copying files to /scratch...
starting benchmark...
/scratch/knn/venv/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  from ._conv import register_converters as _register_converters
running only kgraph
order: [Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 70, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 4, {'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', 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', 20, {'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', 3, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 100, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 2, {'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', 5, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 80, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 40, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 60, {'reverse': -1}, False])]
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 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: 0.0064 accuracy: 1.59623 cost: 0.00633344 M: 10 delta: 1 time: 0.61509 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: 0.841321 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: 1.12814 one-recall: 0.43 one-ratio: 1.1397
iteration: 4 recall: 0.8112 accuracy: 0.0263972 cost: 0.0212911 M: 11.9692 delta: 0.602846 time: 1.43238 one-recall: 0.83 one-ratio: 1.03613
iteration: 5 recall: 0.9496 accuracy: 0.00527856 cost: 0.0304053 M: 16.8655 delta: 0.265485 time: 1.85419 one-recall: 0.97 one-ratio: 1.01155
iteration: 6 recall: 0.9828 accuracy: 0.00117401 cost: 0.0394962 M: 22.5717 delta: 0.10555 time: 2.26806 one-recall: 0.99 one-ratio: 1.00163
iteration: 7 recall: 0.9928 accuracy: 0.000380949 cost: 0.043175 M: 24.4187 delta: 0.0840825 time: 2.49333 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 46.739999999999995
Index size:  100140.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0011150000
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0200000000, query time of that 0.0114122950, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Reject!
1830.97 < 1848.52
  -> Decision False in time 0.1400000000, query time of that 0.0628236970, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Accept!
  -> Decision True in time 2.2900000000, query time of that 1.0723297070, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1400000000, query time of that 0.0125906740, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Accept!
  -> Decision True in time 1.3500000000, query time of that 0.1219965670, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
1963.61 < 2034.1
  -> Decision False in time 1.7400000000, query time of that 0.1557689710, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Accept!
  -> Decision True in time 1.3700000000, query time of that 0.0134745850, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Reject!
1394.22 < 1479.94
  -> Decision False in time 9.7200000000, query time of that 0.0964731050, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
2206.53 < 2299.9
  -> Decision False in time 25.3100000000, query time of that 0.2481926970, 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: 0.0044 accuracy: 1.81839 cost: 0.00633344 M: 10 delta: 1 time: 6.90323 one-recall: 0 one-ratio: 2.07916
iteration: 2 recall: 0.052 accuracy: 0.699626 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1986 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: 14.5027 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: 19.1991 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: 25.8928 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: 32.0949 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.99 accuracy: 0.000358431 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.6839 one-recall: 1 one-ratio: 1
iteration: 8 recall: 0.9912 accuracy: 0.00031344 cost: 0.0443167 M: 24.8843 delta: 0.0806719 time: 35.5912 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 35.94
Index size:  95472.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0200933333
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0200000000, query time of that 0.0046418170, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Reject!
2169.24 < 2280.17
  -> Decision False in time 0.0500000000, query time of that 0.0118310880, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Reject!
1925.21 < 2285.84
  -> Decision False in time 0.0700000000, query time of that 0.0187771150, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1300000000, query time of that 0.0047649880, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Reject!
1971.51 < 2015.37
  -> Decision False in time 0.3600000000, query time of that 0.0133597920, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
2498.93 < 2532.38
  -> Decision False in time 0.2300000000, query time of that 0.0084515260, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Accept!
  -> Decision True in time 1.3800000000, query time of that 0.0059819000, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Reject!
1812.73 < 1858.63
  -> Decision False in time 1.1400000000, query time of that 0.0052892330, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
1687.91 < 1810.92
  -> Decision False in time 0.2100000000, query time of that 0.0011416760, 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: 0.004 accuracy: 1.75774 cost: 0.00633344 M: 10 delta: 1 time: 6.89974 one-recall: 0 one-ratio: 2.03464
iteration: 2 recall: 0.0608 accuracy: 0.646443 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1948 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: 14.4992 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: 19.1936 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: 25.8861 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: 32.0855 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: 34.6726 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 35.03
Index size:  94232.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0030666667
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0200000000, query time of that 0.0083440720, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Accept!
  -> Decision True in time 0.2000000000, query time of that 0.0797374810, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Reject!
2161.54 < 2237.85
  -> Decision False in time 1.3400000000, query time of that 0.5314185860, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1300000000, query time of that 0.0091871340, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Reject!
2003.74 < 2234.01
  -> Decision False in time 1.1900000000, query time of that 0.0832583970, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
1315.99 < 1345.83
  -> Decision False in time 0.0700000000, query time of that 0.0050580500, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Accept!
  -> Decision True in time 1.3800000000, query time of that 0.0113502000, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Accept!
  -> Decision True in time 13.4500000000, query time of that 0.1024325220, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
1822.73 < 2020.16
  -> Decision False in time 2.8500000000, query time of that 0.0223080070, 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: 0.006 accuracy: 1.65471 cost: 0.00633344 M: 10 delta: 1 time: 6.90176 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: 10.1968 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: 14.5003 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: 19.1967 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: 25.8917 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: 32.0957 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.992 accuracy: 0.000590742 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.6839 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 35.049999999999955
Index size:  94228.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0154166667
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0200000000, query time of that 0.0054294620, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Reject!
1792.88 < 2063.78
  -> Decision False in time 0.1500000000, query time of that 0.0429720270, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Reject!
1748.43 < 1963.39
  -> Decision False in time 0.0200000000, query time of that 0.0044309260, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1300000000, query time of that 0.0051256120, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Reject!
1961.51 < 1964.57
  -> Decision False in time 0.0600000000, query time of that 0.0032838950, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
2044.98 < 2080
  -> Decision False in time 0.1900000000, query time of that 0.0078923660, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Reject!
1794.84 < 1832.24
  -> Decision False in time 1.0200000000, query time of that 0.0048495950, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Reject!
1760.12 < 1854.78
  -> Decision False in time 0.4800000000, query time of that 0.0025637890, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
1602.19 < 2092.65
  -> Decision False in time 2.4300000000, query time of that 0.0114698730, 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: 0.0036 accuracy: 1.7649 cost: 0.00633344 M: 10 delta: 1 time: 6.89562 one-recall: 0 one-ratio: 2.10464
iteration: 2 recall: 0.0604 accuracy: 0.635748 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1925 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: 14.4983 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: 19.1954 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: 25.8909 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: 32.096 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: 34.6856 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: 35.593 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 35.94999999999999
Index size:  95472.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0010100000
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0200000000, query time of that 0.0124221990, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Accept!
  -> Decision True in time 0.2400000000, query time of that 0.1128908530, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Reject!
1847.72 < 1891.37
  -> Decision False in time 2.1200000000, query time of that 1.0095693480, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1500000000, query time of that 0.0139202260, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Accept!
  -> Decision True in time 1.3600000000, query time of that 0.1246064520, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Accept!
  -> Decision True in time 13.7500000000, query time of that 1.2568309350, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Accept!
  -> Decision True in time 1.3900000000, query time of that 0.0138062020, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Accept!
  -> Decision True in time 13.6600000000, query time of that 0.1381998520, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
1432.57 < 1495.82
  -> Decision False in time 49.0000000000, query time of that 0.4928034490, 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: 0.0044 accuracy: 1.68691 cost: 0.00633344 M: 10 delta: 1 time: 6.89528 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: 10.1906 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: 14.4944 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: 19.1893 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: 25.8825 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: 32.0854 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.9912 accuracy: 0.000477346 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.673 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 35.01999999999998
Index size:  94232.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0078483333
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0200000000, query time of that 0.0063802910, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Reject!
2170.13 < 2185.42
  -> Decision False in time 0.0200000000, query time of that 0.0084050960, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Reject!
1694.75 < 1819.41
  -> Decision False in time 0.0400000000, query time of that 0.0114951250, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1300000000, query time of that 0.0069931930, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Reject!
1704.75 < 1755.05
  -> Decision False in time 1.2400000000, query time of that 0.0632783410, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
1774.53 < 1812.1
  -> Decision False in time 1.4500000000, query time of that 0.0716034200, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Accept!
  -> Decision True in time 1.3600000000, query time of that 0.0082164270, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Reject!
1355.52 < 1467.16
  -> Decision False in time 2.7700000000, query time of that 0.0148244190, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
1790.54 < 1903.2
  -> Decision False in time 1.8800000000, query time of that 0.0114820640, 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: 0.008 accuracy: 1.74437 cost: 0.00633344 M: 10 delta: 1 time: 6.88886 one-recall: 0 one-ratio: 2.03444
iteration: 2 recall: 0.0652 accuracy: 0.637073 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1823 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: 14.4868 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: 19.1844 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: 25.88 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: 32.0849 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: 34.6737 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 35.01999999999998
Index size:  94232.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0388233333
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0100000000, query time of that 0.0046683460, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Reject!
1786.93 < 2104.46
  -> Decision False in time 0.0100000000, query time of that 0.0011131070, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Reject!
1905.29 < 2248.94
  -> Decision False in time 0.0200000000, query time of that 0.0067266500, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Reject!
1577.91 < 1741.88
  -> Decision False in time 0.0300000000, query time of that 0.0011269540, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Reject!
1969.2 < 1990.09
  -> Decision False in time 0.1700000000, query time of that 0.0068954110, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
2060.11 < 2149.58
  -> Decision False in time 0.1500000000, query time of that 0.0059230610, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Reject!
1488.04 < 1512.48
  -> Decision False in time 0.1500000000, query time of that 0.0008593500, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Reject!
1590.19 < 1811.39
  -> Decision False in time 0.0700000000, query time of that 0.0006082050, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
2154.55 < 2263.11
  -> Decision False in time 0.4200000000, query time of that 0.0019967020, 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: 0.004 accuracy: 1.49738 cost: 0.00633344 M: 10 delta: 1 time: 6.88439 one-recall: 0 one-ratio: 1.99571
iteration: 2 recall: 0.0468 accuracy: 0.631223 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1789 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: 14.4844 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: 19.1805 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: 25.8733 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: 32.0749 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.9884 accuracy: 0.000479649 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.6652 one-recall: 1 one-ratio: 1
iteration: 8 recall: 0.9908 accuracy: 0.000330768 cost: 0.0443167 M: 24.8843 delta: 0.0806719 time: 35.5726 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 35.92999999999995
Index size:  96968.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0251566667
  Testing...
|S| = 20
|T| = 283
Reject!
2322.46 < 2392.78
  -> Decision False in time 0.0000000000, query time of that 0.0006719530, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Reject!
1707.68 < 1773.04
  -> Decision False in time 0.0500000000, query time of that 0.0145108130, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Reject!
2328.26 < 2358.55
  -> Decision False in time 0.0200000000, query time of that 0.0032553210, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Reject!
1500.69 < 1999.32
  -> Decision False in time 0.0500000000, query time of that 0.0022057880, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Reject!
1973.28 < 2116.72
  -> Decision False in time 0.1200000000, query time of that 0.0055318680, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
1367.91 < 1798.63
  -> Decision False in time 0.2300000000, query time of that 0.0087440500, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Reject!
1849.34 < 1890.41
  -> Decision False in time 0.5600000000, query time of that 0.0025915890, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Reject!
1915.71 < 2099.55
  -> Decision False in time 0.6800000000, query time of that 0.0033713960, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
2030.87 < 2053.68
  -> Decision False in time 6.9600000000, query time of that 0.0299626530, 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: 0.0072 accuracy: 1.78614 cost: 0.00633344 M: 10 delta: 1 time: 6.89214 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: 10.1865 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: 14.4915 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: 19.1883 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: 25.8812 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: 32.0851 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 32.40999999999997
Index size:  78996.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0010816667
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0300000000, query time of that 0.0122140190, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Accept!
  -> Decision True in time 0.2300000000, query time of that 0.1137447350, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Accept!
  -> Decision True in time 2.3600000000, query time of that 1.1421604030, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1400000000, query time of that 0.0121938930, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Reject!
1776.33 < 1827.44
  -> Decision False in time 0.1600000000, query time of that 0.0152469230, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Accept!
  -> Decision True in time 13.6900000000, query time of that 1.2741745150, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Accept!
  -> Decision True in time 1.3700000000, query time of that 0.0137447180, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Accept!
  -> Decision True in time 13.4400000000, query time of that 0.1428161760, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
1666.81 < 1751.68
  -> Decision False in time 40.1600000000, query time of that 0.4146946270, 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: 1.58384 cost: 0.00633344 M: 10 delta: 1 time: 6.88627 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: 10.1829 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: 14.489 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: 19.1856 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: 25.8823 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9896 accuracy: 0.000550482 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 32.0872 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.9944 accuracy: 0.000259975 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.6765 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 35.02999999999997
Index size:  83332.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0388550000
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0200000000, query time of that 0.0041350270, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Reject!
1434.97 < 1605.47
  -> Decision False in time 0.0100000000, query time of that 0.0034338190, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Reject!
1575.42 < 1793.47
  -> Decision False in time 0.0300000000, query time of that 0.0069815580, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Reject!
2465.07 < 2535.63
  -> Decision False in time 0.0700000000, query time of that 0.0026637430, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Reject!
1939.78 < 1946.49
  -> Decision False in time 0.1600000000, query time of that 0.0065922030, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
2087.72 < 2131.61
  -> Decision False in time 0.0300000000, query time of that 0.0011430870, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Reject!
1606.17 < 1905.01
  -> Decision False in time 0.0000000000, query time of that 0.0002556960, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Reject!
2038.76 < 2308.19
  -> Decision False in time 0.4100000000, query time of that 0.0017757050, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
1763.51 < 1968.11
  -> Decision False in time 3.4700000000, query time of that 0.0143936760, 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: 0.008 accuracy: 1.72963 cost: 0.00633344 M: 10 delta: 1 time: 6.88255 one-recall: 0 one-ratio: 2.01043
iteration: 2 recall: 0.066 accuracy: 0.635277 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1776 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: 14.4829 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: 19.1804 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: 25.8776 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.992 accuracy: 0.000346113 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 32.0824 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 32.40999999999997
Index size:  78992.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0084066667
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0100000000, query time of that 0.0071057570, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Accept!
  -> Decision True in time 0.1900000000, query time of that 0.0616284660, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Reject!
1392.27 < 1645.21
  -> Decision False in time 0.0700000000, query time of that 0.0237249300, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1300000000, query time of that 0.0063136180, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Reject!
1788.34 < 2130.85
  -> Decision False in time 0.0800000000, query time of that 0.0045673540, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
2013.65 < 2112.37
  -> Decision False in time 0.9600000000, query time of that 0.0492167670, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Accept!
  -> Decision True in time 1.3800000000, query time of that 0.0086146430, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Reject!
1522.93 < 1542.41
  -> Decision False in time 0.9700000000, query time of that 0.0058757050, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
1696.46 < 1728.74
  -> Decision False in time 0.9100000000, query time of that 0.0052324120, 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: 0.004 accuracy: 1.5407 cost: 0.00633344 M: 10 delta: 1 time: 6.88249 one-recall: 0 one-ratio: 2.00156
iteration: 2 recall: 0.0588 accuracy: 0.610834 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1797 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: 14.487 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: 19.1839 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: 25.8787 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: 32.0831 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.9916 accuracy: 0.0004699 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.6704 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 35.00999999999999
Index size:  83324.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0258416667
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0100000000, query time of that 0.0042725170, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Reject!
2285.17 < 2288.62
  -> Decision False in time 0.1100000000, query time of that 0.0281923300, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Reject!
1861.99 < 1867.77
  -> Decision False in time 0.0100000000, query time of that 0.0028889730, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Reject!
1988.89 < 1996.97
  -> Decision False in time 0.0400000000, query time of that 0.0015310960, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Reject!
2514.17 < 2681.46
  -> Decision False in time 0.3000000000, query time of that 0.0113063160, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
2065.29 < 2078.17
  -> Decision False in time 0.0100000000, query time of that 0.0003824620, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Reject!
2225.67 < 2252.75
  -> Decision False in time 0.6100000000, query time of that 0.0031053050, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Reject!
1774.9 < 2369.43
  -> Decision False in time 1.4400000000, query time of that 0.0065555440, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
2333.99 < 2341.75
  -> Decision False in time 1.2600000000, query time of that 0.0053178870, 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: 0.004 accuracy: 1.6918 cost: 0.00633344 M: 10 delta: 1 time: 6.89361 one-recall: 0 one-ratio: 2.12757
iteration: 2 recall: 0.0552 accuracy: 0.670215 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1884 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: 14.4936 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: 19.1904 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: 25.8855 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: 32.0908 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: 34.6794 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 35.02999999999997
Index size:  83324.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0013850000
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0200000000, query time of that 0.0110403960, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Accept!
  -> Decision True in time 0.2200000000, query time of that 0.1012742670, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Reject!
1813.65 < 1927.67
  -> Decision False in time 0.5100000000, query time of that 0.2305298340, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1300000000, query time of that 0.0122087640, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Accept!
  -> Decision True in time 1.3400000000, query time of that 0.1152749780, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
1776.5 < 1875.78
  -> Decision False in time 8.7000000000, query time of that 0.7348381620, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Accept!
  -> Decision True in time 1.3800000000, query time of that 0.0123906590, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Accept!
  -> Decision True in time 13.5600000000, query time of that 0.1283787500, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
1577.58 < 1580.25
  -> Decision False in time 33.5600000000, query time of that 0.3185499560, 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: 0.0056 accuracy: 1.41157 cost: 0.00633344 M: 10 delta: 1 time: 6.89152 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: 10.1872 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: 14.4916 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: 19.1899 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: 25.8857 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9828 accuracy: 0.00106873 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 32.0899 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.99 accuracy: 0.000613841 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.6764 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 35.02999999999997
Index size:  85808.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0024850000
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0200000000, query time of that 0.0078656170, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Accept!
  -> Decision True in time 0.2000000000, query time of that 0.0714235000, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Reject!
1671.52 < 1759.39
  -> Decision False in time 0.2300000000, query time of that 0.0864668140, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1300000000, query time of that 0.0080219870, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Accept!
  -> Decision True in time 1.2800000000, query time of that 0.0806593860, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
1773.98 < 1838.66
  -> Decision False in time 2.7400000000, query time of that 0.1710010250, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Accept!
  -> Decision True in time 1.3800000000, query time of that 0.0094061160, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Reject!
1924.62 < 1962.99
  -> Decision False in time 5.0800000000, query time of that 0.0354009510, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
2013.97 < 2062.37
  -> Decision False in time 4.0600000000, query time of that 0.0273431380, 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: 0.0068 accuracy: 1.63493 cost: 0.00633344 M: 10 delta: 1 time: 6.88651 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: 10.1809 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: 14.4866 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: 19.1844 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: 25.8812 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: 32.0866 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: 34.6776 one-recall: 1 one-ratio: 1
iteration: 8 recall: 0.9932 accuracy: 0.000294001 cost: 0.0443167 M: 24.8843 delta: 0.0806719 time: 35.5858 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 35.940000000000055
Index size:  87048.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0018466667
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0300000000, query time of that 0.0102359670, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Accept!
  -> Decision True in time 0.2100000000, query time of that 0.0891238490, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Accept!
  -> Decision True in time 2.1300000000, query time of that 0.8880949570, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1300000000, query time of that 0.0100586280, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Accept!
  -> Decision True in time 1.3300000000, query time of that 0.1012704040, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
1892.36 < 1893.04
  -> Decision False in time 0.2800000000, query time of that 0.0212426560, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Accept!
  -> Decision True in time 1.3900000000, query time of that 0.0112366630, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Accept!
  -> Decision True in time 13.6000000000, query time of that 0.1123046860, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
1829.03 < 1925.99
  -> Decision False in time 33.4300000000, query time of that 0.2734037330, with c1=5.0000000000, c2=0.1000000000
