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', 10, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 90, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 20, {'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', 4, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 60, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 50, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 40, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 1, {'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', 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', 3, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 30, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 100, {'reverse': -1}, False])]
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: 0.0044 accuracy: 1.65812 cost: 0.00633344 M: 10 delta: 1 time: 6.87783 one-recall: 0 one-ratio: 2.05485
iteration: 2 recall: 0.0664 accuracy: 0.581012 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4916 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: 15.5189 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: 21.47 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: 30.3297 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: 36.0213 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 36.330000000000005
Index size:  98596.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0017520000
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3400000000, query time of that 0.0513705790, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1131.14 < 1137.48
  -> Decision False in time 0.6400000000, query time of that 0.0902199430, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1229.53 < 1239.08
  -> Decision False in time 0.6300000000, query time of that 0.0931084510, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1741.97 < 1768.59
  -> Decision False in time 2.0400000000, query time of that 0.0348010570, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1945.17 < 1950.47
  -> Decision False in time 1.1600000000, query time of that 0.0224137120, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1063.07 < 1103.91
  -> Decision False in time 0.7300000000, query time of that 0.0136529060, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1626.28 < 1660.89
  -> Decision False in time 6.2400000000, query time of that 0.0106363920, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
2068.04 < 2108.54
  -> Decision False in time 9.4700000000, query time of that 0.0193098980, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1026.08 < 1087.9
  -> Decision False in time 8.5800000000, query time of that 0.0152378000, 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: 0.0076 accuracy: 1.64122 cost: 0.00633344 M: 10 delta: 1 time: 6.82395 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: 10.4361 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: 15.4622 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: 21.4134 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 30.539999999999992
Index size:  29680.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0006313333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3700000000, query time of that 0.0737177050, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1181.54 < 1218.83
  -> Decision False in time 1.1300000000, query time of that 0.2221998440, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1839.79 < 1868.05
  -> Decision False in time 8.0500000000, query time of that 1.6004179510, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Accept!
  -> Decision True in time 3.4100000000, query time of that 0.0840932020, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1669.63 < 1842.43
  -> Decision False in time 14.5100000000, query time of that 0.3631681290, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1505.49 < 1539.14
  -> Decision False in time 2.4100000000, query time of that 0.0649290460, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1035.02 < 1059.99
  -> Decision False in time 22.9100000000, query time of that 0.0633997010, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1696.74 < 1702.93
  -> Decision False in time 25.5900000000, query time of that 0.0699465560, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1689.81 < 1753.94
  -> Decision False in time 35.1000000000, query time of that 0.0957495870, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 20, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 20, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.0064 accuracy: 1.75244 cost: 0.00633344 M: 10 delta: 1 time: 6.82638 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: 10.4422 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: 15.4706 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: 21.4236 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: 30.2872 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:  25212.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0022150000
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3400000000, query time of that 0.0462045760, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1010.83 < 1011.41
  -> Decision False in time 2.8900000000, query time of that 0.3955946410, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1462.27 < 1470.33
  -> Decision False in time 1.0100000000, query time of that 0.1453172060, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Accept!
  -> Decision True in time 3.3900000000, query time of that 0.0598036410, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
2213.14 < 2281.33
  -> Decision False in time 0.8900000000, query time of that 0.0157365090, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1615.2 < 1615.79
  -> Decision False in time 0.0200000000, query time of that 0.0009148500, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1964.17 < 2029.59
  -> Decision False in time 0.0400000000, query time of that 0.0004855830, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1348.45 < 1355.17
  -> Decision False in time 1.0900000000, query time of that 0.0028582860, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1686.21 < 1734.55
  -> Decision False in time 13.2000000000, query time of that 0.0233258970, 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: 0.0024 accuracy: 1.88913 cost: 0.00633344 M: 10 delta: 1 time: 6.82517 one-recall: 0 one-ratio: 2.13154
iteration: 2 recall: 0.0708 accuracy: 0.607821 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4379 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: 15.4637 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: 21.4159 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: 30.2802 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9964 accuracy: 0.000106052 cost: 0.046019 M: 21.1579 delta: 0.134134 time: 35.9689 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:  32160.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0047146667
  Testing...
|S| = 98
|T| = 1411
Reject!
1701.3 < 1806.48
  -> Decision False in time 0.1500000000, query time of that 0.0226726250, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Accept!
  -> Decision True in time 3.4600000000, query time of that 0.4879100110, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2607.37 < 2782.13
  -> Decision False in time 0.5700000000, query time of that 0.0798159080, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1614.43 < 1617.84
  -> Decision False in time 0.4300000000, query time of that 0.0074396340, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1342.14 < 1465.47
  -> Decision False in time 0.0600000000, query time of that 0.0015866050, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
2131.5 < 2382.16
  -> Decision False in time 0.1000000000, query time of that 0.0021012180, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1519.71 < 1552.12
  -> Decision False in time 4.9400000000, query time of that 0.0091562930, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1423.51 < 1460.94
  -> Decision False in time 2.4800000000, query time of that 0.0051763570, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
2024.88 < 2067.5
  -> Decision False in time 0.3500000000, query time of that 0.0011683820, 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: 0.0068 accuracy: 1.62174 cost: 0.00633344 M: 10 delta: 1 time: 6.82916 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: 10.4434 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: 15.4717 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: 21.427 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: 30.2954 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: 35.9963 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 36.309999999999945
Index size:  32168.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0026126667
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3400000000, query time of that 0.0480721680, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1206.88 < 1255.21
  -> Decision False in time 0.1700000000, query time of that 0.0242823610, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1888.8 < 1910.34
  -> Decision False in time 1.4000000000, query time of that 0.1978623790, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Accept!
  -> Decision True in time 3.3700000000, query time of that 0.0608802140, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
2249.24 < 2256.46
  -> Decision False in time 4.9400000000, query time of that 0.0849650480, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1711.29 < 1714.22
  -> Decision False in time 1.0900000000, query time of that 0.0202870470, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1704.74 < 1772.97
  -> Decision False in time 0.2800000000, query time of that 0.0006079900, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1636.78 < 1638.04
  -> Decision False in time 7.1300000000, query time of that 0.0138499380, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1600.26 < 1660.53
  -> Decision False in time 5.1000000000, query time of that 0.0102398370, 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: 6.82637 one-recall: 0 one-ratio: 1.95236
iteration: 2 recall: 0.0752 accuracy: 0.554068 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4424 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: 15.4708 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: 21.4261 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: 30.294 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9952 accuracy: 0.000206068 cost: 0.0460244 M: 21.1593 delta: 0.134112 time: 35.9892 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 36.289999999999964
Index size:  32180.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0010383333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3600000000, query time of that 0.0686382950, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
2772.69 < 2826.26
  -> Decision False in time 1.5200000000, query time of that 0.2802526890, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2216.91 < 2369.69
  -> Decision False in time 7.0900000000, query time of that 1.3162248240, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Accept!
  -> Decision True in time 3.3900000000, query time of that 0.0791793490, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1671.38 < 1672.61
  -> Decision False in time 7.3700000000, query time of that 0.1761922830, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1310.66 < 1351.21
  -> Decision False in time 8.6800000000, query time of that 0.2030529710, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Accept!
  -> Decision True in time 32.9500000000, query time of that 0.0821365100, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1331.2 < 1372.77
  -> Decision False in time 6.7100000000, query time of that 0.0179710050, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
2037.41 < 2047.67
  -> Decision False in time 20.2800000000, query time of that 0.0525365390, 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: 6.82819 one-recall: 0 one-ratio: 1.86151
iteration: 2 recall: 0.0744 accuracy: 0.522987 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4412 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: 15.4671 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: 21.419 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: 30.28 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:  25212.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0011073333
  Testing...
|S| = 98
|T| = 1411
Reject!
1798.63 < 1807.75
  -> Decision False in time 0.2000000000, query time of that 0.0338597930, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1532.98 < 1560.24
  -> Decision False in time 0.1500000000, query time of that 0.0232047700, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1699.14 < 1725.54
  -> Decision False in time 4.5600000000, query time of that 0.7590761870, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Accept!
  -> Decision True in time 3.4300000000, query time of that 0.0711183890, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
2008.62 < 2031.71
  -> Decision False in time 7.0800000000, query time of that 0.1489606380, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1771.8 < 1776.62
  -> Decision False in time 0.3000000000, query time of that 0.0067872490, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1692.05 < 1692.93
  -> Decision False in time 6.9100000000, query time of that 0.0151826220, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1233.7 < 1241.28
  -> Decision False in time 4.1600000000, query time of that 0.0092161880, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1039.91 < 1095.58
  -> Decision False in time 5.8500000000, query time of that 0.0139476970, 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: 0.0064 accuracy: 1.66082 cost: 0.00633344 M: 10 delta: 1 time: 6.82407 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: 10.4384 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: 15.4673 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: 21.4219 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: 30.2834 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9956 accuracy: 0.00017291 cost: 0.046016 M: 21.1572 delta: 0.134172 time: 35.9823 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 36.289999999999964
Index size:  32164.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0024300000
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3500000000, query time of that 0.0594439170, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1973.86 < 2430.26
  -> Decision False in time 0.0100000000, query time of that 0.0019152490, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2215.13 < 2389.19
  -> Decision False in time 1.2300000000, query time of that 0.2033875870, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Accept!
  -> Decision True in time 3.3800000000, query time of that 0.0713044220, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1150.78 < 1168.91
  -> Decision False in time 3.0200000000, query time of that 0.0658991690, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
970.335 < 1041.21
  -> Decision False in time 18.1300000000, query time of that 0.3943475470, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1082.22 < 1090.33
  -> Decision False in time 3.7500000000, query time of that 0.0083581610, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1603.59 < 1633.57
  -> Decision False in time 5.1300000000, query time of that 0.0132018770, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1536.83 < 1659.66
  -> Decision False in time 9.4400000000, query time of that 0.0211648710, 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: 1.7295 cost: 0.00633344 M: 10 delta: 1 time: 6.82603 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: 10.4402 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: 15.4704 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: 21.4266 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: 30.299 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 30.58000000000004
Index size:  25216.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0072953333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3500000000, query time of that 0.0481242110, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1389.16 < 1405.84
  -> Decision False in time 0.2000000000, query time of that 0.0280502130, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2407.12 < 2861.98
  -> Decision False in time 0.0000000000, query time of that 0.0010804290, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1583.02 < 1602.76
  -> Decision False in time 1.0200000000, query time of that 0.0182666250, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
2405.83 < 3169.57
  -> Decision False in time 0.8900000000, query time of that 0.0160012790, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1129.44 < 1139.53
  -> Decision False in time 0.3500000000, query time of that 0.0078220160, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1839.29 < 1851.77
  -> Decision False in time 3.6100000000, query time of that 0.0068040630, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1037.13 < 1128.81
  -> Decision False in time 1.4200000000, query time of that 0.0031148060, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1323.09 < 1357.43
  -> Decision False in time 8.4600000000, query time of that 0.0159244070, 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: 6.8244 one-recall: 0 one-ratio: 1.87159
iteration: 2 recall: 0.0744 accuracy: 0.55721 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4374 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: 15.4679 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: 21.4234 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: 30.286 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9948 accuracy: 0.000305712 cost: 0.0460245 M: 21.1589 delta: 0.134158 time: 35.9817 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 36.289999999999964
Index size:  32160.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0004706667
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3700000000, query time of that 0.0710332490, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Accept!
  -> Decision True in time 3.6800000000, query time of that 0.7085008260, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1762.49 < 2123.36
  -> Decision False in time 6.0500000000, query time of that 1.1668608660, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Accept!
  -> Decision True in time 3.4300000000, query time of that 0.0829512740, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1646.57 < 1687.91
  -> Decision False in time 25.5700000000, query time of that 0.6310427070, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1269.12 < 1295.42
  -> Decision False in time 5.5100000000, query time of that 0.1419846380, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Accept!
  -> Decision True in time 33.3400000000, query time of that 0.0826500870, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1776.64 < 1815.86
  -> Decision False in time 38.7200000000, query time of that 0.0999663480, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1175.39 < 1175.71
  -> Decision False in time 45.7800000000, query time of that 0.1160376200, 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: 1.79013 cost: 0.00633344 M: 10 delta: 1 time: 6.8235 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: 10.4386 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: 15.4668 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: 21.4195 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: 30.2807 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:  25212.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0006256667
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3600000000, query time of that 0.0727660080, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Accept!
  -> Decision True in time 3.6300000000, query time of that 0.6920565910, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2261.47 < 2369.69
  -> Decision False in time 1.0200000000, query time of that 0.1963581950, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
2039.96 < 2047.67
  -> Decision False in time 0.3000000000, query time of that 0.0076829420, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1552.54 < 1741.71
  -> Decision False in time 13.8000000000, query time of that 0.3362335120, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
2164.17 < 2238.41
  -> Decision False in time 7.7200000000, query time of that 0.1904750370, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1257.16 < 1282.65
  -> Decision False in time 7.3000000000, query time of that 0.0185950590, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1284.33 < 1312.81
  -> Decision False in time 8.8100000000, query time of that 0.0244895240, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1852.79 < 1979.55
  -> Decision False in time 1.1000000000, query time of that 0.0032389450, 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: 6.82542 one-recall: 0 one-ratio: 1.98489
iteration: 2 recall: 0.066 accuracy: 0.617249 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4401 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: 15.4675 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: 21.4191 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: 30.2792 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: 35.9699 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: 37.3293 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 37.63000000000011
Index size:  35156.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0049670000
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3500000000, query time of that 0.0517653750, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1550.91 < 1566.72
  -> Decision False in time 0.5300000000, query time of that 0.0746385080, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1039.81 < 1048.92
  -> Decision False in time 0.4400000000, query time of that 0.0653820910, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Accept!
  -> Decision True in time 3.3800000000, query time of that 0.0588349560, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1670.79 < 1760.42
  -> Decision False in time 1.2200000000, query time of that 0.0225111380, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1308.42 < 1351.76
  -> Decision False in time 3.8700000000, query time of that 0.0709867380, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1025.22 < 1025.68
  -> Decision False in time 2.1900000000, query time of that 0.0037680960, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1712.44 < 1746.27
  -> Decision False in time 2.1000000000, query time of that 0.0046078200, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
784.337 < 804.105
  -> Decision False in time 10.4900000000, query time of that 0.0193896680, 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: 0.0084 accuracy: 1.69404 cost: 0.00633344 M: 10 delta: 1 time: 6.82226 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: 10.4368 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: 15.4622 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: 21.4119 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: 30.27 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 30.549999999999955
Index size:  25216.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0043096667
  Testing...
|S| = 98
|T| = 1411
Reject!
1094.8 < 1298.81
  -> Decision False in time 0.0700000000, query time of that 0.0102287110, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1625.61 < 1671.25
  -> Decision False in time 0.8400000000, query time of that 0.1093275890, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1070.61 < 1079.68
  -> Decision False in time 0.3400000000, query time of that 0.0437820300, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1083.37 < 1189.14
  -> Decision False in time 0.0900000000, query time of that 0.0015334820, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
686.85 < 1257.56
  -> Decision False in time 3.4200000000, query time of that 0.0557875140, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1574.14 < 1583.83
  -> Decision False in time 0.7800000000, query time of that 0.0126700530, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1122.28 < 1206.22
  -> Decision False in time 7.6600000000, query time of that 0.0132409070, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1601.73 < 1612.19
  -> Decision False in time 10.8800000000, query time of that 0.0189971610, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
2491.58 < 2503.37
  -> Decision False in time 2.4200000000, query time of that 0.0045856210, 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: 0.0056 accuracy: 1.68503 cost: 0.00633344 M: 10 delta: 1 time: 6.82293 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: 10.4381 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: 15.4646 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: 21.4189 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: 30.2849 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:  25216.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0016166667
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3400000000, query time of that 0.0507119560, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1848.11 < 1873.88
  -> Decision False in time 0.1300000000, query time of that 0.0189697960, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2167.17 < 2192.87
  -> Decision False in time 0.3600000000, query time of that 0.0552890380, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1559.63 < 1612.38
  -> Decision False in time 3.0700000000, query time of that 0.0587775000, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
2082.45 < 2141.19
  -> Decision False in time 5.2600000000, query time of that 0.0965545820, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1095.78 < 1190.85
  -> Decision False in time 0.0500000000, query time of that 0.0011688120, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1125.13 < 1132.49
  -> Decision False in time 2.0900000000, query time of that 0.0049123990, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1444.74 < 1454.42
  -> Decision False in time 24.7600000000, query time of that 0.0487705960, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1465.19 < 1496.67
  -> Decision False in time 1.4600000000, query time of that 0.0032160200, 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: 0.006 accuracy: 3.58104 cost: 0.00633344 M: 10 delta: 1 time: 6.82385 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: 10.4373 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: 15.4638 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: 21.4165 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: 30.2774 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:  25208.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0006283333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3700000000, query time of that 0.0755089740, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
2140.57 < 2359.52
  -> Decision False in time 1.3400000000, query time of that 0.2752713810, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2113.47 < 2209.19
  -> Decision False in time 4.4300000000, query time of that 0.9081491020, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Accept!
  -> Decision True in time 3.4100000000, query time of that 0.0917370690, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1572.65 < 1675.27
  -> Decision False in time 3.8600000000, query time of that 0.0978873870, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1043.14 < 1048.88
  -> Decision False in time 1.2500000000, query time of that 0.0332005290, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Accept!
  -> Decision True in time 33.0600000000, query time of that 0.0912611230, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
2066.41 < 2209.19
  -> Decision False in time 7.7600000000, query time of that 0.0239560430, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1635.18 < 1689.94
  -> Decision False in time 45.1100000000, query time of that 0.1227927110, with c1=5.0000000000, c2=0.1000000000
