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', 20, {'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', 100, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 40, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 80, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 3, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 4, {'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', 30, {'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', 50, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 2, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 10, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 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]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 20, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.0044 accuracy: 1.5879 cost: 0.00633344 M: 10 delta: 1 time: 6.84652 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: 10.1274 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: 14.418 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: 19.1012 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: 25.7767 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: 31.9708 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.9932 accuracy: 0.0002248 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.5526 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 34.85
Index size:  100448.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0056770000
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3400000000, query time of that 0.0481222940, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1799.19 < 1813.33
  -> Decision False in time 0.4700000000, query time of that 0.0670457240, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1617.94 < 1622.12
  -> Decision False in time 0.8400000000, query time of that 0.1161087760, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Accept!
  -> Decision True in time 3.3700000000, query time of that 0.0575503990, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1878.22 < 1925.24
  -> Decision False in time 1.0500000000, query time of that 0.0195234480, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
2085.92 < 2100.21
  -> Decision False in time 0.3800000000, query time of that 0.0069883650, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1735.21 < 1771.13
  -> Decision False in time 0.0800000000, query time of that 0.0007680960, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1642.98 < 1673.64
  -> Decision False in time 1.4500000000, query time of that 0.0026956890, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1893.96 < 1932.02
  -> Decision False in time 0.5800000000, query time of that 0.0014207300, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 90, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 90, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.0044 accuracy: 1.81839 cost: 0.00633344 M: 10 delta: 1 time: 6.84512 one-recall: 0 one-ratio: 2.07916
iteration: 2 recall: 0.052 accuracy: 0.699626 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1271 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: 14.4173 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: 19.098 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: 25.7768 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: 31.9636 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.99 accuracy: 0.000358431 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.5438 one-recall: 1 one-ratio: 1
iteration: 8 recall: 0.9912 accuracy: 0.00031344 cost: 0.0443167 M: 24.8843 delta: 0.0806719 time: 35.4472 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 35.75
Index size:  82992.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0015016667
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3800000000, query time of that 0.0755316460, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1851.88 < 1909.71
  -> Decision False in time 0.4300000000, query time of that 0.0867874270, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1618.25 < 1678.74
  -> Decision False in time 1.2100000000, query time of that 0.2421794940, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1813.31 < 1860.6
  -> Decision False in time 1.4500000000, query time of that 0.0371895090, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1931 < 1931.23
  -> Decision False in time 5.6300000000, query time of that 0.1447714640, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1722.45 < 1731.93
  -> Decision False in time 5.7300000000, query time of that 0.1450970380, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1759.29 < 1763.43
  -> Decision False in time 12.3400000000, query time of that 0.0319307470, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1772.98 < 1829.16
  -> Decision False in time 25.0300000000, query time of that 0.0690613880, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1886.9 < 1890.55
  -> Decision False in time 13.9200000000, query time of that 0.0369134510, 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: 0.004 accuracy: 1.75774 cost: 0.00633344 M: 10 delta: 1 time: 6.84318 one-recall: 0 one-ratio: 2.03464
iteration: 2 recall: 0.0608 accuracy: 0.646443 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1258 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: 14.4155 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: 19.1014 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: 25.7818 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: 31.9724 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: 34.5568 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 34.85000000000002
Index size:  81724.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0107390000
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3400000000, query time of that 0.0445961770, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1625.5 < 1665.26
  -> Decision False in time 0.0500000000, query time of that 0.0076675030, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2042.28 < 2049.28
  -> Decision False in time 0.0200000000, query time of that 0.0028184230, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1617.35 < 1618.43
  -> Decision False in time 0.2900000000, query time of that 0.0055643670, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1877.13 < 1888.2
  -> Decision False in time 0.1400000000, query time of that 0.0025261730, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1984.47 < 2017.11
  -> Decision False in time 0.0200000000, query time of that 0.0005586420, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1560.94 < 1596.02
  -> Decision False in time 0.0300000000, query time of that 0.0006511000, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1891.67 < 2077.08
  -> Decision False in time 1.3500000000, query time of that 0.0029616670, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1712.98 < 1743.75
  -> Decision False in time 4.2100000000, query time of that 0.0076648680, 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: 0.006 accuracy: 1.65471 cost: 0.00633344 M: 10 delta: 1 time: 6.84937 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: 10.1313 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: 14.4221 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: 19.1058 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: 25.7873 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: 31.9741 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.992 accuracy: 0.000590742 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.5554 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 34.85000000000002
Index size:  81732.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0014630000
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3800000000, query time of that 0.0751554600, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1971.42 < 2003.86
  -> Decision False in time 1.9000000000, query time of that 0.3941328360, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1920.9 < 1988.96
  -> Decision False in time 14.9300000000, query time of that 3.0792242530, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Accept!
  -> Decision True in time 3.4300000000, query time of that 0.0888137380, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1564 < 1621.96
  -> Decision False in time 0.4000000000, query time of that 0.0127935490, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1984.96 < 2045.58
  -> Decision False in time 0.4800000000, query time of that 0.0129456670, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1889.91 < 1953.78
  -> Decision False in time 3.1500000000, query time of that 0.0083847500, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
2107.66 < 2107.87
  -> Decision False in time 7.6200000000, query time of that 0.0220314010, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1869.68 < 1877.72
  -> Decision False in time 2.7500000000, query time of that 0.0081660030, 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: 0.0036 accuracy: 1.7649 cost: 0.00633344 M: 10 delta: 1 time: 6.84948 one-recall: 0 one-ratio: 2.10464
iteration: 2 recall: 0.0604 accuracy: 0.635748 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1312 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: 14.4189 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: 19.1004 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: 25.7798 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: 31.971 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: 34.5527 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: 35.456 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: 0.0030116667
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3500000000, query time of that 0.0582318730, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1685.38 < 1752.95
  -> Decision False in time 0.2500000000, query time of that 0.0415416900, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1326.52 < 1363.54
  -> Decision False in time 0.6800000000, query time of that 0.1099202670, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1956.92 < 1966.92
  -> Decision False in time 0.8600000000, query time of that 0.0178111610, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1251.67 < 1439.87
  -> Decision False in time 2.5600000000, query time of that 0.0516223970, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1786.84 < 1818.22
  -> Decision False in time 2.2700000000, query time of that 0.0461576660, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1773.49 < 1856.87
  -> Decision False in time 1.7100000000, query time of that 0.0046952060, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1747.04 < 1823.73
  -> Decision False in time 12.1500000000, query time of that 0.0256238560, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
2319.65 < 2320.87
  -> Decision False in time 1.0600000000, query time of that 0.0026971170, 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: 0.0044 accuracy: 1.68691 cost: 0.00633344 M: 10 delta: 1 time: 6.8364 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: 10.1189 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: 14.4086 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: 19.0911 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: 25.7716 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: 31.9626 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.9912 accuracy: 0.000477346 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.5438 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 34.83999999999992
Index size:  81720.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0020226667
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3700000000, query time of that 0.0708426850, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1569.03 < 1594.57
  -> Decision False in time 1.8700000000, query time of that 0.3601152670, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1660.79 < 1704.91
  -> Decision False in time 1.2300000000, query time of that 0.2404304240, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Accept!
  -> Decision True in time 3.3900000000, query time of that 0.0820680760, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
2019.19 < 2048.7
  -> Decision False in time 5.8600000000, query time of that 0.1397093670, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
2129.09 < 2157.08
  -> Decision False in time 0.6600000000, query time of that 0.0159980740, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1660.99 < 1678.37
  -> Decision False in time 3.3700000000, query time of that 0.0099918910, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1887.59 < 1906.25
  -> Decision False in time 9.1700000000, query time of that 0.0228682190, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1893.64 < 1942.81
  -> Decision False in time 7.4900000000, query time of that 0.0206558450, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 3, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 3, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.008 accuracy: 1.74437 cost: 0.00633344 M: 10 delta: 1 time: 6.84747 one-recall: 0 one-ratio: 2.03444
iteration: 2 recall: 0.0652 accuracy: 0.637073 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1284 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: 14.4201 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: 19.1034 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: 25.7836 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: 31.9749 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: 34.5575 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 34.85000000000002
Index size:  81724.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0092930000
  Testing...
|S| = 98
|T| = 1411
Reject!
2111.19 < 2115.46
  -> Decision False in time 0.0800000000, query time of that 0.0105103520, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1682.51 < 1685.9
  -> Decision False in time 0.4900000000, query time of that 0.0642286830, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1697.58 < 1797.93
  -> Decision False in time 0.1400000000, query time of that 0.0193005150, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1887.06 < 2007.64
  -> Decision False in time 0.3100000000, query time of that 0.0055736960, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1818.22 < 1823.1
  -> Decision False in time 0.5700000000, query time of that 0.0087210420, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1752.38 < 1754.69
  -> Decision False in time 0.0100000000, query time of that 0.0006317140, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1826.19 < 1832.75
  -> Decision False in time 0.6800000000, query time of that 0.0016663410, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1990.97 < 1997.05
  -> Decision False in time 0.1900000000, query time of that 0.0004552780, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1612.46 < 1646.79
  -> Decision False in time 4.0400000000, query time of that 0.0075295900, 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: 0.004 accuracy: 1.49738 cost: 0.00633344 M: 10 delta: 1 time: 6.86026 one-recall: 0 one-ratio: 1.99571
iteration: 2 recall: 0.0468 accuracy: 0.631223 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1402 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: 14.429 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: 19.1082 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: 25.7848 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: 31.9734 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.9884 accuracy: 0.000479649 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.5526 one-recall: 1 one-ratio: 1
iteration: 8 recall: 0.9908 accuracy: 0.000330768 cost: 0.0443167 M: 24.8843 delta: 0.0806719 time: 35.4561 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:  83000.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0072696667
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3400000000, query time of that 0.0469847670, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1682.79 < 1706.05
  -> Decision False in time 0.0600000000, query time of that 0.0086194270, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1947.49 < 1970.41
  -> Decision False in time 0.1000000000, query time of that 0.0139275800, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1548.34 < 1568.13
  -> Decision False in time 0.1200000000, query time of that 0.0026390290, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1836.47 < 1868.16
  -> Decision False in time 0.0800000000, query time of that 0.0017281540, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
2125.13 < 2133.02
  -> Decision False in time 0.5500000000, query time of that 0.0090061780, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1564.43 < 1580.35
  -> Decision False in time 2.7700000000, query time of that 0.0046998850, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1621.56 < 1655.8
  -> Decision False in time 2.0000000000, query time of that 0.0032579230, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1642.46 < 1652.83
  -> Decision False in time 1.7000000000, query time of that 0.0028998080, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 5, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 5, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.0072 accuracy: 1.78614 cost: 0.00633344 M: 10 delta: 1 time: 6.85077 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: 10.134 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: 14.4254 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: 19.1102 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: 25.7912 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: 31.9838 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 32.27999999999997
Index size:  77428.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0118173333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3400000000, query time of that 0.0426584440, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
2533.74 < 2574.79
  -> Decision False in time 0.1700000000, query time of that 0.0214705610, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1387.4 < 1402.15
  -> Decision False in time 1.6000000000, query time of that 0.2056878070, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1562.07 < 1567.31
  -> Decision False in time 0.7300000000, query time of that 0.0119841450, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1754.65 < 1760.41
  -> Decision False in time 1.3300000000, query time of that 0.0211123210, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1434.88 < 1466.9
  -> Decision False in time 0.7600000000, query time of that 0.0117524630, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1739.12 < 1742.36
  -> Decision False in time 4.4300000000, query time of that 0.0068866800, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1561.09 < 1645.8
  -> Decision False in time 0.4700000000, query time of that 0.0010773670, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1635.54 < 1667.58
  -> Decision False in time 4.0500000000, query time of that 0.0068753690, 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: 0.0048 accuracy: 1.58384 cost: 0.00633344 M: 10 delta: 1 time: 6.85242 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: 10.1337 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: 14.4209 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: 19.1024 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: 25.7806 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9896 accuracy: 0.000550482 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 31.9732 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.9944 accuracy: 0.000259975 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.5542 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 34.860000000000014
Index size:  81732.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0048390000
  Testing...
|S| = 98
|T| = 1411
Reject!
1811 < 1854.46
  -> Decision False in time 0.3500000000, query time of that 0.0529149130, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1574.29 < 1588.11
  -> Decision False in time 0.5300000000, query time of that 0.0784475420, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1842.31 < 1862.73
  -> Decision False in time 0.2600000000, query time of that 0.0393481520, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1712.61 < 1729.71
  -> Decision False in time 0.9000000000, query time of that 0.0173469110, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1915.91 < 2221.39
  -> Decision False in time 2.0100000000, query time of that 0.0375983890, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
2160.28 < 2196.52
  -> Decision False in time 0.9500000000, query time of that 0.0185642120, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1477.44 < 1492.02
  -> Decision False in time 0.4500000000, query time of that 0.0014110230, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1837.43 < 1867.71
  -> Decision False in time 5.1100000000, query time of that 0.0102437040, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1761.81 < 1776.16
  -> Decision False in time 0.0400000000, query time of that 0.0006676470, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 60, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 60, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.008 accuracy: 1.72963 cost: 0.00633344 M: 10 delta: 1 time: 6.84139 one-recall: 0 one-ratio: 2.01043
iteration: 2 recall: 0.066 accuracy: 0.635277 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1234 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: 14.414 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: 19.0959 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: 25.7767 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.992 accuracy: 0.000346113 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 31.9721 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 32.260000000000105
Index size:  77424.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0034236667
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3600000000, query time of that 0.0607182090, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1740.56 < 1997.71
  -> Decision False in time 1.5900000000, query time of that 0.2659048320, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1928.13 < 1961.77
  -> Decision False in time 0.3400000000, query time of that 0.0577579380, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1577.2 < 1607.84
  -> Decision False in time 0.1800000000, query time of that 0.0042824470, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1446.41 < 1455.36
  -> Decision False in time 1.1900000000, query time of that 0.0248218290, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1526.24 < 1584.01
  -> Decision False in time 0.0000000000, query time of that 0.0006932840, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1832 < 1837.22
  -> Decision False in time 1.7500000000, query time of that 0.0045701540, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1601.16 < 1610.34
  -> Decision False in time 3.5400000000, query time of that 0.0080181260, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1842.11 < 1856.59
  -> Decision False in time 1.8100000000, query time of that 0.0040599220, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 50, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 50, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.004 accuracy: 1.5407 cost: 0.00633344 M: 10 delta: 1 time: 6.8447 one-recall: 0 one-ratio: 2.00156
iteration: 2 recall: 0.0588 accuracy: 0.610834 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1254 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: 14.4161 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: 19.0959 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: 25.7695 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: 31.9606 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.9916 accuracy: 0.0004699 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.5384 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 34.82999999999993
Index size:  81720.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0032233333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3500000000, query time of that 0.0589426540, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1912 < 1936.79
  -> Decision False in time 0.8300000000, query time of that 0.1377127200, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1876.92 < 1928.24
  -> Decision False in time 0.4300000000, query time of that 0.0748028050, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1408.3 < 1416.46
  -> Decision False in time 0.4900000000, query time of that 0.0102556130, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1935.96 < 1947.54
  -> Decision False in time 0.7800000000, query time of that 0.0180128860, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1873.5 < 1911.62
  -> Decision False in time 1.2200000000, query time of that 0.0269117020, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1925.06 < 1959.97
  -> Decision False in time 1.2900000000, query time of that 0.0032635750, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1830.31 < 1938.63
  -> Decision False in time 12.9100000000, query time of that 0.0276807160, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1379.18 < 1381.74
  -> Decision False in time 9.1900000000, query time of that 0.0208274750, 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: 0.004 accuracy: 1.6918 cost: 0.00633344 M: 10 delta: 1 time: 6.85473 one-recall: 0 one-ratio: 2.12757
iteration: 2 recall: 0.0552 accuracy: 0.670215 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1366 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: 14.4292 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: 19.1129 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: 25.7924 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: 31.9834 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: 34.5658 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 34.8599999999999
Index size:  81740.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0101743333
  Testing...
|S| = 98
|T| = 1411
Reject!
1956.19 < 2295.03
  -> Decision False in time 0.0800000000, query time of that 0.0109584230, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1833.82 < 1866.53
  -> Decision False in time 0.5100000000, query time of that 0.0661098040, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1812.8 < 1850.95
  -> Decision False in time 0.2900000000, query time of that 0.0397291380, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1663.75 < 1741.78
  -> Decision False in time 0.4200000000, query time of that 0.0067534700, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1887.06 < 1929.72
  -> Decision False in time 0.3400000000, query time of that 0.0062570880, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1401.14 < 1443.44
  -> Decision False in time 0.3500000000, query time of that 0.0060499490, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1640.83 < 1650.72
  -> Decision False in time 4.1400000000, query time of that 0.0070336800, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1657.64 < 1705.02
  -> Decision False in time 4.1600000000, query time of that 0.0067802270, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
2127.71 < 2156.77
  -> Decision False in time 3.0200000000, query time of that 0.0059332180, 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: 0.0056 accuracy: 1.41157 cost: 0.00633344 M: 10 delta: 1 time: 6.853 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: 10.1337 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: 14.4212 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: 19.1019 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: 25.7766 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9828 accuracy: 0.00106873 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 31.9619 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.99 accuracy: 0.000613841 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.5384 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 34.82999999999993
Index size:  81728.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0075250000
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3400000000, query time of that 0.0477005280, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
2339.1 < 2348.88
  -> Decision False in time 0.0200000000, query time of that 0.0022120580, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1783.08 < 1816.6
  -> Decision False in time 0.0600000000, query time of that 0.0089164060, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1431.7 < 1434.64
  -> Decision False in time 0.3200000000, query time of that 0.0060409910, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1804.17 < 1825.82
  -> Decision False in time 0.8300000000, query time of that 0.0138968490, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1827.53 < 1878.23
  -> Decision False in time 0.2100000000, query time of that 0.0045459410, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1758.36 < 1788.9
  -> Decision False in time 0.7500000000, query time of that 0.0019196830, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1655.34 < 1716.62
  -> Decision False in time 3.3900000000, query time of that 0.0062939980, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
2125.56 < 2151.38
  -> Decision False in time 1.3500000000, query time of that 0.0030286090, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 70, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 70, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.0068 accuracy: 1.63493 cost: 0.00633344 M: 10 delta: 1 time: 6.85088 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: 10.1332 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: 14.4236 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: 19.106 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: 25.7857 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: 31.9789 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: 34.5592 one-recall: 1 one-ratio: 1
iteration: 8 recall: 0.9932 accuracy: 0.000294001 cost: 0.0443167 M: 24.8843 delta: 0.0806719 time: 35.4635 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:  83004.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0018956667
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3600000000, query time of that 0.0687261660, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1607.57 < 1696.81
  -> Decision False in time 1.8300000000, query time of that 0.3423730890, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1813.8 < 1864.24
  -> Decision False in time 2.1500000000, query time of that 0.4030435040, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1770.65 < 1780.13
  -> Decision False in time 1.9600000000, query time of that 0.0477450740, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1684.82 < 1712.64
  -> Decision False in time 1.5200000000, query time of that 0.0368800280, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1712.67 < 1753.22
  -> Decision False in time 6.4700000000, query time of that 0.1504665670, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1660.52 < 1660.91
  -> Decision False in time 0.2200000000, query time of that 0.0008629510, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
2160.98 < 2181.08
  -> Decision False in time 0.8400000000, query time of that 0.0027472750, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1246.95 < 1251.2
  -> Decision False in time 2.3800000000, query time of that 0.0061822910, with c1=5.0000000000, c2=0.1000000000
