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', 1, {'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', 10, {'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', 90, {'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', 30, {'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', 70, {'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', 2, {'reverse': -1}, False])]
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: 1.5879 cost: 0.00633344 M: 10 delta: 1 time: 6.83878 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: 10.1195 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: 14.4113 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: 19.0901 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: 25.7652 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: 32.4776 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.9932 accuracy: 0.0002248 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 35.5665 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 35.92
Index size:  100448.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0107390000
  Testing...
|S| = 98
|T| = 1411
Reject!
2392.06 < 2427.46
  -> Decision False in time 0.0500000000, query time of that 0.0104580930, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
2014.77 < 2048.51
  -> Decision False in time 0.0700000000, query time of that 0.0116992760, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1751.33 < 1765.5
  -> Decision False in time 0.0400000000, query time of that 0.0081803250, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1431.87 < 1457.09
  -> Decision False in time 1.2300000000, query time of that 0.0303814480, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1489.63 < 1496.6
  -> Decision False in time 0.5600000000, query time of that 0.0134791660, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1813.23 < 1855.39
  -> Decision False in time 0.8200000000, query time of that 0.0206765660, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1528.81 < 1557.74
  -> Decision False in time 1.6600000000, query time of that 0.0040311800, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1806.1 < 1818.27
  -> Decision False in time 3.9900000000, query time of that 0.0098546100, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1894.14 < 1902.75
  -> Decision False in time 8.9800000000, query time of that 0.0234647580, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 20, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 20, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.0044 accuracy: 1.81839 cost: 0.00633344 M: 10 delta: 1 time: 6.88425 one-recall: 0 one-ratio: 2.07916
iteration: 2 recall: 0.052 accuracy: 0.699626 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.199 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: 14.5281 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: 19.251 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: 25.9853 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: 32.2483 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.99 accuracy: 0.000358431 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.8714 one-recall: 1 one-ratio: 1
iteration: 8 recall: 0.9912 accuracy: 0.00031344 cost: 0.0443167 M: 24.8843 delta: 0.0806719 time: 35.7929 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 36.109999999999985
Index size:  83004.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0051403333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3400000000, query time of that 0.0507114930, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1775.31 < 1818.6
  -> Decision False in time 0.0200000000, query time of that 0.0034045010, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1971.89 < 2017.92
  -> Decision False in time 0.4500000000, query time of that 0.0648574500, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1638.56 < 1698.13
  -> Decision False in time 0.9700000000, query time of that 0.0195970340, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1845.87 < 1852.55
  -> Decision False in time 0.4000000000, query time of that 0.0071391120, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1447.9 < 1539.08
  -> Decision False in time 1.5100000000, query time of that 0.0286584620, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1852.02 < 1865.47
  -> Decision False in time 0.0500000000, query time of that 0.0006205300, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1757.01 < 1783.32
  -> Decision False in time 0.6900000000, query time of that 0.0021737980, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1438.7 < 1454.17
  -> Decision False in time 3.7700000000, query time of that 0.0076158120, 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: 0.004 accuracy: 1.75774 cost: 0.00633344 M: 10 delta: 1 time: 6.88156 one-recall: 0 one-ratio: 2.03464
iteration: 2 recall: 0.0608 accuracy: 0.646443 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1968 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: 14.5241 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: 19.2462 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: 25.9809 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: 32.2456 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: 34.8685 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:  81728.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0075250000
  Testing...
|S| = 98
|T| = 1411
Reject!
1471.03 < 1675.94
  -> Decision False in time 0.1000000000, query time of that 0.0154642870, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
2037.87 < 2058.87
  -> Decision False in time 0.6400000000, query time of that 0.0900308230, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1640.7 < 1681.67
  -> Decision False in time 0.9000000000, query time of that 0.1279328220, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1614.42 < 1672.59
  -> Decision False in time 0.4800000000, query time of that 0.0096740900, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1604.65 < 1614.41
  -> Decision False in time 1.9100000000, query time of that 0.0350500980, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1420.82 < 1426.35
  -> Decision False in time 1.3400000000, query time of that 0.0246918360, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
2017.48 < 2058.87
  -> Decision False in time 8.8800000000, query time of that 0.0164097330, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1559.66 < 1567.67
  -> Decision False in time 1.1900000000, query time of that 0.0022380890, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1265.68 < 1276.04
  -> Decision False in time 1.4300000000, query time of that 0.0028960460, 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: 0.006 accuracy: 1.65471 cost: 0.00633344 M: 10 delta: 1 time: 6.89276 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: 10.2119 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: 14.5436 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: 19.2709 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: 26.0089 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: 32.2732 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.992 accuracy: 0.000590742 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.897 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 35.22000000000003
Index size:  81740.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0020226667
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3700000000, query time of that 0.0734260510, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1955.74 < 1979.93
  -> Decision False in time 0.9400000000, query time of that 0.1871920530, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2086.33 < 2122.71
  -> Decision False in time 1.9600000000, query time of that 0.3919488870, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Accept!
  -> Decision True in time 3.3900000000, query time of that 0.0883997270, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1629.24 < 1658.76
  -> Decision False in time 0.4100000000, query time of that 0.0119494280, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1654.25 < 1674.3
  -> Decision False in time 0.9500000000, query time of that 0.0252662260, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1632.06 < 1683.08
  -> Decision False in time 2.0700000000, query time of that 0.0068188030, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1838.79 < 1988.09
  -> Decision False in time 2.7000000000, query time of that 0.0083983970, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1832.82 < 1993.23
  -> Decision False in time 1.4400000000, query time of that 0.0039521030, 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: 0.0036 accuracy: 1.7649 cost: 0.00633344 M: 10 delta: 1 time: 6.87153 one-recall: 0 one-ratio: 2.10464
iteration: 2 recall: 0.0604 accuracy: 0.635748 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1872 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: 14.5146 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: 19.2399 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: 25.9762 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: 32.2369 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: 34.8593 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: 35.7802 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 36.099999999999966
Index size:  83004.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0081253333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3400000000, query time of that 0.0473272130, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1579.87 < 1592.41
  -> Decision False in time 1.0600000000, query time of that 0.1444561020, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1386.44 < 1387.64
  -> Decision False in time 0.0600000000, query time of that 0.0083476580, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1372.58 < 1440.83
  -> Decision False in time 1.1700000000, query time of that 0.0197330380, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1974.44 < 2053.92
  -> Decision False in time 0.0500000000, query time of that 0.0013938500, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1476.23 < 1477.01
  -> Decision False in time 0.6700000000, query time of that 0.0122025040, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1996.41 < 2035.24
  -> Decision False in time 0.4000000000, query time of that 0.0011119610, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1708.93 < 1719.87
  -> Decision False in time 9.4000000000, query time of that 0.0171194370, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1647.75 < 1705.6
  -> Decision False in time 3.4000000000, query time of that 0.0064563840, 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: 0.0044 accuracy: 1.68691 cost: 0.00633344 M: 10 delta: 1 time: 6.88336 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: 10.2017 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: 14.5343 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: 19.2605 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: 25.9997 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: 32.2722 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.9912 accuracy: 0.000477346 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.8965 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 35.20999999999998
Index size:  81724.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0016203333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3700000000, query time of that 0.0740791770, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1648.04 < 1656.85
  -> Decision False in time 1.6000000000, query time of that 0.3320007030, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1826.05 < 1916.51
  -> Decision False in time 0.9000000000, query time of that 0.1831305180, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1789.74 < 1793.77
  -> Decision False in time 2.4300000000, query time of that 0.0676267630, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1686.91 < 1691.65
  -> Decision False in time 2.8400000000, query time of that 0.0761737690, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1937.52 < 1940.47
  -> Decision False in time 3.9800000000, query time of that 0.1089823060, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1354.8 < 1376.95
  -> Decision False in time 5.1000000000, query time of that 0.0142456480, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1869.86 < 1894.96
  -> Decision False in time 1.3600000000, query time of that 0.0051848210, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1475.45 < 1486.11
  -> Decision False in time 0.1600000000, query time of that 0.0008130290, 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: 0.008 accuracy: 1.74437 cost: 0.00633344 M: 10 delta: 1 time: 6.86835 one-recall: 0 one-ratio: 2.03444
iteration: 2 recall: 0.0652 accuracy: 0.637073 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1845 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: 14.513 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: 19.2362 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: 25.9709 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: 32.2338 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: 34.8549 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:  81732.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0014630000
  Testing...
|S| = 98
|T| = 1411
Reject!
1478.5 < 1486.97
  -> Decision False in time 0.0600000000, query time of that 0.0115077770, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1849.7 < 1890.28
  -> Decision False in time 0.5000000000, query time of that 0.1090538220, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1901.74 < 1915.35
  -> Decision False in time 3.0500000000, query time of that 0.6431727150, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
2152.9 < 2162.25
  -> Decision False in time 0.7100000000, query time of that 0.0204009160, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1940.91 < 1961.94
  -> Decision False in time 8.0800000000, query time of that 0.2247912270, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1673.15 < 1700.97
  -> Decision False in time 5.6200000000, query time of that 0.1591047610, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1873.2 < 1890.92
  -> Decision False in time 4.4900000000, query time of that 0.0134544620, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1781 < 1790.11
  -> Decision False in time 12.2200000000, query time of that 0.0368670060, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1669.44 < 1704.98
  -> Decision False in time 12.3200000000, query time of that 0.0372376610, 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: 6.88533 one-recall: 0 one-ratio: 1.99571
iteration: 2 recall: 0.0468 accuracy: 0.631223 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.2038 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: 14.5358 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: 19.262 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: 26.0011 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: 32.2647 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.9884 accuracy: 0.000479649 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.8887 one-recall: 1 one-ratio: 1
iteration: 8 recall: 0.9908 accuracy: 0.000330768 cost: 0.0443167 M: 24.8843 delta: 0.0806719 time: 35.8097 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 36.129999999999995
Index size:  83000.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0030116667
  Testing...
|S| = 98
|T| = 1411
Reject!
1578.5 < 1634.26
  -> Decision False in time 0.3600000000, query time of that 0.0576428560, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
2196.42 < 2282.66
  -> Decision False in time 0.1600000000, query time of that 0.0254702780, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1694.02 < 1702.42
  -> Decision False in time 1.9000000000, query time of that 0.3086489720, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1813.65 < 1952.75
  -> Decision False in time 0.1800000000, query time of that 0.0043353620, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1980.25 < 1987.77
  -> Decision False in time 1.6700000000, query time of that 0.0360188640, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1603.2 < 1610.47
  -> Decision False in time 0.6700000000, query time of that 0.0154926710, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1765.95 < 1819.14
  -> Decision False in time 3.4300000000, query time of that 0.0084416780, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1930.29 < 1956.42
  -> Decision False in time 9.8300000000, query time of that 0.0228687430, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1452.23 < 1626.59
  -> Decision False in time 14.5500000000, query time of that 0.0321469260, 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: 0.0072 accuracy: 1.78614 cost: 0.00633344 M: 10 delta: 1 time: 6.90141 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: 10.2207 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: 14.5538 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: 19.2813 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: 26.0262 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: 32.3004 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 32.60000000000002
Index size:  77428.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0062620000
  Testing...
|S| = 98
|T| = 1411
Reject!
2124.12 < 2286.98
  -> Decision False in time 0.1700000000, query time of that 0.0271252580, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1734.03 < 1746.17
  -> Decision False in time 1.0400000000, query time of that 0.1540579280, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1911.38 < 1934.3
  -> Decision False in time 1.2000000000, query time of that 0.1772147850, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1631.98 < 1634.24
  -> Decision False in time 1.3700000000, query time of that 0.0281518740, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1705.96 < 1751.45
  -> Decision False in time 0.9300000000, query time of that 0.0189416630, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1393.6 < 1414.37
  -> Decision False in time 0.4300000000, query time of that 0.0089435590, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1866.92 < 1919.98
  -> Decision False in time 0.7100000000, query time of that 0.0019818410, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1614.94 < 1645.48
  -> Decision False in time 9.0900000000, query time of that 0.0185695830, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1351.62 < 1352.08
  -> Decision False in time 8.5800000000, query time of that 0.0174726370, 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: 0.0048 accuracy: 1.58384 cost: 0.00633344 M: 10 delta: 1 time: 6.89618 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: 10.2154 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: 14.5501 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: 19.2799 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: 26.0235 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9896 accuracy: 0.000550482 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 32.2997 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.9944 accuracy: 0.000259975 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.9308 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 35.25
Index size:  81736.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0078486667
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3400000000, query time of that 0.0445889710, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1376.33 < 1381.55
  -> Decision False in time 0.2800000000, query time of that 0.0385324600, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1644.97 < 1709.93
  -> Decision False in time 0.3300000000, query time of that 0.0455514710, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1785.19 < 1849.63
  -> Decision False in time 0.4900000000, query time of that 0.0092150990, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1651.15 < 1676.23
  -> Decision False in time 0.1800000000, query time of that 0.0036654510, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1878.47 < 1887.06
  -> Decision False in time 0.4100000000, query time of that 0.0069172670, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1897.04 < 1897.44
  -> Decision False in time 0.0200000000, query time of that 0.0004919860, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1907.11 < 1943.99
  -> Decision False in time 2.4600000000, query time of that 0.0044886710, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
2161.92 < 2180.74
  -> Decision False in time 1.7500000000, query time of that 0.0037850940, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 3, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 3, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.008 accuracy: 1.72963 cost: 0.00633344 M: 10 delta: 1 time: 6.89467 one-recall: 0 one-ratio: 2.01043
iteration: 2 recall: 0.066 accuracy: 0.635277 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.2133 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: 14.5433 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: 19.2688 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: 26.0042 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.992 accuracy: 0.000346113 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 32.272 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:  77432.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0121573333
  Testing...
|S| = 98
|T| = 1411
Reject!
1869.31 < 2040.98
  -> Decision False in time 0.0400000000, query time of that 0.0057961510, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
2110.03 < 2239.61
  -> Decision False in time 0.2200000000, query time of that 0.0298043540, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1867.71 < 1885.93
  -> Decision False in time 0.4300000000, query time of that 0.0554722830, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
2029.4 < 2129.22
  -> Decision False in time 0.2600000000, query time of that 0.0045122260, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1987.66 < 2033.88
  -> Decision False in time 0.0100000000, query time of that 0.0005701410, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
2061.87 < 2066.63
  -> Decision False in time 0.0700000000, query time of that 0.0015638780, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1739.14 < 1802.23
  -> Decision False in time 3.0900000000, query time of that 0.0051447440, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1849.66 < 1868.11
  -> Decision False in time 0.3600000000, query time of that 0.0011836650, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1736.4 < 1749.05
  -> Decision False in time 3.8700000000, query time of that 0.0066485400, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 70, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 70, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.004 accuracy: 1.5407 cost: 0.00633344 M: 10 delta: 1 time: 6.89526 one-recall: 0 one-ratio: 2.00156
iteration: 2 recall: 0.0588 accuracy: 0.610834 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.2134 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: 14.5425 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: 19.2668 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: 26.0036 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: 32.2719 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.9916 accuracy: 0.0004699 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.8944 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 35.22000000000003
Index size:  81728.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0021906667
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3700000000, query time of that 0.0695096610, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1533.48 < 1554.24
  -> Decision False in time 0.2800000000, query time of that 0.0531927000, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2013.81 < 2061.73
  -> Decision False in time 0.5300000000, query time of that 0.1014039660, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1759.29 < 1763.43
  -> Decision False in time 1.1100000000, query time of that 0.0277338660, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1871.79 < 1954.8
  -> Decision False in time 3.6200000000, query time of that 0.0906298420, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
2107.79 < 2155.65
  -> Decision False in time 2.1000000000, query time of that 0.0514927040, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1810.73 < 1893.56
  -> Decision False in time 2.7200000000, query time of that 0.0077424920, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
2130.52 < 2209.38
  -> Decision False in time 6.0700000000, query time of that 0.0160251400, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1632.68 < 1644.1
  -> Decision False in time 2.7500000000, query time of that 0.0078040590, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 50, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 50, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.004 accuracy: 1.6918 cost: 0.00633344 M: 10 delta: 1 time: 6.89439 one-recall: 0 one-ratio: 2.12757
iteration: 2 recall: 0.0552 accuracy: 0.670215 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.2138 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: 14.5465 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: 19.2735 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: 26.0161 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: 32.2879 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: 34.9172 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 35.25
Index size:  81716.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0032233333
  Testing...
|S| = 98
|T| = 1411
Reject!
1977.03 < 2055.71
  -> Decision False in time 0.1000000000, query time of that 0.0164507660, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1642.48 < 1759.84
  -> Decision False in time 0.3800000000, query time of that 0.0668611580, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1681.4 < 1773.8
  -> Decision False in time 0.2200000000, query time of that 0.0371112980, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
2221.73 < 2246.09
  -> Decision False in time 0.4800000000, query time of that 0.0108064070, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1553.35 < 1595.27
  -> Decision False in time 0.8700000000, query time of that 0.0193685990, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1789.72 < 1842.89
  -> Decision False in time 1.7300000000, query time of that 0.0401616320, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1633.59 < 1753.04
  -> Decision False in time 1.0400000000, query time of that 0.0031302030, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
2161.08 < 2166.35
  -> Decision False in time 1.7700000000, query time of that 0.0045497510, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1784.21 < 1847.64
  -> Decision False in time 2.7400000000, query time of that 0.0072255080, 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: 0.0056 accuracy: 1.41157 cost: 0.00633344 M: 10 delta: 1 time: 6.88681 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: 10.2071 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: 14.5385 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: 19.2623 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: 25.9991 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9828 accuracy: 0.00106873 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 32.2628 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.99 accuracy: 0.000613841 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.8848 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:  81728.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0026693333
  Testing...
|S| = 98
|T| = 1411
Reject!
1829.03 < 1844.1
  -> Decision False in time 0.1100000000, query time of that 0.0203371860, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
2228.26 < 2333.36
  -> Decision False in time 0.3400000000, query time of that 0.0624132720, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1805.12 < 1806.58
  -> Decision False in time 1.2900000000, query time of that 0.2309294930, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
2009.01 < 2011.82
  -> Decision False in time 0.8400000000, query time of that 0.0208515870, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1795.29 < 1832.42
  -> Decision False in time 1.2000000000, query time of that 0.0293696740, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1889.33 < 2006.8
  -> Decision False in time 1.9100000000, query time of that 0.0460449670, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1887.5 < 1942.01
  -> Decision False in time 2.3600000000, query time of that 0.0069696110, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1885.54 < 1889.52
  -> Decision False in time 3.0300000000, query time of that 0.0080314140, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1741.12 < 1742.1
  -> Decision False in time 6.4100000000, query time of that 0.0160154390, 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: 0.0068 accuracy: 1.63493 cost: 0.00633344 M: 10 delta: 1 time: 6.88708 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: 10.2025 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: 14.5305 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: 19.2551 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: 25.9908 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: 32.257 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: 34.8831 one-recall: 1 one-ratio: 1
iteration: 8 recall: 0.9932 accuracy: 0.000294001 cost: 0.0443167 M: 24.8843 delta: 0.0806719 time: 35.805 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:  82980.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0089603333
  Testing...
|S| = 98
|T| = 1411
Reject!
1910.82 < 2091.6
  -> Decision False in time 0.1300000000, query time of that 0.0186728280, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1992.8 < 1992.82
  -> Decision False in time 0.8100000000, query time of that 0.1101271930, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1767.49 < 2343.71
  -> Decision False in time 0.7100000000, query time of that 0.0983616620, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1694.02 < 1696.09
  -> Decision False in time 0.2200000000, query time of that 0.0038201580, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1613.03 < 1620.27
  -> Decision False in time 0.4300000000, query time of that 0.0081179910, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1527.76 < 1884.17
  -> Decision False in time 0.1400000000, query time of that 0.0028987140, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1327.71 < 1339.95
  -> Decision False in time 0.8300000000, query time of that 0.0016538950, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1449.17 < 1477.91
  -> Decision False in time 2.7700000000, query time of that 0.0052894450, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1305.08 < 1323.42
  -> Decision False in time 0.3900000000, query time of that 0.0010611720, with c1=5.0000000000, c2=0.1000000000
