copying files to /scratch...
starting benchmark...
/scratch/knn/venv/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  from ._conv import register_converters as _register_converters
running only kgraph
order: [Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 20, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 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', 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', 40, {'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', 80, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 50, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 100, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 4, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 5, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 60, {'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', 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]) ...
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: 0.0064 accuracy: 1.59623 cost: 0.00633344 M: 10 delta: 1 time: 0.58665 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: 0.811916 one-recall: 0.05 one-ratio: 1.47978
iteration: 3 recall: 0.358 accuracy: 0.180699 cost: 0.0153111 M: 11.543 delta: 0.827699 time: 1.09778 one-recall: 0.43 one-ratio: 1.1397
iteration: 4 recall: 0.8112 accuracy: 0.0263972 cost: 0.021291 M: 11.9692 delta: 0.602846 time: 1.40236 one-recall: 0.83 one-ratio: 1.03613
iteration: 5 recall: 0.9496 accuracy: 0.00527856 cost: 0.0304051 M: 16.8659 delta: 0.265489 time: 1.82532 one-recall: 0.97 one-ratio: 1.01155
iteration: 6 recall: 0.9828 accuracy: 0.00117401 cost: 0.0394947 M: 22.5704 delta: 0.105604 time: 2.2379 one-recall: 0.99 one-ratio: 1.00163
iteration: 7 recall: 0.9928 accuracy: 0.000380949 cost: 0.0431693 M: 24.4161 delta: 0.0841072 time: 2.46043 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 46.86
Index size:  99984.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0064900000
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0200000000, query time of that 0.0079708300, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Accept!
  -> Decision True in time 0.2000000000, query time of that 0.0787693550, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Reject!
2025.26 < 2201.36
  -> Decision False in time 0.2100000000, query time of that 0.0844426980, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1400000000, query time of that 0.0098738470, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Accept!
  -> Decision True in time 1.2900000000, query time of that 0.0885443400, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
1964.95 < 1969.69
  -> Decision False in time 0.3400000000, query time of that 0.0240138430, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Accept!
  -> Decision True in time 1.3900000000, query time of that 0.0117845850, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Accept!
  -> Decision True in time 13.6300000000, query time of that 0.1133937960, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
1851.97 < 1881.95
  -> Decision False in time 1.9300000000, query time of that 0.0153616350, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 10, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 10, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.0044 accuracy: 1.81839 cost: 0.00633344 M: 10 delta: 1 time: 6.87865 one-recall: 0 one-ratio: 2.07916
iteration: 2 recall: 0.052 accuracy: 0.699626 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1825 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: 14.4971 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: 19.2081 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: 25.92 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: 32.1559 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.99 accuracy: 0.000358431 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.7659 one-recall: 1 one-ratio: 1
iteration: 8 recall: 0.9912 accuracy: 0.00031344 cost: 0.0443167 M: 24.8843 delta: 0.0806719 time: 35.6834 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 36.02000000000001
Index size:  93612.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0131666667
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0200000000, query time of that 0.0049201590, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Reject!
1915.53 < 1993.57
  -> Decision False in time 0.0500000000, query time of that 0.0161498250, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Reject!
1966.02 < 2100.13
  -> Decision False in time 0.0800000000, query time of that 0.0197090880, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Reject!
2026.3 < 2132.73
  -> Decision False in time 0.0100000000, query time of that 0.0007861850, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Reject!
1784.52 < 1799.72
  -> Decision False in time 0.0800000000, query time of that 0.0032585560, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
1925.51 < 1947.22
  -> Decision False in time 0.1500000000, query time of that 0.0065699860, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Reject!
1901.73 < 1967.85
  -> Decision False in time 0.9600000000, query time of that 0.0048851640, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Reject!
1931.1 < 1959.53
  -> Decision False in time 8.3800000000, query time of that 0.0424753580, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
1279.84 < 1440.82
  -> Decision False in time 16.7000000000, query time of that 0.0839252850, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 90, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 90, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.004 accuracy: 1.75774 cost: 0.00633344 M: 10 delta: 1 time: 6.89547 one-recall: 0 one-ratio: 2.03464
iteration: 2 recall: 0.0608 accuracy: 0.646443 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.2023 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: 14.5214 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: 19.2356 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: 25.9581 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: 32.2029 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: 34.8174 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 35.140000000000015
Index size:  92360.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0010516667
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0300000000, query time of that 0.0124963590, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Accept!
  -> Decision True in time 0.2300000000, query time of that 0.1128551570, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Accept!
  -> Decision True in time 2.3300000000, query time of that 1.1212087420, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1400000000, query time of that 0.0136018540, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Reject!
1862.47 < 1922.15
  -> Decision False in time 1.2400000000, query time of that 0.1193389740, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Accept!
  -> Decision True in time 13.5500000000, query time of that 1.2493161500, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Accept!
  -> Decision True in time 1.3700000000, query time of that 0.0146483470, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Accept!
  -> Decision True in time 13.3700000000, query time of that 0.1397850500, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
1496.96 < 1622.86
  -> Decision False in time 42.1300000000, query time of that 0.4388615430, 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: 0.006 accuracy: 1.65471 cost: 0.00633344 M: 10 delta: 1 time: 6.89291 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: 10.1983 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: 14.517 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: 19.2269 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: 25.9459 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: 32.1871 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.992 accuracy: 0.000590742 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.8021 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 35.120000000000005
Index size:  92364.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0388550000
  Testing...
|S| = 20
|T| = 283
Reject!
2219.35 < 2223.09
  -> Decision False in time 0.0100000000, query time of that 0.0040883700, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Reject!
1899.16 < 1951.66
  -> Decision False in time 0.0100000000, query time of that 0.0030363340, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Reject!
1985.5 < 1995.34
  -> Decision False in time 0.0100000000, query time of that 0.0020549880, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Reject!
1654.17 < 1715.42
  -> Decision False in time 0.0600000000, query time of that 0.0022022070, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Reject!
2020.36 < 2192.15
  -> Decision False in time 0.0500000000, query time of that 0.0020588660, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
1930.25 < 2329.25
  -> Decision False in time 0.3000000000, query time of that 0.0102309930, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Reject!
1622.96 < 2140.64
  -> Decision False in time 0.0000000000, query time of that 0.0002233130, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Reject!
1948.81 < 2035.46
  -> Decision False in time 2.9700000000, query time of that 0.0128279610, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
2443.78 < 2461.42
  -> Decision False in time 2.1100000000, query time of that 0.0094377010, 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: 0.0036 accuracy: 1.7649 cost: 0.00633344 M: 10 delta: 1 time: 6.88212 one-recall: 0 one-ratio: 2.10464
iteration: 2 recall: 0.0604 accuracy: 0.635748 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1863 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: 14.5017 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: 19.2123 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: 25.9244 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: 32.1605 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: 34.7726 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: 35.6906 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 36.01999999999998
Index size:  93608.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0055583333
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0200000000, query time of that 0.0078496060, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Accept!
  -> Decision True in time 0.1800000000, query time of that 0.0636599970, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Reject!
2320.09 < 2330.04
  -> Decision False in time 0.0500000000, query time of that 0.0172430390, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1300000000, query time of that 0.0075524090, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Reject!
2180.26 < 2220.33
  -> Decision False in time 0.3500000000, query time of that 0.0203887720, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
1852.63 < 2079.44
  -> Decision False in time 0.9900000000, query time of that 0.0589057770, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Reject!
1854.74 < 2004.21
  -> Decision False in time 0.6800000000, query time of that 0.0052152910, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Reject!
1609.81 < 1618
  -> Decision False in time 8.2100000000, query time of that 0.0539516920, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
1697.97 < 1805.47
  -> Decision False in time 0.4800000000, query time of that 0.0033174420, 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: 6.88103 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: 10.1885 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: 14.5054 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: 19.2141 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: 25.9298 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: 32.1674 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.9912 accuracy: 0.000477346 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.7815 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 35.10999999999996
Index size:  92372.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0024850000
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0200000000, query time of that 0.0072880670, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Accept!
  -> Decision True in time 0.2000000000, query time of that 0.0712933880, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Reject!
1899.64 < 2059.21
  -> Decision False in time 1.4400000000, query time of that 0.5331590050, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1300000000, query time of that 0.0079753870, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Reject!
1916.76 < 1979.27
  -> Decision False in time 0.2200000000, query time of that 0.0146749390, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
1447.02 < 1522.26
  -> Decision False in time 3.7500000000, query time of that 0.2320516940, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Accept!
  -> Decision True in time 1.3800000000, query time of that 0.0098924860, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Reject!
1570.09 < 1734.18
  -> Decision False in time 6.6100000000, query time of that 0.0473707750, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
1766.28 < 1882.37
  -> Decision False in time 25.4900000000, query time of that 0.1809275240, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 3, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 3, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.008 accuracy: 1.74437 cost: 0.00633344 M: 10 delta: 1 time: 6.8836 one-recall: 0 one-ratio: 2.03444
iteration: 2 recall: 0.0652 accuracy: 0.637073 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1896 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: 14.51 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: 19.2209 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: 25.9378 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: 32.1796 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: 34.7927 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 35.120000000000005
Index size:  92356.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0281516667
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0200000000, query time of that 0.0047216410, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Reject!
2060.85 < 2232.06
  -> Decision False in time 0.0300000000, query time of that 0.0090330870, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Reject!
2078.79 < 2141.61
  -> Decision False in time 0.0300000000, query time of that 0.0064253790, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Reject!
1718.32 < 1985.88
  -> Decision False in time 0.0400000000, query time of that 0.0014524380, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Reject!
1964.82 < 2051.54
  -> Decision False in time 0.1100000000, query time of that 0.0047433770, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
2129.34 < 2214.63
  -> Decision False in time 0.0900000000, query time of that 0.0034309850, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Reject!
1795.95 < 1910.37
  -> Decision False in time 0.6800000000, query time of that 0.0032944770, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Reject!
1747.39 < 1754.55
  -> Decision False in time 0.2000000000, query time of that 0.0012394370, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
1391.21 < 1873.14
  -> Decision False in time 0.3500000000, query time of that 0.0018039570, 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: 0.004 accuracy: 1.49738 cost: 0.00633344 M: 10 delta: 1 time: 6.885 one-recall: 0 one-ratio: 1.99571
iteration: 2 recall: 0.0468 accuracy: 0.631223 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1923 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: 14.5133 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: 19.2265 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: 25.9462 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: 32.192 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.9884 accuracy: 0.000479649 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.8055 one-recall: 1 one-ratio: 1
iteration: 8 recall: 0.9908 accuracy: 0.000330768 cost: 0.0443167 M: 24.8843 delta: 0.0806719 time: 35.7245 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 36.039999999999964
Index size:  93624.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0012200000
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0300000000, query time of that 0.0117088160, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Accept!
  -> Decision True in time 0.2200000000, query time of that 0.1054455700, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Accept!
  -> Decision True in time 2.2600000000, query time of that 1.0475236690, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1300000000, query time of that 0.0111779170, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Accept!
  -> Decision True in time 1.3300000000, query time of that 0.1197926010, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
1816.63 < 1922.15
  -> Decision False in time 2.2900000000, query time of that 0.2032285730, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Accept!
  -> Decision True in time 1.3600000000, query time of that 0.0135465010, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Reject!
1739.34 < 2037.67
  -> Decision False in time 4.2800000000, query time of that 0.0430309840, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
1786.33 < 1818.19
  -> Decision False in time 13.4600000000, query time of that 0.1353722950, 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: 0.0072 accuracy: 1.78614 cost: 0.00633344 M: 10 delta: 1 time: 6.8875 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: 10.1937 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: 14.5144 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: 19.2259 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: 25.9452 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: 32.1916 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 32.50999999999999
Index size:  88032.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0038800000
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0200000000, query time of that 0.0087961740, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Accept!
  -> Decision True in time 0.2000000000, query time of that 0.0763302320, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Reject!
2354.44 < 2370.81
  -> Decision False in time 0.0400000000, query time of that 0.0153342990, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1300000000, query time of that 0.0083895170, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Accept!
  -> Decision True in time 1.2800000000, query time of that 0.0853311920, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
2351.06 < 2491.57
  -> Decision False in time 3.3900000000, query time of that 0.2271839880, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Reject!
2185.98 < 2232.66
  -> Decision False in time 0.5500000000, query time of that 0.0049746780, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Reject!
2214.77 < 2270.16
  -> Decision False in time 0.4800000000, query time of that 0.0046952290, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
2208.85 < 2269.13
  -> Decision False in time 3.3200000000, query time of that 0.0258876820, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 100, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 100, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.0048 accuracy: 1.58384 cost: 0.00633344 M: 10 delta: 1 time: 6.88586 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: 10.1929 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: 14.5121 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: 19.2208 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: 25.9381 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9896 accuracy: 0.000550482 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 32.1829 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.9944 accuracy: 0.000259975 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.7976 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 35.120000000000005
Index size:  92352.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0008050000
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0300000000, query time of that 0.0121056480, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Accept!
  -> Decision True in time 0.2400000000, query time of that 0.1218261070, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Accept!
  -> Decision True in time 2.4100000000, query time of that 1.1865865710, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1400000000, query time of that 0.0136274630, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Accept!
  -> Decision True in time 1.3600000000, query time of that 0.1345514420, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
1805.46 < 1862.34
  -> Decision False in time 10.6500000000, query time of that 1.0224413100, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Accept!
  -> Decision True in time 1.3800000000, query time of that 0.0143532850, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Accept!
  -> Decision True in time 13.6200000000, query time of that 0.1493936690, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
1319.61 < 1351.3
  -> Decision False in time 51.9600000000, query time of that 0.5643823290, 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: 0.008 accuracy: 1.72963 cost: 0.00633344 M: 10 delta: 1 time: 6.8856 one-recall: 0 one-ratio: 2.01043
iteration: 2 recall: 0.066 accuracy: 0.635277 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1925 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: 14.5103 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: 19.2188 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: 25.9357 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.992 accuracy: 0.000346113 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 32.1804 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 32.5
Index size:  88028.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0310166667
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0200000000, query time of that 0.0047819030, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Reject!
1841.21 < 2334.74
  -> Decision False in time 0.0300000000, query time of that 0.0075937990, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Reject!
1942.48 < 1963.62
  -> Decision False in time 0.0300000000, query time of that 0.0064774390, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1300000000, query time of that 0.0045881080, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Reject!
1778.89 < 1887.28
  -> Decision False in time 0.0000000000, query time of that 0.0004590480, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
1747.33 < 1798.59
  -> Decision False in time 0.3600000000, query time of that 0.0137754820, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Accept!
  -> Decision True in time 1.3700000000, query time of that 0.0057511650, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Reject!
2167.99 < 2491.08
  -> Decision False in time 0.5500000000, query time of that 0.0027939680, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
1921.37 < 2135.25
  -> Decision False in time 0.0700000000, query time of that 0.0006347070, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 5, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 5, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.004 accuracy: 1.5407 cost: 0.00633344 M: 10 delta: 1 time: 6.8817 one-recall: 0 one-ratio: 2.00156
iteration: 2 recall: 0.0588 accuracy: 0.610834 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1857 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: 14.5029 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: 19.2103 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: 25.9255 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: 32.1652 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.9916 accuracy: 0.0004699 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.7741 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 35.09999999999991
Index size:  92356.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0258416667
  Testing...
|S| = 20
|T| = 283
Reject!
2072.17 < 2098.12
  -> Decision False in time 0.0200000000, query time of that 0.0050957730, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Reject!
2003.77 < 2319.33
  -> Decision False in time 0.0200000000, query time of that 0.0044460230, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Reject!
2119.98 < 2179.45
  -> Decision False in time 0.0500000000, query time of that 0.0135907440, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1300000000, query time of that 0.0051576830, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Reject!
1349.35 < 1579.47
  -> Decision False in time 0.1700000000, query time of that 0.0065343940, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
2033.82 < 2298.7
  -> Decision False in time 0.2000000000, query time of that 0.0073775700, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Accept!
  -> Decision True in time 1.3600000000, query time of that 0.0060858470, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Reject!
2065.36 < 2437.24
  -> Decision False in time 1.4400000000, query time of that 0.0068127500, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
2381.2 < 2402.34
  -> Decision False in time 2.5600000000, query time of that 0.0112595870, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 60, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 60, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.004 accuracy: 1.6918 cost: 0.00633344 M: 10 delta: 1 time: 6.88417 one-recall: 0 one-ratio: 2.12757
iteration: 2 recall: 0.0552 accuracy: 0.670215 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1919 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: 14.5113 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: 19.225 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: 25.9467 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: 32.1917 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: 34.8056 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 35.13999999999987
Index size:  92352.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0020716667
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0300000000, query time of that 0.0096462960, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Accept!
  -> Decision True in time 0.2100000000, query time of that 0.0918272000, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Reject!
1619.07 < 1805.47
  -> Decision False in time 0.2000000000, query time of that 0.0853834060, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1400000000, query time of that 0.0105155670, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Accept!
  -> Decision True in time 1.3100000000, query time of that 0.0977126690, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Accept!
  -> Decision True in time 13.2600000000, query time of that 0.9783932300, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Accept!
  -> Decision True in time 1.3800000000, query time of that 0.0120838270, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Reject!
1868.28 < 1870.11
  -> Decision False in time 2.6900000000, query time of that 0.0232601470, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
1776.12 < 1814.94
  -> Decision False in time 2.2400000000, query time of that 0.0194231610, 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: 0.0056 accuracy: 1.41157 cost: 0.00633344 M: 10 delta: 1 time: 6.88565 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: 10.1942 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: 14.5153 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: 19.2252 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: 25.9411 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9828 accuracy: 0.00106873 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 32.1812 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.99 accuracy: 0.000613841 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.7951 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 35.11999999999989
Index size:  92360.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0014900000
  Testing...
|S| = 20
|T| = 283
Accept!
  -> Decision True in time 0.0200000000, query time of that 0.0105497130, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Accept!
  -> Decision True in time 0.2200000000, query time of that 0.0948451110, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Reject!
1789.31 < 1814.94
  -> Decision False in time 0.2900000000, query time of that 0.1292805490, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1300000000, query time of that 0.0109889430, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Reject!
1742.54 < 1943.91
  -> Decision False in time 1.1700000000, query time of that 0.0995567970, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Accept!
  -> Decision True in time 13.1900000000, query time of that 1.0689090040, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Accept!
  -> Decision True in time 1.3700000000, query time of that 0.0126501530, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Reject!
1775.08 < 1805.42
  -> Decision False in time 5.5400000000, query time of that 0.0499220100, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
1737.51 < 1779.61
  -> Decision False in time 11.4400000000, query time of that 0.1099407150, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 1, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 1, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.0068 accuracy: 1.63493 cost: 0.00633344 M: 10 delta: 1 time: 6.87594 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: 10.1861 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: 14.5068 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: 19.2177 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: 25.9379 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: 32.1853 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: 34.7996 one-recall: 1 one-ratio: 1
iteration: 8 recall: 0.9932 accuracy: 0.000294001 cost: 0.0443167 M: 24.8843 delta: 0.0806719 time: 35.7172 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 36.049999999999955
Index size:  93624.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0346916667
  Testing...
|S| = 20
|T| = 283
Reject!
2180.07 < 2497.98
  -> Decision False in time 0.0100000000, query time of that 0.0040226740, with c1=0.0500000000, c2=0.0010000000
|S| = 196
|T| = 283
Reject!
1853.56 < 2007.38
  -> Decision False in time 0.0100000000, query time of that 0.0024947490, with c1=0.0500000000, c2=0.0100000000
|S| = 1960
|T| = 283
Reject!
1830.25 < 1833.4
  -> Decision False in time 0.0900000000, query time of that 0.0222826860, with c1=0.0500000000, c2=0.1000000000
|S| = 20
|T| = 2821
Accept!
  -> Decision True in time 0.1300000000, query time of that 0.0049448040, with c1=0.5000000000, c2=0.0010000000
|S| = 196
|T| = 2821
Reject!
2224.12 < 2320.94
  -> Decision False in time 0.0200000000, query time of that 0.0010364640, with c1=0.5000000000, c2=0.0100000000
|S| = 1960
|T| = 2821
Reject!
1875.96 < 2285.33
  -> Decision False in time 0.0800000000, query time of that 0.0036212620, with c1=0.5000000000, c2=0.1000000000
|S| = 20
|T| = 28201
Reject!
2239.14 < 2274.83
  -> Decision False in time 0.6800000000, query time of that 0.0033541410, with c1=5.0000000000, c2=0.0010000000
|S| = 196
|T| = 28201
Reject!
1795.07 < 1829.82
  -> Decision False in time 0.4200000000, query time of that 0.0020080020, with c1=5.0000000000, c2=0.0100000000
|S| = 1960
|T| = 28201
Reject!
1898.19 < 1932.3
  -> Decision False in time 3.5700000000, query time of that 0.0162127330, with c1=5.0000000000, c2=0.1000000000
