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', 5, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 1, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 20, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 4, {'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', 70, {'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', 10, {'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', 50, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 2, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 30, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 100, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 80, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 40, {'reverse': -1}, False])]
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: 0.0064 accuracy: 1.59623 cost: 0.00633344 M: 10 delta: 1 time: 0.665778 one-recall: 0.01 one-ratio: 1.95911
iteration: 2 recall: 0.0544 accuracy: 0.62013 cost: 0.0100109 M: 10 delta: 0.854945 time: 0.891444 one-recall: 0.05 one-ratio: 1.47978
iteration: 3 recall: 0.358 accuracy: 0.180699 cost: 0.0153112 M: 11.543 delta: 0.827699 time: 1.18004 one-recall: 0.43 one-ratio: 1.1397
iteration: 4 recall: 0.8112 accuracy: 0.0263972 cost: 0.0212911 M: 11.9693 delta: 0.602846 time: 1.48517 one-recall: 0.83 one-ratio: 1.03613
iteration: 5 recall: 0.9496 accuracy: 0.00527856 cost: 0.0304055 M: 16.8658 delta: 0.265485 time: 1.90843 one-recall: 0.97 one-ratio: 1.01155
iteration: 6 recall: 0.9828 accuracy: 0.00117401 cost: 0.0394971 M: 22.5727 delta: 0.105552 time: 2.32316 one-recall: 0.99 one-ratio: 1.00163
iteration: 7 recall: 0.9928 accuracy: 0.000380949 cost: 0.0431732 M: 24.4191 delta: 0.0840432 time: 2.54824 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 46.54
Index size:  100040.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0223450000
  Testing...
|S| = 20
|T| = 283
Reject!
1325.65 < 1851.95
  -> Decision False in time 0.0100000000, query time of that 0.0021784620, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Accept!
  -> Decision True in time 0.1600000000, query time of that 0.0453442420, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Reject!
1884.84 < 2156.43
  -> Decision False in time 0.0900000000, query time of that 0.0242154560, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1300000000, query time of that 0.0049159100, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Reject!
2127.06 < 2157.24
  -> Decision False in time 0.1900000000, query time of that 0.0084753090, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
2106.48 < 2465.2
  -> Decision False in time 0.6000000000, query time of that 0.0274709580, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Accept!
  -> Decision True in time 1.5800000000, query time of that 0.0087564430, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Reject!
1708.57 < 2079.88
  -> Decision False in time 4.1700000000, query time of that 0.0244104470, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
1638.55 < 1651.64
  -> Decision False in time 1.4400000000, query time of that 0.0081378570, 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: 6.91958 one-recall: 0 one-ratio: 2.07916
iteration: 2 recall: 0.052 accuracy: 0.699626 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.2365 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: 14.5672 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: 19.2904 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: 26.026 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: 32.282 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.99 accuracy: 0.000358431 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.8998 one-recall: 1 one-ratio: 1
iteration: 8 recall: 0.9912 accuracy: 0.00031344 cost: 0.0443167 M: 24.8843 delta: 0.0806719 time: 35.8182 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 36.19999999999999
Index size:  92652.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0346916667
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0200000000, query time of that 0.0045309800, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Reject!
2096.12 < 2221.32
  -> Decision False in time 0.0300000000, query time of that 0.0083686290, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Reject!
1790.68 < 2112.02
  -> Decision False in time 0.0100000000, query time of that 0.0001877910, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Reject!
2386.57 < 2506.11
  -> Decision False in time 0.1100000000, query time of that 0.0047623930, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Reject!
2131.59 < 2157.19
  -> Decision False in time 0.0500000000, query time of that 0.0022767640, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
2056.25 < 2183.07
  -> Decision False in time 0.0100000000, query time of that 0.0006901900, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Accept!
  -> Decision True in time 1.3800000000, query time of that 0.0064779460, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Reject!
2073 < 2172.67
  -> Decision False in time 2.6200000000, query time of that 0.0125603370, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
2296.02 < 2386.01
  -> Decision False in time 6.6200000000, query time of that 0.0307686570, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 20, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 20, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.004 accuracy: 1.75774 cost: 0.00633344 M: 10 delta: 1 time: 6.91457 one-recall: 0 one-ratio: 2.03464
iteration: 2 recall: 0.0608 accuracy: 0.646443 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.2303 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: 14.5592 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: 19.2798 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: 26.0124 one-recall: 0.97 one-ratio: 1.00167
iteration: 6 recall: 0.9844 accuracy: 0.000793035 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 32.2653 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: 34.8788 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 35.25
Index size:  91384.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0078483333
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0200000000, query time of that 0.0060360810, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Reject!
2045.99 < 2061.69
  -> Decision False in time 0.0800000000, query time of that 0.0261785730, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Reject!
1354.3 < 1736.71
  -> Decision False in time 0.2500000000, query time of that 0.0769824710, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1300000000, query time of that 0.0064493640, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Accept!
  -> Decision True in time 1.2600000000, query time of that 0.0623401110, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
1556.64 < 2129.56
  -> Decision False in time 1.1000000000, query time of that 0.0540789780, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Accept!
  -> Decision True in time 1.3600000000, query time of that 0.0077173050, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Reject!
1697.18 < 1747.53
  -> Decision False in time 3.9800000000, query time of that 0.0222929930, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
1941.02 < 1979.38
  -> Decision False in time 1.6000000000, query time of that 0.0095936520, 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: 0.006 accuracy: 1.65471 cost: 0.00633344 M: 10 delta: 1 time: 6.91477 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: 10.2265 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: 14.5493 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: 19.2648 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: 25.9873 one-recall: 0.98 one-ratio: 1.00081
iteration: 6 recall: 0.986 accuracy: 0.000854818 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 32.2262 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.992 accuracy: 0.000590742 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.8339 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 35.21000000000001
Index size:  95428.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0224016667
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0200000000, query time of that 0.0053981730, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Reject!
1780.69 < 1989.78
  -> Decision False in time 0.1300000000, query time of that 0.0338295440, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Reject!
2155.45 < 2185.36
  -> Decision False in time 0.0100000000, query time of that 0.0026798130, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1300000000, query time of that 0.0049923510, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Reject!
2218.38 < 2223.86
  -> Decision False in time 0.2500000000, query time of that 0.0104508870, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
1297.76 < 1991.75
  -> Decision False in time 0.5700000000, query time of that 0.0234305590, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Reject!
2128.32 < 2158.31
  -> Decision False in time 0.9600000000, query time of that 0.0050999430, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Reject!
1813.23 < 2141.21
  -> Decision False in time 0.6800000000, query time of that 0.0038020680, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
1718.08 < 1951.59
  -> Decision False in time 0.4200000000, query time of that 0.0020460350, 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: 0.0036 accuracy: 1.7649 cost: 0.00633344 M: 10 delta: 1 time: 6.91593 one-recall: 0 one-ratio: 2.10464
iteration: 2 recall: 0.0604 accuracy: 0.635748 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.226 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: 14.5494 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: 19.265 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: 25.9872 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: 32.2274 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: 34.8343 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: 35.7503 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 36.120000000000005
Index size:  87240.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0251566667
  Testing...
|S| = 20
|T| = 283
Reject!
2314.55 < 2436.25
  -> Decision False in time 0.0000000000, query time of that 0.0010008820, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Reject!
2147.82 < 2243.11
  -> Decision False in time 0.0500000000, query time of that 0.0111183760, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Reject!
2077.53 < 2304.42
  -> Decision False in time 0.0200000000, query time of that 0.0055668480, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1300000000, query time of that 0.0047921140, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Reject!
2246.86 < 2262.27
  -> Decision False in time 0.0900000000, query time of that 0.0039264640, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
1629.82 < 1978.67
  -> Decision False in time 0.3700000000, query time of that 0.0135489390, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Accept!
  -> Decision True in time 1.3700000000, query time of that 0.0059078720, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Reject!
1683.09 < 1796.44
  -> Decision False in time 1.7200000000, query time of that 0.0083031860, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
2089.94 < 2179.24
  -> Decision False in time 1.6000000000, query time of that 0.0074818350, 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: 0.0044 accuracy: 1.68691 cost: 0.00633344 M: 10 delta: 1 time: 6.9149 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: 10.2276 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: 14.5499 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: 19.2656 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: 25.99 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: 32.2325 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.9912 accuracy: 0.000477346 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.8425 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 35.20999999999998
Index size:  85988.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0014900000
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0200000000, query time of that 0.0103821050, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Reject!
2118.12 < 2275.79
  -> Decision False in time 0.2100000000, query time of that 0.0929141930, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Reject!
1878.72 < 1911.43
  -> Decision False in time 2.0600000000, query time of that 0.9166773070, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1400000000, query time of that 0.0109537500, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Accept!
  -> Decision True in time 1.3300000000, query time of that 0.1089057050, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
1436.91 < 1735.72
  -> Decision False in time 10.3000000000, query time of that 0.8433341520, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Accept!
  -> Decision True in time 1.3800000000, query time of that 0.0137372260, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Accept!
  -> Decision True in time 13.5600000000, query time of that 0.1260621100, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
1703.07 < 1799.45
  -> Decision False in time 62.0600000000, query time of that 0.5712988120, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 60, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 60, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.008 accuracy: 1.74437 cost: 0.00633344 M: 10 delta: 1 time: 6.91793 one-recall: 0 one-ratio: 2.03444
iteration: 2 recall: 0.0652 accuracy: 0.637073 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.2298 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: 14.5541 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: 19.2716 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: 25.9982 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: 32.2428 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: 34.853 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 35.210000000000036
Index size:  85988.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0020716667
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0200000000, query time of that 0.0101868630, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Accept!
  -> Decision True in time 0.2100000000, query time of that 0.0877864590, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Reject!
1727.71 < 1747.86
  -> Decision False in time 1.6500000000, query time of that 0.6908810770, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1300000000, query time of that 0.0099592370, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Accept!
  -> Decision True in time 1.3100000000, query time of that 0.1012021770, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
2400.64 < 2534
  -> Decision False in time 0.1800000000, query time of that 0.0135722410, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Accept!
  -> Decision True in time 1.3700000000, query time of that 0.0121633460, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Reject!
1697.18 < 1736.81
  -> Decision False in time 6.1500000000, query time of that 0.0540811310, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
1665.79 < 1778.24
  -> Decision False in time 1.9000000000, query time of that 0.0161102550, 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: 0.004 accuracy: 1.49738 cost: 0.00633344 M: 10 delta: 1 time: 6.91237 one-recall: 0 one-ratio: 1.99571
iteration: 2 recall: 0.0468 accuracy: 0.631223 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.2243 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: 14.5487 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: 19.2634 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: 25.9882 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: 32.2313 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.9884 accuracy: 0.000479649 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.8392 one-recall: 1 one-ratio: 1
iteration: 8 recall: 0.9908 accuracy: 0.000330768 cost: 0.0443167 M: 24.8843 delta: 0.0806719 time: 35.7547 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 36.110000000000014
Index size:  86400.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0131666667
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0200000000, query time of that 0.0051672190, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Reject!
2116.05 < 2326.25
  -> Decision False in time 0.0900000000, query time of that 0.0289802190, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Reject!
1686.3 < 2040.66
  -> Decision False in time 0.1000000000, query time of that 0.0261368970, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1300000000, query time of that 0.0058771740, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Accept!
  -> Decision True in time 1.2400000000, query time of that 0.0521152710, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
1843.52 < 1859.81
  -> Decision False in time 0.2200000000, query time of that 0.0097498640, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Reject!
1586.01 < 1620.33
  -> Decision False in time 0.1400000000, query time of that 0.0010829100, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Accept!
  -> Decision True in time 13.4000000000, query time of that 0.0670738340, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
1515.78 < 1543.44
  -> Decision False in time 4.9500000000, query time of that 0.0250786880, 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: 0.0072 accuracy: 1.78614 cost: 0.00633344 M: 10 delta: 1 time: 6.91486 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: 10.2267 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: 14.5492 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: 19.2639 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: 25.9873 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: 32.2298 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 32.569999999999936
Index size:  80128.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0013600000
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0200000000, query time of that 0.0114483000, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Accept!
  -> Decision True in time 0.2300000000, query time of that 0.1077916640, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Accept!
  -> Decision True in time 2.2800000000, query time of that 1.0665302480, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1400000000, query time of that 0.0123112580, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Accept!
  -> Decision True in time 1.3300000000, query time of that 0.1212792560, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
1945.2 < 2066.75
  -> Decision False in time 4.2800000000, query time of that 0.3847747010, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Accept!
  -> Decision True in time 1.3700000000, query time of that 0.0141212910, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Reject!
1588.44 < 1628.08
  -> Decision False in time 4.5400000000, query time of that 0.0447269730, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
1980.14 < 2109.68
  -> Decision False in time 12.3400000000, query time of that 0.1240533120, 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: 0.0048 accuracy: 1.58384 cost: 0.00633344 M: 10 delta: 1 time: 6.9128 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: 10.2235 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: 14.5469 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: 19.2619 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: 25.9856 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9896 accuracy: 0.000550482 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 32.2285 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.9944 accuracy: 0.000259975 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.8379 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 35.18999999999994
Index size:  84412.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0030666667
  Testing...
|S| = 20
|T| = 283
Reject!
2544.86 < 2545.11
  -> Decision False in time 0.0100000000, query time of that 0.0025099990, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Accept!
  -> Decision True in time 0.2000000000, query time of that 0.0827041920, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Reject!
2086.08 < 2136.32
  -> Decision False in time 0.9400000000, query time of that 0.3740998930, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1300000000, query time of that 0.0102857030, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Reject!
1763.33 < 1913.21
  -> Decision False in time 0.8000000000, query time of that 0.0566141670, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
2181.64 < 2192.05
  -> Decision False in time 1.4700000000, query time of that 0.1065137070, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Accept!
  -> Decision True in time 1.3600000000, query time of that 0.0113679820, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Accept!
  -> Decision True in time 13.3500000000, query time of that 0.1087543140, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
1576.02 < 1651.48
  -> Decision False in time 7.9300000000, query time of that 0.0654112380, 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: 0.008 accuracy: 1.72963 cost: 0.00633344 M: 10 delta: 1 time: 6.91843 one-recall: 0 one-ratio: 2.01043
iteration: 2 recall: 0.066 accuracy: 0.635277 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.2303 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: 14.5546 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: 19.2712 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: 25.9963 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.992 accuracy: 0.000346113 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 32.2413 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 32.59000000000003
Index size:  80124.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0498883333
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0100000000, query time of that 0.0040144390, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Reject!
2102.41 < 2106.86
  -> Decision False in time 0.0400000000, query time of that 0.0082267360, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Reject!
1738.79 < 1767.87
  -> Decision False in time 0.0100000000, query time of that 0.0024462790, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Reject!
1890.98 < 1928.16
  -> Decision False in time 0.0800000000, query time of that 0.0026047770, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Reject!
2184.53 < 2256.44
  -> Decision False in time 0.0100000000, query time of that 0.0008326870, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
1687.96 < 1769.78
  -> Decision False in time 0.1700000000, query time of that 0.0058415640, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Reject!
1798.69 < 1881.02
  -> Decision False in time 0.2100000000, query time of that 0.0009765750, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Reject!
2108.08 < 2300.66
  -> Decision False in time 0.8200000000, query time of that 0.0033550800, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
1820.1 < 1941.51
  -> Decision False in time 0.7600000000, query time of that 0.0033450910, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 30, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 30, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.004 accuracy: 1.5407 cost: 0.00633344 M: 10 delta: 1 time: 6.91453 one-recall: 0 one-ratio: 2.00156
iteration: 2 recall: 0.0588 accuracy: 0.610834 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.2259 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: 14.5496 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: 19.2664 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: 25.9907 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: 32.2322 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.9916 accuracy: 0.0004699 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.8416 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 35.20999999999992
Index size:  84432.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0065250000
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0200000000, query time of that 0.0076659820, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Reject!
1802.64 < 1851.84
  -> Decision False in time 0.0400000000, query time of that 0.0161069690, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Reject!
1784.46 < 2100.99
  -> Decision False in time 0.0700000000, query time of that 0.0238370130, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1300000000, query time of that 0.0072651210, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Accept!
  -> Decision True in time 1.2600000000, query time of that 0.0710626440, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
1489.85 < 1542.41
  -> Decision False in time 0.0500000000, query time of that 0.0023821280, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Accept!
  -> Decision True in time 1.3700000000, query time of that 0.0088904000, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Reject!
2174.98 < 2199.22
  -> Decision False in time 8.9100000000, query time of that 0.0577162640, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
1676.3 < 1771.98
  -> Decision False in time 0.1500000000, query time of that 0.0015959440, 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: 0.004 accuracy: 1.6918 cost: 0.00633344 M: 10 delta: 1 time: 6.91202 one-recall: 0 one-ratio: 2.12757
iteration: 2 recall: 0.0552 accuracy: 0.670215 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.2236 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: 14.5453 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: 19.2597 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: 25.9818 one-recall: 0.98 one-ratio: 1.00458
iteration: 6 recall: 0.9852 accuracy: 0.00131027 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 32.225 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: 34.8339 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 35.179999999999836
Index size:  83216.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0008050000
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0200000000, query time of that 0.0120767040, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Accept!
  -> Decision True in time 0.2400000000, query time of that 0.1200408060, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Accept!
  -> Decision True in time 2.4300000000, query time of that 1.1949997200, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1400000000, query time of that 0.0124079390, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Accept!
  -> Decision True in time 1.3500000000, query time of that 0.1330473700, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
1604.79 < 1621.76
  -> Decision False in time 1.7800000000, query time of that 0.1706914940, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Accept!
  -> Decision True in time 1.3800000000, query time of that 0.0165267780, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Reject!
1224 < 1231.04
  -> Decision False in time 2.6900000000, query time of that 0.0286508500, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
1816.65 < 1890.94
  -> Decision False in time 4.7200000000, query time of that 0.0535656750, 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: 0.0056 accuracy: 1.41157 cost: 0.00633344 M: 10 delta: 1 time: 6.91308 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: 10.2225 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: 14.5451 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: 19.2586 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: 25.9799 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9828 accuracy: 0.00106873 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 32.2202 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.99 accuracy: 0.000613841 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.8276 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 35.190000000000055
Index size:  83216.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0013850000
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0200000000, query time of that 0.0110687310, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Accept!
  -> Decision True in time 0.2300000000, query time of that 0.1060999340, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Reject!
1577.64 < 1958.28
  -> Decision False in time 0.7900000000, query time of that 0.3699436850, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1400000000, query time of that 0.0115464430, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Accept!
  -> Decision True in time 1.3500000000, query time of that 0.1228574320, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
1999.51 < 2029.2
  -> Decision False in time 4.6800000000, query time of that 0.4142704120, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Accept!
  -> Decision True in time 1.3800000000, query time of that 0.0129331640, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Reject!
2113.01 < 2150.04
  -> Decision False in time 1.2400000000, query time of that 0.0123016020, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
1540.07 < 1543.62
  -> Decision False in time 12.8300000000, query time of that 0.1252867360, 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: 0.0068 accuracy: 1.63493 cost: 0.00633344 M: 10 delta: 1 time: 6.872 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: 10.1825 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: 14.504 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: 19.2196 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: 25.9414 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: 32.1849 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: 34.7928 one-recall: 1 one-ratio: 1
iteration: 8 recall: 0.9932 accuracy: 0.000294001 cost: 0.0443167 M: 24.8843 delta: 0.0806719 time: 35.7078 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 36.069999999999936
Index size:  3284.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0021300000
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0200000000, query time of that 0.0076695100, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Accept!
  -> Decision True in time 0.1900000000, query time of that 0.0742584640, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Reject!
1882.03 < 1962.25
  -> Decision False in time 1.5500000000, query time of that 0.5876471390, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1400000000, query time of that 0.0086604720, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Accept!
  -> Decision True in time 1.2800000000, query time of that 0.0841852650, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
1690.75 < 1728.45
  -> Decision False in time 10.4300000000, query time of that 0.6792122670, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Accept!
  -> Decision True in time 1.3700000000, query time of that 0.0097019150, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Accept!
  -> Decision True in time 13.4600000000, query time of that 0.0991052160, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
1625.32 < 1665.13
  -> Decision False in time 17.2500000000, query time of that 0.1221547580, with c1=5.0000000000, c2=0.1000000000
