copying files to /scratch...
starting benchmark...
/scratch/knn/venv/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  from ._conv import register_converters as _register_converters
running only kgraph
order: [Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 90, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 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', 80, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 40, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 100, {'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', 1, {'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', 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', 3, {'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', 90, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 90, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.0044 accuracy: 1.5879 cost: 0.00633344 M: 10 delta: 1 time: 6.85437 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: 10.1376 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: 14.4296 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: 19.1154 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: 25.7915 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: 32.1069 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.9932 accuracy: 0.0002248 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 35.1859 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 35.53
Index size:  100448.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0016203333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.4000000000, query time of that 0.1055548170, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1707.17 < 1772.24
  -> Decision False in time 0.6100000000, query time of that 0.1549357240, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1662.46 < 1759.75
  -> Decision False in time 2.9100000000, query time of that 0.7542358160, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Accept!
  -> Decision True in time 3.5200000000, query time of that 0.1291918510, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
2002.39 < 2052.46
  -> Decision False in time 5.3000000000, query time of that 0.1897587620, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1768 < 1808.11
  -> Decision False in time 1.9200000000, query time of that 0.0732698620, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1938.95 < 1951.28
  -> Decision False in time 2.1600000000, query time of that 0.0101654610, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1665.47 < 1792.43
  -> Decision False in time 3.7200000000, query time of that 0.0140986950, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1945.71 < 1970.86
  -> Decision False in time 3.5300000000, query time of that 0.0141544330, 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: 0.0044 accuracy: 1.81839 cost: 0.00633344 M: 10 delta: 1 time: 6.87691 one-recall: 0 one-ratio: 2.07916
iteration: 2 recall: 0.052 accuracy: 0.699626 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1975 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: 14.5315 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: 19.2619 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: 26.0096 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: 32.2822 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.99 accuracy: 0.000358431 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.9071 one-recall: 1 one-ratio: 1
iteration: 8 recall: 0.9912 accuracy: 0.00031344 cost: 0.0443167 M: 24.8843 delta: 0.0806719 time: 35.8292 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 36.150000000000006
Index size:  82980.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0068253333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3400000000, query time of that 0.0474287390, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1525.83 < 1531.67
  -> Decision False in time 0.1200000000, query time of that 0.0174138920, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2105.14 < 2240.81
  -> Decision False in time 0.9100000000, query time of that 0.1253861230, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1596.44 < 1602.2
  -> Decision False in time 1.2900000000, query time of that 0.0226464720, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1653.95 < 1666.14
  -> Decision False in time 0.0700000000, query time of that 0.0013168770, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1436.76 < 1440.57
  -> Decision False in time 0.2500000000, query time of that 0.0048461670, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
2162.65 < 2174.57
  -> Decision False in time 1.0200000000, query time of that 0.0024894150, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1753.6 < 1812.66
  -> Decision False in time 0.5400000000, query time of that 0.0010626740, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1883.47 < 1891.28
  -> Decision False in time 1.6100000000, query time of that 0.0026721130, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 30, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 30, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.004 accuracy: 1.75774 cost: 0.00633344 M: 10 delta: 1 time: 6.8767 one-recall: 0 one-ratio: 2.03464
iteration: 2 recall: 0.0608 accuracy: 0.646443 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1935 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: 14.5254 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: 19.254 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: 25.9986 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: 32.265 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: 34.8863 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 35.21000000000001
Index size:  81728.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0048390000
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3500000000, query time of that 0.0549856380, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1772.68 < 1875.9
  -> Decision False in time 1.0900000000, query time of that 0.1712361350, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1417.56 < 1615.79
  -> Decision False in time 0.0600000000, query time of that 0.0093539060, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
2125.13 < 2133.02
  -> Decision False in time 1.8500000000, query time of that 0.0357122270, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1562.86 < 1578.29
  -> Decision False in time 2.3100000000, query time of that 0.0454803620, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1605.32 < 1615.76
  -> Decision False in time 1.4700000000, query time of that 0.0281472990, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1380.63 < 1535.94
  -> Decision False in time 13.3800000000, query time of that 0.0261668190, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1448.03 < 1454.61
  -> Decision False in time 20.9100000000, query time of that 0.0399580740, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1551.08 < 1681.27
  -> Decision False in time 5.7300000000, query time of that 0.0116131850, 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: 0.006 accuracy: 1.65471 cost: 0.00633344 M: 10 delta: 1 time: 6.86988 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: 10.1878 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: 14.5188 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: 19.2481 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: 25.9942 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: 32.2621 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.992 accuracy: 0.000590742 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.8871 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: 0.0056770000
  Testing...
|S| = 98
|T| = 1411
Reject!
1751.53 < 1760.08
  -> Decision False in time 0.0700000000, query time of that 0.0101285120, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1943.43 < 1944.1
  -> Decision False in time 0.4800000000, query time of that 0.0690953830, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1987.65 < 2066.44
  -> Decision False in time 1.0000000000, query time of that 0.1456617990, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1894.53 < 1905.17
  -> Decision False in time 1.0500000000, query time of that 0.0195925940, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1638.5 < 1691.26
  -> Decision False in time 0.7000000000, query time of that 0.0127962840, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1597.88 < 1633.78
  -> Decision False in time 1.4800000000, query time of that 0.0280108420, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1899.75 < 1976.23
  -> Decision False in time 1.0700000000, query time of that 0.0024448730, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1819.39 < 1839.22
  -> Decision False in time 1.7200000000, query time of that 0.0035937880, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1708.85 < 1752.07
  -> Decision False in time 0.3600000000, query time of that 0.0015115750, 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: 0.0036 accuracy: 1.7649 cost: 0.00633344 M: 10 delta: 1 time: 6.87534 one-recall: 0 one-ratio: 2.10464
iteration: 2 recall: 0.0604 accuracy: 0.635748 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1937 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: 14.5242 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: 19.2544 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: 25.9978 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: 32.2623 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: 34.8817 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: 35.802 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:  82992.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0018133333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3700000000, query time of that 0.0756554630, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1684.27 < 1738.32
  -> Decision False in time 1.5900000000, query time of that 0.3194140560, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2282.22 < 2428.18
  -> Decision False in time 3.1000000000, query time of that 0.6151623500, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
2059.64 < 2105.65
  -> Decision False in time 3.3000000000, query time of that 0.0801139140, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1467.63 < 1507.99
  -> Decision False in time 2.2100000000, query time of that 0.0564771510, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
2022.78 < 2082.43
  -> Decision False in time 0.8200000000, query time of that 0.0202722360, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1560.84 < 1587.76
  -> Decision False in time 0.1400000000, query time of that 0.0007157500, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
2005.66 < 2009.56
  -> Decision False in time 1.1900000000, query time of that 0.0034773740, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1399.21 < 1429.47
  -> Decision False in time 5.7600000000, query time of that 0.0163355790, 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: 0.0044 accuracy: 1.68691 cost: 0.00633344 M: 10 delta: 1 time: 6.87522 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: 10.1915 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: 14.525 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: 19.2524 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: 25.9922 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: 32.2597 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.9912 accuracy: 0.000477346 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.8861 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 35.200000000000045
Index size:  81740.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0033383333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3500000000, query time of that 0.0590661630, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1906.51 < 1920.93
  -> Decision False in time 0.0900000000, query time of that 0.0151036040, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1623.41 < 1624.62
  -> Decision False in time 0.7900000000, query time of that 0.1340130640, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
2119.26 < 2135.76
  -> Decision False in time 0.5700000000, query time of that 0.0122226570, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1701.76 < 1708.6
  -> Decision False in time 1.9100000000, query time of that 0.0412853400, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1646.72 < 1652.46
  -> Decision False in time 1.0900000000, query time of that 0.0225419230, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1424.3 < 1455.02
  -> Decision False in time 1.3400000000, query time of that 0.0036583490, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1531.9 < 1532.2
  -> Decision False in time 0.0500000000, query time of that 0.0005780250, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1682.59 < 1731.02
  -> Decision False in time 1.8300000000, query time of that 0.0044825540, 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: 6.87109 one-recall: 0 one-ratio: 2.03444
iteration: 2 recall: 0.0652 accuracy: 0.637073 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1874 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: 14.5199 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: 19.2437 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: 25.979 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: 32.2405 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: 34.8614 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 35.17000000000007
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 0.0804273980, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
2012.76 < 2048.73
  -> Decision False in time 2.6200000000, query time of that 0.5565813800, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1706.84 < 1892.92
  -> Decision False in time 1.0400000000, query time of that 0.2211153070, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1807.92 < 1832.36
  -> Decision False in time 1.7400000000, query time of that 0.0492055010, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1832.82 < 1993.23
  -> Decision False in time 4.0900000000, query time of that 0.1140214860, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1360.77 < 1428.46
  -> Decision False in time 4.8300000000, query time of that 0.1342148970, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1740.09 < 1751.03
  -> Decision False in time 7.6000000000, query time of that 0.0226455850, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1581.15 < 1581.42
  -> Decision False in time 0.3600000000, query time of that 0.0026345760, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1342.32 < 1363.38
  -> Decision False in time 12.0300000000, query time of that 0.0346390350, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 5, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 5, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.004 accuracy: 1.49738 cost: 0.00633344 M: 10 delta: 1 time: 6.87929 one-recall: 0 one-ratio: 1.99571
iteration: 2 recall: 0.0468 accuracy: 0.631223 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1952 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: 14.5224 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: 19.2464 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: 25.9829 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: 32.2406 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.9884 accuracy: 0.000479649 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.8572 one-recall: 1 one-ratio: 1
iteration: 8 recall: 0.9908 accuracy: 0.000330768 cost: 0.0443167 M: 24.8843 delta: 0.0806719 time: 35.776 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 36.08999999999992
Index size:  83000.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0081253333
  Testing...
|S| = 98
|T| = 1411
Reject!
2041.11 < 2054.59
  -> Decision False in time 0.0100000000, query time of that 0.0015580610, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1683.72 < 2334.66
  -> Decision False in time 0.2900000000, query time of that 0.0405462230, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1990.6 < 2057.26
  -> Decision False in time 0.4000000000, query time of that 0.0516096990, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1631.58 < 1649.58
  -> Decision False in time 0.4700000000, query time of that 0.0080940450, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1911.62 < 1912.48
  -> Decision False in time 0.5300000000, query time of that 0.0088879620, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1447.73 < 1451
  -> Decision False in time 0.1800000000, query time of that 0.0033576750, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
2050.49 < 2116.15
  -> Decision False in time 0.3700000000, query time of that 0.0013453680, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1622.81 < 1636.03
  -> Decision False in time 6.8500000000, query time of that 0.0119261140, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1684.57 < 1741.48
  -> Decision False in time 0.7100000000, query time of that 0.0017629380, 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: 0.0072 accuracy: 1.78614 cost: 0.00633344 M: 10 delta: 1 time: 6.87654 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: 10.1921 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: 14.5222 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: 19.2502 one-recall: 0.93 one-ratio: 1.01494
iteration: 5 recall: 0.9824 accuracy: 0.00111758 cost: 0.0303842 M: 16.8423 delta: 0.26712 time: 25.9865 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: 32.2437 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 32.55000000000007
Index size:  77428.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0141410000
  Testing...
|S| = 98
|T| = 1411
Reject!
2469.98 < 2504.61
  -> Decision False in time 0.2700000000, query time of that 0.0358375180, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1644.2 < 1662.35
  -> Decision False in time 0.2200000000, query time of that 0.0292023200, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1214.16 < 1225.7
  -> Decision False in time 0.3800000000, query time of that 0.0493695120, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1423.22 < 1425.32
  -> Decision False in time 0.5000000000, query time of that 0.0086547480, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
2006.36 < 2038.18
  -> Decision False in time 0.6600000000, query time of that 0.0118369420, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1233.72 < 1242.53
  -> Decision False in time 0.4100000000, query time of that 0.0069888620, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1558.12 < 1572.19
  -> Decision False in time 0.8500000000, query time of that 0.0020157380, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1462.32 < 1465.76
  -> Decision False in time 1.0800000000, query time of that 0.0021855690, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
2005.99 < 2116.15
  -> Decision False in time 1.7400000000, query time of that 0.0037973400, 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: 0.0048 accuracy: 1.58384 cost: 0.00633344 M: 10 delta: 1 time: 6.87737 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: 10.1948 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: 14.5237 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: 19.2486 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: 25.9863 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9896 accuracy: 0.000550482 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 32.2506 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.9944 accuracy: 0.000259975 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.8762 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 35.200000000000045
Index size:  81724.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0026693333
  Testing...
|S| = 98
|T| = 1411
Reject!
1881.99 < 1949.22
  -> Decision False in time 0.2900000000, query time of that 0.0527575620, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Accept!
  -> Decision True in time 3.6300000000, query time of that 0.6497802440, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1508.11 < 1513.33
  -> Decision False in time 1.9600000000, query time of that 0.3492468610, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1765.09 < 1805.08
  -> Decision False in time 0.0200000000, query time of that 0.0007786750, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1514.93 < 1610.55
  -> Decision False in time 0.2000000000, query time of that 0.0053075450, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1912.69 < 1933.44
  -> Decision False in time 1.3800000000, query time of that 0.0323294170, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1837.63 < 1842.41
  -> Decision False in time 3.8300000000, query time of that 0.0100706040, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1627.38 < 1655.64
  -> Decision False in time 11.0000000000, query time of that 0.0268674910, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1696.9 < 1761.68
  -> Decision False in time 12.7000000000, query time of that 0.0305577320, 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: 0.008 accuracy: 1.72963 cost: 0.00633344 M: 10 delta: 1 time: 6.89155 one-recall: 0 one-ratio: 2.01043
iteration: 2 recall: 0.066 accuracy: 0.635277 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.2087 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: 14.543 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: 19.2686 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: 26.01 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.992 accuracy: 0.000346113 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 32.277 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 32.569999999999936
Index size:  77428.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0130310000
  Testing...
|S| = 98
|T| = 1411
Reject!
1981.8 < 2017.07
  -> Decision False in time 0.2000000000, query time of that 0.0258484500, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1600.15 < 1737.16
  -> Decision False in time 0.1100000000, query time of that 0.0139119730, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1669.53 < 1746.19
  -> Decision False in time 0.0800000000, query time of that 0.0105792100, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1653.7 < 1672.21
  -> Decision False in time 0.4200000000, query time of that 0.0070082300, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1513.24 < 1700.29
  -> Decision False in time 1.0500000000, query time of that 0.0174017900, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1467.01 < 1498.16
  -> Decision False in time 0.6300000000, query time of that 0.0100599810, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1307.65 < 1327.62
  -> Decision False in time 1.4300000000, query time of that 0.0024286360, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1643.35 < 1710.14
  -> Decision False in time 2.1500000000, query time of that 0.0040071370, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1469 < 1491.53
  -> Decision False in time 0.8200000000, query time of that 0.0016958200, 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: 6.88507 one-recall: 0 one-ratio: 2.00156
iteration: 2 recall: 0.0588 accuracy: 0.610834 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.2016 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: 14.5314 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: 19.2561 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: 25.9933 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: 32.2541 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.9916 accuracy: 0.0004699 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.8716 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 35.18999999999983
Index size:  81704.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0021906667
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3800000000, query time of that 0.0706542970, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1658.77 < 1694.44
  -> Decision False in time 0.0000000000, query time of that 0.0008030930, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1591.49 < 1690.09
  -> Decision False in time 3.5900000000, query time of that 0.6529983260, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
2561.81 < 2565.3
  -> Decision False in time 0.2800000000, query time of that 0.0079538820, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1794.89 < 1798.94
  -> Decision False in time 3.2900000000, query time of that 0.0803876120, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1708.83 < 1759.57
  -> Decision False in time 0.4400000000, query time of that 0.0107355720, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1529.3 < 1651.12
  -> Decision False in time 23.2800000000, query time of that 0.0576117540, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1588.67 < 1610.61
  -> Decision False in time 1.7400000000, query time of that 0.0042621100, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1683.16 < 1689.78
  -> Decision False in time 0.3800000000, query time of that 0.0015115440, 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: 6.88811 one-recall: 0 one-ratio: 2.12757
iteration: 2 recall: 0.0552 accuracy: 0.670215 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.2081 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: 14.5424 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: 19.2722 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: 26.0181 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: 32.2846 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: 34.9107 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 35.22999999999979
Index size:  81724.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0032233333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3600000000, query time of that 0.0602574550, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1341.93 < 1436.32
  -> Decision False in time 0.2800000000, query time of that 0.0470110050, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2130.11 < 2477.86
  -> Decision False in time 0.3700000000, query time of that 0.0645178660, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1824.82 < 1830.67
  -> Decision False in time 0.5500000000, query time of that 0.0126108940, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1564.29 < 1585.55
  -> Decision False in time 0.2500000000, query time of that 0.0062751400, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1822.76 < 1837.29
  -> Decision False in time 2.7800000000, query time of that 0.0604622140, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1858.97 < 1876.35
  -> Decision False in time 3.0800000000, query time of that 0.0076331020, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1826.33 < 1841.99
  -> Decision False in time 2.1600000000, query time of that 0.0054235060, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1574.14 < 1672.52
  -> Decision False in time 3.1100000000, query time of that 0.0079373940, 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: 6.89218 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: 10.2087 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: 14.5408 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: 19.268 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: 26.007 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9828 accuracy: 0.00106873 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 32.2692 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.99 accuracy: 0.000613841 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.893 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: 0.0092930000
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3500000000, query time of that 0.0456341440, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1637.65 < 1648.8
  -> Decision False in time 0.0500000000, query time of that 0.0068947300, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1896.09 < 1905.93
  -> Decision False in time 0.1800000000, query time of that 0.0233845040, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1834.25 < 1867.98
  -> Decision False in time 0.5800000000, query time of that 0.0099557990, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1774 < 1852.66
  -> Decision False in time 1.3100000000, query time of that 0.0226344040, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1889.38 < 1912.42
  -> Decision False in time 0.2400000000, query time of that 0.0042732000, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1605.16 < 1624.51
  -> Decision False in time 2.1300000000, query time of that 0.0043555410, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1678.42 < 1685.83
  -> Decision False in time 0.7600000000, query time of that 0.0015069910, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1407.24 < 1419.53
  -> Decision False in time 1.9000000000, query time of that 0.0033830630, 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: 0.0068 accuracy: 1.63493 cost: 0.00633344 M: 10 delta: 1 time: 6.88774 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: 10.205 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: 14.5369 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: 19.264 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: 25.9991 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: 32.2617 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: 34.8852 one-recall: 1 one-ratio: 1
iteration: 8 recall: 0.9932 accuracy: 0.000294001 cost: 0.0443167 M: 24.8843 delta: 0.0806719 time: 35.8055 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:  82984.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0072696667
  Testing...
|S| = 98
|T| = 1411
Reject!
1642.92 < 1675.57
  -> Decision False in time 0.1300000000, query time of that 0.0180364900, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1903.77 < 1911.76
  -> Decision False in time 0.1400000000, query time of that 0.0188569400, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1286.43 < 1314.28
  -> Decision False in time 0.5400000000, query time of that 0.0739300750, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1316.97 < 1333.29
  -> Decision False in time 1.3200000000, query time of that 0.0211599040, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1859.12 < 1914.19
  -> Decision False in time 1.4400000000, query time of that 0.0252986030, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1834.98 < 1845.77
  -> Decision False in time 0.6900000000, query time of that 0.0109439400, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1759.72 < 1789.37
  -> Decision False in time 3.6300000000, query time of that 0.0070600350, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1971.92 < 2037.81
  -> Decision False in time 6.0900000000, query time of that 0.0104328660, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1792.8 < 1845.35
  -> Decision False in time 1.2800000000, query time of that 0.0022219280, with c1=5.0000000000, c2=0.1000000000
