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', 50, {'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', 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', 40, {'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', 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', 60, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 70, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 2, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 30, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 1, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 80, {'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', 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: 0.0044 accuracy: 1.65812 cost: 0.00633344 M: 10 delta: 1 time: 6.87145 one-recall: 0 one-ratio: 2.05485
iteration: 2 recall: 0.0664 accuracy: 0.581012 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4828 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: 15.5078 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: 21.457 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: 30.3132 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: 36.0022 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 36.32
Index size:  98596.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0007476667
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3600000000, query time of that 0.0622892520, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1723.13 < 1727.81
  -> Decision False in time 3.4200000000, query time of that 0.6131882400, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1502.99 < 1507.6
  -> Decision False in time 4.5800000000, query time of that 0.8150135380, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
2073.01 < 2129.45
  -> Decision False in time 1.5900000000, query time of that 0.0358978720, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
2129.74 < 2134.89
  -> Decision False in time 1.6700000000, query time of that 0.0395263070, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1983.04 < 2019.07
  -> Decision False in time 13.4900000000, query time of that 0.2999682020, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
2289.09 < 2304.82
  -> Decision False in time 19.8100000000, query time of that 0.0448569020, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
2058.5 < 2085.76
  -> Decision False in time 23.4300000000, query time of that 0.0526191280, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1650.46 < 1668.3
  -> Decision False in time 25.5800000000, query time of that 0.0582928140, 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: 1.64122 cost: 0.00633344 M: 10 delta: 1 time: 6.85456 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: 10.4894 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: 15.5608 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: 21.5827 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: 30.6664 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 30.94999999999999
Index size:  29684.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0006293333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.4100000000, query time of that 0.1053141780, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1626.01 < 1645.27
  -> Decision False in time 1.3100000000, query time of that 0.3369054240, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1936.46 < 1993.3
  -> Decision False in time 14.0200000000, query time of that 3.6921539730, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Accept!
  -> Decision True in time 3.5500000000, query time of that 0.1301141400, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
2182.32 < 2271.11
  -> Decision False in time 18.5000000000, query time of that 0.7009778450, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1684.64 < 1705.7
  -> Decision False in time 8.9200000000, query time of that 0.3405550610, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1303.39 < 1335.06
  -> Decision False in time 3.6800000000, query time of that 0.0146209930, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1627.87 < 1655.56
  -> Decision False in time 26.1300000000, query time of that 0.0998512080, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1893.61 < 1939.08
  -> Decision False in time 1.1300000000, query time of that 0.0057383300, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 3, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 3, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.0064 accuracy: 1.75244 cost: 0.00633344 M: 10 delta: 1 time: 6.98585 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: 10.7278 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: 15.9076 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: 22.0337 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: 31.1603 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 31.45999999999998
Index size:  29684.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0043050000
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3500000000, query time of that 0.0458636750, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1465.5 < 1472.77
  -> Decision False in time 0.1500000000, query time of that 0.0195995600, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1025 < 1028.72
  -> Decision False in time 0.1300000000, query time of that 0.0164997210, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1045.2 < 1072.64
  -> Decision False in time 1.9000000000, query time of that 0.0314753180, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1197.23 < 1218.86
  -> Decision False in time 0.4400000000, query time of that 0.0083384680, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1600.26 < 1660.53
  -> Decision False in time 1.0800000000, query time of that 0.0196326210, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1580.76 < 1615.08
  -> Decision False in time 2.1000000000, query time of that 0.0039165390, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1795.49 < 1831.05
  -> Decision False in time 0.6000000000, query time of that 0.0010728500, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1327.11 < 1350.9
  -> Decision False in time 3.1400000000, query time of that 0.0062610780, 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: 0.0024 accuracy: 1.88913 cost: 0.00633344 M: 10 delta: 1 time: 6.98573 one-recall: 0 one-ratio: 2.13154
iteration: 2 recall: 0.0708 accuracy: 0.607821 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.7297 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: 15.9101 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: 22.037 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: 31.1638 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9964 accuracy: 0.000106052 cost: 0.046019 M: 21.1579 delta: 0.134134 time: 37.1089 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 37.43000000000001
Index size:  36636.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0004486667
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3800000000, query time of that 0.0810595880, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
2332.14 < 2348.81
  -> Decision False in time 1.8600000000, query time of that 0.3981627220, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1963.76 < 2509.77
  -> Decision False in time 6.1600000000, query time of that 1.3241340910, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Accept!
  -> Decision True in time 3.4800000000, query time of that 0.0985867440, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1317.92 < 1372.6
  -> Decision False in time 2.9700000000, query time of that 0.0831878470, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1787.81 < 1816.7
  -> Decision False in time 27.2700000000, query time of that 0.7516725340, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1821.34 < 1830.03
  -> Decision False in time 13.2000000000, query time of that 0.0405662020, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
2016.77 < 2102.3
  -> Decision False in time 2.8300000000, query time of that 0.0081323830, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1956.43 < 1976.01
  -> Decision False in time 1.8000000000, query time of that 0.0065095260, 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: 0.0068 accuracy: 1.62174 cost: 0.00633344 M: 10 delta: 1 time: 6.987 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: 10.7289 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: 15.9095 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: 22.0365 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: 31.1625 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: 37.1143 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 37.43999999999994
Index size:  36628.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0024310000
  Testing...
|S| = 98
|T| = 1411
Reject!
2607.46 < 2970.02
  -> Decision False in time 0.0100000000, query time of that 0.0031873450, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
2047.43 < 2477.21
  -> Decision False in time 1.0200000000, query time of that 0.1787761270, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2456.73 < 2698.91
  -> Decision False in time 1.4100000000, query time of that 0.2458988780, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Accept!
  -> Decision True in time 3.4400000000, query time of that 0.0762680380, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
2114.02 < 2114.79
  -> Decision False in time 3.1400000000, query time of that 0.0682887510, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1465.12 < 1467.1
  -> Decision False in time 2.4000000000, query time of that 0.0524682390, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1492.7 < 1493.04
  -> Decision False in time 26.9700000000, query time of that 0.0622971820, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1810.19 < 1847.72
  -> Decision False in time 34.9100000000, query time of that 0.0796290690, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1935.3 < 1937.55
  -> Decision False in time 27.3900000000, query time of that 0.0632023920, 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: 0.0076 accuracy: 1.72037 cost: 0.00633344 M: 10 delta: 1 time: 6.98855 one-recall: 0 one-ratio: 1.95236
iteration: 2 recall: 0.0752 accuracy: 0.554068 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.7328 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: 15.9136 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: 22.0404 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: 31.1675 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9952 accuracy: 0.000206068 cost: 0.0460244 M: 21.1593 delta: 0.134112 time: 37.115 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 37.43999999999994
Index size:  36636.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0015043333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3600000000, query time of that 0.0547581960, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1323.73 < 1332.42
  -> Decision False in time 1.9400000000, query time of that 0.2972392920, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1377.43 < 1388.73
  -> Decision False in time 2.8600000000, query time of that 0.4317966360, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1913.61 < 1923.25
  -> Decision False in time 0.0500000000, query time of that 0.0019991170, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
2161.03 < 2165.65
  -> Decision False in time 1.9600000000, query time of that 0.0369886980, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1645.41 < 1735.83
  -> Decision False in time 5.5000000000, query time of that 0.1050545150, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1849.29 < 1862.13
  -> Decision False in time 2.0600000000, query time of that 0.0045369450, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1567.98 < 1582.74
  -> Decision False in time 5.1300000000, query time of that 0.0100269310, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1217.32 < 1241.84
  -> Decision False in time 18.0200000000, query time of that 0.0343570120, 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: 0.0052 accuracy: 1.54971 cost: 0.00633344 M: 10 delta: 1 time: 6.98707 one-recall: 0 one-ratio: 1.86151
iteration: 2 recall: 0.0744 accuracy: 0.522987 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.7305 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: 15.9129 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: 22.0416 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: 31.1732 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 31.469999999999914
Index size:  29684.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0040023333
  Testing...
|S| = 98
|T| = 1411
Reject!
2197.54 < 2197.81
  -> Decision False in time 0.0400000000, query time of that 0.0056926970, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
880.524 < 900.214
  -> Decision False in time 0.6700000000, query time of that 0.0892554180, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1089.9 < 1116.7
  -> Decision False in time 0.1400000000, query time of that 0.0208628240, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1152.68 < 1202.09
  -> Decision False in time 1.2400000000, query time of that 0.0222847340, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1063.16 < 1070.24
  -> Decision False in time 0.3300000000, query time of that 0.0057030960, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1470.85 < 1542.34
  -> Decision False in time 0.4200000000, query time of that 0.0082638980, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1109.21 < 1119.93
  -> Decision False in time 1.0500000000, query time of that 0.0023624280, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1133.59 < 1159.14
  -> Decision False in time 4.8200000000, query time of that 0.0085356360, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1299.6 < 1352.17
  -> Decision False in time 1.4900000000, query time of that 0.0035418630, 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: 0.0064 accuracy: 1.66082 cost: 0.00633344 M: 10 delta: 1 time: 6.98791 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: 10.733 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: 15.9164 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: 22.0438 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: 31.1673 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9956 accuracy: 0.00017291 cost: 0.046016 M: 21.1572 delta: 0.134172 time: 37.1161 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 37.450000000000045
Index size:  36628.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0017536667
  Testing...
|S| = 98
|T| = 1411
Reject!
1420.79 < 1455.84
  -> Decision False in time 0.1900000000, query time of that 0.0274148680, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1279.55 < 1282.7
  -> Decision False in time 1.5000000000, query time of that 0.2176548910, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
945.518 < 1023.72
  -> Decision False in time 2.0200000000, query time of that 0.2947826760, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1664.87 < 1697.41
  -> Decision False in time 0.0500000000, query time of that 0.0012626020, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1258.18 < 1266.94
  -> Decision False in time 0.2600000000, query time of that 0.0054993960, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1588.05 < 1625.32
  -> Decision False in time 0.0500000000, query time of that 0.0016831430, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1622.45 < 1631.89
  -> Decision False in time 13.9400000000, query time of that 0.0281836890, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1268.46 < 1345.57
  -> Decision False in time 3.5400000000, query time of that 0.0076692410, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1753.93 < 1765.78
  -> Decision False in time 1.9500000000, query time of that 0.0050918180, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 60, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 60, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.006 accuracy: 1.7295 cost: 0.00633344 M: 10 delta: 1 time: 6.9861 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: 10.7301 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: 15.9109 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: 22.0366 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: 31.1664 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 31.45999999999981
Index size:  29688.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0013173333
  Testing...
|S| = 98
|T| = 1411
Reject!
2022.9 < 2047.46
  -> Decision False in time 0.0300000000, query time of that 0.0056712850, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Accept!
  -> Decision True in time 3.6200000000, query time of that 0.6406407920, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1832.62 < 1847.37
  -> Decision False in time 6.3900000000, query time of that 1.1264135080, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1849.29 < 1862.52
  -> Decision False in time 2.5100000000, query time of that 0.0609428270, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1547.7 < 1630.06
  -> Decision False in time 5.5500000000, query time of that 0.1276559960, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1654.59 < 1668.84
  -> Decision False in time 13.8900000000, query time of that 0.3095639080, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1409.52 < 1416.31
  -> Decision False in time 0.0200000000, query time of that 0.0013068110, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1375.15 < 1383.57
  -> Decision False in time 0.7200000000, query time of that 0.0025756390, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
2315.08 < 2333.35
  -> Decision False in time 11.5600000000, query time of that 0.0291025750, 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: 0.0052 accuracy: 1.68406 cost: 0.00633344 M: 10 delta: 1 time: 6.98598 one-recall: 0 one-ratio: 1.87159
iteration: 2 recall: 0.0744 accuracy: 0.55721 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.7284 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: 15.9087 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: 22.034 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: 31.1573 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9948 accuracy: 0.000305712 cost: 0.0460245 M: 21.1589 delta: 0.134158 time: 37.1048 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 37.440000000000055
Index size:  36632.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0004706667
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3800000000, query time of that 0.0746465430, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1472.21 < 1474.52
  -> Decision False in time 3.4200000000, query time of that 0.6690246300, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1398.94 < 1410.97
  -> Decision False in time 3.7200000000, query time of that 0.7209048630, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Accept!
  -> Decision True in time 3.4500000000, query time of that 0.0875132340, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1391.82 < 1412.42
  -> Decision False in time 0.2400000000, query time of that 0.0059671330, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1262.14 < 1269.39
  -> Decision False in time 1.4900000000, query time of that 0.0383843950, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1800.54 < 1922.17
  -> Decision False in time 32.4300000000, query time of that 0.0890303500, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1275.79 < 1325.76
  -> Decision False in time 12.4500000000, query time of that 0.0347714870, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1123.39 < 1145.57
  -> Decision False in time 3.3200000000, query time of that 0.0098218220, 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: 1.79013 cost: 0.00633344 M: 10 delta: 1 time: 6.98872 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: 10.7337 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: 15.9152 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: 22.0414 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: 31.165 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:  29680.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0062736667
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3500000000, query time of that 0.0482465680, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
2764.63 < 2793.2
  -> Decision False in time 0.2900000000, query time of that 0.0380425790, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
945.252 < 948.901
  -> Decision False in time 0.7200000000, query time of that 0.0972607400, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
2160.29 < 2170.15
  -> Decision False in time 0.0100000000, query time of that 0.0007006750, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
2375 < 2437.05
  -> Decision False in time 0.9300000000, query time of that 0.0174454320, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1337.09 < 1349.18
  -> Decision False in time 0.9000000000, query time of that 0.0170263630, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
853.152 < 869.552
  -> Decision False in time 1.0600000000, query time of that 0.0024349010, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1146.56 < 1155.86
  -> Decision False in time 2.5300000000, query time of that 0.0045866830, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1418.61 < 1448.68
  -> Decision False in time 11.3400000000, query time of that 0.0191563300, 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: 0.0072 accuracy: 1.83983 cost: 0.00633344 M: 10 delta: 1 time: 6.98172 one-recall: 0 one-ratio: 1.98489
iteration: 2 recall: 0.066 accuracy: 0.617249 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.7233 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: 15.9036 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: 22.0302 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: 31.154 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: 37.1017 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: 38.5492 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 38.88999999999987
Index size:  39632.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0009643333
  Testing...
|S| = 98
|T| = 1411
Reject!
2196.66 < 2403.39
  -> Decision False in time 0.3300000000, query time of that 0.0541350450, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Accept!
  -> Decision True in time 3.5200000000, query time of that 0.5779028240, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1411.94 < 1426.69
  -> Decision False in time 1.4900000000, query time of that 0.2438928360, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1665.83 < 1674.26
  -> Decision False in time 0.5100000000, query time of that 0.0108847680, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1544.9 < 1579.89
  -> Decision False in time 6.3800000000, query time of that 0.1337993710, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1751.25 < 1778
  -> Decision False in time 2.9500000000, query time of that 0.0590541420, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1570.55 < 1583.81
  -> Decision False in time 2.8700000000, query time of that 0.0072961490, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1589.63 < 1649.06
  -> Decision False in time 4.1500000000, query time of that 0.0092114630, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
2123.17 < 2147.5
  -> Decision False in time 1.0400000000, query time of that 0.0032734040, 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: 0.0084 accuracy: 1.69404 cost: 0.00633344 M: 10 delta: 1 time: 6.98825 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: 10.7319 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: 15.9116 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: 22.0389 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: 31.1631 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 31.460000000000036
Index size:  29684.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0072973333
  Testing...
|S| = 98
|T| = 1411
Reject!
1121.52 < 1140.32
  -> Decision False in time 0.0200000000, query time of that 0.0038149530, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1469.9 < 1565.97
  -> Decision False in time 0.7600000000, query time of that 0.1103985940, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1504.62 < 1504.63
  -> Decision False in time 0.3000000000, query time of that 0.0446859110, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Accept!
  -> Decision True in time 3.4600000000, query time of that 0.0645230760, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1769.23 < 1783.64
  -> Decision False in time 1.1400000000, query time of that 0.0205172270, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1615.99 < 1623.35
  -> Decision False in time 4.4600000000, query time of that 0.0818092080, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1240.23 < 1242.63
  -> Decision False in time 1.3300000000, query time of that 0.0020397680, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1567.81 < 1584.36
  -> Decision False in time 2.8000000000, query time of that 0.0050770360, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1188.47 < 1199.04
  -> Decision False in time 1.5200000000, query time of that 0.0033297170, 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: 0.0056 accuracy: 1.68503 cost: 0.00633344 M: 10 delta: 1 time: 6.98143 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: 10.7232 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: 15.9039 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: 22.03 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: 31.1586 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:  29684.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0006246667
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3700000000, query time of that 0.0702124250, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Accept!
  -> Decision True in time 3.6900000000, query time of that 0.7166438290, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1951.77 < 1965.14
  -> Decision False in time 9.0800000000, query time of that 1.7600458130, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1880.46 < 1922.17
  -> Decision False in time 3.2100000000, query time of that 0.0781513150, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1306.54 < 1326.06
  -> Decision False in time 27.1600000000, query time of that 0.6791251150, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
2136.99 < 2138.85
  -> Decision False in time 10.4200000000, query time of that 0.2536843560, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1477.05 < 1478.06
  -> Decision False in time 0.8200000000, query time of that 0.0021937820, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1932.04 < 1943.28
  -> Decision False in time 49.7500000000, query time of that 0.1302359550, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
2203.01 < 2231.8
  -> Decision False in time 46.1100000000, query time of that 0.1210233620, 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: 0.006 accuracy: 3.58104 cost: 0.00633344 M: 10 delta: 1 time: 6.98627 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: 10.7308 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: 15.915 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: 22.0462 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: 31.1803 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 31.470000000000027
Index size:  29684.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0057600000
  Testing...
|S| = 98
|T| = 1411
Reject!
1627.32 < 1669.8
  -> Decision False in time 0.0600000000, query time of that 0.0089018530, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1195.63 < 1222.64
  -> Decision False in time 1.1100000000, query time of that 0.1503503770, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1099.24 < 1144.01
  -> Decision False in time 1.2900000000, query time of that 0.1760555720, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1179.89 < 1194.59
  -> Decision False in time 0.7900000000, query time of that 0.0132176900, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
2331.25 < 2366.79
  -> Decision False in time 4.2800000000, query time of that 0.0719309980, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1589.12 < 1610.93
  -> Decision False in time 0.0500000000, query time of that 0.0012830240, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
964.96 < 981.162
  -> Decision False in time 0.4100000000, query time of that 0.0013284910, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1916.29 < 1935.49
  -> Decision False in time 1.0400000000, query time of that 0.0023780530, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1959.94 < 1993.3
  -> Decision False in time 1.8500000000, query time of that 0.0034593750, with c1=5.0000000000, c2=0.1000000000
