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', 80, {'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', 90, {'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', 60, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 2, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 40, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 70, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 30, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 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]), 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', 4, {'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', 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]) ...
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: 1.5879 cost: 0.00633344 M: 10 delta: 1 time: 6.87539 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: 10.1595 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: 14.453 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: 19.1424 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: 25.8299 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: 32.0235 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.9932 accuracy: 0.0002248 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.6077 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: 0.0020226667
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3700000000, query time of that 0.0709971760, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Accept!
  -> Decision True in time 3.7100000000, query time of that 0.7082832670, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1598.77 < 1609.74
  -> Decision False in time 1.1700000000, query time of that 0.2265715710, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1973.94 < 1991.9
  -> Decision False in time 0.3100000000, query time of that 0.0067185940, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
2023.82 < 2024.37
  -> Decision False in time 2.1800000000, query time of that 0.0539032190, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1888.19 < 1911.92
  -> Decision False in time 2.5500000000, query time of that 0.0604658670, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1823.66 < 1842.59
  -> Decision False in time 9.3600000000, query time of that 0.0269508090, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1582.67 < 1588.09
  -> Decision False in time 29.9000000000, query time of that 0.0766765910, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
2049.48 < 2053.16
  -> Decision False in time 0.2100000000, query time of that 0.0008496350, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 50, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 50, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.0044 accuracy: 1.81839 cost: 0.00633344 M: 10 delta: 1 time: 6.85725 one-recall: 0 one-ratio: 2.07916
iteration: 2 recall: 0.052 accuracy: 0.699626 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1378 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: 14.4278 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: 19.1094 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: 25.7849 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: 31.968 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.99 accuracy: 0.000358431 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.5446 one-recall: 1 one-ratio: 1
iteration: 8 recall: 0.9912 accuracy: 0.00031344 cost: 0.0443167 M: 24.8843 delta: 0.0806719 time: 35.448 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:  82996.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0028516667
  Testing...
|S| = 98
|T| = 1411
Reject!
1898.64 < 1916.96
  -> Decision False in time 0.1400000000, query time of that 0.0239069680, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1672.36 < 1693.32
  -> Decision False in time 0.0600000000, query time of that 0.0124010040, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1626.53 < 1902.7
  -> Decision False in time 4.1100000000, query time of that 0.7013646690, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1993.78 < 2006.76
  -> Decision False in time 1.3600000000, query time of that 0.0306000800, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1593.81 < 1600.46
  -> Decision False in time 1.9600000000, query time of that 0.0412564120, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
2041.44 < 2069.81
  -> Decision False in time 3.9000000000, query time of that 0.0850340870, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
2097.97 < 2202.84
  -> Decision False in time 7.0700000000, query time of that 0.0160353740, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1626.54 < 1678.8
  -> Decision False in time 0.7100000000, query time of that 0.0021615560, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1666.81 < 1725.98
  -> Decision False in time 0.4800000000, query time of that 0.0014691470, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 90, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 90, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.004 accuracy: 1.75774 cost: 0.00633344 M: 10 delta: 1 time: 6.85353 one-recall: 0 one-ratio: 2.03464
iteration: 2 recall: 0.0608 accuracy: 0.646443 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1357 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: 14.4261 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: 19.1085 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: 25.7857 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: 31.9702 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: 34.5475 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:  81736.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0016203333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3800000000, query time of that 0.0754153410, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1647.16 < 1731.62
  -> Decision False in time 1.8700000000, query time of that 0.3768590480, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1867.3 < 1875.84
  -> Decision False in time 1.1500000000, query time of that 0.2316016130, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1820.9 < 1878.97
  -> Decision False in time 1.7900000000, query time of that 0.0479713250, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
2038.16 < 2055.54
  -> Decision False in time 1.5000000000, query time of that 0.0390696790, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1725.87 < 1801.72
  -> Decision False in time 1.5900000000, query time of that 0.0421793060, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1847.05 < 1868.09
  -> Decision False in time 0.8700000000, query time of that 0.0031055800, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
2071.22 < 2107.87
  -> Decision False in time 1.0200000000, query time of that 0.0035279530, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1904.27 < 1946.87
  -> Decision False in time 27.9500000000, query time of that 0.0738544430, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 20, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 20, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.006 accuracy: 1.65471 cost: 0.00633344 M: 10 delta: 1 time: 6.84736 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: 10.1307 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: 14.4216 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: 19.1043 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: 25.7827 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: 31.9696 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.992 accuracy: 0.000590742 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.5489 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 34.84000000000003
Index size:  81732.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0056770000
  Testing...
|S| = 98
|T| = 1411
Reject!
2032.02 < 2096.27
  -> Decision False in time 0.3100000000, query time of that 0.0440894950, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1384.94 < 1473.01
  -> Decision False in time 0.7300000000, query time of that 0.1049427010, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1828.38 < 1852.28
  -> Decision False in time 0.2700000000, query time of that 0.0379560120, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1847.72 < 1955.49
  -> Decision False in time 0.3200000000, query time of that 0.0060962490, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1421.67 < 1431.45
  -> Decision False in time 0.0600000000, query time of that 0.0013707940, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1789.36 < 1802.16
  -> Decision False in time 0.4500000000, query time of that 0.0081316890, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1906.26 < 1911.62
  -> Decision False in time 0.0700000000, query time of that 0.0007468620, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1567.28 < 1594.44
  -> Decision False in time 3.1300000000, query time of that 0.0064667970, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1687.68 < 1698.44
  -> Decision False in time 0.4300000000, query time of that 0.0014392350, 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: 0.0036 accuracy: 1.7649 cost: 0.00633344 M: 10 delta: 1 time: 6.85569 one-recall: 0 one-ratio: 2.10464
iteration: 2 recall: 0.0604 accuracy: 0.635748 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1379 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: 14.4293 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: 19.1126 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: 25.7889 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: 31.9724 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: 34.5504 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: 35.4541 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 35.76000000000005
Index size:  83000.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0023523333
  Testing...
|S| = 98
|T| = 1411
Reject!
1648.64 < 1661.14
  -> Decision False in time 0.1700000000, query time of that 0.0325082570, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1802.36 < 1822.62
  -> Decision False in time 0.0300000000, query time of that 0.0052103990, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1848.26 < 1907.22
  -> Decision False in time 1.2200000000, query time of that 0.2208692990, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1833.91 < 1841.49
  -> Decision False in time 1.5200000000, query time of that 0.0336659320, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1733.57 < 1796.83
  -> Decision False in time 2.1800000000, query time of that 0.0508711780, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
2110.02 < 2149.38
  -> Decision False in time 0.9900000000, query time of that 0.0232509080, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1917.94 < 2032.14
  -> Decision False in time 1.0300000000, query time of that 0.0039738480, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
2044.37 < 2085.8
  -> Decision False in time 0.0300000000, query time of that 0.0012324700, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1938.02 < 1945.95
  -> Decision False in time 0.0000000000, query time of that 0.0010309990, 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: 6.85233 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: 10.1355 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: 14.4262 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: 19.115 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: 25.7976 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: 31.9859 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.9912 accuracy: 0.000477346 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.5713 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:  81732.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0101743333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3500000000, query time of that 0.0438195000, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1726.4 < 1865.83
  -> Decision False in time 0.1600000000, query time of that 0.0202042790, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1661.15 < 1667.58
  -> Decision False in time 0.7200000000, query time of that 0.0900716240, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1644.05 < 1652.8
  -> Decision False in time 0.0100000000, query time of that 0.0003551750, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1651.72 < 1688.13
  -> Decision False in time 0.3900000000, query time of that 0.0069191410, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1752.46 < 1758.77
  -> Decision False in time 1.1900000000, query time of that 0.0207947590, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1773.49 < 1848.17
  -> Decision False in time 0.0200000000, query time of that 0.0005420940, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1555.58 < 1597.09
  -> Decision False in time 2.4100000000, query time of that 0.0038810010, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1800.02 < 1825.45
  -> Decision False in time 0.2200000000, query time of that 0.0006435790, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 40, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 40, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.008 accuracy: 1.74437 cost: 0.00633344 M: 10 delta: 1 time: 6.8452 one-recall: 0 one-ratio: 2.03444
iteration: 2 recall: 0.0652 accuracy: 0.637073 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1255 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: 14.4165 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: 19.0977 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: 25.7733 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: 31.9571 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: 34.5337 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 34.83000000000004
Index size:  81736.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0033383333
  Testing...
|S| = 98
|T| = 1411
Reject!
1305.28 < 1328.67
  -> Decision False in time 0.1300000000, query time of that 0.0228550800, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1771.52 < 1796.89
  -> Decision False in time 1.4500000000, query time of that 0.2271917120, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1965.34 < 1972.41
  -> Decision False in time 0.8900000000, query time of that 0.1415876780, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1591.49 < 1612.22
  -> Decision False in time 1.6700000000, query time of that 0.0341237610, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
2226.84 < 2286
  -> Decision False in time 2.0300000000, query time of that 0.0408971690, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1809.19 < 1908.49
  -> Decision False in time 0.5500000000, query time of that 0.0113140390, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1830.55 < 1858.97
  -> Decision False in time 6.1400000000, query time of that 0.0139200620, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1745.37 < 1754.25
  -> Decision False in time 4.7200000000, query time of that 0.0087693410, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1756.71 < 1769.8
  -> Decision False in time 1.0200000000, query time of that 0.0030804080, 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: 6.84294 one-recall: 0 one-ratio: 1.99571
iteration: 2 recall: 0.0468 accuracy: 0.631223 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.126 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: 14.4174 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: 19.104 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: 25.7838 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: 31.9694 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.9884 accuracy: 0.000479649 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.5483 one-recall: 1 one-ratio: 1
iteration: 8 recall: 0.9908 accuracy: 0.000330768 cost: 0.0443167 M: 24.8843 delta: 0.0806719 time: 35.4524 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:  82996.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0018956667
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3700000000, query time of that 0.0677841100, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1971.09 < 1987.97
  -> Decision False in time 0.2400000000, query time of that 0.0434404890, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1882.06 < 1934.63
  -> Decision False in time 0.6200000000, query time of that 0.1158366410, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1593.29 < 1595.21
  -> Decision False in time 0.8900000000, query time of that 0.0217389080, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1988.6 < 2031.93
  -> Decision False in time 1.4800000000, query time of that 0.0350197660, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1889.99 < 1911.4
  -> Decision False in time 0.4200000000, query time of that 0.0117169220, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
2180.12 < 2185.28
  -> Decision False in time 3.2900000000, query time of that 0.0089757720, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1751.53 < 1758.93
  -> Decision False in time 0.9900000000, query time of that 0.0028508760, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1846.57 < 1862.37
  -> Decision False in time 9.8000000000, query time of that 0.0249233160, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 30, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 30, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.0072 accuracy: 1.78614 cost: 0.00633344 M: 10 delta: 1 time: 6.85363 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: 10.1343 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: 14.4236 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: 19.1051 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: 25.7797 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: 31.9635 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:  77432.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0062620000
  Testing...
|S| = 98
|T| = 1411
Reject!
1637.05 < 1685.83
  -> Decision False in time 0.0400000000, query time of that 0.0059489630, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1744.26 < 1762.2
  -> Decision False in time 0.1600000000, query time of that 0.0225516450, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2161.03 < 2246.32
  -> Decision False in time 0.3500000000, query time of that 0.0495201740, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Accept!
  -> Decision True in time 3.3700000000, query time of that 0.0632596270, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1969.08 < 1986.27
  -> Decision False in time 0.2200000000, query time of that 0.0046757920, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1473.02 < 1493.14
  -> Decision False in time 0.2000000000, query time of that 0.0039969600, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1268.82 < 1281.5
  -> Decision False in time 0.7800000000, query time of that 0.0018591530, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1848.51 < 1879.35
  -> Decision False in time 0.4300000000, query time of that 0.0013147070, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1568.75 < 1665.45
  -> Decision False in time 8.5100000000, query time of that 0.0178138050, 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: 0.0048 accuracy: 1.58384 cost: 0.00633344 M: 10 delta: 1 time: 6.8621 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: 10.146 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: 14.4404 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: 19.1278 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: 25.8078 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9896 accuracy: 0.000550482 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 31.9944 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.9944 accuracy: 0.000259975 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.5729 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 34.879999999999995
Index size:  81728.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0107390000
  Testing...
|S| = 98
|T| = 1411
Reject!
2214.01 < 2237.84
  -> Decision False in time 0.0600000000, query time of that 0.0087856580, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1986.52 < 1997.71
  -> Decision False in time 0.0100000000, query time of that 0.0004568630, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1801.89 < 1885.93
  -> Decision False in time 0.1200000000, query time of that 0.0163966160, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1560.77 < 1612.19
  -> Decision False in time 0.8600000000, query time of that 0.0140124020, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1637.99 < 1691.07
  -> Decision False in time 0.3800000000, query time of that 0.0075059200, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1482.15 < 1531.76
  -> Decision False in time 0.8300000000, query time of that 0.0142477010, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1511.53 < 1535.97
  -> Decision False in time 4.6800000000, query time of that 0.0079451780, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1767.32 < 1776.07
  -> Decision False in time 2.6000000000, query time of that 0.0044690770, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1580.61 < 1595.37
  -> Decision False in time 3.0000000000, query time of that 0.0053516200, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 3, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 3, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.008 accuracy: 1.72963 cost: 0.00633344 M: 10 delta: 1 time: 6.86053 one-recall: 0 one-ratio: 2.01043
iteration: 2 recall: 0.066 accuracy: 0.635277 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.145 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: 14.439 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: 19.1297 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: 25.8141 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.992 accuracy: 0.000346113 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 32.0078 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 32.30000000000007
Index size:  77428.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0121573333
  Testing...
|S| = 98
|T| = 1411
Reject!
1444.08 < 1458.74
  -> Decision False in time 0.1600000000, query time of that 0.0218353170, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1891.65 < 1908.74
  -> Decision False in time 0.7100000000, query time of that 0.0903691790, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1872.81 < 1901.09
  -> Decision False in time 0.1600000000, query time of that 0.0203564810, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1853.2 < 1927.23
  -> Decision False in time 0.1800000000, query time of that 0.0032267290, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1566.75 < 1667.07
  -> Decision False in time 1.0100000000, query time of that 0.0162157270, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1845.09 < 1903.43
  -> Decision False in time 0.0400000000, query time of that 0.0011497760, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
2104.46 < 2116.8
  -> Decision False in time 1.6800000000, query time of that 0.0030417220, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1830.31 < 1939.69
  -> Decision False in time 0.0200000000, query time of that 0.0005216640, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1644.35 < 1719.33
  -> Decision False in time 1.7800000000, query time of that 0.0034820320, 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: 1.5407 cost: 0.00633344 M: 10 delta: 1 time: 6.85505 one-recall: 0 one-ratio: 2.00156
iteration: 2 recall: 0.0588 accuracy: 0.610834 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1368 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: 14.4271 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: 19.1087 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: 25.7848 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: 31.9716 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.9916 accuracy: 0.0004699 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.5492 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 34.84999999999991
Index size:  81724.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0092153333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3300000000, query time of that 0.0446073580, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1976.73 < 2005.85
  -> Decision False in time 0.0300000000, query time of that 0.0040625900, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1625.31 < 1630.12
  -> Decision False in time 0.0000000000, query time of that 0.0005254590, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
2115.62 < 2138.6
  -> Decision False in time 0.0700000000, query time of that 0.0010758400, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1505.11 < 1531.53
  -> Decision False in time 2.1500000000, query time of that 0.0354486480, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1831.94 < 1925.55
  -> Decision False in time 0.1400000000, query time of that 0.0026971070, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
2013.82 < 2047.01
  -> Decision False in time 0.3500000000, query time of that 0.0009411470, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1683.23 < 1683.3
  -> Decision False in time 2.0200000000, query time of that 0.0041227950, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1640.97 < 1718.02
  -> Decision False in time 2.3700000000, query time of that 0.0039483410, 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: 1.6918 cost: 0.00633344 M: 10 delta: 1 time: 6.85756 one-recall: 0 one-ratio: 2.12757
iteration: 2 recall: 0.0552 accuracy: 0.670215 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.141 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: 14.433 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: 19.1184 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: 25.8001 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: 31.9864 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: 34.5704 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 34.87999999999988
Index size:  81712.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0078486667
  Testing...
|S| = 98
|T| = 1411
Reject!
2421.86 < 2502.36
  -> Decision False in time 0.2100000000, query time of that 0.0289156130, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
2243.4 < 2280.56
  -> Decision False in time 0.1800000000, query time of that 0.0237568190, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2165.64 < 2198.21
  -> Decision False in time 0.3900000000, query time of that 0.0513695570, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1700.74 < 1707.67
  -> Decision False in time 1.8900000000, query time of that 0.0307850420, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1651.52 < 1719.46
  -> Decision False in time 0.4000000000, query time of that 0.0064422670, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1639.48 < 1662.65
  -> Decision False in time 0.4600000000, query time of that 0.0081449130, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1545.83 < 1582.61
  -> Decision False in time 1.0300000000, query time of that 0.0023822960, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1586.93 < 1589.89
  -> Decision False in time 1.8200000000, query time of that 0.0034544280, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1793.11 < 1922.66
  -> Decision False in time 1.4600000000, query time of that 0.0026634380, 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: 6.86281 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: 10.1458 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: 14.4382 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: 25.7973 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9828 accuracy: 0.00106873 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 31.9838 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.99 accuracy: 0.000613841 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.5623 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 34.87000000000012
Index size:  81720.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0075250000
  Testing...
|S| = 98
|T| = 1411
Reject!
1912.84 < 2076.35
  -> Decision False in time 0.0700000000, query time of that 0.0098461390, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1976.67 < 1990.58
  -> Decision False in time 0.0100000000, query time of that 0.0013544960, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2110.16 < 2179.14
  -> Decision False in time 0.0500000000, query time of that 0.0070900030, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1649.3 < 1660.71
  -> Decision False in time 0.2100000000, query time of that 0.0043439490, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1604.31 < 1612.72
  -> Decision False in time 0.6300000000, query time of that 0.0112499320, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1609.6 < 1639.56
  -> Decision False in time 0.1200000000, query time of that 0.0022907500, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1734.44 < 1779.73
  -> Decision False in time 1.6900000000, query time of that 0.0037954920, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1446.87 < 1473.51
  -> Decision False in time 1.0500000000, query time of that 0.0024146750, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1791.72 < 1792.86
  -> Decision False in time 0.2800000000, query time of that 0.0007081940, 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: 0.0068 accuracy: 1.63493 cost: 0.00633344 M: 10 delta: 1 time: 6.86656 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: 10.1509 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: 14.4435 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: 19.1259 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: 25.8048 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: 31.9943 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: 34.5734 one-recall: 1 one-ratio: 1
iteration: 8 recall: 0.9932 accuracy: 0.000294001 cost: 0.0443167 M: 24.8843 delta: 0.0806719 time: 35.4784 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: 0.0013130000
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3900000000, query time of that 0.0816992280, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1596.53 < 1618.64
  -> Decision False in time 3.1200000000, query time of that 0.6540483330, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1681.74 < 1688.16
  -> Decision False in time 1.7200000000, query time of that 0.3562058960, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1980.19 < 1981.56
  -> Decision False in time 0.6600000000, query time of that 0.0185691540, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1917.4 < 1956.17
  -> Decision False in time 3.7200000000, query time of that 0.0977921320, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1843 < 1846.4
  -> Decision False in time 5.3000000000, query time of that 0.1458556360, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1939.26 < 1983.71
  -> Decision False in time 5.1000000000, query time of that 0.0160426790, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1746.4 < 1828.36
  -> Decision False in time 7.5000000000, query time of that 0.0203211450, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1323.91 < 1493.67
  -> Decision False in time 16.1200000000, query time of that 0.0469745690, with c1=5.0000000000, c2=0.1000000000
