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', 70, {'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', 10, {'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', 2, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 3, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 100, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 20, {'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', 1, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 5, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 80, {'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', 4, {'reverse': -1}, False])]
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: 0.008 accuracy: 1.6488 cost: 0.00633344 M: 10 delta: 1 time: 0.590086 one-recall: 0 one-ratio: 1.98824
iteration: 2 recall: 0.0748 accuracy: 0.576643 cost: 0.0102207 M: 10 delta: 0.893264 time: 0.823939 one-recall: 0.07 one-ratio: 1.46524
iteration: 3 recall: 0.4584 accuracy: 0.129751 cost: 0.0167282 M: 11.1226 delta: 0.845946 time: 1.1408 one-recall: 0.46 one-ratio: 1.12263
iteration: 4 recall: 0.914 accuracy: 0.00801315 cost: 0.0248724 M: 11.7203 delta: 0.566036 time: 1.50898 one-recall: 0.97 one-ratio: 1.006
iteration: 5 recall: 0.9892 accuracy: 0.000422819 cost: 0.0376461 M: 17.4217 delta: 0.223964 time: 2.05215 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9932 accuracy: 0.000213504 cost: 0.0459853 M: 21.169 delta: 0.133656 time: 2.43581 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 45.69
Index size:  97484.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0004216667
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0200000000, query time of that 0.0116360700, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Accept!
  -> Decision True in time 0.2400000000, query time of that 0.1101269890, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Accept!
  -> Decision True in time 2.3900000000, query time of that 1.1676254120, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1400000000, query time of that 0.0140897520, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Accept!
  -> Decision True in time 1.3400000000, query time of that 0.1294452880, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Accept!
  -> Decision True in time 13.7100000000, query time of that 1.3221225250, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Accept!
  -> Decision True in time 1.3800000000, query time of that 0.0149921330, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Accept!
  -> Decision True in time 13.5700000000, query time of that 0.1470039860, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
1554.44 < 1648.82
  -> Decision False in time 2.9000000000, query time of that 0.0305725050, 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: 0.0068 accuracy: 2.01414 cost: 0.00633344 M: 10 delta: 1 time: 6.87226 one-recall: 0 one-ratio: 1.99097
iteration: 2 recall: 0.0716 accuracy: 0.619951 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4845 one-recall: 0.06 one-ratio: 1.43257
iteration: 3 recall: 0.4668 accuracy: 0.133582 cost: 0.0167507 M: 11.1153 delta: 0.84578 time: 15.508 one-recall: 0.61 one-ratio: 1.11551
iteration: 4 recall: 0.9108 accuracy: 0.00981295 cost: 0.024913 M: 11.7249 delta: 0.566204 time: 21.4568 one-recall: 0.95 one-ratio: 1.01121
iteration: 5 recall: 0.9872 accuracy: 0.000854153 cost: 0.0376882 M: 17.4243 delta: 0.224544 time: 30.3134 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9968 accuracy: 0.000109885 cost: 0.046028 M: 21.1607 delta: 0.134033 time: 36.0007 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 36.329999999999984
Index size:  99848.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0004700000
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0200000000, query time of that 0.0124236420, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Accept!
  -> Decision True in time 0.2400000000, query time of that 0.1192865290, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Reject!
2245.47 < 2440.53
  -> Decision False in time 1.5800000000, query time of that 0.7706675540, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1500000000, query time of that 0.0137117370, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Accept!
  -> Decision True in time 1.3800000000, query time of that 0.1346082640, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
2010.97 < 2022.31
  -> Decision False in time 4.7100000000, query time of that 0.4528431560, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Accept!
  -> Decision True in time 1.4100000000, query time of that 0.0158881410, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Accept!
  -> Decision True in time 13.7500000000, query time of that 0.1519044450, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
1991.19 < 2001.06
  -> Decision False in time 59.9800000000, query time of that 0.6357598120, 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: 0.0044 accuracy: 1.99064 cost: 0.00633344 M: 10 delta: 1 time: 6.87266 one-recall: 0 one-ratio: 2.05354
iteration: 2 recall: 0.07 accuracy: 0.635303 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4841 one-recall: 0.04 one-ratio: 1.44471
iteration: 3 recall: 0.4792 accuracy: 0.127784 cost: 0.0167507 M: 11.1153 delta: 0.845799 time: 15.5079 one-recall: 0.52 one-ratio: 1.10568
iteration: 4 recall: 0.936 accuracy: 0.00673455 cost: 0.0249118 M: 11.7249 delta: 0.566214 time: 21.4541 one-recall: 0.99 one-ratio: 1.0003
iteration: 5 recall: 0.9916 accuracy: 0.000563931 cost: 0.0376863 M: 17.4233 delta: 0.224548 time: 30.3113 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 30.609999999999957
Index size:  92916.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0014250000
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0200000000, query time of that 0.0066507530, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Accept!
  -> Decision True in time 0.1800000000, query time of that 0.0638999010, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Accept!
  -> Decision True in time 1.8700000000, query time of that 0.6368981870, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1300000000, query time of that 0.0071375720, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Reject!
2068.4 < 2109.59
  -> Decision False in time 0.9500000000, query time of that 0.0550180500, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
1871.34 < 1911.26
  -> Decision False in time 8.7000000000, query time of that 0.4899713730, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Accept!
  -> Decision True in time 1.3800000000, query time of that 0.0086594140, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Accept!
  -> Decision True in time 13.4200000000, query time of that 0.0876587490, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
1279.08 < 1341.27
  -> Decision False in time 16.9500000000, query time of that 0.1071817030, 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: 0.01 accuracy: 1.78335 cost: 0.00633344 M: 10 delta: 1 time: 6.86933 one-recall: 0 one-ratio: 1.95328
iteration: 2 recall: 0.0688 accuracy: 0.580668 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4829 one-recall: 0.07 one-ratio: 1.38579
iteration: 3 recall: 0.4812 accuracy: 0.11642 cost: 0.0167507 M: 11.1153 delta: 0.845795 time: 15.5073 one-recall: 0.52 one-ratio: 1.1049
iteration: 4 recall: 0.9304 accuracy: 0.0070946 cost: 0.0249116 M: 11.7247 delta: 0.566212 time: 21.4567 one-recall: 0.94 one-ratio: 1.01492
iteration: 5 recall: 0.9904 accuracy: 0.000471876 cost: 0.0376897 M: 17.4238 delta: 0.224521 time: 30.3107 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 30.610000000000014
Index size:  92912.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0039433333
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0200000000, query time of that 0.0047188120, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Accept!
  -> Decision True in time 0.1700000000, query time of that 0.0466447190, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Reject!
1288.24 < 1303.94
  -> Decision False in time 1.2700000000, query time of that 0.3535578190, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1300000000, query time of that 0.0057223990, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Accept!
  -> Decision True in time 1.2700000000, query time of that 0.0540800980, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
974.09 < 1117.61
  -> Decision False in time 1.1600000000, query time of that 0.0507234260, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Accept!
  -> Decision True in time 1.3900000000, query time of that 0.0064475400, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Reject!
1559.37 < 1567.89
  -> Decision False in time 10.0800000000, query time of that 0.0513651040, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
1523.73 < 1541.65
  -> Decision False in time 2.8800000000, query time of that 0.0141148960, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 40, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 40, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.0068 accuracy: 1.81766 cost: 0.00633344 M: 10 delta: 1 time: 6.87357 one-recall: 0.01 one-ratio: 1.95719
iteration: 2 recall: 0.0732 accuracy: 0.579543 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4875 one-recall: 0.09 one-ratio: 1.41436
iteration: 3 recall: 0.4584 accuracy: 0.127823 cost: 0.0167507 M: 11.1153 delta: 0.845778 time: 15.5131 one-recall: 0.47 one-ratio: 1.15257
iteration: 4 recall: 0.931999 accuracy: 0.00622453 cost: 0.0249113 M: 11.7247 delta: 0.566237 time: 21.4611 one-recall: 0.95 one-ratio: 1.00213
iteration: 5 recall: 0.9936 accuracy: 0.000341321 cost: 0.0376881 M: 17.4242 delta: 0.224554 time: 30.3162 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 30.630000000000052
Index size:  92912.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0034200000
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0300000000, query time of that 0.0083572930, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Reject!
2344.26 < 2467.88
  -> Decision False in time 0.1200000000, query time of that 0.0477323470, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Reject!
2148.38 < 2618.61
  -> Decision False in time 0.4100000000, query time of that 0.1484034980, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1400000000, query time of that 0.0083596530, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Reject!
2174.69 < 2286.07
  -> Decision False in time 1.2000000000, query time of that 0.0767808290, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
2298.79 < 2373.04
  -> Decision False in time 1.3200000000, query time of that 0.0804518770, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Accept!
  -> Decision True in time 1.3800000000, query time of that 0.0093049070, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Accept!
  -> Decision True in time 13.5900000000, query time of that 0.0954762430, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
1398.45 < 1439.76
  -> Decision False in time 10.5500000000, query time of that 0.0765729590, 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: 0.0048 accuracy: 1.69947 cost: 0.00633344 M: 10 delta: 1 time: 6.86698 one-recall: 0.01 one-ratio: 1.91173
iteration: 2 recall: 0.0708 accuracy: 0.592168 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4804 one-recall: 0.06 one-ratio: 1.39292
iteration: 3 recall: 0.4648 accuracy: 0.130263 cost: 0.0167507 M: 11.1153 delta: 0.845785 time: 15.5062 one-recall: 0.51 one-ratio: 1.11049
iteration: 4 recall: 0.9212 accuracy: 0.00938596 cost: 0.024912 M: 11.7247 delta: 0.566206 time: 21.4559 one-recall: 0.97 one-ratio: 1.00747
iteration: 5 recall: 0.9892 accuracy: 0.000520193 cost: 0.0376867 M: 17.4227 delta: 0.224517 time: 30.3156 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9964 accuracy: 0.000178421 cost: 0.0460221 M: 21.1579 delta: 0.134066 time: 36.0053 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 36.32999999999993
Index size:  99864.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0151250000
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0200000000, query time of that 0.0052953970, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Reject!
2051.06 < 2064.56
  -> Decision False in time 0.0300000000, query time of that 0.0088358010, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Reject!
1900.27 < 1996.68
  -> Decision False in time 0.1100000000, query time of that 0.0291324680, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Reject!
958.157 < 1124.71
  -> Decision False in time 0.0600000000, query time of that 0.0026889380, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Reject!
2449.94 < 2513.64
  -> Decision False in time 0.3500000000, query time of that 0.0150809910, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
2029.81 < 2424.36
  -> Decision False in time 0.1400000000, query time of that 0.0059453710, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Accept!
  -> Decision True in time 1.3800000000, query time of that 0.0063084340, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Reject!
2120.07 < 2188.73
  -> Decision False in time 3.1700000000, query time of that 0.0160867380, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
1483.53 < 1491.01
  -> Decision False in time 2.1800000000, query time of that 0.0106284310, 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: 0.0068 accuracy: 1.74555 cost: 0.00633344 M: 10 delta: 1 time: 6.86722 one-recall: 0 one-ratio: 2.08434
iteration: 2 recall: 0.0736 accuracy: 0.581553 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4784 one-recall: 0.05 one-ratio: 1.44893
iteration: 3 recall: 0.4408 accuracy: 0.135415 cost: 0.0167507 M: 11.1153 delta: 0.845813 time: 15.5012 one-recall: 0.48 one-ratio: 1.14891
iteration: 4 recall: 0.9016 accuracy: 0.0101953 cost: 0.0249122 M: 11.7249 delta: 0.566208 time: 21.4478 one-recall: 0.99 one-ratio: 1.00041
iteration: 5 recall: 0.9848 accuracy: 0.000677226 cost: 0.0376843 M: 17.4222 delta: 0.22459 time: 30.2981 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9916 accuracy: 0.000341237 cost: 0.0460222 M: 21.1567 delta: 0.134183 time: 35.983 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 36.32000000000005
Index size:  99868.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0097500000
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0100000000, query time of that 0.0055026990, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Accept!
  -> Decision True in time 0.1700000000, query time of that 0.0442009940, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Reject!
2214.57 < 2248.49
  -> Decision False in time 0.5800000000, query time of that 0.1538083520, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1300000000, query time of that 0.0051849740, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Accept!
  -> Decision True in time 1.2500000000, query time of that 0.0504391840, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
1298.27 < 1340.49
  -> Decision False in time 0.3400000000, query time of that 0.0135087060, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Accept!
  -> Decision True in time 1.3900000000, query time of that 0.0066742710, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Reject!
1174.28 < 1220.01
  -> Decision False in time 3.1000000000, query time of that 0.0158396410, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
2918.36 < 3259.97
  -> Decision False in time 6.2500000000, query time of that 0.0292303900, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 100, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 100, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.006 accuracy: 1.77586 cost: 0.00633344 M: 10 delta: 1 time: 6.86611 one-recall: 0 one-ratio: 1.98389
iteration: 2 recall: 0.0636 accuracy: 0.57395 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4788 one-recall: 0.07 one-ratio: 1.41388
iteration: 3 recall: 0.452 accuracy: 0.128093 cost: 0.0167507 M: 11.1153 delta: 0.845793 time: 15.5032 one-recall: 0.56 one-ratio: 1.09692
iteration: 4 recall: 0.9132 accuracy: 0.00930924 cost: 0.0249123 M: 11.725 delta: 0.566213 time: 21.4495 one-recall: 0.97 one-ratio: 1.00267
iteration: 5 recall: 0.9896 accuracy: 0.000389975 cost: 0.0376872 M: 17.4233 delta: 0.224535 time: 30.3019 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9956 accuracy: 0.000154583 cost: 0.0460192 M: 21.1567 delta: 0.134125 time: 35.9829 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 36.319999999999936
Index size:  99860.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0004566667
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0300000000, query time of that 0.0130475120, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Accept!
  -> Decision True in time 0.2500000000, query time of that 0.1283363740, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Accept!
  -> Decision True in time 2.4800000000, query time of that 1.2571824980, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1400000000, query time of that 0.0140066170, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Accept!
  -> Decision True in time 1.3700000000, query time of that 0.1470026550, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
1843.58 < 1970.41
  -> Decision False in time 5.8900000000, query time of that 0.6036656450, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Accept!
  -> Decision True in time 1.3700000000, query time of that 0.0151622480, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Accept!
  -> Decision True in time 13.4900000000, query time of that 0.1558855140, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
2136.76 < 2138.5
  -> Decision False in time 107.7300000000, query time of that 1.2442725520, 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: 0.004 accuracy: 1.80842 cost: 0.00633344 M: 10 delta: 1 time: 6.86631 one-recall: 0 one-ratio: 1.98791
iteration: 2 recall: 0.0632 accuracy: 0.593879 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4794 one-recall: 0.08 one-ratio: 1.46239
iteration: 3 recall: 0.4864 accuracy: 0.11711 cost: 0.0167507 M: 11.1153 delta: 0.845795 time: 15.506 one-recall: 0.57 one-ratio: 1.13187
iteration: 4 recall: 0.9268 accuracy: 0.00732192 cost: 0.0249115 M: 11.7244 delta: 0.566206 time: 21.4555 one-recall: 0.94 one-ratio: 1.01557
iteration: 5 recall: 0.9876 accuracy: 0.000673747 cost: 0.0376895 M: 17.4233 delta: 0.224555 time: 30.3172 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9948 accuracy: 0.000136794 cost: 0.0460179 M: 21.156 delta: 0.134168 time: 36.0034 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 36.32000000000005
Index size:  99844.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0017216667
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0200000000, query time of that 0.0072305410, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Accept!
  -> Decision True in time 0.1800000000, query time of that 0.0585457690, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Reject!
2423.12 < 2478.03
  -> Decision False in time 0.3700000000, query time of that 0.1198685380, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1300000000, query time of that 0.0067563830, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Accept!
  -> Decision True in time 1.2600000000, query time of that 0.0669237750, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
1899.42 < 1900.4
  -> Decision False in time 2.4500000000, query time of that 0.1275054070, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Accept!
  -> Decision True in time 1.3700000000, query time of that 0.0087210370, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Reject!
1444.22 < 1472.17
  -> Decision False in time 12.5000000000, query time of that 0.0751879470, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
1572.26 < 1607.79
  -> Decision False in time 15.4600000000, query time of that 0.0906181750, 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: 0.008 accuracy: 1.77879 cost: 0.00633344 M: 10 delta: 1 time: 6.86931 one-recall: 0.02 one-ratio: 2.03412
iteration: 2 recall: 0.0664 accuracy: 0.611814 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4813 one-recall: 0.1 one-ratio: 1.44436
iteration: 3 recall: 0.4648 accuracy: 0.132651 cost: 0.0167507 M: 11.1153 delta: 0.8458 time: 15.5045 one-recall: 0.58 one-ratio: 1.11094
iteration: 4 recall: 0.924 accuracy: 0.00815479 cost: 0.0249111 M: 11.7248 delta: 0.566221 time: 21.4529 one-recall: 0.97 one-ratio: 1.01136
iteration: 5 recall: 0.9872 accuracy: 0.000686875 cost: 0.037685 M: 17.422 delta: 0.224547 time: 30.3081 one-recall: 0.99 one-ratio: 1.00761
iteration: 6 recall: 0.9952 accuracy: 0.000385174 cost: 0.0460185 M: 21.1544 delta: 0.134155 time: 35.9943 one-recall: 0.99 one-ratio: 1.00761
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 36.319999999999936
Index size:  99852.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0007166667
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0200000000, query time of that 0.0090152870, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Accept!
  -> Decision True in time 0.2100000000, query time of that 0.0879717320, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Reject!
2157.38 < 2170.27
  -> Decision False in time 1.7600000000, query time of that 0.7311452850, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1400000000, query time of that 0.0115579540, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Accept!
  -> Decision True in time 1.3100000000, query time of that 0.0959623840, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
2092.1 < 2364.83
  -> Decision False in time 9.0600000000, query time of that 0.6760224620, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Accept!
  -> Decision True in time 1.3700000000, query time of that 0.0118600190, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Reject!
1226.97 < 1233.08
  -> Decision False in time 9.7700000000, query time of that 0.0852639130, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
1382.33 < 1393.26
  -> Decision False in time 81.1100000000, query time of that 0.6692227350, 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: 1.76963 cost: 0.00633344 M: 10 delta: 1 time: 6.86853 one-recall: 0 one-ratio: 1.90844
iteration: 2 recall: 0.0688 accuracy: 0.583997 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4827 one-recall: 0.07 one-ratio: 1.40716
iteration: 3 recall: 0.4812 accuracy: 0.120398 cost: 0.0167507 M: 11.1153 delta: 0.845799 time: 15.5081 one-recall: 0.56 one-ratio: 1.08911
iteration: 4 recall: 0.926 accuracy: 0.00702806 cost: 0.0249108 M: 11.7243 delta: 0.566211 time: 21.4586 one-recall: 0.96 one-ratio: 1.00141
iteration: 5 recall: 0.9868 accuracy: 0.000789418 cost: 0.0376832 M: 17.423 delta: 0.224582 time: 30.3192 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9948 accuracy: 0.000323166 cost: 0.0460137 M: 21.1561 delta: 0.134207 time: 36.0074 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 36.32999999999993
Index size:  99856.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0421266667
  Testing...
|S| = 20
|T| = 283
Reject!
1618.34 < 2640.04
  -> Decision False in time 0.0000000000, query time of that 0.0010288140, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Reject!
2534.07 < 2594.95
  -> Decision False in time 0.0700000000, query time of that 0.0201946280, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Reject!
1453.87 < 1623.74
  -> Decision False in time 0.0100000000, query time of that 0.0041407910, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1400000000, query time of that 0.0060866330, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Reject!
1713.94 < 2994.47
  -> Decision False in time 0.0900000000, query time of that 0.0041639740, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
1057.29 < 1398.96
  -> Decision False in time 0.0100000000, query time of that 0.0007556790, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Reject!
1647.23 < 2053.93
  -> Decision False in time 0.8900000000, query time of that 0.0048948450, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Reject!
2513.54 < 2823.5
  -> Decision False in time 0.7600000000, query time of that 0.0041442460, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
2038.97 < 2057.03
  -> Decision False in time 3.4500000000, query time of that 0.0179925050, 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: 1.78761 cost: 0.00633344 M: 10 delta: 1 time: 6.86661 one-recall: 0.01 one-ratio: 1.94306
iteration: 2 recall: 0.072 accuracy: 0.609225 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4791 one-recall: 0.06 one-ratio: 1.41971
iteration: 3 recall: 0.4808 accuracy: 0.125084 cost: 0.0167507 M: 11.1153 delta: 0.845794 time: 15.5064 one-recall: 0.58 one-ratio: 1.09089
iteration: 4 recall: 0.9252 accuracy: 0.00678269 cost: 0.0249103 M: 11.7246 delta: 0.566184 time: 21.4559 one-recall: 0.96 one-ratio: 1.00458
iteration: 5 recall: 0.9876 accuracy: 0.000689571 cost: 0.037681 M: 17.422 delta: 0.224583 time: 30.3153 one-recall: 0.99 one-ratio: 1.00017
iteration: 6 recall: 0.9956 accuracy: 0.000281846 cost: 0.0460203 M: 21.1585 delta: 0.134152 time: 36.0093 one-recall: 0.99 one-ratio: 1.00017
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 36.32999999999993
Index size:  99856.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0093466667
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0200000000, query time of that 0.0049622970, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Reject!
1902.73 < 2289.75
  -> Decision False in time 0.0300000000, query time of that 0.0083823820, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Reject!
2426.13 < 2680
  -> Decision False in time 0.0600000000, query time of that 0.0147667670, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1300000000, query time of that 0.0050093680, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Reject!
1764.77 < 2203.07
  -> Decision False in time 0.3700000000, query time of that 0.0162693160, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
1853.19 < 2070.99
  -> Decision False in time 1.1300000000, query time of that 0.0458067580, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Accept!
  -> Decision True in time 1.3900000000, query time of that 0.0068234780, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Reject!
1833.75 < 1856.98
  -> Decision False in time 0.4900000000, query time of that 0.0026990040, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
2591.91 < 2848.18
  -> Decision False in time 31.3900000000, query time of that 0.1493612840, 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: 0.006 accuracy: 1.6979 cost: 0.00633344 M: 10 delta: 1 time: 6.87549 one-recall: 0 one-ratio: 1.98558
iteration: 2 recall: 0.0768 accuracy: 0.559769 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.489 one-recall: 0.09 one-ratio: 1.39487
iteration: 3 recall: 0.4884 accuracy: 0.12079 cost: 0.0167507 M: 11.1153 delta: 0.845797 time: 15.5151 one-recall: 0.5 one-ratio: 1.11318
iteration: 4 recall: 0.9388 accuracy: 0.00688035 cost: 0.0249119 M: 11.7249 delta: 0.566225 time: 21.4654 one-recall: 0.99 one-ratio: 1.00254
iteration: 5 recall: 0.994 accuracy: 0.000235388 cost: 0.0376887 M: 17.4241 delta: 0.224517 time: 30.3272 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 30.62999999999988
Index size:  92912.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0005183333
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0200000000, query time of that 0.0104445510, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Reject!
2448.37 < 2514.71
  -> Decision False in time 0.1400000000, query time of that 0.0662239960, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Accept!
  -> Decision True in time 2.2500000000, query time of that 1.0253653740, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1400000000, query time of that 0.0128920540, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Accept!
  -> Decision True in time 1.3500000000, query time of that 0.1179543250, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
1943.06 < 2354.4
  -> Decision False in time 6.0300000000, query time of that 0.5120723020, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Accept!
  -> Decision True in time 1.3800000000, query time of that 0.0130248230, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Accept!
  -> Decision True in time 13.4700000000, query time of that 0.1287539290, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
2149.95 < 2252.21
  -> Decision False in time 38.6500000000, query time of that 0.3753680630, 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: 0.0036 accuracy: 1.66545 cost: 0.00633344 M: 10 delta: 1 time: 6.86783 one-recall: 0 one-ratio: 2.07322
iteration: 2 recall: 0.0692 accuracy: 0.592436 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4799 one-recall: 0.07 one-ratio: 1.45014
iteration: 3 recall: 0.4504 accuracy: 0.128367 cost: 0.0167507 M: 11.1153 delta: 0.84579 time: 15.506 one-recall: 0.58 one-ratio: 1.09854
iteration: 4 recall: 0.888 accuracy: 0.0112001 cost: 0.0249109 M: 11.7246 delta: 0.566229 time: 21.4565 one-recall: 0.98 one-ratio: 1.0028
iteration: 5 recall: 0.9812 accuracy: 0.00142502 cost: 0.0376869 M: 17.4231 delta: 0.224545 time: 30.3177 one-recall: 0.99 one-ratio: 1.00169
iteration: 6 recall: 0.9924 accuracy: 0.000349551 cost: 0.0460289 M: 21.1604 delta: 0.134084 time: 36.012 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 36.350000000000136
Index size:  99844.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0010150000
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0200000000, query time of that 0.0111889900, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Accept!
  -> Decision True in time 0.2200000000, query time of that 0.0971564150, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Accept!
  -> Decision True in time 2.1800000000, query time of that 0.9591505570, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1400000000, query time of that 0.0102169060, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Accept!
  -> Decision True in time 1.3100000000, query time of that 0.1074569530, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
1687.06 < 1751.13
  -> Decision False in time 5.8700000000, query time of that 0.4817822560, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Accept!
  -> Decision True in time 1.3700000000, query time of that 0.0121226760, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Accept!
  -> Decision True in time 13.4000000000, query time of that 0.1136878740, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
1976.17 < 2009.2
  -> Decision False in time 34.1400000000, query time of that 0.3140183530, 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: 0.006 accuracy: 1.65345 cost: 0.00633344 M: 10 delta: 1 time: 6.8717 one-recall: 0.01 one-ratio: 1.94936
iteration: 2 recall: 0.0652 accuracy: 0.576065 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4828 one-recall: 0.09 one-ratio: 1.43566
iteration: 3 recall: 0.4568 accuracy: 0.133971 cost: 0.0167507 M: 11.1153 delta: 0.8458 time: 15.5064 one-recall: 0.56 one-ratio: 1.11408
iteration: 4 recall: 0.9004 accuracy: 0.0109552 cost: 0.0249118 M: 11.7248 delta: 0.566223 time: 21.4526 one-recall: 0.98 one-ratio: 1.00123
iteration: 5 recall: 0.988 accuracy: 0.000761574 cost: 0.0376843 M: 17.4229 delta: 0.224546 time: 30.3083 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9948 accuracy: 0.000260213 cost: 0.0460206 M: 21.1587 delta: 0.134084 time: 35.9927 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 36.319999999999936
Index size:  99864.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0042383333
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0200000000, query time of that 0.0049940530, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Accept!
  -> Decision True in time 0.1700000000, query time of that 0.0474882190, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Reject!
1482.45 < 1713.76
  -> Decision False in time 0.6400000000, query time of that 0.1815134260, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1300000000, query time of that 0.0058726550, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Accept!
  -> Decision True in time 1.2600000000, query time of that 0.0542930100, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
1536.3 < 1574.43
  -> Decision False in time 3.1400000000, query time of that 0.1340667220, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Accept!
  -> Decision True in time 1.3700000000, query time of that 0.0075350790, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Reject!
1699.14 < 1849.66
  -> Decision False in time 2.8700000000, query time of that 0.0149565040, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
2190.7 < 2327.55
  -> Decision False in time 2.7900000000, query time of that 0.0149758790, with c1=5.0000000000, c2=0.1000000000
