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', 100, {'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', 70, {'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', 10, {'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', 20, {'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', 3, {'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', 80, {'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', 4, {'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', 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: 0.0044 accuracy: 1.5879 cost: 0.00633344 M: 10 delta: 1 time: 6.84936 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: 10.1298 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: 14.4173 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: 19.1001 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: 25.7743 one-recall: 0.97 one-ratio: 1.00419
iteration: 6 recall: 0.9856 accuracy: 0.000706031 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 31.9524 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.9932 accuracy: 0.0002248 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.5277 one-recall: 1 one-ratio: 1
Graph completion with reverse edges...

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

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 34.82
Index size:  100448.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0014630000
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3800000000, query time of that 0.0762010480, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Accept!
  -> Decision True in time 3.7500000000, query time of that 0.7836045160, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2085.54 < 2188.66
  -> Decision False in time 0.2800000000, query time of that 0.0587228220, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1628.25 < 1788.93
  -> Decision False in time 1.1000000000, query time of that 0.0298778020, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1846.58 < 1852.97
  -> Decision False in time 0.2200000000, query time of that 0.0066806730, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1892.62 < 2000.41
  -> Decision False in time 1.4700000000, query time of that 0.0420768820, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1814.69 < 1862.79
  -> Decision False in time 11.0100000000, query time of that 0.0312750860, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1790.8 < 1806.19
  -> Decision False in time 1.6800000000, query time of that 0.0045051780, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1845.24 < 1848.93
  -> Decision False in time 11.8400000000, query time of that 0.0328950580, 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: 0.0044 accuracy: 1.81839 cost: 0.00633344 M: 10 delta: 1 time: 6.84645 one-recall: 0 one-ratio: 2.07916
iteration: 2 recall: 0.052 accuracy: 0.699626 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1265 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: 14.4112 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: 19.0918 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: 25.7655 one-recall: 0.99 one-ratio: 1.0012
iteration: 6 recall: 0.9872 accuracy: 0.000561977 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 31.9462 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.99 accuracy: 0.000358431 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.5222 one-recall: 1 one-ratio: 1
iteration: 8 recall: 0.9912 accuracy: 0.00031344 cost: 0.0443167 M: 24.8843 delta: 0.0806719 time: 35.4258 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 35.72
Index size:  90300.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0028516667
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3500000000, query time of that 0.0607862470, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
2006.3 < 2012.49
  -> Decision False in time 1.4300000000, query time of that 0.2413465130, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2029.73 < 2035.24
  -> Decision False in time 0.4000000000, query time of that 0.0697723690, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1871.72 < 1934.52
  -> Decision False in time 0.4800000000, query time of that 0.0112083030, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1519.03 < 1580.33
  -> Decision False in time 4.5000000000, query time of that 0.0965550720, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
2006.92 < 2045.17
  -> Decision False in time 0.5400000000, query time of that 0.0124071100, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1821.11 < 1824.71
  -> Decision False in time 1.0600000000, query time of that 0.0032171130, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1796.55 < 1806.97
  -> Decision False in time 0.7100000000, query time of that 0.0022030230, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1759.01 < 1787.49
  -> Decision False in time 13.1100000000, query time of that 0.0295731380, 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: 1.75774 cost: 0.00633344 M: 10 delta: 1 time: 6.83562 one-recall: 0 one-ratio: 2.03464
iteration: 2 recall: 0.0608 accuracy: 0.646443 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1161 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: 14.4026 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: 19.0843 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: 25.7557 one-recall: 0.97 one-ratio: 1.00167
iteration: 6 recall: 0.9844 accuracy: 0.000793035 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 31.9366 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: 34.5116 one-recall: 1 one-ratio: 1
Graph completion with reverse edges...

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

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 34.81
Index size:  76296.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0021906667
  Testing...
|S| = 98
|T| = 1411
Reject!
1416.01 < 1440.29
  -> Decision False in time 0.2100000000, query time of that 0.0379874510, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1996.26 < 2072.03
  -> Decision False in time 0.0400000000, query time of that 0.0069264900, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1785.19 < 1841.67
  -> Decision False in time 2.9300000000, query time of that 0.5341934520, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1981.48 < 1994.21
  -> Decision False in time 0.3600000000, query time of that 0.0089794580, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1841.71 < 1871.12
  -> Decision False in time 0.4500000000, query time of that 0.0113770010, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1825.79 < 1907.74
  -> Decision False in time 0.2900000000, query time of that 0.0076766980, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1932.6 < 1944.32
  -> Decision False in time 1.0300000000, query time of that 0.0038392170, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1733.51 < 1749.57
  -> Decision False in time 2.3800000000, query time of that 0.0060809650, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1701.52 < 1707.26
  -> Decision False in time 0.3600000000, query time of that 0.0016730040, 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: 0.006 accuracy: 1.65471 cost: 0.00633344 M: 10 delta: 1 time: 6.83119 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: 10.1091 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: 14.394 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: 19.0759 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: 25.7476 one-recall: 0.98 one-ratio: 1.00081
iteration: 6 recall: 0.986 accuracy: 0.000854818 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 31.9269 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.992 accuracy: 0.000590742 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.5019 one-recall: 1 one-ratio: 1
Graph completion with reverse edges...

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

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 34.789999999999964
Index size:  76288.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0048390000
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3500000000, query time of that 0.0530681390, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1685.7 < 1695.97
  -> Decision False in time 0.1900000000, query time of that 0.0294917990, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1569.85 < 1571.21
  -> Decision False in time 0.4700000000, query time of that 0.0669101030, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1650.57 < 1654.21
  -> Decision False in time 1.5800000000, query time of that 0.0288464890, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1753.13 < 1755.85
  -> Decision False in time 0.3500000000, query time of that 0.0072024950, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1503.32 < 1532.81
  -> Decision False in time 1.3300000000, query time of that 0.0250818910, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1439.4 < 1440.55
  -> Decision False in time 16.2900000000, query time of that 0.0317081670, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1656.33 < 1671.2
  -> Decision False in time 0.7100000000, query time of that 0.0018397230, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1511.69 < 1532.37
  -> Decision False in time 0.7500000000, query time of that 0.0017820970, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 10, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 10, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.0036 accuracy: 1.7649 cost: 0.00633344 M: 10 delta: 1 time: 6.83401 one-recall: 0 one-ratio: 2.10464
iteration: 2 recall: 0.0604 accuracy: 0.635748 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1131 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: 14.4043 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: 19.0867 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: 25.7627 one-recall: 0.97 one-ratio: 1.00944
iteration: 6 recall: 0.9812 accuracy: 0.00168385 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 31.9464 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: 34.5239 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: 35.4262 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 35.72000000000003
Index size:  77544.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0068253333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3400000000, query time of that 0.0456500290, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1825.09 < 1864.97
  -> Decision False in time 0.9100000000, query time of that 0.1260229550, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1904.27 < 1946.88
  -> Decision False in time 0.0300000000, query time of that 0.0053443860, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1571.09 < 1636.67
  -> Decision False in time 0.0200000000, query time of that 0.0005842060, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1729.84 < 1779.49
  -> Decision False in time 1.4600000000, query time of that 0.0239299750, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
2124.42 < 2143.76
  -> Decision False in time 0.0100000000, query time of that 0.0007265410, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1619.22 < 1625.26
  -> Decision False in time 0.0400000000, query time of that 0.0007428670, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1501.44 < 1521.17
  -> Decision False in time 2.4500000000, query time of that 0.0048659580, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1907 < 1943.9
  -> Decision False in time 1.6700000000, query time of that 0.0027489860, 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: 0.0044 accuracy: 1.68691 cost: 0.00633344 M: 10 delta: 1 time: 6.83686 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: 10.1138 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: 14.3999 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: 19.0804 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: 25.7516 one-recall: 0.99 one-ratio: 1.00065
iteration: 6 recall: 0.988 accuracy: 0.000656383 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 31.9292 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.9912 accuracy: 0.000477346 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.5014 one-recall: 1 one-ratio: 1
Graph completion with reverse edges...

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

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 34.79000000000008
Index size:  76316.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0092153333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3400000000, query time of that 0.0456040840, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1593.33 < 1758.28
  -> Decision False in time 0.3100000000, query time of that 0.0402358840, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1335.45 < 1969.85
  -> Decision False in time 0.2400000000, query time of that 0.0317396900, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1462.41 < 1702.44
  -> Decision False in time 0.5300000000, query time of that 0.0088392530, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1862.02 < 1866.28
  -> Decision False in time 0.7900000000, query time of that 0.0130922860, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1663.88 < 1714.81
  -> Decision False in time 0.0600000000, query time of that 0.0011547140, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1804.18 < 1812.22
  -> Decision False in time 1.0100000000, query time of that 0.0019933110, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
2000.4 < 2025.28
  -> Decision False in time 0.7100000000, query time of that 0.0016343410, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1761.83 < 1781.75
  -> Decision False in time 2.8100000000, query time of that 0.0045618010, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 1, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 1, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.008 accuracy: 1.74437 cost: 0.00633344 M: 10 delta: 1 time: 6.8311 one-recall: 0 one-ratio: 2.03444
iteration: 2 recall: 0.0652 accuracy: 0.637073 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1088 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: 14.3924 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: 19.0677 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: 25.7327 one-recall: 0.99 one-ratio: 1.00077
iteration: 6 recall: 0.99 accuracy: 0.000758827 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 31.9043 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: 34.4753 one-recall: 0.99 one-ratio: 1.00077
Graph completion with reverse edges...

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

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 34.770000000000095
Index size:  76316.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0107390000
  Testing...
|S| = 98
|T| = 1411
Reject!
1479.67 < 1509.5
  -> Decision False in time 0.0300000000, query time of that 0.0040834780, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1418.19 < 1421.56
  -> Decision False in time 0.1100000000, query time of that 0.0149007370, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1861.85 < 1904.24
  -> Decision False in time 0.1700000000, query time of that 0.0246875440, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
2294.1 < 2349.48
  -> Decision False in time 0.1700000000, query time of that 0.0034123550, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1652.55 < 1654.74
  -> Decision False in time 0.7600000000, query time of that 0.0129385050, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1763.85 < 1858.46
  -> Decision False in time 1.2900000000, query time of that 0.0224164920, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1371.83 < 1432.46
  -> Decision False in time 0.4900000000, query time of that 0.0011610610, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1907.24 < 1961.44
  -> Decision False in time 1.3000000000, query time of that 0.0023803140, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1198.52 < 1227.3
  -> Decision False in time 0.6900000000, query time of that 0.0014075340, 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: 0.004 accuracy: 1.49738 cost: 0.00633344 M: 10 delta: 1 time: 6.83796 one-recall: 0 one-ratio: 1.99571
iteration: 2 recall: 0.0468 accuracy: 0.631223 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1137 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: 14.3977 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: 19.0745 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: 25.7406 one-recall: 0.99 one-ratio: 1.00003
iteration: 6 recall: 0.9824 accuracy: 0.000906052 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 31.9124 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.9884 accuracy: 0.000479649 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.4874 one-recall: 1 one-ratio: 1
iteration: 8 recall: 0.9908 accuracy: 0.000330768 cost: 0.0443167 M: 24.8843 delta: 0.0806719 time: 35.3903 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 35.690000000000055
Index size:  77544.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0051403333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3500000000, query time of that 0.0485170760, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1809.7 < 1904.37
  -> Decision False in time 0.3800000000, query time of that 0.0542056780, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2033.76 < 2138.1
  -> Decision False in time 2.1400000000, query time of that 0.3048710440, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1815.6 < 1820.24
  -> Decision False in time 0.0300000000, query time of that 0.0006399760, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1769.46 < 1777.4
  -> Decision False in time 0.2300000000, query time of that 0.0051720020, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1750 < 1751.31
  -> Decision False in time 0.0800000000, query time of that 0.0020424940, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1798.44 < 1828.01
  -> Decision False in time 2.2800000000, query time of that 0.0046263550, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
2092.47 < 2148.75
  -> Decision False in time 1.0300000000, query time of that 0.0023734310, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1585.48 < 1589.15
  -> Decision False in time 0.6900000000, query time of that 0.0018694400, 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: 0.0072 accuracy: 1.78614 cost: 0.00633344 M: 10 delta: 1 time: 6.83614 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: 10.1157 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: 14.4026 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: 19.082 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: 25.7522 one-recall: 0.99 one-ratio: 1.00036
iteration: 6 recall: 0.9956 accuracy: 0.000240658 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 31.9294 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 32.210000000000036
Index size:  71996.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0130310000
  Testing...
|S| = 98
|T| = 1411
Reject!
2145.9 < 2236.35
  -> Decision False in time 0.0200000000, query time of that 0.0033812530, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1512.28 < 1519.62
  -> Decision False in time 0.4300000000, query time of that 0.0532378490, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1695.7 < 1751.1
  -> Decision False in time 0.5600000000, query time of that 0.0684314730, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
2077.69 < 2122.04
  -> Decision False in time 0.6500000000, query time of that 0.0102125770, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
2047.2 < 2060.83
  -> Decision False in time 1.8600000000, query time of that 0.0291232020, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1649.59 < 1658.75
  -> Decision False in time 0.0700000000, query time of that 0.0015529240, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1593.08 < 1636.32
  -> Decision False in time 0.3700000000, query time of that 0.0011359950, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1825.46 < 1857.35
  -> Decision False in time 0.2600000000, query time of that 0.0005291990, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1409.33 < 1422.55
  -> Decision False in time 2.3900000000, query time of that 0.0041967600, 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: 0.0048 accuracy: 1.58384 cost: 0.00633344 M: 10 delta: 1 time: 6.83674 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: 10.1142 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: 14.4006 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: 19.0796 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: 25.7473 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9896 accuracy: 0.000550482 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 31.9251 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.9944 accuracy: 0.000259975 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.4984 one-recall: 1 one-ratio: 1
Graph completion with reverse edges...

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

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 34.780000000000086
Index size:  76292.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0092930000
  Testing...
|S| = 98
|T| = 1411
Reject!
1642.31 < 1667.07
  -> Decision False in time 0.1700000000, query time of that 0.0232925000, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1655.3 < 1696.56
  -> Decision False in time 0.2100000000, query time of that 0.0286954180, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2544.71 < 2596.92
  -> Decision False in time 0.4500000000, query time of that 0.0584739430, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1734.51 < 1756.67
  -> Decision False in time 0.6800000000, query time of that 0.0111761210, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1925.07 < 1982.09
  -> Decision False in time 1.8100000000, query time of that 0.0295642570, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1815.4 < 1891.05
  -> Decision False in time 0.0100000000, query time of that 0.0005563370, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1244.98 < 1263.71
  -> Decision False in time 3.0600000000, query time of that 0.0060395150, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1442.7 < 1459.3
  -> Decision False in time 0.8200000000, query time of that 0.0014792480, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1778.93 < 1781.02
  -> Decision False in time 3.6700000000, query time of that 0.0063441480, 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: 0.008 accuracy: 1.72963 cost: 0.00633344 M: 10 delta: 1 time: 6.8346 one-recall: 0 one-ratio: 2.01043
iteration: 2 recall: 0.066 accuracy: 0.635277 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1151 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: 14.4026 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: 19.0833 one-recall: 0.94 one-ratio: 1.01843
iteration: 5 recall: 0.9752 accuracy: 0.00155631 cost: 0.0303842 M: 16.8423 delta: 0.26712 time: 25.7571 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.992 accuracy: 0.000346113 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 31.94 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 32.22000000000003
Index size:  71988.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0043010000
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3500000000, query time of that 0.0565523500, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1679.82 < 1701.75
  -> Decision False in time 0.4000000000, query time of that 0.0626532010, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1961.51 < 2019.75
  -> Decision False in time 2.1500000000, query time of that 0.3260957790, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1652 < 1704.67
  -> Decision False in time 0.4900000000, query time of that 0.0102816580, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
2051.6 < 2129.47
  -> Decision False in time 0.9200000000, query time of that 0.0177030710, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1761.32 < 1772.15
  -> Decision False in time 2.8500000000, query time of that 0.0544809360, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1797.14 < 1878.3
  -> Decision False in time 3.7500000000, query time of that 0.0087368200, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1383.69 < 1423.98
  -> Decision False in time 0.3400000000, query time of that 0.0017960270, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1654.02 < 1657.79
  -> Decision False in time 5.4700000000, query time of that 0.0114694590, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 80, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 80, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.004 accuracy: 1.5407 cost: 0.00633344 M: 10 delta: 1 time: 6.828 one-recall: 0 one-ratio: 2.00156
iteration: 2 recall: 0.0588 accuracy: 0.610834 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1045 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: 14.3924 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: 19.0726 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: 25.7446 one-recall: 0.99 one-ratio: 1.00319
iteration: 6 recall: 0.9868 accuracy: 0.000732637 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 31.9245 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.9916 accuracy: 0.0004699 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.501 one-recall: 1 one-ratio: 1
Graph completion with reverse edges...

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

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 34.79000000000008
Index size:  76308.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0020226667
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3700000000, query time of that 0.0701870890, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1886.19 < 1922.78
  -> Decision False in time 0.9700000000, query time of that 0.1804896940, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1707.3 < 1988.71
  -> Decision False in time 1.0500000000, query time of that 0.1993686560, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1480.16 < 1559.75
  -> Decision False in time 1.8600000000, query time of that 0.0453909470, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
2126.73 < 2135.95
  -> Decision False in time 2.7600000000, query time of that 0.0694471030, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1721.47 < 1774.3
  -> Decision False in time 0.7700000000, query time of that 0.0191297930, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1777.45 < 1909.09
  -> Decision False in time 0.0500000000, query time of that 0.0011260010, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1596.73 < 1663.6
  -> Decision False in time 12.2900000000, query time of that 0.0303757530, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1697.56 < 1705.71
  -> Decision False in time 8.8900000000, query time of that 0.0224583030, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 90, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 90, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.004 accuracy: 1.6918 cost: 0.00633344 M: 10 delta: 1 time: 6.83362 one-recall: 0 one-ratio: 2.12757
iteration: 2 recall: 0.0552 accuracy: 0.670215 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1104 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: 14.3954 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: 19.0722 one-recall: 0.92 one-ratio: 1.02262
iteration: 5 recall: 0.9636 accuracy: 0.00320592 cost: 0.0303842 M: 16.8423 delta: 0.26712 time: 25.7427 one-recall: 0.98 one-ratio: 1.00458
iteration: 6 recall: 0.9852 accuracy: 0.00131027 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 31.9167 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: 34.4892 one-recall: 0.99 one-ratio: 1.00454
Graph completion with reverse edges...

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

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 34.77999999999997
Index size:  76296.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0016203333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3700000000, query time of that 0.0752225830, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1647.59 < 1713.95
  -> Decision False in time 0.0700000000, query time of that 0.0139042120, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1475.45 < 1486.11
  -> Decision False in time 0.2200000000, query time of that 0.0427356800, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Accept!
  -> Decision True in time 3.3700000000, query time of that 0.0847599960, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1972.5 < 2076.41
  -> Decision False in time 3.9900000000, query time of that 0.1044408160, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1893.61 < 1924.5
  -> Decision False in time 1.4900000000, query time of that 0.0411835680, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1627.89 < 1678.83
  -> Decision False in time 2.1500000000, query time of that 0.0061264320, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1631.82 < 1642.64
  -> Decision False in time 3.4900000000, query time of that 0.0111903460, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1640.47 < 1703.95
  -> Decision False in time 16.4100000000, query time of that 0.0436997730, 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: 0.0056 accuracy: 1.41157 cost: 0.00633344 M: 10 delta: 1 time: 6.83634 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: 10.1134 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: 14.3985 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: 19.0763 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: 25.7436 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9828 accuracy: 0.00106873 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 31.9159 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.99 accuracy: 0.000613841 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.488 one-recall: 1 one-ratio: 1
Graph completion with reverse edges...

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

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 34.77999999999997
Index size:  76300.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0078486667
  Testing...
|S| = 98
|T| = 1411
Reject!
1783.27 < 1932.7
  -> Decision False in time 0.2300000000, query time of that 0.0293315440, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1733.02 < 1750.37
  -> Decision False in time 0.0200000000, query time of that 0.0017492690, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2425.78 < 2433.98
  -> Decision False in time 0.0700000000, query time of that 0.0098373800, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1579.11 < 1634.24
  -> Decision False in time 1.0300000000, query time of that 0.0162993320, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1893.43 < 1904.26
  -> Decision False in time 0.5200000000, query time of that 0.0078717220, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1413.05 < 1445.19
  -> Decision False in time 0.5200000000, query time of that 0.0087723280, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1915.49 < 1956.13
  -> Decision False in time 0.2900000000, query time of that 0.0005119600, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1438.77 < 1445.85
  -> Decision False in time 0.0400000000, query time of that 0.0006175330, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
2022.78 < 2082.43
  -> Decision False in time 1.7100000000, query time of that 0.0033950190, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 60, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 60, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.0068 accuracy: 1.63493 cost: 0.00633344 M: 10 delta: 1 time: 6.83538 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: 10.1124 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: 14.3964 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: 19.0737 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: 25.7404 one-recall: 0.97 one-ratio: 1.00691
iteration: 6 recall: 0.9844 accuracy: 0.00194847 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 31.9147 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: 34.4928 one-recall: 1 one-ratio: 1
iteration: 8 recall: 0.9932 accuracy: 0.000294001 cost: 0.0443167 M: 24.8843 delta: 0.0806719 time: 35.3955 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 35.690000000000055
Index size:  77564.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0023523333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3600000000, query time of that 0.0621404490, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1554.62 < 1657.4
  -> Decision False in time 0.5000000000, query time of that 0.0876221690, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2115.06 < 2125.9
  -> Decision False in time 2.3500000000, query time of that 0.4098455110, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1994.25 < 2005.4
  -> Decision False in time 1.6000000000, query time of that 0.0364368230, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1816.1 < 1873.48
  -> Decision False in time 1.6100000000, query time of that 0.0354106450, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1914.84 < 1972.53
  -> Decision False in time 1.4300000000, query time of that 0.0317155130, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1839.63 < 1852.9
  -> Decision False in time 2.7400000000, query time of that 0.0066516490, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1803.61 < 1841.26
  -> Decision False in time 2.3700000000, query time of that 0.0063480530, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1591.49 < 1690.09
  -> Decision False in time 20.2400000000, query time of that 0.0466530710, with c1=5.0000000000, c2=0.1000000000
