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', 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', 100, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 90, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 40, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 5, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 3, {'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', 4, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 1, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 30, {'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', 50, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 80, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 2, {'reverse': -1}, False])]
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: 2.29356 cost: 0.00038 M: 10 delta: 1 time: 59.2334 one-recall: 0 one-ratio: 3.52528
iteration: 2 recall: 0.004 accuracy: 1.20332 cost: 0.000637428 M: 10 delta: 0.856032 time: 100.123 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: 149.469 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: 198.391 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: 249.119 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: 306.265 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: 371.788 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: 434.108 one-recall: 1 one-ratio: 1
iteration: 9 recall: 0.9644 accuracy: 0.00221739 cost: 0.00577293 M: 31.2904 delta: 0.0514019 time: 482.915 one-recall: 1 one-ratio: 1
iteration: 10 recall: 0.9764 accuracy: 0.0012952 cost: 0.00625843 M: 33.3974 delta: 0.0372226 time: 516.946 one-recall: 1 one-ratio: 1
iteration: 11 recall: 0.984 accuracy: 0.000915513 cost: 0.00651572 M: 34.4268 delta: 0.0313604 time: 539.863 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.9872 accuracy: 0.000651871 cost: 0.00664321 M: 34.9174 delta: 0.028787 time: 555.694 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.9876 accuracy: 0.000628894 cost: 0.00670536 M: 35.1523 delta: 0.027629 time: 567.282 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.9876 accuracy: 0.000628894 cost: 0.00673551 M: 35.2647 delta: 0.0270893 time: 576.521 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.9876 accuracy: 0.000628894 cost: 0.00675029 M: 35.3194 delta: 0.0268326 time: 584.529 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.9876 accuracy: 0.000628894 cost: 0.00675777 M: 35.3471 delta: 0.0267076 time: 591.914 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.9876 accuracy: 0.000628894 cost: 0.00676154 M: 35.3608 delta: 0.0266443 time: 598.964 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.9876 accuracy: 0.000628894 cost: 0.00676338 M: 35.3677 delta: 0.0266122 time: 605.831 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.9876 accuracy: 0.000628894 cost: 0.00676433 M: 35.3712 delta: 0.0265977 time: 612.606 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.9876 accuracy: 0.000628894 cost: 0.00676485 M: 35.373 delta: 0.02659 time: 619.332 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.988 accuracy: 0.0006087 cost: 0.00676515 M: 35.3742 delta: 0.0265857 time: 626.029 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.988 accuracy: 0.0006087 cost: 0.00676532 M: 35.3749 delta: 0.0265828 time: 632.707 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.988 accuracy: 0.0006087 cost: 0.00676541 M: 35.3753 delta: 0.0265811 time: 639.372 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.988 accuracy: 0.0006087 cost: 0.00676547 M: 35.3755 delta: 0.0265798 time: 646.028 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.988 accuracy: 0.0006087 cost: 0.00676549 M: 35.3756 delta: 0.0265796 time: 652.679 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.988 accuracy: 0.0006087 cost: 0.00676551 M: 35.3757 delta: 0.0265794 time: 659.325 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.988 accuracy: 0.0006087 cost: 0.00676552 M: 35.3757 delta: 0.0265791 time: 665.976 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.988 accuracy: 0.0006087 cost: 0.00676553 M: 35.3757 delta: 0.0265791 time: 672.621 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.988 accuracy: 0.0006087 cost: 0.00676553 M: 35.3757 delta: 0.026579 time: 679.26 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.988 accuracy: 0.0006087 cost: 0.00676553 M: 35.3757 delta: 0.026579 time: 685.905 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 701.88
Index size:  1903140.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0107919000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.0700000000, query time of that 0.0269352860, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Reject!
330.624 < 382.671
  -> Decision False in time 0.1600000000, query time of that 0.0573934900, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
360.076 < 405.89
  -> Decision False in time 0.2300000000, query time of that 0.0799952500, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.5300000000, query time of that 0.0320669680, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
323.657 < 364.423
  -> Decision False in time 4.9500000000, query time of that 0.3003517940, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
391.701 < 404.071
  -> Decision False in time 0.3600000000, query time of that 0.0206652450, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Reject!
386.778 < 413.941
  -> Decision False in time 4.8900000000, query time of that 0.0312210280, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
307.553 < 336.736
  -> Decision False in time 3.2400000000, query time of that 0.0206829170, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
311.707 < 323.368
  -> Decision False in time 12.9200000000, query time of that 0.0830309290, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 70, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 70, {'reverse': -1}, False])
Got a train set of size (1000000 * 128)
Generating control...
Initializing...
iteration: 1 recall: 0 accuracy: 2.6766 cost: 0.00038 M: 10 delta: 1 time: 54.2924 one-recall: 0 one-ratio: 3.66072
iteration: 2 recall: 0.0016 accuracy: 1.38091 cost: 0.000637428 M: 10 delta: 0.856032 time: 92.5377 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: 139.022 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: 185.172 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: 233.125 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: 287.363 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: 349.35 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: 407.452 one-recall: 1 one-ratio: 1
iteration: 9 recall: 0.97 accuracy: 0.00153751 cost: 0.00577336 M: 31.2902 delta: 0.0513331 time: 451.704 one-recall: 1 one-ratio: 1
iteration: 10 recall: 0.978 accuracy: 0.00108745 cost: 0.00625831 M: 33.3952 delta: 0.0371841 time: 481.378 one-recall: 1 one-ratio: 1
iteration: 11 recall: 0.9816 accuracy: 0.000822769 cost: 0.00651495 M: 34.422 delta: 0.0313218 time: 500.527 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.9856 accuracy: 0.000634549 cost: 0.00664356 M: 34.9169 delta: 0.0287556 time: 513.367 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.9868 accuracy: 0.000578651 cost: 0.00670598 M: 35.1529 delta: 0.027595 time: 522.582 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.9876 accuracy: 0.000568954 cost: 0.00673627 M: 35.2664 delta: 0.0270435 time: 529.87 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.9876 accuracy: 0.000568954 cost: 0.00675099 M: 35.3217 delta: 0.0267899 time: 536.152 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.9876 accuracy: 0.000568954 cost: 0.0067584 M: 35.3495 delta: 0.0266568 time: 541.937 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.9876 accuracy: 0.000568954 cost: 0.00676207 M: 35.3629 delta: 0.0265947 time: 547.454 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.9876 accuracy: 0.000568954 cost: 0.00676399 M: 35.37 delta: 0.0265678 time: 552.84 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.9876 accuracy: 0.000568954 cost: 0.00676502 M: 35.3739 delta: 0.0265497 time: 558.152 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.9876 accuracy: 0.000568954 cost: 0.00676557 M: 35.376 delta: 0.026542 time: 563.424 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.9876 accuracy: 0.000568954 cost: 0.00676589 M: 35.3772 delta: 0.026538 time: 568.672 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.9876 accuracy: 0.000568954 cost: 0.00676609 M: 35.378 delta: 0.0265349 time: 573.911 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.9876 accuracy: 0.000568954 cost: 0.00676619 M: 35.3784 delta: 0.0265336 time: 579.136 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.9876 accuracy: 0.000568954 cost: 0.00676626 M: 35.3787 delta: 0.0265323 time: 584.354 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.9876 accuracy: 0.000568954 cost: 0.0067663 M: 35.3789 delta: 0.026532 time: 589.568 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.9876 accuracy: 0.000568954 cost: 0.00676631 M: 35.3789 delta: 0.0265317 time: 594.779 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.9876 accuracy: 0.000568954 cost: 0.00676632 M: 35.379 delta: 0.0265314 time: 599.99 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.9876 accuracy: 0.000568954 cost: 0.00676632 M: 35.379 delta: 0.0265314 time: 605.201 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.9876 accuracy: 0.000568954 cost: 0.00676633 M: 35.379 delta: 0.0265313 time: 610.414 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.9876 accuracy: 0.000568954 cost: 0.00676633 M: 35.379 delta: 0.0265314 time: 615.622 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 630.06
Index size:  261040.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0035803000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.1000000000, query time of that 0.0517817030, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Accept!
  -> Decision True in time 1.0500000000, query time of that 0.6064901920, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
191.69 < 193.331
  -> Decision False in time 6.6200000000, query time of that 3.7439462450, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.5800000000, query time of that 0.0598031740, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Accept!
  -> Decision True in time 5.7800000000, query time of that 0.6930713240, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
336.681 < 351.064
  -> Decision False in time 4.9300000000, query time of that 0.5939591520, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Accept!
  -> Decision True in time 6.6700000000, query time of that 0.0732935040, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
332.334 < 340.313
  -> Decision False in time 14.4400000000, query time of that 0.1833761300, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
255.578 < 256.133
  -> Decision False in time 65.2700000000, query time of that 0.7997105230, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 100, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 100, {'reverse': -1}, False])
Got a train set of size (1000000 * 128)
Generating control...
Initializing...
iteration: 1 recall: 0.0008 accuracy: 2.48177 cost: 0.00038 M: 10 delta: 1 time: 54.2104 one-recall: 0 one-ratio: 3.57151
iteration: 2 recall: 0.006 accuracy: 1.20232 cost: 0.000637428 M: 10 delta: 0.856032 time: 92.4323 one-recall: 0.01 one-ratio: 2.71347
iteration: 3 recall: 0.0428 accuracy: 0.621878 cost: 0.00109521 M: 11.5287 delta: 0.835103 time: 138.896 one-recall: 0.06 one-ratio: 2.24598
iteration: 4 recall: 0.2136 accuracy: 0.28981 cost: 0.00163045 M: 11.8363 delta: 0.783478 time: 185.035 one-recall: 0.26 one-ratio: 1.81277
iteration: 5 recall: 0.5448 accuracy: 0.114318 cost: 0.002236 M: 12.6033 delta: 0.664602 time: 232.972 one-recall: 0.61 one-ratio: 1.37506
iteration: 6 recall: 0.7952 accuracy: 0.0245186 cost: 0.0029798 M: 15.1144 delta: 0.432322 time: 287.19 one-recall: 0.92 one-ratio: 1.07501
iteration: 7 recall: 0.9064 accuracy: 0.00727861 cost: 0.00395526 M: 21.1404 delta: 0.196394 time: 349.173 one-recall: 0.98 one-ratio: 1.00751
iteration: 8 recall: 0.9524 accuracy: 0.00286463 cost: 0.00497971 M: 27.3052 delta: 0.088477 time: 407.253 one-recall: 0.99 one-ratio: 1.00262
iteration: 9 recall: 0.968 accuracy: 0.0016867 cost: 0.0057726 M: 31.289 delta: 0.0513382 time: 451.468 one-recall: 0.99 one-ratio: 1.00262
iteration: 10 recall: 0.9756 accuracy: 0.00119361 cost: 0.00625731 M: 33.3919 delta: 0.0372072 time: 481.115 one-recall: 0.99 one-ratio: 1.00262
iteration: 11 recall: 0.9776 accuracy: 0.00103174 cost: 0.00651498 M: 34.4232 delta: 0.0313255 time: 500.304 one-recall: 0.99 one-ratio: 1.00262
iteration: 12 recall: 0.9788 accuracy: 0.000991152 cost: 0.00664245 M: 34.9147 delta: 0.0287581 time: 513.084 one-recall: 0.99 one-ratio: 1.00262
iteration: 13 recall: 0.9796 accuracy: 0.000952986 cost: 0.00670487 M: 35.1503 delta: 0.0275933 time: 522.3 one-recall: 0.99 one-ratio: 1.00262
iteration: 14 recall: 0.9812 accuracy: 0.000893622 cost: 0.00673507 M: 35.2635 delta: 0.0270471 time: 529.58 one-recall: 0.99 one-ratio: 1.00262
iteration: 15 recall: 0.9812 accuracy: 0.000893622 cost: 0.00674967 M: 35.318 delta: 0.0267891 time: 535.851 one-recall: 0.99 one-ratio: 1.00262
iteration: 16 recall: 0.9816 accuracy: 0.000873917 cost: 0.00675708 M: 35.3455 delta: 0.0266679 time: 541.635 one-recall: 0.99 one-ratio: 1.00262
iteration: 17 recall: 0.9816 accuracy: 0.000873917 cost: 0.00676089 M: 35.3596 delta: 0.0266002 time: 547.164 one-recall: 0.99 one-ratio: 1.00262
iteration: 18 recall: 0.9816 accuracy: 0.000873917 cost: 0.00676275 M: 35.3664 delta: 0.0265726 time: 552.545 one-recall: 0.99 one-ratio: 1.00262
iteration: 19 recall: 0.9816 accuracy: 0.000873917 cost: 0.00676375 M: 35.3702 delta: 0.0265561 time: 557.854 one-recall: 0.99 one-ratio: 1.00262
iteration: 20 recall: 0.9816 accuracy: 0.000873917 cost: 0.00676426 M: 35.3722 delta: 0.0265482 time: 563.119 one-recall: 0.99 one-ratio: 1.00262
iteration: 21 recall: 0.9816 accuracy: 0.000873917 cost: 0.00676456 M: 35.3732 delta: 0.0265447 time: 568.365 one-recall: 0.99 one-ratio: 1.00262
iteration: 22 recall: 0.9816 accuracy: 0.000873917 cost: 0.00676475 M: 35.374 delta: 0.0265421 time: 573.595 one-recall: 0.99 one-ratio: 1.00262
iteration: 23 recall: 0.9816 accuracy: 0.000873917 cost: 0.00676483 M: 35.3743 delta: 0.0265411 time: 578.813 one-recall: 0.99 one-ratio: 1.00262
iteration: 24 recall: 0.9816 accuracy: 0.000873917 cost: 0.00676489 M: 35.3745 delta: 0.0265398 time: 584.03 one-recall: 0.99 one-ratio: 1.00262
iteration: 25 recall: 0.9816 accuracy: 0.000873917 cost: 0.00676494 M: 35.3747 delta: 0.026539 time: 589.244 one-recall: 0.99 one-ratio: 1.00262
iteration: 26 recall: 0.9816 accuracy: 0.000873917 cost: 0.00676496 M: 35.3748 delta: 0.026539 time: 594.452 one-recall: 0.99 one-ratio: 1.00262
iteration: 27 recall: 0.9816 accuracy: 0.000873917 cost: 0.00676496 M: 35.3748 delta: 0.026539 time: 599.663 one-recall: 0.99 one-ratio: 1.00262
iteration: 28 recall: 0.9816 accuracy: 0.000873917 cost: 0.00676497 M: 35.3749 delta: 0.0265386 time: 604.869 one-recall: 0.99 one-ratio: 1.00262
iteration: 29 recall: 0.9816 accuracy: 0.000873917 cost: 0.00676498 M: 35.3749 delta: 0.0265386 time: 610.078 one-recall: 0.99 one-ratio: 1.00262
iteration: 30 recall: 0.9816 accuracy: 0.000873917 cost: 0.00676498 M: 35.3749 delta: 0.0265386 time: 615.284 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 629.7199999999998
Index size:  262920.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0024667000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.1200000000, query time of that 0.0715741950, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Accept!
  -> Decision True in time 1.1200000000, query time of that 0.6682466590, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Accept!
  -> Decision True in time 11.2900000000, query time of that 6.7244942300, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.6000000000, query time of that 0.0767861000, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Accept!
  -> Decision True in time 6.0800000000, query time of that 0.7792521220, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
238.592 < 242.403
  -> Decision False in time 9.4300000000, query time of that 1.2448555270, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Accept!
  -> Decision True in time 6.6800000000, query time of that 0.0929048620, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
246.246 < 286.554
  -> Decision False in time 21.1400000000, query time of that 0.2935010030, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
308.039 < 315.935
  -> Decision False in time 9.0800000000, query time of that 0.1256119860, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 90, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 90, {'reverse': -1}, False])
Got a train set of size (1000000 * 128)
Generating control...
Initializing...
iteration: 1 recall: 0 accuracy: 2.38304 cost: 0.00038 M: 10 delta: 1 time: 54.2083 one-recall: 0 one-ratio: 3.12961
iteration: 2 recall: 0.002 accuracy: 1.21552 cost: 0.000637428 M: 10 delta: 0.856032 time: 92.4333 one-recall: 0 one-ratio: 2.50554
iteration: 3 recall: 0.03 accuracy: 0.637012 cost: 0.00109521 M: 11.5287 delta: 0.835107 time: 138.897 one-recall: 0.04 one-ratio: 2.0044
iteration: 4 recall: 0.2136 accuracy: 0.302483 cost: 0.00163043 M: 11.8362 delta: 0.783461 time: 185.041 one-recall: 0.27 one-ratio: 1.58053
iteration: 5 recall: 0.54 accuracy: 0.102859 cost: 0.00223609 M: 12.6039 delta: 0.664615 time: 232.979 one-recall: 0.63 one-ratio: 1.21308
iteration: 6 recall: 0.7916 accuracy: 0.0237297 cost: 0.00297991 M: 15.1142 delta: 0.432346 time: 287.204 one-recall: 0.88 one-ratio: 1.0418
iteration: 7 recall: 0.9048 accuracy: 0.00733309 cost: 0.00395541 M: 21.1412 delta: 0.196445 time: 349.19 one-recall: 0.93 one-ratio: 1.01948
iteration: 8 recall: 0.952 accuracy: 0.00282404 cost: 0.00498059 M: 27.3083 delta: 0.0884317 time: 407.307 one-recall: 0.97 one-ratio: 1.00385
iteration: 9 recall: 0.9732 accuracy: 0.00139526 cost: 0.00577397 M: 31.2946 delta: 0.0513665 time: 451.55 one-recall: 0.99 one-ratio: 1.00043
iteration: 10 recall: 0.978 accuracy: 0.0011261 cost: 0.00625911 M: 33.4005 delta: 0.0372186 time: 481.214 one-recall: 0.99 one-ratio: 1.00043
iteration: 11 recall: 0.9824 accuracy: 0.000920647 cost: 0.00651709 M: 34.4331 delta: 0.0313 time: 500.422 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.984 accuracy: 0.000814495 cost: 0.00664491 M: 34.9233 delta: 0.0287324 time: 513.219 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.9848 accuracy: 0.000794049 cost: 0.00670662 M: 35.1569 delta: 0.0275677 time: 522.398 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.9852 accuracy: 0.000789393 cost: 0.00673646 M: 35.2691 delta: 0.0270287 time: 529.657 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.9856 accuracy: 0.000774968 cost: 0.00675137 M: 35.3246 delta: 0.0267723 time: 535.95 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.9856 accuracy: 0.000773496 cost: 0.00675878 M: 35.3524 delta: 0.0266461 time: 541.731 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.9856 accuracy: 0.000773496 cost: 0.00676274 M: 35.3672 delta: 0.0265754 time: 547.27 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.9856 accuracy: 0.000773496 cost: 0.00676474 M: 35.3747 delta: 0.026545 time: 552.662 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.9856 accuracy: 0.000773496 cost: 0.00676584 M: 35.3788 delta: 0.0265288 time: 557.982 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.9856 accuracy: 0.000773496 cost: 0.00676648 M: 35.3812 delta: 0.0265174 time: 563.265 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.9856 accuracy: 0.000773496 cost: 0.00676684 M: 35.3826 delta: 0.0265126 time: 568.52 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.9856 accuracy: 0.000773496 cost: 0.00676703 M: 35.3834 delta: 0.0265092 time: 573.756 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.9856 accuracy: 0.000773496 cost: 0.00676714 M: 35.3839 delta: 0.0265077 time: 578.981 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.9856 accuracy: 0.000773496 cost: 0.00676721 M: 35.3841 delta: 0.0265071 time: 584.206 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.9856 accuracy: 0.000773496 cost: 0.00676728 M: 35.3844 delta: 0.0265065 time: 589.423 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.9856 accuracy: 0.000773496 cost: 0.00676731 M: 35.3845 delta: 0.0265056 time: 594.638 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.9856 accuracy: 0.000773496 cost: 0.00676734 M: 35.3846 delta: 0.0265057 time: 599.851 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.9856 accuracy: 0.000773496 cost: 0.00676735 M: 35.3847 delta: 0.0265057 time: 605.065 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.9856 accuracy: 0.000773496 cost: 0.00676737 M: 35.3847 delta: 0.0265053 time: 610.278 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.9856 accuracy: 0.000773496 cost: 0.00676738 M: 35.3848 delta: 0.0265051 time: 615.488 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 629.9200000000001
Index size:  263016.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0027236000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.1000000000, query time of that 0.0618393370, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Accept!
  -> Decision True in time 1.0800000000, query time of that 0.6288161750, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Accept!
  -> Decision True in time 10.8100000000, query time of that 6.1997702380, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.6000000000, query time of that 0.0718049490, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Accept!
  -> Decision True in time 5.8800000000, query time of that 0.7229381340, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
286.944 < 289.086
  -> Decision False in time 24.0200000000, query time of that 2.9748670530, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Accept!
  -> Decision True in time 6.6600000000, query time of that 0.0828126970, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
310.998 < 316.85
  -> Decision False in time 3.2900000000, query time of that 0.0399068440, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
259.38 < 260.069
  -> Decision False in time 29.2600000000, query time of that 0.3729979930, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 40, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 40, {'reverse': -1}, False])
Got a train set of size (1000000 * 128)
Generating control...
Initializing...
iteration: 1 recall: 0.0008 accuracy: 2.99976 cost: 0.00038 M: 10 delta: 1 time: 54.2113 one-recall: 0 one-ratio: 3.62095
iteration: 2 recall: 0.0068 accuracy: 1.33123 cost: 0.000637428 M: 10 delta: 0.856032 time: 92.4258 one-recall: 0.01 one-ratio: 2.80155
iteration: 3 recall: 0.0388 accuracy: 0.654038 cost: 0.00109521 M: 11.5287 delta: 0.835103 time: 138.869 one-recall: 0.09 one-ratio: 2.26323
iteration: 4 recall: 0.2172 accuracy: 0.314837 cost: 0.00163043 M: 11.8362 delta: 0.783454 time: 184.994 one-recall: 0.28 one-ratio: 1.72659
iteration: 5 recall: 0.5488 accuracy: 0.100561 cost: 0.00223606 M: 12.6036 delta: 0.664611 time: 232.912 one-recall: 0.67 one-ratio: 1.28769
iteration: 6 recall: 0.8128 accuracy: 0.0196754 cost: 0.00297985 M: 15.1147 delta: 0.432321 time: 287.107 one-recall: 0.93 one-ratio: 1.0522
iteration: 7 recall: 0.914 accuracy: 0.00680671 cost: 0.00395531 M: 21.1426 delta: 0.196426 time: 349.065 one-recall: 0.96 one-ratio: 1.02079
iteration: 8 recall: 0.9528 accuracy: 0.00345233 cost: 0.00497982 M: 27.3046 delta: 0.0884235 time: 407.123 one-recall: 0.98 one-ratio: 1.00801
iteration: 9 recall: 0.9704 accuracy: 0.00181453 cost: 0.00577269 M: 31.2888 delta: 0.0513716 time: 451.337 one-recall: 0.99 one-ratio: 1.00369
iteration: 10 recall: 0.9788 accuracy: 0.00117021 cost: 0.00625805 M: 33.3952 delta: 0.0372265 time: 481.03 one-recall: 1 one-ratio: 1
iteration: 11 recall: 0.982 accuracy: 0.000930717 cost: 0.00651532 M: 34.4257 delta: 0.0313169 time: 500.204 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.984 accuracy: 0.000848911 cost: 0.00664274 M: 34.9156 delta: 0.0287531 time: 512.971 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.9844 accuracy: 0.000831797 cost: 0.0067044 M: 35.1482 delta: 0.0276086 time: 522.143 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.9848 accuracy: 0.000830786 cost: 0.00673433 M: 35.2597 delta: 0.0270741 time: 529.405 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.9848 accuracy: 0.000830786 cost: 0.00674924 M: 35.3154 delta: 0.0268123 time: 535.701 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.9852 accuracy: 0.000808228 cost: 0.00675674 M: 35.3434 delta: 0.0266906 time: 541.489 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.9852 accuracy: 0.000808228 cost: 0.00676042 M: 35.3571 delta: 0.0266271 time: 547.008 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.9856 accuracy: 0.000789375 cost: 0.00676243 M: 35.3646 delta: 0.0265953 time: 552.398 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.9856 accuracy: 0.000789375 cost: 0.00676345 M: 35.3686 delta: 0.0265784 time: 557.709 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.9856 accuracy: 0.000789375 cost: 0.00676406 M: 35.3709 delta: 0.0265702 time: 562.986 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.9856 accuracy: 0.000789375 cost: 0.00676443 M: 35.3723 delta: 0.0265643 time: 568.239 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.9856 accuracy: 0.000789375 cost: 0.00676464 M: 35.3731 delta: 0.0265613 time: 573.476 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.9856 accuracy: 0.000789375 cost: 0.00676475 M: 35.3735 delta: 0.0265589 time: 578.705 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.9856 accuracy: 0.000789375 cost: 0.00676481 M: 35.3738 delta: 0.0265577 time: 583.925 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.9856 accuracy: 0.000789375 cost: 0.00676484 M: 35.3739 delta: 0.0265569 time: 589.139 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.9856 accuracy: 0.000789375 cost: 0.00676485 M: 35.3739 delta: 0.0265569 time: 594.347 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.9856 accuracy: 0.000789375 cost: 0.00676485 M: 35.3739 delta: 0.0265568 time: 599.557 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.9856 accuracy: 0.000789375 cost: 0.00676485 M: 35.3739 delta: 0.0265567 time: 604.766 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.9856 accuracy: 0.000789375 cost: 0.00676485 M: 35.3739 delta: 0.0265567 time: 609.972 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.9856 accuracy: 0.000789375 cost: 0.00676485 M: 35.3739 delta: 0.0265567 time: 615.179 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 629.6100000000006
Index size:  262808.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0061980000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.0800000000, query time of that 0.0367763840, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Accept!
  -> Decision True in time 0.8100000000, query time of that 0.3572094230, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
314.391 < 356.282
  -> Decision False in time 0.9300000000, query time of that 0.4091262520, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.5500000000, query time of that 0.0424058970, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Accept!
  -> Decision True in time 5.4600000000, query time of that 0.4275601650, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
345.407 < 351.134
  -> Decision False in time 1.4700000000, query time of that 0.1183508890, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Accept!
  -> Decision True in time 6.6300000000, query time of that 0.0544137600, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
276.946 < 279.635
  -> Decision False in time 13.8500000000, query time of that 0.1107148430, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
238.342 < 253.219
  -> Decision False in time 9.3400000000, query time of that 0.0761369580, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 5, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 5, {'reverse': -1}, False])
Got a train set of size (1000000 * 128)
Generating control...
Initializing...
iteration: 1 recall: 0.0004 accuracy: 1.8691 cost: 0.00038 M: 10 delta: 1 time: 54.201 one-recall: 0 one-ratio: 3.32446
iteration: 2 recall: 0.0036 accuracy: 1.08046 cost: 0.000637428 M: 10 delta: 0.856032 time: 92.4316 one-recall: 0 one-ratio: 2.64174
iteration: 3 recall: 0.0372 accuracy: 0.644047 cost: 0.00109521 M: 11.5287 delta: 0.835106 time: 138.889 one-recall: 0.03 one-ratio: 2.15541
iteration: 4 recall: 0.1816 accuracy: 0.33768 cost: 0.00163042 M: 11.8362 delta: 0.78346 time: 185.022 one-recall: 0.23 one-ratio: 1.64814
iteration: 5 recall: 0.5108 accuracy: 0.101217 cost: 0.00223606 M: 12.6038 delta: 0.66461 time: 232.956 one-recall: 0.68 one-ratio: 1.19223
iteration: 6 recall: 0.7756 accuracy: 0.0288425 cost: 0.00297987 M: 15.1142 delta: 0.432333 time: 287.164 one-recall: 0.86 one-ratio: 1.08064
iteration: 7 recall: 0.8876 accuracy: 0.00940746 cost: 0.00395519 M: 21.1405 delta: 0.196413 time: 349.135 one-recall: 0.93 one-ratio: 1.03855
iteration: 8 recall: 0.9356 accuracy: 0.00480809 cost: 0.00497957 M: 27.305 delta: 0.0884287 time: 407.207 one-recall: 0.98 one-ratio: 1.01513
iteration: 9 recall: 0.9596 accuracy: 0.00306771 cost: 0.00577216 M: 31.2889 delta: 0.0513356 time: 451.424 one-recall: 1 one-ratio: 1
iteration: 10 recall: 0.9704 accuracy: 0.00214965 cost: 0.00625672 M: 33.3894 delta: 0.0372061 time: 481.069 one-recall: 1 one-ratio: 1
iteration: 11 recall: 0.9764 accuracy: 0.00189153 cost: 0.00651399 M: 34.4211 delta: 0.0313145 time: 500.241 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.9796 accuracy: 0.00159838 cost: 0.00664163 M: 34.9129 delta: 0.0287468 time: 513.028 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.9812 accuracy: 0.00147381 cost: 0.00670421 M: 35.1499 delta: 0.0275767 time: 522.256 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.982 accuracy: 0.00144994 cost: 0.0067343 M: 35.2627 delta: 0.0270219 time: 529.536 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.9828 accuracy: 0.00140945 cost: 0.00674904 M: 35.3179 delta: 0.026771 time: 535.82 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.9844 accuracy: 0.00117137 cost: 0.00675654 M: 35.3459 delta: 0.0266453 time: 541.611 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.9856 accuracy: 0.00106072 cost: 0.00676042 M: 35.3604 delta: 0.026581 time: 547.146 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.9856 accuracy: 0.00106072 cost: 0.00676236 M: 35.3675 delta: 0.0265496 time: 552.534 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.9856 accuracy: 0.00106072 cost: 0.00676341 M: 35.3713 delta: 0.0265338 time: 557.848 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.9856 accuracy: 0.00106072 cost: 0.00676398 M: 35.3735 delta: 0.0265255 time: 563.121 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.9856 accuracy: 0.00106072 cost: 0.00676428 M: 35.3748 delta: 0.0265203 time: 568.369 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.9856 accuracy: 0.00106072 cost: 0.00676447 M: 35.3756 delta: 0.0265169 time: 573.605 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.9856 accuracy: 0.00106072 cost: 0.00676459 M: 35.3761 delta: 0.0265152 time: 578.834 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.9856 accuracy: 0.00106072 cost: 0.00676464 M: 35.3763 delta: 0.0265144 time: 584.052 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.9856 accuracy: 0.00106072 cost: 0.00676469 M: 35.3765 delta: 0.026514 time: 589.267 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.9856 accuracy: 0.00106072 cost: 0.00676471 M: 35.3766 delta: 0.0265135 time: 594.483 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.9856 accuracy: 0.00106072 cost: 0.00676473 M: 35.3767 delta: 0.0265134 time: 599.694 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.9856 accuracy: 0.00106072 cost: 0.00676474 M: 35.3767 delta: 0.0265132 time: 604.904 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.9856 accuracy: 0.00106072 cost: 0.00676474 M: 35.3767 delta: 0.0265131 time: 610.112 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.9856 accuracy: 0.00106072 cost: 0.00676474 M: 35.3767 delta: 0.0265131 time: 615.323 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 629.75
Index size:  262812.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0112939000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.0600000000, query time of that 0.0168194090, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Reject!
248.755 < 254.851
  -> Decision False in time 0.4700000000, query time of that 0.1226909030, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
365.69 < 395.525
  -> Decision False in time 0.2600000000, query time of that 0.0672366320, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.5300000000, query time of that 0.0194774230, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
354.538 < 410.927
  -> Decision False in time 3.8100000000, query time of that 0.1423211040, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
272.808 < 273.823
  -> Decision False in time 2.8300000000, query time of that 0.1063014630, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Reject!
225.9 < 228.88
  -> Decision False in time 0.9000000000, query time of that 0.0037865660, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
264.095 < 266.231
  -> Decision False in time 3.4100000000, query time of that 0.0146230560, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
253.316 < 256.996
  -> Decision False in time 32.3300000000, query time of that 0.1330896830, 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: 0.0012 accuracy: 1.95623 cost: 0.00038 M: 10 delta: 1 time: 54.1834 one-recall: 0 one-ratio: 3.07341
iteration: 2 recall: 0.0056 accuracy: 1.11449 cost: 0.000637428 M: 10 delta: 0.856032 time: 92.4055 one-recall: 0 one-ratio: 2.44049
iteration: 3 recall: 0.0356 accuracy: 0.596481 cost: 0.00109521 M: 11.5287 delta: 0.835105 time: 138.852 one-recall: 0.04 one-ratio: 1.97306
iteration: 4 recall: 0.1848 accuracy: 0.293092 cost: 0.00163043 M: 11.8363 delta: 0.783458 time: 184.978 one-recall: 0.19 one-ratio: 1.56623
iteration: 5 recall: 0.5104 accuracy: 0.0958911 cost: 0.00223617 M: 12.6042 delta: 0.66462 time: 232.905 one-recall: 0.65 one-ratio: 1.15941
iteration: 6 recall: 0.762 accuracy: 0.0262869 cost: 0.00298004 M: 15.1143 delta: 0.432359 time: 287.109 one-recall: 0.91 one-ratio: 1.03282
iteration: 7 recall: 0.8872 accuracy: 0.00860413 cost: 0.00395504 M: 21.1386 delta: 0.196388 time: 349.051 one-recall: 0.97 one-ratio: 1.00463
iteration: 8 recall: 0.9284 accuracy: 0.00441742 cost: 0.00497966 M: 27.3052 delta: 0.0884856 time: 407.131 one-recall: 0.98 one-ratio: 1.00347
iteration: 9 recall: 0.9444 accuracy: 0.00293923 cost: 0.00577339 M: 31.2919 delta: 0.0513489 time: 451.381 one-recall: 0.99 one-ratio: 1.00286
iteration: 10 recall: 0.956 accuracy: 0.00230469 cost: 0.00625871 M: 33.3983 delta: 0.0372137 time: 481.071 one-recall: 0.99 one-ratio: 1.00286
iteration: 11 recall: 0.9572 accuracy: 0.00220857 cost: 0.00651668 M: 34.4301 delta: 0.0313187 time: 500.277 one-recall: 0.99 one-ratio: 1.00286
iteration: 12 recall: 0.96 accuracy: 0.00207172 cost: 0.00664429 M: 34.9223 delta: 0.028733 time: 513.063 one-recall: 0.99 one-ratio: 1.00286
iteration: 13 recall: 0.9604 accuracy: 0.00205928 cost: 0.00670621 M: 35.156 delta: 0.0275692 time: 522.254 one-recall: 0.99 one-ratio: 1.00286
iteration: 14 recall: 0.9608 accuracy: 0.00203997 cost: 0.00673593 M: 35.2679 delta: 0.0270427 time: 529.511 one-recall: 0.99 one-ratio: 1.00286
iteration: 15 recall: 0.9608 accuracy: 0.00203997 cost: 0.00675071 M: 35.3229 delta: 0.0267855 time: 535.797 one-recall: 0.99 one-ratio: 1.00286
iteration: 16 recall: 0.9612 accuracy: 0.00203059 cost: 0.00675816 M: 35.3508 delta: 0.0266664 time: 541.582 one-recall: 0.99 one-ratio: 1.00286
iteration: 17 recall: 0.9612 accuracy: 0.00203059 cost: 0.00676214 M: 35.3657 delta: 0.0266011 time: 547.122 one-recall: 0.99 one-ratio: 1.00286
iteration: 18 recall: 0.9612 accuracy: 0.00203059 cost: 0.00676414 M: 35.3732 delta: 0.0265708 time: 552.513 one-recall: 0.99 one-ratio: 1.00286
iteration: 19 recall: 0.9612 accuracy: 0.00203059 cost: 0.00676528 M: 35.3777 delta: 0.0265514 time: 557.837 one-recall: 0.99 one-ratio: 1.00286
iteration: 20 recall: 0.9612 accuracy: 0.00203059 cost: 0.00676588 M: 35.3799 delta: 0.0265416 time: 563.113 one-recall: 0.99 one-ratio: 1.00286
iteration: 21 recall: 0.9612 accuracy: 0.00203059 cost: 0.00676622 M: 35.3812 delta: 0.0265355 time: 568.367 one-recall: 0.99 one-ratio: 1.00286
iteration: 22 recall: 0.9612 accuracy: 0.00203059 cost: 0.00676636 M: 35.3817 delta: 0.0265336 time: 573.599 one-recall: 0.99 one-ratio: 1.00286
iteration: 23 recall: 0.9612 accuracy: 0.00203059 cost: 0.00676644 M: 35.382 delta: 0.0265318 time: 578.82 one-recall: 0.99 one-ratio: 1.00286
iteration: 24 recall: 0.9612 accuracy: 0.00203059 cost: 0.00676648 M: 35.3822 delta: 0.0265314 time: 584.037 one-recall: 0.99 one-ratio: 1.00286
iteration: 25 recall: 0.9612 accuracy: 0.00203059 cost: 0.0067665 M: 35.3823 delta: 0.0265312 time: 589.246 one-recall: 0.99 one-ratio: 1.00286
iteration: 26 recall: 0.9612 accuracy: 0.00203059 cost: 0.00676652 M: 35.3823 delta: 0.0265309 time: 594.459 one-recall: 0.99 one-ratio: 1.00286
iteration: 27 recall: 0.9612 accuracy: 0.00203059 cost: 0.00676653 M: 35.3824 delta: 0.0265309 time: 599.671 one-recall: 0.99 one-ratio: 1.00286
iteration: 28 recall: 0.9612 accuracy: 0.00203059 cost: 0.00676653 M: 35.3824 delta: 0.0265308 time: 604.887 one-recall: 0.99 one-ratio: 1.00286
iteration: 29 recall: 0.9612 accuracy: 0.00203059 cost: 0.00676653 M: 35.3824 delta: 0.0265308 time: 610.099 one-recall: 0.99 one-ratio: 1.00286
iteration: 30 recall: 0.9612 accuracy: 0.00203059 cost: 0.00676654 M: 35.3824 delta: 0.0265308 time: 615.308 one-recall: 0.99 one-ratio: 1.00286
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 629.75
Index size:  263036.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0192339000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.0600000000, query time of that 0.0157095980, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Reject!
376.328 < 433.881
  -> Decision False in time 0.0200000000, query time of that 0.0029041520, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
381.572 < 398.377
  -> Decision False in time 0.0000000000, query time of that 0.0009449320, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Reject!
404.822 < 432.238
  -> Decision False in time 0.4000000000, query time of that 0.0136405980, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
283.572 < 293.012
  -> Decision False in time 0.3500000000, query time of that 0.0126721160, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
364.823 < 386.28
  -> Decision False in time 1.7700000000, query time of that 0.0625157640, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Reject!
259.654 < 271.45
  -> Decision False in time 1.2500000000, query time of that 0.0049296870, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
402.615 < 426.592
  -> Decision False in time 4.8800000000, query time of that 0.0197669920, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
289.8 < 292.407
  -> Decision False in time 3.4600000000, query time of that 0.0130743700, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 60, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 60, {'reverse': -1}, False])
Got a train set of size (1000000 * 128)
Generating control...
Initializing...
iteration: 1 recall: 0 accuracy: 1.94486 cost: 0.00038 M: 10 delta: 1 time: 54.2419 one-recall: 0 one-ratio: 3.3161
iteration: 2 recall: 0.002 accuracy: 1.12207 cost: 0.000637428 M: 10 delta: 0.856032 time: 92.4713 one-recall: 0 one-ratio: 2.64138
iteration: 3 recall: 0.0272 accuracy: 0.634098 cost: 0.00109521 M: 11.5287 delta: 0.835106 time: 138.934 one-recall: 0.03 one-ratio: 2.10169
iteration: 4 recall: 0.168 accuracy: 0.31511 cost: 0.00163043 M: 11.8363 delta: 0.783441 time: 185.069 one-recall: 0.28 one-ratio: 1.6345
iteration: 5 recall: 0.4948 accuracy: 0.107571 cost: 0.00223613 M: 12.6039 delta: 0.6646 time: 233.007 one-recall: 0.61 one-ratio: 1.25939
iteration: 6 recall: 0.77 accuracy: 0.0281694 cost: 0.00298001 M: 15.1139 delta: 0.432335 time: 287.221 one-recall: 0.88 one-ratio: 1.05451
iteration: 7 recall: 0.8852 accuracy: 0.00980829 cost: 0.0039552 M: 21.1393 delta: 0.196388 time: 349.193 one-recall: 0.91 one-ratio: 1.03045
iteration: 8 recall: 0.9328 accuracy: 0.00525692 cost: 0.00497961 M: 27.3048 delta: 0.088478 time: 407.262 one-recall: 0.95 one-ratio: 1.02472
iteration: 9 recall: 0.958 accuracy: 0.00297355 cost: 0.00577336 M: 31.293 delta: 0.0513495 time: 451.514 one-recall: 0.96 one-ratio: 1.01657
iteration: 10 recall: 0.9688 accuracy: 0.00230776 cost: 0.006259 M: 33.3991 delta: 0.0371794 time: 481.227 one-recall: 0.96 one-ratio: 1.01657
iteration: 11 recall: 0.9732 accuracy: 0.00195039 cost: 0.0065163 M: 34.4297 delta: 0.031289 time: 500.404 one-recall: 0.97 one-ratio: 1.01616
iteration: 12 recall: 0.9756 accuracy: 0.00173173 cost: 0.00664371 M: 34.9209 delta: 0.0286949 time: 513.181 one-recall: 0.97 one-ratio: 1.01616
iteration: 13 recall: 0.9764 accuracy: 0.00164416 cost: 0.00670522 M: 35.1533 delta: 0.0275429 time: 522.35 one-recall: 0.97 one-ratio: 1.01616
iteration: 14 recall: 0.9776 accuracy: 0.00152171 cost: 0.00673542 M: 35.2665 delta: 0.0270107 time: 529.627 one-recall: 0.97 one-ratio: 1.01616
iteration: 15 recall: 0.978 accuracy: 0.00142186 cost: 0.00675045 M: 35.3232 delta: 0.0267447 time: 535.929 one-recall: 0.97 one-ratio: 1.01616
iteration: 16 recall: 0.978 accuracy: 0.00142186 cost: 0.00675806 M: 35.3506 delta: 0.0266148 time: 541.725 one-recall: 0.97 one-ratio: 1.01616
iteration: 17 recall: 0.978 accuracy: 0.00142186 cost: 0.00676172 M: 35.3639 delta: 0.0265588 time: 547.242 one-recall: 0.97 one-ratio: 1.01616
iteration: 18 recall: 0.978 accuracy: 0.00142186 cost: 0.00676376 M: 35.3715 delta: 0.0265256 time: 552.636 one-recall: 0.97 one-ratio: 1.01616
iteration: 19 recall: 0.978 accuracy: 0.00142186 cost: 0.00676483 M: 35.3757 delta: 0.0265077 time: 557.953 one-recall: 0.97 one-ratio: 1.01616
iteration: 20 recall: 0.978 accuracy: 0.00142186 cost: 0.00676543 M: 35.3777 delta: 0.0264999 time: 563.228 one-recall: 0.97 one-ratio: 1.01616
iteration: 21 recall: 0.978 accuracy: 0.00142186 cost: 0.00676577 M: 35.379 delta: 0.0264949 time: 568.479 one-recall: 0.97 one-ratio: 1.01616
iteration: 22 recall: 0.978 accuracy: 0.00142186 cost: 0.00676596 M: 35.3798 delta: 0.0264914 time: 573.715 one-recall: 0.97 one-ratio: 1.01616
iteration: 23 recall: 0.978 accuracy: 0.00142186 cost: 0.00676608 M: 35.3803 delta: 0.0264897 time: 578.941 one-recall: 0.97 one-ratio: 1.01616
iteration: 24 recall: 0.978 accuracy: 0.00142186 cost: 0.00676614 M: 35.3805 delta: 0.0264889 time: 584.157 one-recall: 0.97 one-ratio: 1.01616
iteration: 25 recall: 0.978 accuracy: 0.00142186 cost: 0.00676617 M: 35.3806 delta: 0.0264884 time: 589.372 one-recall: 0.97 one-ratio: 1.01616
iteration: 26 recall: 0.978 accuracy: 0.00142186 cost: 0.00676619 M: 35.3807 delta: 0.026488 time: 594.585 one-recall: 0.97 one-ratio: 1.01616
iteration: 27 recall: 0.978 accuracy: 0.00142186 cost: 0.0067662 M: 35.3808 delta: 0.026488 time: 599.792 one-recall: 0.97 one-ratio: 1.01616
iteration: 28 recall: 0.978 accuracy: 0.00142186 cost: 0.00676622 M: 35.3808 delta: 0.0264878 time: 605.004 one-recall: 0.97 one-ratio: 1.01616
iteration: 29 recall: 0.978 accuracy: 0.00142186 cost: 0.00676623 M: 35.3808 delta: 0.0264877 time: 610.213 one-recall: 0.97 one-ratio: 1.01616
iteration: 30 recall: 0.978 accuracy: 0.00142186 cost: 0.00676623 M: 35.3808 delta: 0.0264877 time: 615.42 one-recall: 0.97 one-ratio: 1.01616
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 629.8500000000004
Index size:  262896.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0041210000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.0900000000, query time of that 0.0476005290, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Accept!
  -> Decision True in time 0.9100000000, query time of that 0.4589165970, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Accept!
  -> Decision True in time 9.2400000000, query time of that 4.6284672330, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.5800000000, query time of that 0.0556111080, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Accept!
  -> Decision True in time 5.6100000000, query time of that 0.5426574030, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
221.095 < 230.25
  -> Decision False in time 9.4300000000, query time of that 0.9269665960, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Accept!
  -> Decision True in time 6.6500000000, query time of that 0.0662887830, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
302.746 < 327.979
  -> Decision False in time 4.4400000000, query time of that 0.0437665830, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
263.689 < 266.798
  -> Decision False in time 3.1400000000, query time of that 0.0308270430, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 4, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 4, {'reverse': -1}, False])
Got a train set of size (1000000 * 128)
Generating control...
Initializing...
iteration: 1 recall: 0 accuracy: 2.0495 cost: 0.00038 M: 10 delta: 1 time: 54.224 one-recall: 0 one-ratio: 3.13566
iteration: 2 recall: 0.0028 accuracy: 1.20824 cost: 0.000637428 M: 10 delta: 0.856032 time: 92.4497 one-recall: 0 one-ratio: 2.52933
iteration: 3 recall: 0.0296 accuracy: 0.71566 cost: 0.00109521 M: 11.5287 delta: 0.835107 time: 138.906 one-recall: 0.03 one-ratio: 2.05408
iteration: 4 recall: 0.17 accuracy: 0.371899 cost: 0.00163046 M: 11.8362 delta: 0.783461 time: 185.05 one-recall: 0.25 one-ratio: 1.55168
iteration: 5 recall: 0.5108 accuracy: 0.107339 cost: 0.00223604 M: 12.6036 delta: 0.664594 time: 232.983 one-recall: 0.63 one-ratio: 1.16582
iteration: 6 recall: 0.796 accuracy: 0.0251756 cost: 0.00297997 M: 15.1148 delta: 0.432384 time: 287.204 one-recall: 0.93 one-ratio: 1.01736
iteration: 7 recall: 0.916 accuracy: 0.00659905 cost: 0.00395509 M: 21.139 delta: 0.196442 time: 349.161 one-recall: 0.97 one-ratio: 1.00249
iteration: 8 recall: 0.9548 accuracy: 0.00252184 cost: 0.0049795 M: 27.3044 delta: 0.0884616 time: 407.221 one-recall: 0.99 one-ratio: 1.00013
iteration: 9 recall: 0.976 accuracy: 0.0011075 cost: 0.00577256 M: 31.288 delta: 0.0513322 time: 451.45 one-recall: 0.99 one-ratio: 1.00013
iteration: 10 recall: 0.9824 accuracy: 0.00073907 cost: 0.00625653 M: 33.3879 delta: 0.0372171 time: 481.078 one-recall: 0.99 one-ratio: 1.00013
iteration: 11 recall: 0.9836 accuracy: 0.000644393 cost: 0.00651375 M: 34.4208 delta: 0.0313189 time: 500.25 one-recall: 0.99 one-ratio: 1.00013
iteration: 12 recall: 0.9864 accuracy: 0.000532051 cost: 0.00664168 M: 34.9129 delta: 0.0287403 time: 513.053 one-recall: 0.99 one-ratio: 1.00013
iteration: 13 recall: 0.9868 accuracy: 0.000522832 cost: 0.00670386 M: 35.1491 delta: 0.0275839 time: 522.261 one-recall: 0.99 one-ratio: 1.00013
iteration: 14 recall: 0.9876 accuracy: 0.000485721 cost: 0.00673455 M: 35.2639 delta: 0.0270287 time: 529.574 one-recall: 0.99 one-ratio: 1.00013
iteration: 15 recall: 0.9876 accuracy: 0.000485721 cost: 0.00674959 M: 35.3195 delta: 0.0267685 time: 535.877 one-recall: 0.99 one-ratio: 1.00013
iteration: 16 recall: 0.9876 accuracy: 0.000485721 cost: 0.0067573 M: 35.3483 delta: 0.0266403 time: 541.681 one-recall: 0.99 one-ratio: 1.00013
iteration: 17 recall: 0.9876 accuracy: 0.000485721 cost: 0.00676125 M: 35.3626 delta: 0.026576 time: 547.218 one-recall: 0.99 one-ratio: 1.00013
iteration: 18 recall: 0.9876 accuracy: 0.000485721 cost: 0.00676325 M: 35.3698 delta: 0.026542 time: 552.611 one-recall: 0.99 one-ratio: 1.00013
iteration: 19 recall: 0.9876 accuracy: 0.000485721 cost: 0.00676424 M: 35.3733 delta: 0.0265267 time: 557.923 one-recall: 0.99 one-ratio: 1.00013
iteration: 20 recall: 0.9876 accuracy: 0.000485721 cost: 0.00676475 M: 35.3753 delta: 0.0265185 time: 563.194 one-recall: 0.99 one-ratio: 1.00013
iteration: 21 recall: 0.9876 accuracy: 0.000485721 cost: 0.00676505 M: 35.3765 delta: 0.0265154 time: 568.443 one-recall: 0.99 one-ratio: 1.00013
iteration: 22 recall: 0.9876 accuracy: 0.000485721 cost: 0.00676524 M: 35.3773 delta: 0.0265119 time: 573.678 one-recall: 0.99 one-ratio: 1.00013
iteration: 23 recall: 0.9876 accuracy: 0.000485721 cost: 0.00676534 M: 35.3776 delta: 0.0265104 time: 578.902 one-recall: 0.99 one-ratio: 1.00013
iteration: 24 recall: 0.9876 accuracy: 0.000485721 cost: 0.00676541 M: 35.3779 delta: 0.0265096 time: 584.122 one-recall: 0.99 one-ratio: 1.00013
iteration: 25 recall: 0.9876 accuracy: 0.000485721 cost: 0.00676544 M: 35.3781 delta: 0.0265088 time: 589.337 one-recall: 0.99 one-ratio: 1.00013
iteration: 26 recall: 0.9876 accuracy: 0.000485721 cost: 0.00676547 M: 35.3782 delta: 0.0265084 time: 594.55 one-recall: 0.99 one-ratio: 1.00013
iteration: 27 recall: 0.9876 accuracy: 0.000485721 cost: 0.00676548 M: 35.3782 delta: 0.0265082 time: 599.763 one-recall: 0.99 one-ratio: 1.00013
iteration: 28 recall: 0.9876 accuracy: 0.000485721 cost: 0.00676549 M: 35.3782 delta: 0.0265081 time: 604.972 one-recall: 0.99 one-ratio: 1.00013
iteration: 29 recall: 0.9876 accuracy: 0.000485721 cost: 0.00676549 M: 35.3782 delta: 0.0265081 time: 610.184 one-recall: 0.99 one-ratio: 1.00013
iteration: 30 recall: 0.9876 accuracy: 0.000485721 cost: 0.00676549 M: 35.3782 delta: 0.0265081 time: 615.394 one-recall: 0.99 one-ratio: 1.00013
Graph completion with reverse edges...

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

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 629.8199999999997
Index size:  262952.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0114680000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.0700000000, query time of that 0.0165229440, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Reject!
379.03 < 414.513
  -> Decision False in time 0.4100000000, query time of that 0.1029755770, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
336.448 < 396.904
  -> Decision False in time 0.5800000000, query time of that 0.1400277020, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.5200000000, query time of that 0.0181806060, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
237.382 < 266.404
  -> Decision False in time 0.0100000000, query time of that 0.0005713950, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
261.75 < 266.646
  -> Decision False in time 2.8800000000, query time of that 0.1034836050, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Reject!
214.332 < 249.796
  -> Decision False in time 1.2200000000, query time of that 0.0045612720, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
224.678 < 246.854
  -> Decision False in time 3.4900000000, query time of that 0.0141391900, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
429.4 < 431.438
  -> Decision False in time 3.3800000000, query time of that 0.0130685250, 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: 0.0016 accuracy: 2.08437 cost: 0.00038 M: 10 delta: 1 time: 54.2264 one-recall: 0 one-ratio: 3.20702
iteration: 2 recall: 0.0056 accuracy: 1.2131 cost: 0.000637428 M: 10 delta: 0.856032 time: 92.4338 one-recall: 0 one-ratio: 2.5515
iteration: 3 recall: 0.0356 accuracy: 0.69731 cost: 0.00109521 M: 11.5287 delta: 0.835109 time: 138.877 one-recall: 0.05 one-ratio: 2.0801
iteration: 4 recall: 0.1668 accuracy: 0.358035 cost: 0.00163042 M: 11.8362 delta: 0.783455 time: 185.01 one-recall: 0.2 one-ratio: 1.65727
iteration: 5 recall: 0.4808 accuracy: 0.12709 cost: 0.00223611 M: 12.6038 delta: 0.664648 time: 232.941 one-recall: 0.59 one-ratio: 1.33821
iteration: 6 recall: 0.7512 accuracy: 0.0325175 cost: 0.00297987 M: 15.1137 delta: 0.432349 time: 287.14 one-recall: 0.83 one-ratio: 1.09384
iteration: 7 recall: 0.88 accuracy: 0.0102146 cost: 0.00395515 M: 21.1402 delta: 0.196403 time: 349.104 one-recall: 0.95 one-ratio: 1.02927
iteration: 8 recall: 0.934 accuracy: 0.00440885 cost: 0.00497926 M: 27.3042 delta: 0.0884518 time: 407.146 one-recall: 0.98 one-ratio: 1.00957
iteration: 9 recall: 0.9572 accuracy: 0.00227688 cost: 0.0057726 M: 31.2896 delta: 0.0513209 time: 451.378 one-recall: 0.99 one-ratio: 1.0018
iteration: 10 recall: 0.9668 accuracy: 0.00162566 cost: 0.00625741 M: 33.3949 delta: 0.0372032 time: 481.034 one-recall: 0.99 one-ratio: 1.0018
iteration: 11 recall: 0.9712 accuracy: 0.00141875 cost: 0.00651513 M: 34.4274 delta: 0.0312921 time: 500.236 one-recall: 0.99 one-ratio: 1.0018
iteration: 12 recall: 0.972 accuracy: 0.00132082 cost: 0.00664312 M: 34.9193 delta: 0.0287203 time: 513.044 one-recall: 0.99 one-ratio: 1.0018
iteration: 13 recall: 0.9736 accuracy: 0.00126779 cost: 0.00670547 M: 35.1548 delta: 0.0275542 time: 522.257 one-recall: 0.99 one-ratio: 1.0018
iteration: 14 recall: 0.9748 accuracy: 0.00120877 cost: 0.00673594 M: 35.2683 delta: 0.0270057 time: 529.554 one-recall: 0.99 one-ratio: 1.0018
iteration: 15 recall: 0.9752 accuracy: 0.00120357 cost: 0.00675073 M: 35.3234 delta: 0.0267484 time: 535.839 one-recall: 0.99 one-ratio: 1.0018
iteration: 16 recall: 0.9752 accuracy: 0.00120357 cost: 0.00675821 M: 35.3514 delta: 0.0266213 time: 541.628 one-recall: 0.99 one-ratio: 1.0018
iteration: 17 recall: 0.9752 accuracy: 0.00120357 cost: 0.006762 M: 35.3654 delta: 0.0265544 time: 547.155 one-recall: 0.99 one-ratio: 1.0018
iteration: 18 recall: 0.9752 accuracy: 0.00120357 cost: 0.0067639 M: 35.3724 delta: 0.0265257 time: 552.541 one-recall: 0.99 one-ratio: 1.0018
iteration: 19 recall: 0.9752 accuracy: 0.00120357 cost: 0.00676489 M: 35.376 delta: 0.0265102 time: 557.855 one-recall: 0.99 one-ratio: 1.0018
iteration: 20 recall: 0.9752 accuracy: 0.00120357 cost: 0.00676546 M: 35.3782 delta: 0.0265012 time: 563.13 one-recall: 0.99 one-ratio: 1.0018
iteration: 21 recall: 0.9752 accuracy: 0.00120357 cost: 0.00676578 M: 35.3795 delta: 0.0264975 time: 568.38 one-recall: 0.99 one-ratio: 1.0018
iteration: 22 recall: 0.9752 accuracy: 0.00120357 cost: 0.00676596 M: 35.3803 delta: 0.0264941 time: 573.621 one-recall: 0.99 one-ratio: 1.0018
iteration: 23 recall: 0.9752 accuracy: 0.00120357 cost: 0.00676609 M: 35.3807 delta: 0.0264921 time: 578.849 one-recall: 0.99 one-ratio: 1.0018
iteration: 24 recall: 0.9752 accuracy: 0.00120357 cost: 0.00676615 M: 35.381 delta: 0.0264911 time: 584.069 one-recall: 0.99 one-ratio: 1.0018
iteration: 25 recall: 0.9752 accuracy: 0.00120357 cost: 0.00676619 M: 35.3811 delta: 0.0264906 time: 589.287 one-recall: 0.99 one-ratio: 1.0018
iteration: 26 recall: 0.9752 accuracy: 0.00120357 cost: 0.0067662 M: 35.3811 delta: 0.0264903 time: 594.499 one-recall: 0.99 one-ratio: 1.0018
iteration: 27 recall: 0.9752 accuracy: 0.00120357 cost: 0.0067662 M: 35.3811 delta: 0.0264903 time: 599.71 one-recall: 0.99 one-ratio: 1.0018
iteration: 28 recall: 0.9752 accuracy: 0.00120357 cost: 0.0067662 M: 35.3811 delta: 0.0264903 time: 604.918 one-recall: 0.99 one-ratio: 1.0018
iteration: 29 recall: 0.9752 accuracy: 0.00120357 cost: 0.0067662 M: 35.3811 delta: 0.0264903 time: 610.124 one-recall: 0.99 one-ratio: 1.0018
iteration: 30 recall: 0.9752 accuracy: 0.00120357 cost: 0.0067662 M: 35.3811 delta: 0.0264903 time: 615.332 one-recall: 0.99 one-ratio: 1.0018
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 629.7700000000004
Index size:  262864.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.1053729000
  Testing...
|S| = 80
|T| = 1152
Reject!
359.883 < 379.787
  -> Decision False in time 0.0000000000, query time of that 0.0015570080, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Reject!
313.471 < 465.701
  -> Decision False in time 0.0100000000, query time of that 0.0005732780, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
439.687 < 459.456
  -> Decision False in time 0.0000000000, query time of that 0.0005005700, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Reject!
419.513 < 471.949
  -> Decision False in time 0.0300000000, query time of that 0.0010255600, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
391.261 < 425.452
  -> Decision False in time 0.0000000000, query time of that 0.0002394060, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
389.261 < 422.644
  -> Decision False in time 0.0200000000, query time of that 0.0006643670, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Reject!
373.141 < 449.991
  -> Decision False in time 0.0800000000, query time of that 0.0004950860, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
433.332 < 462.923
  -> Decision False in time 3.4800000000, query time of that 0.0138304140, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
400.981 < 450.394
  -> Decision False in time 0.1600000000, query time of that 0.0007919500, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 30, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 30, {'reverse': -1}, False])
Got a train set of size (1000000 * 128)
Generating control...
Initializing...
iteration: 1 recall: 0.0008 accuracy: 2.17979 cost: 0.00038 M: 10 delta: 1 time: 54.2346 one-recall: 0.01 one-ratio: 3.67245
iteration: 2 recall: 0.0036 accuracy: 1.22528 cost: 0.000637428 M: 10 delta: 0.856032 time: 92.4346 one-recall: 0.01 one-ratio: 2.93579
iteration: 3 recall: 0.0304 accuracy: 0.702882 cost: 0.00109521 M: 11.5287 delta: 0.835106 time: 138.873 one-recall: 0.05 one-ratio: 2.34428
iteration: 4 recall: 0.1832 accuracy: 0.334416 cost: 0.00163042 M: 11.8362 delta: 0.783465 time: 184.995 one-recall: 0.22 one-ratio: 1.84264
iteration: 5 recall: 0.5228 accuracy: 0.094868 cost: 0.00223607 M: 12.6037 delta: 0.664614 time: 232.919 one-recall: 0.63 one-ratio: 1.23506
iteration: 6 recall: 0.7876 accuracy: 0.0272047 cost: 0.00297985 M: 15.1141 delta: 0.432336 time: 287.118 one-recall: 0.89 one-ratio: 1.06657
iteration: 7 recall: 0.914 accuracy: 0.00642042 cost: 0.00395492 M: 21.138 delta: 0.196435 time: 349.056 one-recall: 0.96 one-ratio: 1.01929
iteration: 8 recall: 0.9568 accuracy: 0.00288495 cost: 0.00497911 M: 27.3016 delta: 0.0884726 time: 407.094 one-recall: 0.97 one-ratio: 1.00634
iteration: 9 recall: 0.9736 accuracy: 0.0016362 cost: 0.00577143 M: 31.2839 delta: 0.051373 time: 451.287 one-recall: 0.97 one-ratio: 1.00634
iteration: 10 recall: 0.98 accuracy: 0.00113846 cost: 0.00625612 M: 33.3879 delta: 0.0372505 time: 480.944 one-recall: 0.98 one-ratio: 1.00564
iteration: 11 recall: 0.9848 accuracy: 0.00089551 cost: 0.00651352 M: 34.4186 delta: 0.0313485 time: 500.114 one-recall: 0.98 one-ratio: 1.00564
iteration: 12 recall: 0.986 accuracy: 0.000826711 cost: 0.00664098 M: 34.9093 delta: 0.0287904 time: 512.897 one-recall: 0.98 one-ratio: 1.00564
iteration: 13 recall: 0.9868 accuracy: 0.000785816 cost: 0.00670318 M: 35.1445 delta: 0.0276352 time: 522.1 one-recall: 0.98 one-ratio: 1.00564
iteration: 14 recall: 0.9872 accuracy: 0.000749773 cost: 0.00673346 M: 35.259 delta: 0.0270773 time: 529.383 one-recall: 0.98 one-ratio: 1.00564
iteration: 15 recall: 0.9876 accuracy: 0.000734994 cost: 0.00674837 M: 35.314 delta: 0.026814 time: 535.677 one-recall: 0.98 one-ratio: 1.00564
iteration: 16 recall: 0.9876 accuracy: 0.000734994 cost: 0.00675576 M: 35.3414 delta: 0.0266949 time: 541.459 one-recall: 0.98 one-ratio: 1.00564
iteration: 17 recall: 0.988 accuracy: 0.000696574 cost: 0.00675952 M: 35.3555 delta: 0.0266275 time: 546.982 one-recall: 0.98 one-ratio: 1.00564
iteration: 18 recall: 0.988 accuracy: 0.000696574 cost: 0.00676136 M: 35.3623 delta: 0.0265969 time: 552.357 one-recall: 0.98 one-ratio: 1.00564
iteration: 19 recall: 0.988 accuracy: 0.000696574 cost: 0.00676232 M: 35.3658 delta: 0.0265812 time: 557.664 one-recall: 0.98 one-ratio: 1.00564
iteration: 20 recall: 0.988 accuracy: 0.000696574 cost: 0.00676276 M: 35.3675 delta: 0.0265741 time: 562.925 one-recall: 0.98 one-ratio: 1.00564
iteration: 21 recall: 0.988 accuracy: 0.000696574 cost: 0.00676302 M: 35.3686 delta: 0.0265696 time: 568.166 one-recall: 0.98 one-ratio: 1.00564
iteration: 22 recall: 0.988 accuracy: 0.000696574 cost: 0.00676314 M: 35.369 delta: 0.0265677 time: 573.391 one-recall: 0.98 one-ratio: 1.00564
iteration: 23 recall: 0.988 accuracy: 0.000696574 cost: 0.00676322 M: 35.3692 delta: 0.0265668 time: 578.608 one-recall: 0.98 one-ratio: 1.00564
iteration: 24 recall: 0.988 accuracy: 0.000696574 cost: 0.00676324 M: 35.3694 delta: 0.0265661 time: 583.818 one-recall: 0.98 one-ratio: 1.00564
iteration: 25 recall: 0.988 accuracy: 0.000696574 cost: 0.00676326 M: 35.3694 delta: 0.026566 time: 589.025 one-recall: 0.98 one-ratio: 1.00564
iteration: 26 recall: 0.988 accuracy: 0.000696574 cost: 0.00676327 M: 35.3694 delta: 0.0265658 time: 594.234 one-recall: 0.98 one-ratio: 1.00564
iteration: 27 recall: 0.988 accuracy: 0.000696574 cost: 0.00676328 M: 35.3695 delta: 0.0265657 time: 599.441 one-recall: 0.98 one-ratio: 1.00564
iteration: 28 recall: 0.988 accuracy: 0.000696574 cost: 0.00676329 M: 35.3695 delta: 0.0265657 time: 604.652 one-recall: 0.98 one-ratio: 1.00564
iteration: 29 recall: 0.988 accuracy: 0.000696574 cost: 0.00676329 M: 35.3695 delta: 0.0265656 time: 609.86 one-recall: 0.98 one-ratio: 1.00564
iteration: 30 recall: 0.988 accuracy: 0.000696574 cost: 0.00676329 M: 35.3695 delta: 0.0265656 time: 615.068 one-recall: 0.98 one-ratio: 1.00564
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 629.4899999999998
Index size:  263004.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0071788000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.0800000000, query time of that 0.0310985960, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Accept!
  -> Decision True in time 0.7500000000, query time of that 0.3039522300, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
242.72 < 243.452
  -> Decision False in time 3.5700000000, query time of that 1.4173789580, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.5500000000, query time of that 0.0373277950, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
279.136 < 280.749
  -> Decision False in time 0.8800000000, query time of that 0.0590931040, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
291.441 < 297.669
  -> Decision False in time 18.4200000000, query time of that 1.2522672460, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Reject!
284.649 < 287.508
  -> Decision False in time 6.4100000000, query time of that 0.0441233750, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
285.251 < 288.34
  -> Decision False in time 3.0900000000, query time of that 0.0225306890, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
260.452 < 263.355
  -> Decision False in time 14.2100000000, query time of that 0.0986201950, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 20, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 20, {'reverse': -1}, False])
Got a train set of size (1000000 * 128)
Generating control...
Initializing...
iteration: 1 recall: 0 accuracy: 1.84123 cost: 0.00038 M: 10 delta: 1 time: 54.2115 one-recall: 0 one-ratio: 3.67141
iteration: 2 recall: 0.0048 accuracy: 1.04382 cost: 0.000637428 M: 10 delta: 0.856032 time: 92.4346 one-recall: 0.01 one-ratio: 2.8923
iteration: 3 recall: 0.0344 accuracy: 0.616866 cost: 0.00109521 M: 11.5287 delta: 0.835106 time: 138.885 one-recall: 0.05 one-ratio: 2.32929
iteration: 4 recall: 0.1844 accuracy: 0.317893 cost: 0.00163042 M: 11.8362 delta: 0.783464 time: 185.012 one-recall: 0.26 one-ratio: 1.78795
iteration: 5 recall: 0.4924 accuracy: 0.111296 cost: 0.00223606 M: 12.604 delta: 0.664613 time: 232.947 one-recall: 0.69 one-ratio: 1.2553
iteration: 6 recall: 0.7604 accuracy: 0.0287863 cost: 0.00297993 M: 15.1146 delta: 0.432361 time: 287.157 one-recall: 0.89 one-ratio: 1.06745
iteration: 7 recall: 0.8972 accuracy: 0.0080072 cost: 0.00395525 M: 21.1394 delta: 0.196443 time: 349.122 one-recall: 0.99 one-ratio: 1.00314
iteration: 8 recall: 0.952399 accuracy: 0.00276868 cost: 0.00497977 M: 27.3036 delta: 0.0884557 time: 407.195 one-recall: 0.99 one-ratio: 1.00314
iteration: 9 recall: 0.9724 accuracy: 0.00162498 cost: 0.00577236 M: 31.2876 delta: 0.0513602 time: 451.398 one-recall: 0.99 one-ratio: 1.00314
iteration: 10 recall: 0.98 accuracy: 0.00106921 cost: 0.00625667 M: 33.3882 delta: 0.0372063 time: 481.042 one-recall: 0.99 one-ratio: 1.00314
iteration: 11 recall: 0.9836 accuracy: 0.000753891 cost: 0.00651363 M: 34.4205 delta: 0.031321 time: 500.206 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.9852 accuracy: 0.000719434 cost: 0.00664169 M: 34.9124 delta: 0.0287597 time: 513.019 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.9856 accuracy: 0.000709645 cost: 0.00670467 M: 35.1507 delta: 0.027588 time: 522.273 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.9864 accuracy: 0.000686719 cost: 0.00673518 M: 35.2656 delta: 0.0270307 time: 529.573 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.9864 accuracy: 0.000686719 cost: 0.00675018 M: 35.3218 delta: 0.0267735 time: 535.873 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.9864 accuracy: 0.000686719 cost: 0.006758 M: 35.3507 delta: 0.0266407 time: 541.685 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.9864 accuracy: 0.000686719 cost: 0.00676177 M: 35.3649 delta: 0.0265767 time: 547.208 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.9864 accuracy: 0.000686719 cost: 0.00676382 M: 35.3724 delta: 0.0265437 time: 552.603 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.9864 accuracy: 0.000686719 cost: 0.00676485 M: 35.3765 delta: 0.0265274 time: 557.922 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.9864 accuracy: 0.000686719 cost: 0.00676543 M: 35.3788 delta: 0.0265176 time: 563.198 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.9864 accuracy: 0.000686719 cost: 0.00676571 M: 35.3799 delta: 0.0265131 time: 568.446 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.9864 accuracy: 0.000686719 cost: 0.00676585 M: 35.3805 delta: 0.0265109 time: 573.676 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.9864 accuracy: 0.000686719 cost: 0.00676595 M: 35.3809 delta: 0.0265097 time: 578.902 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.9864 accuracy: 0.000686719 cost: 0.00676602 M: 35.3812 delta: 0.0265089 time: 584.123 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.9864 accuracy: 0.000686719 cost: 0.00676606 M: 35.3813 delta: 0.0265084 time: 589.336 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.9864 accuracy: 0.000686719 cost: 0.00676609 M: 35.3814 delta: 0.026508 time: 594.549 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.9864 accuracy: 0.000686719 cost: 0.00676611 M: 35.3815 delta: 0.0265081 time: 599.765 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.9864 accuracy: 0.000686719 cost: 0.00676612 M: 35.3816 delta: 0.0265079 time: 604.975 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.9864 accuracy: 0.000686719 cost: 0.00676613 M: 35.3816 delta: 0.0265078 time: 610.186 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.9864 accuracy: 0.000686719 cost: 0.00676614 M: 35.3816 delta: 0.0265077 time: 615.395 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 629.8299999999999
Index size:  262888.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0091184000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.0700000000, query time of that 0.0257650770, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Accept!
  -> Decision True in time 0.6900000000, query time of that 0.2438628190, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
413.085 < 413.624
  -> Decision False in time 2.6000000000, query time of that 0.9023974320, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Reject!
205.978 < 239.502
  -> Decision False in time 0.2200000000, query time of that 0.0116907360, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
289.893 < 294.156
  -> Decision False in time 2.0900000000, query time of that 0.1179176500, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
276.438 < 294.734
  -> Decision False in time 5.8500000000, query time of that 0.3314333900, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Reject!
248.195 < 253.219
  -> Decision False in time 2.3600000000, query time of that 0.0140790280, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
200.4 < 266.385
  -> Decision False in time 6.4300000000, query time of that 0.0371835480, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
237.632 < 248.536
  -> Decision False in time 14.2500000000, query time of that 0.0826313970, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 50, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 50, {'reverse': -1}, False])
Got a train set of size (1000000 * 128)
Generating control...
Initializing...
iteration: 1 recall: 0 accuracy: 2.13975 cost: 0.00038 M: 10 delta: 1 time: 54.2348 one-recall: 0 one-ratio: 3.75507
iteration: 2 recall: 0.004 accuracy: 1.20304 cost: 0.000637428 M: 10 delta: 0.856032 time: 92.4717 one-recall: 0 one-ratio: 2.95297
iteration: 3 recall: 0.038 accuracy: 0.680856 cost: 0.00109521 M: 11.5287 delta: 0.835113 time: 138.939 one-recall: 0.06 one-ratio: 2.36485
iteration: 4 recall: 0.1972 accuracy: 0.31346 cost: 0.00163045 M: 11.8362 delta: 0.783458 time: 185.067 one-recall: 0.3 one-ratio: 1.79731
iteration: 5 recall: 0.526 accuracy: 0.111799 cost: 0.00223609 M: 12.6038 delta: 0.664581 time: 232.998 one-recall: 0.61 one-ratio: 1.32716
iteration: 6 recall: 0.7884 accuracy: 0.0360911 cost: 0.00298003 M: 15.1152 delta: 0.43235 time: 287.22 one-recall: 0.84 one-ratio: 1.13203
iteration: 7 recall: 0.8944 accuracy: 0.0111992 cost: 0.00395518 M: 21.1388 delta: 0.196433 time: 349.182 one-recall: 0.93 one-ratio: 1.05897
iteration: 8 recall: 0.9436 accuracy: 0.00501912 cost: 0.00497959 M: 27.3036 delta: 0.0884599 time: 407.257 one-recall: 0.96 one-ratio: 1.03877
iteration: 9 recall: 0.9632 accuracy: 0.00277327 cost: 0.00577222 M: 31.2859 delta: 0.0513177 time: 451.465 one-recall: 0.98 one-ratio: 1.01968
iteration: 10 recall: 0.9736 accuracy: 0.00142996 cost: 0.006257 M: 33.3907 delta: 0.0371869 time: 481.124 one-recall: 0.99 one-ratio: 1.00375
iteration: 11 recall: 0.9796 accuracy: 0.00105876 cost: 0.00651366 M: 34.4182 delta: 0.031323 time: 500.283 one-recall: 0.99 one-ratio: 1.00375
iteration: 12 recall: 0.9808 accuracy: 0.00104304 cost: 0.00664014 M: 34.9067 delta: 0.0287461 time: 513.015 one-recall: 0.99 one-ratio: 1.00375
iteration: 13 recall: 0.9808 accuracy: 0.00104304 cost: 0.00670195 M: 35.1409 delta: 0.0275865 time: 522.197 one-recall: 0.99 one-ratio: 1.00375
iteration: 14 recall: 0.9808 accuracy: 0.00104304 cost: 0.00673187 M: 35.2527 delta: 0.0270562 time: 529.458 one-recall: 0.99 one-ratio: 1.00375
iteration: 15 recall: 0.9812 accuracy: 0.00101663 cost: 0.00674671 M: 35.3083 delta: 0.0268013 time: 535.747 one-recall: 0.99 one-ratio: 1.00375
iteration: 16 recall: 0.9812 accuracy: 0.00101663 cost: 0.00675438 M: 35.3372 delta: 0.0266746 time: 541.546 one-recall: 0.99 one-ratio: 1.00375
iteration: 17 recall: 0.9812 accuracy: 0.00101663 cost: 0.00675829 M: 35.352 delta: 0.026605 time: 547.08 one-recall: 0.99 one-ratio: 1.00375
iteration: 18 recall: 0.9812 accuracy: 0.00101663 cost: 0.00676037 M: 35.3598 delta: 0.0265705 time: 552.472 one-recall: 0.99 one-ratio: 1.00375
iteration: 19 recall: 0.9812 accuracy: 0.00101663 cost: 0.00676142 M: 35.3638 delta: 0.0265515 time: 557.786 one-recall: 0.99 one-ratio: 1.00375
iteration: 20 recall: 0.9812 accuracy: 0.00101663 cost: 0.00676197 M: 35.366 delta: 0.0265431 time: 563.056 one-recall: 0.99 one-ratio: 1.00375
iteration: 21 recall: 0.9812 accuracy: 0.00101663 cost: 0.00676228 M: 35.3672 delta: 0.0265385 time: 568.301 one-recall: 0.99 one-ratio: 1.00375
iteration: 22 recall: 0.9812 accuracy: 0.00101663 cost: 0.00676246 M: 35.3679 delta: 0.0265354 time: 573.534 one-recall: 0.99 one-ratio: 1.00375
iteration: 23 recall: 0.9812 accuracy: 0.00101663 cost: 0.00676257 M: 35.3683 delta: 0.0265347 time: 578.757 one-recall: 0.99 one-ratio: 1.00375
iteration: 24 recall: 0.9812 accuracy: 0.00101663 cost: 0.00676265 M: 35.3686 delta: 0.0265335 time: 583.974 one-recall: 0.99 one-ratio: 1.00375
iteration: 25 recall: 0.9812 accuracy: 0.00101663 cost: 0.00676268 M: 35.3687 delta: 0.0265328 time: 589.186 one-recall: 0.99 one-ratio: 1.00375
iteration: 26 recall: 0.9812 accuracy: 0.00101663 cost: 0.0067627 M: 35.3687 delta: 0.0265327 time: 594.395 one-recall: 0.99 one-ratio: 1.00375
iteration: 27 recall: 0.9812 accuracy: 0.00101663 cost: 0.00676271 M: 35.3688 delta: 0.0265324 time: 599.605 one-recall: 0.99 one-ratio: 1.00375
iteration: 28 recall: 0.9812 accuracy: 0.00101663 cost: 0.00676272 M: 35.3688 delta: 0.0265323 time: 604.813 one-recall: 0.99 one-ratio: 1.00375
iteration: 29 recall: 0.9812 accuracy: 0.00101663 cost: 0.00676272 M: 35.3688 delta: 0.0265321 time: 610.017 one-recall: 0.99 one-ratio: 1.00375
iteration: 30 recall: 0.9812 accuracy: 0.00101663 cost: 0.00676273 M: 35.3688 delta: 0.0265321 time: 615.224 one-recall: 0.99 one-ratio: 1.00375
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 629.6499999999978
Index size:  262688.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0049909000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.0800000000, query time of that 0.0423437100, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Reject!
335.538 < 355.505
  -> Decision False in time 0.4300000000, query time of that 0.2063516180, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Accept!
  -> Decision True in time 8.7400000000, query time of that 4.1297309810, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.5700000000, query time of that 0.0512700360, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
267.026 < 376.642
  -> Decision False in time 4.5100000000, query time of that 0.3996227320, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
358.246 < 400.326
  -> Decision False in time 8.9400000000, query time of that 0.7945350100, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Accept!
  -> Decision True in time 6.6400000000, query time of that 0.0591871030, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
237.788 < 239.123
  -> Decision False in time 19.5900000000, query time of that 0.1802199590, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
221.102 < 237.365
  -> Decision False in time 6.2000000000, query time of that 0.0546348830, 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: 2.45547 cost: 0.00038 M: 10 delta: 1 time: 54.1643 one-recall: 0 one-ratio: 3.32561
iteration: 2 recall: 0.0028 accuracy: 1.23324 cost: 0.000637428 M: 10 delta: 0.856032 time: 92.3751 one-recall: 0 one-ratio: 2.60251
iteration: 3 recall: 0.0276 accuracy: 0.709976 cost: 0.00109521 M: 11.5287 delta: 0.835106 time: 138.837 one-recall: 0.05 one-ratio: 2.14312
iteration: 4 recall: 0.1636 accuracy: 0.41242 cost: 0.00163043 M: 11.8363 delta: 0.783442 time: 184.966 one-recall: 0.16 one-ratio: 1.78349
iteration: 5 recall: 0.4936 accuracy: 0.15342 cost: 0.0022361 M: 12.6039 delta: 0.664597 time: 232.892 one-recall: 0.63 one-ratio: 1.23015
iteration: 6 recall: 0.788 accuracy: 0.0244945 cost: 0.00297992 M: 15.1142 delta: 0.432348 time: 287.106 one-recall: 0.91 one-ratio: 1.0491
iteration: 7 recall: 0.9084 accuracy: 0.00678162 cost: 0.00395496 M: 21.138 delta: 0.196419 time: 349.073 one-recall: 0.98 one-ratio: 1.00494
iteration: 8 recall: 0.952 accuracy: 0.00298767 cost: 0.00497895 M: 27.3032 delta: 0.0884179 time: 407.135 one-recall: 0.99 one-ratio: 1.00039
iteration: 9 recall: 0.9684 accuracy: 0.00171464 cost: 0.00577185 M: 31.2893 delta: 0.0513099 time: 451.354 one-recall: 1 one-ratio: 1
iteration: 10 recall: 0.9768 accuracy: 0.00123439 cost: 0.00625664 M: 33.3924 delta: 0.0372069 time: 481.014 one-recall: 1 one-ratio: 1
iteration: 11 recall: 0.9804 accuracy: 0.00104942 cost: 0.00651465 M: 34.4262 delta: 0.0313171 time: 500.223 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.9808 accuracy: 0.00101213 cost: 0.00664294 M: 34.9201 delta: 0.0287515 time: 513.048 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.9812 accuracy: 0.0010121 cost: 0.0067055 M: 35.1564 delta: 0.0275813 time: 522.271 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.9812 accuracy: 0.0010121 cost: 0.00673541 M: 35.2699 delta: 0.0270309 time: 529.536 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.9812 accuracy: 0.0010121 cost: 0.00675025 M: 35.3254 delta: 0.0267736 time: 535.827 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.9812 accuracy: 0.0010121 cost: 0.00675759 M: 35.3529 delta: 0.0266504 time: 541.606 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.9812 accuracy: 0.0010121 cost: 0.00676149 M: 35.3675 delta: 0.0265784 time: 547.139 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.9812 accuracy: 0.0010121 cost: 0.0067634 M: 35.3747 delta: 0.0265479 time: 552.523 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.9812 accuracy: 0.0010121 cost: 0.00676444 M: 35.3785 delta: 0.0265309 time: 557.836 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.9812 accuracy: 0.0010121 cost: 0.00676498 M: 35.3805 delta: 0.0265234 time: 563.106 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.9812 accuracy: 0.0010121 cost: 0.00676525 M: 35.3816 delta: 0.0265183 time: 568.35 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.9812 accuracy: 0.0010121 cost: 0.00676539 M: 35.3822 delta: 0.0265162 time: 573.579 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.9812 accuracy: 0.0010121 cost: 0.00676549 M: 35.3825 delta: 0.0265141 time: 578.8 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.9812 accuracy: 0.0010121 cost: 0.00676555 M: 35.3827 delta: 0.0265132 time: 584.015 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.9812 accuracy: 0.0010121 cost: 0.00676556 M: 35.3828 delta: 0.0265129 time: 589.228 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.9812 accuracy: 0.0010121 cost: 0.00676557 M: 35.3828 delta: 0.0265128 time: 594.439 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.9812 accuracy: 0.0010121 cost: 0.00676557 M: 35.3829 delta: 0.0265129 time: 599.65 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.9812 accuracy: 0.0010121 cost: 0.00676558 M: 35.3829 delta: 0.0265128 time: 604.859 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.9812 accuracy: 0.0010121 cost: 0.00676558 M: 35.3829 delta: 0.0265129 time: 610.067 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.9812 accuracy: 0.0010121 cost: 0.00676558 M: 35.3829 delta: 0.0265128 time: 615.277 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 629.7000000000007
Index size:  262892.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0031625000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.1100000000, query time of that 0.0714804330, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Accept!
  -> Decision True in time 1.0300000000, query time of that 0.5742923850, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
395.63 < 395.808
  -> Decision False in time 10.1300000000, query time of that 5.5853537770, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.6000000000, query time of that 0.0767127710, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Accept!
  -> Decision True in time 5.8400000000, query time of that 0.6763497720, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
272.391 < 281.54
  -> Decision False in time 2.4500000000, query time of that 0.2826821770, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Reject!
250.503 < 265.52
  -> Decision False in time 3.2000000000, query time of that 0.0439230310, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
239.662 < 241.052
  -> Decision False in time 17.0900000000, query time of that 0.2003153830, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
286.669 < 294.834
  -> Decision False in time 51.4300000000, query time of that 0.6092686150, 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 accuracy: 2.02492 cost: 0.00038 M: 10 delta: 1 time: 54.1591 one-recall: 0 one-ratio: 2.99279
iteration: 2 recall: 0.0012 accuracy: 1.09794 cost: 0.000637428 M: 10 delta: 0.856032 time: 92.3727 one-recall: 0 one-ratio: 2.36624
iteration: 3 recall: 0.0272 accuracy: 0.606589 cost: 0.00109521 M: 11.5287 delta: 0.835104 time: 138.821 one-recall: 0.05 one-ratio: 1.91592
iteration: 4 recall: 0.1764 accuracy: 0.306813 cost: 0.00163045 M: 11.8363 delta: 0.783465 time: 184.927 one-recall: 0.2 one-ratio: 1.57073
iteration: 5 recall: 0.506 accuracy: 0.125391 cost: 0.00223611 M: 12.6037 delta: 0.664599 time: 232.842 one-recall: 0.65 one-ratio: 1.23009
iteration: 6 recall: 0.7684 accuracy: 0.0431846 cost: 0.00298003 M: 15.1142 delta: 0.432336 time: 287.056 one-recall: 0.85 one-ratio: 1.07494
iteration: 7 recall: 0.8872 accuracy: 0.00889852 cost: 0.00395518 M: 21.1392 delta: 0.196422 time: 349.048 one-recall: 0.93 one-ratio: 1.01437
iteration: 8 recall: 0.944 accuracy: 0.00381913 cost: 0.00497998 M: 27.3065 delta: 0.0885031 time: 407.137 one-recall: 0.95 one-ratio: 1.00784
iteration: 9 recall: 0.966 accuracy: 0.00184469 cost: 0.00577272 M: 31.2907 delta: 0.0513356 time: 451.346 one-recall: 0.98 one-ratio: 1.00202
iteration: 10 recall: 0.9744 accuracy: 0.00137771 cost: 0.00625753 M: 33.3959 delta: 0.0371953 time: 481.011 one-recall: 0.99 one-ratio: 1.00146
iteration: 11 recall: 0.9784 accuracy: 0.00116982 cost: 0.00651436 M: 34.4229 delta: 0.0313153 time: 500.17 one-recall: 0.99 one-ratio: 1.00146
iteration: 12 recall: 0.98 accuracy: 0.00104169 cost: 0.00664213 M: 34.9147 delta: 0.0287534 time: 512.965 one-recall: 0.99 one-ratio: 1.00146
iteration: 13 recall: 0.9812 accuracy: 0.000991647 cost: 0.00670472 M: 35.1513 delta: 0.0275767 time: 522.192 one-recall: 0.99 one-ratio: 1.00146
iteration: 14 recall: 0.9812 accuracy: 0.000991647 cost: 0.00673476 M: 35.2638 delta: 0.0270344 time: 529.462 one-recall: 0.99 one-ratio: 1.00146
iteration: 15 recall: 0.9812 accuracy: 0.000991647 cost: 0.00674948 M: 35.3189 delta: 0.0267727 time: 535.744 one-recall: 0.99 one-ratio: 1.00146
iteration: 16 recall: 0.9812 accuracy: 0.000991647 cost: 0.00675699 M: 35.3465 delta: 0.0266479 time: 541.534 one-recall: 0.99 one-ratio: 1.00146
iteration: 17 recall: 0.9812 accuracy: 0.000991647 cost: 0.00676082 M: 35.3607 delta: 0.0265816 time: 547.061 one-recall: 0.99 one-ratio: 1.00146
iteration: 18 recall: 0.9812 accuracy: 0.000991647 cost: 0.0067627 M: 35.3677 delta: 0.0265512 time: 552.442 one-recall: 0.99 one-ratio: 1.00146
iteration: 19 recall: 0.9812 accuracy: 0.000991647 cost: 0.00676374 M: 35.3717 delta: 0.0265348 time: 557.755 one-recall: 0.99 one-ratio: 1.00146
iteration: 20 recall: 0.9812 accuracy: 0.000991647 cost: 0.00676425 M: 35.3736 delta: 0.0265284 time: 563.017 one-recall: 0.99 one-ratio: 1.00146
iteration: 21 recall: 0.9812 accuracy: 0.000991647 cost: 0.00676455 M: 35.3747 delta: 0.0265227 time: 568.264 one-recall: 0.99 one-ratio: 1.00146
iteration: 22 recall: 0.9812 accuracy: 0.000991647 cost: 0.0067647 M: 35.3753 delta: 0.0265211 time: 573.494 one-recall: 0.99 one-ratio: 1.00146
iteration: 23 recall: 0.9812 accuracy: 0.000991647 cost: 0.00676481 M: 35.3757 delta: 0.0265191 time: 578.716 one-recall: 0.99 one-ratio: 1.00146
iteration: 24 recall: 0.9812 accuracy: 0.000991647 cost: 0.00676485 M: 35.3759 delta: 0.0265181 time: 583.93 one-recall: 0.99 one-ratio: 1.00146
iteration: 25 recall: 0.9812 accuracy: 0.000991647 cost: 0.00676489 M: 35.3761 delta: 0.026518 time: 589.144 one-recall: 0.99 one-ratio: 1.00146
iteration: 26 recall: 0.9812 accuracy: 0.000991647 cost: 0.00676491 M: 35.3761 delta: 0.0265174 time: 594.354 one-recall: 0.99 one-ratio: 1.00146
iteration: 27 recall: 0.9812 accuracy: 0.000991647 cost: 0.00676492 M: 35.3762 delta: 0.0265174 time: 599.561 one-recall: 0.99 one-ratio: 1.00146
iteration: 28 recall: 0.9812 accuracy: 0.000991647 cost: 0.00676492 M: 35.3762 delta: 0.0265174 time: 604.768 one-recall: 0.99 one-ratio: 1.00146
iteration: 29 recall: 0.9812 accuracy: 0.000991647 cost: 0.00676493 M: 35.3762 delta: 0.0265173 time: 609.976 one-recall: 0.99 one-ratio: 1.00146
iteration: 30 recall: 0.9812 accuracy: 0.000991647 cost: 0.00676493 M: 35.3762 delta: 0.026517 time: 615.188 one-recall: 0.99 one-ratio: 1.00146
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 629.630000000001
Index size:  238264.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.1087529000
  Testing...
|S| = 80
|T| = 1152
Reject!
426.742 < 468.45
  -> Decision False in time 0.0000000000, query time of that 0.0011383800, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Reject!
337.399 < 480.38
  -> Decision False in time 0.0100000000, query time of that 0.0016822630, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
404.54 < 470.388
  -> Decision False in time 0.0100000000, query time of that 0.0023819690, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Reject!
403.839 < 440.692
  -> Decision False in time 0.1300000000, query time of that 0.0049107860, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
370.867 < 453.645
  -> Decision False in time 0.1300000000, query time of that 0.0054087180, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
457.8 < 490.983
  -> Decision False in time 0.0700000000, query time of that 0.0029515550, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Reject!
421.533 < 440.913
  -> Decision False in time 0.9100000000, query time of that 0.0039655800, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
358.707 < 418.881
  -> Decision False in time 0.1600000000, query time of that 0.0007823010, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
435.086 < 448.415
  -> Decision False in time 0.9200000000, query time of that 0.0039502860, with c1=5.0000000000, c2=0.1000000000
