copying files to /scratch...
starting benchmark...
/scratch/knn/venv/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  from ._conv import register_converters as _register_converters
running only kgraph
order: [Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 4, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 1, {'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', 10, {'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', 20, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 40, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 5, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 50, {'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', 90, {'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', 3, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 70, {'reverse': -1}, False])]
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 4, {'reverse': -1}, False]) ...
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: 1.5879 cost: 0.00633344 M: 10 delta: 1 time: 6.85191 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: 10.4088 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: 15.0918 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: 20.182 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: 27.4113 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: 34.2941 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.9932 accuracy: 0.0002248 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 37.3731 one-recall: 1 one-ratio: 1
Graph completion with reverse edges...

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Reranking edges...

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 37.71
Index size:  100448.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0078486667
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3600000000, query time of that 0.0614033860, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1571.32 < 1578.55
  -> Decision False in time 0.9000000000, query time of that 0.1548555160, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2027.3 < 2085.67
  -> Decision False in time 0.1700000000, query time of that 0.0308273160, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1678.85 < 1707.26
  -> Decision False in time 2.0800000000, query time of that 0.0463544350, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1883.29 < 1894.69
  -> Decision False in time 0.6500000000, query time of that 0.0140820750, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1499.94 < 1570.15
  -> Decision False in time 1.1000000000, query time of that 0.0274653010, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1617.35 < 1621.45
  -> Decision False in time 2.4900000000, query time of that 0.0060923550, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1715.96 < 1753.13
  -> Decision False in time 1.5900000000, query time of that 0.0039059930, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1800.21 < 1803.22
  -> Decision False in time 1.0600000000, query time of that 0.0029737770, 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: 0.0044 accuracy: 1.81839 cost: 0.00633344 M: 10 delta: 1 time: 6.87665 one-recall: 0 one-ratio: 2.07916
iteration: 2 recall: 0.052 accuracy: 0.699626 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.193 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: 14.5211 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: 19.2441 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: 25.9804 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: 32.2442 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.99 accuracy: 0.000358431 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.8684 one-recall: 1 one-ratio: 1
iteration: 8 recall: 0.9912 accuracy: 0.00031344 cost: 0.0443167 M: 24.8843 delta: 0.0806719 time: 35.7904 one-recall: 1 one-ratio: 1
Graph completion with reverse edges...

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Reranking edges...

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 36.110000000000014
Index size:  82988.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0095726667
  Testing...
|S| = 98
|T| = 1411
Reject!
1640.68 < 1647.77
  -> Decision False in time 0.1000000000, query time of that 0.0126100080, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
2447.19 < 2476.71
  -> Decision False in time 0.1500000000, query time of that 0.0198217040, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2210.69 < 2228.98
  -> Decision False in time 0.1000000000, query time of that 0.0149515740, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
2301.78 < 2305.07
  -> Decision False in time 0.0100000000, query time of that 0.0005258980, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1907.82 < 1929.7
  -> Decision False in time 1.1600000000, query time of that 0.0188776360, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1879.07 < 1893.62
  -> Decision False in time 0.0500000000, query time of that 0.0011621530, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1321.13 < 1328.33
  -> Decision False in time 7.1000000000, query time of that 0.0128629020, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1967.66 < 1989.76
  -> Decision False in time 3.8500000000, query time of that 0.0073997530, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1389.83 < 1390.54
  -> Decision False in time 7.8000000000, query time of that 0.0146168210, 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: 1.75774 cost: 0.00633344 M: 10 delta: 1 time: 6.8719 one-recall: 0 one-ratio: 2.03464
iteration: 2 recall: 0.0608 accuracy: 0.646443 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1881 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: 14.5176 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: 19.2393 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: 25.9744 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: 32.2397 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: 34.8644 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 35.19
Index size:  81732.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0014630000
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3800000000, query time of that 0.0792831190, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1344.89 < 1363.57
  -> Decision False in time 2.4400000000, query time of that 0.5171181320, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1238.85 < 1262.34
  -> Decision False in time 0.0500000000, query time of that 0.0090285100, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1719.06 < 1748.91
  -> Decision False in time 1.0500000000, query time of that 0.0290072320, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1874.4 < 1875.47
  -> Decision False in time 2.5600000000, query time of that 0.0695213850, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1869.8 < 2060.7
  -> Decision False in time 6.6100000000, query time of that 0.1810618390, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1632.04 < 1734.56
  -> Decision False in time 11.0300000000, query time of that 0.0308876010, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1577.2 < 1607.84
  -> Decision False in time 21.5100000000, query time of that 0.0595455290, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
2066.9 < 2080.81
  -> Decision False in time 2.9000000000, query time of that 0.0088704540, 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: 0.006 accuracy: 1.65471 cost: 0.00633344 M: 10 delta: 1 time: 6.87491 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: 10.193 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: 14.52 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: 19.2422 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: 25.9756 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: 32.2378 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.992 accuracy: 0.000590742 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.8603 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 35.18000000000001
Index size:  81728.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0101743333
  Testing...
|S| = 98
|T| = 1411
Reject!
1674.15 < 1728.66
  -> Decision False in time 0.0900000000, query time of that 0.0140819500, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1770.05 < 1803.48
  -> Decision False in time 0.3000000000, query time of that 0.0395629900, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1518.99 < 1550.48
  -> Decision False in time 0.2800000000, query time of that 0.0371525070, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
2101.9 < 2102.02
  -> Decision False in time 0.5600000000, query time of that 0.0096661310, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1963.33 < 1980.31
  -> Decision False in time 0.5400000000, query time of that 0.0091204500, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1594.3 < 1630.31
  -> Decision False in time 0.7900000000, query time of that 0.0137863480, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1834.08 < 1837.16
  -> Decision False in time 0.0300000000, query time of that 0.0007136220, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1841.48 < 1849.49
  -> Decision False in time 0.3600000000, query time of that 0.0011499380, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1739.12 < 1742.9
  -> Decision False in time 0.4200000000, query time of that 0.0011500070, 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: 0.0036 accuracy: 1.7649 cost: 0.00633344 M: 10 delta: 1 time: 6.88197 one-recall: 0 one-ratio: 2.10464
iteration: 2 recall: 0.0604 accuracy: 0.635748 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1977 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: 14.5249 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: 19.2472 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: 25.9828 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: 32.2458 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: 34.8667 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: 35.7865 one-recall: 1 one-ratio: 1
Graph completion with reverse edges...

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Reranking edges...

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 36.110000000000014
Index size:  82992.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0068253333
  Testing...
|S| = 98
|T| = 1411
Reject!
1730.19 < 1998.52
  -> Decision False in time 0.3100000000, query time of that 0.0417770170, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1639.75 < 1640.28
  -> Decision False in time 0.0700000000, query time of that 0.0104860270, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1948.66 < 1968.88
  -> Decision False in time 0.1900000000, query time of that 0.0268198470, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1417.56 < 1615.79
  -> Decision False in time 1.3000000000, query time of that 0.0237568710, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1662.91 < 1681.47
  -> Decision False in time 0.2500000000, query time of that 0.0051128890, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1550.77 < 1565.04
  -> Decision False in time 0.0300000000, query time of that 0.0006587840, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1435.25 < 1454
  -> Decision False in time 4.5500000000, query time of that 0.0086856690, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
2043.36 < 2073.03
  -> Decision False in time 1.3500000000, query time of that 0.0032648980, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1870.53 < 1897.64
  -> Decision False in time 0.0200000000, query time of that 0.0005619200, 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: 1.68691 cost: 0.00633344 M: 10 delta: 1 time: 6.88548 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: 10.2035 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: 14.5346 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: 19.26 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: 26.0029 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: 32.2763 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.9912 accuracy: 0.000477346 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.9064 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 35.23000000000002
Index size:  81732.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0048390000
  Testing...
|S| = 98
|T| = 1411
Reject!
1755.69 < 1796.41
  -> Decision False in time 0.0500000000, query time of that 0.0078337820, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1871.48 < 1904.3
  -> Decision False in time 1.3600000000, query time of that 0.2056629840, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1610.05 < 1628.53
  -> Decision False in time 0.4900000000, query time of that 0.0742636720, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1841.83 < 1957.27
  -> Decision False in time 2.0200000000, query time of that 0.0405201560, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1747.02 < 1789.92
  -> Decision False in time 0.1800000000, query time of that 0.0043679900, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1570.45 < 1592.63
  -> Decision False in time 1.1300000000, query time of that 0.0232710430, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
2196.3 < 2200.9
  -> Decision False in time 1.7200000000, query time of that 0.0035390220, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
2028.12 < 2064.51
  -> Decision False in time 3.5100000000, query time of that 0.0069870200, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1876.79 < 1947.58
  -> Decision False in time 5.1300000000, query time of that 0.0118684640, 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: 0.008 accuracy: 1.74437 cost: 0.00633344 M: 10 delta: 1 time: 6.87688 one-recall: 0 one-ratio: 2.03444
iteration: 2 recall: 0.0652 accuracy: 0.637073 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1914 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: 14.5177 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: 19.2402 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: 25.9739 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: 32.2352 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: 34.8559 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 35.17999999999995
Index size:  81744.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0056770000
  Testing...
|S| = 98
|T| = 1411
Reject!
1601.07 < 1732.48
  -> Decision False in time 0.0400000000, query time of that 0.0047522600, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1995.25 < 2005.4
  -> Decision False in time 0.1000000000, query time of that 0.0159461410, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1359.73 < 1902.7
  -> Decision False in time 0.8800000000, query time of that 0.1294369120, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1899.38 < 1915.93
  -> Decision False in time 0.0800000000, query time of that 0.0020536560, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1617.04 < 1655.41
  -> Decision False in time 0.4800000000, query time of that 0.0088068620, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1498.11 < 1515.51
  -> Decision False in time 1.2300000000, query time of that 0.0233066020, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1918.7 < 1936.47
  -> Decision False in time 2.0700000000, query time of that 0.0043702110, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1435.71 < 1441.5
  -> Decision False in time 9.9100000000, query time of that 0.0187637800, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1608.34 < 1664.05
  -> Decision False in time 2.1800000000, query time of that 0.0047576670, 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: 0.004 accuracy: 1.49738 cost: 0.00633344 M: 10 delta: 1 time: 6.87703 one-recall: 0 one-ratio: 1.99571
iteration: 2 recall: 0.0468 accuracy: 0.631223 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1922 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: 14.5203 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: 19.2425 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: 25.9772 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: 32.239 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.9884 accuracy: 0.000479649 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.8604 one-recall: 1 one-ratio: 1
iteration: 8 recall: 0.9908 accuracy: 0.000330768 cost: 0.0443167 M: 24.8843 delta: 0.0806719 time: 35.7803 one-recall: 1 one-ratio: 1
Graph completion with reverse edges...

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Reranking edges...

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 36.10000000000002
Index size:  82980.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0030116667
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3500000000, query time of that 0.0596102770, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1657.62 < 1686.5
  -> Decision False in time 0.4500000000, query time of that 0.0750084440, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1436.46 < 1533.44
  -> Decision False in time 0.4500000000, query time of that 0.0728239170, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1875.31 < 1884.81
  -> Decision False in time 2.0300000000, query time of that 0.0444221820, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1827.92 < 1832.36
  -> Decision False in time 0.4600000000, query time of that 0.0107404480, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1908.66 < 1909.05
  -> Decision False in time 0.1800000000, query time of that 0.0044816890, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1756.06 < 1813.95
  -> Decision False in time 10.0500000000, query time of that 0.0231277390, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1534.38 < 1568.32
  -> Decision False in time 0.3700000000, query time of that 0.0014448720, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1668.09 < 1713.47
  -> Decision False in time 4.7600000000, query time of that 0.0097007180, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 5, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 5, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.0072 accuracy: 1.78614 cost: 0.00633344 M: 10 delta: 1 time: 6.88535 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: 10.2036 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: 14.5362 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: 19.2631 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: 26.0066 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: 32.2815 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 32.59000000000003
Index size:  77424.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0118173333
  Testing...
|S| = 98
|T| = 1411
Reject!
2362.79 < 2489.88
  -> Decision False in time 0.1300000000, query time of that 0.0174828540, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1519.39 < 1526.14
  -> Decision False in time 0.1400000000, query time of that 0.0177577990, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2037.47 < 2164.12
  -> Decision False in time 0.1200000000, query time of that 0.0155221750, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1765.2 < 1800.74
  -> Decision False in time 0.0000000000, query time of that 0.0006126280, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1371.86 < 1385.03
  -> Decision False in time 0.0900000000, query time of that 0.0014418560, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
2059.7 < 2380.96
  -> Decision False in time 0.6300000000, query time of that 0.0104918770, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1614.66 < 1622.12
  -> Decision False in time 3.8400000000, query time of that 0.0065738300, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1484.35 < 1598.71
  -> Decision False in time 1.0600000000, query time of that 0.0023033810, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1486.88 < 1513.8
  -> Decision False in time 1.3700000000, query time of that 0.0027008540, 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: 0.0048 accuracy: 1.58384 cost: 0.00633344 M: 10 delta: 1 time: 6.87356 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: 10.1904 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: 14.5196 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: 19.245 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: 25.9839 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9896 accuracy: 0.000550482 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 32.2518 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.9944 accuracy: 0.000259975 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.8785 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 35.210000000000036
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 0.0618132350, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1657.78 < 1661.85
  -> Decision False in time 1.1600000000, query time of that 0.1958539590, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2104.52 < 2116.88
  -> Decision False in time 2.1800000000, query time of that 0.3698594280, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1708.13 < 1709.95
  -> Decision False in time 1.8200000000, query time of that 0.0385791440, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1785.3 < 1884.23
  -> Decision False in time 0.9400000000, query time of that 0.0213527470, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1735.59 < 1795.66
  -> Decision False in time 0.8100000000, query time of that 0.0177427640, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1951.22 < 1989.09
  -> Decision False in time 7.9000000000, query time of that 0.0185092650, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1400.96 < 1475.86
  -> Decision False in time 4.8600000000, query time of that 0.0113738540, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1992.9 < 2066.79
  -> Decision False in time 3.2300000000, query time of that 0.0074621690, 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: 0.008 accuracy: 1.72963 cost: 0.00633344 M: 10 delta: 1 time: 6.87622 one-recall: 0 one-ratio: 2.01043
iteration: 2 recall: 0.066 accuracy: 0.635277 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1906 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: 14.5176 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: 19.2408 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: 25.9747 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.992 accuracy: 0.000346113 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 32.2384 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 32.539999999999964
Index size:  77432.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0034236667
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3600000000, query time of that 0.0622194680, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1673.48 < 1675.53
  -> Decision False in time 0.2800000000, query time of that 0.0482954970, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1578.91 < 1598.1
  -> Decision False in time 1.5700000000, query time of that 0.2773271530, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1685.09 < 1774.93
  -> Decision False in time 0.1800000000, query time of that 0.0046185710, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
2103.84 < 2208.09
  -> Decision False in time 1.6200000000, query time of that 0.0361794560, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1931.99 < 1969.39
  -> Decision False in time 2.0400000000, query time of that 0.0463215350, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1815.49 < 1952.84
  -> Decision False in time 2.0400000000, query time of that 0.0052729330, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
2007.22 < 2025.07
  -> Decision False in time 2.0900000000, query time of that 0.0048950550, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1998.18 < 2077.6
  -> Decision False in time 3.0900000000, query time of that 0.0082229160, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 90, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 90, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.004 accuracy: 1.5407 cost: 0.00633344 M: 10 delta: 1 time: 6.87729 one-recall: 0 one-ratio: 2.00156
iteration: 2 recall: 0.0588 accuracy: 0.610834 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1937 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: 14.5217 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: 19.2473 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: 25.9841 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: 32.2509 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.9916 accuracy: 0.0004699 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.8753 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 35.190000000000055
Index size:  81732.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0016203333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3800000000, query time of that 0.0785190830, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Accept!
  -> Decision True in time 3.7400000000, query time of that 0.7644066400, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1847.12 < 1867.97
  -> Decision False in time 0.5700000000, query time of that 0.1167810030, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Accept!
  -> Decision True in time 3.4600000000, query time of that 0.0894604820, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1891.03 < 1945.6
  -> Decision False in time 0.6000000000, query time of that 0.0169547880, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1980.19 < 1981.56
  -> Decision False in time 2.3800000000, query time of that 0.0625358630, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1791.78 < 1792.39
  -> Decision False in time 8.0500000000, query time of that 0.0211060640, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
2177.86 < 2245.87
  -> Decision False in time 22.5700000000, query time of that 0.0608308030, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1578.47 < 1622.84
  -> Decision False in time 10.2600000000, query time of that 0.0284967240, 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: 6.88404 one-recall: 0 one-ratio: 2.12757
iteration: 2 recall: 0.0552 accuracy: 0.670215 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.2035 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: 14.5345 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: 19.2614 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: 26.0051 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: 32.2787 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: 34.9007 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 35.22000000000003
Index size:  81732.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0020226667
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3700000000, query time of that 0.0760677390, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
2013.93 < 2048.65
  -> Decision False in time 0.4600000000, query time of that 0.0852532260, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1817.89 < 1890.61
  -> Decision False in time 3.5300000000, query time of that 0.6903429790, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
2029.29 < 2029.47
  -> Decision False in time 2.5900000000, query time of that 0.0661488180, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1720.98 < 1724.91
  -> Decision False in time 2.6800000000, query time of that 0.0677298560, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
2104.76 < 2112.56
  -> Decision False in time 4.3100000000, query time of that 0.1119046300, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1531.32 < 1601.62
  -> Decision False in time 2.4000000000, query time of that 0.0064033700, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1546.89 < 1624.28
  -> Decision False in time 0.0500000000, query time of that 0.0011123970, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1561.99 < 1597.25
  -> Decision False in time 4.4600000000, query time of that 0.0127775840, 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: 0.0056 accuracy: 1.41157 cost: 0.00633344 M: 10 delta: 1 time: 6.87907 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: 10.1967 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: 14.524 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: 19.2481 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: 25.9829 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9828 accuracy: 0.00106873 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 32.2454 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.99 accuracy: 0.000613841 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.8683 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 35.179999999999836
Index size:  81740.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0092930000
  Testing...
|S| = 98
|T| = 1411
Reject!
1701.39 < 1726.42
  -> Decision False in time 0.2300000000, query time of that 0.0304748250, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1746.38 < 1787.79
  -> Decision False in time 0.1500000000, query time of that 0.0206192970, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1889.16 < 1922.26
  -> Decision False in time 0.3500000000, query time of that 0.0454381050, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1617.22 < 1622.95
  -> Decision False in time 0.1200000000, query time of that 0.0020987060, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1649.99 < 1686.82
  -> Decision False in time 0.1800000000, query time of that 0.0037739970, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
572.798 < 663.788
  -> Decision False in time 0.0700000000, query time of that 0.0010844220, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1734.13 < 1750.93
  -> Decision False in time 3.4000000000, query time of that 0.0067935140, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1037.63 < 1040.51
  -> Decision False in time 1.7400000000, query time of that 0.0033837550, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1618.85 < 1658.08
  -> Decision False in time 5.4000000000, query time of that 0.0095104250, 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: 6.88762 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: 10.2046 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: 14.5342 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: 19.2576 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: 25.9965 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: 32.2607 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: 34.8855 one-recall: 1 one-ratio: 1
iteration: 8 recall: 0.9932 accuracy: 0.000294001 cost: 0.0443167 M: 24.8843 delta: 0.0806719 time: 35.8074 one-recall: 1 one-ratio: 1
Graph completion with reverse edges...

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Reranking edges...

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 36.12999999999988
Index size:  83000.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0018956667
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3600000000, query time of that 0.0704917620, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1860.89 < 1893.48
  -> Decision False in time 1.3400000000, query time of that 0.2516473030, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1825.08 < 1869.46
  -> Decision False in time 0.6800000000, query time of that 0.1289193600, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1722.31 < 1733.32
  -> Decision False in time 0.2500000000, query time of that 0.0071178830, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1492.22 < 1500.73
  -> Decision False in time 8.9800000000, query time of that 0.2185134160, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
2113.49 < 2167.96
  -> Decision False in time 0.2500000000, query time of that 0.0067309480, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1618.17 < 1668.67
  -> Decision False in time 0.8400000000, query time of that 0.0026576350, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1867.78 < 1926.84
  -> Decision False in time 8.6400000000, query time of that 0.0216343950, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1564.22 < 1624.1
  -> Decision False in time 11.9700000000, query time of that 0.0302310120, with c1=5.0000000000, c2=0.1000000000
