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', 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', 20, {'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', 5, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 3, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 2, {'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', 50, {'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', 1, {'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', 10, {'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: 0.008 accuracy: 1.6488 cost: 0.00633344 M: 10 delta: 1 time: 0.564226 one-recall: 0 one-ratio: 1.98824
iteration: 2 recall: 0.0748 accuracy: 0.576643 cost: 0.0102207 M: 10 delta: 0.893264 time: 0.797157 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: 1.11271 one-recall: 0.46 one-ratio: 1.12263
iteration: 4 recall: 0.914 accuracy: 0.00792383 cost: 0.0248727 M: 11.7199 delta: 0.566054 time: 1.47971 one-recall: 0.97 one-ratio: 1.006
iteration: 5 recall: 0.9892 accuracy: 0.000422819 cost: 0.0376473 M: 17.4211 delta: 0.22394 time: 2.01994 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9932 accuracy: 0.000213504 cost: 0.0459776 M: 21.1652 delta: 0.133685 time: 2.40479 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 45.45
Index size:  97420.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0042616667
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0200000000, query time of that 0.0075514790, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Reject!
2470.16 < 2756.99
  -> Decision False in time 0.0500000000, query time of that 0.0152850780, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Reject!
2326.46 < 2401.7
  -> Decision False in time 0.8000000000, query time of that 0.2713043110, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1300000000, query time of that 0.0072511290, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Reject!
2336.59 < 2361.67
  -> Decision False in time 0.1200000000, query time of that 0.0072391670, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
1622.26 < 1902.54
  -> Decision False in time 0.1200000000, query time of that 0.0083556500, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Accept!
  -> Decision True in time 1.3800000000, query time of that 0.0101338570, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Reject!
1915.02 < 2249.34
  -> Decision False in time 10.1900000000, query time of that 0.0720713100, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
2430.73 < 2767.17
  -> Decision False in time 8.0200000000, query time of that 0.0573293000, 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: 1.63762 cost: 0.00633344 M: 10 delta: 1 time: 6.8742 one-recall: 0.02 one-ratio: 1.92785
iteration: 2 recall: 0.0824 accuracy: 0.532266 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4938 one-recall: 0.08 one-ratio: 1.45867
iteration: 3 recall: 0.4808 accuracy: 0.116397 cost: 0.0167507 M: 11.1153 delta: 0.845801 time: 15.5275 one-recall: 0.5 one-ratio: 1.17188
iteration: 4 recall: 0.9312 accuracy: 0.00643485 cost: 0.0249114 M: 11.7249 delta: 0.56619 time: 21.4909 one-recall: 0.95 one-ratio: 1.04658
iteration: 5 recall: 0.9932 accuracy: 0.00108182 cost: 0.0376784 M: 17.4206 delta: 0.22463 time: 30.3711 one-recall: 0.98 one-ratio: 1.03525
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 30.64
Index size:  88436.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0005316667
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0300000000, query time of that 0.0118540280, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Accept!
  -> Decision True in time 0.2400000000, query time of that 0.1191443670, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Reject!
2241.04 < 2284.28
  -> Decision False in time 0.1100000000, query time of that 0.0563190970, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1400000000, query time of that 0.0125420130, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Accept!
  -> Decision True in time 1.3400000000, query time of that 0.1315729500, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Accept!
  -> Decision True in time 13.5500000000, query time of that 1.3277969990, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Accept!
  -> Decision True in time 1.3700000000, query time of that 0.0153366750, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Accept!
  -> Decision True in time 13.4000000000, query time of that 0.1558127350, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
1554.44 < 1648.82
  -> Decision False in time 74.1700000000, query time of that 0.8417998990, 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: 0.0084 accuracy: 1.78908 cost: 0.00633344 M: 10 delta: 1 time: 6.87251 one-recall: 0.01 one-ratio: 1.937
iteration: 2 recall: 0.0636 accuracy: 0.608546 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4919 one-recall: 0.1 one-ratio: 1.38562
iteration: 3 recall: 0.452 accuracy: 0.12864 cost: 0.0167507 M: 11.1153 delta: 0.845791 time: 15.5252 one-recall: 0.54 one-ratio: 1.11018
iteration: 4 recall: 0.9128 accuracy: 0.00815382 cost: 0.024912 M: 11.7245 delta: 0.566205 time: 21.4853 one-recall: 0.98 one-ratio: 1.00481
iteration: 5 recall: 0.9904 accuracy: 0.000591159 cost: 0.0376883 M: 17.4242 delta: 0.224554 time: 30.3651 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 30.629999999999995
Index size:  90204.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0012500000
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0200000000, query time of that 0.0092925620, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Accept!
  -> Decision True in time 0.2100000000, query time of that 0.0909108310, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Reject!
2286.35 < 2321.86
  -> Decision False in time 0.6600000000, query time of that 0.2805385420, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1400000000, query time of that 0.0110832190, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Accept!
  -> Decision True in time 1.3000000000, query time of that 0.0994824400, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Accept!
  -> Decision True in time 13.1800000000, query time of that 0.9946709090, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Accept!
  -> Decision True in time 1.3700000000, query time of that 0.0122169250, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Accept!
  -> Decision True in time 13.4400000000, query time of that 0.1205141620, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
2440.31 < 2539.87
  -> Decision False in time 109.0700000000, query time of that 0.9563580700, 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: 0.0084 accuracy: 1.92665 cost: 0.00633344 M: 10 delta: 1 time: 6.87085 one-recall: 0.01 one-ratio: 1.84963
iteration: 2 recall: 0.064 accuracy: 0.631431 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.49 one-recall: 0.06 one-ratio: 1.4037
iteration: 3 recall: 0.49 accuracy: 0.122878 cost: 0.0167507 M: 11.1153 delta: 0.84578 time: 15.5231 one-recall: 0.53 one-ratio: 1.07845
iteration: 4 recall: 0.923999 accuracy: 0.00683883 cost: 0.0249121 M: 11.7247 delta: 0.56623 time: 21.4836 one-recall: 0.94 one-ratio: 1.00867
iteration: 5 recall: 0.9864 accuracy: 0.000718044 cost: 0.0376849 M: 17.4221 delta: 0.224602 time: 30.3609 one-recall: 0.99 one-ratio: 1.00031
iteration: 6 recall: 0.992 accuracy: 0.000248968 cost: 0.0460157 M: 21.1528 delta: 0.13422 time: 36.079 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 36.360000000000014
Index size:  97152.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0017033333
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0100000000, query time of that 0.0070575500, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Accept!
  -> Decision True in time 0.1800000000, query time of that 0.0572905900, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Accept!
  -> Decision True in time 1.8100000000, query time of that 0.5962149910, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1300000000, query time of that 0.0067809050, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Reject!
1815.74 < 1900.4
  -> Decision False in time 0.2100000000, query time of that 0.0114830230, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
2296.6 < 2320.86
  -> Decision False in time 1.5800000000, query time of that 0.0851357630, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Accept!
  -> Decision True in time 1.3600000000, query time of that 0.0089118760, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Reject!
1939.74 < 1958.57
  -> Decision False in time 5.5600000000, query time of that 0.0367445180, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
1690.41 < 1692.2
  -> Decision False in time 0.8000000000, query time of that 0.0049310250, 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: 0.0048 accuracy: 1.62606 cost: 0.00633344 M: 10 delta: 1 time: 6.87495 one-recall: 0 one-ratio: 1.8449
iteration: 2 recall: 0.0708 accuracy: 0.560837 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4939 one-recall: 0.1 one-ratio: 1.36019
iteration: 3 recall: 0.492 accuracy: 0.110007 cost: 0.0167507 M: 11.1153 delta: 0.845787 time: 15.5264 one-recall: 0.53 one-ratio: 1.09376
iteration: 4 recall: 0.9188 accuracy: 0.00695364 cost: 0.0249114 M: 11.7243 delta: 0.566209 time: 21.4845 one-recall: 0.96 one-ratio: 1.00216
iteration: 5 recall: 0.9876 accuracy: 0.000479051 cost: 0.0376814 M: 17.4213 delta: 0.224561 time: 30.3593 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9948 accuracy: 0.000137602 cost: 0.0460135 M: 21.1552 delta: 0.134189 time: 36.0763 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 36.360000000000014
Index size:  97160.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0030250000
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0200000000, query time of that 0.0084418420, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Accept!
  -> Decision True in time 0.2000000000, query time of that 0.0782201930, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Reject!
1798.19 < 2852.97
  -> Decision False in time 0.6200000000, query time of that 0.2424793840, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1300000000, query time of that 0.0087702800, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Accept!
  -> Decision True in time 1.3000000000, query time of that 0.0933185370, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
1972.28 < 2400.62
  -> Decision False in time 6.5100000000, query time of that 0.4389942600, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Accept!
  -> Decision True in time 1.3700000000, query time of that 0.0108072470, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Accept!
  -> Decision True in time 13.3500000000, query time of that 0.1106326890, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
1067.35 < 1071.61
  -> Decision False in time 0.6400000000, query time of that 0.0054257460, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 5, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 5, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.0072 accuracy: 1.60168 cost: 0.00633344 M: 10 delta: 1 time: 6.88022 one-recall: 0.01 one-ratio: 1.92537
iteration: 2 recall: 0.064 accuracy: 0.550991 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4995 one-recall: 0.04 one-ratio: 1.38985
iteration: 3 recall: 0.4416 accuracy: 0.126062 cost: 0.0167507 M: 11.1153 delta: 0.845793 time: 15.5323 one-recall: 0.52 one-ratio: 1.15433
iteration: 4 recall: 0.897199 accuracy: 0.0116211 cost: 0.0249109 M: 11.7245 delta: 0.566211 time: 21.4923 one-recall: 0.95 one-ratio: 1.02076
iteration: 5 recall: 0.98 accuracy: 0.00154425 cost: 0.0376838 M: 17.4223 delta: 0.224546 time: 30.3712 one-recall: 0.99 one-ratio: 1.00033
iteration: 6 recall: 0.9884 accuracy: 0.000735292 cost: 0.0460132 M: 21.1561 delta: 0.134108 time: 36.0908 one-recall: 0.99 one-ratio: 1.00033
iteration: 7 recall: 0.9908 accuracy: 0.000460367 cost: 0.0477857 M: 21.8133 delta: 0.126887 time: 37.4566 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 37.75999999999999
Index size:  100148.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0091783333
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0200000000, query time of that 0.0052577330, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Reject!
1629.92 < 2060.69
  -> Decision False in time 0.1000000000, query time of that 0.0254534950, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Reject!
2532.42 < 2889.72
  -> Decision False in time 0.1300000000, query time of that 0.0361761900, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1300000000, query time of that 0.0053677080, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Reject!
2242.05 < 2759.12
  -> Decision False in time 0.0000000000, query time of that 0.0004645120, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
2332.64 < 2962.79
  -> Decision False in time 0.5000000000, query time of that 0.0217959530, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Accept!
  -> Decision True in time 1.3700000000, query time of that 0.0070997800, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Reject!
1682.6 < 2735.26
  -> Decision False in time 4.0900000000, query time of that 0.0214747800, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
1086.06 < 1134.76
  -> Decision False in time 9.1200000000, query time of that 0.0454604400, 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: 0.0072 accuracy: 1.56154 cost: 0.00633344 M: 10 delta: 1 time: 6.87159 one-recall: 0.01 one-ratio: 1.97759
iteration: 2 recall: 0.078 accuracy: 0.515854 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4865 one-recall: 0.09 one-ratio: 1.4234
iteration: 3 recall: 0.5 accuracy: 0.108068 cost: 0.0167507 M: 11.1153 delta: 0.845802 time: 15.5138 one-recall: 0.53 one-ratio: 1.10148
iteration: 4 recall: 0.924399 accuracy: 0.00798161 cost: 0.0249117 M: 11.7246 delta: 0.566215 time: 21.4668 one-recall: 0.99 one-ratio: 1.0033
iteration: 5 recall: 0.9952 accuracy: 0.000100272 cost: 0.0376795 M: 17.4204 delta: 0.224611 time: 30.328 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 30.590000000000032
Index size:  90192.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0124450000
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0200000000, query time of that 0.0046373490, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Reject!
1341.11 < 1516.11
  -> Decision False in time 0.1100000000, query time of that 0.0288643210, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Reject!
1944.04 < 2797.15
  -> Decision False in time 0.1700000000, query time of that 0.0422346670, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1300000000, query time of that 0.0042965610, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Reject!
1864.53 < 2223.09
  -> Decision False in time 0.6000000000, query time of that 0.0217996740, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
1707.98 < 1865.07
  -> Decision False in time 0.3300000000, query time of that 0.0123557980, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Accept!
  -> Decision True in time 1.3600000000, query time of that 0.0064624310, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Reject!
2279.42 < 2286.6
  -> Decision False in time 6.6500000000, query time of that 0.0310838850, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
2167.37 < 2346.52
  -> Decision False in time 0.3500000000, query time of that 0.0019994890, 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: 1.79747 cost: 0.00633344 M: 10 delta: 1 time: 6.85822 one-recall: 0 one-ratio: 2.05584
iteration: 2 recall: 0.0708 accuracy: 0.654695 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4719 one-recall: 0.08 one-ratio: 1.40343
iteration: 3 recall: 0.4692 accuracy: 0.172934 cost: 0.0167507 M: 11.1153 delta: 0.845789 time: 15.4969 one-recall: 0.5 one-ratio: 1.11632
iteration: 4 recall: 0.9044 accuracy: 0.00959984 cost: 0.0249125 M: 11.725 delta: 0.566218 time: 21.4476 one-recall: 0.94 one-ratio: 1.00982
iteration: 5 recall: 0.9852 accuracy: 0.000835266 cost: 0.0376841 M: 17.421 delta: 0.224596 time: 30.3081 one-recall: 0.99 one-ratio: 1.00155
iteration: 6 recall: 0.9924 accuracy: 0.000520624 cost: 0.046026 M: 21.1572 delta: 0.134126 time: 36.0181 one-recall: 0.99 one-ratio: 1.00155
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:  97144.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0151416667
  Testing...
|S| = 20
|T| = 283
Reject!
1453.27 < 2026.61
  -> Decision False in time 0.0200000000, query time of that 0.0047242370, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Reject!
2034.93 < 2105.07
  -> Decision False in time 0.0000000000, query time of that 0.0001882630, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Reject!
2224.01 < 2664.8
  -> Decision False in time 0.0900000000, query time of that 0.0253161770, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1300000000, query time of that 0.0048489950, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Reject!
1611.45 < 1702.88
  -> Decision False in time 0.0200000000, query time of that 0.0010772890, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
1057.66 < 1148.17
  -> Decision False in time 1.8000000000, query time of that 0.0755197620, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Reject!
1706.37 < 1753.45
  -> Decision False in time 0.1500000000, query time of that 0.0009506170, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Reject!
2008.24 < 2337.63
  -> Decision False in time 3.7300000000, query time of that 0.0200880680, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
2895.36 < 2935
  -> Decision False in time 6.0600000000, query time of that 0.0310336300, 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: 1.50785 cost: 0.00633344 M: 10 delta: 1 time: 6.87542 one-recall: 0.01 one-ratio: 1.91384
iteration: 2 recall: 0.0784 accuracy: 0.512676 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4935 one-recall: 0.06 one-ratio: 1.38911
iteration: 3 recall: 0.4556 accuracy: 0.117907 cost: 0.0167507 M: 11.1153 delta: 0.845804 time: 15.5248 one-recall: 0.52 one-ratio: 1.1064
iteration: 4 recall: 0.9 accuracy: 0.0108322 cost: 0.0249121 M: 11.7248 delta: 0.566201 time: 21.4821 one-recall: 0.92 one-ratio: 1.0168
iteration: 5 recall: 0.9876 accuracy: 0.000811248 cost: 0.0376908 M: 17.4254 delta: 0.224509 time: 30.3618 one-recall: 0.99 one-ratio: 1.00194
iteration: 6 recall: 0.9984 accuracy: 5.17272e-05 cost: 0.0460166 M: 21.1583 delta: 0.134099 time: 36.0724 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 36.360000000000014
Index size:  97156.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0004383333
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0300000000, query time of that 0.0114783120, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Accept!
  -> Decision True in time 0.2300000000, query time of that 0.1092384740, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Reject!
2081.66 < 2252.21
  -> Decision False in time 0.9900000000, query time of that 0.4729273380, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1400000000, query time of that 0.0122942150, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Accept!
  -> Decision True in time 1.3500000000, query time of that 0.1315405920, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Accept!
  -> Decision True in time 13.5800000000, query time of that 1.2519385200, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Accept!
  -> Decision True in time 1.3700000000, query time of that 0.0158784780, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Accept!
  -> Decision True in time 13.4400000000, query time of that 0.1435346790, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
1983.59 < 1991.19
  -> Decision False in time 25.0800000000, query time of that 0.2625027710, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 50, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 50, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.0068 accuracy: 1.6671 cost: 0.00633344 M: 10 delta: 1 time: 6.87328 one-recall: 0.01 one-ratio: 1.94374
iteration: 2 recall: 0.072 accuracy: 0.557489 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4874 one-recall: 0.07 one-ratio: 1.39304
iteration: 3 recall: 0.4856 accuracy: 0.117368 cost: 0.0167507 M: 11.1153 delta: 0.8458 time: 15.5157 one-recall: 0.53 one-ratio: 1.10306
iteration: 4 recall: 0.9244 accuracy: 0.00712159 cost: 0.0249124 M: 11.725 delta: 0.566231 time: 21.4731 one-recall: 0.96 one-ratio: 1.00134
iteration: 5 recall: 0.9912 accuracy: 0.000305989 cost: 0.0376841 M: 17.4221 delta: 0.224584 time: 30.3496 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 30.6099999999999
Index size:  90196.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0009933333
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0200000000, query time of that 0.0085660440, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Accept!
  -> Decision True in time 0.2000000000, query time of that 0.0810938520, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Accept!
  -> Decision True in time 2.0200000000, query time of that 0.8086315500, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1400000000, query time of that 0.0093554010, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Accept!
  -> Decision True in time 1.2900000000, query time of that 0.0900055650, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
2031.85 < 2115.14
  -> Decision False in time 4.4400000000, query time of that 0.3077727680, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Accept!
  -> Decision True in time 1.3600000000, query time of that 0.0117940640, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Reject!
2058.85 < 2259.04
  -> Decision False in time 11.0100000000, query time of that 0.0890242910, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
1144.37 < 1155.77
  -> Decision False in time 0.8800000000, query time of that 0.0063859040, 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: 1.82884 cost: 0.00633344 M: 10 delta: 1 time: 6.8605 one-recall: 0 one-ratio: 1.92466
iteration: 2 recall: 0.0788 accuracy: 0.620854 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4741 one-recall: 0.03 one-ratio: 1.40014
iteration: 3 recall: 0.4892 accuracy: 0.162158 cost: 0.0167507 M: 11.1153 delta: 0.845789 time: 15.4988 one-recall: 0.44 one-ratio: 1.16123
iteration: 4 recall: 0.9204 accuracy: 0.00718955 cost: 0.0249114 M: 11.7249 delta: 0.566205 time: 21.4491 one-recall: 0.96 one-ratio: 1.00368
iteration: 5 recall: 0.9884 accuracy: 0.000623288 cost: 0.0376827 M: 17.4227 delta: 0.22458 time: 30.3108 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9948 accuracy: 0.000131622 cost: 0.0460133 M: 21.1544 delta: 0.134145 time: 36.0177 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:  97152.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0010033333
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0200000000, query time of that 0.0078824840, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Accept!
  -> Decision True in time 0.1900000000, query time of that 0.0701476920, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Accept!
  -> Decision True in time 1.8900000000, query time of that 0.6813584440, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1400000000, query time of that 0.0078303920, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Accept!
  -> Decision True in time 1.2700000000, query time of that 0.0760757740, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
1280.32 < 1305.01
  -> Decision False in time 6.2800000000, query time of that 0.3794983160, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Accept!
  -> Decision True in time 1.3600000000, query time of that 0.0098125870, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Reject!
2248.29 < 2252.21
  -> Decision False in time 9.7200000000, query time of that 0.0693316350, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
1701.41 < 1724.46
  -> Decision False in time 33.4800000000, query time of that 0.2401893700, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 1, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 1, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.0072 accuracy: 1.6272 cost: 0.00633344 M: 10 delta: 1 time: 6.87573 one-recall: 0.01 one-ratio: 1.88538
iteration: 2 recall: 0.0716 accuracy: 0.566061 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4943 one-recall: 0.07 one-ratio: 1.38217
iteration: 3 recall: 0.4628 accuracy: 0.122971 cost: 0.0167507 M: 11.1153 delta: 0.845789 time: 15.5262 one-recall: 0.47 one-ratio: 1.1016
iteration: 4 recall: 0.924399 accuracy: 0.00726559 cost: 0.0249123 M: 11.7248 delta: 0.566228 time: 21.4865 one-recall: 0.94 one-ratio: 1.00637
iteration: 5 recall: 0.9884 accuracy: 0.000688203 cost: 0.0376865 M: 17.4235 delta: 0.224554 time: 30.3632 one-recall: 0.99 one-ratio: 1.00131
iteration: 6 recall: 0.9964 accuracy: 0.00021369 cost: 0.0460276 M: 21.1596 delta: 0.134099 time: 36.0883 one-recall: 0.99 one-ratio: 1.00131
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 36.38000000000011
Index size:  97152.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0421366667
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0200000000, query time of that 0.0056274110, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Reject!
2618.81 < 2922
  -> Decision False in time 0.0100000000, query time of that 0.0024396030, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Reject!
2633.64 < 3231.48
  -> Decision False in time 0.0200000000, query time of that 0.0077227880, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1300000000, query time of that 0.0059307220, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Reject!
1053.22 < 2769.46
  -> Decision False in time 0.1700000000, query time of that 0.0080770290, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
2849.57 < 3295.21
  -> Decision False in time 0.0700000000, query time of that 0.0032178290, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Accept!
  -> Decision True in time 1.3600000000, query time of that 0.0078706400, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Reject!
2694.34 < 2865.38
  -> Decision False in time 0.0000000000, query time of that 0.0002962170, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
1377.98 < 1399.73
  -> Decision False in time 0.5300000000, query time of that 0.0029859100, 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: 1.78995 cost: 0.00633344 M: 10 delta: 1 time: 6.87476 one-recall: 0 one-ratio: 1.93059
iteration: 2 recall: 0.0704 accuracy: 0.596717 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4939 one-recall: 0.07 one-ratio: 1.3813
iteration: 3 recall: 0.4848 accuracy: 0.121134 cost: 0.0167507 M: 11.1153 delta: 0.845798 time: 15.5266 one-recall: 0.55 one-ratio: 1.09069
iteration: 4 recall: 0.926 accuracy: 0.00749514 cost: 0.0249112 M: 11.7247 delta: 0.566217 time: 21.4867 one-recall: 0.96 one-ratio: 1.00222
iteration: 5 recall: 0.988 accuracy: 0.000663597 cost: 0.037684 M: 17.4228 delta: 0.224567 time: 30.3646 one-recall: 0.99 one-ratio: 1.00045
iteration: 6 recall: 0.994 accuracy: 0.000328784 cost: 0.046006 M: 21.1533 delta: 0.134207 time: 36.0775 one-recall: 0.99 one-ratio: 1.00045
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 36.36999999999989
Index size:  97160.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0004466667
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0200000000, query time of that 0.0134073390, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Accept!
  -> Decision True in time 0.2400000000, query time of that 0.1198168510, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Accept!
  -> Decision True in time 2.4200000000, query time of that 1.1962644200, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1400000000, query time of that 0.0127457700, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Accept!
  -> Decision True in time 1.3500000000, query time of that 0.1361491840, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Accept!
  -> Decision True in time 13.7200000000, query time of that 1.3315866050, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Reject!
2180.71 < 2284.28
  -> Decision False in time 1.0400000000, query time of that 0.0138478950, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Accept!
  -> Decision True in time 13.4200000000, query time of that 0.1528567940, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
1773.05 < 1784.58
  -> Decision False in time 105.4400000000, query time of that 1.1799904410, 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: 0.0048 accuracy: 1.65454 cost: 0.00633344 M: 10 delta: 1 time: 6.87494 one-recall: 0.01 one-ratio: 1.88091
iteration: 2 recall: 0.0696 accuracy: 0.549211 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4939 one-recall: 0.11 one-ratio: 1.38656
iteration: 3 recall: 0.4796 accuracy: 0.113888 cost: 0.0167507 M: 11.1153 delta: 0.845798 time: 15.5266 one-recall: 0.51 one-ratio: 1.10407
iteration: 4 recall: 0.9316 accuracy: 0.00657476 cost: 0.0249114 M: 11.7247 delta: 0.56624 time: 21.486 one-recall: 0.97 one-ratio: 1.00359
iteration: 5 recall: 0.9912 accuracy: 0.000621991 cost: 0.0376811 M: 17.4221 delta: 0.224594 time: 30.3626 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 30.63000000000011
Index size:  90204.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0039483333
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0100000000, query time of that 0.0053727390, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Reject!
2084.73 < 2128.7
  -> Decision False in time 0.0900000000, query time of that 0.0236389430, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Reject!
993.55 < 1506.32
  -> Decision False in time 0.0500000000, query time of that 0.0132666550, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1300000000, query time of that 0.0055963060, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Accept!
  -> Decision True in time 1.2300000000, query time of that 0.0534706430, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
1496.83 < 1498.22
  -> Decision False in time 2.1400000000, query time of that 0.0917143910, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Accept!
  -> Decision True in time 1.3600000000, query time of that 0.0073030160, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Reject!
1222.68 < 1473.58
  -> Decision False in time 7.4700000000, query time of that 0.0403636560, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
1215.46 < 1382.22
  -> Decision False in time 1.0400000000, query time of that 0.0060666570, 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: 0.008 accuracy: 1.63956 cost: 0.00633344 M: 10 delta: 1 time: 6.87406 one-recall: 0.01 one-ratio: 1.90551
iteration: 2 recall: 0.0768 accuracy: 0.55528 cost: 0.0102345 M: 10 delta: 0.893354 time: 10.4917 one-recall: 0.09 one-ratio: 1.37011
iteration: 3 recall: 0.4964 accuracy: 0.110262 cost: 0.0167507 M: 11.1153 delta: 0.845801 time: 15.5204 one-recall: 0.47 one-ratio: 1.10241
iteration: 4 recall: 0.9184 accuracy: 0.00954065 cost: 0.0249117 M: 11.7251 delta: 0.566232 time: 21.472 one-recall: 0.95 one-ratio: 1.00767
iteration: 5 recall: 0.9888 accuracy: 0.000792879 cost: 0.037684 M: 17.4226 delta: 0.22456 time: 30.344 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.996 accuracy: 0.00018839 cost: 0.0460204 M: 21.158 delta: 0.134134 time: 36.0602 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 36.33999999999992
Index size:  97156.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0004683333
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0200000000, query time of that 0.0104523680, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Accept!
  -> Decision True in time 0.2200000000, query time of that 0.1024103160, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Accept!
  -> Decision True in time 2.2600000000, query time of that 1.0399325860, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1400000000, query time of that 0.0123088040, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Accept!
  -> Decision True in time 1.3200000000, query time of that 0.1156490450, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Accept!
  -> Decision True in time 13.3900000000, query time of that 1.1489521980, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Accept!
  -> Decision True in time 1.3700000000, query time of that 0.0154478890, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Accept!
  -> Decision True in time 13.4100000000, query time of that 0.1390957010, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
1138.32 < 1144.17
  -> Decision False in time 85.3600000000, query time of that 0.8640414700, with c1=5.0000000000, c2=0.1000000000
