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', 30, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 100, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 5, {'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', 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', 2, {'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', 80, {'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', 90, {'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', 20, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 50, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 70, {'reverse': -1}, False])]
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 accuracy: 2.29356 cost: 0.00038 M: 10 delta: 1 time: 54.203 one-recall: 0 one-ratio: 3.52528
iteration: 2 recall: 0.004 accuracy: 1.20332 cost: 0.000637428 M: 10 delta: 0.856032 time: 92.2198 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: 138.307 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: 183.972 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: 231.351 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: 284.818 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: 345.646 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: 402.443 one-recall: 1 one-ratio: 1
iteration: 9 recall: 0.9644 accuracy: 0.00221739 cost: 0.00577293 M: 31.2904 delta: 0.0514019 time: 445.655 one-recall: 1 one-ratio: 1
iteration: 10 recall: 0.9764 accuracy: 0.0012952 cost: 0.00625843 M: 33.3974 delta: 0.0372226 time: 474.7 one-recall: 1 one-ratio: 1
iteration: 11 recall: 0.984 accuracy: 0.000915513 cost: 0.00651572 M: 34.4268 delta: 0.0313604 time: 493.58 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.9872 accuracy: 0.000651871 cost: 0.00664321 M: 34.9174 delta: 0.028787 time: 506.25 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.9876 accuracy: 0.000628894 cost: 0.00670536 M: 35.1523 delta: 0.027629 time: 515.434 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.9876 accuracy: 0.000628894 cost: 0.00673551 M: 35.2647 delta: 0.0270893 time: 522.725 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.9876 accuracy: 0.000628894 cost: 0.00675029 M: 35.3194 delta: 0.0268326 time: 529.017 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.9876 accuracy: 0.000628894 cost: 0.00675777 M: 35.3471 delta: 0.0267076 time: 534.794 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.9876 accuracy: 0.000628894 cost: 0.00676154 M: 35.3608 delta: 0.0266443 time: 540.301 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.9876 accuracy: 0.000628894 cost: 0.00676338 M: 35.3677 delta: 0.0266122 time: 545.674 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.9876 accuracy: 0.000628894 cost: 0.00676433 M: 35.3712 delta: 0.0265977 time: 551.003 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.9876 accuracy: 0.000628894 cost: 0.00676485 M: 35.373 delta: 0.02659 time: 556.276 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.988 accuracy: 0.0006087 cost: 0.00676515 M: 35.3742 delta: 0.0265857 time: 561.53 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.988 accuracy: 0.0006087 cost: 0.00676532 M: 35.3749 delta: 0.0265828 time: 566.768 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.988 accuracy: 0.0006087 cost: 0.00676541 M: 35.3753 delta: 0.0265811 time: 571.996 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.988 accuracy: 0.0006087 cost: 0.00676547 M: 35.3755 delta: 0.0265798 time: 577.216 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.988 accuracy: 0.0006087 cost: 0.00676549 M: 35.3756 delta: 0.0265796 time: 582.429 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.988 accuracy: 0.0006087 cost: 0.00676551 M: 35.3757 delta: 0.0265794 time: 587.648 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.988 accuracy: 0.0006087 cost: 0.00676552 M: 35.3757 delta: 0.0265791 time: 592.865 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.988 accuracy: 0.0006087 cost: 0.00676553 M: 35.3757 delta: 0.0265791 time: 598.078 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.988 accuracy: 0.0006087 cost: 0.00676553 M: 35.3757 delta: 0.026579 time: 603.29 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.988 accuracy: 0.0006087 cost: 0.00676553 M: 35.3757 delta: 0.026579 time: 608.499 one-recall: 1 one-ratio: 1
Graph completion with reverse edges...

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

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 622.9599999999999
Index size:  1903140.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0071767000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.0800000000, query time of that 0.0303656270, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Accept!
  -> Decision True in time 0.7500000000, query time of that 0.2998463870, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
400.189 < 426.735
  -> Decision False in time 0.3100000000, query time of that 0.1224404550, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.5400000000, query time of that 0.0350009280, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
287.071 < 290.283
  -> Decision False in time 2.7300000000, query time of that 0.1845543980, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
277.083 < 277.328
  -> Decision False in time 5.4900000000, query time of that 0.3661664970, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Reject!
247.059 < 250.144
  -> Decision False in time 2.7900000000, query time of that 0.0174983270, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
329.301 < 336.333
  -> Decision False in time 6.1100000000, query time of that 0.0397794930, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
287.856 < 295.117
  -> Decision False in time 9.6400000000, query time of that 0.0630168810, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 100, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 100, {'reverse': -1}, False])
Got a train set of size (1000000 * 128)
Generating control...
Initializing...
iteration: 1 recall: 0 accuracy: 2.6766 cost: 0.00038 M: 10 delta: 1 time: 53.9418 one-recall: 0 one-ratio: 3.66072
iteration: 2 recall: 0.0016 accuracy: 1.38091 cost: 0.000637428 M: 10 delta: 0.856032 time: 91.9957 one-recall: 0 one-ratio: 2.8624
iteration: 3 recall: 0.0388 accuracy: 0.711342 cost: 0.00109521 M: 11.5287 delta: 0.835103 time: 138.224 one-recall: 0.04 one-ratio: 2.27853
iteration: 4 recall: 0.2128 accuracy: 0.331692 cost: 0.00163043 M: 11.8362 delta: 0.783467 time: 184.121 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: 231.82 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: 285.788 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: 347.426 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: 405.152 one-recall: 1 one-ratio: 1
iteration: 9 recall: 0.9708 accuracy: 0.00137405 cost: 0.00577272 M: 31.2865 delta: 0.0513399 time: 449.112 one-recall: 1 one-ratio: 1
iteration: 10 recall: 0.9796 accuracy: 0.000974264 cost: 0.0062576 M: 33.3907 delta: 0.0372004 time: 478.63 one-recall: 1 one-ratio: 1
iteration: 11 recall: 0.982 accuracy: 0.000821148 cost: 0.00651448 M: 34.4194 delta: 0.0313362 time: 497.734 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.9856 accuracy: 0.00063386 cost: 0.0066431 M: 34.9143 delta: 0.0287677 time: 510.559 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.9868 accuracy: 0.000577962 cost: 0.006706 M: 35.1524 delta: 0.0276101 time: 519.805 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.9876 accuracy: 0.000568264 cost: 0.00673624 M: 35.2661 delta: 0.027052 time: 527.068 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.9876 accuracy: 0.000568264 cost: 0.0067511 M: 35.3217 delta: 0.0267951 time: 533.352 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.9876 accuracy: 0.000568264 cost: 0.00675856 M: 35.3495 delta: 0.0266678 time: 539.131 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.9876 accuracy: 0.000568264 cost: 0.00676249 M: 35.3641 delta: 0.0266031 time: 544.697 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.9876 accuracy: 0.000568264 cost: 0.00676447 M: 35.3715 delta: 0.0265723 time: 550.11 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.9876 accuracy: 0.000568264 cost: 0.00676549 M: 35.3754 delta: 0.0265551 time: 555.446 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.9876 accuracy: 0.000568264 cost: 0.00676606 M: 35.3775 delta: 0.0265465 time: 560.741 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.9876 accuracy: 0.000568264 cost: 0.00676636 M: 35.3786 delta: 0.0265425 time: 566.009 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.9876 accuracy: 0.000568264 cost: 0.00676654 M: 35.3792 delta: 0.0265399 time: 571.261 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.9876 accuracy: 0.000568264 cost: 0.00676663 M: 35.3796 delta: 0.0265387 time: 576.506 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.9876 accuracy: 0.000568264 cost: 0.00676668 M: 35.3798 delta: 0.0265377 time: 581.746 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.9876 accuracy: 0.000568264 cost: 0.0067667 M: 35.3799 delta: 0.0265375 time: 586.978 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.9876 accuracy: 0.000568264 cost: 0.00676672 M: 35.3799 delta: 0.0265372 time: 592.212 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.9876 accuracy: 0.000568264 cost: 0.00676674 M: 35.38 delta: 0.026537 time: 597.443 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.9876 accuracy: 0.000568264 cost: 0.00676675 M: 35.38 delta: 0.0265369 time: 602.669 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.9876 accuracy: 0.000568264 cost: 0.00676675 M: 35.38 delta: 0.0265368 time: 607.904 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.9876 accuracy: 0.000568264 cost: 0.00676675 M: 35.38 delta: 0.026537 time: 613.137 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 627.55
Index size:  261124.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0024683000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.1100000000, query time of that 0.0670068420, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Accept!
  -> Decision True in time 1.1100000000, query time of that 0.6617156220, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Accept!
  -> Decision True in time 11.2700000000, query time of that 6.7022784470, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.6000000000, query time of that 0.0753813200, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
265.994 < 270.59
  -> Decision False in time 2.7100000000, query time of that 0.3573314070, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
287.037 < 289.924
  -> Decision False in time 39.6600000000, query time of that 5.2148221190, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Accept!
  -> Decision True in time 6.6500000000, query time of that 0.0878977010, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
276.501 < 299.963
  -> Decision False in time 21.7700000000, query time of that 0.2895857460, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
208.471 < 221.305
  -> Decision False in time 28.4200000000, query time of that 0.3819128710, 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: 2.03157 cost: 0.00038 M: 10 delta: 1 time: 53.9686 one-recall: 0 one-ratio: 3.73501
iteration: 2 recall: 0.0016 accuracy: 1.16247 cost: 0.000637428 M: 10 delta: 0.856032 time: 92.0242 one-recall: 0 one-ratio: 2.91785
iteration: 3 recall: 0.0312 accuracy: 0.695082 cost: 0.00109521 M: 11.5287 delta: 0.835107 time: 138.261 one-recall: 0.02 one-ratio: 2.31464
iteration: 4 recall: 0.1788 accuracy: 0.366826 cost: 0.00163043 M: 11.8362 delta: 0.783463 time: 184.163 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: 231.86 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: 285.821 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: 347.465 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: 405.196 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: 449.175 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: 478.683 one-recall: 1 one-ratio: 1
iteration: 11 recall: 0.978 accuracy: 0.00104776 cost: 0.00651482 M: 34.4253 delta: 0.0313493 time: 497.816 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.9788 accuracy: 0.00100726 cost: 0.00664253 M: 34.9185 delta: 0.0287713 time: 510.593 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.9792 accuracy: 0.000936912 cost: 0.00670467 M: 35.1539 delta: 0.0276173 time: 519.8 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.9792 accuracy: 0.000936912 cost: 0.00673481 M: 35.2674 delta: 0.0270635 time: 527.086 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.9796 accuracy: 0.00088822 cost: 0.00674951 M: 35.3223 delta: 0.0268061 time: 533.372 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.9796 accuracy: 0.00088822 cost: 0.00675718 M: 35.3508 delta: 0.0266787 time: 539.189 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.9796 accuracy: 0.00088822 cost: 0.00676107 M: 35.3653 delta: 0.026612 time: 544.718 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.9796 accuracy: 0.00088822 cost: 0.0067631 M: 35.3729 delta: 0.0265774 time: 550.13 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.9796 accuracy: 0.00088822 cost: 0.00676415 M: 35.3768 delta: 0.026562 time: 555.465 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.9796 accuracy: 0.00088822 cost: 0.00676468 M: 35.3788 delta: 0.0265532 time: 560.756 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.9796 accuracy: 0.00088822 cost: 0.00676498 M: 35.38 delta: 0.0265484 time: 566.028 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.9796 accuracy: 0.00088822 cost: 0.00676515 M: 35.3806 delta: 0.0265463 time: 571.281 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.9796 accuracy: 0.00088822 cost: 0.00676524 M: 35.381 delta: 0.0265443 time: 576.523 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.9796 accuracy: 0.00088822 cost: 0.00676531 M: 35.3812 delta: 0.0265439 time: 581.762 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.9796 accuracy: 0.00088822 cost: 0.00676533 M: 35.3813 delta: 0.0265431 time: 586.999 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.9796 accuracy: 0.00088822 cost: 0.00676536 M: 35.3814 delta: 0.0265432 time: 592.234 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.9796 accuracy: 0.00088822 cost: 0.00676537 M: 35.3815 delta: 0.026543 time: 597.467 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.9796 accuracy: 0.00088822 cost: 0.00676538 M: 35.3815 delta: 0.0265427 time: 602.695 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.9796 accuracy: 0.00088822 cost: 0.00676538 M: 35.3815 delta: 0.0265427 time: 607.925 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.9796 accuracy: 0.00088822 cost: 0.00676538 M: 35.3815 delta: 0.0265426 time: 613.155 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 627.56
Index size:  262896.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0112902000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.0600000000, query time of that 0.0159007050, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Reject!
398.059 < 404.631
  -> Decision False in time 0.0800000000, query time of that 0.0207345160, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
426.074 < 437.002
  -> Decision False in time 0.2100000000, query time of that 0.0520600090, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.5200000000, query time of that 0.0187376300, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
256.131 < 271.409
  -> Decision False in time 3.4900000000, query time of that 0.1270379680, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
366.706 < 371.64
  -> Decision False in time 0.0100000000, query time of that 0.0006150280, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Reject!
285.629 < 289.89
  -> Decision False in time 1.2900000000, query time of that 0.0052516130, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
267.307 < 269.663
  -> Decision False in time 3.3300000000, query time of that 0.0126226480, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
294.223 < 295.171
  -> Decision False in time 5.1800000000, query time of that 0.0201813010, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 1, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 1, {'reverse': -1}, False])
Got a train set of size (1000000 * 128)
Generating control...
Initializing...
iteration: 1 recall: 0 accuracy: 2.04383 cost: 0.00038 M: 10 delta: 1 time: 53.9449 one-recall: 0 one-ratio: 3.38832
iteration: 2 recall: 0.002 accuracy: 1.16107 cost: 0.000637428 M: 10 delta: 0.856032 time: 92.007 one-recall: 0 one-ratio: 2.63215
iteration: 3 recall: 0.0312 accuracy: 0.655445 cost: 0.00109521 M: 11.5287 delta: 0.835104 time: 138.243 one-recall: 0.04 one-ratio: 2.08916
iteration: 4 recall: 0.1716 accuracy: 0.328344 cost: 0.00163044 M: 11.8362 delta: 0.783475 time: 184.157 one-recall: 0.2 one-ratio: 1.64007
iteration: 5 recall: 0.5036 accuracy: 0.113655 cost: 0.00223606 M: 12.6035 delta: 0.66458 time: 231.864 one-recall: 0.6 one-ratio: 1.25817
iteration: 6 recall: 0.7668 accuracy: 0.0331323 cost: 0.00297991 M: 15.1142 delta: 0.432346 time: 285.832 one-recall: 0.85 one-ratio: 1.09688
iteration: 7 recall: 0.8948 accuracy: 0.0120145 cost: 0.00395518 M: 21.1406 delta: 0.196409 time: 347.474 one-recall: 0.93 one-ratio: 1.03705
iteration: 8 recall: 0.9448 accuracy: 0.00380819 cost: 0.00497968 M: 27.3037 delta: 0.0884967 time: 405.226 one-recall: 0.99 one-ratio: 1.00043
iteration: 9 recall: 0.9664 accuracy: 0.00208169 cost: 0.00577228 M: 31.2896 delta: 0.0513611 time: 449.202 one-recall: 1 one-ratio: 1
iteration: 10 recall: 0.9716 accuracy: 0.00178294 cost: 0.00625699 M: 33.3923 delta: 0.0372263 time: 478.714 one-recall: 1 one-ratio: 1
iteration: 11 recall: 0.974 accuracy: 0.00163255 cost: 0.00651451 M: 34.4227 delta: 0.0313379 time: 497.859 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.976 accuracy: 0.00152158 cost: 0.00664177 M: 34.914 delta: 0.028788 time: 510.623 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.9772 accuracy: 0.00146768 cost: 0.00670348 M: 35.1467 delta: 0.027626 time: 519.803 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.9776 accuracy: 0.00146357 cost: 0.00673369 M: 35.2604 delta: 0.0270835 time: 527.055 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.9776 accuracy: 0.00146357 cost: 0.00674885 M: 35.3169 delta: 0.0268232 time: 533.339 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.9776 accuracy: 0.00146357 cost: 0.00675617 M: 35.3444 delta: 0.0266974 time: 539.092 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.9776 accuracy: 0.00146357 cost: 0.00676008 M: 35.3588 delta: 0.0266303 time: 544.604 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.9776 accuracy: 0.00146357 cost: 0.00676207 M: 35.3662 delta: 0.0265987 time: 549.973 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.9776 accuracy: 0.00146357 cost: 0.0067632 M: 35.3706 delta: 0.026581 time: 555.277 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.9776 accuracy: 0.00146357 cost: 0.00676379 M: 35.3729 delta: 0.0265703 time: 560.53 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.9776 accuracy: 0.00146357 cost: 0.00676406 M: 35.3739 delta: 0.0265675 time: 565.769 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.9776 accuracy: 0.00146357 cost: 0.00676422 M: 35.3745 delta: 0.0265651 time: 570.978 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.9776 accuracy: 0.00146357 cost: 0.0067643 M: 35.3748 delta: 0.0265638 time: 576.176 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.9776 accuracy: 0.00146357 cost: 0.00676435 M: 35.375 delta: 0.0265633 time: 581.369 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.9776 accuracy: 0.00146357 cost: 0.00676439 M: 35.3752 delta: 0.026563 time: 586.562 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.9776 accuracy: 0.00146357 cost: 0.00676442 M: 35.3753 delta: 0.0265625 time: 591.753 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.9776 accuracy: 0.00146357 cost: 0.00676444 M: 35.3754 delta: 0.0265626 time: 596.942 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.9776 accuracy: 0.00146357 cost: 0.00676445 M: 35.3754 delta: 0.0265623 time: 602.143 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.9776 accuracy: 0.00146357 cost: 0.00676447 M: 35.3755 delta: 0.0265621 time: 607.33 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.9776 accuracy: 0.00146357 cost: 0.00676447 M: 35.3755 delta: 0.0265621 time: 612.52 one-recall: 1 one-ratio: 1
Graph completion with reverse edges...

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

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 626.8800000000001
Index size:  262704.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.1018693000
  Testing...
|S| = 80
|T| = 1152
Reject!
434.135 < 461.511
  -> Decision False in time 0.0000000000, query time of that 0.0003697450, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Reject!
449.683 < 497.137
  -> Decision False in time 0.0100000000, query time of that 0.0023832050, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
444.97 < 521.113
  -> Decision False in time 0.0000000000, query time of that 0.0005599640, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Reject!
401.269 < 446.993
  -> Decision False in time 0.1500000000, query time of that 0.0047859890, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
309.231 < 446.525
  -> Decision False in time 0.0800000000, query time of that 0.0031188920, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
403.011 < 486.098
  -> Decision False in time 0.0200000000, query time of that 0.0006581240, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Reject!
395.798 < 451.8
  -> Decision False in time 0.0000000000, query time of that 0.0001902810, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
407.511 < 412.93
  -> Decision False in time 4.2200000000, query time of that 0.0148724240, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
374.47 < 404.396
  -> Decision False in time 0.2500000000, query time of that 0.0011591670, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 10, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 10, {'reverse': -1}, False])
Got a train set of size (1000000 * 128)
Generating control...
Initializing...
iteration: 1 recall: 0.0008 accuracy: 2.15707 cost: 0.00038 M: 10 delta: 1 time: 53.9151 one-recall: 0 one-ratio: 3.61267
iteration: 2 recall: 0.0052 accuracy: 1.25567 cost: 0.000637428 M: 10 delta: 0.856032 time: 91.9659 one-recall: 0 one-ratio: 2.80255
iteration: 3 recall: 0.0288 accuracy: 0.746885 cost: 0.00109521 M: 11.5287 delta: 0.835105 time: 138.21 one-recall: 0.07 one-ratio: 2.28326
iteration: 4 recall: 0.1684 accuracy: 0.341227 cost: 0.00163043 M: 11.8363 delta: 0.783442 time: 184.123 one-recall: 0.29 one-ratio: 1.72993
iteration: 5 recall: 0.5092 accuracy: 0.0971546 cost: 0.00223604 M: 12.6036 delta: 0.664591 time: 231.851 one-recall: 0.62 one-ratio: 1.32505
iteration: 6 recall: 0.7728 accuracy: 0.0274841 cost: 0.00297989 M: 15.1146 delta: 0.432353 time: 285.832 one-recall: 0.86 one-ratio: 1.06592
iteration: 7 recall: 0.8892 accuracy: 0.00873724 cost: 0.00395511 M: 21.14 delta: 0.196396 time: 347.48 one-recall: 0.97 one-ratio: 1.01027
iteration: 8 recall: 0.94 accuracy: 0.00319573 cost: 0.00497948 M: 27.3041 delta: 0.0884112 time: 405.214 one-recall: 1 one-ratio: 1
iteration: 9 recall: 0.9596 accuracy: 0.00204819 cost: 0.00577159 M: 31.2854 delta: 0.0513298 time: 449.175 one-recall: 1 one-ratio: 1
iteration: 10 recall: 0.972 accuracy: 0.00137966 cost: 0.0062557 M: 33.3875 delta: 0.0371999 time: 478.674 one-recall: 1 one-ratio: 1
iteration: 11 recall: 0.9748 accuracy: 0.00120289 cost: 0.00651243 M: 34.4172 delta: 0.0313026 time: 497.781 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.9768 accuracy: 0.00106622 cost: 0.00664049 M: 34.9095 delta: 0.0287402 time: 510.576 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.9776 accuracy: 0.00103347 cost: 0.00670296 M: 35.1459 delta: 0.0275752 time: 519.801 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.978 accuracy: 0.00102887 cost: 0.00673348 M: 35.2601 delta: 0.0270307 time: 527.111 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.978 accuracy: 0.00102887 cost: 0.00674804 M: 35.3145 delta: 0.026773 time: 533.395 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.978 accuracy: 0.00102887 cost: 0.00675541 M: 35.3418 delta: 0.0266503 time: 539.191 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.978 accuracy: 0.00102887 cost: 0.00675919 M: 35.356 delta: 0.0265835 time: 544.732 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.978 accuracy: 0.00102887 cost: 0.00676102 M: 35.3628 delta: 0.0265528 time: 550.134 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.978 accuracy: 0.00102887 cost: 0.00676199 M: 35.3666 delta: 0.0265376 time: 555.461 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.978 accuracy: 0.00102887 cost: 0.0067625 M: 35.3685 delta: 0.0265299 time: 560.751 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.978 accuracy: 0.00102887 cost: 0.00676275 M: 35.3695 delta: 0.0265255 time: 566.014 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.978 accuracy: 0.00102887 cost: 0.00676289 M: 35.3701 delta: 0.0265228 time: 571.262 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.978 accuracy: 0.00102887 cost: 0.00676296 M: 35.3704 delta: 0.0265215 time: 576.502 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.978 accuracy: 0.00102887 cost: 0.006763 M: 35.3705 delta: 0.0265212 time: 581.744 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.978 accuracy: 0.00102887 cost: 0.00676303 M: 35.3706 delta: 0.0265207 time: 586.983 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.978 accuracy: 0.00102887 cost: 0.00676305 M: 35.3707 delta: 0.0265203 time: 592.213 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.978 accuracy: 0.00102887 cost: 0.00676306 M: 35.3708 delta: 0.0265201 time: 597.443 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.978 accuracy: 0.00102887 cost: 0.00676307 M: 35.3708 delta: 0.0265201 time: 602.677 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.978 accuracy: 0.00102887 cost: 0.00676307 M: 35.3708 delta: 0.0265201 time: 607.908 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.978 accuracy: 0.00102887 cost: 0.00676307 M: 35.3708 delta: 0.0265201 time: 613.136 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 627.54
Index size:  262900.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0107635000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.0700000000, query time of that 0.0191150830, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Reject!
418.925 < 425.466
  -> Decision False in time 0.0400000000, query time of that 0.0138926000, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
350.286 < 358.876
  -> Decision False in time 0.2300000000, query time of that 0.0658777310, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.5300000000, query time of that 0.0228544050, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
349.14 < 351.295
  -> Decision False in time 0.4300000000, query time of that 0.0188194230, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
358.385 < 377.569
  -> Decision False in time 3.7000000000, query time of that 0.1596524340, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Accept!
  -> Decision True in time 6.5900000000, query time of that 0.0295960890, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
402.006 < 411.298
  -> Decision False in time 7.2900000000, query time of that 0.0311777310, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
342.32 < 343.757
  -> Decision False in time 7.7800000000, query time of that 0.0337568240, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 4, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 4, {'reverse': -1}, False])
Got a train set of size (1000000 * 128)
Generating control...
Initializing...
iteration: 1 recall: 0 accuracy: 1.99988 cost: 0.00038 M: 10 delta: 1 time: 53.9312 one-recall: 0 one-ratio: 3.56408
iteration: 2 recall: 0.004 accuracy: 1.12199 cost: 0.000637428 M: 10 delta: 0.856032 time: 91.9817 one-recall: 0.01 one-ratio: 2.81414
iteration: 3 recall: 0.0332 accuracy: 0.629171 cost: 0.00109521 M: 11.5287 delta: 0.835103 time: 138.211 one-recall: 0.04 one-ratio: 2.30047
iteration: 4 recall: 0.1952 accuracy: 0.293054 cost: 0.00163044 M: 11.8363 delta: 0.783453 time: 184.113 one-recall: 0.27 one-ratio: 1.70801
iteration: 5 recall: 0.5376 accuracy: 0.104926 cost: 0.00223615 M: 12.6038 delta: 0.664604 time: 231.829 one-recall: 0.64 one-ratio: 1.26485
iteration: 6 recall: 0.788 accuracy: 0.0263564 cost: 0.00298004 M: 15.1147 delta: 0.432358 time: 285.822 one-recall: 0.87 one-ratio: 1.10765
iteration: 7 recall: 0.9092 accuracy: 0.00620361 cost: 0.00395518 M: 21.1389 delta: 0.196417 time: 347.472 one-recall: 0.98 one-ratio: 1.00309
iteration: 8 recall: 0.9488 accuracy: 0.00302479 cost: 0.0049797 M: 27.3049 delta: 0.0884507 time: 405.224 one-recall: 0.99 one-ratio: 1.00204
iteration: 9 recall: 0.966 accuracy: 0.00194991 cost: 0.00577292 M: 31.291 delta: 0.0513158 time: 449.22 one-recall: 1 one-ratio: 1
iteration: 10 recall: 0.9764 accuracy: 0.00126976 cost: 0.00625749 M: 33.3934 delta: 0.0371755 time: 478.734 one-recall: 1 one-ratio: 1
iteration: 11 recall: 0.9804 accuracy: 0.00098944 cost: 0.0065147 M: 34.4237 delta: 0.031287 time: 497.862 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.9824 accuracy: 0.000936951 cost: 0.00664254 M: 34.9158 delta: 0.0287196 time: 510.642 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.9828 accuracy: 0.000909176 cost: 0.00670461 M: 35.1507 delta: 0.0275536 time: 519.82 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.984 accuracy: 0.000879307 cost: 0.00673455 M: 35.2621 delta: 0.0270214 time: 527.064 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.984 accuracy: 0.000879307 cost: 0.00674923 M: 35.3177 delta: 0.026753 time: 533.322 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.984 accuracy: 0.000879307 cost: 0.00675659 M: 35.3454 delta: 0.026629 time: 539.078 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.984 accuracy: 0.000879307 cost: 0.00676035 M: 35.3593 delta: 0.0265657 time: 544.581 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.984 accuracy: 0.000879307 cost: 0.00676223 M: 35.3665 delta: 0.0265325 time: 549.944 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.984 accuracy: 0.000879307 cost: 0.00676325 M: 35.3705 delta: 0.0265158 time: 555.252 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.984 accuracy: 0.000879307 cost: 0.00676379 M: 35.3725 delta: 0.0265065 time: 560.508 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.984 accuracy: 0.000879307 cost: 0.0067641 M: 35.3736 delta: 0.0265028 time: 565.739 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.984 accuracy: 0.000879307 cost: 0.00676426 M: 35.3742 delta: 0.0264999 time: 570.952 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.984 accuracy: 0.000879307 cost: 0.00676435 M: 35.3746 delta: 0.0264988 time: 576.173 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.984 accuracy: 0.000879307 cost: 0.0067644 M: 35.3748 delta: 0.0264977 time: 581.366 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.984 accuracy: 0.000879307 cost: 0.00676442 M: 35.3749 delta: 0.0264972 time: 586.565 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.984 accuracy: 0.000879307 cost: 0.00676443 M: 35.3749 delta: 0.0264971 time: 591.759 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.984 accuracy: 0.000879307 cost: 0.00676444 M: 35.3749 delta: 0.026497 time: 596.952 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.984 accuracy: 0.000879307 cost: 0.00676444 M: 35.3749 delta: 0.0264971 time: 602.137 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.984 accuracy: 0.000879307 cost: 0.00676444 M: 35.375 delta: 0.0264969 time: 607.322 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.984 accuracy: 0.000879307 cost: 0.00676445 M: 35.375 delta: 0.0264969 time: 612.519 one-recall: 1 one-ratio: 1
Graph completion with reverse edges...

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

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 626.9000000000005
Index size:  262856.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0115723000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.0600000000, query time of that 0.0156817860, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Accept!
  -> Decision True in time 0.6000000000, query time of that 0.1470605590, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
395.257 < 397.086
  -> Decision False in time 0.6700000000, query time of that 0.1596635290, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.5200000000, query time of that 0.0182444340, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
382.292 < 402.187
  -> Decision False in time 4.4500000000, query time of that 0.1531731640, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
300.277 < 307.672
  -> Decision False in time 13.1500000000, query time of that 0.4589541040, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Reject!
238.227 < 284.8
  -> Decision False in time 1.8100000000, query time of that 0.0062188830, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
185.27 < 190.276
  -> Decision False in time 3.5300000000, query time of that 0.0125243700, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
294.766 < 302.728
  -> Decision False in time 4.5700000000, query time of that 0.0161065200, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 2, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 2, {'reverse': -1}, False])
Got a train set of size (1000000 * 128)
Generating control...
Initializing...
iteration: 1 recall: 0.0004 accuracy: 2.10534 cost: 0.00038 M: 10 delta: 1 time: 53.9441 one-recall: 0 one-ratio: 3.12445
iteration: 2 recall: 0.0024 accuracy: 1.15126 cost: 0.000637428 M: 10 delta: 0.856032 time: 91.9903 one-recall: 0.01 one-ratio: 2.54633
iteration: 3 recall: 0.0368 accuracy: 0.620377 cost: 0.00109521 M: 11.5287 delta: 0.835109 time: 138.236 one-recall: 0.05 one-ratio: 2.04237
iteration: 4 recall: 0.1996 accuracy: 0.289535 cost: 0.00163046 M: 11.8363 delta: 0.783452 time: 184.138 one-recall: 0.24 one-ratio: 1.57117
iteration: 5 recall: 0.5116 accuracy: 0.0993007 cost: 0.00223611 M: 12.6038 delta: 0.664622 time: 231.846 one-recall: 0.64 one-ratio: 1.22556
iteration: 6 recall: 0.7664 accuracy: 0.0322305 cost: 0.00297991 M: 15.1143 delta: 0.43232 time: 285.795 one-recall: 0.84 one-ratio: 1.10283
iteration: 7 recall: 0.8824 accuracy: 0.0108528 cost: 0.00395514 M: 21.1406 delta: 0.196405 time: 347.419 one-recall: 0.94 one-ratio: 1.04017
iteration: 8 recall: 0.9356 accuracy: 0.00490271 cost: 0.00497938 M: 27.3027 delta: 0.0884789 time: 405.144 one-recall: 0.97 one-ratio: 1.00993
iteration: 9 recall: 0.9552 accuracy: 0.00285185 cost: 0.0057718 M: 31.2844 delta: 0.0513382 time: 449.115 one-recall: 0.98 one-ratio: 1.00587
iteration: 10 recall: 0.9684 accuracy: 0.0016967 cost: 0.00625644 M: 33.3897 delta: 0.0372331 time: 478.627 one-recall: 0.99 one-ratio: 1.00027
iteration: 11 recall: 0.9716 accuracy: 0.00137834 cost: 0.00651503 M: 34.4266 delta: 0.031292 time: 497.8 one-recall: 0.99 one-ratio: 1.00027
iteration: 12 recall: 0.9728 accuracy: 0.00131386 cost: 0.00664259 M: 34.9174 delta: 0.0287388 time: 510.572 one-recall: 0.99 one-ratio: 1.00027
iteration: 13 recall: 0.9748 accuracy: 0.00119056 cost: 0.0067051 M: 35.1528 delta: 0.0275718 time: 519.801 one-recall: 0.99 one-ratio: 1.00027
iteration: 14 recall: 0.9748 accuracy: 0.00118962 cost: 0.00673522 M: 35.266 delta: 0.027037 time: 527.083 one-recall: 0.99 one-ratio: 1.00027
iteration: 15 recall: 0.9748 accuracy: 0.00118962 cost: 0.00675035 M: 35.3216 delta: 0.0267736 time: 533.397 one-recall: 0.99 one-ratio: 1.00027
iteration: 16 recall: 0.9748 accuracy: 0.00118962 cost: 0.00675795 M: 35.3499 delta: 0.026646 time: 539.213 one-recall: 0.99 one-ratio: 1.00027
iteration: 17 recall: 0.9748 accuracy: 0.00118962 cost: 0.00676168 M: 35.3635 delta: 0.0265824 time: 544.762 one-recall: 0.99 one-ratio: 1.00027
iteration: 18 recall: 0.9748 accuracy: 0.00118962 cost: 0.00676356 M: 35.3708 delta: 0.02655 time: 550.17 one-recall: 0.99 one-ratio: 1.00027
iteration: 19 recall: 0.9748 accuracy: 0.00118962 cost: 0.00676455 M: 35.3745 delta: 0.0265337 time: 555.502 one-recall: 0.99 one-ratio: 1.00027
iteration: 20 recall: 0.9748 accuracy: 0.00118962 cost: 0.006765 M: 35.3762 delta: 0.0265245 time: 560.786 one-recall: 0.99 one-ratio: 1.00027
iteration: 21 recall: 0.9748 accuracy: 0.00118962 cost: 0.00676523 M: 35.3771 delta: 0.0265216 time: 566.049 one-recall: 0.99 one-ratio: 1.00027
iteration: 22 recall: 0.9748 accuracy: 0.00118962 cost: 0.00676538 M: 35.3777 delta: 0.026519 time: 571.302 one-recall: 0.99 one-ratio: 1.00027
iteration: 23 recall: 0.9748 accuracy: 0.00118962 cost: 0.00676547 M: 35.378 delta: 0.0265181 time: 576.546 one-recall: 0.99 one-ratio: 1.00027
iteration: 24 recall: 0.9748 accuracy: 0.00118962 cost: 0.00676551 M: 35.3782 delta: 0.0265173 time: 581.788 one-recall: 0.99 one-ratio: 1.00027
iteration: 25 recall: 0.9748 accuracy: 0.00118962 cost: 0.00676553 M: 35.3783 delta: 0.0265163 time: 587.025 one-recall: 0.99 one-ratio: 1.00027
iteration: 26 recall: 0.9748 accuracy: 0.00118962 cost: 0.00676554 M: 35.3784 delta: 0.0265162 time: 592.253 one-recall: 0.99 one-ratio: 1.00027
iteration: 27 recall: 0.9748 accuracy: 0.00118962 cost: 0.00676555 M: 35.3784 delta: 0.026516 time: 597.484 one-recall: 0.99 one-ratio: 1.00027
iteration: 28 recall: 0.9748 accuracy: 0.00118962 cost: 0.00676555 M: 35.3784 delta: 0.0265161 time: 602.71 one-recall: 0.99 one-ratio: 1.00027
iteration: 29 recall: 0.9748 accuracy: 0.00118962 cost: 0.00676555 M: 35.3784 delta: 0.026516 time: 607.938 one-recall: 0.99 one-ratio: 1.00027
iteration: 30 recall: 0.9748 accuracy: 0.00118962 cost: 0.00676555 M: 35.3784 delta: 0.026516 time: 613.167 one-recall: 0.99 one-ratio: 1.00027
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 627.5700000000006
Index size:  262856.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.1043278000
  Testing...
|S| = 80
|T| = 1152
Reject!
317.622 < 399.383
  -> Decision False in time 0.0000000000, query time of that 0.0001981770, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Reject!
296.633 < 426.265
  -> Decision False in time 0.0100000000, query time of that 0.0015401190, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
373.236 < 395.601
  -> Decision False in time 0.0000000000, query time of that 0.0009176150, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Reject!
285.205 < 470.311
  -> Decision False in time 0.0500000000, query time of that 0.0018926140, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
436.873 < 462.437
  -> Decision False in time 0.0500000000, query time of that 0.0018680780, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
490.612 < 533.103
  -> Decision False in time 0.0500000000, query time of that 0.0021742150, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Reject!
439.977 < 443.182
  -> Decision False in time 0.1700000000, query time of that 0.0007614460, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
393.972 < 429.432
  -> Decision False in time 0.0900000000, query time of that 0.0004235700, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
398.052 < 471.187
  -> Decision False in time 0.4900000000, query time of that 0.0019332130, 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: 2.71406 cost: 0.00038 M: 10 delta: 1 time: 53.947 one-recall: 0 one-ratio: 3.30861
iteration: 2 recall: 0.0012 accuracy: 1.42289 cost: 0.000637428 M: 10 delta: 0.856032 time: 92.0093 one-recall: 0 one-ratio: 2.66757
iteration: 3 recall: 0.0288 accuracy: 0.74568 cost: 0.00109521 M: 11.5287 delta: 0.835105 time: 138.253 one-recall: 0.03 one-ratio: 2.25818
iteration: 4 recall: 0.1812 accuracy: 0.347902 cost: 0.00163042 M: 11.8362 delta: 0.783455 time: 184.153 one-recall: 0.16 one-ratio: 1.85328
iteration: 5 recall: 0.4856 accuracy: 0.134968 cost: 0.00223603 M: 12.6038 delta: 0.664598 time: 231.865 one-recall: 0.48 one-ratio: 1.40627
iteration: 6 recall: 0.7628 accuracy: 0.0320061 cost: 0.00297995 M: 15.115 delta: 0.43233 time: 285.831 one-recall: 0.8 one-ratio: 1.1049
iteration: 7 recall: 0.8844 accuracy: 0.0103133 cost: 0.00395511 M: 21.1392 delta: 0.196439 time: 347.468 one-recall: 0.92 one-ratio: 1.02955
iteration: 8 recall: 0.9388 accuracy: 0.00451649 cost: 0.00497975 M: 27.3041 delta: 0.0884759 time: 405.192 one-recall: 0.98 one-ratio: 1.0105
iteration: 9 recall: 0.9636 accuracy: 0.00222517 cost: 0.0057727 M: 31.2874 delta: 0.0513514 time: 449.18 one-recall: 1 one-ratio: 1
iteration: 10 recall: 0.974 accuracy: 0.00162403 cost: 0.00625615 M: 33.3874 delta: 0.0372215 time: 478.638 one-recall: 1 one-ratio: 1
iteration: 11 recall: 0.976 accuracy: 0.00154543 cost: 0.00651324 M: 34.4171 delta: 0.0313422 time: 497.764 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.9772 accuracy: 0.00146485 cost: 0.00664055 M: 34.908 delta: 0.0287861 time: 510.543 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.9788 accuracy: 0.00139507 cost: 0.00670306 M: 35.1455 delta: 0.0275955 time: 519.771 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.9796 accuracy: 0.00131272 cost: 0.00673301 M: 35.258 delta: 0.0270535 time: 527.038 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.9796 accuracy: 0.00131272 cost: 0.00674783 M: 35.3134 delta: 0.0267941 time: 533.318 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.9796 accuracy: 0.00131272 cost: 0.00675511 M: 35.3405 delta: 0.0266724 time: 539.096 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.9804 accuracy: 0.00126903 cost: 0.00675877 M: 35.3543 delta: 0.02661 time: 544.626 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.9808 accuracy: 0.00125119 cost: 0.00676064 M: 35.3613 delta: 0.0265816 time: 550.025 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.9816 accuracy: 0.00123711 cost: 0.00676168 M: 35.3654 delta: 0.0265632 time: 555.361 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.9816 accuracy: 0.00123711 cost: 0.0067622 M: 35.3674 delta: 0.0265551 time: 560.652 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.9816 accuracy: 0.00123711 cost: 0.0067625 M: 35.3686 delta: 0.0265511 time: 565.921 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.9816 accuracy: 0.00123711 cost: 0.00676268 M: 35.3694 delta: 0.026548 time: 571.177 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.9816 accuracy: 0.00123711 cost: 0.00676279 M: 35.3697 delta: 0.0265468 time: 576.425 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.9816 accuracy: 0.00123711 cost: 0.00676284 M: 35.3699 delta: 0.0265455 time: 581.664 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.9816 accuracy: 0.00123711 cost: 0.00676286 M: 35.37 delta: 0.0265448 time: 586.895 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.9816 accuracy: 0.00123711 cost: 0.00676287 M: 35.37 delta: 0.0265447 time: 592.131 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.9816 accuracy: 0.00123711 cost: 0.00676287 M: 35.37 delta: 0.0265446 time: 597.365 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.9816 accuracy: 0.00123711 cost: 0.00676288 M: 35.37 delta: 0.0265446 time: 602.594 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.9816 accuracy: 0.00123711 cost: 0.00676288 M: 35.37 delta: 0.0265446 time: 607.828 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.9816 accuracy: 0.00123711 cost: 0.00676288 M: 35.37 delta: 0.0265446 time: 613.054 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 627.4700000000003
Index size:  263156.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0041175000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.1000000000, query time of that 0.0464296840, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Accept!
  -> Decision True in time 0.9000000000, query time of that 0.4577633340, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
261.017 < 285.112
  -> Decision False in time 7.1700000000, query time of that 3.5945521170, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.5700000000, query time of that 0.0543334100, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Accept!
  -> Decision True in time 5.6700000000, query time of that 0.5570364350, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
259.386 < 286.094
  -> Decision False in time 4.8400000000, query time of that 0.4816344120, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Accept!
  -> Decision True in time 6.6700000000, query time of that 0.0654589200, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
228.197 < 228.882
  -> Decision False in time 4.4200000000, query time of that 0.0437127550, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
276.429 < 282.508
  -> Decision False in time 3.8400000000, query time of that 0.0372244060, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 80, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 80, {'reverse': -1}, False])
Got a train set of size (1000000 * 128)
Generating control...
Initializing...
iteration: 1 recall: 0.0004 accuracy: 2.23476 cost: 0.00038 M: 10 delta: 1 time: 53.9252 one-recall: 0 one-ratio: 3.25476
iteration: 2 recall: 0.0052 accuracy: 1.13893 cost: 0.000637428 M: 10 delta: 0.856032 time: 91.9781 one-recall: 0 one-ratio: 2.56817
iteration: 3 recall: 0.03 accuracy: 0.643717 cost: 0.00109521 M: 11.5287 delta: 0.8351 time: 138.208 one-recall: 0.07 one-ratio: 2.08383
iteration: 4 recall: 0.1752 accuracy: 0.322785 cost: 0.00163042 M: 11.8363 delta: 0.783452 time: 184.104 one-recall: 0.23 one-ratio: 1.67021
iteration: 5 recall: 0.468 accuracy: 0.119852 cost: 0.00223598 M: 12.6035 delta: 0.664574 time: 231.8 one-recall: 0.61 one-ratio: 1.23415
iteration: 6 recall: 0.754 accuracy: 0.0322051 cost: 0.00297991 M: 15.1151 delta: 0.432331 time: 285.74 one-recall: 0.85 one-ratio: 1.07722
iteration: 7 recall: 0.8968 accuracy: 0.00804908 cost: 0.00395529 M: 21.141 delta: 0.196434 time: 347.341 one-recall: 0.96 one-ratio: 1.01048
iteration: 8 recall: 0.9508 accuracy: 0.00336588 cost: 0.00497958 M: 27.3053 delta: 0.0884526 time: 405.033 one-recall: 0.98 one-ratio: 1.00582
iteration: 9 recall: 0.9676 accuracy: 0.00215697 cost: 0.00577295 M: 31.2895 delta: 0.0513407 time: 449.014 one-recall: 0.98 one-ratio: 1.00582
iteration: 10 recall: 0.976 accuracy: 0.00151639 cost: 0.00625753 M: 33.3918 delta: 0.0372024 time: 478.518 one-recall: 0.98 one-ratio: 1.00582
iteration: 11 recall: 0.978 accuracy: 0.00124203 cost: 0.00651441 M: 34.4198 delta: 0.0313242 time: 497.633 one-recall: 0.99 one-ratio: 1.002
iteration: 12 recall: 0.9796 accuracy: 0.00115916 cost: 0.00664217 M: 34.9125 delta: 0.0287735 time: 510.398 one-recall: 0.99 one-ratio: 1.002
iteration: 13 recall: 0.9804 accuracy: 0.00107608 cost: 0.00670471 M: 35.1483 delta: 0.027589 time: 519.596 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.9804 accuracy: 0.00107608 cost: 0.00673506 M: 35.2618 delta: 0.0270501 time: 526.863 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.9808 accuracy: 0.0010701 cost: 0.00674997 M: 35.3173 delta: 0.0267881 time: 533.13 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.9808 accuracy: 0.0010701 cost: 0.00675738 M: 35.3447 delta: 0.0266639 time: 538.89 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.9808 accuracy: 0.0010701 cost: 0.00676118 M: 35.3588 delta: 0.0265985 time: 544.397 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.9808 accuracy: 0.0010701 cost: 0.00676319 M: 35.3663 delta: 0.0265665 time: 549.765 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.9808 accuracy: 0.0010701 cost: 0.00676424 M: 35.3701 delta: 0.0265518 time: 555.057 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.9808 accuracy: 0.0010701 cost: 0.00676477 M: 35.3721 delta: 0.0265439 time: 560.308 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.9808 accuracy: 0.0010701 cost: 0.00676506 M: 35.3732 delta: 0.0265405 time: 565.528 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.9808 accuracy: 0.0010701 cost: 0.00676524 M: 35.3739 delta: 0.0265369 time: 570.738 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.9808 accuracy: 0.0010701 cost: 0.00676535 M: 35.3744 delta: 0.0265353 time: 575.942 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.9808 accuracy: 0.0010701 cost: 0.00676541 M: 35.3746 delta: 0.0265345 time: 581.137 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.9808 accuracy: 0.0010701 cost: 0.00676545 M: 35.3747 delta: 0.0265339 time: 586.329 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.9808 accuracy: 0.0010701 cost: 0.00676547 M: 35.3748 delta: 0.0265337 time: 591.517 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.9808 accuracy: 0.0010701 cost: 0.00676548 M: 35.3748 delta: 0.0265338 time: 596.701 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.9808 accuracy: 0.0010701 cost: 0.00676549 M: 35.3749 delta: 0.0265335 time: 601.884 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.9808 accuracy: 0.0010701 cost: 0.00676549 M: 35.3749 delta: 0.0265335 time: 607.069 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.9808 accuracy: 0.0010701 cost: 0.0067655 M: 35.3749 delta: 0.0265335 time: 612.257 one-recall: 1 one-ratio: 1
Graph completion with reverse edges...

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

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 626.6100000000006
Index size:  262904.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0031667000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.1000000000, query time of that 0.0587916510, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Reject!
325.251 < 341.319
  -> Decision False in time 0.8800000000, query time of that 0.4923087530, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
298.367 < 300.538
  -> Decision False in time 4.4000000000, query time of that 2.4301436010, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.6000000000, query time of that 0.0691592660, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Accept!
  -> Decision True in time 5.7400000000, query time of that 0.6571917580, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
299.923 < 399.218
  -> Decision False in time 7.1600000000, query time of that 0.8214033690, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Accept!
  -> Decision True in time 6.6400000000, query time of that 0.0717398510, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
260.271 < 265.236
  -> Decision False in time 23.9800000000, query time of that 0.2775263310, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
271.77 < 278.173
  -> Decision False in time 53.0500000000, query time of that 0.6173621280, 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: 2.23096 cost: 0.00038 M: 10 delta: 1 time: 53.9454 one-recall: 0 one-ratio: 3.26443
iteration: 2 recall: 0.0048 accuracy: 1.22334 cost: 0.000637428 M: 10 delta: 0.856032 time: 91.9975 one-recall: 0.01 one-ratio: 2.50226
iteration: 3 recall: 0.0276 accuracy: 0.632734 cost: 0.00109521 M: 11.5287 delta: 0.835103 time: 138.26 one-recall: 0.03 one-ratio: 2.05552
iteration: 4 recall: 0.1728 accuracy: 0.292353 cost: 0.00163043 M: 11.8362 delta: 0.783459 time: 184.167 one-recall: 0.29 one-ratio: 1.61119
iteration: 5 recall: 0.4956 accuracy: 0.0995393 cost: 0.00223609 M: 12.6039 delta: 0.664603 time: 231.873 one-recall: 0.65 one-ratio: 1.20582
iteration: 6 recall: 0.7648 accuracy: 0.0280037 cost: 0.00297991 M: 15.1144 delta: 0.432355 time: 285.824 one-recall: 0.88 one-ratio: 1.06066
iteration: 7 recall: 0.9016 accuracy: 0.00921966 cost: 0.00395502 M: 21.1386 delta: 0.196412 time: 347.444 one-recall: 0.94 one-ratio: 1.02087
iteration: 8 recall: 0.9472 accuracy: 0.00379385 cost: 0.00497965 M: 27.3047 delta: 0.0884656 time: 405.199 one-recall: 0.96 one-ratio: 1.00509
iteration: 9 recall: 0.9656 accuracy: 0.00217389 cost: 0.00577227 M: 31.2881 delta: 0.0513479 time: 449.149 one-recall: 0.97 one-ratio: 1.00442
iteration: 10 recall: 0.976 accuracy: 0.00156528 cost: 0.00625838 M: 33.3961 delta: 0.0371992 time: 478.71 one-recall: 0.97 one-ratio: 1.00442
iteration: 11 recall: 0.9784 accuracy: 0.00133998 cost: 0.00651641 M: 34.4291 delta: 0.0312962 time: 497.852 one-recall: 0.98 one-ratio: 1.00203
iteration: 12 recall: 0.9792 accuracy: 0.00127889 cost: 0.00664385 M: 34.921 delta: 0.0287389 time: 510.586 one-recall: 0.98 one-ratio: 1.00203
iteration: 13 recall: 0.98 accuracy: 0.00120628 cost: 0.00670664 M: 35.1583 delta: 0.0275587 time: 519.797 one-recall: 0.98 one-ratio: 1.00203
iteration: 14 recall: 0.9804 accuracy: 0.00118679 cost: 0.00673692 M: 35.2711 delta: 0.0270074 time: 527.05 one-recall: 0.98 one-ratio: 1.00203
iteration: 15 recall: 0.9804 accuracy: 0.00118679 cost: 0.00675158 M: 35.3257 delta: 0.0267483 time: 533.311 one-recall: 0.98 one-ratio: 1.00203
iteration: 16 recall: 0.9804 accuracy: 0.00118679 cost: 0.00675909 M: 35.3533 delta: 0.0266236 time: 539.081 one-recall: 0.98 one-ratio: 1.00203
iteration: 17 recall: 0.9804 accuracy: 0.00118679 cost: 0.00676286 M: 35.3674 delta: 0.0265578 time: 544.607 one-recall: 0.98 one-ratio: 1.00203
iteration: 18 recall: 0.9804 accuracy: 0.00118679 cost: 0.0067648 M: 35.3745 delta: 0.0265262 time: 549.976 one-recall: 0.98 one-ratio: 1.00203
iteration: 19 recall: 0.9804 accuracy: 0.00118679 cost: 0.00676576 M: 35.3779 delta: 0.0265099 time: 555.263 one-recall: 0.98 one-ratio: 1.00203
iteration: 20 recall: 0.9804 accuracy: 0.00118679 cost: 0.00676618 M: 35.3795 delta: 0.026502 time: 560.501 one-recall: 0.98 one-ratio: 1.00203
iteration: 21 recall: 0.9804 accuracy: 0.00118679 cost: 0.00676639 M: 35.3803 delta: 0.0264976 time: 565.718 one-recall: 0.98 one-ratio: 1.00203
iteration: 22 recall: 0.9804 accuracy: 0.00118679 cost: 0.00676649 M: 35.3807 delta: 0.0264958 time: 570.924 one-recall: 0.98 one-ratio: 1.00203
iteration: 23 recall: 0.9804 accuracy: 0.00118679 cost: 0.00676655 M: 35.3809 delta: 0.0264951 time: 576.124 one-recall: 0.98 one-ratio: 1.00203
iteration: 24 recall: 0.9804 accuracy: 0.00118679 cost: 0.00676659 M: 35.381 delta: 0.0264942 time: 581.322 one-recall: 0.98 one-ratio: 1.00203
iteration: 25 recall: 0.9804 accuracy: 0.00118679 cost: 0.00676662 M: 35.3811 delta: 0.026494 time: 586.529 one-recall: 0.98 one-ratio: 1.00203
iteration: 26 recall: 0.9804 accuracy: 0.00118679 cost: 0.00676664 M: 35.3812 delta: 0.0264937 time: 591.728 one-recall: 0.98 one-ratio: 1.00203
iteration: 27 recall: 0.9804 accuracy: 0.00118679 cost: 0.00676665 M: 35.3812 delta: 0.0264937 time: 596.922 one-recall: 0.98 one-ratio: 1.00203
iteration: 28 recall: 0.9804 accuracy: 0.00118679 cost: 0.00676666 M: 35.3813 delta: 0.0264935 time: 602.113 one-recall: 0.98 one-ratio: 1.00203
iteration: 29 recall: 0.9804 accuracy: 0.00118679 cost: 0.00676666 M: 35.3813 delta: 0.0264935 time: 607.298 one-recall: 0.98 one-ratio: 1.00203
iteration: 30 recall: 0.9804 accuracy: 0.00118679 cost: 0.00676666 M: 35.3813 delta: 0.0264935 time: 612.486 one-recall: 0.98 one-ratio: 1.00203
Graph completion with reverse edges...

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

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 626.8499999999985
Index size:  201528.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0062131000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.0800000000, query time of that 0.0367464610, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Reject!
294.873 < 298.203
  -> Decision False in time 0.7400000000, query time of that 0.3306043390, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
321.308 < 334.912
  -> Decision False in time 4.1600000000, query time of that 1.8082248000, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.5400000000, query time of that 0.0406700970, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Accept!
  -> Decision True in time 5.5000000000, query time of that 0.4323870830, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
339.156 < 371.935
  -> Decision False in time 5.0400000000, query time of that 0.3905333260, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Accept!
  -> Decision True in time 6.6200000000, query time of that 0.0515204710, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
265.179 < 273.04
  -> Decision False in time 1.7200000000, query time of that 0.0143152470, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
224.47 < 290.277
  -> Decision False in time 11.9700000000, query time of that 0.0935774460, 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: 2.04491 cost: 0.00038 M: 10 delta: 1 time: 53.9397 one-recall: 0 one-ratio: 3.66967
iteration: 2 recall: 0.0052 accuracy: 1.16687 cost: 0.000637428 M: 10 delta: 0.856032 time: 91.9985 one-recall: 0 one-ratio: 2.8496
iteration: 3 recall: 0.0296 accuracy: 0.660347 cost: 0.00109521 M: 11.5287 delta: 0.835108 time: 138.261 one-recall: 0.06 one-ratio: 2.35595
iteration: 4 recall: 0.1756 accuracy: 0.356411 cost: 0.00163044 M: 11.8362 delta: 0.783459 time: 184.166 one-recall: 0.26 one-ratio: 1.82809
iteration: 5 recall: 0.498 accuracy: 0.111396 cost: 0.00223609 M: 12.6037 delta: 0.664598 time: 231.87 one-recall: 0.6 one-ratio: 1.30098
iteration: 6 recall: 0.7528 accuracy: 0.0312512 cost: 0.00298 M: 15.1144 delta: 0.432347 time: 285.831 one-recall: 0.84 one-ratio: 1.08493
iteration: 7 recall: 0.8804 accuracy: 0.0109232 cost: 0.00395508 M: 21.1369 delta: 0.196409 time: 347.449 one-recall: 0.94 one-ratio: 1.02997
iteration: 8 recall: 0.9284 accuracy: 0.00496597 cost: 0.00497957 M: 27.305 delta: 0.0884399 time: 405.203 one-recall: 0.99 one-ratio: 1.00074
iteration: 9 recall: 0.9564 accuracy: 0.00233895 cost: 0.00577261 M: 31.2904 delta: 0.0513531 time: 449.189 one-recall: 1 one-ratio: 1
iteration: 10 recall: 0.9744 accuracy: 0.00129119 cost: 0.00625791 M: 33.396 delta: 0.0372194 time: 478.727 one-recall: 1 one-ratio: 1
iteration: 11 recall: 0.978 accuracy: 0.00114838 cost: 0.00651548 M: 34.4272 delta: 0.0313417 time: 497.867 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.9792 accuracy: 0.00110041 cost: 0.00664367 M: 34.919 delta: 0.0287617 time: 510.657 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.98 accuracy: 0.00107213 cost: 0.00670654 M: 35.157 delta: 0.0275901 time: 519.891 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.98 accuracy: 0.00107213 cost: 0.00673662 M: 35.2697 delta: 0.0270398 time: 527.138 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.9804 accuracy: 0.00106927 cost: 0.00675177 M: 35.3262 delta: 0.0267844 time: 533.425 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.9804 accuracy: 0.00106927 cost: 0.00675937 M: 35.3548 delta: 0.0266513 time: 539.207 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.9804 accuracy: 0.00106927 cost: 0.00676325 M: 35.3691 delta: 0.0265838 time: 544.725 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.9804 accuracy: 0.00106927 cost: 0.00676515 M: 35.3762 delta: 0.0265526 time: 550.09 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.9804 accuracy: 0.00106927 cost: 0.00676613 M: 35.3798 delta: 0.0265358 time: 555.385 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.9804 accuracy: 0.00106927 cost: 0.00676666 M: 35.3818 delta: 0.0265279 time: 560.632 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.9804 accuracy: 0.00106927 cost: 0.00676693 M: 35.3828 delta: 0.0265241 time: 565.873 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.9804 accuracy: 0.00106927 cost: 0.00676709 M: 35.3835 delta: 0.0265217 time: 571.12 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.9804 accuracy: 0.00106927 cost: 0.00676717 M: 35.3838 delta: 0.0265205 time: 576.359 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.9804 accuracy: 0.00106927 cost: 0.00676721 M: 35.3839 delta: 0.0265199 time: 581.591 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.9804 accuracy: 0.00106927 cost: 0.00676725 M: 35.3841 delta: 0.0265193 time: 586.828 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.9804 accuracy: 0.00106927 cost: 0.00676727 M: 35.3841 delta: 0.0265191 time: 592.053 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.9804 accuracy: 0.00106927 cost: 0.00676728 M: 35.3842 delta: 0.0265187 time: 597.293 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.9804 accuracy: 0.00106927 cost: 0.00676729 M: 35.3842 delta: 0.0265186 time: 602.519 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.9804 accuracy: 0.00106927 cost: 0.00676729 M: 35.3842 delta: 0.0265185 time: 607.748 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.9804 accuracy: 0.00106927 cost: 0.00676729 M: 35.3842 delta: 0.0265185 time: 612.97 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 627.3700000000008
Index size:  201436.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0027070000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.1100000000, query time of that 0.0581025360, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Accept!
  -> Decision True in time 1.0500000000, query time of that 0.6127444540, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Accept!
  -> Decision True in time 10.7900000000, query time of that 6.1721076650, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.5900000000, query time of that 0.0705451760, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Accept!
  -> Decision True in time 5.9900000000, query time of that 0.7415816300, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
214.415 < 254.859
  -> Decision False in time 55.6600000000, query time of that 6.9332924710, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Accept!
  -> Decision True in time 6.6700000000, query time of that 0.0815636730, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
383.393 < 396.875
  -> Decision False in time 6.6700000000, query time of that 0.0823854220, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
325.435 < 346.175
  -> Decision False in time 91.7300000000, query time of that 1.1448008370, 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: 1.99828 cost: 0.00038 M: 10 delta: 1 time: 53.9181 one-recall: 0 one-ratio: 3.38047
iteration: 2 recall: 0.0064 accuracy: 1.15823 cost: 0.000637428 M: 10 delta: 0.856032 time: 91.9747 one-recall: 0 one-ratio: 2.59197
iteration: 3 recall: 0.0364 accuracy: 0.651347 cost: 0.00109521 M: 11.5287 delta: 0.835106 time: 138.232 one-recall: 0.04 one-ratio: 2.0806
iteration: 4 recall: 0.1804 accuracy: 0.321669 cost: 0.00163044 M: 11.8362 delta: 0.783454 time: 184.134 one-recall: 0.2 one-ratio: 1.71253
iteration: 5 recall: 0.4948 accuracy: 0.106534 cost: 0.0022361 M: 12.6038 delta: 0.664592 time: 231.828 one-recall: 0.61 one-ratio: 1.26031
iteration: 6 recall: 0.7812 accuracy: 0.0290305 cost: 0.00297992 M: 15.1147 delta: 0.432311 time: 285.78 one-recall: 0.88 one-ratio: 1.04475
iteration: 7 recall: 0.9 accuracy: 0.00746423 cost: 0.00395513 M: 21.1393 delta: 0.196439 time: 347.395 one-recall: 0.96 one-ratio: 1.00428
iteration: 8 recall: 0.9464 accuracy: 0.00317784 cost: 0.00497947 M: 27.303 delta: 0.0884783 time: 405.135 one-recall: 0.97 one-ratio: 1.00261
iteration: 9 recall: 0.9716 accuracy: 0.00134505 cost: 0.00577214 M: 31.2869 delta: 0.0513333 time: 449.087 one-recall: 0.97 one-ratio: 1.00261
iteration: 10 recall: 0.9836 accuracy: 0.000867626 cost: 0.00625631 M: 33.3887 delta: 0.0371924 time: 478.569 one-recall: 0.97 one-ratio: 1.00261
iteration: 11 recall: 0.9864 accuracy: 0.000600833 cost: 0.00651382 M: 34.4193 delta: 0.0313201 time: 497.664 one-recall: 0.98 one-ratio: 1.00201
iteration: 12 recall: 0.9872 accuracy: 0.000540074 cost: 0.00664173 M: 34.9104 delta: 0.0287641 time: 510.418 one-recall: 0.98 one-ratio: 1.00201
iteration: 13 recall: 0.9872 accuracy: 0.000540074 cost: 0.0067043 M: 35.1462 delta: 0.027586 time: 519.599 one-recall: 0.98 one-ratio: 1.00201
iteration: 14 recall: 0.9872 accuracy: 0.000540074 cost: 0.00673468 M: 35.2608 delta: 0.0270382 time: 526.862 one-recall: 0.98 one-ratio: 1.00201
iteration: 15 recall: 0.9872 accuracy: 0.000540074 cost: 0.0067495 M: 35.316 delta: 0.0267777 time: 533.126 one-recall: 0.98 one-ratio: 1.00201
iteration: 16 recall: 0.9872 accuracy: 0.000540074 cost: 0.00675717 M: 35.3446 delta: 0.0266494 time: 538.908 one-recall: 0.98 one-ratio: 1.00201
iteration: 17 recall: 0.9872 accuracy: 0.000540074 cost: 0.00676097 M: 35.3589 delta: 0.0265845 time: 544.41 one-recall: 0.98 one-ratio: 1.00201
iteration: 18 recall: 0.9872 accuracy: 0.000540074 cost: 0.00676282 M: 35.3659 delta: 0.0265541 time: 549.771 one-recall: 0.98 one-ratio: 1.00201
iteration: 19 recall: 0.9872 accuracy: 0.000540074 cost: 0.00676383 M: 35.3696 delta: 0.0265383 time: 555.06 one-recall: 0.98 one-ratio: 1.00201
iteration: 20 recall: 0.9872 accuracy: 0.000540074 cost: 0.00676431 M: 35.3713 delta: 0.0265318 time: 560.305 one-recall: 0.98 one-ratio: 1.00201
iteration: 21 recall: 0.9872 accuracy: 0.000540074 cost: 0.00676461 M: 35.3724 delta: 0.0265285 time: 565.528 one-recall: 0.98 one-ratio: 1.00201
iteration: 22 recall: 0.9872 accuracy: 0.000540074 cost: 0.00676478 M: 35.3731 delta: 0.0265254 time: 570.738 one-recall: 0.98 one-ratio: 1.00201
iteration: 23 recall: 0.9872 accuracy: 0.000540074 cost: 0.00676489 M: 35.3735 delta: 0.0265245 time: 575.942 one-recall: 0.98 one-ratio: 1.00201
iteration: 24 recall: 0.9872 accuracy: 0.000540074 cost: 0.00676495 M: 35.3737 delta: 0.0265238 time: 581.14 one-recall: 0.98 one-ratio: 1.00201
iteration: 25 recall: 0.9872 accuracy: 0.000540074 cost: 0.00676498 M: 35.3739 delta: 0.0265231 time: 586.337 one-recall: 0.98 one-ratio: 1.00201
iteration: 26 recall: 0.9872 accuracy: 0.000540074 cost: 0.00676501 M: 35.374 delta: 0.0265224 time: 591.529 one-recall: 0.98 one-ratio: 1.00201
iteration: 27 recall: 0.9872 accuracy: 0.000540074 cost: 0.00676502 M: 35.374 delta: 0.0265226 time: 596.717 one-recall: 0.98 one-ratio: 1.00201
iteration: 28 recall: 0.9872 accuracy: 0.000540074 cost: 0.00676503 M: 35.3741 delta: 0.0265225 time: 601.904 one-recall: 0.98 one-ratio: 1.00201
iteration: 29 recall: 0.9872 accuracy: 0.000540074 cost: 0.00676504 M: 35.3741 delta: 0.0265223 time: 607.094 one-recall: 0.98 one-ratio: 1.00201
iteration: 30 recall: 0.9872 accuracy: 0.000540074 cost: 0.00676505 M: 35.3741 delta: 0.0265222 time: 612.282 one-recall: 0.98 one-ratio: 1.00201
Graph completion with reverse edges...

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

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 626.6599999999999
Index size:  201124.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0194793000
  Testing...
|S| = 80
|T| = 1152
Reject!
370.613 < 377.96
  -> Decision False in time 0.0000000000, query time of that 0.0004135860, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Reject!
329.021 < 342.787
  -> Decision False in time 0.0300000000, query time of that 0.0073947730, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
343.91 < 460.614
  -> Decision False in time 0.1200000000, query time of that 0.0288796470, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.5200000000, query time of that 0.0179617720, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
309.262 < 313.447
  -> Decision False in time 0.6400000000, query time of that 0.0221158170, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
384.875 < 387.7
  -> Decision False in time 0.3500000000, query time of that 0.0117333530, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Reject!
253.772 < 260.862
  -> Decision False in time 0.3800000000, query time of that 0.0015398210, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
349.567 < 431.13
  -> Decision False in time 2.1500000000, query time of that 0.0078600330, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
303.341 < 334.141
  -> Decision False in time 14.2300000000, query time of that 0.0514498900, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 20, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 20, {'reverse': -1}, False])
Got a train set of size (1000000 * 128)
Generating control...
Initializing...
iteration: 1 recall: 0.0004 accuracy: 2.04811 cost: 0.00038 M: 10 delta: 1 time: 53.9209 one-recall: 0 one-ratio: 3.57092
iteration: 2 recall: 0.0036 accuracy: 1.17789 cost: 0.000637428 M: 10 delta: 0.856032 time: 91.98 one-recall: 0 one-ratio: 2.86937
iteration: 3 recall: 0.0292 accuracy: 0.695405 cost: 0.00109521 M: 11.5287 delta: 0.835109 time: 138.228 one-recall: 0.05 one-ratio: 2.31193
iteration: 4 recall: 0.1496 accuracy: 0.374201 cost: 0.00163042 M: 11.8362 delta: 0.783456 time: 184.131 one-recall: 0.16 one-ratio: 1.87853
iteration: 5 recall: 0.4624 accuracy: 0.140496 cost: 0.00223605 M: 12.6037 delta: 0.664597 time: 231.822 one-recall: 0.54 one-ratio: 1.39418
iteration: 6 recall: 0.7492 accuracy: 0.0331159 cost: 0.00297995 M: 15.1144 delta: 0.432366 time: 285.782 one-recall: 0.82 one-ratio: 1.11497
iteration: 7 recall: 0.8788 accuracy: 0.0118138 cost: 0.00395516 M: 21.1396 delta: 0.196423 time: 347.399 one-recall: 0.92 one-ratio: 1.04315
iteration: 8 recall: 0.93 accuracy: 0.00459188 cost: 0.00497978 M: 27.3048 delta: 0.0884471 time: 405.159 one-recall: 0.95 one-ratio: 1.01542
iteration: 9 recall: 0.9576 accuracy: 0.00277552 cost: 0.00577292 M: 31.292 delta: 0.0512838 time: 449.126 one-recall: 0.97 one-ratio: 1.011
iteration: 10 recall: 0.9676 accuracy: 0.00171291 cost: 0.006258 M: 33.3957 delta: 0.0371521 time: 478.634 one-recall: 0.99 one-ratio: 1.0079
iteration: 11 recall: 0.974 accuracy: 0.00142378 cost: 0.00651582 M: 34.4266 delta: 0.0312651 time: 497.763 one-recall: 0.99 one-ratio: 1.0079
iteration: 12 recall: 0.9772 accuracy: 0.00126087 cost: 0.00664358 M: 34.9193 delta: 0.0287053 time: 510.502 one-recall: 0.99 one-ratio: 1.0079
iteration: 13 recall: 0.9788 accuracy: 0.0011277 cost: 0.00670592 M: 35.1549 delta: 0.0275376 time: 519.682 one-recall: 0.99 one-ratio: 1.0079
iteration: 14 recall: 0.9788 accuracy: 0.0011277 cost: 0.00673626 M: 35.2687 delta: 0.0269884 time: 526.948 one-recall: 0.99 one-ratio: 1.0079
iteration: 15 recall: 0.9788 accuracy: 0.0011277 cost: 0.00675118 M: 35.3245 delta: 0.0267291 time: 533.217 one-recall: 0.99 one-ratio: 1.0079
iteration: 16 recall: 0.9788 accuracy: 0.0011277 cost: 0.00675878 M: 35.3524 delta: 0.0266019 time: 538.995 one-recall: 0.99 one-ratio: 1.0079
iteration: 17 recall: 0.9788 accuracy: 0.0011277 cost: 0.00676258 M: 35.3667 delta: 0.026539 time: 544.504 one-recall: 0.99 one-ratio: 1.0079
iteration: 18 recall: 0.9788 accuracy: 0.0011277 cost: 0.00676452 M: 35.3738 delta: 0.026507 time: 549.877 one-recall: 0.99 one-ratio: 1.0079
iteration: 19 recall: 0.9788 accuracy: 0.0011277 cost: 0.00676554 M: 35.3776 delta: 0.0264905 time: 555.169 one-recall: 0.99 one-ratio: 1.0079
iteration: 20 recall: 0.9788 accuracy: 0.0011277 cost: 0.00676608 M: 35.3797 delta: 0.0264818 time: 560.436 one-recall: 0.99 one-ratio: 1.0079
iteration: 21 recall: 0.9788 accuracy: 0.0011277 cost: 0.0067664 M: 35.381 delta: 0.0264766 time: 565.679 one-recall: 0.99 one-ratio: 1.0079
iteration: 22 recall: 0.9788 accuracy: 0.0011277 cost: 0.0067666 M: 35.3816 delta: 0.0264742 time: 570.899 one-recall: 0.99 one-ratio: 1.0079
iteration: 23 recall: 0.9788 accuracy: 0.0011277 cost: 0.0067667 M: 35.382 delta: 0.0264725 time: 576.107 one-recall: 0.99 one-ratio: 1.0079
iteration: 24 recall: 0.9788 accuracy: 0.0011277 cost: 0.00676676 M: 35.3822 delta: 0.0264715 time: 581.308 one-recall: 0.99 one-ratio: 1.0079
iteration: 25 recall: 0.9788 accuracy: 0.0011277 cost: 0.00676678 M: 35.3823 delta: 0.0264713 time: 586.5 one-recall: 0.99 one-ratio: 1.0079
iteration: 26 recall: 0.9788 accuracy: 0.0011277 cost: 0.00676679 M: 35.3824 delta: 0.0264709 time: 591.694 one-recall: 0.99 one-ratio: 1.0079
iteration: 27 recall: 0.9788 accuracy: 0.0011277 cost: 0.00676679 M: 35.3824 delta: 0.0264709 time: 596.892 one-recall: 0.99 one-ratio: 1.0079
iteration: 28 recall: 0.9788 accuracy: 0.0011277 cost: 0.00676679 M: 35.3824 delta: 0.0264709 time: 602.083 one-recall: 0.99 one-ratio: 1.0079
iteration: 29 recall: 0.9788 accuracy: 0.0011277 cost: 0.00676679 M: 35.3824 delta: 0.0264709 time: 607.274 one-recall: 0.99 one-ratio: 1.0079
iteration: 30 recall: 0.9788 accuracy: 0.0011277 cost: 0.00676679 M: 35.3824 delta: 0.0264709 time: 612.466 one-recall: 0.99 one-ratio: 1.0079
Graph completion with reverse edges...

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

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 626.8400000000001
Index size:  201476.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0091589000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.0700000000, query time of that 0.0248320620, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Reject!
244.293 < 244.471
  -> Decision False in time 0.3200000000, query time of that 0.1117701830, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Reject!
399.905 < 400.971
  -> Decision False in time 1.8300000000, query time of that 0.6306977880, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.5400000000, query time of that 0.0285138230, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Reject!
310.39 < 310.693
  -> Decision False in time 2.2200000000, query time of that 0.1218254940, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
283.383 < 287.649
  -> Decision False in time 1.1000000000, query time of that 0.0628844050, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Reject!
226.863 < 230.922
  -> Decision False in time 4.9800000000, query time of that 0.0263611820, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
226.548 < 227.535
  -> Decision False in time 23.7200000000, query time of that 0.1303913420, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
250.298 < 254.657
  -> Decision False in time 4.6300000000, query time of that 0.0255016140, 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: 2.2112 cost: 0.00038 M: 10 delta: 1 time: 53.941 one-recall: 0 one-ratio: 3.30364
iteration: 2 recall: 0.0024 accuracy: 1.23542 cost: 0.000637428 M: 10 delta: 0.856032 time: 91.9983 one-recall: 0 one-ratio: 2.64068
iteration: 3 recall: 0.0312 accuracy: 0.715724 cost: 0.00109521 M: 11.5287 delta: 0.835105 time: 138.256 one-recall: 0.03 one-ratio: 2.14712
iteration: 4 recall: 0.1888 accuracy: 0.334817 cost: 0.00163044 M: 11.8363 delta: 0.78344 time: 184.156 one-recall: 0.23 one-ratio: 1.6959
iteration: 5 recall: 0.53 accuracy: 0.114945 cost: 0.00223611 M: 12.6041 delta: 0.664619 time: 231.862 one-recall: 0.64 one-ratio: 1.2776
iteration: 6 recall: 0.7644 accuracy: 0.0386794 cost: 0.00298005 M: 15.1152 delta: 0.43235 time: 285.808 one-recall: 0.87 one-ratio: 1.09163
iteration: 7 recall: 0.8872 accuracy: 0.0108685 cost: 0.00395537 M: 21.1396 delta: 0.196397 time: 347.436 one-recall: 0.96 one-ratio: 1.02869
iteration: 8 recall: 0.9408 accuracy: 0.00466633 cost: 0.00497952 M: 27.3002 delta: 0.0884779 time: 405.159 one-recall: 0.97 one-ratio: 1.02038
iteration: 9 recall: 0.964 accuracy: 0.00218179 cost: 0.00577212 M: 31.2834 delta: 0.0513677 time: 449.11 one-recall: 1 one-ratio: 1
iteration: 10 recall: 0.9732 accuracy: 0.00148794 cost: 0.00625746 M: 33.3905 delta: 0.0372208 time: 478.64 one-recall: 1 one-ratio: 1
iteration: 11 recall: 0.9784 accuracy: 0.0011633 cost: 0.00651405 M: 34.4185 delta: 0.0313354 time: 497.736 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.9808 accuracy: 0.0010661 cost: 0.00664104 M: 34.9078 delta: 0.0287845 time: 510.472 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.9812 accuracy: 0.00106022 cost: 0.00670326 M: 35.1436 delta: 0.0276238 time: 519.648 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.9812 accuracy: 0.00106022 cost: 0.00673345 M: 35.2568 delta: 0.0270649 time: 526.932 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.9812 accuracy: 0.00106022 cost: 0.00674828 M: 35.3115 delta: 0.0268151 time: 533.198 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.9812 accuracy: 0.00106022 cost: 0.00675579 M: 35.3393 delta: 0.0266873 time: 538.972 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.9812 accuracy: 0.00106022 cost: 0.00675967 M: 35.3534 delta: 0.0266278 time: 544.483 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.9812 accuracy: 0.00106022 cost: 0.0067616 M: 35.361 delta: 0.0265931 time: 549.853 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.9812 accuracy: 0.00106022 cost: 0.00676261 M: 35.3649 delta: 0.0265759 time: 555.153 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.9812 accuracy: 0.00106022 cost: 0.00676315 M: 35.3669 delta: 0.026567 time: 560.433 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.9812 accuracy: 0.00106022 cost: 0.0067634 M: 35.3679 delta: 0.0265631 time: 565.695 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.9812 accuracy: 0.00106022 cost: 0.00676357 M: 35.3685 delta: 0.0265601 time: 570.944 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.9812 accuracy: 0.00106022 cost: 0.00676362 M: 35.3687 delta: 0.0265594 time: 576.177 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.9812 accuracy: 0.00106022 cost: 0.00676366 M: 35.3688 delta: 0.0265587 time: 581.416 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.9812 accuracy: 0.00106022 cost: 0.00676368 M: 35.3689 delta: 0.026558 time: 586.65 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.9812 accuracy: 0.00106022 cost: 0.00676368 M: 35.3689 delta: 0.0265578 time: 591.876 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.9812 accuracy: 0.00106022 cost: 0.00676369 M: 35.3689 delta: 0.0265577 time: 597.083 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.9812 accuracy: 0.00106022 cost: 0.00676369 M: 35.3689 delta: 0.0265576 time: 602.3 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.9812 accuracy: 0.00106022 cost: 0.00676369 M: 35.3689 delta: 0.0265576 time: 607.527 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.9812 accuracy: 0.00106022 cost: 0.00676369 M: 35.3689 delta: 0.0265576 time: 612.748 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 627.1699999999983
Index size:  201108.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0049856000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.0900000000, query time of that 0.0423340950, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Accept!
  -> Decision True in time 0.8600000000, query time of that 0.4140740300, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Accept!
  -> Decision True in time 8.6800000000, query time of that 4.1074089950, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.5700000000, query time of that 0.0504616350, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Accept!
  -> Decision True in time 5.6500000000, query time of that 0.4926042990, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
260.267 < 271.95
  -> Decision False in time 1.1700000000, query time of that 0.1068979950, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Accept!
  -> Decision True in time 6.6400000000, query time of that 0.0570074680, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
245.367 < 247.691
  -> Decision False in time 21.4500000000, query time of that 0.1848368290, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
250.288 < 255.809
  -> Decision False in time 9.2000000000, query time of that 0.0764656890, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 70, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 70, {'reverse': -1}, False])
Got a train set of size (1000000 * 128)
Generating control...
Initializing...
iteration: 1 recall: 0.0004 accuracy: 1.84126 cost: 0.00038 M: 10 delta: 1 time: 53.9454 one-recall: 0.01 one-ratio: 3.43229
iteration: 2 recall: 0.004 accuracy: 1.03886 cost: 0.000637428 M: 10 delta: 0.856032 time: 91.9997 one-recall: 0.01 one-ratio: 2.72834
iteration: 3 recall: 0.0352 accuracy: 0.610612 cost: 0.00109521 M: 11.5287 delta: 0.835106 time: 138.255 one-recall: 0.04 one-ratio: 2.25623
iteration: 4 recall: 0.1704 accuracy: 0.314012 cost: 0.00163044 M: 11.8362 delta: 0.783465 time: 184.166 one-recall: 0.19 one-ratio: 1.73559
iteration: 5 recall: 0.4752 accuracy: 0.121848 cost: 0.00223606 M: 12.6037 delta: 0.664603 time: 231.869 one-recall: 0.57 one-ratio: 1.31374
iteration: 6 recall: 0.7564 accuracy: 0.0328707 cost: 0.00297991 M: 15.1143 delta: 0.432342 time: 285.82 one-recall: 0.82 one-ratio: 1.10968
iteration: 7 recall: 0.896 accuracy: 0.00888135 cost: 0.00395527 M: 21.1402 delta: 0.196389 time: 347.423 one-recall: 0.96 one-ratio: 1.02526
iteration: 8 recall: 0.9464 accuracy: 0.00398663 cost: 0.00497963 M: 27.3043 delta: 0.0884432 time: 405.131 one-recall: 0.97 one-ratio: 1.02445
iteration: 9 recall: 0.9672 accuracy: 0.001676 cost: 0.00577151 M: 31.2849 delta: 0.0513602 time: 449.046 one-recall: 1 one-ratio: 1
iteration: 10 recall: 0.9752 accuracy: 0.00122075 cost: 0.00625665 M: 33.3895 delta: 0.037212 time: 478.579 one-recall: 1 one-ratio: 1
iteration: 11 recall: 0.9788 accuracy: 0.00106753 cost: 0.00651326 M: 34.4167 delta: 0.0313359 time: 497.654 one-recall: 1 one-ratio: 1
iteration: 12 recall: 0.9816 accuracy: 0.000838159 cost: 0.00664065 M: 34.9081 delta: 0.0287409 time: 510.407 one-recall: 1 one-ratio: 1
iteration: 13 recall: 0.9836 accuracy: 0.000718404 cost: 0.00670239 M: 35.1418 delta: 0.0275853 time: 519.546 one-recall: 1 one-ratio: 1
iteration: 14 recall: 0.9844 accuracy: 0.000697387 cost: 0.00673234 M: 35.2542 delta: 0.0270509 time: 526.795 one-recall: 1 one-ratio: 1
iteration: 15 recall: 0.9844 accuracy: 0.000697387 cost: 0.00674687 M: 35.3087 delta: 0.026787 time: 533.059 one-recall: 1 one-ratio: 1
iteration: 16 recall: 0.9844 accuracy: 0.000697387 cost: 0.00675414 M: 35.3356 delta: 0.0266693 time: 538.834 one-recall: 1 one-ratio: 1
iteration: 17 recall: 0.9844 accuracy: 0.000697387 cost: 0.00675806 M: 35.3501 delta: 0.026608 time: 544.365 one-recall: 1 one-ratio: 1
iteration: 18 recall: 0.9844 accuracy: 0.000697387 cost: 0.00676019 M: 35.3579 delta: 0.0265735 time: 549.763 one-recall: 1 one-ratio: 1
iteration: 19 recall: 0.9844 accuracy: 0.000697387 cost: 0.00676132 M: 35.3621 delta: 0.0265573 time: 555.077 one-recall: 1 one-ratio: 1
iteration: 20 recall: 0.9844 accuracy: 0.000697387 cost: 0.00676188 M: 35.3644 delta: 0.026549 time: 560.348 one-recall: 1 one-ratio: 1
iteration: 21 recall: 0.9844 accuracy: 0.000697387 cost: 0.00676222 M: 35.3657 delta: 0.0265438 time: 565.602 one-recall: 1 one-ratio: 1
iteration: 22 recall: 0.9844 accuracy: 0.000697387 cost: 0.00676239 M: 35.3664 delta: 0.0265396 time: 570.836 one-recall: 1 one-ratio: 1
iteration: 23 recall: 0.9844 accuracy: 0.000697387 cost: 0.00676246 M: 35.3666 delta: 0.0265385 time: 576.051 one-recall: 1 one-ratio: 1
iteration: 24 recall: 0.9844 accuracy: 0.000697387 cost: 0.00676252 M: 35.3668 delta: 0.0265376 time: 581.262 one-recall: 1 one-ratio: 1
iteration: 25 recall: 0.9844 accuracy: 0.000697387 cost: 0.00676254 M: 35.3669 delta: 0.0265376 time: 586.467 one-recall: 1 one-ratio: 1
iteration: 26 recall: 0.9844 accuracy: 0.000697387 cost: 0.00676256 M: 35.367 delta: 0.0265377 time: 591.675 one-recall: 1 one-ratio: 1
iteration: 27 recall: 0.9844 accuracy: 0.000697387 cost: 0.00676258 M: 35.367 delta: 0.0265374 time: 596.881 one-recall: 1 one-ratio: 1
iteration: 28 recall: 0.9844 accuracy: 0.000697387 cost: 0.00676259 M: 35.3671 delta: 0.0265372 time: 602.084 one-recall: 1 one-ratio: 1
iteration: 29 recall: 0.9844 accuracy: 0.000697387 cost: 0.00676259 M: 35.3671 delta: 0.0265372 time: 607.29 one-recall: 1 one-ratio: 1
iteration: 30 recall: 0.9844 accuracy: 0.000697387 cost: 0.0067626 M: 35.3671 delta: 0.0265372 time: 612.487 one-recall: 1 one-ratio: 1
Graph completion with reverse edges...

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

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 626.8600000000006
Index size:  147696.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0035982000
  Testing...
|S| = 80
|T| = 1152
Accept!
  -> Decision True in time 0.1000000000, query time of that 0.0520080270, with c1=0.0500000000, c2=0.0010000000
|S| = 800
|T| = 1152
Accept!
  -> Decision True in time 0.9600000000, query time of that 0.5180223520, with c1=0.0500000000, c2=0.0100000000
|S| = 8000
|T| = 1152
Accept!
  -> Decision True in time 9.7600000000, query time of that 5.1557674180, with c1=0.0500000000, c2=0.1000000000
|S| = 80
|T| = 11513
Accept!
  -> Decision True in time 0.5800000000, query time of that 0.0591108730, with c1=0.5000000000, c2=0.0010000000
|S| = 800
|T| = 11513
Accept!
  -> Decision True in time 5.7000000000, query time of that 0.6085769460, with c1=0.5000000000, c2=0.0100000000
|S| = 8000
|T| = 11513
Reject!
317.526 < 325.189
  -> Decision False in time 51.6300000000, query time of that 5.5202574290, with c1=0.5000000000, c2=0.1000000000
|S| = 80
|T| = 115130
Reject!
236.146 < 239.042
  -> Decision False in time 3.8100000000, query time of that 0.0428408680, with c1=5.0000000000, c2=0.0010000000
|S| = 800
|T| = 115130
Reject!
257.023 < 258.482
  -> Decision False in time 18.9000000000, query time of that 0.1992859500, with c1=5.0000000000, c2=0.0100000000
|S| = 8000
|T| = 115130
Reject!
360.848 < 361.533
  -> Decision False in time 0.8500000000, query time of that 0.0101134450, with c1=5.0000000000, c2=0.1000000000
