copying files to /scratch...
starting benchmark...
/scratch/knn/venv/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  from ._conv import register_converters as _register_converters
running only kgraph
order: [Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 3, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 30, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 2, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 70, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 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', 60, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 20, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 5, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 80, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 100, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 1, {'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', 4, {'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', 3, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 3, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.0044 accuracy: 1.65812 cost: 0.00633344 M: 10 delta: 1 time: 6.88194 one-recall: 0 one-ratio: 2.05485
iteration: 2 recall: 0.0664 accuracy: 0.581012 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4989 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: 15.5263 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: 21.4753 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: 30.3342 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: 36.0283 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 36.34
Index size:  98596.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0027053333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3500000000, query time of that 0.0490048100, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1113.07 < 1127.56
  -> Decision False in time 0.4300000000, query time of that 0.0594584430, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1401.61 < 1406.82
  -> Decision False in time 1.5700000000, query time of that 0.2221537470, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1249.28 < 1257.45
  -> Decision False in time 3.0700000000, query time of that 0.0495001160, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1025.22 < 1025.68
  -> Decision False in time 0.2400000000, query time of that 0.0041973760, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1675.48 < 1677.44
  -> Decision False in time 0.4500000000, query time of that 0.0091613150, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1879.76 < 1888.31
  -> Decision False in time 1.7500000000, query time of that 0.0035744080, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1784.68 < 1796.87
  -> Decision False in time 1.9300000000, query time of that 0.0039856090, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1605.37 < 1614.99
  -> Decision False in time 0.3500000000, query time of that 0.0015064730, 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: 6.83212 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: 10.4474 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: 15.4732 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: 21.4225 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: 30.2873 one-recall: 1 one-ratio: 1
Graph completion with reverse edges...

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

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 30.560000000000002
Index size:  29684.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0016166667
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3400000000, query time of that 0.0523215850, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1525.63 < 1531.22
  -> Decision False in time 1.3300000000, query time of that 0.1989047160, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2155.08 < 2187.53
  -> Decision False in time 2.5600000000, query time of that 0.3831648200, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1801.57 < 1864.53
  -> Decision False in time 1.7000000000, query time of that 0.0308171860, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1451.62 < 1451.8
  -> Decision False in time 0.4200000000, query time of that 0.0082030470, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1387.43 < 1404.2
  -> Decision False in time 1.3000000000, query time of that 0.0244765500, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1231.96 < 1250.77
  -> Decision False in time 2.7000000000, query time of that 0.0067699690, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1560.42 < 1596.72
  -> Decision False in time 17.6100000000, query time of that 0.0339243230, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1340.99 < 1358.98
  -> Decision False in time 1.4000000000, query time of that 0.0029672350, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 2, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 2, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.0064 accuracy: 1.75244 cost: 0.00633344 M: 10 delta: 1 time: 6.82801 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: 10.4409 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: 15.4649 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: 21.413 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: 30.2697 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 30.55000000000001
Index size:  29684.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0062866667
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3400000000, query time of that 0.0460001840, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1138.32 < 1221.07
  -> Decision False in time 0.1900000000, query time of that 0.0245897190, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2413.47 < 2622.37
  -> Decision False in time 0.0300000000, query time of that 0.0051104530, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1266.51 < 1289.32
  -> Decision False in time 0.9400000000, query time of that 0.0144634310, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1585.69 < 1608.29
  -> Decision False in time 0.2200000000, query time of that 0.0037329250, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1426.17 < 1476.3
  -> Decision False in time 0.1200000000, query time of that 0.0021655160, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1237.79 < 1267.27
  -> Decision False in time 7.8700000000, query time of that 0.0135898650, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1488.88 < 1489.91
  -> Decision False in time 1.4400000000, query time of that 0.0029102660, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1052.57 < 1099.87
  -> Decision False in time 4.1300000000, query time of that 0.0077796210, 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: 6.83395 one-recall: 0 one-ratio: 2.13154
iteration: 2 recall: 0.0708 accuracy: 0.607821 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4467 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: 15.4703 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: 21.4173 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: 30.2743 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9964 accuracy: 0.000106052 cost: 0.046019 M: 21.1579 delta: 0.134134 time: 35.9608 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 36.25999999999999
Index size:  36632.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0004723333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3700000000, query time of that 0.0709249890, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
2024.95 < 2031.72
  -> Decision False in time 3.6800000000, query time of that 0.6965184900, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1840.63 < 1942.74
  -> Decision False in time 4.1300000000, query time of that 0.7924832410, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1214.44 < 1227.54
  -> Decision False in time 0.1900000000, query time of that 0.0042984280, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1871.78 < 1903.03
  -> Decision False in time 6.3700000000, query time of that 0.1486607400, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
2042.89 < 2082.65
  -> Decision False in time 0.0700000000, query time of that 0.0015959740, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Accept!
  -> Decision True in time 33.5900000000, query time of that 0.0870426610, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1626.28 < 1660.89
  -> Decision False in time 17.6500000000, query time of that 0.0458212390, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1171.45 < 1221.51
  -> Decision False in time 61.5800000000, query time of that 0.1508338170, 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: 0.0068 accuracy: 1.62174 cost: 0.00633344 M: 10 delta: 1 time: 6.82745 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: 10.4389 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: 15.4626 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: 21.4095 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: 30.2637 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: 35.9554 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 36.25999999999999
Index size:  36632.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0007680000
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3700000000, query time of that 0.0631000400, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1526.93 < 1607.58
  -> Decision False in time 2.8500000000, query time of that 0.4997681240, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1840.63 < 1942.74
  -> Decision False in time 5.6700000000, query time of that 0.9964812010, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Accept!
  -> Decision True in time 3.4100000000, query time of that 0.0756072200, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1892.44 < 1980.52
  -> Decision False in time 0.5300000000, query time of that 0.0129330890, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
2344.43 < 2402.03
  -> Decision False in time 8.2500000000, query time of that 0.1794768600, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1329.49 < 1337.42
  -> Decision False in time 7.2900000000, query time of that 0.0170095210, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1648.46 < 1691.94
  -> Decision False in time 5.8000000000, query time of that 0.0150309960, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
2293.46 < 2297.27
  -> Decision False in time 32.2100000000, query time of that 0.0717851090, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 40, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 40, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.0076 accuracy: 1.72037 cost: 0.00633344 M: 10 delta: 1 time: 6.8299 one-recall: 0 one-ratio: 1.95236
iteration: 2 recall: 0.0752 accuracy: 0.554068 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4433 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: 15.4683 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: 21.417 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: 30.2743 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9952 accuracy: 0.000206068 cost: 0.0460244 M: 21.1593 delta: 0.134112 time: 35.9617 one-recall: 1 one-ratio: 1
Graph completion with reverse edges...

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

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 36.270000000000095
Index size:  36632.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0024480000
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3600000000, query time of that 0.0601231100, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
2368.66 < 2706
  -> Decision False in time 0.9400000000, query time of that 0.1614305380, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2023.32 < 2645.3
  -> Decision False in time 1.3200000000, query time of that 0.2208142790, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Accept!
  -> Decision True in time 3.4300000000, query time of that 0.0735259190, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1891.74 < 1903.03
  -> Decision False in time 14.6600000000, query time of that 0.3074491850, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1751.25 < 1778
  -> Decision False in time 7.5300000000, query time of that 0.1590666360, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1488.91 < 1528.7
  -> Decision False in time 23.9800000000, query time of that 0.0523212130, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1784.54 < 1785.42
  -> Decision False in time 6.3000000000, query time of that 0.0155206280, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
2310.47 < 2326.52
  -> Decision False in time 15.6800000000, query time of that 0.0347773180, 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: 0.0052 accuracy: 1.54971 cost: 0.00633344 M: 10 delta: 1 time: 6.8295 one-recall: 0 one-ratio: 1.86151
iteration: 2 recall: 0.0744 accuracy: 0.522987 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4415 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: 15.4649 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: 21.4153 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: 30.271 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 30.539999999999964
Index size:  29680.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0013196667
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3700000000, query time of that 0.0643093450, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Accept!
  -> Decision True in time 3.6200000000, query time of that 0.6328728210, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2052.53 < 2373.64
  -> Decision False in time 1.0400000000, query time of that 0.1785298410, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1544.9 < 1579.89
  -> Decision False in time 0.0300000000, query time of that 0.0016479850, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1628.61 < 1638.08
  -> Decision False in time 6.0600000000, query time of that 0.1351236650, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
2063.94 < 2081.87
  -> Decision False in time 0.6700000000, query time of that 0.0151328170, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1487.34 < 1500.77
  -> Decision False in time 5.5300000000, query time of that 0.0117467730, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1331.2 < 1372.77
  -> Decision False in time 10.5400000000, query time of that 0.0256524220, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1909.78 < 1911.27
  -> Decision False in time 3.0700000000, query time of that 0.0073508610, 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: 1.66082 cost: 0.00633344 M: 10 delta: 1 time: 6.83015 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: 10.4438 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: 15.4735 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: 21.4231 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: 30.2809 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9956 accuracy: 0.00017291 cost: 0.046016 M: 21.1572 delta: 0.134172 time: 35.9766 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 36.27999999999997
Index size:  36636.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0014896667
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3500000000, query time of that 0.0546516490, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
2213.41 < 2337.92
  -> Decision False in time 1.7900000000, query time of that 0.2663425950, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1013.59 < 1019.61
  -> Decision False in time 0.9000000000, query time of that 0.1321367220, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
2139.03 < 2596.61
  -> Decision False in time 0.0800000000, query time of that 0.0020616700, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1445.32 < 1498.82
  -> Decision False in time 0.9400000000, query time of that 0.0170583980, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1467.2 < 1496.89
  -> Decision False in time 0.8700000000, query time of that 0.0149540790, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1060.22 < 1086.14
  -> Decision False in time 29.3600000000, query time of that 0.0564878210, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1796.29 < 1885.85
  -> Decision False in time 0.7100000000, query time of that 0.0022559990, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1758.63 < 1817.66
  -> Decision False in time 0.7200000000, query time of that 0.0017600250, 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: 6.82688 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: 10.4389 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: 15.4628 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: 21.4109 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: 30.2729 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 30.540000000000077
Index size:  29684.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0057570000
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3400000000, query time of that 0.0435139420, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
2527.22 < 2555.76
  -> Decision False in time 0.0500000000, query time of that 0.0071348240, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1820.86 < 2728.12
  -> Decision False in time 0.0600000000, query time of that 0.0088398980, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1210.94 < 1300.56
  -> Decision False in time 0.8300000000, query time of that 0.0136044800, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1273.84 < 1323.73
  -> Decision False in time 0.4200000000, query time of that 0.0075027150, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1173.9 < 1190.83
  -> Decision False in time 1.7200000000, query time of that 0.0281074560, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1600.32 < 1605.18
  -> Decision False in time 2.7800000000, query time of that 0.0051714760, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1287.79 < 1297.17
  -> Decision False in time 3.1100000000, query time of that 0.0056756840, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
2227.05 < 2319.12
  -> Decision False in time 1.1500000000, query time of that 0.0019233040, 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: 0.0052 accuracy: 1.68406 cost: 0.00633344 M: 10 delta: 1 time: 6.82853 one-recall: 0 one-ratio: 1.87159
iteration: 2 recall: 0.0744 accuracy: 0.55721 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4431 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: 15.4689 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: 21.4197 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: 30.2805 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9948 accuracy: 0.000305712 cost: 0.0460245 M: 21.1589 delta: 0.134158 time: 35.9794 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 36.27999999999997
Index size:  36632.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0004400000
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3800000000, query time of that 0.0786121220, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
2142.76 < 2144.36
  -> Decision False in time 3.7700000000, query time of that 0.7484883440, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1094.49 < 1119.21
  -> Decision False in time 5.5800000000, query time of that 1.1064875330, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Accept!
  -> Decision True in time 3.4500000000, query time of that 0.0871866770, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1747.7 < 1825.69
  -> Decision False in time 3.0800000000, query time of that 0.0799184190, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1480.16 < 1540.77
  -> Decision False in time 15.1500000000, query time of that 0.3857485690, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1821.34 < 1830.03
  -> Decision False in time 17.1600000000, query time of that 0.0458146460, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
2011.55 < 2053.94
  -> Decision False in time 7.9600000000, query time of that 0.0207680170, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1214.98 < 1248.17
  -> Decision False in time 31.6000000000, query time of that 0.0838565940, 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: 6.83121 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: 10.442 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: 15.4653 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: 21.412 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: 30.2647 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: 0.0006290000
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3800000000, query time of that 0.0785755560, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Accept!
  -> Decision True in time 3.7600000000, query time of that 0.7665867420, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2085.75 < 2430.26
  -> Decision False in time 4.3700000000, query time of that 0.8897341030, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Accept!
  -> Decision True in time 3.4400000000, query time of that 0.0883740130, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
2711.59 < 2713.64
  -> Decision False in time 4.1300000000, query time of that 0.1122386840, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
2461.7 < 2592.94
  -> Decision False in time 34.2900000000, query time of that 0.9057488420, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1257.04 < 1279.71
  -> Decision False in time 25.1800000000, query time of that 0.0658790230, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
2107.19 < 2180.38
  -> Decision False in time 16.7800000000, query time of that 0.0438049710, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
2449.96 < 2464.34
  -> Decision False in time 46.3800000000, query time of that 0.1248426810, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 1, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 1, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.0072 accuracy: 1.83983 cost: 0.00633344 M: 10 delta: 1 time: 6.82699 one-recall: 0 one-ratio: 1.98489
iteration: 2 recall: 0.066 accuracy: 0.617249 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4396 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: 15.4649 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: 21.4116 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: 30.2694 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: 35.9589 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: 37.3185 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:  39640.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0063393333
  Testing...
|S| = 98
|T| = 1411
Reject!
3243.11 < 3332.91
  -> Decision False in time 0.2100000000, query time of that 0.0320513910, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
3081.48 < 3291.07
  -> Decision False in time 0.0200000000, query time of that 0.0039305480, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1197.73 < 1214.85
  -> Decision False in time 0.1800000000, query time of that 0.0260163500, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1665.83 < 1676.21
  -> Decision False in time 0.4000000000, query time of that 0.0077025430, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1073.19 < 1089.78
  -> Decision False in time 4.3800000000, query time of that 0.0847774110, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1678.58 < 1700.09
  -> Decision False in time 0.0900000000, query time of that 0.0021339850, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1504.34 < 1511.27
  -> Decision False in time 1.7300000000, query time of that 0.0043801880, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1843.32 < 1843.51
  -> Decision False in time 5.3900000000, query time of that 0.0096306600, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1074.65 < 1089.71
  -> Decision False in time 10.2900000000, query time of that 0.0196885750, 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: 0.0084 accuracy: 1.69404 cost: 0.00633344 M: 10 delta: 1 time: 6.83592 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: 10.4513 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: 15.475 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: 21.4235 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: 30.2837 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:  29688.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0027960000
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3400000000, query time of that 0.0455545740, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1974.83 < 2097.84
  -> Decision False in time 0.1000000000, query time of that 0.0123185820, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1415.19 < 1450.34
  -> Decision False in time 1.6500000000, query time of that 0.2205851420, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1369.95 < 1461.22
  -> Decision False in time 0.7400000000, query time of that 0.0129071580, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1803.91 < 1814.19
  -> Decision False in time 1.7700000000, query time of that 0.0297281770, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1436.67 < 1499.69
  -> Decision False in time 1.9500000000, query time of that 0.0320533580, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1320.71 < 1345.29
  -> Decision False in time 5.6300000000, query time of that 0.0104589360, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1305.86 < 1351.37
  -> Decision False in time 2.0400000000, query time of that 0.0035970340, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1235.85 < 1258.31
  -> Decision False in time 12.5900000000, query time of that 0.0228467720, 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: 0.0056 accuracy: 1.68503 cost: 0.00633344 M: 10 delta: 1 time: 6.83025 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: 10.4455 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: 15.471 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: 21.4228 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: 30.2878 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:  29684.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0040066667
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3400000000, query time of that 0.0493322300, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1619.13 < 1684.22
  -> Decision False in time 0.1500000000, query time of that 0.0203137310, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1112.13 < 1155.37
  -> Decision False in time 0.4100000000, query time of that 0.0558433730, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
725.673 < 1005.79
  -> Decision False in time 1.9200000000, query time of that 0.0346287580, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1927.48 < 1952.81
  -> Decision False in time 0.7100000000, query time of that 0.0112788980, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1113.37 < 1131.1
  -> Decision False in time 2.3200000000, query time of that 0.0370436000, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1479.31 < 1492.94
  -> Decision False in time 1.3500000000, query time of that 0.0028419170, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1916.29 < 1935.49
  -> Decision False in time 5.2300000000, query time of that 0.0083504600, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1150.02 < 1153.96
  -> Decision False in time 5.7000000000, query time of that 0.0091641850, 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: 6.8294 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: 10.443 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: 15.4668 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: 21.4149 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: 30.2699 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 0.0739495550, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Accept!
  -> Decision True in time 3.7100000000, query time of that 0.7321615650, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1633.11 < 1730.97
  -> Decision False in time 3.3000000000, query time of that 0.6505880150, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1578.34 < 1704.81
  -> Decision False in time 3.0000000000, query time of that 0.0780158160, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1711.86 < 1730.11
  -> Decision False in time 0.0800000000, query time of that 0.0029140490, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1392.08 < 1422.11
  -> Decision False in time 19.9300000000, query time of that 0.5077432590, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1794.3 < 1887.78
  -> Decision False in time 9.8300000000, query time of that 0.0269535330, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1278.91 < 1295.91
  -> Decision False in time 39.3700000000, query time of that 0.1084369460, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1269.17 < 1284.53
  -> Decision False in time 2.9100000000, query time of that 0.0072655890, with c1=5.0000000000, c2=0.1000000000
