├── .gitignore
├── .idea
    ├── PyGCN_Visualization.iml
    ├── encodings.xml
    ├── misc.xml
    ├── modules.xml
    └── workspace.xml
├── 2D.png
├── 3D.gif
├── LICENCE
├── README.md
├── data
    └── cora
    │   ├── README
    │   ├── cora.cites
    │   └── cora.content
├── figure.png
├── pygcn
    ├── MNIST_with_t-SNE.py
    ├── S_data_with_t-SNE.py
    ├── __init__.py
    ├── demos.py
    ├── layers.py
    ├── models.py
    ├── test.py
    ├── train.py
    ├── try_of_BuidingGraph.py
    ├── try_of_LoadData.py
    ├── try_of_normalize.py
    ├── try_of_onehot.py
    └── utils.py
└── setup.py


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2 | *.pyc
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/2D.png:
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https://raw.githubusercontent.com/HanGuangXin/Result-Visualization-of-Graph-Convolutional-Networks-in-PyTorch/3f83524de7f00535970b9bbff100dabc28d52c05/2D.png


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/3D.gif:
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https://raw.githubusercontent.com/HanGuangXin/Result-Visualization-of-Graph-Convolutional-Networks-in-PyTorch/3f83524de7f00535970b9bbff100dabc28d52c05/3D.gif


--------------------------------------------------------------------------------
/LICENCE:
--------------------------------------------------------------------------------
 1 | The MIT License
 2 | 
 3 | Copyright (c) 2017 Thomas Kipf
 4 | 
 5 | Permission is hereby granted, free of charge, to any person obtaining a copy
 6 | of this software and associated documentation files (the "Software"), to deal
 7 | in the Software without restriction, including without limitation the rights
 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 | 
12 | The above copyright notice and this permission notice shall be included in
13 | all copies or substantial portions of the Software.
14 | 
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
21 | THE SOFTWARE.


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
 1 | Modification of Graph Convolutional Networks in PyTorch
 2 | ====
 3 | 
 4 | ## introduction
 5 | 
 6 | **Modification of [Graph Convolutional Networks in PyTorch](https://github.com/tkipf/pygcn), visualization of test set results was added in this version with t-SNE algorithm.**
 7 | 
 8 | You can cancel the visualization of ground truth and test set results with `--no_visual` for quick debugging.
 9 | 
10 | Thanks to the original author [Thomas Kipf](https://github.com/tkipf)  for the open source code, which provided me with the opportunity to learn.
11 | 
12 | The visualization results are shown below:
13 | 
14 | ![visualization in 2-dim](2D.png)
15 | 
16 | ![visualization in 3-dim](3D.gif)
17 | 
18 | In addition, the division of the training set in this project is not the same as the division method in the paper. In the paper, 4 samples are selected from each type of sample to form the training set, while 100 samples are randomly selected as the training set in the project.
19 | 
20 | PyTorch implementation of Graph Convolutional Networks (GCNs) for semi-supervised classification [1].
21 | 
22 | For a high-level introduction to GCNs, see: Thomas Kipf, [Graph Convolutional Networks](http://tkipf.github.io/graph-convolutional-networks/) (2016)
23 | 
24 | ![Graph Convolutional Networks](figure.png)
25 | 
26 | Note: There are subtle differences between the TensorFlow implementation in https://github.com/tkipf/gcn and this PyTorch re-implementation. This re-implementation serves as a proof of concept and is not intended for reproduction of the results reported in [1].
27 | 
28 | This implementation makes use of the Cora dataset from [2].
29 | 
30 | ## Installation
31 | 
32 | ```python setup.py install```
33 | 
34 | ## Requirements
35 | 
36 |   * PyTorch 0.4 or 0.5
37 |   * Python 2.7 or 3.6
38 | 
39 | ## Usage
40 | 
41 | ```python train.py```
42 | 
43 | The following optional hyperparameters can be set before running：
44 | 
45 | - no_cuda：Disables CUDA training.
46 | - fastmode：Validate during training pass.
47 | - seed：Random seed.
48 | - epochs：Number of epochs to train.
49 | - lr：Initial learning rate.
50 | - weight_decay：Weight decay (L2 loss on parameters).
51 | - hidden：Number of hidden units.
52 | - dropout：Dropout rate (1 - keep probability).
53 | 
54 | ## References
55 | 
56 | [1] [Kipf & Welling, Semi-Supervised Classification with Graph Convolutional Networks, 2016](https://arxiv.org/abs/1609.02907)
57 | 
58 | [2][Sen et al., Collective Classification in Network Data, AI Magazine 2008](http://linqs.cs.umd.edu/projects/projects/lbc/)
59 | 
60 | 
61 | 


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/data/cora/README:
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 1 | This directory contains the a selection of the Cora dataset (www.research.whizbang.com/data).
 2 | 
 3 | The Cora dataset consists of Machine Learning papers. These papers are classified into one of the following seven classes:
 4 | 		Case_Based
 5 | 		Genetic_Algorithms
 6 | 		Neural_Networks
 7 | 		Probabilistic_Methods
 8 | 		Reinforcement_Learning
 9 | 		Rule_Learning
10 | 		Theory
11 | 
12 | The papers were selected in a way such that in the final corpus every paper cites or is cited by atleast one other paper. There are 2708 papers in the whole corpus. 
13 | 
14 | After stemming and removing stopwords we were left with a vocabulary of size 1433 unique words. All words with document frequency less than 10 were removed.
15 | 
16 | 
17 | THE DIRECTORY CONTAINS TWO FILES:
18 | 
19 | The .content file contains descriptions of the papers in the following format:
20 | 
21 | 		<paper_id> <word_attributes>+ <class_label>
22 | 
23 | The first entry in each line contains the unique string ID of the paper followed by binary values indicating whether each word in the vocabulary is present (indicated by 1) or absent (indicated by 0) in the paper. Finally, the last entry in the line contains the class label of the paper.
24 | 
25 | The .cites file contains the citation graph of the corpus. Each line describes a link in the following format:
26 | 
27 | 		<ID of cited paper> <ID of citing paper>
28 | 
29 | Each line contains two paper IDs. The first entry is the ID of the paper being cited and the second ID stands for the paper which contains the citation. The direction of the link is from right to left. If a line is represented by "paper1 paper2" then the link is "paper2->paper1". 


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/data/cora/cora.cites:
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   1 | 35	1033
   2 | 35	103482
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 356 | 1213	409725
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 358 | 1237	102938
 359 | 1237	1102400
 360 | 1237	1106112
 361 | 1237	143676
 362 | 1246	1102400
 363 | 1246	1104007
 364 | 1246	42207
 365 | 1246	57764
 366 | 1246	6125
 367 | 1272	1102625
 368 | 1272	1108167
 369 | 1272	1120962
 370 | 1272	112378
 371 | 1272	1123867
 372 | 1272	1128256
 373 | 1272	1129208
 374 | 1272	1135125
 375 | 1272	1135358
 376 | 1272	157805
 377 | 1272	18615
 378 | 1272	192734
 379 | 1272	20593
 380 | 1272	254923
 381 | 1272	27230
 382 | 1272	284414
 383 | 1272	30895
 384 | 1272	30901
 385 | 1272	444240
 386 | 1272	520471
 387 | 1272	52835
 388 | 1272	552469
 389 | 1272	560936
 390 | 1272	591016
 391 | 1272	636098
 392 | 1272	65653
 393 | 1272	6917
 394 | 1272	6923
 395 | 1272	85452
 396 | 1272	85688
 397 | 1272	93923
 398 | 1272	97892
 399 | 1365	1031453
 400 | 1365	1102407
 401 | 1365	1105062
 402 | 1365	1106287
 403 | 1365	1108050
 404 | 1365	1110494
 405 | 1365	1110998
 406 | 1365	1113995
 407 | 1365	1114153
 408 | 1365	1114352
 409 | 1365	1114388
 410 | 1365	1114605
 411 | 1365	1116347
 412 | 1365	1116594
 413 | 1365	1117653
 414 | 1365	1119140
 415 | 1365	1120211
 416 | 1365	1120866
 417 | 1365	1128839
 418 | 1365	1129443
 419 | 1365	1130600
 420 | 1365	1131647
 421 | 1365	1131745
 422 | 1365	1131748
 423 | 1365	1132922
 424 | 1365	1132968
 425 | 1365	1135137
 426 | 1365	1135368
 427 | 1365	1136422
 428 | 1365	1136442
 429 | 1365	1152143
 430 | 1365	1152821
 431 | 1365	1154169
 432 | 1365	120084
 433 | 1365	139865
 434 | 1365	157401
 435 | 1365	161221
 436 | 1365	171225
 437 | 1365	184918
 438 | 1365	188318
 439 | 1365	188471
 440 | 1365	22835
 441 | 1365	23448
 442 | 1365	23502
 443 | 1365	23507
 444 | 1365	237521
 445 | 1365	26850
 446 | 1365	330148
 447 | 1365	340075
 448 | 1365	340299
 449 | 1365	39904
 450 | 1365	436796
 451 | 1365	49482
 452 | 1365	560936
 453 | 1365	562123
 454 | 1365	628500
 455 | 1365	648232
 456 | 1365	649731
 457 | 1365	69392
 458 | 1365	7276
 459 | 1365	7297
 460 | 1365	77758
 461 | 1365	782486
 462 | 1365	83826
 463 | 1365	85299
 464 | 1365	853150
 465 | 1365	90888
 466 | 1365	93555
 467 | 1365	948299
 468 | 1365	948846
 469 | 1365	949318
 470 | 1365	949511
 471 | 1365	950052
 472 | 1365	950305
 473 | 1481	1102567
 474 | 1481	1106052
 475 | 1481	1108267
 476 | 1481	1111614
 477 | 1481	1113934
 478 | 1481	1114864
 479 | 1481	1117184
 480 | 1481	1119295
 481 | 1481	1120563
 482 | 1481	1153891
 483 | 1481	13960
 484 | 1481	200480
 485 | 1481	399173
 486 | 1481	4878
 487 | 1481	521207
 488 | 1481	521269
 489 | 1688	1103985
 490 | 1688	1131360
 491 | 1688	1133338
 492 | 1688	1134022
 493 | 1688	1136814
 494 | 1688	1153065
 495 | 1688	152483
 496 | 1688	1694
 497 | 1688	39474
 498 | 1688	44368
 499 | 1688	576725
 500 | 1688	647413
 501 | 1688	69284
 502 | 1688	694759
 503 | 1688	84021
 504 | 1694	39474
 505 | 1717	1115291
 506 | 1717	1116336
 507 | 1717	1135108
 508 | 1717	50381
 509 | 1717	733167
 510 | 1786	35797
 511 | 1817	1114502
 512 | 1919	1108389
 513 | 1919	1109581
 514 | 1949	129042
 515 | 1949	3101
 516 | 1951	3095
 517 | 1952	1107215
 518 | 1952	1111899
 519 | 1952	1949
 520 | 1952	3101
 521 | 1953	1153166
 522 | 1953	1153724
 523 | 1953	1153728
 524 | 1953	1949
 525 | 1955	110163
 526 | 1955	1110390
 527 | 1955	1125386
 528 | 1955	1956
 529 | 1956	101143
 530 | 1956	1111899
 531 | 1956	1118302
 532 | 1956	1153101
 533 | 1956	1153150
 534 | 1956	1817
 535 | 1956	263486
 536 | 1956	83449
 537 | 1959	101143
 538 | 1959	1152896
 539 | 1959	1153728
 540 | 1959	170798
 541 | 1959	207395
 542 | 1959	3097
 543 | 1959	310530
 544 | 1959	38839
 545 | 1959	73327
 546 | 1959	82664
 547 | 1997	1102442
 548 | 1997	1108551
 549 | 1997	1109439
 550 | 1997	1109542
 551 | 1997	129897
 552 | 1997	154982
 553 | 1997	3233
 554 | 1997	49811
 555 | 1997	7032
 556 | 1999	1102442
 557 | 1999	1123068
 558 | 1999	1131471
 559 | 1999	39126
 560 | 1999	6771
 561 | 2354	10186
 562 | 2354	1107140
 563 | 2354	1113852
 564 | 2354	1130539
 565 | 2354	154134
 566 | 2354	40151
 567 | 2354	74749
 568 | 2440	1000012
 569 | 2440	1061127
 570 | 2440	1106388
 571 | 2440	1107095
 572 | 2440	1108551
 573 | 2440	1110426
 574 | 2440	1114512
 575 | 2440	1117786
 576 | 2440	1119295
 577 | 2440	1120650
 578 | 2440	1127619
 579 | 2440	1153254
 580 | 2440	136766
 581 | 2440	136768
 582 | 2440	151430
 583 | 2440	18615
 584 | 2440	212777
 585 | 2440	23546
 586 | 2440	27230
 587 | 2440	49811
 588 | 2440	49843
 589 | 2440	49844
 590 | 2440	49847
 591 | 2440	582343
 592 | 2440	591016
 593 | 2440	591017
 594 | 2440	63931
 595 | 2440	6917
 596 | 2440	6923
 597 | 2440	72908
 598 | 2653	102406
 599 | 2653	107177
 600 | 2653	1104055
 601 | 2653	1116268
 602 | 2653	1116842
 603 | 2653	1118764
 604 | 2653	1119751
 605 | 2653	1152075
 606 | 2653	167656
 607 | 2653	175291
 608 | 2653	197783
 609 | 2653	28641
 610 | 2653	321861
 611 | 2653	350362
 612 | 2653	4660
 613 | 2653	59045
 614 | 2653	6125
 615 | 2653	65650
 616 | 2654	1104851
 617 | 2654	1106630
 618 | 2654	1107861
 619 | 2654	1110438
 620 | 2654	1116842
 621 | 2654	1121867
 622 | 2654	1123926
 623 | 2654	1152075
 624 | 2654	197783
 625 | 2654	211906
 626 | 2654	27250
 627 | 2654	463825
 628 | 2654	52784
 629 | 2658	1115375
 630 | 2658	1130676
 631 | 2658	1130808
 632 | 2658	1131607
 633 | 2658	1132948
 634 | 2658	1135899
 635 | 2658	1140230
 636 | 2658	230884
 637 | 2658	236759
 638 | 2658	282700
 639 | 2658	395540
 640 | 2658	578347
 641 | 2658	696342
 642 | 2658	696345
 643 | 2658	696346
 644 | 2658	751408
 645 | 2658	99023
 646 | 2663	1119671
 647 | 2665	1105574
 648 | 2665	1113035
 649 | 2665	1115375
 650 | 2665	1122704
 651 | 2665	1130676
 652 | 2665	1130808
 653 | 2665	1131607
 654 | 2665	1132486
 655 | 2665	1132948
 656 | 2665	1132968
 657 | 2665	205192
 658 | 2665	230884
 659 | 2665	237489
 660 | 2665	2658
 661 | 2665	395540
 662 | 2665	43639
 663 | 2665	578306
 664 | 2665	578309
 665 | 2665	578337
 666 | 2665	578347
 667 | 2665	582139
 668 | 2665	630817
 669 | 2665	631052
 670 | 2665	696345
 671 | 2665	696346
 672 | 2665	751408
 673 | 2665	763009
 674 | 2665	99023
 675 | 2695	1108169
 676 | 2695	1120197
 677 | 2695	2698
 678 | 2695	342802
 679 | 2696	1108050
 680 | 2696	1108169
 681 | 2696	1113035
 682 | 2696	1114192
 683 | 2696	1118083
 684 | 2696	1120858
 685 | 2696	1123239
 686 | 2696	1133004
 687 | 2696	160705
 688 | 2696	256106
 689 | 2696	2695
 690 | 2696	2698
 691 | 2696	342802
 692 | 2696	469504
 693 | 2696	5348
 694 | 2696	5869
 695 | 2696	99025
 696 | 2698	1114192
 697 | 2698	256106
 698 | 2698	2695
 699 | 2698	342802
 700 | 2698	99025
 701 | 2702	1120777
 702 | 2702	12330
 703 | 2702	395553
 704 | 2702	72056
 705 | 3084	1107171
 706 | 3084	255233
 707 | 3084	33303
 708 | 3085	15889
 709 | 3085	221302
 710 | 3085	30973
 711 | 3085	395725
 712 | 3085	5062
 713 | 3085	83449
 714 | 3095	110162
 715 | 3095	110163
 716 | 3095	218682
 717 | 3095	83449
 718 | 3097	1106236
 719 | 3097	129045
 720 | 3097	1949
 721 | 3097	1952
 722 | 3097	3101
 723 | 3101	110162
 724 | 3101	110163
 725 | 3101	110164
 726 | 3101	70441
 727 | 3112	103529
 728 | 3112	110163
 729 | 3112	110164
 730 | 3112	1152564
 731 | 3112	1949
 732 | 3112	470511
 733 | 3112	77826
 734 | 3112	77829
 735 | 3187	1110000
 736 | 3187	129896
 737 | 3187	129897
 738 | 3187	280876
 739 | 3187	5086
 740 | 3191	105865
 741 | 3191	1106789
 742 | 3191	1127530
 743 | 3191	1131267
 744 | 3191	129896
 745 | 3191	129897
 746 | 3191	137873
 747 | 3191	162664
 748 | 3191	308920
 749 | 3191	310742
 750 | 3191	3192
 751 | 3191	423463
 752 | 3191	5086
 753 | 3191	561364
 754 | 3191	642827
 755 | 3192	105865
 756 | 3192	129896
 757 | 3192	310742
 758 | 3192	3191
 759 | 3192	423463
 760 | 3192	5086
 761 | 3192	561364
 762 | 3192	642827
 763 | 3217	1000012
 764 | 3217	167670
 765 | 3217	238099
 766 | 3217	65653
 767 | 3217	86840
 768 | 3218	1106492
 769 | 3218	1110426
 770 | 3218	1119987
 771 | 3218	1120169
 772 | 3218	1152290
 773 | 3218	1153264
 774 | 3218	187354
 775 | 3218	277263
 776 | 3218	35070
 777 | 3218	417017
 778 | 3218	49843
 779 | 3218	6639
 780 | 3218	66782
 781 | 3218	6767
 782 | 3218	6941
 783 | 3220	1115677
 784 | 3220	1125992
 785 | 3220	1128430
 786 | 3220	1130634
 787 | 3220	1131728
 788 | 3220	1132706
 789 | 3220	120039
 790 | 3220	145215
 791 | 3220	167670
 792 | 3220	346243
 793 | 3220	36620
 794 | 3220	39124
 795 | 3220	40922
 796 | 3220	429805
 797 | 3220	654177
 798 | 3220	69397
 799 | 3220	8832
 800 | 3222	1103737
 801 | 3222	1114222
 802 | 3222	1131137
 803 | 3222	1132157
 804 | 3222	167656
 805 | 3222	964248
 806 | 3223	1132157
 807 | 3223	1153101
 808 | 3223	1153150
 809 | 3223	167656
 810 | 3223	167670
 811 | 3229	100197
 812 | 3229	1050679
 813 | 3229	1103960
 814 | 3229	1105718
 815 | 3229	1106568
 816 | 3229	1108209
 817 | 3229	1108834
 818 | 3229	1109392
 819 | 3229	1112767
 820 | 3229	1125082
 821 | 3229	1125895
 822 | 3229	1126037
 823 | 3229	1128839
 824 | 3229	1128868
 825 | 3229	1130915
 826 | 3229	1130927
 827 | 3229	1130931
 828 | 3229	1131647
 829 | 3229	1131748
 830 | 3229	1132418
 831 | 3229	1140289
 832 | 3229	1152277
 833 | 3229	1152673
 834 | 3229	1154169
 835 | 3229	1154251
 836 | 3229	118559
 837 | 3229	133550
 838 | 3229	157401
 839 | 3229	16461
 840 | 3229	189577
 841 | 3229	23502
 842 | 3229	25181
 843 | 3229	25184
 844 | 3229	27174
 845 | 3229	27631
 846 | 3229	31769
 847 | 3229	33412
 848 | 3229	35343
 849 | 3229	35863
 850 | 3229	35922
 851 | 3229	390922
 852 | 3229	396412
 853 | 3229	444191
 854 | 3229	447250
 855 | 3229	48550
 856 | 3229	49482
 857 | 3229	52515
 858 | 3229	62347
 859 | 3229	641976
 860 | 3229	654326
 861 | 3229	7022
 862 | 3229	72101
 863 | 3229	82087
 864 | 3229	82098
 865 | 3229	83826
 866 | 3229	90888
 867 | 3229	919885
 868 | 3229	92065
 869 | 3229	948299
 870 | 3229	949511
 871 | 3229	96335
 872 | 3231	104840
 873 | 3231	1102761
 874 | 3231	1106330
 875 | 3231	1106370
 876 | 3231	1107067
 877 | 3231	1113926
 878 | 3231	1115471
 879 | 3231	1125386
 880 | 3231	1128536
 881 | 3231	1131471
 882 | 3231	1153169
 883 | 3231	167670
 884 | 3231	180399
 885 | 3231	200480
 886 | 3231	20850
 887 | 3231	25184
 888 | 3231	259126
 889 | 3231	263279
 890 | 3231	3237
 891 | 3231	328370
 892 | 3231	39124
 893 | 3231	39126
 894 | 3231	46431
 895 | 3231	49660
 896 | 3231	6334
 897 | 3231	63477
 898 | 3231	63486
 899 | 3231	636098
 900 | 3231	66594
 901 | 3231	68463
 902 | 3231	8699
 903 | 3231	8821
 904 | 3232	1106330
 905 | 3232	1123068
 906 | 3232	1128536
 907 | 3232	20850
 908 | 3232	20942
 909 | 3232	521251
 910 | 3232	66594
 911 | 3233	1103737
 912 | 3233	1114222
 913 | 3233	1125467
 914 | 3233	192850
 915 | 3233	1997
 916 | 3233	272720
 917 | 3233	509379
 918 | 3233	66594
 919 | 3233	976284
 920 | 3235	1114222
 921 | 3235	65653
 922 | 3236	1125467
 923 | 3236	167670
 924 | 3236	3233
 925 | 3236	3237
 926 | 3236	601561
 927 | 3237	964248
 928 | 3240	1110531
 929 | 3240	1131137
 930 | 3240	3233
 931 | 3240	36620
 932 | 3240	39130
 933 | 3240	964248
 934 | 3243	1103610
 935 | 3243	1104055
 936 | 3243	1110947
 937 | 3243	1113739
 938 | 3243	307336
 939 | 3243	30895
 940 | 3243	30901
 941 | 3243	31932
 942 | 3243	350362
 943 | 3243	368431
 944 | 3243	854434
 945 | 3243	96335
 946 | 3828	1152896
 947 | 3828	1956
 948 | 3828	207395
 949 | 3828	310530
 950 | 3932	5075
 951 | 4274	1105428
 952 | 4274	1114664
 953 | 4274	1119078
 954 | 4329	105899
 955 | 4329	1110768
 956 | 4329	1111304
 957 | 4329	28254
 958 | 4329	395547
 959 | 4329	46468
 960 | 4329	510718
 961 | 4330	1103676
 962 | 4330	1103737
 963 | 4330	1104449
 964 | 4330	1108834
 965 | 4330	11093
 966 | 4330	1109439
 967 | 4330	1110438
 968 | 4330	1112929
 969 | 4330	1114352
 970 | 4330	1132459
 971 | 4330	1132461
 972 | 4330	1135108
 973 | 4330	1136393
 974 | 4330	1136397
 975 | 4330	1152917
 976 | 4330	118436
 977 | 4330	119956
 978 | 4330	120013
 979 | 4330	12946
 980 | 4330	137873
 981 | 4330	151708
 982 | 4330	168410
 983 | 4330	1717
 984 | 4330	299197
 985 | 4330	32688
 986 | 4330	328370
 987 | 4330	35797
 988 | 4330	37884
 989 | 4330	390889
 990 | 4330	395553
 991 | 4330	428610
 992 | 4330	4329
 993 | 4330	46468
 994 | 4330	684986
 995 | 4330	69392
 996 | 4330	69418
 997 | 4330	753265
 998 | 4330	949217
 999 | 4335	1104300
1000 | 4335	1121254
1001 | 4335	1136310
1002 | 4335	1136397
1003 | 4335	1140547
1004 | 4335	116790
1005 | 4335	1717
1006 | 4335	239800
1007 | 4335	32698
1008 | 4335	35922
1009 | 4335	390889
1010 | 4335	46468
1011 | 4335	62274
1012 | 4335	62417
1013 | 4335	755082
1014 | 4335	95718
1015 | 4553	1102548
1016 | 4584	1031453
1017 | 4584	1102550
1018 | 4584	1113739
1019 | 4584	1116347
1020 | 4584	1120020
1021 | 4584	1128839
1022 | 4584	1130567
1023 | 4584	1130600
1024 | 4584	124064
1025 | 4584	1272
1026 | 4584	128
1027 | 4584	132806
1028 | 4584	13885
1029 | 4584	13917
1030 | 4584	168410
1031 | 4584	188471
1032 | 4584	193742
1033 | 4584	28278
1034 | 4584	28287
1035 | 4584	293285
1036 | 4584	36167
1037 | 4584	6151
1038 | 4584	6155
1039 | 4584	6184
1040 | 4584	6196
1041 | 4584	6210
1042 | 4584	6213
1043 | 4584	6214
1044 | 4584	628500
1045 | 4584	628815
1046 | 4584	782486
1047 | 4584	976334
1048 | 4637	164
1049 | 4649	1105887
1050 | 4649	1127851
1051 | 4649	135765
1052 | 4649	135766
1053 | 4649	411092
1054 | 4649	434
1055 | 4649	449841
1056 | 4649	6917
1057 | 4649	8703
1058 | 4660	18615
1059 | 4660	429781
1060 | 4660	5038
1061 | 4660	582343
1062 | 4804	102884
1063 | 4804	1108175
1064 | 4804	1112574
1065 | 4804	1153262
1066 | 4804	12182
1067 | 4804	12210
1068 | 4804	157761
1069 | 4804	164
1070 | 4804	25805
1071 | 4804	45189
1072 | 4804	68505
1073 | 4804	77515
1074 | 4804	976284
1075 | 4804	989397
1076 | 4878	1102567
1077 | 4983	7867
1078 | 5055	1104851
1079 | 5055	1107861
1080 | 5055	1119751
1081 | 5055	1121867
1082 | 5055	129897
1083 | 5055	167656
1084 | 5062	1120563
1085 | 5062	28026
1086 | 5062	5064
1087 | 5062	5069
1088 | 5062	521207
1089 | 5064	1120563
1090 | 5064	1121176
1091 | 5064	1129106
1092 | 5064	272345
1093 | 5064	28026
1094 | 5064	385067
1095 | 5064	5069
1096 | 5069	1120563
1097 | 5069	385067
1098 | 5069	5064
1099 | 5075	1120563
1100 | 5075	5064
1101 | 5075	5069
1102 | 5086	109323
1103 | 5086	1105698
1104 | 5086	129896
1105 | 5086	137849
1106 | 5086	3187
1107 | 5086	3191
1108 | 5086	354004
1109 | 5086	423463
1110 | 5086	440815
1111 | 5086	561364
1112 | 5086	79809
1113 | 5194	1152714
1114 | 5194	133550
1115 | 5194	133563
1116 | 5194	133566
1117 | 5194	140569
1118 | 5348	1108050
1119 | 5348	1114192
1120 | 5348	1118083
1121 | 5348	1120197
1122 | 5348	1120858
1123 | 5348	1123239
1124 | 5348	1132968
1125 | 5348	139547
1126 | 5348	256106
1127 | 5348	2695
1128 | 5348	2696
1129 | 5348	342802
1130 | 5348	75695
1131 | 5348	911198
1132 | 5348	99025
1133 | 5454	1105574
1134 | 5454	1128425
1135 | 5454	1130680
1136 | 5462	1129111
1137 | 5462	119956
1138 | 5462	227286
1139 | 5462	910
1140 | 5600	1022969
1141 | 5600	1117833
1142 | 5600	255233
1143 | 5600	33303
1144 | 5600	77826
1145 | 5869	1110520
1146 | 5869	910
1147 | 5959	1102625
1148 | 5959	152226
1149 | 5959	545647
1150 | 5959	582511
1151 | 5966	1102625
1152 | 5966	1106388
1153 | 5966	1107861
1154 | 5966	1110390
1155 | 5966	1114502
1156 | 5966	68463
1157 | 5966	86840
1158 | 5966	8699
1159 | 6125	1107572
1160 | 6125	12211
1161 | 6125	27612
1162 | 6125	28641
1163 | 6125	385251
1164 | 6130	1132461
1165 | 6130	1154123
1166 | 6130	1154124
1167 | 6130	671269
1168 | 6130	675847
1169 | 6151	1106547
1170 | 6151	1107355
1171 | 6151	1130567
1172 | 6151	1385
1173 | 6151	193742
1174 | 6151	6152
1175 | 6151	6155
1176 | 6151	6213
1177 | 6151	6214
1178 | 6152	299195
1179 | 6155	1126029
1180 | 6163	1106966
1181 | 6163	1111052
1182 | 6163	1113739
1183 | 6163	130
1184 | 6163	134128
1185 | 6163	20534
1186 | 6163	28202
1187 | 6163	96847
1188 | 6169	1106406
1189 | 6169	1114629
1190 | 6169	1130567
1191 | 6169	170338
1192 | 6169	20526
1193 | 6169	7419
1194 | 6184	10981
1195 | 6184	1105718
1196 | 6184	1120731
1197 | 6184	1136422
1198 | 6184	13686
1199 | 6184	27631
1200 | 6184	3229
1201 | 6184	399339
1202 | 6196	1120731
1203 | 6196	1153056
1204 | 6196	28387
1205 | 6196	58540
1206 | 6196	81350
1207 | 6196	976334
1208 | 6209	1108329
1209 | 6209	118259
1210 | 6209	118260
1211 | 6209	168410
1212 | 6209	181782
1213 | 6209	212930
1214 | 6209	28485
1215 | 6209	38480
1216 | 6209	8213
1217 | 6209	8875
1218 | 6209	95579
1219 | 6210	1120731
1220 | 6213	10796
1221 | 6213	1103315
1222 | 6213	1105764
1223 | 6213	1106406
1224 | 6213	1106547
1225 | 6213	1106966
1226 | 6213	1107355
1227 | 6213	1107455
1228 | 6213	1108329
1229 | 6213	1109957
1230 | 6213	1111052
1231 | 6213	1111230
1232 | 6213	1111304
1233 | 6213	1113182
1234 | 6213	1113459
1235 | 6213	1113614
1236 | 6213	1116347
1237 | 6213	1117760
1238 | 6213	1117942
1239 | 6213	1120020
1240 | 6213	1122425
1241 | 6213	1123553
1242 | 6213	1128267
1243 | 6213	1129096
1244 | 6213	1129243
1245 | 6213	1130567
1246 | 6213	124064
1247 | 6213	12576
1248 | 6213	128
1249 | 6213	13193
1250 | 6213	134128
1251 | 6213	1385
1252 | 6213	13960
1253 | 6213	153598
1254 | 6213	161221
1255 | 6213	17208
1256 | 6213	193742
1257 | 6213	195361
1258 | 6213	20526
1259 | 6213	20534
1260 | 6213	218410
1261 | 6213	23774
1262 | 6213	241133
1263 | 6213	28202
1264 | 6213	28227
1265 | 6213	28278
1266 | 6213	28350
1267 | 6213	28471
1268 | 6213	28485
1269 | 6213	293271
1270 | 6213	299195
1271 | 6213	299197
1272 | 6213	353541
1273 | 6213	35863
1274 | 6213	38480
1275 | 6213	399339
1276 | 6213	434
1277 | 6213	55968
1278 | 6213	58540
1279 | 6213	595056
1280 | 6213	6151
1281 | 6213	6155
1282 | 6213	6163
1283 | 6213	6184
1284 | 6213	6210
1285 | 6213	6214
1286 | 6213	6215
1287 | 6213	6220
1288 | 6213	6224
1289 | 6213	628667
1290 | 6213	628668
1291 | 6213	628815
1292 | 6213	672064
1293 | 6213	7419
1294 | 6213	755217
1295 | 6213	91975
1296 | 6214	1105764
1297 | 6214	1106172
1298 | 6214	1109199
1299 | 6214	1115886
1300 | 6214	1126029
1301 | 6214	1130600
1302 | 6214	1152740
1303 | 6214	124064
1304 | 6214	13960
1305 | 6214	149669
1306 | 6214	188471
1307 | 6214	23258
1308 | 6214	27631
1309 | 6214	28278
1310 | 6214	28447
1311 | 6214	345340
1312 | 6214	38480
1313 | 6214	399339
1314 | 6214	6155
1315 | 6214	6184
1316 | 6214	6196
1317 | 6214	6215
1318 | 6214	6224
1319 | 6214	628500
1320 | 6214	6378
1321 | 6214	93320
1322 | 6214	95589
1323 | 6214	97892
1324 | 6215	1120170
1325 | 6215	6224
1326 | 6216	1113739
1327 | 6216	1116347
1328 | 6216	1123553
1329 | 6216	230300
1330 | 6216	96847
1331 | 6216	976334
1332 | 6220	134128
1333 | 6220	6163
1334 | 6220	6224
1335 | 6224	28387
1336 | 6224	345340
1337 | 6224	6163
1338 | 6238	10793
1339 | 6238	1123991
1340 | 6238	1130356
1341 | 6238	84459
1342 | 6311	1114864
1343 | 6311	1115677
1344 | 6311	235776
1345 | 6311	69397
1346 | 6318	1108656
1347 | 6318	1119295
1348 | 6318	1121057
1349 | 6318	20833
1350 | 6334	1107067
1351 | 6334	1116268
1352 | 6334	1152307
1353 | 6334	1152448
1354 | 6334	1152975
1355 | 6334	1153169
1356 | 6334	1153703
1357 | 6334	200480
1358 | 6334	20850
1359 | 6334	3231
1360 | 6334	444240
1361 | 6334	46431
1362 | 6334	68463
1363 | 6343	1125992
1364 | 6343	141868
1365 | 6343	200480
1366 | 6343	359067
1367 | 6343	521252
1368 | 6344	1112665
1369 | 6344	200480
1370 | 6346	1106103
1371 | 6346	1112075
1372 | 6378	1122425
1373 | 6378	1129208
1374 | 6378	28473
1375 | 6385	1103162
1376 | 6385	1129243
1377 | 6385	17476
1378 | 6385	28473
1379 | 6385	55968
1380 | 6385	6378
1381 | 6385	682666
1382 | 6385	892139
1383 | 6539	1102646
1384 | 6639	116084
1385 | 6639	178209
1386 | 6639	568045
1387 | 6639	591017
1388 | 6741	1130637
1389 | 6741	1153160
1390 | 6741	348437
1391 | 6741	411092
1392 | 6741	417017
1393 | 6741	49895
1394 | 6741	51909
1395 | 6741	83725
1396 | 6741	9708
1397 | 6767	1106103
1398 | 6767	1114864
1399 | 6767	1120444
1400 | 6767	235776
1401 | 6767	6334
1402 | 6767	6925
1403 | 6767	69397
1404 | 6771	65653
1405 | 6775	1111240
1406 | 6775	350319
1407 | 6782	1123068
1408 | 6782	463825
1409 | 6784	100961
1410 | 6784	1116629
1411 | 6784	350319
1412 | 6784	35797
1413 | 6784	560936
1414 | 6784	562123
1415 | 6784	60170
1416 | 6784	6775
1417 | 6786	1106789
1418 | 6786	714975
1419 | 6814	1117348
1420 | 6814	1125469
1421 | 6814	158172
1422 | 6814	293974
1423 | 6814	300071
1424 | 6814	315266
1425 | 6814	37884
1426 | 6814	390896
1427 | 6814	445938
1428 | 6818	1105531
1429 | 6818	1121254
1430 | 6818	1128425
1431 | 6818	293974
1432 | 6818	37884
1433 | 6818	390896
1434 | 6818	50336
1435 | 6818	50337
1436 | 6898	1123188
1437 | 6898	124224
1438 | 6898	12631
1439 | 6898	431206
1440 | 6898	6910
1441 | 6898	78994
1442 | 6910	1117219
1443 | 6910	1123188
1444 | 6910	1132083
1445 | 6910	1152150
1446 | 6910	1154103
1447 | 6910	169279
1448 | 6910	263553
1449 | 6910	431206
1450 | 6910	662416
1451 | 6913	1105011
1452 | 6913	1116336
1453 | 6913	1131230
1454 | 6913	1135108
1455 | 6913	1152917
1456 | 6913	1717
1457 | 6913	428610
1458 | 6913	431206
1459 | 6913	4330
1460 | 6913	703953
1461 | 6917	1153811
1462 | 6917	449841
1463 | 6923	1155073
1464 | 6923	187354
1465 | 6923	20857
1466 | 6923	308003
1467 | 6923	6941
1468 | 6923	93923
1469 | 6925	1114526
1470 | 6925	1118658
1471 | 6925	372862
1472 | 6925	57922
1473 | 6925	57948
1474 | 6935	1000012
1475 | 6935	101660
1476 | 6935	1106388
1477 | 6935	1115670
1478 | 6935	1120650
1479 | 6935	18615
1480 | 6935	238099
1481 | 6935	308003
1482 | 6939	1116922
1483 | 6941	372862
1484 | 7032	1109439
1485 | 7032	1110438
1486 | 7032	137873
1487 | 7032	323128
1488 | 7032	52847
1489 | 7041	1120880
1490 | 7047	1135082
1491 | 7047	14549
1492 | 7047	212930
1493 | 7047	2663
1494 | 7047	54844
1495 | 7272	1136446
1496 | 7272	7276
1497 | 7272	7297
1498 | 7272	763181
1499 | 7276	763181
1500 | 7296	763181
1501 | 7297	7276
1502 | 7297	763010
1503 | 7297	763181
1504 | 7419	1105764
1505 | 7419	1107312
1506 | 7419	1109581
1507 | 7419	1113182
1508 | 7419	1117760
1509 | 7419	1118245
1510 | 7419	341188
1511 | 7430	1153287
1512 | 7430	95586
1513 | 7532	1121603
1514 | 7532	1153097
1515 | 7532	141171
1516 | 7532	314459
1517 | 7532	7537
1518 | 7532	80515
1519 | 7537	1113534
1520 | 7537	1131348
1521 | 7537	1153097
1522 | 7537	141171
1523 | 7537	314459
1524 | 7537	409255
1525 | 7537	7532
1526 | 7867	1138027
1527 | 7867	315789
1528 | 8079	105057
1529 | 8079	1103737
1530 | 8079	1121867
1531 | 8079	154982
1532 | 8079	192850
1533 | 8079	27250
1534 | 8079	39124
1535 | 8224	1108728
1536 | 8224	1110426
1537 | 8224	1111788
1538 | 8224	1128531
1539 | 8224	1132815
1540 | 8224	11342
1541 | 8224	1153148
1542 | 8224	1153866
1543 | 8224	133615
1544 | 8224	22431
1545 | 8224	22563
1546 | 8224	23738
1547 | 8224	263279
1548 | 8224	30895
1549 | 8224	368431
1550 | 8224	49843
1551 | 8224	49844
1552 | 8224	49847
1553 | 8224	55403
1554 | 8224	601567
1555 | 8224	62389
1556 | 8224	6311
1557 | 8224	65653
1558 | 8224	6639
1559 | 8224	97377
1560 | 8581	1120059
1561 | 8581	1132968
1562 | 8581	2665
1563 | 8581	75691
1564 | 8581	75695
1565 | 8591	1154525
1566 | 8591	137956
1567 | 8591	167205
1568 | 8591	709113
1569 | 8594	1132809
1570 | 8594	1154525
1571 | 8594	1213
1572 | 8594	180187
1573 | 8594	232605
1574 | 8594	58268
1575 | 8594	709113
1576 | 8594	8591
1577 | 8617	1116268
1578 | 8617	12359
1579 | 8617	175291
1580 | 8617	36145
1581 | 8617	52784
1582 | 8619	175291
1583 | 8619	36145
1584 | 8687	1102751
1585 | 8687	38000
1586 | 8696	104840
1587 | 8696	1102751
1588 | 8699	104840
1589 | 8699	1102751
1590 | 8703	101145
1591 | 8703	104840
1592 | 8703	1102751
1593 | 8703	1105428
1594 | 8703	1119078
1595 | 8703	173884
1596 | 8703	27535
1597 | 8703	308232
1598 | 8703	353541
1599 | 8703	434
1600 | 8703	502574
1601 | 8703	51866
1602 | 8703	51909
1603 | 8703	59244
1604 | 8703	89335
1605 | 8766	409725
1606 | 8821	1102761
1607 | 8821	1153811
1608 | 8832	1102761
1609 | 8865	103515
1610 | 8865	1129570
1611 | 8865	395075
1612 | 8865	608292
1613 | 8865	647408
1614 | 8865	785678
1615 | 8865	8875
1616 | 8872	1104647
1617 | 8872	1125092
1618 | 8872	1152761
1619 | 8872	1153860
1620 | 8872	197054
1621 | 8872	229635
1622 | 8872	395075
1623 | 8872	8875
1624 | 8874	1120643
1625 | 8874	1153816
1626 | 8874	395075
1627 | 8874	608292
1628 | 8874	646809
1629 | 8874	647447
1630 | 8874	8875
1631 | 8875	1129570
1632 | 8875	1152676
1633 | 8875	18582
1634 | 8875	395075
1635 | 8875	608292
1636 | 8961	1103610
1637 | 8961	1133390
1638 | 9513	102879
1639 | 9515	102879
1640 | 9515	4804
1641 | 9515	9513
1642 | 9559	1102794
1643 | 9559	252725
1644 | 9581	1130780
1645 | 9581	633585
1646 | 9586	1120138
1647 | 9586	33818
1648 | 9708	1130539
1649 | 9708	1153003
1650 | 9708	136766
1651 | 9708	136768
1652 | 9708	181782
1653 | 9716	1153262
1654 | 9716	28674
1655 | 9716	6539
1656 | 10169	1107067
1657 | 10169	1123867
1658 | 10169	114189
1659 | 10169	158614
1660 | 10169	17798
1661 | 10169	20850
1662 | 10169	211875
1663 | 10169	259126
1664 | 10169	3231
1665 | 10169	39126
1666 | 10169	39131
1667 | 10169	49660
1668 | 10169	552469
1669 | 10169	6334
1670 | 10169	63477
1671 | 10169	63486
1672 | 10169	636098
1673 | 10169	711598
1674 | 10169	85452
1675 | 10174	10186
1676 | 10177	10169
1677 | 10177	1112574
1678 | 10177	12197
1679 | 10177	12198
1680 | 10177	12210
1681 | 10177	124734
1682 | 10177	15429
1683 | 10177	164
1684 | 10177	249858
1685 | 10177	27606
1686 | 10177	4804
1687 | 10177	67415
1688 | 10177	68495
1689 | 10177	68505
1690 | 10177	9515
1691 | 10183	10186
1692 | 10183	10430
1693 | 10183	1108267
1694 | 10183	1110947
1695 | 10183	1114336
1696 | 10183	1120713
1697 | 10183	259772
1698 | 10183	47570
1699 | 10183	54550
1700 | 10183	6378
1701 | 10186	86840
1702 | 10430	1120713
1703 | 10430	3095
1704 | 10435	1103394
1705 | 10435	112378
1706 | 10435	208345
1707 | 10435	22566
1708 | 10435	41417
1709 | 10435	8703
1710 | 10531	1102850
1711 | 10531	1107567
1712 | 10531	1129442
1713 | 10531	1130634
1714 | 10531	194617
1715 | 10531	31336
1716 | 10531	31349
1717 | 10531	31353
1718 | 10531	36620
1719 | 10531	43698
1720 | 10531	686532
1721 | 10796	1110947
1722 | 10796	1113934
1723 | 10796	1135358
1724 | 10796	1153148
1725 | 10796	180373
1726 | 10796	20942
1727 | 10796	30895
1728 | 10796	31097
1729 | 10796	417017
1730 | 10796	46536
1731 | 10796	601567
1732 | 10796	6217
1733 | 10796	8699
1734 | 10796	93320
1735 | 10798	10183
1736 | 10798	10796
1737 | 10798	1154500
1738 | 10798	18619
1739 | 10798	20193
1740 | 10798	212777
1741 | 10798	252715
1742 | 10798	434
1743 | 10798	601561
1744 | 10798	8703
1745 | 10798	97892
1746 | 10981	1102873
1747 | 10981	23507
1748 | 11093	39199
1749 | 11148	1129835
1750 | 11148	1131719
1751 | 11148	193354
1752 | 11148	25413
1753 | 11326	11339
1754 | 11335	1127657
1755 | 11335	11325
1756 | 11335	1153264
1757 | 11335	20833
1758 | 11335	217139
1759 | 11335	35070
1760 | 11335	49843
1761 | 11335	545647
1762 | 11335	582511
1763 | 11337	1128256
1764 | 11339	11326
1765 | 11339	211432
1766 | 11339	30901
1767 | 11339	44017
1768 | 11339	45061
1769 | 11342	463825
1770 | 11342	65650
1771 | 11342	79809
1772 | 12155	1126350
1773 | 12158	12199
1774 | 12158	148399
1775 | 12165	1107567
1776 | 12165	1112099
1777 | 12169	1119471
1778 | 12169	4637
1779 | 12169	989397
1780 | 12182	1059953
1781 | 12182	1106418
1782 | 12182	1117249
1783 | 12182	1118764
1784 | 12182	1153183
1785 | 12182	12165
1786 | 12182	12211
1787 | 12182	155736
1788 | 12182	157761
1789 | 12182	219239
1790 | 12182	28632
1791 | 12182	28640
1792 | 12182	28641
1793 | 12182	309476
1794 | 12182	321861
1795 | 12182	429781
1796 | 12182	4660
1797 | 12182	5038
1798 | 12182	65650
1799 | 12182	94713
1800 | 12194	27199
1801 | 12195	1107319
1802 | 12195	1107567
1803 | 12195	1112099
1804 | 12195	1131611
1805 | 12195	1132285
1806 | 12195	12165
1807 | 12195	38722
1808 | 12195	51180
1809 | 12197	111866
1810 | 12197	1119471
1811 | 12197	68495
1812 | 12198	1126350
1813 | 12198	12158
1814 | 12198	12199
1815 | 12198	164
1816 | 12198	27612
1817 | 12198	95225
1818 | 12199	1119471
1819 | 12199	12247
1820 | 12210	1112574
1821 | 12210	12182
1822 | 12210	4804
1823 | 12210	57948
1824 | 12210	6125
1825 | 12210	67415
1826 | 12210	77515
1827 | 12211	12165
1828 | 12238	101263
1829 | 12238	1107567
1830 | 12238	1112099
1831 | 12238	12165
1832 | 12247	1119471
1833 | 12247	148399
1834 | 12330	107569
1835 | 12330	1120777
1836 | 12330	1152490
1837 | 12330	1153861
1838 | 12330	156977
1839 | 12330	213279
1840 | 12330	395553
1841 | 12330	400356
1842 | 12330	949217
1843 | 12337	1129683
1844 | 12337	16451
1845 | 12337	16470
1846 | 12337	16474
1847 | 12337	395725
1848 | 12337	57764
1849 | 12347	16451
1850 | 12347	72101
1851 | 12350	101811
1852 | 12350	1104007
1853 | 12350	1107319
1854 | 12350	1131611
1855 | 12350	12158
1856 | 12350	12195
1857 | 12350	12198
1858 | 12350	12210
1859 | 12350	152227
1860 | 12350	157805
1861 | 12350	16451
1862 | 12350	20601
1863 | 12350	20602
1864 | 12350	27612
1865 | 12350	4804
1866 | 12350	6125
1867 | 12350	6539
1868 | 12350	67415
1869 | 12350	68495
1870 | 12359	1131634
1871 | 12359	16451
1872 | 12359	242637
1873 | 12439	105057
1874 | 12439	323128
1875 | 12439	340078
1876 | 12576	1071981
1877 | 12576	1104999
1878 | 12576	1105221
1879 | 12576	1107674
1880 | 12576	1129573
1881 | 12576	127033
1882 | 12576	135130
1883 | 12576	229635
1884 | 12576	28290
1885 | 12576	416455
1886 | 12576	56112
1887 | 12576	56119
1888 | 12576	56709
1889 | 12576	574710
1890 | 12576	575795
1891 | 12576	62718
1892 | 12576	63832
1893 | 12576	83725
1894 | 12576	8875
1895 | 12631	1123188
1896 | 12631	1152150
1897 | 12631	1154103
1898 | 12631	12638
1899 | 12631	662416
1900 | 12631	6898
1901 | 12631	6910
1902 | 12631	78994
1903 | 12638	1123188
1904 | 12638	119761
1905 | 12638	263553
1906 | 12638	4330
1907 | 12638	662416
1908 | 12638	6898
1909 | 12638	6910
1910 | 13024	899119
1911 | 13136	1118083
1912 | 13136	160705
1913 | 13193	17208
1914 | 13193	755217
1915 | 13195	1107312
1916 | 13195	1120731
1917 | 13195	755217
1918 | 13205	1105148
1919 | 13205	13208
1920 | 13208	1105148
1921 | 13208	131318
1922 | 13212	1105148
1923 | 13212	131318
1924 | 13212	13208
1925 | 13212	170338
1926 | 13212	214472
1927 | 13212	358884
1928 | 13212	411005
1929 | 13213	1152143
1930 | 13213	131318
1931 | 13269	17476
1932 | 13652	13654
1933 | 13652	13656
1934 | 13652	83746
1935 | 13654	1115959
1936 | 13654	119686
1937 | 13654	83746
1938 | 13654	987188
1939 | 13656	1120431
1940 | 13658	1115959
1941 | 13658	1120431
1942 | 13658	119686
1943 | 13658	13656
1944 | 13658	198443
1945 | 13658	294239
1946 | 13658	987188
1947 | 13686	1107355
1948 | 13686	1120431
1949 | 13686	1272
1950 | 13686	1385
1951 | 13717	1110531
1952 | 13717	37998
1953 | 13717	38000
1954 | 13885	1104379
1955 | 13885	13917
1956 | 13885	31043
1957 | 13885	42847
1958 | 13885	84459
1959 | 13917	1120020
1960 | 13966	13960
1961 | 13972	1126050
1962 | 13972	20534
1963 | 13982	34979
1964 | 14062	1119708
1965 | 14062	1152358
1966 | 14062	1152421
1967 | 14062	1153280
1968 | 14062	1154459
1969 | 14062	141347
1970 | 14062	33907
1971 | 14062	44368
1972 | 14062	646836
1973 | 14062	97390
1974 | 14062	98693
1975 | 14083	1103016
1976 | 14090	1103016
1977 | 14090	643069
1978 | 14428	1103031
1979 | 14428	1103969
1980 | 14428	14429
1981 | 14428	14431
1982 | 14428	34082
1983 | 14428	73119
1984 | 14429	1103031
1985 | 14429	1103969
1986 | 14429	1119216
1987 | 14429	14431
1988 | 14429	34082
1989 | 14429	73119
1990 | 14430	1103031
1991 | 14430	1103969
1992 | 14430	1119216
1993 | 14430	14429
1994 | 14430	14431
1995 | 14430	34082
1996 | 14430	73119
1997 | 14431	1103031
1998 | 14529	1103038
1999 | 14529	1114864
2000 | 14529	1119987
2001 | 14529	239829
2002 | 14531	1103038
2003 | 14531	1105932
2004 | 14531	1152308
2005 | 14531	56167
2006 | 14531	592830
2007 | 14531	60682
2008 | 14545	1103038
2009 | 14545	14549
2010 | 14549	1103038
2011 | 14549	592830
2012 | 14807	1111240
2013 | 14807	264347
2014 | 15076	25702
2015 | 15076	708945
2016 | 15429	10169
2017 | 15429	10177
2018 | 15429	1104007
2019 | 15429	1107572
2020 | 15429	1114526
2021 | 15429	1153160
2022 | 15429	12197
2023 | 15429	12350
2024 | 15429	175576
2025 | 15429	210309
2026 | 15429	217115
2027 | 15429	219239
2028 | 15429	35854
2029 | 15429	41666
2030 | 15429	57948
2031 | 15429	77515
2032 | 15429	89547
2033 | 15429	9513
2034 | 15429	9515
2035 | 15431	35854
2036 | 15670	12558
2037 | 15889	1110024
2038 | 15889	1118388
2039 | 15889	1152564
2040 | 15889	1153891
2041 | 15889	15892
2042 | 15889	175909
2043 | 15889	221302
2044 | 15892	1116146
2045 | 15892	1118388
2046 | 15984	158098
2047 | 15984	189577
2048 | 15984	308920
2049 | 15984	714975
2050 | 15987	1127530
2051 | 15987	158098
2052 | 15987	3192
2053 | 15987	523394
2054 | 15987	653441
2055 | 15987	714289
2056 | 16008	1105531
2057 | 16008	1131165
2058 | 16008	158098
2059 | 16008	189571
2060 | 16008	189572
2061 | 16008	189577
2062 | 16008	308920
2063 | 16008	3191
2064 | 16008	3192
2065 | 16008	523394
2066 | 16008	642827
2067 | 16437	152219
2068 | 16437	16470
2069 | 16437	16474
2070 | 16437	395725
2071 | 16437	430329
2072 | 16437	51831
2073 | 16451	118436
2074 | 16461	1105603
2075 | 16470	1129621
2076 | 16471	1104787
2077 | 16471	1114992
2078 | 16471	1126044
2079 | 16471	16474
2080 | 16471	273949
2081 | 16474	16471
2082 | 16476	1123087
2083 | 16476	1129621
2084 | 16476	12359
2085 | 16476	16474
2086 | 16485	105856
2087 | 16485	1104787
2088 | 16485	1105603
2089 | 16485	1109891
2090 | 16485	1120049
2091 | 16485	1123087
2092 | 16485	12347
2093 | 16485	286500
2094 | 16819	1131167
2095 | 16819	1131236
2096 | 16819	1131274
2097 | 16819	1131312
2098 | 16819	126793
2099 | 16819	643003
2100 | 16819	643069
2101 | 16819	643221
2102 | 16819	644093
2103 | 16819	644334
2104 | 16819	646195
2105 | 16819	646286
2106 | 16843	1152259
2107 | 16843	1152991
2108 | 16843	643069
2109 | 17201	1107140
2110 | 17201	1118245
2111 | 17201	1126012
2112 | 17201	17208
2113 | 17201	184157
2114 | 17201	32872
2115 | 17201	95435
2116 | 17208	1152633
2117 | 17208	1153262
2118 | 17208	184157
2119 | 17208	32872
2120 | 17208	95435
2121 | 17242	1115291
2122 | 17242	280876
2123 | 17363	444191
2124 | 17476	1103162
2125 | 17476	1105033
2126 | 17476	1122425
2127 | 17476	1129208
2128 | 17476	170338
2129 | 17476	28456
2130 | 17476	33013
2131 | 17476	682666
2132 | 17476	892139
2133 | 17477	1103162
2134 | 17488	1103162
2135 | 17798	1119987
2136 | 17798	1152308
2137 | 17798	3231
2138 | 17798	323128
2139 | 17798	39131
2140 | 17798	56167
2141 | 17811	1107136
2142 | 17811	63486
2143 | 17821	245955
2144 | 18251	1106052
2145 | 18313	249421
2146 | 18532	86923
2147 | 18536	1106854
2148 | 18536	424540
2149 | 18536	86923
2150 | 18615	1000012
2151 | 18615	4660
2152 | 18615	582343
2153 | 18619	1153091
2154 | 18619	18615
2155 | 18619	4660
2156 | 18770	28964
2157 | 18770	531348
2158 | 18770	531351
2159 | 18773	167656
2160 | 18773	88356
2161 | 18774	73146
2162 | 18777	103537
2163 | 18777	1112686
2164 | 18777	116790
2165 | 18777	173863
2166 | 18777	18774
2167 | 18777	33013
2168 | 18777	345340
2169 | 18777	445938
2170 | 18777	510718
2171 | 18777	66794
2172 | 18777	73146
2173 | 18777	79817
2174 | 18777	94713
2175 | 18781	1108175
2176 | 18781	18785
2177 | 18781	86840
2178 | 18811	1106401
2179 | 18811	1112686
2180 | 18811	20920
2181 | 18812	20920
2182 | 18812	510715
2183 | 18815	1104449
2184 | 18815	20920
2185 | 18832	1122425
2186 | 18832	18833
2187 | 18832	682666
2188 | 18832	81350
2189 | 18834	1152740
2190 | 18834	1152944
2191 | 18834	18833
2192 | 19045	593210
2193 | 19045	593328
2194 | 19045	593329
2195 | 19045	87363
2196 | 19231	1107572
2197 | 19231	1153736
2198 | 19231	12960
2199 | 19231	18619
2200 | 19231	30934
2201 | 19231	686061
2202 | 19621	1106287
2203 | 19621	1114153
2204 | 19621	1114442
2205 | 19621	1126044
2206 | 19621	1128846
2207 | 19621	1131464
2208 | 19621	1135082
2209 | 19621	1135137
2210 | 19621	123825
2211 | 19621	184918
2212 | 19621	23448
2213 | 19621	240791
2214 | 19621	330148
2215 | 19621	42847
2216 | 19621	49482
2217 | 19621	6184
2218 | 19621	62347
2219 | 19621	628888
2220 | 19621	631052
2221 | 19621	649730
2222 | 19621	649739
2223 | 19621	66990
2224 | 19621	684372
2225 | 19621	7276
2226 | 19621	7297
2227 | 19621	763009
2228 | 19621	83826
2229 | 19621	853116
2230 | 19621	948147
2231 | 19621	948299
2232 | 19621	950052
2233 | 19697	131318
2234 | 19697	239800
2235 | 19697	32698
2236 | 19697	40124
2237 | 19697	95718
2238 | 20178	38829
2239 | 20178	64271
2240 | 20178	91853
2241 | 20179	20178
2242 | 20179	91852
2243 | 20179	91853
2244 | 20179	95188
2245 | 20180	91853
2246 | 20180	943
2247 | 20193	1108597
2248 | 20193	1114777
2249 | 20193	1116397
2250 | 20193	1116839
2251 | 20193	1119180
2252 | 20193	112813
2253 | 20193	1130653
2254 | 20193	1130657
2255 | 20193	1138091
2256 | 20193	1152244
2257 | 20193	1153877
2258 | 20193	1153879
2259 | 20193	1153889
2260 | 20193	144330
2261 | 20193	3085
2262 | 20193	30973
2263 | 20193	395725
2264 | 20193	5062
2265 | 20193	566488
2266 | 20193	566653
2267 | 20193	566664
2268 | 20193	91853
2269 | 20526	1103315
2270 | 20526	1118092
2271 | 20528	1103315
2272 | 20528	1103676
2273 | 20528	1107067
2274 | 20534	1103315
2275 | 20534	1106966
2276 | 20534	1107455
2277 | 20534	1111052
2278 | 20534	23774
2279 | 20534	70442
2280 | 20534	7419
2281 | 20584	1106849
2282 | 20584	1111788
2283 | 20584	1118823
2284 | 20584	20592
2285 | 20584	20593
2286 | 20584	235776
2287 | 20584	389715
2288 | 20584	6311
2289 | 20592	20593
2290 | 20601	1108167
2291 | 20601	1118209
2292 | 20601	1121537
2293 | 20601	12158
2294 | 20601	12199
2295 | 20601	12247
2296 | 20601	124734
2297 | 20601	20602
2298 | 20601	25805
2299 | 20601	42207
2300 | 20601	95225
2301 | 20602	1108167
2302 | 20602	1121537
2303 | 20602	12155
2304 | 20602	12158
2305 | 20602	12199
2306 | 20602	12247
2307 | 20602	20601
2308 | 20602	42207
2309 | 20602	95225
2310 | 20821	1127851
2311 | 20821	372862
2312 | 20857	1127863
2313 | 20857	1155073
2314 | 20923	1115701
2315 | 20923	1116530
2316 | 20923	20924
2317 | 20923	289885
2318 | 20923	294030
2319 | 20924	1108267
2320 | 20924	1113934
2321 | 20924	1115701
2322 | 20924	289885
2323 | 20924	294030
2324 | 20924	521207
2325 | 20972	1116181
2326 | 22229	1103383
2327 | 22229	1107418
2328 | 22229	1112911
2329 | 22229	1128369
2330 | 22229	144701
2331 | 22229	190697
2332 | 22229	22241
2333 | 22229	243483
2334 | 22229	459216
2335 | 22229	593105
2336 | 22229	595193
2337 | 22241	1103383
2338 | 22241	1107418
2339 | 22241	1128369
2340 | 22241	595193
2341 | 22386	1107010
2342 | 22386	1128407
2343 | 22386	207395
2344 | 22386	310530
2345 | 22386	38839
2346 | 22386	38845
2347 | 22386	38846
2348 | 22386	3932
2349 | 22431	1110426
2350 | 22431	144330
2351 | 22431	49843
2352 | 22431	49844
2353 | 22431	49847
2354 | 22431	6639
2355 | 22563	10435
2356 | 22563	107251
2357 | 22563	107252
2358 | 22563	1103394
2359 | 22563	1120962
2360 | 22563	1121459
2361 | 22563	1123926
2362 | 22563	12439
2363 | 22563	13686
2364 | 22563	1786
2365 | 22563	22564
2366 | 22563	22566
2367 | 22563	3220
2368 | 22563	36140
2369 | 22563	389715
2370 | 22563	63915
2371 | 22563	6767
2372 | 22563	6771
2373 | 22563	94229
2374 | 22564	10435
2375 | 22564	1103394
2376 | 22564	22566
2377 | 22566	10435
2378 | 22566	1103394
2379 | 22835	1110563
2380 | 22835	39904
2381 | 22869	22876
2382 | 22874	22876
2383 | 22875	1110947
2384 | 22875	1113934
2385 | 22875	22876
2386 | 22883	22876
2387 | 22886	1107367
2388 | 22886	1120444
2389 | 22886	28447
2390 | 23069	23070
2391 | 23069	74700
2392 | 23070	1116044
2393 | 23070	134307
2394 | 23070	134316
2395 | 23070	74698
2396 | 23070	87915
2397 | 23116	1105433
2398 | 23116	1111978
2399 | 23116	40124
2400 | 23116	41666
2401 | 23116	576257
2402 | 23258	1106052
2403 | 23258	168410
2404 | 23258	18251
2405 | 23502	184918
2406 | 23502	23507
2407 | 23507	1109392
2408 | 23507	13717
2409 | 23507	152731
2410 | 23507	217852
2411 | 23507	23502
2412 | 23507	649731
2413 | 23507	93320
2414 | 23546	27230
2415 | 23738	101662
2416 | 23738	101811
2417 | 23738	1118764
2418 | 23738	11325
2419 | 23738	11342
2420 | 23738	1153064
2421 | 23738	1153148
2422 | 23738	189574
2423 | 23738	22563
2424 | 23738	27249
2425 | 23738	27250
2426 | 23738	411092
2427 | 23738	65650
2428 | 23738	73146
2429 | 23738	84020
2430 | 23774	1106406
2431 | 23774	1107455
2432 | 23774	7432
2433 | 24043	1111265
2434 | 24043	15076
2435 | 24043	928873
2436 | 24476	1103499
2437 | 24476	1106546
2438 | 24476	1153024
2439 | 24530	1109581
2440 | 24966	1104449
2441 | 24966	1105344
2442 | 24966	1105433
2443 | 24966	1106370
2444 | 24966	1106671
2445 | 24966	1123576
2446 | 24966	1131149
2447 | 24966	1131267
2448 | 24966	1152194
2449 | 24966	1154042
2450 | 24966	124828
2451 | 24966	131042
2452 | 24966	131318
2453 | 24966	145134
2454 | 24966	145176
2455 | 24966	19697
2456 | 24966	197452
2457 | 24966	202639
2458 | 24966	23116
2459 | 24966	239800
2460 | 24966	27627
2461 | 24966	40124
2462 | 24966	63549
2463 | 24966	641976
2464 | 24966	65212
2465 | 24966	671293
2466 | 24966	79809
2467 | 24966	92065
2468 | 24966	95719
2469 | 24974	1104258
2470 | 24974	1112723
2471 | 24974	131042
2472 | 24974	34315
2473 | 24974	40125
2474 | 24974	6771
2475 | 25181	1152277
2476 | 25181	1152673
2477 | 25181	1154251
2478 | 25181	25184
2479 | 25181	27174
2480 | 25181	96335
2481 | 25184	285675
2482 | 25184	385572
2483 | 25702	1108834
2484 | 25702	1118848
2485 | 25702	1136397
2486 | 25702	1153897
2487 | 25702	147870
2488 | 25702	202639
2489 | 25702	675649
2490 | 25702	910
2491 | 25772	1104769
2492 | 25772	1105932
2493 | 25772	1122580
2494 | 25772	1126503
2495 | 25772	1997
2496 | 25772	641956
2497 | 25791	28202
2498 | 25791	45212
2499 | 25794	582343
2500 | 25805	1108167
2501 | 25805	1110028
2502 | 25805	1121063
2503 | 25805	385251
2504 | 26850	1114605
2505 | 27174	180373
2506 | 27174	25184
2507 | 27174	35343
2508 | 27174	96335
2509 | 27199	148399
2510 | 27199	248119
2511 | 27203	1106418
2512 | 27203	1118209
2513 | 27203	148399
2514 | 27230	23545
2515 | 27241	1106492
2516 | 27241	1116268
2517 | 27243	1104851
2518 | 27243	59045
2519 | 27246	1110390
2520 | 27249	368431
2521 | 27249	8079
2522 | 27510	1110998
2523 | 27510	1128990
2524 | 27510	1152143
2525 | 27510	188318
2526 | 27514	1152143
2527 | 27530	1106630
2528 | 27530	1128542
2529 | 27530	27531
2530 | 27530	592826
2531 | 27530	66594
2532 | 27531	1106370
2533 | 27531	763009
2534 | 27535	1112026
2535 | 27535	13686
2536 | 27535	173884
2537 | 27535	308232
2538 | 27535	34961
2539 | 27543	1106630
2540 | 27543	1135125
2541 | 27543	27531
2542 | 27606	111866
2543 | 27606	124734
2544 | 27606	249858
2545 | 27612	1118209
2546 | 27612	12198
2547 | 27623	105899
2548 | 27623	1104182
2549 | 27623	1126029
2550 | 27623	27632
2551 | 27623	28254
2552 | 27623	686015
2553 | 27623	686030
2554 | 27623	686061
2555 | 27623	75674
2556 | 27627	105899
2557 | 27627	116021
2558 | 27627	18619
2559 | 27627	23116
2560 | 27627	27632
2561 | 27627	28254
2562 | 27627	686015
2563 | 27627	686030
2564 | 27627	686061
2565 | 27631	20528
2566 | 27631	28254
2567 | 27631	55968
2568 | 27632	28254
2569 | 27895	105057
2570 | 27895	1103676
2571 | 27895	1122580
2572 | 27895	112378
2573 | 27895	1132706
2574 | 27895	1786
2575 | 27895	23738
2576 | 28026	5064
2577 | 28026	5069
2578 | 28202	1114629
2579 | 28202	1128267
2580 | 28202	325497
2581 | 28227	20526
2582 | 28227	6169
2583 | 28230	1108267
2584 | 28230	1123756
2585 | 28249	1116347
2586 | 28249	1129096
2587 | 28249	628668
2588 | 28254	105899
2589 | 28254	1118092
2590 | 28254	686015
2591 | 28254	686061
2592 | 28265	1152436
2593 | 28265	943
2594 | 28267	1152896
2595 | 28278	1107312
2596 | 28287	1106547
2597 | 28287	1107355
2598 | 28287	128540
2599 | 28287	1385
2600 | 28287	308529
2601 | 28287	387795
2602 | 28287	567005
2603 | 28287	56709
2604 | 28290	1104999
2605 | 28290	1107674
2606 | 28290	28287
2607 | 28290	308529
2608 | 28290	56709
2609 | 28290	63832
2610 | 28336	108047
2611 | 28350	1109957
2612 | 28350	1113459
2613 | 28350	1129243
2614 | 28350	13193
2615 | 28350	241133
2616 | 28350	28227
2617 | 28350	341188
2618 | 28350	6210
2619 | 28350	6220
2620 | 28350	628667
2621 | 28350	628668
2622 | 28350	95579
2623 | 28359	35863
2624 | 28385	1106112
2625 | 28385	1125895
2626 | 28385	118558
2627 | 28387	1107312
2628 | 28389	353541
2629 | 28412	1128430
2630 | 28412	27631
2631 | 28412	6217
2632 | 28412	6220
2633 | 28447	1107367
2634 | 28447	1120444
2635 | 28447	194645
2636 | 28471	91975
2637 | 28485	168410
2638 | 28485	23258
2639 | 28487	1153056
2640 | 28487	128
2641 | 28487	206259
2642 | 28487	8213
2643 | 28487	95586
2644 | 28489	13193
2645 | 28489	6210
2646 | 28491	1152910
2647 | 28491	153598
2648 | 28491	341188
2649 | 28491	782486
2650 | 28504	1139928
2651 | 28504	1153287
2652 | 28504	131315
2653 | 28504	365294
2654 | 28504	7430
2655 | 28504	95589
2656 | 28542	124064
2657 | 28542	206259
2658 | 28632	32260
2659 | 28640	1059953
2660 | 28640	28641
2661 | 28649	12182
2662 | 28649	155736
2663 | 28649	155738
2664 | 28851	54131
2665 | 28851	578669
2666 | 28851	595157
2667 | 28957	103537
2668 | 28957	1105531
2669 | 28957	159897
2670 | 28957	189577
2671 | 28957	28964
2672 | 28957	35922
2673 | 28957	66794
2674 | 28957	88356
2675 | 28957	94713
2676 | 28964	1125402
2677 | 28964	1125944
2678 | 29492	1112426
2679 | 29492	1122574
2680 | 29492	131117
2681 | 29492	144408
2682 | 29492	152219
2683 | 29492	29708
2684 | 29492	3229
2685 | 29492	400473
2686 | 29708	131315
2687 | 29723	131315
2688 | 29723	300071
2689 | 29723	35905
2690 | 29723	6818
2691 | 29738	155277
2692 | 30817	1120211
2693 | 30817	3229
2694 | 30817	631052
2695 | 30817	75691
2696 | 30895	1152277
2697 | 30895	1152673
2698 | 30895	1154251
2699 | 30895	144679
2700 | 30895	30901
2701 | 30934	12960
2702 | 31055	1106630
2703 | 31083	1123068
2704 | 31083	48066
2705 | 31105	1105672
2706 | 31105	175291
2707 | 31336	1129442
2708 | 31336	31349
2709 | 31336	686532
2710 | 31349	686532
2711 | 31353	10531
2712 | 31353	1063773
2713 | 31353	1107567
2714 | 31353	1123576
2715 | 31353	1124844
2716 | 31353	1129442
2717 | 31353	1129608
2718 | 31353	1135746
2719 | 31353	1152162
2720 | 31353	1152272
2721 | 31353	1152904
2722 | 31353	194617
2723 | 31353	286562
2724 | 31353	31336
2725 | 31353	31349
2726 | 31353	31927
2727 | 31353	43698
2728 | 31353	686532
2729 | 31353	686559
2730 | 31479	1115701
2731 | 31479	114189
2732 | 31479	14549
2733 | 31479	20592
2734 | 31479	289885
2735 | 31479	294030
2736 | 31479	39165
2737 | 31483	1131137
2738 | 31483	118682
2739 | 31483	39126
2740 | 31483	39165
2741 | 31489	1130780
2742 | 31489	1152150
2743 | 31489	227286
2744 | 31489	40583
2745 | 31489	40605
2746 | 31489	632796
2747 | 31489	632874
2748 | 31489	632935
2749 | 31489	633585
2750 | 31489	633721
2751 | 31489	9581
2752 | 31769	67245
2753 | 31769	67246
2754 | 31863	358894
2755 | 31863	91581
2756 | 31927	1124844
2757 | 31927	1129572
2758 | 31927	194617
2759 | 31927	31932
2760 | 31932	194617
2761 | 32083	1113995
2762 | 32083	1120962
2763 | 32083	1153933
2764 | 32083	200630
2765 | 32083	31769
2766 | 32083	346292
2767 | 32083	35061
2768 | 32083	45605
2769 | 32083	6771
2770 | 32083	688361
2771 | 32260	62274
2772 | 32260	94713
2773 | 32276	15076
2774 | 32276	174418
2775 | 32276	39199
2776 | 32276	636500
2777 | 32276	636511
2778 | 32276	84695
2779 | 32688	1717
2780 | 32688	69418
2781 | 32698	95719
2782 | 32872	1118245
2783 | 33231	1105531
2784 | 33231	1121254
2785 | 33231	1132887
2786 | 33231	158172
2787 | 33231	293974
2788 | 33231	300071
2789 | 33231	315266
2790 | 33231	445938
2791 | 33301	1022969
2792 | 33301	33303
2793 | 33325	1110256
2794 | 33325	12960
2795 | 33325	429781
2796 | 33412	124296
2797 | 33412	211875
2798 | 33412	34708
2799 | 33818	1120138
2800 | 33818	1123087
2801 | 33818	78549
2802 | 33818	78552
2803 | 33818	78557
2804 | 33823	1119004
2805 | 33823	33818
2806 | 33823	78549
2807 | 33823	78552
2808 | 33823	78557
2809 | 33895	1103960
2810 | 33895	1153577
2811 | 33895	1154176
2812 | 33895	33904
2813 | 33895	593813
2814 | 33895	82087
2815 | 33904	1103960
2816 | 33904	1110546
2817 | 33907	1103960
2818 | 33907	1134022
2819 | 33907	1136814
2820 | 33907	593091
2821 | 33907	593813
2822 | 34082	1103969
2823 | 34082	1119216
2824 | 34257	1106771
2825 | 34257	1111186
2826 | 34257	1114398
2827 | 34257	1115456
2828 | 34257	1116974
2829 | 34257	1122642
2830 | 34257	1153003
2831 | 34257	192870
2832 | 34257	34263
2833 | 34257	34266
2834 | 34257	368605
2835 | 34257	87482
2836 | 34257	90655
2837 | 34263	1122642
2838 | 34263	87482
2839 | 34263	90655
2840 | 34266	1122642
2841 | 34266	34263
2842 | 34266	90655
2843 | 34315	1103979
2844 | 34315	40125
2845 | 34355	1103979
2846 | 34708	102406
2847 | 34708	120039
2848 | 34708	211432
2849 | 34961	22883
2850 | 34961	31043
2851 | 34979	1104007
2852 | 34979	1106418
2853 | 34979	13982
2854 | 35061	1113438
2855 | 35061	1152761
2856 | 35061	1153860
2857 | 35061	144701
2858 | 35061	210871
2859 | 35061	22229
2860 | 35061	240791
2861 | 35061	263498
2862 | 35061	45605
2863 | 35061	48766
2864 | 35061	503871
2865 | 35061	682815
2866 | 35335	168958
2867 | 35335	35343
2868 | 35335	59772
2869 | 35343	1118332
2870 | 35490	1104031
2871 | 35490	1116410
2872 | 35490	64271
2873 | 35490	95188
2874 | 35778	108962
2875 | 35778	108983
2876 | 35778	1108389
2877 | 35778	1125469
2878 | 35778	1919
2879 | 35778	519318
2880 | 35778	73146
2881 | 35797	100961
2882 | 35797	1050679
2883 | 35797	1110998
2884 | 35797	1116629
2885 | 35797	1128974
2886 | 35797	188318
2887 | 35797	399370
2888 | 35797	60169
2889 | 35797	60170
2890 | 35797	627024
2891 | 35852	1035
2892 | 35852	1113438
2893 | 35852	41732
2894 | 35852	486840
2895 | 35863	134060
2896 | 35863	20972
2897 | 35863	28359
2898 | 35905	1152162
2899 | 35905	1152272
2900 | 35905	1152904
2901 | 35905	29723
2902 | 35922	1105116
2903 | 35922	1140547
2904 | 35922	1152162
2905 | 35922	1152272
2906 | 35922	1152904
2907 | 35922	194223
2908 | 35922	390894
2909 | 35922	390896
2910 | 35922	390922
2911 | 35922	396412
2912 | 35922	50336
2913 | 35922	50337
2914 | 35922	66982
2915 | 36131	1152633
2916 | 36131	17208
2917 | 36131	77438
2918 | 36140	105057
2919 | 36140	1104055
2920 | 36140	112378
2921 | 36140	12439
2922 | 36140	22563
2923 | 36140	27249
2924 | 36140	323128
2925 | 36140	350373
2926 | 36140	35797
2927 | 36140	46500
2928 | 36140	57764
2929 | 36140	63915
2930 | 36145	1104055
2931 | 36145	1105672
2932 | 36145	1116268
2933 | 36145	272720
2934 | 36145	52784
2935 | 36162	1132815
2936 | 36162	1152307
2937 | 36162	1152448
2938 | 36162	1152975
2939 | 36162	1153703
2940 | 36162	197452
2941 | 36162	20528
2942 | 36167	20528
2943 | 36620	1128430
2944 | 36620	120039
2945 | 36620	189620
2946 | 36802	1108389
2947 | 36802	130
2948 | 36802	189856
2949 | 36802	37888
2950 | 36802	589923
2951 | 36802	590022
2952 | 37483	1130539
2953 | 37483	217852
2954 | 37483	2440
2955 | 37483	5959
2956 | 37483	63931
2957 | 37541	1119140
2958 | 37541	260979
2959 | 37879	1105116
2960 | 37884	300071
2961 | 37888	1108389
2962 | 37888	1125469
2963 | 37888	1152633
2964 | 37888	293974
2965 | 37888	77438
2966 | 38205	108047
2967 | 38205	1128997
2968 | 38205	1129610
2969 | 38205	1153942
2970 | 38205	117316
2971 | 38205	592975
2972 | 38205	592986
2973 | 38205	593060
2974 | 38205	595056
2975 | 38205	606647
2976 | 38205	61069
2977 | 38480	1104182
2978 | 38537	1131270
2979 | 38537	1131277
2980 | 38537	137868
2981 | 38537	153063
2982 | 38537	642847
2983 | 38537	646195
2984 | 38722	1106172
2985 | 38722	1135746
2986 | 38722	5038
2987 | 38722	51180
2988 | 38722	65650
2989 | 38722	81350
2990 | 38771	1104191
2991 | 38829	1107010
2992 | 38829	1116397
2993 | 38829	1152244
2994 | 38839	310530
2995 | 38845	1107010
2996 | 38845	1110579
2997 | 38846	1128407
2998 | 38846	38845
2999 | 39126	1107067
3000 | 39126	88356
3001 | 39127	1105932
3002 | 39127	1128881
3003 | 39127	1128927
3004 | 39127	1128935
3005 | 39127	116081
3006 | 39127	116084
3007 | 39127	116087
3008 | 39127	195150
3009 | 39127	277263
3010 | 39127	3231
3011 | 39127	46476
3012 | 39127	49660
3013 | 39127	52835
3014 | 39127	592826
3015 | 39127	592830
3016 | 39127	75972
3017 | 39130	346243
3018 | 39130	36620
3019 | 39130	39131
3020 | 39130	521252
3021 | 39131	1106330
3022 | 39131	1131728
3023 | 39131	3231
3024 | 39131	6334
3025 | 39165	1131137
3026 | 39165	118682
3027 | 39165	39126
3028 | 39199	39210
3029 | 39199	66982
3030 | 39199	66990
3031 | 39210	66982
3032 | 39210	66986
3033 | 39403	130
3034 | 39474	1694
3035 | 39890	1123689
3036 | 39890	1154229
3037 | 39890	1154232
3038 | 39890	1154233
3039 | 39890	149669
3040 | 39890	162664
3041 | 39890	189571
3042 | 39890	242663
3043 | 39890	294030
3044 | 39890	3191
3045 | 39890	51045
3046 | 39890	521855
3047 | 39890	523394
3048 | 39890	559804
3049 | 39890	653441
3050 | 39890	714256
3051 | 39890	714289
3052 | 39890	714975
3053 | 40124	1104258
3054 | 40124	1112723
3055 | 40125	1104258
3056 | 40131	118079
3057 | 40135	118079
3058 | 40135	13686
3059 | 40151	1104261
3060 | 40151	1132418
3061 | 40151	1152307
3062 | 40151	1152448
3063 | 40151	1152975
3064 | 40151	1153703
3065 | 40151	288
3066 | 40151	49660
3067 | 40151	671293
3068 | 40605	40583
3069 | 40605	633721
3070 | 40605	884094
3071 | 40886	1121459
3072 | 40886	112378
3073 | 40886	1131728
3074 | 40886	141868
3075 | 40886	145215
3076 | 40886	429805
3077 | 40886	654177
3078 | 41216	1104300
3079 | 41417	1108329
3080 | 41417	1113551
3081 | 41417	128383
3082 | 41666	943087
3083 | 41714	1033
3084 | 41714	1110515
3085 | 41714	144212
3086 | 41714	182094
3087 | 41714	190697
3088 | 41714	190706
3089 | 41714	197054
3090 | 41714	44455
3091 | 41714	568857
3092 | 41714	594047
3093 | 41714	61069
3094 | 42156	1116594
3095 | 42156	1118120
3096 | 42156	1152179
3097 | 42156	340299
3098 | 42207	219239
3099 | 42209	27199
3100 | 42221	248119
3101 | 42221	27199
3102 | 42221	42209
3103 | 42221	57948
3104 | 42221	989397
3105 | 42847	1104379
3106 | 42848	1104379
3107 | 42848	1116835
3108 | 43165	1131195
3109 | 43186	1135894
3110 | 43186	152731
3111 | 43186	16461
3112 | 43639	469504
3113 | 43639	99025
3114 | 43698	1129442
3115 | 43698	31349
3116 | 43698	686532
3117 | 44017	1106492
3118 | 44017	206524
3119 | 44017	48075
3120 | 44121	1104435
3121 | 44368	1128227
3122 | 44455	1128985
3123 | 44455	128540
3124 | 44455	227178
3125 | 44455	387795
3126 | 44455	41714
3127 | 44455	568857
3128 | 44514	210871
3129 | 44514	253971
3130 | 44514	606479
3131 | 45052	1131266
3132 | 45052	27250
3133 | 45188	1130929
3134 | 45188	192734
3135 | 45188	350362
3136 | 45188	45189
3137 | 45188	976284
3138 | 45189	1105450
3139 | 45189	1106401
3140 | 45189	1132385
3141 | 45189	192734
3142 | 45189	192850
3143 | 45189	272720
3144 | 45189	45188
3145 | 45189	976284
3146 | 45212	1105764
3147 | 45212	120013
3148 | 45533	1104495
3149 | 45533	1106771
3150 | 45533	1115456
3151 | 45533	1116974
3152 | 45533	34266
3153 | 45533	87482
3154 | 45603	1104647
3155 | 45603	1125092
3156 | 45605	1033
3157 | 45605	1035
3158 | 45605	1131639
3159 | 45605	144212
3160 | 45605	190706
3161 | 45605	240791
3162 | 45605	503871
3163 | 45605	682815
3164 | 45605	975567
3165 | 46079	1152569
3166 | 46079	69284
3167 | 46452	1153091
3168 | 46452	582343
3169 | 46468	27249
3170 | 46470	1105450
3171 | 46491	107251
3172 | 46491	107252
3173 | 46491	1105672
3174 | 46491	1118823
3175 | 46491	1120169
3176 | 46491	1152290
3177 | 46491	137380
3178 | 46491	308232
3179 | 46491	37541
3180 | 46491	389715
3181 | 46491	46547
3182 | 46491	51834
3183 | 46500	57764
3184 | 46501	1153275
3185 | 46501	57764
3186 | 46547	107251
3187 | 46547	1118823
3188 | 46547	1786
3189 | 46547	389715
3190 | 46887	1111788
3191 | 46887	1153106
3192 | 46887	235776
3193 | 46887	45212
3194 | 46887	6311
3195 | 47570	385067
3196 | 47682	1125393
3197 | 47682	47683
3198 | 47682	47684
3199 | 47839	1124837
3200 | 48550	133563
3201 | 48550	227286
3202 | 48555	133563
3203 | 48555	48550
3204 | 48764	1104647
3205 | 48764	1125092
3206 | 48766	1104647
3207 | 48766	595056
3208 | 48768	1104647
3209 | 48781	1120643
3210 | 48781	1134865
3211 | 48781	397488
3212 | 48781	423816
3213 | 49482	1112369
3214 | 49482	217984
3215 | 49482	631052
3216 | 49482	7297
3217 | 49660	3237
3218 | 49660	66594
3219 | 49720	49753
3220 | 49753	49720
3221 | 49811	3233
3222 | 49811	463825
3223 | 49843	1106388
3224 | 49843	49844
3225 | 49843	49847
3226 | 49844	1127851
3227 | 49847	49844
3228 | 50336	13269
3229 | 50336	35922
3230 | 50336	390894
3231 | 50336	50337
3232 | 50336	683355
3233 | 50337	13269
3234 | 50337	390894
3235 | 50337	396412
3236 | 50354	123556
3237 | 50354	14807
3238 | 50354	289088
3239 | 50381	62274
3240 | 50807	1104749
3241 | 50838	1126503
3242 | 50838	25794
3243 | 50980	73972
3244 | 51045	242663
3245 | 51049	1134320
3246 | 51049	1154232
3247 | 51049	242663
3248 | 51049	39890
3249 | 51049	51045
3250 | 51052	242663
3251 | 51052	51045
3252 | 51180	1107319
3253 | 51180	12195
3254 | 51180	12350
3255 | 51180	50381
3256 | 51866	1119078
3257 | 51866	22883
3258 | 51866	27535
3259 | 51866	34961
3260 | 51879	1114664
3261 | 51879	22883
3262 | 51879	51866
3263 | 51879	51909
3264 | 51909	1105428
3265 | 51909	173884
3266 | 51909	502574
3267 | 51934	14545
3268 | 52000	1104809
3269 | 52000	52007
3270 | 52003	1104809
3271 | 52003	1112194
3272 | 52003	52000
3273 | 52007	1104809
3274 | 52515	300806
3275 | 52515	39199
3276 | 52515	446271
3277 | 52784	1104851
3278 | 52835	1115471
3279 | 52847	1131471
3280 | 52847	1153811
3281 | 53942	1117184
3282 | 53942	56167
3283 | 54129	108047
3284 | 54129	1128291
3285 | 54129	1128319
3286 | 54129	1136791
3287 | 54129	117315
3288 | 54129	117316
3289 | 54129	17477
3290 | 54129	28336
3291 | 54129	578645
3292 | 54129	578646
3293 | 54129	578649
3294 | 54129	578780
3295 | 54129	593022
3296 | 54129	594543
3297 | 54131	1128982
3298 | 54131	1153897
3299 | 54131	141324
3300 | 54131	243483
3301 | 54131	459213
3302 | 54131	459214
3303 | 54131	578780
3304 | 54131	593859
3305 | 54131	593942
3306 | 54131	59715
3307 | 54131	66563
3308 | 54132	1117476
3309 | 54132	1128985
3310 | 54132	578780
3311 | 54132	579008
3312 | 54132	593921
3313 | 54132	594387
3314 | 54132	59715
3315 | 54132	62634
3316 | 54550	5600
3317 | 54844	1104946
3318 | 55403	1128531
3319 | 55770	1114777
3320 | 55770	208345
3321 | 55770	521207
3322 | 55801	208345
3323 | 55968	892139
3324 | 56112	1104999
3325 | 56112	1107674
3326 | 56112	127033
3327 | 56112	56115
3328 | 56112	56119
3329 | 56112	63832
3330 | 56112	83725
3331 | 56115	1104999
3332 | 56115	1107674
3333 | 56115	1129573
3334 | 56115	127033
3335 | 56115	135130
3336 | 56115	56119
3337 | 56115	62718
3338 | 56115	63486
3339 | 56115	63832
3340 | 56115	69284
3341 | 56119	1104999
3342 | 56119	1105221
3343 | 56119	127033
3344 | 56119	63832
3345 | 56167	1117184
3346 | 56167	1119178
3347 | 56167	1152308
3348 | 56167	239810
3349 | 56167	359067
3350 | 56708	28287
3351 | 56708	308529
3352 | 56708	56709
3353 | 56709	1128959
3354 | 56709	18834
3355 | 56709	28287
3356 | 56709	308529
3357 | 56709	416455
3358 | 57119	1129570
3359 | 57119	1132731
3360 | 57119	1152676
3361 | 57119	1153942
3362 | 57119	28456
3363 | 57119	592975
3364 | 57119	592986
3365 | 57119	593060
3366 | 57119	595056
3367 | 57119	606647
3368 | 57119	647408
3369 | 57764	711527
3370 | 57773	1154524
3371 | 57773	235670
3372 | 57922	1118658
3373 | 57922	57948
3374 | 57932	6925
3375 | 57948	1114526
3376 | 57948	1118658
3377 | 57948	57922
3378 | 58268	13024
3379 | 58268	137956
3380 | 58268	899119
3381 | 58436	1114239
3382 | 58436	87417
3383 | 58453	87417
3384 | 58454	144679
3385 | 58454	87417
3386 | 58540	181782
3387 | 58552	1113828
3388 | 58552	248395
3389 | 58758	1128204
3390 | 58758	1128208
3391 | 58758	1688
3392 | 58758	33895
3393 | 58758	576973
3394 | 58758	69284
3395 | 59045	1121867
3396 | 59626	102406
3397 | 59626	124734
3398 | 59626	217115
3399 | 59626	249858
3400 | 59626	6125
3401 | 59715	96845
3402 | 59772	35335
3403 | 59798	1152277
3404 | 59798	1152673
3405 | 59798	1154251
3406 | 59798	180373
3407 | 59798	25181
3408 | 59798	27174
3409 | 59798	30895
3410 | 59798	96335
3411 | 60159	399370
3412 | 60159	60169
3413 | 60169	399370
3414 | 60170	399370
3415 | 60682	63477
3416 | 61069	1107062
3417 | 61069	1129610
3418 | 61069	1153816
3419 | 61069	28456
3420 | 61069	975567
3421 | 61073	975567
3422 | 61312	1105231
3423 | 61417	1106764
3424 | 61417	212777
3425 | 61417	94416
3426 | 62274	32260
3427 | 62274	50381
3428 | 62274	94713
3429 | 62329	1131565
3430 | 62329	195792
3431 | 62329	251756
3432 | 62329	593155
3433 | 62329	650834
3434 | 62347	62333
3435 | 62389	1107325
3436 | 62417	1107558
3437 | 62417	41216
3438 | 62607	294145
3439 | 62634	243483
3440 | 62676	14083
3441 | 62676	312409
3442 | 62718	127033
3443 | 63486	509379
3444 | 63486	83461
3445 | 63486	96845
3446 | 63549	1105344
3447 | 63812	1105360
3448 | 63812	1121057
3449 | 63812	1125393
3450 | 63835	1110531
3451 | 63835	95586
3452 | 63915	145215
3453 | 63915	38000
3454 | 63915	6935
3455 | 63915	6939
3456 | 63931	1117786
3457 | 63931	136766
3458 | 63931	136768
3459 | 63931	13717
3460 | 63931	6939
3461 | 64271	1152244
3462 | 64271	38829
3463 | 64271	73327
3464 | 64271	91852
3465 | 64271	95188
3466 | 64319	111866
3467 | 64319	249858
3468 | 64484	1105394
3469 | 64519	154134
3470 | 64519	37483
3471 | 64519	63835
3472 | 65057	1125909
3473 | 65057	134307
3474 | 65057	519353
3475 | 65057	74698
3476 | 65074	1110209
3477 | 65074	1117920
3478 | 65074	134307
3479 | 65074	134316
3480 | 65074	142268
3481 | 65074	23070
3482 | 65074	65057
3483 | 65074	714748
3484 | 65074	74698
3485 | 65074	87915
3486 | 65212	1105433
3487 | 65212	1154042
3488 | 65212	23116
3489 | 65650	1105450
3490 | 65650	1118764
3491 | 65650	309476
3492 | 65650	5038
3493 | 65653	1105450
3494 | 65653	1112319
3495 | 66556	1129778
3496 | 66556	141324
3497 | 66556	54131
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3499 | 66556	66563
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3501 | 66563	96845
3502 | 66564	141324
3503 | 66564	593859
3504 | 66564	593942
3505 | 66564	66563
3506 | 66594	8821
3507 | 66596	362926
3508 | 66596	8821
3509 | 66751	1135750
3510 | 66751	1138043
3511 | 66751	573535
3512 | 66751	693143
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3518 | 66794	1153031
3519 | 66805	509315
3520 | 66805	82090
3521 | 66809	82090
3522 | 66982	39210
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3525 | 66990	1152490
3526 | 66990	1153861
3527 | 66990	390889
3528 | 66990	39210
3529 | 67245	31769
3530 | 67245	67246
3531 | 67246	31769
3532 | 67292	1105530
3533 | 67415	1110256
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3535 | 67415	12195
3536 | 67415	164
3537 | 67415	171954
3538 | 67415	38722
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3541 | 67584	1127566
3542 | 67584	562067
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3545 | 68115	47684
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3610 | 74427	1106771
3611 | 74427	1116974
3612 | 74427	34257
3613 | 74427	34266
3614 | 74427	368605
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3626 | 74920	1105877
3627 | 74921	1105877
3628 | 74937	1105877
3629 | 74937	74920
3630 | 74937	74921
3631 | 74975	1107041
3632 | 74975	1128407
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3668 | 77438	158172
3669 | 77438	293974
3670 | 77438	37888
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3715 | 80491	7532
3716 | 80491	80515
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3722 | 81350	1106172
3723 | 81350	892139
3724 | 81714	81722
3725 | 82090	82087
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3727 | 82664	1106236
3728 | 82664	3097
3729 | 82666	101143
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3731 | 82666	3097
3732 | 82920	1119708
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3735 | 82920	1128198
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3740 | 82920	1153280
3741 | 82920	1153943
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3743 | 82920	259702
3744 | 82920	273152
3745 | 82920	307015
3746 | 82920	35
3747 | 82920	513189
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3749 | 82920	576691
3750 | 82920	608326
3751 | 82920	70970
3752 | 82920	81714
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3762 | 83725	56119
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4230 | 141171	1121569
4231 | 141324	1113438
4232 | 141324	503877
4233 | 141342	1119708
4234 | 141342	35061
4235 | 141342	40
4236 | 141342	575292
4237 | 141342	608191
4238 | 141342	70970
4239 | 141342	78511
4240 | 141347	1119708
4241 | 141596	40125
4242 | 141868	429805
4243 | 142268	1110209
4244 | 142268	1117920
4245 | 142268	134316
4246 | 143323	1130539
4247 | 143323	154134
4248 | 143323	217139
4249 | 143323	3084
4250 | 143323	84021
4251 | 143476	1112686
4252 | 143676	102938
4253 | 143801	284025
4254 | 144212	1131549
4255 | 144330	63486
4256 | 144408	219446
4257 | 144408	3229
4258 | 144679	1114239
4259 | 144701	1112911
4260 | 144701	1128975
4261 | 144701	1128997
4262 | 144701	1136342
4263 | 144701	22229
4264 | 144701	595063
4265 | 145134	129287
4266 | 145134	641976
4267 | 145176	1112723
4268 | 145176	1132418
4269 | 145176	40124
4270 | 145215	141868
4271 | 145215	429805
4272 | 145215	654177
4273 | 145315	294126
4274 | 145315	649944
4275 | 145384	1153287
4276 | 145384	7430
4277 | 145384	95589
4278 | 147870	1109185
4279 | 147870	1132434
4280 | 147870	636500
4281 | 148170	1109199
4282 | 148170	1128997
4283 | 148341	1109208
4284 | 148341	1131116
4285 | 149139	1123215
4286 | 149669	589923
4287 | 151430	1115670
4288 | 151708	1120786
4289 | 152226	1102400
4290 | 152227	1139928
4291 | 152227	152226
4292 | 152227	987197
4293 | 152483	583318
4294 | 152483	593091
4295 | 152731	1109392
4296 | 152731	23507
4297 | 153063	1131223
4298 | 153063	440815
4299 | 153063	561568
4300 | 153063	561581
4301 | 153063	561593
4302 | 153063	561595
4303 | 153063	561610
4304 | 153063	561613
4305 | 153063	645084
4306 | 153598	1113459
4307 | 154023	1112106
4308 | 154023	1133846
4309 | 154023	154047
4310 | 154047	1112106
4311 | 154047	1133846
4312 | 154047	154023
4313 | 154982	1121867
4314 | 154982	1130927
4315 | 154982	1130931
4316 | 154982	1130934
4317 | 154982	1133028
4318 | 154982	129897
4319 | 155158	397590
4320 | 155158	8687
4321 | 155736	155738
4322 | 155738	155736
4323 | 155738	157761
4324 | 156794	1109581
4325 | 156977	1152490
4326 | 156977	1153861
4327 | 156977	390889
4328 | 156977	395547
4329 | 157401	1118017
4330 | 157761	219239
4331 | 157805	6539
4332 | 158172	445938
4333 | 158614	10169
4334 | 158812	1127812
4335 | 159084	1152711
4336 | 159084	159085
4337 | 159084	241821
4338 | 159085	1152711
4339 | 159085	241821
4340 | 159897	310653
4341 | 159897	683355
4342 | 160732	1128853
4343 | 160732	75969
4344 | 161221	1153922
4345 | 162075	1109830
4346 | 162075	162080
4347 | 162075	737204
4348 | 162080	1109830
4349 | 162080	1135345
4350 | 162080	162075
4351 | 162080	739707
4352 | 162664	118682
4353 | 162664	39165
4354 | 162664	510718
4355 | 162664	531348
4356 | 162664	531351
4357 | 163235	1109873
4358 | 164885	447224
4359 | 166420	1119751
4360 | 166825	1110024
4361 | 166825	1118388
4362 | 166989	1110028
4363 | 166989	1121063
4364 | 166989	25805
4365 | 167670	1123068
4366 | 168332	86359
4367 | 169279	1132083
4368 | 169279	1154103
4369 | 169280	1132083
4370 | 169280	1154103
4371 | 170798	656231
4372 | 171954	1110256
4373 | 171954	1135750
4374 | 171954	1138755
4375 | 171954	820662
4376 | 173863	116790
4377 | 174418	307336
4378 | 174418	636511
4379 | 174425	174418
4380 | 174425	636511
4381 | 175256	1120643
4382 | 175256	1133196
4383 | 175256	682815
4384 | 175291	52784
4385 | 175548	105856
4386 | 175548	1120049
4387 | 175548	689152
4388 | 175548	753070
4389 | 175909	112787
4390 | 175909	1153891
4391 | 175909	221302
4392 | 177115	5069
4393 | 177993	1110515
4394 | 177998	1110515
4395 | 177998	583318
4396 | 178209	1000012
4397 | 178209	116084
4398 | 178209	195150
4399 | 178209	568045
4400 | 178209	75972
4401 | 178718	1110546
4402 | 178718	69296
4403 | 178727	1110546
4404 | 179180	1110563
4405 | 179180	1126037
4406 | 179180	139865
4407 | 179702	1121569
4408 | 179702	1152859
4409 | 179702	141171
4410 | 179702	424540
4411 | 179706	141171
4412 | 180187	1131719
4413 | 180301	1110628
4414 | 180373	180399
4415 | 180399	1120084
4416 | 180399	385572
4417 | 182093	197054
4418 | 182094	1131550
4419 | 182094	197054
4420 | 182094	650814
4421 | 184157	95435
4422 | 184918	171225
4423 | 184918	330148
4424 | 187260	1110950
4425 | 187354	1117786
4426 | 187354	1127851
4427 | 187354	372862
4428 | 188318	1110998
4429 | 188471	628500
4430 | 189566	1153014
4431 | 189566	189856
4432 | 189566	310742
4433 | 189566	36802
4434 | 189566	90470
4435 | 189571	1154232
4436 | 189571	189572
4437 | 189571	521855
4438 | 189571	714748
4439 | 189572	1134348
4440 | 189574	1123689
4441 | 189620	1129994
4442 | 189620	1133417
4443 | 189623	683355
4444 | 189655	559804
4445 | 189655	714260
4446 | 189655	714748
4447 | 189708	149669
4448 | 189721	1129518
4449 | 189774	1134346
4450 | 189774	714879
4451 | 189856	1129518
4452 | 189856	1129994
4453 | 189856	683355
4454 | 189856	683404
4455 | 190698	1114502
4456 | 190698	190697
4457 | 190698	198443
4458 | 190706	144212
4459 | 190706	182094
4460 | 191222	191216
4461 | 191222	708945
4462 | 192850	272720
4463 | 192870	1111186
4464 | 192870	1114398
4465 | 193347	1129835
4466 | 193347	1131719
4467 | 193347	193354
4468 | 193347	612306
4469 | 193352	1129835
4470 | 193352	1131719
4471 | 193352	193347
4472 | 193352	193354
4473 | 193352	612306
4474 | 193354	1129835
4475 | 193742	1111230
4476 | 193742	6152
4477 | 193918	1111240
4478 | 193931	1111240
4479 | 193931	193932
4480 | 193932	1111240
4481 | 194223	1126044
4482 | 194223	390922
4483 | 194609	1128868
4484 | 194609	1129572
4485 | 194609	1154520
4486 | 194609	126128
4487 | 194609	215912
4488 | 194609	31927
4489 | 194609	563613
4490 | 194617	1124844
4491 | 194617	1129572
4492 | 194617	215912
4493 | 194617	31932
4494 | 194645	248823
4495 | 195150	568045
4496 | 195361	1111304
4497 | 195792	1128974
4498 | 195792	19045
4499 | 195792	377303
4500 | 195792	593210
4501 | 195792	682815
4502 | 197054	1125092
4503 | 197452	1135894
4504 | 197452	424
4505 | 198443	190697
4506 | 198653	1113551
4507 | 198653	1130539
4508 | 198653	1152896
4509 | 198653	3084
4510 | 198653	887
4511 | 198866	1153728
4512 | 198866	73327
4513 | 199571	46431
4514 | 200630	643734
4515 | 202520	1115701
4516 | 202520	446271
4517 | 202522	446271
4518 | 202639	1117348
4519 | 202639	1123087
4520 | 202639	1154123
4521 | 202639	1154124
4522 | 202639	144408
4523 | 202639	242637
4524 | 202639	40131
4525 | 202639	643597
4526 | 202639	672064
4527 | 203646	1116569
4528 | 205192	1132968
4529 | 205192	578337
4530 | 205192	99030
4531 | 205196	1112929
4532 | 205196	1130568
4533 | 205196	1130586
4534 | 205196	628751
4535 | 205196	628764
4536 | 205196	628766
4537 | 205196	815096
4538 | 205196	950986
4539 | 206371	69296
4540 | 206524	48075
4541 | 208345	1114777
4542 | 208345	55801
4543 | 210309	1111978
4544 | 210871	1117476
4545 | 210871	1128151
4546 | 210871	1129629
4547 | 210871	198653
4548 | 210871	273152
4549 | 210871	35
4550 | 210871	38205
4551 | 210871	568857
4552 | 210871	575077
4553 | 210871	595157
4554 | 210871	59715
4555 | 210871	81714
4556 | 210871	887
4557 | 210872	1129610
4558 | 210872	210871
4559 | 210872	273152
4560 | 210872	35
4561 | 210872	593260
4562 | 210872	606479
4563 | 212097	1112071
4564 | 212107	1112071
4565 | 213246	1122642
4566 | 213246	34263
4567 | 213279	197783
4568 | 215912	31932
4569 | 216877	1117618
4570 | 216877	334153
4571 | 216878	1117618
4572 | 216878	334153
4573 | 216878	711527
4574 | 217115	1112319
4575 | 217139	154134
4576 | 217984	1112369
4577 | 218410	1152663
4578 | 218410	134128
4579 | 218682	22874
4580 | 219218	1112417
4581 | 219239	12247
4582 | 219239	157761
4583 | 219446	1112426
4584 | 219446	1127812
4585 | 219446	249421
4586 | 219446	567018
4587 | 219976	1116410
4588 | 220420	1128227
4589 | 221302	1153891
4590 | 226698	1112767
4591 | 226698	1122304
4592 | 227178	1114502
4593 | 227178	1128946
4594 | 227178	1128985
4595 | 227178	593240
4596 | 227178	96847
4597 | 227286	1115790
4598 | 228990	228992
4599 | 230300	118260
4600 | 230879	1130808
4601 | 230879	1132486
4602 | 230879	1132948
4603 | 230879	1133428
4604 | 230879	1140230
4605 | 230879	205192
4606 | 230879	230884
4607 | 230879	236759
4608 | 230879	2658
4609 | 230879	578309
4610 | 230879	578337
4611 | 230879	696342
4612 | 230879	696343
4613 | 230879	696345
4614 | 230879	696346
4615 | 230879	751408
4616 | 230879	851968
4617 | 230879	99030
4618 | 230884	1131607
4619 | 230884	1132486
4620 | 230884	1135899
4621 | 230884	751408
4622 | 231198	1153195
4623 | 231198	1153922
4624 | 231198	144408
4625 | 231249	1125492
4626 | 231249	1127913
4627 | 232605	25413
4628 | 232606	1132809
4629 | 232606	232605
4630 | 232606	25413
4631 | 232860	1113084
4632 | 233106	12275
4633 | 233106	630890
4634 | 235678	235670
4635 | 235678	235679
4636 | 235678	689439
4637 | 235679	1154524
4638 | 235679	235670
4639 | 235679	235678
4640 | 235679	57773
4641 | 235679	689439
4642 | 235683	1154524
4643 | 235683	235670
4644 | 235683	235678
4645 | 235683	235679
4646 | 235683	57773
4647 | 237489	1120059
4648 | 237489	2665
4649 | 237521	2665
4650 | 238099	1127851
4651 | 238401	1125386
4652 | 239800	95718
4653 | 239810	359067
4654 | 240321	1132434
4655 | 240321	1132459
4656 | 240321	1136397
4657 | 240321	1154123
4658 | 240321	1154124
4659 | 240321	428610
4660 | 240321	447250
4661 | 240321	675649
4662 | 240321	675847
4663 | 240321	69392
4664 | 240791	1113438
4665 | 240791	45605
4666 | 240791	503871
4667 | 240791	650834
4668 | 241133	1113459
4669 | 241821	1152711
4670 | 241821	159085
4671 | 242637	1113541
4672 | 243483	459214
4673 | 245288	1122460
4674 | 246618	1113742
4675 | 246618	116553
4676 | 248395	1113828
4677 | 248395	58552
4678 | 248425	1113831
4679 | 248425	1121398
4680 | 248425	592975
4681 | 248425	594387
4682 | 248431	1113831
4683 | 248823	682666
4684 | 249421	1127812
4685 | 249421	567018
4686 | 249858	111866
4687 | 250566	1113926
4688 | 251756	1102550
4689 | 251756	195792
4690 | 252715	601561
4691 | 253762	23116
4692 | 253971	1129621
4693 | 254923	108047
4694 | 254923	444240
4695 | 255233	33303
4696 | 255628	1135589
4697 | 255628	1140543
4698 | 255628	120084
4699 | 256106	1114192
4700 | 258259	168958
4701 | 259701	1114331
4702 | 259702	1114331
4703 | 259702	1131116
4704 | 259772	1114336
4705 | 260121	1114352
4706 | 260979	1114388
4707 | 261040	110164
4708 | 261040	1117184
4709 | 262108	1114442
4710 | 262121	1114442
4711 | 262178	1114442
4712 | 262178	1138968
4713 | 262178	85449
4714 | 263069	1131116
4715 | 263069	148341
4716 | 263279	1114502
4717 | 263482	1114512
4718 | 263482	1118302
4719 | 263482	263486
4720 | 263486	1114512
4721 | 263498	1152761
4722 | 263498	1153860
4723 | 263553	1117219
4724 | 263553	662416
4725 | 263553	6910
4726 | 264347	1153183
4727 | 264556	120084
4728 | 264556	289085
4729 | 264556	335042
4730 | 265203	86359
4731 | 267003	75121
4732 | 267824	1153195
4733 | 267824	69198
4734 | 270456	203646
4735 | 270456	63549
4736 | 270600	1114838
4737 | 270600	1153003
4738 | 272720	192850
4739 | 273152	210871
4740 | 273949	1114992
4741 | 278394	627024
4742 | 278394	851968
4743 | 278403	627024
4744 | 280876	1115291
4745 | 282700	1115375
4746 | 284023	1152958
4747 | 284023	1152959
4748 | 284023	119761
4749 | 284023	346292
4750 | 284025	119761
4751 | 284025	949217
4752 | 284414	1115456
4753 | 285687	285675
4754 | 286513	1131557
4755 | 286513	177993
4756 | 287787	1123756
4757 | 287787	1130847
4758 | 287787	1130856
4759 | 287787	415693
4760 | 287787	503883
4761 | 287787	634902
4762 | 287787	634904
4763 | 287787	634938
4764 | 287787	634975
4765 | 287787	647447
4766 | 288107	1123215
4767 | 289085	689152
4768 | 289088	689152
4769 | 289779	289780
4770 | 289779	801170
4771 | 289780	1128453
4772 | 289780	1153784
4773 | 289780	801170
4774 | 289781	1128453
4775 | 289781	1153784
4776 | 289781	289780
4777 | 289781	801170
4778 | 289885	1115701
4779 | 289885	20924
4780 | 289945	48550
4781 | 292277	578845
4782 | 293271	293285
4783 | 293974	1125469
4784 | 294030	20924
4785 | 294126	649944
4786 | 300806	1133010
4787 | 300806	29723
4788 | 300806	396412
4789 | 300806	6818
4790 | 302545	1152858
4791 | 307015	1131198
4792 | 307015	335733
4793 | 307015	39474
4794 | 307015	643199
4795 | 307336	285675
4796 | 307336	708945
4797 | 307656	1131360
4798 | 307656	270085
4799 | 310530	38839
4800 | 310653	159897
4801 | 314459	1121603
4802 | 315789	1138027
4803 | 315789	1139195
4804 | 318071	1153014
4805 | 318187	1116922
4806 | 321004	1117049
4807 | 321861	1117089
4808 | 325314	1122704
4809 | 330148	184918
4810 | 330148	23502
4811 | 330208	469504
4812 | 330208	99025
4813 | 334153	1117618
4814 | 335042	1117653
4815 | 335042	289085
4816 | 335733	643199
4817 | 337766	110162
4818 | 340078	928873
4819 | 340078	949217
4820 | 340299	74427
4821 | 341188	1117942
4822 | 341188	682666
4823 | 342802	2695
4824 | 346243	521251
4825 | 346292	1152958
4826 | 346292	284023
4827 | 348305	193352
4828 | 350319	193932
4829 | 354004	1118546
4830 | 358866	358894
4831 | 358866	79809
4832 | 358884	358894
4833 | 358887	358894
4834 | 358887	79809
4835 | 360028	1118823
4836 | 365294	7430
4837 | 367312	1119211
4838 | 367312	746058
4839 | 368431	30901
4840 | 368605	1153003
4841 | 368657	400455
4842 | 370366	1140040
4843 | 370366	641956
4844 | 370366	928873
4845 | 375605	1119505
4846 | 375825	1119623
4847 | 376704	1119654
4848 | 377303	1129572
4849 | 379288	67633
4850 | 380341	60560
4851 | 384428	1120019
4852 | 385067	272345
4853 | 385572	1120084
4854 | 387795	128540
4855 | 389715	107251
4856 | 389715	107252
4857 | 390693	1131639
4858 | 395540	1130676
4859 | 395553	395547
4860 | 395553	684972
4861 | 395725	1135115
4862 | 395725	1153933
4863 | 397488	1120643
4864 | 397488	1134865
4865 | 397590	1120650
4866 | 399339	1120731
4867 | 399370	60159
4868 | 400356	1120777
4869 | 400356	66990
4870 | 400455	368657
4871 | 400473	131117
4872 | 408885	1154173
4873 | 409255	1153097
4874 | 415693	254923
4875 | 416867	1121537
4876 | 416964	127940
4877 | 421481	375825
4878 | 423816	1134865
4879 | 423816	48781
4880 | 424540	48555
4881 | 427606	1050679
4882 | 427606	1131634
4883 | 427606	1138968
4884 | 427606	62329
4885 | 430329	127940
4886 | 430574	1105622
4887 | 430711	1132416
4888 | 430711	671052
4889 | 431206	662572
4890 | 440815	645571
4891 | 444191	1154068
4892 | 444191	6910
4893 | 445938	315266
4894 | 446610	300071
4895 | 447224	164885
4896 | 449841	1123093
4897 | 458439	1123493
4898 | 459206	1123530
4899 | 459213	1123530
4900 | 459214	1123530
4901 | 459216	1123530
4902 | 459216	593210
4903 | 466170	66794
4904 | 467383	1128977
4905 | 467383	116553
4906 | 486840	1128975
4907 | 486840	595063
4908 | 503877	1131374
4909 | 503877	1153577
4910 | 503877	1154076
4911 | 503883	1131374
4912 | 503883	1153577
4913 | 503883	1154076
4914 | 503883	577331
4915 | 503883	646809
4916 | 503883	646836
4917 | 503883	646913
4918 | 503883	647413
4919 | 503883	647447
4920 | 503893	1153577
4921 | 503893	33904
4922 | 503893	646837
4923 | 509379	1125393
4924 | 513189	1125597
4925 | 513189	573964
4926 | 513189	576691
4927 | 513189	787016
4928 | 519318	1125906
4929 | 519353	1125909
4930 | 520471	1125953
4931 | 521183	1125992
4932 | 521183	521207
4933 | 521183	521251
4934 | 521207	521269
4935 | 521251	1125992
4936 | 521252	1125992
4937 | 521269	1125993
4938 | 521855	519353
4939 | 522338	1132864
4940 | 522338	683360
4941 | 523010	1126044
4942 | 523010	1140543
4943 | 523394	714975
4944 | 523574	1154229
4945 | 523574	523394
4946 | 523574	653441
4947 | 523574	714289
4948 | 529165	1126315
4949 | 531348	531351
4950 | 531351	531348
4951 | 545647	582511
4952 | 559804	653441
4953 | 559804	714289
4954 | 560936	562123
4955 | 560936	91581
4956 | 561364	642827
4957 | 561568	153063
4958 | 561581	561582
4959 | 561581	646440
4960 | 561582	561581
4961 | 561593	1131223
4962 | 561595	561581
4963 | 561595	561582
4964 | 561610	561595
4965 | 561611	561595
4966 | 561611	561610
4967 | 561613	153063
4968 | 561613	561568
4969 | 561674	1127541
4970 | 561674	561581
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4991 | 573535	693143
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5421 | 853115	19621
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5425 | 853116	19621
5426 | 853116	853155
5427 | 853118	1140289
5428 | 853155	853118
5429 | 954315	1155073
5430 | 


--------------------------------------------------------------------------------
/figure.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/HanGuangXin/Result-Visualization-of-Graph-Convolutional-Networks-in-PyTorch/3f83524de7f00535970b9bbff100dabc28d52c05/figure.png


--------------------------------------------------------------------------------
/pygcn/MNIST_with_t-SNE.py:
--------------------------------------------------------------------------------
 1 | # coding='utf-8'
 2 | """t-SNE 对手写数字进行可视化"""
 3 | from time import time
 4 | import numpy as np
 5 | import matplotlib.pyplot as plt
 6 | 
 7 | from sklearn import datasets
 8 | from sklearn.manifold import TSNE
 9 | 
10 | 
11 | def get_data():
12 |     digits = datasets.load_digits(n_class=6)        # 获得6类的样本及标签
13 |     data = digits.data                              # 获取sample
14 |     label = digits.target                           # 获取label
15 |     n_samples, n_features = data.shape              # 获取number of sample 和 number of feature
16 |     return data, label, n_samples, n_features
17 | 
18 | 
19 | def plot_embedding(data, label, title):
20 |     x_min, x_max = np.min(data, 0), np.max(data, 0)
21 |     data = (data - x_min) / (x_max - x_min)
22 | 
23 |     fig = plt.figure()
24 |     ax = plt.subplot(111)
25 |     for i in range(data.shape[0]):              # 即1083
26 |         plt.text(data[i, 0], data[i, 1], str(label[i]),         # x,y坐标及label
27 |                  color=plt.cm.Set1(label[i] / 10.),
28 |                  fontdict={'weight': 'bold', 'size': 9})
29 |     plt.xticks([])
30 |     plt.yticks([])
31 |     plt.title(title)
32 |     return fig
33 | 
34 | 
35 | if __name__ == '__main__':
36 |     data, label, n_samples, n_features = get_data()
37 |     print('Computing t-SNE embedding')
38 |     tsne = TSNE(n_components=2, init='pca', random_state=0)
39 |     t0 = time()
40 |     result = tsne.fit_transform(data)
41 |     fig = plot_embedding(result, label,
42 |                          't-SNE embedding of the digits (time %.2fs)'
43 |                          % (time() - t0))
44 |     # print(fig)
45 |     plt.show()


--------------------------------------------------------------------------------
/pygcn/S_data_with_t-SNE.py:
--------------------------------------------------------------------------------
 1 | # coding='utf-8'
 2 | """# 一个对 S 曲线数据集上进行各种降维的说明。"""
 3 | from time import time
 4 | 
 5 | import matplotlib.pyplot as plt
 6 | from mpl_toolkits.mplot3d import Axes3D
 7 | from matplotlib.ticker import NullFormatter
 8 | 
 9 | from sklearn import manifold, datasets
10 | 
11 | # # Next line to silence pyflakes. This import is needed.
12 | # Axes3D
13 | 
14 | n_points = 1000
15 | # X 是一个(1000, 3)的 2 维数据(1000个点，XYZ三个坐标的数据)，color 是一个(1000,)的 1 维数据
16 | X, color = datasets.samples_generator.make_s_curve(n_points, random_state=0)
17 | n_neighbors = 10
18 | n_components = 2        # 降低到的低维为2维
19 | 
20 | fig = plt.figure(figsize=(8, 8))
21 | # 创建了一个 figure，标题为"Manifold Learning with 1000 points, 10 neighbors"
22 | plt.suptitle("Manifold Learning with %i points, %i neighbors"
23 |              % (1000, n_neighbors), fontsize=14)
24 | 
25 | '''绘制 S 曲线的 3D 图像'''
26 | ax = fig.add_subplot(211, projection='3d')
27 | ax.scatter(X[:, 0], X[:, 1], X[:, 2], c=color, cmap=plt.cm.Spectral)
28 | ax.view_init(4, -72)  # 初始化视角
29 | 
30 | '''t-SNE'''
31 | t0 = time()
32 | tsne = manifold.TSNE(n_components=n_components, init='pca', random_state=0)
33 | Y = tsne.fit_transform(X)  # 转换后的输出，维度1000×2（XY轴的坐标）
34 | t1 = time()
35 | print("t-SNE: %.2g sec" % (t1 - t0))  # 算法用时
36 | 
37 | '''绘制 S 曲线的降维到 2 维的图像'''
38 | ax = fig.add_subplot(2, 1, 2)
39 | plt.scatter(Y[:, 0], Y[:, 1], c=color, cmap=plt.cm.Spectral)
40 | plt.title("t-SNE (%.2g sec)" % (t1 - t0))
41 | ax.xaxis.set_major_formatter(NullFormatter())  # 设置标签显示格式为空
42 | ax.yaxis.set_major_formatter(NullFormatter())
43 | # plt.axis('tight')
44 | 
45 | plt.show()


--------------------------------------------------------------------------------
/pygcn/__init__.py:
--------------------------------------------------------------------------------
1 | from __future__ import print_function
2 | from __future__ import division
3 | 
4 | from .layers import *
5 | from .models import *
6 | from .utils import *


--------------------------------------------------------------------------------
/pygcn/demos.py:
--------------------------------------------------------------------------------
1 | import numpy as np
2 | import scipy.sparse as sp
3 | from pygcn.utils import normalize,sparse_mx_to_torch_sparse_tensor,encode_onehot
4 | import torch
5 | 
6 | '''测试论文编号处理'''
7 | a = np.arange(9).reshape((3,3))
8 | print(np.where(a==np.max(a,axis=1)))


--------------------------------------------------------------------------------
/pygcn/layers.py:
--------------------------------------------------------------------------------
 1 | import math
 2 | 
 3 | import torch
 4 | 
 5 | from torch.nn.parameter import Parameter
 6 | from torch.nn.modules.module import Module
 7 | 
 8 | 
 9 | class GraphConvolution(Module):
10 |     """
11 |     Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
12 |     """
13 |     '''定义对象的属性'''
14 |     def __init__(self, in_features, out_features, bias=True):
15 |         super(GraphConvolution, self).__init__()
16 |         self.in_features = in_features
17 |         self.out_features = out_features
18 |         self.weight = Parameter(torch.FloatTensor(in_features, out_features))           # in_features × out_features
19 |         if bias:
20 |             self.bias = Parameter(torch.FloatTensor(out_features))
21 |         else:
22 |             self.register_parameter('bias', None)
23 |         self.reset_parameters()
24 | 
25 |     '''生成权重'''
26 |     def reset_parameters(self):
27 |         stdv = 1. / math.sqrt(self.weight.size(1))
28 |         self.weight.data.uniform_(-stdv, stdv)          # .uniform()：将tensor用从均匀分布中抽样得到的值填充。
29 |         if self.bias is not None:
30 |             self.bias.data.uniform_(-stdv, stdv)
31 | 
32 |     '''前向传播 of 一层之内：即本层的计算方法：A * X * W '''
33 |     def forward(self, input, adj):
34 |         support = torch.mm(input, self.weight)          # torch.mm：Matrix multiply，input和weight实现矩阵点乘。
35 |         output = torch.spmm(adj, support)               # torch.spmm：稀疏矩阵乘法，sp即sparse。
36 |         if self.bias is not None:
37 |             return output + self.bias
38 |         else:
39 |             return output
40 | 
41 |     '''把一个对象用字符串的形式表达出来以便辨认，在终端调用的时候会显示信息'''
42 |     def __repr__(self):
43 |         return self.__class__.__name__ + ' (' \
44 |                + str(self.in_features) + ' -> ' \
45 |                + str(self.out_features) + ')'
46 | 


--------------------------------------------------------------------------------
/pygcn/models.py:
--------------------------------------------------------------------------------
 1 | import torch.nn as nn
 2 | import torch.nn.functional as F
 3 | from pygcn.layers import GraphConvolution
 4 | 
 5 | '''GCN类'''
 6 | class GCN(nn.Module):
 7 |     def __init__(self, nfeat, nhid, nclass, dropout):
 8 |         super(GCN, self).__init__()
 9 | 
10 |         self.gc1 = GraphConvolution(nfeat, nhid)        # 第一层
11 |         self.gc2 = GraphConvolution(nhid, nclass)       # 第二层
12 |         self.dropout = dropout                          # 定义dropout
13 | 
14 |     '''前向传播 of 层间：整个网络的前向传播的方式：relu(gc1) --> dropout --> gc2 --> log_softmax'''
15 |     def forward(self, x, adj):
16 |         x = F.relu(self.gc1(x, adj))
17 |         x = F.dropout(x, self.dropout, training=self.training)
18 |         x = self.gc2(x, adj)
19 |         return F.log_softmax(x, dim=1)
20 | 


--------------------------------------------------------------------------------
/pygcn/test.py:
--------------------------------------------------------------------------------
 1 | import numpy as np
 2 | import scipy.sparse as sp
 3 | import torch
 4 | 
 5 | def encode_onehot(labels):
 6 |     classes = set(labels)       # set() 函数创建一个无序不重复元素集
 7 |     print('number of classes:',classes)
 8 |     # number of classes: {'Genetic_Algorithms', 'Reinforcement_Learning', 'Case_Based',
 9 |     # 'Probabilistic_Methods', 'Theory', 'Neural_Networks', 'Rule_Learning'}
10 |     # 一共7个类别。
11 | 
12 |     # enumerate()函数生成序列，带有索引i和值c。
13 |     # 这一句将string类型的label变为int类型的label，建立映射关系
14 |     classes_dict = {c: np.identity(len(classes))[i, :] for i, c in
15 |                     enumerate(classes)}
16 |     # map() 会根据提供的函数对指定序列做映射。
17 |     # 这一句将string类型的label替换为int类型的label
18 | 
19 |     labels_onehot = np.array(list(map(classes_dict.get, labels)),
20 |                              dtype=np.int32)
21 |     # 返回int类型的label
22 |     return labels_onehot
23 | 
24 | # # demo of torch.mm()：矩阵点乘
25 | # markov_states = torch.Tensor([[1,1,1],[2,2,2],[3,3,3],[4,4,4],[5,5,5]])
26 | # transition_matrix = torch.Tensor([[1,1,1],[2,2,2],[3,3,3]])
27 | #
28 | # # Apply one transition
29 | # new_state = torch.mm(markov_states, transition_matrix)
30 | # print(markov_states)
31 | # print(transition_matrix)
32 | # print(new_state)
33 | # print(new_state.shape)      # torch.Size([5, 3])
34 | # ----------------------------------------------------------------
35 | # a = torch.Tensor([[1,2,3,4,5,6,7],[8,9,10,11,12,13,14]])
36 | # print(a[:,1:-1])
37 | # print(a[:,-1])
38 | # ----------------------------------------------------------------
39 | path="C:/Users/73416/PycharmProjects/PyGCN_Visualization/data/cora/"
40 | dataset="cora"
41 | edges_unordered = np.genfromtxt("{}{}.cites".format(path, dataset),
42 |                                     dtype=np.int32)
43 | # print(edges_unordered)
44 | # # [[     35    1033]
45 | # #  [     35  103482]
46 | # #  [     35  103515]
47 | # #  ...
48 | # #  [ 853118 1140289]
49 | # #  [ 853155  853118]
50 | # #  [ 954315 1155073]]
51 | 
52 | # print(type(edges_unordered))
53 | # # <class 'numpy.ndarray'>
54 | 
55 | # print(edges_unordered.shape)
56 | # # (5429, 2)
57 | 
58 | idx_features_labels = np.genfromtxt("{}{}.content".format(path, dataset),
59 |                                         dtype=np.dtype(str))
60 | features = sp.csr_matrix(idx_features_labels[:, 1:-1], dtype=np.float32)
61 | idx = np.array(idx_features_labels[:, 0], dtype=np.int32)
62 | labels = encode_onehot(idx_features_labels[:, -1])
63 | print('labels:\n',labels)
64 | print('labels.shape:\n',labels.shape)
65 | 
66 | idx_map = {j: i for i, j in enumerate(idx)}
67 | # print(idx_map)
68 | # # {31336: 0, 1061127: 1, ...24043: 2707}，2707+1=2708 --> 样本数。
69 | '''论文编号没有用，需要重新的其进行编号（从0开始），然后对原编号进行替换。
70 | 所以目的是把离散的原始的编号，变成0-2707的连续编号'''
71 | edges = np.array(list(map(idx_map.get, edges_unordered.flatten())),
72 |                      dtype=np.int32).reshape(edges_unordered.shape)
73 | # print(edges)
74 | # print(edges.shape)
75 | # # (5429, 2)


--------------------------------------------------------------------------------
/pygcn/train.py:
--------------------------------------------------------------------------------
  1 | from __future__ import division
  2 | from __future__ import print_function
  3 | 
  4 | # 路径初始化
  5 | import os, sys
  6 | curPath = os.path.abspath(os.path.dirname(__file__))
  7 | rootPath = os.path.split(curPath)[0]
  8 | sys.path.append(rootPath)
  9 | sys.path.append('E:\\Anaconda\\lib\\site-packages\\')
 10 | # print(sys.path)
 11 | print('Path initialization finished！\n')
 12 | 
 13 | # 可视化增加路径
 14 | from time import time
 15 | from sklearn import manifold, datasets
 16 | 
 17 | # visdom显示模块
 18 | from visdom import Visdom
 19 | 
 20 | import time
 21 | import argparse
 22 | import numpy as np
 23 | 
 24 | import torch
 25 | import torch.nn.functional as F
 26 | import torch.optim as optim
 27 | 
 28 | from pygcn.utils import load_data, accuracy
 29 | from pygcn.models import GCN
 30 | 
 31 | def show_Hyperparameter(args):
 32 |     argsDict = args.__dict__
 33 |     print('the settings are as following:')
 34 |     for key in argsDict:
 35 |         print(key,':',argsDict[key])
 36 | 
 37 | def train(epoch):
 38 |     t = time.time()
 39 |     '''将模型转为训练模式，并将优化器梯度置零'''
 40 |     model.train()
 41 |     optimizer.zero_grad()
 42 |     '''计算输出时，对所有的节点计算输出'''
 43 | 
 44 |     # ----------------------------------debug----------------------------------
 45 |     # print(features.size(),adj.size())
 46 |     # torch.Size([2708, 1433]) torch.Size([2708, 2708])
 47 |     # 也就是说，每次对整个图进行计算，但优化的时候，仅仅针对训练集样本产生的损失。
 48 |     # ----------------------------------debug----------------------------------
 49 |     output = model(features, adj)
 50 | 
 51 |     '''损失函数，仅对训练集节点计算，即：优化仅对训练集数据进行'''
 52 |     loss_train = F.nll_loss(output[idx_train], labels[idx_train])
 53 |     # 计算准确率
 54 |     acc_train = accuracy(output[idx_train], labels[idx_train])
 55 |     # 反向传播
 56 |     loss_train.backward()
 57 |     # 优化
 58 |     optimizer.step()
 59 | 
 60 |     '''fastmode ? '''
 61 |     if not args.fastmode:
 62 |         # Evaluate validation set performance separately,
 63 |         # deactivates dropout during validation run.
 64 |         model.eval()
 65 |         output = model(features, adj)
 66 | 
 67 |     '''验证集 loss 和 accuracy '''
 68 |     loss_val = F.nll_loss(output[idx_val], labels[idx_val])
 69 |     acc_val = accuracy(output[idx_val], labels[idx_val])
 70 |     '''输出训练集+验证集的 loss 和 accuracy '''
 71 |     print('Epoch: {:04d}'.format(epoch+1),
 72 |           'loss_train: {:.4f}'.format(loss_train.item()),
 73 |           'acc_train: {:.4f}'.format(acc_train.item()),
 74 |           'loss_val: {:.4f}'.format(loss_val.item()),
 75 |           'acc_val: {:.4f}'.format(acc_val.item()),
 76 |           'time: {:.4f}s'.format(time.time() - t))
 77 | 
 78 | def test():
 79 |     model.eval()    # model转为测试模式
 80 |     output = model(features, adj)
 81 |     loss_test = F.nll_loss(output[idx_test], labels[idx_test])
 82 |     acc_test = accuracy(output[idx_test], labels[idx_test])
 83 |     print("Test set results:",
 84 |           "loss= {:.4f}".format(loss_test.item()),
 85 |           "accuracy= {:.4f}".format(acc_test.item()))
 86 |     return output   # 可视化返回output
 87 | 
 88 | # t-SNE 降维
 89 | def t_SNE(output, dimention):
 90 |     # output:待降维的数据
 91 |     # dimention：降低到的维度
 92 |     tsne = manifold.TSNE(n_components=dimention, init='pca', random_state=0)
 93 |     result = tsne.fit_transform(output)
 94 |     return result
 95 | 
 96 | # Visualization with visdom
 97 | def Visualization(vis, result, labels,title):
 98 |     # vis: Visdom对象
 99 |     # result: 待显示的数据，这里为t_SNE()函数的输出
100 |     # label: 待显示数据的标签
101 |     # title: 标题
102 |     vis.scatter(
103 |         X = result,
104 |         Y = labels+1,           # 将label的最小值从0变为1，显示时label不可为0
105 |        opts=dict(markersize=4,title=title),
106 |     )
107 | 
108 | '''代码主函数开始'''
109 | # Training settings
110 | parser = argparse.ArgumentParser()
111 | parser.add_argument('--no_cuda', action='store_true', default=False,
112 |                     help='Disables CUDA training.')
113 | parser.add_argument('--fastmode', action='store_true', default=False,
114 |                     help='Validate during training pass.')
115 | parser.add_argument('--seed', type=int, default=42, help='Random seed.')
116 | parser.add_argument('--epochs', type=int, default=200,
117 |                     help='Number of epochs to train.')
118 | parser.add_argument('--lr', type=float, default=0.01,
119 |                     help='Initial learning rate.')
120 | parser.add_argument('--weight_decay', type=float, default=5e-4,
121 |                     help='Weight decay (L2 loss on parameters).')
122 | parser.add_argument('--hidden', type=int, default=16,
123 |                     help='Number of hidden units.')
124 | parser.add_argument('--dropout', type=float, default=0.5,
125 |                     help='Dropout rate (1 - keep probability).')
126 | parser.add_argument('--no_visual', action='store_true', default=False,
127 |                     help='visualization of ground truth and test result')
128 | 
129 | args = parser.parse_args()
130 | 
131 | 
132 | # 显示args
133 | show_Hyperparameter(args)
134 | 
135 | # 是否使用CUDA
136 | args.cuda = not args.no_cuda and torch.cuda.is_available()
137 | 
138 | # 设置随机数种子
139 | np.random.seed(args.seed)
140 | torch.manual_seed(args.seed)
141 | if args.cuda:
142 |     torch.cuda.manual_seed(args.seed)
143 | 
144 | # Load data
145 | adj, features, labels, idx_train, idx_val, idx_test = load_data()           # 返回可视化要用的labels
146 | 
147 | # Model
148 | model = GCN(nfeat=features.shape[1],
149 |             nhid=args.hidden,
150 |             nclass=labels.max().item() + 1,     # 对Cora数据集，为7，即类别总数。
151 |             dropout=args.dropout)
152 | # optimizer
153 | optimizer = optim.Adam(model.parameters(),
154 |                        lr=args.lr, weight_decay=args.weight_decay)
155 | 
156 | # to CUDA
157 | if args.cuda:
158 |     model.cuda()
159 |     features = features.cuda()
160 |     adj = adj.cuda()
161 |     labels = labels.cuda()
162 |     idx_train = idx_train.cuda()
163 |     idx_val = idx_val.cuda()
164 |     idx_test = idx_test.cuda()
165 | 
166 | # Train model
167 | t_total = time.time()
168 | for epoch in range(args.epochs):
169 |     train(epoch)
170 | print("Optimization Finished!")
171 | print("Total time elapsed: {:.4f}s".format(time.time() - t_total))
172 | 
173 | # Testing
174 | output=test()           # 返回output
175 | 
176 | if not args.no_visual:
177 |     # 计算预测值
178 |     preds = output.max(1)[1].type_as(labels)
179 | 
180 |     # output的格式转换
181 |     output = output.cpu().detach().numpy()
182 |     labels = labels.cpu().detach().numpy()
183 |     preds = preds.cpu().detach().numpy()
184 | 
185 |     # Visualization with visdom
186 |     vis = Visdom(env='pyGCN Visualization')
187 | 
188 |     # ground truth 可视化
189 |     result_all_2d = t_SNE(output, 2)
190 |     Visualization(vis, result_all_2d, labels,
191 |                   title='[ground truth of all samples]\n Dimension reduction to %dD' % (result_all_2d.shape[1]))
192 |     result_all_3d = t_SNE(output, 3)
193 |     Visualization(vis, result_all_3d, labels,
194 |                   title='[ground truth of all samples]\n Dimension reduction to %dD' % (result_all_3d.shape[1]))
195 | 
196 |     # 预测结果可视化
197 |     result_test_2d = t_SNE(output[idx_test.cpu().detach().numpy()], 2)
198 |     Visualization(vis, result_test_2d, preds[idx_test.cpu().detach().numpy()],
199 |                   title='[prediction of test set]\n Dimension reduction to %dD' % (result_test_2d.shape[1]))
200 |     result_test_3d = t_SNE(output[idx_test.cpu().detach().numpy()], 3)
201 |     Visualization(vis, result_test_3d, preds[idx_test.cpu().detach().numpy()],
202 |                   title='[prediction of test set]\n Dimension reduction to %dD' % (result_test_3d.shape[1]))
203 | 
204 | 
205 | 
206 | 
207 | 


--------------------------------------------------------------------------------
/pygcn/try_of_BuidingGraph.py:
--------------------------------------------------------------------------------
 1 | import numpy as np
 2 | import scipy.sparse as sp
 3 | from pygcn.utils import normalize
 4 | 
 5 | '''测试论文编号处理'''
 6 | # 读取原始数据集
 7 | path="C:/Users/73416/PycharmProjects/PyGCN_Visualization/data/cora/"
 8 | dataset = "cora"
 9 | idx_features_labels = np.genfromtxt("{}{}.content".format(path, dataset),
10 |                                         dtype=np.dtype(str))
11 | 
12 | # build graph
13 | # idx_features_labelsidx_features_labels[:, 0]表示取论文编号
14 | idx = np.array(idx_features_labels[:, 0], dtype=np.int32)
15 | 
16 | # 通过建立论文序号的序列，得到论文序号的字典
17 | idx_map = {j: i for i, j in enumerate(idx)}
18 | 
19 | # 读取图的边（论文间的引用关系）
20 | # cora.cites共5429行， 每一行有两个论文编号，表示第一个编号的论文先写，第二个编号的论文引用第一个编号的论文。
21 | edges_unordered = np.genfromtxt("{}{}.cites".format(path, dataset),
22 |                                     dtype=np.int32)
23 | 
24 | # 进行一次论文序号的映射
25 | # 论文编号没有用，需要重新的其进行编号（从0开始），然后对原编号进行替换。
26 | # 所以目的是把离散的原始的编号，变成0 - 2707的连续编号
27 | edges = np.array(list(map(idx_map.get, edges_unordered.flatten())),
28 |                      dtype=np.int32).reshape(edges_unordered.shape)
29 | 
30 | # coo_matrix()：系数矩阵的压缩。分别定义有那些非零元素，以及各个非零元素对应的row和col，最后定义稀疏矩阵的shape。
31 | adj = sp.coo_matrix((np.ones(edges.shape[0]), (edges[:, 0], edges[:, 1])),
32 |                         shape=(2708, 2708),
33 |                         dtype=np.float32)
34 | 
35 | # build symmetric adjacency matrix
36 | adj_sysm = adj + adj.T.multiply(adj.T > adj) - adj.multiply(adj.T > adj)
37 | 
38 | adj_sysm_self= adj_sysm + sp.eye(adj.shape[0])
39 | 
40 | adj_norm = normalize(adj_sysm_self)


--------------------------------------------------------------------------------
/pygcn/try_of_LoadData.py:
--------------------------------------------------------------------------------
 1 | import numpy as np
 2 | import scipy.sparse as sp
 3 | from pygcn.utils import normalize,sparse_mx_to_torch_sparse_tensor,encode_onehot
 4 | import torch
 5 | 
 6 | '''测试论文编号处理'''
 7 | # 读取原始数据集
 8 | path="C:/Users/73416/PycharmProjects/PyGCN_Visualization/data/cora/"
 9 | dataset = "cora"
10 | idx_features_labels = np.genfromtxt("{}{}.content".format(path, dataset),
11 |                                         dtype=np.dtype(str))
12 | 
13 | # build graph
14 | # idx_features_labelsidx_features_labels[:, 0]表示取论文编号
15 | idx = np.array(idx_features_labels[:, 0], dtype=np.int32)
16 | 
17 | # 通过建立论文序号的序列，得到论文序号的字典
18 | idx_map = {j: i for i, j in enumerate(idx)}
19 | 
20 | # 读取图的边（论文间的引用关系）
21 | # cora.cites共5429行， 每一行有两个论文编号，表示第一个编号的论文先写，第二个编号的论文引用第一个编号的论文。
22 | edges_unordered = np.genfromtxt("{}{}.cites".format(path, dataset),
23 |                                     dtype=np.int32)
24 | 
25 | # 进行一次论文序号的映射
26 | # 论文编号没有用，需要重新的其进行编号（从0开始），然后对原编号进行替换。
27 | # 所以目的是把离散的原始的编号，变成0 - 2707的连续编号
28 | edges = np.array(list(map(idx_map.get, edges_unordered.flatten())),
29 |                      dtype=np.int32).reshape(edges_unordered.shape)
30 | 
31 | # coo_matrix()：系数矩阵的压缩。分别定义有那些非零元素，以及各个非零元素对应的row和col，最后定义稀疏矩阵的shape。
32 | adj = sp.coo_matrix((np.ones(edges.shape[0]), (edges[:, 0], edges[:, 1])),
33 |                         shape=(2708, 2708),
34 |                         dtype=np.float32)
35 | 
36 | # build symmetric adjacency matrix
37 | adj_sysm = adj + adj.T.multiply(adj.T > adj) - adj.multiply(adj.T > adj)
38 | 
39 | # 引入自环
40 | adj_sysm_self= adj + sp.eye(adj.shape[0])
41 | 
42 | # 归一化
43 | adj_norm = normalize(adj_sysm_self)
44 | 
45 | features = sp.csr_matrix(idx_features_labels[:, 1:-1], dtype=np.float32)
46 | features = normalize(features)
47 | 
48 | labels = encode_onehot(idx_features_labels[:, -1])
49 | 
50 | # 数据类型转tensor
51 | features = torch.FloatTensor(np.array(features.todense()))
52 | labels = torch.LongTensor(np.where(labels)[1])
53 | adj_norm = sparse_mx_to_torch_sparse_tensor(adj_norm)
54 | 
55 | 
56 | # 测试sparse_mx_to_torch_sparse_tensor(sparse_mx)函数
57 | # sparse_mx = adj_norm.tocoo().astype(np.float32)
58 | # indices = torch.from_numpy(
59 | #     np.vstack((sparse_mx.row, sparse_mx.col)).astype(np.int64))
60 | # values = torch.from_numpy(sparse_mx.data)
61 | # shape = torch.Size(sparse_mx.shape)


--------------------------------------------------------------------------------
/pygcn/try_of_normalize.py:
--------------------------------------------------------------------------------
 1 | import numpy as np
 2 | import scipy.sparse as sp
 3 | from pygcn.utils import normalize
 4 | 
 5 | '''测试归一化函数'''
 6 | # 读取原始数据集
 7 | path="C:/Users/73416/PycharmProjects/PyGCN_Visualization/data/cora/"
 8 | dataset = "cora"
 9 | idx_features_labels = np.genfromtxt("{}{}.content".format(path, dataset),
10 |                                         dtype=np.dtype(str))
11 | 
12 | RawFeature = idx_features_labels[:, 1:-1]
13 | RawFeature=RawFeature.astype(int)
14 | sample1_label=RawFeature[0,:]
15 | sumA=sample1_label.sum()
16 | print("原始的feature\n",RawFeature)
17 | # type ndarray
18 | # [['0' '0' '0'... '0' '0' '0']
19 | #  ['0' '0' '0'... '0' '0' '0']
20 | #  ['0' '0' '0'...'0' '0' '0']
21 | #  ...
22 | #  ['0' '0' '0'...'0' '0' '0']
23 | # ['0' '0' '0'... '0' '0' '0']
24 | # ['0' '0' '0'...'0' '0' '0']]
25 | print(RawFeature.shape)
26 | # (2708, 1433)
27 | 
28 | features = sp.csr_matrix(idx_features_labels[:, 1:-1], dtype=np.float32)
29 | # <2708x1433 sparse matrix of type '<class 'numpy.float32'>'
30 | # 	with 49216 stored elements in Compressed Sparse Row format>
31 | print("csr_matrix之后的feature\n",features)
32 | # type csr_matrix
33 | # (0, 0)          0.0
34 | # (0, 1)          0.0
35 | # (0, 2)          0.0
36 | # (0, 3)          0.0
37 | # (0, 4)          0.0
38 | # ::
39 | # (2707, 1428)    0.0
40 | # (2707, 1429)    0.0
41 | # (2707, 1430)    0.0
42 | # (2707, 1431)    0.0
43 | # (2707, 1432)    0.0
44 | print(features.shape)
45 | # (2708, 1433)
46 | 
47 | # features = normalize(features)
48 | rowsum = np.array(features.sum(1))      # (2708, 1)
49 | r_inv = np.power(rowsum, -1).flatten()  # (2708,)
50 | r_inv[np.isinf(r_inv)] = 0.             # 处理除数为0导致的inf
51 | r_mat_inv = sp.diags(r_inv)
52 | # <2708x2708 sparse matrix of type '<class 'numpy.float32'>'
53 | # 	with 2708 stored elements (1 diagonals) in DIAgonal format>
54 | mx = r_mat_inv.dot(features)
55 | print('normalization之后的feature\n',mx)
56 | # (0, 176)    0.05
57 | # (0, 125)    0.05
58 | # (0, 118)    0.05
59 | # (1, 1425)   0.05882353
60 | # (1, 1389)   0.05882353
61 | # (1, 1263)   0.05882353
62 | # ::
63 | # (2707, 136)   0.05263158
64 | # (2707, 67)    0.05263158
65 | # (2707, 19)    0.05263158
66 | 
67 | 
68 | 


--------------------------------------------------------------------------------
/pygcn/try_of_onehot.py:
--------------------------------------------------------------------------------
 1 | from pygcn.utils import encode_onehot
 2 | import numpy as np
 3 | 
 4 | '''测试onehot编码'''
 5 | '''labels的onehot编码，前后结果对比'''
 6 | # 读取原始数据集
 7 | path="C:/Users/73416/PycharmProjects/PyGCN_Visualization/data/cora/"
 8 | dataset = "cora"
 9 | idx_features_labels = np.genfromtxt("{}{}.content".format(path, dataset),
10 |                                         dtype=np.dtype(str))
11 | 
12 | RawLabels=idx_features_labels[:, -1]
13 | print("原始论文类别（label）：\n",RawLabels)
14 | # ['Neural_Networks' 'Rule_Learning' 'Reinforcement_Learning' ...
15 | # 'Genetic_Algorithms' 'Case_Based' 'Neural_Networks']
16 | print(len(RawLabels))       # 2708
17 | 
18 | classes = set(RawLabels)       # set() 函数创建一个无序不重复元素集
19 | print("原始标签的无序不重复元素集\n", classes)
20 | # {'Genetic_Algorithms', 'Probabilistic_Methods', 'Reinforcement_Learning', 'Neural_Networks', 'Theory', 'Case_Based', 'Rule_Learning'}
21 | 
22 | 
23 | # enumerate()函数生成序列，带有索引i和值c。
24 | # 这一句将string类型的label变为onehot编码的label，建立映射关系
25 | classes_dict = {c: np.identity(len(classes))[i, :] for i, c in
26 |                     enumerate(classes)}
27 | print("原始标签与onehot编码结果的映射字典\n",classes_dict)
28 | #  {'Genetic_Algorithms': array([1., 0., 0., 0., 0., 0., 0.]), 'Probabilistic_Methods': array([0., 1., 0., 0., 0., 0., 0.]),
29 | #   'Reinforcement_Learning': array([0., 0., 1., 0., 0., 0., 0.]), 'Neural_Networks': array([0., 0., 0., 1., 0., 0., 0.]),
30 | #   'Theory': array([0., 0., 0., 0., 1., 0., 0.]), 'Case_Based': array([0., 0., 0., 0., 0., 1., 0.]),
31 | #   'Rule_Learning': array([0., 0., 0., 0., 0., 0., 1.])}
32 | 
33 | # map() 会根据提供的函数对指定序列做映射。
34 | # 这一句将string类型的label替换为onehot编码的label
35 | labels_onehot = np.array(list(map(classes_dict.get, RawLabels)),
36 |                              dtype=np.int32)
37 | print("onehot编码的论文类别（label）：\n",labels_onehot)
38 | # [[0 0 0... 0 0 0]
39 | #  [0 0 0... 1 0 0]
40 | #  [0 1 0 ... 0 0 0]
41 | #  ...
42 | #  [0 0 0 ... 0 0 1]
43 | #  [0 0 1 ... 0 0 0]
44 | #  [0 0 0 ... 0 0 0]]
45 | print(labels_onehot.shape)
46 | # (2708, 7)


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/pygcn/utils.py:
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  1 | import numpy as np
  2 | import scipy.sparse as sp
  3 | import torch
  4 | 
  5 | 
  6 | def encode_onehot(labels):
  7 |     classes = set(labels)       # set() 函数创建一个无序不重复元素集
  8 | 
  9 |     # enumerate()函数生成序列，带有索引i和值c。
 10 |     # 这一句将string类型的label变为onehot编码的label，建立映射关系
 11 |     classes_dict = {c: np.identity(len(classes))[i, :] for i, c in
 12 |                     enumerate(classes)}
 13 |     # map() 会根据提供的函数对指定序列做映射。
 14 |     # 这一句将string类型的label替换为onehot编码的label
 15 |     labels_onehot = np.array(list(map(classes_dict.get, labels)),
 16 |                              dtype=np.int32)
 17 |     # 返回int类型的label
 18 |     return labels_onehot
 19 | 
 20 | '''数据读取'''
 21 | # 更改路径。由../改为C:\Users\73416\PycharmProjects\PyGCN
 22 | def load_data(path="C:/Users/73416/PycharmProjects/PyGCN_Visualization/data/cora/", dataset="cora"):
 23 |     """Load citation network dataset (cora only for now)"""
 24 |     print('Loading {} dataset...'.format(dataset))
 25 | 
 26 |     ''' 
 27 |     cora.content 介绍：
 28 |     cora.content共有2708行，每一行代表一个样本点，即一篇论文。
 29 |     每一行由三部分组成:
 30 |     是论文的编号，如31336；
 31 |     论文的词向量，一个有1433位的二进制；
 32 |     论文的类别，如Neural_Networks。总共7种类别（label）
 33 |     第一个是论文编号，最后一个是论文类别，中间是自己的信息（feature）
 34 |     '''
 35 | 
 36 |     '''读取feature和label'''
 37 |     # 以字符串形式读取数据集文件：各自的信息。
 38 |     idx_features_labels = np.genfromtxt("{}{}.content".format(path, dataset),
 39 |                                         dtype=np.dtype(str))
 40 | 
 41 |     # csr_matrix：Compressed Sparse Row marix，稀疏np.array的压缩
 42 |     # idx_features_labels[:, 1:-1]表明跳过论文编号和论文类别，只取自己的信息（feature of node）
 43 |     features = sp.csr_matrix(idx_features_labels[:, 1:-1], dtype=np.float32)
 44 | 
 45 |     # idx_features_labels[:, -1]表示只取最后一个，即论文类别，得到的返回值为int类型的label
 46 |     labels = encode_onehot(idx_features_labels[:, -1])
 47 | 
 48 |     # build graph
 49 |     # idx_features_labelsidx_features_labels[:, 0]表示取论文编号
 50 |     idx = np.array(idx_features_labels[:, 0], dtype=np.int32)
 51 | 
 52 |     # 通过建立论文序号的序列，得到论文序号的字典
 53 |     idx_map = {j: i for i, j in enumerate(idx)}
 54 |     edges_unordered = np.genfromtxt("{}{}.cites".format(path, dataset),
 55 |                                     dtype=np.int32)
 56 |     # 进行一次论文序号的映射
 57 |     # 论文编号没有用，需要重新的其进行编号（从0开始），然后对原编号进行替换。
 58 |     # 所以目的是把离散的原始的编号，变成0 - 2707的连续编号
 59 |     edges = np.array(list(map(idx_map.get, edges_unordered.flatten())),
 60 |                      dtype=np.int32).reshape(edges_unordered.shape)
 61 | 
 62 |     # coo_matrix()：系数矩阵的压缩。分别定义有那些非零元素，以及各个非零元素对应的row和col，最后定义稀疏矩阵的shape。
 63 |     adj = sp.coo_matrix((np.ones(edges.shape[0]), (edges[:, 0], edges[:, 1])),
 64 |                         shape=(labels.shape[0], labels.shape[0]),
 65 |                         dtype=np.float32)
 66 | 
 67 |     # build symmetric adjacency matrix
 68 |     # np.multiply()函数，数组和矩阵对应位置相乘，输出与相乘数组/矩阵的大小一致
 69 |     adj = adj + adj.T.multiply(adj.T > adj) - adj.multiply(adj.T > adj)
 70 | 
 71 |     # feature和adj归一化
 72 |     features = normalize(features)
 73 |     adj = normalize(adj + sp.eye(adj.shape[0]))     # adj在归一化之前，先引入自环
 74 | 
 75 |     # train set, validation set, test set的分组。
 76 |     idx_train = range(140)
 77 |     idx_val = range(200, 500)
 78 |     idx_test = range(500, 1500)
 79 | 
 80 |     # 数据类型转tensor
 81 |     features = torch.FloatTensor(np.array(features.todense()))
 82 |     labels = torch.LongTensor(np.where(labels)[1])
 83 |     adj = sparse_mx_to_torch_sparse_tensor(adj)
 84 | 
 85 |     idx_train = torch.LongTensor(idx_train)
 86 |     idx_val = torch.LongTensor(idx_val)
 87 |     idx_test = torch.LongTensor(idx_test)
 88 | 
 89 |     # 返回数据
 90 |     return adj, features, labels, idx_train, idx_val, idx_test
 91 | 
 92 | 
 93 | '''归一化函数'''
 94 | def normalize(mx):
 95 |     """Row-normalize sparse matrix"""
 96 |     rowsum = np.array(mx.sum(1))                # (2708, 1)
 97 |     r_inv = np.power(rowsum, -1).flatten()      # (2708,)
 98 |     r_inv[np.isinf(r_inv)] = 0.                 # 处理除数为0导致的inf
 99 |     r_mat_inv = sp.diags(r_inv)
100 |     mx = r_mat_inv.dot(mx)
101 |     return mx
102 | 
103 | '''计算accuracy'''
104 | def accuracy(output, labels):
105 |     preds = output.max(1)[1].type_as(labels)
106 |     correct = preds.eq(labels).double()
107 |     correct = correct.sum()
108 |     return correct / len(labels)
109 | 
110 | '''稀疏矩阵转稀疏张量'''
111 | def sparse_mx_to_torch_sparse_tensor(sparse_mx):
112 |     """Convert a scipy sparse matrix to a torch sparse tensor."""
113 |     sparse_mx = sparse_mx.tocoo().astype(np.float32)
114 |     indices = torch.from_numpy(
115 |         np.vstack((sparse_mx.row, sparse_mx.col)).astype(np.int64))
116 |     values = torch.from_numpy(sparse_mx.data)
117 |     shape = torch.Size(sparse_mx.shape)
118 |     return torch.sparse.FloatTensor(indices, values, shape)
119 | 


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/setup.py:
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 1 | from setuptools import setup
 2 | from setuptools import find_packages
 3 | 
 4 | setup(name='pygcn',
 5 |       version='0.1',
 6 |       description='Graph Convolutional Networks in PyTorch',
 7 |       author='Thomas Kipf',
 8 |       author_email='thomas.kipf@gmail.com',
 9 |       url='https://tkipf.github.io',
10 |       download_url='https://github.com/tkipf/pygcn',
11 |       license='MIT',
12 |       install_requires=['numpy',
13 |                         'torch',
14 |                         'scipy'
15 |                         ],
16 |       package_data={'pygcn': ['README.md']},
17 |       packages=find_packages())


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