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', 40, {'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', 30, {'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', 1, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 2, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 10, {'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', 50, {'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', 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]), 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', 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: 0.0064 accuracy: 1.59623 cost: 0.00633344 M: 10 delta: 1 time: 0.570438 one-recall: 0.01 one-ratio: 1.95911
iteration: 2 recall: 0.0544 accuracy: 0.62013 cost: 0.0100109 M: 10 delta: 0.854945 time: 0.787674 one-recall: 0.05 one-ratio: 1.47978
iteration: 3 recall: 0.358 accuracy: 0.180699 cost: 0.0153111 M: 11.543 delta: 0.827699 time: 1.06577 one-recall: 0.43 one-ratio: 1.1397
iteration: 4 recall: 0.8112 accuracy: 0.0263972 cost: 0.021291 M: 11.9692 delta: 0.602847 time: 1.36321 one-recall: 0.83 one-ratio: 1.03613
iteration: 5 recall: 0.9496 accuracy: 0.00527856 cost: 0.0304052 M: 16.8661 delta: 0.265477 time: 1.78075 one-recall: 0.97 one-ratio: 1.01155
iteration: 6 recall: 0.9828 accuracy: 0.00117401 cost: 0.0394975 M: 22.5723 delta: 0.105562 time: 2.18776 one-recall: 0.99 one-ratio: 1.00163
iteration: 7 recall: 0.9928 accuracy: 0.000380949 cost: 0.0431749 M: 24.4189 delta: 0.0840572 time: 2.40071 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 45.5
Index size:  100292.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0022316667
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0200000000, query time of that 0.0097387310, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Accept!
  -> Decision True in time 0.2000000000, query time of that 0.0814670780, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Accept!
  -> Decision True in time 2.0100000000, query time of that 0.8006828020, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1300000000, query time of that 0.0088274070, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Accept!
  -> Decision True in time 1.2900000000, query time of that 0.0901850380, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
1705.79 < 1886.14
  -> Decision False in time 2.3500000000, query time of that 0.1641944280, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Accept!
  -> Decision True in time 1.3600000000, query time of that 0.0105415020, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Reject!
1708.65 < 1827.66
  -> Decision False in time 7.9500000000, query time of that 0.0626340670, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
1475.21 < 1485.65
  -> Decision False in time 41.5400000000, query time of that 0.3193997200, 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: 0.0044 accuracy: 1.81839 cost: 0.00633344 M: 10 delta: 1 time: 6.84666 one-recall: 0 one-ratio: 2.07916
iteration: 2 recall: 0.052 accuracy: 0.699626 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1323 one-recall: 0.09 one-ratio: 1.52392
iteration: 3 recall: 0.3332 accuracy: 0.210691 cost: 0.0152986 M: 11.5308 delta: 0.828164 time: 14.4295 one-recall: 0.33 one-ratio: 1.20405
iteration: 4 recall: 0.8208 accuracy: 0.0243909 cost: 0.0212851 M: 11.9674 delta: 0.604399 time: 19.1148 one-recall: 0.9 one-ratio: 1.02615
iteration: 5 recall: 0.966 accuracy: 0.00248577 cost: 0.0303842 M: 16.8423 delta: 0.26712 time: 25.8005 one-recall: 0.99 one-ratio: 1.0012
iteration: 6 recall: 0.9872 accuracy: 0.000561977 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 31.9945 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.99 accuracy: 0.000358431 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.5766 one-recall: 1 one-ratio: 1
iteration: 8 recall: 0.9912 accuracy: 0.00031344 cost: 0.0443167 M: 24.8843 delta: 0.0806719 time: 35.4823 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 35.81
Index size:  93744.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0012200000
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0300000000, query time of that 0.0108665200, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Accept!
  -> Decision True in time 0.2200000000, query time of that 0.1039139410, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Accept!
  -> Decision True in time 2.2500000000, query time of that 1.0379660180, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1400000000, query time of that 0.0111719560, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Accept!
  -> Decision True in time 1.3100000000, query time of that 0.1140326840, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
1918.04 < 2073.97
  -> Decision False in time 4.8400000000, query time of that 0.4165961260, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Accept!
  -> Decision True in time 1.3600000000, query time of that 0.0134061820, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Accept!
  -> Decision True in time 13.3400000000, query time of that 0.1284713750, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
1777.62 < 1829.56
  -> Decision False in time 33.6400000000, query time of that 0.3210199980, 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: 0.004 accuracy: 1.75774 cost: 0.00633344 M: 10 delta: 1 time: 6.83793 one-recall: 0 one-ratio: 2.03464
iteration: 2 recall: 0.0608 accuracy: 0.646443 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1202 one-recall: 0.09 one-ratio: 1.47702
iteration: 3 recall: 0.3568 accuracy: 0.180946 cost: 0.0152986 M: 11.5308 delta: 0.828164 time: 14.4118 one-recall: 0.38 one-ratio: 1.17399
iteration: 4 recall: 0.8284 accuracy: 0.0237156 cost: 0.0212851 M: 11.9674 delta: 0.604399 time: 19.094 one-recall: 0.86 one-ratio: 1.02268
iteration: 5 recall: 0.9608 accuracy: 0.00313867 cost: 0.0303842 M: 16.8423 delta: 0.26712 time: 25.7703 one-recall: 0.97 one-ratio: 1.00167
iteration: 6 recall: 0.9844 accuracy: 0.000793035 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 31.9543 one-recall: 0.99 one-ratio: 1.00002
iteration: 7 recall: 0.9908 accuracy: 0.000356182 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.5316 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 34.85000000000002
Index size:  92496.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0065250000
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0200000000, query time of that 0.0068452810, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Accept!
  -> Decision True in time 0.1800000000, query time of that 0.0617695550, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Reject!
1866.68 < 1921.86
  -> Decision False in time 0.1600000000, query time of that 0.0565207830, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1300000000, query time of that 0.0067767880, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Reject!
1991.79 < 2225.77
  -> Decision False in time 0.7200000000, query time of that 0.0410967970, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
1737.81 < 2004.21
  -> Decision False in time 0.0800000000, query time of that 0.0042380860, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Accept!
  -> Decision True in time 1.3700000000, query time of that 0.0088448280, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Reject!
1496 < 1551.17
  -> Decision False in time 4.4400000000, query time of that 0.0281630660, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
2035.5 < 2091.24
  -> Decision False in time 2.2300000000, query time of that 0.0134904400, 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: 0.006 accuracy: 1.65471 cost: 0.00633344 M: 10 delta: 1 time: 6.85609 one-recall: 0.01 one-ratio: 2.0093
iteration: 2 recall: 0.0564 accuracy: 0.608626 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1382 one-recall: 0.12 one-ratio: 1.44684
iteration: 3 recall: 0.3732 accuracy: 0.17047 cost: 0.0152986 M: 11.5308 delta: 0.828164 time: 14.4291 one-recall: 0.44 one-ratio: 1.13883
iteration: 4 recall: 0.8276 accuracy: 0.0241551 cost: 0.0212851 M: 11.9674 delta: 0.604399 time: 19.1111 one-recall: 0.9 one-ratio: 1.02079
iteration: 5 recall: 0.9644 accuracy: 0.00292808 cost: 0.0303842 M: 16.8423 delta: 0.26712 time: 25.7884 one-recall: 0.98 one-ratio: 1.00081
iteration: 6 recall: 0.986 accuracy: 0.000854818 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 31.974 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.992 accuracy: 0.000590742 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.5511 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 34.870000000000005
Index size:  92496.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0020716667
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0200000000, query time of that 0.0090412510, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Accept!
  -> Decision True in time 0.2100000000, query time of that 0.0872234450, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Reject!
2013.61 < 2025.9
  -> Decision False in time 0.6200000000, query time of that 0.2617239440, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1400000000, query time of that 0.0090904590, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Accept!
  -> Decision True in time 1.2900000000, query time of that 0.0973606700, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
2047 < 2136.32
  -> Decision False in time 3.6900000000, query time of that 0.2728364400, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Accept!
  -> Decision True in time 1.3700000000, query time of that 0.0112518630, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Accept!
  -> Decision True in time 13.3100000000, query time of that 0.1114422310, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
2196.87 < 2223.66
  -> Decision False in time 26.4000000000, query time of that 0.2177543380, 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: 0.0036 accuracy: 1.7649 cost: 0.00633344 M: 10 delta: 1 time: 6.85236 one-recall: 0 one-ratio: 2.10464
iteration: 2 recall: 0.0604 accuracy: 0.635748 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1365 one-recall: 0.06 one-ratio: 1.52118
iteration: 3 recall: 0.3776 accuracy: 0.169024 cost: 0.0152986 M: 11.5308 delta: 0.828164 time: 14.4321 one-recall: 0.46 one-ratio: 1.20421
iteration: 4 recall: 0.8328 accuracy: 0.0231786 cost: 0.0212851 M: 11.9674 delta: 0.604399 time: 19.1159 one-recall: 0.89 one-ratio: 1.04324
iteration: 5 recall: 0.9596 accuracy: 0.00447756 cost: 0.0303842 M: 16.8423 delta: 0.26712 time: 25.7975 one-recall: 0.97 one-ratio: 1.00944
iteration: 6 recall: 0.9812 accuracy: 0.00168385 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 31.9846 one-recall: 0.99 one-ratio: 1.00557
iteration: 7 recall: 0.9892 accuracy: 0.000901436 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.5614 one-recall: 0.99 one-ratio: 1.00557
iteration: 8 recall: 0.992 accuracy: 0.000417126 cost: 0.0443167 M: 24.8843 delta: 0.0806719 time: 35.4647 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 35.79000000000002
Index size:  93752.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0346916667
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0200000000, query time of that 0.0049991930, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Reject!
2127.83 < 2363.6
  -> Decision False in time 0.0000000000, query time of that 0.0009234040, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Reject!
1941.48 < 2178.27
  -> Decision False in time 0.0100000000, query time of that 0.0026279590, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1300000000, query time of that 0.0048283160, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Reject!
1767.3 < 1860.24
  -> Decision False in time 0.2500000000, query time of that 0.0096851070, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
2086.15 < 2115.99
  -> Decision False in time 0.0700000000, query time of that 0.0027554640, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Reject!
2155.7 < 2364.44
  -> Decision False in time 0.4800000000, query time of that 0.0025428630, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Reject!
1722.45 < 2331.49
  -> Decision False in time 3.4900000000, query time of that 0.0156390300, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
1777.51 < 2270.29
  -> Decision False in time 0.8300000000, query time of that 0.0037112260, 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: 0.0044 accuracy: 1.68691 cost: 0.00633344 M: 10 delta: 1 time: 6.85525 one-recall: 0.02 one-ratio: 1.95821
iteration: 2 recall: 0.0608 accuracy: 0.628781 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1412 one-recall: 0.12 one-ratio: 1.41714
iteration: 3 recall: 0.398 accuracy: 0.170086 cost: 0.0152986 M: 11.5308 delta: 0.828164 time: 14.4362 one-recall: 0.48 one-ratio: 1.09856
iteration: 4 recall: 0.8544 accuracy: 0.0205763 cost: 0.0212851 M: 11.9674 delta: 0.604399 time: 19.1222 one-recall: 0.9 one-ratio: 1.01567
iteration: 5 recall: 0.9676 accuracy: 0.00268387 cost: 0.0303842 M: 16.8423 delta: 0.26712 time: 25.8028 one-recall: 0.99 one-ratio: 1.00065
iteration: 6 recall: 0.988 accuracy: 0.000656383 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 31.9936 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.9912 accuracy: 0.000477346 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.5737 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 34.879999999999995
Index size:  92492.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0388550000
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0200000000, query time of that 0.0044816240, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Reject!
2326.57 < 2406.96
  -> Decision False in time 0.0200000000, query time of that 0.0047421960, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Reject!
1962.21 < 2546.33
  -> Decision False in time 0.0000000000, query time of that 0.0002038960, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1200000000, query time of that 0.0043801740, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Reject!
1952.21 < 2036.26
  -> Decision False in time 0.1700000000, query time of that 0.0059439930, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
1645.13 < 2463.77
  -> Decision False in time 0.1800000000, query time of that 0.0069024790, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Reject!
2112.42 < 2310.49
  -> Decision False in time 0.2700000000, query time of that 0.0014154720, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Reject!
1778.62 < 1825.64
  -> Decision False in time 0.0000000000, query time of that 0.0001919990, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
1284.97 < 1726.56
  -> Decision False in time 2.7800000000, query time of that 0.0112704210, 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: 0.008 accuracy: 1.74437 cost: 0.00633344 M: 10 delta: 1 time: 6.85601 one-recall: 0 one-ratio: 2.03444
iteration: 2 recall: 0.0652 accuracy: 0.637073 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1398 one-recall: 0.06 one-ratio: 1.4837
iteration: 3 recall: 0.3984 accuracy: 0.166915 cost: 0.0152986 M: 11.5308 delta: 0.828164 time: 14.4341 one-recall: 0.49 one-ratio: 1.13879
iteration: 4 recall: 0.862 accuracy: 0.0177386 cost: 0.0212851 M: 11.9674 delta: 0.604399 time: 19.1199 one-recall: 0.92 one-ratio: 1.01668
iteration: 5 recall: 0.976 accuracy: 0.00199604 cost: 0.0303842 M: 16.8423 delta: 0.26712 time: 25.805 one-recall: 0.99 one-ratio: 1.00077
iteration: 6 recall: 0.99 accuracy: 0.000758827 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 31.9948 one-recall: 0.99 one-ratio: 1.00077
iteration: 7 recall: 0.9932 accuracy: 0.000473901 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.576 one-recall: 0.99 one-ratio: 1.00077
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 34.8900000000001
Index size:  92500.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0154166667
  Testing...
|S| = 20
|T| = 283
Reject!
1227.03 < 2056.8
  -> Decision False in time 0.0100000000, query time of that 0.0015861500, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Accept!
  -> Decision True in time 0.1600000000, query time of that 0.0456947030, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Reject!
1884.02 < 1886.78
  -> Decision False in time 0.0800000000, query time of that 0.0198115320, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1200000000, query time of that 0.0051815890, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Reject!
2274.56 < 2319.82
  -> Decision False in time 0.3000000000, query time of that 0.0130795420, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
2051.97 < 2168.04
  -> Decision False in time 1.0000000000, query time of that 0.0411065730, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Reject!
1826.63 < 2025.86
  -> Decision False in time 0.6100000000, query time of that 0.0036688490, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Reject!
1775.62 < 1916.69
  -> Decision False in time 6.4600000000, query time of that 0.0315849150, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
1697.2 < 1906.97
  -> Decision False in time 2.9000000000, query time of that 0.0148639880, 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: 0.004 accuracy: 1.49738 cost: 0.00633344 M: 10 delta: 1 time: 6.86624 one-recall: 0 one-ratio: 1.99571
iteration: 2 recall: 0.0468 accuracy: 0.631223 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1506 one-recall: 0.02 one-ratio: 1.52908
iteration: 3 recall: 0.3512 accuracy: 0.191174 cost: 0.0152986 M: 11.5308 delta: 0.828164 time: 14.4422 one-recall: 0.32 one-ratio: 1.17563
iteration: 4 recall: 0.8116 accuracy: 0.0284491 cost: 0.0212851 M: 11.9674 delta: 0.604399 time: 19.1269 one-recall: 0.86 one-ratio: 1.02276
iteration: 5 recall: 0.9532 accuracy: 0.00452028 cost: 0.0303842 M: 16.8423 delta: 0.26712 time: 25.8059 one-recall: 0.99 one-ratio: 1.00003
iteration: 6 recall: 0.9824 accuracy: 0.000906052 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 31.9953 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.9884 accuracy: 0.000479649 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.5729 one-recall: 1 one-ratio: 1
iteration: 8 recall: 0.9908 accuracy: 0.000330768 cost: 0.0443167 M: 24.8843 delta: 0.0806719 time: 35.4766 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 35.789999999999964
Index size:  93736.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0010100000
  Testing...
|S| = 20
|T| = 283
Reject!
1879.12 < 1965.45
  -> Decision False in time 0.0100000000, query time of that 0.0050157490, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Accept!
  -> Decision True in time 0.2300000000, query time of that 0.1130098650, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Reject!
2034.07 < 2364.79
  -> Decision False in time 1.9900000000, query time of that 0.9486964100, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1400000000, query time of that 0.0130476000, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Accept!
  -> Decision True in time 1.3400000000, query time of that 0.1252691870, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
1810.61 < 1908.22
  -> Decision False in time 0.0600000000, query time of that 0.0070835830, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Accept!
  -> Decision True in time 1.3800000000, query time of that 0.0149598900, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Accept!
  -> Decision True in time 13.5300000000, query time of that 0.1392863540, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
1355.48 < 1400.45
  -> Decision False in time 50.8100000000, query time of that 0.5217039720, 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: 0.0072 accuracy: 1.78614 cost: 0.00633344 M: 10 delta: 1 time: 6.8554 one-recall: 0.01 one-ratio: 2.04356
iteration: 2 recall: 0.0672 accuracy: 0.657772 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.139 one-recall: 0.08 one-ratio: 1.47674
iteration: 3 recall: 0.3812 accuracy: 0.178449 cost: 0.0152986 M: 11.5308 delta: 0.828164 time: 14.4306 one-recall: 0.38 one-ratio: 1.16785
iteration: 4 recall: 0.878799 accuracy: 0.0154212 cost: 0.0212851 M: 11.9674 delta: 0.604399 time: 19.1134 one-recall: 0.93 one-ratio: 1.01494
iteration: 5 recall: 0.9824 accuracy: 0.00111758 cost: 0.0303842 M: 16.8423 delta: 0.26712 time: 25.7912 one-recall: 0.99 one-ratio: 1.00036
iteration: 6 recall: 0.9956 accuracy: 0.000240658 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 31.9747 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 32.280000000000086
Index size:  88172.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0038800000
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0200000000, query time of that 0.0081271180, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Accept!
  -> Decision True in time 0.1900000000, query time of that 0.0771839500, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Reject!
2046.96 < 2202.45
  -> Decision False in time 0.0500000000, query time of that 0.0157311960, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1300000000, query time of that 0.0083633630, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Accept!
  -> Decision True in time 1.2700000000, query time of that 0.0854620850, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
2145.3 < 2234.01
  -> Decision False in time 0.6000000000, query time of that 0.0417933760, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Accept!
  -> Decision True in time 1.3700000000, query time of that 0.0107850490, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Reject!
1722.87 < 1725.92
  -> Decision False in time 5.1000000000, query time of that 0.0378249010, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
2318.11 < 2491.57
  -> Decision False in time 9.8200000000, query time of that 0.0692961550, 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: 0.0048 accuracy: 1.58384 cost: 0.00633344 M: 10 delta: 1 time: 6.862 one-recall: 0.01 one-ratio: 2.0718
iteration: 2 recall: 0.0596 accuracy: 0.628493 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1438 one-recall: 0.07 one-ratio: 1.47418
iteration: 3 recall: 0.3788 accuracy: 0.170552 cost: 0.0152986 M: 11.5308 delta: 0.828164 time: 14.4391 one-recall: 0.5 one-ratio: 1.15294
iteration: 4 recall: 0.8484 accuracy: 0.0207751 cost: 0.0212851 M: 11.9674 delta: 0.604399 time: 19.1218 one-recall: 0.92 one-ratio: 1.01538
iteration: 5 recall: 0.9736 accuracy: 0.00176498 cost: 0.0303842 M: 16.8423 delta: 0.26712 time: 25.7989 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9896 accuracy: 0.000550482 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 31.9861 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.9944 accuracy: 0.000259975 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.5638 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 34.870000000000005
Index size:  92496.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0258416667
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0200000000, query time of that 0.0052670640, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Reject!
1429.51 < 1605.47
  -> Decision False in time 0.0000000000, query time of that 0.0015225770, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Reject!
2263.58 < 2293.56
  -> Decision False in time 0.0200000000, query time of that 0.0034918590, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Reject!
1771.61 < 1832.92
  -> Decision False in time 0.0400000000, query time of that 0.0015331760, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Reject!
1765.07 < 1805.22
  -> Decision False in time 0.3100000000, query time of that 0.0120365760, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
1949.61 < 1964.57
  -> Decision False in time 0.1100000000, query time of that 0.0050147380, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Reject!
1904.31 < 1968.29
  -> Decision False in time 1.0900000000, query time of that 0.0045554200, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Reject!
2058.7 < 2168.64
  -> Decision False in time 0.0700000000, query time of that 0.0005313910, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
1848.92 < 1972.92
  -> Decision False in time 2.2700000000, query time of that 0.0100130870, 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: 0.008 accuracy: 1.72963 cost: 0.00633344 M: 10 delta: 1 time: 6.8616 one-recall: 0 one-ratio: 2.01043
iteration: 2 recall: 0.066 accuracy: 0.635277 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1445 one-recall: 0.1 one-ratio: 1.42655
iteration: 3 recall: 0.4096 accuracy: 0.162884 cost: 0.0152986 M: 11.5308 delta: 0.828164 time: 14.4372 one-recall: 0.55 one-ratio: 1.08928
iteration: 4 recall: 0.8664 accuracy: 0.0168036 cost: 0.0212851 M: 11.9674 delta: 0.604399 time: 19.1203 one-recall: 0.94 one-ratio: 1.01843
iteration: 5 recall: 0.9752 accuracy: 0.00155631 cost: 0.0303842 M: 16.8423 delta: 0.26712 time: 25.8021 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.992 accuracy: 0.000346113 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 31.9917 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 32.30000000000007
Index size:  88172.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0395933333
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0200000000, query time of that 0.0039978350, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Reject!
1633.87 < 1917.04
  -> Decision False in time 0.0000000000, query time of that 0.0002808740, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Reject!
2146.05 < 2227.09
  -> Decision False in time 0.0100000000, query time of that 0.0038125070, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Reject!
2050.94 < 2450.77
  -> Decision False in time 0.0800000000, query time of that 0.0030513780, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Reject!
1386.57 < 1928.6
  -> Decision False in time 0.0200000000, query time of that 0.0006328670, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
2245.83 < 2342.97
  -> Decision False in time 0.0200000000, query time of that 0.0009449010, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Reject!
1833.39 < 2074.42
  -> Decision False in time 1.0300000000, query time of that 0.0042882320, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Reject!
1629.78 < 1748.47
  -> Decision False in time 2.4900000000, query time of that 0.0107850710, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
1403.69 < 1431.67
  -> Decision False in time 0.6400000000, query time of that 0.0027630680, 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: 0.004 accuracy: 1.5407 cost: 0.00633344 M: 10 delta: 1 time: 6.8588 one-recall: 0 one-ratio: 2.00156
iteration: 2 recall: 0.0588 accuracy: 0.610834 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1408 one-recall: 0.06 one-ratio: 1.45495
iteration: 3 recall: 0.358 accuracy: 0.174056 cost: 0.0152986 M: 11.5308 delta: 0.828164 time: 14.4342 one-recall: 0.39 one-ratio: 1.14383
iteration: 4 recall: 0.8292 accuracy: 0.0240465 cost: 0.0212851 M: 11.9674 delta: 0.604399 time: 19.1159 one-recall: 0.87 one-ratio: 1.02334
iteration: 5 recall: 0.964 accuracy: 0.00325969 cost: 0.0303842 M: 16.8423 delta: 0.26712 time: 25.7919 one-recall: 0.99 one-ratio: 1.00319
iteration: 6 recall: 0.9868 accuracy: 0.000732637 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 31.9786 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.9916 accuracy: 0.0004699 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.5587 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 34.86999999999989
Index size:  92492.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0014900000
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0200000000, query time of that 0.0100796610, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Accept!
  -> Decision True in time 0.2200000000, query time of that 0.0968397970, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Reject!
2419.55 < 2443.09
  -> Decision False in time 1.9000000000, query time of that 0.8345882860, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1400000000, query time of that 0.0105947030, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Accept!
  -> Decision True in time 1.3000000000, query time of that 0.1048920040, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
1672.95 < 1728.24
  -> Decision False in time 10.1300000000, query time of that 0.8096998580, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Accept!
  -> Decision True in time 1.3600000000, query time of that 0.0115956220, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Reject!
1910.09 < 1931.71
  -> Decision False in time 4.0300000000, query time of that 0.0380355450, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
1836.5 < 1900.7
  -> Decision False in time 7.4300000000, query time of that 0.0667826090, 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: 0.004 accuracy: 1.6918 cost: 0.00633344 M: 10 delta: 1 time: 6.8641 one-recall: 0 one-ratio: 2.12757
iteration: 2 recall: 0.0552 accuracy: 0.670215 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1465 one-recall: 0.09 one-ratio: 1.58026
iteration: 3 recall: 0.38 accuracy: 0.177198 cost: 0.0152986 M: 11.5308 delta: 0.828164 time: 14.4378 one-recall: 0.48 one-ratio: 1.17305
iteration: 4 recall: 0.846 accuracy: 0.0222695 cost: 0.0212851 M: 11.9674 delta: 0.604399 time: 19.12 one-recall: 0.92 one-ratio: 1.02262
iteration: 5 recall: 0.9636 accuracy: 0.00320592 cost: 0.0303842 M: 16.8423 delta: 0.26712 time: 25.7957 one-recall: 0.98 one-ratio: 1.00458
iteration: 6 recall: 0.9852 accuracy: 0.00131027 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 31.9795 one-recall: 0.98 one-ratio: 1.00458
iteration: 7 recall: 0.9912 accuracy: 0.000846318 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.5557 one-recall: 0.99 one-ratio: 1.00454
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 34.87000000000012
Index size:  92492.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0224016667
  Testing...
|S| = 20
|T| = 283
Reject!
1746.47 < 2030.91
  -> Decision False in time 0.0000000000, query time of that 0.0020816220, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Reject!
1337.81 < 1747.56
  -> Decision False in time 0.1100000000, query time of that 0.0253700400, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Reject!
1780.27 < 2022
  -> Decision False in time 0.0400000000, query time of that 0.0120015910, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Reject!
2196.15 < 2340.97
  -> Decision False in time 0.0200000000, query time of that 0.0005873590, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Reject!
1583.4 < 1640.75
  -> Decision False in time 0.5500000000, query time of that 0.0209149750, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
2541.56 < 2587.16
  -> Decision False in time 0.0900000000, query time of that 0.0031479020, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Reject!
2294.51 < 2297.29
  -> Decision False in time 1.2300000000, query time of that 0.0055051480, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Reject!
1819.44 < 1863.01
  -> Decision False in time 0.6200000000, query time of that 0.0029291400, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
1759.6 < 1761.1
  -> Decision False in time 1.6700000000, query time of that 0.0077819080, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 100, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 100, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.0056 accuracy: 1.41157 cost: 0.00633344 M: 10 delta: 1 time: 6.86964 one-recall: 0.01 one-ratio: 1.99155
iteration: 2 recall: 0.0544 accuracy: 0.60534 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1515 one-recall: 0.04 one-ratio: 1.45143
iteration: 3 recall: 0.3392 accuracy: 0.190145 cost: 0.0152986 M: 11.5308 delta: 0.828164 time: 14.4427 one-recall: 0.37 one-ratio: 1.15719
iteration: 4 recall: 0.7912 accuracy: 0.03203 cost: 0.0212851 M: 11.9674 delta: 0.604399 time: 19.1265 one-recall: 0.87 one-ratio: 1.01805
iteration: 5 recall: 0.9604 accuracy: 0.00284024 cost: 0.0303842 M: 16.8423 delta: 0.26712 time: 25.8022 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9828 accuracy: 0.00106873 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 31.9876 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.99 accuracy: 0.000613841 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.5659 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 34.88000000000011
Index size:  92500.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0008050000
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0200000000, query time of that 0.0123610610, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Accept!
  -> Decision True in time 0.2400000000, query time of that 0.1191438560, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Accept!
  -> Decision True in time 2.3900000000, query time of that 1.1679696020, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1500000000, query time of that 0.0142388580, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Accept!
  -> Decision True in time 1.3500000000, query time of that 0.1304104880, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Accept!
  -> Decision True in time 13.6400000000, query time of that 1.2904760370, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Accept!
  -> Decision True in time 1.3800000000, query time of that 0.0145848590, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Reject!
1666.81 < 1751.68
  -> Decision False in time 6.7800000000, query time of that 0.0746097480, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
1558.04 < 1581.32
  -> Decision False in time 128.5700000000, query time of that 1.3592241490, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 20, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 20, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.0068 accuracy: 1.63493 cost: 0.00633344 M: 10 delta: 1 time: 6.86628 one-recall: 0.01 one-ratio: 2.05973
iteration: 2 recall: 0.0572 accuracy: 0.643555 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1486 one-recall: 0.09 one-ratio: 1.46203
iteration: 3 recall: 0.3896 accuracy: 0.166479 cost: 0.0152986 M: 11.5308 delta: 0.828164 time: 14.4399 one-recall: 0.48 one-ratio: 1.15249
iteration: 4 recall: 0.850399 accuracy: 0.0212351 cost: 0.0212851 M: 11.9674 delta: 0.604399 time: 19.1229 one-recall: 0.88 one-ratio: 1.02536
iteration: 5 recall: 0.9692 accuracy: 0.00334765 cost: 0.0303842 M: 16.8423 delta: 0.26712 time: 25.7985 one-recall: 0.97 one-ratio: 1.00691
iteration: 6 recall: 0.9844 accuracy: 0.00194847 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 31.9826 one-recall: 0.99 one-ratio: 1.00353
iteration: 7 recall: 0.9884 accuracy: 0.00121479 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.5636 one-recall: 1 one-ratio: 1
iteration: 8 recall: 0.9932 accuracy: 0.000294001 cost: 0.0443167 M: 24.8843 delta: 0.0806719 time: 35.4699 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 35.79000000000019
Index size:  93740.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0066416667
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0200000000, query time of that 0.0059499300, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Accept!
  -> Decision True in time 0.1800000000, query time of that 0.0546995970, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Reject!
1922.5 < 1936.86
  -> Decision False in time 0.2200000000, query time of that 0.0696568670, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1300000000, query time of that 0.0057983000, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Reject!
1781.19 < 2065.93
  -> Decision False in time 0.5400000000, query time of that 0.0259450910, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
2293.12 < 2330.66
  -> Decision False in time 0.3800000000, query time of that 0.0191575660, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Accept!
  -> Decision True in time 1.3600000000, query time of that 0.0075869780, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Reject!
1593.13 < 1615.41
  -> Decision False in time 6.0500000000, query time of that 0.0348811190, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
1731.75 < 1758.27
  -> Decision False in time 10.1300000000, query time of that 0.0546246800, with c1=5.0000000000, c2=0.1000000000
