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', 20, {'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', 10, {'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', 80, {'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', 3, {'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', 40, {'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', 60, {'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', 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', 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 accuracy: 2.29356 cost: 0.00038 M: 10 delta: 1 time: 54.3727 one-recall: 0 one-ratio: 3.52528
iteration: 2 recall: 0.004 accuracy: 1.20332 cost: 0.000637428 M: 10 delta: 0.856032 time: 92.4509 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: 138.586 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: 184.29 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: 231.709 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: 285.251 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: 346.27 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: 403.286 one-recall: 1 one-ratio: 1
iteration: 9 recall: 0.9644 accuracy: 0.00221739 cost: 0.00577293 M: 31.2904 delta: 0.0514019 time: 446.7 one-recall: 1 one-ratio: 1
iteration: 10 recall: 0.9764 accuracy: 0.0012952 cost: 0.00625843 M: 33.3974 delta: 0.0372226 time: 475.924 one-recall: 1 one-ratio: 1
iteration: 11 recall: 0.984 accuracy: 0.000915513 cost: 0.00651572 M: 34.4268 delta: 0.0313604 time: 494.917 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.9872 accuracy: 0.000651871 cost: 0.00664321 M: 34.9174 delta: 0.028787 time: 507.642 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.9876 accuracy: 0.000628894 cost: 0.00670536 M: 35.1523 delta: 0.027629 time: 516.845 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.9876 accuracy: 0.000628894 cost: 0.00673551 M: 35.2647 delta: 0.0270893 time: 524.128 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.9876 accuracy: 0.000628894 cost: 0.00675029 M: 35.3194 delta: 0.0268326 time: 530.429 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.9876 accuracy: 0.000628894 cost: 0.00675777 M: 35.3471 delta: 0.0267076 time: 536.234 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.9876 accuracy: 0.000628894 cost: 0.00676154 M: 35.3608 delta: 0.0266443 time: 541.77 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.9876 accuracy: 0.000628894 cost: 0.00676338 M: 35.3677 delta: 0.0266122 time: 547.162 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.9876 accuracy: 0.000628894 cost: 0.00676433 M: 35.3712 delta: 0.0265977 time: 552.482 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.9876 accuracy: 0.000628894 cost: 0.00676485 M: 35.373 delta: 0.02659 time: 557.762 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.988 accuracy: 0.0006087 cost: 0.00676515 M: 35.3742 delta: 0.0265857 time: 563.019 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.988 accuracy: 0.0006087 cost: 0.00676532 M: 35.3749 delta: 0.0265828 time: 568.263 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.988 accuracy: 0.0006087 cost: 0.00676541 M: 35.3753 delta: 0.0265811 time: 573.5 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.988 accuracy: 0.0006087 cost: 0.00676547 M: 35.3755 delta: 0.0265798 time: 578.732 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.988 accuracy: 0.0006087 cost: 0.00676549 M: 35.3756 delta: 0.0265796 time: 583.971 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.988 accuracy: 0.0006087 cost: 0.00676551 M: 35.3757 delta: 0.0265794 time: 589.192 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.988 accuracy: 0.0006087 cost: 0.00676552 M: 35.3757 delta: 0.0265791 time: 594.417 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.988 accuracy: 0.0006087 cost: 0.00676553 M: 35.3757 delta: 0.0265791 time: 599.638 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.988 accuracy: 0.0006087 cost: 0.00676553 M: 35.3757 delta: 0.026579 time: 604.858 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.988 accuracy: 0.0006087 cost: 0.00676553 M: 35.3757 delta: 0.026579 time: 610.078 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 624.65
Index size:  1903140.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0091459000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.0700000000, query time of that 0.0251664780, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Reject!
401.374 < 449.263
  -> Decision False in time 0.2200000000, query time of that 0.0785364220, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
324.627 < 431.877
  -> Decision False in time 0.0600000000, query time of that 0.0208391360, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.5300000000, query time of that 0.0306862590, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
354.223 < 354.67
  -> Decision False in time 1.3300000000, query time of that 0.0742372720, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
257.668 < 258.449
  -> Decision False in time 0.8000000000, query time of that 0.0423290250, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Accept!
  -> Decision True in time 6.6400000000, query time of that 0.0361240530, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
254.498 < 258.126
  -> Decision False in time 1.3600000000, query time of that 0.0075763140, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
223.477 < 232.129
  -> Decision False in time 10.2800000000, query time of that 0.0574880710, 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: 54.0591 one-recall: 0 one-ratio: 3.66072
iteration: 2 recall: 0.0016 accuracy: 1.38091 cost: 0.000637428 M: 10 delta: 0.856032 time: 92.1518 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: 138.294 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: 184.022 one-recall: 0.31 one-ratio: 1.61045
iteration: 5 recall: 0.5252 accuracy: 0.113981 cost: 0.00223605 M: 12.6037 delta: 0.664585 time: 231.498 one-recall: 0.65 one-ratio: 1.31249
iteration: 6 recall: 0.7812 accuracy: 0.0320711 cost: 0.00298004 M: 15.1149 delta: 0.432336 time: 285.117 one-recall: 0.89 one-ratio: 1.10605
iteration: 7 recall: 0.8988 accuracy: 0.00878774 cost: 0.00395536 M: 21.1393 delta: 0.196453 time: 346.18 one-recall: 0.98 one-ratio: 1.00852
iteration: 8 recall: 0.9544 accuracy: 0.00247446 cost: 0.00498001 M: 27.3049 delta: 0.0884608 time: 403.214 one-recall: 1 one-ratio: 1
iteration: 9 recall: 0.9716 accuracy: 0.00132818 cost: 0.00577298 M: 31.2888 delta: 0.0513401 time: 446.609 one-recall: 1 one-ratio: 1
iteration: 10 recall: 0.9796 accuracy: 0.000968214 cost: 0.00625808 M: 33.3946 delta: 0.0371771 time: 475.828 one-recall: 1 one-ratio: 1
iteration: 11 recall: 0.982 accuracy: 0.000821838 cost: 0.00651467 M: 34.4217 delta: 0.0313219 time: 494.814 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.986 accuracy: 0.000602846 cost: 0.0066429 M: 34.9154 delta: 0.0287606 time: 507.595 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.9868 accuracy: 0.000578651 cost: 0.00670514 M: 35.151 delta: 0.0275927 time: 516.81 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.9876 accuracy: 0.000568954 cost: 0.00673522 M: 35.2641 delta: 0.0270442 time: 524.099 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.9876 accuracy: 0.000568954 cost: 0.00675001 M: 35.3194 delta: 0.0267932 time: 530.4 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.9876 accuracy: 0.000568954 cost: 0.00675766 M: 35.3477 delta: 0.0266612 time: 536.214 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.9876 accuracy: 0.000568954 cost: 0.00676147 M: 35.3616 delta: 0.0265961 time: 541.748 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.9876 accuracy: 0.000568954 cost: 0.00676338 M: 35.3687 delta: 0.0265672 time: 547.142 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.9876 accuracy: 0.000568954 cost: 0.00676437 M: 35.3725 delta: 0.0265502 time: 552.461 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.9876 accuracy: 0.000568954 cost: 0.00676489 M: 35.3744 delta: 0.0265429 time: 557.737 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.9876 accuracy: 0.000568954 cost: 0.00676515 M: 35.3754 delta: 0.0265379 time: 562.986 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.9876 accuracy: 0.000568954 cost: 0.0067653 M: 35.376 delta: 0.0265357 time: 568.223 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.9876 accuracy: 0.000568954 cost: 0.00676539 M: 35.3764 delta: 0.026535 time: 573.449 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.9876 accuracy: 0.000568954 cost: 0.00676544 M: 35.3766 delta: 0.026534 time: 578.669 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.9876 accuracy: 0.000568954 cost: 0.00676548 M: 35.3767 delta: 0.0265334 time: 583.889 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.9876 accuracy: 0.000568954 cost: 0.00676549 M: 35.3768 delta: 0.0265335 time: 589.102 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.9876 accuracy: 0.000568954 cost: 0.0067655 M: 35.3769 delta: 0.0265334 time: 594.315 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.9876 accuracy: 0.000568954 cost: 0.00676552 M: 35.3769 delta: 0.0265333 time: 599.532 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.9876 accuracy: 0.000568954 cost: 0.00676552 M: 35.377 delta: 0.0265331 time: 604.741 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.9876 accuracy: 0.000568954 cost: 0.00676553 M: 35.377 delta: 0.026533 time: 609.954 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 624.44
Index size:  261020.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0114810000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.0700000000, query time of that 0.0155517920, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Accept!
  -> Decision True in time 0.5900000000, query time of that 0.1496381920, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
312.096 < 350.999
  -> Decision False in time 0.3900000000, query time of that 0.0954742480, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.5100000000, query time of that 0.0177796950, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Accept!
  -> Decision True in time 5.0700000000, query time of that 0.1733546260, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
325.22 < 426.972
  -> Decision False in time 1.7200000000, query time of that 0.0597876300, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Reject!
215.685 < 220.993
  -> Decision False in time 0.0500000000, query time of that 0.0002915970, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
245.856 < 247.582
  -> Decision False in time 11.0800000000, query time of that 0.0387207360, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
336.355 < 355.907
  -> Decision False in time 10.7500000000, query time of that 0.0391053840, 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: 1.87201 cost: 0.00038 M: 10 delta: 1 time: 54.0285 one-recall: 0 one-ratio: 3.61113
iteration: 2 recall: 0.0052 accuracy: 1.05427 cost: 0.000637428 M: 10 delta: 0.856032 time: 92.127 one-recall: 0 one-ratio: 2.87272
iteration: 3 recall: 0.03 accuracy: 0.622933 cost: 0.00109521 M: 11.5287 delta: 0.835107 time: 138.284 one-recall: 0.02 one-ratio: 2.38985
iteration: 4 recall: 0.1776 accuracy: 0.310901 cost: 0.00163044 M: 11.8363 delta: 0.783456 time: 184.03 one-recall: 0.18 one-ratio: 1.82114
iteration: 5 recall: 0.4908 accuracy: 0.11976 cost: 0.00223603 M: 12.6037 delta: 0.664582 time: 231.538 one-recall: 0.58 one-ratio: 1.38176
iteration: 6 recall: 0.7628 accuracy: 0.03258 cost: 0.00297996 M: 15.1151 delta: 0.432334 time: 285.165 one-recall: 0.88 one-ratio: 1.12982
iteration: 7 recall: 0.8916 accuracy: 0.0106852 cost: 0.00395529 M: 21.1405 delta: 0.196374 time: 346.218 one-recall: 0.96 one-ratio: 1.031
iteration: 8 recall: 0.9324 accuracy: 0.00640053 cost: 0.00497972 M: 27.3048 delta: 0.0884794 time: 403.244 one-recall: 0.98 one-ratio: 1.02545
iteration: 9 recall: 0.9604 accuracy: 0.00301819 cost: 0.00577234 M: 31.2853 delta: 0.0513748 time: 446.627 one-recall: 0.99 one-ratio: 1.00236
iteration: 10 recall: 0.9692 accuracy: 0.00177367 cost: 0.00625711 M: 33.3921 delta: 0.0372379 time: 475.835 one-recall: 0.99 one-ratio: 1.00236
iteration: 11 recall: 0.9744 accuracy: 0.00140968 cost: 0.00651429 M: 34.4206 delta: 0.0313219 time: 494.855 one-recall: 0.99 one-ratio: 1.00236
iteration: 12 recall: 0.9752 accuracy: 0.00132162 cost: 0.00664145 M: 34.9115 delta: 0.0287454 time: 507.596 one-recall: 0.99 one-ratio: 1.00236
iteration: 13 recall: 0.9768 accuracy: 0.00120535 cost: 0.00670341 M: 35.145 delta: 0.0275914 time: 516.797 one-recall: 0.99 one-ratio: 1.00236
iteration: 14 recall: 0.9776 accuracy: 0.00116461 cost: 0.00673361 M: 35.2584 delta: 0.0270536 time: 524.092 one-recall: 0.99 one-ratio: 1.00236
iteration: 15 recall: 0.978 accuracy: 0.00114167 cost: 0.00674855 M: 35.3139 delta: 0.0267988 time: 530.402 one-recall: 0.99 one-ratio: 1.00236
iteration: 16 recall: 0.978 accuracy: 0.00114167 cost: 0.00675607 M: 35.3418 delta: 0.0266672 time: 536.206 one-recall: 0.99 one-ratio: 1.00236
iteration: 17 recall: 0.978 accuracy: 0.00114167 cost: 0.00675983 M: 35.3558 delta: 0.0266042 time: 541.739 one-recall: 0.99 one-ratio: 1.00236
iteration: 18 recall: 0.978 accuracy: 0.00114167 cost: 0.00676172 M: 35.3628 delta: 0.0265718 time: 547.127 one-recall: 0.99 one-ratio: 1.00236
iteration: 19 recall: 0.978 accuracy: 0.00114167 cost: 0.00676269 M: 35.3663 delta: 0.0265569 time: 552.444 one-recall: 0.99 one-ratio: 1.00236
iteration: 20 recall: 0.978 accuracy: 0.00114167 cost: 0.00676316 M: 35.3681 delta: 0.0265486 time: 557.715 one-recall: 0.99 one-ratio: 1.00236
iteration: 21 recall: 0.978 accuracy: 0.00114167 cost: 0.00676342 M: 35.3692 delta: 0.0265448 time: 562.96 one-recall: 0.99 one-ratio: 1.00236
iteration: 22 recall: 0.978 accuracy: 0.00114167 cost: 0.00676357 M: 35.3699 delta: 0.0265419 time: 568.198 one-recall: 0.99 one-ratio: 1.00236
iteration: 23 recall: 0.978 accuracy: 0.00114167 cost: 0.00676364 M: 35.3701 delta: 0.0265409 time: 573.422 one-recall: 0.99 one-ratio: 1.00236
iteration: 24 recall: 0.978 accuracy: 0.00114167 cost: 0.00676368 M: 35.3703 delta: 0.0265401 time: 578.641 one-recall: 0.99 one-ratio: 1.00236
iteration: 25 recall: 0.978 accuracy: 0.00114167 cost: 0.00676372 M: 35.3705 delta: 0.0265395 time: 583.859 one-recall: 0.99 one-ratio: 1.00236
iteration: 26 recall: 0.978 accuracy: 0.00114167 cost: 0.00676374 M: 35.3706 delta: 0.0265391 time: 589.074 one-recall: 0.99 one-ratio: 1.00236
iteration: 27 recall: 0.978 accuracy: 0.00114167 cost: 0.00676376 M: 35.3706 delta: 0.0265391 time: 594.289 one-recall: 0.99 one-ratio: 1.00236
iteration: 28 recall: 0.978 accuracy: 0.00114167 cost: 0.00676377 M: 35.3706 delta: 0.0265388 time: 599.504 one-recall: 0.99 one-ratio: 1.00236
iteration: 29 recall: 0.978 accuracy: 0.00114167 cost: 0.00676377 M: 35.3706 delta: 0.0265388 time: 604.72 one-recall: 0.99 one-ratio: 1.00236
iteration: 30 recall: 0.978 accuracy: 0.00114167 cost: 0.00676377 M: 35.3706 delta: 0.0265388 time: 609.935 one-recall: 0.99 one-ratio: 1.00236
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 624.4200000000001
Index size:  262844.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0108460000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.0700000000, query time of that 0.0186654590, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Reject!
377.285 < 378.342
  -> Decision False in time 0.4000000000, query time of that 0.1203029860, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
243.895 < 248.719
  -> Decision False in time 0.2000000000, query time of that 0.0568046140, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.5200000000, query time of that 0.0225294450, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
296.268 < 296.973
  -> Decision False in time 1.8600000000, query time of that 0.0823458290, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
368.755 < 371.763
  -> Decision False in time 1.3700000000, query time of that 0.0597026560, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Reject!
360.056 < 386.491
  -> Decision False in time 0.1700000000, query time of that 0.0011231350, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
212.579 < 330.66
  -> Decision False in time 14.1000000000, query time of that 0.0598686140, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
277.067 < 286.793
  -> Decision False in time 4.6800000000, query time of that 0.0202683850, 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: 0.0008 accuracy: 2.2106 cost: 0.00038 M: 10 delta: 1 time: 54.1058 one-recall: 0 one-ratio: 2.97853
iteration: 2 recall: 0.0044 accuracy: 1.25375 cost: 0.000637428 M: 10 delta: 0.856032 time: 92.186 one-recall: 0 one-ratio: 2.31723
iteration: 3 recall: 0.0356 accuracy: 0.702915 cost: 0.00109521 M: 11.5287 delta: 0.835106 time: 138.344 one-recall: 0.01 one-ratio: 1.91265
iteration: 4 recall: 0.1696 accuracy: 0.325202 cost: 0.00163043 M: 11.8362 delta: 0.783455 time: 184.084 one-recall: 0.19 one-ratio: 1.46808
iteration: 5 recall: 0.5112 accuracy: 0.11316 cost: 0.00223603 M: 12.6037 delta: 0.664598 time: 231.591 one-recall: 0.61 one-ratio: 1.1693
iteration: 6 recall: 0.7748 accuracy: 0.0274846 cost: 0.00297987 M: 15.114 delta: 0.432339 time: 285.223 one-recall: 0.88 one-ratio: 1.0472
iteration: 7 recall: 0.8948 accuracy: 0.00802979 cost: 0.00395523 M: 21.1413 delta: 0.196431 time: 346.288 one-recall: 0.95 one-ratio: 1.02084
iteration: 8 recall: 0.9388 accuracy: 0.00359352 cost: 0.00497964 M: 27.3057 delta: 0.0884443 time: 403.316 one-recall: 0.98 one-ratio: 1.00173
iteration: 9 recall: 0.9644 accuracy: 0.0015577 cost: 0.0057731 M: 31.2917 delta: 0.0513843 time: 446.738 one-recall: 0.99 one-ratio: 1.00032
iteration: 10 recall: 0.9752 accuracy: 0.00107381 cost: 0.00625849 M: 33.3971 delta: 0.037185 time: 475.96 one-recall: 0.99 one-ratio: 1.00032
iteration: 11 recall: 0.9768 accuracy: 0.00100775 cost: 0.00651555 M: 34.4261 delta: 0.0313061 time: 494.973 one-recall: 0.99 one-ratio: 1.00032
iteration: 12 recall: 0.978 accuracy: 0.000969636 cost: 0.00664334 M: 34.9178 delta: 0.0287484 time: 507.738 one-recall: 0.99 one-ratio: 1.00032
iteration: 13 recall: 0.9788 accuracy: 0.000929637 cost: 0.00670545 M: 35.153 delta: 0.0275836 time: 516.945 one-recall: 0.99 one-ratio: 1.00032
iteration: 14 recall: 0.9792 accuracy: 0.000837422 cost: 0.00673515 M: 35.2639 delta: 0.0270433 time: 524.214 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.9796 accuracy: 0.000756272 cost: 0.00674971 M: 35.3184 delta: 0.026787 time: 530.5 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.9804 accuracy: 0.000694181 cost: 0.00675699 M: 35.3452 delta: 0.0266649 time: 536.286 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.9804 accuracy: 0.000694181 cost: 0.00676072 M: 35.3594 delta: 0.0266053 time: 541.814 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.9804 accuracy: 0.000694181 cost: 0.0067626 M: 35.3667 delta: 0.0265733 time: 547.204 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.9804 accuracy: 0.000694181 cost: 0.00676359 M: 35.3704 delta: 0.026558 time: 552.52 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.9804 accuracy: 0.000694181 cost: 0.00676417 M: 35.3726 delta: 0.0265496 time: 557.8 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.9804 accuracy: 0.000694181 cost: 0.00676447 M: 35.3737 delta: 0.0265453 time: 563.053 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.9804 accuracy: 0.000694181 cost: 0.00676469 M: 35.3746 delta: 0.0265422 time: 568.296 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.9804 accuracy: 0.000694181 cost: 0.00676481 M: 35.375 delta: 0.0265402 time: 573.528 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.9804 accuracy: 0.000694181 cost: 0.00676488 M: 35.3753 delta: 0.0265394 time: 578.752 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.9804 accuracy: 0.000694181 cost: 0.00676492 M: 35.3755 delta: 0.0265385 time: 583.973 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.9804 accuracy: 0.000694181 cost: 0.00676495 M: 35.3756 delta: 0.0265383 time: 589.189 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.9804 accuracy: 0.000694181 cost: 0.00676496 M: 35.3756 delta: 0.0265382 time: 594.406 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.9804 accuracy: 0.000694181 cost: 0.00676497 M: 35.3756 delta: 0.0265379 time: 599.62 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.9804 accuracy: 0.000694181 cost: 0.00676498 M: 35.3757 delta: 0.0265379 time: 604.836 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.9804 accuracy: 0.000694181 cost: 0.00676498 M: 35.3757 delta: 0.0265377 time: 610.049 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 624.5500000000002
Index size:  262784.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0071704000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.0800000000, query time of that 0.0323097480, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Accept!
  -> Decision True in time 0.7500000000, query time of that 0.3068656270, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Accept!
  -> Decision True in time 7.7000000000, query time of that 3.0956134290, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.5500000000, query time of that 0.0379882060, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
238.749 < 239.157
  -> Decision False in time 0.9600000000, query time of that 0.0655300720, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
267.619 < 268.28
  -> Decision False in time 11.5400000000, query time of that 0.7822909200, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Reject!
269.121 < 269.327
  -> Decision False in time 4.1100000000, query time of that 0.0261202290, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
286.873 < 288.47
  -> Decision False in time 11.3300000000, query time of that 0.0737412420, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
290.126 < 294.083
  -> Decision False in time 6.7800000000, query time of that 0.0446704320, 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 accuracy: 2.07844 cost: 0.00038 M: 10 delta: 1 time: 54.0337 one-recall: 0 one-ratio: 2.78539
iteration: 2 recall: 0.0036 accuracy: 1.11996 cost: 0.000637428 M: 10 delta: 0.856032 time: 92.1296 one-recall: 0.01 one-ratio: 2.20444
iteration: 3 recall: 0.0304 accuracy: 0.579597 cost: 0.00109521 M: 11.5287 delta: 0.835108 time: 138.293 one-recall: 0.05 one-ratio: 1.78519
iteration: 4 recall: 0.204 accuracy: 0.259961 cost: 0.00163042 M: 11.8362 delta: 0.783449 time: 184.043 one-recall: 0.29 one-ratio: 1.40437
iteration: 5 recall: 0.536 accuracy: 0.085737 cost: 0.00223607 M: 12.604 delta: 0.6646 time: 231.55 one-recall: 0.56 one-ratio: 1.16157
iteration: 6 recall: 0.7668 accuracy: 0.0274417 cost: 0.00297991 M: 15.1146 delta: 0.432369 time: 285.184 one-recall: 0.83 one-ratio: 1.04906
iteration: 7 recall: 0.8816 accuracy: 0.00916023 cost: 0.00395517 M: 21.1388 delta: 0.196444 time: 346.249 one-recall: 0.97 one-ratio: 1.01464
iteration: 8 recall: 0.9344 accuracy: 0.00397144 cost: 0.00498 M: 27.3066 delta: 0.0884356 time: 403.313 one-recall: 0.99 one-ratio: 1.00366
iteration: 9 recall: 0.9604 accuracy: 0.0022551 cost: 0.00577215 M: 31.2892 delta: 0.0513519 time: 446.681 one-recall: 0.99 one-ratio: 1.00366
iteration: 10 recall: 0.9692 accuracy: 0.0016219 cost: 0.00625789 M: 33.3953 delta: 0.0372114 time: 475.923 one-recall: 0.99 one-ratio: 1.00366
iteration: 11 recall: 0.9732 accuracy: 0.00120326 cost: 0.00651535 M: 34.4258 delta: 0.031341 time: 494.947 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.976 accuracy: 0.0011072 cost: 0.00664368 M: 34.9204 delta: 0.0287434 time: 507.739 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.9772 accuracy: 0.00109533 cost: 0.00670562 M: 35.154 delta: 0.0275911 time: 516.941 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.9784 accuracy: 0.00102944 cost: 0.00673587 M: 35.2676 delta: 0.0270408 time: 524.245 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.9784 accuracy: 0.00102944 cost: 0.00675074 M: 35.3228 delta: 0.0267841 time: 530.553 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.9784 accuracy: 0.00102944 cost: 0.00675818 M: 35.3507 delta: 0.0266497 time: 536.355 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.9784 accuracy: 0.00102944 cost: 0.00676175 M: 35.3637 delta: 0.0265897 time: 541.878 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.9784 accuracy: 0.00102944 cost: 0.00676359 M: 35.3704 delta: 0.0265609 time: 547.267 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.9784 accuracy: 0.00102944 cost: 0.00676458 M: 35.3741 delta: 0.0265442 time: 552.585 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.9784 accuracy: 0.00102944 cost: 0.00676505 M: 35.3758 delta: 0.026537 time: 557.857 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.9784 accuracy: 0.00102944 cost: 0.00676532 M: 35.3768 delta: 0.0265322 time: 563.106 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.9784 accuracy: 0.00102944 cost: 0.00676545 M: 35.3773 delta: 0.02653 time: 568.34 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.9784 accuracy: 0.00102944 cost: 0.00676553 M: 35.3777 delta: 0.026529 time: 573.58 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.9784 accuracy: 0.00102944 cost: 0.00676557 M: 35.3778 delta: 0.0265283 time: 578.803 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.9784 accuracy: 0.00102944 cost: 0.00676559 M: 35.3779 delta: 0.0265275 time: 584.019 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.9784 accuracy: 0.00102944 cost: 0.0067656 M: 35.378 delta: 0.0265274 time: 589.232 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.9784 accuracy: 0.00102944 cost: 0.00676561 M: 35.378 delta: 0.0265274 time: 594.443 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.9784 accuracy: 0.00102944 cost: 0.00676561 M: 35.378 delta: 0.0265274 time: 599.656 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.9784 accuracy: 0.00102944 cost: 0.00676561 M: 35.378 delta: 0.0265274 time: 604.87 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.9784 accuracy: 0.00102944 cost: 0.00676562 M: 35.378 delta: 0.0265274 time: 610.083 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 624.5799999999999
Index size:  262892.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0031884000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.1300000000, query time of that 0.0844579770, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Accept!
  -> Decision True in time 1.2800000000, query time of that 0.8216747010, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
265.906 < 267.729
  -> Decision False in time 9.0300000000, query time of that 5.7703410320, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.6600000000, query time of that 0.0993143780, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Accept!
  -> Decision True in time 6.4700000000, query time of that 0.9627551120, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
410.946 < 433.375
  -> Decision False in time 53.0800000000, query time of that 8.0322010390, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Reject!
264.951 < 266.889
  -> Decision False in time 1.3000000000, query time of that 0.0161764180, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
272.13 < 273.222
  -> Decision False in time 16.8000000000, query time of that 0.2348954190, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
169.812 < 204.695
  -> Decision False in time 15.7400000000, query time of that 0.2157055240, 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: 0.0004 accuracy: 2.25676 cost: 0.00038 M: 10 delta: 1 time: 63.6602 one-recall: 0 one-ratio: 3.19408
iteration: 2 recall: 0.004 accuracy: 1.23162 cost: 0.000637428 M: 10 delta: 0.856032 time: 107.656 one-recall: 0 one-ratio: 2.56629
iteration: 3 recall: 0.0348 accuracy: 0.695782 cost: 0.00109521 M: 11.5287 delta: 0.835105 time: 160.967 one-recall: 0.03 one-ratio: 2.04759
iteration: 4 recall: 0.1792 accuracy: 0.344623 cost: 0.00163044 M: 11.8362 delta: 0.783447 time: 214.032 one-recall: 0.17 one-ratio: 1.6662
iteration: 5 recall: 0.5148 accuracy: 0.106918 cost: 0.00223613 M: 12.6039 delta: 0.664628 time: 269.224 one-recall: 0.62 one-ratio: 1.25335
iteration: 6 recall: 0.7688 accuracy: 0.0284859 cost: 0.00297994 M: 15.1138 delta: 0.432335 time: 331.466 one-recall: 0.87 one-ratio: 1.05363
iteration: 7 recall: 0.8892 accuracy: 0.00962431 cost: 0.00395519 M: 21.1401 delta: 0.196422 time: 403.647 one-recall: 0.94 one-ratio: 1.01545
iteration: 8 recall: 0.936 accuracy: 0.00400395 cost: 0.0049796 M: 27.3039 delta: 0.0884709 time: 473.23 one-recall: 0.97 one-ratio: 1.00434
iteration: 9 recall: 0.9552 accuracy: 0.00268599 cost: 0.005773 M: 31.2901 delta: 0.0513783 time: 527.945 one-recall: 0.97 one-ratio: 1.00434
iteration: 10 recall: 0.9628 accuracy: 0.00203448 cost: 0.00625802 M: 33.3952 delta: 0.0372335 time: 565.908 one-recall: 0.97 one-ratio: 1.00434
iteration: 11 recall: 0.9672 accuracy: 0.00178371 cost: 0.00651573 M: 34.4257 delta: 0.0313266 time: 591.321 one-recall: 0.98 one-ratio: 1.00272
iteration: 12 recall: 0.9684 accuracy: 0.00172219 cost: 0.00664342 M: 34.9181 delta: 0.0287574 time: 608.692 one-recall: 0.98 one-ratio: 1.00272
iteration: 13 recall: 0.9684 accuracy: 0.00172219 cost: 0.0067056 M: 35.153 delta: 0.0275986 time: 621.365 one-recall: 0.98 one-ratio: 1.00272
iteration: 14 recall: 0.9684 accuracy: 0.00172219 cost: 0.00673573 M: 35.2651 delta: 0.0270622 time: 631.419 one-recall: 0.98 one-ratio: 1.00272
iteration: 15 recall: 0.9684 accuracy: 0.00172219 cost: 0.00675047 M: 35.3203 delta: 0.0268024 time: 640.109 one-recall: 0.98 one-ratio: 1.00272
iteration: 16 recall: 0.9684 accuracy: 0.00171989 cost: 0.00675803 M: 35.3484 delta: 0.0266736 time: 648.112 one-recall: 0.98 one-ratio: 1.00272
iteration: 17 recall: 0.9688 accuracy: 0.00170152 cost: 0.00676181 M: 35.3623 delta: 0.0266103 time: 655.737 one-recall: 0.98 one-ratio: 1.00272
iteration: 18 recall: 0.9692 accuracy: 0.00169731 cost: 0.00676373 M: 35.3695 delta: 0.0265779 time: 663.168 one-recall: 0.98 one-ratio: 1.00272
iteration: 19 recall: 0.9696 accuracy: 0.0016615 cost: 0.00676476 M: 35.3734 delta: 0.0265609 time: 670.497 one-recall: 0.98 one-ratio: 1.00272
iteration: 20 recall: 0.9696 accuracy: 0.0016615 cost: 0.00676531 M: 35.3755 delta: 0.0265534 time: 677.765 one-recall: 0.98 one-ratio: 1.00272
iteration: 21 recall: 0.9696 accuracy: 0.0016615 cost: 0.0067656 M: 35.3767 delta: 0.0265476 time: 684.997 one-recall: 0.98 one-ratio: 1.00272
iteration: 22 recall: 0.9696 accuracy: 0.0016615 cost: 0.00676578 M: 35.3774 delta: 0.0265447 time: 692.215 one-recall: 0.98 one-ratio: 1.00272
iteration: 23 recall: 0.9696 accuracy: 0.0016615 cost: 0.00676587 M: 35.3778 delta: 0.0265425 time: 699.417 one-recall: 0.98 one-ratio: 1.00272
iteration: 24 recall: 0.9696 accuracy: 0.0016615 cost: 0.00676592 M: 35.378 delta: 0.026542 time: 706.607 one-recall: 0.98 one-ratio: 1.00272
iteration: 25 recall: 0.9696 accuracy: 0.0016615 cost: 0.00676595 M: 35.3781 delta: 0.0265417 time: 713.79 one-recall: 0.98 one-ratio: 1.00272
iteration: 26 recall: 0.9696 accuracy: 0.0016615 cost: 0.00676597 M: 35.3782 delta: 0.0265417 time: 720.976 one-recall: 0.98 one-ratio: 1.00272
iteration: 27 recall: 0.9696 accuracy: 0.0016615 cost: 0.00676599 M: 35.3783 delta: 0.0265415 time: 728.16 one-recall: 0.98 one-ratio: 1.00272
iteration: 28 recall: 0.9696 accuracy: 0.0016615 cost: 0.006766 M: 35.3783 delta: 0.0265413 time: 735.337 one-recall: 0.98 one-ratio: 1.00272
iteration: 29 recall: 0.9696 accuracy: 0.0016615 cost: 0.006766 M: 35.3784 delta: 0.0265413 time: 742.517 one-recall: 0.98 one-ratio: 1.00272
iteration: 30 recall: 0.9696 accuracy: 0.0016615 cost: 0.006766 M: 35.3784 delta: 0.0265413 time: 749.697 one-recall: 0.98 one-ratio: 1.00272
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 768.4900000000007
Index size:  262784.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0027279000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.1300000000, query time of that 0.0892669410, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Accept!
  -> Decision True in time 1.3600000000, query time of that 0.9039270660, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Accept!
  -> Decision True in time 13.5200000000, query time of that 8.8792781130, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.6600000000, query time of that 0.1013970960, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Accept!
  -> Decision True in time 6.5700000000, query time of that 1.0278054210, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
293.237 < 299.194
  -> Decision False in time 17.5200000000, query time of that 2.7948521560, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Accept!
  -> Decision True in time 8.1600000000, query time of that 0.1206091990, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
353.259 < 355.027
  -> Decision False in time 4.6500000000, query time of that 0.0684793670, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
240.822 < 248.753
  -> Decision False in time 20.6500000000, query time of that 0.3084700940, 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 accuracy: 2.14011 cost: 0.00038 M: 10 delta: 1 time: 63.635 one-recall: 0 one-ratio: 3.73875
iteration: 2 recall: 0.0032 accuracy: 1.2257 cost: 0.000637428 M: 10 delta: 0.856032 time: 107.629 one-recall: 0 one-ratio: 2.95285
iteration: 3 recall: 0.028 accuracy: 0.725569 cost: 0.00109521 M: 11.5287 delta: 0.835107 time: 160.92 one-recall: 0.03 one-ratio: 2.37186
iteration: 4 recall: 0.1876 accuracy: 0.387478 cost: 0.00163043 M: 11.8362 delta: 0.783464 time: 213.977 one-recall: 0.17 one-ratio: 1.89599
iteration: 5 recall: 0.5024 accuracy: 0.148124 cost: 0.00223606 M: 12.6036 delta: 0.664603 time: 269.17 one-recall: 0.58 one-ratio: 1.3865
iteration: 6 recall: 0.7688 accuracy: 0.036321 cost: 0.00298005 M: 15.1152 delta: 0.43235 time: 331.467 one-recall: 0.85 one-ratio: 1.0919
iteration: 7 recall: 0.9088 accuracy: 0.0072617 cost: 0.0039553 M: 21.139 delta: 0.196447 time: 403.664 one-recall: 0.99 one-ratio: 1.0007
iteration: 8 recall: 0.9512 accuracy: 0.00294352 cost: 0.0049797 M: 27.303 delta: 0.0884156 time: 473.247 one-recall: 1 one-ratio: 1
iteration: 9 recall: 0.9652 accuracy: 0.00197855 cost: 0.00577254 M: 31.2879 delta: 0.0513114 time: 527.94 one-recall: 1 one-ratio: 1
iteration: 10 recall: 0.9768 accuracy: 0.00103263 cost: 0.00625676 M: 33.3886 delta: 0.0371747 time: 565.848 one-recall: 1 one-ratio: 1
iteration: 11 recall: 0.9812 accuracy: 0.000739525 cost: 0.00651286 M: 34.4146 delta: 0.0312843 time: 591.179 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.9836 accuracy: 0.00064223 cost: 0.00664025 M: 34.9061 delta: 0.0287184 time: 608.53 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.9844 accuracy: 0.000628953 cost: 0.00670252 M: 35.142 delta: 0.0275626 time: 621.2 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.9848 accuracy: 0.000602461 cost: 0.00673281 M: 35.256 delta: 0.0270127 time: 631.268 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.9852 accuracy: 0.000600702 cost: 0.00674782 M: 35.3118 delta: 0.0267557 time: 639.974 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.9852 accuracy: 0.000600702 cost: 0.00675572 M: 35.3411 delta: 0.0266309 time: 648.005 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.9852 accuracy: 0.000600702 cost: 0.00675974 M: 35.356 delta: 0.0265605 time: 655.651 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.9852 accuracy: 0.000600702 cost: 0.00676177 M: 35.3638 delta: 0.0265293 time: 663.092 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.9852 accuracy: 0.000600702 cost: 0.00676293 M: 35.3683 delta: 0.0265115 time: 670.439 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.9852 accuracy: 0.000600702 cost: 0.00676358 M: 35.3708 delta: 0.0265016 time: 677.718 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.9852 accuracy: 0.000600702 cost: 0.00676396 M: 35.3723 delta: 0.0264962 time: 684.959 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.9852 accuracy: 0.000600702 cost: 0.00676415 M: 35.373 delta: 0.0264932 time: 692.178 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.9852 accuracy: 0.000600702 cost: 0.00676426 M: 35.3734 delta: 0.0264915 time: 699.379 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.9852 accuracy: 0.000600702 cost: 0.00676432 M: 35.3737 delta: 0.0264906 time: 706.572 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.9852 accuracy: 0.000600702 cost: 0.00676438 M: 35.3739 delta: 0.0264897 time: 713.762 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.9852 accuracy: 0.000600702 cost: 0.0067644 M: 35.374 delta: 0.0264893 time: 720.943 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.9852 accuracy: 0.000600702 cost: 0.00676441 M: 35.374 delta: 0.0264893 time: 728.122 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.9852 accuracy: 0.000600702 cost: 0.00676442 M: 35.374 delta: 0.0264891 time: 735.3 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.9852 accuracy: 0.000600702 cost: 0.00676442 M: 35.374 delta: 0.0264891 time: 742.487 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.9852 accuracy: 0.000600702 cost: 0.00676442 M: 35.374 delta: 0.0264891 time: 749.661 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 768.4599999999991
Index size:  262952.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0192889000
  Testing...
|S| = 80
|T| = 1152
Reject!
382.95 < 432.868
  -> Decision False in time 0.0300000000, query time of that 0.0093156680, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Reject!
380.824 < 399.711
  -> Decision False in time 0.1600000000, query time of that 0.0515714070, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
264.63 < 420.1
  -> Decision False in time 0.0100000000, query time of that 0.0026605820, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Reject!
404.995 < 405.512
  -> Decision False in time 0.2700000000, query time of that 0.0129171650, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
323.478 < 365.49
  -> Decision False in time 0.7300000000, query time of that 0.0330356350, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
395.362 < 444.999
  -> Decision False in time 1.1800000000, query time of that 0.0558855010, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Reject!
256.418 < 268.289
  -> Decision False in time 2.8900000000, query time of that 0.0123891120, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
413.31 < 427.226
  -> Decision False in time 0.0100000000, query time of that 0.0002976480, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
263.833 < 269.944
  -> Decision False in time 0.5000000000, query time of that 0.0023003670, 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: 0.0008 accuracy: 2.04542 cost: 0.00038 M: 10 delta: 1 time: 63.6439 one-recall: 0 one-ratio: 3.24623
iteration: 2 recall: 0.0048 accuracy: 1.11486 cost: 0.000637428 M: 10 delta: 0.856032 time: 107.634 one-recall: 0 one-ratio: 2.60658
iteration: 3 recall: 0.0284 accuracy: 0.643841 cost: 0.00109521 M: 11.5287 delta: 0.835108 time: 160.91 one-recall: 0.04 one-ratio: 2.11458
iteration: 4 recall: 0.19 accuracy: 0.30994 cost: 0.00163044 M: 11.8363 delta: 0.783451 time: 213.94 one-recall: 0.3 one-ratio: 1.61103
iteration: 5 recall: 0.5168 accuracy: 0.0959969 cost: 0.00223609 M: 12.6036 delta: 0.66459 time: 269.097 one-recall: 0.66 one-ratio: 1.22858
iteration: 6 recall: 0.7756 accuracy: 0.027401 cost: 0.00297995 M: 15.1137 delta: 0.43235 time: 331.33 one-recall: 0.84 one-ratio: 1.04854
iteration: 7 recall: 0.8952 accuracy: 0.00863105 cost: 0.00395518 M: 21.1402 delta: 0.196406 time: 403.486 one-recall: 0.96 one-ratio: 1.01931
iteration: 8 recall: 0.9404 accuracy: 0.00483902 cost: 0.00497978 M: 27.3049 delta: 0.0884776 time: 473.037 one-recall: 0.96 one-ratio: 1.01918
iteration: 9 recall: 0.9632 accuracy: 0.00287532 cost: 0.00577289 M: 31.2898 delta: 0.0513655 time: 527.727 one-recall: 0.97 one-ratio: 1.0146
iteration: 10 recall: 0.9708 accuracy: 0.0023917 cost: 0.00625838 M: 33.397 delta: 0.0372484 time: 565.709 one-recall: 0.97 one-ratio: 1.0146
iteration: 11 recall: 0.9744 accuracy: 0.00186641 cost: 0.00651621 M: 34.4286 delta: 0.0313401 time: 591.138 one-recall: 0.98 one-ratio: 1.00718
iteration: 12 recall: 0.9748 accuracy: 0.00184983 cost: 0.00664395 M: 34.9205 delta: 0.0287694 time: 608.511 one-recall: 0.98 one-ratio: 1.00718
iteration: 13 recall: 0.9748 accuracy: 0.00184983 cost: 0.00670633 M: 35.1565 delta: 0.0276161 time: 621.19 one-recall: 0.98 one-ratio: 1.00718
iteration: 14 recall: 0.9748 accuracy: 0.00184983 cost: 0.00673656 M: 35.2692 delta: 0.0270723 time: 631.254 one-recall: 0.98 one-ratio: 1.00718
iteration: 15 recall: 0.9752 accuracy: 0.00184258 cost: 0.0067515 M: 35.3252 delta: 0.0268091 time: 639.956 one-recall: 0.98 one-ratio: 1.00718
iteration: 16 recall: 0.9752 accuracy: 0.00184258 cost: 0.00675903 M: 35.3532 delta: 0.0266848 time: 647.955 one-recall: 0.98 one-ratio: 1.00718
iteration: 17 recall: 0.9752 accuracy: 0.00184258 cost: 0.00676296 M: 35.3679 delta: 0.0266195 time: 655.593 one-recall: 0.98 one-ratio: 1.00718
iteration: 18 recall: 0.9752 accuracy: 0.00184258 cost: 0.00676495 M: 35.3752 delta: 0.0265893 time: 663.025 one-recall: 0.98 one-ratio: 1.00718
iteration: 19 recall: 0.9752 accuracy: 0.00184258 cost: 0.00676599 M: 35.3791 delta: 0.0265722 time: 670.347 one-recall: 0.98 one-ratio: 1.00718
iteration: 20 recall: 0.9752 accuracy: 0.00184258 cost: 0.00676656 M: 35.3814 delta: 0.0265625 time: 677.615 one-recall: 0.98 one-ratio: 1.00718
iteration: 21 recall: 0.9752 accuracy: 0.00184258 cost: 0.00676684 M: 35.3825 delta: 0.0265575 time: 684.842 one-recall: 0.98 one-ratio: 1.00718
iteration: 22 recall: 0.9752 accuracy: 0.00184258 cost: 0.00676703 M: 35.3832 delta: 0.0265547 time: 692.055 one-recall: 0.98 one-ratio: 1.00718
iteration: 23 recall: 0.9752 accuracy: 0.00184258 cost: 0.00676711 M: 35.3836 delta: 0.0265531 time: 699.26 one-recall: 0.98 one-ratio: 1.00718
iteration: 24 recall: 0.9752 accuracy: 0.00184258 cost: 0.00676715 M: 35.3837 delta: 0.0265524 time: 706.45 one-recall: 0.98 one-ratio: 1.00718
iteration: 25 recall: 0.9752 accuracy: 0.00184258 cost: 0.00676716 M: 35.3838 delta: 0.0265523 time: 713.63 one-recall: 0.98 one-ratio: 1.00718
iteration: 26 recall: 0.9752 accuracy: 0.00184258 cost: 0.00676717 M: 35.3838 delta: 0.0265521 time: 720.809 one-recall: 0.98 one-ratio: 1.00718
iteration: 27 recall: 0.9752 accuracy: 0.00184258 cost: 0.00676718 M: 35.3838 delta: 0.0265521 time: 727.984 one-recall: 0.98 one-ratio: 1.00718
iteration: 28 recall: 0.9752 accuracy: 0.00184258 cost: 0.00676719 M: 35.3839 delta: 0.0265521 time: 735.162 one-recall: 0.98 one-ratio: 1.00718
iteration: 29 recall: 0.9752 accuracy: 0.00184258 cost: 0.00676719 M: 35.3839 delta: 0.0265521 time: 742.336 one-recall: 0.98 one-ratio: 1.00718
iteration: 30 recall: 0.9752 accuracy: 0.00184258 cost: 0.00676719 M: 35.3839 delta: 0.0265519 time: 749.515 one-recall: 0.98 one-ratio: 1.00718
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 768.2999999999993
Index size:  263224.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0024718000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.1400000000, query time of that 0.0927634820, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Accept!
  -> Decision True in time 1.4300000000, query time of that 0.9725533110, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Accept!
  -> Decision True in time 14.1700000000, query time of that 9.5286085410, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.6900000000, query time of that 0.1189299600, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Accept!
  -> Decision True in time 6.7800000000, query time of that 1.1340055640, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
308.713 < 310.419
  -> Decision False in time 54.8500000000, query time of that 9.2996464350, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Accept!
  -> Decision True in time 8.1700000000, query time of that 0.1309891430, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
253.419 < 258.764
  -> Decision False in time 7.9800000000, query time of that 0.1307862550, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
256.979 < 258.662
  -> Decision False in time 21.8300000000, query time of that 0.3489255170, with c1=5.0000000000, c2=0.1000000000
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: 1.94128 cost: 0.00038 M: 10 delta: 1 time: 63.583 one-recall: 0 one-ratio: 3.20005
iteration: 2 recall: 0.0056 accuracy: 1.05974 cost: 0.000637428 M: 10 delta: 0.856032 time: 107.572 one-recall: 0 one-ratio: 2.5115
iteration: 3 recall: 0.032 accuracy: 0.599663 cost: 0.00109521 M: 11.5287 delta: 0.835103 time: 160.844 one-recall: 0.03 one-ratio: 2.06782
iteration: 4 recall: 0.19 accuracy: 0.288962 cost: 0.00163043 M: 11.8362 delta: 0.783459 time: 213.878 one-recall: 0.26 one-ratio: 1.56616
iteration: 5 recall: 0.4912 accuracy: 0.103169 cost: 0.00223606 M: 12.6037 delta: 0.664595 time: 269.03 one-recall: 0.61 one-ratio: 1.22458
iteration: 6 recall: 0.7536 accuracy: 0.0320998 cost: 0.00297987 M: 15.114 delta: 0.432351 time: 331.24 one-recall: 0.83 one-ratio: 1.06274
iteration: 7 recall: 0.878 accuracy: 0.0126779 cost: 0.00395513 M: 21.1392 delta: 0.196415 time: 403.38 one-recall: 0.93 one-ratio: 1.02746
iteration: 8 recall: 0.9328 accuracy: 0.00526799 cost: 0.00497981 M: 27.3045 delta: 0.0884215 time: 472.889 one-recall: 0.98 one-ratio: 1.00188
iteration: 9 recall: 0.9592 accuracy: 0.00330086 cost: 0.00577335 M: 31.2915 delta: 0.0512943 time: 527.592 one-recall: 0.98 one-ratio: 1.00188
iteration: 10 recall: 0.9676 accuracy: 0.00257584 cost: 0.00625856 M: 33.3945 delta: 0.0371909 time: 565.567 one-recall: 0.98 one-ratio: 1.00051
iteration: 11 recall: 0.9724 accuracy: 0.00216306 cost: 0.00651578 M: 34.4245 delta: 0.0313074 time: 590.986 one-recall: 0.98 one-ratio: 1.00051
iteration: 12 recall: 0.9744 accuracy: 0.00196963 cost: 0.00664351 M: 34.9154 delta: 0.0287333 time: 608.377 one-recall: 0.98 one-ratio: 1.00051
iteration: 13 recall: 0.9764 accuracy: 0.0017539 cost: 0.00670635 M: 35.1533 delta: 0.0275757 time: 621.115 one-recall: 0.98 one-ratio: 1.00051
iteration: 14 recall: 0.9772 accuracy: 0.0016922 cost: 0.00673701 M: 35.2678 delta: 0.0270392 time: 631.234 one-recall: 0.98 one-ratio: 1.00051
iteration: 15 recall: 0.9772 accuracy: 0.0016922 cost: 0.00675231 M: 35.3249 delta: 0.0267664 time: 639.99 one-recall: 0.98 one-ratio: 1.00051
iteration: 16 recall: 0.9772 accuracy: 0.0016922 cost: 0.00676004 M: 35.3534 delta: 0.026641 time: 648.03 one-recall: 0.98 one-ratio: 1.00051
iteration: 17 recall: 0.9772 accuracy: 0.0016922 cost: 0.00676396 M: 35.3681 delta: 0.0265745 time: 655.698 one-recall: 0.98 one-ratio: 1.00051
iteration: 18 recall: 0.9772 accuracy: 0.0016922 cost: 0.006766 M: 35.3759 delta: 0.0265417 time: 663.157 one-recall: 0.98 one-ratio: 1.00051
iteration: 19 recall: 0.9772 accuracy: 0.0016922 cost: 0.00676707 M: 35.38 delta: 0.0265231 time: 670.51 one-recall: 0.98 one-ratio: 1.00051
iteration: 20 recall: 0.9772 accuracy: 0.0016922 cost: 0.00676769 M: 35.3824 delta: 0.0265138 time: 677.805 one-recall: 0.98 one-ratio: 1.00051
iteration: 21 recall: 0.9772 accuracy: 0.0016922 cost: 0.00676805 M: 35.3838 delta: 0.0265086 time: 685.066 one-recall: 0.98 one-ratio: 1.00051
iteration: 22 recall: 0.9772 accuracy: 0.0016922 cost: 0.00676825 M: 35.3846 delta: 0.0265052 time: 692.306 one-recall: 0.98 one-ratio: 1.00051
iteration: 23 recall: 0.9772 accuracy: 0.0016922 cost: 0.00676834 M: 35.3849 delta: 0.0265034 time: 699.528 one-recall: 0.98 one-ratio: 1.00051
iteration: 24 recall: 0.9772 accuracy: 0.0016922 cost: 0.00676839 M: 35.3851 delta: 0.0265029 time: 706.74 one-recall: 0.98 one-ratio: 1.00051
iteration: 25 recall: 0.9772 accuracy: 0.0016922 cost: 0.00676841 M: 35.3852 delta: 0.0265022 time: 713.944 one-recall: 0.98 one-ratio: 1.00051
iteration: 26 recall: 0.9772 accuracy: 0.0016922 cost: 0.00676842 M: 35.3852 delta: 0.0265021 time: 721.143 one-recall: 0.98 one-ratio: 1.00051
iteration: 27 recall: 0.9772 accuracy: 0.0016922 cost: 0.00676842 M: 35.3853 delta: 0.0265021 time: 728.339 one-recall: 0.98 one-ratio: 1.00051
iteration: 28 recall: 0.9772 accuracy: 0.0016922 cost: 0.00676842 M: 35.3853 delta: 0.0265021 time: 735.533 one-recall: 0.98 one-ratio: 1.00051
iteration: 29 recall: 0.9772 accuracy: 0.0016922 cost: 0.00676842 M: 35.3853 delta: 0.0265021 time: 742.731 one-recall: 0.98 one-ratio: 1.00051
iteration: 30 recall: 0.9772 accuracy: 0.0016922 cost: 0.00676843 M: 35.3853 delta: 0.0265022 time: 749.923 one-recall: 0.98 one-ratio: 1.00051
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 768.7299999999996
Index size:  256588.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0062420000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.1100000000, query time of that 0.0537618160, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Accept!
  -> Decision True in time 0.9600000000, query time of that 0.5163062060, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
326.009 < 388.626
  -> Decision False in time 4.0700000000, query time of that 2.1595280790, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.6100000000, query time of that 0.0616022530, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Accept!
  -> Decision True in time 5.9900000000, query time of that 0.6226721160, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
343.894 < 413.853
  -> Decision False in time 10.2400000000, query time of that 1.0639056540, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Reject!
260.808 < 266.282
  -> Decision False in time 4.0700000000, query time of that 0.0373530620, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
290.639 < 363.971
  -> Decision False in time 15.6200000000, query time of that 0.1467503810, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
325.401 < 327.26
  -> Decision False in time 17.2700000000, query time of that 0.1644763080, 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: 1.76901 cost: 0.00038 M: 10 delta: 1 time: 63.6055 one-recall: 0 one-ratio: 2.9461
iteration: 2 recall: 0.0032 accuracy: 0.985861 cost: 0.000637428 M: 10 delta: 0.856032 time: 107.597 one-recall: 0 one-ratio: 2.35996
iteration: 3 recall: 0.0336 accuracy: 0.559775 cost: 0.00109521 M: 11.5287 delta: 0.835109 time: 160.866 one-recall: 0.01 one-ratio: 1.94207
iteration: 4 recall: 0.1748 accuracy: 0.280333 cost: 0.00163043 M: 11.8362 delta: 0.783464 time: 213.872 one-recall: 0.16 one-ratio: 1.57548
iteration: 5 recall: 0.4772 accuracy: 0.102993 cost: 0.00223606 M: 12.6038 delta: 0.664585 time: 268.996 one-recall: 0.54 one-ratio: 1.24275
iteration: 6 recall: 0.74 accuracy: 0.0313889 cost: 0.00298002 M: 15.1152 delta: 0.432356 time: 331.211 one-recall: 0.78 one-ratio: 1.09211
iteration: 7 recall: 0.8696 accuracy: 0.0114839 cost: 0.00395545 M: 21.1401 delta: 0.196437 time: 403.329 one-recall: 0.93 one-ratio: 1.03086
iteration: 8 recall: 0.926 accuracy: 0.00501085 cost: 0.00498013 M: 27.3066 delta: 0.0884568 time: 472.808 one-recall: 0.95 one-ratio: 1.01265
iteration: 9 recall: 0.9456 accuracy: 0.00393183 cost: 0.00577355 M: 31.2951 delta: 0.0513113 time: 527.5 one-recall: 0.95 one-ratio: 1.01265
iteration: 10 recall: 0.9608 accuracy: 0.00285693 cost: 0.00625848 M: 33.3984 delta: 0.0371829 time: 565.466 one-recall: 0.96 one-ratio: 1.01229
iteration: 11 recall: 0.9696 accuracy: 0.00187911 cost: 0.00651672 M: 34.4326 delta: 0.0312774 time: 590.931 one-recall: 0.98 one-ratio: 1.00756
iteration: 12 recall: 0.974 accuracy: 0.00158618 cost: 0.00664486 M: 34.9266 delta: 0.0287108 time: 608.353 one-recall: 0.98 one-ratio: 1.00756
iteration: 13 recall: 0.9756 accuracy: 0.00125523 cost: 0.00670766 M: 35.1633 delta: 0.0275403 time: 621.086 one-recall: 0.99 one-ratio: 1.00061
iteration: 14 recall: 0.978 accuracy: 0.00103955 cost: 0.00673806 M: 35.277 delta: 0.0269948 time: 631.185 one-recall: 0.99 one-ratio: 1.00054
iteration: 15 recall: 0.9784 accuracy: 0.00103187 cost: 0.00675309 M: 35.3331 delta: 0.0267314 time: 639.924 one-recall: 0.99 one-ratio: 1.00054
iteration: 16 recall: 0.9784 accuracy: 0.00103187 cost: 0.00676086 M: 35.3619 delta: 0.0265976 time: 647.972 one-recall: 0.99 one-ratio: 1.00054
iteration: 17 recall: 0.9784 accuracy: 0.00103187 cost: 0.00676474 M: 35.3758 delta: 0.0265354 time: 655.631 one-recall: 0.99 one-ratio: 1.00054
iteration: 18 recall: 0.9784 accuracy: 0.00103187 cost: 0.00676667 M: 35.3829 delta: 0.0265043 time: 663.08 one-recall: 0.99 one-ratio: 1.00054
iteration: 19 recall: 0.9784 accuracy: 0.00103187 cost: 0.00676764 M: 35.3867 delta: 0.0264879 time: 670.422 one-recall: 0.99 one-ratio: 1.00054
iteration: 20 recall: 0.9784 accuracy: 0.00103187 cost: 0.00676817 M: 35.3887 delta: 0.0264796 time: 677.712 one-recall: 0.99 one-ratio: 1.00054
iteration: 21 recall: 0.9784 accuracy: 0.00103187 cost: 0.00676847 M: 35.3898 delta: 0.026475 time: 684.971 one-recall: 0.99 one-ratio: 1.00054
iteration: 22 recall: 0.9784 accuracy: 0.00103187 cost: 0.00676861 M: 35.3904 delta: 0.0264734 time: 692.209 one-recall: 0.99 one-ratio: 1.00054
iteration: 23 recall: 0.9784 accuracy: 0.00103187 cost: 0.00676869 M: 35.3908 delta: 0.0264714 time: 699.432 one-recall: 0.99 one-ratio: 1.00054
iteration: 24 recall: 0.9784 accuracy: 0.00103187 cost: 0.00676873 M: 35.391 delta: 0.0264707 time: 706.647 one-recall: 0.99 one-ratio: 1.00054
iteration: 25 recall: 0.9784 accuracy: 0.00103187 cost: 0.00676876 M: 35.391 delta: 0.0264704 time: 713.854 one-recall: 0.99 one-ratio: 1.00054
iteration: 26 recall: 0.9784 accuracy: 0.00103187 cost: 0.00676876 M: 35.3911 delta: 0.0264702 time: 721.057 one-recall: 0.99 one-ratio: 1.00054
iteration: 27 recall: 0.9784 accuracy: 0.00103187 cost: 0.00676877 M: 35.3911 delta: 0.0264702 time: 728.263 one-recall: 0.99 one-ratio: 1.00054
iteration: 28 recall: 0.9784 accuracy: 0.00103187 cost: 0.00676877 M: 35.3911 delta: 0.0264702 time: 735.467 one-recall: 0.99 one-ratio: 1.00054
iteration: 29 recall: 0.9784 accuracy: 0.00103187 cost: 0.00676877 M: 35.3911 delta: 0.0264703 time: 742.671 one-recall: 0.99 one-ratio: 1.00054
iteration: 30 recall: 0.9784 accuracy: 0.00103187 cost: 0.00676878 M: 35.3912 delta: 0.0264704 time: 749.875 one-recall: 0.99 one-ratio: 1.00054
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 768.7000000000007
Index size:  256292.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.1107721000
  Testing...
|S| = 80
|T| = 1152
Reject!
412.15 < 413.723
  -> Decision False in time 0.0100000000, query time of that 0.0025124220, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Reject!
444.563 < 495.558
  -> Decision False in time 0.0100000000, query time of that 0.0043581010, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
432.688 < 443.521
  -> Decision False in time 0.0000000000, query time of that 0.0008160930, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Reject!
396.265 < 461.338
  -> Decision False in time 0.0000000000, query time of that 0.0001587250, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
523.428 < 540.545
  -> Decision False in time 0.0700000000, query time of that 0.0024001870, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
440.304 < 480.794
  -> Decision False in time 0.0400000000, query time of that 0.0019954150, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Reject!
433.147 < 442.436
  -> Decision False in time 0.0000000000, query time of that 0.0002305680, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
273.733 < 275.069
  -> Decision False in time 2.7600000000, query time of that 0.0125227390, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
361.217 < 388.305
  -> Decision False in time 0.3100000000, query time of that 0.0018912880, 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: 2.17115 cost: 0.00038 M: 10 delta: 1 time: 63.6012 one-recall: 0 one-ratio: 3.39971
iteration: 2 recall: 0.004 accuracy: 1.14115 cost: 0.000637428 M: 10 delta: 0.856032 time: 107.582 one-recall: 0 one-ratio: 2.66323
iteration: 3 recall: 0.034 accuracy: 0.648915 cost: 0.00109521 M: 11.5287 delta: 0.835104 time: 160.837 one-recall: 0.02 one-ratio: 2.15543
iteration: 4 recall: 0.2 accuracy: 0.324713 cost: 0.00163043 M: 11.8362 delta: 0.783463 time: 213.835 one-recall: 0.21 one-ratio: 1.63036
iteration: 5 recall: 0.5276 accuracy: 0.0988228 cost: 0.00223605 M: 12.6038 delta: 0.664589 time: 268.959 one-recall: 0.61 one-ratio: 1.2455
iteration: 6 recall: 0.7896 accuracy: 0.0292407 cost: 0.00297988 M: 15.1145 delta: 0.432344 time: 331.152 one-recall: 0.86 one-ratio: 1.08923
iteration: 7 recall: 0.9088 accuracy: 0.0072859 cost: 0.00395523 M: 21.1404 delta: 0.196444 time: 403.244 one-recall: 0.94 one-ratio: 1.01446
iteration: 8 recall: 0.9504 accuracy: 0.00321865 cost: 0.00498011 M: 27.3051 delta: 0.0884924 time: 472.698 one-recall: 0.97 one-ratio: 1.00492
iteration: 9 recall: 0.968 accuracy: 0.00187234 cost: 0.0057732 M: 31.2901 delta: 0.0513213 time: 527.366 one-recall: 0.98 one-ratio: 1.00155
iteration: 10 recall: 0.974 accuracy: 0.00145791 cost: 0.00625804 M: 33.3954 delta: 0.0372083 time: 565.321 one-recall: 0.98 one-ratio: 1.00155
iteration: 11 recall: 0.9768 accuracy: 0.00128862 cost: 0.00651524 M: 34.4253 delta: 0.0313365 time: 590.733 one-recall: 0.98 one-ratio: 1.00155
iteration: 12 recall: 0.978 accuracy: 0.00124526 cost: 0.00664323 M: 34.9173 delta: 0.0287602 time: 608.137 one-recall: 0.98 one-ratio: 1.00155
iteration: 13 recall: 0.9788 accuracy: 0.00121791 cost: 0.00670549 M: 35.152 delta: 0.0276047 time: 620.83 one-recall: 0.98 one-ratio: 1.00155
iteration: 14 recall: 0.9792 accuracy: 0.00120398 cost: 0.00673603 M: 35.2671 delta: 0.0270468 time: 630.943 one-recall: 0.98 one-ratio: 1.00155
iteration: 15 recall: 0.9792 accuracy: 0.00120398 cost: 0.00675093 M: 35.3231 delta: 0.0267815 time: 639.674 one-recall: 0.98 one-ratio: 1.00155
iteration: 16 recall: 0.9792 accuracy: 0.00120398 cost: 0.00675846 M: 35.3512 delta: 0.0266583 time: 647.698 one-recall: 0.98 one-ratio: 1.00155
iteration: 17 recall: 0.9792 accuracy: 0.00120398 cost: 0.0067623 M: 35.3653 delta: 0.0265911 time: 655.35 one-recall: 0.98 one-ratio: 1.00155
iteration: 18 recall: 0.9792 accuracy: 0.00120398 cost: 0.00676418 M: 35.3725 delta: 0.0265613 time: 662.794 one-recall: 0.98 one-ratio: 1.00155
iteration: 19 recall: 0.9792 accuracy: 0.00120398 cost: 0.00676512 M: 35.3759 delta: 0.0265453 time: 670.135 one-recall: 0.98 one-ratio: 1.00155
iteration: 20 recall: 0.9792 accuracy: 0.00120398 cost: 0.0067656 M: 35.3778 delta: 0.0265361 time: 677.419 one-recall: 0.98 one-ratio: 1.00155
iteration: 21 recall: 0.9792 accuracy: 0.00120398 cost: 0.00676587 M: 35.3789 delta: 0.026533 time: 684.671 one-recall: 0.98 one-ratio: 1.00155
iteration: 22 recall: 0.9792 accuracy: 0.00120398 cost: 0.00676604 M: 35.3796 delta: 0.0265302 time: 691.908 one-recall: 0.98 one-ratio: 1.00155
iteration: 23 recall: 0.9792 accuracy: 0.00120398 cost: 0.00676613 M: 35.38 delta: 0.0265284 time: 699.133 one-recall: 0.98 one-ratio: 1.00155
iteration: 24 recall: 0.9792 accuracy: 0.00120398 cost: 0.00676618 M: 35.3803 delta: 0.0265274 time: 706.349 one-recall: 0.98 one-ratio: 1.00155
iteration: 25 recall: 0.9792 accuracy: 0.00120398 cost: 0.00676622 M: 35.3804 delta: 0.0265265 time: 713.558 one-recall: 0.98 one-ratio: 1.00155
iteration: 26 recall: 0.9792 accuracy: 0.00120398 cost: 0.00676624 M: 35.3805 delta: 0.0265263 time: 720.76 one-recall: 0.98 one-ratio: 1.00155
iteration: 27 recall: 0.9792 accuracy: 0.00120398 cost: 0.00676624 M: 35.3805 delta: 0.0265261 time: 727.961 one-recall: 0.98 one-ratio: 1.00155
iteration: 28 recall: 0.9792 accuracy: 0.00120398 cost: 0.00676625 M: 35.3806 delta: 0.026526 time: 735.162 one-recall: 0.98 one-ratio: 1.00155
iteration: 29 recall: 0.9792 accuracy: 0.00120398 cost: 0.00676625 M: 35.3806 delta: 0.0265259 time: 742.36 one-recall: 0.98 one-ratio: 1.00155
iteration: 30 recall: 0.9792 accuracy: 0.00120398 cost: 0.00676625 M: 35.3806 delta: 0.0265259 time: 749.56 one-recall: 0.98 one-ratio: 1.00155
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 768.3500000000004
Index size:  255940.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0049741000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.1100000000, query time of that 0.0641229660, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Accept!
  -> Decision True in time 1.0500000000, query time of that 0.5986178100, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
282.841 < 297.55
  -> Decision False in time 1.4200000000, query time of that 0.7968067940, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.6100000000, query time of that 0.0701570880, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Accept!
  -> Decision True in time 6.1500000000, query time of that 0.7020628600, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
278.971 < 291.179
  -> Decision False in time 3.8000000000, query time of that 0.4398954350, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Accept!
  -> Decision True in time 8.1400000000, query time of that 0.0823239770, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
378.554 < 427.439
  -> Decision False in time 3.1600000000, query time of that 0.0348711740, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
195.036 < 255.873
  -> Decision False in time 39.5600000000, query time of that 0.4228203990, 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 accuracy: 2.03473 cost: 0.00038 M: 10 delta: 1 time: 63.582 one-recall: 0 one-ratio: 3.55816
iteration: 2 recall: 0.0028 accuracy: 1.152 cost: 0.000637428 M: 10 delta: 0.856032 time: 107.552 one-recall: 0 one-ratio: 2.83424
iteration: 3 recall: 0.028 accuracy: 0.65698 cost: 0.00109521 M: 11.5287 delta: 0.835106 time: 160.801 one-recall: 0.02 one-ratio: 2.27978
iteration: 4 recall: 0.1692 accuracy: 0.316753 cost: 0.00163042 M: 11.8362 delta: 0.783461 time: 213.801 one-recall: 0.17 one-ratio: 1.75597
iteration: 5 recall: 0.5136 accuracy: 0.0978883 cost: 0.00223607 M: 12.6037 delta: 0.664593 time: 268.917 one-recall: 0.58 one-ratio: 1.29568
iteration: 6 recall: 0.7796 accuracy: 0.0272716 cost: 0.00297996 M: 15.1144 delta: 0.43235 time: 331.091 one-recall: 0.81 one-ratio: 1.11574
iteration: 7 recall: 0.9024 accuracy: 0.00833379 cost: 0.00395536 M: 21.1402 delta: 0.19644 time: 403.169 one-recall: 0.93 one-ratio: 1.0195
iteration: 8 recall: 0.9428 accuracy: 0.00411388 cost: 0.00498003 M: 27.3065 delta: 0.0884545 time: 472.619 one-recall: 0.97 one-ratio: 1.00963
iteration: 9 recall: 0.9628 accuracy: 0.00273854 cost: 0.00577328 M: 31.2922 delta: 0.0513136 time: 527.286 one-recall: 0.98 one-ratio: 1.00911
iteration: 10 recall: 0.972 accuracy: 0.00220732 cost: 0.00625801 M: 33.3952 delta: 0.0372174 time: 565.234 one-recall: 0.98 one-ratio: 1.00911
iteration: 11 recall: 0.9756 accuracy: 0.00195922 cost: 0.00651543 M: 34.4275 delta: 0.0313172 time: 590.653 one-recall: 0.98 one-ratio: 1.00911
iteration: 12 recall: 0.9788 accuracy: 0.0013493 cost: 0.0066428 M: 34.9173 delta: 0.0287503 time: 608.025 one-recall: 0.99 one-ratio: 1.00019
iteration: 13 recall: 0.9796 accuracy: 0.00127227 cost: 0.00670477 M: 35.1522 delta: 0.0275935 time: 620.701 one-recall: 0.99 one-ratio: 1.00019
iteration: 14 recall: 0.98 accuracy: 0.00124571 cost: 0.00673461 M: 35.2651 delta: 0.0270478 time: 630.755 one-recall: 0.99 one-ratio: 1.00019
iteration: 15 recall: 0.98 accuracy: 0.00124571 cost: 0.00674952 M: 35.3212 delta: 0.0267899 time: 639.472 one-recall: 0.99 one-ratio: 1.00019
iteration: 16 recall: 0.98 accuracy: 0.00124571 cost: 0.00675711 M: 35.349 delta: 0.0266678 time: 647.496 one-recall: 0.99 one-ratio: 1.00019
iteration: 17 recall: 0.98 accuracy: 0.00124571 cost: 0.00676105 M: 35.3636 delta: 0.0266052 time: 655.155 one-recall: 0.99 one-ratio: 1.00019
iteration: 18 recall: 0.98 accuracy: 0.00124571 cost: 0.00676317 M: 35.3716 delta: 0.0265728 time: 662.62 one-recall: 0.99 one-ratio: 1.00019
iteration: 19 recall: 0.98 accuracy: 0.00124571 cost: 0.00676425 M: 35.3757 delta: 0.0265558 time: 669.971 one-recall: 0.99 one-ratio: 1.00019
iteration: 20 recall: 0.98 accuracy: 0.00124571 cost: 0.00676488 M: 35.378 delta: 0.0265452 time: 677.265 one-recall: 0.99 one-ratio: 1.00019
iteration: 21 recall: 0.98 accuracy: 0.00124571 cost: 0.00676516 M: 35.3791 delta: 0.0265415 time: 684.515 one-recall: 0.99 one-ratio: 1.00019
iteration: 22 recall: 0.98 accuracy: 0.00124571 cost: 0.00676536 M: 35.3798 delta: 0.0265388 time: 691.752 one-recall: 0.99 one-ratio: 1.00019
iteration: 23 recall: 0.98 accuracy: 0.00124571 cost: 0.00676546 M: 35.3802 delta: 0.0265377 time: 698.969 one-recall: 0.99 one-ratio: 1.00019
iteration: 24 recall: 0.98 accuracy: 0.00124571 cost: 0.00676552 M: 35.3804 delta: 0.0265361 time: 706.178 one-recall: 0.99 one-ratio: 1.00019
iteration: 25 recall: 0.98 accuracy: 0.00124571 cost: 0.00676556 M: 35.3805 delta: 0.0265357 time: 713.388 one-recall: 0.99 one-ratio: 1.00019
iteration: 26 recall: 0.98 accuracy: 0.00124571 cost: 0.00676558 M: 35.3806 delta: 0.0265358 time: 720.585 one-recall: 0.99 one-ratio: 1.00019
iteration: 27 recall: 0.98 accuracy: 0.00124571 cost: 0.00676559 M: 35.3806 delta: 0.0265356 time: 727.782 one-recall: 0.99 one-ratio: 1.00019
iteration: 28 recall: 0.98 accuracy: 0.00124571 cost: 0.00676559 M: 35.3806 delta: 0.0265354 time: 734.981 one-recall: 0.99 one-ratio: 1.00019
iteration: 29 recall: 0.98 accuracy: 0.00124571 cost: 0.00676559 M: 35.3806 delta: 0.0265354 time: 742.182 one-recall: 0.99 one-ratio: 1.00019
iteration: 30 recall: 0.98 accuracy: 0.00124571 cost: 0.00676559 M: 35.3806 delta: 0.0265353 time: 749.376 one-recall: 0.99 one-ratio: 1.00019
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 768.1599999999999
Index size:  256360.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0041183000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.0900000000, query time of that 0.0489239790, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Accept!
  -> Decision True in time 0.9300000000, query time of that 0.4784396130, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
388.779 < 409.298
  -> Decision False in time 0.1800000000, query time of that 0.0969410820, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Reject!
248.642 < 254.973
  -> Decision False in time 0.4700000000, query time of that 0.0483189600, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
285.368 < 288.396
  -> Decision False in time 3.5700000000, query time of that 0.3597437850, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
252.784 < 254.163
  -> Decision False in time 33.5200000000, query time of that 3.4061290330, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Accept!
  -> Decision True in time 6.7100000000, query time of that 0.0680050760, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
243.731 < 246.258
  -> Decision False in time 4.8700000000, query time of that 0.0504574400, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
266.039 < 272.119
  -> Decision False in time 16.3200000000, query time of that 0.1649037170, 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: 2.60388 cost: 0.00038 M: 10 delta: 1 time: 54.0043 one-recall: 0 one-ratio: 3.59551
iteration: 2 recall: 0.0044 accuracy: 1.24597 cost: 0.000637428 M: 10 delta: 0.856032 time: 92.0956 one-recall: 0 one-ratio: 2.77158
iteration: 3 recall: 0.0328 accuracy: 0.671585 cost: 0.00109521 M: 11.5287 delta: 0.835106 time: 138.24 one-recall: 0.03 one-ratio: 2.20148
iteration: 4 recall: 0.1808 accuracy: 0.31383 cost: 0.00163045 M: 11.8363 delta: 0.783478 time: 183.973 one-recall: 0.27 one-ratio: 1.66302
iteration: 5 recall: 0.4876 accuracy: 0.113895 cost: 0.00223608 M: 12.6033 delta: 0.664613 time: 231.433 one-recall: 0.57 one-ratio: 1.34115
iteration: 6 recall: 0.764 accuracy: 0.028598 cost: 0.00298 M: 15.1146 delta: 0.4323 time: 285.009 one-recall: 0.85 one-ratio: 1.06875
iteration: 7 recall: 0.888 accuracy: 0.00937119 cost: 0.00395528 M: 21.1402 delta: 0.196434 time: 346.028 one-recall: 0.94 one-ratio: 1.02547
iteration: 8 recall: 0.9396 accuracy: 0.0047714 cost: 0.00497949 M: 27.3023 delta: 0.088449 time: 402.999 one-recall: 0.95 one-ratio: 1.02006
iteration: 9 recall: 0.9612 accuracy: 0.00219641 cost: 0.00577181 M: 31.2861 delta: 0.0513848 time: 446.331 one-recall: 0.98 one-ratio: 1.00231
iteration: 10 recall: 0.9692 accuracy: 0.00175038 cost: 0.00625635 M: 33.3905 delta: 0.0372212 time: 475.5 one-recall: 0.98 one-ratio: 1.00231
iteration: 11 recall: 0.974 accuracy: 0.00127348 cost: 0.00651397 M: 34.4207 delta: 0.0313312 time: 494.504 one-recall: 0.99 one-ratio: 1.00013
iteration: 12 recall: 0.976 accuracy: 0.00117531 cost: 0.00664123 M: 34.9098 delta: 0.028782 time: 507.218 one-recall: 0.99 one-ratio: 1.00013
iteration: 13 recall: 0.9772 accuracy: 0.0011415 cost: 0.00670326 M: 35.145 delta: 0.0276158 time: 516.397 one-recall: 0.99 one-ratio: 1.00013
iteration: 14 recall: 0.9772 accuracy: 0.0011415 cost: 0.00673314 M: 35.2567 delta: 0.0270766 time: 523.652 one-recall: 0.99 one-ratio: 1.00013
iteration: 15 recall: 0.9772 accuracy: 0.0011415 cost: 0.00674808 M: 35.3126 delta: 0.0268177 time: 529.944 one-recall: 0.99 one-ratio: 1.00013
iteration: 16 recall: 0.9776 accuracy: 0.00112371 cost: 0.00675571 M: 35.341 delta: 0.0266886 time: 535.739 one-recall: 0.99 one-ratio: 1.00013
iteration: 17 recall: 0.9776 accuracy: 0.00112371 cost: 0.0067596 M: 35.3554 delta: 0.0266267 time: 541.267 one-recall: 0.99 one-ratio: 1.00013
iteration: 18 recall: 0.9776 accuracy: 0.00112371 cost: 0.00676158 M: 35.3628 delta: 0.0265945 time: 546.647 one-recall: 0.99 one-ratio: 1.00013
iteration: 19 recall: 0.9776 accuracy: 0.00112371 cost: 0.0067626 M: 35.3667 delta: 0.0265765 time: 551.951 one-recall: 0.99 one-ratio: 1.00013
iteration: 20 recall: 0.9776 accuracy: 0.00112371 cost: 0.00676316 M: 35.3688 delta: 0.026568 time: 557.212 one-recall: 0.99 one-ratio: 1.00013
iteration: 21 recall: 0.9776 accuracy: 0.00112371 cost: 0.00676346 M: 35.37 delta: 0.0265625 time: 562.448 one-recall: 0.99 one-ratio: 1.00013
iteration: 22 recall: 0.9776 accuracy: 0.00112371 cost: 0.00676361 M: 35.3705 delta: 0.0265596 time: 567.667 one-recall: 0.99 one-ratio: 1.00013
iteration: 23 recall: 0.9776 accuracy: 0.00112371 cost: 0.00676368 M: 35.3708 delta: 0.0265591 time: 572.875 one-recall: 0.99 one-ratio: 1.00013
iteration: 24 recall: 0.9776 accuracy: 0.00112371 cost: 0.00676372 M: 35.371 delta: 0.0265579 time: 578.082 one-recall: 0.99 one-ratio: 1.00013
iteration: 25 recall: 0.9776 accuracy: 0.00112371 cost: 0.00676374 M: 35.3711 delta: 0.0265574 time: 583.285 one-recall: 0.99 one-ratio: 1.00013
iteration: 26 recall: 0.9776 accuracy: 0.00112371 cost: 0.00676375 M: 35.3711 delta: 0.0265573 time: 588.486 one-recall: 0.99 one-ratio: 1.00013
iteration: 27 recall: 0.9776 accuracy: 0.00112371 cost: 0.00676376 M: 35.3711 delta: 0.0265572 time: 593.685 one-recall: 0.99 one-ratio: 1.00013
iteration: 28 recall: 0.9776 accuracy: 0.00112371 cost: 0.00676376 M: 35.3711 delta: 0.0265571 time: 598.884 one-recall: 0.99 one-ratio: 1.00013
iteration: 29 recall: 0.9776 accuracy: 0.00112371 cost: 0.00676376 M: 35.3712 delta: 0.026557 time: 604.083 one-recall: 0.99 one-ratio: 1.00013
iteration: 30 recall: 0.9776 accuracy: 0.00112371 cost: 0.00676376 M: 35.3712 delta: 0.026557 time: 609.281 one-recall: 0.99 one-ratio: 1.00013
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 623.7099999999991
Index size:  256208.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0113284000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.0600000000, query time of that 0.0180575940, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Reject!
340.845 < 354.82
  -> Decision False in time 0.4800000000, query time of that 0.1255877400, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
349.289 < 402.837
  -> Decision False in time 0.9200000000, query time of that 0.2364275530, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.5100000000, query time of that 0.0189739910, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
248.481 < 259.424
  -> Decision False in time 2.0200000000, query time of that 0.0767077370, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
367.493 < 368.01
  -> Decision False in time 1.3300000000, query time of that 0.0496003870, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Accept!
  -> Decision True in time 6.5900000000, query time of that 0.0248659010, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
402.877 < 414.681
  -> Decision False in time 1.0000000000, query time of that 0.0040519650, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
260.356 < 261.545
  -> Decision False in time 6.1100000000, query time of that 0.0229918340, 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: 0.0008 accuracy: 2.01866 cost: 0.00038 M: 10 delta: 1 time: 53.9757 one-recall: 0 one-ratio: 3.21154
iteration: 2 recall: 0.004 accuracy: 1.07531 cost: 0.000637428 M: 10 delta: 0.856032 time: 92.0398 one-recall: 0 one-ratio: 2.50797
iteration: 3 recall: 0.0344 accuracy: 0.612596 cost: 0.00109521 M: 11.5287 delta: 0.835101 time: 138.152 one-recall: 0.05 one-ratio: 2.04448
iteration: 4 recall: 0.1712 accuracy: 0.31562 cost: 0.00163043 M: 11.8362 delta: 0.783462 time: 183.845 one-recall: 0.17 one-ratio: 1.71457
iteration: 5 recall: 0.4684 accuracy: 0.110857 cost: 0.00223603 M: 12.6038 delta: 0.664612 time: 231.285 one-recall: 0.55 one-ratio: 1.29596
iteration: 6 recall: 0.7424 accuracy: 0.0373445 cost: 0.00297992 M: 15.1143 delta: 0.432317 time: 284.85 one-recall: 0.84 one-ratio: 1.09789
iteration: 7 recall: 0.8756 accuracy: 0.0129879 cost: 0.00395525 M: 21.1402 delta: 0.196365 time: 345.862 one-recall: 0.93 one-ratio: 1.03981
iteration: 8 recall: 0.9344 accuracy: 0.00488211 cost: 0.0049795 M: 27.3041 delta: 0.0884699 time: 402.818 one-recall: 0.96 one-ratio: 1.02388
iteration: 9 recall: 0.9588 accuracy: 0.00201347 cost: 0.00577223 M: 31.2879 delta: 0.051333 time: 446.151 one-recall: 0.99 one-ratio: 1.00339
iteration: 10 recall: 0.9688 accuracy: 0.00142783 cost: 0.00625815 M: 33.3974 delta: 0.0372111 time: 475.372 one-recall: 0.99 one-ratio: 1.00339
iteration: 11 recall: 0.9748 accuracy: 0.0010933 cost: 0.00651586 M: 34.4289 delta: 0.0313205 time: 494.383 one-recall: 0.99 one-ratio: 1.00339
iteration: 12 recall: 0.9764 accuracy: 0.00102865 cost: 0.00664384 M: 34.9227 delta: 0.0287555 time: 507.137 one-recall: 0.99 one-ratio: 1.00339
iteration: 13 recall: 0.9768 accuracy: 0.00101877 cost: 0.00670612 M: 35.1577 delta: 0.0275661 time: 516.335 one-recall: 0.99 one-ratio: 1.00339
iteration: 14 recall: 0.9772 accuracy: 0.00101373 cost: 0.00673622 M: 35.2703 delta: 0.027027 time: 523.609 one-recall: 0.99 one-ratio: 1.00339
iteration: 15 recall: 0.9772 accuracy: 0.00101373 cost: 0.00675104 M: 35.3259 delta: 0.0267682 time: 529.9 one-recall: 0.99 one-ratio: 1.00339
iteration: 16 recall: 0.9776 accuracy: 0.000983017 cost: 0.00675858 M: 35.354 delta: 0.0266428 time: 535.692 one-recall: 0.99 one-ratio: 1.00339
iteration: 17 recall: 0.9776 accuracy: 0.000983017 cost: 0.00676245 M: 35.3684 delta: 0.026577 time: 541.219 one-recall: 0.99 one-ratio: 1.00339
iteration: 18 recall: 0.9776 accuracy: 0.000983017 cost: 0.00676453 M: 35.3762 delta: 0.0265431 time: 546.613 one-recall: 0.99 one-ratio: 1.00339
iteration: 19 recall: 0.9776 accuracy: 0.000983017 cost: 0.00676559 M: 35.3801 delta: 0.0265274 time: 551.92 one-recall: 0.99 one-ratio: 1.00339
iteration: 20 recall: 0.9776 accuracy: 0.000983017 cost: 0.00676613 M: 35.3823 delta: 0.0265194 time: 557.186 one-recall: 0.99 one-ratio: 1.00339
iteration: 21 recall: 0.9776 accuracy: 0.000983017 cost: 0.00676649 M: 35.3837 delta: 0.0265139 time: 562.431 one-recall: 0.99 one-ratio: 1.00339
iteration: 22 recall: 0.9776 accuracy: 0.000983017 cost: 0.0067667 M: 35.3843 delta: 0.0265111 time: 567.657 one-recall: 0.99 one-ratio: 1.00339
iteration: 23 recall: 0.9776 accuracy: 0.000983017 cost: 0.0067668 M: 35.3847 delta: 0.0265091 time: 572.873 one-recall: 0.99 one-ratio: 1.00339
iteration: 24 recall: 0.9776 accuracy: 0.000983017 cost: 0.00676686 M: 35.385 delta: 0.0265081 time: 578.081 one-recall: 0.99 one-ratio: 1.00339
iteration: 25 recall: 0.9776 accuracy: 0.000983017 cost: 0.00676688 M: 35.385 delta: 0.0265078 time: 583.283 one-recall: 0.99 one-ratio: 1.00339
iteration: 26 recall: 0.9776 accuracy: 0.000983017 cost: 0.0067669 M: 35.3851 delta: 0.0265076 time: 588.489 one-recall: 0.99 one-ratio: 1.00339
iteration: 27 recall: 0.9776 accuracy: 0.000983017 cost: 0.00676691 M: 35.3852 delta: 0.0265075 time: 593.692 one-recall: 0.99 one-ratio: 1.00339
iteration: 28 recall: 0.9776 accuracy: 0.000983017 cost: 0.00676692 M: 35.3852 delta: 0.0265076 time: 598.89 one-recall: 0.99 one-ratio: 1.00339
iteration: 29 recall: 0.9776 accuracy: 0.000983017 cost: 0.00676692 M: 35.3852 delta: 0.0265075 time: 604.106 one-recall: 0.99 one-ratio: 1.00339
iteration: 30 recall: 0.9776 accuracy: 0.000983017 cost: 0.00676693 M: 35.3852 delta: 0.0265074 time: 609.306 one-recall: 0.99 one-ratio: 1.00339
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 623.75
Index size:  256140.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.1035212000
  Testing...
|S| = 80
|T| = 1152
Reject!
433.771 < 439.179
  -> Decision False in time 0.0000000000, query time of that 0.0003770130, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Reject!
368.778 < 418.381
  -> Decision False in time 0.0000000000, query time of that 0.0003956030, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
322.394 < 433.569
  -> Decision False in time 0.0100000000, query time of that 0.0018116830, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Reject!
471.776 < 496.161
  -> Decision False in time 0.0200000000, query time of that 0.0006493300, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
453.464 < 514.69
  -> Decision False in time 0.2400000000, query time of that 0.0087782910, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
329.811 < 454.725
  -> Decision False in time 0.0800000000, query time of that 0.0030490060, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Reject!
394.195 < 449.771
  -> Decision False in time 0.0000000000, query time of that 0.0002323210, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
423.506 < 439.124
  -> Decision False in time 0.0800000000, query time of that 0.0004347480, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
422.605 < 461.518
  -> Decision False in time 1.4100000000, query time of that 0.0056339420, 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: 1.97678 cost: 0.00038 M: 10 delta: 1 time: 53.9622 one-recall: 0 one-ratio: 3.50178
iteration: 2 recall: 0.006 accuracy: 1.12512 cost: 0.000637428 M: 10 delta: 0.856032 time: 92.0272 one-recall: 0 one-ratio: 2.65627
iteration: 3 recall: 0.0332 accuracy: 0.663805 cost: 0.00109521 M: 11.5287 delta: 0.835106 time: 138.148 one-recall: 0.04 one-ratio: 2.19697
iteration: 4 recall: 0.1452 accuracy: 0.363498 cost: 0.00163045 M: 11.8363 delta: 0.783458 time: 183.858 one-recall: 0.18 one-ratio: 1.77786
iteration: 5 recall: 0.4584 accuracy: 0.118641 cost: 0.00223609 M: 12.6035 delta: 0.66459 time: 231.318 one-recall: 0.62 one-ratio: 1.2824
iteration: 6 recall: 0.7532 accuracy: 0.0270109 cost: 0.00297995 M: 15.1139 delta: 0.432336 time: 284.895 one-recall: 0.88 one-ratio: 1.04349
iteration: 7 recall: 0.8828 accuracy: 0.00927565 cost: 0.00395515 M: 21.1389 delta: 0.196387 time: 345.902 one-recall: 0.95 one-ratio: 1.01226
iteration: 8 recall: 0.941999 accuracy: 0.00430492 cost: 0.00497952 M: 27.3039 delta: 0.0884974 time: 402.867 one-recall: 0.97 one-ratio: 1.00933
iteration: 9 recall: 0.9604 accuracy: 0.002893 cost: 0.00577178 M: 31.2859 delta: 0.0513696 time: 446.188 one-recall: 0.99 one-ratio: 1.00502
iteration: 10 recall: 0.9704 accuracy: 0.00221401 cost: 0.00625759 M: 33.3931 delta: 0.0371937 time: 475.392 one-recall: 0.99 one-ratio: 1.00502
iteration: 11 recall: 0.9756 accuracy: 0.00176745 cost: 0.00651476 M: 34.4232 delta: 0.0313197 time: 494.377 one-recall: 0.99 one-ratio: 1.00502
iteration: 12 recall: 0.9768 accuracy: 0.00154626 cost: 0.00664234 M: 34.9147 delta: 0.0287606 time: 507.111 one-recall: 0.99 one-ratio: 1.00502
iteration: 13 recall: 0.9784 accuracy: 0.00135324 cost: 0.00670465 M: 35.1503 delta: 0.0275909 time: 516.318 one-recall: 0.99 one-ratio: 1.00502
iteration: 14 recall: 0.9784 accuracy: 0.00135324 cost: 0.00673492 M: 35.2631 delta: 0.0270515 time: 523.598 one-recall: 0.99 one-ratio: 1.00502
iteration: 15 recall: 0.9788 accuracy: 0.00130013 cost: 0.00674989 M: 35.3187 delta: 0.0267932 time: 529.891 one-recall: 0.99 one-ratio: 1.00502
iteration: 16 recall: 0.9788 accuracy: 0.00130013 cost: 0.00675729 M: 35.3465 delta: 0.0266674 time: 535.673 one-recall: 0.99 one-ratio: 1.00502
iteration: 17 recall: 0.9788 accuracy: 0.00130013 cost: 0.00676114 M: 35.3609 delta: 0.0266009 time: 541.197 one-recall: 0.99 one-ratio: 1.00502
iteration: 18 recall: 0.9788 accuracy: 0.00130013 cost: 0.0067631 M: 35.3683 delta: 0.026569 time: 546.579 one-recall: 0.99 one-ratio: 1.00502
iteration: 19 recall: 0.9788 accuracy: 0.00130013 cost: 0.00676421 M: 35.3726 delta: 0.0265503 time: 551.892 one-recall: 0.99 one-ratio: 1.00502
iteration: 20 recall: 0.9788 accuracy: 0.00130013 cost: 0.00676477 M: 35.3747 delta: 0.0265415 time: 557.16 one-recall: 0.99 one-ratio: 1.00502
iteration: 21 recall: 0.9788 accuracy: 0.00130013 cost: 0.00676507 M: 35.3759 delta: 0.026537 time: 562.402 one-recall: 0.99 one-ratio: 1.00502
iteration: 22 recall: 0.9788 accuracy: 0.00130013 cost: 0.00676524 M: 35.3766 delta: 0.0265341 time: 567.624 one-recall: 0.99 one-ratio: 1.00502
iteration: 23 recall: 0.9788 accuracy: 0.00130013 cost: 0.00676532 M: 35.3769 delta: 0.0265331 time: 572.835 one-recall: 0.99 one-ratio: 1.00502
iteration: 24 recall: 0.9788 accuracy: 0.00130013 cost: 0.00676539 M: 35.3772 delta: 0.0265323 time: 578.045 one-recall: 0.99 one-ratio: 1.00502
iteration: 25 recall: 0.9788 accuracy: 0.00130013 cost: 0.00676542 M: 35.3773 delta: 0.0265315 time: 583.251 one-recall: 0.99 one-ratio: 1.00502
iteration: 26 recall: 0.9788 accuracy: 0.00130013 cost: 0.00676544 M: 35.3774 delta: 0.0265316 time: 588.453 one-recall: 0.99 one-ratio: 1.00502
iteration: 27 recall: 0.9788 accuracy: 0.00130013 cost: 0.00676546 M: 35.3775 delta: 0.0265311 time: 593.659 one-recall: 0.99 one-ratio: 1.00502
iteration: 28 recall: 0.9788 accuracy: 0.00130013 cost: 0.00676548 M: 35.3776 delta: 0.0265309 time: 598.861 one-recall: 0.99 one-ratio: 1.00502
iteration: 29 recall: 0.9788 accuracy: 0.00130013 cost: 0.00676548 M: 35.3777 delta: 0.0265306 time: 604.063 one-recall: 0.99 one-ratio: 1.00502
iteration: 30 recall: 0.9788 accuracy: 0.00130013 cost: 0.00676549 M: 35.3777 delta: 0.0265304 time: 609.265 one-recall: 0.99 one-ratio: 1.00502
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 623.7100000000028
Index size:  256092.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0035927000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.1200000000, query time of that 0.0725935570, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Accept!
  -> Decision True in time 1.1900000000, query time of that 0.7403207970, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Accept!
  -> Decision True in time 12.1200000000, query time of that 7.4946629760, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.6600000000, query time of that 0.0895024200, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
251.119 < 257.701
  -> Decision False in time 4.8200000000, query time of that 0.6560351160, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
221.75 < 240.069
  -> Decision False in time 4.7000000000, query time of that 0.6599471300, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Accept!
  -> Decision True in time 8.1800000000, query time of that 0.1029863120, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Accept!
  -> Decision True in time 81.8600000000, query time of that 1.0373683350, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
238.914 < 240.535
  -> Decision False in time 55.2600000000, query time of that 0.7050818000, with c1=5.0000000000, c2=0.1000000000
