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', 50, {'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', 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', 80, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 40, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 90, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 4, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 100, {'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', 3, {'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', 20, {'reverse': -1}, False])]
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: 1.5879 cost: 0.00633344 M: 10 delta: 1 time: 6.84911 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: 10.1314 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: 14.4223 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: 19.1066 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: 25.7898 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: 31.9767 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.9932 accuracy: 0.0002248 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.556 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 34.86
Index size:  100448.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0032233333
  Testing...
|S| = 98
|T| = 1411
Reject!
2008.91 < 2041.04
  -> Decision False in time 0.0200000000, query time of that 0.0048251060, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1796.2 < 1827.25
  -> Decision False in time 0.4800000000, query time of that 0.0797995880, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2072.09 < 2236.69
  -> Decision False in time 0.5500000000, query time of that 0.0918897410, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1602.33 < 1630.53
  -> Decision False in time 1.7100000000, query time of that 0.0371587030, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1919.82 < 2055.71
  -> Decision False in time 1.3200000000, query time of that 0.0273580510, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1627 < 1643.91
  -> Decision False in time 0.8500000000, query time of that 0.0179868540, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1866.28 < 1882.92
  -> Decision False in time 6.1100000000, query time of that 0.0144252800, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1565.87 < 1575.32
  -> Decision False in time 1.2200000000, query time of that 0.0028574380, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1702.01 < 1705.37
  -> Decision False in time 4.5400000000, query time of that 0.0090459000, 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: 0.0044 accuracy: 1.81839 cost: 0.00633344 M: 10 delta: 1 time: 6.83603 one-recall: 0 one-ratio: 2.07916
iteration: 2 recall: 0.052 accuracy: 0.699626 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1174 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: 14.4068 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: 19.0906 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: 25.765 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: 31.952 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.99 accuracy: 0.000358431 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.5326 one-recall: 1 one-ratio: 1
iteration: 8 recall: 0.9912 accuracy: 0.00031344 cost: 0.0443167 M: 24.8843 delta: 0.0806719 time: 35.4361 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 35.74000000000001
Index size:  82992.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0095726667
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3400000000, query time of that 0.0446361150, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
2206.36 < 2476.48
  -> Decision False in time 0.3800000000, query time of that 0.0519270930, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2780.58 < 2815.66
  -> Decision False in time 0.0400000000, query time of that 0.0051511160, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
2068.95 < 2121.51
  -> Decision False in time 1.0300000000, query time of that 0.0171808840, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1887.1 < 1922.85
  -> Decision False in time 0.4200000000, query time of that 0.0075384130, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1939.79 < 2094.48
  -> Decision False in time 0.4900000000, query time of that 0.0085755590, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1735.09 < 1748.91
  -> Decision False in time 2.0200000000, query time of that 0.0041239550, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
2101.35 < 2101.72
  -> Decision False in time 3.6700000000, query time of that 0.0069243950, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1688.52 < 1710.91
  -> Decision False in time 0.4900000000, query time of that 0.0013605270, 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: 1.75774 cost: 0.00633344 M: 10 delta: 1 time: 6.84034 one-recall: 0 one-ratio: 2.03464
iteration: 2 recall: 0.0608 accuracy: 0.646443 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1259 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: 14.4216 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: 19.1129 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: 25.8017 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: 31.9978 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: 34.5787 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: 0.0092153333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3400000000, query time of that 0.0420484280, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
2063.95 < 2132.43
  -> Decision False in time 0.0500000000, query time of that 0.0070724910, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1676.39 < 1720.56
  -> Decision False in time 0.0800000000, query time of that 0.0106098140, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1406.04 < 1428.46
  -> Decision False in time 1.2200000000, query time of that 0.0192446030, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1634.54 < 1645.36
  -> Decision False in time 1.2900000000, query time of that 0.0216851050, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1623.79 < 1848.31
  -> Decision False in time 0.1400000000, query time of that 0.0030422760, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1473.54 < 1506.77
  -> Decision False in time 2.2300000000, query time of that 0.0035408530, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1746.98 < 1759.16
  -> Decision False in time 2.7900000000, query time of that 0.0049948330, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1467.85 < 1471.16
  -> Decision False in time 4.4200000000, query time of that 0.0081766340, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 30, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 30, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.006 accuracy: 1.65471 cost: 0.00633344 M: 10 delta: 1 time: 6.8385 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: 10.1226 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: 14.4146 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: 19.0986 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: 25.7765 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: 31.9642 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.992 accuracy: 0.000590742 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.5444 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:  81724.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0048390000
  Testing...
|S| = 98
|T| = 1411
Reject!
1653.44 < 1672.28
  -> Decision False in time 0.0400000000, query time of that 0.0080769610, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1915.53 < 1937.44
  -> Decision False in time 1.2100000000, query time of that 0.1814036370, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1492.84 < 1551.85
  -> Decision False in time 3.2600000000, query time of that 0.4925358440, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1265.5 < 1273.77
  -> Decision False in time 2.9700000000, query time of that 0.0562909990, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
2077.26 < 2138.96
  -> Decision False in time 1.8200000000, query time of that 0.0337367050, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
2041.83 < 2058.87
  -> Decision False in time 0.3700000000, query time of that 0.0069403920, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1744.11 < 1784.99
  -> Decision False in time 3.0700000000, query time of that 0.0067684420, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1763.79 < 1805.88
  -> Decision False in time 5.1900000000, query time of that 0.0097081080, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1606.46 < 1614.15
  -> Decision False in time 14.9400000000, query time of that 0.0302776270, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 80, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 80, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.0036 accuracy: 1.7649 cost: 0.00633344 M: 10 delta: 1 time: 6.83634 one-recall: 0 one-ratio: 2.10464
iteration: 2 recall: 0.0604 accuracy: 0.635748 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1201 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: 14.4121 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: 19.0962 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: 25.775 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: 31.9664 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: 34.5464 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: 35.4502 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: 0.0018133333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3700000000, query time of that 0.0712210290, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1904.16 < 1947.65
  -> Decision False in time 3.0900000000, query time of that 0.6082024740, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1605.65 < 1943.08
  -> Decision False in time 0.9300000000, query time of that 0.1816421480, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1200.81 < 1236.24
  -> Decision False in time 2.6900000000, query time of that 0.0646736800, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1744.88 < 1750.09
  -> Decision False in time 5.3400000000, query time of that 0.1312994170, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1523.82 < 1668.46
  -> Decision False in time 0.3100000000, query time of that 0.0079039740, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1399.33 < 1432
  -> Decision False in time 2.4400000000, query time of that 0.0073219480, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1706.88 < 1728.33
  -> Decision False in time 4.4400000000, query time of that 0.0119061650, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1792.8 < 1845.35
  -> Decision False in time 33.7900000000, query time of that 0.0875150730, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 40, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 40, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.0044 accuracy: 1.68691 cost: 0.00633344 M: 10 delta: 1 time: 6.84257 one-recall: 0.02 one-ratio: 1.95821
iteration: 2 recall: 0.0608 accuracy: 0.628781 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1264 one-recall: 0.12 one-ratio: 1.41714
iteration: 3 recall: 0.398 accuracy: 0.170086 cost: 0.0152986 M: 11.5308 delta: 0.828164 time: 14.4164 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: 19.1019 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: 25.7858 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: 31.984 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.9912 accuracy: 0.000477346 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.5695 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:  81736.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0033383333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3500000000, query time of that 0.0554886510, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1872.11 < 1876.18
  -> Decision False in time 0.0300000000, query time of that 0.0051193450, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1826.33 < 1839.23
  -> Decision False in time 1.3100000000, query time of that 0.2102636330, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1887.14 < 1894.92
  -> Decision False in time 1.3500000000, query time of that 0.0264056450, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1971.31 < 2032.59
  -> Decision False in time 0.2400000000, query time of that 0.0056429740, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1620.29 < 1737.24
  -> Decision False in time 0.8100000000, query time of that 0.0176296010, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
2053.62 < 2083.96
  -> Decision False in time 3.7800000000, query time of that 0.0076389820, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1501.68 < 1529.49
  -> Decision False in time 1.1900000000, query time of that 0.0028595710, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1547.54 < 1552.38
  -> Decision False in time 2.3200000000, query time of that 0.0049292310, 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: 0.008 accuracy: 1.74437 cost: 0.00633344 M: 10 delta: 1 time: 6.83865 one-recall: 0 one-ratio: 2.03444
iteration: 2 recall: 0.0652 accuracy: 0.637073 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1211 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: 14.4133 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: 19.0993 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: 25.7796 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: 31.9714 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: 34.553 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:  81712.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0016203333
  Testing...
|S| = 98
|T| = 1411
Reject!
1445.1 < 1451.11
  -> Decision False in time 0.2400000000, query time of that 0.0466176840, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1582.32 < 1610.45
  -> Decision False in time 1.7200000000, query time of that 0.3404022940, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1814.83 < 1844.23
  -> Decision False in time 0.3200000000, query time of that 0.0656662170, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
2078.83 < 2099.27
  -> Decision False in time 1.5100000000, query time of that 0.0395254520, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1626.28 < 1711.89
  -> Decision False in time 8.1700000000, query time of that 0.2093306550, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1814.83 < 1844.23
  -> Decision False in time 2.0100000000, query time of that 0.0500099170, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1596.53 < 1618.64
  -> Decision False in time 18.2200000000, query time of that 0.0485443490, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1461.73 < 1514.36
  -> Decision False in time 12.8600000000, query time of that 0.0350472640, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
2209.54 < 2237.65
  -> Decision False in time 5.1200000000, query time of that 0.0139067700, 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: 6.83828 one-recall: 0 one-ratio: 1.99571
iteration: 2 recall: 0.0468 accuracy: 0.631223 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1194 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: 14.4088 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: 19.0911 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: 25.7666 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: 31.9533 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.9884 accuracy: 0.000479649 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.5329 one-recall: 1 one-ratio: 1
iteration: 8 recall: 0.9908 accuracy: 0.000330768 cost: 0.0443167 M: 24.8843 delta: 0.0806719 time: 35.4383 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: 0.0072696667
  Testing...
|S| = 98
|T| = 1411
Reject!
1612.47 < 1635.61
  -> Decision False in time 0.2200000000, query time of that 0.0302985970, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
2119.26 < 2138.72
  -> Decision False in time 0.5200000000, query time of that 0.0697106940, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1899.89 < 1903.65
  -> Decision False in time 0.1600000000, query time of that 0.0200243010, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1782.57 < 1827.14
  -> Decision False in time 1.2700000000, query time of that 0.0205479430, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1687.71 < 1733.03
  -> Decision False in time 2.1000000000, query time of that 0.0356022810, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1499.21 < 1502.24
  -> Decision False in time 2.5200000000, query time of that 0.0417456650, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1665.27 < 1673.12
  -> Decision False in time 3.7400000000, query time of that 0.0072065240, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1766.22 < 1785.79
  -> Decision False in time 7.1200000000, query time of that 0.0121857290, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1565.85 < 1569.86
  -> Decision False in time 2.4900000000, query time of that 0.0042004550, 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: 0.0072 accuracy: 1.78614 cost: 0.00633344 M: 10 delta: 1 time: 6.83855 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: 10.1245 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: 14.4177 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: 19.1031 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: 25.7856 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: 31.9802 one-recall: 1 one-ratio: 1
Graph completion with reverse edges...

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

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 32.25999999999999
Index size:  77432.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0019170000
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3700000000, query time of that 0.0762687400, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
2050.38 < 2134.44
  -> Decision False in time 2.5500000000, query time of that 0.5187712700, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1845.76 < 1883.93
  -> Decision False in time 0.0800000000, query time of that 0.0153689660, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1789.17 < 1903.27
  -> Decision False in time 2.7000000000, query time of that 0.0684865030, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1798.38 < 1799.5
  -> Decision False in time 0.1500000000, query time of that 0.0043362650, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1672.7 < 1759.75
  -> Decision False in time 2.5700000000, query time of that 0.0665400080, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1529.31 < 1591.35
  -> Decision False in time 5.8100000000, query time of that 0.0160178720, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1873.48 < 1901.16
  -> Decision False in time 5.1400000000, query time of that 0.0151866700, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1785.43 < 1883.06
  -> Decision False in time 11.2100000000, query time of that 0.0304617200, 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: 0.0048 accuracy: 1.58384 cost: 0.00633344 M: 10 delta: 1 time: 6.85066 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: 10.1362 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: 14.4315 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: 19.1201 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: 25.8041 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9896 accuracy: 0.000550482 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 32.0003 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.9944 accuracy: 0.000259975 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.5857 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:  81736.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0101743333
  Testing...
|S| = 98
|T| = 1411
Reject!
1729.7 < 1741.1
  -> Decision False in time 0.1400000000, query time of that 0.0183171820, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
2007.82 < 2011.16
  -> Decision False in time 0.1100000000, query time of that 0.0144144230, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1711.27 < 1989.48
  -> Decision False in time 0.0800000000, query time of that 0.0106623830, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1562.53 < 1682.28
  -> Decision False in time 0.6900000000, query time of that 0.0110251620, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1567.53 < 1585.39
  -> Decision False in time 0.3600000000, query time of that 0.0060953470, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1543.11 < 1584.38
  -> Decision False in time 0.2600000000, query time of that 0.0050620840, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1102.37 < 1193.78
  -> Decision False in time 1.5800000000, query time of that 0.0028608770, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1738.1 < 1840.5
  -> Decision False in time 0.0600000000, query time of that 0.0005786470, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1155.72 < 1244.73
  -> Decision False in time 0.3700000000, query time of that 0.0010151250, 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: 0.008 accuracy: 1.72963 cost: 0.00633344 M: 10 delta: 1 time: 6.84841 one-recall: 0 one-ratio: 2.01043
iteration: 2 recall: 0.066 accuracy: 0.635277 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1323 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: 14.426 one-recall: 0.55 one-ratio: 1.08928
iteration: 4 recall: 0.8664 accuracy: 0.0168036 cost: 0.0212851 M: 11.9674 delta: 0.604399 time: 19.1119 one-recall: 0.94 one-ratio: 1.01843
iteration: 5 recall: 0.9752 accuracy: 0.00155631 cost: 0.0303842 M: 16.8423 delta: 0.26712 time: 25.792 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.992 accuracy: 0.000346113 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 31.988 one-recall: 1 one-ratio: 1
Graph completion with reverse edges...

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

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 32.25999999999999
Index size:  77432.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0094886667
  Testing...
|S| = 98
|T| = 1411
Reject!
1655.33 < 1664.3
  -> Decision False in time 0.0300000000, query time of that 0.0042394680, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
2107.15 < 2246.22
  -> Decision False in time 0.5100000000, query time of that 0.0658865780, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1909.9 < 2381.65
  -> Decision False in time 0.3200000000, query time of that 0.0412694130, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1349.06 < 1425.61
  -> Decision False in time 0.5200000000, query time of that 0.0088254270, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1850.5 < 1933.4
  -> Decision False in time 0.0300000000, query time of that 0.0006088310, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1710.23 < 1724.18
  -> Decision False in time 0.3500000000, query time of that 0.0059425790, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1706.66 < 1838.81
  -> Decision False in time 0.6200000000, query time of that 0.0012909450, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1707.62 < 1777.78
  -> Decision False in time 0.1500000000, query time of that 0.0005427980, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1669.39 < 1687.94
  -> Decision False in time 2.4400000000, query time of that 0.0041902960, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 70, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 70, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.004 accuracy: 1.5407 cost: 0.00633344 M: 10 delta: 1 time: 6.84293 one-recall: 0 one-ratio: 2.00156
iteration: 2 recall: 0.0588 accuracy: 0.610834 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1266 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: 14.4194 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: 19.1074 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: 25.7895 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: 31.9839 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.9916 accuracy: 0.0004699 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.5682 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 34.8599999999999
Index size:  81740.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0021906667
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3700000000, query time of that 0.0662522370, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
2214.58 < 2226.87
  -> Decision False in time 1.7300000000, query time of that 0.3168110520, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2139.87 < 2171.8
  -> Decision False in time 1.4400000000, query time of that 0.2651096500, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1544.68 < 1545.48
  -> Decision False in time 2.2500000000, query time of that 0.0518456710, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1649.2 < 1665.65
  -> Decision False in time 0.1900000000, query time of that 0.0049355880, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1709.38 < 1729.65
  -> Decision False in time 1.3900000000, query time of that 0.0341313080, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1782.65 < 1786.65
  -> Decision False in time 2.4400000000, query time of that 0.0066670400, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1610.89 < 1701.31
  -> Decision False in time 1.8400000000, query time of that 0.0049246510, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
2139.87 < 2171.8
  -> Decision False in time 1.4000000000, query time of that 0.0043485190, 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: 0.004 accuracy: 1.6918 cost: 0.00633344 M: 10 delta: 1 time: 6.83991 one-recall: 0 one-ratio: 2.12757
iteration: 2 recall: 0.0552 accuracy: 0.670215 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1236 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: 14.4153 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: 19.0986 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: 25.7781 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: 31.9674 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: 34.5463 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 34.850000000000136
Index size:  81724.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0092930000
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3400000000, query time of that 0.0450476280, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1675.48 < 1703.49
  -> Decision False in time 0.0400000000, query time of that 0.0048169280, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1478.5 < 1492.33
  -> Decision False in time 0.2200000000, query time of that 0.0291447500, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1442.16 < 1482.35
  -> Decision False in time 2.2200000000, query time of that 0.0365706400, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1561.2 < 1592.42
  -> Decision False in time 1.3200000000, query time of that 0.0208518310, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1527.76 < 1534.3
  -> Decision False in time 0.1400000000, query time of that 0.0023748860, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1676.37 < 1741.42
  -> Decision False in time 2.4000000000, query time of that 0.0042585060, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1835.55 < 1875.18
  -> Decision False in time 2.7400000000, query time of that 0.0051062420, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1373.84 < 1410.49
  -> Decision False in time 3.8800000000, query time of that 0.0073633440, 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: 0.0056 accuracy: 1.41157 cost: 0.00633344 M: 10 delta: 1 time: 6.85401 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: 10.1368 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: 14.4286 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: 19.1156 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: 25.7923 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9828 accuracy: 0.00106873 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 31.9849 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.99 accuracy: 0.000613841 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.5681 one-recall: 1 one-ratio: 1
Graph completion with reverse edges...

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

0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
***************************************************
Built index in 34.86999999999989
Index size:  81740.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0026693333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3600000000, query time of that 0.0623105970, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1679.97 < 1704.22
  -> Decision False in time 0.8700000000, query time of that 0.1542615210, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1948.65 < 1975.61
  -> Decision False in time 1.8800000000, query time of that 0.3313902470, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1962.22 < 1984.25
  -> Decision False in time 0.6500000000, query time of that 0.0144819000, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1806.1 < 1818.27
  -> Decision False in time 2.3600000000, query time of that 0.0527327680, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
2100.53 < 2137.1
  -> Decision False in time 7.5100000000, query time of that 0.1692423130, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1651.12 < 1663.79
  -> Decision False in time 2.0700000000, query time of that 0.0059764030, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1942.21 < 2125.26
  -> Decision False in time 2.0700000000, query time of that 0.0054976330, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1783.35 < 1787.53
  -> Decision False in time 1.4100000000, query time of that 0.0041988730, 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: 0.0068 accuracy: 1.63493 cost: 0.00633344 M: 10 delta: 1 time: 6.85621 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: 10.1419 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: 14.4363 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: 19.123 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: 25.8061 one-recall: 0.97 one-ratio: 1.00691
iteration: 6 recall: 0.9844 accuracy: 0.00194847 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 32.0009 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: 34.5853 one-recall: 1 one-ratio: 1
iteration: 8 recall: 0.9932 accuracy: 0.000294001 cost: 0.0443167 M: 24.8843 delta: 0.0806719 time: 35.4906 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 35.799999999999955
Index size:  82996.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0051403333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3400000000, query time of that 0.0516122800, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1601.98 < 1640.28
  -> Decision False in time 2.3700000000, query time of that 0.3377906890, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1835.45 < 1836.67
  -> Decision False in time 0.2200000000, query time of that 0.0334501330, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
2039.42 < 2055.1
  -> Decision False in time 0.4300000000, query time of that 0.0082996390, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1766.8 < 1819.29
  -> Decision False in time 1.5100000000, query time of that 0.0262665910, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1732.99 < 1740.18
  -> Decision False in time 0.7700000000, query time of that 0.0145912380, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1455.62 < 1532.62
  -> Decision False in time 2.7100000000, query time of that 0.0053266790, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1566.9 < 1585.66
  -> Decision False in time 2.4100000000, query time of that 0.0046660530, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1913.42 < 1924.45
  -> Decision False in time 1.1200000000, query time of that 0.0025554280, with c1=5.0000000000, c2=0.1000000000
