432189 0.04866168 0.06444727
0.02
5085
25 0.0
2533085
0.
0.05
308703 0.02539854 0.02270809 0.044889
0.0
4766016 0.0086368
0.0
1501699
0. 0.0
3107868
0.0054
221
0. 0.
0.005
96081
0.01
183977 0.00159761 0.11435876 0.03988501 0.05128991
0.0
4558135 0.02303469 0.05092032 0.04700221 0.00927644
0.00
84190
3
0. 0.0
3243633 0.04514374 0.05170213
]
References
----------
...
...
@@ -283,7 +283,7 @@ 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)
0.0
98021233955
9
0.0
88441481190
9
References
----------
...
...
@@ -359,31 +359,31 @@ def eigentrust(g, trust_map, vprop=None, norm=False, epslon=1e-6, max_iter=0,
>>> trust.get_array()[:] = random(g.num_edges())*42
>>> t = gt.eigentrust(g, trust, norm=True)
>>> print t.get_array()
[
1.04935746
e-0
2
2.82745068e-02 0.00000000e+00 1.81121002
e-0
2
0.00000000e+00
3.70898521e-03 1.00108703e-03 1.29620638
e-0
2
1.
7187404
7e-02
7.07523828e-03 8.29873222
e-0
3
1.79259666
e-0
3
4.08925756
e-0
2
1.
55855653e-02 2.92256968
e-0
3
1.71520782
e-0
3
5.04335865
e-0
3
1.25678184e-02 1.92903241
e-0
2
2
.4
6642649
e-0
2
1.76431290
e-0
4
1.
85066489
e-0
4
0.00000000e+00
4.52686439
e-0
4
7.13943855
e-03
2
.36
002975
e-03
1.44366165e-02 4.3963254
3e-0
4
7.50316
67
1
e-0
3
8
.1
3521884
e-0
3
3.98083843e-03 1.04883920
e-0
2
7.
420996
89e-03
2.46651355e-03 2.08148781e-02 8.02104873
e-03
2.
5
93
66573
e-02
2.11125347
e-0
2
7.45781416e-03 6.62338254
e-0
3
0.00000000e+00
0.00000000e+00 1.72521147
e-02
4
.7
43464
99e-0
2
8.10
5
936
68
e-03
2.27229702
e-0
2
2.2152558
6e-0
3
6.24223052
e-0
3
2
.5
9753300e-03 9.15181124e-03 3.67310718e-03 1.18998211
e-02
1.
66177496e-02 6.44748287e-03 8.01978992
e-03 1.
48621102
e-02
6
.6
5606246
e-0
3
3.39887550
e-0
3
1.
20188240e-02 3.51012614
e-03
2.79661104e-02 7.90103914
e-0
5
1.1
8015521e-03 8.17179744
e-0
3
1.05694658
e-0
2
0.00000000e+00
4.49123443e-04 9.80728243
e-0
4
2.
70933271
e-0
3
1.61865322
e-0
2
2.
1350412
4e-02 0.00000000e+00
1.17773123
e-0
2
4
.6
349020
3e-0
3
1.
79331966
e-0
2
1.46366115
e-02
3.26856602e-02 4.31126006
e-0
3
1.68787878e-02 2.02752156
e-02
1.
48203062e-02 1.17346898e-03 7.87933309
e-03 0.00000000e+00
1.13274458e-03 2.25418313e-03 1.27966643e-02 2.46154526
e-02
7.15248968
e-0
3
8.35660945
e-0
3
3.
88259360e-03 5.95428313
e-03
1.16751480
e-0
4
5.
78637193e-03 6.50575506e-03 1.47111816
e-03
1.22855215e-02 1.34294277
e-0
2
4
.0
3141738
e-0
2
2.77313687
e-02]
[
5.51422638
e-0
3
1.12397965e-02 2.34959294e-04 6.32738574
e-0
3
0.00000000e+00
6.34804836e-03 2.67885424e-03 4.02497751
e-0
3
1.
6794346
7e-02
6.46196106e-03 1.92402451
e-0
2
9.04032352
e-0
4
9.70843104
e-0
3
1.
40319816e-02 1.04995777
e-0
2
2.86712231
e-0
2
2.47285894
e-0
2
2.38394469e-02 7.06936059
e-0
3
9
.4
5794717
e-0
3
2.09970054
e-0
5
1.
64768298
e-0
3
0.00000000e+00
1.19346706
e-0
3
6.88434371
e-03
5
.36
337333
e-03
2.08428677e-02 2.8581378
3e-0
3
1.10564
67
0
e-0
2
3
.1
6345060
e-0
4
5.25737238e-03 5.43761445
e-0
3
7.
980483
89e-03
7.95939648e-03 2.23891858e-02 5.68630666
e-03
2.
0
93
00588
e-02
4.28902068
e-0
3
1.70833078e-03 2.37814042
e-0
2
0.00000000e+00
1.20805010e-03 1.29713483
e-02
5
.7
3021
99
2
e-0
3
8.
7
10936
74
e-03
7.77661067
e-0
3
8.7648980
6e-0
4
2.38519385
e-0
2
3
.5
3225723e-03 8.46948906e-03 5.09874234e-03 2.44547150
e-02
1.
32342629e-02 1.80085559e-03 4.37189381
e-03 1.
18195253
e-02
1
.6
2748861
e-0
2
1.83200678
e-0
4
1.
09745025e-02 1.47544090
e-03
3.34512926e-02 1.58885132
e-0
3
1.1
3128910e-03 3.04944830
e-0
2
4.22684975
e-0
3
0.00000000e+00
9.89654274e-04 4.25927156
e-0
3
2.
34516214
e-0
2
4.91370905
e-0
3
2.
2936666
4e-02 0.00000000e+00
6.83407601
e-0
3
1
.6
050875
3e-0
2
1.
62762068
e-0
3
3.94324856
e-02
2.84109571e-02 8.81167727
e-0
4
2.16999908e-02 1.28688125
e-02
1.
10825963e-02 2.64915564e-03 2.88711928
e-03 0.00000000e+00
4.24392252e-03 9.38398819e-03 0.00000000e+00 1.74508371
e-02
3.26594153
e-0
2
4.07188867
e-0
2
3.
20678152e-03 6.35046287
e-03
8.07061556
e-0
3
5.
08505374e-03 3.27300367e-03 3.30989070
e-03
2.30651195e-02 4.20338525
e-0
3
5
.0
4332662
e-0
3
3.58731532
e-02]
References
----------
...
...
@@ -477,23 +477,23 @@ def absolute_trust(g, trust_map, source, target = None, vprop=None):
>>> trust.a = random(g.num_edges())
>>> t = gt.absolute_trust(g, trust, source=g.vertex(0))
>>> print t.a
[ 0.
05927703 0.06133836 0. 0.05630559
0. 0.0
3317174
0.03
488483 0.15920558 0.16940159
0.0
971
6039 0.
1485169
0.0
1202
87
0.0
3787312 0.37284274 0.00646336 0.0084941 0.0379645 0.07997339
0.1
0733769 0.10053845 0.00283938 0.05224064
0. 0.
16523684
0.0
393326 0.25853808 0.14682555 0.03254906 0.12124144 0.0118341
0.
18110
839 0.
18513216 0.05031324 0.04484457 0.17197674 0.08569659
0.1
7523371 0.22435776 0.33916191 0.07980329
0. 0.
0.0
9750183 0.09811054 0.14574289 0.0085499 0.34593499 0.03151408
0.
083739 0.05409947 0.09161205 0.19921201 0.10647812 0.21597
25
3
0.0
6266044 0.8738786 0.11239455 0.09493216 0.19073287 0.11968616
0.1
3409125
0.006
26821 0.05857625 0.05917779 0.05673643
0.
0.0
2682173 0.00355514 0.17475858 0.15113517 0.13247358
0.
0.0
4003866 0.00997401 0.11126411 0.07400706 0.11247583 0.10125886
0.1
6028191
0.0430
0862 0.03259707 0.0225482
0.
05538721 0.
0.
06715919 0.0701153
0.
02999368 0.04675702 0.06310919 0.01722603
0.
18455906 0.08034113 0.00376382 0.10041304 0.3437539 0.105302
38
0.1
1654855 0.09495419 0.05317485 0.1072776
7]
[ 0.
16260667 0.04129912 0.13735376 0.19146125
0. 0.0
9147461
0.
1
03
71912 0.12465511 0.24631221
0.06039
16
0.
2375385
0.0
6637
87
9
0.0
8897662 0.0800988 0.05250601 0.66759022 0.09368793 0.08275437
0.1
3674709 0.15553915 0.01376162 0.417068
0. 0.
06096886
0.0
8746817 0.39380693 0.09215297 0.09575144 0.15594162 0.04008874
0.
054
839
72
0.
05691086 0.13571077 0.32376012 0.22477937 0.06347962
0.1
0445085 0.19447845 0.38007043 0.13810585
0. 0.
08451096
0.0
6648153 0.18479174 0.13003649 0.14850631 0.00320603 0.1074644
0.
12088162 0.06792678 0.08472666 0.2002143 0.25963204 0.378384
25
0.0
3089371 0.18389694 0.39420339 0.03348093 0.11483196 0.0656204
0.1
4206403
0.0
7
06
6434 0.25168986 0.07040126 0.04870569
0.
0.0
9861349 0.03882069 0.1105267 0.07951823 0.08748441
0.
0.0
8393443 0.11121719 0.21903223 0.25529628 0.0414386 0.03695558
0.1
7664854
0.0
51
430
33 0.11735779 0.06525968
0.
19600919 0. 0.1220922
0.
33330041 0.
0.
28595961 0.14526678 0.12514885 0.089524
0.
40738962 0.03719195 0.54409979 0.06247424 0.10660136 0.11674
38
5
0.1
3218144 0.02214988 0.2321593
7]
"""
if
vprop
==
None
:
...
...
src/graph_tool/clustering/__init__.py
View file @
33f7f0bb
...
...
@@ -112,7 +112,7 @@ def local_clustering(g, prop=None, undirected=False):
>>> g = gt.random_graph(1000, lambda: (5,5))
>>> clust = gt.local_clustering(g)
>>> print gt.vertex_average(g, clust)
(0.005
4000000000000003, 0.00046091213913282869
)
(0.005
2816666666666671, 0.00046415526317530143
)
References
----------
...
...
@@ -174,7 +174,7 @@ def global_clustering(g):
>>> seed(42)
>>> g = gt.random_graph(1000, lambda: (5,5))
>>> print gt.global_clustering(g)
(0.00
91250670960815895, 0.00044046497971164271
)
(0.00
75696677384780283, 0.00039465997422993533
)
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])
...
(0.005
4000000000000003, 0.00046091213913282869
)
(0.02
735
333333333333
4
, 0.0010
35315646983512
5)
(0.11
857833333333334, 0.002002602051082541
)
(0.40
064, 0.0030508380196631575
)
(0.42
819166666666664, 0.0030905005774865082
)
(0.005
2816666666666671, 0.00046415526317530143
)
(0.02
654
333333333333
2
, 0.0010
40537419904840
5)
(0.11
648, 0.0019761350156302583
)
(0.40
672499999999995, 0.0031128844140121867
)
(0.42
646999999999996, 0.0030644539462829075
)
References
----------
...
...
@@ -330,9 +330,10 @@ def motifs(g, k, p=1.0, motif_list=None, undirected=None):
>>> g = gt.random_graph(1000, lambda: (5,5))
>>> motifs, counts = gt.motifs(g, 4, undirected=True)
>>> print len(motifs)
1
3
1
1
>>> print counts
[114750, 387884, 875, 1004, 2254, 3162, 770, 14, 8, 7, 16, 2, 5]
[115780, 390603, 667, 764, 2666, 1694, 821, 5, 9, 4, 4]
References
----------
...
...
@@ -499,7 +500,7 @@ def motif_significance(g, k, n_shuffles=100, p=1.0, motif_list=None,
>>> print len(motifs)
12
>>> print zscores
[
-
0.
16076033462706543
,
-
0.
16176522339544466, 0.010436730454036206
, 0.
0944284162896325
, 0.
20360864249886917
, 0.
20675511240829708
, -0.
4
24
78047102781324, -0.10000000000000001, -0.10000000000000001
, -0.2
8
00000000000000
3
, -0.14000000000000001, -0.01]
[0.
84493166311546375
, 0.
83875258032645894, 1.2117425659306142
,
-
0.
20405718722884647
,
-
0.
69886762637118316
,
-
0.
68343227667794837
, -0.
9
24
81997609648403, -0.11, -0.14999999999999999
, -0.2
6
00000000000000
1
, -0.14000000000000001, -0.01]
"""
s_ms
,
counts
=
motifs
(
g
,
k
,
p
,
motif_list
,
undirected
)
...
...
src/graph_tool/correlations/__init__.py
View file @
33f7f0bb
...
...
@@ -105,7 +105,7 @@ def assortativity(g, deg):
>>> g = gt.random_graph(1000, lambda: sample_k(40),
... lambda i,k: 1.0/(1+abs(i-k)), directed=False)
>>> gt.assortativity(g, "out")
(0.1
4264767490573943, 0.0050939131827104651
)
(0.1
5379786566227244, 0.0052484342042414195
)
References
----------
...
...
@@ -174,12 +174,12 @@ def scalar_assortativity(g, deg):
>>> g = gt.random_graph(1000, lambda: sample_k(40), lambda i,k: abs(i-k),
... directed=False)
>>> gt.scalar_assortativity(g, "out")
(-0.4
7490876200787641, 0.010404791498106225
)
(-0.4
6612545377150078, 0.010365307846181516
)
>>> g = gt.random_graph(1000, lambda: sample_k(40),
... lambda i,k: 1.0/(1+abs(i-k)),
... directed=False)
>>> gt.scalar_assortativity(g, "out")
(0.6
2401111084836425, 0.011049051007488318
)
(0.6
3254355342678503, 0.011015440807502176
)
References
----------
...
...
src/graph_tool/flow/__init__.py
View file @
33f7f0bb
...
...
@@ -85,44 +85,44 @@ def edmonds_karp_max_flow(g, source, target, capacity, residual=None):
Examples
--------
>>> from numpy.random import seed, random
>>> seed(4
2
)
>>> seed(4
3
)
>>> g = gt.random_graph(100, lambda: (2,2))
>>> c = g.new_edge_property("double")
>>> c.a = random(len(c.a))
>>> res = gt.edmonds_karp_max_flow(g, g.vertex(0), g.vertex(1), c)
>>> res.a = c.a - res.a # the actual flow
>>> print res.a[0:g.num_edges()]
[ 0.39416408 0.72865707 0. 0. 0.02419415 0.05808361
0. 0. 0.70807258 0.02058449 0.02058449 0.
0.21233911 0.18182497 0.01658783 0. 0. 0. 0.
0. 0. 0.11572935 0. 0.1483536 0.5083988
0.19967378 0.02058449 0. 0. 0. 0.11572935
0. 0. 0.47060682 0. 0. 0. 0.
[ 0. 0.24058962 0. 0. 0. 0. 0.
0.05688494 0.39495002 0.03148888 0. 0.05688494 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.03148888
0. 0. 0. 0. 0.52856316 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0.3571333 0. 0.
0. 0. 0.11118128 0.0884925 0. 0. 0.
0. 0. 0. 0. 0.14837338 0. 0.
0.0884925 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0.20875992 0.46564427 0. 0.02058449 0. 0. 0.
0.23696565 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0.13361314 0.
0.03148888 0. 0.13361314 0. 0. 0.
0.10335251 0. 0. 0. 0. 0.
0.03148888 0. 0. 0. 0. 0. 0.
0. 0.80064166 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0.61693698 0.10335251
0.13723711 0. 0. 0.05688494 0. 0. 0.
0. 0. 0. 0. 0.08837382 0. 0.
0. 0. 0. 0. 0.03148888 0. 0.
0. 0. 0. 0. 0. 0. 0.
0.01658783 0. 0. 0. 0. 0. 0.
0. 0.20875992 0. 0.09303057 0. 0.
0.09303057 0.22879817 0. 0. 0. 0. 0.
0. 0.05808361 0. 0.18657006 0. 0.11118128
0. 0. 0. 0. 0. 0.13361314
0. 0.23696565 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0.11572935 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.
0.12776911 0. 0. 0. 0. 0. 0.
0. 0.02058449 0. 0.11572935 0. 0.32182874
0.18657006 0. 0. 0. 0. 0.31729067
0. 0.14837338 0. 0.11572935 0.09028977 0. 0.
0. 0. 0. 0. 0.01658783 0.08227776
0. 0. 0. 0. 0. 0.14837338
0. 0.20875992 0.11347352 0.09886559 0.65221433 0. 0.
0. 0. 0. 0. 0. 0. 0.
0.05808361 0. 0. 0. 0. 0. 0.
0. ]
0.13361314 0. 0.16872599 0. 0. 0.
0.13361314 0.13723711 0. 0. 0. 0. 0.
0.13723711 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.13361314
0. 0.40569164 0. 0. 0. 0. 0.
0. 0. 0.08837382 0. 0. 0. ]
References
----------
...
...
@@ -177,46 +177,46 @@ def push_relabel_max_flow(g, source, target, capacity, residual=None):
Examples
--------
>>> from numpy.random import seed, random
>>> seed(4
2
)
>>> seed(4
3
)
>>> g = gt.random_graph(100, lambda: (2,2))
>>> c = g.new_edge_property("double")
>>> c.a = random(len(c.a))
>>> res = gt.push_relabel_max_flow(g, g.vertex(0), g.vertex(1), c)
>>> res.a = c.a - res.a # the actual flow
>>> print res.a[0:g.num_edges()]
[ 0.
39416408 0.72865707
0. 0. 0.
10582673
0. 0.
0.
0.70807258 0.02058449 0.02058449 0. 0.21233911
0.1
8182
497 0.0
4005893
0. 0.
0.02354897 0.03688695
0.
0.04645041 0.01722617 0.08333736 0.02058449 0.64459363
0.06
347894 0.0574714
4 0. 0.0
4645041 0.
0.
01722617
0.
06505159 0. 0.56093603 0. 0.06245346 0.04645041
0. 0. 0.0
7377389 0.06505159
0. 0.0
314291
9
0. 0.0
6505159 0.023471
1
0.
07377389
0. 0.
04645041
0.
44746251 0.02816465 0.03688695 0.03688695
0. 0.
0.
06347894 0.04645041 0.04522729 0. 0.04005893 0. 0.023471
1
0.
0.1561659 0.03688
69
5
0.
03688695 0.125932
4 0.
0.03688695
0. 0.
04645041
0. 0.
04645041 0.
0.
0. 0.0
6505159
0. 0.
0.29129
66
1
0.
37531506
0.
0.05747144 0.03688695 0.01722617 0. 0.03142919
0.0
0862975 0.0464504
1 0. 0.00
86297
5 0.01
722617 0.04005893
0. 0. 0.
02816465 0. 0. 0.
0.0
3142919 0.03688695 0.25440966
0.
08
852
27
0.
23718349 0. 0.
0.
26065459 0.22879817 0. 0. 0. 0.
0.0
4645041 0. 0.05508016
0. 0.
18657006
0.
0.0
4645041
0. 0.13
245284 0. 0.0234711 0.
0.0
3142919
0. 0.
0. 0.13245284 0.08227776
0.0
2585591 0.0234711
0. 0.
0. 0.
0.0
6245346 0. 0. 0.06245346 0.04645041 0. 0.
0.
04069727 0.03688695 0.06505159 0.03142919
0. 0.
02058449
0.0
3688695 0.08227776 0. 0.48945276 0.18657006 0.06505159
0. 0. 0.
01722617 0.35473057 0. 0.20261631
0.
0.2147306 0.0729211 0.04069727 0.02354897 0.0916777
0.
04077514 0.04077514 0. 0.01658783 0.08227776
0. 0.
0.
0. 0. 0.12800126
0. 0.
25440966
0.
11347352 0.09886559 0.56188512 0. 0.0234711 0.
0.0
3688695
0. 0.
06505159
0. 0. 0.0
3688695
0.04
645041 0.
0. 0.0
3688695
0. 0.0
3688695
0.
04645041 0.03688695
]
[ 0.
00508328 0.24058962
0. 0. 0.
07640118
0. 0.
0149749
0.
00476207 0.39495002 0.06036354 0.07755781 0.05688494 0.00984535
0.
0
1497
49
0.0
0984535 0.06594114
0. 0.
0149749
0.
0.
0.
1383694 0.00984535 0.07755781 0. 0. 0.0149749
0.06
03635
4 0. 0.0
0512955 0.0702089 0.
0.
63637368
0.
13988182 0.12852405 0.00476207 0. 0. 0.00512955
0.05247866
0. 0. 0.0
1940656
0. 0.0
515922
9
0.00984535
0. 0.0
775578
1 0.
19097437
0. 0.
0.
05159229 0.00984535 0. 0.0227834 0.05247866
0. 0.
0.
0.20608185 0. 0.10979179 0.01073172 0.0775578
1
0.
2159272 0.13988182 0. 0.14805
69
1
0.
0.022783
4 0.
0. 0.
0. 0. 0.
00984535 0.04127632
0.02525962
0. 0.0
0984535
0. 0.
800641
66 0.
02416862
0.
06440315 0.00508328 0.06372057 0.00512955 0.00508328 0.
0.0
775578
1 0. 0.00
98453
5 0.01
49749 0.06232401 0.07755781
0.02525962
0. 0. 0.
61693698 0.10335251 0.13723711
0.0
447044 0.00508328 0.00476207
0.
12
852
405
0.
07755781 0.06277679
0.
06232401 0. 0.00476207 0.04093717 0.02183962 0.02057707
0.0
0476207 0.01802133 0.
0. 0.
00730949
0.
0.0
0476207
0. 0.13
83694 0.00476207 0.00730949 0.04851461
0.0
0476207
0. 0.
0149749 0.00984535 0.06036354 0.
0.0
0476207
0. 0.
00984535
0. 0.
15790227 0.
0.0
5582411 0.0149749 0.04023452 0.07755781 0.1383694 0.10352007
0.
0. 0.07755781 0.
0. 0.
0.0
4127632 0. 0.05247866 0.02596227 0. 0.12408411
0.00512955
0. 0. 0.
0.05247866 0.
0.
07755781 0.30420045 0.05247866 0.21471727 0. 0. 0.1139163
0.
33016596 0.1445466 0. 0.01802133
0. 0.
01715485
0.
02416862 0.14962989 0. 0.00508328 0.
0. 0.
0.
00730949 0. 0.0227834 0. 0. 0.00476207
0.0
7755781
0. 0.
40569164
0. 0. 0.0
0476207
0.04
874567 0.00512955
0. 0.0
227834
0. 0.0
0730949
0.
0.00730949
]
References
----------
...
...
@@ -271,46 +271,46 @@ def kolmogorov_max_flow(g, source, target, capacity, residual=None):
Examples
--------
>>> from numpy.random import seed, random
>>> seed(4
2
)
>>> seed(4
3
)
>>> g = gt.random_graph(100, lambda: (2,2))
>>> c = g.new_edge_property("double")
>>> c.a = random(len(c.a))
>>> res = gt.push_relabel_max_flow(g, g.vertex(0), g.vertex(1), c)
>>> res.a = c.a - res.a # the actual flow
>>> print res.a[0:g.num_edges()]
[ 0.
39416408 0.72865707
0. 0. 0.
10582673
0. 0.
0.
0.70807258 0.02058449 0.02058449 0. 0.21233911
0.1
8182
497 0.0
4005893
0. 0.
0.02354897 0.03688695
0.
0.04645041 0.01722617 0.08333736 0.02058449 0.64459363
0.06
347894 0.0574714
4 0. 0.0
4645041 0.
0.
01722617
0.
06505159 0. 0.56093603 0. 0.06245346 0.04645041
0. 0. 0.0
7377389 0.06505159
0. 0.0
314291
9
0. 0.0
6505159 0.023471
1
0.
07377389
0. 0.
04645041
0.
44746251 0.02816465 0.03688695 0.03688695
0. 0.
0.
06347894 0.04645041 0.04522729 0. 0.04005893 0. 0.023471
1
0.
0.1561659 0.03688
69
5
0.
03688695 0.125932
4 0.
0.03688695
0. 0.
04645041
0. 0.
04645041 0.
0.
0. 0.0
6505159
0. 0.
0.29129
66
1
0.
37531506
0.
0.05747144 0.03688695 0.01722617 0. 0.03142919
0.0
0862975 0.0464504
1 0. 0.00
86297
5 0.01
722617 0.04005893
0. 0. 0.
02816465 0. 0. 0.
0.0
3142919 0.03688695 0.25440966
0.
08
852
27
0.
23718349 0. 0.
0.
26065459 0.22879817 0. 0. 0. 0.
0.0
4645041 0. 0.05508016
0. 0.
18657006
0.
0.0
4645041
0. 0.13
245284 0. 0.0234711 0.
0.0
3142919
0. 0.
0. 0.13245284 0.08227776
0.0
2585591 0.0234711
0. 0.
0. 0.
0.0
6245346 0. 0. 0.06245346 0.04645041 0. 0.
0.
04069727 0.03688695 0.06505159 0.03142919
0. 0.
02058449
0.0
3688695 0.08227776 0. 0.48945276 0.18657006 0.06505159
0. 0. 0.
01722617 0.35473057 0. 0.20261631
0.
0.2147306 0.0729211 0.04069727 0.02354897 0.0916777
0.
04077514 0.04077514 0. 0.01658783 0.08227776
0. 0.
0.
0. 0. 0.12800126
0. 0.
25440966
0.
11347352 0.09886559 0.56188512 0. 0.0234711 0.
0.0
3688695
0. 0.
06505159
0. 0. 0.0
3688695
0.04
645041 0.
0. 0.0
3688695
0. 0.0
3688695
0.
04645041 0.03688695
]
[ 0.
00508328 0.24058962
0. 0. 0.
07640118
0. 0.
0149749
0.
00476207 0.39495002 0.06036354 0.07755781 0.05688494 0.00984535
0.
0
1497
49
0.0
0984535 0.06594114
0. 0.
0149749
0.
0.
0.
1383694 0.00984535 0.07755781 0. 0. 0.0149749
0.06
03635
4 0. 0.0
0512955 0.0702089 0.
0.
63637368
0.
13988182 0.12852405 0.00476207 0. 0. 0.00512955
0.05247866
0. 0. 0.0
1940656
0. 0.0
515922
9
0.00984535
0. 0.0
775578
1 0.
19097437
0. 0.
0.
05159229 0.00984535 0. 0.0227834 0.05247866
0. 0.
0.
0.20608185 0. 0.10979179 0.01073172 0.0775578
1
0.
2159272 0.13988182 0. 0.14805
69
1
0.
0.022783
4 0.
0. 0.
0. 0. 0.
00984535 0.04127632
0.02525962
0. 0.0
0984535
0. 0.
800641
66 0.
02416862
0.
06440315 0.00508328 0.06372057 0.00512955 0.00508328 0.
0.0
775578
1 0. 0.00
98453
5 0.01
49749 0.06232401 0.07755781
0.02525962
0. 0. 0.
61693698 0.10335251 0.13723711
0.0
447044 0.00508328 0.00476207
0.
12
852
405
0.
07755781 0.06277679
0.
06232401 0. 0.00476207 0.04093717 0.02183962 0.02057707
0.0
0476207 0.01802133 0.
0. 0.
00730949
0.
0.0
0476207
0. 0.13
83694 0.00476207 0.00730949 0.04851461
0.0
0476207
0. 0.
0149749 0.00984535 0.06036354 0.
0.0
0476207
0. 0.
00984535
0. 0.
15790227 0.
0.0
5582411 0.0149749 0.04023452 0.07755781 0.1383694 0.10352007
0.
0. 0.07755781 0.
0. 0.
0.0
4127632 0. 0.05247866 0.02596227 0. 0.12408411
0.00512955
0. 0. 0.
0.05247866 0.
0.
07755781 0.30420045 0.05247866 0.21471727 0. 0. 0.1139163
0.
33016596 0.1445466 0. 0.01802133
0. 0.
01715485
0.
02416862 0.14962989 0. 0.00508328 0.
0. 0.
0.
00730949 0. 0.0227834 0. 0. 0.00476207
0.0
7755781
0. 0.
40569164
0. 0. 0.0
0476207
0.04
874567 0.00512955
0. 0.0
227834
0. 0.0
0730949
0.
0.00730949
]
References
----------
...
...
@@ -363,7 +363,7 @@ def max_cardinality_matching(g, match=None):
Examples
--------
>>> from numpy.random import seed, random
>>> seed(4
2
)
>>> seed(4
3
)
>>> g = gt.random_graph(100, lambda: (2,2))
>>> res = gt.max_cardinality_matching(g)
>>> print res[1]
...
...
src/graph_tool/generation/__init__.py
View file @
33f7f0bb
...
...
@@ -135,7 +135,7 @@ def random_graph(N, deg_sampler, deg_corr=None, directed=True,
>>> g = gt.random_graph(1000, lambda: sample_k(40),
... lambda i,k: 1.0/(1+abs(i-k)), directed=False)
>>> gt.scalar_assortativity(g, "out")
(0.6
3243885897121965, 0.011153551018567562
)
(0.6
2318897995178757, 0.011431222500824638
)
The following samples an in,out-degree pair from the joint distribution:
...
...
src/graph_tool/spectral/__init__.py
View file @
33f7f0bb
...
...
@@ -80,11 +80,11 @@ def adjacency(g, sparse=True, weight=None):
>>> g = gt.random_graph(100, lambda: (10,10))
>>> m = gt.adjacency(g)
>>> print m.todense()
[[ 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. 1.
0
.]
[ 0. 0. 0. ..., 0. 1.
1
.]
[ 0. 0. 0. ..., 0. 0. 0.]
[ 0. 0. 0. ..., 0. 0. 0.]]
...
...
@@ -186,11 +186,11 @@ def laplacian(g, deg="total", normalized=True, sparse=True, weight=None):
>>> g = gt.random_graph(100, lambda: (10,10))
>>> m = gt.laplacian(g)
>>> print m.todense()
[[ 1. 0. 0. ..., 0.
05
0. 0. ]
[[ 1. 0. 0. ..., 0.
0. 0. ]
[ 0. 1. 0. ..., 0. 0. 0. ]
[ 0.
05
0. 1. ..., 0. 0. 0. ]
[ 0.
0. 1. ..., 0. 0. 0. ]
...,
[ 0. 0. 0. ..., 1. 0.05 0.
]
[ 0. 0. 0. ..., 1. 0.05 0.
05
]
[ 0. 0. 0. ..., 0. 1. 0. ]
[ 0. 0. 0. ..., 0. 0. 1. ]]
...
...
@@ -274,7 +274,7 @@ def incidence(g, sparse=True):
>>> print m.todense()
[[ 0. 0. 0. ..., 0. 0. 0.]
[ 0. 0. 0. ..., 0. 0. 0.]
[ 0. 0. 0. ...,
1
. 0.
0
.]
[ 0. 0. 0. ...,
0
. 0.
1
.]
...,
[ 0. 0. 0. ..., 0. 0. 0.]
[ 0. 0. 0. ..., 0. 0. 0.]
...
...
src/graph_tool/stats/__init__.py
View file @
33f7f0bb
...
...
@@ -158,7 +158,7 @@ def edge_hist(g, eprop, bins=[1], float_count=True):
>>> eprop = g.new_edge_property("double")
>>> eprop.get_array()[:] = random(g.num_edges())
>>> print gt.edge_hist(g, eprop, arange(0, 1, 0.1))
[array([ 525., 504., 502., 502., 46
7
., 499., 531., 471., 520., 47
9
.]), array([ 0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9])]
[array([ 525., 504., 502., 502., 46
8
., 499., 531., 471., 520., 47
8
.]), array([ 0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9])]
"""
...
...
@@ -252,7 +252,7 @@ def edge_average(g, eprop):
>>> eprop = g.new_edge_property("double")
>>> eprop.get_array()[:] = random(g.num_edges())
>>> print gt.edge_average(g, eprop)
(0.496
835810070708
87, 0.00409
5
60
77241228531
)
(0.496
740354341301
87, 0.00409
4
60
40690938677
)
"""
ret
=
libgraph_tool_stats
.
\
...
...
@@ -354,10 +354,10 @@ def distance_histogram(g, weight=None, bins=[1], samples=None,
>>> g = gt.random_graph(100, lambda: (3, 3))
>>> hist = gt.distance_histogram(g)
>>> print hist
[array([ 0., 300., 8
62
., 21
47
., 3
766
., 25
88
.,
237
.]), array([0, 1, 2, 3, 4, 5, 6], dtype=uint64)]
[array([ 0., 300., 8
57
., 21
86
., 3
894
., 25
11
.,
152
.]), array([0, 1, 2, 3, 4, 5, 6], dtype=uint64)]
>>> hist = gt.distance_histogram(g, samples=10)
>>> print hist
[array([ 0., 30., 8
4
., 2
10
., 3
75
., 2
64
.,
27
.]), array([0, 1, 2, 3, 4, 5, 6], dtype=uint64)]
[array([ 0., 30., 8
8
., 2
22
., 3
84
., 2
51
.,
15
.]), array([0, 1, 2, 3, 4, 5, 6], dtype=uint64)]
"""
if
samples
!=
None
:
seed
=
numpy
.
random
.
randint
(
0
,
sys
.
maxint
)
...
...
src/graph_tool/topology/__init__.py
View file @
33f7f0bb
...
...
@@ -99,7 +99,7 @@ def subgraph_isomorphism(sub, g):
>>> sub = gt.random_graph(10, lambda: (poisson(1.8), poisson(1.9)))
>>> vm, em = gt.subgraph_isomorphism(sub, g)
>>> print len(vm)
46
175
>>> for i in xrange(len(vm)):
... g.set_vertex_filter(None)
... g.set_edge_filter(None)
...
...
@@ -221,20 +221,20 @@ def min_spanning_tree(g, weights=None, root=None, tree_map=None):
>>> g = gt.random_graph(100, lambda: (5, 5))
>>> tree = gt.min_spanning_tree(g)
>>> print tree.a
[0 0 0 0 1 0 0 0 0 0
0 0 0 1
1 0 0 0
0 1
1 0 0
0
0 0 0 0 0 1
1 1
0 0 1 0
0
0
1 1
0 0
0
1
1
0 0 0 0 1
0
0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0 0 0
0
0 0 0 0 0
1 0 0 0 0 0 0 0
0 0 1 0 0 0
0
1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
0 0 0
1 0 0 0 0 1
0 0 0 0 0 1 0 0
0
1 0 0 0 0
1
0
0
0 1
1
0 0 0 0 1 0 0
0