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', 50, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 10, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 80, {'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', 1, {'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', 90, {'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', 20, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 5, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 2, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 30, {'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', 70, {'reverse': -1}, False])]
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: 2.29356 cost: 0.00038 M: 10 delta: 1 time: 54.5686 one-recall: 0 one-ratio: 3.52528
iteration: 2 recall: 0.004 accuracy: 1.20332 cost: 0.000637428 M: 10 delta: 0.856032 time: 92.7501 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: 139.135 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: 185.174 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: 232.941 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: 286.745 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: 347.917 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: 404.986 one-recall: 1 one-ratio: 1
iteration: 9 recall: 0.9644 accuracy: 0.00221739 cost: 0.00577293 M: 31.2904 delta: 0.0514019 time: 448.441 one-recall: 1 one-ratio: 1
iteration: 10 recall: 0.9764 accuracy: 0.0012952 cost: 0.00625843 M: 33.3974 delta: 0.0372226 time: 477.641 one-recall: 1 one-ratio: 1
iteration: 11 recall: 0.984 accuracy: 0.000915513 cost: 0.00651572 M: 34.4268 delta: 0.0313604 time: 496.589 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.9872 accuracy: 0.000651871 cost: 0.00664321 M: 34.9174 delta: 0.028787 time: 509.269 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.9876 accuracy: 0.000628894 cost: 0.00670536 M: 35.1523 delta: 0.027629 time: 518.435 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.9876 accuracy: 0.000628894 cost: 0.00673551 M: 35.2647 delta: 0.0270893 time: 525.698 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.9876 accuracy: 0.000628894 cost: 0.00675029 M: 35.3194 delta: 0.0268326 time: 531.983 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.9876 accuracy: 0.000628894 cost: 0.00675777 M: 35.3471 delta: 0.0267076 time: 537.781 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.9876 accuracy: 0.000628894 cost: 0.00676154 M: 35.3608 delta: 0.0266443 time: 543.312 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.9876 accuracy: 0.000628894 cost: 0.00676338 M: 35.3677 delta: 0.0266122 time: 548.702 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.9876 accuracy: 0.000628894 cost: 0.00676433 M: 35.3712 delta: 0.0265977 time: 554.02 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.9876 accuracy: 0.000628894 cost: 0.00676485 M: 35.373 delta: 0.02659 time: 559.293 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.988 accuracy: 0.0006087 cost: 0.00676515 M: 35.3742 delta: 0.0265857 time: 564.55 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.988 accuracy: 0.0006087 cost: 0.00676532 M: 35.3749 delta: 0.0265828 time: 569.787 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.988 accuracy: 0.0006087 cost: 0.00676541 M: 35.3753 delta: 0.0265811 time: 575.017 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.988 accuracy: 0.0006087 cost: 0.00676547 M: 35.3755 delta: 0.0265798 time: 580.24 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.988 accuracy: 0.0006087 cost: 0.00676549 M: 35.3756 delta: 0.0265796 time: 585.46 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.988 accuracy: 0.0006087 cost: 0.00676551 M: 35.3757 delta: 0.0265794 time: 590.677 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.988 accuracy: 0.0006087 cost: 0.00676552 M: 35.3757 delta: 0.0265791 time: 595.893 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.988 accuracy: 0.0006087 cost: 0.00676553 M: 35.3757 delta: 0.0265791 time: 601.112 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.988 accuracy: 0.0006087 cost: 0.00676553 M: 35.3757 delta: 0.026579 time: 606.327 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.988 accuracy: 0.0006087 cost: 0.00676553 M: 35.3757 delta: 0.026579 time: 611.544 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 626.13
Index size:  1903140.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0049871000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.0900000000, query time of that 0.0423482460, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Accept!
  -> Decision True in time 0.8600000000, query time of that 0.4122009740, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
399.964 < 431.442
  -> Decision False in time 5.3500000000, query time of that 2.5197211860, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.5600000000, query time of that 0.0474284440, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
246.26 < 294.43
  -> Decision False in time 2.9100000000, query time of that 0.2563957480, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
280.393 < 281.528
  -> Decision False in time 3.1300000000, query time of that 0.2703487750, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Accept!
  -> Decision True in time 6.6600000000, query time of that 0.0569540820, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
247.566 < 247.611
  -> Decision False in time 16.6900000000, query time of that 0.1426232800, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
184.128 < 184.448
  -> Decision False in time 6.3400000000, query time of that 0.0529969010, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 10, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 10, {'reverse': -1}, False])
Got a train set of size (1000000 * 128)
Generating control...
Initializing...
iteration: 1 recall: 0 accuracy: 2.6766 cost: 0.00038 M: 10 delta: 1 time: 54.1938 one-recall: 0 one-ratio: 3.66072
iteration: 2 recall: 0.0016 accuracy: 1.38091 cost: 0.000637428 M: 10 delta: 0.856032 time: 92.4097 one-recall: 0 one-ratio: 2.8624
iteration: 3 recall: 0.0388 accuracy: 0.711342 cost: 0.00109521 M: 11.5287 delta: 0.835103 time: 138.811 one-recall: 0.04 one-ratio: 2.27853
iteration: 4 recall: 0.2128 accuracy: 0.331692 cost: 0.00163043 M: 11.8362 delta: 0.783467 time: 184.85 one-recall: 0.31 one-ratio: 1.61045
iteration: 5 recall: 0.5256 accuracy: 0.113941 cost: 0.00223606 M: 12.6037 delta: 0.664583 time: 232.656 one-recall: 0.65 one-ratio: 1.31249
iteration: 6 recall: 0.7816 accuracy: 0.0320499 cost: 0.00298007 M: 15.115 delta: 0.432327 time: 286.676 one-recall: 0.89 one-ratio: 1.10605
iteration: 7 recall: 0.898 accuracy: 0.00893238 cost: 0.00395539 M: 21.1392 delta: 0.196451 time: 348.335 one-recall: 0.98 one-ratio: 1.00852
iteration: 8 recall: 0.9536 accuracy: 0.00250703 cost: 0.00497983 M: 27.3039 delta: 0.0884677 time: 406.07 one-recall: 1 one-ratio: 1
iteration: 9 recall: 0.9708 accuracy: 0.00137405 cost: 0.00577272 M: 31.2865 delta: 0.0513399 time: 450.047 one-recall: 1 one-ratio: 1
iteration: 10 recall: 0.9796 accuracy: 0.000974264 cost: 0.0062576 M: 33.3907 delta: 0.0372004 time: 479.579 one-recall: 1 one-ratio: 1
iteration: 11 recall: 0.982 accuracy: 0.000821148 cost: 0.00651448 M: 34.4194 delta: 0.0313362 time: 498.7 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.9856 accuracy: 0.00063386 cost: 0.0066431 M: 34.9143 delta: 0.0287677 time: 511.542 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.9868 accuracy: 0.000577962 cost: 0.006706 M: 35.1524 delta: 0.0276101 time: 520.801 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.9876 accuracy: 0.000568264 cost: 0.00673624 M: 35.2661 delta: 0.027052 time: 528.118 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.9876 accuracy: 0.000568264 cost: 0.0067511 M: 35.3217 delta: 0.0267951 time: 534.424 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.9876 accuracy: 0.000568264 cost: 0.00675856 M: 35.3495 delta: 0.0266678 time: 540.227 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.9876 accuracy: 0.000568264 cost: 0.00676249 M: 35.3641 delta: 0.0266031 time: 545.777 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.9876 accuracy: 0.000568264 cost: 0.00676447 M: 35.3715 delta: 0.0265723 time: 551.18 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.9876 accuracy: 0.000568264 cost: 0.00676549 M: 35.3754 delta: 0.0265551 time: 556.504 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.9876 accuracy: 0.000568264 cost: 0.00676606 M: 35.3775 delta: 0.0265465 time: 561.785 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.9876 accuracy: 0.000568264 cost: 0.00676636 M: 35.3786 delta: 0.0265425 time: 567.043 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.9876 accuracy: 0.000568264 cost: 0.00676654 M: 35.3792 delta: 0.0265399 time: 572.287 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.9876 accuracy: 0.000568264 cost: 0.00676663 M: 35.3796 delta: 0.0265387 time: 577.517 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.9876 accuracy: 0.000568264 cost: 0.00676668 M: 35.3798 delta: 0.0265377 time: 582.737 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.9876 accuracy: 0.000568264 cost: 0.0067667 M: 35.3799 delta: 0.0265375 time: 587.958 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.9876 accuracy: 0.000568264 cost: 0.00676672 M: 35.3799 delta: 0.0265372 time: 593.176 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.9876 accuracy: 0.000568264 cost: 0.00676674 M: 35.38 delta: 0.026537 time: 598.395 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.9876 accuracy: 0.000568264 cost: 0.00676675 M: 35.38 delta: 0.0265369 time: 603.613 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.9876 accuracy: 0.000568264 cost: 0.00676675 M: 35.38 delta: 0.0265368 time: 608.827 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.9876 accuracy: 0.000568264 cost: 0.00676675 M: 35.38 delta: 0.026537 time: 614.044 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 628.56
Index size:  261124.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0107383000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.0600000000, query time of that 0.0194747960, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Reject!
385.916 < 406.074
  -> Decision False in time 0.1800000000, query time of that 0.0533078450, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
388.693 < 398.182
  -> Decision False in time 0.2800000000, query time of that 0.0797097670, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.5200000000, query time of that 0.0209980710, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
267.311 < 271.389
  -> Decision False in time 3.0300000000, query time of that 0.1339981060, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
393.999 < 408.922
  -> Decision False in time 1.9000000000, query time of that 0.0829071970, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Reject!
214.653 < 217.067
  -> Decision False in time 0.5400000000, query time of that 0.0023936060, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
266.291 < 267.72
  -> Decision False in time 0.0700000000, query time of that 0.0004222430, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
295.451 < 295.466
  -> Decision False in time 1.9900000000, query time of that 0.0091009940, 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: 0.0004 accuracy: 2.03157 cost: 0.00038 M: 10 delta: 1 time: 54.2069 one-recall: 0 one-ratio: 3.73501
iteration: 2 recall: 0.0016 accuracy: 1.16247 cost: 0.000637428 M: 10 delta: 0.856032 time: 92.4105 one-recall: 0 one-ratio: 2.91785
iteration: 3 recall: 0.0312 accuracy: 0.695082 cost: 0.00109521 M: 11.5287 delta: 0.835107 time: 138.813 one-recall: 0.02 one-ratio: 2.31464
iteration: 4 recall: 0.1788 accuracy: 0.366826 cost: 0.00163043 M: 11.8362 delta: 0.783463 time: 184.854 one-recall: 0.23 one-ratio: 1.76576
iteration: 5 recall: 0.5216 accuracy: 0.143431 cost: 0.00223603 M: 12.6036 delta: 0.66458 time: 232.659 one-recall: 0.64 one-ratio: 1.2844
iteration: 6 recall: 0.7932 accuracy: 0.0303005 cost: 0.00297998 M: 15.115 delta: 0.432349 time: 286.677 one-recall: 0.86 one-ratio: 1.07535
iteration: 7 recall: 0.9056 accuracy: 0.00755459 cost: 0.00395521 M: 21.1404 delta: 0.196412 time: 348.335 one-recall: 0.94 one-ratio: 1.02107
iteration: 8 recall: 0.9516 accuracy: 0.00295481 cost: 0.0049799 M: 27.3064 delta: 0.088488 time: 406.08 one-recall: 0.97 one-ratio: 1.0135
iteration: 9 recall: 0.9676 accuracy: 0.00188151 cost: 0.00577298 M: 31.2905 delta: 0.0513513 time: 450.051 one-recall: 0.98 one-ratio: 1.00582
iteration: 10 recall: 0.974 accuracy: 0.00123469 cost: 0.00625759 M: 33.3948 delta: 0.0372401 time: 479.562 one-recall: 1 one-ratio: 1
iteration: 11 recall: 0.978 accuracy: 0.00104776 cost: 0.00651482 M: 34.4253 delta: 0.0313493 time: 498.697 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.9788 accuracy: 0.00100726 cost: 0.00664253 M: 34.9185 delta: 0.0287713 time: 511.492 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.9792 accuracy: 0.000936912 cost: 0.00670467 M: 35.1539 delta: 0.0276173 time: 520.712 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.9792 accuracy: 0.000936912 cost: 0.00673481 M: 35.2674 delta: 0.0270635 time: 528.007 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.9796 accuracy: 0.00088822 cost: 0.00674951 M: 35.3223 delta: 0.0268061 time: 534.303 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.9796 accuracy: 0.00088822 cost: 0.00675718 M: 35.3508 delta: 0.0266787 time: 540.12 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.9796 accuracy: 0.00088822 cost: 0.00676107 M: 35.3653 delta: 0.026612 time: 545.665 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.9796 accuracy: 0.00088822 cost: 0.0067631 M: 35.3729 delta: 0.0265774 time: 551.069 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.9796 accuracy: 0.00088822 cost: 0.00676415 M: 35.3768 delta: 0.026562 time: 556.394 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.9796 accuracy: 0.00088822 cost: 0.00676468 M: 35.3788 delta: 0.0265532 time: 561.673 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.9796 accuracy: 0.00088822 cost: 0.00676498 M: 35.38 delta: 0.0265484 time: 566.933 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.9796 accuracy: 0.00088822 cost: 0.00676515 M: 35.3806 delta: 0.0265463 time: 572.176 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.9796 accuracy: 0.00088822 cost: 0.00676524 M: 35.381 delta: 0.0265443 time: 577.414 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.9796 accuracy: 0.00088822 cost: 0.00676531 M: 35.3812 delta: 0.0265439 time: 582.64 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.9796 accuracy: 0.00088822 cost: 0.00676533 M: 35.3813 delta: 0.0265431 time: 587.865 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.9796 accuracy: 0.00088822 cost: 0.00676536 M: 35.3814 delta: 0.0265432 time: 593.089 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.9796 accuracy: 0.00088822 cost: 0.00676537 M: 35.3815 delta: 0.026543 time: 598.309 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.9796 accuracy: 0.00088822 cost: 0.00676538 M: 35.3815 delta: 0.0265427 time: 603.533 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.9796 accuracy: 0.00088822 cost: 0.00676538 M: 35.3815 delta: 0.0265427 time: 608.753 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.9796 accuracy: 0.00088822 cost: 0.00676538 M: 35.3815 delta: 0.0265426 time: 613.973 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 628.5900000000001
Index size:  262896.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0031640000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.1200000000, query time of that 0.0785364920, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Accept!
  -> Decision True in time 1.2500000000, query time of that 0.7971363740, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
319.165 < 336.517
  -> Decision False in time 6.5600000000, query time of that 4.1452518660, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.6800000000, query time of that 0.0969673080, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Accept!
  -> Decision True in time 6.5800000000, query time of that 0.9647185010, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
304.634 < 342.656
  -> Decision False in time 5.4700000000, query time of that 0.8286986480, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Accept!
  -> Decision True in time 8.2100000000, query time of that 0.1145113750, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
296.773 < 302.424
  -> Decision False in time 11.0700000000, query time of that 0.1479887500, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
240.291 < 248.363
  -> Decision False in time 24.0200000000, query time of that 0.3209234650, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 3, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 3, {'reverse': -1}, False])
Got a train set of size (1000000 * 128)
Generating control...
Initializing...
iteration: 1 recall: 0 accuracy: 2.04383 cost: 0.00038 M: 10 delta: 1 time: 63.9568 one-recall: 0 one-ratio: 3.38832
iteration: 2 recall: 0.002 accuracy: 1.16107 cost: 0.000637428 M: 10 delta: 0.856032 time: 108.201 one-recall: 0 one-ratio: 2.63215
iteration: 3 recall: 0.0312 accuracy: 0.655445 cost: 0.00109521 M: 11.5287 delta: 0.835103 time: 161.909 one-recall: 0.04 one-ratio: 2.08916
iteration: 4 recall: 0.1716 accuracy: 0.328344 cost: 0.00163044 M: 11.8362 delta: 0.783473 time: 215.435 one-recall: 0.2 one-ratio: 1.64007
iteration: 5 recall: 0.5024 accuracy: 0.113848 cost: 0.00223606 M: 12.6035 delta: 0.66458 time: 271.079 one-recall: 0.6 one-ratio: 1.25817
iteration: 6 recall: 0.7652 accuracy: 0.0332385 cost: 0.00297989 M: 15.1141 delta: 0.432341 time: 333.859 one-recall: 0.85 one-ratio: 1.09688
iteration: 7 recall: 0.8944 accuracy: 0.0120521 cost: 0.00395515 M: 21.1404 delta: 0.196423 time: 406.801 one-recall: 0.93 one-ratio: 1.03705
iteration: 8 recall: 0.9448 accuracy: 0.00380819 cost: 0.00497964 M: 27.3035 delta: 0.0885009 time: 477.235 one-recall: 0.99 one-ratio: 1.00043
iteration: 9 recall: 0.9664 accuracy: 0.00208169 cost: 0.00577234 M: 31.2891 delta: 0.0513885 time: 532.614 one-recall: 1 one-ratio: 1
iteration: 10 recall: 0.972 accuracy: 0.00174838 cost: 0.00625718 M: 33.3927 delta: 0.0372235 time: 570.947 one-recall: 1 one-ratio: 1
iteration: 11 recall: 0.9748 accuracy: 0.00152981 cost: 0.00651493 M: 34.4234 delta: 0.031346 time: 596.5 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.9772 accuracy: 0.00141242 cost: 0.00664239 M: 34.9155 delta: 0.0287909 time: 613.897 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.9788 accuracy: 0.00138062 cost: 0.00670462 M: 35.1506 delta: 0.0276247 time: 626.57 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.9788 accuracy: 0.00138062 cost: 0.00673495 M: 35.2643 delta: 0.0270882 time: 636.647 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.9788 accuracy: 0.00138062 cost: 0.00674998 M: 35.32 delta: 0.0268258 time: 645.361 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.9788 accuracy: 0.00138046 cost: 0.00675749 M: 35.3483 delta: 0.0266968 time: 653.367 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.9792 accuracy: 0.00137847 cost: 0.00676142 M: 35.3629 delta: 0.0266305 time: 661.013 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.9792 accuracy: 0.00137847 cost: 0.00676334 M: 35.3701 delta: 0.0265993 time: 668.45 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.9792 accuracy: 0.00137847 cost: 0.00676438 M: 35.374 delta: 0.0265837 time: 675.789 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.9792 accuracy: 0.00137847 cost: 0.00676496 M: 35.3762 delta: 0.0265739 time: 683.066 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.9792 accuracy: 0.00137847 cost: 0.00676524 M: 35.3773 delta: 0.026569 time: 690.302 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.9792 accuracy: 0.00137847 cost: 0.00676542 M: 35.3779 delta: 0.0265667 time: 697.521 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.9792 accuracy: 0.00137847 cost: 0.00676551 M: 35.3783 delta: 0.0265656 time: 704.723 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.9792 accuracy: 0.00137847 cost: 0.00676555 M: 35.3785 delta: 0.0265649 time: 711.919 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.9792 accuracy: 0.00137847 cost: 0.00676559 M: 35.3786 delta: 0.0265643 time: 719.111 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.9792 accuracy: 0.00137847 cost: 0.00676561 M: 35.3786 delta: 0.0265641 time: 726.301 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.9792 accuracy: 0.00137847 cost: 0.00676563 M: 35.3787 delta: 0.0265638 time: 733.492 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.9792 accuracy: 0.00137847 cost: 0.00676564 M: 35.3788 delta: 0.0265635 time: 740.677 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.9792 accuracy: 0.00137847 cost: 0.00676565 M: 35.3788 delta: 0.0265633 time: 747.859 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.9792 accuracy: 0.00137847 cost: 0.00676565 M: 35.3788 delta: 0.0265633 time: 755.045 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 773.8499999999995
Index size:  262800.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0194282000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.0700000000, query time of that 0.0221791120, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Reject!
404.62 < 416.577
  -> Decision False in time 0.1400000000, query time of that 0.0423627970, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
430.355 < 445.224
  -> Decision False in time 0.0000000000, query time of that 0.0009265160, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Reject!
404.66 < 424.919
  -> Decision False in time 0.1400000000, query time of that 0.0052498910, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
471.485 < 475.144
  -> Decision False in time 0.3300000000, query time of that 0.0143749370, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
415.359 < 449.843
  -> Decision False in time 0.5800000000, query time of that 0.0264212340, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Reject!
357.318 < 429.493
  -> Decision False in time 7.1700000000, query time of that 0.0287662120, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
409.826 < 420.544
  -> Decision False in time 14.1700000000, query time of that 0.0595427720, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
219.014 < 227.299
  -> Decision False in time 3.7900000000, query time of that 0.0155750790, 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: 0.0004 accuracy: 2.1619 cost: 0.00038 M: 10 delta: 1 time: 63.9856 one-recall: 0 one-ratio: 3.45348
iteration: 2 recall: 0.004 accuracy: 1.22149 cost: 0.000637428 M: 10 delta: 0.856032 time: 108.235 one-recall: 0 one-ratio: 2.78112
iteration: 3 recall: 0.0268 accuracy: 0.71027 cost: 0.00109521 M: 11.5287 delta: 0.835109 time: 161.965 one-recall: 0.08 one-ratio: 2.23853
iteration: 4 recall: 0.1796 accuracy: 0.378712 cost: 0.00163042 M: 11.8362 delta: 0.783463 time: 215.472 one-recall: 0.27 one-ratio: 1.7164
iteration: 5 recall: 0.5152 accuracy: 0.108971 cost: 0.00223602 M: 12.6036 delta: 0.664581 time: 271.106 one-recall: 0.68 one-ratio: 1.224
iteration: 6 recall: 0.7696 accuracy: 0.0273808 cost: 0.00298 M: 15.1149 delta: 0.432331 time: 333.885 one-recall: 0.9 one-ratio: 1.06228
iteration: 7 recall: 0.8888 accuracy: 0.00939868 cost: 0.00395519 M: 21.1397 delta: 0.19642 time: 406.825 one-recall: 0.98 one-ratio: 1.01002
iteration: 8 recall: 0.9388 accuracy: 0.00403161 cost: 0.00497953 M: 27.304 delta: 0.0884704 time: 477.246 one-recall: 0.98 one-ratio: 1.01002
iteration: 9 recall: 0.9588 accuracy: 0.00257855 cost: 0.00577276 M: 31.2906 delta: 0.0513307 time: 532.658 one-recall: 0.98 one-ratio: 1.01002
iteration: 10 recall: 0.968 accuracy: 0.00183742 cost: 0.0062581 M: 33.3947 delta: 0.0371653 time: 571.02 one-recall: 0.99 one-ratio: 1.00902
iteration: 11 recall: 0.9704 accuracy: 0.00172743 cost: 0.00651535 M: 34.4244 delta: 0.031282 time: 596.553 one-recall: 0.99 one-ratio: 1.00902
iteration: 12 recall: 0.9724 accuracy: 0.00159718 cost: 0.00664262 M: 34.9149 delta: 0.0287189 time: 613.924 one-recall: 0.99 one-ratio: 1.00902
iteration: 13 recall: 0.9748 accuracy: 0.0015222 cost: 0.00670485 M: 35.1497 delta: 0.0275566 time: 626.6 one-recall: 0.99 one-ratio: 1.00902
iteration: 14 recall: 0.9752 accuracy: 0.0015087 cost: 0.0067347 M: 35.2622 delta: 0.027024 time: 636.638 one-recall: 0.99 one-ratio: 1.00902
iteration: 15 recall: 0.9752 accuracy: 0.0015087 cost: 0.00674962 M: 35.3178 delta: 0.0267645 time: 645.335 one-recall: 0.99 one-ratio: 1.00902
iteration: 16 recall: 0.9752 accuracy: 0.0015087 cost: 0.00675717 M: 35.3459 delta: 0.0266415 time: 653.337 one-recall: 0.99 one-ratio: 1.00902
iteration: 17 recall: 0.9752 accuracy: 0.0015087 cost: 0.00676107 M: 35.3603 delta: 0.0265776 time: 660.977 one-recall: 0.99 one-ratio: 1.00902
iteration: 18 recall: 0.9752 accuracy: 0.0015087 cost: 0.00676305 M: 35.3677 delta: 0.0265475 time: 668.412 one-recall: 0.99 one-ratio: 1.00902
iteration: 19 recall: 0.9752 accuracy: 0.0015087 cost: 0.0067642 M: 35.3722 delta: 0.0265297 time: 675.756 one-recall: 0.99 one-ratio: 1.00902
iteration: 20 recall: 0.9752 accuracy: 0.0015087 cost: 0.00676481 M: 35.3745 delta: 0.0265213 time: 683.032 one-recall: 0.99 one-ratio: 1.00902
iteration: 21 recall: 0.9752 accuracy: 0.0015087 cost: 0.00676515 M: 35.3759 delta: 0.0265164 time: 690.28 one-recall: 0.99 one-ratio: 1.00902
iteration: 22 recall: 0.9752 accuracy: 0.0015087 cost: 0.00676532 M: 35.3766 delta: 0.0265128 time: 697.502 one-recall: 0.99 one-ratio: 1.00902
iteration: 23 recall: 0.9752 accuracy: 0.0015087 cost: 0.00676542 M: 35.377 delta: 0.0265115 time: 704.705 one-recall: 0.99 one-ratio: 1.00902
iteration: 24 recall: 0.9752 accuracy: 0.0015087 cost: 0.0067655 M: 35.3773 delta: 0.0265101 time: 711.903 one-recall: 0.99 one-ratio: 1.00902
iteration: 25 recall: 0.9752 accuracy: 0.0015087 cost: 0.00676553 M: 35.3774 delta: 0.0265096 time: 719.092 one-recall: 0.99 one-ratio: 1.00902
iteration: 26 recall: 0.9752 accuracy: 0.0015087 cost: 0.00676555 M: 35.3775 delta: 0.0265091 time: 726.275 one-recall: 0.99 one-ratio: 1.00902
iteration: 27 recall: 0.9752 accuracy: 0.0015087 cost: 0.00676555 M: 35.3775 delta: 0.0265088 time: 733.456 one-recall: 0.99 one-ratio: 1.00902
iteration: 28 recall: 0.9752 accuracy: 0.0015087 cost: 0.00676555 M: 35.3775 delta: 0.0265088 time: 740.636 one-recall: 0.99 one-ratio: 1.00902
iteration: 29 recall: 0.9752 accuracy: 0.0015087 cost: 0.00676555 M: 35.3775 delta: 0.0265088 time: 747.815 one-recall: 0.99 one-ratio: 1.00902
iteration: 30 recall: 0.9752 accuracy: 0.0015087 cost: 0.00676555 M: 35.3775 delta: 0.0265088 time: 754.996 one-recall: 0.99 one-ratio: 1.00902
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 773.79
Index size:  262784.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.1014406000
  Testing...
|S| = 80
|T| = 1152
Reject!
412.317 < 443
  -> Decision False in time 0.0000000000, query time of that 0.0002754660, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Reject!
356.93 < 463.605
  -> Decision False in time 0.0000000000, query time of that 0.0001767780, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
454.249 < 457.314
  -> Decision False in time 0.0100000000, query time of that 0.0017551820, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Reject!
433.648 < 439.532
  -> Decision False in time 0.0600000000, query time of that 0.0028587940, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
399.517 < 472.114
  -> Decision False in time 0.0200000000, query time of that 0.0015013880, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
342.25 < 403.9
  -> Decision False in time 0.0900000000, query time of that 0.0041166270, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Reject!
408.485 < 408.913
  -> Decision False in time 0.0100000000, query time of that 0.0002210610, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
421.942 < 440.356
  -> Decision False in time 0.0000000000, query time of that 0.0002356370, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
425.323 < 432.208
  -> Decision False in time 0.2000000000, query time of that 0.0009046830, 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: 0.0008 accuracy: 1.8609 cost: 0.00038 M: 10 delta: 1 time: 63.9901 one-recall: 0 one-ratio: 3.12001
iteration: 2 recall: 0.0044 accuracy: 1.05832 cost: 0.000637428 M: 10 delta: 0.856032 time: 108.25 one-recall: 0 one-ratio: 2.51313
iteration: 3 recall: 0.036 accuracy: 0.613865 cost: 0.00109521 M: 11.5287 delta: 0.835103 time: 161.982 one-recall: 0.02 one-ratio: 2.05456
iteration: 4 recall: 0.1744 accuracy: 0.313546 cost: 0.00163045 M: 11.8363 delta: 0.783463 time: 215.502 one-recall: 0.23 one-ratio: 1.63208
iteration: 5 recall: 0.4896 accuracy: 0.102563 cost: 0.00223611 M: 12.604 delta: 0.664586 time: 271.154 one-recall: 0.66 one-ratio: 1.19476
iteration: 6 recall: 0.774 accuracy: 0.0265632 cost: 0.00297997 M: 15.1147 delta: 0.432386 time: 333.941 one-recall: 0.8 one-ratio: 1.07763
iteration: 7 recall: 0.8916 accuracy: 0.0102538 cost: 0.00395511 M: 21.1383 delta: 0.196428 time: 406.885 one-recall: 0.88 one-ratio: 1.04734
iteration: 8 recall: 0.9452 accuracy: 0.00383875 cost: 0.00498003 M: 27.3046 delta: 0.088476 time: 477.334 one-recall: 0.97 one-ratio: 1.01474
iteration: 9 recall: 0.9672 accuracy: 0.00185459 cost: 0.00577257 M: 31.2873 delta: 0.0513336 time: 532.705 one-recall: 0.98 one-ratio: 1.00289
iteration: 10 recall: 0.974 accuracy: 0.00150476 cost: 0.00625738 M: 33.392 delta: 0.0371935 time: 571.049 one-recall: 0.98 one-ratio: 1.00289
iteration: 11 recall: 0.9764 accuracy: 0.00143858 cost: 0.00651492 M: 34.4233 delta: 0.0313021 time: 596.598 one-recall: 0.98 one-ratio: 1.00289
iteration: 12 recall: 0.9776 accuracy: 0.00125118 cost: 0.00664187 M: 34.9133 delta: 0.0287386 time: 613.948 one-recall: 0.99 one-ratio: 1.0015
iteration: 13 recall: 0.9784 accuracy: 0.0011797 cost: 0.0067042 M: 35.1498 delta: 0.0275658 time: 626.627 one-recall: 0.99 one-ratio: 1.0015
iteration: 14 recall: 0.9792 accuracy: 0.00113123 cost: 0.00673456 M: 35.263 delta: 0.0270243 time: 636.71 one-recall: 0.99 one-ratio: 1.0015
iteration: 15 recall: 0.9792 accuracy: 0.00113123 cost: 0.00674952 M: 35.3193 delta: 0.0267602 time: 645.417 one-recall: 0.99 one-ratio: 1.0015
iteration: 16 recall: 0.9792 accuracy: 0.00113123 cost: 0.00675706 M: 35.3473 delta: 0.0266314 time: 653.423 one-recall: 0.99 one-ratio: 1.0015
iteration: 17 recall: 0.9792 accuracy: 0.00113123 cost: 0.00676099 M: 35.3619 delta: 0.0265639 time: 661.068 one-recall: 0.99 one-ratio: 1.0015
iteration: 18 recall: 0.9792 accuracy: 0.00113123 cost: 0.00676291 M: 35.3688 delta: 0.0265305 time: 668.505 one-recall: 0.99 one-ratio: 1.0015
iteration: 19 recall: 0.9792 accuracy: 0.00113123 cost: 0.0067639 M: 35.3727 delta: 0.0265146 time: 675.836 one-recall: 0.99 one-ratio: 1.0015
iteration: 20 recall: 0.9792 accuracy: 0.00113123 cost: 0.00676441 M: 35.3748 delta: 0.0265075 time: 683.108 one-recall: 0.99 one-ratio: 1.0015
iteration: 21 recall: 0.9792 accuracy: 0.00113123 cost: 0.00676469 M: 35.3759 delta: 0.026504 time: 690.352 one-recall: 0.99 one-ratio: 1.0015
iteration: 22 recall: 0.9792 accuracy: 0.00113123 cost: 0.00676486 M: 35.3766 delta: 0.0265018 time: 697.569 one-recall: 0.99 one-ratio: 1.0015
iteration: 23 recall: 0.9792 accuracy: 0.00113123 cost: 0.006765 M: 35.3771 delta: 0.0264988 time: 704.783 one-recall: 0.99 one-ratio: 1.0015
iteration: 24 recall: 0.9792 accuracy: 0.00113123 cost: 0.00676506 M: 35.3774 delta: 0.0264979 time: 711.982 one-recall: 0.99 one-ratio: 1.0015
iteration: 25 recall: 0.9792 accuracy: 0.00113123 cost: 0.0067651 M: 35.3775 delta: 0.0264977 time: 719.18 one-recall: 0.99 one-ratio: 1.0015
iteration: 26 recall: 0.9792 accuracy: 0.00113123 cost: 0.00676513 M: 35.3776 delta: 0.026497 time: 726.371 one-recall: 0.99 one-ratio: 1.0015
iteration: 27 recall: 0.9792 accuracy: 0.00113123 cost: 0.00676514 M: 35.3777 delta: 0.0264967 time: 733.56 one-recall: 0.99 one-ratio: 1.0015
iteration: 28 recall: 0.9792 accuracy: 0.00113123 cost: 0.00676515 M: 35.3777 delta: 0.0264969 time: 740.744 one-recall: 0.99 one-ratio: 1.0015
iteration: 29 recall: 0.9792 accuracy: 0.00113123 cost: 0.00676515 M: 35.3778 delta: 0.0264966 time: 747.933 one-recall: 0.99 one-ratio: 1.0015
iteration: 30 recall: 0.9792 accuracy: 0.00113123 cost: 0.00676516 M: 35.3778 delta: 0.0264965 time: 755.119 one-recall: 0.99 one-ratio: 1.0015
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 773.9199999999992
Index size:  262776.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0041092000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.1100000000, query time of that 0.0644624850, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Accept!
  -> Decision True in time 1.1000000000, query time of that 0.6510817410, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
301.068 < 308.183
  -> Decision False in time 4.6800000000, query time of that 2.7347427590, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.6400000000, query time of that 0.0775807890, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Accept!
  -> Decision True in time 6.3700000000, query time of that 0.8171420920, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
222.216 < 225.635
  -> Decision False in time 3.1300000000, query time of that 0.3970056880, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Accept!
  -> Decision True in time 8.1800000000, query time of that 0.0935179040, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
280.508 < 283.514
  -> Decision False in time 23.4500000000, query time of that 0.2729482670, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
256.478 < 291.608
  -> Decision False in time 7.2900000000, query time of that 0.0807299230, 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: 2.50365 cost: 0.00038 M: 10 delta: 1 time: 63.9824 one-recall: 0 one-ratio: 3.30295
iteration: 2 recall: 0.006 accuracy: 1.36501 cost: 0.000637428 M: 10 delta: 0.856032 time: 108.236 one-recall: 0 one-ratio: 2.5809
iteration: 3 recall: 0.0384 accuracy: 0.781478 cost: 0.00109521 M: 11.5287 delta: 0.835109 time: 161.957 one-recall: 0.04 one-ratio: 2.09511
iteration: 4 recall: 0.22 accuracy: 0.330172 cost: 0.00163042 M: 11.8362 delta: 0.783474 time: 215.485 one-recall: 0.27 one-ratio: 1.63695
iteration: 5 recall: 0.5556 accuracy: 0.0959265 cost: 0.00223602 M: 12.6033 delta: 0.664588 time: 271.102 one-recall: 0.65 one-ratio: 1.24331
iteration: 6 recall: 0.8036 accuracy: 0.0224397 cost: 0.00297985 M: 15.1141 delta: 0.432322 time: 333.893 one-recall: 0.86 one-ratio: 1.0578
iteration: 7 recall: 0.9052 accuracy: 0.00712937 cost: 0.00395512 M: 21.1402 delta: 0.196396 time: 406.83 one-recall: 0.95 one-ratio: 1.00706
iteration: 8 recall: 0.9428 accuracy: 0.00375352 cost: 0.00498017 M: 27.3084 delta: 0.0884062 time: 477.285 one-recall: 0.96 one-ratio: 1.00503
iteration: 9 recall: 0.9672 accuracy: 0.00192485 cost: 0.00577345 M: 31.294 delta: 0.0513207 time: 532.7 one-recall: 0.98 one-ratio: 1.00119
iteration: 10 recall: 0.9764 accuracy: 0.00137343 cost: 0.00625944 M: 33.4012 delta: 0.0371738 time: 571.088 one-recall: 0.98 one-ratio: 1.00119
iteration: 11 recall: 0.9812 accuracy: 0.0010981 cost: 0.00651718 M: 34.4332 delta: 0.0312599 time: 596.661 one-recall: 0.98 one-ratio: 1.00119
iteration: 12 recall: 0.9824 accuracy: 0.000931678 cost: 0.00664476 M: 34.9245 delta: 0.0286826 time: 614.057 one-recall: 0.98 one-ratio: 1.00119
iteration: 13 recall: 0.9836 accuracy: 0.000814771 cost: 0.00670641 M: 35.1574 delta: 0.0275301 time: 626.689 one-recall: 0.98 one-ratio: 1.00119
iteration: 14 recall: 0.984 accuracy: 0.000788319 cost: 0.00673615 M: 35.2683 delta: 0.0269947 time: 636.709 one-recall: 0.98 one-ratio: 1.00119
iteration: 15 recall: 0.984 accuracy: 0.000787812 cost: 0.00675073 M: 35.3229 delta: 0.0267385 time: 645.381 one-recall: 0.98 one-ratio: 1.00119
iteration: 16 recall: 0.984 accuracy: 0.000787812 cost: 0.00675821 M: 35.3506 delta: 0.0266175 time: 653.38 one-recall: 0.98 one-ratio: 1.00119
iteration: 17 recall: 0.984 accuracy: 0.000787812 cost: 0.00676209 M: 35.3648 delta: 0.0265556 time: 661.021 one-recall: 0.98 one-ratio: 1.00119
iteration: 18 recall: 0.984 accuracy: 0.000787812 cost: 0.00676414 M: 35.3723 delta: 0.0265239 time: 668.468 one-recall: 0.98 one-ratio: 1.00119
iteration: 19 recall: 0.984 accuracy: 0.000787812 cost: 0.00676522 M: 35.3764 delta: 0.0265055 time: 675.803 one-recall: 0.98 one-ratio: 1.00119
iteration: 20 recall: 0.984 accuracy: 0.000787812 cost: 0.00676578 M: 35.3785 delta: 0.0264963 time: 683.075 one-recall: 0.98 one-ratio: 1.00119
iteration: 21 recall: 0.984 accuracy: 0.000787812 cost: 0.00676605 M: 35.3796 delta: 0.026492 time: 690.309 one-recall: 0.98 one-ratio: 1.00119
iteration: 22 recall: 0.984 accuracy: 0.000787812 cost: 0.0067662 M: 35.3802 delta: 0.0264888 time: 697.527 one-recall: 0.98 one-ratio: 1.00119
iteration: 23 recall: 0.984 accuracy: 0.000787812 cost: 0.00676627 M: 35.3805 delta: 0.0264871 time: 704.724 one-recall: 0.98 one-ratio: 1.00119
iteration: 24 recall: 0.984 accuracy: 0.000787812 cost: 0.00676632 M: 35.3807 delta: 0.0264865 time: 711.918 one-recall: 0.98 one-ratio: 1.00119
iteration: 25 recall: 0.984 accuracy: 0.000787812 cost: 0.00676634 M: 35.3808 delta: 0.0264861 time: 719.112 one-recall: 0.98 one-ratio: 1.00119
iteration: 26 recall: 0.984 accuracy: 0.000787812 cost: 0.00676636 M: 35.3809 delta: 0.0264857 time: 726.299 one-recall: 0.98 one-ratio: 1.00119
iteration: 27 recall: 0.984 accuracy: 0.000787812 cost: 0.00676636 M: 35.3809 delta: 0.0264855 time: 733.479 one-recall: 0.98 one-ratio: 1.00119
iteration: 28 recall: 0.984 accuracy: 0.000787812 cost: 0.00676636 M: 35.3809 delta: 0.0264856 time: 740.659 one-recall: 0.98 one-ratio: 1.00119
iteration: 29 recall: 0.984 accuracy: 0.000787812 cost: 0.00676636 M: 35.3809 delta: 0.0264855 time: 747.839 one-recall: 0.98 one-ratio: 1.00119
iteration: 30 recall: 0.984 accuracy: 0.000787812 cost: 0.00676636 M: 35.3809 delta: 0.0264855 time: 755.022 one-recall: 0.98 one-ratio: 1.00119
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 773.8199999999997
Index size:  262920.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0115364000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.0700000000, query time of that 0.0226642910, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Accept!
  -> Decision True in time 0.6500000000, query time of that 0.1996999260, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
435.792 < 441.889
  -> Decision False in time 0.1200000000, query time of that 0.0363839130, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Reject!
408.333 < 429.617
  -> Decision False in time 0.0200000000, query time of that 0.0010753740, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
304.528 < 358.948
  -> Decision False in time 0.3200000000, query time of that 0.0141128230, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
396.083 < 401.506
  -> Decision False in time 5.9800000000, query time of that 0.2775220100, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Accept!
  -> Decision True in time 8.0700000000, query time of that 0.0335948820, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
370.172 < 370.24
  -> Decision False in time 13.4100000000, query time of that 0.0554487700, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
227.236 < 230.517
  -> Decision False in time 3.1300000000, query time of that 0.0134365890, 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: 2.07558 cost: 0.00038 M: 10 delta: 1 time: 63.9836 one-recall: 0 one-ratio: 3.14867
iteration: 2 recall: 0.0044 accuracy: 1.1408 cost: 0.000637428 M: 10 delta: 0.856032 time: 108.24 one-recall: 0 one-ratio: 2.47924
iteration: 3 recall: 0.0392 accuracy: 0.596669 cost: 0.00109521 M: 11.5287 delta: 0.835108 time: 161.97 one-recall: 0.04 one-ratio: 1.89092
iteration: 4 recall: 0.1928 accuracy: 0.269496 cost: 0.00163045 M: 11.8363 delta: 0.78347 time: 215.482 one-recall: 0.22 one-ratio: 1.51006
iteration: 5 recall: 0.5732 accuracy: 0.0718463 cost: 0.00223604 M: 12.6034 delta: 0.664584 time: 271.115 one-recall: 0.67 one-ratio: 1.11848
iteration: 6 recall: 0.8088 accuracy: 0.0193101 cost: 0.00297987 M: 15.1142 delta: 0.432316 time: 333.918 one-recall: 0.88 one-ratio: 1.03834
iteration: 7 recall: 0.8988 accuracy: 0.00757953 cost: 0.00395498 M: 21.139 delta: 0.196417 time: 406.859 one-recall: 0.95 one-ratio: 1.01567
iteration: 8 recall: 0.9428 accuracy: 0.00368352 cost: 0.00497912 M: 27.3016 delta: 0.0884998 time: 477.251 one-recall: 0.97 one-ratio: 1.0147
iteration: 9 recall: 0.9652 accuracy: 0.00208763 cost: 0.00577155 M: 31.2837 delta: 0.0513563 time: 532.609 one-recall: 0.98 one-ratio: 1.00896
iteration: 10 recall: 0.9708 accuracy: 0.00172832 cost: 0.0062559 M: 33.3863 delta: 0.0372345 time: 570.913 one-recall: 0.98 one-ratio: 1.00896
iteration: 11 recall: 0.974 accuracy: 0.0015352 cost: 0.00651305 M: 34.4154 delta: 0.0313692 time: 596.443 one-recall: 0.98 one-ratio: 1.00896
iteration: 12 recall: 0.976 accuracy: 0.00138288 cost: 0.00664058 M: 34.9077 delta: 0.0287937 time: 613.836 one-recall: 0.98 one-ratio: 1.00896
iteration: 13 recall: 0.9764 accuracy: 0.00138064 cost: 0.00670308 M: 35.1447 delta: 0.0276232 time: 626.528 one-recall: 0.98 one-ratio: 1.00896
iteration: 14 recall: 0.9768 accuracy: 0.00135075 cost: 0.0067333 M: 35.2582 delta: 0.0270821 time: 636.588 one-recall: 0.98 one-ratio: 1.00896
iteration: 15 recall: 0.9772 accuracy: 0.00133555 cost: 0.00674818 M: 35.314 delta: 0.0268176 time: 645.292 one-recall: 0.98 one-ratio: 1.00896
iteration: 16 recall: 0.9772 accuracy: 0.00133555 cost: 0.00675574 M: 35.3418 delta: 0.0266933 time: 653.302 one-recall: 0.98 one-ratio: 1.00896
iteration: 17 recall: 0.9772 accuracy: 0.00133555 cost: 0.00675959 M: 35.356 delta: 0.0266269 time: 660.94 one-recall: 0.98 one-ratio: 1.00896
iteration: 18 recall: 0.9772 accuracy: 0.00133555 cost: 0.00676146 M: 35.3629 delta: 0.0265958 time: 668.378 one-recall: 0.98 one-ratio: 1.00896
iteration: 19 recall: 0.9772 accuracy: 0.00133555 cost: 0.00676255 M: 35.3669 delta: 0.0265801 time: 675.717 one-recall: 0.98 one-ratio: 1.00896
iteration: 20 recall: 0.9772 accuracy: 0.00133555 cost: 0.00676306 M: 35.3689 delta: 0.0265712 time: 682.99 one-recall: 0.98 one-ratio: 1.00896
iteration: 21 recall: 0.9772 accuracy: 0.00133555 cost: 0.0067634 M: 35.3701 delta: 0.0265661 time: 690.235 one-recall: 0.98 one-ratio: 1.00896
iteration: 22 recall: 0.9772 accuracy: 0.00133555 cost: 0.0067636 M: 35.3709 delta: 0.0265634 time: 697.462 one-recall: 0.98 one-ratio: 1.00896
iteration: 23 recall: 0.9772 accuracy: 0.00133555 cost: 0.0067637 M: 35.3713 delta: 0.0265615 time: 704.671 one-recall: 0.98 one-ratio: 1.00896
iteration: 24 recall: 0.9772 accuracy: 0.00133555 cost: 0.00676376 M: 35.3715 delta: 0.0265606 time: 711.865 one-recall: 0.98 one-ratio: 1.00896
iteration: 25 recall: 0.9772 accuracy: 0.00133555 cost: 0.00676378 M: 35.3716 delta: 0.0265601 time: 719.056 one-recall: 0.98 one-ratio: 1.00896
iteration: 26 recall: 0.9772 accuracy: 0.00133555 cost: 0.00676379 M: 35.3716 delta: 0.0265599 time: 726.234 one-recall: 0.98 one-ratio: 1.00896
iteration: 27 recall: 0.9772 accuracy: 0.00133555 cost: 0.00676379 M: 35.3716 delta: 0.02656 time: 733.418 one-recall: 0.98 one-ratio: 1.00896
iteration: 28 recall: 0.9772 accuracy: 0.00133555 cost: 0.00676379 M: 35.3716 delta: 0.0265598 time: 740.598 one-recall: 0.98 one-ratio: 1.00896
iteration: 29 recall: 0.9772 accuracy: 0.00133555 cost: 0.00676379 M: 35.3716 delta: 0.0265598 time: 747.777 one-recall: 0.98 one-ratio: 1.00896
iteration: 30 recall: 0.9772 accuracy: 0.00133555 cost: 0.00676379 M: 35.3716 delta: 0.0265598 time: 754.957 one-recall: 0.98 one-ratio: 1.00896
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 773.7699999999986
Index size:  262908.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0027253000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.1300000000, query time of that 0.0809759450, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Accept!
  -> Decision True in time 1.3000000000, query time of that 0.8577560940, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
320.822 < 322.012
  -> Decision False in time 4.7500000000, query time of that 3.0854836090, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.7000000000, query time of that 0.1044353520, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
309.443 < 312.501
  -> Decision False in time 0.6200000000, query time of that 0.0947564930, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
282.064 < 283.884
  -> Decision False in time 12.2100000000, query time of that 1.9372301860, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Accept!
  -> Decision True in time 8.2200000000, query time of that 0.1130689370, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
266.644 < 299.221
  -> Decision False in time 35.8000000000, query time of that 0.5283958730, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
299.244 < 306.637
  -> Decision False in time 8.3500000000, query time of that 0.1246485490, 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 accuracy: 2.20056 cost: 0.00038 M: 10 delta: 1 time: 63.9238 one-recall: 0 one-ratio: 3.24926
iteration: 2 recall: 0.0036 accuracy: 1.18859 cost: 0.000637428 M: 10 delta: 0.856032 time: 108.149 one-recall: 0 one-ratio: 2.55748
iteration: 3 recall: 0.0304 accuracy: 0.666815 cost: 0.00109521 M: 11.5287 delta: 0.835107 time: 161.85 one-recall: 0.05 one-ratio: 2.1129
iteration: 4 recall: 0.1972 accuracy: 0.330926 cost: 0.00163043 M: 11.8363 delta: 0.783459 time: 215.344 one-recall: 0.2 one-ratio: 1.65067
iteration: 5 recall: 0.5252 accuracy: 0.0910092 cost: 0.00223608 M: 12.6034 delta: 0.664579 time: 270.954 one-recall: 0.65 one-ratio: 1.14942
iteration: 6 recall: 0.7724 accuracy: 0.0254933 cost: 0.00297992 M: 15.1143 delta: 0.432351 time: 333.714 one-recall: 0.88 one-ratio: 1.04183
iteration: 7 recall: 0.886 accuracy: 0.00999636 cost: 0.00395519 M: 21.1401 delta: 0.196465 time: 406.601 one-recall: 0.95 one-ratio: 1.0172
iteration: 8 recall: 0.938 accuracy: 0.00477448 cost: 0.00497964 M: 27.3043 delta: 0.0884844 time: 476.999 one-recall: 0.98 one-ratio: 1.00405
iteration: 9 recall: 0.9632 accuracy: 0.00245147 cost: 0.0057721 M: 31.2878 delta: 0.0513266 time: 532.352 one-recall: 1 one-ratio: 1
iteration: 10 recall: 0.9732 accuracy: 0.00171023 cost: 0.00625666 M: 33.3904 delta: 0.0372167 time: 570.665 one-recall: 1 one-ratio: 1
iteration: 11 recall: 0.9756 accuracy: 0.00155144 cost: 0.00651406 M: 34.4204 delta: 0.031306 time: 596.19 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.978 accuracy: 0.00144793 cost: 0.00664156 M: 34.9106 delta: 0.0287437 time: 613.578 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.978 accuracy: 0.00144793 cost: 0.00670367 M: 35.1464 delta: 0.0275932 time: 626.228 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.978 accuracy: 0.00144793 cost: 0.00673413 M: 35.2608 delta: 0.0270465 time: 636.316 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.9784 accuracy: 0.00144104 cost: 0.00674917 M: 35.3169 delta: 0.0267815 time: 645.031 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.9784 accuracy: 0.00144104 cost: 0.00675678 M: 35.3449 delta: 0.0266554 time: 653.044 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.9784 accuracy: 0.00144104 cost: 0.00676074 M: 35.3597 delta: 0.0265902 time: 660.696 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.9784 accuracy: 0.00144104 cost: 0.00676269 M: 35.3669 delta: 0.0265581 time: 668.136 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.9784 accuracy: 0.00144104 cost: 0.00676371 M: 35.3708 delta: 0.0265402 time: 675.47 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.9784 accuracy: 0.00144104 cost: 0.00676426 M: 35.3729 delta: 0.0265311 time: 682.745 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.9784 accuracy: 0.00144104 cost: 0.00676456 M: 35.3741 delta: 0.0265262 time: 689.986 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.9784 accuracy: 0.00144104 cost: 0.00676471 M: 35.3746 delta: 0.0265227 time: 697.204 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.9784 accuracy: 0.00144104 cost: 0.00676476 M: 35.3749 delta: 0.0265213 time: 704.403 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.9784 accuracy: 0.00144104 cost: 0.00676479 M: 35.375 delta: 0.0265209 time: 711.594 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.9784 accuracy: 0.00144104 cost: 0.00676481 M: 35.3751 delta: 0.0265207 time: 718.786 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.9784 accuracy: 0.00144104 cost: 0.00676482 M: 35.3751 delta: 0.0265204 time: 725.973 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.9784 accuracy: 0.00144104 cost: 0.00676483 M: 35.3752 delta: 0.0265204 time: 733.161 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.9784 accuracy: 0.00144104 cost: 0.00676483 M: 35.3752 delta: 0.0265204 time: 740.347 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.9784 accuracy: 0.00144104 cost: 0.00676484 M: 35.3752 delta: 0.0265203 time: 747.541 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.9784 accuracy: 0.00144104 cost: 0.00676484 M: 35.3752 delta: 0.0265203 time: 754.723 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 773.5200000000004
Index size:  262848.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0024620000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.1400000000, query time of that 0.0956724260, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Accept!
  -> Decision True in time 1.3800000000, query time of that 0.9286921990, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Accept!
  -> Decision True in time 13.8700000000, query time of that 9.2415746970, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.6900000000, query time of that 0.1117645610, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
212.782 < 212.962
  -> Decision False in time 5.3100000000, query time of that 0.8841233170, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
223.35 < 228.467
  -> Decision False in time 19.4200000000, query time of that 3.2471502250, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Accept!
  -> Decision True in time 8.1700000000, query time of that 0.1349149820, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
249.433 < 252.65
  -> Decision False in time 25.2700000000, query time of that 0.4024200500, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
214.206 < 231.309
  -> Decision False in time 151.4400000000, query time of that 2.3775557770, 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: 0.0004 accuracy: 2.08054 cost: 0.00038 M: 10 delta: 1 time: 63.9534 one-recall: 0 one-ratio: 3.45004
iteration: 2 recall: 0.0036 accuracy: 1.14691 cost: 0.000637428 M: 10 delta: 0.856033 time: 108.202 one-recall: 0 one-ratio: 2.77168
iteration: 3 recall: 0.0348 accuracy: 0.667325 cost: 0.00109521 M: 11.5287 delta: 0.835107 time: 161.903 one-recall: 0.03 one-ratio: 2.19454
iteration: 4 recall: 0.1892 accuracy: 0.318219 cost: 0.00163043 M: 11.8362 delta: 0.783447 time: 215.412 one-recall: 0.23 one-ratio: 1.68217
iteration: 5 recall: 0.5188 accuracy: 0.108321 cost: 0.0022361 M: 12.6038 delta: 0.664593 time: 271.027 one-recall: 0.61 one-ratio: 1.31947
iteration: 6 recall: 0.776 accuracy: 0.0284261 cost: 0.00297986 M: 15.114 delta: 0.432352 time: 333.814 one-recall: 0.85 one-ratio: 1.07293
iteration: 7 recall: 0.9068 accuracy: 0.00732291 cost: 0.00395502 M: 21.1391 delta: 0.196432 time: 406.754 one-recall: 0.97 one-ratio: 1.0104
iteration: 8 recall: 0.9488 accuracy: 0.00348185 cost: 0.00498009 M: 27.307 delta: 0.088485 time: 477.178 one-recall: 0.98 one-ratio: 1.00346
iteration: 9 recall: 0.9676 accuracy: 0.00204209 cost: 0.00577265 M: 31.2898 delta: 0.0513134 time: 532.546 one-recall: 0.98 one-ratio: 1.00346
iteration: 10 recall: 0.9756 accuracy: 0.00151897 cost: 0.00625733 M: 33.3922 delta: 0.0371867 time: 570.871 one-recall: 0.98 one-ratio: 1.00346
iteration: 11 recall: 0.98 accuracy: 0.00109295 cost: 0.00651437 M: 34.4206 delta: 0.0313073 time: 596.374 one-recall: 0.99 one-ratio: 1.00034
iteration: 12 recall: 0.9812 accuracy: 0.000996788 cost: 0.0066416 M: 34.9105 delta: 0.0287568 time: 613.747 one-recall: 0.99 one-ratio: 1.00034
iteration: 13 recall: 0.9812 accuracy: 0.000995415 cost: 0.00670438 M: 35.147 delta: 0.027581 time: 626.451 one-recall: 0.99 one-ratio: 1.00034
iteration: 14 recall: 0.9812 accuracy: 0.000995415 cost: 0.0067345 M: 35.2597 delta: 0.0270358 time: 636.507 one-recall: 0.99 one-ratio: 1.00034
iteration: 15 recall: 0.9812 accuracy: 0.000995415 cost: 0.00674942 M: 35.3156 delta: 0.0267762 time: 645.21 one-recall: 0.99 one-ratio: 1.00034
iteration: 16 recall: 0.9812 accuracy: 0.000995415 cost: 0.00675671 M: 35.343 delta: 0.0266477 time: 653.196 one-recall: 0.99 one-ratio: 1.00034
iteration: 17 recall: 0.9812 accuracy: 0.000995415 cost: 0.00676055 M: 35.3573 delta: 0.0265825 time: 660.83 one-recall: 0.99 one-ratio: 1.00034
iteration: 18 recall: 0.9812 accuracy: 0.000995415 cost: 0.00676236 M: 35.364 delta: 0.026555 time: 668.253 one-recall: 0.99 one-ratio: 1.00034
iteration: 19 recall: 0.9816 accuracy: 0.000986984 cost: 0.00676335 M: 35.3678 delta: 0.0265382 time: 675.583 one-recall: 0.99 one-ratio: 1.00034
iteration: 20 recall: 0.9816 accuracy: 0.000986984 cost: 0.00676386 M: 35.3699 delta: 0.0265306 time: 682.854 one-recall: 0.99 one-ratio: 1.00034
iteration: 21 recall: 0.9816 accuracy: 0.000986984 cost: 0.00676416 M: 35.3711 delta: 0.0265263 time: 690.094 one-recall: 0.99 one-ratio: 1.00034
iteration: 22 recall: 0.9816 accuracy: 0.000986984 cost: 0.00676437 M: 35.372 delta: 0.0265228 time: 697.318 one-recall: 0.99 one-ratio: 1.00034
iteration: 23 recall: 0.9816 accuracy: 0.000986984 cost: 0.00676449 M: 35.3724 delta: 0.026521 time: 704.529 one-recall: 0.99 one-ratio: 1.00034
iteration: 24 recall: 0.9816 accuracy: 0.000986984 cost: 0.00676455 M: 35.3727 delta: 0.0265199 time: 711.726 one-recall: 0.99 one-ratio: 1.00034
iteration: 25 recall: 0.9816 accuracy: 0.000986984 cost: 0.00676459 M: 35.3728 delta: 0.0265196 time: 718.919 one-recall: 0.99 one-ratio: 1.00034
iteration: 26 recall: 0.9816 accuracy: 0.000986984 cost: 0.00676461 M: 35.3728 delta: 0.0265193 time: 726.108 one-recall: 0.99 one-ratio: 1.00034
iteration: 27 recall: 0.9816 accuracy: 0.000986984 cost: 0.00676463 M: 35.3729 delta: 0.0265191 time: 733.297 one-recall: 0.99 one-ratio: 1.00034
iteration: 28 recall: 0.9816 accuracy: 0.000986984 cost: 0.00676465 M: 35.3729 delta: 0.0265189 time: 740.483 one-recall: 0.99 one-ratio: 1.00034
iteration: 29 recall: 0.9816 accuracy: 0.000986984 cost: 0.00676465 M: 35.373 delta: 0.0265191 time: 747.667 one-recall: 0.99 one-ratio: 1.00034
iteration: 30 recall: 0.9816 accuracy: 0.000986984 cost: 0.00676466 M: 35.373 delta: 0.026519 time: 754.853 one-recall: 0.99 one-ratio: 1.00034
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 773.6399999999994
Index size:  262712.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0091462000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.0800000000, query time of that 0.0350044700, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Reject!
377.492 < 437.057
  -> Decision False in time 0.1300000000, query time of that 0.0553643670, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
390.611 < 418.349
  -> Decision False in time 0.1800000000, query time of that 0.0768533310, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.5900000000, query time of that 0.0419113560, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
390.095 < 395.125
  -> Decision False in time 3.4500000000, query time of that 0.2586162400, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
328.853 < 374.683
  -> Decision False in time 2.6000000000, query time of that 0.1974863720, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Accept!
  -> Decision True in time 8.1100000000, query time of that 0.0539240570, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
308.701 < 308.827
  -> Decision False in time 0.4200000000, query time of that 0.0030272760, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
255.767 < 267.563
  -> Decision False in time 9.8900000000, query time of that 0.0618933870, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 5, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 5, {'reverse': -1}, False])
Got a train set of size (1000000 * 128)
Generating control...
Initializing...
iteration: 1 recall: 0.0004 accuracy: 2.04174 cost: 0.00038 M: 10 delta: 1 time: 63.9304 one-recall: 0 one-ratio: 3.89367
iteration: 2 recall: 0.0036 accuracy: 1.15209 cost: 0.000637428 M: 10 delta: 0.856032 time: 108.153 one-recall: 0 one-ratio: 3.12412
iteration: 3 recall: 0.0364 accuracy: 0.675083 cost: 0.00109521 M: 11.5287 delta: 0.835111 time: 161.841 one-recall: 0.02 one-ratio: 2.49182
iteration: 4 recall: 0.1892 accuracy: 0.361106 cost: 0.00163042 M: 11.8363 delta: 0.783461 time: 215.327 one-recall: 0.23 one-ratio: 1.97093
iteration: 5 recall: 0.518 accuracy: 0.11355 cost: 0.00223606 M: 12.6036 delta: 0.66457 time: 270.952 one-recall: 0.65 one-ratio: 1.29758
iteration: 6 recall: 0.798 accuracy: 0.024412 cost: 0.00297991 M: 15.1143 delta: 0.432327 time: 333.709 one-recall: 0.92 one-ratio: 1.03139
iteration: 7 recall: 0.9084 accuracy: 0.00794053 cost: 0.00395522 M: 21.1406 delta: 0.196431 time: 406.639 one-recall: 0.96 one-ratio: 1.01786
iteration: 8 recall: 0.948 accuracy: 0.00372915 cost: 0.00497946 M: 27.3035 delta: 0.0884808 time: 477.046 one-recall: 0.97 one-ratio: 1.00607
iteration: 9 recall: 0.968 accuracy: 0.0020282 cost: 0.00577258 M: 31.2888 delta: 0.0513379 time: 532.441 one-recall: 0.98 one-ratio: 1.00099
iteration: 10 recall: 0.9776 accuracy: 0.00121191 cost: 0.0062576 M: 33.3953 delta: 0.0372265 time: 570.768 one-recall: 1 one-ratio: 1
iteration: 11 recall: 0.9808 accuracy: 0.00101671 cost: 0.00651549 M: 34.4265 delta: 0.0313125 time: 596.333 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.9828 accuracy: 0.000868644 cost: 0.00664329 M: 34.9173 delta: 0.0287597 time: 613.741 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.9844 accuracy: 0.000784086 cost: 0.00670598 M: 35.1541 delta: 0.0276011 time: 626.446 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.9852 accuracy: 0.000745158 cost: 0.00673669 M: 35.2684 delta: 0.0270548 time: 636.545 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.9852 accuracy: 0.000745158 cost: 0.00675175 M: 35.3246 delta: 0.0267916 time: 645.261 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.9852 accuracy: 0.000745158 cost: 0.00675953 M: 35.3531 delta: 0.0266625 time: 653.289 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.9852 accuracy: 0.000745158 cost: 0.00676352 M: 35.368 delta: 0.0265957 time: 660.94 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.9852 accuracy: 0.000745158 cost: 0.0067656 M: 35.3756 delta: 0.0265639 time: 668.394 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.9852 accuracy: 0.000745158 cost: 0.00676665 M: 35.3797 delta: 0.0265461 time: 675.731 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.9852 accuracy: 0.000745158 cost: 0.00676726 M: 35.3821 delta: 0.0265359 time: 683.009 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.9852 accuracy: 0.000745158 cost: 0.00676756 M: 35.3833 delta: 0.0265321 time: 690.25 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.9852 accuracy: 0.000745158 cost: 0.00676771 M: 35.3838 delta: 0.0265297 time: 697.469 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.9852 accuracy: 0.000745158 cost: 0.0067678 M: 35.3842 delta: 0.0265283 time: 704.672 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.9852 accuracy: 0.000745158 cost: 0.00676784 M: 35.3844 delta: 0.0265279 time: 711.864 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.9852 accuracy: 0.000745158 cost: 0.00676786 M: 35.3845 delta: 0.0265273 time: 719.052 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.9852 accuracy: 0.000745158 cost: 0.00676788 M: 35.3845 delta: 0.0265272 time: 726.24 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.9852 accuracy: 0.000745158 cost: 0.00676789 M: 35.3845 delta: 0.026527 time: 733.427 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.9852 accuracy: 0.000745158 cost: 0.00676789 M: 35.3846 delta: 0.026527 time: 740.61 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.9852 accuracy: 0.000745158 cost: 0.00676789 M: 35.3846 delta: 0.026527 time: 747.792 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.9852 accuracy: 0.000745158 cost: 0.00676789 M: 35.3846 delta: 0.026527 time: 754.976 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 773.7800000000007
Index size:  262936.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0112902000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.0700000000, query time of that 0.0234460360, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Reject!
378.937 < 385.116
  -> Decision False in time 0.0200000000, query time of that 0.0085837340, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
422.386 < 427.341
  -> Decision False in time 0.0400000000, query time of that 0.0106048320, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.5600000000, query time of that 0.0256375340, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Accept!
  -> Decision True in time 5.4300000000, query time of that 0.2653181490, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
388.96 < 389.835
  -> Decision False in time 0.4900000000, query time of that 0.0241566580, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Reject!
249.267 < 274.009
  -> Decision False in time 5.2500000000, query time of that 0.0239216130, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
278.124 < 279.339
  -> Decision False in time 5.0700000000, query time of that 0.0210117430, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
281.771 < 285.734
  -> Decision False in time 9.3400000000, query time of that 0.0419319670, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 2, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 2, {'reverse': -1}, False])
Got a train set of size (1000000 * 128)
Generating control...
Initializing...
iteration: 1 recall: 0.0004 accuracy: 2.25787 cost: 0.00038 M: 10 delta: 1 time: 63.9439 one-recall: 0 one-ratio: 3.45912
iteration: 2 recall: 0.004 accuracy: 1.24424 cost: 0.000637428 M: 10 delta: 0.856032 time: 108.178 one-recall: 0 one-ratio: 2.63883
iteration: 3 recall: 0.036 accuracy: 0.668253 cost: 0.00109521 M: 11.5287 delta: 0.835104 time: 161.871 one-recall: 0.05 one-ratio: 2.08354
iteration: 4 recall: 0.2124 accuracy: 0.309538 cost: 0.00163043 M: 11.8362 delta: 0.783465 time: 215.362 one-recall: 0.22 one-ratio: 1.63512
iteration: 5 recall: 0.5484 accuracy: 0.0871193 cost: 0.0022361 M: 12.604 delta: 0.664596 time: 270.982 one-recall: 0.7 one-ratio: 1.20675
iteration: 6 recall: 0.788 accuracy: 0.0268801 cost: 0.00297997 M: 15.1144 delta: 0.432306 time: 333.74 one-recall: 0.89 one-ratio: 1.08357
iteration: 7 recall: 0.8956 accuracy: 0.00920915 cost: 0.00395518 M: 21.1403 delta: 0.196446 time: 406.627 one-recall: 0.95 one-ratio: 1.0216
iteration: 8 recall: 0.9456 accuracy: 0.00396928 cost: 0.00497966 M: 27.3039 delta: 0.0884451 time: 477.021 one-recall: 0.97 one-ratio: 1.00659
iteration: 9 recall: 0.9644 accuracy: 0.00220898 cost: 0.00577245 M: 31.2869 delta: 0.051342 time: 532.4 one-recall: 0.99 one-ratio: 1.00432
iteration: 10 recall: 0.9776 accuracy: 0.000947233 cost: 0.00625726 M: 33.3902 delta: 0.0372397 time: 570.717 one-recall: 1 one-ratio: 1
iteration: 11 recall: 0.982 accuracy: 0.000817179 cost: 0.0065147 M: 34.4231 delta: 0.0313351 time: 596.254 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.9824 accuracy: 0.000782284 cost: 0.0066423 M: 34.9132 delta: 0.0287718 time: 613.652 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.9828 accuracy: 0.000743851 cost: 0.00670464 M: 35.1485 delta: 0.0276004 time: 626.328 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.9828 accuracy: 0.000743851 cost: 0.00673493 M: 35.2617 delta: 0.0270538 time: 636.399 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.9828 accuracy: 0.000743851 cost: 0.00674997 M: 35.318 delta: 0.0268012 time: 645.117 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.9828 accuracy: 0.000743851 cost: 0.00675754 M: 35.3461 delta: 0.0266707 time: 653.128 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.9828 accuracy: 0.000743851 cost: 0.00676133 M: 35.3605 delta: 0.0266061 time: 660.762 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.9828 accuracy: 0.000743851 cost: 0.00676329 M: 35.3679 delta: 0.0265709 time: 668.205 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.9828 accuracy: 0.000743851 cost: 0.00676432 M: 35.3719 delta: 0.0265548 time: 675.539 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.9828 accuracy: 0.000743851 cost: 0.00676487 M: 35.3739 delta: 0.0265475 time: 682.815 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.9828 accuracy: 0.000743851 cost: 0.00676521 M: 35.3752 delta: 0.0265418 time: 690.064 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.9828 accuracy: 0.000743851 cost: 0.00676539 M: 35.3759 delta: 0.0265374 time: 697.287 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.9828 accuracy: 0.000743851 cost: 0.00676548 M: 35.3762 delta: 0.0265359 time: 704.492 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.9828 accuracy: 0.000743851 cost: 0.00676551 M: 35.3763 delta: 0.0265354 time: 711.685 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.9828 accuracy: 0.000743851 cost: 0.00676553 M: 35.3764 delta: 0.0265351 time: 718.876 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.9828 accuracy: 0.000743851 cost: 0.00676555 M: 35.3765 delta: 0.0265349 time: 726.072 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.9828 accuracy: 0.000743851 cost: 0.00676556 M: 35.3765 delta: 0.0265346 time: 733.257 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.9828 accuracy: 0.000743851 cost: 0.00676557 M: 35.3765 delta: 0.0265346 time: 740.442 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.9828 accuracy: 0.000743851 cost: 0.00676557 M: 35.3765 delta: 0.0265346 time: 747.628 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.9828 accuracy: 0.000743851 cost: 0.00676557 M: 35.3765 delta: 0.0265346 time: 754.809 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 773.6100000000006
Index size:  262808.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.1067205000
  Testing...
|S| = 80
|T| = 1152
Reject!
471.859 < 476.371
  -> Decision False in time 0.0100000000, query time of that 0.0028992930, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Reject!
427.645 < 456.453
  -> Decision False in time 0.0000000000, query time of that 0.0002367370, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
242.078 < 453.33
  -> Decision False in time 0.0000000000, query time of that 0.0009011790, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Reject!
376.871 < 454.091
  -> Decision False in time 0.1300000000, query time of that 0.0063644470, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
339.15 < 470.657
  -> Decision False in time 0.2900000000, query time of that 0.0135481380, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
406.537 < 442.697
  -> Decision False in time 0.1700000000, query time of that 0.0078015980, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Reject!
455.387 < 468.887
  -> Decision False in time 0.1000000000, query time of that 0.0008120440, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
416.838 < 420.063
  -> Decision False in time 1.8200000000, query time of that 0.0079521270, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
359.347 < 391.464
  -> Decision False in time 1.1100000000, query time of that 0.0049569870, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 30, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 30, {'reverse': -1}, False])
Got a train set of size (1000000 * 128)
Generating control...
Initializing...
iteration: 1 recall: 0.0004 accuracy: 2.22098 cost: 0.00038 M: 10 delta: 1 time: 63.7172 one-recall: 0 one-ratio: 3.35958
iteration: 2 recall: 0.0024 accuracy: 1.29793 cost: 0.000637428 M: 10 delta: 0.856032 time: 107.737 one-recall: 0 one-ratio: 2.65486
iteration: 3 recall: 0.0328 accuracy: 0.752481 cost: 0.00109521 M: 11.5287 delta: 0.835108 time: 161.192 one-recall: 0.02 one-ratio: 2.15882
iteration: 4 recall: 0.1708 accuracy: 0.32873 cost: 0.00163045 M: 11.8362 delta: 0.783463 time: 214.445 one-recall: 0.21 one-ratio: 1.63589
iteration: 5 recall: 0.498 accuracy: 0.101051 cost: 0.00223604 M: 12.6036 delta: 0.664587 time: 269.799 one-recall: 0.67 one-ratio: 1.20423
iteration: 6 recall: 0.7672 accuracy: 0.0275466 cost: 0.00297988 M: 15.1145 delta: 0.43233 time: 332.264 one-recall: 0.87 one-ratio: 1.0768
iteration: 7 recall: 0.8904 accuracy: 0.0109279 cost: 0.0039549 M: 21.1379 delta: 0.196433 time: 404.755 one-recall: 0.93 one-ratio: 1.04102
iteration: 8 recall: 0.9352 accuracy: 0.00486119 cost: 0.00497917 M: 27.3036 delta: 0.0884691 time: 474.788 one-recall: 0.97 one-ratio: 1.00743
iteration: 9 recall: 0.9572 accuracy: 0.00272258 cost: 0.0057718 M: 31.2859 delta: 0.0513548 time: 529.935 one-recall: 0.97 one-ratio: 1.00386
iteration: 10 recall: 0.9696 accuracy: 0.00178789 cost: 0.00625641 M: 33.3885 delta: 0.0371854 time: 568.132 one-recall: 0.98 one-ratio: 1.00199
iteration: 11 recall: 0.9744 accuracy: 0.0012215 cost: 0.00651364 M: 34.4185 delta: 0.0313021 time: 593.626 one-recall: 0.99 one-ratio: 1.00011
iteration: 12 recall: 0.9772 accuracy: 0.00112338 cost: 0.00664149 M: 34.9107 delta: 0.0287366 time: 611.054 one-recall: 0.99 one-ratio: 1.00011
iteration: 13 recall: 0.9788 accuracy: 0.000949045 cost: 0.00670333 M: 35.1447 delta: 0.02758 time: 623.733 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.9792 accuracy: 0.000924701 cost: 0.00673379 M: 35.2594 delta: 0.027028 time: 633.856 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.9796 accuracy: 0.000904694 cost: 0.00674845 M: 35.3144 delta: 0.0267684 time: 642.585 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.9796 accuracy: 0.000904694 cost: 0.00675618 M: 35.3433 delta: 0.0266431 time: 650.658 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.98 accuracy: 0.000866025 cost: 0.00675999 M: 35.3574 delta: 0.0265736 time: 658.345 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.9808 accuracy: 0.000801143 cost: 0.00676196 M: 35.3645 delta: 0.0265438 time: 665.839 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.9824 accuracy: 0.00074382 cost: 0.00676308 M: 35.3686 delta: 0.0265271 time: 673.229 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.9828 accuracy: 0.000737809 cost: 0.00676358 M: 35.3705 delta: 0.0265188 time: 680.555 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.9828 accuracy: 0.000737809 cost: 0.00676386 M: 35.3715 delta: 0.0265146 time: 687.848 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.9828 accuracy: 0.000737809 cost: 0.00676402 M: 35.3721 delta: 0.0265118 time: 695.122 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.9828 accuracy: 0.000737809 cost: 0.00676411 M: 35.3725 delta: 0.0265106 time: 702.379 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.9828 accuracy: 0.000737809 cost: 0.00676416 M: 35.3727 delta: 0.0265099 time: 709.635 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.9828 accuracy: 0.000737809 cost: 0.00676418 M: 35.3727 delta: 0.0265095 time: 716.876 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.9828 accuracy: 0.000737809 cost: 0.00676418 M: 35.3727 delta: 0.026509 time: 724.115 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.9828 accuracy: 0.000737809 cost: 0.00676418 M: 35.3727 delta: 0.026509 time: 731.351 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.9828 accuracy: 0.000737809 cost: 0.00676418 M: 35.3727 delta: 0.026509 time: 738.589 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.9828 accuracy: 0.000737809 cost: 0.00676418 M: 35.3727 delta: 0.026509 time: 745.825 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.9828 accuracy: 0.000737809 cost: 0.00676418 M: 35.3727 delta: 0.026509 time: 753.063 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 771.9200000000019
Index size:  196032.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0071937000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.1000000000, query time of that 0.0463207120, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Reject!
330.392 < 366.287
  -> Decision False in time 0.1300000000, query time of that 0.0655045670, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
284.405 < 284.415
  -> Decision False in time 1.8300000000, query time of that 0.8686035900, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.6100000000, query time of that 0.0523849920, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Accept!
  -> Decision True in time 5.9300000000, query time of that 0.5222321020, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
269.42 < 270.396
  -> Decision False in time 1.2100000000, query time of that 0.1076222340, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Accept!
  -> Decision True in time 8.0900000000, query time of that 0.0635246240, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
285.935 < 287.185
  -> Decision False in time 12.2900000000, query time of that 0.0965928010, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
246.4 < 250.186
  -> Decision False in time 15.2600000000, query time of that 0.1225021520, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 40, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 40, {'reverse': -1}, False])
Got a train set of size (1000000 * 128)
Generating control...
Initializing...
iteration: 1 recall: 0 accuracy: 3.43512 cost: 0.00038 M: 10 delta: 1 time: 63.7142 one-recall: 0 one-ratio: 3.45247
iteration: 2 recall: 0.004 accuracy: 1.50103 cost: 0.000637428 M: 10 delta: 0.856032 time: 107.751 one-recall: 0 one-ratio: 2.74823
iteration: 3 recall: 0.0416 accuracy: 0.736298 cost: 0.00109521 M: 11.5287 delta: 0.835103 time: 161.211 one-recall: 0.05 one-ratio: 2.19363
iteration: 4 recall: 0.2104 accuracy: 0.344883 cost: 0.00163043 M: 11.8362 delta: 0.78346 time: 214.453 one-recall: 0.28 one-ratio: 1.70752
iteration: 5 recall: 0.5124 accuracy: 0.11757 cost: 0.00223601 M: 12.6033 delta: 0.664575 time: 269.786 one-recall: 0.61 one-ratio: 1.28621
iteration: 6 recall: 0.7496 accuracy: 0.0307658 cost: 0.00297995 M: 15.1149 delta: 0.432368 time: 332.24 one-recall: 0.88 one-ratio: 1.06106
iteration: 7 recall: 0.8784 accuracy: 0.0102874 cost: 0.0039554 M: 21.1397 delta: 0.196412 time: 404.751 one-recall: 0.93 one-ratio: 1.02492
iteration: 8 recall: 0.9328 accuracy: 0.00503454 cost: 0.00497963 M: 27.3054 delta: 0.0884433 time: 474.784 one-recall: 0.95 one-ratio: 1.01768
iteration: 9 recall: 0.956 accuracy: 0.00363589 cost: 0.00577185 M: 31.2873 delta: 0.0513618 time: 529.911 one-recall: 0.95 one-ratio: 1.01768
iteration: 10 recall: 0.9608 accuracy: 0.00334517 cost: 0.00625609 M: 33.3874 delta: 0.0372357 time: 568.104 one-recall: 0.95 one-ratio: 1.01768
iteration: 11 recall: 0.9628 accuracy: 0.00313514 cost: 0.00651341 M: 34.4185 delta: 0.0313417 time: 593.601 one-recall: 0.95 one-ratio: 1.01768
iteration: 12 recall: 0.9644 accuracy: 0.00304571 cost: 0.00664143 M: 34.9109 delta: 0.0287574 time: 611.038 one-recall: 0.96 one-ratio: 1.01738
iteration: 13 recall: 0.9652 accuracy: 0.00301358 cost: 0.00670338 M: 35.1448 delta: 0.0276153 time: 623.718 one-recall: 0.96 one-ratio: 1.01738
iteration: 14 recall: 0.9652 accuracy: 0.00301358 cost: 0.00673356 M: 35.2582 delta: 0.0270683 time: 633.827 one-recall: 0.96 one-ratio: 1.01738
iteration: 15 recall: 0.9664 accuracy: 0.00296668 cost: 0.00674869 M: 35.3149 delta: 0.0268077 time: 642.599 one-recall: 0.96 one-ratio: 1.01738
iteration: 16 recall: 0.9664 accuracy: 0.00296668 cost: 0.00675632 M: 35.3433 delta: 0.0266806 time: 650.664 one-recall: 0.96 one-ratio: 1.01738
iteration: 17 recall: 0.9668 accuracy: 0.00293678 cost: 0.00676013 M: 35.3573 delta: 0.0266169 time: 658.349 one-recall: 0.96 one-ratio: 1.01738
iteration: 18 recall: 0.9672 accuracy: 0.00290752 cost: 0.0067621 M: 35.3644 delta: 0.0265852 time: 665.838 one-recall: 0.96 one-ratio: 1.01738
iteration: 19 recall: 0.9672 accuracy: 0.00290752 cost: 0.00676316 M: 35.3682 delta: 0.026569 time: 673.23 one-recall: 0.96 one-ratio: 1.01738
iteration: 20 recall: 0.9672 accuracy: 0.00290752 cost: 0.00676367 M: 35.3701 delta: 0.026561 time: 680.551 one-recall: 0.96 one-ratio: 1.01738
iteration: 21 recall: 0.9672 accuracy: 0.00290752 cost: 0.00676394 M: 35.3712 delta: 0.0265576 time: 687.837 one-recall: 0.96 one-ratio: 1.01738
iteration: 22 recall: 0.9672 accuracy: 0.00290752 cost: 0.00676409 M: 35.3718 delta: 0.0265537 time: 695.105 one-recall: 0.96 one-ratio: 1.01738
iteration: 23 recall: 0.9672 accuracy: 0.00290752 cost: 0.00676418 M: 35.3722 delta: 0.0265525 time: 702.363 one-recall: 0.96 one-ratio: 1.01738
iteration: 24 recall: 0.9672 accuracy: 0.00290752 cost: 0.00676424 M: 35.3724 delta: 0.0265518 time: 709.613 one-recall: 0.96 one-ratio: 1.01738
iteration: 25 recall: 0.9672 accuracy: 0.00290752 cost: 0.00676429 M: 35.3726 delta: 0.0265518 time: 716.855 one-recall: 0.96 one-ratio: 1.01738
iteration: 26 recall: 0.9672 accuracy: 0.00290752 cost: 0.00676432 M: 35.3727 delta: 0.0265514 time: 724.098 one-recall: 0.96 one-ratio: 1.01738
iteration: 27 recall: 0.9672 accuracy: 0.00290752 cost: 0.00676434 M: 35.3728 delta: 0.0265511 time: 731.339 one-recall: 0.96 one-ratio: 1.01738
iteration: 28 recall: 0.9672 accuracy: 0.00290752 cost: 0.00676435 M: 35.3728 delta: 0.0265509 time: 738.574 one-recall: 0.96 one-ratio: 1.01738
iteration: 29 recall: 0.9672 accuracy: 0.00290752 cost: 0.00676435 M: 35.3728 delta: 0.0265509 time: 745.807 one-recall: 0.96 one-ratio: 1.01738
iteration: 30 recall: 0.9672 accuracy: 0.00290752 cost: 0.00676436 M: 35.3729 delta: 0.0265509 time: 753.044 one-recall: 0.96 one-ratio: 1.01738
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 771.9200000000019
Index size:  196160.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0062601000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.1000000000, query time of that 0.0503517330, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Accept!
  -> Decision True in time 0.9300000000, query time of that 0.4874248070, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
287.056 < 398.506
  -> Decision False in time 7.2700000000, query time of that 3.7673926240, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.6100000000, query time of that 0.0616952660, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
253.677 < 263.243
  -> Decision False in time 3.9700000000, query time of that 0.4165214770, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
265.65 < 269.529
  -> Decision False in time 15.8700000000, query time of that 1.6401836770, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Accept!
  -> Decision True in time 8.1500000000, query time of that 0.0779972100, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
256.531 < 257.453
  -> Decision False in time 1.6500000000, query time of that 0.0152619160, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
259.76 < 266.179
  -> Decision False in time 44.0100000000, query time of that 0.4066243320, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 70, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 70, {'reverse': -1}, False])
Got a train set of size (1000000 * 128)
Generating control...
Initializing...
iteration: 1 recall: 0.0004 accuracy: 2.2747 cost: 0.00038 M: 10 delta: 1 time: 63.7122 one-recall: 0 one-ratio: 3.58484
iteration: 2 recall: 0.0068 accuracy: 1.22456 cost: 0.000637428 M: 10 delta: 0.856032 time: 107.755 one-recall: 0 one-ratio: 2.70598
iteration: 3 recall: 0.0368 accuracy: 0.682315 cost: 0.00109521 M: 11.5287 delta: 0.835107 time: 161.218 one-recall: 0.04 one-ratio: 2.21888
iteration: 4 recall: 0.1956 accuracy: 0.341545 cost: 0.00163045 M: 11.8362 delta: 0.783451 time: 214.461 one-recall: 0.2 one-ratio: 1.76695
iteration: 5 recall: 0.5496 accuracy: 0.112852 cost: 0.00223608 M: 12.6035 delta: 0.664591 time: 269.811 one-recall: 0.65 one-ratio: 1.3055
iteration: 6 recall: 0.808 accuracy: 0.0238629 cost: 0.00297996 M: 15.1147 delta: 0.432336 time: 332.286 one-recall: 0.89 one-ratio: 1.02744
iteration: 7 recall: 0.9168 accuracy: 0.00581847 cost: 0.00395513 M: 21.1393 delta: 0.196423 time: 404.796 one-recall: 0.96 one-ratio: 1.00462
iteration: 8 recall: 0.9468 accuracy: 0.00323799 cost: 0.00497964 M: 27.3047 delta: 0.0884402 time: 474.841 one-recall: 0.96 one-ratio: 1.00462
iteration: 9 recall: 0.9632 accuracy: 0.00215948 cost: 0.00577166 M: 31.2833 delta: 0.0513247 time: 529.963 one-recall: 0.97 one-ratio: 1.00434
iteration: 10 recall: 0.97 accuracy: 0.00171413 cost: 0.00625555 M: 33.3854 delta: 0.0371919 time: 568.133 one-recall: 0.98 one-ratio: 1.0043
iteration: 11 recall: 0.974 accuracy: 0.00144138 cost: 0.00651347 M: 34.4168 delta: 0.0313041 time: 593.67 one-recall: 0.98 one-ratio: 1.0043
iteration: 12 recall: 0.9752 accuracy: 0.00139393 cost: 0.00664106 M: 34.9083 delta: 0.0287429 time: 611.082 one-recall: 0.98 one-ratio: 1.0043
iteration: 13 recall: 0.976 accuracy: 0.00139254 cost: 0.00670366 M: 35.1451 delta: 0.0275697 time: 623.815 one-recall: 0.98 one-ratio: 1.0043
iteration: 14 recall: 0.976 accuracy: 0.00139254 cost: 0.00673379 M: 35.2574 delta: 0.0270248 time: 633.91 one-recall: 0.98 one-ratio: 1.0043
iteration: 15 recall: 0.9764 accuracy: 0.00137037 cost: 0.00674894 M: 35.313 delta: 0.0267653 time: 642.674 one-recall: 0.98 one-ratio: 1.0043
iteration: 16 recall: 0.9764 accuracy: 0.00137037 cost: 0.00675668 M: 35.3419 delta: 0.0266401 time: 650.745 one-recall: 0.98 one-ratio: 1.0043
iteration: 17 recall: 0.9764 accuracy: 0.00137037 cost: 0.00676063 M: 35.3566 delta: 0.026573 time: 658.443 one-recall: 0.98 one-ratio: 1.0043
iteration: 18 recall: 0.9764 accuracy: 0.00137037 cost: 0.00676263 M: 35.364 delta: 0.0265424 time: 665.936 one-recall: 0.98 one-ratio: 1.0043
iteration: 19 recall: 0.9764 accuracy: 0.00137037 cost: 0.00676372 M: 35.3682 delta: 0.0265258 time: 673.324 one-recall: 0.98 one-ratio: 1.0043
iteration: 20 recall: 0.9764 accuracy: 0.00137037 cost: 0.0067643 M: 35.3703 delta: 0.0265167 time: 680.657 one-recall: 0.98 one-ratio: 1.0043
iteration: 21 recall: 0.9764 accuracy: 0.00137037 cost: 0.00676463 M: 35.3716 delta: 0.0265114 time: 687.955 one-recall: 0.98 one-ratio: 1.0043
iteration: 22 recall: 0.9764 accuracy: 0.00137037 cost: 0.00676479 M: 35.3722 delta: 0.0265092 time: 695.222 one-recall: 0.98 one-ratio: 1.0043
iteration: 23 recall: 0.9764 accuracy: 0.00137037 cost: 0.00676487 M: 35.3725 delta: 0.0265078 time: 702.481 one-recall: 0.98 one-ratio: 1.0043
iteration: 24 recall: 0.9764 accuracy: 0.00137037 cost: 0.00676494 M: 35.3728 delta: 0.0265073 time: 709.734 one-recall: 0.98 one-ratio: 1.0043
iteration: 25 recall: 0.9764 accuracy: 0.00137037 cost: 0.00676498 M: 35.373 delta: 0.0265064 time: 716.981 one-recall: 0.98 one-ratio: 1.0043
iteration: 26 recall: 0.9764 accuracy: 0.00137037 cost: 0.00676501 M: 35.3731 delta: 0.0265062 time: 724.229 one-recall: 0.98 one-ratio: 1.0043
iteration: 27 recall: 0.9764 accuracy: 0.00137037 cost: 0.00676503 M: 35.3732 delta: 0.0265058 time: 731.475 one-recall: 0.98 one-ratio: 1.0043
iteration: 28 recall: 0.9764 accuracy: 0.00137037 cost: 0.00676504 M: 35.3732 delta: 0.0265056 time: 738.718 one-recall: 0.98 one-ratio: 1.0043
iteration: 29 recall: 0.9764 accuracy: 0.00137037 cost: 0.00676505 M: 35.3732 delta: 0.0265056 time: 745.958 one-recall: 0.98 one-ratio: 1.0043
iteration: 30 recall: 0.9764 accuracy: 0.00137037 cost: 0.00676505 M: 35.3732 delta: 0.0265056 time: 753.196 one-recall: 0.98 one-ratio: 1.0043
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 772.0499999999993
Index size:  196108.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0035790000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.1200000000, query time of that 0.0758323500, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Accept!
  -> Decision True in time 1.1600000000, query time of that 0.7031216520, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
261.54 < 268.818
  -> Decision False in time 9.9700000000, query time of that 6.0729918740, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.6600000000, query time of that 0.0906825160, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
270.599 < 270.806
  -> Decision False in time 5.4200000000, query time of that 0.7343358990, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
335.951 < 356.497
  -> Decision False in time 14.0900000000, query time of that 1.9348140890, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Reject!
288.246 < 288.413
  -> Decision False in time 7.3100000000, query time of that 0.0880964270, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
252.962 < 255.83
  -> Decision False in time 0.8200000000, query time of that 0.0121574500, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
296.604 < 302.392
  -> Decision False in time 18.1500000000, query time of that 0.2357086160, with c1=5.0000000000, c2=0.1000000000
