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', 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', 20, {'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', 5, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 80, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 10, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 70, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 60, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 100, {'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', 30, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 1, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 3, {'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', 4, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 4, {'reverse': -1}, False])
Got a train set of size (1000000 * 128)
Generating control...
Initializing...
iteration: 1 recall: 0 accuracy: 2.29356 cost: 0.00038 M: 10 delta: 1 time: 59.3176 one-recall: 0 one-ratio: 3.52528
iteration: 2 recall: 0.004 accuracy: 1.20332 cost: 0.000637428 M: 10 delta: 0.856032 time: 100.273 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: 149.801 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: 198.969 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: 249.942 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: 307.297 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: 373.028 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: 435.538 one-recall: 1 one-ratio: 1
iteration: 9 recall: 0.9644 accuracy: 0.00221739 cost: 0.00577293 M: 31.2904 delta: 0.0514019 time: 484.508 one-recall: 1 one-ratio: 1
iteration: 10 recall: 0.9764 accuracy: 0.0012952 cost: 0.00625843 M: 33.3974 delta: 0.0372226 time: 518.684 one-recall: 1 one-ratio: 1
iteration: 11 recall: 0.984 accuracy: 0.000915513 cost: 0.00651572 M: 34.4268 delta: 0.0313604 time: 541.741 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.9872 accuracy: 0.000651871 cost: 0.00664321 M: 34.9174 delta: 0.028787 time: 557.643 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.9876 accuracy: 0.000628894 cost: 0.00670536 M: 35.1523 delta: 0.027629 time: 569.283 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.9876 accuracy: 0.000628894 cost: 0.00673551 M: 35.2647 delta: 0.0270893 time: 578.554 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.9876 accuracy: 0.000628894 cost: 0.00675029 M: 35.3194 delta: 0.0268326 time: 586.582 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.9876 accuracy: 0.000628894 cost: 0.00675777 M: 35.3471 delta: 0.0267076 time: 593.982 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.9876 accuracy: 0.000628894 cost: 0.00676154 M: 35.3608 delta: 0.0266443 time: 601.039 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.9876 accuracy: 0.000628894 cost: 0.00676338 M: 35.3677 delta: 0.0266122 time: 607.916 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.9876 accuracy: 0.000628894 cost: 0.00676433 M: 35.3712 delta: 0.0265977 time: 614.701 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.9876 accuracy: 0.000628894 cost: 0.00676485 M: 35.373 delta: 0.02659 time: 621.433 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.988 accuracy: 0.0006087 cost: 0.00676515 M: 35.3742 delta: 0.0265857 time: 628.139 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.988 accuracy: 0.0006087 cost: 0.00676532 M: 35.3749 delta: 0.0265828 time: 634.832 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.988 accuracy: 0.0006087 cost: 0.00676541 M: 35.3753 delta: 0.0265811 time: 641.507 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.988 accuracy: 0.0006087 cost: 0.00676547 M: 35.3755 delta: 0.0265798 time: 648.174 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.988 accuracy: 0.0006087 cost: 0.00676549 M: 35.3756 delta: 0.0265796 time: 654.837 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.988 accuracy: 0.0006087 cost: 0.00676551 M: 35.3757 delta: 0.0265794 time: 661.5 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.988 accuracy: 0.0006087 cost: 0.00676552 M: 35.3757 delta: 0.0265791 time: 668.159 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.988 accuracy: 0.0006087 cost: 0.00676553 M: 35.3757 delta: 0.0265791 time: 674.818 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.988 accuracy: 0.0006087 cost: 0.00676553 M: 35.3757 delta: 0.026579 time: 681.473 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.988 accuracy: 0.0006087 cost: 0.00676553 M: 35.3757 delta: 0.026579 time: 688.131 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 704.2099999999999
Index size:  1903140.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0115286000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.0700000000, query time of that 0.0211656720, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Reject!
341.423 < 403.492
  -> Decision False in time 0.3400000000, query time of that 0.1068541670, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
372.649 < 384.129
  -> Decision False in time 0.4700000000, query time of that 0.1439269910, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.5300000000, query time of that 0.0277564910, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
264.199 < 268.881
  -> Decision False in time 0.7000000000, query time of that 0.0343603810, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
378.262 < 416.083
  -> Decision False in time 3.2500000000, query time of that 0.1572513440, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Accept!
  -> Decision True in time 6.6200000000, query time of that 0.0347813670, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
263.701 < 273.847
  -> Decision False in time 13.7400000000, query time of that 0.0702072960, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
249.904 < 255.552
  -> Decision False in time 9.9600000000, query time of that 0.0538624980, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 40, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 40, {'reverse': -1}, False])
Got a train set of size (1000000 * 128)
Generating control...
Initializing...
iteration: 1 recall: 0 accuracy: 2.6766 cost: 0.00038 M: 10 delta: 1 time: 53.8082 one-recall: 0 one-ratio: 3.66072
iteration: 2 recall: 0.0016 accuracy: 1.38091 cost: 0.000637428 M: 10 delta: 0.856032 time: 91.8741 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: 137.952 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: 183.62 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: 231.025 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: 284.538 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: 345.44 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: 402.291 one-recall: 1 one-ratio: 1
iteration: 9 recall: 0.97 accuracy: 0.00153751 cost: 0.00577336 M: 31.2902 delta: 0.0513331 time: 445.584 one-recall: 1 one-ratio: 1
iteration: 10 recall: 0.978 accuracy: 0.00108745 cost: 0.00625831 M: 33.3952 delta: 0.0371841 time: 474.681 one-recall: 1 one-ratio: 1
iteration: 11 recall: 0.9816 accuracy: 0.000822769 cost: 0.00651495 M: 34.422 delta: 0.0313218 time: 493.543 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.9856 accuracy: 0.000634549 cost: 0.00664356 M: 34.9169 delta: 0.0287556 time: 506.243 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.9868 accuracy: 0.000578651 cost: 0.00670598 M: 35.1529 delta: 0.027595 time: 515.401 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.9876 accuracy: 0.000568954 cost: 0.00673627 M: 35.2664 delta: 0.0270435 time: 522.673 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.9876 accuracy: 0.000568954 cost: 0.00675099 M: 35.3217 delta: 0.0267899 time: 528.957 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.9876 accuracy: 0.000568954 cost: 0.0067584 M: 35.3495 delta: 0.0266568 time: 534.752 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.9876 accuracy: 0.000568954 cost: 0.00676207 M: 35.3629 delta: 0.0265947 time: 540.29 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.9876 accuracy: 0.000568954 cost: 0.00676399 M: 35.37 delta: 0.0265678 time: 545.695 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.9876 accuracy: 0.000568954 cost: 0.00676502 M: 35.3739 delta: 0.0265497 time: 551.028 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.9876 accuracy: 0.000568954 cost: 0.00676557 M: 35.376 delta: 0.026542 time: 556.32 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.9876 accuracy: 0.000568954 cost: 0.00676589 M: 35.3772 delta: 0.026538 time: 561.587 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.9876 accuracy: 0.000568954 cost: 0.00676609 M: 35.378 delta: 0.0265349 time: 566.846 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.9876 accuracy: 0.000568954 cost: 0.00676619 M: 35.3784 delta: 0.0265336 time: 572.098 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.9876 accuracy: 0.000568954 cost: 0.00676626 M: 35.3787 delta: 0.0265323 time: 577.345 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.9876 accuracy: 0.000568954 cost: 0.0067663 M: 35.3789 delta: 0.026532 time: 582.583 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.9876 accuracy: 0.000568954 cost: 0.00676631 M: 35.3789 delta: 0.0265317 time: 587.821 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.9876 accuracy: 0.000568954 cost: 0.00676632 M: 35.379 delta: 0.0265314 time: 593.058 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.9876 accuracy: 0.000568954 cost: 0.00676632 M: 35.379 delta: 0.0265314 time: 598.296 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.9876 accuracy: 0.000568954 cost: 0.00676633 M: 35.379 delta: 0.0265313 time: 603.532 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.9876 accuracy: 0.000568954 cost: 0.00676633 M: 35.379 delta: 0.0265314 time: 608.763 one-recall: 1 one-ratio: 1
Graph completion with reverse edges...

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

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 623.29
Index size:  260980.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0062231000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.0800000000, query time of that 0.0369532110, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Accept!
  -> Decision True in time 0.8200000000, query time of that 0.3731869780, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
307.12 < 362.802
  -> Decision False in time 2.4500000000, query time of that 1.0896365740, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.5500000000, query time of that 0.0424946780, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
260.642 < 263.395
  -> Decision False in time 1.5000000000, query time of that 0.1190549940, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
415.347 < 431.656
  -> Decision False in time 3.2800000000, query time of that 0.2626706980, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Reject!
260.455 < 270.25
  -> Decision False in time 1.9000000000, query time of that 0.0158424740, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
269.063 < 272.776
  -> Decision False in time 43.8600000000, query time of that 0.3633755950, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
252.931 < 252.976
  -> Decision False in time 2.2300000000, query time of that 0.0175127240, 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: 0.0008 accuracy: 2.48177 cost: 0.00038 M: 10 delta: 1 time: 53.7964 one-recall: 0 one-ratio: 3.57151
iteration: 2 recall: 0.006 accuracy: 1.20232 cost: 0.000637428 M: 10 delta: 0.856032 time: 91.8428 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: 137.908 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: 183.548 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: 230.927 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: 284.401 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: 345.254 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: 402.07 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: 445.275 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: 474.364 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: 493.269 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: 505.922 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: 515.072 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: 522.312 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: 528.588 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: 534.385 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: 539.932 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: 545.332 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: 550.66 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: 555.942 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: 561.201 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: 566.448 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: 571.688 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: 576.922 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: 582.153 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: 587.375 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: 592.605 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: 597.833 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: 603.062 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: 608.29 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 622.8000000000002
Index size:  262924.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0091521000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.0700000000, query time of that 0.0258210760, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Accept!
  -> Decision True in time 0.7000000000, query time of that 0.2535821360, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
387.394 < 388.117
  -> Decision False in time 0.6300000000, query time of that 0.2219433730, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.5300000000, query time of that 0.0286019450, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
240.747 < 243.216
  -> Decision False in time 0.3800000000, query time of that 0.0232283080, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
377.142 < 383.226
  -> Decision False in time 0.3600000000, query time of that 0.0193465940, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Reject!
226.473 < 257.965
  -> Decision False in time 5.8900000000, query time of that 0.0354591580, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
258.287 < 262.339
  -> Decision False in time 2.3200000000, query time of that 0.0133642770, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
327.831 < 365.075
  -> Decision False in time 53.5700000000, query time of that 0.3161206710, 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: 2.75563 cost: 0.00038 M: 10 delta: 1 time: 53.8079 one-recall: 0 one-ratio: 3.20375
iteration: 2 recall: 0.0036 accuracy: 1.28835 cost: 0.000637428 M: 10 delta: 0.856032 time: 91.8617 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: 137.931 one-recall: 0.04 one-ratio: 2.18955
iteration: 4 recall: 0.188 accuracy: 0.300992 cost: 0.00163043 M: 11.8362 delta: 0.78346 time: 183.583 one-recall: 0.15 one-ratio: 1.72886
iteration: 5 recall: 0.4932 accuracy: 0.113832 cost: 0.00223606 M: 12.6036 delta: 0.664589 time: 230.973 one-recall: 0.55 one-ratio: 1.31529
iteration: 6 recall: 0.746 accuracy: 0.0314189 cost: 0.00297996 M: 15.1149 delta: 0.43236 time: 284.473 one-recall: 0.82 one-ratio: 1.08068
iteration: 7 recall: 0.8672 accuracy: 0.010611 cost: 0.00395521 M: 21.1403 delta: 0.196429 time: 345.346 one-recall: 0.94 one-ratio: 1.01691
iteration: 8 recall: 0.9224 accuracy: 0.00563282 cost: 0.00497945 M: 27.3029 delta: 0.0885035 time: 402.164 one-recall: 0.97 one-ratio: 1.01391
iteration: 9 recall: 0.947199 accuracy: 0.00299932 cost: 0.00577183 M: 31.2855 delta: 0.0513971 time: 445.381 one-recall: 0.99 one-ratio: 1.00022
iteration: 10 recall: 0.9592 accuracy: 0.00227026 cost: 0.00625696 M: 33.3914 delta: 0.0372729 time: 474.483 one-recall: 0.99 one-ratio: 1.00022
iteration: 11 recall: 0.9636 accuracy: 0.00199197 cost: 0.00651441 M: 34.4225 delta: 0.0313612 time: 493.391 one-recall: 0.99 one-ratio: 1.00022
iteration: 12 recall: 0.9664 accuracy: 0.00187919 cost: 0.00664216 M: 34.9141 delta: 0.0287902 time: 506.048 one-recall: 0.99 one-ratio: 1.00022
iteration: 13 recall: 0.968 accuracy: 0.0018145 cost: 0.00670434 M: 35.1492 delta: 0.027634 time: 515.194 one-recall: 0.99 one-ratio: 1.00022
iteration: 14 recall: 0.968 accuracy: 0.0018145 cost: 0.00673502 M: 35.2633 delta: 0.0270735 time: 522.484 one-recall: 0.99 one-ratio: 1.00022
iteration: 15 recall: 0.968 accuracy: 0.0018145 cost: 0.00674955 M: 35.3182 delta: 0.0268176 time: 528.762 one-recall: 0.99 one-ratio: 1.00022
iteration: 16 recall: 0.968 accuracy: 0.0018145 cost: 0.00675688 M: 35.3456 delta: 0.0266927 time: 534.552 one-recall: 0.99 one-ratio: 1.00022
iteration: 17 recall: 0.9684 accuracy: 0.00178448 cost: 0.00676054 M: 35.3594 delta: 0.0266336 time: 540.094 one-recall: 0.99 one-ratio: 1.00022
iteration: 18 recall: 0.9684 accuracy: 0.00178448 cost: 0.00676258 M: 35.3668 delta: 0.026599 time: 545.507 one-recall: 0.99 one-ratio: 1.00022
iteration: 19 recall: 0.9684 accuracy: 0.00178448 cost: 0.00676361 M: 35.3708 delta: 0.0265803 time: 550.838 one-recall: 0.99 one-ratio: 1.00022
iteration: 20 recall: 0.9684 accuracy: 0.00178448 cost: 0.00676418 M: 35.3729 delta: 0.0265718 time: 556.126 one-recall: 0.99 one-ratio: 1.00022
iteration: 21 recall: 0.9684 accuracy: 0.00178448 cost: 0.00676447 M: 35.3741 delta: 0.0265668 time: 561.386 one-recall: 0.99 one-ratio: 1.00022
iteration: 22 recall: 0.9684 accuracy: 0.00178448 cost: 0.00676462 M: 35.3747 delta: 0.0265652 time: 566.627 one-recall: 0.99 one-ratio: 1.00022
iteration: 23 recall: 0.9684 accuracy: 0.00178448 cost: 0.00676469 M: 35.3749 delta: 0.0265639 time: 571.861 one-recall: 0.99 one-ratio: 1.00022
iteration: 24 recall: 0.9684 accuracy: 0.00178448 cost: 0.00676472 M: 35.375 delta: 0.026563 time: 577.092 one-recall: 0.99 one-ratio: 1.00022
iteration: 25 recall: 0.9684 accuracy: 0.00178448 cost: 0.00676473 M: 35.3751 delta: 0.026563 time: 582.32 one-recall: 0.99 one-ratio: 1.00022
iteration: 26 recall: 0.9684 accuracy: 0.00178448 cost: 0.00676474 M: 35.3751 delta: 0.0265627 time: 587.55 one-recall: 0.99 one-ratio: 1.00022
iteration: 27 recall: 0.9684 accuracy: 0.00178448 cost: 0.00676474 M: 35.3751 delta: 0.0265627 time: 592.774 one-recall: 0.99 one-ratio: 1.00022
iteration: 28 recall: 0.9684 accuracy: 0.00178448 cost: 0.00676474 M: 35.3751 delta: 0.0265626 time: 597.997 one-recall: 0.99 one-ratio: 1.00022
iteration: 29 recall: 0.9684 accuracy: 0.00178448 cost: 0.00676475 M: 35.3751 delta: 0.0265624 time: 603.222 one-recall: 0.99 one-ratio: 1.00022
iteration: 30 recall: 0.9684 accuracy: 0.00178448 cost: 0.00676475 M: 35.3751 delta: 0.0265624 time: 608.45 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 622.96
Index size:  262800.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.1073082000
  Testing...
|S| = 80
|T| = 1152
Reject!
328.831 < 420.918
  -> Decision False in time 0.0000000000, query time of that 0.0002070640, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Reject!
358.548 < 444.536
  -> Decision False in time 0.0100000000, query time of that 0.0022500360, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
556.004 < 569.395
  -> Decision False in time 0.0000000000, query time of that 0.0005056100, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Reject!
415.582 < 435.195
  -> Decision False in time 0.0700000000, query time of that 0.0023375910, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
360.642 < 379.191
  -> Decision False in time 0.0000000000, query time of that 0.0002838670, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
408.412 < 451.961
  -> Decision False in time 0.0200000000, query time of that 0.0008741470, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Reject!
456.134 < 461.591
  -> Decision False in time 0.5800000000, query time of that 0.0025103210, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
445.714 < 471.915
  -> Decision False in time 0.5800000000, query time of that 0.0027050920, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
366.28 < 407.151
  -> Decision False in time 0.1700000000, query time of that 0.0007950280, 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: 2.38371 cost: 0.00038 M: 10 delta: 1 time: 53.8277 one-recall: 0 one-ratio: 3.41607
iteration: 2 recall: 0.002 accuracy: 1.28323 cost: 0.000637428 M: 10 delta: 0.856032 time: 91.8544 one-recall: 0 one-ratio: 2.77149
iteration: 3 recall: 0.0312 accuracy: 0.723596 cost: 0.00109521 M: 11.5287 delta: 0.835105 time: 137.91 one-recall: 0.06 one-ratio: 2.15225
iteration: 4 recall: 0.1752 accuracy: 0.395304 cost: 0.00163043 M: 11.8362 delta: 0.78347 time: 183.552 one-recall: 0.28 one-ratio: 1.71038
iteration: 5 recall: 0.4992 accuracy: 0.102045 cost: 0.00223601 M: 12.6037 delta: 0.664571 time: 230.937 one-recall: 0.65 one-ratio: 1.25973
iteration: 6 recall: 0.758 accuracy: 0.0320828 cost: 0.00297995 M: 15.1153 delta: 0.432326 time: 284.412 one-recall: 0.83 one-ratio: 1.09167
iteration: 7 recall: 0.8816 accuracy: 0.0111449 cost: 0.0039552 M: 21.1397 delta: 0.196414 time: 345.258 one-recall: 0.93 one-ratio: 1.02641
iteration: 8 recall: 0.9408 accuracy: 0.00409358 cost: 0.00498008 M: 27.3069 delta: 0.0883961 time: 402.062 one-recall: 0.97 one-ratio: 1.00433
iteration: 9 recall: 0.9616 accuracy: 0.00210106 cost: 0.00577281 M: 31.2917 delta: 0.0513212 time: 445.256 one-recall: 0.98 one-ratio: 1.0026
iteration: 10 recall: 0.9704 accuracy: 0.0015283 cost: 0.0062576 M: 33.3958 delta: 0.0371998 time: 474.347 one-recall: 0.98 one-ratio: 1.0026
iteration: 11 recall: 0.9752 accuracy: 0.00114867 cost: 0.0065146 M: 34.426 delta: 0.0313184 time: 493.25 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.9784 accuracy: 0.000988028 cost: 0.00664233 M: 34.9156 delta: 0.028758 time: 505.933 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.9792 accuracy: 0.000984121 cost: 0.0067052 M: 35.1523 delta: 0.02759 time: 515.153 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.98 accuracy: 0.000949828 cost: 0.0067357 M: 35.2653 delta: 0.0270433 time: 522.455 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.98 accuracy: 0.000949828 cost: 0.00675051 M: 35.3211 delta: 0.0267825 time: 528.768 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.9808 accuracy: 0.00086411 cost: 0.00675783 M: 35.3484 delta: 0.0266539 time: 534.572 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.9808 accuracy: 0.00086411 cost: 0.00676152 M: 35.3621 delta: 0.0265905 time: 540.118 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.9808 accuracy: 0.00086411 cost: 0.00676342 M: 35.3693 delta: 0.0265613 time: 545.525 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.9808 accuracy: 0.00086411 cost: 0.00676438 M: 35.373 delta: 0.0265447 time: 550.858 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.9808 accuracy: 0.00086411 cost: 0.00676491 M: 35.375 delta: 0.026538 time: 556.15 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.9808 accuracy: 0.00086411 cost: 0.00676518 M: 35.3762 delta: 0.026533 time: 561.423 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.9808 accuracy: 0.00086411 cost: 0.00676535 M: 35.3769 delta: 0.0265297 time: 566.681 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.9808 accuracy: 0.00086411 cost: 0.00676545 M: 35.3773 delta: 0.0265285 time: 571.931 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.9808 accuracy: 0.00086411 cost: 0.00676551 M: 35.3775 delta: 0.0265274 time: 577.177 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.9808 accuracy: 0.00086411 cost: 0.00676554 M: 35.3777 delta: 0.026527 time: 582.419 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.9808 accuracy: 0.00086411 cost: 0.00676557 M: 35.3778 delta: 0.0265266 time: 587.658 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.9808 accuracy: 0.00086411 cost: 0.00676558 M: 35.3778 delta: 0.0265264 time: 592.896 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.9808 accuracy: 0.00086411 cost: 0.00676558 M: 35.3778 delta: 0.0265263 time: 598.135 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.9808 accuracy: 0.00086411 cost: 0.00676559 M: 35.3778 delta: 0.0265262 time: 603.374 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.9808 accuracy: 0.00086411 cost: 0.00676559 M: 35.3778 delta: 0.0265262 time: 608.616 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 623.1399999999994
Index size:  262840.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0114501000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.0700000000, query time of that 0.0170691980, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Reject!
304.897 < 381.186
  -> Decision False in time 0.0700000000, query time of that 0.0194906540, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
395.522 < 409.845
  -> Decision False in time 0.7500000000, query time of that 0.1961989360, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Reject!
441.376 < 448.128
  -> Decision False in time 0.1300000000, query time of that 0.0053614050, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
257.928 < 260.862
  -> Decision False in time 0.9900000000, query time of that 0.0366609870, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
272.36 < 272.998
  -> Decision False in time 1.3700000000, query time of that 0.0525472820, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Accept!
  -> Decision True in time 6.5900000000, query time of that 0.0272646620, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
275.599 < 292.983
  -> Decision False in time 1.2300000000, query time of that 0.0051457560, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
178.983 < 202.49
  -> Decision False in time 8.3200000000, query time of that 0.0347815770, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 80, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 80, {'reverse': -1}, False])
Got a train set of size (1000000 * 128)
Generating control...
Initializing...
iteration: 1 recall: 0.0008 accuracy: 2.92922 cost: 0.00038 M: 10 delta: 1 time: 53.8221 one-recall: 0 one-ratio: 4.21155
iteration: 2 recall: 0.004 accuracy: 1.49012 cost: 0.000637428 M: 10 delta: 0.856032 time: 91.8518 one-recall: 0 one-ratio: 3.0998
iteration: 3 recall: 0.0336 accuracy: 0.817186 cost: 0.00109521 M: 11.5287 delta: 0.835105 time: 137.915 one-recall: 0.06 one-ratio: 2.50799
iteration: 4 recall: 0.196 accuracy: 0.39141 cost: 0.00163043 M: 11.8362 delta: 0.783457 time: 183.565 one-recall: 0.25 one-ratio: 1.91377
iteration: 5 recall: 0.5336 accuracy: 0.0998286 cost: 0.00223611 M: 12.6037 delta: 0.664592 time: 230.946 one-recall: 0.73 one-ratio: 1.21909
iteration: 6 recall: 0.8036 accuracy: 0.0244561 cost: 0.00298 M: 15.1148 delta: 0.432317 time: 284.452 one-recall: 0.92 one-ratio: 1.05745
iteration: 7 recall: 0.914 accuracy: 0.00707247 cost: 0.00395542 M: 21.1408 delta: 0.196419 time: 345.335 one-recall: 0.96 one-ratio: 1.02375
iteration: 8 recall: 0.9588 accuracy: 0.00305021 cost: 0.00498021 M: 27.3046 delta: 0.0884338 time: 402.155 one-recall: 0.97 one-ratio: 1.01967
iteration: 9 recall: 0.9732 accuracy: 0.00198931 cost: 0.00577264 M: 31.2863 delta: 0.0513476 time: 445.342 one-recall: 0.99 one-ratio: 1.01567
iteration: 10 recall: 0.9816 accuracy: 0.00095726 cost: 0.00625691 M: 33.388 delta: 0.0372198 time: 474.41 one-recall: 1 one-ratio: 1
iteration: 11 recall: 0.986 accuracy: 0.000499954 cost: 0.00651388 M: 34.4174 delta: 0.0313529 time: 493.315 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.9868 accuracy: 0.000452938 cost: 0.00664086 M: 34.908 delta: 0.0287829 time: 505.998 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.9876 accuracy: 0.000418723 cost: 0.00670291 M: 35.1427 delta: 0.0276241 time: 515.18 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.9876 accuracy: 0.000418723 cost: 0.00673288 M: 35.2552 delta: 0.0270724 time: 522.446 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.988 accuracy: 0.00041175 cost: 0.00674777 M: 35.31 delta: 0.0268061 time: 528.765 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.988 accuracy: 0.00041175 cost: 0.0067552 M: 35.338 delta: 0.0266788 time: 534.576 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.988 accuracy: 0.00041175 cost: 0.00675882 M: 35.3511 delta: 0.0266197 time: 540.121 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.988 accuracy: 0.00041175 cost: 0.00676073 M: 35.3581 delta: 0.0265885 time: 545.533 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.988 accuracy: 0.00041175 cost: 0.00676174 M: 35.362 delta: 0.0265717 time: 550.875 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.988 accuracy: 0.00041175 cost: 0.00676231 M: 35.3643 delta: 0.0265632 time: 556.176 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.988 accuracy: 0.00041175 cost: 0.00676261 M: 35.3654 delta: 0.0265598 time: 561.451 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.988 accuracy: 0.00041175 cost: 0.00676277 M: 35.3661 delta: 0.0265565 time: 566.717 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.988 accuracy: 0.00041175 cost: 0.00676289 M: 35.3666 delta: 0.0265548 time: 571.969 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.988 accuracy: 0.00041175 cost: 0.00676297 M: 35.3669 delta: 0.0265535 time: 577.216 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.988 accuracy: 0.00041175 cost: 0.006763 M: 35.367 delta: 0.0265529 time: 582.457 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.988 accuracy: 0.00041175 cost: 0.00676301 M: 35.367 delta: 0.026553 time: 587.695 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.988 accuracy: 0.00041175 cost: 0.00676302 M: 35.3671 delta: 0.0265526 time: 592.935 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.988 accuracy: 0.00041175 cost: 0.00676302 M: 35.3671 delta: 0.0265525 time: 598.175 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.988 accuracy: 0.00041175 cost: 0.00676302 M: 35.3671 delta: 0.0265525 time: 603.416 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.988 accuracy: 0.00041175 cost: 0.00676302 M: 35.3671 delta: 0.0265525 time: 608.656 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 623.2000000000007
Index size:  262820.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0031703000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.1100000000, query time of that 0.0575970850, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Accept!
  -> Decision True in time 1.0200000000, query time of that 0.5762023750, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
367.47 < 377.025
  -> Decision False in time 7.1400000000, query time of that 3.9516977650, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.5900000000, query time of that 0.0647610970, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Accept!
  -> Decision True in time 5.7800000000, query time of that 0.6655195740, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Accept!
  -> Decision True in time 57.4000000000, query time of that 6.6957735080, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Accept!
  -> Decision True in time 6.6500000000, query time of that 0.0794486660, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
227.119 < 229.159
  -> Decision False in time 4.9100000000, query time of that 0.0606284300, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
263.331 < 264.221
  -> Decision False in time 28.8200000000, query time of that 0.3476454120, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 10, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 10, {'reverse': -1}, False])
Got a train set of size (1000000 * 128)
Generating control...
Initializing...
iteration: 1 recall: 0 accuracy: 2.21465 cost: 0.00038 M: 10 delta: 1 time: 53.8103 one-recall: 0 one-ratio: 3.57287
iteration: 2 recall: 0.0048 accuracy: 1.14976 cost: 0.000637428 M: 10 delta: 0.856032 time: 91.8286 one-recall: 0 one-ratio: 2.76746
iteration: 3 recall: 0.0396 accuracy: 0.613566 cost: 0.00109521 M: 11.5287 delta: 0.835106 time: 137.878 one-recall: 0.04 one-ratio: 2.22771
iteration: 4 recall: 0.21 accuracy: 0.294946 cost: 0.00163043 M: 11.8362 delta: 0.783459 time: 183.513 one-recall: 0.24 one-ratio: 1.75219
iteration: 5 recall: 0.5208 accuracy: 0.110449 cost: 0.00223606 M: 12.6035 delta: 0.664596 time: 230.873 one-recall: 0.61 one-ratio: 1.30656
iteration: 6 recall: 0.784 accuracy: 0.0258177 cost: 0.00297987 M: 15.1139 delta: 0.432354 time: 284.335 one-recall: 0.88 one-ratio: 1.04498
iteration: 7 recall: 0.8984 accuracy: 0.00892343 cost: 0.00395505 M: 21.1386 delta: 0.196455 time: 345.177 one-recall: 0.95 one-ratio: 1.0259
iteration: 8 recall: 0.941999 accuracy: 0.0041276 cost: 0.00497883 M: 27.3 delta: 0.0884752 time: 401.932 one-recall: 0.99 one-ratio: 1.01291
iteration: 9 recall: 0.9668 accuracy: 0.00196648 cost: 0.0057707 M: 31.2809 delta: 0.0514235 time: 445.079 one-recall: 1 one-ratio: 1
iteration: 10 recall: 0.9752 accuracy: 0.00136899 cost: 0.00625542 M: 33.3862 delta: 0.0372937 time: 474.15 one-recall: 1 one-ratio: 1
iteration: 11 recall: 0.9788 accuracy: 0.00116132 cost: 0.00651324 M: 34.4175 delta: 0.0313798 time: 493.094 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.9808 accuracy: 0.00100576 cost: 0.00664101 M: 34.9115 delta: 0.0288142 time: 505.794 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.9812 accuracy: 0.00100146 cost: 0.00670313 M: 35.1472 delta: 0.0276437 time: 514.98 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.9816 accuracy: 0.000988489 cost: 0.00673336 M: 35.2607 delta: 0.0271012 time: 522.262 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.9816 accuracy: 0.000988489 cost: 0.00674843 M: 35.3167 delta: 0.0268361 time: 528.578 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.9816 accuracy: 0.000988489 cost: 0.00675598 M: 35.345 delta: 0.026704 time: 534.394 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.982 accuracy: 0.000980722 cost: 0.00675982 M: 35.3591 delta: 0.0266414 time: 539.947 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.982 accuracy: 0.000980722 cost: 0.00676176 M: 35.3664 delta: 0.0266092 time: 545.357 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.982 accuracy: 0.000980722 cost: 0.00676273 M: 35.3698 delta: 0.0265936 time: 550.689 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.982 accuracy: 0.000980722 cost: 0.00676321 M: 35.3716 delta: 0.0265863 time: 555.979 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.982 accuracy: 0.000980722 cost: 0.00676348 M: 35.3726 delta: 0.026583 time: 561.253 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.982 accuracy: 0.000980722 cost: 0.00676362 M: 35.3732 delta: 0.0265811 time: 566.505 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.982 accuracy: 0.000980722 cost: 0.0067637 M: 35.3735 delta: 0.0265801 time: 571.748 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.982 accuracy: 0.000980722 cost: 0.00676376 M: 35.3738 delta: 0.0265795 time: 576.995 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.982 accuracy: 0.000980722 cost: 0.00676381 M: 35.374 delta: 0.0265786 time: 582.228 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.982 accuracy: 0.000980722 cost: 0.00676383 M: 35.3741 delta: 0.0265785 time: 587.471 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.982 accuracy: 0.000980722 cost: 0.00676384 M: 35.3741 delta: 0.0265779 time: 592.709 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.982 accuracy: 0.000980722 cost: 0.00676385 M: 35.3742 delta: 0.0265779 time: 597.946 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.982 accuracy: 0.000980722 cost: 0.00676386 M: 35.3742 delta: 0.0265779 time: 603.183 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.982 accuracy: 0.000980722 cost: 0.00676387 M: 35.3742 delta: 0.0265777 time: 608.421 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 622.96
Index size:  262900.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0107422000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.0700000000, query time of that 0.0190907140, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Accept!
  -> Decision True in time 0.6300000000, query time of that 0.1883214750, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
434.94 < 445.201
  -> Decision False in time 1.6600000000, query time of that 0.4805138120, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.5300000000, query time of that 0.0225137690, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
343.658 < 381.552
  -> Decision False in time 0.1000000000, query time of that 0.0045173320, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
341.741 < 343.932
  -> Decision False in time 0.5400000000, query time of that 0.0245123080, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Accept!
  -> Decision True in time 6.6700000000, query time of that 0.0321297640, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
265.934 < 276.272
  -> Decision False in time 1.7800000000, query time of that 0.0083048580, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
356.185 < 361.422
  -> Decision False in time 6.1000000000, query time of that 0.0292105770, 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: 0.0004 accuracy: 2.31332 cost: 0.00038 M: 10 delta: 1 time: 53.8158 one-recall: 0 one-ratio: 3.37922
iteration: 2 recall: 0.0036 accuracy: 1.25401 cost: 0.000637428 M: 10 delta: 0.856032 time: 91.8346 one-recall: 0.02 one-ratio: 2.62682
iteration: 3 recall: 0.0404 accuracy: 0.716027 cost: 0.00109521 M: 11.5287 delta: 0.835106 time: 137.888 one-recall: 0.08 one-ratio: 2.09659
iteration: 4 recall: 0.1944 accuracy: 0.39112 cost: 0.00163045 M: 11.8363 delta: 0.783453 time: 183.526 one-recall: 0.25 one-ratio: 1.72461
iteration: 5 recall: 0.5176 accuracy: 0.1596 cost: 0.002236 M: 12.6031 delta: 0.664586 time: 230.885 one-recall: 0.61 one-ratio: 1.314
iteration: 6 recall: 0.776 accuracy: 0.031221 cost: 0.0029799 M: 15.1149 delta: 0.432334 time: 284.37 one-recall: 0.86 one-ratio: 1.08233
iteration: 7 recall: 0.9004 accuracy: 0.00774704 cost: 0.00395544 M: 21.141 delta: 0.19644 time: 345.233 one-recall: 0.97 one-ratio: 1.0171
iteration: 8 recall: 0.9464 accuracy: 0.00327191 cost: 0.00498012 M: 27.3058 delta: 0.0884328 time: 402.026 one-recall: 0.98 one-ratio: 1.0026
iteration: 9 recall: 0.9664 accuracy: 0.00167686 cost: 0.0057727 M: 31.2895 delta: 0.051304 time: 445.208 one-recall: 0.99 one-ratio: 1.0014
iteration: 10 recall: 0.9772 accuracy: 0.00107445 cost: 0.00625732 M: 33.392 delta: 0.0371847 time: 474.267 one-recall: 1 one-ratio: 1
iteration: 11 recall: 0.982 accuracy: 0.000884681 cost: 0.00651501 M: 34.4228 delta: 0.0313195 time: 493.192 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.9828 accuracy: 0.000695118 cost: 0.00664295 M: 34.9149 delta: 0.0287542 time: 505.892 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.9836 accuracy: 0.000689913 cost: 0.00670482 M: 35.1492 delta: 0.0275938 time: 515.057 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.984 accuracy: 0.000675274 cost: 0.00673468 M: 35.2616 delta: 0.0270461 time: 522.311 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.984 accuracy: 0.000675274 cost: 0.00674952 M: 35.317 delta: 0.0267891 time: 528.616 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.984 accuracy: 0.000675274 cost: 0.00675693 M: 35.3444 delta: 0.026664 time: 534.414 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.984 accuracy: 0.000675274 cost: 0.00676077 M: 35.3585 delta: 0.0266018 time: 539.956 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.984 accuracy: 0.000675274 cost: 0.00676268 M: 35.3654 delta: 0.0265738 time: 545.353 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.984 accuracy: 0.000675274 cost: 0.0067637 M: 35.3692 delta: 0.0265546 time: 550.672 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.984 accuracy: 0.000675274 cost: 0.0067642 M: 35.3713 delta: 0.0265461 time: 555.948 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.984 accuracy: 0.000675274 cost: 0.00676448 M: 35.3723 delta: 0.0265423 time: 561.204 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.984 accuracy: 0.000675274 cost: 0.00676462 M: 35.3729 delta: 0.0265399 time: 566.449 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.984 accuracy: 0.000675274 cost: 0.00676471 M: 35.3733 delta: 0.0265385 time: 571.68 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.984 accuracy: 0.000675274 cost: 0.00676476 M: 35.3734 delta: 0.0265376 time: 576.91 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.984 accuracy: 0.000675274 cost: 0.00676477 M: 35.3735 delta: 0.0265371 time: 582.137 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.984 accuracy: 0.000675274 cost: 0.00676479 M: 35.3736 delta: 0.026537 time: 587.358 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.984 accuracy: 0.000675274 cost: 0.0067648 M: 35.3736 delta: 0.0265367 time: 592.578 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.984 accuracy: 0.000675274 cost: 0.00676481 M: 35.3737 delta: 0.0265364 time: 597.805 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.984 accuracy: 0.000675274 cost: 0.00676481 M: 35.3737 delta: 0.0265363 time: 603.029 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.984 accuracy: 0.000675274 cost: 0.00676481 M: 35.3737 delta: 0.0265363 time: 608.249 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 622.7700000000004
Index size:  262748.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0035799000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.1000000000, query time of that 0.0538724230, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Accept!
  -> Decision True in time 0.9800000000, query time of that 0.5258655520, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
358.893 < 379.357
  -> Decision False in time 0.7600000000, query time of that 0.4057643390, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.5800000000, query time of that 0.0599450820, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
365.561 < 366.414
  -> Decision False in time 5.6500000000, query time of that 0.5981547930, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
343.102 < 355.907
  -> Decision False in time 17.2000000000, query time of that 1.8513680940, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Accept!
  -> Decision True in time 6.6600000000, query time of that 0.0692466480, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
290.666 < 294.022
  -> Decision False in time 18.5300000000, query time of that 0.2039712680, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
253.618 < 253.831
  -> Decision False in time 112.1100000000, query time of that 1.2225553900, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 60, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 60, {'reverse': -1}, False])
Got a train set of size (1000000 * 128)
Generating control...
Initializing...
iteration: 1 recall: 0.0004 accuracy: 2.80314 cost: 0.00038 M: 10 delta: 1 time: 53.793 one-recall: 0 one-ratio: 3.57671
iteration: 2 recall: 0.0052 accuracy: 1.31236 cost: 0.000637428 M: 10 delta: 0.856032 time: 91.8237 one-recall: 0.01 one-ratio: 2.76605
iteration: 3 recall: 0.0424 accuracy: 0.706103 cost: 0.00109521 M: 11.5287 delta: 0.835112 time: 137.88 one-recall: 0.02 one-ratio: 2.20269
iteration: 4 recall: 0.2056 accuracy: 0.373532 cost: 0.00163043 M: 11.8362 delta: 0.783464 time: 183.522 one-recall: 0.2 one-ratio: 1.77734
iteration: 5 recall: 0.546 accuracy: 0.10652 cost: 0.00223609 M: 12.6038 delta: 0.664577 time: 230.889 one-recall: 0.67 one-ratio: 1.25788
iteration: 6 recall: 0.8172 accuracy: 0.020148 cost: 0.00297995 M: 15.1143 delta: 0.432337 time: 284.381 one-recall: 0.9 one-ratio: 1.0322
iteration: 7 recall: 0.9092 accuracy: 0.00720346 cost: 0.00395524 M: 21.1399 delta: 0.196405 time: 345.239 one-recall: 1 one-ratio: 1
iteration: 8 recall: 0.9444 accuracy: 0.00360144 cost: 0.00497966 M: 27.3046 delta: 0.0884556 time: 402.022 one-recall: 1 one-ratio: 1
iteration: 9 recall: 0.964 accuracy: 0.00187058 cost: 0.00577299 M: 31.2905 delta: 0.0513683 time: 445.224 one-recall: 1 one-ratio: 1
iteration: 10 recall: 0.9728 accuracy: 0.00140608 cost: 0.00625896 M: 33.3978 delta: 0.0372545 time: 474.353 one-recall: 1 one-ratio: 1
iteration: 11 recall: 0.9756 accuracy: 0.00127805 cost: 0.00651717 M: 34.4315 delta: 0.031352 time: 493.32 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.9776 accuracy: 0.00107714 cost: 0.00664495 M: 34.923 delta: 0.0287886 time: 506.029 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.978 accuracy: 0.00104919 cost: 0.00670816 M: 35.1623 delta: 0.0276194 time: 515.283 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.9784 accuracy: 0.000999112 cost: 0.00673895 M: 35.2777 delta: 0.0270713 time: 522.594 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.9784 accuracy: 0.000999112 cost: 0.00675377 M: 35.3329 delta: 0.026804 time: 528.905 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.9784 accuracy: 0.000999112 cost: 0.00676129 M: 35.361 delta: 0.0266778 time: 534.722 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.9784 accuracy: 0.000999112 cost: 0.00676505 M: 35.3749 delta: 0.0266172 time: 540.271 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.9784 accuracy: 0.000999112 cost: 0.00676687 M: 35.3816 delta: 0.0265875 time: 545.676 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.9784 accuracy: 0.000999112 cost: 0.00676786 M: 35.3855 delta: 0.02657 time: 551.018 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.9784 accuracy: 0.000999112 cost: 0.00676842 M: 35.3877 delta: 0.0265629 time: 556.322 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.9784 accuracy: 0.000999112 cost: 0.00676873 M: 35.389 delta: 0.0265577 time: 561.602 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.9784 accuracy: 0.000999112 cost: 0.0067689 M: 35.3896 delta: 0.0265543 time: 566.866 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.9784 accuracy: 0.000999112 cost: 0.00676897 M: 35.3899 delta: 0.0265533 time: 572.119 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.9784 accuracy: 0.000999112 cost: 0.00676902 M: 35.3901 delta: 0.0265524 time: 577.366 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.9784 accuracy: 0.000999112 cost: 0.00676905 M: 35.3902 delta: 0.0265518 time: 582.615 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.9784 accuracy: 0.000999112 cost: 0.00676907 M: 35.3903 delta: 0.0265518 time: 587.857 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.9784 accuracy: 0.000999112 cost: 0.00676909 M: 35.3903 delta: 0.0265517 time: 593.105 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.9784 accuracy: 0.000999112 cost: 0.0067691 M: 35.3904 delta: 0.0265516 time: 598.347 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.9784 accuracy: 0.000999112 cost: 0.0067691 M: 35.3904 delta: 0.0265515 time: 603.588 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.9784 accuracy: 0.000999112 cost: 0.00676911 M: 35.3904 delta: 0.0265515 time: 608.832 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 623.3899999999994
Index size:  263200.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0040999000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.1000000000, query time of that 0.0476022650, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Accept!
  -> Decision True in time 0.9200000000, query time of that 0.4720906620, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
289.254 < 328.186
  -> Decision False in time 1.0000000000, query time of that 0.4998760100, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Reject!
151.601 < 154.916
  -> Decision False in time 0.5200000000, query time of that 0.0488728860, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Accept!
  -> Decision True in time 5.7000000000, query time of that 0.5603842580, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
301.846 < 303.135
  -> Decision False in time 13.3700000000, query time of that 1.3306460760, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Accept!
  -> Decision True in time 6.6600000000, query time of that 0.0670705960, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
271.663 < 272.632
  -> Decision False in time 6.0200000000, query time of that 0.0623645080, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
296.056 < 298.968
  -> Decision False in time 25.3200000000, query time of that 0.2600613300, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 100, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 100, {'reverse': -1}, False])
Got a train set of size (1000000 * 128)
Generating control...
Initializing...
iteration: 1 recall: 0.0004 accuracy: 1.90706 cost: 0.00038 M: 10 delta: 1 time: 53.8459 one-recall: 0 one-ratio: 3.23609
iteration: 2 recall: 0.004 accuracy: 1.08816 cost: 0.000637428 M: 10 delta: 0.856032 time: 91.8818 one-recall: 0 one-ratio: 2.553
iteration: 3 recall: 0.0272 accuracy: 0.639525 cost: 0.00109521 M: 11.5287 delta: 0.835106 time: 137.934 one-recall: 0.04 one-ratio: 2.08392
iteration: 4 recall: 0.1668 accuracy: 0.324049 cost: 0.00163042 M: 11.8362 delta: 0.78345 time: 183.579 one-recall: 0.22 one-ratio: 1.61779
iteration: 5 recall: 0.4912 accuracy: 0.105401 cost: 0.00223608 M: 12.604 delta: 0.664612 time: 230.95 one-recall: 0.59 one-ratio: 1.22558
iteration: 6 recall: 0.7568 accuracy: 0.0291349 cost: 0.00297994 M: 15.1138 delta: 0.432361 time: 284.438 one-recall: 0.86 one-ratio: 1.06145
iteration: 7 recall: 0.8832 accuracy: 0.00939113 cost: 0.00395511 M: 21.1394 delta: 0.196459 time: 345.289 one-recall: 0.97 one-ratio: 1.00996
iteration: 8 recall: 0.9428 accuracy: 0.00299661 cost: 0.00497959 M: 27.3061 delta: 0.0883981 time: 402.084 one-recall: 0.99 one-ratio: 1.00038
iteration: 9 recall: 0.9668 accuracy: 0.0014997 cost: 0.00577255 M: 31.2902 delta: 0.0512997 time: 445.285 one-recall: 1 one-ratio: 1
iteration: 10 recall: 0.9728 accuracy: 0.00114476 cost: 0.00625767 M: 33.397 delta: 0.0371545 time: 474.378 one-recall: 1 one-ratio: 1
iteration: 11 recall: 0.9764 accuracy: 0.000958012 cost: 0.00651518 M: 34.4265 delta: 0.0312579 time: 493.311 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.9784 accuracy: 0.000849395 cost: 0.00664266 M: 34.9173 delta: 0.0287031 time: 505.995 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.9788 accuracy: 0.000843972 cost: 0.00670508 M: 35.1537 delta: 0.0275374 time: 515.189 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.9792 accuracy: 0.000815621 cost: 0.00673511 M: 35.2661 delta: 0.0270021 time: 522.451 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.9792 accuracy: 0.000815621 cost: 0.00675021 M: 35.3225 delta: 0.0267397 time: 528.768 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.9792 accuracy: 0.000815621 cost: 0.00675787 M: 35.351 delta: 0.0266111 time: 534.597 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.9792 accuracy: 0.000815621 cost: 0.00676192 M: 35.3662 delta: 0.0265395 time: 540.156 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.9792 accuracy: 0.000815621 cost: 0.00676386 M: 35.3735 delta: 0.0265078 time: 545.568 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.9792 accuracy: 0.000815621 cost: 0.00676488 M: 35.3775 delta: 0.0264888 time: 550.899 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.9792 accuracy: 0.000815621 cost: 0.00676545 M: 35.3796 delta: 0.0264798 time: 556.2 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.9792 accuracy: 0.000815621 cost: 0.00676571 M: 35.3805 delta: 0.0264756 time: 561.463 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.9792 accuracy: 0.000815621 cost: 0.00676584 M: 35.381 delta: 0.0264737 time: 566.72 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.9792 accuracy: 0.000815621 cost: 0.00676592 M: 35.3813 delta: 0.0264721 time: 571.969 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.9792 accuracy: 0.000815621 cost: 0.00676598 M: 35.3816 delta: 0.0264713 time: 577.209 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.9792 accuracy: 0.000815621 cost: 0.006766 M: 35.3816 delta: 0.0264713 time: 582.431 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.9792 accuracy: 0.000815621 cost: 0.00676601 M: 35.3817 delta: 0.0264711 time: 587.664 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.9792 accuracy: 0.000815621 cost: 0.00676603 M: 35.3818 delta: 0.0264707 time: 592.893 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.9792 accuracy: 0.000815621 cost: 0.00676604 M: 35.3818 delta: 0.0264705 time: 598.129 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.9792 accuracy: 0.000815621 cost: 0.00676604 M: 35.3818 delta: 0.0264705 time: 603.36 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.9792 accuracy: 0.000815621 cost: 0.00676604 M: 35.3818 delta: 0.0264705 time: 608.586 one-recall: 1 one-ratio: 1
Graph completion with reverse edges...

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

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 623.1400000000012
Index size:  263032.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0024671000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.1100000000, query time of that 0.0696470090, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Accept!
  -> Decision True in time 1.1300000000, query time of that 0.6808365020, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
388.51 < 408.47
  -> Decision False in time 5.2400000000, query time of that 3.1002464760, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.6000000000, query time of that 0.0790116860, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Accept!
  -> Decision True in time 5.9000000000, query time of that 0.7700581850, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
248.143 < 256.938
  -> Decision False in time 48.3200000000, query time of that 6.4350620230, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Accept!
  -> Decision True in time 6.6600000000, query time of that 0.0888280760, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Accept!
  -> Decision True in time 66.6500000000, query time of that 0.9120287860, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
139.614 < 139.789
  -> Decision False in time 5.2600000000, query time of that 0.0695901470, 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: 2.23046 cost: 0.00038 M: 10 delta: 1 time: 53.8454 one-recall: 0 one-ratio: 3.49545
iteration: 2 recall: 0.0044 accuracy: 1.33523 cost: 0.000637428 M: 10 delta: 0.856032 time: 91.8747 one-recall: 0 one-ratio: 2.73647
iteration: 3 recall: 0.0396 accuracy: 0.755534 cost: 0.00109521 M: 11.5287 delta: 0.835105 time: 137.934 one-recall: 0.06 one-ratio: 2.11178
iteration: 4 recall: 0.1968 accuracy: 0.332188 cost: 0.00163044 M: 11.8362 delta: 0.783467 time: 183.578 one-recall: 0.25 one-ratio: 1.67942
iteration: 5 recall: 0.5296 accuracy: 0.109875 cost: 0.00223605 M: 12.6034 delta: 0.664594 time: 230.953 one-recall: 0.56 one-ratio: 1.34305
iteration: 6 recall: 0.784 accuracy: 0.0238755 cost: 0.00297997 M: 15.1145 delta: 0.432366 time: 284.436 one-recall: 0.89 one-ratio: 1.05152
iteration: 7 recall: 0.9036 accuracy: 0.00698132 cost: 0.00395514 M: 21.139 delta: 0.196421 time: 345.317 one-recall: 0.97 one-ratio: 1.01562
iteration: 8 recall: 0.954 accuracy: 0.00360516 cost: 0.00497983 M: 27.3058 delta: 0.0884564 time: 402.117 one-recall: 0.97 one-ratio: 1.01274
iteration: 9 recall: 0.9724 accuracy: 0.00175554 cost: 0.0057725 M: 31.2896 delta: 0.0513217 time: 445.315 one-recall: 0.98 one-ratio: 1.01131
iteration: 10 recall: 0.9804 accuracy: 0.0010936 cost: 0.00625736 M: 33.3932 delta: 0.0371719 time: 474.394 one-recall: 0.99 one-ratio: 1.00646
iteration: 11 recall: 0.9836 accuracy: 0.000781714 cost: 0.00651327 M: 34.4178 delta: 0.0313064 time: 493.259 one-recall: 0.99 one-ratio: 1.00646
iteration: 12 recall: 0.9852 accuracy: 0.000711939 cost: 0.00664056 M: 34.9092 delta: 0.0287492 time: 505.939 one-recall: 0.99 one-ratio: 1.00646
iteration: 13 recall: 0.9856 accuracy: 0.000701642 cost: 0.00670337 M: 35.1464 delta: 0.027571 time: 515.161 one-recall: 0.99 one-ratio: 1.00646
iteration: 14 recall: 0.986 accuracy: 0.000627938 cost: 0.00673346 M: 35.2591 delta: 0.0270194 time: 522.455 one-recall: 0.99 one-ratio: 1.00646
iteration: 15 recall: 0.9864 accuracy: 0.000623045 cost: 0.00674807 M: 35.3136 delta: 0.0267638 time: 528.757 one-recall: 0.99 one-ratio: 1.00646
iteration: 16 recall: 0.9864 accuracy: 0.000623045 cost: 0.00675555 M: 35.3413 delta: 0.0266457 time: 534.572 one-recall: 0.99 one-ratio: 1.00646
iteration: 17 recall: 0.9864 accuracy: 0.000623045 cost: 0.00675942 M: 35.3559 delta: 0.0265773 time: 540.125 one-recall: 0.99 one-ratio: 1.00646
iteration: 18 recall: 0.9864 accuracy: 0.000623045 cost: 0.00676128 M: 35.3627 delta: 0.0265491 time: 545.534 one-recall: 0.99 one-ratio: 1.00646
iteration: 19 recall: 0.9864 accuracy: 0.000623045 cost: 0.00676224 M: 35.3665 delta: 0.026531 time: 550.873 one-recall: 0.99 one-ratio: 1.00646
iteration: 20 recall: 0.9864 accuracy: 0.000623045 cost: 0.00676274 M: 35.3684 delta: 0.0265238 time: 556.172 one-recall: 0.99 one-ratio: 1.00646
iteration: 21 recall: 0.9864 accuracy: 0.000623045 cost: 0.00676302 M: 35.3695 delta: 0.02652 time: 561.444 one-recall: 0.99 one-ratio: 1.00646
iteration: 22 recall: 0.9864 accuracy: 0.000623045 cost: 0.00676318 M: 35.3701 delta: 0.0265176 time: 566.703 one-recall: 0.99 one-ratio: 1.00646
iteration: 23 recall: 0.9864 accuracy: 0.000623045 cost: 0.00676328 M: 35.3705 delta: 0.0265158 time: 571.959 one-recall: 0.99 one-ratio: 1.00646
iteration: 24 recall: 0.9864 accuracy: 0.000623045 cost: 0.00676332 M: 35.3706 delta: 0.0265152 time: 577.205 one-recall: 0.99 one-ratio: 1.00646
iteration: 25 recall: 0.9864 accuracy: 0.000623045 cost: 0.00676333 M: 35.3707 delta: 0.0265151 time: 582.453 one-recall: 0.99 one-ratio: 1.00646
iteration: 26 recall: 0.9864 accuracy: 0.000623045 cost: 0.00676334 M: 35.3707 delta: 0.026515 time: 587.692 one-recall: 0.99 one-ratio: 1.00646
iteration: 27 recall: 0.9864 accuracy: 0.000623045 cost: 0.00676335 M: 35.3708 delta: 0.0265149 time: 592.936 one-recall: 0.99 one-ratio: 1.00646
iteration: 28 recall: 0.9864 accuracy: 0.000623045 cost: 0.00676335 M: 35.3708 delta: 0.0265147 time: 598.173 one-recall: 0.99 one-ratio: 1.00646
iteration: 29 recall: 0.9864 accuracy: 0.000623045 cost: 0.00676336 M: 35.3708 delta: 0.0265147 time: 603.413 one-recall: 0.99 one-ratio: 1.00646
iteration: 30 recall: 0.9864 accuracy: 0.000623045 cost: 0.00676336 M: 35.3708 delta: 0.0265146 time: 608.65 one-recall: 0.99 one-ratio: 1.00646
Graph completion with reverse edges...

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

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 623.1800000000003
Index size:  262844.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0050135000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.0900000000, query time of that 0.0422993650, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Accept!
  -> Decision True in time 0.8800000000, query time of that 0.4266430500, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
353.84 < 395.938
  -> Decision False in time 6.7000000000, query time of that 3.1852139520, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.5500000000, query time of that 0.0484846060, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Accept!
  -> Decision True in time 5.5400000000, query time of that 0.4941046990, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
264.687 < 270.376
  -> Decision False in time 7.6600000000, query time of that 0.6765652400, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Accept!
  -> Decision True in time 6.6500000000, query time of that 0.0602818980, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
283.198 < 287.057
  -> Decision False in time 7.9100000000, query time of that 0.0706869010, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
385.977 < 394.977
  -> Decision False in time 6.6500000000, query time of that 0.0615174610, 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: 2.92322 cost: 0.00038 M: 10 delta: 1 time: 53.8298 one-recall: 0 one-ratio: 3.60024
iteration: 2 recall: 0.0028 accuracy: 1.44131 cost: 0.000637428 M: 10 delta: 0.856032 time: 91.8639 one-recall: 0 one-ratio: 2.79215
iteration: 3 recall: 0.0384 accuracy: 0.807059 cost: 0.00109521 M: 11.5287 delta: 0.835104 time: 137.919 one-recall: 0.06 one-ratio: 2.20169
iteration: 4 recall: 0.2084 accuracy: 0.318582 cost: 0.00163046 M: 11.8363 delta: 0.783463 time: 183.562 one-recall: 0.32 one-ratio: 1.72826
iteration: 5 recall: 0.5516 accuracy: 0.0958214 cost: 0.00223606 M: 12.6036 delta: 0.664573 time: 230.924 one-recall: 0.75 one-ratio: 1.19176
iteration: 6 recall: 0.7936 accuracy: 0.0275771 cost: 0.00297996 M: 15.1152 delta: 0.432335 time: 284.412 one-recall: 0.89 one-ratio: 1.10076
iteration: 7 recall: 0.9012 accuracy: 0.00845842 cost: 0.00395522 M: 21.1391 delta: 0.196422 time: 345.242 one-recall: 0.97 one-ratio: 1.01643
iteration: 8 recall: 0.94 accuracy: 0.00396351 cost: 0.0049798 M: 27.3051 delta: 0.0884829 time: 402.003 one-recall: 0.98 one-ratio: 1.00158
iteration: 9 recall: 0.9628 accuracy: 0.00207873 cost: 0.00577349 M: 31.291 delta: 0.0513297 time: 445.207 one-recall: 1 one-ratio: 1
iteration: 10 recall: 0.9712 accuracy: 0.00168933 cost: 0.00625847 M: 33.3965 delta: 0.0371858 time: 474.29 one-recall: 1 one-ratio: 1
iteration: 11 recall: 0.9776 accuracy: 0.00138114 cost: 0.00651548 M: 34.426 delta: 0.03129 time: 493.201 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.9804 accuracy: 0.00128995 cost: 0.00664237 M: 34.9147 delta: 0.0287236 time: 505.869 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.982 accuracy: 0.00100243 cost: 0.00670433 M: 35.1489 delta: 0.0275643 time: 515.042 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.984 accuracy: 0.000820686 cost: 0.00673428 M: 35.2602 delta: 0.0270321 time: 522.313 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.9848 accuracy: 0.000663202 cost: 0.00674912 M: 35.3165 delta: 0.0267735 time: 528.621 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.9852 accuracy: 0.000642367 cost: 0.00675672 M: 35.3445 delta: 0.0266445 time: 534.437 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.9852 accuracy: 0.000642367 cost: 0.00676059 M: 35.3588 delta: 0.0265808 time: 539.987 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.9852 accuracy: 0.000642367 cost: 0.00676258 M: 35.3658 delta: 0.0265499 time: 545.394 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.9852 accuracy: 0.000642367 cost: 0.00676355 M: 35.3693 delta: 0.0265349 time: 550.72 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.9852 accuracy: 0.000642367 cost: 0.00676406 M: 35.3713 delta: 0.0265276 time: 556.006 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.9852 accuracy: 0.000642367 cost: 0.00676436 M: 35.3725 delta: 0.0265216 time: 561.274 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.9852 accuracy: 0.000642367 cost: 0.00676453 M: 35.3732 delta: 0.0265195 time: 566.528 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.9852 accuracy: 0.000642367 cost: 0.00676464 M: 35.3735 delta: 0.0265182 time: 571.773 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.9852 accuracy: 0.000642367 cost: 0.00676469 M: 35.3737 delta: 0.0265174 time: 577.011 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.9852 accuracy: 0.000642367 cost: 0.00676471 M: 35.3738 delta: 0.0265168 time: 582.244 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.9852 accuracy: 0.000642367 cost: 0.00676472 M: 35.3738 delta: 0.0265168 time: 587.477 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.9852 accuracy: 0.000642367 cost: 0.00676473 M: 35.3739 delta: 0.0265166 time: 592.707 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.9852 accuracy: 0.000642367 cost: 0.00676473 M: 35.3739 delta: 0.0265166 time: 597.935 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.9852 accuracy: 0.000642367 cost: 0.00676473 M: 35.3739 delta: 0.0265166 time: 603.164 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.9852 accuracy: 0.000642367 cost: 0.00676473 M: 35.3739 delta: 0.0265166 time: 608.394 one-recall: 1 one-ratio: 1
Graph completion with reverse edges...

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

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 622.9099999999999
Index size:  262864.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0071594000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.0800000000, query time of that 0.0324326430, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Reject!
299.149 < 302.311
  -> Decision False in time 0.2200000000, query time of that 0.0932073450, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
347.724 < 367.966
  -> Decision False in time 2.1000000000, query time of that 0.8489692500, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.5400000000, query time of that 0.0354159660, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Accept!
  -> Decision True in time 5.4200000000, query time of that 0.3648634370, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
262.073 < 277.007
  -> Decision False in time 6.8400000000, query time of that 0.4666695200, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Accept!
  -> Decision True in time 6.6300000000, query time of that 0.0463308930, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
260.739 < 269.633
  -> Decision False in time 1.3600000000, query time of that 0.0098681260, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
225.599 < 249.363
  -> Decision False in time 7.8100000000, query time of that 0.0600542020, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 1, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 1, {'reverse': -1}, False])
Got a train set of size (1000000 * 128)
Generating control...
Initializing...
iteration: 1 recall: 0 accuracy: 2.07046 cost: 0.00038 M: 10 delta: 1 time: 53.8215 one-recall: 0 one-ratio: 3.53467
iteration: 2 recall: 0.0048 accuracy: 1.12509 cost: 0.000637428 M: 10 delta: 0.856032 time: 91.856 one-recall: 0.02 one-ratio: 2.78911
iteration: 3 recall: 0.044 accuracy: 0.618678 cost: 0.00109521 M: 11.5287 delta: 0.835106 time: 137.907 one-recall: 0.05 one-ratio: 2.28431
iteration: 4 recall: 0.2204 accuracy: 0.296348 cost: 0.00163045 M: 11.8363 delta: 0.783448 time: 183.539 one-recall: 0.27 one-ratio: 1.68696
iteration: 5 recall: 0.5452 accuracy: 0.109521 cost: 0.0022361 M: 12.6035 delta: 0.664602 time: 230.901 one-recall: 0.7 one-ratio: 1.22841
iteration: 6 recall: 0.7864 accuracy: 0.0239684 cost: 0.00298 M: 15.1147 delta: 0.432344 time: 284.362 one-recall: 0.92 one-ratio: 1.04524
iteration: 7 recall: 0.896799 accuracy: 0.00813853 cost: 0.00395526 M: 21.1395 delta: 0.196395 time: 345.194 one-recall: 0.95 one-ratio: 1.02903
iteration: 8 recall: 0.938 accuracy: 0.00382393 cost: 0.00497977 M: 27.3058 delta: 0.0884754 time: 401.947 one-recall: 0.97 one-ratio: 1.01336
iteration: 9 recall: 0.9632 accuracy: 0.0023565 cost: 0.00577285 M: 31.29 delta: 0.0513423 time: 445.137 one-recall: 0.98 one-ratio: 1.00866
iteration: 10 recall: 0.9708 accuracy: 0.0017758 cost: 0.00625759 M: 33.3927 delta: 0.0372233 time: 474.211 one-recall: 0.98 one-ratio: 1.00866
iteration: 11 recall: 0.9768 accuracy: 0.00154949 cost: 0.0065147 M: 34.424 delta: 0.0313205 time: 493.127 one-recall: 0.98 one-ratio: 1.00866
iteration: 12 recall: 0.9792 accuracy: 0.00129422 cost: 0.00664229 M: 34.9158 delta: 0.0287498 time: 505.833 one-recall: 0.99 one-ratio: 1.00791
iteration: 13 recall: 0.9816 accuracy: 0.00115279 cost: 0.00670429 M: 35.1503 delta: 0.0275927 time: 515.012 one-recall: 0.99 one-ratio: 1.00791
iteration: 14 recall: 0.9816 accuracy: 0.00115279 cost: 0.0067343 M: 35.2623 delta: 0.0270551 time: 522.282 one-recall: 0.99 one-ratio: 1.00791
iteration: 15 recall: 0.9816 accuracy: 0.00115279 cost: 0.00674913 M: 35.3177 delta: 0.0267926 time: 528.586 one-recall: 0.99 one-ratio: 1.00791
iteration: 16 recall: 0.9816 accuracy: 0.00115279 cost: 0.00675656 M: 35.3451 delta: 0.0266712 time: 534.39 one-recall: 0.99 one-ratio: 1.00791
iteration: 17 recall: 0.9816 accuracy: 0.00115279 cost: 0.00676039 M: 35.3591 delta: 0.0266052 time: 539.937 one-recall: 0.99 one-ratio: 1.00791
iteration: 18 recall: 0.9816 accuracy: 0.00115279 cost: 0.00676225 M: 35.3659 delta: 0.0265734 time: 545.337 one-recall: 0.99 one-ratio: 1.00791
iteration: 19 recall: 0.9816 accuracy: 0.00115279 cost: 0.00676322 M: 35.3695 delta: 0.026557 time: 550.669 one-recall: 0.99 one-ratio: 1.00791
iteration: 20 recall: 0.9816 accuracy: 0.00115279 cost: 0.00676371 M: 35.3712 delta: 0.026549 time: 555.959 one-recall: 0.99 one-ratio: 1.00791
iteration: 21 recall: 0.9816 accuracy: 0.00115279 cost: 0.00676392 M: 35.3721 delta: 0.0265458 time: 561.219 one-recall: 0.99 one-ratio: 1.00791
iteration: 22 recall: 0.9816 accuracy: 0.00115279 cost: 0.00676406 M: 35.3726 delta: 0.0265435 time: 566.473 one-recall: 0.99 one-ratio: 1.00791
iteration: 23 recall: 0.9816 accuracy: 0.00115279 cost: 0.00676415 M: 35.373 delta: 0.0265427 time: 571.721 one-recall: 0.99 one-ratio: 1.00791
iteration: 24 recall: 0.9816 accuracy: 0.00115279 cost: 0.00676421 M: 35.3733 delta: 0.0265414 time: 576.965 one-recall: 0.99 one-ratio: 1.00791
iteration: 25 recall: 0.9816 accuracy: 0.00115279 cost: 0.00676424 M: 35.3734 delta: 0.0265411 time: 582.2 one-recall: 0.99 one-ratio: 1.00791
iteration: 26 recall: 0.9816 accuracy: 0.00115279 cost: 0.00676425 M: 35.3734 delta: 0.0265409 time: 587.434 one-recall: 0.99 one-ratio: 1.00791
iteration: 27 recall: 0.9816 accuracy: 0.00115279 cost: 0.00676426 M: 35.3735 delta: 0.0265407 time: 592.667 one-recall: 0.99 one-ratio: 1.00791
iteration: 28 recall: 0.9816 accuracy: 0.00115279 cost: 0.00676426 M: 35.3735 delta: 0.0265408 time: 597.898 one-recall: 0.99 one-ratio: 1.00791
iteration: 29 recall: 0.9816 accuracy: 0.00115279 cost: 0.00676426 M: 35.3735 delta: 0.0265408 time: 603.137 one-recall: 0.99 one-ratio: 1.00791
iteration: 30 recall: 0.9816 accuracy: 0.00115279 cost: 0.00676427 M: 35.3735 delta: 0.0265407 time: 608.363 one-recall: 0.99 one-ratio: 1.00791
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 622.8599999999988
Index size:  262744.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.1061349000
  Testing...
|S| = 80
|T| = 1152
Reject!
393.897 < 470.531
  -> Decision False in time 0.0100000000, query time of that 0.0035021830, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Reject!
446.381 < 486.82
  -> Decision False in time 0.0100000000, query time of that 0.0023819840, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
297.755 < 461.615
  -> Decision False in time 0.0000000000, query time of that 0.0001804120, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Reject!
412.822 < 414.033
  -> Decision False in time 0.0200000000, query time of that 0.0010384400, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
402.367 < 463.682
  -> Decision False in time 0.1000000000, query time of that 0.0039259250, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
275.334 < 280.836
  -> Decision False in time 0.0100000000, query time of that 0.0002711970, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Reject!
338.133 < 351.363
  -> Decision False in time 0.1300000000, query time of that 0.0004912500, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
419.613 < 451.557
  -> Decision False in time 0.4100000000, query time of that 0.0020393040, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
424.388 < 432.979
  -> Decision False in time 0.4200000000, query time of that 0.0022589040, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 3, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 3, {'reverse': -1}, False])
Got a train set of size (1000000 * 128)
Generating control...
Initializing...
iteration: 1 recall: 0 accuracy: 2.42982 cost: 0.00038 M: 10 delta: 1 time: 53.8221 one-recall: 0 one-ratio: 3.80584
iteration: 2 recall: 0.004 accuracy: 1.40237 cost: 0.000637428 M: 10 delta: 0.856032 time: 91.8497 one-recall: 0 one-ratio: 3.02538
iteration: 3 recall: 0.034 accuracy: 0.819879 cost: 0.00109521 M: 11.5287 delta: 0.835105 time: 137.905 one-recall: 0.03 one-ratio: 2.44459
iteration: 4 recall: 0.1952 accuracy: 0.353701 cost: 0.00163043 M: 11.8363 delta: 0.783467 time: 183.545 one-recall: 0.17 one-ratio: 1.92636
iteration: 5 recall: 0.502 accuracy: 0.109436 cost: 0.00223606 M: 12.6034 delta: 0.664583 time: 230.905 one-recall: 0.59 one-ratio: 1.32966
iteration: 6 recall: 0.764 accuracy: 0.0271792 cost: 0.00297992 M: 15.1142 delta: 0.432351 time: 284.371 one-recall: 0.89 one-ratio: 1.05384
iteration: 7 recall: 0.8872 accuracy: 0.00825071 cost: 0.00395528 M: 21.1398 delta: 0.196386 time: 345.208 one-recall: 0.96 one-ratio: 1.01507
iteration: 8 recall: 0.9332 accuracy: 0.00453195 cost: 0.00497947 M: 27.3037 delta: 0.0884536 time: 401.954 one-recall: 0.98 one-ratio: 1.01224
iteration: 9 recall: 0.9548 accuracy: 0.00294662 cost: 0.00577271 M: 31.2908 delta: 0.0513275 time: 445.16 one-recall: 0.98 one-ratio: 1.01087
iteration: 10 recall: 0.9648 accuracy: 0.00194579 cost: 0.00625816 M: 33.3969 delta: 0.0371975 time: 474.272 one-recall: 0.99 one-ratio: 1.00196
iteration: 11 recall: 0.9708 accuracy: 0.00164212 cost: 0.00651622 M: 34.4286 delta: 0.0312819 time: 493.244 one-recall: 0.99 one-ratio: 1.00196
iteration: 12 recall: 0.9752 accuracy: 0.00125608 cost: 0.00664321 M: 34.9179 delta: 0.028724 time: 505.946 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.976 accuracy: 0.00123773 cost: 0.00670529 M: 35.1536 delta: 0.0275617 time: 515.145 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.976 accuracy: 0.00123773 cost: 0.00673559 M: 35.2672 delta: 0.0270201 time: 522.449 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.976 accuracy: 0.00123773 cost: 0.0067507 M: 35.3232 delta: 0.0267592 time: 528.78 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.9764 accuracy: 0.00121697 cost: 0.00675835 M: 35.3515 delta: 0.026627 time: 534.608 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.9764 accuracy: 0.00121697 cost: 0.00676214 M: 35.3656 delta: 0.0265648 time: 540.161 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.9764 accuracy: 0.00121697 cost: 0.00676415 M: 35.3731 delta: 0.0265341 time: 545.582 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.9764 accuracy: 0.00121697 cost: 0.00676519 M: 35.377 delta: 0.0265181 time: 550.92 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.9764 accuracy: 0.00121697 cost: 0.00676577 M: 35.3792 delta: 0.0265085 time: 556.223 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.9764 accuracy: 0.00121697 cost: 0.00676607 M: 35.3804 delta: 0.0265044 time: 561.496 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.9764 accuracy: 0.00121697 cost: 0.00676624 M: 35.3811 delta: 0.0265012 time: 566.757 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.9764 accuracy: 0.00121697 cost: 0.00676634 M: 35.3815 delta: 0.0264995 time: 572.007 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.9764 accuracy: 0.00121697 cost: 0.00676639 M: 35.3817 delta: 0.0264984 time: 577.251 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.9764 accuracy: 0.00121697 cost: 0.00676642 M: 35.3819 delta: 0.0264978 time: 582.496 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.9764 accuracy: 0.00121697 cost: 0.00676645 M: 35.3819 delta: 0.0264975 time: 587.733 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.9764 accuracy: 0.00121697 cost: 0.00676645 M: 35.382 delta: 0.0264974 time: 592.973 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.9764 accuracy: 0.00121697 cost: 0.00676645 M: 35.382 delta: 0.0264974 time: 598.211 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.9764 accuracy: 0.00121697 cost: 0.00676646 M: 35.382 delta: 0.0264973 time: 603.453 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.9764 accuracy: 0.00121697 cost: 0.00676646 M: 35.382 delta: 0.0264974 time: 608.695 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 623.2099999999991
Index size:  263040.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0191175000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.0600000000, query time of that 0.0161381930, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Reject!
423.719 < 446.293
  -> Decision False in time 0.0500000000, query time of that 0.0114977910, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
404.11 < 405.858
  -> Decision False in time 0.1400000000, query time of that 0.0366602310, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Reject!
418.331 < 420
  -> Decision False in time 0.0300000000, query time of that 0.0008456470, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
282.675 < 285.014
  -> Decision False in time 2.7500000000, query time of that 0.0995555270, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
364.683 < 439.499
  -> Decision False in time 0.2800000000, query time of that 0.0105238180, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Reject!
275.4 < 284.628
  -> Decision False in time 0.1100000000, query time of that 0.0005501420, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
335.407 < 455.269
  -> Decision False in time 1.2500000000, query time of that 0.0051622680, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
409.566 < 449.604
  -> Decision False in time 7.5100000000, query time of that 0.0296461850, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 90, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 90, {'reverse': -1}, False])
Got a train set of size (1000000 * 128)
Generating control...
Initializing...
iteration: 1 recall: 0.0004 accuracy: 1.95606 cost: 0.00038 M: 10 delta: 1 time: 53.8437 one-recall: 0 one-ratio: 3.097
iteration: 2 recall: 0.0056 accuracy: 0.993329 cost: 0.000637428 M: 10 delta: 0.856032 time: 91.8692 one-recall: 0 one-ratio: 2.39197
iteration: 3 recall: 0.0384 accuracy: 0.544347 cost: 0.00109521 M: 11.5287 delta: 0.835104 time: 137.92 one-recall: 0.03 one-ratio: 1.98549
iteration: 4 recall: 0.1936 accuracy: 0.266265 cost: 0.00163044 M: 11.8363 delta: 0.783471 time: 183.559 one-recall: 0.23 one-ratio: 1.59853
iteration: 5 recall: 0.5436 accuracy: 0.0992239 cost: 0.00223608 M: 12.6037 delta: 0.664591 time: 230.919 one-recall: 0.6 one-ratio: 1.25181
iteration: 6 recall: 0.7988 accuracy: 0.0258187 cost: 0.00297991 M: 15.1145 delta: 0.432351 time: 284.381 one-recall: 0.82 one-ratio: 1.08254
iteration: 7 recall: 0.9112 accuracy: 0.00802604 cost: 0.00395495 M: 21.1387 delta: 0.196416 time: 345.202 one-recall: 0.94 one-ratio: 1.01949
iteration: 8 recall: 0.9496 accuracy: 0.00416341 cost: 0.00498004 M: 27.3048 delta: 0.0884701 time: 401.979 one-recall: 0.95 one-ratio: 1.01621
iteration: 9 recall: 0.9692 accuracy: 0.00243074 cost: 0.00577308 M: 31.2879 delta: 0.0513896 time: 445.158 one-recall: 0.96 one-ratio: 1.01342
iteration: 10 recall: 0.9768 accuracy: 0.00198199 cost: 0.00625813 M: 33.3938 delta: 0.0372289 time: 474.241 one-recall: 0.96 one-ratio: 1.01342
iteration: 11 recall: 0.9808 accuracy: 0.00173854 cost: 0.0065153 M: 34.4245 delta: 0.031359 time: 493.15 one-recall: 0.96 one-ratio: 1.01342
iteration: 12 recall: 0.9832 accuracy: 0.00134824 cost: 0.00664295 M: 34.9157 delta: 0.0287789 time: 505.852 one-recall: 0.97 one-ratio: 1.00918
iteration: 13 recall: 0.9844 accuracy: 0.00115894 cost: 0.00670515 M: 35.1506 delta: 0.0276258 time: 515.041 one-recall: 0.98 one-ratio: 1.00659
iteration: 14 recall: 0.9852 accuracy: 0.00105128 cost: 0.00673548 M: 35.2651 delta: 0.027087 time: 522.343 one-recall: 0.98 one-ratio: 1.00659
iteration: 15 recall: 0.9852 accuracy: 0.00105128 cost: 0.00675083 M: 35.3223 delta: 0.0268227 time: 528.684 one-recall: 0.98 one-ratio: 1.00659
iteration: 16 recall: 0.9852 accuracy: 0.00105128 cost: 0.00675856 M: 35.3516 delta: 0.0266863 time: 534.513 one-recall: 0.98 one-ratio: 1.00659
iteration: 17 recall: 0.9852 accuracy: 0.00105128 cost: 0.00676246 M: 35.3659 delta: 0.02662 time: 540.079 one-recall: 0.98 one-ratio: 1.00659
iteration: 18 recall: 0.9852 accuracy: 0.00105128 cost: 0.00676434 M: 35.3729 delta: 0.0265902 time: 545.481 one-recall: 0.98 one-ratio: 1.00659
iteration: 19 recall: 0.9852 accuracy: 0.00105128 cost: 0.0067653 M: 35.3767 delta: 0.0265756 time: 550.823 one-recall: 0.98 one-ratio: 1.00659
iteration: 20 recall: 0.9852 accuracy: 0.00105128 cost: 0.00676583 M: 35.3787 delta: 0.0265675 time: 556.122 one-recall: 0.98 one-ratio: 1.00659
iteration: 21 recall: 0.9852 accuracy: 0.00105128 cost: 0.00676619 M: 35.3801 delta: 0.0265618 time: 561.399 one-recall: 0.98 one-ratio: 1.00659
iteration: 22 recall: 0.9852 accuracy: 0.00105128 cost: 0.00676637 M: 35.3808 delta: 0.026559 time: 566.667 one-recall: 0.98 one-ratio: 1.00659
iteration: 23 recall: 0.9852 accuracy: 0.00105128 cost: 0.00676646 M: 35.3811 delta: 0.0265574 time: 571.919 one-recall: 0.98 one-ratio: 1.00659
iteration: 24 recall: 0.9852 accuracy: 0.00105128 cost: 0.00676651 M: 35.3813 delta: 0.0265564 time: 577.163 one-recall: 0.98 one-ratio: 1.00659
iteration: 25 recall: 0.9852 accuracy: 0.00105128 cost: 0.00676654 M: 35.3814 delta: 0.0265555 time: 582.403 one-recall: 0.98 one-ratio: 1.00659
iteration: 26 recall: 0.9852 accuracy: 0.00105128 cost: 0.00676656 M: 35.3815 delta: 0.0265552 time: 587.642 one-recall: 0.98 one-ratio: 1.00659
iteration: 27 recall: 0.9852 accuracy: 0.00105128 cost: 0.00676657 M: 35.3815 delta: 0.0265552 time: 592.873 one-recall: 0.98 one-ratio: 1.00659
iteration: 28 recall: 0.9852 accuracy: 0.00105128 cost: 0.00676657 M: 35.3815 delta: 0.0265552 time: 598.103 one-recall: 0.98 one-ratio: 1.00659
iteration: 29 recall: 0.9852 accuracy: 0.00105128 cost: 0.00676657 M: 35.3815 delta: 0.0265551 time: 603.347 one-recall: 0.98 one-ratio: 1.00659
iteration: 30 recall: 0.9852 accuracy: 0.00105128 cost: 0.00676657 M: 35.3816 delta: 0.0265551 time: 608.587 one-recall: 0.98 one-ratio: 1.00659
Graph completion with reverse edges...

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

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 623.1100000000006
Index size:  263044.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0027103000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.1100000000, query time of that 0.0622009430, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Accept!
  -> Decision True in time 1.0800000000, query time of that 0.6249769420, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Accept!
  -> Decision True in time 10.9900000000, query time of that 6.3839366700, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.6000000000, query time of that 0.0761946980, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Accept!
  -> Decision True in time 5.9700000000, query time of that 0.7501035350, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
330.767 < 350.207
  -> Decision False in time 4.7200000000, query time of that 0.5871764810, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Accept!
  -> Decision True in time 6.6800000000, query time of that 0.0838148410, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
272.437 < 286.855
  -> Decision False in time 65.5600000000, query time of that 0.8343830370, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
264.826 < 265.586
  -> Decision False in time 49.7800000000, query time of that 0.6370349940, with c1=5.0000000000, c2=0.1000000000
