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', 3, {'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', 60, {'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', 4, {'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', 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', 20, {'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', 80, {'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', 5, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 90, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 1, {'reverse': -1}, False])]
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 3, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 3, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.0044 accuracy: 1.5879 cost: 0.00633344 M: 10 delta: 1 time: 6.87749 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: 10.161 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: 14.4544 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: 19.1414 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: 25.8209 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: 32.014 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.9932 accuracy: 0.0002248 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.6007 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 34.91
Index size:  100448.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0092930000
  Testing...
|S| = 98
|T| = 1411
Reject!
2532.05 < 2682.4
  -> Decision False in time 0.1100000000, query time of that 0.0153699800, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
2301.46 < 2431.28
  -> Decision False in time 0.1900000000, query time of that 0.0248564780, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1731.2 < 1766.42
  -> Decision False in time 0.1600000000, query time of that 0.0207897170, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1616.49 < 1638.39
  -> Decision False in time 0.1900000000, query time of that 0.0033984060, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1924.5 < 1931.4
  -> Decision False in time 0.8900000000, query time of that 0.0152990610, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
2025.9 < 2071.44
  -> Decision False in time 0.1600000000, query time of that 0.0027675550, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1923.62 < 1925.85
  -> Decision False in time 5.8000000000, query time of that 0.0102897900, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1485.33 < 1489.78
  -> Decision False in time 0.8100000000, query time of that 0.0019672970, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1815.6 < 1820.24
  -> Decision False in time 0.8300000000, query time of that 0.0015298120, 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: 6.84588 one-recall: 0 one-ratio: 2.07916
iteration: 2 recall: 0.052 accuracy: 0.699626 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1299 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: 14.425 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: 19.1111 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: 25.7938 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: 31.9842 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.99 accuracy: 0.000358431 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.5678 one-recall: 1 one-ratio: 1
iteration: 8 recall: 0.9912 accuracy: 0.00031344 cost: 0.0443167 M: 24.8843 delta: 0.0806719 time: 35.4744 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 35.78
Index size:  82996.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0068253333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3500000000, query time of that 0.0463466310, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
2217.71 < 2258.78
  -> Decision False in time 0.5400000000, query time of that 0.0766946250, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1955.17 < 1986.04
  -> Decision False in time 0.5200000000, query time of that 0.0718600100, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1265.67 < 1277.07
  -> Decision False in time 0.2700000000, query time of that 0.0042524580, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1364.14 < 1380.87
  -> Decision False in time 0.4000000000, query time of that 0.0067580510, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1609.08 < 1661.19
  -> Decision False in time 0.7300000000, query time of that 0.0128851150, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1838.71 < 1881.27
  -> Decision False in time 2.1400000000, query time of that 0.0042774490, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1950.66 < 1964.46
  -> Decision False in time 0.0700000000, query time of that 0.0006954090, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
2003.46 < 2078.48
  -> Decision False in time 3.2700000000, query time of that 0.0058995850, 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: 0.004 accuracy: 1.75774 cost: 0.00633344 M: 10 delta: 1 time: 6.86064 one-recall: 0 one-ratio: 2.03464
iteration: 2 recall: 0.0608 accuracy: 0.646443 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1452 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: 14.4395 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: 19.1313 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: 25.8162 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: 32.0062 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: 34.5887 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 34.900000000000006
Index size:  81720.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0026693333
  Testing...
|S| = 98
|T| = 1411
Reject!
1637.93 < 1724.91
  -> Decision False in time 0.2500000000, query time of that 0.0447461140, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
2071.67 < 2095.82
  -> Decision False in time 1.5000000000, query time of that 0.2685859460, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2010.2 < 2018.94
  -> Decision False in time 1.8000000000, query time of that 0.3257200430, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1647.32 < 1734.33
  -> Decision False in time 2.3800000000, query time of that 0.0551192830, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
2110.46 < 2126.7
  -> Decision False in time 0.2100000000, query time of that 0.0054357080, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1730.04 < 1741.73
  -> Decision False in time 1.7700000000, query time of that 0.0410186760, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1582.74 < 1598.23
  -> Decision False in time 2.1500000000, query time of that 0.0052650970, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
2028.81 < 2069.48
  -> Decision False in time 5.1000000000, query time of that 0.0126321030, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1772.82 < 1851.92
  -> Decision False in time 2.7000000000, query time of that 0.0074830060, 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: 0.006 accuracy: 1.65471 cost: 0.00633344 M: 10 delta: 1 time: 6.85421 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: 10.1414 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: 14.4352 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: 19.1226 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: 25.811 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: 32.0063 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.992 accuracy: 0.000590742 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.5898 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 34.889999999999986
Index size:  81724.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0033383333
  Testing...
|S| = 98
|T| = 1411
Reject!
2038.78 < 2056.87
  -> Decision False in time 0.2100000000, query time of that 0.0325574440, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1901.36 < 1951.57
  -> Decision False in time 0.1800000000, query time of that 0.0316369120, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1462.65 < 1500.04
  -> Decision False in time 0.1000000000, query time of that 0.0160249830, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Accept!
  -> Decision True in time 3.3700000000, query time of that 0.0678521780, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1841.73 < 1852.24
  -> Decision False in time 1.8100000000, query time of that 0.0370073400, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1909.62 < 1975.09
  -> Decision False in time 2.1200000000, query time of that 0.0423413190, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1530.21 < 1563.86
  -> Decision False in time 2.4500000000, query time of that 0.0056800730, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1873.29 < 1883.72
  -> Decision False in time 0.0500000000, query time of that 0.0008367740, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
2083.99 < 2098.21
  -> Decision False in time 0.7600000000, query time of that 0.0019865310, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 4, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 4, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.0036 accuracy: 1.7649 cost: 0.00633344 M: 10 delta: 1 time: 6.85976 one-recall: 0 one-ratio: 2.10464
iteration: 2 recall: 0.0604 accuracy: 0.635748 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1416 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: 14.4319 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: 19.1152 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: 25.7927 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: 31.9801 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: 34.5592 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: 35.4634 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 35.76999999999998
Index size:  83000.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0072696667
  Testing...
|S| = 98
|T| = 1411
Reject!
1521.55 < 1571.87
  -> Decision False in time 0.2200000000, query time of that 0.0283530050, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1484.45 < 1537.54
  -> Decision False in time 0.2100000000, query time of that 0.0264566390, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1600.42 < 1609.8
  -> Decision False in time 0.2500000000, query time of that 0.0333771590, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1862.16 < 1871.1
  -> Decision False in time 0.0300000000, query time of that 0.0004336350, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1457.13 < 1485.62
  -> Decision False in time 0.6500000000, query time of that 0.0115236790, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1851.57 < 1864.24
  -> Decision False in time 0.0100000000, query time of that 0.0005510630, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1878.61 < 1913.43
  -> Decision False in time 3.9300000000, query time of that 0.0067333070, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1985.16 < 2003.42
  -> Decision False in time 0.1200000000, query time of that 0.0008136820, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1912.33 < 1981.23
  -> Decision False in time 7.9000000000, query time of that 0.0128281670, 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: 0.0044 accuracy: 1.68691 cost: 0.00633344 M: 10 delta: 1 time: 6.84336 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: 10.1264 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: 14.4183 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: 19.1043 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: 25.7833 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: 31.9704 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.9912 accuracy: 0.000477346 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.5503 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 34.849999999999966
Index size:  81728.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0101743333
  Testing...
|S| = 98
|T| = 1411
Reject!
1842.31 < 1892.78
  -> Decision False in time 0.1200000000, query time of that 0.0161044670, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1567.53 < 1585.39
  -> Decision False in time 0.0200000000, query time of that 0.0029374150, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1488.37 < 1510.93
  -> Decision False in time 0.1200000000, query time of that 0.0158649740, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1476.78 < 1599.78
  -> Decision False in time 0.3400000000, query time of that 0.0051948660, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
2065.6 < 2126.18
  -> Decision False in time 0.7200000000, query time of that 0.0112349780, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1613.54 < 1628.51
  -> Decision False in time 0.7300000000, query time of that 0.0125420780, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1970.78 < 1981.82
  -> Decision False in time 5.5600000000, query time of that 0.0088760270, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1815.58 < 1883.28
  -> Decision False in time 0.5100000000, query time of that 0.0011631420, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1575.97 < 1653.25
  -> Decision False in time 2.4000000000, query time of that 0.0040128150, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 50, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 50, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.008 accuracy: 1.74437 cost: 0.00633344 M: 10 delta: 1 time: 6.85754 one-recall: 0 one-ratio: 2.03444
iteration: 2 recall: 0.0652 accuracy: 0.637073 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1434 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: 14.4335 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: 19.117 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: 25.7947 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: 31.98 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: 34.5605 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 34.849999999999966
Index size:  81716.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0032233333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3500000000, query time of that 0.0599568740, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
2112.49 < 2112.56
  -> Decision False in time 0.7800000000, query time of that 0.1301172880, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1585.34 < 1595.15
  -> Decision False in time 1.0900000000, query time of that 0.1844317750, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
2133.92 < 2181.83
  -> Decision False in time 1.6600000000, query time of that 0.0360605370, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1762.84 < 1791.55
  -> Decision False in time 1.0000000000, query time of that 0.0212063240, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1723.06 < 1756.58
  -> Decision False in time 0.8900000000, query time of that 0.0203611640, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1808.24 < 1813.29
  -> Decision False in time 1.1700000000, query time of that 0.0030555780, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1771.52 < 1795.08
  -> Decision False in time 15.5000000000, query time of that 0.0351988280, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
2003.67 < 2023.98
  -> Decision False in time 4.4800000000, query time of that 0.0105922720, 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: 1.49738 cost: 0.00633344 M: 10 delta: 1 time: 6.85135 one-recall: 0 one-ratio: 1.99571
iteration: 2 recall: 0.0468 accuracy: 0.631223 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1348 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: 14.4255 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: 19.1096 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: 25.7866 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: 31.9724 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.9884 accuracy: 0.000479649 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.5514 one-recall: 1 one-ratio: 1
iteration: 8 recall: 0.9908 accuracy: 0.000330768 cost: 0.0443167 M: 24.8843 delta: 0.0806719 time: 35.4557 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 35.75999999999999
Index size:  83004.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0018956667
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3700000000, query time of that 0.0710782790, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1528.35 < 1537.48
  -> Decision False in time 1.9400000000, query time of that 0.3578170050, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1962.77 < 1968.71
  -> Decision False in time 2.4700000000, query time of that 0.4588069510, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Accept!
  -> Decision True in time 3.4100000000, query time of that 0.0794147240, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1597.88 < 1633.78
  -> Decision False in time 3.3000000000, query time of that 0.0786817640, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1957.45 < 2029.74
  -> Decision False in time 2.3700000000, query time of that 0.0576891520, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1987.67 < 2054.58
  -> Decision False in time 8.1800000000, query time of that 0.0214526780, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1538.37 < 1556.84
  -> Decision False in time 3.7800000000, query time of that 0.0105987170, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1289.85 < 1297.72
  -> Decision False in time 6.4500000000, query time of that 0.0153964880, 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: 0.0072 accuracy: 1.78614 cost: 0.00633344 M: 10 delta: 1 time: 6.8448 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: 10.1289 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: 14.4212 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: 19.107 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: 25.788 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: 31.9765 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 32.25999999999999
Index size:  77440.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0073453333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3500000000, query time of that 0.0485725240, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1929.87 < 1933.35
  -> Decision False in time 0.4000000000, query time of that 0.0523800270, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1919.86 < 2023.99
  -> Decision False in time 0.3100000000, query time of that 0.0412602160, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
2143.64 < 2163.91
  -> Decision False in time 0.6400000000, query time of that 0.0112380500, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
2122.51 < 2361.83
  -> Decision False in time 0.0000000000, query time of that 0.0007314790, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1942.05 < 1976.21
  -> Decision False in time 0.2300000000, query time of that 0.0043460730, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
2001.7 < 2065.92
  -> Decision False in time 1.8000000000, query time of that 0.0035435680, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1568.34 < 1636.02
  -> Decision False in time 3.4300000000, query time of that 0.0068537410, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1423.53 < 1466.58
  -> Decision False in time 0.6800000000, query time of that 0.0017691750, 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: 0.0048 accuracy: 1.58384 cost: 0.00633344 M: 10 delta: 1 time: 6.85723 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: 10.1415 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: 14.4345 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: 19.1188 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: 25.7988 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9896 accuracy: 0.000550482 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 31.9853 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.9944 accuracy: 0.000259975 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.565 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 34.870000000000005
Index size:  81740.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0014630000
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3800000000, query time of that 0.0774548750, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1789.07 < 1795.48
  -> Decision False in time 2.2000000000, query time of that 0.4522897130, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1790.82 < 2100.21
  -> Decision False in time 2.2900000000, query time of that 0.4804103070, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1895.32 < 1903.43
  -> Decision False in time 2.9800000000, query time of that 0.0808863040, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1839.63 < 1852.9
  -> Decision False in time 0.9300000000, query time of that 0.0255345830, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1916.62 < 1920.99
  -> Decision False in time 2.1900000000, query time of that 0.0599516040, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1678.57 < 1763.08
  -> Decision False in time 0.0300000000, query time of that 0.0010349080, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
2100.67 < 2112.45
  -> Decision False in time 1.2100000000, query time of that 0.0043943200, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1904.72 < 2021.56
  -> Decision False in time 11.7800000000, query time of that 0.0329655280, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 80, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 80, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.008 accuracy: 1.72963 cost: 0.00633344 M: 10 delta: 1 time: 6.84757 one-recall: 0 one-ratio: 2.01043
iteration: 2 recall: 0.066 accuracy: 0.635277 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1315 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: 14.4262 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: 19.1119 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: 25.7985 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.992 accuracy: 0.000346113 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 31.9912 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 32.289999999999964
Index size:  77420.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0026180000
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3800000000, query time of that 0.0705375450, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1593.08 < 1636.32
  -> Decision False in time 0.8300000000, query time of that 0.1631674430, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1842.75 < 2004.28
  -> Decision False in time 0.0500000000, query time of that 0.0078152780, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Accept!
  -> Decision True in time 3.4100000000, query time of that 0.0836300040, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1805.51 < 1819.14
  -> Decision False in time 1.1300000000, query time of that 0.0288271540, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1683.86 < 1767.86
  -> Decision False in time 1.8900000000, query time of that 0.0453742460, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1520.23 < 1534.54
  -> Decision False in time 15.3400000000, query time of that 0.0366770170, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1651.15 < 1651.96
  -> Decision False in time 8.2100000000, query time of that 0.0209744510, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1561.2 < 1592.42
  -> Decision False in time 6.1200000000, query time of that 0.0170332320, 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: 1.5407 cost: 0.00633344 M: 10 delta: 1 time: 6.8483 one-recall: 0 one-ratio: 2.00156
iteration: 2 recall: 0.0588 accuracy: 0.610834 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1338 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: 14.4276 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: 19.113 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: 25.7943 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: 31.9828 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.9916 accuracy: 0.0004699 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.5626 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 34.86999999999989
Index size:  81732.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0048390000
  Testing...
|S| = 98
|T| = 1411
Reject!
2051.69 < 2094.83
  -> Decision False in time 0.2900000000, query time of that 0.0434818570, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1587.37 < 1610.63
  -> Decision False in time 0.4400000000, query time of that 0.0662253430, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1728.6 < 1773.36
  -> Decision False in time 0.3900000000, query time of that 0.0573140760, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1846.82 < 1863.91
  -> Decision False in time 0.4600000000, query time of that 0.0088131550, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1863.91 < 1884.51
  -> Decision False in time 2.6100000000, query time of that 0.0496544690, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1976.15 < 2004.2
  -> Decision False in time 1.7100000000, query time of that 0.0307319920, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1892.8 < 1892.9
  -> Decision False in time 1.3100000000, query time of that 0.0028923890, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1552.66 < 1568.76
  -> Decision False in time 2.7500000000, query time of that 0.0059612940, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1334.94 < 1474.57
  -> Decision False in time 0.7400000000, query time of that 0.0021178700, 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: 1.6918 cost: 0.00633344 M: 10 delta: 1 time: 6.84987 one-recall: 0 one-ratio: 2.12757
iteration: 2 recall: 0.0552 accuracy: 0.670215 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1361 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: 14.4342 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: 19.1248 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: 25.8103 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: 32.0061 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: 34.5895 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 34.88999999999987
Index size:  81740.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0092153333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3400000000, query time of that 0.0438755180, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
2240.93 < 2286.75
  -> Decision False in time 0.2900000000, query time of that 0.0377974480, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1750.5 < 1773.65
  -> Decision False in time 0.1800000000, query time of that 0.0249789900, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1754.53 < 1759.44
  -> Decision False in time 0.0500000000, query time of that 0.0011250030, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1498.56 < 1519.97
  -> Decision False in time 0.4900000000, query time of that 0.0079724200, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1606.23 < 1606.64
  -> Decision False in time 0.6000000000, query time of that 0.0100613810, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1624.15 < 1672.65
  -> Decision False in time 0.6900000000, query time of that 0.0018983380, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1898.13 < 1906.85
  -> Decision False in time 0.4500000000, query time of that 0.0011786520, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1477.71 < 1484.98
  -> Decision False in time 2.3900000000, query time of that 0.0047102400, 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: 0.0056 accuracy: 1.41157 cost: 0.00633344 M: 10 delta: 1 time: 6.85205 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: 10.1376 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: 14.4334 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: 19.1201 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: 25.8051 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9828 accuracy: 0.00106873 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 31.998 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.99 accuracy: 0.000613841 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.5783 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 34.88000000000011
Index size:  81732.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0016203333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3700000000, query time of that 0.0761820770, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1593.92 < 1672.65
  -> Decision False in time 0.5100000000, query time of that 0.1018864490, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1685.4 < 1695.38
  -> Decision False in time 3.7600000000, query time of that 0.7542468620, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Accept!
  -> Decision True in time 3.4200000000, query time of that 0.0889331190, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1788.34 < 1849.53
  -> Decision False in time 5.3700000000, query time of that 0.1387989020, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
2003.62 < 2010.95
  -> Decision False in time 0.1100000000, query time of that 0.0032942040, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1351.66 < 1359.71
  -> Decision False in time 0.3400000000, query time of that 0.0017473340, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1701.52 < 1703.58
  -> Decision False in time 3.4300000000, query time of that 0.0089514380, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1510.05 < 1526.42
  -> Decision False in time 0.7700000000, query time of that 0.0033269800, 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: 0.0068 accuracy: 1.63493 cost: 0.00633344 M: 10 delta: 1 time: 6.86385 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: 10.1526 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: 14.4513 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: 19.1418 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: 25.8302 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: 32.0288 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: 34.6128 one-recall: 1 one-ratio: 1
iteration: 8 recall: 0.9932 accuracy: 0.000294001 cost: 0.0443167 M: 24.8843 delta: 0.0806719 time: 35.5188 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 35.819999999999936
Index size:  82992.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0095726667
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3500000000, query time of that 0.0484428710, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1652.93 < 1656.95
  -> Decision False in time 0.0100000000, query time of that 0.0021859690, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2207.56 < 2325.8
  -> Decision False in time 0.5000000000, query time of that 0.0681800730, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1999.45 < 2015.68
  -> Decision False in time 0.3100000000, query time of that 0.0058027320, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1693.31 < 1759.94
  -> Decision False in time 0.2000000000, query time of that 0.0035632770, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1546.79 < 1609.26
  -> Decision False in time 0.4100000000, query time of that 0.0076104420, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1825.73 < 1829.34
  -> Decision False in time 0.9100000000, query time of that 0.0016848150, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1682.62 < 1723.74
  -> Decision False in time 6.2300000000, query time of that 0.0101702920, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1780.22 < 1805.08
  -> Decision False in time 4.4300000000, query time of that 0.0085192240, with c1=5.0000000000, c2=0.1000000000
