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', 90, {'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', 80, {'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', 30, {'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', 40, {'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', 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', 3, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 20, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 60, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 100, {'reverse': -1}, False]), Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 50, {'reverse': -1}, False])]
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 90, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 90, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.0044 accuracy: 1.5879 cost: 0.00633344 M: 10 delta: 1 time: 7.07328 one-recall: 0.01 one-ratio: 1.96869
iteration: 2 recall: 0.0536 accuracy: 0.617143 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.6913 one-recall: 0.07 one-ratio: 1.46688
iteration: 3 recall: 0.3584 accuracy: 0.174439 cost: 0.0152986 M: 11.5308 delta: 0.828164 time: 15.3771 one-recall: 0.42 one-ratio: 1.14581
iteration: 4 recall: 0.824 accuracy: 0.0224227 cost: 0.0212851 M: 11.9674 delta: 0.604399 time: 20.4642 one-recall: 0.92 one-ratio: 1.01239
iteration: 5 recall: 0.9608 accuracy: 0.00282107 cost: 0.0303842 M: 16.8423 delta: 0.26712 time: 27.696 one-recall: 0.97 one-ratio: 1.00419
iteration: 6 recall: 0.9856 accuracy: 0.000706031 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 34.582 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.9932 accuracy: 0.0002248 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 37.6612 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 38.0
Index size:  100448.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0016203333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.4000000000, query time of that 0.1029271150, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1628.28 < 1645.6
  -> Decision False in time 1.2500000000, query time of that 0.3305498720, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2098.23 < 2137.06
  -> Decision False in time 0.5800000000, query time of that 0.1551868650, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1759.43 < 1843.13
  -> Decision False in time 0.0100000000, query time of that 0.0012977440, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1566.58 < 1941.04
  -> Decision False in time 1.6300000000, query time of that 0.0601306040, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
2200.11 < 2323.31
  -> Decision False in time 2.5100000000, query time of that 0.0919550240, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1873.19 < 1877.43
  -> Decision False in time 5.9500000000, query time of that 0.0252501530, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1617.68 < 1690.24
  -> Decision False in time 0.3600000000, query time of that 0.0024361980, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1845.43 < 1890.56
  -> Decision False in time 1.5000000000, query time of that 0.0064131070, 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: 1.81839 cost: 0.00633344 M: 10 delta: 1 time: 6.8678 one-recall: 0 one-ratio: 2.07916
iteration: 2 recall: 0.052 accuracy: 0.699626 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1861 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: 14.5169 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: 19.2413 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: 25.9809 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: 32.2534 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.99 accuracy: 0.000358431 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.8807 one-recall: 1 one-ratio: 1
iteration: 8 recall: 0.9912 accuracy: 0.00031344 cost: 0.0443167 M: 24.8843 delta: 0.0806719 time: 35.8036 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 36.13000000000001
Index size:  82980.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0018956667
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3700000000, query time of that 0.0704974170, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
2277.71 < 2280.37
  -> Decision False in time 1.7500000000, query time of that 0.3319563490, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1840.78 < 1902.45
  -> Decision False in time 1.2500000000, query time of that 0.2419415010, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1924.14 < 1949.89
  -> Decision False in time 0.1100000000, query time of that 0.0028007560, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1922.12 < 1943.34
  -> Decision False in time 2.9300000000, query time of that 0.0721418590, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
2033.36 < 2075.15
  -> Decision False in time 1.3600000000, query time of that 0.0367620920, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1754.25 < 1793.39
  -> Decision False in time 15.8200000000, query time of that 0.0411798690, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1520.78 < 1642.59
  -> Decision False in time 3.5400000000, query time of that 0.0099640540, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1610.24 < 1644.83
  -> Decision False in time 8.6100000000, query time of that 0.0233514780, 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: 1.75774 cost: 0.00633344 M: 10 delta: 1 time: 6.88644 one-recall: 0 one-ratio: 2.03464
iteration: 2 recall: 0.0608 accuracy: 0.646443 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.2078 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: 14.5427 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: 19.273 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: 26.0188 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: 32.2998 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: 34.9301 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:  81728.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0020226667
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3800000000, query time of that 0.0713447210, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
2194.77 < 2199.25
  -> Decision False in time 3.5600000000, query time of that 0.6924410630, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1840.59 < 1899.68
  -> Decision False in time 0.1500000000, query time of that 0.0295568230, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1949.48 < 1970.31
  -> Decision False in time 0.3100000000, query time of that 0.0088143860, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1682.79 < 1706.05
  -> Decision False in time 7.1300000000, query time of that 0.1855452150, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1589.88 < 1597.94
  -> Decision False in time 0.6100000000, query time of that 0.0173491090, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1881.64 < 1895.32
  -> Decision False in time 3.5000000000, query time of that 0.0101039100, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1528.47 < 1622.63
  -> Decision False in time 2.9300000000, query time of that 0.0080551960, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
2098.23 < 2137.06
  -> Decision False in time 9.2000000000, query time of that 0.0245939170, 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: 0.006 accuracy: 1.65471 cost: 0.00633344 M: 10 delta: 1 time: 6.87091 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: 10.1895 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: 14.5215 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: 19.25 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: 25.9959 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: 32.2728 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.992 accuracy: 0.000590742 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.9004 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 35.23000000000002
Index size:  81728.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0107390000
  Testing...
|S| = 98
|T| = 1411
Reject!
1528.77 < 1532.96
  -> Decision False in time 0.0400000000, query time of that 0.0054446650, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
2262.75 < 2286.74
  -> Decision False in time 0.2300000000, query time of that 0.0314307130, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1752.25 < 1838.04
  -> Decision False in time 0.2400000000, query time of that 0.0334944210, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1880.65 < 1887.3
  -> Decision False in time 0.0400000000, query time of that 0.0010410330, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1760.84 < 1795.46
  -> Decision False in time 0.9000000000, query time of that 0.0167707170, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1817.61 < 2117.15
  -> Decision False in time 0.1700000000, query time of that 0.0036719880, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1492.86 < 1539.72
  -> Decision False in time 2.5400000000, query time of that 0.0054424530, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1682.57 < 1684
  -> Decision False in time 3.3900000000, query time of that 0.0063229780, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1676.66 < 1725.09
  -> Decision False in time 3.0500000000, query time of that 0.0062335680, 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: 6.87565 one-recall: 0 one-ratio: 2.10464
iteration: 2 recall: 0.0604 accuracy: 0.635748 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.1946 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: 14.5262 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: 19.2556 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: 26.0015 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: 32.2749 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: 34.9046 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: 35.8276 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 36.160000000000025
Index size:  82972.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0043696667
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3500000000, query time of that 0.0542797840, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
2219.41 < 2594.81
  -> Decision False in time 0.1100000000, query time of that 0.0181332220, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1775.15 < 2553.71
  -> Decision False in time 1.6800000000, query time of that 0.2669934730, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1379.56 < 1387.8
  -> Decision False in time 1.0900000000, query time of that 0.0233821080, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1665.76 < 1715.58
  -> Decision False in time 0.2600000000, query time of that 0.0051323390, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1728.29 < 1735.52
  -> Decision False in time 0.8700000000, query time of that 0.0178699710, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1858.99 < 1869.77
  -> Decision False in time 6.3700000000, query time of that 0.0137354440, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1356.85 < 1381.52
  -> Decision False in time 0.7900000000, query time of that 0.0025724700, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1957.07 < 2024.48
  -> Decision False in time 2.8200000000, query time of that 0.0068625020, 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: 1.68691 cost: 0.00633344 M: 10 delta: 1 time: 6.87601 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: 10.1945 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: 14.5272 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: 19.2548 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: 25.9936 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: 32.2608 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.9912 accuracy: 0.000477346 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.8863 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 35.22000000000003
Index size:  81724.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0075250000
  Testing...
|S| = 98
|T| = 1411
Reject!
1704.36 < 1725.48
  -> Decision False in time 0.2500000000, query time of that 0.0328041130, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1383.65 < 1405.15
  -> Decision False in time 0.7100000000, query time of that 0.0964304860, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1683.24 < 1727.39
  -> Decision False in time 0.4400000000, query time of that 0.0605506180, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1900.17 < 1921.76
  -> Decision False in time 0.5200000000, query time of that 0.0086050800, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
2039.18 < 2041.63
  -> Decision False in time 0.7500000000, query time of that 0.0132537440, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1680.36 < 1692.28
  -> Decision False in time 0.2800000000, query time of that 0.0050484960, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1707.17 < 1772.24
  -> Decision False in time 4.9700000000, query time of that 0.0098235550, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1398.69 < 1432.02
  -> Decision False in time 0.2000000000, query time of that 0.0006232610, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1745.72 < 1749.14
  -> Decision False in time 0.4800000000, query time of that 0.0013102220, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 40, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 40, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.008 accuracy: 1.74437 cost: 0.00633344 M: 10 delta: 1 time: 6.88864 one-recall: 0 one-ratio: 2.03444
iteration: 2 recall: 0.0652 accuracy: 0.637073 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.2102 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: 14.5463 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: 19.2774 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: 26.0231 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: 32.2972 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: 34.9254 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 35.25
Index size:  81728.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0033383333
  Testing...
|S| = 98
|T| = 1411
Reject!
2174.98 < 2334.14
  -> Decision False in time 0.1200000000, query time of that 0.0218125550, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1460.57 < 1535.37
  -> Decision False in time 0.0600000000, query time of that 0.0096565010, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1654.52 < 1757.47
  -> Decision False in time 0.0700000000, query time of that 0.0115765060, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1797.8 < 1860.56
  -> Decision False in time 1.4800000000, query time of that 0.0315360610, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1529.53 < 1584.18
  -> Decision False in time 0.4200000000, query time of that 0.0093502450, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
2177.86 < 2256.08
  -> Decision False in time 0.2100000000, query time of that 0.0041519380, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1681.93 < 1708.9
  -> Decision False in time 2.4200000000, query time of that 0.0060525110, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
2057.02 < 2090.5
  -> Decision False in time 0.3500000000, query time of that 0.0017466600, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1447.38 < 1450.13
  -> Decision False in time 2.0500000000, query time of that 0.0054214340, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 2, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 2, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.004 accuracy: 1.49738 cost: 0.00633344 M: 10 delta: 1 time: 6.8799 one-recall: 0 one-ratio: 1.99571
iteration: 2 recall: 0.0468 accuracy: 0.631223 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.2012 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: 14.5395 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: 19.2735 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: 26.0243 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: 32.3067 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.9884 accuracy: 0.000479649 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 34.9361 one-recall: 1 one-ratio: 1
iteration: 8 recall: 0.9908 accuracy: 0.000330768 cost: 0.0443167 M: 24.8843 delta: 0.0806719 time: 35.8596 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 36.180000000000064
Index size:  82976.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0089603333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3400000000, query time of that 0.0461001070, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1820.85 < 1832.23
  -> Decision False in time 0.2600000000, query time of that 0.0348510300, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1513.92 < 1517.68
  -> Decision False in time 0.2000000000, query time of that 0.0282014460, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1256.36 < 1261.51
  -> Decision False in time 0.1800000000, query time of that 0.0031204070, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1095.05 < 1111.81
  -> Decision False in time 0.0300000000, query time of that 0.0004608590, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1641.51 < 1668.06
  -> Decision False in time 1.1500000000, query time of that 0.0199915250, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
2002.45 < 2016.03
  -> Decision False in time 0.3700000000, query time of that 0.0013038550, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1683.79 < 1788.62
  -> Decision False in time 0.1100000000, query time of that 0.0006901100, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1464.96 < 1489.58
  -> Decision False in time 0.0900000000, query time of that 0.0008046130, 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: 0.0072 accuracy: 1.78614 cost: 0.00633344 M: 10 delta: 1 time: 6.87227 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: 10.1889 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: 14.5217 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: 19.2498 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: 25.992 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: 32.2643 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 32.57999999999993
Index size:  77432.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0100203333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3500000000, query time of that 0.0457986870, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
2280 < 2295.1
  -> Decision False in time 0.4700000000, query time of that 0.0601298370, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1511.93 < 1529.37
  -> Decision False in time 0.3900000000, query time of that 0.0512697610, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1700.95 < 1703.4
  -> Decision False in time 0.5700000000, query time of that 0.0094347720, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1790.83 < 1809.51
  -> Decision False in time 1.5300000000, query time of that 0.0259350570, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
2408.35 < 2460.44
  -> Decision False in time 0.1800000000, query time of that 0.0033109770, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1679.36 < 1691.66
  -> Decision False in time 10.9500000000, query time of that 0.0189318790, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
2087.24 < 2148.54
  -> Decision False in time 0.3400000000, query time of that 0.0009907070, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
2039.53 < 2144.14
  -> Decision False in time 1.7100000000, query time of that 0.0033914860, 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: 0.0048 accuracy: 1.58384 cost: 0.00633344 M: 10 delta: 1 time: 6.90205 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: 10.2461 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: 14.6218 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: 19.396 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: 26.2486 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9896 accuracy: 0.000550482 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 32.7852 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.9944 accuracy: 0.000259975 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 35.6018 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 35.91999999999996
Index size:  81716.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0092153333
  Testing...
|S| = 98
|T| = 1411
Reject!
1853.32 < 1886.42
  -> Decision False in time 0.2100000000, query time of that 0.0281205260, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1621.11 < 1626.3
  -> Decision False in time 0.1100000000, query time of that 0.0149829490, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2206.7 < 2286.75
  -> Decision False in time 0.0800000000, query time of that 0.0114815670, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1644.96 < 1706.57
  -> Decision False in time 0.3900000000, query time of that 0.0067113340, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1825.53 < 1843.46
  -> Decision False in time 0.0000000000, query time of that 0.0008513970, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1991.37 < 2002.03
  -> Decision False in time 0.8600000000, query time of that 0.0148779320, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1695.95 < 1698.28
  -> Decision False in time 4.6600000000, query time of that 0.0083847280, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1759.58 < 1764.57
  -> Decision False in time 3.0600000000, query time of that 0.0062357760, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1511.77 < 1514.98
  -> Decision False in time 0.3400000000, query time of that 0.0006336950, with c1=5.0000000000, c2=0.1000000000
Definition(algorithm='kgraph', constructor='KGraph', module='ann_benchmarks.algorithms.kgraph', docker_tag='ann-benchmarks-kgraph', arguments=['euclidean', 3, {'reverse': -1}, False]) ...
Trying to instantiate ann_benchmarks.algorithms.kgraph.KGraph(['euclidean', 3, {'reverse': -1}, False])
Got a train set of size (60000 * 784)
Generating control...
Initializing...
iteration: 1 recall: 0.008 accuracy: 1.72963 cost: 0.00633344 M: 10 delta: 1 time: 6.90532 one-recall: 0 one-ratio: 2.01043
iteration: 2 recall: 0.066 accuracy: 0.635277 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.252 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: 14.6265 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: 19.4027 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: 26.2578 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.992 accuracy: 0.000346113 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 32.797 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 33.10000000000002
Index size:  77432.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0121573333
  Testing...
|S| = 98
|T| = 1411
Reject!
1624.24 < 1650.22
  -> Decision False in time 0.1400000000, query time of that 0.0186675050, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1483.32 < 1487.67
  -> Decision False in time 0.8900000000, query time of that 0.1203401030, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1854.17 < 1885.93
  -> Decision False in time 0.1900000000, query time of that 0.0241921220, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1675.2 < 1720.72
  -> Decision False in time 0.1100000000, query time of that 0.0024295370, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1576.56 < 1608.6
  -> Decision False in time 0.6500000000, query time of that 0.0113452930, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1088.76 < 1096.5
  -> Decision False in time 0.1200000000, query time of that 0.0022550370, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1567.22 < 1584.04
  -> Decision False in time 0.8200000000, query time of that 0.0016340570, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1797.62 < 1798.31
  -> Decision False in time 0.6800000000, query time of that 0.0016516200, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
2215.32 < 2588.24
  -> Decision False in time 0.0100000000, query time of that 0.0006796420, 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: 1.5407 cost: 0.00633344 M: 10 delta: 1 time: 6.91865 one-recall: 0 one-ratio: 2.00156
iteration: 2 recall: 0.0588 accuracy: 0.610834 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.2631 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: 14.64 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: 19.417 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: 26.2767 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: 32.8147 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.9916 accuracy: 0.0004699 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 35.6373 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 35.960000000000036
Index size:  81712.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0056770000
  Testing...
|S| = 98
|T| = 1411
Reject!
1708.93 < 1952.36
  -> Decision False in time 0.2100000000, query time of that 0.0311596250, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1833.91 < 1841.49
  -> Decision False in time 0.0800000000, query time of that 0.0120398150, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
2022.79 < 2058.7
  -> Decision False in time 2.3100000000, query time of that 0.3435099670, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1782.52 < 1823.52
  -> Decision False in time 0.9400000000, query time of that 0.0176164190, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1676.28 < 1700.78
  -> Decision False in time 1.2200000000, query time of that 0.0230152840, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1413 < 1454.02
  -> Decision False in time 2.9100000000, query time of that 0.0544298580, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1569.62 < 1579.34
  -> Decision False in time 0.5800000000, query time of that 0.0011163020, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1965.34 < 1972.41
  -> Decision False in time 4.2500000000, query time of that 0.0086643000, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1755.94 < 1807.8
  -> Decision False in time 4.1300000000, query time of that 0.0085712200, 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: 6.91758 one-recall: 0 one-ratio: 2.12757
iteration: 2 recall: 0.0552 accuracy: 0.670215 cost: 0.0100076 M: 10 delta: 0.854952 time: 10.263 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: 14.6383 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: 19.4162 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: 26.2792 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: 32.8284 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: 35.6511 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 35.97000000000003
Index size:  81724.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0026693333
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3600000000, query time of that 0.0661091060, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1781.5 < 1839.23
  -> Decision False in time 0.6900000000, query time of that 0.1249150830, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1994.48 < 2032.59
  -> Decision False in time 0.0600000000, query time of that 0.0118360410, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Accept!
  -> Decision True in time 3.3800000000, query time of that 0.0809227120, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
2002.39 < 2057.02
  -> Decision False in time 0.6400000000, query time of that 0.0168832510, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1864.76 < 1879.69
  -> Decision False in time 1.9000000000, query time of that 0.0448873710, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1737.47 < 1780.69
  -> Decision False in time 0.1300000000, query time of that 0.0009420230, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1733.56 < 1742.57
  -> Decision False in time 0.1100000000, query time of that 0.0011204080, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1589.44 < 1599.77
  -> Decision False in time 0.5100000000, query time of that 0.0018794750, 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: 0.0056 accuracy: 1.41157 cost: 0.00633344 M: 10 delta: 1 time: 6.91567 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: 10.2582 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: 14.6333 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: 19.4078 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: 26.2609 one-recall: 1 one-ratio: 1
iteration: 6 recall: 0.9828 accuracy: 0.00106873 cost: 0.0394649 M: 22.5291 delta: 0.106382 time: 32.7956 one-recall: 1 one-ratio: 1
iteration: 7 recall: 0.99 accuracy: 0.000613841 cost: 0.0431501 M: 24.3677 delta: 0.0850189 time: 35.6131 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 35.930000000000064
Index size:  81724.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0014630000
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3700000000, query time of that 0.0792758750, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Reject!
1985.24 < 2107.28
  -> Decision False in time 1.7900000000, query time of that 0.3859615850, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1783.35 < 1787.53
  -> Decision False in time 3.1900000000, query time of that 0.6863338900, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Accept!
  -> Decision True in time 3.4000000000, query time of that 0.0956638500, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1770.39 < 1771.08
  -> Decision False in time 0.5100000000, query time of that 0.0142728350, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1931.5 < 1975.43
  -> Decision False in time 12.9400000000, query time of that 0.3798770540, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
2026.38 < 2039.76
  -> Decision False in time 19.1800000000, query time of that 0.0584437970, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
1468.18 < 1476.21
  -> Decision False in time 2.0700000000, query time of that 0.0072245900, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1978.48 < 1978.63
  -> Decision False in time 18.4700000000, query time of that 0.0560199620, 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: 0.0068 accuracy: 1.63493 cost: 0.00633344 M: 10 delta: 1 time: 6.91695 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: 10.2614 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: 14.6344 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: 19.4085 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: 26.26 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: 32.7974 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: 35.6143 one-recall: 1 one-ratio: 1
iteration: 8 recall: 0.9932 accuracy: 0.000294001 cost: 0.0443167 M: 24.8843 delta: 0.0806719 time: 36.6162 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 36.940000000000055
Index size:  82992.0
Run 1/1...
  Calculating distance...
  -> Distance: 0.0028516667
  Testing...
|S| = 98
|T| = 1411
Accept!
  -> Decision True in time 0.3600000000, query time of that 0.0631879400, with c1=0.0500000000, c2=0.0010000000
|S| = 980
|T| = 1411
Accept!
  -> Decision True in time 3.5900000000, query time of that 0.6190050290, with c1=0.0500000000, c2=0.0100000000
|S| = 9798
|T| = 1411
Reject!
1983.38 < 1993.85
  -> Decision False in time 1.9400000000, query time of that 0.3342174300, with c1=0.0500000000, c2=0.1000000000
|S| = 98
|T| = 14101
Reject!
1840.32 < 1915.76
  -> Decision False in time 0.2800000000, query time of that 0.0065316950, with c1=0.5000000000, c2=0.0010000000
|S| = 980
|T| = 14101
Reject!
1976.32 < 1984.63
  -> Decision False in time 2.2700000000, query time of that 0.0496162090, with c1=0.5000000000, c2=0.0100000000
|S| = 9798
|T| = 14101
Reject!
1965.34 < 1972.41
  -> Decision False in time 1.8400000000, query time of that 0.0427292080, with c1=0.5000000000, c2=0.1000000000
|S| = 98
|T| = 141004
Reject!
1920.68 < 1928.27
  -> Decision False in time 2.0700000000, query time of that 0.0055172300, with c1=5.0000000000, c2=0.0010000000
|S| = 980
|T| = 141004
Reject!
2056.87 < 2081.59
  -> Decision False in time 0.7500000000, query time of that 0.0025144780, with c1=5.0000000000, c2=0.0100000000
|S| = 9798
|T| = 141004
Reject!
1289.85 < 1297.72
  -> Decision False in time 0.4000000000, query time of that 0.0017866860, with c1=5.0000000000, c2=0.1000000000
