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', 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', 50, {'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', 20, {'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', 10, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 1, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 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', 100, {'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', 40, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 60, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 90, {'reverse': -1}, False])]
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: 0.0044 accuracy: 1.65812 cost: 0.00633344 M: 10 delta: 1 time: 6.87386 one-recall: 0 one-ratio: 2.05485
iteration: 2 recall: 0.0664 accuracy: 0.581012 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4867 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: 15.5116 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: 21.46 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: 30.316 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: 36.0058 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: 0.0046996667
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3500000000, query time of that 0.0472073060, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1900.92 < 1922.63
  -> Decision False in time 1.1300000000, query time of that 0.1624334410, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2460.02 < 2515.5
  -> Decision False in time 0.1300000000, query time of that 0.0191678250, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1174.48 < 1197.24
  -> Decision False in time 0.1600000000, query time of that 0.0034121250, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1827.11 < 1827.11
  -> Decision False in time 7.8800000000, query time of that 0.1381537490, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1318.14 < 1328.04
  -> Decision False in time 4.8600000000, query time of that 0.0836936870, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
2006.39 < 2085.58
  -> Decision False in time 0.3800000000, query time of that 0.0010801040, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1631.52 < 1633.22
  -> Decision False in time 0.0400000000, query time of that 0.0006502140, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1730.39 < 1756.2
  -> Decision False in time 3.4800000000, query time of that 0.0068719680, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 30, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 30, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.0076 accuracy: 1.64122 cost: 0.00633344 M: 10 delta: 1 time: 6.82527 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: 10.4363 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: 15.4607 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: 21.4101 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: 30.2673 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 30.540000000000006
Index size:  29680.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0016166667
  Testing...
|S| = 98
|T| = 1411
Reject!
1576.89 < 1621.11
  -> Decision False in time 0.1700000000, query time of that 0.0251858150, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1177.82 < 1234.47
  -> Decision False in time 2.4200000000, query time of that 0.3572459520, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2089.38 < 2177.41
  -> Decision False in time 0.1800000000, query time of that 0.0283629520, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1667.99 < 1813.53
  -> Decision False in time 0.6500000000, query time of that 0.0126736710, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1131.27 < 1152.32
  -> Decision False in time 0.6900000000, query time of that 0.0123680280, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1694.86 < 1763.2
  -> Decision False in time 0.1900000000, query time of that 0.0042175820, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1443.82 < 1451.22
  -> Decision False in time 4.8600000000, query time of that 0.0087169000, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1343.12 < 1346.08
  -> Decision False in time 1.0500000000, query time of that 0.0021392530, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1543.42 < 1553.15
  -> Decision False in time 5.4200000000, query time of that 0.0100003310, 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: 0.0064 accuracy: 1.75244 cost: 0.00633344 M: 10 delta: 1 time: 6.82648 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: 10.4376 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: 15.4625 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: 21.4106 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: 30.2713 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 30.54000000000002
Index size:  29680.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0011053333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3600000000, query time of that 0.0631652870, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1863.49 < 1864.61
  -> Decision False in time 0.9300000000, query time of that 0.1593299240, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1557.4 < 1704.93
  -> Decision False in time 2.9200000000, query time of that 0.4828483480, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1683.4 < 1695.76
  -> Decision False in time 0.2500000000, query time of that 0.0048057850, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
966.63 < 971.512
  -> Decision False in time 0.8100000000, query time of that 0.0171249030, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
2004.68 < 2062.79
  -> Decision False in time 1.4000000000, query time of that 0.0297040440, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1943.73 < 1994.94
  -> Decision False in time 4.6300000000, query time of that 0.0103552850, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1758.63 < 1817.66
  -> Decision False in time 17.5800000000, query time of that 0.0385364510, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1683.4 < 1695.76
  -> Decision False in time 21.8400000000, query time of that 0.0471025610, 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: 0.0024 accuracy: 1.88913 cost: 0.00633344 M: 10 delta: 1 time: 6.83196 one-recall: 0 one-ratio: 2.13154
iteration: 2 recall: 0.0708 accuracy: 0.607821 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4413 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: 15.4639 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: 21.4115 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: 30.2647 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9964 accuracy: 0.000106052 cost: 0.046019 M: 21.1579 delta: 0.134134 time: 35.9469 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 36.25
Index size:  36636.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0004723333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3700000000, query time of that 0.0736026440, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Accept!
  -> Decision True in time 3.6800000000, query time of that 0.7185483950, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1839.71 < 1873.88
  -> Decision False in time 1.2900000000, query time of that 0.2470783810, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1173.91 < 1175.71
  -> Decision False in time 1.7000000000, query time of that 0.0410160410, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1636.07 < 1691.94
  -> Decision False in time 14.2800000000, query time of that 0.3512698410, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1857.82 < 1886.37
  -> Decision False in time 1.0800000000, query time of that 0.0268410980, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1440.39 < 1461.41
  -> Decision False in time 14.3100000000, query time of that 0.0366688820, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1814.68 < 1827.77
  -> Decision False in time 9.5300000000, query time of that 0.0238551080, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1405.11 < 1456.69
  -> Decision False in time 13.2800000000, query time of that 0.0337081910, 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: 0.0068 accuracy: 1.62174 cost: 0.00633344 M: 10 delta: 1 time: 6.82408 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: 10.4344 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: 15.4576 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: 21.4047 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: 30.2566 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: 35.9441 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 36.25
Index size:  36636.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0015063333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3500000000, query time of that 0.0523573960, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1786.11 < 1878.33
  -> Decision False in time 2.5600000000, query time of that 0.3841084340, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1324.98 < 1333.37
  -> Decision False in time 2.1300000000, query time of that 0.3167778970, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Accept!
  -> Decision True in time 3.3800000000, query time of that 0.0605720980, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1274 < 1282.58
  -> Decision False in time 1.4200000000, query time of that 0.0265395880, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1306.89 < 1327.41
  -> Decision False in time 1.7100000000, query time of that 0.0328503780, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1059.17 < 1072.42
  -> Decision False in time 4.1000000000, query time of that 0.0087827790, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1192.49 < 1197.72
  -> Decision False in time 0.3500000000, query time of that 0.0015144520, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1633.11 < 1730.97
  -> Decision False in time 6.4600000000, query time of that 0.0130918580, 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: 0.0076 accuracy: 1.72037 cost: 0.00633344 M: 10 delta: 1 time: 6.82254 one-recall: 0 one-ratio: 1.95236
iteration: 2 recall: 0.0752 accuracy: 0.554068 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4318 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: 15.4539 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: 21.4001 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: 30.2537 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9952 accuracy: 0.000206068 cost: 0.0460244 M: 21.1593 delta: 0.134112 time: 35.9382 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 36.24000000000001
Index size:  36636.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0004413333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3800000000, query time of that 0.0725251680, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1579.81 < 1595.69
  -> Decision False in time 1.2500000000, query time of that 0.2499486560, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2299.62 < 2400.39
  -> Decision False in time 6.9000000000, query time of that 1.3694822040, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Accept!
  -> Decision True in time 3.4500000000, query time of that 0.0905915630, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1550.55 < 1559.88
  -> Decision False in time 3.1600000000, query time of that 0.0823314160, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1440.39 < 1461.41
  -> Decision False in time 2.4100000000, query time of that 0.0632467170, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1939.74 < 2021.51
  -> Decision False in time 12.0300000000, query time of that 0.0330930330, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1548.39 < 1555.66
  -> Decision False in time 4.4600000000, query time of that 0.0126238560, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1671.29 < 1734.53
  -> Decision False in time 20.5900000000, query time of that 0.0551549160, 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: 0.0052 accuracy: 1.54971 cost: 0.00633344 M: 10 delta: 1 time: 6.82312 one-recall: 0 one-ratio: 1.86151
iteration: 2 recall: 0.0744 accuracy: 0.522987 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4328 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: 15.4561 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: 21.4017 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: 30.2572 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 30.529999999999973
Index size:  29684.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0027950000
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3400000000, query time of that 0.0447062980, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1875.69 < 1877.47
  -> Decision False in time 1.1900000000, query time of that 0.1599474550, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1533.9 < 1588.39
  -> Decision False in time 0.0300000000, query time of that 0.0048406640, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1409.51 < 1413.69
  -> Decision False in time 0.0200000000, query time of that 0.0006272320, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1132.01 < 1134.54
  -> Decision False in time 0.0200000000, query time of that 0.0007837590, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1429.24 < 1432.78
  -> Decision False in time 4.7200000000, query time of that 0.0802818550, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1045.36 < 1088.63
  -> Decision False in time 0.3400000000, query time of that 0.0011990150, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
855.79 < 896.321
  -> Decision False in time 0.0900000000, query time of that 0.0003251500, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1976.05 < 2078.78
  -> Decision False in time 4.0900000000, query time of that 0.0071846670, 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: 0.0064 accuracy: 1.66082 cost: 0.00633344 M: 10 delta: 1 time: 6.82282 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: 10.4341 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: 15.4567 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: 21.4028 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: 30.2573 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9956 accuracy: 0.00017291 cost: 0.046016 M: 21.1572 delta: 0.134172 time: 35.9501 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 36.25
Index size:  36624.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0061050000
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3500000000, query time of that 0.0541272510, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
2524.26 < 3429.95
  -> Decision False in time 1.3000000000, query time of that 0.1908092950, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2930.47 < 3133.16
  -> Decision False in time 0.1900000000, query time of that 0.0285935570, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1535.89 < 1556.78
  -> Decision False in time 2.1000000000, query time of that 0.0399963750, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1042.44 < 1082.54
  -> Decision False in time 1.8200000000, query time of that 0.0348949110, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1046.33 < 1048.9
  -> Decision False in time 1.4900000000, query time of that 0.0277636190, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1430.13 < 1430.7
  -> Decision False in time 0.6100000000, query time of that 0.0014292420, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1721.25 < 1742.25
  -> Decision False in time 2.3300000000, query time of that 0.0042774730, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1228.27 < 1240.75
  -> Decision False in time 1.4000000000, query time of that 0.0036541440, 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: 1.7295 cost: 0.00633344 M: 10 delta: 1 time: 6.82106 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: 10.4296 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: 15.4526 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: 21.401 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: 30.2634 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 30.539999999999964
Index size:  29688.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0057570000
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3400000000, query time of that 0.0431298860, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1530.48 < 1557.65
  -> Decision False in time 0.5900000000, query time of that 0.0754898220, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2125.43 < 2796.82
  -> Decision False in time 0.6100000000, query time of that 0.0803691840, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1800.88 < 1812.61
  -> Decision False in time 0.0100000000, query time of that 0.0003875590, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1532.9 < 1560.51
  -> Decision False in time 1.6500000000, query time of that 0.0268180410, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1610.39 < 1619.93
  -> Decision False in time 0.5900000000, query time of that 0.0085609910, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1422.94 < 1423.64
  -> Decision False in time 2.0500000000, query time of that 0.0034660820, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1828.89 < 1873.43
  -> Decision False in time 2.8100000000, query time of that 0.0051236060, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
2541.51 < 2559.69
  -> Decision False in time 2.8000000000, query time of that 0.0051315400, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 3, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 3, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.0052 accuracy: 1.68406 cost: 0.00633344 M: 10 delta: 1 time: 6.82335 one-recall: 0 one-ratio: 1.87159
iteration: 2 recall: 0.0744 accuracy: 0.55721 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4326 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: 15.457 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: 21.4047 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: 30.2595 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9948 accuracy: 0.000305712 cost: 0.0460245 M: 21.1589 delta: 0.134158 time: 35.9492 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 36.260000000000105
Index size:  36632.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0027223333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3400000000, query time of that 0.0472049040, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1355.05 < 1500.83
  -> Decision False in time 0.3400000000, query time of that 0.0476149850, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1738.23 < 1775.96
  -> Decision False in time 0.9000000000, query time of that 0.1257713960, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1057.26 < 1064.63
  -> Decision False in time 0.1100000000, query time of that 0.0021168010, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1451.21 < 1458.53
  -> Decision False in time 0.5600000000, query time of that 0.0096601540, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1323.63 < 1342.7
  -> Decision False in time 1.1600000000, query time of that 0.0198617230, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
2127.32 < 2162.39
  -> Decision False in time 9.2400000000, query time of that 0.0169451780, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
2307.26 < 2341.12
  -> Decision False in time 5.6300000000, query time of that 0.0102888100, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1259.27 < 1262.56
  -> Decision False in time 3.2800000000, query time of that 0.0060613600, 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: 0.0056 accuracy: 1.79013 cost: 0.00633344 M: 10 delta: 1 time: 6.82144 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: 10.4319 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: 15.4532 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: 21.3993 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: 30.2492 one-recall: 1 one-ratio: 1
Graph completion with reverse edges...

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Reranking edges...

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 30.50999999999999
Index size:  29680.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0006290000
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3800000000, query time of that 0.0787050680, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Accept!
  -> Decision True in time 3.7100000000, query time of that 0.7646885150, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1888.72 < 1899.57
  -> Decision False in time 2.9500000000, query time of that 0.6123652990, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1722.21 < 1740.96
  -> Decision False in time 2.3900000000, query time of that 0.0644614350, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1302.52 < 1389
  -> Decision False in time 5.0100000000, query time of that 0.1385260920, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
2241.04 < 2369.69
  -> Decision False in time 5.4200000000, query time of that 0.1506445730, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
2322.89 < 2330.58
  -> Decision False in time 12.2400000000, query time of that 0.0336970220, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1129.65 < 1130.39
  -> Decision False in time 7.4800000000, query time of that 0.0216446220, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1747.03 < 1762.46
  -> Decision False in time 45.1600000000, query time of that 0.1241836780, 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: 0.0072 accuracy: 1.83983 cost: 0.00633344 M: 10 delta: 1 time: 6.82623 one-recall: 0 one-ratio: 1.98489
iteration: 2 recall: 0.066 accuracy: 0.617249 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4373 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: 15.4608 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: 21.408 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: 30.2615 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: 35.9484 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: 37.3041 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 37.62000000000012
Index size:  39624.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0023310000
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3500000000, query time of that 0.0524271520, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Accept!
  -> Decision True in time 3.4400000000, query time of that 0.5060979800, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1742.48 < 1750.13
  -> Decision False in time 0.8200000000, query time of that 0.1208879740, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1270.19 < 1286.35
  -> Decision False in time 0.8700000000, query time of that 0.0157528010, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1040.61 < 1053.56
  -> Decision False in time 0.4000000000, query time of that 0.0079869230, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1876.61 < 1892.7
  -> Decision False in time 0.0200000000, query time of that 0.0009621530, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1631.6 < 1647.49
  -> Decision False in time 0.5100000000, query time of that 0.0012101790, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1712.69 < 1735.39
  -> Decision False in time 9.1200000000, query time of that 0.0175454960, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1375.15 < 1383.57
  -> Decision False in time 2.8600000000, query time of that 0.0059371950, 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: 0.0084 accuracy: 1.69404 cost: 0.00633344 M: 10 delta: 1 time: 6.82593 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: 10.437 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: 15.4607 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: 21.4091 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: 30.2658 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 30.54000000000019
Index size:  29684.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0028936667
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3500000000, query time of that 0.0569309140, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
2135.5 < 2726.44
  -> Decision False in time 0.0700000000, query time of that 0.0108511250, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2519.33 < 2756.43
  -> Decision False in time 0.2400000000, query time of that 0.0376659620, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1354.41 < 1362.1
  -> Decision False in time 1.1100000000, query time of that 0.0231412010, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1949.65 < 2354.62
  -> Decision False in time 4.9200000000, query time of that 0.0960177810, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
2033.7 < 2092.54
  -> Decision False in time 2.2100000000, query time of that 0.0433982380, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1362.28 < 1367.63
  -> Decision False in time 0.8000000000, query time of that 0.0018272120, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1991.98 < 2007.12
  -> Decision False in time 2.7800000000, query time of that 0.0058824210, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1899.45 < 1956.35
  -> Decision False in time 3.2000000000, query time of that 0.0078900920, 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: 0.0056 accuracy: 1.68503 cost: 0.00633344 M: 10 delta: 1 time: 6.82527 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: 10.4373 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: 15.4627 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: 21.4125 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: 30.274 one-recall: 1 one-ratio: 1
Graph completion with reverse edges...

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Reranking edges...

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 30.559999999999945
Index size:  29688.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0013173333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3600000000, query time of that 0.0652537480, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
2513.45 < 2617.58
  -> Decision False in time 2.1200000000, query time of that 0.3710334630, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2376.13 < 2617.58
  -> Decision False in time 9.0500000000, query time of that 1.5686175610, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1487.39 < 1498.13
  -> Decision False in time 0.1400000000, query time of that 0.0041628400, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
2365.33 < 2373.64
  -> Decision False in time 1.3600000000, query time of that 0.0303481420, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1411.94 < 1426.69
  -> Decision False in time 0.8800000000, query time of that 0.0188335130, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
2464.76 < 2470.07
  -> Decision False in time 9.9900000000, query time of that 0.0228725510, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1487.39 < 1498.13
  -> Decision False in time 19.9800000000, query time of that 0.0454385940, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1466.99 < 1474.48
  -> Decision False in time 5.4500000000, query time of that 0.0124952710, 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: 0.006 accuracy: 3.58104 cost: 0.00633344 M: 10 delta: 1 time: 6.82661 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: 10.4381 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: 15.462 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: 21.4109 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: 30.2715 one-recall: 1 one-ratio: 1
Graph completion with reverse edges...

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Reranking edges...

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 30.549999999999955
Index size:  29680.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0006293333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3700000000, query time of that 0.0761654670, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Accept!
  -> Decision True in time 3.7200000000, query time of that 0.7476540380, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1568.81 < 1598.48
  -> Decision False in time 18.4800000000, query time of that 3.6735931830, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Accept!
  -> Decision True in time 3.4300000000, query time of that 0.0881286800, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
2133.82 < 2141.19
  -> Decision False in time 1.3400000000, query time of that 0.0362407070, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1576.23 < 1742.7
  -> Decision False in time 0.6300000000, query time of that 0.0165625940, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Accept!
  -> Decision True in time 33.1100000000, query time of that 0.0856596910, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1711.8 < 1751.76
  -> Decision False in time 36.8500000000, query time of that 0.0979899700, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1298.49 < 1308.39
  -> Decision False in time 9.9200000000, query time of that 0.0272439420, with c1=5.0000000000, c2=0.1000000000
