copying files to /scratch...
starting benchmark...
/scratch/knn/venv/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  from ._conv import register_converters as _register_converters
running only kgraph
order: [Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 4, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 60, {'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', 5, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 80, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 100, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 90, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 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', 40, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 70, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 2, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 30, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 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]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 4, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.0044 accuracy: 1.65812 cost: 0.00633344 M: 10 delta: 1 time: 6.83005 one-recall: 0 one-ratio: 2.05485
iteration: 2 recall: 0.0664 accuracy: 0.581012 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4332 one-recall: 0.06 one-ratio: 1.4263
iteration: 3 recall: 0.4744 accuracy: 0.12877 cost: 0.0167507 M: 11.1153 delta: 0.84579 time: 15.4443 one-recall: 0.5 one-ratio: 1.12294
iteration: 4 recall: 0.9176 accuracy: 0.0084085 cost: 0.0249119 M: 11.725 delta: 0.566221 time: 21.3789 one-recall: 0.96 one-ratio: 1.01168
iteration: 5 recall: 0.9868 accuracy: 0.000693834 cost: 0.0376863 M: 17.4234 delta: 0.224531 time: 30.2122 one-recall: 0.99 one-ratio: 1.00139
iteration: 6 recall: 0.994 accuracy: 0.00016126 cost: 0.0460272 M: 21.1608 delta: 0.13404 time: 35.8875 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 36.15
Index size:  98596.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0025976667
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3500000000, query time of that 0.0499506950, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Accept!
  -> Decision True in time 3.4300000000, query time of that 0.4828333010, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2573.95 < 2602.55
  -> Decision False in time 0.2000000000, query time of that 0.0300980610, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1951.11 < 1966.65
  -> Decision False in time 2.9800000000, query time of that 0.0554423090, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1735.93 < 1781.3
  -> Decision False in time 2.6100000000, query time of that 0.0482343960, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
2265.17 < 2319.98
  -> Decision False in time 3.4700000000, query time of that 0.0652536100, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1121.52 < 1140.32
  -> Decision False in time 1.5800000000, query time of that 0.0034529640, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
2088.97 < 2111.6
  -> Decision False in time 1.0800000000, query time of that 0.0030533220, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1426.17 < 1476.3
  -> Decision False in time 9.1100000000, query time of that 0.0170267390, 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 (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.0076 accuracy: 1.64122 cost: 0.00633344 M: 10 delta: 1 time: 6.8064 one-recall: 0.01 one-ratio: 1.95526
iteration: 2 recall: 0.0692 accuracy: 0.554222 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.408 one-recall: 0.05 one-ratio: 1.45285
iteration: 3 recall: 0.4832 accuracy: 0.11812 cost: 0.0167507 M: 11.1153 delta: 0.845801 time: 15.4172 one-recall: 0.47 one-ratio: 1.13561
iteration: 4 recall: 0.9304 accuracy: 0.006495 cost: 0.0249121 M: 11.725 delta: 0.566235 time: 21.351 one-recall: 0.97 one-ratio: 1.00229
iteration: 5 recall: 0.9908 accuracy: 0.000488781 cost: 0.0376853 M: 17.4219 delta: 0.224592 time: 30.1838 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 30.419999999999987
Index size:  29680.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0013196667
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3500000000, query time of that 0.0625085050, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1130.58 < 1144.01
  -> Decision False in time 0.2700000000, query time of that 0.0461824200, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1411.68 < 1422.17
  -> Decision False in time 1.4500000000, query time of that 0.2549924970, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Accept!
  -> Decision True in time 3.3700000000, query time of that 0.0755951910, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
2186.44 < 2190.6
  -> Decision False in time 0.2700000000, query time of that 0.0064008010, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1448.84 < 1497.84
  -> Decision False in time 0.6300000000, query time of that 0.0131603970, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Accept!
  -> Decision True in time 33.1200000000, query time of that 0.0756376180, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1618.82 < 1625.32
  -> Decision False in time 5.8400000000, query time of that 0.0140258570, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
2123.17 < 2145.75
  -> Decision False in time 7.7400000000, query time of that 0.0191783470, 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 (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.0064 accuracy: 1.75244 cost: 0.00633344 M: 10 delta: 1 time: 6.80706 one-recall: 0.02 one-ratio: 2.06497
iteration: 2 recall: 0.0704 accuracy: 0.599505 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4084 one-recall: 0.08 one-ratio: 1.50582
iteration: 3 recall: 0.4672 accuracy: 0.137491 cost: 0.0167507 M: 11.1153 delta: 0.845791 time: 15.4186 one-recall: 0.53 one-ratio: 1.13475
iteration: 4 recall: 0.9236 accuracy: 0.00814146 cost: 0.0249119 M: 11.7248 delta: 0.566209 time: 21.3521 one-recall: 0.96 one-ratio: 1.00661
iteration: 5 recall: 0.9908 accuracy: 0.000433967 cost: 0.0376896 M: 17.424 delta: 0.224556 time: 30.1855 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 30.420000000000016
Index size:  29684.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0043050000
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3400000000, query time of that 0.0446657950, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1073.7 < 1075.85
  -> Decision False in time 0.0400000000, query time of that 0.0053596680, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1469.7 < 1504.85
  -> Decision False in time 1.2200000000, query time of that 0.1607669750, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1280.19 < 1284.98
  -> Decision False in time 0.1600000000, query time of that 0.0028887650, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1812.23 < 1814.65
  -> Decision False in time 0.3200000000, query time of that 0.0055886890, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1839.29 < 1851.77
  -> Decision False in time 0.2200000000, query time of that 0.0035547800, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1608.08 < 1626.4
  -> Decision False in time 3.7400000000, query time of that 0.0065064400, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
2177.48 < 2181.91
  -> Decision False in time 4.1500000000, query time of that 0.0065568680, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1229.17 < 1241.79
  -> Decision False in time 1.4100000000, query time of that 0.0029512880, 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 (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.0024 accuracy: 1.88913 cost: 0.00633344 M: 10 delta: 1 time: 6.80827 one-recall: 0 one-ratio: 2.13154
iteration: 2 recall: 0.0708 accuracy: 0.607821 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4087 one-recall: 0.02 one-ratio: 1.46012
iteration: 3 recall: 0.5004 accuracy: 0.122723 cost: 0.0167507 M: 11.1153 delta: 0.845786 time: 15.4184 one-recall: 0.5 one-ratio: 1.13355
iteration: 4 recall: 0.9256 accuracy: 0.00929663 cost: 0.0249119 M: 11.7251 delta: 0.566223 time: 21.3523 one-recall: 0.98 one-ratio: 1.00342
iteration: 5 recall: 0.988 accuracy: 0.000724423 cost: 0.0376879 M: 17.4236 delta: 0.224543 time: 30.1845 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9964 accuracy: 0.000106052 cost: 0.046019 M: 21.1579 delta: 0.134134 time: 35.856 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 36.11999999999995
Index size:  36632.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0044353333
  Testing...
|S| = 98
|T| = 1411
Reject!
1779.56 < 3106.3
  -> Decision False in time 0.0700000000, query time of that 0.0095344480, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
2501.43 < 2728.49
  -> Decision False in time 0.6400000000, query time of that 0.0870265150, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2516.72 < 2728.49
  -> Decision False in time 0.3900000000, query time of that 0.0573120110, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
2614.26 < 2826.08
  -> Decision False in time 0.9700000000, query time of that 0.0174848020, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1303.54 < 1341.65
  -> Decision False in time 0.0700000000, query time of that 0.0022900410, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
2554.89 < 2592.94
  -> Decision False in time 1.2200000000, query time of that 0.0227569630, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1069.71 < 1117.96
  -> Decision False in time 0.7300000000, query time of that 0.0014581200, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1449.19 < 1449.25
  -> Decision False in time 1.3700000000, query time of that 0.0034669530, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1588.44 < 1610.03
  -> Decision False in time 24.2700000000, query time of that 0.0433282830, 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 (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.0068 accuracy: 1.62174 cost: 0.00633344 M: 10 delta: 1 time: 6.80998 one-recall: 0.02 one-ratio: 1.90484
iteration: 2 recall: 0.0664 accuracy: 0.547944 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4114 one-recall: 0.09 one-ratio: 1.42732
iteration: 3 recall: 0.4464 accuracy: 0.118778 cost: 0.0167507 M: 11.1153 delta: 0.845785 time: 15.422 one-recall: 0.58 one-ratio: 1.12031
iteration: 4 recall: 0.903199 accuracy: 0.00897681 cost: 0.0249116 M: 11.725 delta: 0.5662 time: 21.3567 one-recall: 0.94 one-ratio: 1.01872
iteration: 5 recall: 0.9872 accuracy: 0.000682428 cost: 0.0376863 M: 17.4235 delta: 0.224539 time: 30.1889 one-recall: 0.99 one-ratio: 1.00032
iteration: 6 recall: 0.9948 accuracy: 0.000163326 cost: 0.0460258 M: 21.1582 delta: 0.134144 time: 35.8672 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 36.129999999999995
Index size:  36632.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0004423333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3700000000, query time of that 0.0738690200, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Accept!
  -> Decision True in time 3.6900000000, query time of that 0.7432425010, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1393.71 < 1485.58
  -> Decision False in time 0.5000000000, query time of that 0.1011568780, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Accept!
  -> Decision True in time 3.3900000000, query time of that 0.0888912060, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1216.86 < 1241.84
  -> Decision False in time 2.0300000000, query time of that 0.0566419940, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1721.83 < 1739.56
  -> Decision False in time 3.7800000000, query time of that 0.1032692110, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Accept!
  -> Decision True in time 32.8300000000, query time of that 0.0835879030, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1422.47 < 1459.17
  -> Decision False in time 11.5700000000, query time of that 0.0305686660, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1644.7 < 1658.97
  -> Decision False in time 17.6900000000, query time of that 0.0500976250, 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 (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.0076 accuracy: 1.72037 cost: 0.00633344 M: 10 delta: 1 time: 6.81222 one-recall: 0 one-ratio: 1.95236
iteration: 2 recall: 0.0752 accuracy: 0.554068 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4138 one-recall: 0.08 one-ratio: 1.39984
iteration: 3 recall: 0.4832 accuracy: 0.11899 cost: 0.0167507 M: 11.1153 delta: 0.845793 time: 15.4247 one-recall: 0.55 one-ratio: 1.10712
iteration: 4 recall: 0.9212 accuracy: 0.00763316 cost: 0.0249122 M: 11.7246 delta: 0.566185 time: 21.3592 one-recall: 0.98 one-ratio: 1.00157
iteration: 5 recall: 0.9896 accuracy: 0.000596889 cost: 0.0376911 M: 17.4247 delta: 0.224532 time: 30.1961 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9952 accuracy: 0.000206068 cost: 0.0460244 M: 21.1593 delta: 0.134112 time: 35.8737 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 36.139999999999986
Index size:  36636.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0004676667
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3800000000, query time of that 0.0826418260, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Accept!
  -> Decision True in time 3.7900000000, query time of that 0.8045995630, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1675.43 < 1769.59
  -> Decision False in time 2.2300000000, query time of that 0.4821241060, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Accept!
  -> Decision True in time 3.4400000000, query time of that 0.0995941660, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1628.76 < 1696.66
  -> Decision False in time 25.4200000000, query time of that 0.7139580830, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1665.48 < 1702.9
  -> Decision False in time 5.6500000000, query time of that 0.1621425700, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
2098.1 < 2102.3
  -> Decision False in time 4.8400000000, query time of that 0.0150616050, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1741.32 < 1790.53
  -> Decision False in time 45.5300000000, query time of that 0.1357947960, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
2367.47 < 2398.05
  -> Decision False in time 26.7800000000, query time of that 0.0821151430, 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 (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.0052 accuracy: 1.54971 cost: 0.00633344 M: 10 delta: 1 time: 6.81901 one-recall: 0 one-ratio: 1.86151
iteration: 2 recall: 0.0744 accuracy: 0.522987 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4223 one-recall: 0.12 one-ratio: 1.29068
iteration: 3 recall: 0.4888 accuracy: 0.104907 cost: 0.0167507 M: 11.1153 delta: 0.845796 time: 15.4343 one-recall: 0.54 one-ratio: 1.07353
iteration: 4 recall: 0.9388 accuracy: 0.00552285 cost: 0.0249124 M: 11.7248 delta: 0.566211 time: 21.3724 one-recall: 0.96 one-ratio: 1.01268
iteration: 5 recall: 0.9908 accuracy: 0.000742896 cost: 0.0376881 M: 17.4235 delta: 0.224514 time: 30.2093 one-recall: 0.99 one-ratio: 1.00591
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 30.43999999999994
Index size:  29688.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0006310000
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3800000000, query time of that 0.0751481170, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Accept!
  -> Decision True in time 3.7000000000, query time of that 0.7383667780, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2315.79 < 2511.35
  -> Decision False in time 11.3200000000, query time of that 2.2311095640, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
2402.46 < 2541.06
  -> Decision False in time 0.4900000000, query time of that 0.0116497220, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1840.63 < 1942.74
  -> Decision False in time 1.9400000000, query time of that 0.0517019870, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1819.93 < 1830.43
  -> Decision False in time 12.9500000000, query time of that 0.3305835610, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1437.51 < 1448.92
  -> Decision False in time 9.8400000000, query time of that 0.0237113840, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1726.78 < 1760.17
  -> Decision False in time 13.6100000000, query time of that 0.0361049360, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1443.54 < 1444.41
  -> Decision False in time 1.3900000000, query time of that 0.0036401250, 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 (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.0064 accuracy: 1.66082 cost: 0.00633344 M: 10 delta: 1 time: 6.8137 one-recall: 0.01 one-ratio: 1.90085
iteration: 2 recall: 0.0708 accuracy: 0.53255 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4187 one-recall: 0.05 one-ratio: 1.4435
iteration: 3 recall: 0.4588 accuracy: 0.113732 cost: 0.0167507 M: 11.1153 delta: 0.845792 time: 15.4307 one-recall: 0.51 one-ratio: 1.14386
iteration: 4 recall: 0.9112 accuracy: 0.0082794 cost: 0.0249104 M: 11.7243 delta: 0.56624 time: 21.3658 one-recall: 0.98 one-ratio: 1.00026
iteration: 5 recall: 0.9868 accuracy: 0.000811656 cost: 0.0376787 M: 17.4214 delta: 0.224591 time: 30.197 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9956 accuracy: 0.00017291 cost: 0.046016 M: 21.1572 delta: 0.134172 time: 35.8744 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 36.129999999999995
Index size:  36632.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0014896667
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3400000000, query time of that 0.0525588440, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1266.12 < 1296.5
  -> Decision False in time 1.8900000000, query time of that 0.2856198120, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1608.05 < 1636.53
  -> Decision False in time 1.4500000000, query time of that 0.2145261540, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1810.25 < 1819.82
  -> Decision False in time 0.8600000000, query time of that 0.0153479020, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1305.86 < 1351.37
  -> Decision False in time 3.3600000000, query time of that 0.0641207320, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1215.2 < 1219.28
  -> Decision False in time 3.2400000000, query time of that 0.0604837800, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1916.42 < 1920.83
  -> Decision False in time 1.7100000000, query time of that 0.0034476240, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1784.46 < 1794.16
  -> Decision False in time 0.2600000000, query time of that 0.0006714150, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1996.66 < 1997.18
  -> Decision False in time 0.3900000000, query time of that 0.0012747760, 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 (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.006 accuracy: 1.7295 cost: 0.00633344 M: 10 delta: 1 time: 6.81883 one-recall: 0.01 one-ratio: 1.9021
iteration: 2 recall: 0.0748 accuracy: 0.551656 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4215 one-recall: 0.04 one-ratio: 1.40392
iteration: 3 recall: 0.5004 accuracy: 0.115386 cost: 0.0167507 M: 11.1153 delta: 0.845803 time: 15.4374 one-recall: 0.61 one-ratio: 1.10382
iteration: 4 recall: 0.9288 accuracy: 0.00745011 cost: 0.024911 M: 11.7246 delta: 0.566202 time: 21.3787 one-recall: 0.95 one-ratio: 1.00729
iteration: 5 recall: 0.9908 accuracy: 0.000576763 cost: 0.0376936 M: 17.4264 delta: 0.224481 time: 30.2277 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 30.45000000000016
Index size:  29688.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0011063333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3500000000, query time of that 0.0585002060, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1259.24 < 1266.12
  -> Decision False in time 1.8200000000, query time of that 0.3055242720, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1398.22 < 1403.09
  -> Decision False in time 0.6900000000, query time of that 0.1119040290, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Accept!
  -> Decision True in time 3.4000000000, query time of that 0.0733389540, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1830.02 < 1849.45
  -> Decision False in time 4.9800000000, query time of that 0.1064945740, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
2221.92 < 2315.82
  -> Decision False in time 1.5900000000, query time of that 0.0345475330, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1683.4 < 1695.76
  -> Decision False in time 2.7200000000, query time of that 0.0070236140, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1743.39 < 1781.09
  -> Decision False in time 43.6200000000, query time of that 0.0978697670, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1513.92 < 1515.61
  -> Decision False in time 20.1200000000, query time of that 0.0450270170, 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 (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.0052 accuracy: 1.68406 cost: 0.00633344 M: 10 delta: 1 time: 6.82422 one-recall: 0 one-ratio: 1.87159
iteration: 2 recall: 0.0744 accuracy: 0.55721 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4282 one-recall: 0.1 one-ratio: 1.35669
iteration: 3 recall: 0.4484 accuracy: 0.130534 cost: 0.0167507 M: 11.1153 delta: 0.845785 time: 15.4404 one-recall: 0.41 one-ratio: 1.09378
iteration: 4 recall: 0.9072 accuracy: 0.00910488 cost: 0.0249122 M: 11.7252 delta: 0.566203 time: 21.3769 one-recall: 0.98 one-ratio: 1.00155
iteration: 5 recall: 0.9868 accuracy: 0.000862666 cost: 0.0376858 M: 17.423 delta: 0.224529 time: 30.2129 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9948 accuracy: 0.000305712 cost: 0.0460245 M: 21.1589 delta: 0.134158 time: 35.8941 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 36.16000000000008
Index size:  36632.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0024466667
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3600000000, query time of that 0.0604596180, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
2635.28 < 2828.01
  -> Decision False in time 1.3000000000, query time of that 0.2188147770, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2203.73 < 3024.17
  -> Decision False in time 0.5300000000, query time of that 0.0880343720, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
2043.77 < 2080.34
  -> Decision False in time 2.7500000000, query time of that 0.0589861430, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
2806.13 < 2827.8
  -> Decision False in time 3.5000000000, query time of that 0.0764173460, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1457.64 < 1493.65
  -> Decision False in time 0.7400000000, query time of that 0.0173890220, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1954.82 < 2008.83
  -> Decision False in time 6.4000000000, query time of that 0.0140025100, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1121.13 < 1124.25
  -> Decision False in time 8.7600000000, query time of that 0.0197542930, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1238.5 < 1254.76
  -> Decision False in time 1.7100000000, query time of that 0.0048761880, 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 (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.0056 accuracy: 1.79013 cost: 0.00633344 M: 10 delta: 1 time: 6.82064 one-recall: 0.01 one-ratio: 1.97702
iteration: 2 recall: 0.0652 accuracy: 0.64255 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4263 one-recall: 0.06 one-ratio: 1.41678
iteration: 3 recall: 0.4536 accuracy: 0.144686 cost: 0.0167507 M: 11.1153 delta: 0.845791 time: 15.4415 one-recall: 0.52 one-ratio: 1.08149
iteration: 4 recall: 0.9144 accuracy: 0.00963387 cost: 0.0249104 M: 11.7247 delta: 0.566208 time: 21.3812 one-recall: 0.97 one-ratio: 1.00315
iteration: 5 recall: 0.9916 accuracy: 0.00057723 cost: 0.0376796 M: 17.4217 delta: 0.224629 time: 30.2243 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 30.460000000000036
Index size:  29684.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0006920000
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3600000000, query time of that 0.0643868680, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Accept!
  -> Decision True in time 3.6500000000, query time of that 0.6648122570, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1556.45 < 1559.08
  -> Decision False in time 5.0700000000, query time of that 0.9335406030, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1585.69 < 1605.56
  -> Decision False in time 0.4100000000, query time of that 0.0109494570, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1554.44 < 1637.27
  -> Decision False in time 1.7100000000, query time of that 0.0433471440, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1983.66 < 2054.62
  -> Decision False in time 9.2100000000, query time of that 0.2200680040, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1221.94 < 1252.15
  -> Decision False in time 27.2000000000, query time of that 0.0634668440, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
2008.62 < 2031.42
  -> Decision False in time 10.2100000000, query time of that 0.0274520370, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1570.22 < 1579.29
  -> Decision False in time 9.5500000000, query time of that 0.0231158250, 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 (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.0072 accuracy: 1.83983 cost: 0.00633344 M: 10 delta: 1 time: 6.82143 one-recall: 0 one-ratio: 1.98489
iteration: 2 recall: 0.066 accuracy: 0.617249 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4264 one-recall: 0.1 one-ratio: 1.43897
iteration: 3 recall: 0.4668 accuracy: 0.132149 cost: 0.0167507 M: 11.1153 delta: 0.845821 time: 15.4359 one-recall: 0.57 one-ratio: 1.12599
iteration: 4 recall: 0.922 accuracy: 0.00789148 cost: 0.0249114 M: 11.7246 delta: 0.566206 time: 21.3683 one-recall: 0.97 one-ratio: 1.00894
iteration: 5 recall: 0.9828 accuracy: 0.00113275 cost: 0.0376855 M: 17.4226 delta: 0.224568 time: 30.1992 one-recall: 0.98 one-ratio: 1.00558
iteration: 6 recall: 0.9888 accuracy: 0.000807707 cost: 0.0460215 M: 21.1587 delta: 0.134107 time: 35.874 one-recall: 0.98 one-ratio: 1.00388
iteration: 7 recall: 0.9908 accuracy: 0.000534688 cost: 0.047801 M: 21.8184 delta: 0.126888 time: 37.2274 one-recall: 0.99 one-ratio: 1.00024
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 37.5
Index size:  39636.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0052630000
  Testing...
|S| = 98
|T| = 1411
Reject!
1266.5 < 1291.4
  -> Decision False in time 0.1700000000, query time of that 0.0247475640, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
2128.62 < 2480.33
  -> Decision False in time 0.5400000000, query time of that 0.0784515680, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2536.05 < 2584.42
  -> Decision False in time 0.0300000000, query time of that 0.0048790430, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
2466.14 < 2780.34
  -> Decision False in time 0.0000000000, query time of that 0.0005670370, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1039.81 < 1048.92
  -> Decision False in time 2.4100000000, query time of that 0.0418277910, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1377.43 < 1388.73
  -> Decision False in time 0.8200000000, query time of that 0.0142827040, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1723.05 < 1770.87
  -> Decision False in time 0.4200000000, query time of that 0.0015900640, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1970.2 < 1991.27
  -> Decision False in time 2.5000000000, query time of that 0.0049821160, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1549.31 < 1566.32
  -> Decision False in time 3.1800000000, query time of that 0.0061961600, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 30, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 30, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.0084 accuracy: 1.69404 cost: 0.00633344 M: 10 delta: 1 time: 6.81666 one-recall: 0.01 one-ratio: 1.89077
iteration: 2 recall: 0.0708 accuracy: 0.556105 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4182 one-recall: 0.05 one-ratio: 1.35404
iteration: 3 recall: 0.4804 accuracy: 0.114292 cost: 0.0167507 M: 11.1153 delta: 0.845789 time: 15.4336 one-recall: 0.5 one-ratio: 1.11433
iteration: 4 recall: 0.9352 accuracy: 0.00553785 cost: 0.0249123 M: 11.7249 delta: 0.566213 time: 21.3753 one-recall: 0.96 one-ratio: 1.00492
iteration: 5 recall: 0.9928 accuracy: 0.00052315 cost: 0.0376874 M: 17.4235 delta: 0.224538 time: 30.2202 one-recall: 0.99 one-ratio: 1.00011
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 30.450000000000045
Index size:  29684.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0016126667
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3400000000, query time of that 0.0510211750, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
2030.88 < 2034.81
  -> Decision False in time 0.7100000000, query time of that 0.1033569270, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1756.57 < 1758.9
  -> Decision False in time 1.0500000000, query time of that 0.1609169700, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1707.89 < 1740.25
  -> Decision False in time 1.9800000000, query time of that 0.0371032580, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1230.84 < 1233.79
  -> Decision False in time 2.5400000000, query time of that 0.0499099380, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1854.81 < 1862.52
  -> Decision False in time 0.9600000000, query time of that 0.0169760630, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1396.27 < 1432.25
  -> Decision False in time 0.3500000000, query time of that 0.0014037940, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
2052.78 < 2120.46
  -> Decision False in time 1.4000000000, query time of that 0.0027832740, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1107.2 < 1110.59
  -> Decision False in time 3.0400000000, query time of that 0.0063568250, 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 (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.0056 accuracy: 1.68503 cost: 0.00633344 M: 10 delta: 1 time: 6.82006 one-recall: 0.01 one-ratio: 2.00468
iteration: 2 recall: 0.0708 accuracy: 0.558371 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4239 one-recall: 0.05 one-ratio: 1.40872
iteration: 3 recall: 0.4696 accuracy: 0.123969 cost: 0.0167507 M: 11.1153 delta: 0.84581 time: 15.4386 one-recall: 0.49 one-ratio: 1.11078
iteration: 4 recall: 0.9268 accuracy: 0.0074375 cost: 0.024912 M: 11.7249 delta: 0.566239 time: 21.3774 one-recall: 0.97 one-ratio: 1.00338
iteration: 5 recall: 0.9924 accuracy: 0.000469506 cost: 0.0376885 M: 17.4234 delta: 0.224531 time: 30.2166 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 30.450000000000045
Index size:  29684.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0072986667
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3400000000, query time of that 0.0488608490, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1524.87 < 1526.52
  -> Decision False in time 0.3900000000, query time of that 0.0532437480, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1609.28 < 1653.12
  -> Decision False in time 0.0500000000, query time of that 0.0085106500, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1560.95 < 1561.15
  -> Decision False in time 0.1900000000, query time of that 0.0035707030, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1465.03 < 1477.43
  -> Decision False in time 1.1000000000, query time of that 0.0210385340, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1850.99 < 2078.78
  -> Decision False in time 0.7700000000, query time of that 0.0147117320, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1110.33 < 1204.31
  -> Decision False in time 1.0300000000, query time of that 0.0025804850, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1232.66 < 1257.68
  -> Decision False in time 0.0400000000, query time of that 0.0004405380, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1087.45 < 1088.95
  -> Decision False in time 1.1900000000, query time of that 0.0022705220, 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 (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.006 accuracy: 3.58104 cost: 0.00633344 M: 10 delta: 1 time: 6.8064 one-recall: 0.02 one-ratio: 1.86465
iteration: 2 recall: 0.08 accuracy: 1.54664 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4073 one-recall: 0.09 one-ratio: 1.3487
iteration: 3 recall: 0.482 accuracy: 0.737125 cost: 0.0167507 M: 11.1153 delta: 0.845786 time: 15.4193 one-recall: 0.52 one-ratio: 1.1126
iteration: 4 recall: 0.9248 accuracy: 0.00833091 cost: 0.0249112 M: 11.7247 delta: 0.566218 time: 21.3561 one-recall: 0.97 one-ratio: 1.00491
iteration: 5 recall: 0.9936 accuracy: 0.000395707 cost: 0.037687 M: 17.4228 delta: 0.224562 time: 30.2013 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 30.440000000000055
Index size:  29684.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0027963333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3400000000, query time of that 0.0445919630, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
2063.07 < 2077.44
  -> Decision False in time 0.7500000000, query time of that 0.1019117690, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1530.31 < 1538.1
  -> Decision False in time 0.3800000000, query time of that 0.0506149190, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1240.83 < 1243.31
  -> Decision False in time 0.9200000000, query time of that 0.0150844860, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1701.27 < 1735.76
  -> Decision False in time 0.0300000000, query time of that 0.0007128420, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1291.33 < 1293.93
  -> Decision False in time 1.3700000000, query time of that 0.0235305610, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1224.69 < 1252.9
  -> Decision False in time 3.3700000000, query time of that 0.0067397480, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1570.21 < 1617.14
  -> Decision False in time 4.3800000000, query time of that 0.0076639420, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1488.22 < 1525.52
  -> Decision False in time 1.6900000000, query time of that 0.0041361420, with c1=5.0000000000, c2=0.1000000000
