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', 3, {'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', 80, {'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', 90, {'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', 2, {'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', 70, {'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', 20, {'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', 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', 4, {'reverse': -1}, False])]
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: 2.29356 cost: 0.00038 M: 10 delta: 1 time: 54.3379 one-recall: 0 one-ratio: 3.52528
iteration: 2 recall: 0.004 accuracy: 1.20332 cost: 0.000637428 M: 10 delta: 0.856032 time: 92.4433 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: 138.612 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: 184.343 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: 231.778 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: 285.292 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: 346.198 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: 403.058 one-recall: 1 one-ratio: 1
iteration: 9 recall: 0.9644 accuracy: 0.00221739 cost: 0.00577293 M: 31.2904 delta: 0.0514019 time: 446.368 one-recall: 1 one-ratio: 1
iteration: 10 recall: 0.9764 accuracy: 0.0012952 cost: 0.00625843 M: 33.3974 delta: 0.0372226 time: 475.531 one-recall: 1 one-ratio: 1
iteration: 11 recall: 0.984 accuracy: 0.000915513 cost: 0.00651572 M: 34.4268 delta: 0.0313604 time: 494.502 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.9872 accuracy: 0.000651871 cost: 0.00664321 M: 34.9174 delta: 0.028787 time: 507.231 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.9876 accuracy: 0.000628894 cost: 0.00670536 M: 35.1523 delta: 0.027629 time: 516.439 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.9876 accuracy: 0.000628894 cost: 0.00673551 M: 35.2647 delta: 0.0270893 time: 523.735 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.9876 accuracy: 0.000628894 cost: 0.00675029 M: 35.3194 delta: 0.0268326 time: 530.037 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.9876 accuracy: 0.000628894 cost: 0.00675777 M: 35.3471 delta: 0.0267076 time: 535.845 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.9876 accuracy: 0.000628894 cost: 0.00676154 M: 35.3608 delta: 0.0266443 time: 541.394 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.9876 accuracy: 0.000628894 cost: 0.00676338 M: 35.3677 delta: 0.0266122 time: 546.801 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.9876 accuracy: 0.000628894 cost: 0.00676433 M: 35.3712 delta: 0.0265977 time: 552.127 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.9876 accuracy: 0.000628894 cost: 0.00676485 M: 35.373 delta: 0.02659 time: 557.424 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.988 accuracy: 0.0006087 cost: 0.00676515 M: 35.3742 delta: 0.0265857 time: 562.695 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.988 accuracy: 0.0006087 cost: 0.00676532 M: 35.3749 delta: 0.0265828 time: 567.969 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.988 accuracy: 0.0006087 cost: 0.00676541 M: 35.3753 delta: 0.0265811 time: 573.219 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.988 accuracy: 0.0006087 cost: 0.00676547 M: 35.3755 delta: 0.0265798 time: 578.462 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.988 accuracy: 0.0006087 cost: 0.00676549 M: 35.3756 delta: 0.0265796 time: 583.701 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.988 accuracy: 0.0006087 cost: 0.00676551 M: 35.3757 delta: 0.0265794 time: 588.943 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.988 accuracy: 0.0006087 cost: 0.00676552 M: 35.3757 delta: 0.0265791 time: 594.179 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.988 accuracy: 0.0006087 cost: 0.00676553 M: 35.3757 delta: 0.0265791 time: 599.41 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.988 accuracy: 0.0006087 cost: 0.00676553 M: 35.3757 delta: 0.026579 time: 604.642 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.988 accuracy: 0.0006087 cost: 0.00676553 M: 35.3757 delta: 0.026579 time: 609.875 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 624.53
Index size:  1903140.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0201719000
  Testing...
|S| = 80
|T| = 1152
Reject!
257.785 < 419.782
  -> Decision False in time 0.0300000000, query time of that 0.0086225510, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Reject!
213.631 < 417.594
  -> Decision False in time 0.2600000000, query time of that 0.0632126740, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
394.601 < 406.148
  -> Decision False in time 0.0500000000, query time of that 0.0120743270, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Reject!
331.454 < 470.623
  -> Decision False in time 0.3400000000, query time of that 0.0120313330, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
416.064 < 417.183
  -> Decision False in time 1.1800000000, query time of that 0.0406992260, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
404.256 < 426.978
  -> Decision False in time 0.8600000000, query time of that 0.0304825620, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Reject!
309.243 < 312.994
  -> Decision False in time 0.8000000000, query time of that 0.0026369150, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
230.295 < 234.953
  -> Decision False in time 1.0200000000, query time of that 0.0035212070, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
267.022 < 267.378
  -> Decision False in time 7.7000000000, query time of that 0.0255243750, 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: 2.6766 cost: 0.00038 M: 10 delta: 1 time: 53.8601 one-recall: 0 one-ratio: 3.66072
iteration: 2 recall: 0.0016 accuracy: 1.38091 cost: 0.000637428 M: 10 delta: 0.856032 time: 91.9579 one-recall: 0 one-ratio: 2.8624
iteration: 3 recall: 0.0388 accuracy: 0.711342 cost: 0.00109521 M: 11.5287 delta: 0.835102 time: 138.1 one-recall: 0.04 one-ratio: 2.27853
iteration: 4 recall: 0.2128 accuracy: 0.331692 cost: 0.00163043 M: 11.8362 delta: 0.783466 time: 183.835 one-recall: 0.31 one-ratio: 1.61045
iteration: 5 recall: 0.5252 accuracy: 0.113966 cost: 0.00223606 M: 12.6037 delta: 0.664581 time: 231.305 one-recall: 0.65 one-ratio: 1.31249
iteration: 6 recall: 0.7808 accuracy: 0.0321567 cost: 0.00298004 M: 15.1148 delta: 0.432336 time: 284.9 one-recall: 0.89 one-ratio: 1.10787
iteration: 7 recall: 0.8984 accuracy: 0.00889378 cost: 0.00395541 M: 21.1397 delta: 0.196458 time: 345.904 one-recall: 0.98 one-ratio: 1.00852
iteration: 8 recall: 0.9532 accuracy: 0.00261054 cost: 0.00498005 M: 27.3053 delta: 0.0884561 time: 402.865 one-recall: 1 one-ratio: 1
iteration: 9 recall: 0.97 accuracy: 0.00153751 cost: 0.00577336 M: 31.2902 delta: 0.0513331 time: 446.246 one-recall: 1 one-ratio: 1
iteration: 10 recall: 0.978 accuracy: 0.00108745 cost: 0.00625831 M: 33.3952 delta: 0.0371841 time: 475.428 one-recall: 1 one-ratio: 1
iteration: 11 recall: 0.9816 accuracy: 0.000822769 cost: 0.00651495 M: 34.422 delta: 0.0313218 time: 494.377 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.9856 accuracy: 0.000634549 cost: 0.00664356 M: 34.9169 delta: 0.0287556 time: 507.158 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.9868 accuracy: 0.000578651 cost: 0.00670598 M: 35.1529 delta: 0.027595 time: 516.364 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.9876 accuracy: 0.000568954 cost: 0.00673627 M: 35.2664 delta: 0.0270435 time: 523.647 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.9876 accuracy: 0.000568954 cost: 0.00675099 M: 35.3217 delta: 0.0267899 time: 529.937 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.9876 accuracy: 0.000568954 cost: 0.0067584 M: 35.3495 delta: 0.0266568 time: 535.734 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.9876 accuracy: 0.000568954 cost: 0.00676207 M: 35.3629 delta: 0.0265947 time: 541.268 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.9876 accuracy: 0.000568954 cost: 0.00676399 M: 35.37 delta: 0.0265678 time: 546.67 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.9876 accuracy: 0.000568954 cost: 0.00676502 M: 35.3739 delta: 0.0265497 time: 552.002 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.9876 accuracy: 0.000568954 cost: 0.00676557 M: 35.376 delta: 0.026542 time: 557.294 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.9876 accuracy: 0.000568954 cost: 0.00676589 M: 35.3772 delta: 0.026538 time: 562.562 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.9876 accuracy: 0.000568954 cost: 0.00676609 M: 35.378 delta: 0.0265349 time: 567.817 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.9876 accuracy: 0.000568954 cost: 0.00676619 M: 35.3784 delta: 0.0265336 time: 573.059 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.9876 accuracy: 0.000568954 cost: 0.00676626 M: 35.3787 delta: 0.0265323 time: 578.297 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.9876 accuracy: 0.000568954 cost: 0.0067663 M: 35.3789 delta: 0.026532 time: 583.532 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.9876 accuracy: 0.000568954 cost: 0.00676631 M: 35.3789 delta: 0.0265317 time: 588.764 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.9876 accuracy: 0.000568954 cost: 0.00676632 M: 35.379 delta: 0.0265314 time: 593.998 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.9876 accuracy: 0.000568954 cost: 0.00676632 M: 35.379 delta: 0.0265314 time: 599.228 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.9876 accuracy: 0.000568954 cost: 0.00676633 M: 35.379 delta: 0.0265313 time: 604.46 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.9876 accuracy: 0.000568954 cost: 0.00676633 M: 35.379 delta: 0.0265314 time: 609.688 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 624.28
Index size:  260980.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0049890000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.0900000000, query time of that 0.0419365260, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Accept!
  -> Decision True in time 0.8700000000, query time of that 0.4248677040, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
386.717 < 400.478
  -> Decision False in time 2.8000000000, query time of that 1.3267644090, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.5700000000, query time of that 0.0493177960, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Accept!
  -> Decision True in time 5.6000000000, query time of that 0.5046707920, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
284.593 < 292.67
  -> Decision False in time 8.1200000000, query time of that 0.7231969210, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Accept!
  -> Decision True in time 6.6800000000, query time of that 0.0575622040, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
261.08 < 261.257
  -> Decision False in time 30.0700000000, query time of that 0.2581213980, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
278.519 < 279.004
  -> Decision False in time 19.4600000000, query time of that 0.1683802990, 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: 0.0008 accuracy: 2.48177 cost: 0.00038 M: 10 delta: 1 time: 53.8536 one-recall: 0 one-ratio: 3.57151
iteration: 2 recall: 0.006 accuracy: 1.20232 cost: 0.000637428 M: 10 delta: 0.856032 time: 91.9348 one-recall: 0.01 one-ratio: 2.71347
iteration: 3 recall: 0.0428 accuracy: 0.621878 cost: 0.00109521 M: 11.5287 delta: 0.835103 time: 138.066 one-recall: 0.06 one-ratio: 2.24598
iteration: 4 recall: 0.2128 accuracy: 0.290189 cost: 0.00163045 M: 11.8363 delta: 0.783464 time: 183.788 one-recall: 0.26 one-ratio: 1.81407
iteration: 5 recall: 0.5444 accuracy: 0.114619 cost: 0.00223604 M: 12.6037 delta: 0.664599 time: 231.245 one-recall: 0.61 one-ratio: 1.37506
iteration: 6 recall: 0.7952 accuracy: 0.0249012 cost: 0.00297982 M: 15.1143 delta: 0.432325 time: 284.834 one-recall: 0.91 one-ratio: 1.08532
iteration: 7 recall: 0.906 accuracy: 0.00733108 cost: 0.00395503 M: 21.1391 delta: 0.196401 time: 345.841 one-recall: 0.98 one-ratio: 1.00751
iteration: 8 recall: 0.9524 accuracy: 0.0028175 cost: 0.00497976 M: 27.3062 delta: 0.0884487 time: 402.807 one-recall: 0.99 one-ratio: 1.00262
iteration: 9 recall: 0.9672 accuracy: 0.00177927 cost: 0.00577206 M: 31.2885 delta: 0.0513769 time: 446.152 one-recall: 0.99 one-ratio: 1.00262
iteration: 10 recall: 0.9756 accuracy: 0.00118986 cost: 0.00625738 M: 33.3953 delta: 0.037235 time: 475.33 one-recall: 0.99 one-ratio: 1.00262
iteration: 11 recall: 0.9776 accuracy: 0.00103341 cost: 0.00651547 M: 34.4285 delta: 0.0313111 time: 494.33 one-recall: 0.99 one-ratio: 1.00262
iteration: 12 recall: 0.98 accuracy: 0.00090889 cost: 0.00664324 M: 34.9202 delta: 0.0287412 time: 507.073 one-recall: 0.99 one-ratio: 1.00262
iteration: 13 recall: 0.9804 accuracy: 0.000905174 cost: 0.00670538 M: 35.1542 delta: 0.0275694 time: 516.242 one-recall: 0.99 one-ratio: 1.00262
iteration: 14 recall: 0.9808 accuracy: 0.00088451 cost: 0.00673527 M: 35.2657 delta: 0.0270449 time: 523.498 one-recall: 0.99 one-ratio: 1.00262
iteration: 15 recall: 0.9812 accuracy: 0.000864632 cost: 0.00674995 M: 35.3209 delta: 0.0267893 time: 529.783 one-recall: 0.99 one-ratio: 1.00262
iteration: 16 recall: 0.9812 accuracy: 0.000864632 cost: 0.0067575 M: 35.3491 delta: 0.0266618 time: 535.592 one-recall: 0.99 one-ratio: 1.00262
iteration: 17 recall: 0.9812 accuracy: 0.000864632 cost: 0.00676138 M: 35.3634 delta: 0.0265954 time: 541.157 one-recall: 0.99 one-ratio: 1.00262
iteration: 18 recall: 0.9812 accuracy: 0.000864632 cost: 0.00676333 M: 35.3707 delta: 0.0265682 time: 546.563 one-recall: 0.99 one-ratio: 1.00262
iteration: 19 recall: 0.9812 accuracy: 0.000864632 cost: 0.00676441 M: 35.3747 delta: 0.0265468 time: 551.895 one-recall: 0.99 one-ratio: 1.00262
iteration: 20 recall: 0.9812 accuracy: 0.000864632 cost: 0.00676491 M: 35.3766 delta: 0.0265395 time: 557.184 one-recall: 0.99 one-ratio: 1.00262
iteration: 21 recall: 0.9812 accuracy: 0.000864632 cost: 0.00676518 M: 35.3777 delta: 0.0265346 time: 562.451 one-recall: 0.99 one-ratio: 1.00262
iteration: 22 recall: 0.9812 accuracy: 0.000864632 cost: 0.00676533 M: 35.3783 delta: 0.0265334 time: 567.703 one-recall: 0.99 one-ratio: 1.00262
iteration: 23 recall: 0.9812 accuracy: 0.000864632 cost: 0.00676543 M: 35.3786 delta: 0.0265323 time: 572.949 one-recall: 0.99 one-ratio: 1.00262
iteration: 24 recall: 0.9812 accuracy: 0.000864632 cost: 0.0067655 M: 35.3789 delta: 0.0265312 time: 578.189 one-recall: 0.99 one-ratio: 1.00262
iteration: 25 recall: 0.9812 accuracy: 0.000864632 cost: 0.00676553 M: 35.379 delta: 0.0265308 time: 583.428 one-recall: 0.99 one-ratio: 1.00262
iteration: 26 recall: 0.9812 accuracy: 0.000864632 cost: 0.00676556 M: 35.3791 delta: 0.0265303 time: 588.661 one-recall: 0.99 one-ratio: 1.00262
iteration: 27 recall: 0.9812 accuracy: 0.000864632 cost: 0.00676557 M: 35.3791 delta: 0.0265304 time: 593.892 one-recall: 0.99 one-ratio: 1.00262
iteration: 28 recall: 0.9812 accuracy: 0.000864632 cost: 0.00676558 M: 35.3792 delta: 0.0265302 time: 599.122 one-recall: 0.99 one-ratio: 1.00262
iteration: 29 recall: 0.9812 accuracy: 0.000864632 cost: 0.00676559 M: 35.3792 delta: 0.02653 time: 604.354 one-recall: 0.99 one-ratio: 1.00262
iteration: 30 recall: 0.9812 accuracy: 0.000864632 cost: 0.00676559 M: 35.3792 delta: 0.02653 time: 609.586 one-recall: 0.99 one-ratio: 1.00262
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 624.1700000000001
Index size:  262924.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0031866000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.1000000000, query time of that 0.0592960580, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Accept!
  -> Decision True in time 1.0200000000, query time of that 0.5761530150, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
319.282 < 351.226
  -> Decision False in time 3.8800000000, query time of that 2.1539170910, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.5800000000, query time of that 0.0654149510, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Accept!
  -> Decision True in time 5.7700000000, query time of that 0.6722104430, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
336.749 < 342.143
  -> Decision False in time 4.4000000000, query time of that 0.5124387370, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Accept!
  -> Decision True in time 6.6900000000, query time of that 0.0753368950, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
240.225 < 240.275
  -> Decision False in time 65.0200000000, query time of that 0.7427868140, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
236.694 < 241.514
  -> Decision False in time 42.5100000000, query time of that 0.4883976050, 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: 2.75563 cost: 0.00038 M: 10 delta: 1 time: 53.8571 one-recall: 0 one-ratio: 3.20375
iteration: 2 recall: 0.0036 accuracy: 1.28835 cost: 0.000637428 M: 10 delta: 0.856032 time: 91.9503 one-recall: 0.01 one-ratio: 2.58347
iteration: 3 recall: 0.036 accuracy: 0.648029 cost: 0.00109521 M: 11.5287 delta: 0.835107 time: 138.091 one-recall: 0.04 one-ratio: 2.18955
iteration: 4 recall: 0.188 accuracy: 0.300992 cost: 0.00163043 M: 11.8362 delta: 0.78346 time: 183.819 one-recall: 0.15 one-ratio: 1.72886
iteration: 5 recall: 0.4932 accuracy: 0.113832 cost: 0.00223606 M: 12.6036 delta: 0.664589 time: 231.287 one-recall: 0.55 one-ratio: 1.31529
iteration: 6 recall: 0.746 accuracy: 0.0314189 cost: 0.00297996 M: 15.1149 delta: 0.43236 time: 284.883 one-recall: 0.82 one-ratio: 1.08068
iteration: 7 recall: 0.8672 accuracy: 0.010611 cost: 0.00395521 M: 21.1403 delta: 0.196429 time: 345.891 one-recall: 0.94 one-ratio: 1.01691
iteration: 8 recall: 0.9224 accuracy: 0.00563282 cost: 0.00497945 M: 27.3029 delta: 0.0885035 time: 402.839 one-recall: 0.97 one-ratio: 1.01391
iteration: 9 recall: 0.947199 accuracy: 0.00299932 cost: 0.00577183 M: 31.2855 delta: 0.0513971 time: 446.176 one-recall: 0.99 one-ratio: 1.00022
iteration: 10 recall: 0.9592 accuracy: 0.00227026 cost: 0.00625696 M: 33.3914 delta: 0.0372729 time: 475.362 one-recall: 0.99 one-ratio: 1.00022
iteration: 11 recall: 0.9636 accuracy: 0.00199197 cost: 0.00651441 M: 34.4225 delta: 0.0313612 time: 494.358 one-recall: 0.99 one-ratio: 1.00022
iteration: 12 recall: 0.9664 accuracy: 0.00187919 cost: 0.00664216 M: 34.9141 delta: 0.0287902 time: 507.111 one-recall: 0.99 one-ratio: 1.00022
iteration: 13 recall: 0.968 accuracy: 0.0018145 cost: 0.00670434 M: 35.1492 delta: 0.027634 time: 516.296 one-recall: 0.99 one-ratio: 1.00022
iteration: 14 recall: 0.968 accuracy: 0.0018145 cost: 0.00673502 M: 35.2633 delta: 0.0270735 time: 523.611 one-recall: 0.99 one-ratio: 1.00022
iteration: 15 recall: 0.968 accuracy: 0.0018145 cost: 0.00674955 M: 35.3182 delta: 0.0268176 time: 529.895 one-recall: 0.99 one-ratio: 1.00022
iteration: 16 recall: 0.968 accuracy: 0.0018145 cost: 0.00675688 M: 35.3456 delta: 0.0266927 time: 535.691 one-recall: 0.99 one-ratio: 1.00022
iteration: 17 recall: 0.9684 accuracy: 0.00178448 cost: 0.00676054 M: 35.3594 delta: 0.0266336 time: 541.224 one-recall: 0.99 one-ratio: 1.00022
iteration: 18 recall: 0.9684 accuracy: 0.00178448 cost: 0.00676258 M: 35.3668 delta: 0.026599 time: 546.636 one-recall: 0.99 one-ratio: 1.00022
iteration: 19 recall: 0.9684 accuracy: 0.00178448 cost: 0.00676361 M: 35.3708 delta: 0.0265803 time: 551.969 one-recall: 0.99 one-ratio: 1.00022
iteration: 20 recall: 0.9684 accuracy: 0.00178448 cost: 0.00676418 M: 35.3729 delta: 0.0265718 time: 557.264 one-recall: 0.99 one-ratio: 1.00022
iteration: 21 recall: 0.9684 accuracy: 0.00178448 cost: 0.00676447 M: 35.3741 delta: 0.0265668 time: 562.531 one-recall: 0.99 one-ratio: 1.00022
iteration: 22 recall: 0.9684 accuracy: 0.00178448 cost: 0.00676462 M: 35.3747 delta: 0.0265652 time: 567.78 one-recall: 0.99 one-ratio: 1.00022
iteration: 23 recall: 0.9684 accuracy: 0.00178448 cost: 0.00676469 M: 35.3749 delta: 0.0265639 time: 573.018 one-recall: 0.99 one-ratio: 1.00022
iteration: 24 recall: 0.9684 accuracy: 0.00178448 cost: 0.00676472 M: 35.375 delta: 0.026563 time: 578.252 one-recall: 0.99 one-ratio: 1.00022
iteration: 25 recall: 0.9684 accuracy: 0.00178448 cost: 0.00676473 M: 35.3751 delta: 0.026563 time: 583.489 one-recall: 0.99 one-ratio: 1.00022
iteration: 26 recall: 0.9684 accuracy: 0.00178448 cost: 0.00676474 M: 35.3751 delta: 0.0265627 time: 588.719 one-recall: 0.99 one-ratio: 1.00022
iteration: 27 recall: 0.9684 accuracy: 0.00178448 cost: 0.00676474 M: 35.3751 delta: 0.0265627 time: 593.953 one-recall: 0.99 one-ratio: 1.00022
iteration: 28 recall: 0.9684 accuracy: 0.00178448 cost: 0.00676474 M: 35.3751 delta: 0.0265626 time: 599.181 one-recall: 0.99 one-ratio: 1.00022
iteration: 29 recall: 0.9684 accuracy: 0.00178448 cost: 0.00676475 M: 35.3751 delta: 0.0265624 time: 604.409 one-recall: 0.99 one-ratio: 1.00022
iteration: 30 recall: 0.9684 accuracy: 0.00178448 cost: 0.00676475 M: 35.3751 delta: 0.0265624 time: 609.637 one-recall: 0.99 one-ratio: 1.00022
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 624.2199999999993
Index size:  262800.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0024679000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.1100000000, query time of that 0.0666369950, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Accept!
  -> Decision True in time 1.1300000000, query time of that 0.6781478750, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Accept!
  -> Decision True in time 11.3400000000, query time of that 6.7196637900, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.6100000000, query time of that 0.0784116960, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Accept!
  -> Decision True in time 5.9500000000, query time of that 0.7793047740, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
260.062 < 263.92
  -> Decision False in time 25.2500000000, query time of that 3.3745117270, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Reject!
225.805 < 236.066
  -> Decision False in time 5.8800000000, query time of that 0.0822203030, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
373.738 < 374.339
  -> Decision False in time 9.9000000000, query time of that 0.1365812490, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
345.55 < 354.48
  -> Decision False in time 25.9600000000, query time of that 0.3526185250, 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: 2.38371 cost: 0.00038 M: 10 delta: 1 time: 53.8908 one-recall: 0 one-ratio: 3.41607
iteration: 2 recall: 0.002 accuracy: 1.28323 cost: 0.000637428 M: 10 delta: 0.856032 time: 91.9815 one-recall: 0 one-ratio: 2.77149
iteration: 3 recall: 0.0312 accuracy: 0.723596 cost: 0.00109521 M: 11.5287 delta: 0.835104 time: 138.122 one-recall: 0.06 one-ratio: 2.15225
iteration: 4 recall: 0.1752 accuracy: 0.395304 cost: 0.00163044 M: 11.8363 delta: 0.78347 time: 183.858 one-recall: 0.28 one-ratio: 1.71038
iteration: 5 recall: 0.4992 accuracy: 0.102045 cost: 0.00223601 M: 12.6037 delta: 0.664571 time: 231.312 one-recall: 0.65 one-ratio: 1.25973
iteration: 6 recall: 0.758 accuracy: 0.0320828 cost: 0.00297995 M: 15.1153 delta: 0.432326 time: 284.892 one-recall: 0.83 one-ratio: 1.09167
iteration: 7 recall: 0.8816 accuracy: 0.0111449 cost: 0.0039552 M: 21.1397 delta: 0.196415 time: 345.882 one-recall: 0.93 one-ratio: 1.02641
iteration: 8 recall: 0.9408 accuracy: 0.00409358 cost: 0.00498008 M: 27.3069 delta: 0.0883963 time: 402.839 one-recall: 0.97 one-ratio: 1.00433
iteration: 9 recall: 0.9616 accuracy: 0.00210106 cost: 0.00577281 M: 31.2917 delta: 0.0513199 time: 446.195 one-recall: 0.98 one-ratio: 1.0026
iteration: 10 recall: 0.9704 accuracy: 0.0015283 cost: 0.00625749 M: 33.3955 delta: 0.0371987 time: 475.354 one-recall: 0.98 one-ratio: 1.0026
iteration: 11 recall: 0.9752 accuracy: 0.00114867 cost: 0.00651449 M: 34.4258 delta: 0.0313162 time: 494.314 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.9784 accuracy: 0.000988028 cost: 0.00664233 M: 34.9157 delta: 0.0287569 time: 507.062 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.9792 accuracy: 0.000984121 cost: 0.00670514 M: 35.1524 delta: 0.0275889 time: 516.29 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.98 accuracy: 0.000949828 cost: 0.00673562 M: 35.2655 delta: 0.0270439 time: 523.586 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.98 accuracy: 0.000949828 cost: 0.00675046 M: 35.3215 delta: 0.0267825 time: 529.89 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.9808 accuracy: 0.00086411 cost: 0.00675785 M: 35.3489 delta: 0.0266533 time: 535.687 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.9808 accuracy: 0.00086411 cost: 0.00676155 M: 35.3627 delta: 0.0265892 time: 541.225 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.9808 accuracy: 0.00086411 cost: 0.00676343 M: 35.3698 delta: 0.026561 time: 546.626 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.9808 accuracy: 0.00086411 cost: 0.00676441 M: 35.3735 delta: 0.0265443 time: 551.956 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.9808 accuracy: 0.00086411 cost: 0.00676496 M: 35.3755 delta: 0.0265375 time: 557.246 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.9808 accuracy: 0.00086411 cost: 0.00676525 M: 35.3767 delta: 0.0265324 time: 562.516 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.9808 accuracy: 0.00086411 cost: 0.00676542 M: 35.3775 delta: 0.0265289 time: 567.771 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.9808 accuracy: 0.00086411 cost: 0.00676551 M: 35.3778 delta: 0.0265277 time: 573.019 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.9808 accuracy: 0.00086411 cost: 0.00676557 M: 35.3781 delta: 0.0265266 time: 578.259 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.9808 accuracy: 0.00086411 cost: 0.0067656 M: 35.3782 delta: 0.0265262 time: 583.495 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.9808 accuracy: 0.00086411 cost: 0.00676563 M: 35.3783 delta: 0.0265258 time: 588.731 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.9808 accuracy: 0.00086411 cost: 0.00676564 M: 35.3784 delta: 0.0265256 time: 593.963 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.9808 accuracy: 0.00086411 cost: 0.00676564 M: 35.3784 delta: 0.0265255 time: 599.194 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.9808 accuracy: 0.00086411 cost: 0.00676565 M: 35.3784 delta: 0.0265254 time: 604.426 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.9808 accuracy: 0.00086411 cost: 0.00676565 M: 35.3784 delta: 0.0265254 time: 609.66 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 624.3800000000001
Index size:  262936.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0027165000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.1300000000, query time of that 0.0870127520, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Accept!
  -> Decision True in time 1.3100000000, query time of that 0.8628877100, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Accept!
  -> Decision True in time 13.3300000000, query time of that 8.7073366490, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.6700000000, query time of that 0.1064259960, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Accept!
  -> Decision True in time 6.7900000000, query time of that 1.0816261760, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
280.507 < 281.386
  -> Decision False in time 6.8200000000, query time of that 1.0844630080, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Accept!
  -> Decision True in time 8.1800000000, query time of that 0.1252963410, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
289.52 < 290.463
  -> Decision False in time 13.1300000000, query time of that 0.1998489330, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
314.971 < 318.58
  -> Decision False in time 21.7200000000, query time of that 0.3334944070, 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: 0.0008 accuracy: 2.35075 cost: 0.00038 M: 10 delta: 1 time: 63.5972 one-recall: 0 one-ratio: 3.40698
iteration: 2 recall: 0.0036 accuracy: 1.29003 cost: 0.000637428 M: 10 delta: 0.856032 time: 107.618 one-recall: 0 one-ratio: 2.70123
iteration: 3 recall: 0.028 accuracy: 0.707178 cost: 0.00109521 M: 11.5287 delta: 0.835108 time: 160.931 one-recall: 0.04 one-ratio: 2.1146
iteration: 4 recall: 0.1872 accuracy: 0.331024 cost: 0.00163044 M: 11.8362 delta: 0.783467 time: 213.984 one-recall: 0.22 one-ratio: 1.62748
iteration: 5 recall: 0.546 accuracy: 0.109222 cost: 0.00223606 M: 12.6034 delta: 0.664593 time: 269.126 one-recall: 0.7 one-ratio: 1.22343
iteration: 6 recall: 0.8088 accuracy: 0.0256977 cost: 0.00297998 M: 15.115 delta: 0.432326 time: 331.344 one-recall: 0.9 one-ratio: 1.03514
iteration: 7 recall: 0.92 accuracy: 0.00679889 cost: 0.00395532 M: 21.1401 delta: 0.196454 time: 403.449 one-recall: 0.98 one-ratio: 1.00242
iteration: 8 recall: 0.9592 accuracy: 0.00300145 cost: 0.00497996 M: 27.3047 delta: 0.0884442 time: 472.884 one-recall: 0.99 one-ratio: 1.0019
iteration: 9 recall: 0.974 accuracy: 0.00176275 cost: 0.00577325 M: 31.2911 delta: 0.0513364 time: 527.469 one-recall: 1 one-ratio: 1
iteration: 10 recall: 0.9804 accuracy: 0.00125731 cost: 0.00625771 M: 33.3946 delta: 0.0371907 time: 565.309 one-recall: 1 one-ratio: 1
iteration: 11 recall: 0.984 accuracy: 0.000924382 cost: 0.00651592 M: 34.4275 delta: 0.0312963 time: 590.669 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.986 accuracy: 0.000801036 cost: 0.00664346 M: 34.9188 delta: 0.0287257 time: 607.969 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.9868 accuracy: 0.00077308 cost: 0.00670575 M: 35.1547 delta: 0.0275527 time: 620.625 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.9868 accuracy: 0.00077308 cost: 0.00673605 M: 35.2685 delta: 0.0270038 time: 630.676 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.9868 accuracy: 0.00077308 cost: 0.00675097 M: 35.3242 delta: 0.0267414 time: 639.347 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.9868 accuracy: 0.00077308 cost: 0.00675847 M: 35.3518 delta: 0.0266184 time: 647.325 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.9868 accuracy: 0.00077308 cost: 0.00676234 M: 35.3664 delta: 0.0265545 time: 654.953 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.9868 accuracy: 0.00077308 cost: 0.00676426 M: 35.3737 delta: 0.0265222 time: 662.381 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.9868 accuracy: 0.00077308 cost: 0.00676536 M: 35.3778 delta: 0.0265036 time: 669.721 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.9868 accuracy: 0.00077308 cost: 0.0067659 M: 35.3799 delta: 0.0264972 time: 676.995 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.9868 accuracy: 0.00077308 cost: 0.00676621 M: 35.3811 delta: 0.0264931 time: 684.237 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.9868 accuracy: 0.00077308 cost: 0.00676639 M: 35.3818 delta: 0.0264902 time: 691.456 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.9868 accuracy: 0.00077308 cost: 0.00676651 M: 35.3822 delta: 0.0264888 time: 698.667 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.9868 accuracy: 0.00077308 cost: 0.00676656 M: 35.3824 delta: 0.0264877 time: 705.871 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.9868 accuracy: 0.00077308 cost: 0.00676659 M: 35.3825 delta: 0.0264867 time: 713.069 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.9868 accuracy: 0.00077308 cost: 0.0067666 M: 35.3826 delta: 0.0264864 time: 720.263 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.9868 accuracy: 0.00077308 cost: 0.0067666 M: 35.3826 delta: 0.0264864 time: 727.458 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.9868 accuracy: 0.00077308 cost: 0.0067666 M: 35.3826 delta: 0.0264863 time: 734.649 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.9868 accuracy: 0.00077308 cost: 0.0067666 M: 35.3826 delta: 0.0264864 time: 741.843 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.9868 accuracy: 0.00077308 cost: 0.00676661 M: 35.3826 delta: 0.0264863 time: 749.03 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 767.8600000000006
Index size:  263112.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0112754000
  Testing...
|S| = 80
|T| = 1152
Reject!
419.525 < 433.144
  -> Decision False in time 0.0100000000, query time of that 0.0046395910, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Reject!
425.628 < 468.953
  -> Decision False in time 0.1300000000, query time of that 0.0449267410, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
362.523 < 363.808
  -> Decision False in time 0.0700000000, query time of that 0.0223929330, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.5500000000, query time of that 0.0278537380, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
373.042 < 387.097
  -> Decision False in time 0.5200000000, query time of that 0.0262034060, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
352.908 < 353.432
  -> Decision False in time 0.3900000000, query time of that 0.0198132250, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Reject!
275.225 < 280.777
  -> Decision False in time 0.8100000000, query time of that 0.0034868210, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
282.514 < 286.384
  -> Decision False in time 4.2800000000, query time of that 0.0198120030, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
279.962 < 280.129
  -> Decision False in time 19.0400000000, query time of that 0.0905704710, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 2, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 2, {'reverse': -1}, False])
Got a train set of size (1000000 * 128)
Generating control...
Initializing...
iteration: 1 recall: 0.0004 accuracy: 2.08783 cost: 0.00038 M: 10 delta: 1 time: 63.5872 one-recall: 0 one-ratio: 3.28615
iteration: 2 recall: 0.0032 accuracy: 1.12077 cost: 0.000637428 M: 10 delta: 0.856032 time: 107.606 one-recall: 0.01 one-ratio: 2.60388
iteration: 3 recall: 0.0304 accuracy: 0.620824 cost: 0.00109521 M: 11.5287 delta: 0.835102 time: 160.9 one-recall: 0.04 one-ratio: 2.10915
iteration: 4 recall: 0.1836 accuracy: 0.312046 cost: 0.00163045 M: 11.8362 delta: 0.783478 time: 213.949 one-recall: 0.3 one-ratio: 1.61042
iteration: 5 recall: 0.486 accuracy: 0.106251 cost: 0.00223607 M: 12.6034 delta: 0.664591 time: 269.08 one-recall: 0.65 one-ratio: 1.21993
iteration: 6 recall: 0.7624 accuracy: 0.0289784 cost: 0.00297998 M: 15.1148 delta: 0.432339 time: 331.297 one-recall: 0.85 one-ratio: 1.07761
iteration: 7 recall: 0.8996 accuracy: 0.00941952 cost: 0.00395523 M: 21.1403 delta: 0.196397 time: 403.376 one-recall: 0.96 one-ratio: 1.02874
iteration: 8 recall: 0.9412 accuracy: 0.00477824 cost: 0.00497972 M: 27.3047 delta: 0.0884883 time: 472.811 one-recall: 0.99 one-ratio: 1.02373
iteration: 9 recall: 0.9628 accuracy: 0.00306446 cost: 0.0057727 M: 31.2888 delta: 0.0513913 time: 527.4 one-recall: 0.99 one-ratio: 1.02373
iteration: 10 recall: 0.9728 accuracy: 0.00130521 cost: 0.00625745 M: 33.3927 delta: 0.037257 time: 565.261 one-recall: 1 one-ratio: 1
iteration: 11 recall: 0.9764 accuracy: 0.00112049 cost: 0.00651494 M: 34.4252 delta: 0.0313628 time: 590.594 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.978 accuracy: 0.000924722 cost: 0.00664257 M: 34.9154 delta: 0.0288022 time: 607.906 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.9788 accuracy: 0.00088002 cost: 0.00670469 M: 35.1496 delta: 0.0276372 time: 620.543 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.9788 accuracy: 0.00088002 cost: 0.00673464 M: 35.2621 delta: 0.0270873 time: 630.552 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.9792 accuracy: 0.000868212 cost: 0.00674942 M: 35.3173 delta: 0.0268324 time: 639.227 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.9796 accuracy: 0.000857554 cost: 0.00675696 M: 35.3457 delta: 0.0267068 time: 647.215 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.9804 accuracy: 0.000791918 cost: 0.00676078 M: 35.36 delta: 0.026644 time: 654.838 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.9804 accuracy: 0.000791918 cost: 0.00676272 M: 35.3674 delta: 0.0266096 time: 662.268 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.9804 accuracy: 0.000791918 cost: 0.00676377 M: 35.3713 delta: 0.0265918 time: 669.595 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.9812 accuracy: 0.0007663 cost: 0.00676433 M: 35.3734 delta: 0.0265837 time: 676.872 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.9812 accuracy: 0.0007663 cost: 0.00676462 M: 35.3745 delta: 0.0265783 time: 684.116 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.9812 accuracy: 0.0007663 cost: 0.0067648 M: 35.3751 delta: 0.0265761 time: 691.338 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.9812 accuracy: 0.0007663 cost: 0.00676493 M: 35.3756 delta: 0.0265742 time: 698.551 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.9812 accuracy: 0.0007663 cost: 0.00676501 M: 35.3759 delta: 0.0265732 time: 705.754 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.9812 accuracy: 0.0007663 cost: 0.00676504 M: 35.376 delta: 0.0265726 time: 712.959 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.9812 accuracy: 0.0007663 cost: 0.00676507 M: 35.3761 delta: 0.0265726 time: 720.157 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.9812 accuracy: 0.0007663 cost: 0.0067651 M: 35.3763 delta: 0.0265719 time: 727.355 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.9812 accuracy: 0.0007663 cost: 0.00676512 M: 35.3763 delta: 0.0265717 time: 734.547 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.9812 accuracy: 0.0007663 cost: 0.00676513 M: 35.3764 delta: 0.0265716 time: 741.741 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.9812 accuracy: 0.0007663 cost: 0.00676514 M: 35.3764 delta: 0.0265714 time: 748.933 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 767.7800000000007
Index size:  262784.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.1025392000
  Testing...
|S| = 80
|T| = 1152
Reject!
443.937 < 466.08
  -> Decision False in time 0.0000000000, query time of that 0.0002997020, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Reject!
318.305 < 442.624
  -> Decision False in time 0.0000000000, query time of that 0.0010889940, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
398.678 < 436.198
  -> Decision False in time 0.0100000000, query time of that 0.0019843920, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Reject!
327.843 < 418.814
  -> Decision False in time 0.0000000000, query time of that 0.0003260420, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
444.223 < 461.983
  -> Decision False in time 0.1000000000, query time of that 0.0048910260, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
421.383 < 477.537
  -> Decision False in time 0.1200000000, query time of that 0.0060946440, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Reject!
466.091 < 471.104
  -> Decision False in time 0.6100000000, query time of that 0.0030929370, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
359.256 < 506.621
  -> Decision False in time 0.3100000000, query time of that 0.0018350630, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
381.901 < 407.486
  -> Decision False in time 1.3100000000, query time of that 0.0061639130, 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: 2.42643 cost: 0.00038 M: 10 delta: 1 time: 63.5802 one-recall: 0 one-ratio: 3.41159
iteration: 2 recall: 0.0036 accuracy: 1.2376 cost: 0.000637428 M: 10 delta: 0.856032 time: 107.616 one-recall: 0.01 one-ratio: 2.61181
iteration: 3 recall: 0.0412 accuracy: 0.674588 cost: 0.00109521 M: 11.5287 delta: 0.835108 time: 160.924 one-recall: 0.05 one-ratio: 2.06172
iteration: 4 recall: 0.2204 accuracy: 0.313751 cost: 0.00163043 M: 11.8362 delta: 0.783445 time: 213.965 one-recall: 0.24 one-ratio: 1.61173
iteration: 5 recall: 0.552 accuracy: 0.0877383 cost: 0.00223614 M: 12.6041 delta: 0.66462 time: 269.106 one-recall: 0.66 one-ratio: 1.17587
iteration: 6 recall: 0.7956 accuracy: 0.0258084 cost: 0.00298 M: 15.114 delta: 0.432356 time: 331.309 one-recall: 0.84 one-ratio: 1.07338
iteration: 7 recall: 0.9088 accuracy: 0.00741468 cost: 0.00395529 M: 21.1392 delta: 0.196397 time: 403.384 one-recall: 0.97 one-ratio: 1.01719
iteration: 8 recall: 0.9472 accuracy: 0.00399412 cost: 0.00497939 M: 27.3033 delta: 0.0884961 time: 472.778 one-recall: 0.98 one-ratio: 1.00562
iteration: 9 recall: 0.9632 accuracy: 0.00262239 cost: 0.00577254 M: 31.2888 delta: 0.0513808 time: 527.379 one-recall: 0.98 one-ratio: 1.00562
iteration: 10 recall: 0.9724 accuracy: 0.00191675 cost: 0.00625867 M: 33.3984 delta: 0.0372125 time: 565.309 one-recall: 0.98 one-ratio: 1.00562
iteration: 11 recall: 0.976 accuracy: 0.00163242 cost: 0.00651585 M: 34.4277 delta: 0.0313515 time: 590.595 one-recall: 0.99 one-ratio: 1.00518
iteration: 12 recall: 0.9784 accuracy: 0.00151674 cost: 0.00664373 M: 34.9198 delta: 0.0287904 time: 607.914 one-recall: 0.99 one-ratio: 1.00518
iteration: 13 recall: 0.9784 accuracy: 0.00151465 cost: 0.00670625 M: 35.1562 delta: 0.0276138 time: 620.582 one-recall: 0.99 one-ratio: 1.00518
iteration: 14 recall: 0.9784 accuracy: 0.00151465 cost: 0.00673639 M: 35.2695 delta: 0.0270649 time: 630.591 one-recall: 0.99 one-ratio: 1.00518
iteration: 15 recall: 0.9784 accuracy: 0.00151465 cost: 0.00675125 M: 35.325 delta: 0.0268058 time: 639.264 one-recall: 0.99 one-ratio: 1.00518
iteration: 16 recall: 0.9784 accuracy: 0.00151465 cost: 0.0067586 M: 35.352 delta: 0.0266775 time: 647.233 one-recall: 0.99 one-ratio: 1.00518
iteration: 17 recall: 0.9784 accuracy: 0.00151465 cost: 0.00676237 M: 35.366 delta: 0.0266133 time: 654.851 one-recall: 0.99 one-ratio: 1.00518
iteration: 18 recall: 0.9784 accuracy: 0.00151465 cost: 0.00676414 M: 35.3725 delta: 0.0265806 time: 662.264 one-recall: 0.99 one-ratio: 1.00518
iteration: 19 recall: 0.9784 accuracy: 0.00151465 cost: 0.0067651 M: 35.3761 delta: 0.0265666 time: 669.589 one-recall: 0.99 one-ratio: 1.00518
iteration: 20 recall: 0.9784 accuracy: 0.00151465 cost: 0.00676566 M: 35.3783 delta: 0.0265583 time: 676.865 one-recall: 0.99 one-ratio: 1.00518
iteration: 21 recall: 0.9784 accuracy: 0.00151465 cost: 0.00676595 M: 35.3794 delta: 0.026554 time: 684.104 one-recall: 0.99 one-ratio: 1.00518
iteration: 22 recall: 0.9784 accuracy: 0.00151465 cost: 0.00676612 M: 35.38 delta: 0.0265516 time: 691.327 one-recall: 0.99 one-ratio: 1.00518
iteration: 23 recall: 0.9784 accuracy: 0.00151465 cost: 0.00676621 M: 35.3803 delta: 0.0265503 time: 698.541 one-recall: 0.99 one-ratio: 1.00518
iteration: 24 recall: 0.9784 accuracy: 0.00151465 cost: 0.00676626 M: 35.3805 delta: 0.0265499 time: 705.732 one-recall: 0.99 one-ratio: 1.00518
iteration: 25 recall: 0.9784 accuracy: 0.00151465 cost: 0.0067663 M: 35.3806 delta: 0.0265498 time: 712.931 one-recall: 0.99 one-ratio: 1.00518
iteration: 26 recall: 0.9784 accuracy: 0.00151465 cost: 0.00676631 M: 35.3807 delta: 0.0265493 time: 720.127 one-recall: 0.99 one-ratio: 1.00518
iteration: 27 recall: 0.9784 accuracy: 0.00151465 cost: 0.00676631 M: 35.3807 delta: 0.0265493 time: 727.311 one-recall: 0.99 one-ratio: 1.00518
iteration: 28 recall: 0.9784 accuracy: 0.00151465 cost: 0.00676631 M: 35.3807 delta: 0.0265492 time: 734.519 one-recall: 0.99 one-ratio: 1.00518
iteration: 29 recall: 0.9784 accuracy: 0.00151465 cost: 0.00676632 M: 35.3807 delta: 0.0265492 time: 741.704 one-recall: 0.99 one-ratio: 1.00518
iteration: 30 recall: 0.9784 accuracy: 0.00151465 cost: 0.00676632 M: 35.3807 delta: 0.0265492 time: 748.889 one-recall: 0.99 one-ratio: 1.00518
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 767.7300000000014
Index size:  262880.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0041143000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.1200000000, query time of that 0.0677760650, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Reject!
159.025 < 216.553
  -> Decision False in time 0.3900000000, query time of that 0.2312910160, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
263.315 < 279.414
  -> Decision False in time 7.1600000000, query time of that 4.1787943920, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.6300000000, query time of that 0.0782080240, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Accept!
  -> Decision True in time 6.2700000000, query time of that 0.8062995870, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
290.295 < 291.926
  -> Decision False in time 4.8500000000, query time of that 0.6218016070, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Reject!
217.931 < 219.738
  -> Decision False in time 0.4400000000, query time of that 0.0064243610, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
268.641 < 273.545
  -> Decision False in time 48.7800000000, query time of that 0.5616037450, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
261.038 < 262.391
  -> Decision False in time 79.7300000000, query time of that 0.9357701710, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 70, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 70, {'reverse': -1}, False])
Got a train set of size (1000000 * 128)
Generating control...
Initializing...
iteration: 1 recall: 0 accuracy: 2.48649 cost: 0.00038 M: 10 delta: 1 time: 63.5892 one-recall: 0 one-ratio: 3.50372
iteration: 2 recall: 0.0044 accuracy: 1.22446 cost: 0.000637428 M: 10 delta: 0.856032 time: 107.608 one-recall: 0.01 one-ratio: 2.74654
iteration: 3 recall: 0.0312 accuracy: 0.645306 cost: 0.00109521 M: 11.5287 delta: 0.835104 time: 160.923 one-recall: 0.03 one-ratio: 2.20734
iteration: 4 recall: 0.1752 accuracy: 0.333345 cost: 0.00163043 M: 11.8362 delta: 0.783464 time: 213.969 one-recall: 0.19 one-ratio: 1.80411
iteration: 5 recall: 0.4988 accuracy: 0.110638 cost: 0.00223606 M: 12.6036 delta: 0.664556 time: 269.11 one-recall: 0.57 one-ratio: 1.27349
iteration: 6 recall: 0.7824 accuracy: 0.0252185 cost: 0.00298007 M: 15.116 delta: 0.432347 time: 331.329 one-recall: 0.87 one-ratio: 1.08115
iteration: 7 recall: 0.908 accuracy: 0.0072622 cost: 0.00395543 M: 21.1399 delta: 0.196437 time: 403.412 one-recall: 0.97 one-ratio: 1.01387
iteration: 8 recall: 0.9608 accuracy: 0.00221648 cost: 0.00497979 M: 27.3053 delta: 0.0884729 time: 472.818 one-recall: 0.99 one-ratio: 1.00685
iteration: 9 recall: 0.9772 accuracy: 0.00105725 cost: 0.0057724 M: 31.2873 delta: 0.0513682 time: 527.386 one-recall: 1 one-ratio: 1
iteration: 10 recall: 0.9836 accuracy: 0.000549887 cost: 0.00625739 M: 33.3922 delta: 0.037231 time: 565.266 one-recall: 1 one-ratio: 1
iteration: 11 recall: 0.9872 accuracy: 0.000385133 cost: 0.00651424 M: 34.4202 delta: 0.0313409 time: 590.534 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.988 accuracy: 0.000327907 cost: 0.00664205 M: 34.9128 delta: 0.0287686 time: 607.886 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.9884 accuracy: 0.000309604 cost: 0.00670427 M: 35.1478 delta: 0.0276076 time: 620.505 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.9888 accuracy: 0.000292052 cost: 0.00673436 M: 35.2605 delta: 0.0270656 time: 630.499 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.9888 accuracy: 0.000292052 cost: 0.00674928 M: 35.3164 delta: 0.0268071 time: 639.165 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.9888 accuracy: 0.000292052 cost: 0.00675698 M: 35.3451 delta: 0.026675 time: 647.163 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.9888 accuracy: 0.000292052 cost: 0.00676076 M: 35.3594 delta: 0.0266102 time: 654.783 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.9888 accuracy: 0.000292052 cost: 0.00676273 M: 35.3669 delta: 0.0265813 time: 662.217 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.9888 accuracy: 0.000292052 cost: 0.0067638 M: 35.3709 delta: 0.0265624 time: 669.554 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.9888 accuracy: 0.000292052 cost: 0.00676432 M: 35.3728 delta: 0.0265559 time: 676.819 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.9888 accuracy: 0.000292052 cost: 0.00676468 M: 35.3742 delta: 0.02655 time: 684.066 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.9888 accuracy: 0.000292052 cost: 0.00676487 M: 35.3748 delta: 0.0265462 time: 691.29 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.9888 accuracy: 0.000292052 cost: 0.00676496 M: 35.3752 delta: 0.0265445 time: 698.497 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.9888 accuracy: 0.000292052 cost: 0.00676499 M: 35.3754 delta: 0.0265437 time: 705.693 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.9888 accuracy: 0.000292052 cost: 0.00676501 M: 35.3754 delta: 0.0265435 time: 712.883 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.9888 accuracy: 0.000292052 cost: 0.00676504 M: 35.3755 delta: 0.0265433 time: 720.078 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.9888 accuracy: 0.000292052 cost: 0.00676506 M: 35.3756 delta: 0.026543 time: 727.268 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.9888 accuracy: 0.000292052 cost: 0.00676507 M: 35.3756 delta: 0.0265428 time: 734.456 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.9888 accuracy: 0.000292052 cost: 0.00676507 M: 35.3757 delta: 0.0265428 time: 741.64 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.9888 accuracy: 0.000292052 cost: 0.00676508 M: 35.3757 delta: 0.0265428 time: 748.831 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 767.6700000000001
Index size:  262848.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0035870000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.1200000000, query time of that 0.0759931470, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Accept!
  -> Decision True in time 1.1800000000, query time of that 0.7370099940, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Accept!
  -> Decision True in time 11.9300000000, query time of that 7.2791090920, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.6500000000, query time of that 0.0836511120, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Accept!
  -> Decision True in time 6.3300000000, query time of that 0.8866539690, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
290.986 < 293.096
  -> Decision False in time 7.4000000000, query time of that 1.0386622670, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Accept!
  -> Decision True in time 8.1500000000, query time of that 0.0995757440, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
262.648 < 277.806
  -> Decision False in time 21.3700000000, query time of that 0.2844138780, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
293.07 < 301.992
  -> Decision False in time 29.2600000000, query time of that 0.3847912430, 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 accuracy: 2.02032 cost: 0.00038 M: 10 delta: 1 time: 63.5601 one-recall: 0 one-ratio: 3.17453
iteration: 2 recall: 0.0024 accuracy: 1.18523 cost: 0.000637428 M: 10 delta: 0.856032 time: 107.593 one-recall: 0 one-ratio: 2.53654
iteration: 3 recall: 0.0336 accuracy: 0.682876 cost: 0.00109521 M: 11.5287 delta: 0.835108 time: 160.908 one-recall: 0.03 one-ratio: 2.02802
iteration: 4 recall: 0.174 accuracy: 0.327285 cost: 0.00163043 M: 11.8362 delta: 0.783454 time: 213.963 one-recall: 0.21 one-ratio: 1.6436
iteration: 5 recall: 0.5132 accuracy: 0.109061 cost: 0.00223607 M: 12.6039 delta: 0.664599 time: 269.109 one-recall: 0.63 one-ratio: 1.21763
iteration: 6 recall: 0.77 accuracy: 0.0272776 cost: 0.00298007 M: 15.115 delta: 0.432351 time: 331.329 one-recall: 0.88 one-ratio: 1.05657
iteration: 7 recall: 0.8856 accuracy: 0.00864257 cost: 0.00395529 M: 21.1393 delta: 0.196451 time: 403.417 one-recall: 0.93 one-ratio: 1.01443
iteration: 8 recall: 0.936 accuracy: 0.00434341 cost: 0.00497996 M: 27.3049 delta: 0.0884942 time: 472.857 one-recall: 0.95 one-ratio: 1.00463
iteration: 9 recall: 0.9584 accuracy: 0.00276988 cost: 0.00577244 M: 31.2868 delta: 0.0513444 time: 527.421 one-recall: 0.97 one-ratio: 1.00409
iteration: 10 recall: 0.968 accuracy: 0.00200228 cost: 0.00625713 M: 33.3904 delta: 0.037224 time: 565.279 one-recall: 0.98 one-ratio: 1.00391
iteration: 11 recall: 0.9704 accuracy: 0.00177783 cost: 0.00651463 M: 34.4235 delta: 0.0313445 time: 590.591 one-recall: 0.99 one-ratio: 1.00352
iteration: 12 recall: 0.972 accuracy: 0.00171765 cost: 0.00664224 M: 34.9143 delta: 0.0287785 time: 607.897 one-recall: 0.99 one-ratio: 1.00352
iteration: 13 recall: 0.9732 accuracy: 0.00152845 cost: 0.00670434 M: 35.1491 delta: 0.027614 time: 620.488 one-recall: 0.99 one-ratio: 1.00352
iteration: 14 recall: 0.9732 accuracy: 0.00152845 cost: 0.00673469 M: 35.2628 delta: 0.0270641 time: 630.503 one-recall: 0.99 one-ratio: 1.00352
iteration: 15 recall: 0.9732 accuracy: 0.00152845 cost: 0.00674968 M: 35.3186 delta: 0.0268033 time: 639.176 one-recall: 0.99 one-ratio: 1.00352
iteration: 16 recall: 0.9732 accuracy: 0.00152845 cost: 0.00675739 M: 35.3472 delta: 0.026679 time: 647.175 one-recall: 0.99 one-ratio: 1.00352
iteration: 17 recall: 0.9736 accuracy: 0.00152146 cost: 0.00676125 M: 35.3613 delta: 0.026615 time: 654.799 one-recall: 0.99 one-ratio: 1.00352
iteration: 18 recall: 0.9736 accuracy: 0.00152146 cost: 0.00676319 M: 35.3685 delta: 0.0265813 time: 662.224 one-recall: 0.99 one-ratio: 1.00352
iteration: 19 recall: 0.9736 accuracy: 0.00152146 cost: 0.00676425 M: 35.3725 delta: 0.0265638 time: 669.557 one-recall: 0.99 one-ratio: 1.00352
iteration: 20 recall: 0.9736 accuracy: 0.00152146 cost: 0.00676482 M: 35.3746 delta: 0.026556 time: 676.836 one-recall: 0.99 one-ratio: 1.00352
iteration: 21 recall: 0.9736 accuracy: 0.00152146 cost: 0.00676512 M: 35.3757 delta: 0.0265508 time: 684.08 one-recall: 0.99 one-ratio: 1.00352
iteration: 22 recall: 0.9736 accuracy: 0.00152146 cost: 0.0067653 M: 35.3764 delta: 0.0265479 time: 691.306 one-recall: 0.99 one-ratio: 1.00352
iteration: 23 recall: 0.9736 accuracy: 0.00152146 cost: 0.0067654 M: 35.3768 delta: 0.0265466 time: 698.519 one-recall: 0.99 one-ratio: 1.00352
iteration: 24 recall: 0.9736 accuracy: 0.00152146 cost: 0.00676546 M: 35.377 delta: 0.0265458 time: 705.726 one-recall: 0.99 one-ratio: 1.00352
iteration: 25 recall: 0.9736 accuracy: 0.00152146 cost: 0.00676551 M: 35.3772 delta: 0.0265451 time: 712.923 one-recall: 0.99 one-ratio: 1.00352
iteration: 26 recall: 0.9736 accuracy: 0.00152146 cost: 0.00676553 M: 35.3773 delta: 0.0265449 time: 720.115 one-recall: 0.99 one-ratio: 1.00352
iteration: 27 recall: 0.9736 accuracy: 0.00152146 cost: 0.00676555 M: 35.3773 delta: 0.0265441 time: 727.307 one-recall: 0.99 one-ratio: 1.00352
iteration: 28 recall: 0.9736 accuracy: 0.00152146 cost: 0.00676556 M: 35.3773 delta: 0.0265441 time: 734.498 one-recall: 0.99 one-ratio: 1.00352
iteration: 29 recall: 0.9736 accuracy: 0.00152146 cost: 0.00676556 M: 35.3774 delta: 0.026544 time: 741.686 one-recall: 0.99 one-ratio: 1.00352
iteration: 30 recall: 0.9736 accuracy: 0.00152146 cost: 0.00676556 M: 35.3774 delta: 0.026544 time: 748.871 one-recall: 0.99 one-ratio: 1.00352
Graph completion with reverse edges...

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

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 767.7200000000012
Index size:  263020.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.1045999000
  Testing...
|S| = 80
|T| = 1152
Reject!
394.458 < 417.72
  -> Decision False in time 0.0200000000, query time of that 0.0053516900, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Reject!
358.367 < 463.592
  -> Decision False in time 0.0100000000, query time of that 0.0031453010, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
506.222 < 519.599
  -> Decision False in time 0.0000000000, query time of that 0.0004507780, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Reject!
303.427 < 454.19
  -> Decision False in time 0.1000000000, query time of that 0.0051598660, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
418.166 < 448.266
  -> Decision False in time 0.0100000000, query time of that 0.0006244550, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
454.714 < 472.264
  -> Decision False in time 0.2100000000, query time of that 0.0101741990, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Reject!
411.464 < 519.14
  -> Decision False in time 0.9100000000, query time of that 0.0044854970, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
239.132 < 425.256
  -> Decision False in time 0.0100000000, query time of that 0.0002530880, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
344.867 < 469.537
  -> Decision False in time 1.5100000000, query time of that 0.0070107010, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 20, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 20, {'reverse': -1}, False])
Got a train set of size (1000000 * 128)
Generating control...
Initializing...
iteration: 1 recall: 0 accuracy: 2.0953 cost: 0.00038 M: 10 delta: 1 time: 63.5546 one-recall: 0 one-ratio: 3.33718
iteration: 2 recall: 0.0028 accuracy: 1.19891 cost: 0.000637428 M: 10 delta: 0.856032 time: 107.588 one-recall: 0.01 one-ratio: 2.69122
iteration: 3 recall: 0.0348 accuracy: 0.681409 cost: 0.00109521 M: 11.5287 delta: 0.835107 time: 160.898 one-recall: 0.07 one-ratio: 2.10933
iteration: 4 recall: 0.1896 accuracy: 0.358775 cost: 0.00163043 M: 11.8362 delta: 0.783452 time: 213.956 one-recall: 0.27 one-ratio: 1.65839
iteration: 5 recall: 0.5276 accuracy: 0.10081 cost: 0.00223606 M: 12.6035 delta: 0.664589 time: 269.111 one-recall: 0.66 one-ratio: 1.25158
iteration: 6 recall: 0.7924 accuracy: 0.0263885 cost: 0.00298004 M: 15.1149 delta: 0.43234 time: 331.338 one-recall: 0.9 one-ratio: 1.05579
iteration: 7 recall: 0.9032 accuracy: 0.00843122 cost: 0.00395545 M: 21.1404 delta: 0.196425 time: 403.424 one-recall: 0.97 one-ratio: 1.01869
iteration: 8 recall: 0.9512 accuracy: 0.00326286 cost: 0.00498027 M: 27.3056 delta: 0.088441 time: 472.877 one-recall: 1 one-ratio: 1
iteration: 9 recall: 0.9728 accuracy: 0.00146391 cost: 0.00577274 M: 31.2874 delta: 0.0513224 time: 527.44 one-recall: 1 one-ratio: 1
iteration: 10 recall: 0.9788 accuracy: 0.00100875 cost: 0.00625791 M: 33.3937 delta: 0.0372 time: 565.325 one-recall: 1 one-ratio: 1
iteration: 11 recall: 0.9816 accuracy: 0.000868918 cost: 0.00651508 M: 34.424 delta: 0.0313081 time: 590.616 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.9828 accuracy: 0.000826579 cost: 0.00664255 M: 34.9134 delta: 0.0287735 time: 607.913 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.9848 accuracy: 0.00067951 cost: 0.00670417 M: 35.1469 delta: 0.0276109 time: 620.508 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.9848 accuracy: 0.00067951 cost: 0.00673404 M: 35.2586 delta: 0.0270765 time: 630.49 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.9848 accuracy: 0.00067951 cost: 0.00674886 M: 35.3135 delta: 0.0268156 time: 639.154 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.9848 accuracy: 0.00067951 cost: 0.00675644 M: 35.3417 delta: 0.0266922 time: 647.142 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.9848 accuracy: 0.00067951 cost: 0.0067603 M: 35.3557 delta: 0.0266283 time: 654.768 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.9848 accuracy: 0.00067951 cost: 0.00676228 M: 35.3632 delta: 0.0265945 time: 662.201 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.9848 accuracy: 0.00067951 cost: 0.00676338 M: 35.3674 delta: 0.0265773 time: 669.537 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.9848 accuracy: 0.00067951 cost: 0.00676395 M: 35.3695 delta: 0.0265693 time: 676.811 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.9848 accuracy: 0.00067951 cost: 0.00676425 M: 35.3707 delta: 0.0265635 time: 684.05 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.9848 accuracy: 0.00067951 cost: 0.00676441 M: 35.3714 delta: 0.026561 time: 691.269 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.9848 accuracy: 0.00067951 cost: 0.00676451 M: 35.3717 delta: 0.0265593 time: 698.475 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.9848 accuracy: 0.00067951 cost: 0.00676456 M: 35.3719 delta: 0.0265583 time: 705.683 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.9848 accuracy: 0.00067951 cost: 0.00676459 M: 35.372 delta: 0.026558 time: 712.879 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.9848 accuracy: 0.00067951 cost: 0.0067646 M: 35.3721 delta: 0.026558 time: 720.072 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.9848 accuracy: 0.00067951 cost: 0.00676461 M: 35.3721 delta: 0.0265577 time: 727.262 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.9848 accuracy: 0.00067951 cost: 0.00676462 M: 35.3721 delta: 0.0265578 time: 734.451 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.9848 accuracy: 0.00067951 cost: 0.00676462 M: 35.3721 delta: 0.0265577 time: 741.639 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.9848 accuracy: 0.00067951 cost: 0.00676463 M: 35.3722 delta: 0.0265578 time: 748.826 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 767.6800000000003
Index size:  263028.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0091560000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.0800000000, query time of that 0.0372048250, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Reject!
372.103 < 428.684
  -> Decision False in time 0.0200000000, query time of that 0.0081680650, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
396.628 < 432.778
  -> Decision False in time 0.8900000000, query time of that 0.3829189490, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Reject!
368.448 < 391.005
  -> Decision False in time 0.2900000000, query time of that 0.0192953230, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Accept!
  -> Decision True in time 5.6600000000, query time of that 0.4313156960, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
356.912 < 368.365
  -> Decision False in time 2.3000000000, query time of that 0.1782898060, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Reject!
330.965 < 405.09
  -> Decision False in time 6.2600000000, query time of that 0.0417025940, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
245.784 < 247.036
  -> Decision False in time 15.2700000000, query time of that 0.1046520740, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
286.725 < 293.3
  -> Decision False in time 3.6600000000, query time of that 0.0243308230, 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: 2.23021 cost: 0.00038 M: 10 delta: 1 time: 63.6219 one-recall: 0 one-ratio: 3.39212
iteration: 2 recall: 0.0052 accuracy: 1.29042 cost: 0.000637428 M: 10 delta: 0.856032 time: 107.649 one-recall: 0 one-ratio: 2.58536
iteration: 3 recall: 0.0324 accuracy: 0.698313 cost: 0.00109521 M: 11.5287 delta: 0.835106 time: 160.959 one-recall: 0.04 one-ratio: 2.06257
iteration: 4 recall: 0.1852 accuracy: 0.36008 cost: 0.00163042 M: 11.8361 delta: 0.783443 time: 213.993 one-recall: 0.2 one-ratio: 1.611
iteration: 5 recall: 0.5272 accuracy: 0.0910941 cost: 0.00223607 M: 12.604 delta: 0.664589 time: 269.142 one-recall: 0.56 one-ratio: 1.22373
iteration: 6 recall: 0.7944 accuracy: 0.0255801 cost: 0.00297995 M: 15.1151 delta: 0.432352 time: 331.358 one-recall: 0.84 one-ratio: 1.09442
iteration: 7 recall: 0.8952 accuracy: 0.0102518 cost: 0.00395524 M: 21.1403 delta: 0.196416 time: 403.435 one-recall: 0.93 one-ratio: 1.02624
iteration: 8 recall: 0.9408 accuracy: 0.00485427 cost: 0.00498012 M: 27.3063 delta: 0.0884376 time: 472.884 one-recall: 0.97 one-ratio: 1.01403
iteration: 9 recall: 0.9668 accuracy: 0.0026138 cost: 0.00577302 M: 31.2891 delta: 0.0513433 time: 527.464 one-recall: 0.97 one-ratio: 1.01403
iteration: 10 recall: 0.9788 accuracy: 0.00135518 cost: 0.00625762 M: 33.3919 delta: 0.0372287 time: 565.315 one-recall: 0.99 one-ratio: 1.00003
iteration: 11 recall: 0.982 accuracy: 0.00109805 cost: 0.00651475 M: 34.4227 delta: 0.0313161 time: 590.609 one-recall: 0.99 one-ratio: 1.00003
iteration: 12 recall: 0.9844 accuracy: 0.000955023 cost: 0.00664211 M: 34.9133 delta: 0.028753 time: 607.901 one-recall: 0.99 one-ratio: 1.00003
iteration: 13 recall: 0.9848 accuracy: 0.000941248 cost: 0.00670447 M: 35.1486 delta: 0.0276035 time: 620.549 one-recall: 0.99 one-ratio: 1.00003
iteration: 14 recall: 0.986 accuracy: 0.00084076 cost: 0.00673454 M: 35.2613 delta: 0.0270528 time: 630.558 one-recall: 0.99 one-ratio: 1.00003
iteration: 15 recall: 0.9868 accuracy: 0.000802487 cost: 0.00674927 M: 35.3169 delta: 0.0268039 time: 639.224 one-recall: 0.99 one-ratio: 1.00003
iteration: 16 recall: 0.9868 accuracy: 0.000802487 cost: 0.00675681 M: 35.3452 delta: 0.026674 time: 647.213 one-recall: 0.99 one-ratio: 1.00003
iteration: 17 recall: 0.9868 accuracy: 0.000802487 cost: 0.00676072 M: 35.3598 delta: 0.0266063 time: 654.843 one-recall: 0.99 one-ratio: 1.00003
iteration: 18 recall: 0.9868 accuracy: 0.000802487 cost: 0.00676271 M: 35.3673 delta: 0.0265754 time: 662.279 one-recall: 0.99 one-ratio: 1.00003
iteration: 19 recall: 0.9868 accuracy: 0.000802487 cost: 0.00676373 M: 35.3711 delta: 0.0265563 time: 669.608 one-recall: 0.99 one-ratio: 1.00003
iteration: 20 recall: 0.9868 accuracy: 0.000802487 cost: 0.00676427 M: 35.3729 delta: 0.026548 time: 676.88 one-recall: 0.99 one-ratio: 1.00003
iteration: 21 recall: 0.9868 accuracy: 0.000802487 cost: 0.00676452 M: 35.3739 delta: 0.0265432 time: 684.112 one-recall: 0.99 one-ratio: 1.00003
iteration: 22 recall: 0.9868 accuracy: 0.000802487 cost: 0.00676465 M: 35.3744 delta: 0.0265408 time: 691.329 one-recall: 0.99 one-ratio: 1.00003
iteration: 23 recall: 0.9868 accuracy: 0.000802487 cost: 0.00676472 M: 35.3746 delta: 0.0265404 time: 698.532 one-recall: 0.99 one-ratio: 1.00003
iteration: 24 recall: 0.9868 accuracy: 0.000802487 cost: 0.00676476 M: 35.3748 delta: 0.0265395 time: 705.728 one-recall: 0.99 one-ratio: 1.00003
iteration: 25 recall: 0.9868 accuracy: 0.000802487 cost: 0.00676478 M: 35.3749 delta: 0.026539 time: 712.924 one-recall: 0.99 one-ratio: 1.00003
iteration: 26 recall: 0.9868 accuracy: 0.000802487 cost: 0.00676479 M: 35.3749 delta: 0.0265389 time: 720.112 one-recall: 0.99 one-ratio: 1.00003
iteration: 27 recall: 0.9868 accuracy: 0.000802487 cost: 0.0067648 M: 35.375 delta: 0.0265387 time: 727.297 one-recall: 0.99 one-ratio: 1.00003
iteration: 28 recall: 0.9868 accuracy: 0.000802487 cost: 0.0067648 M: 35.375 delta: 0.0265385 time: 734.486 one-recall: 0.99 one-ratio: 1.00003
iteration: 29 recall: 0.9868 accuracy: 0.000802487 cost: 0.0067648 M: 35.375 delta: 0.0265384 time: 741.674 one-recall: 0.99 one-ratio: 1.00003
iteration: 30 recall: 0.9868 accuracy: 0.000802487 cost: 0.0067648 M: 35.375 delta: 0.0265384 time: 748.863 one-recall: 0.99 one-ratio: 1.00003
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 767.7199999999975
Index size:  262892.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0062472000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.1000000000, query time of that 0.0532598540, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Reject!
268.529 < 269.748
  -> Decision False in time 0.9200000000, query time of that 0.4830128960, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
424.35 < 425.852
  -> Decision False in time 5.7300000000, query time of that 2.9840849860, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.6100000000, query time of that 0.0643020830, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Accept!
  -> Decision True in time 5.9800000000, query time of that 0.6243319910, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
238.751 < 239.395
  -> Decision False in time 15.5900000000, query time of that 1.6337298440, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Accept!
  -> Decision True in time 8.1300000000, query time of that 0.0740563980, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
251.434 < 265.533
  -> Decision False in time 10.6700000000, query time of that 0.1000341370, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
297.219 < 301.369
  -> Decision False in time 5.7500000000, query time of that 0.0503948880, 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: 1.86144 cost: 0.00038 M: 10 delta: 1 time: 63.6289 one-recall: 0 one-ratio: 3.14431
iteration: 2 recall: 0.0052 accuracy: 1.02168 cost: 0.000637428 M: 10 delta: 0.856032 time: 107.662 one-recall: 0.01 one-ratio: 2.504
iteration: 3 recall: 0.0352 accuracy: 0.571611 cost: 0.00109521 M: 11.5287 delta: 0.835108 time: 160.977 one-recall: 0.05 one-ratio: 2.04382
iteration: 4 recall: 0.1808 accuracy: 0.280373 cost: 0.00163045 M: 11.8362 delta: 0.783464 time: 214.028 one-recall: 0.2 one-ratio: 1.66818
iteration: 5 recall: 0.5008 accuracy: 0.102153 cost: 0.00223611 M: 12.6035 delta: 0.664601 time: 269.167 one-recall: 0.55 one-ratio: 1.29218
iteration: 6 recall: 0.7616 accuracy: 0.028441 cost: 0.0029801 M: 15.1153 delta: 0.432359 time: 331.382 one-recall: 0.82 one-ratio: 1.09526
iteration: 7 recall: 0.88 accuracy: 0.00972768 cost: 0.00395545 M: 21.14 delta: 0.196415 time: 403.43 one-recall: 0.92 one-ratio: 1.01048
iteration: 8 recall: 0.9364 accuracy: 0.0043777 cost: 0.00498005 M: 27.3071 delta: 0.0884538 time: 472.841 one-recall: 0.96 one-ratio: 1.00682
iteration: 9 recall: 0.956 accuracy: 0.00283091 cost: 0.00577309 M: 31.2933 delta: 0.0513682 time: 527.424 one-recall: 0.97 one-ratio: 1.00564
iteration: 10 recall: 0.9684 accuracy: 0.00204548 cost: 0.00625912 M: 33.4024 delta: 0.0372211 time: 565.345 one-recall: 0.97 one-ratio: 1.00564
iteration: 11 recall: 0.9728 accuracy: 0.00176134 cost: 0.00651643 M: 34.434 delta: 0.0313266 time: 590.671 one-recall: 0.97 one-ratio: 1.00564
iteration: 12 recall: 0.9752 accuracy: 0.00153081 cost: 0.00664454 M: 34.9279 delta: 0.0287433 time: 608.02 one-recall: 0.98 one-ratio: 1.00504
iteration: 13 recall: 0.9764 accuracy: 0.00152161 cost: 0.00670674 M: 35.1627 delta: 0.0275936 time: 620.65 one-recall: 0.98 one-ratio: 1.00504
iteration: 14 recall: 0.9768 accuracy: 0.00150385 cost: 0.0067372 M: 35.2773 delta: 0.0270297 time: 630.696 one-recall: 0.98 one-ratio: 1.00504
iteration: 15 recall: 0.9772 accuracy: 0.00146795 cost: 0.00675208 M: 35.332 delta: 0.0267757 time: 639.387 one-recall: 0.98 one-ratio: 1.00504
iteration: 16 recall: 0.9772 accuracy: 0.00146795 cost: 0.00675965 M: 35.3601 delta: 0.0266508 time: 647.386 one-recall: 0.98 one-ratio: 1.00504
iteration: 17 recall: 0.9772 accuracy: 0.00146795 cost: 0.00676347 M: 35.3743 delta: 0.0265832 time: 655.011 one-recall: 0.98 one-ratio: 1.00504
iteration: 18 recall: 0.9772 accuracy: 0.00146795 cost: 0.00676539 M: 35.3813 delta: 0.0265511 time: 662.443 one-recall: 0.98 one-ratio: 1.00504
iteration: 19 recall: 0.9772 accuracy: 0.00146795 cost: 0.00676637 M: 35.3848 delta: 0.0265369 time: 669.769 one-recall: 0.98 one-ratio: 1.00504
iteration: 20 recall: 0.9772 accuracy: 0.00146795 cost: 0.00676686 M: 35.3866 delta: 0.0265297 time: 677.039 one-recall: 0.98 one-ratio: 1.00504
iteration: 21 recall: 0.9772 accuracy: 0.00146795 cost: 0.00676717 M: 35.3877 delta: 0.0265252 time: 684.285 one-recall: 0.98 one-ratio: 1.00504
iteration: 22 recall: 0.9772 accuracy: 0.00146795 cost: 0.00676732 M: 35.3882 delta: 0.0265226 time: 691.51 one-recall: 0.98 one-ratio: 1.00504
iteration: 23 recall: 0.9772 accuracy: 0.00146795 cost: 0.00676739 M: 35.3885 delta: 0.0265218 time: 698.714 one-recall: 0.98 one-ratio: 1.00504
iteration: 24 recall: 0.9772 accuracy: 0.00146795 cost: 0.00676743 M: 35.3886 delta: 0.0265206 time: 705.914 one-recall: 0.98 one-ratio: 1.00504
iteration: 25 recall: 0.9772 accuracy: 0.00146795 cost: 0.00676745 M: 35.3887 delta: 0.0265205 time: 713.105 one-recall: 0.98 one-ratio: 1.00504
iteration: 26 recall: 0.9772 accuracy: 0.00146795 cost: 0.00676746 M: 35.3887 delta: 0.0265205 time: 720.295 one-recall: 0.98 one-ratio: 1.00504
iteration: 27 recall: 0.9772 accuracy: 0.00146795 cost: 0.00676746 M: 35.3888 delta: 0.0265205 time: 727.488 one-recall: 0.98 one-ratio: 1.00504
iteration: 28 recall: 0.9772 accuracy: 0.00146795 cost: 0.00676746 M: 35.3888 delta: 0.0265202 time: 734.678 one-recall: 0.98 one-ratio: 1.00504
iteration: 29 recall: 0.9772 accuracy: 0.00146795 cost: 0.00676747 M: 35.3888 delta: 0.0265201 time: 741.865 one-recall: 0.98 one-ratio: 1.00504
iteration: 30 recall: 0.9772 accuracy: 0.00146795 cost: 0.00676747 M: 35.3888 delta: 0.0265201 time: 749.06 one-recall: 0.98 one-ratio: 1.00504
Graph completion with reverse edges...

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

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 767.8899999999994
Index size:  263296.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0107546000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.0800000000, query time of that 0.0276261940, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Accept!
  -> Decision True in time 0.7000000000, query time of that 0.2595787240, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
408.874 < 418.085
  -> Decision False in time 0.8600000000, query time of that 0.3062150490, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Reject!
381.101 < 400.936
  -> Decision False in time 0.0700000000, query time of that 0.0040904000, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
284.529 < 290.119
  -> Decision False in time 3.7400000000, query time of that 0.2256837400, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
244.399 < 253.612
  -> Decision False in time 1.4200000000, query time of that 0.0839834460, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Accept!
  -> Decision True in time 8.1000000000, query time of that 0.0428948320, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
262.58 < 264.707
  -> Decision False in time 0.3800000000, query time of that 0.0016842180, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
261.92 < 266.816
  -> Decision False in time 5.2800000000, query time of that 0.0289084550, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 30, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 30, {'reverse': -1}, False])
Got a train set of size (1000000 * 128)
Generating control...
Initializing...
iteration: 1 recall: 0 accuracy: 2.20782 cost: 0.00038 M: 10 delta: 1 time: 63.6166 one-recall: 0 one-ratio: 3.62478
iteration: 2 recall: 0.0036 accuracy: 1.17926 cost: 0.000637428 M: 10 delta: 0.856032 time: 107.647 one-recall: 0 one-ratio: 2.82006
iteration: 3 recall: 0.0332 accuracy: 0.661008 cost: 0.00109521 M: 11.5287 delta: 0.835106 time: 160.97 one-recall: 0.05 one-ratio: 2.29821
iteration: 4 recall: 0.1828 accuracy: 0.329717 cost: 0.00163045 M: 11.8364 delta: 0.783458 time: 214.036 one-recall: 0.3 one-ratio: 1.77968
iteration: 5 recall: 0.5108 accuracy: 0.133477 cost: 0.0022361 M: 12.6039 delta: 0.664597 time: 269.187 one-recall: 0.67 one-ratio: 1.29935
iteration: 6 recall: 0.7776 accuracy: 0.0286082 cost: 0.00298004 M: 15.1151 delta: 0.432372 time: 331.423 one-recall: 0.89 one-ratio: 1.07724
iteration: 7 recall: 0.8988 accuracy: 0.00887345 cost: 0.00395526 M: 21.1379 delta: 0.196461 time: 403.52 one-recall: 0.97 one-ratio: 1.04021
iteration: 8 recall: 0.9528 accuracy: 0.003916 cost: 0.00497932 M: 27.3029 delta: 0.0884695 time: 472.922 one-recall: 0.98 one-ratio: 1.0145
iteration: 9 recall: 0.972 accuracy: 0.00172308 cost: 0.00577169 M: 31.286 delta: 0.0513521 time: 527.481 one-recall: 1 one-ratio: 1
iteration: 10 recall: 0.9788 accuracy: 0.00113504 cost: 0.00625628 M: 33.3891 delta: 0.0372164 time: 565.324 one-recall: 1 one-ratio: 1
iteration: 11 recall: 0.9836 accuracy: 0.000899743 cost: 0.00651394 M: 34.4216 delta: 0.0313218 time: 590.658 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.9844 accuracy: 0.000866588 cost: 0.00664233 M: 34.9144 delta: 0.0287555 time: 608.015 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.9856 accuracy: 0.0007799 cost: 0.00670448 M: 35.1491 delta: 0.0276021 time: 620.639 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.9856 accuracy: 0.000779169 cost: 0.00673468 M: 35.2624 delta: 0.0270654 time: 630.649 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.9856 accuracy: 0.000779169 cost: 0.00674975 M: 35.3187 delta: 0.0268013 time: 639.352 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.9856 accuracy: 0.000779169 cost: 0.00675723 M: 35.3466 delta: 0.0266659 time: 647.332 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.9856 accuracy: 0.000779169 cost: 0.00676092 M: 35.3603 delta: 0.0266033 time: 654.939 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.9856 accuracy: 0.000779169 cost: 0.00676286 M: 35.3675 delta: 0.0265739 time: 662.367 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.986 accuracy: 0.000776963 cost: 0.00676387 M: 35.3714 delta: 0.0265576 time: 669.692 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.986 accuracy: 0.000776963 cost: 0.00676446 M: 35.3736 delta: 0.0265479 time: 676.972 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.986 accuracy: 0.000776963 cost: 0.00676477 M: 35.3749 delta: 0.0265431 time: 684.211 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.986 accuracy: 0.000776963 cost: 0.00676493 M: 35.3755 delta: 0.0265402 time: 691.43 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.986 accuracy: 0.000776963 cost: 0.006765 M: 35.3757 delta: 0.0265387 time: 698.637 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.986 accuracy: 0.000776963 cost: 0.00676506 M: 35.3759 delta: 0.0265384 time: 705.837 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.986 accuracy: 0.000776963 cost: 0.00676509 M: 35.376 delta: 0.0265382 time: 713.03 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.986 accuracy: 0.000776963 cost: 0.00676511 M: 35.3761 delta: 0.0265377 time: 720.221 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.986 accuracy: 0.000776963 cost: 0.00676512 M: 35.3761 delta: 0.0265375 time: 727.411 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.986 accuracy: 0.000776963 cost: 0.00676512 M: 35.3761 delta: 0.0265375 time: 734.596 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.986 accuracy: 0.000776963 cost: 0.00676512 M: 35.3761 delta: 0.0265375 time: 741.787 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.986 accuracy: 0.000776963 cost: 0.00676512 M: 35.3761 delta: 0.0265375 time: 748.973 one-recall: 1 one-ratio: 1
Graph completion with reverse edges...

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

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 767.8199999999997
Index size:  262864.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0071923000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.0900000000, query time of that 0.0438319020, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Reject!
257.016 < 259.781
  -> Decision False in time 0.8200000000, query time of that 0.4022125440, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
315.344 < 372.655
  -> Decision False in time 0.7400000000, query time of that 0.3529718160, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.5900000000, query time of that 0.0529026110, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Accept!
  -> Decision True in time 5.8600000000, query time of that 0.5369806960, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
271.45 < 279.753
  -> Decision False in time 17.9600000000, query time of that 1.6403441580, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Accept!
  -> Decision True in time 8.1200000000, query time of that 0.0655145480, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
231.551 < 241.845
  -> Decision False in time 10.1400000000, query time of that 0.0825132110, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
267.67 < 270.771
  -> Decision False in time 0.5800000000, query time of that 0.0054015370, 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: 0.0008 accuracy: 2.18449 cost: 0.00038 M: 10 delta: 1 time: 63.6203 one-recall: 0 one-ratio: 3.34949
iteration: 2 recall: 0.0048 accuracy: 1.09808 cost: 0.000637428 M: 10 delta: 0.856032 time: 107.651 one-recall: 0.01 one-ratio: 2.60664
iteration: 3 recall: 0.036 accuracy: 0.601347 cost: 0.00109521 M: 11.5287 delta: 0.835112 time: 160.971 one-recall: 0.07 one-ratio: 2.08121
iteration: 4 recall: 0.2008 accuracy: 0.302616 cost: 0.00163044 M: 11.8362 delta: 0.783461 time: 214.035 one-recall: 0.22 one-ratio: 1.67445
iteration: 5 recall: 0.522 accuracy: 0.0913812 cost: 0.00223603 M: 12.6034 delta: 0.664592 time: 269.173 one-recall: 0.56 one-ratio: 1.27976
iteration: 6 recall: 0.7976 accuracy: 0.0222611 cost: 0.00297994 M: 15.1148 delta: 0.432324 time: 331.399 one-recall: 0.87 one-ratio: 1.06702
iteration: 7 recall: 0.9092 accuracy: 0.00665105 cost: 0.00395549 M: 21.1429 delta: 0.196368 time: 403.505 one-recall: 0.99 one-ratio: 1.00528
iteration: 8 recall: 0.9472 accuracy: 0.00325069 cost: 0.00497988 M: 27.3047 delta: 0.0884478 time: 472.928 one-recall: 0.99 one-ratio: 1.00528
iteration: 9 recall: 0.9668 accuracy: 0.00203549 cost: 0.00577279 M: 31.2885 delta: 0.0513452 time: 527.52 one-recall: 1 one-ratio: 1
iteration: 10 recall: 0.9744 accuracy: 0.00165267 cost: 0.00625768 M: 33.3943 delta: 0.0371819 time: 565.382 one-recall: 1 one-ratio: 1
iteration: 11 recall: 0.9776 accuracy: 0.0014069 cost: 0.00651477 M: 34.4239 delta: 0.0312962 time: 590.662 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.978 accuracy: 0.00137047 cost: 0.00664287 M: 34.9172 delta: 0.0287446 time: 608.004 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.9784 accuracy: 0.00130778 cost: 0.00670509 M: 35.1523 delta: 0.0275456 time: 620.626 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.9788 accuracy: 0.00129436 cost: 0.00673544 M: 35.2661 delta: 0.0270046 time: 630.647 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.9788 accuracy: 0.00129436 cost: 0.00675031 M: 35.3219 delta: 0.0267467 time: 639.318 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.9788 accuracy: 0.00129436 cost: 0.00675783 M: 35.3496 delta: 0.0266167 time: 647.3 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.9792 accuracy: 0.00125259 cost: 0.00676164 M: 35.3638 delta: 0.0265552 time: 654.918 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.9792 accuracy: 0.00125259 cost: 0.00676362 M: 35.3711 delta: 0.0265267 time: 662.348 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.9792 accuracy: 0.00125259 cost: 0.00676466 M: 35.3751 delta: 0.0265091 time: 669.682 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.9792 accuracy: 0.00125259 cost: 0.00676526 M: 35.3774 delta: 0.0265002 time: 676.963 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.9792 accuracy: 0.00125259 cost: 0.00676558 M: 35.3787 delta: 0.0264956 time: 684.21 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.9792 accuracy: 0.00125259 cost: 0.00676576 M: 35.3794 delta: 0.0264928 time: 691.434 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.9792 accuracy: 0.00125259 cost: 0.00676587 M: 35.3798 delta: 0.0264913 time: 698.65 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.9792 accuracy: 0.00125259 cost: 0.00676593 M: 35.38 delta: 0.0264904 time: 705.854 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.9792 accuracy: 0.00125259 cost: 0.00676595 M: 35.3801 delta: 0.02649 time: 713.049 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.9792 accuracy: 0.00125259 cost: 0.00676598 M: 35.3802 delta: 0.0264897 time: 720.245 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.9792 accuracy: 0.00125259 cost: 0.00676599 M: 35.3803 delta: 0.0264895 time: 727.446 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.9792 accuracy: 0.00125259 cost: 0.00676599 M: 35.3803 delta: 0.0264893 time: 734.632 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.9792 accuracy: 0.00125259 cost: 0.00676599 M: 35.3803 delta: 0.0264893 time: 741.823 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.9792 accuracy: 0.00125259 cost: 0.00676599 M: 35.3803 delta: 0.0264893 time: 749.009 one-recall: 1 one-ratio: 1
Graph completion with reverse edges...

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

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 767.8600000000006
Index size:  263208.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0114603000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.0700000000, query time of that 0.0222414140, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Reject!
414.578 < 424.66
  -> Decision False in time 0.2600000000, query time of that 0.0812396470, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
249.978 < 252.646
  -> Decision False in time 1.1300000000, query time of that 0.3489736200, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.5600000000, query time of that 0.0251538700, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
355.675 < 360.505
  -> Decision False in time 0.0300000000, query time of that 0.0013283550, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
337.55 < 408.74
  -> Decision False in time 3.1600000000, query time of that 0.1532005010, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Accept!
  -> Decision True in time 8.0500000000, query time of that 0.0356507490, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
255.045 < 255.374
  -> Decision False in time 28.0700000000, query time of that 0.1250810600, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
243.516 < 262.479
  -> Decision False in time 10.7300000000, query time of that 0.0471870360, with c1=5.0000000000, c2=0.1000000000
