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', 100, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 10, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 4, {'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', 5, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 3, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 30, {'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', 40, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 50, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 60, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 90, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 1, {'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', 20, {'reverse': -1}, False])]
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: 2.29356 cost: 0.00038 M: 10 delta: 1 time: 54.2169 one-recall: 0 one-ratio: 3.52528
iteration: 2 recall: 0.004 accuracy: 1.20332 cost: 0.000637428 M: 10 delta: 0.856032 time: 92.2786 one-recall: 0 one-ratio: 2.78298
iteration: 3 recall: 0.0276 accuracy: 0.674478 cost: 0.00109521 M: 11.5287 delta: 0.835105 time: 138.359 one-recall: 0.03 one-ratio: 2.30487
iteration: 4 recall: 0.1804 accuracy: 0.313238 cost: 0.00163043 M: 11.8364 delta: 0.783443 time: 184.013 one-recall: 0.21 one-ratio: 1.82894
iteration: 5 recall: 0.4868 accuracy: 0.121046 cost: 0.00223612 M: 12.6038 delta: 0.664615 time: 231.37 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: 284.821 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: 345.669 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: 402.455 one-recall: 1 one-ratio: 1
iteration: 9 recall: 0.9644 accuracy: 0.00221739 cost: 0.00577293 M: 31.2904 delta: 0.0514019 time: 445.668 one-recall: 1 one-ratio: 1
iteration: 10 recall: 0.9764 accuracy: 0.0012952 cost: 0.00625843 M: 33.3974 delta: 0.0372226 time: 474.728 one-recall: 1 one-ratio: 1
iteration: 11 recall: 0.984 accuracy: 0.000915513 cost: 0.00651572 M: 34.4268 delta: 0.0313604 time: 493.607 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.9872 accuracy: 0.000651871 cost: 0.00664321 M: 34.9174 delta: 0.028787 time: 506.242 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.9876 accuracy: 0.000628894 cost: 0.00670536 M: 35.1523 delta: 0.027629 time: 515.376 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.9876 accuracy: 0.000628894 cost: 0.00673551 M: 35.2647 delta: 0.0270893 time: 522.613 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.9876 accuracy: 0.000628894 cost: 0.00675029 M: 35.3194 delta: 0.0268326 time: 528.883 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.9876 accuracy: 0.000628894 cost: 0.00675777 M: 35.3471 delta: 0.0267076 time: 534.665 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.9876 accuracy: 0.000628894 cost: 0.00676154 M: 35.3608 delta: 0.0266443 time: 540.184 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.9876 accuracy: 0.000628894 cost: 0.00676338 M: 35.3677 delta: 0.0266122 time: 545.561 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.9876 accuracy: 0.000628894 cost: 0.00676433 M: 35.3712 delta: 0.0265977 time: 550.878 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.9876 accuracy: 0.000628894 cost: 0.00676485 M: 35.373 delta: 0.02659 time: 556.162 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.988 accuracy: 0.0006087 cost: 0.00676515 M: 35.3742 delta: 0.0265857 time: 561.424 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.988 accuracy: 0.0006087 cost: 0.00676532 M: 35.3749 delta: 0.0265828 time: 566.666 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.988 accuracy: 0.0006087 cost: 0.00676541 M: 35.3753 delta: 0.0265811 time: 571.909 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.988 accuracy: 0.0006087 cost: 0.00676547 M: 35.3755 delta: 0.0265798 time: 577.132 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.988 accuracy: 0.0006087 cost: 0.00676549 M: 35.3756 delta: 0.0265796 time: 582.355 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.988 accuracy: 0.0006087 cost: 0.00676551 M: 35.3757 delta: 0.0265794 time: 587.58 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.988 accuracy: 0.0006087 cost: 0.00676552 M: 35.3757 delta: 0.0265791 time: 592.812 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.988 accuracy: 0.0006087 cost: 0.00676553 M: 35.3757 delta: 0.0265791 time: 598.035 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.988 accuracy: 0.0006087 cost: 0.00676553 M: 35.3757 delta: 0.026579 time: 603.248 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.988 accuracy: 0.0006087 cost: 0.00676553 M: 35.3757 delta: 0.026579 time: 608.467 one-recall: 1 one-ratio: 1
Graph completion with reverse edges...

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

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 623.0
Index size:  1903140.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0024709000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.1400000000, query time of that 0.0975762920, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Accept!
  -> Decision True in time 1.3900000000, query time of that 0.9357967540, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Accept!
  -> Decision True in time 13.9500000000, query time of that 9.3666168020, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.6600000000, query time of that 0.1055525240, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
321.902 < 330.22
  -> Decision False in time 2.8400000000, query time of that 0.4829404020, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
316.65 < 318.893
  -> Decision False in time 8.0800000000, query time of that 1.3715825960, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Accept!
  -> Decision True in time 7.9800000000, query time of that 0.1291982690, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Accept!
  -> Decision True in time 80.1100000000, query time of that 1.3253862700, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
269.23 < 272.099
  -> Decision False in time 36.0500000000, query time of that 0.6047045580, 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: 63.5314 one-recall: 0 one-ratio: 3.66072
iteration: 2 recall: 0.0016 accuracy: 1.38091 cost: 0.000637428 M: 10 delta: 0.856032 time: 107.297 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: 160.322 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: 213.14 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: 268.039 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: 330.044 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: 402.002 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: 471.406 one-recall: 1 one-ratio: 1
iteration: 9 recall: 0.9708 accuracy: 0.00137405 cost: 0.00577272 M: 31.2865 delta: 0.0513399 time: 525.93 one-recall: 1 one-ratio: 1
iteration: 10 recall: 0.9796 accuracy: 0.000974264 cost: 0.0062576 M: 33.3907 delta: 0.0372004 time: 563.697 one-recall: 1 one-ratio: 1
iteration: 11 recall: 0.982 accuracy: 0.000821148 cost: 0.00651448 M: 34.4194 delta: 0.0313362 time: 588.789 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.9856 accuracy: 0.00063386 cost: 0.0066431 M: 34.9143 delta: 0.0287677 time: 605.897 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.9868 accuracy: 0.000577962 cost: 0.006706 M: 35.1524 delta: 0.0276101 time: 618.268 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.9876 accuracy: 0.000568264 cost: 0.00673624 M: 35.2661 delta: 0.027052 time: 627.98 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.9876 accuracy: 0.000568264 cost: 0.0067511 M: 35.3217 delta: 0.0267951 time: 636.313 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.9876 accuracy: 0.000568264 cost: 0.00675856 M: 35.3495 delta: 0.0266678 time: 643.942 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.9876 accuracy: 0.000568264 cost: 0.00676249 M: 35.3641 delta: 0.0266031 time: 651.222 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.9876 accuracy: 0.000568264 cost: 0.00676447 M: 35.3715 delta: 0.0265723 time: 658.293 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.9876 accuracy: 0.000568264 cost: 0.00676549 M: 35.3754 delta: 0.0265551 time: 665.258 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.9876 accuracy: 0.000568264 cost: 0.00676606 M: 35.3775 delta: 0.0265465 time: 672.167 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.9876 accuracy: 0.000568264 cost: 0.00676636 M: 35.3786 delta: 0.0265425 time: 679.038 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.9876 accuracy: 0.000568264 cost: 0.00676654 M: 35.3792 delta: 0.0265399 time: 685.895 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.9876 accuracy: 0.000568264 cost: 0.00676663 M: 35.3796 delta: 0.0265387 time: 692.738 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.9876 accuracy: 0.000568264 cost: 0.00676668 M: 35.3798 delta: 0.0265377 time: 699.568 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.9876 accuracy: 0.000568264 cost: 0.0067667 M: 35.3799 delta: 0.0265375 time: 706.396 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.9876 accuracy: 0.000568264 cost: 0.00676672 M: 35.3799 delta: 0.0265372 time: 713.227 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.9876 accuracy: 0.000568264 cost: 0.00676674 M: 35.38 delta: 0.026537 time: 720.054 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.9876 accuracy: 0.000568264 cost: 0.00676675 M: 35.38 delta: 0.0265369 time: 726.879 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.9876 accuracy: 0.000568264 cost: 0.00676675 M: 35.38 delta: 0.0265368 time: 733.701 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.9876 accuracy: 0.000568264 cost: 0.00676675 M: 35.38 delta: 0.026537 time: 740.524 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 758.6699999999998
Index size:  261124.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0107383000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.0700000000, query time of that 0.0281069050, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Reject!
341.757 < 388.847
  -> Decision False in time 0.6100000000, query time of that 0.2256719620, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
264.278 < 438.724
  -> Decision False in time 0.0400000000, query time of that 0.0162552540, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.5400000000, query time of that 0.0315644520, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Accept!
  -> Decision True in time 5.3400000000, query time of that 0.3188094530, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
403.306 < 409.123
  -> Decision False in time 2.2200000000, query time of that 0.1351673180, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Reject!
227.337 < 229.007
  -> Decision False in time 5.3100000000, query time of that 0.0330745610, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
255.822 < 257.206
  -> Decision False in time 7.4000000000, query time of that 0.0427679130, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
249.944 < 256.156
  -> Decision False in time 6.6200000000, query time of that 0.0409755720, 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: 0.0004 accuracy: 2.03157 cost: 0.00038 M: 10 delta: 1 time: 63.5605 one-recall: 0 one-ratio: 3.73501
iteration: 2 recall: 0.0016 accuracy: 1.16247 cost: 0.000637428 M: 10 delta: 0.856032 time: 107.328 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: 160.362 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: 213.18 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: 268.089 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: 330.1 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: 402.064 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: 471.487 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: 526.031 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: 563.749 one-recall: 1 one-ratio: 1
iteration: 11 recall: 0.978 accuracy: 0.00104776 cost: 0.00651482 M: 34.4253 delta: 0.0313493 time: 588.828 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.9788 accuracy: 0.00100726 cost: 0.00664253 M: 34.9185 delta: 0.0287713 time: 605.836 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.9792 accuracy: 0.000936912 cost: 0.00670467 M: 35.1539 delta: 0.0276173 time: 618.119 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.9792 accuracy: 0.000936912 cost: 0.00673481 M: 35.2674 delta: 0.0270635 time: 627.778 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.9796 accuracy: 0.00088822 cost: 0.00674951 M: 35.3223 delta: 0.0268061 time: 636.049 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.9796 accuracy: 0.00088822 cost: 0.00675718 M: 35.3508 delta: 0.0266787 time: 643.643 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.9796 accuracy: 0.00088822 cost: 0.00676107 M: 35.3653 delta: 0.026612 time: 650.867 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.9796 accuracy: 0.00088822 cost: 0.0067631 M: 35.3729 delta: 0.0265774 time: 657.886 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.9796 accuracy: 0.00088822 cost: 0.00676415 M: 35.3768 delta: 0.026562 time: 664.797 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.9796 accuracy: 0.00088822 cost: 0.00676468 M: 35.3788 delta: 0.0265532 time: 671.644 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.9796 accuracy: 0.00088822 cost: 0.00676498 M: 35.38 delta: 0.0265484 time: 678.467 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.9796 accuracy: 0.00088822 cost: 0.00676515 M: 35.3806 delta: 0.0265463 time: 685.265 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.9796 accuracy: 0.00088822 cost: 0.00676524 M: 35.381 delta: 0.0265443 time: 692.045 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.9796 accuracy: 0.00088822 cost: 0.00676531 M: 35.3812 delta: 0.0265439 time: 698.83 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.9796 accuracy: 0.00088822 cost: 0.00676533 M: 35.3813 delta: 0.0265431 time: 705.62 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.9796 accuracy: 0.00088822 cost: 0.00676536 M: 35.3814 delta: 0.0265432 time: 712.395 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.9796 accuracy: 0.00088822 cost: 0.00676537 M: 35.3815 delta: 0.026543 time: 719.168 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.9796 accuracy: 0.00088822 cost: 0.00676538 M: 35.3815 delta: 0.0265427 time: 725.938 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.9796 accuracy: 0.00088822 cost: 0.00676538 M: 35.3815 delta: 0.0265427 time: 732.709 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.9796 accuracy: 0.00088822 cost: 0.00676538 M: 35.3815 delta: 0.0265426 time: 739.479 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 757.54
Index size:  262896.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0114686000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.0700000000, query time of that 0.0216237720, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Reject!
337.025 < 354.815
  -> Decision False in time 0.2300000000, query time of that 0.0731536750, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
406.421 < 415.465
  -> Decision False in time 0.4200000000, query time of that 0.1294354680, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.5400000000, query time of that 0.0247806820, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
386.566 < 392.784
  -> Decision False in time 0.9400000000, query time of that 0.0459894520, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
283.002 < 291.796
  -> Decision False in time 5.7900000000, query time of that 0.2834301070, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Accept!
  -> Decision True in time 7.1000000000, query time of that 0.0347725090, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
271.135 < 272.833
  -> Decision False in time 2.1700000000, query time of that 0.0109264880, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
247.473 < 249.716
  -> Decision False in time 0.8200000000, query time of that 0.0045283570, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 80, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 80, {'reverse': -1}, False])
Got a train set of size (1000000 * 128)
Generating control...
Initializing...
iteration: 1 recall: 0 accuracy: 2.04383 cost: 0.00038 M: 10 delta: 1 time: 63.5455 one-recall: 0 one-ratio: 3.38832
iteration: 2 recall: 0.002 accuracy: 1.16107 cost: 0.000637428 M: 10 delta: 0.856032 time: 107.295 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: 160.326 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: 213.138 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: 268.038 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: 330.041 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: 401.994 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: 471.398 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: 525.942 one-recall: 1 one-ratio: 1
iteration: 10 recall: 0.972 accuracy: 0.00174838 cost: 0.00625718 M: 33.3927 delta: 0.0372235 time: 563.675 one-recall: 1 one-ratio: 1
iteration: 11 recall: 0.9748 accuracy: 0.00152981 cost: 0.00651493 M: 34.4234 delta: 0.031346 time: 588.789 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.9772 accuracy: 0.00141242 cost: 0.00664239 M: 34.9155 delta: 0.0287909 time: 605.794 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.9788 accuracy: 0.00138062 cost: 0.00670462 M: 35.1506 delta: 0.0276247 time: 618.077 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.9788 accuracy: 0.00138062 cost: 0.00673495 M: 35.2643 delta: 0.0270882 time: 627.752 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.9788 accuracy: 0.00138062 cost: 0.00674998 M: 35.32 delta: 0.0268258 time: 636.053 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.9788 accuracy: 0.00138046 cost: 0.00675749 M: 35.3483 delta: 0.0266968 time: 643.636 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.9792 accuracy: 0.00137847 cost: 0.00676142 M: 35.3629 delta: 0.0266305 time: 650.861 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.9792 accuracy: 0.00137847 cost: 0.00676334 M: 35.3701 delta: 0.0265993 time: 657.881 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.9792 accuracy: 0.00137847 cost: 0.00676438 M: 35.374 delta: 0.0265837 time: 664.802 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.9792 accuracy: 0.00137847 cost: 0.00676496 M: 35.3762 delta: 0.0265739 time: 671.664 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.9792 accuracy: 0.00137847 cost: 0.00676524 M: 35.3773 delta: 0.026569 time: 678.49 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.9792 accuracy: 0.00137847 cost: 0.00676542 M: 35.3779 delta: 0.0265667 time: 685.295 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.9792 accuracy: 0.00137847 cost: 0.00676551 M: 35.3783 delta: 0.0265656 time: 692.077 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.9792 accuracy: 0.00137847 cost: 0.00676555 M: 35.3785 delta: 0.0265649 time: 698.853 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.9792 accuracy: 0.00137847 cost: 0.00676559 M: 35.3786 delta: 0.0265643 time: 705.621 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.9792 accuracy: 0.00137847 cost: 0.00676561 M: 35.3786 delta: 0.0265641 time: 712.387 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.9792 accuracy: 0.00137847 cost: 0.00676563 M: 35.3787 delta: 0.0265638 time: 719.153 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.9792 accuracy: 0.00137847 cost: 0.00676564 M: 35.3788 delta: 0.0265635 time: 725.918 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.9792 accuracy: 0.00137847 cost: 0.00676565 M: 35.3788 delta: 0.0265633 time: 732.687 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.9792 accuracy: 0.00137847 cost: 0.00676565 M: 35.3788 delta: 0.0265633 time: 739.447 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 757.5199999999995
Index size:  262872.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0031742000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.1300000000, query time of that 0.0842972180, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Accept!
  -> Decision True in time 1.2700000000, query time of that 0.8150586230, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
306.317 < 396.825
  -> Decision False in time 0.3700000000, query time of that 0.2369685370, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.6300000000, query time of that 0.0954367710, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
284.549 < 351.685
  -> Decision False in time 2.9400000000, query time of that 0.4582269960, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
244.405 < 351.296
  -> Decision False in time 5.2000000000, query time of that 0.8295511640, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Accept!
  -> Decision True in time 7.1400000000, query time of that 0.1109465540, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
229.227 < 242.716
  -> Decision False in time 33.6700000000, query time of that 0.5402892000, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
228.171 < 230.82
  -> Decision False in time 2.3100000000, query time of that 0.0377496270, 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: 2.1619 cost: 0.00038 M: 10 delta: 1 time: 63.5392 one-recall: 0 one-ratio: 3.45348
iteration: 2 recall: 0.004 accuracy: 1.22149 cost: 0.000637428 M: 10 delta: 0.856032 time: 107.31 one-recall: 0 one-ratio: 2.78112
iteration: 3 recall: 0.0268 accuracy: 0.71027 cost: 0.00109521 M: 11.5287 delta: 0.83511 time: 160.334 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: 213.153 one-recall: 0.27 one-ratio: 1.7164
iteration: 5 recall: 0.5152 accuracy: 0.108971 cost: 0.00223602 M: 12.6035 delta: 0.664581 time: 268.054 one-recall: 0.68 one-ratio: 1.224
iteration: 6 recall: 0.7696 accuracy: 0.0273925 cost: 0.00298 M: 15.1149 delta: 0.432327 time: 330.061 one-recall: 0.9 one-ratio: 1.06228
iteration: 7 recall: 0.8892 accuracy: 0.00933819 cost: 0.00395517 M: 21.1397 delta: 0.196411 time: 402.018 one-recall: 0.98 one-ratio: 1.01002
iteration: 8 recall: 0.9388 accuracy: 0.00402025 cost: 0.0049795 M: 27.3038 delta: 0.088478 time: 471.412 one-recall: 0.98 one-ratio: 1.01002
iteration: 9 recall: 0.9592 accuracy: 0.00254933 cost: 0.00577282 M: 31.2909 delta: 0.0513333 time: 525.991 one-recall: 0.98 one-ratio: 1.01002
iteration: 10 recall: 0.9684 accuracy: 0.00181386 cost: 0.00625799 M: 33.3938 delta: 0.0371705 time: 563.735 one-recall: 0.99 one-ratio: 1.00902
iteration: 11 recall: 0.9708 accuracy: 0.00171023 cost: 0.00651492 M: 34.4224 delta: 0.0312862 time: 588.834 one-recall: 0.99 one-ratio: 1.00902
iteration: 12 recall: 0.9732 accuracy: 0.00157349 cost: 0.00664213 M: 34.9124 delta: 0.0287266 time: 605.83 one-recall: 0.99 one-ratio: 1.00902
iteration: 13 recall: 0.9756 accuracy: 0.00149851 cost: 0.00670423 M: 35.147 delta: 0.0275637 time: 618.143 one-recall: 0.99 one-ratio: 1.00902
iteration: 14 recall: 0.976 accuracy: 0.00148624 cost: 0.00673419 M: 35.2597 delta: 0.0270352 time: 627.828 one-recall: 0.99 one-ratio: 1.00902
iteration: 15 recall: 0.976 accuracy: 0.00148624 cost: 0.00674924 M: 35.3155 delta: 0.0267745 time: 636.164 one-recall: 0.99 one-ratio: 1.00902
iteration: 16 recall: 0.976 accuracy: 0.00148624 cost: 0.00675686 M: 35.3438 delta: 0.02665 time: 643.803 one-recall: 0.99 one-ratio: 1.00902
iteration: 17 recall: 0.976 accuracy: 0.00148624 cost: 0.00676078 M: 35.3581 delta: 0.026587 time: 651.071 one-recall: 0.99 one-ratio: 1.00902
iteration: 18 recall: 0.976 accuracy: 0.00148624 cost: 0.00676278 M: 35.3657 delta: 0.0265555 time: 658.144 one-recall: 0.99 one-ratio: 1.00902
iteration: 19 recall: 0.976 accuracy: 0.00148624 cost: 0.00676394 M: 35.37 delta: 0.0265389 time: 665.118 one-recall: 0.99 one-ratio: 1.00902
iteration: 20 recall: 0.976 accuracy: 0.00148624 cost: 0.00676453 M: 35.3723 delta: 0.0265289 time: 672.027 one-recall: 0.99 one-ratio: 1.00902
iteration: 21 recall: 0.976 accuracy: 0.00148624 cost: 0.00676485 M: 35.3735 delta: 0.0265253 time: 678.899 one-recall: 0.99 one-ratio: 1.00902
iteration: 22 recall: 0.976 accuracy: 0.00148624 cost: 0.00676503 M: 35.3743 delta: 0.0265221 time: 685.765 one-recall: 0.99 one-ratio: 1.00902
iteration: 23 recall: 0.9764 accuracy: 0.00145591 cost: 0.00676513 M: 35.3746 delta: 0.0265203 time: 692.606 one-recall: 0.99 one-ratio: 1.00902
iteration: 24 recall: 0.9764 accuracy: 0.00145591 cost: 0.00676519 M: 35.3749 delta: 0.0265192 time: 699.44 one-recall: 0.99 one-ratio: 1.00902
iteration: 25 recall: 0.9764 accuracy: 0.00145591 cost: 0.00676523 M: 35.375 delta: 0.0265185 time: 706.261 one-recall: 0.99 one-ratio: 1.00902
iteration: 26 recall: 0.9764 accuracy: 0.00145591 cost: 0.00676524 M: 35.3751 delta: 0.0265181 time: 713.08 one-recall: 0.99 one-ratio: 1.00902
iteration: 27 recall: 0.9764 accuracy: 0.00145591 cost: 0.00676525 M: 35.3751 delta: 0.026518 time: 719.899 one-recall: 0.99 one-ratio: 1.00902
iteration: 28 recall: 0.9764 accuracy: 0.00145591 cost: 0.00676526 M: 35.3751 delta: 0.0265179 time: 726.712 one-recall: 0.99 one-ratio: 1.00902
iteration: 29 recall: 0.9764 accuracy: 0.00145591 cost: 0.00676526 M: 35.3751 delta: 0.0265179 time: 733.532 one-recall: 0.99 one-ratio: 1.00902
iteration: 30 recall: 0.9764 accuracy: 0.00145591 cost: 0.00676526 M: 35.3751 delta: 0.026518 time: 740.347 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 758.4900000000007
Index size:  262768.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0113556000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.0700000000, query time of that 0.0229336880, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Reject!
409.805 < 444.595
  -> Decision False in time 0.0200000000, query time of that 0.0064775660, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
328.171 < 394.036
  -> Decision False in time 0.4500000000, query time of that 0.1436003340, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.5300000000, query time of that 0.0278105740, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
385.205 < 436.198
  -> Decision False in time 2.2100000000, query time of that 0.1120071630, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
238.558 < 250.523
  -> Decision False in time 0.9900000000, query time of that 0.0515672310, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Reject!
227.976 < 236.478
  -> Decision False in time 6.1600000000, query time of that 0.0298548630, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
200.265 < 201.837
  -> Decision False in time 2.7400000000, query time of that 0.0141108010, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
354.021 < 395.948
  -> Decision False in time 3.7200000000, query time of that 0.0193769170, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 3, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 3, {'reverse': -1}, False])
Got a train set of size (1000000 * 128)
Generating control...
Initializing...
iteration: 1 recall: 0 accuracy: 1.95708 cost: 0.00038 M: 10 delta: 1 time: 63.5455 one-recall: 0 one-ratio: 3.32328
iteration: 2 recall: 0.0032 accuracy: 1.10844 cost: 0.000637428 M: 10 delta: 0.856032 time: 107.312 one-recall: 0.01 one-ratio: 2.58946
iteration: 3 recall: 0.0328 accuracy: 0.616014 cost: 0.00109521 M: 11.5287 delta: 0.835105 time: 160.346 one-recall: 0.05 one-ratio: 2.03401
iteration: 4 recall: 0.1856 accuracy: 0.301847 cost: 0.00163043 M: 11.8363 delta: 0.783461 time: 213.16 one-recall: 0.24 one-ratio: 1.64713
iteration: 5 recall: 0.5172 accuracy: 0.100067 cost: 0.00223605 M: 12.6036 delta: 0.664608 time: 268.068 one-recall: 0.67 one-ratio: 1.19914
iteration: 6 recall: 0.7816 accuracy: 0.0262961 cost: 0.00297991 M: 15.1146 delta: 0.432353 time: 330.073 one-recall: 0.89 one-ratio: 1.03927
iteration: 7 recall: 0.8876 accuracy: 0.0100138 cost: 0.00395518 M: 21.14 delta: 0.196418 time: 402.031 one-recall: 0.96 one-ratio: 1.02032
iteration: 8 recall: 0.9376 accuracy: 0.00484691 cost: 0.00497979 M: 27.3056 delta: 0.0884494 time: 471.44 one-recall: 0.97 one-ratio: 1.01084
iteration: 9 recall: 0.9572 accuracy: 0.0027696 cost: 0.00577263 M: 31.2901 delta: 0.0513218 time: 525.971 one-recall: 0.99 one-ratio: 1.00154
iteration: 10 recall: 0.9672 accuracy: 0.0017295 cost: 0.00625707 M: 33.3933 delta: 0.0372014 time: 563.685 one-recall: 1 one-ratio: 1
iteration: 11 recall: 0.97 accuracy: 0.00158639 cost: 0.00651433 M: 34.4235 delta: 0.031321 time: 588.773 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.9704 accuracy: 0.00157168 cost: 0.00664229 M: 34.9163 delta: 0.0287276 time: 605.801 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.9708 accuracy: 0.00156816 cost: 0.00670454 M: 35.1515 delta: 0.0275808 time: 618.084 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.9708 accuracy: 0.00156816 cost: 0.0067348 M: 35.2651 delta: 0.0270351 time: 627.757 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.9712 accuracy: 0.00154515 cost: 0.00674998 M: 35.321 delta: 0.0267707 time: 636.071 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.9712 accuracy: 0.00154515 cost: 0.00675768 M: 35.3496 delta: 0.0266357 time: 643.686 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.9712 accuracy: 0.00154515 cost: 0.00676147 M: 35.3637 delta: 0.0265743 time: 650.909 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.9712 accuracy: 0.00154515 cost: 0.00676346 M: 35.3711 delta: 0.02654 time: 657.98 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.9712 accuracy: 0.00154515 cost: 0.00676447 M: 35.375 delta: 0.0265223 time: 664.931 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.9712 accuracy: 0.00154515 cost: 0.00676497 M: 35.3771 delta: 0.0265135 time: 671.837 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.9712 accuracy: 0.00154515 cost: 0.00676525 M: 35.3781 delta: 0.0265084 time: 678.699 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.9712 accuracy: 0.00154515 cost: 0.00676538 M: 35.3786 delta: 0.0265078 time: 685.549 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.9712 accuracy: 0.00154515 cost: 0.00676546 M: 35.3789 delta: 0.0265059 time: 692.371 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.9712 accuracy: 0.00154515 cost: 0.00676551 M: 35.3791 delta: 0.0265055 time: 699.203 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.9712 accuracy: 0.00154515 cost: 0.00676554 M: 35.3793 delta: 0.026505 time: 706.032 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.9712 accuracy: 0.00154515 cost: 0.00676556 M: 35.3793 delta: 0.0265045 time: 712.858 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.9712 accuracy: 0.00154515 cost: 0.00676557 M: 35.3794 delta: 0.0265043 time: 719.684 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.9712 accuracy: 0.00154515 cost: 0.00676557 M: 35.3794 delta: 0.0265041 time: 726.502 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.9712 accuracy: 0.00154515 cost: 0.00676557 M: 35.3794 delta: 0.0265041 time: 733.326 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.9712 accuracy: 0.00154515 cost: 0.00676557 M: 35.3794 delta: 0.0265041 time: 740.134 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 758.2800000000007
Index size:  262964.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0184249000
  Testing...
|S| = 80
|T| = 1152
Reject!
418.815 < 432.582
  -> Decision False in time 0.0200000000, query time of that 0.0076045680, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Reject!
382.324 < 406.379
  -> Decision False in time 0.0300000000, query time of that 0.0073944380, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
396.17 < 442.509
  -> Decision False in time 0.6100000000, query time of that 0.1879403260, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Reject!
355.124 < 451.341
  -> Decision False in time 0.4700000000, query time of that 0.0227070590, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
352.989 < 391.721
  -> Decision False in time 0.0400000000, query time of that 0.0020438520, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
406.685 < 431.39
  -> Decision False in time 0.1300000000, query time of that 0.0069724970, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Reject!
275.922 < 277.222
  -> Decision False in time 2.3600000000, query time of that 0.0114469070, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
411.862 < 420.854
  -> Decision False in time 6.7700000000, query time of that 0.0326239640, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
292.366 < 299.328
  -> Decision False in time 2.5900000000, query time of that 0.0135508580, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 30, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 30, {'reverse': -1}, False])
Got a train set of size (1000000 * 128)
Generating control...
Initializing...
iteration: 1 recall: 0.0008 accuracy: 2.24217 cost: 0.00038 M: 10 delta: 1 time: 63.5712 one-recall: 0 one-ratio: 3.33435
iteration: 2 recall: 0.004 accuracy: 1.14832 cost: 0.000637428 M: 10 delta: 0.856032 time: 107.34 one-recall: 0 one-ratio: 2.62876
iteration: 3 recall: 0.0308 accuracy: 0.650813 cost: 0.00109521 M: 11.5287 delta: 0.835109 time: 160.387 one-recall: 0.05 one-ratio: 2.11296
iteration: 4 recall: 0.19 accuracy: 0.302001 cost: 0.00163041 M: 11.8361 delta: 0.783446 time: 213.214 one-recall: 0.3 one-ratio: 1.56589
iteration: 5 recall: 0.5288 accuracy: 0.0906925 cost: 0.00223603 M: 12.6038 delta: 0.664586 time: 268.132 one-recall: 0.67 one-ratio: 1.19486
iteration: 6 recall: 0.788 accuracy: 0.0242574 cost: 0.00297996 M: 15.1145 delta: 0.432357 time: 330.145 one-recall: 0.85 one-ratio: 1.08362
iteration: 7 recall: 0.908 accuracy: 0.0083829 cost: 0.00395534 M: 21.1396 delta: 0.196412 time: 402.112 one-recall: 0.93 one-ratio: 1.05257
iteration: 8 recall: 0.9508 accuracy: 0.00430125 cost: 0.00498023 M: 27.3072 delta: 0.0884329 time: 471.547 one-recall: 0.96 one-ratio: 1.0441
iteration: 9 recall: 0.9652 accuracy: 0.00265834 cost: 0.00577251 M: 31.2881 delta: 0.0513361 time: 526.071 one-recall: 0.98 one-ratio: 1.02468
iteration: 10 recall: 0.9712 accuracy: 0.0021574 cost: 0.00625736 M: 33.3915 delta: 0.0372214 time: 563.809 one-recall: 0.98 one-ratio: 1.02468
iteration: 11 recall: 0.9756 accuracy: 0.00184529 cost: 0.00651522 M: 34.4242 delta: 0.0313344 time: 588.917 one-recall: 0.98 one-ratio: 1.02446
iteration: 12 recall: 0.978 accuracy: 0.00164178 cost: 0.00664336 M: 34.9166 delta: 0.0287426 time: 605.976 one-recall: 0.98 one-ratio: 1.02183
iteration: 13 recall: 0.9788 accuracy: 0.00156006 cost: 0.00670473 M: 35.1494 delta: 0.0275807 time: 618.213 one-recall: 0.98 one-ratio: 1.02183
iteration: 14 recall: 0.98 accuracy: 0.00144257 cost: 0.00673426 M: 35.2601 delta: 0.0270511 time: 627.828 one-recall: 0.98 one-ratio: 1.02183
iteration: 15 recall: 0.98 accuracy: 0.00144257 cost: 0.00674863 M: 35.3139 delta: 0.0267984 time: 636.12 one-recall: 0.98 one-ratio: 1.02183
iteration: 16 recall: 0.9804 accuracy: 0.00141142 cost: 0.0067561 M: 35.3422 delta: 0.0266772 time: 643.747 one-recall: 0.98 one-ratio: 1.02183
iteration: 17 recall: 0.9804 accuracy: 0.00141142 cost: 0.00675996 M: 35.3566 delta: 0.0266099 time: 651.012 one-recall: 0.98 one-ratio: 1.02183
iteration: 18 recall: 0.9804 accuracy: 0.00141142 cost: 0.00676193 M: 35.364 delta: 0.026576 time: 658.081 one-recall: 0.98 one-ratio: 1.02183
iteration: 19 recall: 0.9804 accuracy: 0.00141142 cost: 0.00676303 M: 35.3681 delta: 0.0265584 time: 665.049 one-recall: 0.98 one-ratio: 1.02183
iteration: 20 recall: 0.9804 accuracy: 0.00141142 cost: 0.00676357 M: 35.3702 delta: 0.0265492 time: 671.927 one-recall: 0.98 one-ratio: 1.02183
iteration: 21 recall: 0.9804 accuracy: 0.00141142 cost: 0.00676383 M: 35.3712 delta: 0.0265458 time: 678.751 one-recall: 0.98 one-ratio: 1.02183
iteration: 22 recall: 0.9804 accuracy: 0.00141142 cost: 0.00676398 M: 35.3717 delta: 0.026543 time: 685.605 one-recall: 0.98 one-ratio: 1.02183
iteration: 23 recall: 0.9804 accuracy: 0.00141142 cost: 0.00676406 M: 35.372 delta: 0.0265422 time: 692.44 one-recall: 0.98 one-ratio: 1.02183
iteration: 24 recall: 0.9804 accuracy: 0.00141142 cost: 0.00676409 M: 35.3722 delta: 0.0265413 time: 699.261 one-recall: 0.98 one-ratio: 1.02183
iteration: 25 recall: 0.9804 accuracy: 0.00141142 cost: 0.00676411 M: 35.3722 delta: 0.0265414 time: 706.083 one-recall: 0.98 one-ratio: 1.02183
iteration: 26 recall: 0.9804 accuracy: 0.00141142 cost: 0.00676412 M: 35.3722 delta: 0.0265411 time: 712.907 one-recall: 0.98 one-ratio: 1.02183
iteration: 27 recall: 0.9804 accuracy: 0.00141142 cost: 0.00676414 M: 35.3723 delta: 0.0265409 time: 719.732 one-recall: 0.98 one-ratio: 1.02183
iteration: 28 recall: 0.9804 accuracy: 0.00141142 cost: 0.00676416 M: 35.3724 delta: 0.0265406 time: 726.554 one-recall: 0.98 one-ratio: 1.02183
iteration: 29 recall: 0.9804 accuracy: 0.00141142 cost: 0.00676417 M: 35.3725 delta: 0.0265406 time: 733.377 one-recall: 0.98 one-ratio: 1.02183
iteration: 30 recall: 0.9804 accuracy: 0.00141142 cost: 0.00676418 M: 35.3725 delta: 0.0265407 time: 740.195 one-recall: 0.98 one-ratio: 1.02183
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 758.3400000000001
Index size:  262940.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0072060000
  Testing...
|S| = 80
|T| = 1152
Reject!
341.69 < 370.493
  -> Decision False in time 0.0900000000, query time of that 0.0398943480, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Accept!
  -> Decision True in time 0.8700000000, query time of that 0.4242343650, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Accept!
  -> Decision True in time 8.8200000000, query time of that 4.2402331670, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.5800000000, query time of that 0.0528850670, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
291.441 < 329.865
  -> Decision False in time 4.5200000000, query time of that 0.4167731350, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
351.694 < 373.985
  -> Decision False in time 9.1900000000, query time of that 0.8626819900, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Accept!
  -> Decision True in time 7.1100000000, query time of that 0.0620371810, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
265.511 < 270.871
  -> Decision False in time 15.6500000000, query time of that 0.1435309800, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
247.661 < 260.594
  -> Decision False in time 8.9400000000, query time of that 0.0819366020, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 70, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 70, {'reverse': -1}, False])
Got a train set of size (1000000 * 128)
Generating control...
Initializing...
iteration: 1 recall: 0 accuracy: 2.97095 cost: 0.00038 M: 10 delta: 1 time: 63.5432 one-recall: 0 one-ratio: 3.60395
iteration: 2 recall: 0.0036 accuracy: 1.41785 cost: 0.000637428 M: 10 delta: 0.856032 time: 107.309 one-recall: 0 one-ratio: 2.76724
iteration: 3 recall: 0.0388 accuracy: 0.750892 cost: 0.00109521 M: 11.5287 delta: 0.835106 time: 160.337 one-recall: 0.02 one-ratio: 2.24115
iteration: 4 recall: 0.2036 accuracy: 0.339905 cost: 0.00163044 M: 11.8364 delta: 0.783461 time: 213.151 one-recall: 0.25 one-ratio: 1.74996
iteration: 5 recall: 0.5428 accuracy: 0.100926 cost: 0.00223607 M: 12.6038 delta: 0.664609 time: 268.055 one-recall: 0.7 one-ratio: 1.25965
iteration: 6 recall: 0.7864 accuracy: 0.0298143 cost: 0.00298001 M: 15.1148 delta: 0.432313 time: 330.061 one-recall: 0.89 one-ratio: 1.09525
iteration: 7 recall: 0.892 accuracy: 0.0114988 cost: 0.00395545 M: 21.1418 delta: 0.1964 time: 402.026 one-recall: 0.94 one-ratio: 1.02252
iteration: 8 recall: 0.9468 accuracy: 0.00326675 cost: 0.0049798 M: 27.3057 delta: 0.08839 time: 471.422 one-recall: 0.99 one-ratio: 1.00583
iteration: 9 recall: 0.9696 accuracy: 0.00168427 cost: 0.00577305 M: 31.2894 delta: 0.0513136 time: 525.987 one-recall: 1 one-ratio: 1
iteration: 10 recall: 0.9776 accuracy: 0.00115933 cost: 0.0062575 M: 33.3918 delta: 0.0371811 time: 563.705 one-recall: 1 one-ratio: 1
iteration: 11 recall: 0.9816 accuracy: 0.0010223 cost: 0.00651504 M: 34.4232 delta: 0.0312776 time: 588.804 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.9828 accuracy: 0.000947262 cost: 0.00664272 M: 34.9158 delta: 0.0287059 time: 605.812 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.9828 accuracy: 0.000947262 cost: 0.00670497 M: 35.1506 delta: 0.0275563 time: 618.084 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.9832 accuracy: 0.000938423 cost: 0.00673535 M: 35.2646 delta: 0.0270092 time: 627.755 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.9832 accuracy: 0.000938423 cost: 0.00675046 M: 35.3206 delta: 0.0267477 time: 636.046 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.9832 accuracy: 0.000938423 cost: 0.00675794 M: 35.3483 delta: 0.0266209 time: 643.625 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.9832 accuracy: 0.000938423 cost: 0.00676174 M: 35.362 delta: 0.0265597 time: 650.836 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.9832 accuracy: 0.000938423 cost: 0.00676368 M: 35.3693 delta: 0.0265245 time: 657.85 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.9832 accuracy: 0.000938423 cost: 0.00676468 M: 35.373 delta: 0.0265089 time: 664.759 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.9832 accuracy: 0.000938423 cost: 0.00676516 M: 35.3748 delta: 0.0265012 time: 671.606 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.9832 accuracy: 0.000938423 cost: 0.00676543 M: 35.3759 delta: 0.0264965 time: 678.413 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.9832 accuracy: 0.000938423 cost: 0.00676555 M: 35.3763 delta: 0.0264946 time: 685.205 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.9832 accuracy: 0.000938423 cost: 0.0067656 M: 35.3765 delta: 0.0264934 time: 691.977 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.9832 accuracy: 0.000938423 cost: 0.00676564 M: 35.3766 delta: 0.0264933 time: 698.745 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.9832 accuracy: 0.000938423 cost: 0.00676567 M: 35.3767 delta: 0.0264928 time: 705.511 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.9832 accuracy: 0.000938423 cost: 0.00676567 M: 35.3767 delta: 0.0264926 time: 712.278 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.9832 accuracy: 0.000938423 cost: 0.00676568 M: 35.3768 delta: 0.0264925 time: 719.038 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.9832 accuracy: 0.000938423 cost: 0.00676568 M: 35.3768 delta: 0.0264924 time: 725.799 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.9832 accuracy: 0.000938423 cost: 0.00676568 M: 35.3768 delta: 0.0264924 time: 732.553 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.9832 accuracy: 0.000938423 cost: 0.00676568 M: 35.3768 delta: 0.0264924 time: 739.311 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 757.3700000000008
Index size:  262972.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0035684000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.1300000000, query time of that 0.0752967500, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Accept!
  -> Decision True in time 1.1700000000, query time of that 0.7274682550, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
365.49 < 372.949
  -> Decision False in time 4.7800000000, query time of that 2.9431445440, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.6300000000, query time of that 0.0903194340, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Accept!
  -> Decision True in time 6.1400000000, query time of that 0.9006659460, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
284.018 < 359.362
  -> Decision False in time 7.6400000000, query time of that 1.1166757440, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Accept!
  -> Decision True in time 7.1700000000, query time of that 0.1049243180, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
260.759 < 265.313
  -> Decision False in time 2.7400000000, query time of that 0.0418063700, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
223.087 < 229.257
  -> Decision False in time 7.3200000000, query time of that 0.1112275050, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 40, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 40, {'reverse': -1}, False])
Got a train set of size (1000000 * 128)
Generating control...
Initializing...
iteration: 1 recall: 0 accuracy: 2.35956 cost: 0.00038 M: 10 delta: 1 time: 63.5367 one-recall: 0 one-ratio: 3.54069
iteration: 2 recall: 0.0064 accuracy: 1.22546 cost: 0.000637428 M: 10 delta: 0.856032 time: 107.303 one-recall: 0.01 one-ratio: 2.76249
iteration: 3 recall: 0.0388 accuracy: 0.657027 cost: 0.00109521 M: 11.5287 delta: 0.835105 time: 160.331 one-recall: 0.06 one-ratio: 2.24519
iteration: 4 recall: 0.2164 accuracy: 0.304259 cost: 0.00163044 M: 11.8362 delta: 0.783448 time: 213.147 one-recall: 0.32 one-ratio: 1.68369
iteration: 5 recall: 0.5584 accuracy: 0.0886674 cost: 0.00223608 M: 12.6037 delta: 0.664602 time: 268.055 one-recall: 0.65 one-ratio: 1.22213
iteration: 6 recall: 0.8036 accuracy: 0.0269521 cost: 0.00297999 M: 15.1148 delta: 0.432375 time: 330.055 one-recall: 0.84 one-ratio: 1.08719
iteration: 7 recall: 0.894 accuracy: 0.00887214 cost: 0.0039552 M: 21.1384 delta: 0.196396 time: 402.007 one-recall: 0.92 one-ratio: 1.01572
iteration: 8 recall: 0.9384 accuracy: 0.00433626 cost: 0.00497968 M: 27.3042 delta: 0.0884871 time: 471.415 one-recall: 0.95 one-ratio: 1.01014
iteration: 9 recall: 0.9556 accuracy: 0.00287752 cost: 0.00577267 M: 31.289 delta: 0.0513514 time: 525.98 one-recall: 0.95 one-ratio: 1.01014
iteration: 10 recall: 0.9644 accuracy: 0.00232121 cost: 0.00625728 M: 33.3951 delta: 0.0372204 time: 563.709 one-recall: 0.95 one-ratio: 1.01014
iteration: 11 recall: 0.9708 accuracy: 0.00184301 cost: 0.00651588 M: 34.4302 delta: 0.0313053 time: 588.901 one-recall: 0.97 one-ratio: 1.00923
iteration: 12 recall: 0.9752 accuracy: 0.00150606 cost: 0.00664409 M: 34.9232 delta: 0.0287449 time: 605.98 one-recall: 0.97 one-ratio: 1.00923
iteration: 13 recall: 0.9772 accuracy: 0.00137042 cost: 0.00670681 M: 35.1618 delta: 0.0275708 time: 618.344 one-recall: 0.97 one-ratio: 1.00923
iteration: 14 recall: 0.978 accuracy: 0.00132791 cost: 0.00673724 M: 35.2757 delta: 0.0270174 time: 628.077 one-recall: 0.97 one-ratio: 1.00923
iteration: 15 recall: 0.978 accuracy: 0.00132791 cost: 0.00675234 M: 35.3321 delta: 0.0267558 time: 636.437 one-recall: 0.97 one-ratio: 1.00923
iteration: 16 recall: 0.978 accuracy: 0.00132791 cost: 0.00675996 M: 35.3603 delta: 0.0266258 time: 644.087 one-recall: 0.97 one-ratio: 1.00923
iteration: 17 recall: 0.978 accuracy: 0.00132791 cost: 0.00676395 M: 35.375 delta: 0.0265592 time: 651.368 one-recall: 0.97 one-ratio: 1.00923
iteration: 18 recall: 0.9784 accuracy: 0.00130382 cost: 0.00676601 M: 35.3826 delta: 0.0265265 time: 658.447 one-recall: 0.97 one-ratio: 1.00923
iteration: 19 recall: 0.9784 accuracy: 0.00130382 cost: 0.00676697 M: 35.3861 delta: 0.0265125 time: 665.417 one-recall: 0.97 one-ratio: 1.00923
iteration: 20 recall: 0.9784 accuracy: 0.00130382 cost: 0.00676752 M: 35.3882 delta: 0.0265041 time: 672.331 one-recall: 0.97 one-ratio: 1.00923
iteration: 21 recall: 0.9784 accuracy: 0.00130382 cost: 0.00676786 M: 35.3895 delta: 0.0265001 time: 679.211 one-recall: 0.97 one-ratio: 1.00923
iteration: 22 recall: 0.9784 accuracy: 0.00130382 cost: 0.00676807 M: 35.3903 delta: 0.0264967 time: 686.072 one-recall: 0.97 one-ratio: 1.00923
iteration: 23 recall: 0.9784 accuracy: 0.00130382 cost: 0.00676819 M: 35.3908 delta: 0.0264947 time: 692.92 one-recall: 0.97 one-ratio: 1.00923
iteration: 24 recall: 0.9784 accuracy: 0.00130382 cost: 0.00676825 M: 35.3911 delta: 0.0264937 time: 699.758 one-recall: 0.97 one-ratio: 1.00923
iteration: 25 recall: 0.9784 accuracy: 0.00130382 cost: 0.00676828 M: 35.3912 delta: 0.0264932 time: 706.586 one-recall: 0.97 one-ratio: 1.00923
iteration: 26 recall: 0.9784 accuracy: 0.00130382 cost: 0.00676831 M: 35.3913 delta: 0.026493 time: 713.414 one-recall: 0.97 one-ratio: 1.00923
iteration: 27 recall: 0.9784 accuracy: 0.00130382 cost: 0.00676832 M: 35.3914 delta: 0.0264927 time: 720.239 one-recall: 0.97 one-ratio: 1.00923
iteration: 28 recall: 0.9784 accuracy: 0.00130382 cost: 0.00676832 M: 35.3914 delta: 0.0264926 time: 727.078 one-recall: 0.97 one-ratio: 1.00923
iteration: 29 recall: 0.9784 accuracy: 0.00130382 cost: 0.00676832 M: 35.3914 delta: 0.0264926 time: 733.903 one-recall: 0.97 one-ratio: 1.00923
iteration: 30 recall: 0.9784 accuracy: 0.00130382 cost: 0.00676833 M: 35.3914 delta: 0.0264926 time: 740.73 one-recall: 0.97 one-ratio: 1.00923
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 758.8899999999994
Index size:  263384.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0062335000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.0900000000, query time of that 0.0497319680, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Accept!
  -> Decision True in time 0.9700000000, query time of that 0.5111526890, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
368.557 < 398.359
  -> Decision False in time 0.6000000000, query time of that 0.3185928470, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.5800000000, query time of that 0.0594112610, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
298.206 < 301.815
  -> Decision False in time 5.5100000000, query time of that 0.5878661570, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
345.432 < 352.099
  -> Decision False in time 4.3400000000, query time of that 0.4684368020, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Reject!
422.064 < 438.705
  -> Decision False in time 2.9400000000, query time of that 0.0299501620, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
223.397 < 225.892
  -> Decision False in time 0.3100000000, query time of that 0.0034602260, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
290.991 < 295.296
  -> Decision False in time 6.1900000000, query time of that 0.0679502100, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 50, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 50, {'reverse': -1}, False])
Got a train set of size (1000000 * 128)
Generating control...
Initializing...
iteration: 1 recall: 0 accuracy: 2.25507 cost: 0.00038 M: 10 delta: 1 time: 63.5772 one-recall: 0 one-ratio: 3.43724
iteration: 2 recall: 0.0048 accuracy: 1.3137 cost: 0.000637428 M: 10 delta: 0.856033 time: 107.354 one-recall: 0.01 one-ratio: 2.58612
iteration: 3 recall: 0.03 accuracy: 0.759924 cost: 0.00109521 M: 11.5287 delta: 0.835109 time: 160.406 one-recall: 0.02 one-ratio: 2.15849
iteration: 4 recall: 0.2032 accuracy: 0.375118 cost: 0.00163043 M: 11.8362 delta: 0.783454 time: 213.235 one-recall: 0.24 one-ratio: 1.65968
iteration: 5 recall: 0.5452 accuracy: 0.095648 cost: 0.00223606 M: 12.6038 delta: 0.664597 time: 268.15 one-recall: 0.65 one-ratio: 1.22931
iteration: 6 recall: 0.8024 accuracy: 0.0263904 cost: 0.00297993 M: 15.1142 delta: 0.432345 time: 330.171 one-recall: 0.9 one-ratio: 1.05007
iteration: 7 recall: 0.9148 accuracy: 0.00613128 cost: 0.00395504 M: 21.1384 delta: 0.196421 time: 402.141 one-recall: 0.97 one-ratio: 1.01117
iteration: 8 recall: 0.9536 accuracy: 0.00287991 cost: 0.00497966 M: 27.3032 delta: 0.0884585 time: 471.575 one-recall: 0.99 one-ratio: 1.00542
iteration: 9 recall: 0.9696 accuracy: 0.0014466 cost: 0.00577331 M: 31.2907 delta: 0.0513633 time: 526.176 one-recall: 1 one-ratio: 1
iteration: 10 recall: 0.9784 accuracy: 0.00094683 cost: 0.00625845 M: 33.3968 delta: 0.0371964 time: 563.937 one-recall: 1 one-ratio: 1
iteration: 11 recall: 0.9804 accuracy: 0.000874649 cost: 0.00651497 M: 34.4238 delta: 0.0313452 time: 588.975 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.9824 accuracy: 0.00078899 cost: 0.00664273 M: 34.9166 delta: 0.0287611 time: 605.977 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.9836 accuracy: 0.00073211 cost: 0.00670497 M: 35.152 delta: 0.0276079 time: 618.271 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.984 accuracy: 0.000726122 cost: 0.00673527 M: 35.2652 delta: 0.0270632 time: 627.949 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.984 accuracy: 0.000726122 cost: 0.00675023 M: 35.3212 delta: 0.0267972 time: 636.244 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.984 accuracy: 0.000726122 cost: 0.0067577 M: 35.3488 delta: 0.0266769 time: 643.824 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.984 accuracy: 0.000726122 cost: 0.00676154 M: 35.3631 delta: 0.0266137 time: 651.048 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.984 accuracy: 0.000726122 cost: 0.00676347 M: 35.3703 delta: 0.0265817 time: 658.068 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.984 accuracy: 0.000726122 cost: 0.00676447 M: 35.374 delta: 0.0265649 time: 664.988 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.984 accuracy: 0.000726122 cost: 0.00676501 M: 35.3761 delta: 0.0265572 time: 671.851 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.984 accuracy: 0.000726122 cost: 0.00676531 M: 35.3772 delta: 0.0265537 time: 678.679 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.984 accuracy: 0.000726122 cost: 0.00676552 M: 35.378 delta: 0.0265496 time: 685.494 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.984 accuracy: 0.000726122 cost: 0.0067656 M: 35.3783 delta: 0.0265484 time: 692.302 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.984 accuracy: 0.000726122 cost: 0.00676564 M: 35.3785 delta: 0.0265475 time: 699.091 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.984 accuracy: 0.000726122 cost: 0.00676566 M: 35.3785 delta: 0.0265471 time: 705.882 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.984 accuracy: 0.000726122 cost: 0.00676567 M: 35.3786 delta: 0.0265471 time: 712.661 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.984 accuracy: 0.000726122 cost: 0.00676568 M: 35.3786 delta: 0.026547 time: 719.448 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.984 accuracy: 0.000726122 cost: 0.00676569 M: 35.3786 delta: 0.0265469 time: 726.228 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.984 accuracy: 0.000726122 cost: 0.00676569 M: 35.3786 delta: 0.0265469 time: 733.006 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.984 accuracy: 0.000726122 cost: 0.00676569 M: 35.3786 delta: 0.0265469 time: 739.776 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 757.8700000000008
Index size:  262924.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0049731000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.1100000000, query time of that 0.0627378820, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Accept!
  -> Decision True in time 1.0400000000, query time of that 0.5896500450, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
232.755 < 235.096
  -> Decision False in time 0.3900000000, query time of that 0.2211421280, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Reject!
302.339 < 313.401
  -> Decision False in time 0.4200000000, query time of that 0.0482523910, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Accept!
  -> Decision True in time 5.9400000000, query time of that 0.7130714260, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
269.735 < 273.81
  -> Decision False in time 18.9200000000, query time of that 2.3208884750, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Accept!
  -> Decision True in time 7.1500000000, query time of that 0.0872976190, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
406.743 < 430.249
  -> Decision False in time 0.9900000000, query time of that 0.0111965670, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
344.015 < 349.431
  -> Decision False in time 12.8100000000, query time of that 0.1490520040, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 60, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 60, {'reverse': -1}, False])
Got a train set of size (1000000 * 128)
Generating control...
Initializing...
iteration: 1 recall: 0 accuracy: 2.35603 cost: 0.00038 M: 10 delta: 1 time: 63.5697 one-recall: 0 one-ratio: 3.52762
iteration: 2 recall: 0.0028 accuracy: 1.19801 cost: 0.000637428 M: 10 delta: 0.856032 time: 107.333 one-recall: 0 one-ratio: 2.79656
iteration: 3 recall: 0.0352 accuracy: 0.67725 cost: 0.00109521 M: 11.5287 delta: 0.835107 time: 160.374 one-recall: 0.03 one-ratio: 2.2565
iteration: 4 recall: 0.1936 accuracy: 0.340216 cost: 0.00163044 M: 11.8362 delta: 0.783458 time: 213.193 one-recall: 0.22 one-ratio: 1.75304
iteration: 5 recall: 0.5068 accuracy: 0.104869 cost: 0.00223608 M: 12.6039 delta: 0.664584 time: 268.115 one-recall: 0.73 one-ratio: 1.19642
iteration: 6 recall: 0.7676 accuracy: 0.0320912 cost: 0.00297999 M: 15.1142 delta: 0.432317 time: 330.132 one-recall: 0.9 one-ratio: 1.06804
iteration: 7 recall: 0.902 accuracy: 0.007818 cost: 0.00395526 M: 21.1404 delta: 0.1964 time: 402.111 one-recall: 0.97 one-ratio: 1.0048
iteration: 8 recall: 0.9544 accuracy: 0.00283423 cost: 0.00497986 M: 27.3068 delta: 0.0884453 time: 471.531 one-recall: 0.99 one-ratio: 1.00181
iteration: 9 recall: 0.9692 accuracy: 0.00163485 cost: 0.00577323 M: 31.2908 delta: 0.0513818 time: 526.108 one-recall: 1 one-ratio: 1
iteration: 10 recall: 0.9768 accuracy: 0.001231 cost: 0.00625881 M: 33.398 delta: 0.0372048 time: 563.88 one-recall: 1 one-ratio: 1
iteration: 11 recall: 0.9824 accuracy: 0.000919789 cost: 0.00651646 M: 34.4294 delta: 0.0313137 time: 588.988 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.984 accuracy: 0.000840621 cost: 0.0066442 M: 34.921 delta: 0.0287542 time: 605.995 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.9848 accuracy: 0.000822025 cost: 0.00670673 M: 35.1558 delta: 0.027577 time: 618.309 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.9848 accuracy: 0.000822025 cost: 0.00673671 M: 35.268 delta: 0.0270389 time: 627.986 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.9848 accuracy: 0.000822025 cost: 0.00675151 M: 35.3231 delta: 0.0267835 time: 636.29 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.9852 accuracy: 0.000802364 cost: 0.00675887 M: 35.3502 delta: 0.0266602 time: 643.891 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.9852 accuracy: 0.000802364 cost: 0.00676268 M: 35.3644 delta: 0.0266003 time: 651.14 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.9852 accuracy: 0.000802364 cost: 0.00676468 M: 35.3719 delta: 0.0265695 time: 658.212 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.9852 accuracy: 0.000802364 cost: 0.00676577 M: 35.376 delta: 0.0265486 time: 665.177 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.9852 accuracy: 0.000802364 cost: 0.00676636 M: 35.3783 delta: 0.0265395 time: 672.075 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.9852 accuracy: 0.000802364 cost: 0.00676664 M: 35.3794 delta: 0.0265339 time: 678.932 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.9852 accuracy: 0.000802364 cost: 0.00676681 M: 35.3801 delta: 0.0265315 time: 685.768 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.9852 accuracy: 0.000802364 cost: 0.00676689 M: 35.3804 delta: 0.0265304 time: 692.6 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.9852 accuracy: 0.000802364 cost: 0.00676692 M: 35.3805 delta: 0.02653 time: 699.422 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.9852 accuracy: 0.000802364 cost: 0.00676695 M: 35.3805 delta: 0.0265294 time: 706.234 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.9852 accuracy: 0.000802364 cost: 0.00676695 M: 35.3806 delta: 0.0265292 time: 713.041 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.9852 accuracy: 0.000802364 cost: 0.00676696 M: 35.3806 delta: 0.0265291 time: 719.848 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.9852 accuracy: 0.000802364 cost: 0.00676696 M: 35.3806 delta: 0.0265291 time: 726.66 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.9852 accuracy: 0.000802364 cost: 0.00676696 M: 35.3806 delta: 0.0265291 time: 733.47 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.9852 accuracy: 0.000802364 cost: 0.00676696 M: 35.3806 delta: 0.0265291 time: 740.278 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 758.409999999998
Index size:  262788.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0041305000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.1100000000, query time of that 0.0654613010, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Accept!
  -> Decision True in time 1.1100000000, query time of that 0.6627675380, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
329.559 < 335.081
  -> Decision False in time 7.8200000000, query time of that 4.6501867470, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.6100000000, query time of that 0.0795351920, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Accept!
  -> Decision True in time 6.0400000000, query time of that 0.8001042180, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
236.859 < 238.849
  -> Decision False in time 17.8800000000, query time of that 2.3749494300, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Accept!
  -> Decision True in time 7.1400000000, query time of that 0.0893084200, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
281.991 < 290.233
  -> Decision False in time 3.3600000000, query time of that 0.0452758380, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
276.215 < 277.8
  -> Decision False in time 1.2900000000, query time of that 0.0184875610, 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: 2.63903 cost: 0.00038 M: 10 delta: 1 time: 63.551 one-recall: 0 one-ratio: 3.42587
iteration: 2 recall: 0.0032 accuracy: 1.20621 cost: 0.000637428 M: 10 delta: 0.856032 time: 107.309 one-recall: 0 one-ratio: 2.78034
iteration: 3 recall: 0.0276 accuracy: 0.643724 cost: 0.00109521 M: 11.5287 delta: 0.835108 time: 160.328 one-recall: 0.02 one-ratio: 2.27903
iteration: 4 recall: 0.1792 accuracy: 0.321169 cost: 0.00163041 M: 11.8361 delta: 0.783443 time: 213.136 one-recall: 0.24 one-ratio: 1.70896
iteration: 5 recall: 0.486 accuracy: 0.10468 cost: 0.00223604 M: 12.6036 delta: 0.66462 time: 268.032 one-recall: 0.63 one-ratio: 1.25943
iteration: 6 recall: 0.7544 accuracy: 0.0308753 cost: 0.00297988 M: 15.1145 delta: 0.432371 time: 330.033 one-recall: 0.83 one-ratio: 1.0961
iteration: 7 recall: 0.8804 accuracy: 0.00963993 cost: 0.0039553 M: 21.1403 delta: 0.196417 time: 401.995 one-recall: 0.96 one-ratio: 1.01521
iteration: 8 recall: 0.9352 accuracy: 0.00407707 cost: 0.00497963 M: 27.3054 delta: 0.0884656 time: 471.383 one-recall: 0.98 one-ratio: 1.00213
iteration: 9 recall: 0.962 accuracy: 0.00202291 cost: 0.00577254 M: 31.2897 delta: 0.0513775 time: 525.933 one-recall: 0.98 one-ratio: 1.00213
iteration: 10 recall: 0.9712 accuracy: 0.00154237 cost: 0.00625737 M: 33.3944 delta: 0.0372322 time: 563.686 one-recall: 0.98 one-ratio: 1.00213
iteration: 11 recall: 0.9752 accuracy: 0.00140156 cost: 0.00651451 M: 34.4235 delta: 0.03133 time: 588.812 one-recall: 0.98 one-ratio: 1.00213
iteration: 12 recall: 0.9768 accuracy: 0.00124177 cost: 0.00664184 M: 34.9144 delta: 0.0287576 time: 605.833 one-recall: 0.99 one-ratio: 1.00167
iteration: 13 recall: 0.9772 accuracy: 0.0012255 cost: 0.00670363 M: 35.1475 delta: 0.0276003 time: 618.121 one-recall: 0.99 one-ratio: 1.00167
iteration: 14 recall: 0.9772 accuracy: 0.00122128 cost: 0.00673354 M: 35.2598 delta: 0.0270528 time: 627.786 one-recall: 0.99 one-ratio: 1.00167
iteration: 15 recall: 0.9772 accuracy: 0.00122128 cost: 0.00674807 M: 35.314 delta: 0.0268027 time: 636.099 one-recall: 0.99 one-ratio: 1.00167
iteration: 16 recall: 0.9776 accuracy: 0.00119084 cost: 0.00675549 M: 35.3418 delta: 0.0266829 time: 643.708 one-recall: 0.99 one-ratio: 1.00167
iteration: 17 recall: 0.9776 accuracy: 0.00119084 cost: 0.00675933 M: 35.3559 delta: 0.0266179 time: 650.968 one-recall: 0.99 one-ratio: 1.00167
iteration: 18 recall: 0.9776 accuracy: 0.00119084 cost: 0.0067613 M: 35.3633 delta: 0.0265849 time: 658.025 one-recall: 0.99 one-ratio: 1.00167
iteration: 19 recall: 0.9776 accuracy: 0.00119084 cost: 0.00676238 M: 35.3675 delta: 0.026569 time: 664.982 one-recall: 0.99 one-ratio: 1.00167
iteration: 20 recall: 0.9776 accuracy: 0.00119084 cost: 0.00676293 M: 35.3695 delta: 0.0265594 time: 671.875 one-recall: 0.99 one-ratio: 1.00167
iteration: 21 recall: 0.9776 accuracy: 0.00119084 cost: 0.00676324 M: 35.3706 delta: 0.0265549 time: 678.719 one-recall: 0.99 one-ratio: 1.00167
iteration: 22 recall: 0.9776 accuracy: 0.00119084 cost: 0.00676339 M: 35.3712 delta: 0.026552 time: 685.553 one-recall: 0.99 one-ratio: 1.00167
iteration: 23 recall: 0.9776 accuracy: 0.00119084 cost: 0.00676348 M: 35.3715 delta: 0.0265506 time: 692.377 one-recall: 0.99 one-ratio: 1.00167
iteration: 24 recall: 0.9776 accuracy: 0.00119084 cost: 0.00676352 M: 35.3717 delta: 0.0265497 time: 699.187 one-recall: 0.99 one-ratio: 1.00167
iteration: 25 recall: 0.9776 accuracy: 0.00119084 cost: 0.00676353 M: 35.3718 delta: 0.0265494 time: 705.978 one-recall: 0.99 one-ratio: 1.00167
iteration: 26 recall: 0.9776 accuracy: 0.00119084 cost: 0.00676354 M: 35.3719 delta: 0.0265492 time: 712.768 one-recall: 0.99 one-ratio: 1.00167
iteration: 27 recall: 0.9776 accuracy: 0.00119084 cost: 0.00676355 M: 35.3719 delta: 0.0265492 time: 719.548 one-recall: 0.99 one-ratio: 1.00167
iteration: 28 recall: 0.9776 accuracy: 0.00119084 cost: 0.00676355 M: 35.3719 delta: 0.0265492 time: 726.328 one-recall: 0.99 one-ratio: 1.00167
iteration: 29 recall: 0.9776 accuracy: 0.00119084 cost: 0.00676355 M: 35.3719 delta: 0.0265491 time: 733.11 one-recall: 0.99 one-ratio: 1.00167
iteration: 30 recall: 0.9776 accuracy: 0.00119084 cost: 0.00676355 M: 35.3719 delta: 0.0265491 time: 739.895 one-recall: 0.99 one-ratio: 1.00167
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 758.0
Index size:  262732.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0027133000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.1300000000, query time of that 0.0896164640, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Reject!
364.707 < 435.001
  -> Decision False in time 1.1500000000, query time of that 0.7615145750, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Accept!
  -> Decision True in time 13.4600000000, query time of that 8.8911595430, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.6400000000, query time of that 0.1070206330, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Accept!
  -> Decision True in time 6.3600000000, query time of that 1.0456744830, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
300.401 < 302.483
  -> Decision False in time 7.3100000000, query time of that 1.2238396600, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Accept!
  -> Decision True in time 7.1600000000, query time of that 0.1201308450, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
318.36 < 320.036
  -> Decision False in time 13.8200000000, query time of that 0.2445450140, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
344.253 < 365.048
  -> Decision False in time 51.9700000000, query time of that 0.8877721830, 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: 2.42026 cost: 0.00038 M: 10 delta: 1 time: 63.572 one-recall: 0 one-ratio: 3.25976
iteration: 2 recall: 0.0052 accuracy: 1.3217 cost: 0.000637428 M: 10 delta: 0.856032 time: 107.343 one-recall: 0 one-ratio: 2.5333
iteration: 3 recall: 0.0328 accuracy: 0.711518 cost: 0.00109521 M: 11.5287 delta: 0.835108 time: 160.374 one-recall: 0 one-ratio: 2.02875
iteration: 4 recall: 0.1984 accuracy: 0.346673 cost: 0.00163044 M: 11.8363 delta: 0.783448 time: 213.196 one-recall: 0.18 one-ratio: 1.6133
iteration: 5 recall: 0.548 accuracy: 0.0941023 cost: 0.00223609 M: 12.6039 delta: 0.664627 time: 268.122 one-recall: 0.6 one-ratio: 1.22187
iteration: 6 recall: 0.7972 accuracy: 0.0259827 cost: 0.00298 M: 15.1148 delta: 0.432365 time: 330.149 one-recall: 0.89 one-ratio: 1.08509
iteration: 7 recall: 0.9084 accuracy: 0.00763015 cost: 0.00395521 M: 21.1398 delta: 0.196426 time: 402.125 one-recall: 0.96 one-ratio: 1.02016
iteration: 8 recall: 0.9512 accuracy: 0.00357844 cost: 0.00497964 M: 27.3042 delta: 0.0884669 time: 471.546 one-recall: 0.98 one-ratio: 1.01352
iteration: 9 recall: 0.9688 accuracy: 0.00225542 cost: 0.00577297 M: 31.2919 delta: 0.051365 time: 526.141 one-recall: 0.98 one-ratio: 1.01352
iteration: 10 recall: 0.974 accuracy: 0.00189021 cost: 0.00625821 M: 33.3985 delta: 0.0372157 time: 563.93 one-recall: 0.98 one-ratio: 1.01352
iteration: 11 recall: 0.9768 accuracy: 0.00172096 cost: 0.00651591 M: 34.4292 delta: 0.0313268 time: 589.066 one-recall: 0.98 one-ratio: 1.01352
iteration: 12 recall: 0.9772 accuracy: 0.0016797 cost: 0.00664358 M: 34.9206 delta: 0.0287707 time: 606.116 one-recall: 0.98 one-ratio: 1.01352
iteration: 13 recall: 0.9792 accuracy: 0.00138971 cost: 0.00670591 M: 35.1556 delta: 0.0276259 time: 618.456 one-recall: 0.98 one-ratio: 1.01352
iteration: 14 recall: 0.9796 accuracy: 0.00127358 cost: 0.00673609 M: 35.2684 delta: 0.0270786 time: 628.165 one-recall: 0.98 one-ratio: 1.01352
iteration: 15 recall: 0.9796 accuracy: 0.00127358 cost: 0.00675081 M: 35.3239 delta: 0.0268206 time: 636.488 one-recall: 0.98 one-ratio: 1.01352
iteration: 16 recall: 0.9796 accuracy: 0.00127358 cost: 0.00675824 M: 35.3514 delta: 0.0266951 time: 644.12 one-recall: 0.98 one-ratio: 1.01352
iteration: 17 recall: 0.98 accuracy: 0.00124295 cost: 0.00676194 M: 35.365 delta: 0.026634 time: 651.373 one-recall: 0.98 one-ratio: 1.01352
iteration: 18 recall: 0.98 accuracy: 0.00124295 cost: 0.00676383 M: 35.372 delta: 0.0266007 time: 658.435 one-recall: 0.98 one-ratio: 1.01352
iteration: 19 recall: 0.98 accuracy: 0.00124295 cost: 0.00676478 M: 35.3756 delta: 0.0265846 time: 665.389 one-recall: 0.98 one-ratio: 1.01352
iteration: 20 recall: 0.98 accuracy: 0.00124295 cost: 0.00676525 M: 35.3773 delta: 0.0265776 time: 672.289 one-recall: 0.98 one-ratio: 1.01352
iteration: 21 recall: 0.98 accuracy: 0.00124295 cost: 0.00676552 M: 35.3783 delta: 0.0265738 time: 679.159 one-recall: 0.98 one-ratio: 1.01352
iteration: 22 recall: 0.98 accuracy: 0.00124295 cost: 0.00676568 M: 35.3789 delta: 0.0265715 time: 686.011 one-recall: 0.98 one-ratio: 1.01352
iteration: 23 recall: 0.98 accuracy: 0.00124295 cost: 0.00676575 M: 35.3792 delta: 0.0265707 time: 692.845 one-recall: 0.98 one-ratio: 1.01352
iteration: 24 recall: 0.98 accuracy: 0.00124295 cost: 0.0067658 M: 35.3795 delta: 0.0265697 time: 699.685 one-recall: 0.98 one-ratio: 1.01352
iteration: 25 recall: 0.98 accuracy: 0.00124295 cost: 0.00676583 M: 35.3796 delta: 0.0265686 time: 706.513 one-recall: 0.98 one-ratio: 1.01352
iteration: 26 recall: 0.98 accuracy: 0.00124295 cost: 0.00676585 M: 35.3797 delta: 0.0265687 time: 713.339 one-recall: 0.98 one-ratio: 1.01352
iteration: 27 recall: 0.98 accuracy: 0.00124295 cost: 0.00676586 M: 35.3797 delta: 0.0265684 time: 720.17 one-recall: 0.98 one-ratio: 1.01352
iteration: 28 recall: 0.98 accuracy: 0.00124295 cost: 0.00676588 M: 35.3798 delta: 0.0265683 time: 726.991 one-recall: 0.98 one-ratio: 1.01352
iteration: 29 recall: 0.98 accuracy: 0.00124295 cost: 0.00676589 M: 35.3798 delta: 0.0265682 time: 733.815 one-recall: 0.98 one-ratio: 1.01352
iteration: 30 recall: 0.98 accuracy: 0.00124295 cost: 0.00676589 M: 35.3798 delta: 0.026568 time: 740.639 one-recall: 0.98 one-ratio: 1.01352
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 758.7900000000009
Index size:  262908.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.1023770000
  Testing...
|S| = 80
|T| = 1152
Reject!
393.929 < 457.822
  -> Decision False in time 0.0100000000, query time of that 0.0026878260, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Reject!
398.175 < 439.961
  -> Decision False in time 0.0100000000, query time of that 0.0032192480, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
401.894 < 456.507
  -> Decision False in time 0.0100000000, query time of that 0.0046652570, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Reject!
432.253 < 440.926
  -> Decision False in time 0.0100000000, query time of that 0.0004909290, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
384.574 < 415.106
  -> Decision False in time 0.0000000000, query time of that 0.0002289000, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
365.34 < 373.807
  -> Decision False in time 0.0600000000, query time of that 0.0027503350, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Reject!
378.656 < 465.063
  -> Decision False in time 0.6200000000, query time of that 0.0030270350, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
427.445 < 456.336
  -> Decision False in time 2.0400000000, query time of that 0.0101499580, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
477.119 < 531.674
  -> Decision False in time 0.9800000000, query time of that 0.0055418070, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 2, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 2, {'reverse': -1}, False])
Got a train set of size (1000000 * 128)
Generating control...
Initializing...
iteration: 1 recall: 0 accuracy: 2.46378 cost: 0.00038 M: 10 delta: 1 time: 63.5692 one-recall: 0 one-ratio: 3.5748
iteration: 2 recall: 0.002 accuracy: 1.21316 cost: 0.000637428 M: 10 delta: 0.856032 time: 107.339 one-recall: 0 one-ratio: 2.89589
iteration: 3 recall: 0.04 accuracy: 0.653815 cost: 0.00109521 M: 11.5287 delta: 0.835109 time: 160.377 one-recall: 0.07 one-ratio: 2.33292
iteration: 4 recall: 0.2012 accuracy: 0.34268 cost: 0.00163044 M: 11.8362 delta: 0.783471 time: 213.202 one-recall: 0.22 one-ratio: 1.87684
iteration: 5 recall: 0.5388 accuracy: 0.0899586 cost: 0.00223603 M: 12.6036 delta: 0.664598 time: 268.127 one-recall: 0.66 one-ratio: 1.25715
iteration: 6 recall: 0.7968 accuracy: 0.0243695 cost: 0.00297991 M: 15.1143 delta: 0.432348 time: 330.147 one-recall: 0.88 one-ratio: 1.06768
iteration: 7 recall: 0.906 accuracy: 0.00813727 cost: 0.00395515 M: 21.1409 delta: 0.196402 time: 402.125 one-recall: 0.94 one-ratio: 1.02395
iteration: 8 recall: 0.9492 accuracy: 0.0034371 cost: 0.00497951 M: 27.3056 delta: 0.088426 time: 471.538 one-recall: 1 one-ratio: 1
iteration: 9 recall: 0.97 accuracy: 0.00176883 cost: 0.00577175 M: 31.2892 delta: 0.0513143 time: 526.065 one-recall: 1 one-ratio: 1
iteration: 10 recall: 0.98 accuracy: 0.000933432 cost: 0.00625676 M: 33.3935 delta: 0.0372138 time: 563.813 one-recall: 1 one-ratio: 1
iteration: 11 recall: 0.9824 accuracy: 0.000862029 cost: 0.006514 M: 34.4234 delta: 0.0313325 time: 588.89 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.9836 accuracy: 0.000801984 cost: 0.00664139 M: 34.9134 delta: 0.0287422 time: 605.884 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.9836 accuracy: 0.000801984 cost: 0.00670378 M: 35.1496 delta: 0.0275793 time: 618.171 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.9844 accuracy: 0.000770923 cost: 0.00673413 M: 35.2632 delta: 0.0270427 time: 627.84 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.9844 accuracy: 0.000770923 cost: 0.00674928 M: 35.3201 delta: 0.0267818 time: 636.14 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.9844 accuracy: 0.000770923 cost: 0.00675691 M: 35.3482 delta: 0.0266523 time: 643.719 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.9844 accuracy: 0.000770923 cost: 0.00676078 M: 35.3626 delta: 0.0265867 time: 650.925 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.9844 accuracy: 0.000770923 cost: 0.00676274 M: 35.3699 delta: 0.0265538 time: 657.932 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.9844 accuracy: 0.000770923 cost: 0.00676377 M: 35.3738 delta: 0.026537 time: 664.84 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.9844 accuracy: 0.000770923 cost: 0.00676433 M: 35.3759 delta: 0.0265293 time: 671.692 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.9844 accuracy: 0.000770923 cost: 0.00676464 M: 35.3771 delta: 0.0265232 time: 678.506 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.9844 accuracy: 0.000770923 cost: 0.00676478 M: 35.3775 delta: 0.0265209 time: 685.301 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.9844 accuracy: 0.000770923 cost: 0.00676484 M: 35.3778 delta: 0.0265199 time: 692.079 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.9844 accuracy: 0.000770923 cost: 0.00676489 M: 35.378 delta: 0.0265193 time: 698.848 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.9844 accuracy: 0.000770923 cost: 0.00676491 M: 35.3781 delta: 0.0265188 time: 705.619 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.9844 accuracy: 0.000770923 cost: 0.00676492 M: 35.3781 delta: 0.0265185 time: 712.388 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.9844 accuracy: 0.000770923 cost: 0.00676492 M: 35.3781 delta: 0.0265184 time: 719.147 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.9844 accuracy: 0.000770923 cost: 0.00676492 M: 35.3781 delta: 0.0265184 time: 725.908 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.9844 accuracy: 0.000770923 cost: 0.00676492 M: 35.3781 delta: 0.0265184 time: 732.67 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.9844 accuracy: 0.000770923 cost: 0.00676492 M: 35.3781 delta: 0.0265184 time: 739.432 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 757.510000000002
Index size:  262892.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.1134151000
  Testing...
|S| = 80
|T| = 1152
Reject!
390.452 < 413.886
  -> Decision False in time 0.0100000000, query time of that 0.0023433470, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Reject!
406.223 < 485.433
  -> Decision False in time 0.0000000000, query time of that 0.0021403920, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
357.905 < 485.048
  -> Decision False in time 0.0100000000, query time of that 0.0039954840, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Reject!
410.267 < 442.249
  -> Decision False in time 0.0300000000, query time of that 0.0014747900, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
288.272 < 463.808
  -> Decision False in time 0.0600000000, query time of that 0.0032776080, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
363.475 < 465.219
  -> Decision False in time 0.0800000000, query time of that 0.0040111140, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Reject!
400.219 < 469.805
  -> Decision False in time 0.2700000000, query time of that 0.0014932450, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
255.939 < 258.598
  -> Decision False in time 0.3700000000, query time of that 0.0023776710, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
429.093 < 456.63
  -> Decision False in time 0.1800000000, query time of that 0.0012494900, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 20, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 20, {'reverse': -1}, False])
Got a train set of size (1000000 * 128)
Generating control...
Initializing...
iteration: 1 recall: 0 accuracy: 2.35751 cost: 0.00038 M: 10 delta: 1 time: 63.5851 one-recall: 0 one-ratio: 3.25583
iteration: 2 recall: 0.0044 accuracy: 1.34579 cost: 0.000637428 M: 10 delta: 0.856032 time: 107.356 one-recall: 0.01 one-ratio: 2.56417
iteration: 3 recall: 0.0392 accuracy: 0.72329 cost: 0.00109521 M: 11.5287 delta: 0.835102 time: 160.402 one-recall: 0.01 one-ratio: 2.03273
iteration: 4 recall: 0.1916 accuracy: 0.338165 cost: 0.00163043 M: 11.8362 delta: 0.783447 time: 213.228 one-recall: 0.19 one-ratio: 1.54825
iteration: 5 recall: 0.52 accuracy: 0.110471 cost: 0.00223601 M: 12.6036 delta: 0.664575 time: 268.152 one-recall: 0.66 one-ratio: 1.16637
iteration: 6 recall: 0.778 accuracy: 0.023806 cost: 0.00297994 M: 15.1148 delta: 0.432321 time: 330.177 one-recall: 0.92 one-ratio: 1.04362
iteration: 7 recall: 0.8872 accuracy: 0.00964694 cost: 0.00395536 M: 21.141 delta: 0.19641 time: 402.16 one-recall: 0.96 one-ratio: 1.02576
iteration: 8 recall: 0.9272 accuracy: 0.00475137 cost: 0.00497981 M: 27.3056 delta: 0.0884757 time: 471.577 one-recall: 0.99 one-ratio: 1.01283
iteration: 9 recall: 0.956 accuracy: 0.00274351 cost: 0.00577268 M: 31.2891 delta: 0.0513591 time: 526.148 one-recall: 0.99 one-ratio: 1.01283
iteration: 10 recall: 0.9696 accuracy: 0.00196395 cost: 0.00625768 M: 33.3912 delta: 0.0372329 time: 563.894 one-recall: 0.99 one-ratio: 1.01283
iteration: 11 recall: 0.974 accuracy: 0.00178865 cost: 0.00651537 M: 34.4226 delta: 0.0313501 time: 589.001 one-recall: 0.99 one-ratio: 1.01283
iteration: 12 recall: 0.9748 accuracy: 0.00176807 cost: 0.00664302 M: 34.9133 delta: 0.0287837 time: 606.003 one-recall: 0.99 one-ratio: 1.01283
iteration: 13 recall: 0.9756 accuracy: 0.00174823 cost: 0.00670556 M: 35.1494 delta: 0.0276323 time: 618.341 one-recall: 0.99 one-ratio: 1.01283
iteration: 14 recall: 0.976 accuracy: 0.00170245 cost: 0.00673593 M: 35.2633 delta: 0.027083 time: 628.039 one-recall: 0.99 one-ratio: 1.01283
iteration: 15 recall: 0.9764 accuracy: 0.00129859 cost: 0.00675094 M: 35.3195 delta: 0.0268173 time: 636.386 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.978 accuracy: 0.0011223 cost: 0.00675849 M: 35.3475 delta: 0.0266856 time: 644.013 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.978 accuracy: 0.0011223 cost: 0.00676231 M: 35.362 delta: 0.0266216 time: 651.277 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.978 accuracy: 0.0011223 cost: 0.00676427 M: 35.3693 delta: 0.0265928 time: 658.348 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.978 accuracy: 0.0011223 cost: 0.00676535 M: 35.3733 delta: 0.0265766 time: 665.317 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.978 accuracy: 0.0011223 cost: 0.00676591 M: 35.3755 delta: 0.0265687 time: 672.229 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.978 accuracy: 0.0011223 cost: 0.00676625 M: 35.377 delta: 0.0265635 time: 679.104 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.978 accuracy: 0.0011223 cost: 0.00676647 M: 35.3778 delta: 0.0265597 time: 685.966 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.978 accuracy: 0.0011223 cost: 0.00676656 M: 35.3782 delta: 0.0265588 time: 692.803 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.978 accuracy: 0.0011223 cost: 0.00676663 M: 35.3784 delta: 0.0265578 time: 699.639 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.978 accuracy: 0.0011223 cost: 0.00676668 M: 35.3787 delta: 0.0265569 time: 706.472 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.978 accuracy: 0.0011223 cost: 0.00676672 M: 35.3789 delta: 0.0265565 time: 713.302 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.978 accuracy: 0.0011223 cost: 0.00676674 M: 35.379 delta: 0.026556 time: 720.131 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.978 accuracy: 0.0011223 cost: 0.00676676 M: 35.379 delta: 0.0265558 time: 726.956 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.978 accuracy: 0.0011223 cost: 0.00676676 M: 35.379 delta: 0.0265557 time: 733.781 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.978 accuracy: 0.0011223 cost: 0.00676677 M: 35.3791 delta: 0.0265556 time: 740.604 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 758.75
Index size:  262912.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0091231000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.0800000000, query time of that 0.0382256050, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Reject!
375.084 < 411.545
  -> Decision False in time 0.5400000000, query time of that 0.2349810220, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
262.895 < 264.958
  -> Decision False in time 0.0500000000, query time of that 0.0232366640, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.5600000000, query time of that 0.0415350770, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
222.142 < 222.533
  -> Decision False in time 2.9200000000, query time of that 0.2219587470, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
389.718 < 392.921
  -> Decision False in time 0.9000000000, query time of that 0.0712426130, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Accept!
  -> Decision True in time 7.0900000000, query time of that 0.0525118380, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
220.266 < 222.823
  -> Decision False in time 3.7100000000, query time of that 0.0276081640, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
233.529 < 236.504
  -> Decision False in time 2.1400000000, query time of that 0.0152746940, with c1=5.0000000000, c2=0.1000000000
