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', 90, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 30, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 1, {'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', 4, {'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', 50, {'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', 60, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 20, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 80, {'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', 100, {'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', 70, {'reverse': -1}, False])]
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 90, {'reverse': -1}, False]) ...
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: 0.0044 accuracy: 1.5879 cost: 0.00633344 M: 10 delta: 1 time: 6.82332 one-recall: 0.01 one-ratio: 1.96869
iteration: 2 recall: 0.0536 accuracy: 0.617143 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.0976 one-recall: 0.07 one-ratio: 1.46688
iteration: 3 recall: 0.3584 accuracy: 0.174439 cost: 0.0152986 M: 11.5308 delta: 0.828164 time: 14.3756 one-recall: 0.42 one-ratio: 1.14581
iteration: 4 recall: 0.824 accuracy: 0.0224227 cost: 0.0212851 M: 11.9674 delta: 0.604399 time: 19.0434 one-recall: 0.92 one-ratio: 1.01239
iteration: 5 recall: 0.9608 accuracy: 0.00282107 cost: 0.0303842 M: 16.8423 delta: 0.26712 time: 25.6975 one-recall: 0.97 one-ratio: 1.00419
iteration: 6 recall: 0.9856 accuracy: 0.000706031 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 31.8584 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.9932 accuracy: 0.0002248 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.4237 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 34.73
Index size:  100448.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0016203333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3700000000, query time of that 0.0745268220, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1862.31 < 1890.61
  -> Decision False in time 1.9000000000, query time of that 0.3813278300, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1756.39 < 1769.71
  -> Decision False in time 0.8400000000, query time of that 0.1673798010, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1915.09 < 1932.76
  -> Decision False in time 0.1600000000, query time of that 0.0045580710, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1866.25 < 1884.07
  -> Decision False in time 4.7700000000, query time of that 0.1226752660, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1868.65 < 1913.18
  -> Decision False in time 0.3900000000, query time of that 0.0118378370, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1606.19 < 1629.8
  -> Decision False in time 8.4100000000, query time of that 0.0224510760, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1602.5 < 1624.65
  -> Decision False in time 1.7700000000, query time of that 0.0064956000, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1735.51 < 1751
  -> Decision False in time 24.6200000000, query time of that 0.0648402360, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 30, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 30, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.0044 accuracy: 1.81839 cost: 0.00633344 M: 10 delta: 1 time: 6.81613 one-recall: 0 one-ratio: 2.07916
iteration: 2 recall: 0.052 accuracy: 0.699626 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.0901 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: 14.368 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: 19.0393 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: 25.6995 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: 31.8678 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.99 accuracy: 0.000358431 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.44 one-recall: 1 one-ratio: 1
iteration: 8 recall: 0.9912 accuracy: 0.00031344 cost: 0.0443167 M: 24.8843 delta: 0.0806719 time: 35.3409 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 35.639999999999986
Index size:  82980.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0043696667
  Testing...
|S| = 98
|T| = 1411
Reject!
1736.92 < 1757.62
  -> Decision False in time 0.1600000000, query time of that 0.0240397030, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1865.6 < 1956.15
  -> Decision False in time 0.0700000000, query time of that 0.0121481870, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1878.73 < 1916.44
  -> Decision False in time 0.1800000000, query time of that 0.0264125910, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
2020.2 < 2073.39
  -> Decision False in time 2.6500000000, query time of that 0.0503361760, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1605.51 < 1621.16
  -> Decision False in time 0.3200000000, query time of that 0.0066458180, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1838.55 < 1947.84
  -> Decision False in time 0.8500000000, query time of that 0.0158462290, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1802.59 < 1856.11
  -> Decision False in time 2.2000000000, query time of that 0.0045681580, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
2029.6 < 2040.52
  -> Decision False in time 0.0300000000, query time of that 0.0008541190, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
2051.6 < 2129.47
  -> Decision False in time 2.3700000000, query time of that 0.0050966990, 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: 0.004 accuracy: 1.75774 cost: 0.00633344 M: 10 delta: 1 time: 6.81686 one-recall: 0 one-ratio: 2.03464
iteration: 2 recall: 0.0608 accuracy: 0.646443 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.09 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: 14.3658 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: 19.0359 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: 25.6914 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: 31.8551 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: 34.4214 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 34.72
Index size:  81732.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0107390000
  Testing...
|S| = 98
|T| = 1411
Reject!
1964.85 < 2223.37
  -> Decision False in time 0.3300000000, query time of that 0.0449306690, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
2107.19 < 2465.97
  -> Decision False in time 0.2600000000, query time of that 0.0349135020, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1665.44 < 1682.72
  -> Decision False in time 0.0700000000, query time of that 0.0091019900, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1681.74 < 1688.16
  -> Decision False in time 0.4900000000, query time of that 0.0086586770, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1887.92 < 1930.23
  -> Decision False in time 3.4700000000, query time of that 0.0573295990, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1568.64 < 1594.9
  -> Decision False in time 0.6200000000, query time of that 0.0113283610, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1343.93 < 1346.38
  -> Decision False in time 9.3600000000, query time of that 0.0168718440, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1878.19 < 1922.17
  -> Decision False in time 3.7900000000, query time of that 0.0066225500, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1727.44 < 1732.85
  -> Decision False in time 0.3600000000, query time of that 0.0014102140, 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: 6.82205 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: 10.0966 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: 14.3733 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: 19.0409 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: 25.6941 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: 31.8525 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.992 accuracy: 0.000590742 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.4193 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 34.71999999999997
Index size:  81724.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0075250000
  Testing...
|S| = 98
|T| = 1411
Reject!
1993.7 < 2002.88
  -> Decision False in time 0.1200000000, query time of that 0.0156484670, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
2019.49 < 2042.02
  -> Decision False in time 0.1400000000, query time of that 0.0196711960, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2226.64 < 2284.82
  -> Decision False in time 0.1600000000, query time of that 0.0223144750, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1649.49 < 1683.5
  -> Decision False in time 0.4000000000, query time of that 0.0069839170, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
2144.31 < 2148.76
  -> Decision False in time 0.4400000000, query time of that 0.0077967500, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1447.15 < 1460.02
  -> Decision False in time 0.1200000000, query time of that 0.0027771770, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1953.48 < 1975.57
  -> Decision False in time 4.5600000000, query time of that 0.0080275120, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1651.12 < 1663.79
  -> Decision False in time 2.1800000000, query time of that 0.0037566260, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1476.79 < 1490.39
  -> Decision False in time 1.4200000000, query time of that 0.0033426180, 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: 0.0036 accuracy: 1.7649 cost: 0.00633344 M: 10 delta: 1 time: 6.8218 one-recall: 0 one-ratio: 2.10464
iteration: 2 recall: 0.0604 accuracy: 0.635748 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.0945 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: 14.3745 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: 19.0468 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: 25.709 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: 31.8792 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: 34.45 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: 35.3499 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 35.660000000000025
Index size:  82996.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0072696667
  Testing...
|S| = 98
|T| = 1411
Reject!
1812.53 < 1820.68
  -> Decision False in time 0.2600000000, query time of that 0.0341834200, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
2105.1 < 2175.96
  -> Decision False in time 0.1300000000, query time of that 0.0181494810, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1937.35 < 1938.65
  -> Decision False in time 0.0600000000, query time of that 0.0074226060, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
2089.63 < 2174.22
  -> Decision False in time 0.1300000000, query time of that 0.0021352850, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1519.91 < 1551.41
  -> Decision False in time 1.2000000000, query time of that 0.0198633600, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1589.14 < 1673.47
  -> Decision False in time 0.1100000000, query time of that 0.0024681540, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1511.46 < 1521.74
  -> Decision False in time 0.0800000000, query time of that 0.0007319910, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1686.25 < 1717.52
  -> Decision False in time 3.1300000000, query time of that 0.0063232620, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1358.96 < 1412.62
  -> Decision False in time 3.7500000000, query time of that 0.0069421900, 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: 0.0044 accuracy: 1.68691 cost: 0.00633344 M: 10 delta: 1 time: 6.81554 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: 10.0883 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: 14.3669 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: 19.0392 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: 25.7006 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: 31.8706 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.9912 accuracy: 0.000477346 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.4429 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 34.75
Index size:  81732.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0092930000
  Testing...
|S| = 98
|T| = 1411
Reject!
1558.7 < 1600.18
  -> Decision False in time 0.0100000000, query time of that 0.0014669090, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1503.62 < 1518.45
  -> Decision False in time 0.3900000000, query time of that 0.0522554460, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2076 < 2155.83
  -> Decision False in time 0.4600000000, query time of that 0.0599087270, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1633.86 < 1651.46
  -> Decision False in time 0.1300000000, query time of that 0.0018450660, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1236.39 < 1253.3
  -> Decision False in time 1.1800000000, query time of that 0.0196348680, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1995.68 < 2031.64
  -> Decision False in time 0.9200000000, query time of that 0.0153040740, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
2224.7 < 2251.9
  -> Decision False in time 2.3900000000, query time of that 0.0046412390, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1314.97 < 1343.68
  -> Decision False in time 1.4800000000, query time of that 0.0031087190, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1572.4 < 1592.34
  -> Decision False in time 2.3800000000, query time of that 0.0047216730, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 50, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 50, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.008 accuracy: 1.74437 cost: 0.00633344 M: 10 delta: 1 time: 6.82065 one-recall: 0 one-ratio: 2.03444
iteration: 2 recall: 0.0652 accuracy: 0.637073 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.0932 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: 14.371 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: 19.0421 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: 25.6979 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: 31.8617 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: 34.4287 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 34.73000000000002
Index size:  81720.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0032233333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3600000000, query time of that 0.0609310090, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1506.43 < 1513.63
  -> Decision False in time 1.9300000000, query time of that 0.3274529410, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1855.49 < 1875.16
  -> Decision False in time 0.2500000000, query time of that 0.0426405140, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1710.15 < 1722.45
  -> Decision False in time 0.3700000000, query time of that 0.0087686490, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1934.33 < 2001.42
  -> Decision False in time 1.0700000000, query time of that 0.0229320160, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1763.52 < 1772.71
  -> Decision False in time 0.4800000000, query time of that 0.0105394690, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1921.6 < 1952.67
  -> Decision False in time 4.0600000000, query time of that 0.0111254570, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1935.44 < 1952.4
  -> Decision False in time 4.7300000000, query time of that 0.0106289870, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1676.1 < 1724.54
  -> Decision False in time 1.3100000000, query time of that 0.0029232400, 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: 0.004 accuracy: 1.49738 cost: 0.00633344 M: 10 delta: 1 time: 6.82239 one-recall: 0 one-ratio: 1.99571
iteration: 2 recall: 0.0468 accuracy: 0.631223 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.0956 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: 14.3722 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: 19.0415 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: 25.6962 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: 31.8574 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.9884 accuracy: 0.000479649 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.4204 one-recall: 1 one-ratio: 1
iteration: 8 recall: 0.9908 accuracy: 0.000330768 cost: 0.0443167 M: 24.8843 delta: 0.0806719 time: 35.3183 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 35.610000000000014
Index size:  82988.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0089603333
  Testing...
|S| = 98
|T| = 1411
Reject!
1847.74 < 1861.69
  -> Decision False in time 0.3300000000, query time of that 0.0412626810, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1793.62 < 1799.61
  -> Decision False in time 1.0500000000, query time of that 0.1364050600, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1657.89 < 1697.41
  -> Decision False in time 0.0900000000, query time of that 0.0120631470, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1340.76 < 1410.54
  -> Decision False in time 0.4600000000, query time of that 0.0071214370, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1563.73 < 1575.55
  -> Decision False in time 0.4200000000, query time of that 0.0072331710, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
2122.96 < 2181.71
  -> Decision False in time 0.6500000000, query time of that 0.0108254380, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1785.39 < 1797.96
  -> Decision False in time 2.8400000000, query time of that 0.0048820570, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1999.89 < 2033.01
  -> Decision False in time 0.3400000000, query time of that 0.0012004140, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1827.8 < 1841.59
  -> Decision False in time 4.5700000000, query time of that 0.0077246150, 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: 0.0072 accuracy: 1.78614 cost: 0.00633344 M: 10 delta: 1 time: 6.8198 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: 10.0924 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: 14.369 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: 19.04 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: 25.7008 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: 31.871 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 32.15999999999997
Index size:  77424.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0034236667
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3600000000, query time of that 0.0590815740, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1683.46 < 1750.23
  -> Decision False in time 2.0200000000, query time of that 0.3397720360, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2057.22 < 2097.46
  -> Decision False in time 1.3100000000, query time of that 0.2217545480, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1841.43 < 1844.9
  -> Decision False in time 0.9500000000, query time of that 0.0217723910, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1571.32 < 1647.92
  -> Decision False in time 0.2000000000, query time of that 0.0050627810, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1658.88 < 1673.7
  -> Decision False in time 3.6900000000, query time of that 0.0780257820, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
2054.89 < 2092.24
  -> Decision False in time 0.0900000000, query time of that 0.0008264460, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1988.07 < 2048.14
  -> Decision False in time 1.8000000000, query time of that 0.0045144330, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1828.29 < 1969.25
  -> Decision False in time 2.1200000000, query time of that 0.0056517960, 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: 0.0048 accuracy: 1.58384 cost: 0.00633344 M: 10 delta: 1 time: 6.8146 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: 10.0898 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: 14.3673 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: 19.0347 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: 25.6876 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9896 accuracy: 0.000550482 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 31.8473 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.9944 accuracy: 0.000259975 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.4116 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 34.710000000000036
Index size:  81732.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0056770000
  Testing...
|S| = 98
|T| = 1411
Reject!
1746.61 < 1752.97
  -> Decision False in time 0.2900000000, query time of that 0.0406400450, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1799.04 < 1985.81
  -> Decision False in time 0.5800000000, query time of that 0.0830168010, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1987.13 < 2063.7
  -> Decision False in time 2.0300000000, query time of that 0.2867530280, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1489.99 < 1657.29
  -> Decision False in time 1.2000000000, query time of that 0.0217544360, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1635.4 < 1767.47
  -> Decision False in time 1.2200000000, query time of that 0.0225440220, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1395.61 < 1421.69
  -> Decision False in time 0.2200000000, query time of that 0.0049239220, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1547.96 < 1568.83
  -> Decision False in time 2.7300000000, query time of that 0.0056045480, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1582.67 < 1588.09
  -> Decision False in time 0.1600000000, query time of that 0.0006368350, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1707.36 < 1725.55
  -> Decision False in time 4.7200000000, query time of that 0.0097715550, 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: 0.008 accuracy: 1.72963 cost: 0.00633344 M: 10 delta: 1 time: 6.82511 one-recall: 0 one-ratio: 2.01043
iteration: 2 recall: 0.066 accuracy: 0.635277 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.0976 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: 14.3766 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: 19.0493 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: 25.712 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.992 accuracy: 0.000346113 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 31.8816 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 32.17000000000007
Index size:  77428.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0026180000
  Testing...
|S| = 98
|T| = 1411
Reject!
1573.03 < 1643.97
  -> Decision False in time 0.1900000000, query time of that 0.0357388510, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1900.51 < 1928.07
  -> Decision False in time 0.6300000000, query time of that 0.1193702930, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2027.9 < 2042.02
  -> Decision False in time 0.0500000000, query time of that 0.0097210270, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1900.9 < 1939.86
  -> Decision False in time 0.3200000000, query time of that 0.0078128310, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1348.55 < 1350.76
  -> Decision False in time 1.0000000000, query time of that 0.0243396820, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1659.54 < 1664.32
  -> Decision False in time 0.6300000000, query time of that 0.0159322450, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1627.14 < 1670.75
  -> Decision False in time 3.7600000000, query time of that 0.0104222520, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1635.98 < 1743.47
  -> Decision False in time 5.8200000000, query time of that 0.0140826360, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
2137.48 < 2152.96
  -> Decision False in time 12.1100000000, query time of that 0.0301360150, 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: 6.83189 one-recall: 0 one-ratio: 2.00156
iteration: 2 recall: 0.0588 accuracy: 0.610834 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1046 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: 14.3824 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: 19.0507 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: 25.7068 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: 31.8715 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.9916 accuracy: 0.0004699 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.4354 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 34.739999999999895
Index size:  81724.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0092153333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3400000000, query time of that 0.0435561920, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1524.43 < 1593.74
  -> Decision False in time 0.2900000000, query time of that 0.0366285570, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1912.39 < 1921.46
  -> Decision False in time 0.2900000000, query time of that 0.0370058130, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1814.66 < 1819.67
  -> Decision False in time 0.2900000000, query time of that 0.0046927970, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1874.27 < 1877.46
  -> Decision False in time 0.0200000000, query time of that 0.0006791580, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1452.47 < 1484.79
  -> Decision False in time 0.2500000000, query time of that 0.0046658040, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1785.97 < 1833.33
  -> Decision False in time 6.1800000000, query time of that 0.0110262010, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1423.15 < 1433.91
  -> Decision False in time 3.4500000000, query time of that 0.0059124600, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1372.58 < 1440.83
  -> Decision False in time 5.1600000000, query time of that 0.0089472050, 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: 0.004 accuracy: 1.6918 cost: 0.00633344 M: 10 delta: 1 time: 6.82545 one-recall: 0 one-ratio: 2.12757
iteration: 2 recall: 0.0552 accuracy: 0.670215 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.098 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: 14.3752 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: 19.0435 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: 25.6997 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: 31.8614 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: 34.4294 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 34.73000000000002
Index size:  81736.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0014630000
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3800000000, query time of that 0.0796073960, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1903.32 < 1907.87
  -> Decision False in time 0.1000000000, query time of that 0.0208489570, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1888.19 < 1911.92
  -> Decision False in time 2.1100000000, query time of that 0.4318713840, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1732.56 < 1756.49
  -> Decision False in time 0.9300000000, query time of that 0.0224629860, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
2095.09 < 2126.6
  -> Decision False in time 1.9000000000, query time of that 0.0526188660, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1805.1 < 1811.97
  -> Decision False in time 1.7900000000, query time of that 0.0483327720, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1789.74 < 1793.77
  -> Decision False in time 5.5500000000, query time of that 0.0156400050, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1680.23 < 1725.09
  -> Decision False in time 8.4900000000, query time of that 0.0245948220, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1687.38 < 1727.15
  -> Decision False in time 6.5500000000, query time of that 0.0183694370, 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: 6.82487 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: 10.0999 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: 14.3802 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: 19.0486 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: 25.7072 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9828 accuracy: 0.00106873 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 31.8731 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.99 accuracy: 0.000613841 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.4441 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 34.74000000000001
Index size:  81732.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0033383333
  Testing...
|S| = 98
|T| = 1411
Reject!
1960.84 < 1967.24
  -> Decision False in time 0.1500000000, query time of that 0.0235587400, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1805.52 < 1808.25
  -> Decision False in time 1.4100000000, query time of that 0.2219469630, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2087.85 < 2226.12
  -> Decision False in time 0.0400000000, query time of that 0.0064698120, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1510.99 < 1527.54
  -> Decision False in time 0.1600000000, query time of that 0.0032188600, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1596.59 < 1618.85
  -> Decision False in time 1.7900000000, query time of that 0.0348674850, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1923.3 < 1930.7
  -> Decision False in time 1.8100000000, query time of that 0.0373927770, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
2042.28 < 2044.53
  -> Decision False in time 11.6100000000, query time of that 0.0233786920, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1789.72 < 1842.9
  -> Decision False in time 2.3800000000, query time of that 0.0056021420, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
2048.56 < 2087.76
  -> Decision False in time 4.2000000000, query time of that 0.0095309750, 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: 0.0068 accuracy: 1.63493 cost: 0.00633344 M: 10 delta: 1 time: 6.82906 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: 10.1033 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: 14.3825 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: 19.0544 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: 25.7145 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: 31.8816 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: 34.4532 one-recall: 1 one-ratio: 1
iteration: 8 recall: 0.9932 accuracy: 0.000294001 cost: 0.0443167 M: 24.8843 delta: 0.0806719 time: 35.3538 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 35.649999999999864
Index size:  83000.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0018956667
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3700000000, query time of that 0.0692746360, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1755.4 < 1987.73
  -> Decision False in time 0.2900000000, query time of that 0.0528838860, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1424.81 < 1457.39
  -> Decision False in time 2.6100000000, query time of that 0.4858156440, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1995.06 < 1997.4
  -> Decision False in time 0.4700000000, query time of that 0.0106788960, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1668.17 < 1669.92
  -> Decision False in time 1.2100000000, query time of that 0.0290887300, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1955.6 < 1972.02
  -> Decision False in time 2.4500000000, query time of that 0.0590140090, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1612.53 < 1620.19
  -> Decision False in time 5.5900000000, query time of that 0.0155514490, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1913.31 < 1946.45
  -> Decision False in time 7.4800000000, query time of that 0.0190524020, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1496.92 < 1519.03
  -> Decision False in time 2.8000000000, query time of that 0.0067612090, with c1=5.0000000000, c2=0.1000000000
