copying files to /scratch...
starting benchmark...
/scratch/knn/venv/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  from ._conv import register_converters as _register_converters
running only kgraph
order: [Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 40, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 4, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 1, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 70, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 60, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 90, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 30, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 2, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 50, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 5, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 80, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 10, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 100, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 20, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 3, {'reverse': -1}, False])]
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 40, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 40, {'reverse': -1}, False])
Got a train set of size (1000000 * 128)
Generating control...
Initializing...
iteration: 1 recall: 0 accuracy: 2.29356 cost: 0.00038 M: 10 delta: 1 time: 61.9495 one-recall: 0 one-ratio: 3.52528
iteration: 2 recall: 0.004 accuracy: 1.20332 cost: 0.000637428 M: 10 delta: 0.856032 time: 106.044 one-recall: 0 one-ratio: 2.78298
iteration: 3 recall: 0.0276 accuracy: 0.674478 cost: 0.00109521 M: 11.5287 delta: 0.835105 time: 159.394 one-recall: 0.03 one-ratio: 2.30487
iteration: 4 recall: 0.1804 accuracy: 0.313238 cost: 0.00163043 M: 11.8364 delta: 0.783443 time: 212.358 one-recall: 0.21 one-ratio: 1.82894
iteration: 5 recall: 0.4868 accuracy: 0.121046 cost: 0.00223612 M: 12.6038 delta: 0.664615 time: 267.341 one-recall: 0.6 one-ratio: 1.34572
iteration: 6 recall: 0.7564 accuracy: 0.03077 cost: 0.00297993 M: 15.114 delta: 0.432357 time: 329.306 one-recall: 0.9 one-ratio: 1.05903
iteration: 7 recall: 0.8824 accuracy: 0.0110308 cost: 0.00395537 M: 21.1402 delta: 0.196426 time: 400.96 one-recall: 0.96 one-ratio: 1.02932
iteration: 8 recall: 0.938 accuracy: 0.00400482 cost: 0.00497983 M: 27.3045 delta: 0.0885137 time: 469.818 one-recall: 1 one-ratio: 1
iteration: 9 recall: 0.9644 accuracy: 0.00221739 cost: 0.00577293 M: 31.2904 delta: 0.0514019 time: 523.821 one-recall: 1 one-ratio: 1
iteration: 10 recall: 0.9764 accuracy: 0.0012952 cost: 0.00625843 M: 33.3974 delta: 0.0372226 time: 561.153 one-recall: 1 one-ratio: 1
iteration: 11 recall: 0.984 accuracy: 0.000915513 cost: 0.00651572 M: 34.4268 delta: 0.0313604 time: 585.951 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.9872 accuracy: 0.000651871 cost: 0.00664321 M: 34.9174 delta: 0.028787 time: 602.804 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.9876 accuracy: 0.000628894 cost: 0.00670536 M: 35.1523 delta: 0.027629 time: 615.04 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.9876 accuracy: 0.000628894 cost: 0.00673551 M: 35.2647 delta: 0.0270893 time: 624.705 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.9876 accuracy: 0.000628894 cost: 0.00675029 M: 35.3194 delta: 0.0268326 time: 633.028 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.9876 accuracy: 0.000628894 cost: 0.00675777 M: 35.3471 delta: 0.0267076 time: 640.669 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.9876 accuracy: 0.000628894 cost: 0.00676154 M: 35.3608 delta: 0.0266443 time: 647.941 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.9876 accuracy: 0.000628894 cost: 0.00676338 M: 35.3677 delta: 0.0266122 time: 655.014 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.9876 accuracy: 0.000628894 cost: 0.00676433 M: 35.3712 delta: 0.0265977 time: 661.988 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.9876 accuracy: 0.000628894 cost: 0.00676485 M: 35.373 delta: 0.02659 time: 668.912 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.988 accuracy: 0.0006087 cost: 0.00676515 M: 35.3742 delta: 0.0265857 time: 675.806 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.988 accuracy: 0.0006087 cost: 0.00676532 M: 35.3749 delta: 0.0265828 time: 682.685 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.988 accuracy: 0.0006087 cost: 0.00676541 M: 35.3753 delta: 0.0265811 time: 689.546 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.988 accuracy: 0.0006087 cost: 0.00676547 M: 35.3755 delta: 0.0265798 time: 696.393 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.988 accuracy: 0.0006087 cost: 0.00676549 M: 35.3756 delta: 0.0265796 time: 703.245 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.988 accuracy: 0.0006087 cost: 0.00676551 M: 35.3757 delta: 0.0265794 time: 710.09 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.988 accuracy: 0.0006087 cost: 0.00676552 M: 35.3757 delta: 0.0265791 time: 716.931 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.988 accuracy: 0.0006087 cost: 0.00676553 M: 35.3757 delta: 0.0265791 time: 723.772 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.988 accuracy: 0.0006087 cost: 0.00676553 M: 35.3757 delta: 0.026579 time: 730.618 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.988 accuracy: 0.0006087 cost: 0.00676553 M: 35.3757 delta: 0.026579 time: 737.457 one-recall: 1 one-ratio: 1
Graph completion with reverse edges...

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|----|----|----|----|----|----|----|----|----|----|
***************************************************
Reranking edges...

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 756.02
Index size:  1903140.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0062330000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.0900000000, query time of that 0.0518522790, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Accept!
  -> Decision True in time 0.9600000000, query time of that 0.4992559070, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
391.111 < 426.604
  -> Decision False in time 0.4000000000, query time of that 0.2070577170, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.5700000000, query time of that 0.0576404980, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
352.38 < 355.244
  -> Decision False in time 5.0300000000, query time of that 0.5410581550, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
235.034 < 238.743
  -> Decision False in time 2.1500000000, query time of that 0.2361058310, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Accept!
  -> Decision True in time 6.7500000000, query time of that 0.0767408040, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
346.157 < 349.072
  -> Decision False in time 2.3700000000, query time of that 0.0296798260, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
278.214 < 279.943
  -> Decision False in time 6.1900000000, query time of that 0.0701694840, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 4, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 4, {'reverse': -1}, False])
Got a train set of size (1000000 * 128)
Generating control...
Initializing...
iteration: 1 recall: 0 accuracy: 2.6766 cost: 0.00038 M: 10 delta: 1 time: 61.6166 one-recall: 0 one-ratio: 3.66072
iteration: 2 recall: 0.0016 accuracy: 1.38091 cost: 0.000637428 M: 10 delta: 0.856032 time: 103.936 one-recall: 0 one-ratio: 2.8624
iteration: 3 recall: 0.0388 accuracy: 0.711342 cost: 0.00109521 M: 11.5287 delta: 0.835103 time: 155.118 one-recall: 0.04 one-ratio: 2.27853
iteration: 4 recall: 0.2128 accuracy: 0.331692 cost: 0.00163043 M: 11.8362 delta: 0.783467 time: 205.989 one-recall: 0.31 one-ratio: 1.61045
iteration: 5 recall: 0.5256 accuracy: 0.113941 cost: 0.00223606 M: 12.6037 delta: 0.664583 time: 258.839 one-recall: 0.65 one-ratio: 1.31249
iteration: 6 recall: 0.7816 accuracy: 0.0320499 cost: 0.00298007 M: 15.115 delta: 0.432327 time: 318.557 one-recall: 0.89 one-ratio: 1.10605
iteration: 7 recall: 0.898 accuracy: 0.00893238 cost: 0.00395539 M: 21.1392 delta: 0.196451 time: 387.515 one-recall: 0.98 one-ratio: 1.00852
iteration: 8 recall: 0.9536 accuracy: 0.00250703 cost: 0.00497983 M: 27.3039 delta: 0.0884677 time: 453.714 one-recall: 1 one-ratio: 1
iteration: 9 recall: 0.9708 accuracy: 0.00137405 cost: 0.00577272 M: 31.2865 delta: 0.0513399 time: 505.723 one-recall: 1 one-ratio: 1
iteration: 10 recall: 0.9796 accuracy: 0.000974264 cost: 0.0062576 M: 33.3907 delta: 0.0372004 time: 541.817 one-recall: 1 one-ratio: 1
iteration: 11 recall: 0.982 accuracy: 0.000821148 cost: 0.00651448 M: 34.4194 delta: 0.0313362 time: 565.895 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.9856 accuracy: 0.00063386 cost: 0.0066431 M: 34.9143 delta: 0.0287677 time: 582.389 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.9868 accuracy: 0.000577962 cost: 0.006706 M: 35.1524 delta: 0.0276101 time: 594.38 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.9876 accuracy: 0.000568264 cost: 0.00673624 M: 35.2661 delta: 0.027052 time: 603.845 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.9876 accuracy: 0.000568264 cost: 0.0067511 M: 35.3217 delta: 0.0267951 time: 612.001 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.9876 accuracy: 0.000568264 cost: 0.00675856 M: 35.3495 delta: 0.0266678 time: 619.492 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.9876 accuracy: 0.000568264 cost: 0.00676249 M: 35.3641 delta: 0.0266031 time: 626.644 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.9876 accuracy: 0.000568264 cost: 0.00676447 M: 35.3715 delta: 0.0265723 time: 633.602 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.9876 accuracy: 0.000568264 cost: 0.00676549 M: 35.3754 delta: 0.0265551 time: 640.461 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.9876 accuracy: 0.000568264 cost: 0.00676606 M: 35.3775 delta: 0.0265465 time: 647.267 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.9876 accuracy: 0.000568264 cost: 0.00676636 M: 35.3786 delta: 0.0265425 time: 654.039 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.9876 accuracy: 0.000568264 cost: 0.00676654 M: 35.3792 delta: 0.0265399 time: 660.795 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.9876 accuracy: 0.000568264 cost: 0.00676663 M: 35.3796 delta: 0.0265387 time: 667.528 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.9876 accuracy: 0.000568264 cost: 0.00676668 M: 35.3798 delta: 0.0265377 time: 674.258 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.9876 accuracy: 0.000568264 cost: 0.0067667 M: 35.3799 delta: 0.0265375 time: 680.985 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.9876 accuracy: 0.000568264 cost: 0.00676672 M: 35.3799 delta: 0.0265372 time: 687.705 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.9876 accuracy: 0.000568264 cost: 0.00676674 M: 35.38 delta: 0.026537 time: 694.426 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.9876 accuracy: 0.000568264 cost: 0.00676675 M: 35.38 delta: 0.0265369 time: 701.155 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.9876 accuracy: 0.000568264 cost: 0.00676675 M: 35.38 delta: 0.0265368 time: 707.877 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.9876 accuracy: 0.000568264 cost: 0.00676675 M: 35.38 delta: 0.026537 time: 714.595 one-recall: 1 one-ratio: 1
Graph completion with reverse edges...

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Reranking edges...

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 732.49
Index size:  261124.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0114657000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.0700000000, query time of that 0.0209328530, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Reject!
426.432 < 428.67
  -> Decision False in time 0.4600000000, query time of that 0.1446614420, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
317.758 < 347.648
  -> Decision False in time 0.2000000000, query time of that 0.0605128210, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.5400000000, query time of that 0.0259225930, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
391.962 < 423.425
  -> Decision False in time 3.7000000000, query time of that 0.1814169390, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
353.435 < 363.48
  -> Decision False in time 0.5700000000, query time of that 0.0283473620, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Reject!
271.297 < 271.402
  -> Decision False in time 5.1800000000, query time of that 0.0267856700, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
223.267 < 228.559
  -> Decision False in time 5.7700000000, query time of that 0.0295226760, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
328.699 < 335.585
  -> Decision False in time 7.6800000000, query time of that 0.0395143870, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 1, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 1, {'reverse': -1}, False])
Got a train set of size (1000000 * 128)
Generating control...
Initializing...
iteration: 1 recall: 0.0004 accuracy: 2.03157 cost: 0.00038 M: 10 delta: 1 time: 61.6913 one-recall: 0 one-ratio: 3.73501
iteration: 2 recall: 0.0016 accuracy: 1.16247 cost: 0.000637428 M: 10 delta: 0.856032 time: 104.045 one-recall: 0 one-ratio: 2.91785
iteration: 3 recall: 0.0312 accuracy: 0.695082 cost: 0.00109521 M: 11.5287 delta: 0.835107 time: 155.304 one-recall: 0.02 one-ratio: 2.31464
iteration: 4 recall: 0.1788 accuracy: 0.366826 cost: 0.00163043 M: 11.8362 delta: 0.783463 time: 206.253 one-recall: 0.23 one-ratio: 1.76576
iteration: 5 recall: 0.5216 accuracy: 0.143431 cost: 0.00223603 M: 12.6036 delta: 0.66458 time: 259.201 one-recall: 0.64 one-ratio: 1.2844
iteration: 6 recall: 0.7932 accuracy: 0.0303005 cost: 0.00297998 M: 15.115 delta: 0.432349 time: 318.97 one-recall: 0.86 one-ratio: 1.07535
iteration: 7 recall: 0.9056 accuracy: 0.00755459 cost: 0.00395521 M: 21.1404 delta: 0.196412 time: 387.997 one-recall: 0.94 one-ratio: 1.02107
iteration: 8 recall: 0.9516 accuracy: 0.00295481 cost: 0.0049799 M: 27.3064 delta: 0.088488 time: 454.268 one-recall: 0.97 one-ratio: 1.0135
iteration: 9 recall: 0.9676 accuracy: 0.00188151 cost: 0.00577298 M: 31.2905 delta: 0.0513513 time: 506.33 one-recall: 0.98 one-ratio: 1.00582
iteration: 10 recall: 0.974 accuracy: 0.00123469 cost: 0.00625759 M: 33.3948 delta: 0.0372401 time: 542.427 one-recall: 1 one-ratio: 1
iteration: 11 recall: 0.978 accuracy: 0.00104776 cost: 0.00651482 M: 34.4253 delta: 0.0313493 time: 566.524 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.9788 accuracy: 0.00100726 cost: 0.00664253 M: 34.9185 delta: 0.0287713 time: 582.954 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.9792 accuracy: 0.000936912 cost: 0.00670467 M: 35.1539 delta: 0.0276173 time: 594.897 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.9792 accuracy: 0.000936912 cost: 0.00673481 M: 35.2674 delta: 0.0270635 time: 604.351 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.9796 accuracy: 0.00088822 cost: 0.00674951 M: 35.3223 delta: 0.0268061 time: 612.494 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.9796 accuracy: 0.00088822 cost: 0.00675718 M: 35.3508 delta: 0.0266787 time: 620.002 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.9796 accuracy: 0.00088822 cost: 0.00676107 M: 35.3653 delta: 0.026612 time: 627.152 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.9796 accuracy: 0.00088822 cost: 0.0067631 M: 35.3729 delta: 0.0265774 time: 634.112 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.9796 accuracy: 0.00088822 cost: 0.00676415 M: 35.3768 delta: 0.026562 time: 640.969 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.9796 accuracy: 0.00088822 cost: 0.00676468 M: 35.3788 delta: 0.0265532 time: 647.769 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.9796 accuracy: 0.00088822 cost: 0.00676498 M: 35.38 delta: 0.0265484 time: 654.539 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.9796 accuracy: 0.00088822 cost: 0.00676515 M: 35.3806 delta: 0.0265463 time: 661.288 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.9796 accuracy: 0.00088822 cost: 0.00676524 M: 35.381 delta: 0.0265443 time: 668.029 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.9796 accuracy: 0.00088822 cost: 0.00676531 M: 35.3812 delta: 0.0265439 time: 674.775 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.9796 accuracy: 0.00088822 cost: 0.00676533 M: 35.3813 delta: 0.0265431 time: 681.498 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.9796 accuracy: 0.00088822 cost: 0.00676536 M: 35.3814 delta: 0.0265432 time: 688.223 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.9796 accuracy: 0.00088822 cost: 0.00676537 M: 35.3815 delta: 0.026543 time: 694.947 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.9796 accuracy: 0.00088822 cost: 0.00676538 M: 35.3815 delta: 0.0265427 time: 701.669 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.9796 accuracy: 0.00088822 cost: 0.00676538 M: 35.3815 delta: 0.0265427 time: 708.387 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.9796 accuracy: 0.00088822 cost: 0.00676538 M: 35.3815 delta: 0.0265426 time: 715.11 one-recall: 1 one-ratio: 1
Graph completion with reverse edges...

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Reranking edges...

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 732.9899999999998
Index size:  262940.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.1022548000
  Testing...
|S| = 80
|T| = 1152
Reject!
457.491 < 483.057
  -> Decision False in time 0.0000000000, query time of that 0.0022285140, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Reject!
378.167 < 485.991
  -> Decision False in time 0.0100000000, query time of that 0.0041077030, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
358.525 < 430.488
  -> Decision False in time 0.0100000000, query time of that 0.0017286070, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Reject!
399.483 < 454.042
  -> Decision False in time 0.0500000000, query time of that 0.0023612340, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
247.465 < 249.936
  -> Decision False in time 0.1500000000, query time of that 0.0072827320, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
421.34 < 427.092
  -> Decision False in time 0.0200000000, query time of that 0.0008193580, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Reject!
407.641 < 456.999
  -> Decision False in time 2.5800000000, query time of that 0.0128921660, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
413.01 < 438.925
  -> Decision False in time 0.2600000000, query time of that 0.0014431160, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
299.531 < 300.706
  -> Decision False in time 0.1500000000, query time of that 0.0008699150, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 70, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 70, {'reverse': -1}, False])
Got a train set of size (1000000 * 128)
Generating control...
Initializing...
iteration: 1 recall: 0 accuracy: 2.04383 cost: 0.00038 M: 10 delta: 1 time: 61.6587 one-recall: 0 one-ratio: 3.38832
iteration: 2 recall: 0.002 accuracy: 1.16107 cost: 0.000637428 M: 10 delta: 0.856032 time: 104.019 one-recall: 0 one-ratio: 2.63215
iteration: 3 recall: 0.0312 accuracy: 0.655445 cost: 0.00109521 M: 11.5287 delta: 0.835103 time: 155.264 one-recall: 0.04 one-ratio: 2.08916
iteration: 4 recall: 0.1716 accuracy: 0.328344 cost: 0.00163044 M: 11.8362 delta: 0.783473 time: 206.211 one-recall: 0.2 one-ratio: 1.64007
iteration: 5 recall: 0.5024 accuracy: 0.113848 cost: 0.00223606 M: 12.6035 delta: 0.66458 time: 259.135 one-recall: 0.6 one-ratio: 1.25817
iteration: 6 recall: 0.7652 accuracy: 0.0332385 cost: 0.00297989 M: 15.1141 delta: 0.432341 time: 318.874 one-recall: 0.85 one-ratio: 1.09688
iteration: 7 recall: 0.8944 accuracy: 0.0120521 cost: 0.00395515 M: 21.1404 delta: 0.196423 time: 387.883 one-recall: 0.93 one-ratio: 1.03705
iteration: 8 recall: 0.9448 accuracy: 0.00380819 cost: 0.00497964 M: 27.3035 delta: 0.0885009 time: 454.125 one-recall: 0.99 one-ratio: 1.00043
iteration: 9 recall: 0.9664 accuracy: 0.00208169 cost: 0.00577234 M: 31.2891 delta: 0.0513885 time: 506.155 one-recall: 1 one-ratio: 1
iteration: 10 recall: 0.972 accuracy: 0.00174838 cost: 0.00625718 M: 33.3927 delta: 0.0372235 time: 542.236 one-recall: 1 one-ratio: 1
iteration: 11 recall: 0.9748 accuracy: 0.00152981 cost: 0.00651493 M: 34.4234 delta: 0.031346 time: 566.343 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.9772 accuracy: 0.00141242 cost: 0.00664239 M: 34.9155 delta: 0.0287909 time: 582.764 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.9788 accuracy: 0.00138062 cost: 0.00670462 M: 35.1506 delta: 0.0276247 time: 594.707 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.9788 accuracy: 0.00138062 cost: 0.00673495 M: 35.2643 delta: 0.0270882 time: 604.175 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.9788 accuracy: 0.00138062 cost: 0.00674998 M: 35.32 delta: 0.0268258 time: 612.345 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.9788 accuracy: 0.00138046 cost: 0.00675749 M: 35.3483 delta: 0.0266968 time: 619.835 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.9792 accuracy: 0.00137847 cost: 0.00676142 M: 35.3629 delta: 0.0266305 time: 626.988 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.9792 accuracy: 0.00137847 cost: 0.00676334 M: 35.3701 delta: 0.0265993 time: 633.945 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.9792 accuracy: 0.00137847 cost: 0.00676438 M: 35.374 delta: 0.0265837 time: 640.806 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.9792 accuracy: 0.00137847 cost: 0.00676496 M: 35.3762 delta: 0.0265739 time: 647.615 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.9792 accuracy: 0.00137847 cost: 0.00676524 M: 35.3773 delta: 0.026569 time: 654.385 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.9792 accuracy: 0.00137847 cost: 0.00676542 M: 35.3779 delta: 0.0265667 time: 661.141 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.9792 accuracy: 0.00137847 cost: 0.00676551 M: 35.3783 delta: 0.0265656 time: 667.878 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.9792 accuracy: 0.00137847 cost: 0.00676555 M: 35.3785 delta: 0.0265649 time: 674.609 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.9792 accuracy: 0.00137847 cost: 0.00676559 M: 35.3786 delta: 0.0265643 time: 681.348 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.9792 accuracy: 0.00137847 cost: 0.00676561 M: 35.3786 delta: 0.0265641 time: 688.069 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.9792 accuracy: 0.00137847 cost: 0.00676563 M: 35.3787 delta: 0.0265638 time: 694.791 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.9792 accuracy: 0.00137847 cost: 0.00676564 M: 35.3788 delta: 0.0265635 time: 701.514 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.9792 accuracy: 0.00137847 cost: 0.00676565 M: 35.3788 delta: 0.0265633 time: 708.236 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.9792 accuracy: 0.00137847 cost: 0.00676565 M: 35.3788 delta: 0.0265633 time: 714.957 one-recall: 1 one-ratio: 1
Graph completion with reverse edges...

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Reranking edges...

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 732.8400000000001
Index size:  262768.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0035863000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.1200000000, query time of that 0.0706690510, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Accept!
  -> Decision True in time 1.1700000000, query time of that 0.7163483260, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Accept!
  -> Decision True in time 11.8800000000, query time of that 7.2063347430, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.6300000000, query time of that 0.0888534440, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Accept!
  -> Decision True in time 6.1300000000, query time of that 0.8849813390, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
315.911 < 322.112
  -> Decision False in time 4.1000000000, query time of that 0.6043872070, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Reject!
299.424 < 311.437
  -> Decision False in time 3.8500000000, query time of that 0.0591024180, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
272.877 < 283.575
  -> Decision False in time 65.1900000000, query time of that 0.9892587790, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
331.159 < 333.56
  -> Decision False in time 3.6800000000, query time of that 0.0536984330, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 60, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 60, {'reverse': -1}, False])
Got a train set of size (1000000 * 128)
Generating control...
Initializing...
iteration: 1 recall: 0.0004 accuracy: 2.1619 cost: 0.00038 M: 10 delta: 1 time: 61.6508 one-recall: 0 one-ratio: 3.45348
iteration: 2 recall: 0.004 accuracy: 1.22149 cost: 0.000637428 M: 10 delta: 0.856032 time: 103.999 one-recall: 0 one-ratio: 2.78112
iteration: 3 recall: 0.0268 accuracy: 0.71027 cost: 0.00109521 M: 11.5287 delta: 0.83511 time: 155.19 one-recall: 0.08 one-ratio: 2.23853
iteration: 4 recall: 0.1796 accuracy: 0.378712 cost: 0.00163042 M: 11.8362 delta: 0.783463 time: 206.108 one-recall: 0.27 one-ratio: 1.7164
iteration: 5 recall: 0.5152 accuracy: 0.108971 cost: 0.00223602 M: 12.6035 delta: 0.664581 time: 259.016 one-recall: 0.68 one-ratio: 1.224
iteration: 6 recall: 0.7696 accuracy: 0.0273925 cost: 0.00298 M: 15.1149 delta: 0.432327 time: 318.76 one-recall: 0.9 one-ratio: 1.06228
iteration: 7 recall: 0.8892 accuracy: 0.00933819 cost: 0.00395517 M: 21.1397 delta: 0.196411 time: 387.772 one-recall: 0.98 one-ratio: 1.01002
iteration: 8 recall: 0.9392 accuracy: 0.00397903 cost: 0.0049795 M: 27.3038 delta: 0.0884791 time: 454.004 one-recall: 0.98 one-ratio: 1.01002
iteration: 9 recall: 0.9592 accuracy: 0.00254933 cost: 0.00577282 M: 31.2909 delta: 0.0513342 time: 506.071 one-recall: 0.98 one-ratio: 1.01002
iteration: 10 recall: 0.9684 accuracy: 0.00181386 cost: 0.00625809 M: 33.3942 delta: 0.0371693 time: 542.182 one-recall: 0.99 one-ratio: 1.00902
iteration: 11 recall: 0.9708 accuracy: 0.00171023 cost: 0.00651503 M: 34.4225 delta: 0.031284 time: 566.258 one-recall: 0.99 one-ratio: 1.00902
iteration: 12 recall: 0.9732 accuracy: 0.00157349 cost: 0.00664211 M: 34.9125 delta: 0.0287266 time: 582.644 one-recall: 0.99 one-ratio: 1.00902
iteration: 13 recall: 0.9756 accuracy: 0.00149851 cost: 0.00670424 M: 35.1471 delta: 0.0275633 time: 594.576 one-recall: 0.99 one-ratio: 1.00902
iteration: 14 recall: 0.976 accuracy: 0.00148624 cost: 0.00673425 M: 35.2598 delta: 0.027034 time: 604.018 one-recall: 0.99 one-ratio: 1.00902
iteration: 15 recall: 0.976 accuracy: 0.00148624 cost: 0.00674927 M: 35.3159 delta: 0.0267727 time: 612.181 one-recall: 0.99 one-ratio: 1.00902
iteration: 16 recall: 0.976 accuracy: 0.00148624 cost: 0.00675691 M: 35.3442 delta: 0.0266494 time: 619.682 one-recall: 0.99 one-ratio: 1.00902
iteration: 17 recall: 0.976 accuracy: 0.00148624 cost: 0.00676085 M: 35.3585 delta: 0.0265854 time: 626.831 one-recall: 0.99 one-ratio: 1.00902
iteration: 18 recall: 0.976 accuracy: 0.00148624 cost: 0.00676284 M: 35.366 delta: 0.0265537 time: 633.786 one-recall: 0.99 one-ratio: 1.00902
iteration: 19 recall: 0.976 accuracy: 0.00148624 cost: 0.006764 M: 35.3704 delta: 0.026537 time: 640.654 one-recall: 0.99 one-ratio: 1.00902
iteration: 20 recall: 0.976 accuracy: 0.00148624 cost: 0.00676458 M: 35.3726 delta: 0.0265273 time: 647.461 one-recall: 0.99 one-ratio: 1.00902
iteration: 21 recall: 0.976 accuracy: 0.00148624 cost: 0.0067649 M: 35.3738 delta: 0.0265235 time: 654.236 one-recall: 0.99 one-ratio: 1.00902
iteration: 22 recall: 0.976 accuracy: 0.00148624 cost: 0.00676508 M: 35.3746 delta: 0.0265206 time: 660.987 one-recall: 0.99 one-ratio: 1.00902
iteration: 23 recall: 0.9764 accuracy: 0.00145591 cost: 0.00676518 M: 35.3749 delta: 0.0265188 time: 667.72 one-recall: 0.99 one-ratio: 1.00902
iteration: 24 recall: 0.9764 accuracy: 0.00145591 cost: 0.00676524 M: 35.3752 delta: 0.0265177 time: 674.453 one-recall: 0.99 one-ratio: 1.00902
iteration: 25 recall: 0.9764 accuracy: 0.00145591 cost: 0.00676526 M: 35.3753 delta: 0.0265171 time: 681.173 one-recall: 0.99 one-ratio: 1.00902
iteration: 26 recall: 0.9764 accuracy: 0.00145591 cost: 0.00676528 M: 35.3753 delta: 0.0265167 time: 687.899 one-recall: 0.99 one-ratio: 1.00902
iteration: 27 recall: 0.9764 accuracy: 0.00145591 cost: 0.0067653 M: 35.3754 delta: 0.0265165 time: 694.618 one-recall: 0.99 one-ratio: 1.00902
iteration: 28 recall: 0.9764 accuracy: 0.00145591 cost: 0.0067653 M: 35.3754 delta: 0.0265165 time: 701.337 one-recall: 0.99 one-ratio: 1.00902
iteration: 29 recall: 0.9764 accuracy: 0.00145591 cost: 0.00676531 M: 35.3754 delta: 0.0265165 time: 708.06 one-recall: 0.99 one-ratio: 1.00902
iteration: 30 recall: 0.9764 accuracy: 0.00145591 cost: 0.00676531 M: 35.3754 delta: 0.0265165 time: 714.775 one-recall: 0.99 one-ratio: 1.00902
Graph completion with reverse edges...

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Reranking edges...

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 732.6600000000008
Index size:  262728.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0041095000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.1100000000, query time of that 0.0665459510, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Accept!
  -> Decision True in time 1.1100000000, query time of that 0.6573926580, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
315.609 < 353.586
  -> Decision False in time 2.7400000000, query time of that 1.5920567910, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.6200000000, query time of that 0.0832545500, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Accept!
  -> Decision True in time 5.9800000000, query time of that 0.8074451730, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
285.011 < 291.137
  -> Decision False in time 11.3800000000, query time of that 1.5126813230, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Reject!
259.69 < 268.125
  -> Decision False in time 6.4400000000, query time of that 0.0879469570, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
248.036 < 248.038
  -> Decision False in time 20.5300000000, query time of that 0.2879388450, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
291.885 < 294.919
  -> Decision False in time 28.0800000000, query time of that 0.3872254610, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 90, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 90, {'reverse': -1}, False])
Got a train set of size (1000000 * 128)
Generating control...
Initializing...
iteration: 1 recall: 0 accuracy: 2.10708 cost: 0.00038 M: 10 delta: 1 time: 61.6445 one-recall: 0 one-ratio: 3.67441
iteration: 2 recall: 0.0056 accuracy: 1.19197 cost: 0.000637428 M: 10 delta: 0.856032 time: 104.002 one-recall: 0.01 one-ratio: 2.8386
iteration: 3 recall: 0.0392 accuracy: 0.678384 cost: 0.00109521 M: 11.5287 delta: 0.835104 time: 155.255 one-recall: 0.03 one-ratio: 2.32478
iteration: 4 recall: 0.196 accuracy: 0.342816 cost: 0.00163044 M: 11.8362 delta: 0.783458 time: 206.18 one-recall: 0.28 one-ratio: 1.76844
iteration: 5 recall: 0.496 accuracy: 0.122429 cost: 0.00223606 M: 12.6034 delta: 0.66459 time: 259.113 one-recall: 0.58 one-ratio: 1.29367
iteration: 6 recall: 0.7676 accuracy: 0.0273541 cost: 0.00297997 M: 15.1151 delta: 0.432345 time: 318.867 one-recall: 0.85 one-ratio: 1.05931
iteration: 7 recall: 0.8856 accuracy: 0.00957075 cost: 0.00395541 M: 21.141 delta: 0.196435 time: 387.901 one-recall: 0.91 one-ratio: 1.02194
iteration: 8 recall: 0.9356 accuracy: 0.00494001 cost: 0.00497953 M: 27.3035 delta: 0.0884893 time: 454.164 one-recall: 0.94 one-ratio: 1.01358
iteration: 9 recall: 0.9588 accuracy: 0.00273252 cost: 0.0057724 M: 31.2868 delta: 0.0513254 time: 506.204 one-recall: 0.97 one-ratio: 1.00389
iteration: 10 recall: 0.9692 accuracy: 0.0017512 cost: 0.00625718 M: 33.3927 delta: 0.0372059 time: 542.292 one-recall: 0.98 one-ratio: 1.0026
iteration: 11 recall: 0.9716 accuracy: 0.00155241 cost: 0.00651468 M: 34.4239 delta: 0.0312871 time: 566.386 one-recall: 0.98 one-ratio: 1.0026
iteration: 12 recall: 0.9752 accuracy: 0.00136415 cost: 0.00664196 M: 34.9136 delta: 0.0287213 time: 582.796 one-recall: 0.98 one-ratio: 1.0026
iteration: 13 recall: 0.978 accuracy: 0.00124943 cost: 0.0067041 M: 35.1497 delta: 0.0275657 time: 594.729 one-recall: 0.98 one-ratio: 1.0026
iteration: 14 recall: 0.9788 accuracy: 0.00107735 cost: 0.00673416 M: 35.2627 delta: 0.0270236 time: 604.176 one-recall: 0.99 one-ratio: 1.00157
iteration: 15 recall: 0.9788 accuracy: 0.00107735 cost: 0.00674899 M: 35.3186 delta: 0.0267609 time: 612.332 one-recall: 0.99 one-ratio: 1.00157
iteration: 16 recall: 0.9792 accuracy: 0.00106107 cost: 0.00675653 M: 35.3466 delta: 0.0266365 time: 619.831 one-recall: 0.99 one-ratio: 1.00157
iteration: 17 recall: 0.9796 accuracy: 0.00103227 cost: 0.00676031 M: 35.3606 delta: 0.0265693 time: 626.972 one-recall: 0.99 one-ratio: 1.00157
iteration: 18 recall: 0.9796 accuracy: 0.00103227 cost: 0.00676228 M: 35.368 delta: 0.0265365 time: 633.928 one-recall: 0.99 one-ratio: 1.00157
iteration: 19 recall: 0.9796 accuracy: 0.00103227 cost: 0.00676334 M: 35.3718 delta: 0.0265203 time: 640.787 one-recall: 0.99 one-ratio: 1.00157
iteration: 20 recall: 0.9796 accuracy: 0.00103227 cost: 0.00676388 M: 35.3739 delta: 0.0265109 time: 647.589 one-recall: 0.99 one-ratio: 1.00157
iteration: 21 recall: 0.9796 accuracy: 0.00103227 cost: 0.00676413 M: 35.3749 delta: 0.0265059 time: 654.353 one-recall: 0.99 one-ratio: 1.00157
iteration: 22 recall: 0.9796 accuracy: 0.00103227 cost: 0.00676427 M: 35.3754 delta: 0.0265034 time: 661.101 one-recall: 0.99 one-ratio: 1.00157
iteration: 23 recall: 0.9796 accuracy: 0.00103227 cost: 0.00676432 M: 35.3756 delta: 0.0265027 time: 667.833 one-recall: 0.99 one-ratio: 1.00157
iteration: 24 recall: 0.9796 accuracy: 0.00103227 cost: 0.00676435 M: 35.3757 delta: 0.0265022 time: 674.562 one-recall: 0.99 one-ratio: 1.00157
iteration: 25 recall: 0.9796 accuracy: 0.00103227 cost: 0.00676437 M: 35.3758 delta: 0.0265018 time: 681.284 one-recall: 0.99 one-ratio: 1.00157
iteration: 26 recall: 0.9796 accuracy: 0.00103227 cost: 0.00676437 M: 35.3758 delta: 0.0265016 time: 688.002 one-recall: 0.99 one-ratio: 1.00157
iteration: 27 recall: 0.9796 accuracy: 0.00103227 cost: 0.00676437 M: 35.3758 delta: 0.0265015 time: 694.719 one-recall: 0.99 one-ratio: 1.00157
iteration: 28 recall: 0.9796 accuracy: 0.00103227 cost: 0.00676437 M: 35.3758 delta: 0.0265015 time: 701.436 one-recall: 0.99 one-ratio: 1.00157
iteration: 29 recall: 0.9796 accuracy: 0.00103227 cost: 0.00676437 M: 35.3758 delta: 0.0265015 time: 708.154 one-recall: 0.99 one-ratio: 1.00157
iteration: 30 recall: 0.9796 accuracy: 0.00103227 cost: 0.00676437 M: 35.3758 delta: 0.0265015 time: 714.874 one-recall: 0.99 one-ratio: 1.00157
Graph completion with reverse edges...

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Reranking edges...

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 732.7700000000004
Index size:  262736.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0027253000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.1400000000, query time of that 0.0876849470, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Accept!
  -> Decision True in time 1.3200000000, query time of that 0.8637915420, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
258.925 < 261.948
  -> Decision False in time 9.1400000000, query time of that 5.9425999470, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.6400000000, query time of that 0.1100077710, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Accept!
  -> Decision True in time 6.3600000000, query time of that 1.0634757170, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
172.769 < 202.183
  -> Decision False in time 24.4100000000, query time of that 4.1096167820, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Accept!
  -> Decision True in time 6.9700000000, query time of that 0.1290012820, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
275.035 < 276.682
  -> Decision False in time 20.9000000000, query time of that 0.3674612910, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
237.177 < 237.211
  -> Decision False in time 1.9500000000, query time of that 0.0343620920, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 30, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 30, {'reverse': -1}, False])
Got a train set of size (1000000 * 128)
Generating control...
Initializing...
iteration: 1 recall: 0 accuracy: 2.09431 cost: 0.00038 M: 10 delta: 1 time: 61.6457 one-recall: 0 one-ratio: 3.48588
iteration: 2 recall: 0.0024 accuracy: 1.19492 cost: 0.000637428 M: 10 delta: 0.856032 time: 104.005 one-recall: 0.01 one-ratio: 2.71398
iteration: 3 recall: 0.0276 accuracy: 0.664277 cost: 0.00109521 M: 11.5287 delta: 0.835106 time: 155.236 one-recall: 0.02 one-ratio: 2.16347
iteration: 4 recall: 0.174 accuracy: 0.328036 cost: 0.00163044 M: 11.8362 delta: 0.783457 time: 206.173 one-recall: 0.21 one-ratio: 1.69666
iteration: 5 recall: 0.5032 accuracy: 0.111347 cost: 0.0022361 M: 12.6037 delta: 0.66458 time: 259.119 one-recall: 0.58 one-ratio: 1.28498
iteration: 6 recall: 0.7888 accuracy: 0.0230663 cost: 0.00298002 M: 15.1146 delta: 0.432322 time: 318.87 one-recall: 0.89 one-ratio: 1.03851
iteration: 7 recall: 0.9096 accuracy: 0.00761404 cost: 0.00395537 M: 21.14 delta: 0.196447 time: 387.91 one-recall: 0.97 one-ratio: 1.01051
iteration: 8 recall: 0.9488 accuracy: 0.00366381 cost: 0.00498002 M: 27.3061 delta: 0.0884576 time: 454.181 one-recall: 0.99 one-ratio: 1.00381
iteration: 9 recall: 0.9692 accuracy: 0.00181706 cost: 0.00577247 M: 31.2887 delta: 0.0513636 time: 506.201 one-recall: 1 one-ratio: 1
iteration: 10 recall: 0.9784 accuracy: 0.00111085 cost: 0.00625764 M: 33.3938 delta: 0.0371829 time: 542.314 one-recall: 1 one-ratio: 1
iteration: 11 recall: 0.982 accuracy: 0.000889756 cost: 0.0065142 M: 34.4204 delta: 0.031305 time: 566.361 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.9832 accuracy: 0.000834125 cost: 0.00664166 M: 34.9109 delta: 0.0287392 time: 582.773 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.984 accuracy: 0.000817913 cost: 0.00670325 M: 35.1442 delta: 0.0275806 time: 594.662 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.9844 accuracy: 0.000766461 cost: 0.00673307 M: 35.256 delta: 0.027034 time: 604.084 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.9844 accuracy: 0.000766461 cost: 0.00674773 M: 35.3106 delta: 0.0267826 time: 612.214 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.9844 accuracy: 0.000766461 cost: 0.00675511 M: 35.3378 delta: 0.0266608 time: 619.691 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.9844 accuracy: 0.000766461 cost: 0.00675876 M: 35.3514 delta: 0.0265978 time: 626.815 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.9844 accuracy: 0.000766461 cost: 0.00676069 M: 35.3585 delta: 0.026567 time: 633.767 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.9844 accuracy: 0.000766461 cost: 0.00676169 M: 35.3623 delta: 0.026551 time: 640.619 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.9844 accuracy: 0.000766461 cost: 0.00676219 M: 35.3642 delta: 0.0265417 time: 647.412 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.9844 accuracy: 0.000766461 cost: 0.00676245 M: 35.3652 delta: 0.0265376 time: 654.177 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.9844 accuracy: 0.000766461 cost: 0.00676261 M: 35.3658 delta: 0.0265355 time: 660.924 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.9844 accuracy: 0.000766461 cost: 0.00676271 M: 35.3662 delta: 0.0265339 time: 667.66 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.9844 accuracy: 0.000766461 cost: 0.00676278 M: 35.3665 delta: 0.026533 time: 674.39 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.9844 accuracy: 0.000766461 cost: 0.00676282 M: 35.3666 delta: 0.0265326 time: 681.121 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.9844 accuracy: 0.000766461 cost: 0.00676284 M: 35.3667 delta: 0.0265323 time: 687.844 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.9844 accuracy: 0.000766461 cost: 0.00676285 M: 35.3667 delta: 0.026532 time: 694.565 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.9844 accuracy: 0.000766461 cost: 0.00676285 M: 35.3667 delta: 0.026532 time: 701.284 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.9844 accuracy: 0.000766461 cost: 0.00676285 M: 35.3667 delta: 0.026532 time: 707.995 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.9844 accuracy: 0.000766461 cost: 0.00676285 M: 35.3667 delta: 0.026532 time: 714.708 one-recall: 1 one-ratio: 1
Graph completion with reverse edges...

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Reranking edges...

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 732.5900000000001
Index size:  262708.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0072170000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.0900000000, query time of that 0.0452685750, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Accept!
  -> Decision True in time 0.8900000000, query time of that 0.4262852600, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
376.717 < 377.176
  -> Decision False in time 2.6500000000, query time of that 1.2660293230, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.5800000000, query time of that 0.0528024000, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
264.994 < 273.339
  -> Decision False in time 2.1100000000, query time of that 0.1988241840, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
247.366 < 251.893
  -> Decision False in time 15.1600000000, query time of that 1.4260279110, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Reject!
275.786 < 279.426
  -> Decision False in time 4.7600000000, query time of that 0.0454992210, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
236.54 < 252.731
  -> Decision False in time 27.1900000000, query time of that 0.2522072210, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
221.935 < 224.678
  -> Decision False in time 21.8900000000, query time of that 0.2085013610, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 2, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 2, {'reverse': -1}, False])
Got a train set of size (1000000 * 128)
Generating control...
Initializing...
iteration: 1 recall: 0.0004 accuracy: 2.61165 cost: 0.00038 M: 10 delta: 1 time: 61.6091 one-recall: 0 one-ratio: 3.29715
iteration: 2 recall: 0.004 accuracy: 1.22562 cost: 0.000637428 M: 10 delta: 0.856032 time: 103.87 one-recall: 0.01 one-ratio: 2.63567
iteration: 3 recall: 0.0324 accuracy: 0.621311 cost: 0.00109521 M: 11.5287 delta: 0.835106 time: 155.006 one-recall: 0.04 one-ratio: 2.13451
iteration: 4 recall: 0.1968 accuracy: 0.295331 cost: 0.00163045 M: 11.8363 delta: 0.783468 time: 205.858 one-recall: 0.23 one-ratio: 1.64677
iteration: 5 recall: 0.53 accuracy: 0.0945309 cost: 0.00223612 M: 12.6035 delta: 0.664581 time: 258.687 one-recall: 0.63 one-ratio: 1.21897
iteration: 6 recall: 0.7976 accuracy: 0.0245431 cost: 0.00298006 M: 15.1147 delta: 0.432316 time: 318.336 one-recall: 0.83 one-ratio: 1.09297
iteration: 7 recall: 0.9128 accuracy: 0.007256 cost: 0.00395542 M: 21.1401 delta: 0.196432 time: 387.272 one-recall: 0.93 one-ratio: 1.0205
iteration: 8 recall: 0.9604 accuracy: 0.00273874 cost: 0.00498003 M: 27.3047 delta: 0.0884606 time: 453.452 one-recall: 0.98 one-ratio: 1.01422
iteration: 9 recall: 0.9752 accuracy: 0.00166986 cost: 0.00577332 M: 31.2898 delta: 0.0513298 time: 505.492 one-recall: 0.98 one-ratio: 1.01422
iteration: 10 recall: 0.9804 accuracy: 0.00139323 cost: 0.00625816 M: 33.3942 delta: 0.0371925 time: 541.589 one-recall: 0.98 one-ratio: 1.01419
iteration: 11 recall: 0.982 accuracy: 0.00126471 cost: 0.00651499 M: 34.4229 delta: 0.0312882 time: 565.665 one-recall: 0.98 one-ratio: 1.01419
iteration: 12 recall: 0.9836 accuracy: 0.0011271 cost: 0.00664198 M: 34.9127 delta: 0.028732 time: 582.054 one-recall: 0.98 one-ratio: 1.01309
iteration: 13 recall: 0.986 accuracy: 0.000954906 cost: 0.00670404 M: 35.1484 delta: 0.0275723 time: 593.985 one-recall: 0.99 one-ratio: 1.01198
iteration: 14 recall: 0.9868 accuracy: 0.000922564 cost: 0.00673393 M: 35.2602 delta: 0.0270302 time: 603.419 one-recall: 0.99 one-ratio: 1.01198
iteration: 15 recall: 0.9872 accuracy: 0.000909219 cost: 0.00674901 M: 35.3163 delta: 0.0267743 time: 611.592 one-recall: 0.99 one-ratio: 1.01198
iteration: 16 recall: 0.9872 accuracy: 0.000909219 cost: 0.00675646 M: 35.3443 delta: 0.0266422 time: 619.082 one-recall: 0.99 one-ratio: 1.01198
iteration: 17 recall: 0.9872 accuracy: 0.000909219 cost: 0.00676038 M: 35.3593 delta: 0.0265748 time: 626.232 one-recall: 0.99 one-ratio: 1.01198
iteration: 18 recall: 0.9872 accuracy: 0.000909219 cost: 0.0067623 M: 35.3664 delta: 0.0265442 time: 633.18 one-recall: 0.99 one-ratio: 1.01198
iteration: 19 recall: 0.9872 accuracy: 0.000909219 cost: 0.0067633 M: 35.3703 delta: 0.0265267 time: 640.04 one-recall: 0.99 one-ratio: 1.01198
iteration: 20 recall: 0.9872 accuracy: 0.000909219 cost: 0.00676383 M: 35.3723 delta: 0.0265197 time: 646.842 one-recall: 0.99 one-ratio: 1.01198
iteration: 21 recall: 0.9872 accuracy: 0.000909219 cost: 0.00676416 M: 35.3735 delta: 0.0265141 time: 653.618 one-recall: 0.99 one-ratio: 1.01198
iteration: 22 recall: 0.9872 accuracy: 0.000909219 cost: 0.0067643 M: 35.374 delta: 0.0265119 time: 660.368 one-recall: 0.99 one-ratio: 1.01198
iteration: 23 recall: 0.9872 accuracy: 0.000909219 cost: 0.00676439 M: 35.3744 delta: 0.0265106 time: 667.101 one-recall: 0.99 one-ratio: 1.01198
iteration: 24 recall: 0.9872 accuracy: 0.000909219 cost: 0.00676445 M: 35.3746 delta: 0.0265098 time: 673.829 one-recall: 0.99 one-ratio: 1.01198
iteration: 25 recall: 0.9872 accuracy: 0.000909219 cost: 0.00676447 M: 35.3747 delta: 0.0265094 time: 680.553 one-recall: 0.99 one-ratio: 1.01198
iteration: 26 recall: 0.9872 accuracy: 0.000909219 cost: 0.0067645 M: 35.3748 delta: 0.0265092 time: 687.278 one-recall: 0.99 one-ratio: 1.01198
iteration: 27 recall: 0.9872 accuracy: 0.000909219 cost: 0.00676451 M: 35.3748 delta: 0.0265089 time: 693.996 one-recall: 0.99 one-ratio: 1.01198
iteration: 28 recall: 0.9872 accuracy: 0.000909219 cost: 0.00676451 M: 35.3748 delta: 0.0265089 time: 700.719 one-recall: 0.99 one-ratio: 1.01198
iteration: 29 recall: 0.9872 accuracy: 0.000909219 cost: 0.00676451 M: 35.3748 delta: 0.0265089 time: 707.437 one-recall: 0.99 one-ratio: 1.01198
iteration: 30 recall: 0.9872 accuracy: 0.000909219 cost: 0.00676451 M: 35.3748 delta: 0.0265089 time: 714.155 one-recall: 0.99 one-ratio: 1.01198
Graph completion with reverse edges...

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Reranking edges...

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 732.039999999999
Index size:  198592.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.1040744000
  Testing...
|S| = 80
|T| = 1152
Reject!
424.453 < 453.262
  -> Decision False in time 0.0000000000, query time of that 0.0005828890, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Reject!
421.758 < 441.622
  -> Decision False in time 0.0100000000, query time of that 0.0046700480, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
339.128 < 453.3
  -> Decision False in time 0.0000000000, query time of that 0.0002277190, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Reject!
387.519 < 448.346
  -> Decision False in time 0.1000000000, query time of that 0.0047281030, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
294.102 < 443.588
  -> Decision False in time 0.0000000000, query time of that 0.0002648720, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
283.47 < 420.001
  -> Decision False in time 0.0100000000, query time of that 0.0003741690, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Reject!
397.042 < 451.07
  -> Decision False in time 0.0000000000, query time of that 0.0002884530, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
443.193 < 479.091
  -> Decision False in time 1.1300000000, query time of that 0.0060876900, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
402.913 < 481.326
  -> Decision False in time 3.2600000000, query time of that 0.0175096570, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 50, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 50, {'reverse': -1}, False])
Got a train set of size (1000000 * 128)
Generating control...
Initializing...
iteration: 1 recall: 0 accuracy: 2.23492 cost: 0.00038 M: 10 delta: 1 time: 61.6279 one-recall: 0 one-ratio: 3.46025
iteration: 2 recall: 0.0044 accuracy: 1.21664 cost: 0.000637428 M: 10 delta: 0.856032 time: 103.856 one-recall: 0 one-ratio: 2.64013
iteration: 3 recall: 0.038 accuracy: 0.68822 cost: 0.00109521 M: 11.5287 delta: 0.835105 time: 154.94 one-recall: 0.03 one-ratio: 2.11067
iteration: 4 recall: 0.1904 accuracy: 0.345736 cost: 0.00163045 M: 11.8362 delta: 0.783462 time: 205.75 one-recall: 0.14 one-ratio: 1.66685
iteration: 5 recall: 0.5228 accuracy: 0.107495 cost: 0.002236 M: 12.6033 delta: 0.664569 time: 258.559 one-recall: 0.58 one-ratio: 1.26966
iteration: 6 recall: 0.7804 accuracy: 0.0254533 cost: 0.00297994 M: 15.1152 delta: 0.432335 time: 318.196 one-recall: 0.84 one-ratio: 1.06962
iteration: 7 recall: 0.9056 accuracy: 0.00704364 cost: 0.00395525 M: 21.1402 delta: 0.196433 time: 387.101 one-recall: 0.97 one-ratio: 1.01245
iteration: 8 recall: 0.9452 accuracy: 0.00340885 cost: 0.00497979 M: 27.3049 delta: 0.0884374 time: 453.286 one-recall: 0.97 one-ratio: 1.01245
iteration: 9 recall: 0.966 accuracy: 0.00210836 cost: 0.0057721 M: 31.2875 delta: 0.0513153 time: 505.278 one-recall: 0.97 one-ratio: 1.01245
iteration: 10 recall: 0.9772 accuracy: 0.00128424 cost: 0.00625721 M: 33.3919 delta: 0.0372024 time: 541.385 one-recall: 0.99 one-ratio: 1.00758
iteration: 11 recall: 0.9804 accuracy: 0.00113353 cost: 0.00651493 M: 34.4251 delta: 0.0312988 time: 565.52 one-recall: 0.99 one-ratio: 1.00758
iteration: 12 recall: 0.9824 accuracy: 0.000784233 cost: 0.00664305 M: 34.9193 delta: 0.0287195 time: 581.997 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.9828 accuracy: 0.000733889 cost: 0.00670565 M: 35.1548 delta: 0.0275462 time: 593.959 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.9836 accuracy: 0.000652581 cost: 0.00673546 M: 35.266 delta: 0.0270039 time: 603.394 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.9836 accuracy: 0.000652581 cost: 0.00675 M: 35.3206 delta: 0.026752 time: 611.526 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.9836 accuracy: 0.000652581 cost: 0.00675735 M: 35.3483 delta: 0.0266304 time: 619.005 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.9836 accuracy: 0.000652581 cost: 0.00676131 M: 35.363 delta: 0.0265606 time: 626.165 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.9836 accuracy: 0.000652581 cost: 0.00676321 M: 35.3701 delta: 0.0265313 time: 633.121 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.9836 accuracy: 0.000652581 cost: 0.00676422 M: 35.374 delta: 0.0265148 time: 639.977 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.9836 accuracy: 0.000652581 cost: 0.00676472 M: 35.3759 delta: 0.0265074 time: 646.778 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.9836 accuracy: 0.000652581 cost: 0.00676498 M: 35.3769 delta: 0.0265021 time: 653.548 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.9836 accuracy: 0.000652581 cost: 0.00676513 M: 35.3774 delta: 0.0265 time: 660.298 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.9836 accuracy: 0.000652581 cost: 0.0067652 M: 35.3777 delta: 0.0264989 time: 667.034 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.9836 accuracy: 0.000652581 cost: 0.00676525 M: 35.3779 delta: 0.0264982 time: 673.772 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.9836 accuracy: 0.000652581 cost: 0.00676528 M: 35.3779 delta: 0.0264981 time: 680.503 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.9836 accuracy: 0.000652581 cost: 0.0067653 M: 35.378 delta: 0.0264977 time: 687.224 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.9836 accuracy: 0.000652581 cost: 0.0067653 M: 35.378 delta: 0.0264975 time: 693.953 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.9836 accuracy: 0.000652581 cost: 0.0067653 M: 35.378 delta: 0.0264975 time: 700.677 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.9836 accuracy: 0.000652581 cost: 0.0067653 M: 35.378 delta: 0.0264975 time: 707.4 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.9836 accuracy: 0.000652581 cost: 0.0067653 M: 35.378 delta: 0.0264975 time: 714.123 one-recall: 1 one-ratio: 1
Graph completion with reverse edges...

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Reranking edges...

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 732.0200000000004
Index size:  198696.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0049886000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.1100000000, query time of that 0.0610315440, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Reject!
220.098 < 226.597
  -> Decision False in time 0.0900000000, query time of that 0.0519014700, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
289.531 < 356.282
  -> Decision False in time 2.5800000000, query time of that 1.4301518990, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.6000000000, query time of that 0.0696198340, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Accept!
  -> Decision True in time 5.9300000000, query time of that 0.7206928980, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
232.463 < 240.46
  -> Decision False in time 11.2200000000, query time of that 1.3945615040, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Accept!
  -> Decision True in time 6.9400000000, query time of that 0.0876358530, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
280.548 < 282.652
  -> Decision False in time 2.2100000000, query time of that 0.0283218590, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
283.424 < 285.2
  -> Decision False in time 7.3600000000, query time of that 0.0919462570, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 5, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 5, {'reverse': -1}, False])
Got a train set of size (1000000 * 128)
Generating control...
Initializing...
iteration: 1 recall: 0.0004 accuracy: 1.97127 cost: 0.00038 M: 10 delta: 1 time: 61.6073 one-recall: 0 one-ratio: 3.65225
iteration: 2 recall: 0.0032 accuracy: 1.13988 cost: 0.000637428 M: 10 delta: 0.856033 time: 103.854 one-recall: 0 one-ratio: 2.87408
iteration: 3 recall: 0.0324 accuracy: 0.647702 cost: 0.00109521 M: 11.5287 delta: 0.83511 time: 154.998 one-recall: 0.04 one-ratio: 2.30264
iteration: 4 recall: 0.1896 accuracy: 0.314334 cost: 0.00163045 M: 11.8362 delta: 0.783459 time: 205.799 one-recall: 0.23 one-ratio: 1.77641
iteration: 5 recall: 0.514 accuracy: 0.089975 cost: 0.00223607 M: 12.6035 delta: 0.66459 time: 258.62 one-recall: 0.69 one-ratio: 1.2161
iteration: 6 recall: 0.7988 accuracy: 0.022973 cost: 0.00297989 M: 15.1142 delta: 0.432335 time: 318.268 one-recall: 0.9 one-ratio: 1.05346
iteration: 7 recall: 0.9152 accuracy: 0.00606149 cost: 0.00395516 M: 21.14 delta: 0.196434 time: 387.184 one-recall: 0.99 one-ratio: 1.002
iteration: 8 recall: 0.9536 accuracy: 0.00258427 cost: 0.0049796 M: 27.3034 delta: 0.088504 time: 453.369 one-recall: 1 one-ratio: 1
iteration: 9 recall: 0.9704 accuracy: 0.00142406 cost: 0.00577248 M: 31.2897 delta: 0.0513342 time: 505.379 one-recall: 1 one-ratio: 1
iteration: 10 recall: 0.9764 accuracy: 0.00118508 cost: 0.00625767 M: 33.3935 delta: 0.0372554 time: 541.499 one-recall: 1 one-ratio: 1
iteration: 11 recall: 0.9796 accuracy: 0.00088773 cost: 0.00651527 M: 34.4243 delta: 0.0313534 time: 565.635 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.9812 accuracy: 0.000801238 cost: 0.00664315 M: 34.9177 delta: 0.0287745 time: 582.098 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.9816 accuracy: 0.000791111 cost: 0.00670556 M: 35.154 delta: 0.0276141 time: 594.059 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.982 accuracy: 0.000785553 cost: 0.00673586 M: 35.2683 delta: 0.0270746 time: 603.53 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.9824 accuracy: 0.000778254 cost: 0.00675072 M: 35.3237 delta: 0.0268042 time: 611.695 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.9824 accuracy: 0.000778254 cost: 0.00675803 M: 35.351 delta: 0.0266813 time: 619.175 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.9824 accuracy: 0.000778254 cost: 0.00676184 M: 35.3649 delta: 0.0266207 time: 626.319 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.9824 accuracy: 0.000778254 cost: 0.00676376 M: 35.372 delta: 0.0265854 time: 633.272 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.9824 accuracy: 0.000778254 cost: 0.00676466 M: 35.3755 delta: 0.0265715 time: 640.126 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.9824 accuracy: 0.000778254 cost: 0.00676518 M: 35.3774 delta: 0.0265633 time: 646.932 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.9824 accuracy: 0.000778254 cost: 0.00676545 M: 35.3784 delta: 0.0265581 time: 653.702 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.9824 accuracy: 0.000778254 cost: 0.00676561 M: 35.3791 delta: 0.026555 time: 660.457 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.9824 accuracy: 0.000778254 cost: 0.00676568 M: 35.3794 delta: 0.0265533 time: 667.197 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.9824 accuracy: 0.000778254 cost: 0.00676571 M: 35.3794 delta: 0.0265533 time: 673.932 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.9824 accuracy: 0.000778254 cost: 0.00676572 M: 35.3795 delta: 0.0265527 time: 680.66 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.9824 accuracy: 0.000778254 cost: 0.00676573 M: 35.3795 delta: 0.0265527 time: 687.384 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.9824 accuracy: 0.000778254 cost: 0.00676573 M: 35.3795 delta: 0.0265527 time: 694.108 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.9824 accuracy: 0.000778254 cost: 0.00676573 M: 35.3796 delta: 0.0265525 time: 700.835 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.9824 accuracy: 0.000778254 cost: 0.00676573 M: 35.3796 delta: 0.0265526 time: 707.558 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.9824 accuracy: 0.000778254 cost: 0.00676574 M: 35.3796 delta: 0.0265525 time: 714.281 one-recall: 1 one-ratio: 1
Graph completion with reverse edges...

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Reranking edges...

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 732.1800000000003
Index size:  198752.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0112955000
  Testing...
|S| = 80
|T| = 1152
Reject!
381.454 < 383.346
  -> Decision False in time 0.0700000000, query time of that 0.0223309430, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Accept!
  -> Decision True in time 0.6700000000, query time of that 0.2155439170, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
344.735 < 345.73
  -> Decision False in time 0.3200000000, query time of that 0.1020666250, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.5400000000, query time of that 0.0270630270, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
328.688 < 341.893
  -> Decision False in time 1.5200000000, query time of that 0.0781029310, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
348.735 < 399.055
  -> Decision False in time 6.9700000000, query time of that 0.3604711870, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Reject!
265.509 < 269.611
  -> Decision False in time 0.7300000000, query time of that 0.0038671520, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
274.563 < 277.908
  -> Decision False in time 1.2200000000, query time of that 0.0074916490, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
440.918 < 465.637
  -> Decision False in time 9.0100000000, query time of that 0.0476326230, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 80, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 80, {'reverse': -1}, False])
Got a train set of size (1000000 * 128)
Generating control...
Initializing...
iteration: 1 recall: 0.0012 accuracy: 2.39916 cost: 0.00038 M: 10 delta: 1 time: 61.6197 one-recall: 0 one-ratio: 3.30721
iteration: 2 recall: 0.0056 accuracy: 1.25232 cost: 0.000637428 M: 10 delta: 0.856032 time: 103.881 one-recall: 0 one-ratio: 2.56221
iteration: 3 recall: 0.0364 accuracy: 0.64839 cost: 0.00109521 M: 11.5287 delta: 0.835108 time: 155.014 one-recall: 0 one-ratio: 2.10731
iteration: 4 recall: 0.196 accuracy: 0.294221 cost: 0.00163044 M: 11.8361 delta: 0.783463 time: 205.85 one-recall: 0.23 one-ratio: 1.60942
iteration: 5 recall: 0.528 accuracy: 0.0927021 cost: 0.00223607 M: 12.6036 delta: 0.664585 time: 258.679 one-recall: 0.62 one-ratio: 1.23277
iteration: 6 recall: 0.7876 accuracy: 0.0257046 cost: 0.00297993 M: 15.1142 delta: 0.432371 time: 318.34 one-recall: 0.89 one-ratio: 1.07163
iteration: 7 recall: 0.9112 accuracy: 0.00874674 cost: 0.00395511 M: 21.1387 delta: 0.196382 time: 387.264 one-recall: 0.96 one-ratio: 1.03605
iteration: 8 recall: 0.9532 accuracy: 0.00267917 cost: 0.00497935 M: 27.3028 delta: 0.0884699 time: 453.454 one-recall: 0.99 one-ratio: 1.0007
iteration: 9 recall: 0.9724 accuracy: 0.0015912 cost: 0.00577185 M: 31.2845 delta: 0.0513488 time: 505.462 one-recall: 0.99 one-ratio: 1.0007
iteration: 10 recall: 0.9796 accuracy: 0.0011662 cost: 0.00625643 M: 33.3887 delta: 0.0372096 time: 541.543 one-recall: 1 one-ratio: 1
iteration: 11 recall: 0.9824 accuracy: 0.000926638 cost: 0.00651392 M: 34.4202 delta: 0.0313231 time: 565.651 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.984 accuracy: 0.000827234 cost: 0.00664177 M: 34.911 delta: 0.0287569 time: 582.097 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.9844 accuracy: 0.000801737 cost: 0.00670383 M: 35.1453 delta: 0.0275971 time: 594.03 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.9852 accuracy: 0.000720058 cost: 0.00673407 M: 35.2593 delta: 0.027054 time: 603.493 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.9852 accuracy: 0.000720058 cost: 0.00674911 M: 35.3148 delta: 0.0267888 time: 611.665 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.9852 accuracy: 0.000720058 cost: 0.0067566 M: 35.3423 delta: 0.0266661 time: 619.158 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.9852 accuracy: 0.000720058 cost: 0.0067605 M: 35.3567 delta: 0.0266034 time: 626.31 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.9852 accuracy: 0.000720058 cost: 0.00676241 M: 35.3639 delta: 0.0265696 time: 633.267 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.9852 accuracy: 0.000720058 cost: 0.00676342 M: 35.3677 delta: 0.0265539 time: 640.126 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.9852 accuracy: 0.000720058 cost: 0.00676396 M: 35.3697 delta: 0.0265461 time: 646.929 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.9852 accuracy: 0.000720058 cost: 0.00676422 M: 35.3707 delta: 0.0265415 time: 653.697 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.9852 accuracy: 0.000720058 cost: 0.00676436 M: 35.3713 delta: 0.0265385 time: 660.447 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.9852 accuracy: 0.000720058 cost: 0.00676445 M: 35.3716 delta: 0.0265369 time: 667.183 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.9852 accuracy: 0.000720058 cost: 0.00676449 M: 35.3717 delta: 0.0265365 time: 673.915 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.9852 accuracy: 0.000720058 cost: 0.00676452 M: 35.3719 delta: 0.0265362 time: 680.645 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.9852 accuracy: 0.000720058 cost: 0.00676454 M: 35.3719 delta: 0.0265359 time: 687.366 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.9852 accuracy: 0.000720058 cost: 0.00676454 M: 35.3719 delta: 0.0265357 time: 694.089 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.9852 accuracy: 0.000720058 cost: 0.00676454 M: 35.3719 delta: 0.0265357 time: 700.809 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.9852 accuracy: 0.000720058 cost: 0.00676454 M: 35.3719 delta: 0.0265357 time: 707.531 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.9852 accuracy: 0.000720058 cost: 0.00676454 M: 35.3719 delta: 0.0265357 time: 714.254 one-recall: 1 one-ratio: 1
Graph completion with reverse edges...

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Reranking edges...

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 732.1399999999994
Index size:  198488.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0031850000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.1300000000, query time of that 0.0781223650, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Accept!
  -> Decision True in time 1.2600000000, query time of that 0.8015525190, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
377.387 < 395.808
  -> Decision False in time 6.2900000000, query time of that 3.9642339390, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Reject!
267.838 < 272.097
  -> Decision False in time 0.4100000000, query time of that 0.0631652720, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Accept!
  -> Decision True in time 6.2000000000, query time of that 0.9698045160, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
378.852 < 407.15
  -> Decision False in time 7.6300000000, query time of that 1.2031994240, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Accept!
  -> Decision True in time 6.9500000000, query time of that 0.1165141750, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
267.496 < 280.332
  -> Decision False in time 3.9800000000, query time of that 0.0648162870, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
232.914 < 244.583
  -> Decision False in time 17.8400000000, query time of that 0.2939202590, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 10, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 10, {'reverse': -1}, False])
Got a train set of size (1000000 * 128)
Generating control...
Initializing...
iteration: 1 recall: 0 accuracy: 2.08997 cost: 0.00038 M: 10 delta: 1 time: 61.6267 one-recall: 0 one-ratio: 3.15017
iteration: 2 recall: 0.0036 accuracy: 1.11207 cost: 0.000637428 M: 10 delta: 0.856032 time: 103.888 one-recall: 0.01 one-ratio: 2.5285
iteration: 3 recall: 0.0328 accuracy: 0.612049 cost: 0.00109521 M: 11.5287 delta: 0.835107 time: 155.021 one-recall: 0.06 one-ratio: 2.03264
iteration: 4 recall: 0.1708 accuracy: 0.290035 cost: 0.00163043 M: 11.8363 delta: 0.783451 time: 205.871 one-recall: 0.22 one-ratio: 1.66658
iteration: 5 recall: 0.4888 accuracy: 0.103155 cost: 0.00223605 M: 12.6033 delta: 0.664594 time: 258.701 one-recall: 0.61 one-ratio: 1.2989
iteration: 6 recall: 0.7448 accuracy: 0.0300249 cost: 0.0029801 M: 15.1148 delta: 0.432348 time: 318.365 one-recall: 0.9 one-ratio: 1.04473
iteration: 7 recall: 0.8732 accuracy: 0.0108453 cost: 0.00395554 M: 21.1411 delta: 0.196411 time: 387.342 one-recall: 0.98 one-ratio: 1.01448
iteration: 8 recall: 0.9236 accuracy: 0.00529885 cost: 0.00497992 M: 27.3048 delta: 0.0884569 time: 453.551 one-recall: 0.99 one-ratio: 1
iteration: 9 recall: 0.9508 accuracy: 0.00311665 cost: 0.00577251 M: 31.2865 delta: 0.0513395 time: 505.569 one-recall: 1 one-ratio: 1
iteration: 10 recall: 0.9632 accuracy: 0.00227629 cost: 0.00625732 M: 33.3907 delta: 0.0372026 time: 541.66 one-recall: 1 one-ratio: 1
iteration: 11 recall: 0.9684 accuracy: 0.00203663 cost: 0.00651428 M: 34.4185 delta: 0.0313234 time: 565.755 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.9724 accuracy: 0.00168299 cost: 0.0066415 M: 34.9095 delta: 0.0287727 time: 582.167 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.9732 accuracy: 0.00160605 cost: 0.00670341 M: 35.1428 delta: 0.0276081 time: 594.086 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.9756 accuracy: 0.00147973 cost: 0.00673337 M: 35.2543 delta: 0.0270616 time: 603.518 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.9756 accuracy: 0.00147805 cost: 0.00674827 M: 35.3101 delta: 0.0267998 time: 611.675 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.976 accuracy: 0.00146556 cost: 0.00675565 M: 35.3376 delta: 0.0266763 time: 619.156 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.976 accuracy: 0.00146556 cost: 0.00675955 M: 35.3516 delta: 0.0266129 time: 626.304 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.976 accuracy: 0.00146556 cost: 0.00676147 M: 35.3585 delta: 0.0265806 time: 633.255 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.976 accuracy: 0.00146556 cost: 0.00676245 M: 35.3623 delta: 0.0265673 time: 640.107 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.976 accuracy: 0.00146556 cost: 0.00676301 M: 35.3645 delta: 0.0265574 time: 646.909 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.976 accuracy: 0.00146556 cost: 0.00676333 M: 35.3657 delta: 0.0265544 time: 653.695 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.976 accuracy: 0.00146556 cost: 0.0067635 M: 35.3663 delta: 0.0265513 time: 660.446 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.976 accuracy: 0.00146556 cost: 0.00676358 M: 35.3666 delta: 0.0265498 time: 667.184 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.976 accuracy: 0.00146556 cost: 0.00676363 M: 35.3668 delta: 0.0265491 time: 673.916 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.976 accuracy: 0.00146556 cost: 0.00676366 M: 35.367 delta: 0.0265489 time: 680.645 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.976 accuracy: 0.00146556 cost: 0.00676368 M: 35.367 delta: 0.0265483 time: 687.364 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.976 accuracy: 0.00146556 cost: 0.00676369 M: 35.3671 delta: 0.0265482 time: 694.088 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.976 accuracy: 0.00146556 cost: 0.0067637 M: 35.3671 delta: 0.026548 time: 700.809 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.976 accuracy: 0.00146556 cost: 0.0067637 M: 35.3672 delta: 0.026548 time: 707.527 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.976 accuracy: 0.00146556 cost: 0.00676371 M: 35.3672 delta: 0.026548 time: 714.243 one-recall: 1 one-ratio: 1
Graph completion with reverse edges...

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Reranking edges...

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 732.119999999999
Index size:  198408.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0108558000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.0700000000, query time of that 0.0269366840, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Reject!
402.029 < 406.438
  -> Decision False in time 0.5200000000, query time of that 0.1928327820, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
221.33 < 233.617
  -> Decision False in time 0.5900000000, query time of that 0.2108940260, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.5500000000, query time of that 0.0343920800, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
431.877 < 439.653
  -> Decision False in time 4.3300000000, query time of that 0.2624637700, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
266.978 < 268.594
  -> Decision False in time 3.7800000000, query time of that 0.2285696050, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Reject!
251.324 < 256.655
  -> Decision False in time 1.9800000000, query time of that 0.0124874290, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
264.785 < 279.891
  -> Decision False in time 7.3100000000, query time of that 0.0453839510, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
269.564 < 272.853
  -> Decision False in time 2.3300000000, query time of that 0.0150431160, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 100, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 100, {'reverse': -1}, False])
Got a train set of size (1000000 * 128)
Generating control...
Initializing...
iteration: 1 recall: 0.0004 accuracy: 2.09076 cost: 0.00038 M: 10 delta: 1 time: 61.6508 one-recall: 0 one-ratio: 3.11087
iteration: 2 recall: 0.0028 accuracy: 1.1345 cost: 0.000637428 M: 10 delta: 0.856032 time: 103.993 one-recall: 0 one-ratio: 2.50025
iteration: 3 recall: 0.038 accuracy: 0.614839 cost: 0.00109521 M: 11.5287 delta: 0.835108 time: 155.227 one-recall: 0.03 one-ratio: 1.94388
iteration: 4 recall: 0.1856 accuracy: 0.285971 cost: 0.00163043 M: 11.8362 delta: 0.783457 time: 206.121 one-recall: 0.2 one-ratio: 1.51173
iteration: 5 recall: 0.5088 accuracy: 0.0926285 cost: 0.00223604 M: 12.6038 delta: 0.664611 time: 259.015 one-recall: 0.61 one-ratio: 1.22707
iteration: 6 recall: 0.7716 accuracy: 0.0268479 cost: 0.00297994 M: 15.1146 delta: 0.432324 time: 318.766 one-recall: 0.84 one-ratio: 1.07736
iteration: 7 recall: 0.886 accuracy: 0.00883519 cost: 0.00395518 M: 21.1388 delta: 0.196425 time: 387.75 one-recall: 0.94 one-ratio: 1.02021
iteration: 8 recall: 0.9372 accuracy: 0.00380393 cost: 0.0049797 M: 27.3032 delta: 0.0884726 time: 453.997 one-recall: 0.97 one-ratio: 1.00662
iteration: 9 recall: 0.9624 accuracy: 0.00212853 cost: 0.00577232 M: 31.2852 delta: 0.0513277 time: 506.015 one-recall: 0.98 one-ratio: 1.0052
iteration: 10 recall: 0.9692 accuracy: 0.00164916 cost: 0.00625743 M: 33.3888 delta: 0.037201 time: 542.145 one-recall: 0.98 one-ratio: 1.0052
iteration: 11 recall: 0.9728 accuracy: 0.00149705 cost: 0.00651433 M: 34.4186 delta: 0.0313503 time: 566.227 one-recall: 0.98 one-ratio: 1.0052
iteration: 12 recall: 0.9732 accuracy: 0.00149326 cost: 0.00664222 M: 34.9111 delta: 0.0287844 time: 582.67 one-recall: 0.98 one-ratio: 1.0052
iteration: 13 recall: 0.9732 accuracy: 0.00149326 cost: 0.00670418 M: 35.145 delta: 0.0276177 time: 594.595 one-recall: 0.98 one-ratio: 1.0052
iteration: 14 recall: 0.9736 accuracy: 0.00147449 cost: 0.00673462 M: 35.259 delta: 0.0270759 time: 604.069 one-recall: 0.98 one-ratio: 1.0052
iteration: 15 recall: 0.9736 accuracy: 0.00147449 cost: 0.00674939 M: 35.3141 delta: 0.0268234 time: 612.222 one-recall: 0.98 one-ratio: 1.0052
iteration: 16 recall: 0.974 accuracy: 0.00145395 cost: 0.00675677 M: 35.3418 delta: 0.026698 time: 619.705 one-recall: 0.98 one-ratio: 1.0052
iteration: 17 recall: 0.974 accuracy: 0.00145395 cost: 0.00676062 M: 35.3561 delta: 0.0266322 time: 626.85 one-recall: 0.98 one-ratio: 1.0052
iteration: 18 recall: 0.974 accuracy: 0.00145395 cost: 0.00676251 M: 35.3632 delta: 0.0266044 time: 633.797 one-recall: 0.98 one-ratio: 1.0052
iteration: 19 recall: 0.974 accuracy: 0.00145395 cost: 0.00676363 M: 35.3674 delta: 0.0265864 time: 640.666 one-recall: 0.98 one-ratio: 1.0052
iteration: 20 recall: 0.974 accuracy: 0.00145395 cost: 0.00676415 M: 35.3694 delta: 0.0265778 time: 647.464 one-recall: 0.98 one-ratio: 1.0052
iteration: 21 recall: 0.974 accuracy: 0.00145395 cost: 0.00676447 M: 35.3707 delta: 0.0265724 time: 654.247 one-recall: 0.98 one-ratio: 1.0052
iteration: 22 recall: 0.974 accuracy: 0.00145395 cost: 0.00676463 M: 35.3713 delta: 0.0265698 time: 660.996 one-recall: 0.98 one-ratio: 1.0052
iteration: 23 recall: 0.974 accuracy: 0.00145395 cost: 0.00676471 M: 35.3716 delta: 0.0265683 time: 667.74 one-recall: 0.98 one-ratio: 1.0052
iteration: 24 recall: 0.974 accuracy: 0.00145395 cost: 0.00676476 M: 35.3718 delta: 0.026568 time: 674.473 one-recall: 0.98 one-ratio: 1.0052
iteration: 25 recall: 0.974 accuracy: 0.00145395 cost: 0.00676479 M: 35.3719 delta: 0.0265677 time: 681.201 one-recall: 0.98 one-ratio: 1.0052
iteration: 26 recall: 0.974 accuracy: 0.00145395 cost: 0.00676481 M: 35.3721 delta: 0.0265673 time: 687.923 one-recall: 0.98 one-ratio: 1.0052
iteration: 27 recall: 0.974 accuracy: 0.00145395 cost: 0.00676482 M: 35.3721 delta: 0.0265671 time: 694.648 one-recall: 0.98 one-ratio: 1.0052
iteration: 28 recall: 0.974 accuracy: 0.00145395 cost: 0.00676483 M: 35.3722 delta: 0.0265671 time: 701.37 one-recall: 0.98 one-ratio: 1.0052
iteration: 29 recall: 0.974 accuracy: 0.00145395 cost: 0.00676484 M: 35.3722 delta: 0.0265669 time: 708.093 one-recall: 0.98 one-ratio: 1.0052
iteration: 30 recall: 0.974 accuracy: 0.00145395 cost: 0.00676484 M: 35.3722 delta: 0.0265669 time: 714.813 one-recall: 0.98 one-ratio: 1.0052
Graph completion with reverse edges...

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Reranking edges...

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 732.6800000000003
Index size:  232724.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0024644000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.1400000000, query time of that 0.0940464360, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Accept!
  -> Decision True in time 1.4100000000, query time of that 0.9515229060, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Accept!
  -> Decision True in time 14.0200000000, query time of that 9.3714124540, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.6500000000, query time of that 0.1153285970, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
201.326 < 245.324
  -> Decision False in time 3.3700000000, query time of that 0.5930055080, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
328.743 < 349.123
  -> Decision False in time 21.5700000000, query time of that 3.8369114680, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Reject!
279.534 < 284.944
  -> Decision False in time 4.8000000000, query time of that 0.0881014850, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Accept!
  -> Decision True in time 70.0700000000, query time of that 1.3064937530, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
279.364 < 280.582
  -> Decision False in time 1.3900000000, query time of that 0.0260922260, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 20, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 20, {'reverse': -1}, False])
Got a train set of size (1000000 * 128)
Generating control...
Initializing...
iteration: 1 recall: 0.0004 accuracy: 2.22176 cost: 0.00038 M: 10 delta: 1 time: 61.6477 one-recall: 0 one-ratio: 3.35915
iteration: 2 recall: 0.0036 accuracy: 1.14529 cost: 0.000637428 M: 10 delta: 0.856032 time: 104.009 one-recall: 0 one-ratio: 2.63842
iteration: 3 recall: 0.0332 accuracy: 0.61232 cost: 0.00109521 M: 11.5287 delta: 0.835111 time: 155.217 one-recall: 0.03 one-ratio: 2.0949
iteration: 4 recall: 0.2072 accuracy: 0.274858 cost: 0.00163043 M: 11.8362 delta: 0.783446 time: 206.097 one-recall: 0.26 one-ratio: 1.62065
iteration: 5 recall: 0.5364 accuracy: 0.0991907 cost: 0.00223605 M: 12.6037 delta: 0.664587 time: 258.988 one-recall: 0.59 one-ratio: 1.28849
iteration: 6 recall: 0.7748 accuracy: 0.032973 cost: 0.00298003 M: 15.1147 delta: 0.432345 time: 318.716 one-recall: 0.81 one-ratio: 1.09175
iteration: 7 recall: 0.8856 accuracy: 0.010341 cost: 0.00395517 M: 21.1383 delta: 0.196458 time: 387.693 one-recall: 0.95 one-ratio: 1.01309
iteration: 8 recall: 0.9368 accuracy: 0.00437559 cost: 0.00497924 M: 27.3012 delta: 0.0884939 time: 453.888 one-recall: 0.99 one-ratio: 1.00212
iteration: 9 recall: 0.9576 accuracy: 0.00258913 cost: 0.00577164 M: 31.286 delta: 0.0514181 time: 505.89 one-recall: 1 one-ratio: 1
iteration: 10 recall: 0.9692 accuracy: 0.00165046 cost: 0.00625768 M: 33.3955 delta: 0.0372605 time: 542.041 one-recall: 1 one-ratio: 1
iteration: 11 recall: 0.9736 accuracy: 0.00139008 cost: 0.00651537 M: 34.4256 delta: 0.0313459 time: 566.157 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.9764 accuracy: 0.00118886 cost: 0.00664279 M: 34.9162 delta: 0.028782 time: 582.573 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.9772 accuracy: 0.00117053 cost: 0.00670475 M: 35.1511 delta: 0.027616 time: 594.51 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.9776 accuracy: 0.00115388 cost: 0.00673517 M: 35.2646 delta: 0.0270593 time: 603.988 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.9776 accuracy: 0.00115388 cost: 0.0067501 M: 35.3202 delta: 0.0268026 time: 612.156 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.9776 accuracy: 0.00115388 cost: 0.00675759 M: 35.3475 delta: 0.0266767 time: 619.648 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.9776 accuracy: 0.00115388 cost: 0.00676141 M: 35.3615 delta: 0.0266178 time: 626.794 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.9776 accuracy: 0.00115388 cost: 0.0067634 M: 35.369 delta: 0.0265831 time: 633.755 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.9776 accuracy: 0.00115388 cost: 0.00676442 M: 35.3729 delta: 0.0265663 time: 640.621 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.9776 accuracy: 0.00115388 cost: 0.00676496 M: 35.3748 delta: 0.0265584 time: 647.428 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.9776 accuracy: 0.00115388 cost: 0.00676523 M: 35.3758 delta: 0.0265547 time: 654.199 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.9776 accuracy: 0.00115388 cost: 0.00676539 M: 35.3764 delta: 0.0265521 time: 660.962 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.9776 accuracy: 0.00115388 cost: 0.00676547 M: 35.3767 delta: 0.0265507 time: 667.704 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.9776 accuracy: 0.00115388 cost: 0.00676552 M: 35.3769 delta: 0.0265496 time: 674.443 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.9776 accuracy: 0.00115388 cost: 0.00676555 M: 35.377 delta: 0.0265494 time: 681.174 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.9776 accuracy: 0.00115388 cost: 0.00676557 M: 35.3771 delta: 0.0265492 time: 687.904 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.9776 accuracy: 0.00115388 cost: 0.00676559 M: 35.3772 delta: 0.0265488 time: 694.628 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.9776 accuracy: 0.00115388 cost: 0.00676559 M: 35.3772 delta: 0.0265485 time: 701.351 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.9776 accuracy: 0.00115388 cost: 0.0067656 M: 35.3772 delta: 0.0265484 time: 708.075 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.9776 accuracy: 0.00115388 cost: 0.0067656 M: 35.3772 delta: 0.0265484 time: 714.796 one-recall: 1 one-ratio: 1
Graph completion with reverse edges...

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Reranking edges...

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 732.6800000000003
Index size:  232900.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0091481000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.0800000000, query time of that 0.0365230120, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Reject!
327.047 < 342.396
  -> Decision False in time 0.0400000000, query time of that 0.0172837670, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
392 < 407.795
  -> Decision False in time 0.2100000000, query time of that 0.0927386400, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.5600000000, query time of that 0.0426347090, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Accept!
  -> Decision True in time 5.5800000000, query time of that 0.4385535320, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
325.008 < 410.623
  -> Decision False in time 0.2500000000, query time of that 0.0192045220, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Accept!
  -> Decision True in time 6.9100000000, query time of that 0.0546117430, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
399.346 < 438.724
  -> Decision False in time 8.4800000000, query time of that 0.0685377310, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
244.121 < 246.751
  -> Decision False in time 1.5900000000, query time of that 0.0143784070, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 3, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 3, {'reverse': -1}, False])
Got a train set of size (1000000 * 128)
Generating control...
Initializing...
iteration: 1 recall: 0.0004 accuracy: 2.18575 cost: 0.00038 M: 10 delta: 1 time: 61.6416 one-recall: 0 one-ratio: 3.23856
iteration: 2 recall: 0.0036 accuracy: 1.1727 cost: 0.000637428 M: 10 delta: 0.856032 time: 103.99 one-recall: 0 one-ratio: 2.56389
iteration: 3 recall: 0.0368 accuracy: 0.633524 cost: 0.00109521 M: 11.5287 delta: 0.835102 time: 155.218 one-recall: 0.03 one-ratio: 2.04124
iteration: 4 recall: 0.21 accuracy: 0.287474 cost: 0.00163045 M: 11.8362 delta: 0.783454 time: 206.112 one-recall: 0.27 one-ratio: 1.60797
iteration: 5 recall: 0.548 accuracy: 0.0897616 cost: 0.00223607 M: 12.6033 delta: 0.664601 time: 259.019 one-recall: 0.62 one-ratio: 1.21145
iteration: 6 recall: 0.7968 accuracy: 0.0220066 cost: 0.00297999 M: 15.1146 delta: 0.432349 time: 318.741 one-recall: 0.88 one-ratio: 1.04075
iteration: 7 recall: 0.906 accuracy: 0.00797475 cost: 0.00395529 M: 21.1405 delta: 0.196377 time: 387.75 one-recall: 0.91 one-ratio: 1.02624
iteration: 8 recall: 0.9492 accuracy: 0.00391272 cost: 0.00497949 M: 27.3052 delta: 0.088401 time: 453.953 one-recall: 0.95 one-ratio: 1.01412
iteration: 9 recall: 0.9664 accuracy: 0.00278699 cost: 0.00577226 M: 31.2895 delta: 0.0513244 time: 505.971 one-recall: 0.96 one-ratio: 1.01322
iteration: 10 recall: 0.9724 accuracy: 0.0025017 cost: 0.00625729 M: 33.3938 delta: 0.0371809 time: 542.09 one-recall: 0.96 one-ratio: 1.01322
iteration: 11 recall: 0.9776 accuracy: 0.00186059 cost: 0.00651506 M: 34.4233 delta: 0.0313004 time: 566.232 one-recall: 0.97 one-ratio: 1.00828
iteration: 12 recall: 0.9796 accuracy: 0.00162858 cost: 0.00664267 M: 34.9153 delta: 0.0287211 time: 582.657 one-recall: 0.97 one-ratio: 1.00828
iteration: 13 recall: 0.9816 accuracy: 0.00153049 cost: 0.0067046 M: 35.1502 delta: 0.0275669 time: 594.585 one-recall: 0.97 one-ratio: 1.00828
iteration: 14 recall: 0.9816 accuracy: 0.00153049 cost: 0.00673483 M: 35.2635 delta: 0.0270205 time: 604.053 one-recall: 0.97 one-ratio: 1.00828
iteration: 15 recall: 0.9816 accuracy: 0.00153049 cost: 0.00674996 M: 35.3197 delta: 0.0267566 time: 612.235 one-recall: 0.97 one-ratio: 1.00828
iteration: 16 recall: 0.982 accuracy: 0.00151055 cost: 0.00675774 M: 35.3488 delta: 0.0266304 time: 619.762 one-recall: 0.97 one-ratio: 1.00828
iteration: 17 recall: 0.982 accuracy: 0.00151055 cost: 0.00676159 M: 35.3632 delta: 0.0265675 time: 626.912 one-recall: 0.97 one-ratio: 1.00828
iteration: 18 recall: 0.982 accuracy: 0.00151055 cost: 0.00676354 M: 35.3706 delta: 0.0265339 time: 633.868 one-recall: 0.97 one-ratio: 1.00828
iteration: 19 recall: 0.982 accuracy: 0.00151055 cost: 0.00676454 M: 35.3744 delta: 0.0265186 time: 640.724 one-recall: 0.97 one-ratio: 1.00828
iteration: 20 recall: 0.982 accuracy: 0.00151055 cost: 0.00676507 M: 35.3765 delta: 0.0265109 time: 647.536 one-recall: 0.97 one-ratio: 1.00828
iteration: 21 recall: 0.982 accuracy: 0.00151055 cost: 0.00676538 M: 35.3777 delta: 0.0265058 time: 654.317 one-recall: 0.97 one-ratio: 1.00828
iteration: 22 recall: 0.982 accuracy: 0.00151055 cost: 0.00676553 M: 35.3783 delta: 0.0265037 time: 661.071 one-recall: 0.97 one-ratio: 1.00828
iteration: 23 recall: 0.982 accuracy: 0.00151055 cost: 0.00676561 M: 35.3786 delta: 0.026503 time: 667.808 one-recall: 0.97 one-ratio: 1.00828
iteration: 24 recall: 0.982 accuracy: 0.00151055 cost: 0.00676567 M: 35.3789 delta: 0.0265016 time: 674.553 one-recall: 0.97 one-ratio: 1.00828
iteration: 25 recall: 0.982 accuracy: 0.00151055 cost: 0.00676571 M: 35.3791 delta: 0.0265013 time: 681.286 one-recall: 0.97 one-ratio: 1.00828
iteration: 26 recall: 0.982 accuracy: 0.00151055 cost: 0.00676575 M: 35.3793 delta: 0.0265008 time: 688.012 one-recall: 0.97 one-ratio: 1.00828
iteration: 27 recall: 0.982 accuracy: 0.00151055 cost: 0.00676577 M: 35.3794 delta: 0.0265001 time: 694.74 one-recall: 0.97 one-ratio: 1.00828
iteration: 28 recall: 0.982 accuracy: 0.00151055 cost: 0.00676578 M: 35.3794 delta: 0.0264998 time: 701.461 one-recall: 0.97 one-ratio: 1.00828
iteration: 29 recall: 0.982 accuracy: 0.00151055 cost: 0.00676578 M: 35.3794 delta: 0.0264998 time: 708.181 one-recall: 0.97 one-ratio: 1.00828
iteration: 30 recall: 0.982 accuracy: 0.00151055 cost: 0.00676578 M: 35.3794 delta: 0.0264998 time: 714.911 one-recall: 0.97 one-ratio: 1.00828
Graph completion with reverse edges...

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Reranking edges...

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 732.7700000000004
Index size:  232936.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0189798000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.0700000000, query time of that 0.0216099820, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Reject!
430.867 < 460.621
  -> Decision False in time 0.0200000000, query time of that 0.0055149990, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
383.887 < 393.512
  -> Decision False in time 0.0900000000, query time of that 0.0267423950, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.5400000000, query time of that 0.0264875930, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
297.486 < 298.528
  -> Decision False in time 1.0600000000, query time of that 0.0510118140, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
430.164 < 454.623
  -> Decision False in time 1.2400000000, query time of that 0.0584036240, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Reject!
333.12 < 407.379
  -> Decision False in time 1.8000000000, query time of that 0.0093032980, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
269.674 < 272.771
  -> Decision False in time 2.5600000000, query time of that 0.0132925630, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
270.087 < 270.548
  -> Decision False in time 0.1800000000, query time of that 0.0012515720, with c1=5.0000000000, c2=0.1000000000
