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', 90, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 5, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 2, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 40, {'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', 50, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 1, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 30, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 60, {'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', 10, {'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', 70, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 4, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 80, {'reverse': -1}, False])]
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 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: 0.0044 accuracy: 1.65812 cost: 0.00633344 M: 10 delta: 1 time: 7.35576 one-recall: 0 one-ratio: 2.05485
iteration: 2 recall: 0.0664 accuracy: 0.581012 cost: 0.0102345 M: 10 delta: 0.893354 time: 11.3067 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: 16.7439 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: 23.1472 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: 32.6772 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: 39.0789 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 39.42
Index size:  98596.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0004320000
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.4200000000, query time of that 0.1190102840, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Accept!
  -> Decision True in time 4.1300000000, query time of that 1.1189207370, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2558.44 < 2614.1
  -> Decision False in time 3.9100000000, query time of that 1.0409452270, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Accept!
  -> Decision True in time 3.5300000000, query time of that 0.1318672260, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
2132.36 < 2135.99
  -> Decision False in time 2.9600000000, query time of that 0.1132384220, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1676.6 < 1893.05
  -> Decision False in time 28.3400000000, query time of that 1.0777400730, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
2167.17 < 2192.87
  -> Decision False in time 13.5100000000, query time of that 0.0554480410, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1331.2 < 1372.77
  -> Decision False in time 56.1000000000, query time of that 0.2279709240, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
2056.95 < 2137
  -> Decision False in time 40.5500000000, query time of that 0.1610900120, 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: 0.0076 accuracy: 1.64122 cost: 0.00633344 M: 10 delta: 1 time: 7.06134 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: 10.8209 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: 16.0701 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: 22.2738 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: 31.4716 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 31.75999999999999
Index size:  29680.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0057600000
  Testing...
|S| = 98
|T| = 1411
Reject!
1773.59 < 2778.48
  -> Decision False in time 0.1100000000, query time of that 0.0194629090, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1633.22 < 1644.15
  -> Decision False in time 0.8100000000, query time of that 0.1390174590, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2284.3 < 2317.57
  -> Decision False in time 0.4500000000, query time of that 0.0777082810, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1389.83 < 1430.54
  -> Decision False in time 1.3900000000, query time of that 0.0334933370, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
2604.83 < 2835.71
  -> Decision False in time 2.3900000000, query time of that 0.0547880930, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1296.65 < 1299.97
  -> Decision False in time 0.0500000000, query time of that 0.0014177670, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1611.16 < 1743.81
  -> Decision False in time 1.5100000000, query time of that 0.0038118810, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1296.04 < 1336.93
  -> Decision False in time 4.7200000000, query time of that 0.0119924450, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1309.45 < 1398.79
  -> Decision False in time 5.4500000000, query time of that 0.0124793170, 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: 0.0064 accuracy: 1.75244 cost: 0.00633344 M: 10 delta: 1 time: 6.81821 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: 10.4251 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: 15.4463 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: 21.3913 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: 30.2427 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 30.50999999999999
Index size:  29684.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0062866667
  Testing...
|S| = 98
|T| = 1411
Reject!
2139.03 < 2598.85
  -> Decision False in time 0.0100000000, query time of that 0.0014278080, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
2086.82 < 2437.05
  -> Decision False in time 0.0500000000, query time of that 0.0078461520, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2792.57 < 3022.76
  -> Decision False in time 1.2200000000, query time of that 0.1642309180, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1290.04 < 1330.18
  -> Decision False in time 0.4100000000, query time of that 0.0079948710, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1255.34 < 1262.28
  -> Decision False in time 0.0900000000, query time of that 0.0020356890, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1607.5 < 1638.17
  -> Decision False in time 0.6900000000, query time of that 0.0125758520, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1301.53 < 1312.75
  -> Decision False in time 4.0200000000, query time of that 0.0071743820, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1542.23 < 1549.74
  -> Decision False in time 0.4000000000, query time of that 0.0010811710, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1533.48 < 1573.99
  -> Decision False in time 0.3300000000, query time of that 0.0006229850, 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: 0.0024 accuracy: 1.88913 cost: 0.00633344 M: 10 delta: 1 time: 6.81585 one-recall: 0 one-ratio: 2.13154
iteration: 2 recall: 0.0708 accuracy: 0.607821 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4239 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: 15.4451 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: 21.3905 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: 30.2409 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9964 accuracy: 0.000106052 cost: 0.046019 M: 21.1579 delta: 0.134134 time: 35.9205 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 36.21999999999997
Index size:  36632.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0024473333
  Testing...
|S| = 98
|T| = 1411
Reject!
2257.35 < 2981.4
  -> Decision False in time 0.3000000000, query time of that 0.0518719970, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
2094.07 < 2252.23
  -> Decision False in time 2.0100000000, query time of that 0.3445627820, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2399.32 < 3024.17
  -> Decision False in time 4.4700000000, query time of that 0.7487515710, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1359.96 < 1400.15
  -> Decision False in time 1.3700000000, query time of that 0.0285663940, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1784.65 < 1808.77
  -> Decision False in time 4.5400000000, query time of that 0.0970625570, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1364.79 < 1394.4
  -> Decision False in time 0.4600000000, query time of that 0.0119271810, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1090.52 < 1100.6
  -> Decision False in time 11.2300000000, query time of that 0.0246764470, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1538.96 < 1592.54
  -> Decision False in time 3.9200000000, query time of that 0.0087482830, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1710.73 < 1905.67
  -> Decision False in time 6.2000000000, query time of that 0.0144232110, 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: 0.0068 accuracy: 1.62174 cost: 0.00633344 M: 10 delta: 1 time: 6.89751 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: 10.5231 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: 15.6125 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: 21.6327 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: 30.5515 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: 36.2621 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 36.56000000000006
Index size:  36636.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0004683333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3900000000, query time of that 0.0837199860, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
2172.13 < 2552.37
  -> Decision False in time 3.0200000000, query time of that 0.6467086170, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1318.62 < 1373.39
  -> Decision False in time 19.3600000000, query time of that 4.1681675840, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
2136.37 < 2248.45
  -> Decision False in time 1.0600000000, query time of that 0.0311513020, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1944.79 < 1948.87
  -> Decision False in time 31.0600000000, query time of that 0.8726248900, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1548.39 < 1555.66
  -> Decision False in time 8.4600000000, query time of that 0.2373725540, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Accept!
  -> Decision True in time 33.2300000000, query time of that 0.0988225630, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1646.57 < 1686.89
  -> Decision False in time 26.9000000000, query time of that 0.0834572320, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1688.72 < 1713.96
  -> Decision False in time 19.1500000000, query time of that 0.0587417040, 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: 0.0076 accuracy: 1.72037 cost: 0.00633344 M: 10 delta: 1 time: 6.81692 one-recall: 0 one-ratio: 1.95236
iteration: 2 recall: 0.0752 accuracy: 0.554068 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4273 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: 15.4476 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: 21.3934 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: 30.2456 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9952 accuracy: 0.000206068 cost: 0.0460244 M: 21.1593 delta: 0.134112 time: 35.9261 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 36.22000000000003
Index size:  36628.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0007696667
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3600000000, query time of that 0.0625383450, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Accept!
  -> Decision True in time 3.6100000000, query time of that 0.6353099610, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1598.03 < 1993.92
  -> Decision False in time 3.3100000000, query time of that 0.5840101920, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Accept!
  -> Decision True in time 3.4200000000, query time of that 0.0788516700, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1663.96 < 1673.46
  -> Decision False in time 0.9900000000, query time of that 0.0214706430, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1788.4 < 1839.04
  -> Decision False in time 6.8000000000, query time of that 0.1484906070, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1483.13 < 1495.68
  -> Decision False in time 12.9900000000, query time of that 0.0298478920, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1447.7 < 1451.86
  -> Decision False in time 19.4400000000, query time of that 0.0437212650, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1495.81 < 1499.09
  -> Decision False in time 30.9500000000, query time of that 0.0726449920, 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: 0.0052 accuracy: 1.54971 cost: 0.00633344 M: 10 delta: 1 time: 6.81846 one-recall: 0 one-ratio: 1.86151
iteration: 2 recall: 0.0744 accuracy: 0.522987 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4267 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: 15.4486 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: 21.3935 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: 30.2419 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 30.50999999999999
Index size:  29684.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0072973333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3400000000, query time of that 0.0470284890, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1842.58 < 1857.38
  -> Decision False in time 0.3000000000, query time of that 0.0404584810, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2907.43 < 3206.75
  -> Decision False in time 0.7300000000, query time of that 0.1017441120, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1385.23 < 1387.8
  -> Decision False in time 1.1200000000, query time of that 0.0196235280, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1085.62 < 1117.56
  -> Decision False in time 0.2500000000, query time of that 0.0047907400, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1719.77 < 1722.68
  -> Decision False in time 0.2300000000, query time of that 0.0047450380, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
945.251 < 971.73
  -> Decision False in time 1.5100000000, query time of that 0.0032546020, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1768.47 < 1779.33
  -> Decision False in time 1.6300000000, query time of that 0.0029460470, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
2092.32 < 2110
  -> Decision False in time 0.8100000000, query time of that 0.0019694040, 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: 0.0064 accuracy: 1.66082 cost: 0.00633344 M: 10 delta: 1 time: 6.89901 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: 10.5229 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: 15.6117 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: 21.6325 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: 30.5539 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9956 accuracy: 0.00017291 cost: 0.046016 M: 21.1572 delta: 0.134172 time: 36.2647 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 36.57000000000005
Index size:  36620.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0010736667
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3500000000, query time of that 0.0554345740, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Accept!
  -> Decision True in time 3.4800000000, query time of that 0.5499175020, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1279.08 < 1434.41
  -> Decision False in time 3.3900000000, query time of that 0.5402901020, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1464.58 < 1539.36
  -> Decision False in time 2.8700000000, query time of that 0.0601040350, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1549.6 < 1577.36
  -> Decision False in time 0.8800000000, query time of that 0.0173526490, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1398.94 < 1410.97
  -> Decision False in time 11.1300000000, query time of that 0.2253089930, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1278.91 < 1295.91
  -> Decision False in time 3.4800000000, query time of that 0.0073630880, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1850.22 < 1879.85
  -> Decision False in time 6.3100000000, query time of that 0.0127524410, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1225.07 < 1227.08
  -> Decision False in time 0.6400000000, query time of that 0.0018279430, 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: 0.006 accuracy: 1.7295 cost: 0.00633344 M: 10 delta: 1 time: 6.8164 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: 10.4245 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: 15.4459 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: 21.3907 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: 30.2431 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 30.519999999999982
Index size:  29684.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0013173333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3600000000, query time of that 0.0639533020, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
2527.88 < 2654.05
  -> Decision False in time 1.6100000000, query time of that 0.2797601060, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2539.8 < 2731.62
  -> Decision False in time 9.5400000000, query time of that 1.6453522970, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Accept!
  -> Decision True in time 3.4200000000, query time of that 0.0745161830, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1612.66 < 1618.68
  -> Decision False in time 0.5900000000, query time of that 0.0143714690, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1784.72 < 1798.63
  -> Decision False in time 0.3400000000, query time of that 0.0077338410, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
2331.71 < 2660.08
  -> Decision False in time 18.3900000000, query time of that 0.0427073560, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
2205.86 < 2216.18
  -> Decision False in time 10.9100000000, query time of that 0.0249991850, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1795.13 < 1811.07
  -> Decision False in time 6.3400000000, query time of that 0.0145622190, 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: 0.0052 accuracy: 1.68406 cost: 0.00633344 M: 10 delta: 1 time: 6.8178 one-recall: 0 one-ratio: 1.87159
iteration: 2 recall: 0.0744 accuracy: 0.55721 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4252 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: 15.4456 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: 21.3908 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: 30.241 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9948 accuracy: 0.000305712 cost: 0.0460245 M: 21.1589 delta: 0.134158 time: 35.9235 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 36.23000000000002
Index size:  36624.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0015026667
  Testing...
|S| = 98
|T| = 1411
Reject!
1936.41 < 1942.98
  -> Decision False in time 0.1900000000, query time of that 0.0291409000, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1762.49 < 2123.36
  -> Decision False in time 0.2700000000, query time of that 0.0378296030, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1793.12 < 1806.65
  -> Decision False in time 3.4000000000, query time of that 0.5052941050, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
941.755 < 945.384
  -> Decision False in time 1.9700000000, query time of that 0.0362399120, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1253.75 < 1392.98
  -> Decision False in time 0.0500000000, query time of that 0.0013810700, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1898.93 < 1925.25
  -> Decision False in time 0.9300000000, query time of that 0.0176025840, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
2326.3 < 2340.96
  -> Decision False in time 15.8300000000, query time of that 0.0307458500, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1601 < 1616.42
  -> Decision False in time 7.2300000000, query time of that 0.0144268620, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1443.27 < 1466.4
  -> Decision False in time 7.8000000000, query time of that 0.0155495760, 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: 0.0056 accuracy: 1.79013 cost: 0.00633344 M: 10 delta: 1 time: 6.81272 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: 10.4193 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: 15.4403 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: 21.3848 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: 30.2311 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 30.5
Index size:  29684.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0027913333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3400000000, query time of that 0.0449335510, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1372.32 < 1372.51
  -> Decision False in time 1.2700000000, query time of that 0.1663336600, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1491.88 < 1529.53
  -> Decision False in time 0.0500000000, query time of that 0.0063954720, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1351.17 < 1359.24
  -> Decision False in time 0.3800000000, query time of that 0.0078182910, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1509.94 < 1522.95
  -> Decision False in time 0.9400000000, query time of that 0.0148273570, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1820.05 < 1859.04
  -> Decision False in time 3.2400000000, query time of that 0.0529825090, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1353.04 < 1354.79
  -> Decision False in time 1.7000000000, query time of that 0.0038722970, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1267.46 < 1282.44
  -> Decision False in time 2.3800000000, query time of that 0.0046064770, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1256.39 < 1266.86
  -> Decision False in time 12.6400000000, query time of that 0.0214126820, 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: 0.0072 accuracy: 1.83983 cost: 0.00633344 M: 10 delta: 1 time: 6.89686 one-recall: 0 one-ratio: 1.98489
iteration: 2 recall: 0.066 accuracy: 0.617249 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.5213 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: 15.6089 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: 21.6299 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: 30.5528 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: 36.2628 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: 37.6181 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 37.930000000000064
Index size:  39628.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0024203333
  Testing...
|S| = 98
|T| = 1411
Reject!
2046.38 < 2685.71
  -> Decision False in time 0.2000000000, query time of that 0.0289412960, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1759.82 < 1814.98
  -> Decision False in time 0.0300000000, query time of that 0.0055968340, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1280.59 < 1338.08
  -> Decision False in time 1.2500000000, query time of that 0.1736745540, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1616.03 < 1616.18
  -> Decision False in time 1.9700000000, query time of that 0.0366589440, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1482.24 < 1517.76
  -> Decision False in time 1.3000000000, query time of that 0.0232379400, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1527.74 < 1536.37
  -> Decision False in time 0.8600000000, query time of that 0.0158019850, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1286.46 < 1321.01
  -> Decision False in time 15.2000000000, query time of that 0.0287850590, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1624.11 < 1670.48
  -> Decision False in time 3.5000000000, query time of that 0.0066237160, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1625.61 < 1671.25
  -> Decision False in time 3.8800000000, query time of that 0.0074884610, 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: 0.0084 accuracy: 1.69404 cost: 0.00633344 M: 10 delta: 1 time: 6.81903 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: 10.4278 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: 15.4474 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: 21.3914 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: 30.2405 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 30.50999999999999
Index size:  29688.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0006886667
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3600000000, query time of that 0.0651816320, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
2228.68 < 2235.78
  -> Decision False in time 2.5600000000, query time of that 0.4668713440, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1910.91 < 1924.09
  -> Decision False in time 5.8400000000, query time of that 1.0730734970, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Accept!
  -> Decision True in time 3.4000000000, query time of that 0.0787114830, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
2325.84 < 2347.21
  -> Decision False in time 2.0800000000, query time of that 0.0501833500, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1538.16 < 1539.24
  -> Decision False in time 1.8800000000, query time of that 0.0460744770, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1707.99 < 1721.33
  -> Decision False in time 9.1800000000, query time of that 0.0234237380, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1730.09 < 1752.56
  -> Decision False in time 3.8200000000, query time of that 0.0094242930, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1548.46 < 1577.08
  -> Decision False in time 41.5100000000, query time of that 0.0985803150, 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 (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.0056 accuracy: 1.68503 cost: 0.00633344 M: 10 delta: 1 time: 6.81422 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: 10.422 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: 15.4406 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: 21.3841 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: 30.2318 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 30.5
Index size:  29688.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0040066667
  Testing...
|S| = 98
|T| = 1411
Reject!
947.687 < 958.702
  -> Decision False in time 0.0900000000, query time of that 0.0119428680, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1816.74 < 1912.58
  -> Decision False in time 1.4000000000, query time of that 0.1834000380, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1768.96 < 1776.84
  -> Decision False in time 1.8800000000, query time of that 0.2478270470, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1808.89 < 1869.96
  -> Decision False in time 2.1000000000, query time of that 0.0346076760, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1606.56 < 1652.56
  -> Decision False in time 0.9600000000, query time of that 0.0162584470, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1258.18 < 1266.94
  -> Decision False in time 0.2700000000, query time of that 0.0041685080, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
962.483 < 994.379
  -> Decision False in time 3.4200000000, query time of that 0.0065207180, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1743.51 < 1791.32
  -> Decision False in time 2.4600000000, query time of that 0.0046055200, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
2167.17 < 2192.87
  -> Decision False in time 1.7700000000, query time of that 0.0035445930, 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: 0.006 accuracy: 3.58104 cost: 0.00633344 M: 10 delta: 1 time: 6.81988 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: 10.4274 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: 15.447 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: 21.3893 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: 30.233 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 30.50999999999999
Index size:  29684.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0006246667
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3700000000, query time of that 0.0710573290, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1120.72 < 1154.48
  -> Decision False in time 0.2000000000, query time of that 0.0374756500, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1755.35 < 1765.8
  -> Decision False in time 11.0400000000, query time of that 2.1106752030, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Accept!
  -> Decision True in time 3.4100000000, query time of that 0.0853024030, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1736.85 < 1763.79
  -> Decision False in time 16.0800000000, query time of that 0.3919608150, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1999.54 < 2004.6
  -> Decision False in time 1.3100000000, query time of that 0.0325268710, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
889.806 < 906.221
  -> Decision False in time 26.1300000000, query time of that 0.0688228080, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1913.61 < 1922.16
  -> Decision False in time 22.8200000000, query time of that 0.0605124550, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1492.69 < 1495.39
  -> Decision False in time 14.6100000000, query time of that 0.0388637570, with c1=5.0000000000, c2=0.1000000000
