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', 3, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 30, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 70, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 10, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 90, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 100, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 40, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 60, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 80, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 20, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 5, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 1, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 4, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 2, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 50, {'reverse': -1}, False])]
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: 0.0044 accuracy: 1.65812 cost: 0.00633344 M: 10 delta: 1 time: 6.88323 one-recall: 0 one-ratio: 2.05485
iteration: 2 recall: 0.0664 accuracy: 0.581012 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.499 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: 15.5272 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: 21.4802 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: 30.9966 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: 37.4133 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 37.78
Index size:  98596.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0027053333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3600000000, query time of that 0.0675682030, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1770 < 1800.77
  -> Decision False in time 1.6400000000, query time of that 0.3082810950, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1380.98 < 1405.36
  -> Decision False in time 1.5100000000, query time of that 0.2866332080, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1284.88 < 1311.15
  -> Decision False in time 0.2700000000, query time of that 0.0075108290, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
2125.25 < 2128.04
  -> Decision False in time 1.4600000000, query time of that 0.0357592430, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1768.63 < 1770.38
  -> Decision False in time 0.3300000000, query time of that 0.0087012740, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1426.24 < 1437.83
  -> Decision False in time 5.8700000000, query time of that 0.0179571160, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1289.71 < 1301.19
  -> Decision False in time 5.9900000000, query time of that 0.0150061690, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1733.15 < 1822.52
  -> Decision False in time 25.4600000000, query time of that 0.0653383430, 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: 0.0076 accuracy: 1.64122 cost: 0.00633344 M: 10 delta: 1 time: 6.98086 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: 10.7229 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: 15.903 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: 22.0293 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: 31.1526 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 31.44999999999999
Index size:  29684.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0016166667
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3500000000, query time of that 0.0529910200, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1384.33 < 1406.73
  -> Decision False in time 1.7200000000, query time of that 0.2628000380, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1194.97 < 1228.76
  -> Decision False in time 1.0700000000, query time of that 0.1661570870, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1129.32 < 1138.91
  -> Decision False in time 0.1600000000, query time of that 0.0036897870, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1258.34 < 1261.28
  -> Decision False in time 0.5900000000, query time of that 0.0116749270, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1570.65 < 1583.41
  -> Decision False in time 1.5900000000, query time of that 0.0301933380, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1125.94 < 1134.17
  -> Decision False in time 17.5300000000, query time of that 0.0350166870, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1173.04 < 1181.28
  -> Decision False in time 1.5700000000, query time of that 0.0035141940, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1438.7 < 1469.69
  -> Decision False in time 3.0400000000, query time of that 0.0062268540, 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: 0.0064 accuracy: 1.75244 cost: 0.00633344 M: 10 delta: 1 time: 6.98301 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: 10.7252 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: 15.9066 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: 22.0357 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: 31.167 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 31.460000000000008
Index size:  29684.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0006896667
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3700000000, query time of that 0.0669641800, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Accept!
  -> Decision True in time 3.6900000000, query time of that 0.6785497320, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2009.07 < 2074.18
  -> Decision False in time 5.7300000000, query time of that 1.0642022320, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Accept!
  -> Decision True in time 3.5100000000, query time of that 0.0822265070, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
2310.31 < 2342.85
  -> Decision False in time 11.3500000000, query time of that 0.2729325320, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1479.56 < 1585.97
  -> Decision False in time 0.9300000000, query time of that 0.0226305450, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1337.16 < 1351.44
  -> Decision False in time 15.0000000000, query time of that 0.0372411240, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1168.61 < 1170.06
  -> Decision False in time 46.6500000000, query time of that 0.1149280080, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1878.41 < 1885.28
  -> Decision False in time 14.0100000000, query time of that 0.0362132090, 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: 0.0024 accuracy: 1.88913 cost: 0.00633344 M: 10 delta: 1 time: 6.98357 one-recall: 0 one-ratio: 2.13154
iteration: 2 recall: 0.0708 accuracy: 0.607821 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.7258 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: 15.9083 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: 22.036 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: 31.1681 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9964 accuracy: 0.000106052 cost: 0.046019 M: 21.1579 delta: 0.134134 time: 37.123 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 37.44
Index size:  36636.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0017703333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3500000000, query time of that 0.0518649470, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1618.95 < 1643.27
  -> Decision False in time 0.6500000000, query time of that 0.0963315810, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1630.21 < 1666.35
  -> Decision False in time 0.6000000000, query time of that 0.0866489280, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Accept!
  -> Decision True in time 3.4800000000, query time of that 0.0662436990, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1786.82 < 1797.72
  -> Decision False in time 0.8500000000, query time of that 0.0158155740, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1981.05 < 1994.23
  -> Decision False in time 4.4400000000, query time of that 0.0827587660, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1280.32 < 1378.05
  -> Decision False in time 2.8000000000, query time of that 0.0056876170, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1505.08 < 1510.59
  -> Decision False in time 4.3500000000, query time of that 0.0096878530, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1988.97 < 2031.88
  -> Decision False in time 0.9200000000, query time of that 0.0020650850, 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: 0.0068 accuracy: 1.62174 cost: 0.00633344 M: 10 delta: 1 time: 6.98203 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: 10.7224 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: 15.903 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: 22.0305 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: 31.156 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: 37.1124 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 37.44
Index size:  36628.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0004493333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3800000000, query time of that 0.0808430230, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1803.61 < 1808.26
  -> Decision False in time 0.4700000000, query time of that 0.0960701370, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2083.77 < 2094.27
  -> Decision False in time 2.5200000000, query time of that 0.5342644120, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Accept!
  -> Decision True in time 3.4800000000, query time of that 0.0920204090, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1704.83 < 1721.25
  -> Decision False in time 19.4300000000, query time of that 0.5474707370, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1634.3 < 1649.98
  -> Decision False in time 4.6000000000, query time of that 0.1320345630, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1571.45 < 1636.4
  -> Decision False in time 0.5400000000, query time of that 0.0022941550, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1457.76 < 1459.96
  -> Decision False in time 141.1600000000, query time of that 0.3996238730, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
2042.68 < 2047.11
  -> Decision False in time 31.0000000000, query time of that 0.0938459200, 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: 6.97636 one-recall: 0 one-ratio: 1.95236
iteration: 2 recall: 0.0752 accuracy: 0.554068 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.7185 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: 15.8969 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: 22.0208 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: 31.1455 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9952 accuracy: 0.000206068 cost: 0.0460244 M: 21.1593 delta: 0.134112 time: 37.0937 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 37.41999999999996
Index size:  36636.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0004676667
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3900000000, query time of that 0.0884329720, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
2157.28 < 2195.19
  -> Decision False in time 3.7500000000, query time of that 0.8231711830, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1325.86 < 1334.63
  -> Decision False in time 19.7200000000, query time of that 4.3310643960, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Accept!
  -> Decision True in time 3.4800000000, query time of that 0.0994759550, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Accept!
  -> Decision True in time 34.7900000000, query time of that 1.0144495900, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1337.91 < 1395.22
  -> Decision False in time 8.7100000000, query time of that 0.2506590900, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Accept!
  -> Decision True in time 33.7700000000, query time of that 0.1002025890, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1921.12 < 1949.49
  -> Decision False in time 79.6900000000, query time of that 0.2433349880, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
2199.71 < 2209.7
  -> Decision False in time 156.1400000000, query time of that 0.4634061910, 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: 1.54971 cost: 0.00633344 M: 10 delta: 1 time: 6.97862 one-recall: 0 one-ratio: 1.86151
iteration: 2 recall: 0.0744 accuracy: 0.522987 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.72 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: 15.9 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: 22.0257 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: 31.1485 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 31.450000000000045
Index size:  29684.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0028816667
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3600000000, query time of that 0.0570736370, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1913.63 < 2513.23
  -> Decision False in time 1.2200000000, query time of that 0.1926930380, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2089.79 < 2104.99
  -> Decision False in time 0.4600000000, query time of that 0.0722917250, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1673.87 < 1753.94
  -> Decision False in time 1.2600000000, query time of that 0.0261237780, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1576.57 < 1581.14
  -> Decision False in time 9.9000000000, query time of that 0.2048225920, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1531.78 < 1547.59
  -> Decision False in time 0.6400000000, query time of that 0.0133590120, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
2318.86 < 2336.64
  -> Decision False in time 3.5600000000, query time of that 0.0078891770, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1461.19 < 1477.29
  -> Decision False in time 10.7300000000, query time of that 0.0224142220, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1157.76 < 1227.47
  -> Decision False in time 8.3800000000, query time of that 0.0183833760, 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: 0.0064 accuracy: 1.66082 cost: 0.00633344 M: 10 delta: 1 time: 6.98462 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: 10.7269 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: 15.9073 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: 22.0341 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: 31.1606 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9956 accuracy: 0.00017291 cost: 0.046016 M: 21.1572 delta: 0.134172 time: 37.1193 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 37.44999999999982
Index size:  36620.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0010213333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3700000000, query time of that 0.0702038940, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Accept!
  -> Decision True in time 3.6500000000, query time of that 0.6980866610, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1495.8 < 1517.51
  -> Decision False in time 4.6400000000, query time of that 0.8825976460, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Accept!
  -> Decision True in time 3.4500000000, query time of that 0.0832760370, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1814.68 < 1827.77
  -> Decision False in time 9.9900000000, query time of that 0.2419395650, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1654.59 < 1668.84
  -> Decision False in time 1.1100000000, query time of that 0.0305033210, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1533.48 < 1573.99
  -> Decision False in time 18.9800000000, query time of that 0.0502317240, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1411.68 < 1422.17
  -> Decision False in time 4.7100000000, query time of that 0.0112984840, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1519.21 < 1526.51
  -> Decision False in time 18.3300000000, query time of that 0.0466038370, 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: 1.7295 cost: 0.00633344 M: 10 delta: 1 time: 6.97876 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: 10.7203 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: 15.9012 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: 22.0266 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: 31.1587 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 31.450000000000045
Index size:  29688.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0006243333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3600000000, query time of that 0.0681998160, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Accept!
  -> Decision True in time 3.7600000000, query time of that 0.7198612480, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1888.38 < 1898.78
  -> Decision False in time 0.7000000000, query time of that 0.1377032940, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1611.41 < 1614.01
  -> Decision False in time 1.6300000000, query time of that 0.0443783830, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1739.04 < 1756.69
  -> Decision False in time 20.5800000000, query time of that 0.5254370040, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1648.46 < 1691.94
  -> Decision False in time 16.6700000000, query time of that 0.4183098930, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Accept!
  -> Decision True in time 33.9400000000, query time of that 0.0858911420, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
2161.32 < 2168.39
  -> Decision False in time 14.2000000000, query time of that 0.0392676940, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1547.45 < 1619.2
  -> Decision False in time 38.6500000000, query time of that 0.1022060490, 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: 6.98086 one-recall: 0 one-ratio: 1.87159
iteration: 2 recall: 0.0744 accuracy: 0.55721 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.7222 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: 15.9011 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: 22.0253 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: 31.1506 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9948 accuracy: 0.000305712 cost: 0.0460245 M: 21.1589 delta: 0.134158 time: 37.1024 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 37.430000000000064
Index size:  36636.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0015026667
  Testing...
|S| = 98
|T| = 1411
Reject!
1867.64 < 1913.13
  -> Decision False in time 0.0400000000, query time of that 0.0077573830, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Accept!
  -> Decision True in time 3.5200000000, query time of that 0.5291148930, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1378.86 < 1415.36
  -> Decision False in time 2.8800000000, query time of that 0.4388326810, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1563.45 < 1589.2
  -> Decision False in time 2.5900000000, query time of that 0.0501432230, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1994.65 < 2004.49
  -> Decision False in time 8.5600000000, query time of that 0.1662474780, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1674.37 < 1678.59
  -> Decision False in time 4.6700000000, query time of that 0.0907811950, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
2335.09 < 2391.85
  -> Decision False in time 18.1800000000, query time of that 0.0347370290, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1866.44 < 1868.87
  -> Decision False in time 16.4600000000, query time of that 0.0334094320, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1513.24 < 1531.47
  -> Decision False in time 5.9200000000, query time of that 0.0125809090, 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: 0.0056 accuracy: 1.79013 cost: 0.00633344 M: 10 delta: 1 time: 6.97935 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: 10.7196 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: 15.8991 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: 22.0232 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: 31.1453 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 31.440000000000055
Index size:  29680.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0057553333
  Testing...
|S| = 98
|T| = 1411
Reject!
2514.63 < 2568.89
  -> Decision False in time 0.3200000000, query time of that 0.0433494230, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1239.34 < 1277.64
  -> Decision False in time 0.4000000000, query time of that 0.0541173760, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1465.66 < 1469.53
  -> Decision False in time 1.1100000000, query time of that 0.1451741830, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1380.99 < 1406.3
  -> Decision False in time 0.5200000000, query time of that 0.0084681390, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1467.82 < 1477.14
  -> Decision False in time 0.5600000000, query time of that 0.0096045140, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1337.36 < 1341.53
  -> Decision False in time 0.1200000000, query time of that 0.0025630660, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1683.82 < 1697.65
  -> Decision False in time 0.7200000000, query time of that 0.0020685120, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1351.36 < 1369.02
  -> Decision False in time 1.0900000000, query time of that 0.0022433880, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1093.82 < 1126.36
  -> Decision False in time 1.8200000000, query time of that 0.0036345170, 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: 0.0072 accuracy: 1.83983 cost: 0.00633344 M: 10 delta: 1 time: 6.97821 one-recall: 0 one-ratio: 1.98489
iteration: 2 recall: 0.066 accuracy: 0.617249 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.7188 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: 15.8976 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: 22.0232 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: 31.1479 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: 37.0987 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: 38.5479 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 38.87999999999988
Index size:  39632.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0063393333
  Testing...
|S| = 98
|T| = 1411
Reject!
2189.47 < 3072.52
  -> Decision False in time 0.2800000000, query time of that 0.0442417680, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1605.65 < 1610.86
  -> Decision False in time 0.7600000000, query time of that 0.1174608860, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2137.76 < 2166.85
  -> Decision False in time 0.0600000000, query time of that 0.0100808480, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
2163.5 < 2176.24
  -> Decision False in time 0.9800000000, query time of that 0.0212108440, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
3040 < 3211.09
  -> Decision False in time 0.9500000000, query time of that 0.0199407990, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1198.66 < 1223.59
  -> Decision False in time 3.2000000000, query time of that 0.0616863390, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1449.19 < 1449.25
  -> Decision False in time 1.0600000000, query time of that 0.0029663670, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1904.98 < 1913.73
  -> Decision False in time 4.8800000000, query time of that 0.0109223980, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1188 < 1282.83
  -> Decision False in time 2.4900000000, query time of that 0.0054677490, 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: 0.0084 accuracy: 1.69404 cost: 0.00633344 M: 10 delta: 1 time: 6.9781 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: 10.7197 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: 15.9005 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: 22.025 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: 31.1487 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 31.439999999999827
Index size:  29688.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0040033333
  Testing...
|S| = 98
|T| = 1411
Reject!
2617.09 < 2823.2
  -> Decision False in time 0.1600000000, query time of that 0.0216860080, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1902.5 < 1937.79
  -> Decision False in time 0.7200000000, query time of that 0.0967405420, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1922.31 < 1942.5
  -> Decision False in time 0.4600000000, query time of that 0.0609733140, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1010.93 < 1011.05
  -> Decision False in time 1.0100000000, query time of that 0.0168686180, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1294.44 < 1323.75
  -> Decision False in time 1.1300000000, query time of that 0.0218741430, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1727.47 < 1741.93
  -> Decision False in time 1.5700000000, query time of that 0.0269944520, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
694.084 < 700.164
  -> Decision False in time 2.1000000000, query time of that 0.0045012350, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1696.23 < 1703.81
  -> Decision False in time 5.4800000000, query time of that 0.0094792660, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1035.74 < 1077.32
  -> Decision False in time 0.7500000000, query time of that 0.0018979760, 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: 0.0056 accuracy: 1.68503 cost: 0.00633344 M: 10 delta: 1 time: 6.98219 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: 10.7234 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: 15.9042 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: 22.0304 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: 31.1588 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 31.449999999999818
Index size:  29684.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0062766667
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3500000000, query time of that 0.0463229850, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1406.84 < 1480.59
  -> Decision False in time 0.6900000000, query time of that 0.0965055960, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1241.43 < 1253.18
  -> Decision False in time 0.0900000000, query time of that 0.0134670670, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1147.96 < 1189.89
  -> Decision False in time 0.1600000000, query time of that 0.0031173130, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1364.04 < 1399.25
  -> Decision False in time 0.1900000000, query time of that 0.0043235230, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
977.61 < 1015.39
  -> Decision False in time 1.1600000000, query time of that 0.0211725920, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1756.4 < 1757.94
  -> Decision False in time 1.7700000000, query time of that 0.0035666390, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1688.41 < 1708.5
  -> Decision False in time 0.3600000000, query time of that 0.0011888360, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1159.25 < 1163.27
  -> Decision False in time 1.1700000000, query time of that 0.0023149710, 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: 3.58104 cost: 0.00633344 M: 10 delta: 1 time: 6.97955 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: 10.7191 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: 15.9011 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: 22.0274 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: 31.1585 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 31.450000000000273
Index size:  29684.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0011050000
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3600000000, query time of that 0.0626410050, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Accept!
  -> Decision True in time 3.5800000000, query time of that 0.6176670440, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2094.92 < 2098.22
  -> Decision False in time 0.0700000000, query time of that 0.0134000700, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Accept!
  -> Decision True in time 3.4700000000, query time of that 0.0771539080, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1407.59 < 1424.96
  -> Decision False in time 7.4700000000, query time of that 0.1604766210, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1651.87 < 1663.88
  -> Decision False in time 4.3800000000, query time of that 0.1001287390, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1984.4 < 2035.71
  -> Decision False in time 7.8300000000, query time of that 0.0169475370, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1777.35 < 1808.98
  -> Decision False in time 10.0700000000, query time of that 0.0222567510, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1582.65 < 1587.23
  -> Decision False in time 13.5200000000, query time of that 0.0310384590, with c1=5.0000000000, c2=0.1000000000
