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', 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', 30, {'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', 100, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 60, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 3, {'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', 1, {'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', 50, {'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', 10, {'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', 70, {'reverse': -1}, False])]
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: 0.0044 accuracy: 1.65812 cost: 0.00633344 M: 10 delta: 1 time: 6.87788 one-recall: 0 one-ratio: 2.05485
iteration: 2 recall: 0.0664 accuracy: 0.581012 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4927 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: 15.521 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: 30.342 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: 36.0492 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 36.36
Index size:  98596.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0025976667
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3400000000, query time of that 0.0490310930, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1353.29 < 1386.87
  -> Decision False in time 0.1700000000, query time of that 0.0240855700, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1233.41 < 1243.56
  -> Decision False in time 0.8700000000, query time of that 0.1239969140, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
2204.43 < 2204.93
  -> Decision False in time 0.3300000000, query time of that 0.0066111210, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1674.02 < 1691.96
  -> Decision False in time 1.8700000000, query time of that 0.0327453280, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1375.88 < 1379.45
  -> Decision False in time 2.9800000000, query time of that 0.0542272910, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1259.21 < 1286.48
  -> Decision False in time 0.1500000000, query time of that 0.0006032510, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1408.64 < 1440.39
  -> Decision False in time 2.5100000000, query time of that 0.0048590100, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1588.81 < 1600.38
  -> Decision False in time 0.3500000000, query time of that 0.0011800660, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 90, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 90, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.0076 accuracy: 1.64122 cost: 0.00633344 M: 10 delta: 1 time: 6.82504 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: 10.4357 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: 15.4598 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: 21.4129 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: 30.2737 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 30.549999999999997
Index size:  29680.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0006313333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3700000000, query time of that 0.0729676980, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
2233.28 < 2315.82
  -> Decision False in time 0.5600000000, query time of that 0.1097821910, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1837.25 < 2511.35
  -> Decision False in time 10.4700000000, query time of that 2.0717113200, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Accept!
  -> Decision True in time 3.4100000000, query time of that 0.0860233960, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1673.74 < 1684.6
  -> Decision False in time 0.9800000000, query time of that 0.0270183070, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1423.96 < 1426.32
  -> Decision False in time 12.6900000000, query time of that 0.3166785600, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1560.41 < 1648.12
  -> Decision False in time 24.1000000000, query time of that 0.0605054020, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1958.37 < 1963.06
  -> Decision False in time 15.5600000000, query time of that 0.0420963290, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1959.94 < 1974.52
  -> Decision False in time 6.0800000000, query time of that 0.0161140520, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 30, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 30, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.0064 accuracy: 1.75244 cost: 0.00633344 M: 10 delta: 1 time: 6.82665 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: 10.4378 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: 15.4619 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: 21.4104 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: 30.2703 one-recall: 1 one-ratio: 1
Graph completion with reverse edges...

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

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 30.539999999999992
Index size:  29684.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0016100000
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3500000000, query time of that 0.0523389720, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1879.65 < 1880.06
  -> Decision False in time 0.3500000000, query time of that 0.0541462670, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2264.34 < 2568.2
  -> Decision False in time 3.1900000000, query time of that 0.4783977040, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1254.34 < 1295.56
  -> Decision False in time 1.6200000000, query time of that 0.0301917150, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1757.64 < 1774.38
  -> Decision False in time 1.2300000000, query time of that 0.0230424580, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1393.92 < 1444.75
  -> Decision False in time 0.4200000000, query time of that 0.0083932530, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1958.94 < 1962.83
  -> Decision False in time 5.5000000000, query time of that 0.0117231320, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
2127.92 < 2184.57
  -> Decision False in time 0.6900000000, query time of that 0.0021096770, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1797.6 < 1914.29
  -> Decision False in time 0.1600000000, query time of that 0.0007223390, 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: 0.0024 accuracy: 1.88913 cost: 0.00633344 M: 10 delta: 1 time: 6.82419 one-recall: 0 one-ratio: 2.13154
iteration: 2 recall: 0.0708 accuracy: 0.607821 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4373 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: 15.4615 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: 21.4099 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: 30.2686 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9964 accuracy: 0.000106052 cost: 0.046019 M: 21.1579 delta: 0.134134 time: 35.9598 one-recall: 1 one-ratio: 1
Graph completion with reverse edges...

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

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 36.26999999999998
Index size:  36632.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0024473333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3600000000, query time of that 0.0609826080, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1970.06 < 2761.4
  -> Decision False in time 1.0900000000, query time of that 0.1837653750, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1901.85 < 1906.29
  -> Decision False in time 0.0900000000, query time of that 0.0142030470, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Accept!
  -> Decision True in time 3.3900000000, query time of that 0.0703746090, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1532.22 < 1533.97
  -> Decision False in time 5.6200000000, query time of that 0.1174186610, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1825.08 < 1832.22
  -> Decision False in time 2.2000000000, query time of that 0.0467672090, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Accept!
  -> Decision True in time 32.9700000000, query time of that 0.0734291370, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1291.36 < 1305.22
  -> Decision False in time 23.2400000000, query time of that 0.0495400550, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1780.78 < 1861.25
  -> Decision False in time 9.1300000000, query time of that 0.0198376400, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 100, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 100, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.0068 accuracy: 1.62174 cost: 0.00633344 M: 10 delta: 1 time: 6.82197 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: 10.4333 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: 15.4561 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: 21.4037 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: 30.2596 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: 35.9528 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 36.26000000000005
Index size:  36624.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0004683333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3800000000, query time of that 0.0828350730, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Accept!
  -> Decision True in time 3.7900000000, query time of that 0.8190751470, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1875.14 < 1905.52
  -> Decision False in time 5.4600000000, query time of that 1.1723577510, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
2357.01 < 2385.04
  -> Decision False in time 1.6400000000, query time of that 0.0457837270, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1636.07 < 1749.2
  -> Decision False in time 6.7100000000, query time of that 0.1858358180, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1936.46 < 1993.3
  -> Decision False in time 1.6300000000, query time of that 0.0472249320, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Accept!
  -> Decision True in time 33.3600000000, query time of that 0.0998194710, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1347.81 < 1359.81
  -> Decision False in time 49.4400000000, query time of that 0.1438332870, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1595.36 < 1621.97
  -> Decision False in time 1.3900000000, query time of that 0.0049069830, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 60, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 60, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.0076 accuracy: 1.72037 cost: 0.00633344 M: 10 delta: 1 time: 6.82833 one-recall: 0 one-ratio: 1.95236
iteration: 2 recall: 0.0752 accuracy: 0.554068 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4402 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: 15.4632 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: 21.4111 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: 30.2692 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9952 accuracy: 0.000206068 cost: 0.0460244 M: 21.1593 delta: 0.134112 time: 35.9574 one-recall: 1 one-ratio: 1
Graph completion with reverse edges...

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

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 36.26999999999998
Index size:  36644.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0010383333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3700000000, query time of that 0.0655648970, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
2162.77 < 2477.21
  -> Decision False in time 0.8800000000, query time of that 0.1629647380, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2085.86 < 2163.21
  -> Decision False in time 11.9700000000, query time of that 2.2440663810, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1556.73 < 1656.13
  -> Decision False in time 2.0900000000, query time of that 0.0511911220, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
2318.86 < 2336.64
  -> Decision False in time 0.7200000000, query time of that 0.0167811820, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1832.17 < 1864.97
  -> Decision False in time 2.8800000000, query time of that 0.0668743470, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
2097.89 < 2122.84
  -> Decision False in time 3.0700000000, query time of that 0.0078249840, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1303.18 < 1352.12
  -> Decision False in time 10.5700000000, query time of that 0.0260857190, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
2120.78 < 2132.31
  -> Decision False in time 11.8800000000, query time of that 0.0272862570, 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: 1.54971 cost: 0.00633344 M: 10 delta: 1 time: 6.82665 one-recall: 0 one-ratio: 1.86151
iteration: 2 recall: 0.0744 accuracy: 0.522987 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4398 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: 15.4657 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: 21.417 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: 30.2791 one-recall: 0.99 one-ratio: 1.00591
Graph completion with reverse edges...

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

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 30.549999999999955
Index size:  29684.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0043106667
  Testing...
|S| = 98
|T| = 1411
Reject!
1156.57 < 1207.48
  -> Decision False in time 0.1100000000, query time of that 0.0144515660, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1633.68 < 1737.65
  -> Decision False in time 0.8300000000, query time of that 0.1114040680, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1900.4 < 1930.03
  -> Decision False in time 1.6800000000, query time of that 0.2189699690, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1606.77 < 1610.3
  -> Decision False in time 0.4400000000, query time of that 0.0066200020, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1239.81 < 1307.55
  -> Decision False in time 1.8400000000, query time of that 0.0299930930, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1389.18 < 1464.02
  -> Decision False in time 0.6600000000, query time of that 0.0105174100, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1208.32 < 1209.54
  -> Decision False in time 0.0400000000, query time of that 0.0007312620, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1063.39 < 1096.02
  -> Decision False in time 3.7600000000, query time of that 0.0063854940, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1247.56 < 1257.9
  -> Decision False in time 0.3900000000, query time of that 0.0009753270, 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: 0.0064 accuracy: 1.66082 cost: 0.00633344 M: 10 delta: 1 time: 6.8239 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: 10.4357 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: 15.4599 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: 21.4095 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: 30.2674 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9956 accuracy: 0.00017291 cost: 0.046016 M: 21.1572 delta: 0.134172 time: 35.9615 one-recall: 1 one-ratio: 1
Graph completion with reverse edges...

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

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 36.26999999999998
Index size:  36620.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0044230000
  Testing...
|S| = 98
|T| = 1411
Reject!
2156.02 < 2260.8
  -> Decision False in time 0.1000000000, query time of that 0.0181594150, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
849.975 < 851.475
  -> Decision False in time 0.2800000000, query time of that 0.0530994040, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1180.41 < 1201.81
  -> Decision False in time 0.6700000000, query time of that 0.1215607990, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
730.54 < 781.103
  -> Decision False in time 0.0300000000, query time of that 0.0005016600, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
800.226 < 811.814
  -> Decision False in time 1.0400000000, query time of that 0.0247624880, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1305.67 < 1318.77
  -> Decision False in time 0.5800000000, query time of that 0.0141764270, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1733.46 < 1734.58
  -> Decision False in time 2.8200000000, query time of that 0.0072514120, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1152.12 < 1156.83
  -> Decision False in time 1.8300000000, query time of that 0.0048829560, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1387.46 < 1424.21
  -> Decision False in time 0.0400000000, query time of that 0.0014045000, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 1, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 1, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.006 accuracy: 1.7295 cost: 0.00633344 M: 10 delta: 1 time: 6.98751 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: 10.7305 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: 15.9125 one-recall: 0.61 one-ratio: 1.10382
iteration: 4 recall: 0.9288 accuracy: 0.00745011 cost: 0.024911 M: 11.7246 delta: 0.566202 time: 22.0422 one-recall: 0.95 one-ratio: 1.00729
iteration: 5 recall: 0.9908 accuracy: 0.000576763 cost: 0.0376936 M: 17.4264 delta: 0.224481 time: 31.1794 one-recall: 1 one-ratio: 1
Graph completion with reverse edges...

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

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 31.470000000000027
Index size:  29688.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0072953333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3400000000, query time of that 0.0508502020, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1725.97 < 3182.92
  -> Decision False in time 0.6100000000, query time of that 0.0858273690, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
3241.36 < 3371.68
  -> Decision False in time 0.3800000000, query time of that 0.0566273040, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1293.19 < 1295.99
  -> Decision False in time 0.8900000000, query time of that 0.0173525160, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1173.24 < 1184.34
  -> Decision False in time 0.3200000000, query time of that 0.0063248550, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1153.41 < 1159.73
  -> Decision False in time 0.7800000000, query time of that 0.0149189580, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1156.81 < 1162.72
  -> Decision False in time 0.0100000000, query time of that 0.0005958410, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1156.66 < 1243.12
  -> Decision False in time 0.0500000000, query time of that 0.0008969220, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1347.45 < 1362.57
  -> Decision False in time 7.7200000000, query time of that 0.0148860980, 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: 0.0052 accuracy: 1.68406 cost: 0.00633344 M: 10 delta: 1 time: 6.97914 one-recall: 0 one-ratio: 1.87159
iteration: 2 recall: 0.0744 accuracy: 0.55721 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.7215 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: 15.9032 one-recall: 0.41 one-ratio: 1.09378
iteration: 4 recall: 0.9072 accuracy: 0.00910488 cost: 0.0249122 M: 11.7252 delta: 0.566203 time: 22.0325 one-recall: 0.98 one-ratio: 1.00155
iteration: 5 recall: 0.9868 accuracy: 0.000862666 cost: 0.0376858 M: 17.423 delta: 0.224529 time: 31.1612 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9948 accuracy: 0.000305712 cost: 0.0460245 M: 21.1589 delta: 0.134158 time: 37.1182 one-recall: 1 one-ratio: 1
Graph completion with reverse edges...

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

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 37.440000000000055
Index size:  36632.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0047166667
  Testing...
|S| = 98
|T| = 1411
Reject!
2942.16 < 2992.23
  -> Decision False in time 0.0300000000, query time of that 0.0049898320, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1510.67 < 1531.99
  -> Decision False in time 2.2000000000, query time of that 0.3180899100, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1616.24 < 1933.06
  -> Decision False in time 0.0700000000, query time of that 0.0090212770, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1889.67 < 1908.36
  -> Decision False in time 1.0500000000, query time of that 0.0204572020, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1571.48 < 1581.43
  -> Decision False in time 2.1400000000, query time of that 0.0408192280, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1222.8 < 1225.5
  -> Decision False in time 0.9500000000, query time of that 0.0182764930, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1228.61 < 1239.42
  -> Decision False in time 2.7800000000, query time of that 0.0061912090, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1700.84 < 1736.65
  -> Decision False in time 1.8200000000, query time of that 0.0041426210, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1742.29 < 1746.25
  -> Decision False in time 3.5100000000, query time of that 0.0076326770, 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: 0.0056 accuracy: 1.79013 cost: 0.00633344 M: 10 delta: 1 time: 6.98186 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: 10.7255 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: 15.9078 one-recall: 0.52 one-ratio: 1.08149
iteration: 4 recall: 0.9144 accuracy: 0.00963387 cost: 0.0249104 M: 11.7247 delta: 0.566208 time: 22.0358 one-recall: 0.97 one-ratio: 1.00315
iteration: 5 recall: 0.9916 accuracy: 0.00057723 cost: 0.0376796 M: 17.4217 delta: 0.224629 time: 31.1615 one-recall: 1 one-ratio: 1
Graph completion with reverse edges...

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

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 31.450000000000045
Index size:  29684.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0011070000
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3500000000, query time of that 0.0609660260, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Accept!
  -> Decision True in time 3.5700000000, query time of that 0.6210716280, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1347.81 < 1359.81
  -> Decision False in time 1.2500000000, query time of that 0.2103542080, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
979.828 < 1027.33
  -> Decision False in time 1.2500000000, query time of that 0.0269967480, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1800.54 < 1922.17
  -> Decision False in time 0.6300000000, query time of that 0.0146268110, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1235 < 1239.19
  -> Decision False in time 7.0900000000, query time of that 0.1563167120, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1692.05 < 1692.93
  -> Decision False in time 17.1600000000, query time of that 0.0372894810, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1519.81 < 1520.51
  -> Decision False in time 8.3800000000, query time of that 0.0189441580, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1168.18 < 1237.31
  -> Decision False in time 3.2100000000, query time of that 0.0072014640, 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: 0.0072 accuracy: 1.83983 cost: 0.00633344 M: 10 delta: 1 time: 6.9828 one-recall: 0 one-ratio: 1.98489
iteration: 2 recall: 0.066 accuracy: 0.617249 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.7258 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: 15.9079 one-recall: 0.57 one-ratio: 1.12599
iteration: 4 recall: 0.922 accuracy: 0.00789148 cost: 0.0249114 M: 11.7246 delta: 0.566206 time: 22.0366 one-recall: 0.97 one-ratio: 1.00894
iteration: 5 recall: 0.9828 accuracy: 0.00113275 cost: 0.0376855 M: 17.4226 delta: 0.224568 time: 31.1642 one-recall: 0.98 one-ratio: 1.00558
iteration: 6 recall: 0.9888 accuracy: 0.000807707 cost: 0.0460215 M: 21.1587 delta: 0.134107 time: 37.119 one-recall: 0.98 one-ratio: 1.00388
iteration: 7 recall: 0.9908 accuracy: 0.000534688 cost: 0.047801 M: 21.8184 delta: 0.126888 time: 38.5706 one-recall: 0.99 one-ratio: 1.00024
Graph completion with reverse edges...

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

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 38.90000000000009
Index size:  39624.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0013566667
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3500000000, query time of that 0.0535504750, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
2129.74 < 2135.15
  -> Decision False in time 2.7600000000, query time of that 0.4245748510, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1802.81 < 1813.97
  -> Decision False in time 8.0400000000, query time of that 1.2333165840, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1021.73 < 1034.71
  -> Decision False in time 0.2000000000, query time of that 0.0041045900, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1879.45 < 1895.43
  -> Decision False in time 1.7000000000, query time of that 0.0314231320, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1846.48 < 1868.23
  -> Decision False in time 0.4700000000, query time of that 0.0098488340, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1536.65 < 1551.59
  -> Decision False in time 16.4700000000, query time of that 0.0312292380, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1722.83 < 1725.75
  -> Decision False in time 6.5400000000, query time of that 0.0137920420, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1412.88 < 1423.74
  -> Decision False in time 5.2800000000, query time of that 0.0104196580, 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: 6.9807 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: 10.7239 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: 15.9077 one-recall: 0.5 one-ratio: 1.11433
iteration: 4 recall: 0.9352 accuracy: 0.00553785 cost: 0.0249123 M: 11.7249 delta: 0.566213 time: 22.0363 one-recall: 0.96 one-ratio: 1.00492
iteration: 5 recall: 0.9928 accuracy: 0.00052315 cost: 0.0376874 M: 17.4235 delta: 0.224538 time: 31.166 one-recall: 0.99 one-ratio: 1.00011
Graph completion with reverse edges...

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

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 31.4699999999998
Index size:  29684.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0027960000
  Testing...
|S| = 98
|T| = 1411
Reject!
1980.36 < 2015.74
  -> Decision False in time 0.1300000000, query time of that 0.0169226140, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1540.3 < 1696.99
  -> Decision False in time 0.7400000000, query time of that 0.0984035660, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2077.44 < 2138.29
  -> Decision False in time 0.3100000000, query time of that 0.0401755320, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1753.16 < 1767.59
  -> Decision False in time 1.5500000000, query time of that 0.0269618970, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1370.85 < 1373.46
  -> Decision False in time 0.2700000000, query time of that 0.0053176540, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1437.51 < 1450.16
  -> Decision False in time 2.1500000000, query time of that 0.0370780810, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1402.42 < 1435.41
  -> Decision False in time 0.0200000000, query time of that 0.0007966370, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1834.47 < 1862.58
  -> Decision False in time 0.0700000000, query time of that 0.0005334520, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1600.17 < 1613.83
  -> Decision False in time 1.1300000000, query time of that 0.0026580160, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 80, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 80, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.0056 accuracy: 1.68503 cost: 0.00633344 M: 10 delta: 1 time: 6.98454 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: 10.727 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: 15.9085 one-recall: 0.49 one-ratio: 1.11078
iteration: 4 recall: 0.9268 accuracy: 0.0074375 cost: 0.024912 M: 11.7249 delta: 0.566239 time: 22.0388 one-recall: 0.97 one-ratio: 1.00338
iteration: 5 recall: 0.9924 accuracy: 0.000469506 cost: 0.0376885 M: 17.4234 delta: 0.224531 time: 31.1684 one-recall: 1 one-ratio: 1
Graph completion with reverse edges...

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

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 31.470000000000027
Index size:  29684.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0006246667
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3700000000, query time of that 0.0708217400, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1357.37 < 1358.43
  -> Decision False in time 0.8300000000, query time of that 0.1631310480, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1440.39 < 1461.41
  -> Decision False in time 10.6500000000, query time of that 2.0529878940, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Accept!
  -> Decision True in time 3.4900000000, query time of that 0.0886856610, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1880.71 < 1945.54
  -> Decision False in time 0.9600000000, query time of that 0.0237630190, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
2081.57 < 2105.09
  -> Decision False in time 7.6800000000, query time of that 0.1923777100, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1239.34 < 1277.64
  -> Decision False in time 7.0400000000, query time of that 0.0180481440, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1040.61 < 1053.56
  -> Decision False in time 12.6700000000, query time of that 0.0301572470, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1565.42 < 1658.52
  -> Decision False in time 24.0000000000, query time of that 0.0586915510, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 70, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 70, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.006 accuracy: 3.58104 cost: 0.00633344 M: 10 delta: 1 time: 6.98356 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: 10.7265 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: 15.9083 one-recall: 0.52 one-ratio: 1.1126
iteration: 4 recall: 0.9248 accuracy: 0.00833091 cost: 0.0249112 M: 11.7247 delta: 0.566218 time: 22.0401 one-recall: 0.97 one-ratio: 1.00491
iteration: 5 recall: 0.9936 accuracy: 0.000395707 cost: 0.037687 M: 17.4228 delta: 0.224562 time: 31.17 one-recall: 1 one-ratio: 1
Graph completion with reverse edges...

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

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 31.470000000000027
Index size:  29684.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0006893333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3600000000, query time of that 0.0688567330, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1636.27 < 1658.26
  -> Decision False in time 0.1400000000, query time of that 0.0277434620, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1788.4 < 1839.04
  -> Decision False in time 1.9200000000, query time of that 0.3608923230, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Accept!
  -> Decision True in time 3.4700000000, query time of that 0.0842613750, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1878.41 < 1885.28
  -> Decision False in time 7.0300000000, query time of that 0.1662433240, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1816.22 < 1934.73
  -> Decision False in time 2.3400000000, query time of that 0.0592770770, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1556.45 < 1559.08
  -> Decision False in time 22.1300000000, query time of that 0.0555508140, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
996.144 < 1010.5
  -> Decision False in time 6.0200000000, query time of that 0.0159700690, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1226.59 < 1234.65
  -> Decision False in time 7.5900000000, query time of that 0.0196895400, with c1=5.0000000000, c2=0.1000000000
