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', 1, {'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', 10, {'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', 70, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 20, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 60, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 90, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 30, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 3, {'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', 40, {'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', 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: 2.29356 cost: 0.00038 M: 10 delta: 1 time: 54.3102 one-recall: 0 one-ratio: 3.52528
iteration: 2 recall: 0.004 accuracy: 1.20332 cost: 0.000637428 M: 10 delta: 0.856032 time: 92.421 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: 138.589 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: 184.331 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: 231.785 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: 285.315 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: 346.236 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: 403.117 one-recall: 1 one-ratio: 1
iteration: 9 recall: 0.9644 accuracy: 0.00221739 cost: 0.00577293 M: 31.2904 delta: 0.0514019 time: 446.419 one-recall: 1 one-ratio: 1
iteration: 10 recall: 0.9764 accuracy: 0.0012952 cost: 0.00625843 M: 33.3974 delta: 0.0372226 time: 475.562 one-recall: 1 one-ratio: 1
iteration: 11 recall: 0.984 accuracy: 0.000915513 cost: 0.00651572 M: 34.4268 delta: 0.0313604 time: 494.522 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.9872 accuracy: 0.000651871 cost: 0.00664321 M: 34.9174 delta: 0.028787 time: 507.244 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.9876 accuracy: 0.000628894 cost: 0.00670536 M: 35.1523 delta: 0.027629 time: 516.456 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.9876 accuracy: 0.000628894 cost: 0.00673551 M: 35.2647 delta: 0.0270893 time: 523.747 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.9876 accuracy: 0.000628894 cost: 0.00675029 M: 35.3194 delta: 0.0268326 time: 530.05 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.9876 accuracy: 0.000628894 cost: 0.00675777 M: 35.3471 delta: 0.0267076 time: 535.862 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.9876 accuracy: 0.000628894 cost: 0.00676154 M: 35.3608 delta: 0.0266443 time: 541.408 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.9876 accuracy: 0.000628894 cost: 0.00676338 M: 35.3677 delta: 0.0266122 time: 546.807 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.9876 accuracy: 0.000628894 cost: 0.00676433 M: 35.3712 delta: 0.0265977 time: 552.133 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.9876 accuracy: 0.000628894 cost: 0.00676485 M: 35.373 delta: 0.02659 time: 557.427 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.988 accuracy: 0.0006087 cost: 0.00676515 M: 35.3742 delta: 0.0265857 time: 562.694 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.988 accuracy: 0.0006087 cost: 0.00676532 M: 35.3749 delta: 0.0265828 time: 567.943 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.988 accuracy: 0.0006087 cost: 0.00676541 M: 35.3753 delta: 0.0265811 time: 573.185 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.988 accuracy: 0.0006087 cost: 0.00676547 M: 35.3755 delta: 0.0265798 time: 578.417 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.988 accuracy: 0.0006087 cost: 0.00676549 M: 35.3756 delta: 0.0265796 time: 583.642 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.988 accuracy: 0.0006087 cost: 0.00676551 M: 35.3757 delta: 0.0265794 time: 588.868 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.988 accuracy: 0.0006087 cost: 0.00676552 M: 35.3757 delta: 0.0265791 time: 594.092 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.988 accuracy: 0.0006087 cost: 0.00676553 M: 35.3757 delta: 0.0265791 time: 599.312 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.988 accuracy: 0.0006087 cost: 0.00676553 M: 35.3757 delta: 0.026579 time: 604.535 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.988 accuracy: 0.0006087 cost: 0.00676553 M: 35.3757 delta: 0.026579 time: 609.756 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 624.31
Index size:  1903140.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.1034248000
  Testing...
|S| = 80
|T| = 1152
Reject!
318.583 < 457.83
  -> Decision False in time 0.0200000000, query time of that 0.0038913900, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Reject!
385.169 < 435.055
  -> Decision False in time 0.0000000000, query time of that 0.0001281820, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
454.794 < 468.6
  -> Decision False in time 0.0000000000, query time of that 0.0003125150, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Reject!
220.425 < 233.814
  -> Decision False in time 0.0100000000, query time of that 0.0001833370, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
404.907 < 465.061
  -> Decision False in time 0.0900000000, query time of that 0.0034382480, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
363.497 < 419.653
  -> Decision False in time 0.0200000000, query time of that 0.0008179280, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Reject!
451.709 < 457.953
  -> Decision False in time 0.0000000000, query time of that 0.0001764630, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
385.332 < 477.93
  -> Decision False in time 0.5800000000, query time of that 0.0020295520, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
331.142 < 488.169
  -> Decision False in time 0.0100000000, query time of that 0.0001725990, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 80, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 80, {'reverse': -1}, False])
Got a train set of size (1000000 * 128)
Generating control...
Initializing...
iteration: 1 recall: 0 accuracy: 2.6766 cost: 0.00038 M: 10 delta: 1 time: 53.8423 one-recall: 0 one-ratio: 3.66072
iteration: 2 recall: 0.0016 accuracy: 1.38091 cost: 0.000637428 M: 10 delta: 0.856032 time: 91.9453 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: 138.112 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: 183.87 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: 231.352 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: 284.951 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: 345.954 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: 402.901 one-recall: 1 one-ratio: 1
iteration: 9 recall: 0.97 accuracy: 0.00153751 cost: 0.00577336 M: 31.2902 delta: 0.0513331 time: 446.261 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: 494.398 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.9856 accuracy: 0.000634549 cost: 0.00664356 M: 34.9169 delta: 0.0287556 time: 507.201 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.9868 accuracy: 0.000578651 cost: 0.00670598 M: 35.1529 delta: 0.027595 time: 516.432 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.9876 accuracy: 0.000568954 cost: 0.00673627 M: 35.2664 delta: 0.0270435 time: 523.746 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.9876 accuracy: 0.000568954 cost: 0.00675099 M: 35.3217 delta: 0.0267899 time: 530.056 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.9876 accuracy: 0.000568954 cost: 0.0067584 M: 35.3495 delta: 0.0266568 time: 535.867 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.9876 accuracy: 0.000568954 cost: 0.00676207 M: 35.3629 delta: 0.0265947 time: 541.408 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.9876 accuracy: 0.000568954 cost: 0.00676399 M: 35.37 delta: 0.0265678 time: 546.81 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.9876 accuracy: 0.000568954 cost: 0.00676502 M: 35.3739 delta: 0.0265497 time: 552.14 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.9876 accuracy: 0.000568954 cost: 0.00676557 M: 35.376 delta: 0.026542 time: 557.427 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.9876 accuracy: 0.000568954 cost: 0.00676589 M: 35.3772 delta: 0.026538 time: 562.687 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.9876 accuracy: 0.000568954 cost: 0.00676609 M: 35.378 delta: 0.0265349 time: 567.935 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.9876 accuracy: 0.000568954 cost: 0.00676619 M: 35.3784 delta: 0.0265336 time: 573.174 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.9876 accuracy: 0.000568954 cost: 0.00676626 M: 35.3787 delta: 0.0265323 time: 578.406 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.9876 accuracy: 0.000568954 cost: 0.0067663 M: 35.3789 delta: 0.026532 time: 583.632 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.9876 accuracy: 0.000568954 cost: 0.00676631 M: 35.3789 delta: 0.0265317 time: 588.857 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.9876 accuracy: 0.000568954 cost: 0.00676632 M: 35.379 delta: 0.0265314 time: 594.08 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.9876 accuracy: 0.000568954 cost: 0.00676632 M: 35.379 delta: 0.0265314 time: 599.317 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.9876 accuracy: 0.000568954 cost: 0.00676633 M: 35.379 delta: 0.0265313 time: 604.536 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.9876 accuracy: 0.000568954 cost: 0.00676633 M: 35.379 delta: 0.0265314 time: 609.756 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 624.2199999999999
Index size:  260980.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0031756000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.1100000000, query time of that 0.0682043330, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Accept!
  -> Decision True in time 1.0300000000, query time of that 0.5767169570, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Accept!
  -> Decision True in time 10.4500000000, query time of that 5.8530958940, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Reject!
335.362 < 350.32
  -> Decision False in time 0.1000000000, query time of that 0.0135602370, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Accept!
  -> Decision True in time 5.7900000000, query time of that 0.6782210610, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
291.803 < 294.873
  -> Decision False in time 9.0100000000, query time of that 1.0393439970, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Reject!
291.417 < 295.946
  -> Decision False in time 4.1800000000, query time of that 0.0552712300, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
273.134 < 274.082
  -> Decision False in time 4.6400000000, query time of that 0.0540046300, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
268.82 < 272.848
  -> Decision False in time 11.6900000000, query time of that 0.1342130330, 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: 2.48177 cost: 0.00038 M: 10 delta: 1 time: 53.8663 one-recall: 0 one-ratio: 3.57151
iteration: 2 recall: 0.006 accuracy: 1.20232 cost: 0.000637428 M: 10 delta: 0.856032 time: 91.9679 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: 138.119 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: 183.877 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: 231.357 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: 284.933 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: 345.913 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: 402.85 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: 446.157 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: 475.332 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: 494.365 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: 507.137 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: 516.357 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: 523.647 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: 529.961 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: 535.78 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: 541.336 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: 546.747 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: 552.081 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: 557.366 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: 562.629 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: 567.876 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: 573.112 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: 578.344 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: 583.573 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: 588.8 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: 594.026 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: 599.254 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: 604.477 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: 609.7 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: 0.0024772000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.1500000000, query time of that 0.0992534790, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Accept!
  -> Decision True in time 1.4300000000, query time of that 0.9788038860, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Accept!
  -> Decision True in time 14.2700000000, query time of that 9.6440131190, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.6900000000, query time of that 0.1154704280, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Accept!
  -> Decision True in time 6.8200000000, query time of that 1.1507152870, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
359.594 < 396.935
  -> Decision False in time 23.8500000000, query time of that 4.0931039500, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Accept!
  -> Decision True in time 8.2000000000, query time of that 0.1337170120, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Accept!
  -> Decision True in time 82.4100000000, query time of that 1.3244578190, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
241.711 < 247.792
  -> Decision False in time 44.5700000000, query time of that 0.7250031070, 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: 0.0008 accuracy: 2.75563 cost: 0.00038 M: 10 delta: 1 time: 63.6616 one-recall: 0 one-ratio: 3.20375
iteration: 2 recall: 0.0036 accuracy: 1.28835 cost: 0.000637428 M: 10 delta: 0.856032 time: 107.741 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: 161.126 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: 214.25 one-recall: 0.15 one-ratio: 1.72886
iteration: 5 recall: 0.4932 accuracy: 0.113832 cost: 0.00223606 M: 12.6036 delta: 0.664588 time: 269.449 one-recall: 0.55 one-ratio: 1.31529
iteration: 6 recall: 0.746 accuracy: 0.0314189 cost: 0.00297996 M: 15.1149 delta: 0.432359 time: 331.7 one-recall: 0.82 one-ratio: 1.08068
iteration: 7 recall: 0.8676 accuracy: 0.0106082 cost: 0.0039552 M: 21.1403 delta: 0.196427 time: 403.807 one-recall: 0.94 one-ratio: 1.01691
iteration: 8 recall: 0.9224 accuracy: 0.00563543 cost: 0.00497947 M: 27.3031 delta: 0.0885044 time: 473.27 one-recall: 0.97 one-ratio: 1.01391
iteration: 9 recall: 0.9476 accuracy: 0.0029921 cost: 0.00577188 M: 31.2856 delta: 0.0513972 time: 527.899 one-recall: 0.99 one-ratio: 1.00022
iteration: 10 recall: 0.9596 accuracy: 0.00226055 cost: 0.0062569 M: 33.3914 delta: 0.0372701 time: 565.839 one-recall: 0.99 one-ratio: 1.00022
iteration: 11 recall: 0.9636 accuracy: 0.00200393 cost: 0.00651437 M: 34.4225 delta: 0.0313597 time: 591.23 one-recall: 0.99 one-ratio: 1.00022
iteration: 12 recall: 0.9664 accuracy: 0.00187919 cost: 0.00664206 M: 34.9138 delta: 0.0287899 time: 608.606 one-recall: 0.99 one-ratio: 1.00022
iteration: 13 recall: 0.968 accuracy: 0.0018145 cost: 0.00670424 M: 35.1488 delta: 0.0276308 time: 621.291 one-recall: 0.99 one-ratio: 1.00022
iteration: 14 recall: 0.968 accuracy: 0.0018145 cost: 0.0067349 M: 35.2629 delta: 0.0270725 time: 631.41 one-recall: 0.99 one-ratio: 1.00022
iteration: 15 recall: 0.968 accuracy: 0.0018145 cost: 0.00674943 M: 35.3177 delta: 0.0268174 time: 640.101 one-recall: 0.99 one-ratio: 1.00022
iteration: 16 recall: 0.968 accuracy: 0.0018145 cost: 0.00675678 M: 35.3452 delta: 0.0266916 time: 648.107 one-recall: 0.99 one-ratio: 1.00022
iteration: 17 recall: 0.9684 accuracy: 0.00178448 cost: 0.00676047 M: 35.3591 delta: 0.026633 time: 655.748 one-recall: 0.99 one-ratio: 1.00022
iteration: 18 recall: 0.9684 accuracy: 0.00178448 cost: 0.00676253 M: 35.3666 delta: 0.0265977 time: 663.217 one-recall: 0.99 one-ratio: 1.00022
iteration: 19 recall: 0.9684 accuracy: 0.00178448 cost: 0.00676355 M: 35.3706 delta: 0.0265794 time: 670.567 one-recall: 0.99 one-ratio: 1.00022
iteration: 20 recall: 0.9684 accuracy: 0.00178448 cost: 0.00676408 M: 35.3726 delta: 0.0265712 time: 677.851 one-recall: 0.99 one-ratio: 1.00022
iteration: 21 recall: 0.9684 accuracy: 0.00178448 cost: 0.00676436 M: 35.3737 delta: 0.0265661 time: 685.093 one-recall: 0.99 one-ratio: 1.00022
iteration: 22 recall: 0.9684 accuracy: 0.00178448 cost: 0.0067645 M: 35.3742 delta: 0.0265644 time: 692.31 one-recall: 0.99 one-ratio: 1.00022
iteration: 23 recall: 0.9684 accuracy: 0.00178448 cost: 0.00676455 M: 35.3744 delta: 0.0265631 time: 699.513 one-recall: 0.99 one-ratio: 1.00022
iteration: 24 recall: 0.9684 accuracy: 0.00178448 cost: 0.00676458 M: 35.3745 delta: 0.0265625 time: 706.712 one-recall: 0.99 one-ratio: 1.00022
iteration: 25 recall: 0.9684 accuracy: 0.00178448 cost: 0.00676459 M: 35.3745 delta: 0.0265624 time: 713.903 one-recall: 0.99 one-ratio: 1.00022
iteration: 26 recall: 0.9684 accuracy: 0.00178448 cost: 0.0067646 M: 35.3745 delta: 0.0265622 time: 721.095 one-recall: 0.99 one-ratio: 1.00022
iteration: 27 recall: 0.9684 accuracy: 0.00178448 cost: 0.0067646 M: 35.3745 delta: 0.0265622 time: 728.283 one-recall: 0.99 one-ratio: 1.00022
iteration: 28 recall: 0.9684 accuracy: 0.00178448 cost: 0.0067646 M: 35.3746 delta: 0.0265621 time: 735.485 one-recall: 0.99 one-ratio: 1.00022
iteration: 29 recall: 0.9684 accuracy: 0.00178448 cost: 0.0067646 M: 35.3746 delta: 0.0265619 time: 742.671 one-recall: 0.99 one-ratio: 1.00022
iteration: 30 recall: 0.9684 accuracy: 0.00178448 cost: 0.0067646 M: 35.3746 delta: 0.0265619 time: 749.862 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 768.6199999999999
Index size:  262848.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0107715000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.0700000000, query time of that 0.0289698960, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Reject!
263.608 < 341.918
  -> Decision False in time 0.3800000000, query time of that 0.1436290110, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
227.521 < 398.394
  -> Decision False in time 0.5300000000, query time of that 0.1910217530, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.5700000000, query time of that 0.0324829230, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Accept!
  -> Decision True in time 5.5900000000, query time of that 0.3343233500, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
256.295 < 263.01
  -> Decision False in time 0.1800000000, query time of that 0.0093104430, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Accept!
  -> Decision True in time 8.0900000000, query time of that 0.0435884630, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
196.777 < 199.762
  -> Decision False in time 6.1500000000, query time of that 0.0323418940, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
305.239 < 305.328
  -> Decision False in time 18.4300000000, query time of that 0.0970461200, 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: 0.0004 accuracy: 2.29894 cost: 0.00038 M: 10 delta: 1 time: 63.7 one-recall: 0 one-ratio: 3.10797
iteration: 2 recall: 0.0044 accuracy: 1.16986 cost: 0.000637428 M: 10 delta: 0.856032 time: 107.773 one-recall: 0 one-ratio: 2.49046
iteration: 3 recall: 0.0392 accuracy: 0.626236 cost: 0.00109521 M: 11.5287 delta: 0.835103 time: 161.159 one-recall: 0.02 one-ratio: 2.00884
iteration: 4 recall: 0.212 accuracy: 0.311593 cost: 0.00163042 M: 11.8362 delta: 0.783442 time: 214.29 one-recall: 0.28 one-ratio: 1.51788
iteration: 5 recall: 0.5272 accuracy: 0.0921518 cost: 0.00223607 M: 12.6038 delta: 0.664591 time: 269.489 one-recall: 0.72 one-ratio: 1.12941
iteration: 6 recall: 0.8028 accuracy: 0.0213253 cost: 0.00297995 M: 15.1148 delta: 0.432318 time: 331.746 one-recall: 0.91 one-ratio: 1.03513
iteration: 7 recall: 0.9108 accuracy: 0.00637034 cost: 0.00395519 M: 21.1402 delta: 0.19643 time: 403.854 one-recall: 0.98 one-ratio: 1.00242
iteration: 8 recall: 0.9504 accuracy: 0.00292536 cost: 0.00498002 M: 27.3075 delta: 0.0884363 time: 473.327 one-recall: 0.99 one-ratio: 1.00007
iteration: 9 recall: 0.9708 accuracy: 0.00157553 cost: 0.00577288 M: 31.2899 delta: 0.0513432 time: 527.959 one-recall: 0.99 one-ratio: 1.00007
iteration: 10 recall: 0.9788 accuracy: 0.00100886 cost: 0.00625814 M: 33.3968 delta: 0.037225 time: 565.901 one-recall: 1 one-ratio: 1
iteration: 11 recall: 0.9844 accuracy: 0.000697259 cost: 0.00651591 M: 34.4298 delta: 0.0313184 time: 591.31 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.986 accuracy: 0.000632687 cost: 0.00664341 M: 34.9202 delta: 0.02877 time: 608.675 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.9864 accuracy: 0.000610065 cost: 0.00670542 M: 35.1563 delta: 0.0275857 time: 621.349 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.9868 accuracy: 0.000590251 cost: 0.00673511 M: 35.2677 delta: 0.0270517 time: 631.396 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.9868 accuracy: 0.000590251 cost: 0.00674981 M: 35.3226 delta: 0.026792 time: 640.111 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.9868 accuracy: 0.000590251 cost: 0.00675728 M: 35.3507 delta: 0.0266654 time: 648.134 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.9868 accuracy: 0.000590251 cost: 0.006761 M: 35.3645 delta: 0.0266008 time: 655.791 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.9868 accuracy: 0.000590251 cost: 0.00676291 M: 35.3718 delta: 0.0265695 time: 663.255 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.9868 accuracy: 0.000590251 cost: 0.00676398 M: 35.3758 delta: 0.026555 time: 670.616 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.9868 accuracy: 0.000590251 cost: 0.00676458 M: 35.378 delta: 0.0265461 time: 677.902 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.9868 accuracy: 0.000590251 cost: 0.00676489 M: 35.3791 delta: 0.0265411 time: 685.164 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.9868 accuracy: 0.000590251 cost: 0.00676506 M: 35.3798 delta: 0.0265385 time: 692.403 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.9868 accuracy: 0.000590251 cost: 0.00676515 M: 35.3801 delta: 0.0265371 time: 699.615 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.9868 accuracy: 0.000590251 cost: 0.0067652 M: 35.3804 delta: 0.0265361 time: 706.817 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.9868 accuracy: 0.000590251 cost: 0.00676521 M: 35.3804 delta: 0.0265365 time: 714.01 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.9868 accuracy: 0.000590251 cost: 0.00676523 M: 35.3805 delta: 0.0265364 time: 721.205 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.9868 accuracy: 0.000590251 cost: 0.00676524 M: 35.3805 delta: 0.0265364 time: 728.401 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.9868 accuracy: 0.000590251 cost: 0.00676525 M: 35.3806 delta: 0.026536 time: 735.591 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.9868 accuracy: 0.000590251 cost: 0.00676527 M: 35.3807 delta: 0.026536 time: 742.785 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.9868 accuracy: 0.000590251 cost: 0.00676527 M: 35.3807 delta: 0.0265358 time: 749.974 one-recall: 1 one-ratio: 1
Graph completion with reverse edges...

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

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 768.7200000000003
Index size:  262720.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0049810000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.1000000000, query time of that 0.0588044160, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Reject!
282.558 < 365.227
  -> Decision False in time 0.8800000000, query time of that 0.5056523420, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
354.415 < 381.898
  -> Decision False in time 6.6800000000, query time of that 3.7765521500, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.6200000000, query time of that 0.0721913710, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
257.332 < 258.006
  -> Decision False in time 3.2600000000, query time of that 0.3761890090, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
360.157 < 372.438
  -> Decision False in time 2.0600000000, query time of that 0.2415563060, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Accept!
  -> Decision True in time 8.1100000000, query time of that 0.0832201530, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
252.903 < 257.451
  -> Decision False in time 51.4400000000, query time of that 0.5520730560, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
278.97 < 280.248
  -> Decision False in time 59.6100000000, query time of that 0.6321652970, 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: 0.0008 accuracy: 2.09128 cost: 0.00038 M: 10 delta: 1 time: 63.6707 one-recall: 0 one-ratio: 3.14009
iteration: 2 recall: 0.0036 accuracy: 1.12737 cost: 0.000637428 M: 10 delta: 0.856032 time: 107.755 one-recall: 0.01 one-ratio: 2.46171
iteration: 3 recall: 0.036 accuracy: 0.618127 cost: 0.00109521 M: 11.5287 delta: 0.835109 time: 161.143 one-recall: 0.02 one-ratio: 2.01024
iteration: 4 recall: 0.188 accuracy: 0.303909 cost: 0.00163041 M: 11.8362 delta: 0.783453 time: 214.266 one-recall: 0.22 one-ratio: 1.64726
iteration: 5 recall: 0.5096 accuracy: 0.0918748 cost: 0.00223606 M: 12.6039 delta: 0.66461 time: 269.491 one-recall: 0.62 one-ratio: 1.21199
iteration: 6 recall: 0.7592 accuracy: 0.0294594 cost: 0.00297983 M: 15.1139 delta: 0.432376 time: 331.746 one-recall: 0.84 one-ratio: 1.08419
iteration: 7 recall: 0.8808 accuracy: 0.00929804 cost: 0.00395501 M: 21.1387 delta: 0.1964 time: 403.853 one-recall: 0.97 one-ratio: 1.00694
iteration: 8 recall: 0.9356 accuracy: 0.0041806 cost: 0.00497967 M: 27.3052 delta: 0.0884747 time: 473.342 one-recall: 1 one-ratio: 1
iteration: 9 recall: 0.9608 accuracy: 0.00226473 cost: 0.00577189 M: 31.2883 delta: 0.0513442 time: 527.951 one-recall: 1 one-ratio: 1
iteration: 10 recall: 0.9708 accuracy: 0.00163061 cost: 0.00625611 M: 33.3902 delta: 0.0372236 time: 565.844 one-recall: 1 one-ratio: 1
iteration: 11 recall: 0.9752 accuracy: 0.00130203 cost: 0.00651394 M: 34.4214 delta: 0.0313237 time: 591.246 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.9772 accuracy: 0.00120338 cost: 0.00664214 M: 34.9133 delta: 0.0287398 time: 608.644 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.9788 accuracy: 0.00109261 cost: 0.00670393 M: 35.1478 delta: 0.0275819 time: 621.295 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.9804 accuracy: 0.00104282 cost: 0.00673388 M: 35.2605 delta: 0.0270438 time: 631.35 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.9808 accuracy: 0.00103648 cost: 0.00674897 M: 35.3171 delta: 0.0267895 time: 640.089 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.9812 accuracy: 0.00102201 cost: 0.00675677 M: 35.3463 delta: 0.0266548 time: 648.135 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.9812 accuracy: 0.00102201 cost: 0.00676083 M: 35.3614 delta: 0.0265847 time: 655.807 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.9812 accuracy: 0.00102201 cost: 0.00676282 M: 35.3689 delta: 0.026552 time: 663.266 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.9812 accuracy: 0.00102201 cost: 0.0067639 M: 35.3728 delta: 0.026535 time: 670.623 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.9812 accuracy: 0.00102201 cost: 0.00676447 M: 35.3749 delta: 0.0265276 time: 677.918 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.9812 accuracy: 0.00102201 cost: 0.00676478 M: 35.3761 delta: 0.0265227 time: 685.172 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.9812 accuracy: 0.00102201 cost: 0.00676492 M: 35.3766 delta: 0.0265194 time: 692.404 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.9812 accuracy: 0.00102201 cost: 0.00676498 M: 35.3769 delta: 0.0265186 time: 699.619 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.9812 accuracy: 0.00102201 cost: 0.00676502 M: 35.377 delta: 0.0265176 time: 706.823 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.9812 accuracy: 0.00102201 cost: 0.00676503 M: 35.3771 delta: 0.0265173 time: 714.018 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.9812 accuracy: 0.00102201 cost: 0.00676504 M: 35.3771 delta: 0.026517 time: 721.21 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.9812 accuracy: 0.00102201 cost: 0.00676504 M: 35.3771 delta: 0.0265169 time: 728.406 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.9812 accuracy: 0.00102201 cost: 0.00676504 M: 35.3771 delta: 0.0265169 time: 735.6 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.9812 accuracy: 0.00102201 cost: 0.00676504 M: 35.3771 delta: 0.0265169 time: 742.788 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.9812 accuracy: 0.00102201 cost: 0.00676504 M: 35.3771 delta: 0.0265169 time: 749.982 one-recall: 1 one-ratio: 1
Graph completion with reverse edges...

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

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 768.75
Index size:  262844.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0035730000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.1200000000, query time of that 0.0757366460, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Accept!
  -> Decision True in time 1.2000000000, query time of that 0.7526448700, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
327.735 < 383.284
  -> Decision False in time 11.0200000000, query time of that 6.8102664900, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.6400000000, query time of that 0.0853199240, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
263.276 < 270.379
  -> Decision False in time 2.0300000000, query time of that 0.2760034450, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
249.938 < 254.295
  -> Decision False in time 33.3000000000, query time of that 4.6188839120, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Accept!
  -> Decision True in time 8.1700000000, query time of that 0.1105051610, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
286.26 < 288.281
  -> Decision False in time 27.6600000000, query time of that 0.3557763220, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
278.519 < 280.487
  -> Decision False in time 41.5700000000, query time of that 0.5234193900, 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: 1.85731 cost: 0.00038 M: 10 delta: 1 time: 63.6836 one-recall: 0 one-ratio: 3.1228
iteration: 2 recall: 0.0052 accuracy: 1.08473 cost: 0.000637428 M: 10 delta: 0.856032 time: 107.767 one-recall: 0.02 one-ratio: 2.44798
iteration: 3 recall: 0.0408 accuracy: 0.583119 cost: 0.00109521 M: 11.5287 delta: 0.835106 time: 161.152 one-recall: 0.13 one-ratio: 1.95365
iteration: 4 recall: 0.1952 accuracy: 0.301565 cost: 0.00163044 M: 11.8363 delta: 0.783462 time: 214.29 one-recall: 0.24 one-ratio: 1.61392
iteration: 5 recall: 0.4836 accuracy: 0.115058 cost: 0.00223603 M: 12.6037 delta: 0.664609 time: 269.499 one-recall: 0.57 one-ratio: 1.28187
iteration: 6 recall: 0.7432 accuracy: 0.0333429 cost: 0.00297986 M: 15.1139 delta: 0.432344 time: 331.755 one-recall: 0.84 one-ratio: 1.06329
iteration: 7 recall: 0.8724 accuracy: 0.011716 cost: 0.00395492 M: 21.139 delta: 0.196385 time: 403.855 one-recall: 0.92 one-ratio: 1.01405
iteration: 8 recall: 0.9256 accuracy: 0.00508544 cost: 0.00497925 M: 27.3041 delta: 0.0884736 time: 473.31 one-recall: 0.99 one-ratio: 1.00493
iteration: 9 recall: 0.9524 accuracy: 0.00304974 cost: 0.00577218 M: 31.289 delta: 0.0513377 time: 527.962 one-recall: 0.99 one-ratio: 1.00493
iteration: 10 recall: 0.9604 accuracy: 0.00241156 cost: 0.00625743 M: 33.395 delta: 0.0371953 time: 565.908 one-recall: 0.99 one-ratio: 1.00493
iteration: 11 recall: 0.9676 accuracy: 0.00183386 cost: 0.00651506 M: 34.4237 delta: 0.0313133 time: 591.303 one-recall: 0.99 one-ratio: 1.00493
iteration: 12 recall: 0.9708 accuracy: 0.00168375 cost: 0.00664295 M: 34.9165 delta: 0.0287367 time: 608.695 one-recall: 0.99 one-ratio: 1.00493
iteration: 13 recall: 0.9728 accuracy: 0.00158757 cost: 0.0067055 M: 35.1522 delta: 0.0275803 time: 621.402 one-recall: 0.99 one-ratio: 1.00493
iteration: 14 recall: 0.9732 accuracy: 0.00156542 cost: 0.00673596 M: 35.2664 delta: 0.0270279 time: 631.492 one-recall: 0.99 one-ratio: 1.00493
iteration: 15 recall: 0.9736 accuracy: 0.0014829 cost: 0.00675081 M: 35.3217 delta: 0.0267676 time: 640.208 one-recall: 0.99 one-ratio: 1.00493
iteration: 16 recall: 0.9744 accuracy: 0.00134556 cost: 0.00675826 M: 35.3493 delta: 0.0266481 time: 648.221 one-recall: 0.99 one-ratio: 1.00493
iteration: 17 recall: 0.9744 accuracy: 0.00134556 cost: 0.00676215 M: 35.3636 delta: 0.0265824 time: 655.878 one-recall: 0.99 one-ratio: 1.00493
iteration: 18 recall: 0.9744 accuracy: 0.00134556 cost: 0.00676415 M: 35.3711 delta: 0.0265483 time: 663.336 one-recall: 0.99 one-ratio: 1.00493
iteration: 19 recall: 0.9744 accuracy: 0.00134556 cost: 0.00676523 M: 35.3752 delta: 0.0265307 time: 670.689 one-recall: 0.99 one-ratio: 1.00493
iteration: 20 recall: 0.9744 accuracy: 0.00134556 cost: 0.00676576 M: 35.3772 delta: 0.0265212 time: 677.971 one-recall: 0.99 one-ratio: 1.00493
iteration: 21 recall: 0.9744 accuracy: 0.00134556 cost: 0.00676606 M: 35.3783 delta: 0.0265175 time: 685.218 one-recall: 0.99 one-ratio: 1.00493
iteration: 22 recall: 0.9744 accuracy: 0.00134556 cost: 0.00676625 M: 35.379 delta: 0.0265151 time: 692.445 one-recall: 0.99 one-ratio: 1.00493
iteration: 23 recall: 0.9744 accuracy: 0.00134556 cost: 0.00676634 M: 35.3794 delta: 0.0265127 time: 699.658 one-recall: 0.99 one-ratio: 1.00493
iteration: 24 recall: 0.9744 accuracy: 0.00134556 cost: 0.0067664 M: 35.3796 delta: 0.0265119 time: 706.864 one-recall: 0.99 one-ratio: 1.00493
iteration: 25 recall: 0.9744 accuracy: 0.00134556 cost: 0.00676643 M: 35.3797 delta: 0.0265116 time: 714.068 one-recall: 0.99 one-ratio: 1.00493
iteration: 26 recall: 0.9744 accuracy: 0.00134556 cost: 0.00676646 M: 35.3798 delta: 0.0265112 time: 721.261 one-recall: 0.99 one-ratio: 1.00493
iteration: 27 recall: 0.9744 accuracy: 0.00134556 cost: 0.00676646 M: 35.3798 delta: 0.0265111 time: 728.452 one-recall: 0.99 one-ratio: 1.00493
iteration: 28 recall: 0.9744 accuracy: 0.00134556 cost: 0.00676646 M: 35.3798 delta: 0.0265112 time: 735.645 one-recall: 0.99 one-ratio: 1.00493
iteration: 29 recall: 0.9744 accuracy: 0.00134556 cost: 0.00676647 M: 35.3798 delta: 0.0265111 time: 742.834 one-recall: 0.99 one-ratio: 1.00493
iteration: 30 recall: 0.9744 accuracy: 0.00134556 cost: 0.00676647 M: 35.3798 delta: 0.0265111 time: 750.022 one-recall: 0.99 one-ratio: 1.00493
Graph completion with reverse edges...

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

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 768.7800000000007
Index size:  263184.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0091352000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.0800000000, query time of that 0.0348954910, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Reject!
373.782 < 374.979
  -> Decision False in time 0.1900000000, query time of that 0.0867428120, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
250.843 < 253.444
  -> Decision False in time 0.3100000000, query time of that 0.1351269970, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.5800000000, query time of that 0.0421751430, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Accept!
  -> Decision True in time 5.6900000000, query time of that 0.4337826560, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
283.404 < 291.462
  -> Decision False in time 0.9100000000, query time of that 0.0707136430, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Reject!
294.02 < 298.941
  -> Decision False in time 0.8700000000, query time of that 0.0055949680, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
226.252 < 226.888
  -> Decision False in time 0.5500000000, query time of that 0.0049236680, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
250.721 < 252.351
  -> Decision False in time 2.2100000000, query time of that 0.0150432830, 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: 3.08977 cost: 0.00038 M: 10 delta: 1 time: 63.6624 one-recall: 0 one-ratio: 3.4719
iteration: 2 recall: 0.0056 accuracy: 1.26291 cost: 0.000637428 M: 10 delta: 0.856032 time: 107.739 one-recall: 0.02 one-ratio: 2.69911
iteration: 3 recall: 0.0332 accuracy: 0.633261 cost: 0.00109521 M: 11.5287 delta: 0.835107 time: 161.131 one-recall: 0.06 one-ratio: 2.15061
iteration: 4 recall: 0.196 accuracy: 0.303263 cost: 0.00163046 M: 11.8363 delta: 0.783451 time: 214.269 one-recall: 0.27 one-ratio: 1.71694
iteration: 5 recall: 0.5248 accuracy: 0.0991725 cost: 0.00223612 M: 12.6039 delta: 0.664594 time: 269.479 one-recall: 0.66 one-ratio: 1.26155
iteration: 6 recall: 0.7888 accuracy: 0.0236097 cost: 0.00298 M: 15.1144 delta: 0.432347 time: 331.734 one-recall: 0.91 one-ratio: 1.04963
iteration: 7 recall: 0.91 accuracy: 0.00840575 cost: 0.00395517 M: 21.1393 delta: 0.196413 time: 403.846 one-recall: 0.94 one-ratio: 1.03701
iteration: 8 recall: 0.9484 accuracy: 0.00419254 cost: 0.00497967 M: 27.3048 delta: 0.0884119 time: 473.326 one-recall: 0.96 one-ratio: 1.02084
iteration: 9 recall: 0.9684 accuracy: 0.0021917 cost: 0.00577202 M: 31.2891 delta: 0.0513904 time: 527.944 one-recall: 0.98 one-ratio: 1.01317
iteration: 10 recall: 0.9752 accuracy: 0.00173034 cost: 0.00625737 M: 33.3957 delta: 0.037238 time: 565.899 one-recall: 0.98 one-ratio: 1.01317
iteration: 11 recall: 0.9776 accuracy: 0.00164802 cost: 0.00651549 M: 34.4283 delta: 0.0313313 time: 591.334 one-recall: 0.98 one-ratio: 1.01317
iteration: 12 recall: 0.9788 accuracy: 0.00128486 cost: 0.00664284 M: 34.9186 delta: 0.0287716 time: 608.692 one-recall: 0.99 one-ratio: 1.00677
iteration: 13 recall: 0.9804 accuracy: 0.00123246 cost: 0.0067051 M: 35.1538 delta: 0.0276064 time: 621.384 one-recall: 0.99 one-ratio: 1.00677
iteration: 14 recall: 0.9804 accuracy: 0.00122718 cost: 0.00673532 M: 35.2673 delta: 0.0270569 time: 631.473 one-recall: 0.99 one-ratio: 1.00677
iteration: 15 recall: 0.9804 accuracy: 0.00122718 cost: 0.00675008 M: 35.3221 delta: 0.026795 time: 640.174 one-recall: 0.99 one-ratio: 1.00677
iteration: 16 recall: 0.9808 accuracy: 0.00118001 cost: 0.00675744 M: 35.3492 delta: 0.0266724 time: 648.185 one-recall: 0.99 one-ratio: 1.00677
iteration: 17 recall: 0.9808 accuracy: 0.00118001 cost: 0.0067612 M: 35.3628 delta: 0.0266124 time: 655.831 one-recall: 0.99 one-ratio: 1.00677
iteration: 18 recall: 0.9808 accuracy: 0.00118001 cost: 0.00676322 M: 35.3702 delta: 0.0265812 time: 663.301 one-recall: 0.99 one-ratio: 1.00677
iteration: 19 recall: 0.9808 accuracy: 0.00118001 cost: 0.00676433 M: 35.3741 delta: 0.0265624 time: 670.655 one-recall: 0.99 one-ratio: 1.00677
iteration: 20 recall: 0.9808 accuracy: 0.00118001 cost: 0.00676485 M: 35.3762 delta: 0.0265528 time: 677.936 one-recall: 0.99 one-ratio: 1.00677
iteration: 21 recall: 0.9808 accuracy: 0.00118001 cost: 0.0067651 M: 35.3771 delta: 0.0265487 time: 685.179 one-recall: 0.99 one-ratio: 1.00677
iteration: 22 recall: 0.9808 accuracy: 0.00118001 cost: 0.00676521 M: 35.3775 delta: 0.0265473 time: 692.392 one-recall: 0.99 one-ratio: 1.00677
iteration: 23 recall: 0.9808 accuracy: 0.00118001 cost: 0.00676528 M: 35.3778 delta: 0.0265471 time: 699.604 one-recall: 0.99 one-ratio: 1.00677
iteration: 24 recall: 0.9808 accuracy: 0.00118001 cost: 0.00676531 M: 35.3779 delta: 0.0265464 time: 706.809 one-recall: 0.99 one-ratio: 1.00677
iteration: 25 recall: 0.9808 accuracy: 0.00118001 cost: 0.00676534 M: 35.3781 delta: 0.0265461 time: 714.01 one-recall: 0.99 one-ratio: 1.00677
iteration: 26 recall: 0.9808 accuracy: 0.00118001 cost: 0.00676536 M: 35.3781 delta: 0.0265458 time: 721.21 one-recall: 0.99 one-ratio: 1.00677
iteration: 27 recall: 0.9808 accuracy: 0.00118001 cost: 0.00676536 M: 35.3781 delta: 0.0265456 time: 728.404 one-recall: 0.99 one-ratio: 1.00677
iteration: 28 recall: 0.9808 accuracy: 0.00118001 cost: 0.00676537 M: 35.3782 delta: 0.0265456 time: 735.595 one-recall: 0.99 one-ratio: 1.00677
iteration: 29 recall: 0.9808 accuracy: 0.00118001 cost: 0.00676537 M: 35.3782 delta: 0.0265456 time: 742.785 one-recall: 0.99 one-ratio: 1.00677
iteration: 30 recall: 0.9808 accuracy: 0.00118001 cost: 0.00676537 M: 35.3782 delta: 0.0265456 time: 749.969 one-recall: 0.99 one-ratio: 1.00677
Graph completion with reverse edges...

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

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 768.7299999999996
Index size:  262756.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0041311000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.1200000000, query time of that 0.0690176820, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Accept!
  -> Decision True in time 1.1100000000, query time of that 0.6675001020, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Accept!
  -> Decision True in time 11.3500000000, query time of that 6.7536607890, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.6500000000, query time of that 0.0843971900, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Accept!
  -> Decision True in time 6.4000000000, query time of that 0.8294988380, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
246.52 < 250.356
  -> Decision False in time 7.2300000000, query time of that 0.9377475700, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Accept!
  -> Decision True in time 8.1500000000, query time of that 0.0933803250, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
246.816 < 249.455
  -> Decision False in time 17.1600000000, query time of that 0.1994507420, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
302.255 < 310.989
  -> Decision False in time 21.5600000000, query time of that 0.2479826130, 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: 0.0008 accuracy: 2.57336 cost: 0.00038 M: 10 delta: 1 time: 63.6772 one-recall: 0 one-ratio: 3.13368
iteration: 2 recall: 0.0048 accuracy: 1.17517 cost: 0.000637428 M: 10 delta: 0.856032 time: 107.75 one-recall: 0.01 one-ratio: 2.53184
iteration: 3 recall: 0.04 accuracy: 0.618833 cost: 0.00109521 M: 11.5287 delta: 0.835109 time: 161.136 one-recall: 0.06 one-ratio: 2.06007
iteration: 4 recall: 0.2172 accuracy: 0.311472 cost: 0.00163044 M: 11.8363 delta: 0.783451 time: 214.262 one-recall: 0.25 one-ratio: 1.60478
iteration: 5 recall: 0.4976 accuracy: 0.10963 cost: 0.00223612 M: 12.604 delta: 0.664596 time: 269.471 one-recall: 0.57 one-ratio: 1.32211
iteration: 6 recall: 0.7492 accuracy: 0.0323727 cost: 0.00298008 M: 15.1148 delta: 0.432346 time: 331.728 one-recall: 0.85 one-ratio: 1.0931
iteration: 7 recall: 0.8728 accuracy: 0.0116361 cost: 0.00395534 M: 21.1405 delta: 0.196376 time: 403.835 one-recall: 0.94 one-ratio: 1.03789
iteration: 8 recall: 0.9312 accuracy: 0.00550647 cost: 0.00497991 M: 27.3056 delta: 0.0884589 time: 473.31 one-recall: 0.96 one-ratio: 1.02388
iteration: 9 recall: 0.9596 accuracy: 0.00309198 cost: 0.00577303 M: 31.2905 delta: 0.0513521 time: 527.961 one-recall: 0.98 one-ratio: 1.01288
iteration: 10 recall: 0.9716 accuracy: 0.00164397 cost: 0.00625819 M: 33.3966 delta: 0.037201 time: 565.912 one-recall: 0.99 one-ratio: 1.0011
iteration: 11 recall: 0.9768 accuracy: 0.0012875 cost: 0.00651583 M: 34.4296 delta: 0.031297 time: 591.313 one-recall: 0.99 one-ratio: 1.0011
iteration: 12 recall: 0.9792 accuracy: 0.00110369 cost: 0.00664358 M: 34.9215 delta: 0.0287377 time: 608.706 one-recall: 0.99 one-ratio: 1.0011
iteration: 13 recall: 0.98 accuracy: 0.00108831 cost: 0.00670616 M: 35.1579 delta: 0.0275672 time: 621.436 one-recall: 0.99 one-ratio: 1.0011
iteration: 14 recall: 0.9804 accuracy: 0.00105174 cost: 0.00673659 M: 35.2716 delta: 0.0270194 time: 631.549 one-recall: 0.99 one-ratio: 1.0011
iteration: 15 recall: 0.9804 accuracy: 0.0010513 cost: 0.00675122 M: 35.3269 delta: 0.0267597 time: 640.263 one-recall: 0.99 one-ratio: 1.0011
iteration: 16 recall: 0.9804 accuracy: 0.0010513 cost: 0.00675858 M: 35.3543 delta: 0.0266364 time: 648.278 one-recall: 0.99 one-ratio: 1.0011
iteration: 17 recall: 0.9804 accuracy: 0.0010513 cost: 0.00676222 M: 35.3681 delta: 0.0265697 time: 655.921 one-recall: 0.99 one-ratio: 1.0011
iteration: 18 recall: 0.9804 accuracy: 0.0010513 cost: 0.00676402 M: 35.3748 delta: 0.026539 time: 663.378 one-recall: 0.99 one-ratio: 1.0011
iteration: 19 recall: 0.9804 accuracy: 0.0010513 cost: 0.00676498 M: 35.3784 delta: 0.0265246 time: 670.734 one-recall: 0.99 one-ratio: 1.0011
iteration: 20 recall: 0.9804 accuracy: 0.0010513 cost: 0.00676543 M: 35.3803 delta: 0.026517 time: 678.007 one-recall: 0.99 one-ratio: 1.0011
iteration: 21 recall: 0.9804 accuracy: 0.0010513 cost: 0.00676569 M: 35.3813 delta: 0.0265128 time: 685.267 one-recall: 0.99 one-ratio: 1.0011
iteration: 22 recall: 0.9804 accuracy: 0.0010513 cost: 0.00676585 M: 35.3819 delta: 0.0265107 time: 692.506 one-recall: 0.99 one-ratio: 1.0011
iteration: 23 recall: 0.9804 accuracy: 0.0010513 cost: 0.00676593 M: 35.3822 delta: 0.0265093 time: 699.724 one-recall: 0.99 one-ratio: 1.0011
iteration: 24 recall: 0.9804 accuracy: 0.0010513 cost: 0.00676597 M: 35.3823 delta: 0.0265087 time: 706.932 one-recall: 0.99 one-ratio: 1.0011
iteration: 25 recall: 0.9804 accuracy: 0.0010513 cost: 0.00676599 M: 35.3824 delta: 0.0265083 time: 714.142 one-recall: 0.99 one-ratio: 1.0011
iteration: 26 recall: 0.9804 accuracy: 0.0010513 cost: 0.00676599 M: 35.3824 delta: 0.0265082 time: 721.341 one-recall: 0.99 one-ratio: 1.0011
iteration: 27 recall: 0.9804 accuracy: 0.0010513 cost: 0.006766 M: 35.3825 delta: 0.0265082 time: 728.533 one-recall: 0.99 one-ratio: 1.0011
iteration: 28 recall: 0.9804 accuracy: 0.0010513 cost: 0.006766 M: 35.3825 delta: 0.0265081 time: 735.73 one-recall: 0.99 one-ratio: 1.0011
iteration: 29 recall: 0.9804 accuracy: 0.0010513 cost: 0.006766 M: 35.3825 delta: 0.026508 time: 742.923 one-recall: 0.99 one-ratio: 1.0011
iteration: 30 recall: 0.9804 accuracy: 0.0010513 cost: 0.00676601 M: 35.3825 delta: 0.026508 time: 750.118 one-recall: 0.99 one-ratio: 1.0011
Graph completion with reverse edges...

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

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 768.8900000000012
Index size:  262880.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0027249000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.1400000000, query time of that 0.0932448850, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Accept!
  -> Decision True in time 1.3300000000, query time of that 0.8716479960, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
259.187 < 259.937
  -> Decision False in time 2.2200000000, query time of that 1.4695161160, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.6700000000, query time of that 0.1040152750, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Accept!
  -> Decision True in time 6.8000000000, query time of that 1.0803139570, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Accept!
  -> Decision True in time 65.4400000000, query time of that 10.5927478840, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Accept!
  -> Decision True in time 8.1900000000, query time of that 0.1243225700, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
230.204 < 236.038
  -> Decision False in time 57.4200000000, query time of that 0.8598399690, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
305.748 < 307.257
  -> Decision False in time 17.2300000000, query time of that 0.2629383940, 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: 0.0004 accuracy: 2.30133 cost: 0.00038 M: 10 delta: 1 time: 63.6681 one-recall: 0 one-ratio: 3.32786
iteration: 2 recall: 0.006 accuracy: 1.19241 cost: 0.000637428 M: 10 delta: 0.856032 time: 107.745 one-recall: 0.01 one-ratio: 2.55179
iteration: 3 recall: 0.0384 accuracy: 0.649868 cost: 0.00109521 M: 11.5287 delta: 0.835109 time: 161.127 one-recall: 0.05 one-ratio: 2.13868
iteration: 4 recall: 0.222 accuracy: 0.320438 cost: 0.00163043 M: 11.8363 delta: 0.783463 time: 214.256 one-recall: 0.19 one-ratio: 1.6882
iteration: 5 recall: 0.56 accuracy: 0.0942755 cost: 0.00223608 M: 12.6038 delta: 0.664608 time: 269.464 one-recall: 0.64 one-ratio: 1.20598
iteration: 6 recall: 0.8084 accuracy: 0.0254334 cost: 0.00297998 M: 15.1146 delta: 0.432347 time: 331.721 one-recall: 0.87 one-ratio: 1.05162
iteration: 7 recall: 0.9028 accuracy: 0.00811158 cost: 0.00395505 M: 21.137 delta: 0.196393 time: 403.821 one-recall: 0.95 one-ratio: 1.01734
iteration: 8 recall: 0.9488 accuracy: 0.00369685 cost: 0.00497928 M: 27.3003 delta: 0.0884831 time: 473.29 one-recall: 0.95 one-ratio: 1.01734
iteration: 9 recall: 0.9696 accuracy: 0.00221783 cost: 0.0057716 M: 31.2846 delta: 0.0513492 time: 527.914 one-recall: 0.97 one-ratio: 1.01348
iteration: 10 recall: 0.9796 accuracy: 0.00148673 cost: 0.00625616 M: 33.3865 delta: 0.0372064 time: 565.85 one-recall: 0.98 one-ratio: 1.0091
iteration: 11 recall: 0.9816 accuracy: 0.00144117 cost: 0.00651342 M: 34.4165 delta: 0.0313324 time: 591.246 one-recall: 0.98 one-ratio: 1.0091
iteration: 12 recall: 0.9824 accuracy: 0.00108752 cost: 0.00664148 M: 34.9096 delta: 0.0287535 time: 608.654 one-recall: 0.99 one-ratio: 1.00861
iteration: 13 recall: 0.9824 accuracy: 0.00108752 cost: 0.00670371 M: 35.1452 delta: 0.0275874 time: 621.35 one-recall: 0.99 one-ratio: 1.00861
iteration: 14 recall: 0.9824 accuracy: 0.00108752 cost: 0.00673404 M: 35.2587 delta: 0.0270357 time: 631.451 one-recall: 0.99 one-ratio: 1.00861
iteration: 15 recall: 0.9828 accuracy: 0.00108135 cost: 0.00674867 M: 35.3133 delta: 0.0267854 time: 640.159 one-recall: 0.99 one-ratio: 1.00861
iteration: 16 recall: 0.9828 accuracy: 0.00108135 cost: 0.00675617 M: 35.3415 delta: 0.0266594 time: 648.192 one-recall: 0.99 one-ratio: 1.00861
iteration: 17 recall: 0.9828 accuracy: 0.00108135 cost: 0.00675995 M: 35.3559 delta: 0.026591 time: 655.856 one-recall: 0.99 one-ratio: 1.00861
iteration: 18 recall: 0.9828 accuracy: 0.00108135 cost: 0.00676187 M: 35.363 delta: 0.0265619 time: 663.318 one-recall: 0.99 one-ratio: 1.00861
iteration: 19 recall: 0.9828 accuracy: 0.00108135 cost: 0.0067629 M: 35.367 delta: 0.0265456 time: 670.685 one-recall: 0.99 one-ratio: 1.00861
iteration: 20 recall: 0.9828 accuracy: 0.00108135 cost: 0.00676345 M: 35.3692 delta: 0.0265371 time: 678.001 one-recall: 0.99 one-ratio: 1.00861
iteration: 21 recall: 0.9828 accuracy: 0.00108135 cost: 0.00676379 M: 35.3705 delta: 0.0265323 time: 685.278 one-recall: 0.99 one-ratio: 1.00861
iteration: 22 recall: 0.9828 accuracy: 0.00108135 cost: 0.00676399 M: 35.3712 delta: 0.0265291 time: 692.534 one-recall: 0.99 one-ratio: 1.00861
iteration: 23 recall: 0.9828 accuracy: 0.00108135 cost: 0.00676411 M: 35.3716 delta: 0.026528 time: 699.774 one-recall: 0.99 one-ratio: 1.00861
iteration: 24 recall: 0.9828 accuracy: 0.00108135 cost: 0.00676416 M: 35.3718 delta: 0.0265269 time: 706.998 one-recall: 0.99 one-ratio: 1.00861
iteration: 25 recall: 0.9828 accuracy: 0.00108135 cost: 0.00676421 M: 35.372 delta: 0.0265265 time: 714.224 one-recall: 0.99 one-ratio: 1.00861
iteration: 26 recall: 0.9828 accuracy: 0.00108135 cost: 0.00676422 M: 35.3721 delta: 0.0265261 time: 721.45 one-recall: 0.99 one-ratio: 1.00861
iteration: 27 recall: 0.9828 accuracy: 0.00108135 cost: 0.00676424 M: 35.3721 delta: 0.0265261 time: 728.672 one-recall: 0.99 one-ratio: 1.00861
iteration: 28 recall: 0.9828 accuracy: 0.00108135 cost: 0.00676425 M: 35.3722 delta: 0.026526 time: 735.899 one-recall: 0.99 one-ratio: 1.00861
iteration: 29 recall: 0.9828 accuracy: 0.00108135 cost: 0.00676425 M: 35.3722 delta: 0.0265259 time: 743.115 one-recall: 0.99 one-ratio: 1.00861
iteration: 30 recall: 0.9828 accuracy: 0.00108135 cost: 0.00676426 M: 35.3722 delta: 0.026526 time: 750.33 one-recall: 0.99 one-ratio: 1.00861
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 769.1399999999994
Index size:  262824.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0071937000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.0900000000, query time of that 0.0440590510, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Accept!
  -> Decision True in time 0.8900000000, query time of that 0.4423000730, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
365.568 < 372.69
  -> Decision False in time 5.9300000000, query time of that 2.8834372660, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.6000000000, query time of that 0.0540423560, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
287.442 < 299.815
  -> Decision False in time 2.9800000000, query time of that 0.2689154110, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
290.393 < 294.459
  -> Decision False in time 4.5200000000, query time of that 0.4047141240, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Reject!
336.918 < 352.033
  -> Decision False in time 2.0300000000, query time of that 0.0172451970, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
214.278 < 252.182
  -> Decision False in time 0.8400000000, query time of that 0.0071014940, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
241.18 < 256.944
  -> Decision False in time 24.6800000000, query time of that 0.1999032570, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 3, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 3, {'reverse': -1}, False])
Got a train set of size (1000000 * 128)
Generating control...
Initializing...
iteration: 1 recall: 0 accuracy: 1.9103 cost: 0.00038 M: 10 delta: 1 time: 63.6621 one-recall: 0 one-ratio: 3.25077
iteration: 2 recall: 0.0048 accuracy: 1.06183 cost: 0.000637428 M: 10 delta: 0.856032 time: 107.748 one-recall: 0 one-ratio: 2.58319
iteration: 3 recall: 0.024 accuracy: 0.618906 cost: 0.00109521 M: 11.5287 delta: 0.835107 time: 161.137 one-recall: 0.02 one-ratio: 2.21049
iteration: 4 recall: 0.1596 accuracy: 0.3068 cost: 0.00163042 M: 11.8362 delta: 0.783453 time: 214.262 one-recall: 0.22 one-ratio: 1.70773
iteration: 5 recall: 0.478 accuracy: 0.111532 cost: 0.00223606 M: 12.6038 delta: 0.664608 time: 269.468 one-recall: 0.6 one-ratio: 1.26612
iteration: 6 recall: 0.7328 accuracy: 0.03635 cost: 0.00297978 M: 15.1132 delta: 0.432375 time: 331.72 one-recall: 0.77 one-ratio: 1.12723
iteration: 7 recall: 0.8668 accuracy: 0.012424 cost: 0.0039549 M: 21.1388 delta: 0.196428 time: 403.838 one-recall: 0.92 one-ratio: 1.04552
iteration: 8 recall: 0.9304 accuracy: 0.00521978 cost: 0.00497911 M: 27.3015 delta: 0.0884839 time: 473.308 one-recall: 0.97 one-ratio: 1.01558
iteration: 9 recall: 0.9568 accuracy: 0.00223453 cost: 0.00577169 M: 31.2873 delta: 0.051354 time: 527.951 one-recall: 1 one-ratio: 1
iteration: 10 recall: 0.966 accuracy: 0.00173669 cost: 0.00625636 M: 33.3889 delta: 0.0372057 time: 565.866 one-recall: 1 one-ratio: 1
iteration: 11 recall: 0.97 accuracy: 0.00142367 cost: 0.00651373 M: 34.4197 delta: 0.0313075 time: 591.242 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.9724 accuracy: 0.00138286 cost: 0.00664103 M: 34.9102 delta: 0.0287426 time: 608.59 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.9728 accuracy: 0.00132251 cost: 0.00670288 M: 35.1434 delta: 0.0275815 time: 621.245 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.974 accuracy: 0.00124788 cost: 0.00673294 M: 35.2561 delta: 0.0270334 time: 631.31 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.974 accuracy: 0.00124788 cost: 0.00674762 M: 35.3108 delta: 0.0267788 time: 640.007 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.974 accuracy: 0.00124788 cost: 0.00675513 M: 35.3387 delta: 0.0266543 time: 648.015 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.974 accuracy: 0.00124788 cost: 0.00675891 M: 35.3527 delta: 0.0265911 time: 655.659 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.974 accuracy: 0.00124788 cost: 0.00676094 M: 35.3602 delta: 0.026557 time: 663.12 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.974 accuracy: 0.00124788 cost: 0.006762 M: 35.3641 delta: 0.0265427 time: 670.486 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.974 accuracy: 0.00124788 cost: 0.00676257 M: 35.3663 delta: 0.0265339 time: 677.778 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.974 accuracy: 0.00124788 cost: 0.00676287 M: 35.3674 delta: 0.026529 time: 685.034 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.974 accuracy: 0.00124788 cost: 0.00676303 M: 35.368 delta: 0.0265265 time: 692.256 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.974 accuracy: 0.00124788 cost: 0.00676311 M: 35.3683 delta: 0.0265255 time: 699.467 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.974 accuracy: 0.00124788 cost: 0.00676317 M: 35.3685 delta: 0.0265251 time: 706.672 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.974 accuracy: 0.00124788 cost: 0.0067632 M: 35.3686 delta: 0.0265245 time: 713.869 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.974 accuracy: 0.00124788 cost: 0.00676322 M: 35.3687 delta: 0.0265243 time: 721.064 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.974 accuracy: 0.00124788 cost: 0.00676323 M: 35.3687 delta: 0.0265238 time: 728.261 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.974 accuracy: 0.00124788 cost: 0.00676324 M: 35.3688 delta: 0.0265239 time: 735.456 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.974 accuracy: 0.00124788 cost: 0.00676324 M: 35.3688 delta: 0.0265237 time: 742.651 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.974 accuracy: 0.00124788 cost: 0.00676325 M: 35.3689 delta: 0.0265237 time: 749.842 one-recall: 1 one-ratio: 1
Graph completion with reverse edges...

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

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 768.619999999999
Index size:  262768.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0183438000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.0700000000, query time of that 0.0226341170, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Reject!
405.222 < 445.394
  -> Decision False in time 0.1500000000, query time of that 0.0438489020, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
385.861 < 447.95
  -> Decision False in time 0.0300000000, query time of that 0.0104087820, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Reject!
277.321 < 404.505
  -> Decision False in time 0.5000000000, query time of that 0.0236285200, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
426 < 477.911
  -> Decision False in time 0.4000000000, query time of that 0.0194149030, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
352.586 < 399.881
  -> Decision False in time 0.2700000000, query time of that 0.0116688480, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Reject!
282.388 < 288.253
  -> Decision False in time 2.0400000000, query time of that 0.0094207780, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
299.615 < 300.882
  -> Decision False in time 6.6100000000, query time of that 0.0275605640, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
230.948 < 234.989
  -> Decision False in time 4.0900000000, query time of that 0.0183740800, 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: 2.08968 cost: 0.00038 M: 10 delta: 1 time: 63.6908 one-recall: 0 one-ratio: 3.38432
iteration: 2 recall: 0.0036 accuracy: 1.17512 cost: 0.000637428 M: 10 delta: 0.856032 time: 107.76 one-recall: 0 one-ratio: 2.66119
iteration: 3 recall: 0.0332 accuracy: 0.666701 cost: 0.00109521 M: 11.5287 delta: 0.835105 time: 161.145 one-recall: 0.03 one-ratio: 2.18205
iteration: 4 recall: 0.1792 accuracy: 0.305083 cost: 0.00163043 M: 11.8363 delta: 0.783459 time: 214.272 one-recall: 0.32 one-ratio: 1.6105
iteration: 5 recall: 0.492 accuracy: 0.105439 cost: 0.00223602 M: 12.6036 delta: 0.664567 time: 269.477 one-recall: 0.68 one-ratio: 1.23591
iteration: 6 recall: 0.7588 accuracy: 0.0309188 cost: 0.00297995 M: 15.1153 delta: 0.432347 time: 331.737 one-recall: 0.88 one-ratio: 1.05225
iteration: 7 recall: 0.8816 accuracy: 0.0102073 cost: 0.0039553 M: 21.1393 delta: 0.19642 time: 403.847 one-recall: 0.98 one-ratio: 1.00476
iteration: 8 recall: 0.9384 accuracy: 0.00432147 cost: 0.00497961 M: 27.3031 delta: 0.0885011 time: 473.3 one-recall: 1 one-ratio: 1
iteration: 9 recall: 0.9652 accuracy: 0.00208229 cost: 0.00577223 M: 31.2866 delta: 0.051382 time: 527.924 one-recall: 1 one-ratio: 1
iteration: 10 recall: 0.974 accuracy: 0.00134975 cost: 0.00625699 M: 33.3902 delta: 0.0372562 time: 565.857 one-recall: 1 one-ratio: 1
iteration: 11 recall: 0.9764 accuracy: 0.001249 cost: 0.00651407 M: 34.4204 delta: 0.0313556 time: 591.231 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.9784 accuracy: 0.00103998 cost: 0.00664205 M: 34.9111 delta: 0.0288157 time: 608.62 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.9796 accuracy: 0.000983664 cost: 0.00670482 M: 35.1485 delta: 0.0276194 time: 621.357 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.98 accuracy: 0.000978767 cost: 0.00673464 M: 35.2599 delta: 0.0270841 time: 631.413 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.98 accuracy: 0.000978767 cost: 0.00674932 M: 35.3149 delta: 0.0268324 time: 640.122 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.98 accuracy: 0.000978767 cost: 0.00675703 M: 35.3441 delta: 0.0267013 time: 648.174 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.98 accuracy: 0.000978767 cost: 0.00676091 M: 35.3586 delta: 0.0266374 time: 655.847 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.98 accuracy: 0.000978767 cost: 0.00676296 M: 35.3662 delta: 0.0266084 time: 663.317 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.98 accuracy: 0.000978767 cost: 0.00676406 M: 35.3703 delta: 0.0265874 time: 670.678 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.98 accuracy: 0.000978767 cost: 0.00676456 M: 35.3723 delta: 0.026579 time: 677.966 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.98 accuracy: 0.000978767 cost: 0.00676484 M: 35.3733 delta: 0.0265767 time: 685.222 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.98 accuracy: 0.000978767 cost: 0.00676497 M: 35.3738 delta: 0.0265739 time: 692.445 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.98 accuracy: 0.000978767 cost: 0.00676506 M: 35.3742 delta: 0.0265719 time: 699.665 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.98 accuracy: 0.000978767 cost: 0.0067651 M: 35.3743 delta: 0.0265712 time: 706.871 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.98 accuracy: 0.000978767 cost: 0.00676514 M: 35.3745 delta: 0.0265705 time: 714.068 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.98 accuracy: 0.000978767 cost: 0.00676515 M: 35.3745 delta: 0.0265702 time: 721.265 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.98 accuracy: 0.000978767 cost: 0.00676517 M: 35.3746 delta: 0.0265699 time: 728.469 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.98 accuracy: 0.000978767 cost: 0.00676517 M: 35.3746 delta: 0.0265699 time: 735.667 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.98 accuracy: 0.000978767 cost: 0.00676518 M: 35.3747 delta: 0.0265698 time: 742.862 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.98 accuracy: 0.000978767 cost: 0.00676518 M: 35.3747 delta: 0.0265698 time: 750.053 one-recall: 1 one-ratio: 1
Graph completion with reverse edges...

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

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 768.8199999999997
Index size:  262996.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0115195000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.0700000000, query time of that 0.0229258180, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Accept!
  -> Decision True in time 0.6600000000, query time of that 0.2095280750, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
383.008 < 422.345
  -> Decision False in time 1.2600000000, query time of that 0.3907762230, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.5600000000, query time of that 0.0257382860, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
382.783 < 422.768
  -> Decision False in time 4.5100000000, query time of that 0.2145187230, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
390.115 < 414.211
  -> Decision False in time 2.4800000000, query time of that 0.1158345460, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Accept!
  -> Decision True in time 8.0600000000, query time of that 0.0355775390, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
275.236 < 275.641
  -> Decision False in time 7.6200000000, query time of that 0.0336727410, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
219.461 < 231.976
  -> Decision False in time 11.6700000000, query time of that 0.0509430530, 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: 0.0008 accuracy: 2.55541 cost: 0.00038 M: 10 delta: 1 time: 63.6544 one-recall: 0 one-ratio: 3.35634
iteration: 2 recall: 0.0056 accuracy: 1.22181 cost: 0.000637428 M: 10 delta: 0.856032 time: 107.72 one-recall: 0 one-ratio: 2.68233
iteration: 3 recall: 0.0344 accuracy: 0.657319 cost: 0.00109521 M: 11.5287 delta: 0.835107 time: 161.108 one-recall: 0.05 one-ratio: 2.18418
iteration: 4 recall: 0.1932 accuracy: 0.312536 cost: 0.00163043 M: 11.8363 delta: 0.783462 time: 214.242 one-recall: 0.25 one-ratio: 1.68467
iteration: 5 recall: 0.5148 accuracy: 0.114718 cost: 0.00223609 M: 12.6036 delta: 0.664606 time: 269.456 one-recall: 0.62 one-ratio: 1.30151
iteration: 6 recall: 0.7616 accuracy: 0.0352814 cost: 0.00298003 M: 15.1148 delta: 0.432343 time: 331.719 one-recall: 0.82 one-ratio: 1.12504
iteration: 7 recall: 0.8912 accuracy: 0.00874392 cost: 0.00395538 M: 21.141 delta: 0.196394 time: 403.837 one-recall: 0.97 one-ratio: 1.00951
iteration: 8 recall: 0.938 accuracy: 0.00510377 cost: 0.00498001 M: 27.3067 delta: 0.0884632 time: 473.326 one-recall: 0.97 one-ratio: 1.00951
iteration: 9 recall: 0.9624 accuracy: 0.00293193 cost: 0.00577292 M: 31.2912 delta: 0.0513042 time: 527.966 one-recall: 0.98 one-ratio: 1.00381
iteration: 10 recall: 0.9716 accuracy: 0.00183103 cost: 0.00625747 M: 33.3932 delta: 0.0371875 time: 565.895 one-recall: 0.98 one-ratio: 1.00381
iteration: 11 recall: 0.9752 accuracy: 0.00120819 cost: 0.00651517 M: 34.4264 delta: 0.0312677 time: 591.313 one-recall: 0.99 one-ratio: 1.00053
iteration: 12 recall: 0.9756 accuracy: 0.00116795 cost: 0.00664245 M: 34.9173 delta: 0.028701 time: 608.668 one-recall: 0.99 one-ratio: 1.00053
iteration: 13 recall: 0.9764 accuracy: 0.00107525 cost: 0.00670461 M: 35.1513 delta: 0.0275361 time: 621.356 one-recall: 0.99 one-ratio: 1.00053
iteration: 14 recall: 0.9768 accuracy: 0.00105733 cost: 0.00673446 M: 35.2639 delta: 0.0269969 time: 631.417 one-recall: 0.99 one-ratio: 1.00053
iteration: 15 recall: 0.9772 accuracy: 0.0010461 cost: 0.00674942 M: 35.3195 delta: 0.0267363 time: 640.148 one-recall: 0.99 one-ratio: 1.00053
iteration: 16 recall: 0.9772 accuracy: 0.0010461 cost: 0.00675713 M: 35.3484 delta: 0.0266067 time: 648.189 one-recall: 0.99 one-ratio: 1.00053
iteration: 17 recall: 0.9772 accuracy: 0.0010461 cost: 0.00676098 M: 35.3627 delta: 0.0265404 time: 655.845 one-recall: 0.99 one-ratio: 1.00053
iteration: 18 recall: 0.9772 accuracy: 0.0010461 cost: 0.0067629 M: 35.37 delta: 0.0265095 time: 663.298 one-recall: 0.99 one-ratio: 1.00053
iteration: 19 recall: 0.9772 accuracy: 0.0010461 cost: 0.00676386 M: 35.3736 delta: 0.0264923 time: 670.644 one-recall: 0.99 one-ratio: 1.00053
iteration: 20 recall: 0.9772 accuracy: 0.0010461 cost: 0.00676439 M: 35.3757 delta: 0.0264854 time: 677.924 one-recall: 0.99 one-ratio: 1.00053
iteration: 21 recall: 0.9772 accuracy: 0.0010461 cost: 0.00676468 M: 35.3768 delta: 0.0264801 time: 685.171 one-recall: 0.99 one-ratio: 1.00053
iteration: 22 recall: 0.9772 accuracy: 0.0010461 cost: 0.00676487 M: 35.3775 delta: 0.0264767 time: 692.4 one-recall: 0.99 one-ratio: 1.00053
iteration: 23 recall: 0.9772 accuracy: 0.0010461 cost: 0.00676496 M: 35.3778 delta: 0.0264757 time: 699.618 one-recall: 0.99 one-ratio: 1.00053
iteration: 24 recall: 0.9772 accuracy: 0.0010461 cost: 0.00676502 M: 35.3781 delta: 0.0264754 time: 706.829 one-recall: 0.99 one-ratio: 1.00053
iteration: 25 recall: 0.9772 accuracy: 0.0010461 cost: 0.00676507 M: 35.3783 delta: 0.0264743 time: 714.036 one-recall: 0.99 one-ratio: 1.00053
iteration: 26 recall: 0.9772 accuracy: 0.0010461 cost: 0.0067651 M: 35.3784 delta: 0.0264738 time: 721.24 one-recall: 0.99 one-ratio: 1.00053
iteration: 27 recall: 0.9772 accuracy: 0.0010461 cost: 0.00676511 M: 35.3784 delta: 0.0264737 time: 728.44 one-recall: 0.99 one-ratio: 1.00053
iteration: 28 recall: 0.9772 accuracy: 0.0010461 cost: 0.00676512 M: 35.3785 delta: 0.0264735 time: 735.644 one-recall: 0.99 one-ratio: 1.00053
iteration: 29 recall: 0.9772 accuracy: 0.0010461 cost: 0.00676513 M: 35.3785 delta: 0.0264735 time: 742.841 one-recall: 0.99 one-ratio: 1.00053
iteration: 30 recall: 0.9772 accuracy: 0.0010461 cost: 0.00676513 M: 35.3785 delta: 0.0264735 time: 750.039 one-recall: 0.99 one-ratio: 1.00053
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 768.8299999999981
Index size:  262944.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0062218000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.1000000000, query time of that 0.0531835550, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Reject!
241.564 < 249.112
  -> Decision False in time 0.4400000000, query time of that 0.2408451450, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
296.965 < 400.022
  -> Decision False in time 1.9400000000, query time of that 1.0260213970, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.6200000000, query time of that 0.0645972540, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
237.67 < 246.426
  -> Decision False in time 1.1700000000, query time of that 0.1233311870, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
401.042 < 443.508
  -> Decision False in time 2.9600000000, query time of that 0.3092323330, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Reject!
285.281 < 300.762
  -> Decision False in time 4.3500000000, query time of that 0.0386307270, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
231.322 < 236.129
  -> Decision False in time 0.1500000000, query time of that 0.0014913100, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
249.958 < 251.388
  -> Decision False in time 19.1800000000, query time of that 0.1829909850, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 5, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 5, {'reverse': -1}, False])
Got a train set of size (1000000 * 128)
Generating control...
Initializing...
iteration: 1 recall: 0.0004 accuracy: 2.03946 cost: 0.00038 M: 10 delta: 1 time: 63.6912 one-recall: 0 one-ratio: 3.30843
iteration: 2 recall: 0.0028 accuracy: 1.12847 cost: 0.000637428 M: 10 delta: 0.856032 time: 107.768 one-recall: 0 one-ratio: 2.66557
iteration: 3 recall: 0.0356 accuracy: 0.644 cost: 0.00109521 M: 11.5287 delta: 0.835107 time: 161.163 one-recall: 0.02 one-ratio: 2.12008
iteration: 4 recall: 0.2052 accuracy: 0.345135 cost: 0.00163044 M: 11.8363 delta: 0.783453 time: 214.297 one-recall: 0.2 one-ratio: 1.66655
iteration: 5 recall: 0.5252 accuracy: 0.115858 cost: 0.00223605 M: 12.6037 delta: 0.66461 time: 269.507 one-recall: 0.58 one-ratio: 1.23339
iteration: 6 recall: 0.7908 accuracy: 0.0253603 cost: 0.00297993 M: 15.1139 delta: 0.432364 time: 331.772 one-recall: 0.91 one-ratio: 1.02118
iteration: 7 recall: 0.8984 accuracy: 0.00841058 cost: 0.00395508 M: 21.1371 delta: 0.19646 time: 403.88 one-recall: 0.97 one-ratio: 1.00175
iteration: 8 recall: 0.9448 accuracy: 0.00402758 cost: 0.00497964 M: 27.304 delta: 0.0884755 time: 473.353 one-recall: 0.98 one-ratio: 1.00103
iteration: 9 recall: 0.9632 accuracy: 0.00247143 cost: 0.00577267 M: 31.2899 delta: 0.0513354 time: 528.002 one-recall: 0.98 one-ratio: 1.00103
iteration: 10 recall: 0.9724 accuracy: 0.00176654 cost: 0.006258 M: 33.3967 delta: 0.0371923 time: 565.973 one-recall: 0.98 one-ratio: 1.00103
iteration: 11 recall: 0.9776 accuracy: 0.00147491 cost: 0.006516 M: 34.431 delta: 0.031295 time: 591.407 one-recall: 0.98 one-ratio: 1.00103
iteration: 12 recall: 0.9788 accuracy: 0.00141261 cost: 0.00664431 M: 34.9234 delta: 0.0287269 time: 608.819 one-recall: 0.98 one-ratio: 1.00103
iteration: 13 recall: 0.9796 accuracy: 0.00130902 cost: 0.00670661 M: 35.1596 delta: 0.027563 time: 621.515 one-recall: 0.99 one-ratio: 1.0003
iteration: 14 recall: 0.9796 accuracy: 0.00130902 cost: 0.00673646 M: 35.2714 delta: 0.0270185 time: 631.581 one-recall: 0.99 one-ratio: 1.0003
iteration: 15 recall: 0.9796 accuracy: 0.00130902 cost: 0.00675125 M: 35.3266 delta: 0.0267558 time: 640.304 one-recall: 0.99 one-ratio: 1.0003
iteration: 16 recall: 0.9796 accuracy: 0.00130902 cost: 0.00675901 M: 35.3555 delta: 0.0266326 time: 648.349 one-recall: 0.99 one-ratio: 1.0003
iteration: 17 recall: 0.9796 accuracy: 0.00130902 cost: 0.00676288 M: 35.3698 delta: 0.0265677 time: 656.016 one-recall: 0.99 one-ratio: 1.0003
iteration: 18 recall: 0.9796 accuracy: 0.00130902 cost: 0.00676493 M: 35.3775 delta: 0.0265324 time: 663.491 one-recall: 0.99 one-ratio: 1.0003
iteration: 19 recall: 0.9796 accuracy: 0.00130902 cost: 0.00676595 M: 35.3813 delta: 0.0265158 time: 670.853 one-recall: 0.99 one-ratio: 1.0003
iteration: 20 recall: 0.9796 accuracy: 0.00130902 cost: 0.00676644 M: 35.3831 delta: 0.0265082 time: 678.16 one-recall: 0.99 one-ratio: 1.0003
iteration: 21 recall: 0.9796 accuracy: 0.00130902 cost: 0.0067667 M: 35.3841 delta: 0.0265039 time: 685.426 one-recall: 0.99 one-ratio: 1.0003
iteration: 22 recall: 0.9796 accuracy: 0.00130902 cost: 0.00676683 M: 35.3846 delta: 0.0265003 time: 692.664 one-recall: 0.99 one-ratio: 1.0003
iteration: 23 recall: 0.9796 accuracy: 0.00130902 cost: 0.00676691 M: 35.3849 delta: 0.0264994 time: 699.875 one-recall: 0.99 one-ratio: 1.0003
iteration: 24 recall: 0.9796 accuracy: 0.00130902 cost: 0.00676696 M: 35.3851 delta: 0.0264987 time: 707.093 one-recall: 0.99 one-ratio: 1.0003
iteration: 25 recall: 0.9796 accuracy: 0.00130902 cost: 0.006767 M: 35.3853 delta: 0.0264981 time: 714.296 one-recall: 0.99 one-ratio: 1.0003
iteration: 26 recall: 0.9796 accuracy: 0.00130902 cost: 0.00676701 M: 35.3854 delta: 0.0264977 time: 721.493 one-recall: 0.99 one-ratio: 1.0003
iteration: 27 recall: 0.9796 accuracy: 0.00130902 cost: 0.00676702 M: 35.3854 delta: 0.0264979 time: 728.69 one-recall: 0.99 one-ratio: 1.0003
iteration: 28 recall: 0.9796 accuracy: 0.00130902 cost: 0.00676703 M: 35.3854 delta: 0.0264977 time: 735.891 one-recall: 0.99 one-ratio: 1.0003
iteration: 29 recall: 0.9796 accuracy: 0.00130902 cost: 0.00676703 M: 35.3854 delta: 0.0264977 time: 743.082 one-recall: 0.99 one-ratio: 1.0003
iteration: 30 recall: 0.9796 accuracy: 0.00130902 cost: 0.00676703 M: 35.3854 delta: 0.0264976 time: 750.283 one-recall: 0.99 one-ratio: 1.0003
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 769.1000000000022
Index size:  263088.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0113460000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.0700000000, query time of that 0.0226920090, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Reject!
378.747 < 422.972
  -> Decision False in time 0.0200000000, query time of that 0.0067245750, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
389.423 < 430.264
  -> Decision False in time 0.0300000000, query time of that 0.0084339180, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.5500000000, query time of that 0.0280128970, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
407.432 < 426.765
  -> Decision False in time 0.3900000000, query time of that 0.0196723090, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
220.22 < 225.624
  -> Decision False in time 5.6800000000, query time of that 0.2860718840, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Accept!
  -> Decision True in time 8.0900000000, query time of that 0.0368992980, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
368.101 < 386.736
  -> Decision False in time 2.5400000000, query time of that 0.0125146550, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
270.797 < 278.943
  -> Decision False in time 1.3800000000, query time of that 0.0063796450, 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: 0.0008 accuracy: 2.30144 cost: 0.00038 M: 10 delta: 1 time: 63.6453 one-recall: 0 one-ratio: 3.36738
iteration: 2 recall: 0.002 accuracy: 1.24913 cost: 0.000637428 M: 10 delta: 0.856032 time: 107.705 one-recall: 0 one-ratio: 2.62974
iteration: 3 recall: 0.0356 accuracy: 0.676115 cost: 0.00109521 M: 11.5287 delta: 0.835104 time: 161.099 one-recall: 0.02 one-ratio: 2.02414
iteration: 4 recall: 0.1956 accuracy: 0.324636 cost: 0.00163042 M: 11.8362 delta: 0.783451 time: 214.233 one-recall: 0.23 one-ratio: 1.57009
iteration: 5 recall: 0.5116 accuracy: 0.10599 cost: 0.00223604 M: 12.6036 delta: 0.664644 time: 269.443 one-recall: 0.63 one-ratio: 1.21567
iteration: 6 recall: 0.7908 accuracy: 0.0251423 cost: 0.00297991 M: 15.1139 delta: 0.432362 time: 331.703 one-recall: 0.87 one-ratio: 1.06129
iteration: 7 recall: 0.9068 accuracy: 0.00811147 cost: 0.00395512 M: 21.14 delta: 0.196348 time: 403.816 one-recall: 0.96 one-ratio: 1.02793
iteration: 8 recall: 0.9528 accuracy: 0.00292716 cost: 0.00497953 M: 27.3036 delta: 0.0884324 time: 473.285 one-recall: 1 one-ratio: 1
iteration: 9 recall: 0.9752 accuracy: 0.00110033 cost: 0.00577215 M: 31.2869 delta: 0.0513151 time: 527.9 one-recall: 1 one-ratio: 1
iteration: 10 recall: 0.9828 accuracy: 0.000859618 cost: 0.00625749 M: 33.3909 delta: 0.0371719 time: 565.854 one-recall: 1 one-ratio: 1
iteration: 11 recall: 0.986 accuracy: 0.000765526 cost: 0.00651544 M: 34.4255 delta: 0.0312622 time: 591.262 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.9876 accuracy: 0.000634127 cost: 0.00664353 M: 34.9192 delta: 0.0287028 time: 608.673 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.988 accuracy: 0.000596104 cost: 0.00670618 M: 35.1566 delta: 0.0275344 time: 621.38 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.988 accuracy: 0.000596104 cost: 0.00673625 M: 35.2694 delta: 0.0269929 time: 631.444 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.988 accuracy: 0.000596104 cost: 0.00675115 M: 35.3262 delta: 0.0267289 time: 640.166 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.988 accuracy: 0.000596104 cost: 0.00675877 M: 35.3548 delta: 0.0266009 time: 648.198 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.988 accuracy: 0.000596104 cost: 0.00676279 M: 35.3698 delta: 0.0265356 time: 655.867 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.988 accuracy: 0.000596104 cost: 0.00676472 M: 35.3771 delta: 0.0265045 time: 663.318 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.988 accuracy: 0.000596104 cost: 0.00676574 M: 35.3809 delta: 0.0264909 time: 670.656 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.988 accuracy: 0.000596104 cost: 0.00676635 M: 35.3833 delta: 0.0264802 time: 677.945 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.988 accuracy: 0.000596104 cost: 0.00676669 M: 35.3847 delta: 0.0264746 time: 685.196 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.988 accuracy: 0.000596104 cost: 0.00676686 M: 35.3854 delta: 0.0264718 time: 692.426 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.9884 accuracy: 0.000590766 cost: 0.00676696 M: 35.3858 delta: 0.0264699 time: 699.641 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.9884 accuracy: 0.000590766 cost: 0.00676701 M: 35.3861 delta: 0.0264689 time: 706.843 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.9884 accuracy: 0.000590766 cost: 0.00676706 M: 35.3862 delta: 0.0264684 time: 714.044 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.9884 accuracy: 0.000590766 cost: 0.00676709 M: 35.3863 delta: 0.0264681 time: 721.243 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.9884 accuracy: 0.000590766 cost: 0.0067671 M: 35.3864 delta: 0.0264679 time: 728.433 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.9884 accuracy: 0.000590766 cost: 0.00676711 M: 35.3864 delta: 0.0264677 time: 735.628 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.9884 accuracy: 0.000590766 cost: 0.00676712 M: 35.3864 delta: 0.0264676 time: 742.821 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.9884 accuracy: 0.000590766 cost: 0.00676712 M: 35.3864 delta: 0.0264677 time: 750.017 one-recall: 1 one-ratio: 1
Graph completion with reverse edges...

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

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 768.7999999999993
Index size:  262896.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.1078595000
  Testing...
|S| = 80
|T| = 1152
Reject!
407.822 < 486.643
  -> Decision False in time 0.0000000000, query time of that 0.0007230660, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Reject!
393.667 < 436.627
  -> Decision False in time 0.0100000000, query time of that 0.0005261420, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
473.221 < 492.826
  -> Decision False in time 0.0200000000, query time of that 0.0076595450, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Reject!
390.48 < 412.941
  -> Decision False in time 0.1400000000, query time of that 0.0071445400, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
430.003 < 446.6
  -> Decision False in time 0.0200000000, query time of that 0.0009515370, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
452.639 < 463.461
  -> Decision False in time 0.0400000000, query time of that 0.0018503040, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Reject!
358.898 < 446.542
  -> Decision False in time 0.5000000000, query time of that 0.0024591040, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
439.331 < 445.215
  -> Decision False in time 0.6100000000, query time of that 0.0027910080, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
373.977 < 447.935
  -> Decision False in time 0.4100000000, query time of that 0.0022859800, with c1=5.0000000000, c2=0.1000000000
