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', 20, {'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', 30, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 80, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 100, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 60, {'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', 2, {'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', 3, {'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', 5, {'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', 10, {'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', 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: 0.0044 accuracy: 1.65812 cost: 0.00633344 M: 10 delta: 1 time: 6.87525 one-recall: 0 one-ratio: 2.05485
iteration: 2 recall: 0.0664 accuracy: 0.581012 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4858 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: 15.5097 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: 21.461 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: 30.3252 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: 36.0234 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 36.35
Index size:  98596.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0014883333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3400000000, query time of that 0.0499153050, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1277.44 < 1335.13
  -> Decision False in time 0.7500000000, query time of that 0.1108974850, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1415.24 < 1424.61
  -> Decision False in time 0.6100000000, query time of that 0.0913688550, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1208.42 < 1231.95
  -> Decision False in time 0.5600000000, query time of that 0.0105794160, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1582.49 < 1639.8
  -> Decision False in time 2.2600000000, query time of that 0.0437959760, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1441.37 < 1444.56
  -> Decision False in time 6.4300000000, query time of that 0.1257035840, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
2273.57 < 2287.34
  -> Decision False in time 0.6900000000, query time of that 0.0021128540, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
2261.38 < 2285.88
  -> Decision False in time 3.1000000000, query time of that 0.0061142060, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1691.1 < 1732.85
  -> Decision False in time 4.1400000000, query time of that 0.0090867560, 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: 0.0076 accuracy: 1.64122 cost: 0.00633344 M: 10 delta: 1 time: 6.82743 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: 10.4391 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: 15.4633 one-recall: 0.47 one-ratio: 1.13561
iteration: 4 recall: 0.9304 accuracy: 0.006495 cost: 0.0249121 M: 11.725 delta: 0.566235 time: 21.4135 one-recall: 0.97 one-ratio: 1.00229
iteration: 5 recall: 0.9908 accuracy: 0.000488781 cost: 0.0376853 M: 17.4219 delta: 0.224592 time: 30.2716 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 30.560000000000002
Index size:  29684.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0028823333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3500000000, query time of that 0.0560438010, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1515.6 < 1562.03
  -> Decision False in time 1.5400000000, query time of that 0.2370779070, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2647.32 < 3020.03
  -> Decision False in time 1.4600000000, query time of that 0.2251014130, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Accept!
  -> Decision True in time 3.4200000000, query time of that 0.0696350040, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1632.13 < 1664.83
  -> Decision False in time 2.1100000000, query time of that 0.0445044760, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1959.38 < 1968.16
  -> Decision False in time 6.7100000000, query time of that 0.1387792600, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1606.77 < 1609.17
  -> Decision False in time 0.0600000000, query time of that 0.0008443780, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1676.58 < 1696.55
  -> Decision False in time 0.1000000000, query time of that 0.0007560440, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1376.09 < 1381.21
  -> Decision False in time 2.5700000000, query time of that 0.0057445140, 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: 1.75244 cost: 0.00633344 M: 10 delta: 1 time: 6.82794 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: 10.4392 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: 15.4624 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: 21.4114 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: 30.2725 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 30.560000000000002
Index size:  29684.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0016100000
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3500000000, query time of that 0.0511816790, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1618.48 < 1832.63
  -> Decision False in time 0.4800000000, query time of that 0.0706870090, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1373.26 < 1480.26
  -> Decision False in time 0.7400000000, query time of that 0.1103890390, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
2045.93 < 2074.83
  -> Decision False in time 0.4500000000, query time of that 0.0093485940, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
2498.65 < 2503.37
  -> Decision False in time 3.2400000000, query time of that 0.0630904280, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1296.11 < 1338.34
  -> Decision False in time 0.2100000000, query time of that 0.0050290360, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1554.3 < 1620.22
  -> Decision False in time 3.4700000000, query time of that 0.0076578090, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1260.04 < 1287.83
  -> Decision False in time 8.6000000000, query time of that 0.0164133140, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1047.21 < 1077.82
  -> Decision False in time 2.8000000000, query time of that 0.0063030910, 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: 0.0024 accuracy: 1.88913 cost: 0.00633344 M: 10 delta: 1 time: 6.83815 one-recall: 0 one-ratio: 2.13154
iteration: 2 recall: 0.0708 accuracy: 0.607821 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4502 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: 15.475 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: 21.4253 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: 30.2888 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9964 accuracy: 0.000106052 cost: 0.046019 M: 21.1579 delta: 0.134134 time: 35.9799 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 36.29000000000002
Index size:  36636.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0004416667
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3700000000, query time of that 0.0757724320, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Accept!
  -> Decision True in time 3.7600000000, query time of that 0.7571304700, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2126.23 < 2183.94
  -> Decision False in time 2.3800000000, query time of that 0.4872090680, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Accept!
  -> Decision True in time 3.4000000000, query time of that 0.0894653930, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
2473.34 < 2497.98
  -> Decision False in time 1.4200000000, query time of that 0.0385745400, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1324.46 < 1336.55
  -> Decision False in time 24.2600000000, query time of that 0.6340507910, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
2078.18 < 2144.36
  -> Decision False in time 16.2000000000, query time of that 0.0434852610, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
2082.45 < 2140.89
  -> Decision False in time 9.7100000000, query time of that 0.0254060050, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1400.25 < 1401.42
  -> Decision False in time 36.1100000000, query time of that 0.0986104850, 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: 6.83226 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: 10.4433 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: 15.4679 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: 21.4166 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: 30.2728 one-recall: 0.99 one-ratio: 1.00032
iteration: 6 recall: 0.9948 accuracy: 0.000163326 cost: 0.0460258 M: 21.1582 delta: 0.134144 time: 35.9654 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 36.26999999999998
Index size:  36632.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0004683333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3900000000, query time of that 0.0834138670, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Accept!
  -> Decision True in time 3.8600000000, query time of that 0.8274777630, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2111.53 < 2122.67
  -> Decision False in time 7.7900000000, query time of that 1.6734553070, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Accept!
  -> Decision True in time 3.4400000000, query time of that 0.0994705670, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
2049.76 < 2138.85
  -> Decision False in time 1.5100000000, query time of that 0.0437706490, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1932.02 < 2005.36
  -> Decision False in time 29.3500000000, query time of that 0.8221400940, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1702.79 < 1746.76
  -> Decision False in time 25.0000000000, query time of that 0.0779460530, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1877.64 < 1906.88
  -> Decision False in time 14.0100000000, query time of that 0.0430959180, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1382.24 < 1631.75
  -> Decision False in time 14.2400000000, query time of that 0.0433577160, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 60, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 60, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.0076 accuracy: 1.72037 cost: 0.00633344 M: 10 delta: 1 time: 6.92153 one-recall: 0 one-ratio: 1.95236
iteration: 2 recall: 0.0752 accuracy: 0.554068 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.55 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: 15.641 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: 21.6665 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: 30.5947 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9952 accuracy: 0.000206068 cost: 0.0460244 M: 21.1593 delta: 0.134112 time: 36.3116 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 36.620000000000005
Index size:  36636.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0010383333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3600000000, query time of that 0.0681364350, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
2174.42 < 2208.78
  -> Decision False in time 3.2600000000, query time of that 0.6165099650, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2003.97 < 2005.38
  -> Decision False in time 1.5500000000, query time of that 0.2889460790, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1632.13 < 1664.22
  -> Decision False in time 2.2000000000, query time of that 0.0515992990, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
2319.37 < 2326.52
  -> Decision False in time 5.0400000000, query time of that 0.1190834290, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1720.88 < 1730.41
  -> Decision False in time 1.8400000000, query time of that 0.0453041560, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
2329.33 < 2423.67
  -> Decision False in time 30.5100000000, query time of that 0.0765370520, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1528.66 < 1530.56
  -> Decision False in time 23.9200000000, query time of that 0.0638054950, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1264.78 < 1280.31
  -> Decision False in time 27.7400000000, query time of that 0.0686281920, 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: 0.0052 accuracy: 1.54971 cost: 0.00633344 M: 10 delta: 1 time: 6.82961 one-recall: 0 one-ratio: 1.86151
iteration: 2 recall: 0.0744 accuracy: 0.522987 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4431 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: 15.4667 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: 21.4178 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: 30.2758 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 30.549999999999955
Index size:  29684.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0011073333
  Testing...
|S| = 98
|T| = 1411
Reject!
1622.45 < 1631.89
  -> Decision False in time 0.2500000000, query time of that 0.0419538310, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Accept!
  -> Decision True in time 3.5800000000, query time of that 0.6096379840, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1835.7 < 1867.06
  -> Decision False in time 0.9100000000, query time of that 0.1590307230, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1870.47 < 1930.92
  -> Decision False in time 2.5000000000, query time of that 0.0538618210, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1296.62 < 1306.17
  -> Decision False in time 1.6900000000, query time of that 0.0355356940, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1793.12 < 1806.65
  -> Decision False in time 0.6500000000, query time of that 0.0148730890, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
2068.4 < 2119.45
  -> Decision False in time 3.2100000000, query time of that 0.0081504060, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
2056 < 2074.18
  -> Decision False in time 9.5700000000, query time of that 0.0221877520, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1456.86 < 1492.54
  -> Decision False in time 6.6600000000, query time of that 0.0144630480, 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: 1.66082 cost: 0.00633344 M: 10 delta: 1 time: 6.82698 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: 10.4383 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: 15.4602 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: 21.4074 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: 30.2616 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9956 accuracy: 0.00017291 cost: 0.046016 M: 21.1572 delta: 0.134172 time: 35.9534 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 36.270000000000095
Index size:  36636.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0047053333
  Testing...
|S| = 98
|T| = 1411
Reject!
2492.67 < 2739.5
  -> Decision False in time 0.1400000000, query time of that 0.0195277850, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1976.76 < 1981.7
  -> Decision False in time 0.0700000000, query time of that 0.0106022790, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1778.78 < 1808.81
  -> Decision False in time 0.3600000000, query time of that 0.0536202680, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1447.7 < 1451.86
  -> Decision False in time 0.6300000000, query time of that 0.0116478550, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1344.87 < 1354.95
  -> Decision False in time 1.5700000000, query time of that 0.0287700970, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1999.71 < 2021.46
  -> Decision False in time 1.4000000000, query time of that 0.0270270140, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1280.14 < 1313.48
  -> Decision False in time 1.2400000000, query time of that 0.0024876760, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1315.28 < 1354.58
  -> Decision False in time 5.7300000000, query time of that 0.0113451780, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
800.019 < 803.584
  -> Decision False in time 2.7600000000, query time of that 0.0050126210, 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: 0.006 accuracy: 1.7295 cost: 0.00633344 M: 10 delta: 1 time: 6.8263 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: 10.439 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: 15.4636 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: 21.4133 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: 30.2788 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 30.560000000000173
Index size:  29688.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0006900000
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3600000000, query time of that 0.0650189640, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
2077.89 < 2082.25
  -> Decision False in time 1.9100000000, query time of that 0.3536105860, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1016.81 < 1019.07
  -> Decision False in time 5.1000000000, query time of that 0.9331184200, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1364.74 < 1391.15
  -> Decision False in time 2.7000000000, query time of that 0.0647312160, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
2463.57 < 2518.47
  -> Decision False in time 1.2600000000, query time of that 0.0299815350, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1461.16 < 1550.82
  -> Decision False in time 3.9900000000, query time of that 0.0939050480, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1958.37 < 1963.06
  -> Decision False in time 0.7900000000, query time of that 0.0025410310, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1885.2 < 1905.87
  -> Decision False in time 9.8700000000, query time of that 0.0249274520, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1676.25 < 1696.66
  -> Decision False in time 12.2100000000, query time of that 0.0290754710, 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: 0.0052 accuracy: 1.68406 cost: 0.00633344 M: 10 delta: 1 time: 6.82953 one-recall: 0 one-ratio: 1.87159
iteration: 2 recall: 0.0744 accuracy: 0.55721 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4416 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: 15.4642 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: 21.4129 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: 30.2703 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9948 accuracy: 0.000305712 cost: 0.0460245 M: 21.1589 delta: 0.134158 time: 35.9603 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 36.26999999999998
Index size:  36624.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0027223333
  Testing...
|S| = 98
|T| = 1411
Reject!
1535.52 < 1565.27
  -> Decision False in time 0.0200000000, query time of that 0.0024009580, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1793.32 < 1820.21
  -> Decision False in time 1.0600000000, query time of that 0.1505080540, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1068.37 < 1094.12
  -> Decision False in time 0.5500000000, query time of that 0.0796924050, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1231.43 < 1248.37
  -> Decision False in time 0.1300000000, query time of that 0.0026445870, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1069.21 < 1077.54
  -> Decision False in time 2.0600000000, query time of that 0.0364041760, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
2586.63 < 2610.49
  -> Decision False in time 0.6700000000, query time of that 0.0118946690, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1097.5 < 1168.68
  -> Decision False in time 13.6700000000, query time of that 0.0263749780, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1478.96 < 1504.62
  -> Decision False in time 0.7100000000, query time of that 0.0022310980, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1239.13 < 1343.42
  -> Decision False in time 2.5700000000, query time of that 0.0042794670, 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: 0.0056 accuracy: 1.79013 cost: 0.00633344 M: 10 delta: 1 time: 6.82688 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: 10.439 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: 15.4625 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: 21.4109 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: 30.266 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 30.550000000000182
Index size:  29684.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0006306667
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3700000000, query time of that 0.0748349560, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1849.15 < 2440.61
  -> Decision False in time 0.5900000000, query time of that 0.1180122850, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1661.92 < 1662.44
  -> Decision False in time 15.0000000000, query time of that 3.0066146600, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Accept!
  -> Decision True in time 3.3800000000, query time of that 0.0863012080, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1443.71 < 1445.37
  -> Decision False in time 27.6400000000, query time of that 0.7183851510, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1747.56 < 1754.15
  -> Decision False in time 1.7700000000, query time of that 0.0468975360, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1780.89 < 1827.55
  -> Decision False in time 8.5500000000, query time of that 0.0244511950, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1739.4 < 1756.36
  -> Decision False in time 7.3900000000, query time of that 0.0192929940, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1760.81 < 1816.27
  -> Decision False in time 22.3600000000, query time of that 0.0621516410, 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: 0.0072 accuracy: 1.83983 cost: 0.00633344 M: 10 delta: 1 time: 6.82685 one-recall: 0 one-ratio: 1.98489
iteration: 2 recall: 0.066 accuracy: 0.617249 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4377 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: 15.4619 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: 21.4127 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: 30.2681 one-recall: 0.98 one-ratio: 1.00558
iteration: 6 recall: 0.9888 accuracy: 0.000807707 cost: 0.0460215 M: 21.1587 delta: 0.134107 time: 35.9572 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: 37.3151 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 37.62999999999988
Index size:  39636.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0049670000
  Testing...
|S| = 98
|T| = 1411
Reject!
1273.82 < 1309.5
  -> Decision False in time 0.3100000000, query time of that 0.0447245230, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
2438.88 < 2691.09
  -> Decision False in time 0.0900000000, query time of that 0.0138420090, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
833.32 < 848.428
  -> Decision False in time 0.4700000000, query time of that 0.0679254880, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1728.66 < 1737.15
  -> Decision False in time 1.2000000000, query time of that 0.0211583710, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1063.86 < 1069.83
  -> Decision False in time 0.4500000000, query time of that 0.0074058170, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1648.99 < 2303.8
  -> Decision False in time 0.4500000000, query time of that 0.0094641450, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1657.28 < 1670.65
  -> Decision False in time 12.1200000000, query time of that 0.0233789870, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1365.82 < 1393.8
  -> Decision False in time 7.8300000000, query time of that 0.0155843730, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
2031.02 < 2086.11
  -> Decision False in time 2.2700000000, query time of that 0.0036629350, 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: 6.82564 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: 10.4361 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: 15.4592 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: 21.4076 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: 30.2635 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 30.539999999999964
Index size:  29688.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0040033333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3400000000, query time of that 0.0460767200, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1621.79 < 1665.67
  -> Decision False in time 0.5800000000, query time of that 0.0797650990, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2033.84 < 2047.12
  -> Decision False in time 0.1100000000, query time of that 0.0150699030, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1293.07 < 1316.68
  -> Decision False in time 0.5100000000, query time of that 0.0084833050, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1555.17 < 1655.73
  -> Decision False in time 0.4900000000, query time of that 0.0080022550, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
893.507 < 921.881
  -> Decision False in time 3.5600000000, query time of that 0.0619104140, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1473.36 < 1485.73
  -> Decision False in time 0.2600000000, query time of that 0.0006314140, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
783.859 < 883.723
  -> Decision False in time 10.5000000000, query time of that 0.0187477430, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1073.94 < 1121.85
  -> Decision False in time 4.0900000000, query time of that 0.0075517080, 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: 1.68503 cost: 0.00633344 M: 10 delta: 1 time: 6.83592 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: 10.4485 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: 15.4727 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: 21.4247 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: 30.2894 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 30.559999999999945
Index size:  29688.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0027943333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3500000000, query time of that 0.0492359670, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1789.12 < 1810.42
  -> Decision False in time 0.7100000000, query time of that 0.0979982010, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1267.46 < 1282.44
  -> Decision False in time 3.1300000000, query time of that 0.4181384720, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1393.8 < 1422.05
  -> Decision False in time 2.0900000000, query time of that 0.0352738490, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1086.91 < 1088.51
  -> Decision False in time 2.7700000000, query time of that 0.0490983000, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1227.35 < 1250.62
  -> Decision False in time 0.5900000000, query time of that 0.0097575850, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1298.06 < 1345.38
  -> Decision False in time 5.0900000000, query time of that 0.0097403100, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1401.61 < 1406.82
  -> Decision False in time 1.2100000000, query time of that 0.0028225070, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1258.28 < 1259.7
  -> Decision False in time 8.7000000000, query time of that 0.0156201890, 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: 0.006 accuracy: 3.58104 cost: 0.00633344 M: 10 delta: 1 time: 6.83344 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: 10.4446 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: 15.4675 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: 21.415 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: 30.2718 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 30.549999999999955
Index size:  29680.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0073000000
  Testing...
|S| = 98
|T| = 1411
Reject!
2026.52 < 2085.98
  -> Decision False in time 0.2400000000, query time of that 0.0338327910, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1613.32 < 1614.76
  -> Decision False in time 0.0600000000, query time of that 0.0082463840, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
3115.8 < 3169.57
  -> Decision False in time 0.4300000000, query time of that 0.0609272340, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1274.63 < 1283.3
  -> Decision False in time 0.4800000000, query time of that 0.0087435550, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1862.2 < 1877.15
  -> Decision False in time 1.1900000000, query time of that 0.0217600570, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1329.86 < 1332.45
  -> Decision False in time 0.9700000000, query time of that 0.0180685220, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1610.55 < 1627.8
  -> Decision False in time 1.4800000000, query time of that 0.0028957360, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1055.85 < 1066.04
  -> Decision False in time 2.8500000000, query time of that 0.0055276370, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
808.161 < 809.3
  -> Decision False in time 0.1100000000, query time of that 0.0005677110, with c1=5.0000000000, c2=0.1000000000
