From ee33264442f76cfaab2c14aa8fdd79875dc925b4 Mon Sep 17 00:00:00 2001 From: Alex Klibisz Date: Sat, 31 Aug 2024 04:19:25 +0000 Subject: [PATCH 1/4] Partial results for a gridsearch of parameters on AWS --- ann-benchmarks/config.yml | 38 +- docs/pages/performance/fashion-mnist/plot.png | Bin 46755 -> 38999 bytes docs/pages/performance/fashion-mnist/plot.txt | 694 ++++++++++++++++++ .../performance/fashion-mnist/results.md | 355 ++++++++- 4 files changed, 1060 insertions(+), 27 deletions(-) create mode 100644 docs/pages/performance/fashion-mnist/plot.txt diff --git a/ann-benchmarks/config.yml b/ann-benchmarks/config.yml index ffd6d718..d8689a1b 100644 --- a/ann-benchmarks/config.yml +++ b/ann-benchmarks/config.yml @@ -1,22 +1,22 @@ float: any: - - base_args: ['@metric', '@dimension'] - constructor: Exact - disabled: true - docker_tag: ann-benchmarks-elastiknn - module: ann_benchmarks.algorithms.elastiknn - name: elastiknn-exact - run_groups: - exact: - args: [] + - base_args: ['@metric', '@dimension'] + constructor: Exact + disabled: true + docker_tag: ann-benchmarks-elastiknn + module: ann_benchmarks.algorithms.elastiknn + name: elastiknn-exact + run_groups: + exact: + args: [] euclidean: - - base_args: [] - constructor: L2Lsh - disabled: true - docker_tag: ann-benchmarks-elastiknn - module: ann_benchmarks.algorithms.elastiknn - name: elastiknn-l2lsh - run_groups: - elastiknn-l2lsh: - args: [[100], [4], [1024, 2048]] - query_args: [[500, 1000], [0, 3]] + - base_args: [] + constructor: L2Lsh + disabled: true + docker_tag: ann-benchmarks-elastiknn + module: ann_benchmarks.algorithms.elastiknn + name: elastiknn-l2lsh + run_groups: + elastiknn-l2lsh: + args: [[125,150,175,200], [7,8,9], [3900,4000,4100]] + query_args: [[500,750,1000,1250], [0,1,2]] diff --git a/docs/pages/performance/fashion-mnist/plot.png b/docs/pages/performance/fashion-mnist/plot.png index 7432ee2956e1e5e8854bd920bae3d9f284818e73..4745f151e2aafb8a5afabc756b8f0ffeb21b92c0 100644 GIT binary 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eknn-l2lsh-L=175-k=7-w=3900_candidates=1000_probes=1 0.982 173.397 +Found cached result +132: eknn-l2lsh-L=200-k=7-w=4100_candidates=500_probes=2 0.979 139.828 +Found cached result +133: eknn-l2lsh-L=175-k=9-w=3900_candidates=750_probes=2 0.967 175.534 +Found cached result +134: eknn-l2lsh-L=125-k=8-w=4100_candidates=1250_probes=2 0.981 173.280 +Found cached result +135: eknn-l2lsh-L=150-k=8-w=4000_candidates=500_probes=0 0.891 280.175 +Found cached result +136: eknn-l2lsh-L=150-k=8-w=4000_candidates=1250_probes=0 0.952 232.135 +Found cached result +137: eknn-l2lsh-L=125-k=8-w=3900_candidates=500_probes=1 0.917 242.059 +Found cached result +138: eknn-l2lsh-L=175-k=7-w=3900_candidates=1250_probes=0 0.970 212.504 +Found cached result +139: eknn-l2lsh-L=150-k=9-w=4000_candidates=750_probes=0 0.906 275.675 +Found cached result +140: eknn-l2lsh-L=175-k=9-w=4100_candidates=500_probes=0 0.895 277.871 +Found cached result +141: eknn-l2lsh-L=175-k=9-w=3900_candidates=1000_probes=1 0.964 190.995 +Found cached result +142: eknn-l2lsh-L=150-k=9-w=4000_candidates=500_probes=2 0.943 198.876 +Found cached result +143: eknn-l2lsh-L=125-k=8-w=4100_candidates=1000_probes=1 0.964 203.809 +Found cached result +144: eknn-l2lsh-L=175-k=8-w=3900_candidates=500_probes=1 0.945 203.812 +Found cached result +145: eknn-l2lsh-L=200-k=9-w=4000_candidates=1250_probes=1 0.978 172.968 +Found cached result +146: eknn-l2lsh-L=175-k=9-w=4000_candidates=1000_probes=2 0.978 159.401 +Found cached result +147: eknn-l2lsh-L=200-k=7-w=4000_candidates=1000_probes=2 0.992 133.459 +Found cached result +148: eknn-l2lsh-L=175-k=8-w=3900_candidates=1000_probes=1 0.974 182.369 +Found cached result +149: eknn-l2lsh-L=150-k=8-w=4000_candidates=1000_probes=0 0.942 245.700 +Found cached result +150: eknn-l2lsh-L=125-k=7-w=4100_candidates=1000_probes=0 0.946 244.874 +Found cached result +151: eknn-l2lsh-L=200-k=8-w=4100_candidates=750_probes=2 0.984 148.909 +Computing knn metrics +152: eknn-l2lsh-L=200-k=8-w=4100_candidates=1250_probes=1 0.987 164.391 +Found cached result +153: eknn-l2lsh-L=175-k=7-w=4000_candidates=750_probes=0 0.952 235.437 +Found cached result +154: eknn-l2lsh-L=175-k=9-w=4100_candidates=1250_probes=2 0.984 152.925 +Found cached result +155: eknn-l2lsh-L=150-k=8-w=3900_candidates=500_probes=2 0.952 189.909 +Found cached result +156: eknn-l2lsh-L=150-k=8-w=3900_candidates=1000_probes=0 0.937 246.914 +Found cached result +157: eknn-l2lsh-L=125-k=9-w=4100_candidates=500_probes=0 0.851 311.906 +Found cached result +158: eknn-l2lsh-L=150-k=8-w=4000_candidates=1250_probes=1 0.976 188.064 +Found cached result +159: eknn-l2lsh-L=200-k=9-w=4000_candidates=1000_probes=2 0.982 153.807 +Found cached result +160: eknn-l2lsh-L=175-k=8-w=3900_candidates=1250_probes=0 0.959 224.310 +Found cached result +161: eknn-l2lsh-L=175-k=7-w=3900_candidates=1250_probes=2 0.992 142.170 +Found cached result +162: eknn-l2lsh-L=150-k=8-w=4000_candidates=1000_probes=2 0.980 168.212 +Found cached result +163: eknn-l2lsh-L=150-k=8-w=4100_candidates=1250_probes=2 0.986 160.360 +Found cached result +164: eknn-l2lsh-L=200-k=7-w=3900_candidates=500_probes=2 0.977 139.436 +Found cached result +165: eknn-l2lsh-L=200-k=9-w=4100_candidates=1250_probes=1 0.980 169.150 +Found cached result +166: eknn-l2lsh-L=150-k=8-w=4000_candidates=750_probes=2 0.973 180.931 +Found cached result +167: eknn-l2lsh-L=175-k=8-w=3900_candidates=1250_probes=1 0.980 177.663 +Found cached result +168: eknn-l2lsh-L=200-k=7-w=4000_candidates=500_probes=0 0.937 237.096 +Found cached result +169: eknn-l2lsh-L=150-k=9-w=4100_candidates=1000_probes=0 0.930 256.525 +Found cached result +170: eknn-l2lsh-L=200-k=9-w=3900_candidates=750_probes=0 0.927 258.428 +Found cached result +171: eknn-l2lsh-L=150-k=9-w=4100_candidates=500_probes=0 0.877 285.484 +Found cached result +172: eknn-l2lsh-L=125-k=7-w=4100_candidates=1000_probes=2 0.983 168.819 +Found cached result +173: eknn-l2lsh-L=150-k=9-w=4100_candidates=1000_probes=1 0.962 202.603 +Found cached result +174: eknn-l2lsh-L=150-k=7-w=4100_candidates=1000_probes=1 0.981 182.734 +Found cached result +175: eknn-l2lsh-L=125-k=7-w=4100_candidates=1000_probes=1 0.973 196.158 +Found cached result +176: eknn-l2lsh-L=150-k=8-w=3900_candidates=1000_probes=2 0.979 173.302 +Found cached result +177: eknn-l2lsh-L=175-k=9-w=4000_candidates=500_probes=1 0.934 214.027 +Found cached result +178: eknn-l2lsh-L=200-k=9-w=3900_candidates=500_probes=2 0.957 170.595 +Found cached result +179: eknn-l2lsh-L=175-k=7-w=4000_candidates=750_probes=1 0.976 176.770 +Found cached result +180: eknn-l2lsh-L=125-k=8-w=4100_candidates=500_probes=2 0.946 201.687 +Found cached result +181: eknn-l2lsh-L=150-k=9-w=4000_candidates=1000_probes=0 0.925 257.201 +Found cached result +182: eknn-l2lsh-L=200-k=9-w=4100_candidates=750_probes=1 0.966 187.450 +Found cached result +183: eknn-l2lsh-L=150-k=8-w=4000_candidates=1250_probes=2 0.985 163.613 +Found cached result +184: eknn-l2lsh-L=150-k=7-w=3900_candidates=750_probes=1 0.968 197.120 +Found cached result +185: eknn-l2lsh-L=125-k=8-w=3900_candidates=1000_probes=1 0.958 208.477 +Found cached result +186: eknn-l2lsh-L=150-k=7-w=4100_candidates=1250_probes=0 0.968 217.790 +Found cached result +187: eknn-l2lsh-L=175-k=8-w=3900_candidates=500_probes=0 0.904 272.791 +Found cached result +188: eknn-l2lsh-L=175-k=8-w=4000_candidates=500_probes=1 0.948 202.060 +Found cached result +189: eknn-l2lsh-L=175-k=7-w=3900_candidates=500_probes=2 0.971 158.635 +Found cached result +190: eknn-l2lsh-L=200-k=7-w=3900_candidates=750_probes=1 0.980 169.016 +Found cached result +191: eknn-l2lsh-L=175-k=9-w=3900_candidates=500_probes=1 0.929 215.929 +Found cached result +192: eknn-l2lsh-L=125-k=7-w=3900_candidates=500_probes=1 0.933 225.817 +Found cached result +193: eknn-l2lsh-L=200-k=7-w=4100_candidates=750_probes=1 0.982 160.596 +Found cached result +194: eknn-l2lsh-L=175-k=7-w=4000_candidates=500_probes=2 0.973 158.915 +Found cached result +195: eknn-l2lsh-L=125-k=7-w=3900_candidates=1250_probes=1 0.976 192.985 +Found cached result +196: eknn-l2lsh-L=200-k=7-w=4100_candidates=500_probes=0 0.939 232.796 +Found cached result +197: eknn-l2lsh-L=150-k=8-w=4000_candidates=500_probes=2 0.955 180.825 +Found cached result +198: eknn-l2lsh-L=150-k=9-w=3900_candidates=750_probes=1 0.942 220.811 +Found cached result +199: eknn-l2lsh-L=175-k=7-w=4000_candidates=1000_probes=1 0.984 172.790 +Found cached result +200: eknn-l2lsh-L=175-k=7-w=3900_candidates=1000_probes=2 0.989 148.949 +Found cached result +201: eknn-l2lsh-L=200-k=9-w=4000_candidates=1250_probes=2 0.986 147.703 +Found cached result +202: eknn-l2lsh-L=175-k=9-w=4100_candidates=1000_probes=0 0.943 242.498 +Found cached result +203: eknn-l2lsh-L=200-k=9-w=3900_candidates=1250_probes=2 0.984 142.014 +Found cached result +204: eknn-l2lsh-L=200-k=7-w=4100_candidates=750_probes=0 0.963 214.423 +Found cached result +205: eknn-l2lsh-L=150-k=8-w=3900_candidates=500_probes=0 0.886 289.585 +Found cached result +206: eknn-l2lsh-L=150-k=7-w=3900_candidates=500_probes=0 0.906 264.575 +Found cached result +207: eknn-l2lsh-L=200-k=7-w=3900_candidates=750_probes=2 0.987 141.367 +Found cached result +208: eknn-l2lsh-L=150-k=8-w=3900_candidates=750_probes=1 0.956 213.143 +Found cached result +209: eknn-l2lsh-L=200-k=9-w=4100_candidates=750_probes=0 0.937 250.330 +Found cached result +210: eknn-l2lsh-L=150-k=8-w=4100_candidates=500_probes=2 0.958 183.474 +Found cached result +211: eknn-l2lsh-L=125-k=9-w=3900_candidates=750_probes=0 0.877 292.179 +Found cached result +212: eknn-l2lsh-L=150-k=8-w=4100_candidates=750_probes=1 0.962 205.715 +Found cached result +213: eknn-l2lsh-L=125-k=9-w=3900_candidates=750_probes=1 0.927 239.317 +Found cached result +214: eknn-l2lsh-L=150-k=9-w=3900_candidates=500_probes=1 0.916 233.020 +Found cached result +215: eknn-l2lsh-L=150-k=9-w=3900_candidates=750_probes=0 0.900 277.357 +Found cached result +216: eknn-l2lsh-L=200-k=9-w=4000_candidates=750_probes=1 0.963 189.180 +Found cached result +217: eknn-l2lsh-L=200-k=7-w=3900_candidates=1000_probes=2 0.991 132.193 +Found cached result +218: eknn-l2lsh-L=175-k=9-w=4000_candidates=500_probes=0 0.889 286.607 +Found cached result +219: eknn-l2lsh-L=200-k=9-w=4000_candidates=500_probes=1 0.944 199.125 +Found cached result +220: eknn-l2lsh-L=125-k=7-w=3900_candidates=1000_probes=0 0.939 250.426 +Found cached result +221: eknn-l2lsh-L=125-k=8-w=4100_candidates=500_probes=0 0.874 298.451 +Found cached result +222: eknn-l2lsh-L=150-k=9-w=4100_candidates=1000_probes=2 0.975 178.600 +Found cached result +223: eknn-l2lsh-L=125-k=8-w=3900_candidates=1250_probes=1 0.966 203.273 +Found cached result +224: eknn-l2lsh-L=150-k=7-w=3900_candidates=1000_probes=0 0.953 234.020 +Found cached result +225: eknn-l2lsh-L=200-k=7-w=4000_candidates=750_probes=0 0.960 221.518 +Found cached result +226: eknn-l2lsh-L=150-k=7-w=4100_candidates=500_probes=2 0.968 169.566 +Found cached result +227: eknn-l2lsh-L=150-k=8-w=4100_candidates=750_probes=0 0.929 260.596 +Found cached result +228: eknn-l2lsh-L=125-k=7-w=3900_candidates=750_probes=2 0.972 184.359 +Found cached result +229: eknn-l2lsh-L=125-k=8-w=4100_candidates=750_probes=2 0.967 190.711 +Found cached result +230: eknn-l2lsh-L=175-k=9-w=4000_candidates=1250_probes=1 0.973 183.447 +Found cached result +231: eknn-l2lsh-L=200-k=8-w=4100_candidates=750_probes=1 0.975 177.736 +Found cached result +232: eknn-l2lsh-L=200-k=9-w=4000_candidates=500_probes=0 0.903 269.983 +Found cached result +233: eknn-l2lsh-L=175-k=9-w=3900_candidates=1250_probes=0 0.943 232.425 +Found cached result +234: eknn-l2lsh-L=175-k=9-w=4000_candidates=750_probes=2 0.970 172.460 +Found cached result +235: eknn-l2lsh-L=200-k=9-w=3900_candidates=500_probes=1 0.940 203.171 +Found cached result +236: eknn-l2lsh-L=125-k=8-w=4100_candidates=750_probes=1 0.951 219.195 +Found cached result +237: eknn-l2lsh-L=175-k=7-w=4000_candidates=1250_probes=0 0.972 211.460 +Found cached result +238: eknn-l2lsh-L=150-k=9-w=3900_candidates=1250_probes=2 0.975 173.881 +Found cached result +239: eknn-l2lsh-L=150-k=7-w=3900_candidates=500_probes=1 0.948 206.707 +Found cached result +240: eknn-l2lsh-L=125-k=7-w=3900_candidates=500_probes=0 0.884 289.416 +Found cached result +241: eknn-l2lsh-L=200-k=7-w=4100_candidates=1250_probes=2 0.995 126.016 +Found cached result +242: eknn-l2lsh-L=150-k=9-w=4100_candidates=1250_probes=0 0.942 240.364 +Found cached result +243: eknn-l2lsh-L=150-k=9-w=4100_candidates=1250_probes=1 0.970 195.084 +Found cached result +244: eknn-l2lsh-L=125-k=8-w=3900_candidates=1250_probes=0 0.935 244.842 +Found cached result +245: eknn-l2lsh-L=150-k=7-w=3900_candidates=500_probes=2 0.964 176.316 +Found cached result +246: eknn-l2lsh-L=175-k=8-w=4000_candidates=750_probes=1 0.967 193.286 +Found cached result +247: eknn-l2lsh-L=200-k=9-w=3900_candidates=1000_probes=0 0.942 238.436 +Found cached result +248: eknn-l2lsh-L=200-k=7-w=3900_candidates=1250_probes=1 0.990 154.557 +Found cached result +249: eknn-l2lsh-L=200-k=7-w=4100_candidates=1000_probes=2 0.993 130.024 +Found cached result +250: eknn-l2lsh-L=150-k=8-w=3900_candidates=1250_probes=1 0.974 190.636 +Found cached result +251: eknn-l2lsh-L=175-k=7-w=4000_candidates=1250_probes=2 0.993 140.242 +Found cached result +252: eknn-l2lsh-L=200-k=7-w=3900_candidates=1000_probes=0 0.969 209.121 +Found cached result +253: eknn-l2lsh-L=150-k=8-w=3900_candidates=750_probes=0 0.920 269.915 +Found cached result +254: eknn-l2lsh-L=175-k=8-w=3900_candidates=500_probes=2 0.962 174.431 +Found cached result +255: eknn-l2lsh-L=125-k=9-w=3900_candidates=1250_probes=1 0.952 213.370 +Found cached result +256: eknn-l2lsh-L=125-k=7-w=3900_candidates=750_probes=1 0.958 213.409 +Found cached result +257: eknn-l2lsh-L=150-k=8-w=4000_candidates=750_probes=0 0.925 269.618 +Found cached result +258: eknn-l2lsh-L=200-k=7-w=3900_candidates=500_probes=0 0.934 243.251 +Found cached result +259: eknn-l2lsh-L=200-k=7-w=4100_candidates=1250_probes=0 0.979 197.590 +Found cached result +260: eknn-l2lsh-L=150-k=9-w=4000_candidates=500_probes=1 0.921 232.303 +Found cached result +261: eknn-l2lsh-L=150-k=7-w=3900_candidates=1000_probes=1 0.977 175.861 +Found cached result +262: eknn-l2lsh-L=175-k=7-w=3900_candidates=500_probes=1 0.958 194.054 +Found cached result +263: eknn-l2lsh-L=200-k=9-w=4100_candidates=1000_probes=0 0.951 234.136 +Found cached result +264: eknn-l2lsh-L=175-k=8-w=4000_candidates=750_probes=0 0.938 253.939 +Found cached result +265: eknn-l2lsh-L=200-k=7-w=3900_candidates=1250_probes=0 0.976 195.296 +Found cached result +266: eknn-l2lsh-L=150-k=8-w=4100_candidates=500_probes=0 0.896 284.495 +Found cached result +267: eknn-l2lsh-L=150-k=9-w=4100_candidates=750_probes=2 0.966 184.985 +Found cached result +268: eknn-l2lsh-L=175-k=9-w=4100_candidates=1000_probes=2 0.980 159.869 +Found cached result +269: eknn-l2lsh-L=150-k=9-w=4000_candidates=1250_probes=0 0.937 242.487 +Found cached result +270: eknn-l2lsh-L=125-k=9-w=3900_candidates=1250_probes=0 0.914 259.536 +Found cached result +271: eknn-l2lsh-L=175-k=8-w=4000_candidates=500_probes=2 0.964 170.576 +Found cached result +272: eknn-l2lsh-L=125-k=7-w=4100_candidates=500_probes=2 0.957 186.862 +Found cached result +273: eknn-l2lsh-L=200-k=7-w=4000_candidates=1250_probes=2 0.994 129.749 +Found cached result +274: eknn-l2lsh-L=125-k=9-w=3900_candidates=1000_probes=1 0.943 227.127 +Found cached result +275: eknn-l2lsh-L=200-k=8-w=4100_candidates=500_probes=0 0.926 248.573 +Found cached result +276: eknn-l2lsh-L=200-k=9-w=4000_candidates=500_probes=2 0.961 165.952 +Found cached result +277: eknn-l2lsh-L=125-k=9-w=4100_candidates=500_probes=2 0.932 213.809 +Found cached result +278: eknn-l2lsh-L=125-k=7-w=3900_candidates=750_probes=0 0.920 269.825 +Found cached result +279: eknn-l2lsh-L=125-k=7-w=4000_candidates=500_probes=2 0.955 191.810 +Found cached result +280: eknn-l2lsh-L=125-k=7-w=4100_candidates=750_probes=1 0.962 204.999 +Found cached result +281: eknn-l2lsh-L=150-k=9-w=4100_candidates=750_probes=1 0.950 213.884 +Found cached result +282: eknn-l2lsh-L=125-k=9-w=3900_candidates=1000_probes=0 0.900 276.969 +Found cached result +283: eknn-l2lsh-L=150-k=8-w=4100_candidates=1250_probes=0 0.956 231.391 +Found cached result +284: eknn-l2lsh-L=150-k=8-w=4000_candidates=500_probes=1 0.937 209.724 +Found cached result +285: eknn-l2lsh-L=125-k=7-w=4100_candidates=500_probes=0 0.892 277.219 +Found cached result +286: eknn-l2lsh-L=200-k=7-w=4000_candidates=500_probes=2 0.978 141.893 +Found cached result +287: eknn-l2lsh-L=175-k=8-w=3900_candidates=1000_probes=2 0.983 157.473 +Found cached result +288: eknn-l2lsh-L=175-k=7-w=3900_candidates=1000_probes=0 0.962 222.959 +Found cached result +289: eknn-l2lsh-L=125-k=8-w=4100_candidates=1000_probes=2 0.976 179.341 +Found cached result +290: eknn-l2lsh-L=125-k=8-w=4100_candidates=1250_probes=1 0.971 195.239 +Found cached result +291: eknn-l2lsh-L=200-k=7-w=4000_candidates=1000_probes=0 0.971 209.860 +Found cached result +292: eknn-l2lsh-L=175-k=9-w=3900_candidates=1000_probes=2 0.975 167.418 +Found cached result +293: eknn-l2lsh-L=125-k=8-w=4100_candidates=1000_probes=0 0.932 251.971 +Found cached result +294: eknn-l2lsh-L=125-k=7-w=3900_candidates=1250_probes=0 0.951 235.937 +Found cached result +295: eknn-l2lsh-L=200-k=9-w=3900_candidates=750_probes=2 0.973 163.680 +Found cached result +296: eknn-l2lsh-L=175-k=9-w=4000_candidates=1250_probes=0 0.948 225.770 +Found cached result +297: eknn-l2lsh-L=200-k=9-w=3900_candidates=1000_probes=1 0.969 184.638 +Found cached result +298: eknn-l2lsh-L=200-k=9-w=4100_candidates=1000_probes=2 0.984 152.098 +Found cached result +299: eknn-l2lsh-L=125-k=7-w=4100_candidates=1250_probes=1 0.980 182.451 +Found cached result +300: eknn-l2lsh-L=175-k=9-w=3900_candidates=1000_probes=0 0.933 248.932 +Found cached result +301: eknn-l2lsh-L=150-k=7-w=4100_candidates=1250_probes=1 0.985 173.447 +Found cached result +302: eknn-l2lsh-L=175-k=9-w=3900_candidates=500_probes=0 0.882 282.479 +Found cached result +303: eknn-l2lsh-L=200-k=9-w=3900_candidates=1250_probes=0 0.952 216.606 +Found cached result +304: eknn-l2lsh-L=125-k=9-w=4100_candidates=1250_probes=2 0.973 183.616 +Found cached result +305: eknn-l2lsh-L=175-k=8-w=4000_candidates=1250_probes=0 0.962 222.790 +Found cached result +306: eknn-l2lsh-L=175-k=8-w=3900_candidates=750_probes=2 0.976 166.383 +Found cached result +307: eknn-l2lsh-L=175-k=9-w=4000_candidates=1000_probes=1 0.967 187.608 +Found cached result +308: eknn-l2lsh-L=200-k=8-w=4100_candidates=750_probes=0 0.951 237.657 +Found cached result +309: eknn-l2lsh-L=175-k=7-w=3900_candidates=1250_probes=1 0.987 165.410 +Found cached result +310: eknn-l2lsh-L=125-k=7-w=4000_candidates=750_probes=1 0.960 209.464 +Found cached result +311: eknn-l2lsh-L=175-k=9-w=4100_candidates=500_probes=2 0.956 177.716 +Found cached result +312: eknn-l2lsh-L=150-k=9-w=4000_candidates=1250_probes=2 0.978 172.674 +Found cached result +313: eknn-l2lsh-L=200-k=9-w=4100_candidates=500_probes=1 0.947 197.100 +Found cached result +314: eknn-l2lsh-L=175-k=8-w=4000_candidates=1000_probes=2 0.985 156.827 +Found cached result +315: eknn-l2lsh-L=150-k=7-w=4100_candidates=750_probes=2 0.982 161.400 +Found cached result +316: eknn-l2lsh-L=125-k=9-w=3900_candidates=1250_probes=2 0.968 189.159 +Found cached result +317: eknn-l2lsh-L=175-k=9-w=3900_candidates=500_probes=2 0.949 183.198 +Found cached result +318: eknn-l2lsh-L=125-k=7-w=4000_candidates=1250_probes=1 0.978 189.013 +Found cached result +319: eknn-l2lsh-L=150-k=8-w=4100_candidates=750_probes=2 0.975 175.020 +Found cached result +320: eknn-l2lsh-L=200-k=9-w=4000_candidates=1000_probes=0 0.947 238.454 +Found cached result +321: eknn-l2lsh-L=125-k=9-w=4100_candidates=1250_probes=0 0.926 250.285 +Found cached result +322: eknn-l2lsh-L=125-k=8-w=3900_candidates=1000_probes=0 0.922 261.355 +Found cached result +323: eknn-l2lsh-L=200-k=9-w=4000_candidates=750_probes=0 0.932 245.158 +Found cached result +324: eknn-l2lsh-L=175-k=9-w=4000_candidates=750_probes=0 0.921 263.695 +Found cached result +325: eknn-l2lsh-L=125-k=7-w=3900_candidates=1000_probes=1 0.970 200.830 +Found cached result +326: eknn-l2lsh-L=200-k=9-w=4000_candidates=750_probes=2 0.975 159.587 +Found cached result +327: eknn-l2lsh-L=125-k=9-w=4100_candidates=1000_probes=1 0.951 219.441 +Found cached result +328: eknn-l2lsh-L=150-k=7-w=4100_candidates=1000_probes=2 0.988 154.745 +Found cached result +329: eknn-l2lsh-L=175-k=9-w=3900_candidates=750_probes=0 0.915 267.361 +Found cached result +330: eknn-l2lsh-L=150-k=8-w=4100_candidates=1000_probes=0 0.945 245.532 +Found cached result +331: eknn-l2lsh-L=125-k=8-w=3900_candidates=750_probes=0 0.901 278.315 +Found cached result +332: eknn-l2lsh-L=150-k=9-w=3900_candidates=1250_probes=1 0.963 198.103 +Found cached result +333: eknn-l2lsh-L=150-k=8-w=3900_candidates=1250_probes=0 0.948 237.059 +Found cached result +334: eknn-l2lsh-L=125-k=9-w=3900_candidates=1000_probes=2 0.960 201.594 +Found cached result +335: eknn-l2lsh-L=150-k=9-w=4100_candidates=500_probes=1 0.926 222.971 +Found cached result +336: eknn-l2lsh-L=150-k=9-w=3900_candidates=1250_probes=0 0.931 244.528 +Found cached result +337: eknn-l2lsh-L=175-k=7-w=3900_candidates=750_probes=0 0.949 234.738 +Found cached result +338: eknn-l2lsh-L=150-k=7-w=4100_candidates=500_probes=1 0.953 203.767 +Found cached result +339: eknn-l2lsh-L=125-k=7-w=3900_candidates=1250_probes=2 0.985 166.714 +Found cached result +340: eknn-l2lsh-L=200-k=9-w=3900_candidates=1000_probes=2 0.980 149.577 +Found cached result +341: eknn-l2lsh-L=200-k=7-w=4100_candidates=1000_probes=1 0.988 154.212 +Found cached result +342: eknn-l2lsh-L=150-k=9-w=3900_candidates=1000_probes=1 0.955 209.590 +Found cached result +343: eknn-l2lsh-L=200-k=9-w=4100_candidates=750_probes=2 0.977 158.559 +Found cached result +344: eknn-l2lsh-L=150-k=8-w=4000_candidates=750_probes=1 0.959 210.001 +Found cached result +345: eknn-l2lsh-L=125-k=9-w=4100_candidates=1000_probes=0 0.912 270.403 +Found cached result +346: eknn-l2lsh-L=125-k=7-w=4000_candidates=1000_probes=0 0.943 246.386 diff --git a/docs/pages/performance/fashion-mnist/results.md b/docs/pages/performance/fashion-mnist/results.md index 06509969..164174a4 100644 --- a/docs/pages/performance/fashion-mnist/results.md +++ b/docs/pages/performance/fashion-mnist/results.md @@ -1,10 +1,349 @@ |Model|Parameters|Recall|Queries per Second| |---|---|---|---| -|eknn-l2lsh|L=100 k=4 w=1024 candidates=500 probes=0|0.378|373.943| -|eknn-l2lsh|L=100 k=4 w=1024 candidates=1000 probes=0|0.447|322.600| -|eknn-l2lsh|L=100 k=4 w=1024 candidates=500 probes=3|0.635|278.750| -|eknn-l2lsh|L=100 k=4 w=1024 candidates=1000 probes=3|0.717|248.708| -|eknn-l2lsh|L=100 k=4 w=2048 candidates=500 probes=0|0.767|328.214| -|eknn-l2lsh|L=100 k=4 w=2048 candidates=1000 probes=0|0.847|291.762| -|eknn-l2lsh|L=100 k=4 w=2048 candidates=500 probes=3|0.922|217.030| -|eknn-l2lsh|L=100 k=4 w=2048 candidates=1000 probes=3|0.960|197.218| +|eknn-l2lsh|L=125 k=9 w=3900 candidates=500 probes=0|0.837|323.650| +|eknn-l2lsh|L=125 k=9 w=4100 candidates=500 probes=0|0.851|311.906| +|eknn-l2lsh|L=125 k=8 w=3900 candidates=500 probes=0|0.862|309.353| +|eknn-l2lsh|L=150 k=9 w=3900 candidates=500 probes=0|0.863|302.352| +|eknn-l2lsh|L=150 k=9 w=4000 candidates=500 probes=0|0.870|298.232| +|eknn-l2lsh|L=125 k=8 w=4100 candidates=500 probes=0|0.874|298.451| +|eknn-l2lsh|L=150 k=9 w=4100 candidates=500 probes=0|0.877|285.484| +|eknn-l2lsh|L=125 k=9 w=3900 candidates=750 probes=0|0.877|292.179| +|eknn-l2lsh|L=175 k=9 w=3900 candidates=500 probes=0|0.882|282.479| +|eknn-l2lsh|L=125 k=7 w=3900 candidates=500 probes=0|0.884|289.416| +|eknn-l2lsh|L=150 k=8 w=3900 candidates=500 probes=0|0.886|289.585| +|eknn-l2lsh|L=125 k=7 w=4000 candidates=500 probes=0|0.889|282.787| +|eknn-l2lsh|L=175 k=9 w=4000 candidates=500 probes=0|0.889|286.607| +|eknn-l2lsh|L=150 k=8 w=4000 candidates=500 probes=0|0.891|280.175| +|eknn-l2lsh|L=125 k=9 w=4100 candidates=750 probes=0|0.891|288.164| +|eknn-l2lsh|L=125 k=7 w=4100 candidates=500 probes=0|0.892|277.219| +|eknn-l2lsh|L=175 k=9 w=4100 candidates=500 probes=0|0.895|277.871| +|eknn-l2lsh|L=125 k=9 w=3900 candidates=500 probes=1|0.896|253.335| +|eknn-l2lsh|L=150 k=8 w=4100 candidates=500 probes=0|0.896|284.495| +|eknn-l2lsh|L=200 k=9 w=3900 candidates=500 probes=0|0.897|273.516| +|eknn-l2lsh|L=125 k=9 w=3900 candidates=1000 probes=0|0.900|276.969| +|eknn-l2lsh|L=150 k=9 w=3900 candidates=750 probes=0|0.900|277.357| +|eknn-l2lsh|L=125 k=8 w=3900 candidates=750 probes=0|0.901|278.315| +|eknn-l2lsh|L=200 k=9 w=4000 candidates=500 probes=0|0.903|269.983| +|eknn-l2lsh|L=175 k=8 w=3900 candidates=500 probes=0|0.904|272.791| +|eknn-l2lsh|L=150 k=7 w=3900 candidates=500 probes=0|0.906|264.575| +|eknn-l2lsh|L=150 k=9 w=4000 candidates=750 probes=0|0.906|275.675| +|eknn-l2lsh|L=125 k=9 w=4100 candidates=500 probes=1|0.907|248.027| +|eknn-l2lsh|L=200 k=9 w=4100 candidates=500 probes=0|0.909|260.129| +|eknn-l2lsh|L=175 k=8 w=4000 candidates=500 probes=0|0.909|272.299| +|eknn-l2lsh|L=150 k=9 w=4100 candidates=750 probes=0|0.912|266.915| +|eknn-l2lsh|L=125 k=9 w=4100 candidates=1000 probes=0|0.912|270.403| +|eknn-l2lsh|L=125 k=8 w=4100 candidates=750 probes=0|0.912|275.778| +|eknn-l2lsh|L=125 k=9 w=3900 candidates=1250 probes=0|0.914|259.536| +|eknn-l2lsh|L=150 k=7 w=4100 candidates=500 probes=0|0.914|264.401| +|eknn-l2lsh|L=175 k=9 w=3900 candidates=750 probes=0|0.915|267.361| +|eknn-l2lsh|L=150 k=9 w=3900 candidates=500 probes=1|0.916|233.020| +|eknn-l2lsh|L=125 k=8 w=3900 candidates=500 probes=1|0.917|242.059| +|eknn-l2lsh|L=150 k=9 w=3900 candidates=1000 probes=0|0.919|262.989| +|eknn-l2lsh|L=125 k=7 w=3900 candidates=750 probes=0|0.920|269.825| +|eknn-l2lsh|L=150 k=8 w=3900 candidates=750 probes=0|0.920|269.915| +|eknn-l2lsh|L=150 k=9 w=4000 candidates=500 probes=1|0.921|232.303| +|eknn-l2lsh|L=175 k=7 w=3900 candidates=500 probes=0|0.921|257.151| +|eknn-l2lsh|L=175 k=9 w=4000 candidates=750 probes=0|0.921|263.695| +|eknn-l2lsh|L=125 k=8 w=3900 candidates=1000 probes=0|0.922|261.355| +|eknn-l2lsh|L=125 k=9 w=3900 candidates=500 probes=2|0.924|217.381| +|eknn-l2lsh|L=125 k=8 w=4100 candidates=500 probes=1|0.925|236.820| +|eknn-l2lsh|L=175 k=7 w=4000 candidates=500 probes=0|0.925|254.374| +|eknn-l2lsh|L=150 k=9 w=4000 candidates=1000 probes=0|0.925|257.201| +|eknn-l2lsh|L=125 k=7 w=4000 candidates=750 probes=0|0.925|266.401| +|eknn-l2lsh|L=150 k=8 w=4000 candidates=750 probes=0|0.925|269.618| +|eknn-l2lsh|L=150 k=9 w=4100 candidates=500 probes=1|0.926|222.971| +|eknn-l2lsh|L=200 k=8 w=4100 candidates=500 probes=0|0.926|248.573| +|eknn-l2lsh|L=125 k=9 w=4100 candidates=1250 probes=0|0.926|250.285| +|eknn-l2lsh|L=125 k=9 w=3900 candidates=750 probes=1|0.927|239.317| +|eknn-l2lsh|L=175 k=9 w=4100 candidates=750 probes=0|0.927|256.851| +|eknn-l2lsh|L=200 k=9 w=3900 candidates=750 probes=0|0.927|258.428| +|eknn-l2lsh|L=125 k=7 w=4100 candidates=750 probes=0|0.928|258.768| +|eknn-l2lsh|L=175 k=9 w=3900 candidates=500 probes=1|0.929|215.929| +|eknn-l2lsh|L=150 k=8 w=4100 candidates=750 probes=0|0.929|260.596| +|eknn-l2lsh|L=150 k=9 w=4100 candidates=1000 probes=0|0.930|256.525| +|eknn-l2lsh|L=150 k=9 w=3900 candidates=1250 probes=0|0.931|244.528| +|eknn-l2lsh|L=125 k=9 w=4100 candidates=500 probes=2|0.932|213.809| +|eknn-l2lsh|L=200 k=9 w=4000 candidates=750 probes=0|0.932|245.158| +|eknn-l2lsh|L=125 k=8 w=4100 candidates=1000 probes=0|0.932|251.971| +|eknn-l2lsh|L=150 k=8 w=3900 candidates=500 probes=1|0.933|224.721| +|eknn-l2lsh|L=125 k=7 w=3900 candidates=500 probes=1|0.933|225.817| +|eknn-l2lsh|L=175 k=9 w=3900 candidates=1000 probes=0|0.933|248.932| +|eknn-l2lsh|L=175 k=9 w=4000 candidates=500 probes=1|0.934|214.027| +|eknn-l2lsh|L=200 k=7 w=3900 candidates=500 probes=0|0.934|243.251| +|eknn-l2lsh|L=175 k=8 w=3900 candidates=750 probes=0|0.934|256.273| +|eknn-l2lsh|L=125 k=8 w=3900 candidates=1250 probes=0|0.935|244.842| +|eknn-l2lsh|L=125 k=7 w=4000 candidates=500 probes=1|0.936|222.739| +|eknn-l2lsh|L=125 k=9 w=4100 candidates=750 probes=1|0.936|232.567| +|eknn-l2lsh|L=150 k=8 w=4000 candidates=500 probes=1|0.937|209.724| +|eknn-l2lsh|L=200 k=7 w=4000 candidates=500 probes=0|0.937|237.096| +|eknn-l2lsh|L=150 k=9 w=4000 candidates=1250 probes=0|0.937|242.487| +|eknn-l2lsh|L=150 k=8 w=3900 candidates=1000 probes=0|0.937|246.914| +|eknn-l2lsh|L=200 k=9 w=4100 candidates=750 probes=0|0.937|250.330| +|eknn-l2lsh|L=175 k=9 w=4100 candidates=500 probes=1|0.938|208.918| +|eknn-l2lsh|L=175 k=9 w=4000 candidates=1000 probes=0|0.938|235.525| +|eknn-l2lsh|L=175 k=8 w=4000 candidates=750 probes=0|0.938|253.939| +|eknn-l2lsh|L=150 k=7 w=3900 candidates=750 probes=0|0.938|255.499| +|eknn-l2lsh|L=150 k=9 w=3900 candidates=500 probes=2|0.939|199.486| +|eknn-l2lsh|L=125 k=7 w=4100 candidates=500 probes=1|0.939|218.346| +|eknn-l2lsh|L=200 k=7 w=4100 candidates=500 probes=0|0.939|232.796| +|eknn-l2lsh|L=125 k=7 w=3900 candidates=1000 probes=0|0.939|250.426| +|eknn-l2lsh|L=200 k=9 w=3900 candidates=500 probes=1|0.940|203.171| +|eknn-l2lsh|L=125 k=8 w=3900 candidates=500 probes=2|0.940|209.130| +|eknn-l2lsh|L=150 k=8 w=4100 candidates=500 probes=1|0.940|214.884| +|eknn-l2lsh|L=150 k=9 w=3900 candidates=750 probes=1|0.942|220.811| +|eknn-l2lsh|L=200 k=9 w=3900 candidates=1000 probes=0|0.942|238.436| +|eknn-l2lsh|L=150 k=9 w=4100 candidates=1250 probes=0|0.942|240.364| +|eknn-l2lsh|L=150 k=8 w=4000 candidates=1000 probes=0|0.942|245.700| +|eknn-l2lsh|L=150 k=9 w=4000 candidates=500 probes=2|0.943|198.876| +|eknn-l2lsh|L=125 k=9 w=3900 candidates=1000 probes=1|0.943|227.127| +|eknn-l2lsh|L=175 k=9 w=3900 candidates=1250 probes=0|0.943|232.425| +|eknn-l2lsh|L=175 k=9 w=4100 candidates=1000 probes=0|0.943|242.498| +|eknn-l2lsh|L=125 k=7 w=4000 candidates=1000 probes=0|0.943|246.386| +|eknn-l2lsh|L=200 k=9 w=4000 candidates=500 probes=1|0.944|199.125| +|eknn-l2lsh|L=125 k=8 w=3900 candidates=750 probes=1|0.944|217.569| +|eknn-l2lsh|L=125 k=8 w=4100 candidates=1250 probes=0|0.944|239.834| +|eknn-l2lsh|L=150 k=7 w=4100 candidates=750 probes=0|0.944|247.966| +|eknn-l2lsh|L=175 k=8 w=3900 candidates=500 probes=1|0.945|203.812| +|eknn-l2lsh|L=150 k=8 w=4100 candidates=1000 probes=0|0.945|245.532| +|eknn-l2lsh|L=125 k=8 w=4100 candidates=500 probes=2|0.946|201.687| +|eknn-l2lsh|L=125 k=7 w=4100 candidates=1000 probes=0|0.946|244.874| +|eknn-l2lsh|L=150 k=9 w=4100 candidates=500 probes=2|0.947|188.774| +|eknn-l2lsh|L=200 k=9 w=4100 candidates=500 probes=1|0.947|197.100| +|eknn-l2lsh|L=150 k=9 w=4000 candidates=750 probes=1|0.947|219.975| +|eknn-l2lsh|L=200 k=9 w=4000 candidates=1000 probes=0|0.947|238.454| +|eknn-l2lsh|L=175 k=8 w=4000 candidates=500 probes=1|0.948|202.060| +|eknn-l2lsh|L=150 k=7 w=3900 candidates=500 probes=1|0.948|206.707| +|eknn-l2lsh|L=125 k=9 w=3900 candidates=750 probes=2|0.948|209.180| +|eknn-l2lsh|L=175 k=9 w=4000 candidates=1250 probes=0|0.948|225.770| +|eknn-l2lsh|L=150 k=8 w=3900 candidates=1250 probes=0|0.948|237.059| +|eknn-l2lsh|L=175 k=9 w=3900 candidates=500 probes=2|0.949|183.198| +|eknn-l2lsh|L=175 k=7 w=3900 candidates=750 probes=0|0.949|234.738| +|eknn-l2lsh|L=175 k=8 w=3900 candidates=1000 probes=0|0.949|240.218| +|eknn-l2lsh|L=150 k=9 w=4100 candidates=750 probes=1|0.950|213.884| +|eknn-l2lsh|L=125 k=8 w=4100 candidates=750 probes=1|0.951|219.195| +|eknn-l2lsh|L=125 k=9 w=4100 candidates=1000 probes=1|0.951|219.441| +|eknn-l2lsh|L=200 k=9 w=4100 candidates=1000 probes=0|0.951|234.136| +|eknn-l2lsh|L=125 k=7 w=3900 candidates=1250 probes=0|0.951|235.937| +|eknn-l2lsh|L=200 k=8 w=4100 candidates=750 probes=0|0.951|237.657| +|eknn-l2lsh|L=150 k=8 w=3900 candidates=500 probes=2|0.952|189.909| +|eknn-l2lsh|L=175 k=9 w=3900 candidates=750 probes=1|0.952|206.997| +|eknn-l2lsh|L=125 k=9 w=3900 candidates=1250 probes=1|0.952|213.370| +|eknn-l2lsh|L=200 k=9 w=3900 candidates=1250 probes=0|0.952|216.606| +|eknn-l2lsh|L=150 k=8 w=4000 candidates=1250 probes=0|0.952|232.135| +|eknn-l2lsh|L=175 k=7 w=4000 candidates=750 probes=0|0.952|235.437| +|eknn-l2lsh|L=175 k=9 w=4000 candidates=500 probes=2|0.953|181.086| +|eknn-l2lsh|L=125 k=7 w=3900 candidates=500 probes=2|0.953|196.345| +|eknn-l2lsh|L=150 k=7 w=4100 candidates=500 probes=1|0.953|203.767| +|eknn-l2lsh|L=175 k=9 w=4100 candidates=1250 probes=0|0.953|227.474| +|eknn-l2lsh|L=150 k=7 w=3900 candidates=1000 probes=0|0.953|234.020| +|eknn-l2lsh|L=175 k=8 w=4000 candidates=1000 probes=0|0.953|239.020| +|eknn-l2lsh|L=125 k=7 w=4000 candidates=1250 probes=0|0.954|231.635| +|eknn-l2lsh|L=150 k=8 w=4000 candidates=500 probes=2|0.955|180.825| +|eknn-l2lsh|L=125 k=7 w=4000 candidates=500 probes=2|0.955|191.810| +|eknn-l2lsh|L=125 k=9 w=4100 candidates=750 probes=2|0.955|201.907| +|eknn-l2lsh|L=150 k=9 w=3900 candidates=1000 probes=1|0.955|209.590| +|eknn-l2lsh|L=175 k=9 w=4100 candidates=500 probes=2|0.956|177.716| +|eknn-l2lsh|L=175 k=9 w=4000 candidates=750 probes=1|0.956|200.126| +|eknn-l2lsh|L=150 k=8 w=3900 candidates=750 probes=1|0.956|213.143| +|eknn-l2lsh|L=200 k=9 w=4000 candidates=1250 probes=0|0.956|221.315| +|eknn-l2lsh|L=150 k=8 w=4100 candidates=1250 probes=0|0.956|231.391| +|eknn-l2lsh|L=200 k=9 w=3900 candidates=500 probes=2|0.957|170.595| +|eknn-l2lsh|L=125 k=7 w=4100 candidates=500 probes=2|0.957|186.862| +|eknn-l2lsh|L=125 k=7 w=4100 candidates=1250 probes=0|0.957|224.911| +|eknn-l2lsh|L=150 k=8 w=4100 candidates=500 probes=2|0.958|183.474| +|eknn-l2lsh|L=175 k=7 w=3900 candidates=500 probes=1|0.958|194.054| +|eknn-l2lsh|L=125 k=8 w=3900 candidates=1000 probes=1|0.958|208.477| +|eknn-l2lsh|L=125 k=7 w=3900 candidates=750 probes=1|0.958|213.409| +|eknn-l2lsh|L=200 k=7 w=3900 candidates=750 probes=0|0.958|224.903| +|eknn-l2lsh|L=200 k=8 w=4100 candidates=500 probes=1|0.959|187.307| +|eknn-l2lsh|L=150 k=9 w=3900 candidates=750 probes=2|0.959|192.285| +|eknn-l2lsh|L=175 k=9 w=4100 candidates=750 probes=1|0.959|195.500| +|eknn-l2lsh|L=150 k=9 w=4000 candidates=1000 probes=1|0.959|205.758| +|eknn-l2lsh|L=150 k=8 w=4000 candidates=750 probes=1|0.959|210.001| +|eknn-l2lsh|L=175 k=8 w=3900 candidates=1250 probes=0|0.959|224.310| +|eknn-l2lsh|L=150 k=7 w=4100 candidates=1000 probes=0|0.959|226.857| +|eknn-l2lsh|L=175 k=7 w=4000 candidates=500 probes=1|0.960|189.476| +|eknn-l2lsh|L=200 k=9 w=3900 candidates=750 probes=1|0.960|191.764| +|eknn-l2lsh|L=125 k=9 w=3900 candidates=1000 probes=2|0.960|201.594| +|eknn-l2lsh|L=125 k=9 w=4100 candidates=1250 probes=1|0.960|206.423| +|eknn-l2lsh|L=125 k=7 w=4000 candidates=750 probes=1|0.960|209.464| +|eknn-l2lsh|L=200 k=9 w=4100 candidates=1250 probes=0|0.960|221.266| +|eknn-l2lsh|L=200 k=7 w=4000 candidates=750 probes=0|0.960|221.518| +|eknn-l2lsh|L=200 k=9 w=4000 candidates=500 probes=2|0.961|165.952| +|eknn-l2lsh|L=125 k=8 w=3900 candidates=750 probes=2|0.961|195.683| +|eknn-l2lsh|L=175 k=8 w=3900 candidates=500 probes=2|0.962|174.431| +|eknn-l2lsh|L=150 k=9 w=4000 candidates=750 probes=2|0.962|190.600| +|eknn-l2lsh|L=150 k=9 w=4100 candidates=1000 probes=1|0.962|202.603| +|eknn-l2lsh|L=125 k=7 w=4100 candidates=750 probes=1|0.962|204.999| +|eknn-l2lsh|L=150 k=8 w=4100 candidates=750 probes=1|0.962|205.715| +|eknn-l2lsh|L=175 k=8 w=4000 candidates=1250 probes=0|0.962|222.790| +|eknn-l2lsh|L=175 k=7 w=3900 candidates=1000 probes=0|0.962|222.959| +|eknn-l2lsh|L=200 k=9 w=4100 candidates=500 probes=2|0.963|165.919| +|eknn-l2lsh|L=200 k=9 w=4000 candidates=750 probes=1|0.963|189.180| +|eknn-l2lsh|L=150 k=9 w=3900 candidates=1250 probes=1|0.963|198.103| +|eknn-l2lsh|L=150 k=7 w=3900 candidates=1250 probes=0|0.963|211.024| +|eknn-l2lsh|L=200 k=7 w=4100 candidates=750 probes=0|0.963|214.423| +|eknn-l2lsh|L=175 k=8 w=4000 candidates=500 probes=2|0.964|170.576| +|eknn-l2lsh|L=150 k=7 w=3900 candidates=500 probes=2|0.964|176.316| +|eknn-l2lsh|L=175 k=9 w=3900 candidates=1000 probes=1|0.964|190.995| +|eknn-l2lsh|L=125 k=8 w=4100 candidates=1000 probes=1|0.964|203.809| +|eknn-l2lsh|L=200 k=8 w=4100 candidates=1000 probes=0|0.964|220.637| +|eknn-l2lsh|L=200 k=7 w=3900 candidates=500 probes=1|0.965|170.608| +|eknn-l2lsh|L=175 k=8 w=3900 candidates=750 probes=1|0.965|193.101| +|eknn-l2lsh|L=175 k=7 w=4000 candidates=1000 probes=0|0.965|222.720| +|eknn-l2lsh|L=150 k=9 w=4100 candidates=750 probes=2|0.966|184.985| +|eknn-l2lsh|L=200 k=9 w=4100 candidates=750 probes=1|0.966|187.450| +|eknn-l2lsh|L=125 k=9 w=4100 candidates=1000 probes=2|0.966|192.040| +|eknn-l2lsh|L=125 k=8 w=3900 candidates=1250 probes=1|0.966|203.273| +|eknn-l2lsh|L=200 k=7 w=4000 candidates=500 probes=1|0.967|172.951| +|eknn-l2lsh|L=175 k=9 w=3900 candidates=750 probes=2|0.967|175.534| +|eknn-l2lsh|L=175 k=9 w=4000 candidates=1000 probes=1|0.967|187.608| +|eknn-l2lsh|L=125 k=8 w=4100 candidates=750 probes=2|0.967|190.711| +|eknn-l2lsh|L=175 k=8 w=4000 candidates=750 probes=1|0.967|193.286| +|eknn-l2lsh|L=150 k=9 w=4000 candidates=1250 probes=1|0.967|195.426| +|eknn-l2lsh|L=150 k=7 w=4100 candidates=500 probes=2|0.968|169.566| +|eknn-l2lsh|L=125 k=9 w=3900 candidates=1250 probes=2|0.968|189.159| +|eknn-l2lsh|L=150 k=8 w=3900 candidates=1000 probes=1|0.968|195.836| +|eknn-l2lsh|L=150 k=7 w=3900 candidates=750 probes=1|0.968|197.120| +|eknn-l2lsh|L=150 k=7 w=4100 candidates=1250 probes=0|0.968|217.790| +|eknn-l2lsh|L=200 k=7 w=4100 candidates=500 probes=1|0.969|169.462| +|eknn-l2lsh|L=150 k=9 w=3900 candidates=1000 probes=2|0.969|177.270| +|eknn-l2lsh|L=200 k=9 w=3900 candidates=1000 probes=1|0.969|184.638| +|eknn-l2lsh|L=200 k=7 w=3900 candidates=1000 probes=0|0.969|209.121| +|eknn-l2lsh|L=175 k=9 w=4000 candidates=750 probes=2|0.970|172.460| +|eknn-l2lsh|L=150 k=8 w=3900 candidates=750 probes=2|0.970|181.551| +|eknn-l2lsh|L=175 k=9 w=3900 candidates=1250 probes=1|0.970|183.826| +|eknn-l2lsh|L=175 k=9 w=4100 candidates=1000 probes=1|0.970|187.352| +|eknn-l2lsh|L=150 k=9 w=4100 candidates=1250 probes=1|0.970|195.084| +|eknn-l2lsh|L=150 k=8 w=4000 candidates=1000 probes=1|0.970|197.122| +|eknn-l2lsh|L=125 k=7 w=3900 candidates=1000 probes=1|0.970|200.830| +|eknn-l2lsh|L=175 k=7 w=3900 candidates=1250 probes=0|0.970|212.504| +|eknn-l2lsh|L=175 k=7 w=3900 candidates=500 probes=2|0.971|158.635| +|eknn-l2lsh|L=125 k=8 w=4100 candidates=1250 probes=1|0.971|195.239| +|eknn-l2lsh|L=200 k=8 w=4100 candidates=1250 probes=0|0.971|209.429| +|eknn-l2lsh|L=200 k=7 w=4000 candidates=1000 probes=0|0.971|209.860| +|eknn-l2lsh|L=200 k=8 w=4100 candidates=500 probes=2|0.972|156.062| +|eknn-l2lsh|L=175 k=9 w=4100 candidates=750 probes=2|0.972|167.739| +|eknn-l2lsh|L=200 k=9 w=4000 candidates=1000 probes=1|0.972|180.587| +|eknn-l2lsh|L=150 k=9 w=4000 candidates=1000 probes=2|0.972|180.912| +|eknn-l2lsh|L=125 k=7 w=3900 candidates=750 probes=2|0.972|184.359| +|eknn-l2lsh|L=125 k=8 w=3900 candidates=1000 probes=2|0.972|184.815| +|eknn-l2lsh|L=150 k=7 w=4100 candidates=750 probes=1|0.972|188.104| +|eknn-l2lsh|L=125 k=7 w=4000 candidates=1000 probes=1|0.972|197.987| +|eknn-l2lsh|L=175 k=7 w=4000 candidates=1250 probes=0|0.972|211.460| +|eknn-l2lsh|L=175 k=7 w=4000 candidates=500 probes=2|0.973|158.915| +|eknn-l2lsh|L=200 k=9 w=3900 candidates=750 probes=2|0.973|163.680| +|eknn-l2lsh|L=150 k=8 w=4000 candidates=750 probes=2|0.973|180.931| +|eknn-l2lsh|L=175 k=9 w=4000 candidates=1250 probes=1|0.973|183.447| +|eknn-l2lsh|L=125 k=9 w=4100 candidates=1250 probes=2|0.973|183.616| +|eknn-l2lsh|L=150 k=8 w=4100 candidates=1000 probes=1|0.973|194.179| +|eknn-l2lsh|L=125 k=7 w=4100 candidates=1000 probes=1|0.973|196.158| +|eknn-l2lsh|L=200 k=7 w=4100 candidates=1000 probes=0|0.973|206.000| +|eknn-l2lsh|L=125 k=7 w=4000 candidates=750 probes=2|0.974|181.437| +|eknn-l2lsh|L=175 k=8 w=3900 candidates=1000 probes=1|0.974|182.369| +|eknn-l2lsh|L=150 k=8 w=3900 candidates=1250 probes=1|0.974|190.636| +|eknn-l2lsh|L=200 k=9 w=4000 candidates=750 probes=2|0.975|159.587| +|eknn-l2lsh|L=200 k=9 w=3900 candidates=1250 probes=1|0.975|164.301| +|eknn-l2lsh|L=175 k=9 w=3900 candidates=1000 probes=2|0.975|167.418| +|eknn-l2lsh|L=150 k=9 w=3900 candidates=1250 probes=2|0.975|173.881| +|eknn-l2lsh|L=150 k=8 w=4100 candidates=750 probes=2|0.975|175.020| +|eknn-l2lsh|L=125 k=7 w=4100 candidates=750 probes=2|0.975|177.351| +|eknn-l2lsh|L=200 k=8 w=4100 candidates=750 probes=1|0.975|177.736| +|eknn-l2lsh|L=150 k=9 w=4100 candidates=1000 probes=2|0.975|178.600| +|eknn-l2lsh|L=200 k=9 w=4100 candidates=1000 probes=1|0.975|178.742| +|eknn-l2lsh|L=175 k=7 w=3900 candidates=750 probes=1|0.975|179.769| +|eknn-l2lsh|L=175 k=8 w=3900 candidates=750 probes=2|0.976|166.383| +|eknn-l2lsh|L=175 k=8 w=4000 candidates=1000 probes=1|0.976|176.214| +|eknn-l2lsh|L=175 k=7 w=4000 candidates=750 probes=1|0.976|176.770| +|eknn-l2lsh|L=175 k=9 w=4100 candidates=1250 probes=1|0.976|178.650| +|eknn-l2lsh|L=125 k=8 w=4100 candidates=1000 probes=2|0.976|179.341| +|eknn-l2lsh|L=150 k=8 w=4000 candidates=1250 probes=1|0.976|188.064| +|eknn-l2lsh|L=125 k=7 w=3900 candidates=1250 probes=1|0.976|192.985| +|eknn-l2lsh|L=200 k=7 w=3900 candidates=1250 probes=0|0.976|195.296| +|eknn-l2lsh|L=200 k=7 w=3900 candidates=500 probes=2|0.977|139.436| +|eknn-l2lsh|L=200 k=9 w=4100 candidates=750 probes=2|0.977|158.559| +|eknn-l2lsh|L=150 k=7 w=3900 candidates=1000 probes=1|0.977|175.861| +|eknn-l2lsh|L=200 k=7 w=4000 candidates=500 probes=2|0.978|141.893| +|eknn-l2lsh|L=175 k=9 w=4000 candidates=1000 probes=2|0.978|159.401| +|eknn-l2lsh|L=150 k=9 w=4000 candidates=1250 probes=2|0.978|172.674| 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w=3900 candidates=750 probes=1|0.980|169.016| +|eknn-l2lsh|L=200 k=9 w=4100 candidates=1250 probes=1|0.980|169.150| +|eknn-l2lsh|L=150 k=9 w=4100 candidates=1250 probes=2|0.980|171.101| +|eknn-l2lsh|L=125 k=7 w=3900 candidates=1000 probes=2|0.980|176.214| +|eknn-l2lsh|L=175 k=8 w=3900 candidates=1250 probes=1|0.980|177.663| +|eknn-l2lsh|L=125 k=7 w=4100 candidates=1250 probes=1|0.980|182.451| +|eknn-l2lsh|L=200 k=7 w=4000 candidates=750 probes=1|0.981|168.508| +|eknn-l2lsh|L=125 k=8 w=4100 candidates=1250 probes=2|0.981|173.280| +|eknn-l2lsh|L=150 k=7 w=4100 candidates=1000 probes=1|0.981|182.734| +|eknn-l2lsh|L=200 k=9 w=4000 candidates=1000 probes=2|0.982|153.807| +|eknn-l2lsh|L=175 k=9 w=4000 candidates=1250 probes=2|0.982|157.920| +|eknn-l2lsh|L=200 k=7 w=4100 candidates=750 probes=1|0.982|160.596| +|eknn-l2lsh|L=150 k=7 w=4100 candidates=750 probes=2|0.982|161.400| +|eknn-l2lsh|L=150 k=8 w=4100 candidates=1000 probes=2|0.982|161.581| +|eknn-l2lsh|L=200 k=8 w=4100 candidates=1000 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candidates=1000 probes=2|0.991|132.193| +|eknn-l2lsh|L=150 k=7 w=4100 candidates=1250 probes=2|0.991|149.119| +|eknn-l2lsh|L=200 k=7 w=4100 candidates=1250 probes=1|0.991|149.197| +|eknn-l2lsh|L=200 k=7 w=4000 candidates=1000 probes=2|0.992|133.459| +|eknn-l2lsh|L=175 k=7 w=3900 candidates=1250 probes=2|0.992|142.170| +|eknn-l2lsh|L=200 k=7 w=4100 candidates=1000 probes=2|0.993|130.024| +|eknn-l2lsh|L=175 k=7 w=4000 candidates=1250 probes=2|0.993|140.242| +|eknn-l2lsh|L=200 k=7 w=4000 candidates=1250 probes=2|0.994|129.749| +|eknn-l2lsh|L=200 k=7 w=3900 candidates=1250 probes=2|0.994|130.837| +|eknn-l2lsh|L=200 k=7 w=4100 candidates=1250 probes=2|0.995|126.016| From c7efcf14a59c4cdcd15636042aeb6f4e381634ac Mon Sep 17 00:00:00 2001 From: Alex Klibisz Date: Sat, 31 Aug 2024 04:35:21 +0000 Subject: [PATCH 2/4] latest --- docs/pages/performance/fashion-mnist/plot.png | Bin 38999 -> 38986 bytes docs/pages/performance/fashion-mnist/plot.txt | 620 +++++++++--------- 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b/docs/pages/performance/fashion-mnist/plot.txt @@ -93,602 +93,620 @@ Found cached result Found cached result 46: eknn-l2lsh-L=200-k=9-w=3900_candidates=500_probes=0 0.897 273.516 Found cached result - 47: eknn-l2lsh-L=175-k=8-w=4000_candidates=500_probes=0 0.909 272.299 + 47: eknn-l2lsh-L=125-k=9-w=4000_candidates=750_probes=1 0.932 238.150 Found cached result - 48: eknn-l2lsh-L=200-k=7-w=4000_candidates=1000_probes=1 0.987 158.015 + 48: eknn-l2lsh-L=175-k=8-w=4000_candidates=500_probes=0 0.909 272.299 Found cached result - 49: eknn-l2lsh-L=200-k=9-w=4100_candidates=1250_probes=2 0.987 142.536 + 49: eknn-l2lsh-L=200-k=7-w=4000_candidates=1000_probes=1 0.987 158.015 Found cached result - 50: eknn-l2lsh-L=150-k=8-w=4100_candidates=1250_probes=1 0.978 184.691 + 50: eknn-l2lsh-L=200-k=9-w=4100_candidates=1250_probes=2 0.987 142.536 Found cached result - 51: eknn-l2lsh-L=200-k=9-w=4100_candidates=500_probes=2 0.963 165.919 + 51: eknn-l2lsh-L=150-k=8-w=4100_candidates=1250_probes=1 0.978 184.691 Found cached result - 52: eknn-l2lsh-L=175-k=9-w=4100_candidates=750_probes=1 0.959 195.500 + 52: eknn-l2lsh-L=200-k=9-w=4100_candidates=500_probes=2 0.963 165.919 Found cached result - 53: eknn-l2lsh-L=175-k=8-w=3900_candidates=750_probes=0 0.934 256.273 + 53: eknn-l2lsh-L=125-k=9-w=4000_candidates=1000_probes=0 0.907 274.705 Found cached result - 54: eknn-l2lsh-L=125-k=9-w=3900_candidates=750_probes=2 0.948 209.180 + 54: eknn-l2lsh-L=175-k=9-w=4100_candidates=750_probes=1 0.959 195.500 Found cached result - 55: eknn-l2lsh-L=200-k=9-w=4100_candidates=500_probes=0 0.909 260.129 + 55: eknn-l2lsh-L=175-k=8-w=3900_candidates=750_probes=0 0.934 256.273 Found cached result - 56: eknn-l2lsh-L=150-k=9-w=4100_candidates=500_probes=2 0.947 188.774 + 56: eknn-l2lsh-L=125-k=9-w=3900_candidates=750_probes=2 0.948 209.180 Found cached result - 57: eknn-l2lsh-L=150-k=7-w=4100_candidates=1250_probes=2 0.991 149.119 + 57: eknn-l2lsh-L=200-k=9-w=4100_candidates=500_probes=0 0.909 260.129 Found cached result - 58: eknn-l2lsh-L=150-k=9-w=4000_candidates=500_probes=0 0.870 298.232 + 58: eknn-l2lsh-L=150-k=9-w=4100_candidates=500_probes=2 0.947 188.774 Found cached result - 59: eknn-l2lsh-L=150-k=8-w=3900_candidates=500_probes=1 0.933 224.721 + 59: eknn-l2lsh-L=150-k=7-w=4100_candidates=1250_probes=2 0.991 149.119 Found cached result - 60: eknn-l2lsh-L=175-k=7-w=4000_candidates=750_probes=2 0.985 150.557 + 60: eknn-l2lsh-L=150-k=9-w=4000_candidates=500_probes=0 0.870 298.232 Found cached result - 61: eknn-l2lsh-L=175-k=7-w=4000_candidates=500_probes=1 0.960 189.476 + 61: eknn-l2lsh-L=150-k=8-w=3900_candidates=500_probes=1 0.933 224.721 Found cached result - 62: eknn-l2lsh-L=125-k=8-w=4100_candidates=500_probes=1 0.925 236.820 + 62: eknn-l2lsh-L=175-k=7-w=4000_candidates=750_probes=2 0.985 150.557 Found cached result - 63: eknn-l2lsh-L=150-k=9-w=4100_candidates=750_probes=0 0.912 266.915 + 63: eknn-l2lsh-L=175-k=7-w=4000_candidates=500_probes=1 0.960 189.476 Found cached result - 64: eknn-l2lsh-L=125-k=8-w=4100_candidates=1250_probes=0 0.944 239.834 + 64: eknn-l2lsh-L=125-k=8-w=4100_candidates=500_probes=1 0.925 236.820 Found cached result - 65: eknn-l2lsh-L=150-k=9-w=3900_candidates=500_probes=2 0.939 199.486 + 65: eknn-l2lsh-L=150-k=9-w=4100_candidates=750_probes=0 0.912 266.915 Found cached result - 66: eknn-l2lsh-L=175-k=9-w=4000_candidates=1250_probes=2 0.982 157.920 + 66: eknn-l2lsh-L=125-k=8-w=4100_candidates=1250_probes=0 0.944 239.834 Found cached result - 67: eknn-l2lsh-L=125-k=7-w=4100_candidates=1250_probes=0 0.957 224.911 + 67: eknn-l2lsh-L=150-k=9-w=3900_candidates=500_probes=2 0.939 199.486 Found cached result - 68: eknn-l2lsh-L=150-k=8-w=4100_candidates=1000_probes=2 0.982 161.581 + 68: eknn-l2lsh-L=175-k=9-w=4000_candidates=1250_probes=2 0.982 157.920 Found cached result - 69: eknn-l2lsh-L=175-k=8-w=4000_candidates=1000_probes=0 0.953 239.020 + 69: eknn-l2lsh-L=125-k=7-w=4100_candidates=1250_probes=0 0.957 224.911 Found cached result - 70: eknn-l2lsh-L=200-k=7-w=4100_candidates=1250_probes=1 0.991 149.197 + 70: eknn-l2lsh-L=150-k=8-w=4100_candidates=1000_probes=2 0.982 161.581 Found cached result - 71: eknn-l2lsh-L=125-k=7-w=4000_candidates=750_probes=0 0.925 266.401 + 71: eknn-l2lsh-L=175-k=8-w=4000_candidates=1000_probes=0 0.953 239.020 Found cached result - 72: eknn-l2lsh-L=150-k=8-w=3900_candidates=1250_probes=2 0.983 165.732 + 72: eknn-l2lsh-L=200-k=7-w=4100_candidates=1250_probes=1 0.991 149.197 Found cached result - 73: eknn-l2lsh-L=175-k=9-w=3900_candidates=750_probes=1 0.952 206.997 + 73: eknn-l2lsh-L=125-k=7-w=4000_candidates=750_probes=0 0.925 266.401 Found cached result - 74: eknn-l2lsh-L=175-k=9-w=3900_candidates=1250_probes=2 0.980 159.601 + 74: eknn-l2lsh-L=150-k=8-w=3900_candidates=1250_probes=2 0.983 165.732 Found cached result - 75: eknn-l2lsh-L=200-k=9-w=4000_candidates=1000_probes=1 0.972 180.587 + 75: eknn-l2lsh-L=175-k=9-w=3900_candidates=750_probes=1 0.952 206.997 Found cached result - 76: eknn-l2lsh-L=125-k=7-w=4000_candidates=500_probes=1 0.936 222.739 + 76: eknn-l2lsh-L=175-k=9-w=3900_candidates=1250_probes=2 0.980 159.601 Found cached result - 77: eknn-l2lsh-L=175-k=7-w=4000_candidates=1000_probes=2 0.990 144.779 + 77: eknn-l2lsh-L=200-k=9-w=4000_candidates=1000_probes=1 0.972 180.587 Found cached result - 78: eknn-l2lsh-L=200-k=9-w=4100_candidates=1250_probes=0 0.960 221.266 + 78: eknn-l2lsh-L=125-k=7-w=4000_candidates=500_probes=1 0.936 222.739 Found cached result - 79: eknn-l2lsh-L=125-k=7-w=4100_candidates=750_probes=0 0.928 258.768 + 79: eknn-l2lsh-L=175-k=7-w=4000_candidates=1000_probes=2 0.990 144.779 Found cached result - 80: eknn-l2lsh-L=200-k=7-w=4000_candidates=500_probes=1 0.967 172.951 + 80: eknn-l2lsh-L=200-k=9-w=4100_candidates=1250_probes=0 0.960 221.266 Found cached result - 81: eknn-l2lsh-L=200-k=7-w=4100_candidates=750_probes=2 0.989 130.462 + 81: eknn-l2lsh-L=125-k=7-w=4100_candidates=750_probes=0 0.928 258.768 Found cached result - 82: eknn-l2lsh-L=175-k=9-w=4100_candidates=1250_probes=1 0.976 178.650 + 82: eknn-l2lsh-L=200-k=7-w=4000_candidates=500_probes=1 0.967 172.951 Found cached result - 83: eknn-l2lsh-L=150-k=7-w=3900_candidates=1250_probes=2 0.989 145.575 + 83: eknn-l2lsh-L=200-k=7-w=4100_candidates=750_probes=2 0.989 130.462 Found cached result - 84: eknn-l2lsh-L=125-k=9-w=4100_candidates=750_probes=0 0.891 288.164 + 84: eknn-l2lsh-L=175-k=9-w=4100_candidates=1250_probes=1 0.976 178.650 Found cached result - 85: eknn-l2lsh-L=150-k=7-w=4100_candidates=1000_probes=0 0.959 226.857 + 85: eknn-l2lsh-L=150-k=7-w=3900_candidates=1250_probes=2 0.989 145.575 Found cached result - 86: eknn-l2lsh-L=125-k=9-w=4100_candidates=1000_probes=2 0.966 192.040 + 86: eknn-l2lsh-L=125-k=9-w=4100_candidates=750_probes=0 0.891 288.164 Found cached result - 87: eknn-l2lsh-L=175-k=8-w=4000_candidates=1250_probes=1 0.982 174.306 + 87: eknn-l2lsh-L=150-k=7-w=4100_candidates=1000_probes=0 0.959 226.857 Found cached result - 88: eknn-l2lsh-L=150-k=7-w=3900_candidates=1000_probes=2 0.986 152.082 + 88: eknn-l2lsh-L=125-k=9-w=4100_candidates=1000_probes=2 0.966 192.040 Found cached result - 89: eknn-l2lsh-L=125-k=7-w=4000_candidates=1250_probes=2 0.986 165.114 + 89: eknn-l2lsh-L=175-k=8-w=4000_candidates=1250_probes=1 0.982 174.306 Found cached result - 90: eknn-l2lsh-L=200-k=8-w=4100_candidates=1000_probes=0 0.964 220.637 + 90: eknn-l2lsh-L=150-k=7-w=3900_candidates=1000_probes=2 0.986 152.082 Found cached result - 91: eknn-l2lsh-L=150-k=7-w=3900_candidates=1250_probes=0 0.963 211.024 + 91: eknn-l2lsh-L=125-k=7-w=4000_candidates=1250_probes=2 0.986 165.114 Found cached result - 92: eknn-l2lsh-L=200-k=7-w=3900_candidates=1250_probes=2 0.994 130.837 + 92: eknn-l2lsh-L=200-k=8-w=4100_candidates=1000_probes=0 0.964 220.637 Found cached result - 93: eknn-l2lsh-L=125-k=8-w=3900_candidates=500_probes=2 0.940 209.130 + 93: eknn-l2lsh-L=150-k=7-w=3900_candidates=1250_probes=0 0.963 211.024 Found cached result - 94: eknn-l2lsh-L=175-k=7-w=3900_candidates=500_probes=0 0.921 257.151 + 94: eknn-l2lsh-L=200-k=7-w=3900_candidates=1250_probes=2 0.994 130.837 Found cached result - 95: eknn-l2lsh-L=175-k=7-w=4000_candidates=500_probes=0 0.925 254.374 + 95: eknn-l2lsh-L=125-k=8-w=3900_candidates=500_probes=2 0.940 209.130 Found cached result - 96: eknn-l2lsh-L=150-k=7-w=3900_candidates=1250_probes=1 0.983 167.695 + 96: eknn-l2lsh-L=175-k=7-w=3900_candidates=500_probes=0 0.921 257.151 Found cached result - 97: eknn-l2lsh-L=150-k=9-w=3900_candidates=1000_probes=2 0.969 177.270 + 97: eknn-l2lsh-L=175-k=7-w=4000_candidates=500_probes=0 0.925 254.374 Found cached result - 98: eknn-l2lsh-L=125-k=9-w=3900_candidates=500_probes=2 0.924 217.381 + 98: eknn-l2lsh-L=150-k=7-w=3900_candidates=1250_probes=1 0.983 167.695 Found cached result - 99: eknn-l2lsh-L=125-k=7-w=4000_candidates=750_probes=2 0.974 181.437 + 99: eknn-l2lsh-L=150-k=9-w=3900_candidates=1000_probes=2 0.969 177.270 Found cached result -100: eknn-l2lsh-L=175-k=9-w=4100_candidates=1250_probes=0 0.953 227.474 +100: eknn-l2lsh-L=125-k=9-w=3900_candidates=500_probes=2 0.924 217.381 Found cached result -101: eknn-l2lsh-L=200-k=9-w=3900_candidates=750_probes=1 0.960 191.764 +101: eknn-l2lsh-L=125-k=7-w=4000_candidates=750_probes=2 0.974 181.437 Found cached result -102: eknn-l2lsh-L=150-k=9-w=3900_candidates=500_probes=0 0.863 302.352 +102: eknn-l2lsh-L=175-k=9-w=4100_candidates=1250_probes=0 0.953 227.474 Found cached result -103: eknn-l2lsh-L=175-k=9-w=4100_candidates=750_probes=0 0.927 256.851 +103: eknn-l2lsh-L=200-k=9-w=3900_candidates=750_probes=1 0.960 191.764 Found cached result -104: eknn-l2lsh-L=175-k=9-w=4000_candidates=750_probes=1 0.956 200.126 +104: eknn-l2lsh-L=150-k=9-w=3900_candidates=500_probes=0 0.863 302.352 Found cached result -105: eknn-l2lsh-L=150-k=9-w=3900_candidates=750_probes=2 0.959 192.285 +105: eknn-l2lsh-L=175-k=9-w=4100_candidates=750_probes=0 0.927 256.851 Found cached result -106: eknn-l2lsh-L=175-k=9-w=4100_candidates=1000_probes=1 0.970 187.352 +106: eknn-l2lsh-L=175-k=9-w=4000_candidates=750_probes=1 0.956 200.126 Found cached result -107: eknn-l2lsh-L=125-k=7-w=4000_candidates=1250_probes=0 0.954 231.635 +107: eknn-l2lsh-L=150-k=9-w=3900_candidates=750_probes=2 0.959 192.285 Found cached result -108: eknn-l2lsh-L=125-k=8-w=4100_candidates=750_probes=0 0.912 275.778 +108: eknn-l2lsh-L=175-k=9-w=4100_candidates=1000_probes=1 0.970 187.352 Found cached result -109: eknn-l2lsh-L=150-k=9-w=4000_candidates=750_probes=1 0.947 219.975 +109: eknn-l2lsh-L=125-k=7-w=4000_candidates=1250_probes=0 0.954 231.635 Found cached result -110: eknn-l2lsh-L=125-k=7-w=4000_candidates=1000_probes=2 0.982 169.063 +110: eknn-l2lsh-L=125-k=8-w=4100_candidates=750_probes=0 0.912 275.778 Found cached result -111: eknn-l2lsh-L=125-k=7-w=4000_candidates=500_probes=0 0.889 282.787 +111: eknn-l2lsh-L=150-k=9-w=4000_candidates=750_probes=1 0.947 219.975 Found cached result -112: eknn-l2lsh-L=125-k=9-w=3900_candidates=500_probes=0 0.837 323.650 +112: eknn-l2lsh-L=125-k=7-w=4000_candidates=1000_probes=2 0.982 169.063 Found cached result -113: eknn-l2lsh-L=125-k=9-w=4100_candidates=750_probes=1 0.936 232.567 +113: eknn-l2lsh-L=125-k=7-w=4000_candidates=500_probes=0 0.889 282.787 Found cached result -114: eknn-l2lsh-L=200-k=8-w=4100_candidates=500_probes=1 0.959 187.307 +114: eknn-l2lsh-L=125-k=9-w=3900_candidates=500_probes=0 0.837 323.650 Found cached result -115: eknn-l2lsh-L=150-k=7-w=4100_candidates=750_probes=1 0.972 188.104 +115: eknn-l2lsh-L=125-k=9-w=4100_candidates=750_probes=1 0.936 232.567 Found cached result -116: eknn-l2lsh-L=125-k=8-w=3900_candidates=500_probes=0 0.862 309.353 +116: eknn-l2lsh-L=200-k=8-w=4100_candidates=500_probes=1 0.959 187.307 Found cached result -117: eknn-l2lsh-L=150-k=8-w=4100_candidates=1000_probes=1 0.973 194.179 +117: eknn-l2lsh-L=150-k=7-w=4100_candidates=750_probes=1 0.972 188.104 Found cached result -118: eknn-l2lsh-L=125-k=8-w=3900_candidates=1250_probes=2 0.978 179.267 +118: eknn-l2lsh-L=125-k=8-w=3900_candidates=500_probes=0 0.862 309.353 Found cached result -119: eknn-l2lsh-L=175-k=8-w=3900_candidates=1000_probes=0 0.949 240.218 +119: eknn-l2lsh-L=150-k=8-w=4100_candidates=1000_probes=1 0.973 194.179 Found cached result -120: eknn-l2lsh-L=200-k=9-w=4100_candidates=1000_probes=1 0.975 178.742 +120: eknn-l2lsh-L=125-k=8-w=3900_candidates=1250_probes=2 0.978 179.267 Found cached result -121: eknn-l2lsh-L=200-k=8-w=4100_candidates=500_probes=2 0.972 156.062 +121: eknn-l2lsh-L=175-k=8-w=3900_candidates=1000_probes=0 0.949 240.218 Found cached result -122: eknn-l2lsh-L=200-k=7-w=3900_candidates=750_probes=0 0.958 224.903 +122: eknn-l2lsh-L=200-k=9-w=4100_candidates=1000_probes=1 0.975 178.742 Found cached result -123: eknn-l2lsh-L=175-k=7-w=3900_candidates=750_probes=1 0.975 179.769 +123: eknn-l2lsh-L=200-k=8-w=4100_candidates=500_probes=2 0.972 156.062 Found cached result -124: eknn-l2lsh-L=200-k=9-w=3900_candidates=1250_probes=1 0.975 164.301 +124: eknn-l2lsh-L=200-k=7-w=3900_candidates=750_probes=0 0.958 224.903 Found cached result -125: eknn-l2lsh-L=150-k=9-w=4000_candidates=1000_probes=1 0.959 205.758 +125: eknn-l2lsh-L=175-k=7-w=3900_candidates=750_probes=1 0.975 179.769 Found cached result -126: eknn-l2lsh-L=150-k=8-w=3900_candidates=750_probes=2 0.970 181.551 +126: eknn-l2lsh-L=125-k=9-w=4000_candidates=500_probes=2 0.928 220.280 Found cached result -127: eknn-l2lsh-L=175-k=9-w=4100_candidates=750_probes=2 0.972 167.739 +127: eknn-l2lsh-L=200-k=9-w=3900_candidates=1250_probes=1 0.975 164.301 Found cached result -128: eknn-l2lsh-L=200-k=7-w=4000_candidates=750_probes=2 0.988 138.887 +128: eknn-l2lsh-L=150-k=9-w=4000_candidates=1000_probes=1 0.959 205.758 Found cached result -129: eknn-l2lsh-L=200-k=8-w=4100_candidates=1250_probes=0 0.971 209.429 +129: eknn-l2lsh-L=150-k=8-w=3900_candidates=750_probes=2 0.970 181.551 Found cached result -130: eknn-l2lsh-L=200-k=7-w=3900_candidates=500_probes=1 0.965 170.608 +130: eknn-l2lsh-L=175-k=9-w=4100_candidates=750_probes=2 0.972 167.739 Found cached result -131: eknn-l2lsh-L=175-k=7-w=3900_candidates=1000_probes=1 0.982 173.397 +131: eknn-l2lsh-L=200-k=7-w=4000_candidates=750_probes=2 0.988 138.887 Found cached result -132: eknn-l2lsh-L=200-k=7-w=4100_candidates=500_probes=2 0.979 139.828 +132: eknn-l2lsh-L=200-k=8-w=4100_candidates=1250_probes=0 0.971 209.429 Found cached result -133: eknn-l2lsh-L=175-k=9-w=3900_candidates=750_probes=2 0.967 175.534 +133: eknn-l2lsh-L=200-k=7-w=3900_candidates=500_probes=1 0.965 170.608 Found cached result -134: eknn-l2lsh-L=125-k=8-w=4100_candidates=1250_probes=2 0.981 173.280 +134: eknn-l2lsh-L=175-k=7-w=3900_candidates=1000_probes=1 0.982 173.397 Found cached result -135: eknn-l2lsh-L=150-k=8-w=4000_candidates=500_probes=0 0.891 280.175 +135: eknn-l2lsh-L=200-k=7-w=4100_candidates=500_probes=2 0.979 139.828 Found cached result -136: eknn-l2lsh-L=150-k=8-w=4000_candidates=1250_probes=0 0.952 232.135 +136: eknn-l2lsh-L=175-k=9-w=3900_candidates=750_probes=2 0.967 175.534 Found cached result -137: eknn-l2lsh-L=125-k=8-w=3900_candidates=500_probes=1 0.917 242.059 +137: eknn-l2lsh-L=125-k=8-w=4100_candidates=1250_probes=2 0.981 173.280 Found cached result -138: eknn-l2lsh-L=175-k=7-w=3900_candidates=1250_probes=0 0.970 212.504 +138: eknn-l2lsh-L=150-k=8-w=4000_candidates=500_probes=0 0.891 280.175 Found cached result -139: eknn-l2lsh-L=150-k=9-w=4000_candidates=750_probes=0 0.906 275.675 +139: eknn-l2lsh-L=150-k=8-w=4000_candidates=1250_probes=0 0.952 232.135 Found cached result -140: eknn-l2lsh-L=175-k=9-w=4100_candidates=500_probes=0 0.895 277.871 +140: eknn-l2lsh-L=125-k=8-w=3900_candidates=500_probes=1 0.917 242.059 Found cached result -141: eknn-l2lsh-L=175-k=9-w=3900_candidates=1000_probes=1 0.964 190.995 +141: eknn-l2lsh-L=175-k=7-w=3900_candidates=1250_probes=0 0.970 212.504 Found cached result -142: eknn-l2lsh-L=150-k=9-w=4000_candidates=500_probes=2 0.943 198.876 +142: eknn-l2lsh-L=150-k=9-w=4000_candidates=750_probes=0 0.906 275.675 Found cached result -143: eknn-l2lsh-L=125-k=8-w=4100_candidates=1000_probes=1 0.964 203.809 +143: eknn-l2lsh-L=175-k=9-w=4100_candidates=500_probes=0 0.895 277.871 Found cached result -144: eknn-l2lsh-L=175-k=8-w=3900_candidates=500_probes=1 0.945 203.812 +144: eknn-l2lsh-L=175-k=9-w=3900_candidates=1000_probes=1 0.964 190.995 Found cached result -145: eknn-l2lsh-L=200-k=9-w=4000_candidates=1250_probes=1 0.978 172.968 +145: eknn-l2lsh-L=150-k=9-w=4000_candidates=500_probes=2 0.943 198.876 Found cached result -146: eknn-l2lsh-L=175-k=9-w=4000_candidates=1000_probes=2 0.978 159.401 +146: eknn-l2lsh-L=125-k=8-w=4100_candidates=1000_probes=1 0.964 203.809 Found cached result -147: eknn-l2lsh-L=200-k=7-w=4000_candidates=1000_probes=2 0.992 133.459 +147: eknn-l2lsh-L=175-k=8-w=3900_candidates=500_probes=1 0.945 203.812 Found cached result -148: eknn-l2lsh-L=175-k=8-w=3900_candidates=1000_probes=1 0.974 182.369 +148: eknn-l2lsh-L=200-k=9-w=4000_candidates=1250_probes=1 0.978 172.968 Found cached result -149: eknn-l2lsh-L=150-k=8-w=4000_candidates=1000_probes=0 0.942 245.700 +149: eknn-l2lsh-L=175-k=9-w=4000_candidates=1000_probes=2 0.978 159.401 Found cached result -150: eknn-l2lsh-L=125-k=7-w=4100_candidates=1000_probes=0 0.946 244.874 +150: eknn-l2lsh-L=200-k=7-w=4000_candidates=1000_probes=2 0.992 133.459 Found cached result -151: eknn-l2lsh-L=200-k=8-w=4100_candidates=750_probes=2 0.984 148.909 -Computing knn metrics -152: eknn-l2lsh-L=200-k=8-w=4100_candidates=1250_probes=1 0.987 164.391 +151: eknn-l2lsh-L=175-k=8-w=3900_candidates=1000_probes=1 0.974 182.369 +Found cached result +152: eknn-l2lsh-L=150-k=8-w=4000_candidates=1000_probes=0 0.942 245.700 +Found cached result +153: eknn-l2lsh-L=125-k=7-w=4100_candidates=1000_probes=0 0.946 244.874 +Found cached result +154: eknn-l2lsh-L=200-k=8-w=4100_candidates=750_probes=2 0.984 148.909 +Found cached result +155: eknn-l2lsh-L=200-k=8-w=4100_candidates=1250_probes=1 0.987 164.391 +Found cached result +156: eknn-l2lsh-L=175-k=7-w=4000_candidates=750_probes=0 0.952 235.437 Found cached result -153: eknn-l2lsh-L=175-k=7-w=4000_candidates=750_probes=0 0.952 235.437 +157: eknn-l2lsh-L=175-k=9-w=4100_candidates=1250_probes=2 0.984 152.925 Found cached result -154: eknn-l2lsh-L=175-k=9-w=4100_candidates=1250_probes=2 0.984 152.925 +158: eknn-l2lsh-L=150-k=8-w=3900_candidates=500_probes=2 0.952 189.909 Found cached result -155: eknn-l2lsh-L=150-k=8-w=3900_candidates=500_probes=2 0.952 189.909 +159: eknn-l2lsh-L=150-k=8-w=3900_candidates=1000_probes=0 0.937 246.914 Found cached result -156: eknn-l2lsh-L=150-k=8-w=3900_candidates=1000_probes=0 0.937 246.914 +160: eknn-l2lsh-L=125-k=9-w=4100_candidates=500_probes=0 0.851 311.906 Found cached result -157: eknn-l2lsh-L=125-k=9-w=4100_candidates=500_probes=0 0.851 311.906 +161: eknn-l2lsh-L=150-k=8-w=4000_candidates=1250_probes=1 0.976 188.064 Found cached result -158: eknn-l2lsh-L=150-k=8-w=4000_candidates=1250_probes=1 0.976 188.064 +162: eknn-l2lsh-L=200-k=9-w=4000_candidates=1000_probes=2 0.982 153.807 Found cached result -159: eknn-l2lsh-L=200-k=9-w=4000_candidates=1000_probes=2 0.982 153.807 +163: eknn-l2lsh-L=175-k=8-w=3900_candidates=1250_probes=0 0.959 224.310 Found cached result -160: eknn-l2lsh-L=175-k=8-w=3900_candidates=1250_probes=0 0.959 224.310 +164: eknn-l2lsh-L=175-k=7-w=3900_candidates=1250_probes=2 0.992 142.170 Found cached result -161: eknn-l2lsh-L=175-k=7-w=3900_candidates=1250_probes=2 0.992 142.170 +165: eknn-l2lsh-L=150-k=8-w=4000_candidates=1000_probes=2 0.980 168.212 Found cached result -162: eknn-l2lsh-L=150-k=8-w=4000_candidates=1000_probes=2 0.980 168.212 +166: eknn-l2lsh-L=150-k=8-w=4100_candidates=1250_probes=2 0.986 160.360 Found cached result -163: eknn-l2lsh-L=150-k=8-w=4100_candidates=1250_probes=2 0.986 160.360 +167: eknn-l2lsh-L=200-k=7-w=3900_candidates=500_probes=2 0.977 139.436 Found cached result -164: eknn-l2lsh-L=200-k=7-w=3900_candidates=500_probes=2 0.977 139.436 +168: eknn-l2lsh-L=200-k=9-w=4100_candidates=1250_probes=1 0.980 169.150 Found cached result -165: eknn-l2lsh-L=200-k=9-w=4100_candidates=1250_probes=1 0.980 169.150 +169: eknn-l2lsh-L=150-k=8-w=4000_candidates=750_probes=2 0.973 180.931 Found cached result -166: eknn-l2lsh-L=150-k=8-w=4000_candidates=750_probes=2 0.973 180.931 +170: eknn-l2lsh-L=175-k=8-w=3900_candidates=1250_probes=1 0.980 177.663 Found cached result -167: eknn-l2lsh-L=175-k=8-w=3900_candidates=1250_probes=1 0.980 177.663 +171: eknn-l2lsh-L=200-k=7-w=4000_candidates=500_probes=0 0.937 237.096 Found cached result -168: eknn-l2lsh-L=200-k=7-w=4000_candidates=500_probes=0 0.937 237.096 +172: eknn-l2lsh-L=150-k=9-w=4100_candidates=1000_probes=0 0.930 256.525 Found cached result -169: eknn-l2lsh-L=150-k=9-w=4100_candidates=1000_probes=0 0.930 256.525 +173: eknn-l2lsh-L=200-k=9-w=3900_candidates=750_probes=0 0.927 258.428 Found cached result -170: eknn-l2lsh-L=200-k=9-w=3900_candidates=750_probes=0 0.927 258.428 +174: eknn-l2lsh-L=150-k=9-w=4100_candidates=500_probes=0 0.877 285.484 Found cached result -171: eknn-l2lsh-L=150-k=9-w=4100_candidates=500_probes=0 0.877 285.484 +175: eknn-l2lsh-L=125-k=7-w=4100_candidates=1000_probes=2 0.983 168.819 Found cached result -172: eknn-l2lsh-L=125-k=7-w=4100_candidates=1000_probes=2 0.983 168.819 +176: eknn-l2lsh-L=150-k=9-w=4100_candidates=1000_probes=1 0.962 202.603 Found cached result -173: eknn-l2lsh-L=150-k=9-w=4100_candidates=1000_probes=1 0.962 202.603 +177: eknn-l2lsh-L=150-k=7-w=4100_candidates=1000_probes=1 0.981 182.734 Found cached result -174: eknn-l2lsh-L=150-k=7-w=4100_candidates=1000_probes=1 0.981 182.734 +178: eknn-l2lsh-L=125-k=7-w=4100_candidates=1000_probes=1 0.973 196.158 Found cached result -175: eknn-l2lsh-L=125-k=7-w=4100_candidates=1000_probes=1 0.973 196.158 +179: eknn-l2lsh-L=150-k=8-w=3900_candidates=1000_probes=2 0.979 173.302 Found cached result -176: eknn-l2lsh-L=150-k=8-w=3900_candidates=1000_probes=2 0.979 173.302 +180: eknn-l2lsh-L=175-k=9-w=4000_candidates=500_probes=1 0.934 214.027 Found cached result -177: eknn-l2lsh-L=175-k=9-w=4000_candidates=500_probes=1 0.934 214.027 +181: eknn-l2lsh-L=200-k=9-w=3900_candidates=500_probes=2 0.957 170.595 Found cached result -178: eknn-l2lsh-L=200-k=9-w=3900_candidates=500_probes=2 0.957 170.595 +182: eknn-l2lsh-L=175-k=7-w=4000_candidates=750_probes=1 0.976 176.770 Found cached result -179: eknn-l2lsh-L=175-k=7-w=4000_candidates=750_probes=1 0.976 176.770 +183: eknn-l2lsh-L=125-k=8-w=4100_candidates=500_probes=2 0.946 201.687 Found cached result -180: eknn-l2lsh-L=125-k=8-w=4100_candidates=500_probes=2 0.946 201.687 +184: eknn-l2lsh-L=150-k=9-w=4000_candidates=1000_probes=0 0.925 257.201 Found cached result -181: eknn-l2lsh-L=150-k=9-w=4000_candidates=1000_probes=0 0.925 257.201 +185: eknn-l2lsh-L=200-k=9-w=4100_candidates=750_probes=1 0.966 187.450 Found cached result -182: eknn-l2lsh-L=200-k=9-w=4100_candidates=750_probes=1 0.966 187.450 +186: eknn-l2lsh-L=150-k=8-w=4000_candidates=1250_probes=2 0.985 163.613 Found cached result -183: eknn-l2lsh-L=150-k=8-w=4000_candidates=1250_probes=2 0.985 163.613 +187: eknn-l2lsh-L=150-k=7-w=3900_candidates=750_probes=1 0.968 197.120 Found cached result -184: eknn-l2lsh-L=150-k=7-w=3900_candidates=750_probes=1 0.968 197.120 +188: eknn-l2lsh-L=125-k=8-w=3900_candidates=1000_probes=1 0.958 208.477 Found cached result -185: eknn-l2lsh-L=125-k=8-w=3900_candidates=1000_probes=1 0.958 208.477 +189: eknn-l2lsh-L=150-k=7-w=4100_candidates=1250_probes=0 0.968 217.790 Found cached result -186: eknn-l2lsh-L=150-k=7-w=4100_candidates=1250_probes=0 0.968 217.790 +190: eknn-l2lsh-L=175-k=8-w=3900_candidates=500_probes=0 0.904 272.791 Found cached result -187: eknn-l2lsh-L=175-k=8-w=3900_candidates=500_probes=0 0.904 272.791 +191: eknn-l2lsh-L=175-k=8-w=4000_candidates=500_probes=1 0.948 202.060 Found cached result -188: eknn-l2lsh-L=175-k=8-w=4000_candidates=500_probes=1 0.948 202.060 +192: eknn-l2lsh-L=175-k=7-w=3900_candidates=500_probes=2 0.971 158.635 Found cached result -189: eknn-l2lsh-L=175-k=7-w=3900_candidates=500_probes=2 0.971 158.635 +193: eknn-l2lsh-L=200-k=7-w=3900_candidates=750_probes=1 0.980 169.016 Found cached result -190: eknn-l2lsh-L=200-k=7-w=3900_candidates=750_probes=1 0.980 169.016 +194: eknn-l2lsh-L=175-k=9-w=3900_candidates=500_probes=1 0.929 215.929 Found cached result -191: eknn-l2lsh-L=175-k=9-w=3900_candidates=500_probes=1 0.929 215.929 +195: eknn-l2lsh-L=125-k=7-w=3900_candidates=500_probes=1 0.933 225.817 Found cached result -192: eknn-l2lsh-L=125-k=7-w=3900_candidates=500_probes=1 0.933 225.817 +196: eknn-l2lsh-L=200-k=7-w=4100_candidates=750_probes=1 0.982 160.596 Found cached result -193: eknn-l2lsh-L=200-k=7-w=4100_candidates=750_probes=1 0.982 160.596 +197: eknn-l2lsh-L=175-k=7-w=4000_candidates=500_probes=2 0.973 158.915 Found cached result -194: eknn-l2lsh-L=175-k=7-w=4000_candidates=500_probes=2 0.973 158.915 +198: eknn-l2lsh-L=125-k=7-w=3900_candidates=1250_probes=1 0.976 192.985 Found cached result -195: eknn-l2lsh-L=125-k=7-w=3900_candidates=1250_probes=1 0.976 192.985 +199: eknn-l2lsh-L=200-k=7-w=4100_candidates=500_probes=0 0.939 232.796 Found cached result -196: eknn-l2lsh-L=200-k=7-w=4100_candidates=500_probes=0 0.939 232.796 +200: eknn-l2lsh-L=150-k=8-w=4000_candidates=500_probes=2 0.955 180.825 Found cached result -197: eknn-l2lsh-L=150-k=8-w=4000_candidates=500_probes=2 0.955 180.825 +201: eknn-l2lsh-L=150-k=9-w=3900_candidates=750_probes=1 0.942 220.811 Found cached result -198: eknn-l2lsh-L=150-k=9-w=3900_candidates=750_probes=1 0.942 220.811 +202: eknn-l2lsh-L=175-k=7-w=4000_candidates=1000_probes=1 0.984 172.790 Found cached result -199: eknn-l2lsh-L=175-k=7-w=4000_candidates=1000_probes=1 0.984 172.790 +203: eknn-l2lsh-L=175-k=7-w=3900_candidates=1000_probes=2 0.989 148.949 Found cached result -200: eknn-l2lsh-L=175-k=7-w=3900_candidates=1000_probes=2 0.989 148.949 +204: eknn-l2lsh-L=200-k=9-w=4000_candidates=1250_probes=2 0.986 147.703 Found cached result -201: eknn-l2lsh-L=200-k=9-w=4000_candidates=1250_probes=2 0.986 147.703 +205: eknn-l2lsh-L=175-k=9-w=4100_candidates=1000_probes=0 0.943 242.498 Found cached result -202: eknn-l2lsh-L=175-k=9-w=4100_candidates=1000_probes=0 0.943 242.498 +206: eknn-l2lsh-L=200-k=9-w=3900_candidates=1250_probes=2 0.984 142.014 Found cached result -203: eknn-l2lsh-L=200-k=9-w=3900_candidates=1250_probes=2 0.984 142.014 +207: eknn-l2lsh-L=200-k=7-w=4100_candidates=750_probes=0 0.963 214.423 Found cached result -204: eknn-l2lsh-L=200-k=7-w=4100_candidates=750_probes=0 0.963 214.423 +208: eknn-l2lsh-L=150-k=8-w=3900_candidates=500_probes=0 0.886 289.585 Found cached result -205: eknn-l2lsh-L=150-k=8-w=3900_candidates=500_probes=0 0.886 289.585 +209: eknn-l2lsh-L=150-k=7-w=3900_candidates=500_probes=0 0.906 264.575 Found cached result -206: eknn-l2lsh-L=150-k=7-w=3900_candidates=500_probes=0 0.906 264.575 +210: eknn-l2lsh-L=200-k=7-w=3900_candidates=750_probes=2 0.987 141.367 Found cached result -207: eknn-l2lsh-L=200-k=7-w=3900_candidates=750_probes=2 0.987 141.367 +211: eknn-l2lsh-L=150-k=8-w=3900_candidates=750_probes=1 0.956 213.143 Found cached result -208: eknn-l2lsh-L=150-k=8-w=3900_candidates=750_probes=1 0.956 213.143 +212: eknn-l2lsh-L=200-k=9-w=4100_candidates=750_probes=0 0.937 250.330 Found cached result -209: eknn-l2lsh-L=200-k=9-w=4100_candidates=750_probes=0 0.937 250.330 +213: eknn-l2lsh-L=150-k=8-w=4100_candidates=500_probes=2 0.958 183.474 Found cached result -210: eknn-l2lsh-L=150-k=8-w=4100_candidates=500_probes=2 0.958 183.474 +214: eknn-l2lsh-L=125-k=9-w=3900_candidates=750_probes=0 0.877 292.179 Found cached result -211: eknn-l2lsh-L=125-k=9-w=3900_candidates=750_probes=0 0.877 292.179 +215: eknn-l2lsh-L=150-k=8-w=4100_candidates=750_probes=1 0.962 205.715 Found cached result -212: eknn-l2lsh-L=150-k=8-w=4100_candidates=750_probes=1 0.962 205.715 +216: eknn-l2lsh-L=125-k=9-w=3900_candidates=750_probes=1 0.927 239.317 Found cached result -213: eknn-l2lsh-L=125-k=9-w=3900_candidates=750_probes=1 0.927 239.317 +217: eknn-l2lsh-L=150-k=9-w=3900_candidates=500_probes=1 0.916 233.020 Found cached result -214: eknn-l2lsh-L=150-k=9-w=3900_candidates=500_probes=1 0.916 233.020 +218: eknn-l2lsh-L=150-k=9-w=3900_candidates=750_probes=0 0.900 277.357 Found cached result -215: eknn-l2lsh-L=150-k=9-w=3900_candidates=750_probes=0 0.900 277.357 +219: eknn-l2lsh-L=200-k=9-w=4000_candidates=750_probes=1 0.963 189.180 Found cached result -216: eknn-l2lsh-L=200-k=9-w=4000_candidates=750_probes=1 0.963 189.180 +220: eknn-l2lsh-L=200-k=7-w=3900_candidates=1000_probes=2 0.991 132.193 Found cached result -217: eknn-l2lsh-L=200-k=7-w=3900_candidates=1000_probes=2 0.991 132.193 +221: eknn-l2lsh-L=175-k=9-w=4000_candidates=500_probes=0 0.889 286.607 Found cached result -218: eknn-l2lsh-L=175-k=9-w=4000_candidates=500_probes=0 0.889 286.607 +222: eknn-l2lsh-L=200-k=9-w=4000_candidates=500_probes=1 0.944 199.125 Found cached result -219: eknn-l2lsh-L=200-k=9-w=4000_candidates=500_probes=1 0.944 199.125 +223: eknn-l2lsh-L=125-k=7-w=3900_candidates=1000_probes=0 0.939 250.426 Found cached result -220: eknn-l2lsh-L=125-k=7-w=3900_candidates=1000_probes=0 0.939 250.426 +224: eknn-l2lsh-L=125-k=8-w=4100_candidates=500_probes=0 0.874 298.451 Found cached result -221: eknn-l2lsh-L=125-k=8-w=4100_candidates=500_probes=0 0.874 298.451 +225: eknn-l2lsh-L=150-k=9-w=4100_candidates=1000_probes=2 0.975 178.600 Found cached result -222: eknn-l2lsh-L=150-k=9-w=4100_candidates=1000_probes=2 0.975 178.600 +226: eknn-l2lsh-L=125-k=8-w=3900_candidates=1250_probes=1 0.966 203.273 Found cached result -223: eknn-l2lsh-L=125-k=8-w=3900_candidates=1250_probes=1 0.966 203.273 +227: eknn-l2lsh-L=150-k=7-w=3900_candidates=1000_probes=0 0.953 234.020 Found cached result -224: eknn-l2lsh-L=150-k=7-w=3900_candidates=1000_probes=0 0.953 234.020 +228: eknn-l2lsh-L=200-k=7-w=4000_candidates=750_probes=0 0.960 221.518 Found cached result -225: eknn-l2lsh-L=200-k=7-w=4000_candidates=750_probes=0 0.960 221.518 +229: eknn-l2lsh-L=150-k=7-w=4100_candidates=500_probes=2 0.968 169.566 Found cached result -226: eknn-l2lsh-L=150-k=7-w=4100_candidates=500_probes=2 0.968 169.566 +230: eknn-l2lsh-L=150-k=8-w=4100_candidates=750_probes=0 0.929 260.596 +Found cached result +231: eknn-l2lsh-L=125-k=7-w=3900_candidates=750_probes=2 0.972 184.359 +Found cached result +232: eknn-l2lsh-L=125-k=8-w=4100_candidates=750_probes=2 0.967 190.711 +Found cached result +233: eknn-l2lsh-L=175-k=9-w=4000_candidates=1250_probes=1 0.973 183.447 +Found cached result +234: eknn-l2lsh-L=200-k=8-w=4100_candidates=750_probes=1 0.975 177.736 +Computing knn metrics +235: eknn-l2lsh-L=125-k=9-w=4000_candidates=1000_probes=1 0.947 225.280 Found cached result -227: eknn-l2lsh-L=150-k=8-w=4100_candidates=750_probes=0 0.929 260.596 +236: eknn-l2lsh-L=200-k=9-w=4000_candidates=500_probes=0 0.903 269.983 Found cached result -228: eknn-l2lsh-L=125-k=7-w=3900_candidates=750_probes=2 0.972 184.359 +237: eknn-l2lsh-L=175-k=9-w=3900_candidates=1250_probes=0 0.943 232.425 Found cached result -229: eknn-l2lsh-L=125-k=8-w=4100_candidates=750_probes=2 0.967 190.711 +238: eknn-l2lsh-L=175-k=9-w=4000_candidates=750_probes=2 0.970 172.460 Found cached result -230: eknn-l2lsh-L=175-k=9-w=4000_candidates=1250_probes=1 0.973 183.447 +239: eknn-l2lsh-L=200-k=9-w=3900_candidates=500_probes=1 0.940 203.171 Found cached result -231: eknn-l2lsh-L=200-k=8-w=4100_candidates=750_probes=1 0.975 177.736 +240: eknn-l2lsh-L=125-k=8-w=4100_candidates=750_probes=1 0.951 219.195 Found cached result -232: eknn-l2lsh-L=200-k=9-w=4000_candidates=500_probes=0 0.903 269.983 +241: eknn-l2lsh-L=175-k=7-w=4000_candidates=1250_probes=0 0.972 211.460 Found cached result -233: eknn-l2lsh-L=175-k=9-w=3900_candidates=1250_probes=0 0.943 232.425 +242: eknn-l2lsh-L=150-k=9-w=3900_candidates=1250_probes=2 0.975 173.881 Found cached result -234: eknn-l2lsh-L=175-k=9-w=4000_candidates=750_probes=2 0.970 172.460 +243: eknn-l2lsh-L=150-k=7-w=3900_candidates=500_probes=1 0.948 206.707 Found cached result -235: eknn-l2lsh-L=200-k=9-w=3900_candidates=500_probes=1 0.940 203.171 +244: eknn-l2lsh-L=125-k=7-w=3900_candidates=500_probes=0 0.884 289.416 Found cached result -236: eknn-l2lsh-L=125-k=8-w=4100_candidates=750_probes=1 0.951 219.195 +245: eknn-l2lsh-L=200-k=7-w=4100_candidates=1250_probes=2 0.995 126.016 Found cached result -237: eknn-l2lsh-L=175-k=7-w=4000_candidates=1250_probes=0 0.972 211.460 +246: eknn-l2lsh-L=150-k=9-w=4100_candidates=1250_probes=0 0.942 240.364 Found cached result -238: eknn-l2lsh-L=150-k=9-w=3900_candidates=1250_probes=2 0.975 173.881 +247: eknn-l2lsh-L=150-k=9-w=4100_candidates=1250_probes=1 0.970 195.084 Found cached result -239: eknn-l2lsh-L=150-k=7-w=3900_candidates=500_probes=1 0.948 206.707 +248: eknn-l2lsh-L=125-k=8-w=3900_candidates=1250_probes=0 0.935 244.842 Found cached result -240: eknn-l2lsh-L=125-k=7-w=3900_candidates=500_probes=0 0.884 289.416 +249: eknn-l2lsh-L=150-k=7-w=3900_candidates=500_probes=2 0.964 176.316 Found cached result -241: eknn-l2lsh-L=200-k=7-w=4100_candidates=1250_probes=2 0.995 126.016 +250: eknn-l2lsh-L=175-k=8-w=4000_candidates=750_probes=1 0.967 193.286 Found cached result -242: eknn-l2lsh-L=150-k=9-w=4100_candidates=1250_probes=0 0.942 240.364 +251: eknn-l2lsh-L=200-k=9-w=3900_candidates=1000_probes=0 0.942 238.436 Found cached result -243: eknn-l2lsh-L=150-k=9-w=4100_candidates=1250_probes=1 0.970 195.084 +252: eknn-l2lsh-L=200-k=7-w=3900_candidates=1250_probes=1 0.990 154.557 Found cached result -244: eknn-l2lsh-L=125-k=8-w=3900_candidates=1250_probes=0 0.935 244.842 +253: eknn-l2lsh-L=200-k=7-w=4100_candidates=1000_probes=2 0.993 130.024 Found cached result -245: eknn-l2lsh-L=150-k=7-w=3900_candidates=500_probes=2 0.964 176.316 +254: eknn-l2lsh-L=150-k=8-w=3900_candidates=1250_probes=1 0.974 190.636 Found cached result -246: eknn-l2lsh-L=175-k=8-w=4000_candidates=750_probes=1 0.967 193.286 +255: eknn-l2lsh-L=175-k=7-w=4000_candidates=1250_probes=2 0.993 140.242 Found cached result -247: eknn-l2lsh-L=200-k=9-w=3900_candidates=1000_probes=0 0.942 238.436 +256: eknn-l2lsh-L=200-k=7-w=3900_candidates=1000_probes=0 0.969 209.121 Found cached result -248: eknn-l2lsh-L=200-k=7-w=3900_candidates=1250_probes=1 0.990 154.557 +257: eknn-l2lsh-L=150-k=8-w=3900_candidates=750_probes=0 0.920 269.915 Found cached result -249: eknn-l2lsh-L=200-k=7-w=4100_candidates=1000_probes=2 0.993 130.024 +258: eknn-l2lsh-L=175-k=8-w=3900_candidates=500_probes=2 0.962 174.431 Found cached result -250: eknn-l2lsh-L=150-k=8-w=3900_candidates=1250_probes=1 0.974 190.636 +259: eknn-l2lsh-L=125-k=9-w=3900_candidates=1250_probes=1 0.952 213.370 Found cached result -251: eknn-l2lsh-L=175-k=7-w=4000_candidates=1250_probes=2 0.993 140.242 +260: eknn-l2lsh-L=125-k=7-w=3900_candidates=750_probes=1 0.958 213.409 Found cached result -252: eknn-l2lsh-L=200-k=7-w=3900_candidates=1000_probes=0 0.969 209.121 +261: eknn-l2lsh-L=150-k=8-w=4000_candidates=750_probes=0 0.925 269.618 Found cached result -253: eknn-l2lsh-L=150-k=8-w=3900_candidates=750_probes=0 0.920 269.915 +262: eknn-l2lsh-L=200-k=7-w=3900_candidates=500_probes=0 0.934 243.251 Found cached result -254: eknn-l2lsh-L=175-k=8-w=3900_candidates=500_probes=2 0.962 174.431 +263: eknn-l2lsh-L=200-k=7-w=4100_candidates=1250_probes=0 0.979 197.590 Found cached result -255: eknn-l2lsh-L=125-k=9-w=3900_candidates=1250_probes=1 0.952 213.370 +264: eknn-l2lsh-L=150-k=9-w=4000_candidates=500_probes=1 0.921 232.303 Found cached result -256: eknn-l2lsh-L=125-k=7-w=3900_candidates=750_probes=1 0.958 213.409 +265: eknn-l2lsh-L=150-k=7-w=3900_candidates=1000_probes=1 0.977 175.861 Found cached result -257: eknn-l2lsh-L=150-k=8-w=4000_candidates=750_probes=0 0.925 269.618 +266: eknn-l2lsh-L=175-k=7-w=3900_candidates=500_probes=1 0.958 194.054 Found cached result -258: eknn-l2lsh-L=200-k=7-w=3900_candidates=500_probes=0 0.934 243.251 +267: eknn-l2lsh-L=125-k=9-w=4000_candidates=750_probes=2 0.952 208.470 Found cached result -259: eknn-l2lsh-L=200-k=7-w=4100_candidates=1250_probes=0 0.979 197.590 +268: eknn-l2lsh-L=200-k=9-w=4100_candidates=1000_probes=0 0.951 234.136 Found cached result -260: eknn-l2lsh-L=150-k=9-w=4000_candidates=500_probes=1 0.921 232.303 +269: eknn-l2lsh-L=175-k=8-w=4000_candidates=750_probes=0 0.938 253.939 Found cached result -261: eknn-l2lsh-L=150-k=7-w=3900_candidates=1000_probes=1 0.977 175.861 +270: eknn-l2lsh-L=200-k=7-w=3900_candidates=1250_probes=0 0.976 195.296 Found cached result -262: eknn-l2lsh-L=175-k=7-w=3900_candidates=500_probes=1 0.958 194.054 +271: eknn-l2lsh-L=150-k=8-w=4100_candidates=500_probes=0 0.896 284.495 Found cached result -263: eknn-l2lsh-L=200-k=9-w=4100_candidates=1000_probes=0 0.951 234.136 +272: eknn-l2lsh-L=150-k=9-w=4100_candidates=750_probes=2 0.966 184.985 Found cached result -264: eknn-l2lsh-L=175-k=8-w=4000_candidates=750_probes=0 0.938 253.939 +273: eknn-l2lsh-L=175-k=9-w=4100_candidates=1000_probes=2 0.980 159.869 Found cached result -265: eknn-l2lsh-L=200-k=7-w=3900_candidates=1250_probes=0 0.976 195.296 +274: eknn-l2lsh-L=150-k=9-w=4000_candidates=1250_probes=0 0.937 242.487 Found cached result -266: eknn-l2lsh-L=150-k=8-w=4100_candidates=500_probes=0 0.896 284.495 +275: eknn-l2lsh-L=125-k=9-w=3900_candidates=1250_probes=0 0.914 259.536 Found cached result -267: eknn-l2lsh-L=150-k=9-w=4100_candidates=750_probes=2 0.966 184.985 +276: eknn-l2lsh-L=175-k=8-w=4000_candidates=500_probes=2 0.964 170.576 Found cached result -268: eknn-l2lsh-L=175-k=9-w=4100_candidates=1000_probes=2 0.980 159.869 +277: eknn-l2lsh-L=125-k=7-w=4100_candidates=500_probes=2 0.957 186.862 Found cached result -269: eknn-l2lsh-L=150-k=9-w=4000_candidates=1250_probes=0 0.937 242.487 +278: eknn-l2lsh-L=200-k=7-w=4000_candidates=1250_probes=2 0.994 129.749 Found cached result -270: eknn-l2lsh-L=125-k=9-w=3900_candidates=1250_probes=0 0.914 259.536 +279: eknn-l2lsh-L=125-k=9-w=3900_candidates=1000_probes=1 0.943 227.127 Found cached result -271: eknn-l2lsh-L=175-k=8-w=4000_candidates=500_probes=2 0.964 170.576 +280: eknn-l2lsh-L=200-k=8-w=4100_candidates=500_probes=0 0.926 248.573 Found cached result -272: eknn-l2lsh-L=125-k=7-w=4100_candidates=500_probes=2 0.957 186.862 +281: eknn-l2lsh-L=200-k=9-w=4000_candidates=500_probes=2 0.961 165.952 Found cached result -273: eknn-l2lsh-L=200-k=7-w=4000_candidates=1250_probes=2 0.994 129.749 +282: eknn-l2lsh-L=125-k=9-w=4100_candidates=500_probes=2 0.932 213.809 Found cached result -274: eknn-l2lsh-L=125-k=9-w=3900_candidates=1000_probes=1 0.943 227.127 +283: eknn-l2lsh-L=125-k=7-w=3900_candidates=750_probes=0 0.920 269.825 Found cached result -275: eknn-l2lsh-L=200-k=8-w=4100_candidates=500_probes=0 0.926 248.573 +284: eknn-l2lsh-L=125-k=7-w=4000_candidates=500_probes=2 0.955 191.810 Found cached result -276: eknn-l2lsh-L=200-k=9-w=4000_candidates=500_probes=2 0.961 165.952 +285: eknn-l2lsh-L=125-k=7-w=4100_candidates=750_probes=1 0.962 204.999 Found cached result -277: eknn-l2lsh-L=125-k=9-w=4100_candidates=500_probes=2 0.932 213.809 +286: eknn-l2lsh-L=125-k=9-w=4000_candidates=500_probes=1 0.902 255.236 Found cached result -278: eknn-l2lsh-L=125-k=7-w=3900_candidates=750_probes=0 0.920 269.825 +287: eknn-l2lsh-L=150-k=9-w=4100_candidates=750_probes=1 0.950 213.884 Found cached result -279: eknn-l2lsh-L=125-k=7-w=4000_candidates=500_probes=2 0.955 191.810 +288: eknn-l2lsh-L=125-k=9-w=3900_candidates=1000_probes=0 0.900 276.969 Found cached result -280: eknn-l2lsh-L=125-k=7-w=4100_candidates=750_probes=1 0.962 204.999 +289: eknn-l2lsh-L=150-k=8-w=4100_candidates=1250_probes=0 0.956 231.391 Found cached result -281: eknn-l2lsh-L=150-k=9-w=4100_candidates=750_probes=1 0.950 213.884 +290: eknn-l2lsh-L=150-k=8-w=4000_candidates=500_probes=1 0.937 209.724 Found cached result -282: eknn-l2lsh-L=125-k=9-w=3900_candidates=1000_probes=0 0.900 276.969 +291: eknn-l2lsh-L=125-k=7-w=4100_candidates=500_probes=0 0.892 277.219 Found cached result -283: eknn-l2lsh-L=150-k=8-w=4100_candidates=1250_probes=0 0.956 231.391 +292: eknn-l2lsh-L=200-k=7-w=4000_candidates=500_probes=2 0.978 141.893 Found cached result -284: eknn-l2lsh-L=150-k=8-w=4000_candidates=500_probes=1 0.937 209.724 +293: eknn-l2lsh-L=175-k=8-w=3900_candidates=1000_probes=2 0.983 157.473 Found cached result -285: eknn-l2lsh-L=125-k=7-w=4100_candidates=500_probes=0 0.892 277.219 +294: eknn-l2lsh-L=175-k=7-w=3900_candidates=1000_probes=0 0.962 222.959 Found cached result -286: eknn-l2lsh-L=200-k=7-w=4000_candidates=500_probes=2 0.978 141.893 +295: eknn-l2lsh-L=125-k=8-w=4100_candidates=1000_probes=2 0.976 179.341 Found cached result -287: eknn-l2lsh-L=175-k=8-w=3900_candidates=1000_probes=2 0.983 157.473 +296: eknn-l2lsh-L=125-k=8-w=4100_candidates=1250_probes=1 0.971 195.239 Found cached result -288: eknn-l2lsh-L=175-k=7-w=3900_candidates=1000_probes=0 0.962 222.959 +297: eknn-l2lsh-L=200-k=7-w=4000_candidates=1000_probes=0 0.971 209.860 Found cached result -289: eknn-l2lsh-L=125-k=8-w=4100_candidates=1000_probes=2 0.976 179.341 +298: eknn-l2lsh-L=175-k=9-w=3900_candidates=1000_probes=2 0.975 167.418 Found cached result -290: eknn-l2lsh-L=125-k=8-w=4100_candidates=1250_probes=1 0.971 195.239 +299: eknn-l2lsh-L=125-k=8-w=4100_candidates=1000_probes=0 0.932 251.971 Found cached result -291: eknn-l2lsh-L=200-k=7-w=4000_candidates=1000_probes=0 0.971 209.860 +300: eknn-l2lsh-L=125-k=7-w=3900_candidates=1250_probes=0 0.951 235.937 Found cached result -292: eknn-l2lsh-L=175-k=9-w=3900_candidates=1000_probes=2 0.975 167.418 +301: eknn-l2lsh-L=200-k=9-w=3900_candidates=750_probes=2 0.973 163.680 Found cached result -293: eknn-l2lsh-L=125-k=8-w=4100_candidates=1000_probes=0 0.932 251.971 +302: eknn-l2lsh-L=175-k=9-w=4000_candidates=1250_probes=0 0.948 225.770 Found cached result -294: eknn-l2lsh-L=125-k=7-w=3900_candidates=1250_probes=0 0.951 235.937 +303: eknn-l2lsh-L=200-k=9-w=3900_candidates=1000_probes=1 0.969 184.638 Found cached result -295: eknn-l2lsh-L=200-k=9-w=3900_candidates=750_probes=2 0.973 163.680 +304: eknn-l2lsh-L=200-k=9-w=4100_candidates=1000_probes=2 0.984 152.098 Found cached result -296: eknn-l2lsh-L=175-k=9-w=4000_candidates=1250_probes=0 0.948 225.770 +305: eknn-l2lsh-L=125-k=7-w=4100_candidates=1250_probes=1 0.980 182.451 Found cached result -297: eknn-l2lsh-L=200-k=9-w=3900_candidates=1000_probes=1 0.969 184.638 +306: eknn-l2lsh-L=175-k=9-w=3900_candidates=1000_probes=0 0.933 248.932 Found cached result -298: eknn-l2lsh-L=200-k=9-w=4100_candidates=1000_probes=2 0.984 152.098 +307: eknn-l2lsh-L=125-k=9-w=4000_candidates=500_probes=0 0.845 319.979 Found cached result -299: eknn-l2lsh-L=125-k=7-w=4100_candidates=1250_probes=1 0.980 182.451 +308: eknn-l2lsh-L=150-k=7-w=4100_candidates=1250_probes=1 0.985 173.447 Found cached result -300: eknn-l2lsh-L=175-k=9-w=3900_candidates=1000_probes=0 0.933 248.932 +309: eknn-l2lsh-L=175-k=9-w=3900_candidates=500_probes=0 0.882 282.479 Found cached result -301: eknn-l2lsh-L=150-k=7-w=4100_candidates=1250_probes=1 0.985 173.447 +310: eknn-l2lsh-L=200-k=9-w=3900_candidates=1250_probes=0 0.952 216.606 Found cached result -302: eknn-l2lsh-L=175-k=9-w=3900_candidates=500_probes=0 0.882 282.479 +311: eknn-l2lsh-L=125-k=9-w=4100_candidates=1250_probes=2 0.973 183.616 Found cached result -303: eknn-l2lsh-L=200-k=9-w=3900_candidates=1250_probes=0 0.952 216.606 +312: eknn-l2lsh-L=175-k=8-w=4000_candidates=1250_probes=0 0.962 222.790 Found cached result -304: eknn-l2lsh-L=125-k=9-w=4100_candidates=1250_probes=2 0.973 183.616 +313: eknn-l2lsh-L=175-k=8-w=3900_candidates=750_probes=2 0.976 166.383 Found cached result -305: eknn-l2lsh-L=175-k=8-w=4000_candidates=1250_probes=0 0.962 222.790 +314: eknn-l2lsh-L=175-k=9-w=4000_candidates=1000_probes=1 0.967 187.608 Found cached result -306: eknn-l2lsh-L=175-k=8-w=3900_candidates=750_probes=2 0.976 166.383 +315: eknn-l2lsh-L=200-k=8-w=4100_candidates=750_probes=0 0.951 237.657 Found cached result -307: eknn-l2lsh-L=175-k=9-w=4000_candidates=1000_probes=1 0.967 187.608 +316: eknn-l2lsh-L=175-k=7-w=3900_candidates=1250_probes=1 0.987 165.410 Found cached result -308: eknn-l2lsh-L=200-k=8-w=4100_candidates=750_probes=0 0.951 237.657 +317: eknn-l2lsh-L=125-k=7-w=4000_candidates=750_probes=1 0.960 209.464 Found cached result -309: eknn-l2lsh-L=175-k=7-w=3900_candidates=1250_probes=1 0.987 165.410 +318: eknn-l2lsh-L=125-k=9-w=4000_candidates=750_probes=0 0.885 294.792 Found cached result -310: eknn-l2lsh-L=125-k=7-w=4000_candidates=750_probes=1 0.960 209.464 +319: 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eknn-l2lsh-L=175-k=9-w=3900_candidates=500_probes=2 0.949 183.198 Found cached result -317: eknn-l2lsh-L=175-k=9-w=3900_candidates=500_probes=2 0.949 183.198 +326: eknn-l2lsh-L=125-k=7-w=4000_candidates=1250_probes=1 0.978 189.013 Found cached result -318: eknn-l2lsh-L=125-k=7-w=4000_candidates=1250_probes=1 0.978 189.013 +327: eknn-l2lsh-L=150-k=8-w=4100_candidates=750_probes=2 0.975 175.020 Found cached result -319: eknn-l2lsh-L=150-k=8-w=4100_candidates=750_probes=2 0.975 175.020 +328: eknn-l2lsh-L=200-k=9-w=4000_candidates=1000_probes=0 0.947 238.454 Found cached result -320: eknn-l2lsh-L=200-k=9-w=4000_candidates=1000_probes=0 0.947 238.454 +329: eknn-l2lsh-L=200-k=8-w=4100_candidates=1250_probes=2 0.992 137.316 Found cached result -321: eknn-l2lsh-L=125-k=9-w=4100_candidates=1250_probes=0 0.926 250.285 +330: eknn-l2lsh-L=125-k=9-w=4100_candidates=1250_probes=0 0.926 250.285 Found cached result -322: eknn-l2lsh-L=125-k=8-w=3900_candidates=1000_probes=0 0.922 261.355 +331: eknn-l2lsh-L=125-k=8-w=3900_candidates=1000_probes=0 0.922 261.355 Found cached result -323: eknn-l2lsh-L=200-k=9-w=4000_candidates=750_probes=0 0.932 245.158 +332: eknn-l2lsh-L=200-k=9-w=4000_candidates=750_probes=0 0.932 245.158 Found cached result -324: eknn-l2lsh-L=175-k=9-w=4000_candidates=750_probes=0 0.921 263.695 +333: eknn-l2lsh-L=175-k=9-w=4000_candidates=750_probes=0 0.921 263.695 Found cached result -325: eknn-l2lsh-L=125-k=7-w=3900_candidates=1000_probes=1 0.970 200.830 +334: eknn-l2lsh-L=125-k=7-w=3900_candidates=1000_probes=1 0.970 200.830 Found cached result -326: eknn-l2lsh-L=200-k=9-w=4000_candidates=750_probes=2 0.975 159.587 +335: eknn-l2lsh-L=200-k=9-w=4000_candidates=750_probes=2 0.975 159.587 Found cached result -327: eknn-l2lsh-L=125-k=9-w=4100_candidates=1000_probes=1 0.951 219.441 +336: eknn-l2lsh-L=125-k=9-w=4100_candidates=1000_probes=1 0.951 219.441 Found cached result -328: eknn-l2lsh-L=150-k=7-w=4100_candidates=1000_probes=2 0.988 154.745 +337: eknn-l2lsh-L=150-k=7-w=4100_candidates=1000_probes=2 0.988 154.745 Found cached result -329: eknn-l2lsh-L=175-k=9-w=3900_candidates=750_probes=0 0.915 267.361 +338: eknn-l2lsh-L=175-k=9-w=3900_candidates=750_probes=0 0.915 267.361 Found cached result -330: eknn-l2lsh-L=150-k=8-w=4100_candidates=1000_probes=0 0.945 245.532 +339: eknn-l2lsh-L=150-k=8-w=4100_candidates=1000_probes=0 0.945 245.532 Found cached result -331: eknn-l2lsh-L=125-k=8-w=3900_candidates=750_probes=0 0.901 278.315 +340: eknn-l2lsh-L=125-k=8-w=3900_candidates=750_probes=0 0.901 278.315 Found cached result -332: eknn-l2lsh-L=150-k=9-w=3900_candidates=1250_probes=1 0.963 198.103 +341: eknn-l2lsh-L=150-k=9-w=3900_candidates=1250_probes=1 0.963 198.103 Found cached result -333: eknn-l2lsh-L=150-k=8-w=3900_candidates=1250_probes=0 0.948 237.059 +342: eknn-l2lsh-L=150-k=8-w=3900_candidates=1250_probes=0 0.948 237.059 Found cached result -334: eknn-l2lsh-L=125-k=9-w=3900_candidates=1000_probes=2 0.960 201.594 +343: eknn-l2lsh-L=125-k=9-w=3900_candidates=1000_probes=2 0.960 201.594 Found cached result -335: eknn-l2lsh-L=150-k=9-w=4100_candidates=500_probes=1 0.926 222.971 +344: eknn-l2lsh-L=150-k=9-w=4100_candidates=500_probes=1 0.926 222.971 Found cached result -336: eknn-l2lsh-L=150-k=9-w=3900_candidates=1250_probes=0 0.931 244.528 +345: eknn-l2lsh-L=150-k=9-w=3900_candidates=1250_probes=0 0.931 244.528 Found cached result -337: eknn-l2lsh-L=175-k=7-w=3900_candidates=750_probes=0 0.949 234.738 +346: eknn-l2lsh-L=175-k=7-w=3900_candidates=750_probes=0 0.949 234.738 Found cached result -338: eknn-l2lsh-L=150-k=7-w=4100_candidates=500_probes=1 0.953 203.767 +347: eknn-l2lsh-L=150-k=7-w=4100_candidates=500_probes=1 0.953 203.767 Found cached result -339: eknn-l2lsh-L=125-k=7-w=3900_candidates=1250_probes=2 0.985 166.714 +348: eknn-l2lsh-L=125-k=7-w=3900_candidates=1250_probes=2 0.985 166.714 Found cached result -340: eknn-l2lsh-L=200-k=9-w=3900_candidates=1000_probes=2 0.980 149.577 +349: eknn-l2lsh-L=200-k=9-w=3900_candidates=1000_probes=2 0.980 149.577 Found cached result -341: eknn-l2lsh-L=200-k=7-w=4100_candidates=1000_probes=1 0.988 154.212 +350: eknn-l2lsh-L=200-k=7-w=4100_candidates=1000_probes=1 0.988 154.212 Found cached result -342: eknn-l2lsh-L=150-k=9-w=3900_candidates=1000_probes=1 0.955 209.590 +351: eknn-l2lsh-L=150-k=9-w=3900_candidates=1000_probes=1 0.955 209.590 Found cached result -343: eknn-l2lsh-L=200-k=9-w=4100_candidates=750_probes=2 0.977 158.559 +352: eknn-l2lsh-L=200-k=9-w=4100_candidates=750_probes=2 0.977 158.559 Found cached result -344: eknn-l2lsh-L=150-k=8-w=4000_candidates=750_probes=1 0.959 210.001 +353: eknn-l2lsh-L=150-k=8-w=4000_candidates=750_probes=1 0.959 210.001 Found cached result -345: eknn-l2lsh-L=125-k=9-w=4100_candidates=1000_probes=0 0.912 270.403 +354: eknn-l2lsh-L=125-k=9-w=4100_candidates=1000_probes=0 0.912 270.403 Found cached result -346: eknn-l2lsh-L=125-k=7-w=4000_candidates=1000_probes=0 0.943 246.386 +355: eknn-l2lsh-L=125-k=7-w=4000_candidates=1000_probes=0 0.943 246.386 diff --git a/docs/pages/performance/fashion-mnist/results.md b/docs/pages/performance/fashion-mnist/results.md index 164174a4..896da4bb 100644 --- a/docs/pages/performance/fashion-mnist/results.md +++ b/docs/pages/performance/fashion-mnist/results.md @@ -1,6 +1,7 @@ |Model|Parameters|Recall|Queries per Second| |---|---|---|---| |eknn-l2lsh|L=125 k=9 w=3900 candidates=500 probes=0|0.837|323.650| +|eknn-l2lsh|L=125 k=9 w=4000 candidates=500 probes=0|0.845|319.979| |eknn-l2lsh|L=125 k=9 w=4100 candidates=500 probes=0|0.851|311.906| |eknn-l2lsh|L=125 k=8 w=3900 candidates=500 probes=0|0.862|309.353| |eknn-l2lsh|L=150 k=9 w=3900 candidates=500 probes=0|0.863|302.352| @@ -10,6 +11,7 @@ |eknn-l2lsh|L=125 k=9 w=3900 candidates=750 probes=0|0.877|292.179| |eknn-l2lsh|L=175 k=9 w=3900 candidates=500 probes=0|0.882|282.479| |eknn-l2lsh|L=125 k=7 w=3900 candidates=500 probes=0|0.884|289.416| +|eknn-l2lsh|L=125 k=9 w=4000 candidates=750 probes=0|0.885|294.792| |eknn-l2lsh|L=150 k=8 w=3900 candidates=500 probes=0|0.886|289.585| |eknn-l2lsh|L=125 k=7 w=4000 candidates=500 probes=0|0.889|282.787| |eknn-l2lsh|L=175 k=9 w=4000 candidates=500 probes=0|0.889|286.607| @@ -23,11 +25,13 @@ |eknn-l2lsh|L=125 k=9 w=3900 candidates=1000 probes=0|0.900|276.969| |eknn-l2lsh|L=150 k=9 w=3900 candidates=750 probes=0|0.900|277.357| |eknn-l2lsh|L=125 k=8 w=3900 candidates=750 probes=0|0.901|278.315| +|eknn-l2lsh|L=125 k=9 w=4000 candidates=500 probes=1|0.902|255.236| |eknn-l2lsh|L=200 k=9 w=4000 candidates=500 probes=0|0.903|269.983| |eknn-l2lsh|L=175 k=8 w=3900 candidates=500 probes=0|0.904|272.791| |eknn-l2lsh|L=150 k=7 w=3900 candidates=500 probes=0|0.906|264.575| |eknn-l2lsh|L=150 k=9 w=4000 candidates=750 probes=0|0.906|275.675| |eknn-l2lsh|L=125 k=9 w=4100 candidates=500 probes=1|0.907|248.027| +|eknn-l2lsh|L=125 k=9 w=4000 candidates=1000 probes=0|0.907|274.705| |eknn-l2lsh|L=200 k=9 w=4100 candidates=500 probes=0|0.909|260.129| |eknn-l2lsh|L=175 k=8 w=4000 candidates=500 probes=0|0.909|272.299| |eknn-l2lsh|L=150 k=9 w=4100 candidates=750 probes=0|0.912|266.915| @@ -57,12 +61,14 @@ |eknn-l2lsh|L=125 k=9 w=3900 candidates=750 probes=1|0.927|239.317| |eknn-l2lsh|L=175 k=9 w=4100 candidates=750 probes=0|0.927|256.851| |eknn-l2lsh|L=200 k=9 w=3900 candidates=750 probes=0|0.927|258.428| +|eknn-l2lsh|L=125 k=9 w=4000 candidates=500 probes=2|0.928|220.280| |eknn-l2lsh|L=125 k=7 w=4100 candidates=750 probes=0|0.928|258.768| |eknn-l2lsh|L=175 k=9 w=3900 candidates=500 probes=1|0.929|215.929| |eknn-l2lsh|L=150 k=8 w=4100 candidates=750 probes=0|0.929|260.596| |eknn-l2lsh|L=150 k=9 w=4100 candidates=1000 probes=0|0.930|256.525| |eknn-l2lsh|L=150 k=9 w=3900 candidates=1250 probes=0|0.931|244.528| |eknn-l2lsh|L=125 k=9 w=4100 candidates=500 probes=2|0.932|213.809| +|eknn-l2lsh|L=125 k=9 w=4000 candidates=750 probes=1|0.932|238.150| |eknn-l2lsh|L=200 k=9 w=4000 candidates=750 probes=0|0.932|245.158| |eknn-l2lsh|L=125 k=8 w=4100 candidates=1000 probes=0|0.932|251.971| |eknn-l2lsh|L=150 k=8 w=3900 candidates=500 probes=1|0.933|224.721| @@ -110,6 +116,7 @@ |eknn-l2lsh|L=150 k=9 w=4100 candidates=500 probes=2|0.947|188.774| |eknn-l2lsh|L=200 k=9 w=4100 candidates=500 probes=1|0.947|197.100| |eknn-l2lsh|L=150 k=9 w=4000 candidates=750 probes=1|0.947|219.975| +|eknn-l2lsh|L=125 k=9 w=4000 candidates=1000 probes=1|0.947|225.280| |eknn-l2lsh|L=200 k=9 w=4000 candidates=1000 probes=0|0.947|238.454| |eknn-l2lsh|L=175 k=8 w=4000 candidates=500 probes=1|0.948|202.060| |eknn-l2lsh|L=150 k=7 w=3900 candidates=500 probes=1|0.948|206.707| @@ -127,6 +134,7 @@ |eknn-l2lsh|L=200 k=8 w=4100 candidates=750 probes=0|0.951|237.657| |eknn-l2lsh|L=150 k=8 w=3900 candidates=500 probes=2|0.952|189.909| |eknn-l2lsh|L=175 k=9 w=3900 candidates=750 probes=1|0.952|206.997| +|eknn-l2lsh|L=125 k=9 w=4000 candidates=750 probes=2|0.952|208.470| |eknn-l2lsh|L=125 k=9 w=3900 candidates=1250 probes=1|0.952|213.370| |eknn-l2lsh|L=200 k=9 w=3900 candidates=1250 probes=0|0.952|216.606| |eknn-l2lsh|L=150 k=8 w=4000 candidates=1250 probes=0|0.952|232.135| @@ -341,6 +349,7 @@ |eknn-l2lsh|L=150 k=7 w=4100 candidates=1250 probes=2|0.991|149.119| |eknn-l2lsh|L=200 k=7 w=4100 candidates=1250 probes=1|0.991|149.197| |eknn-l2lsh|L=200 k=7 w=4000 candidates=1000 probes=2|0.992|133.459| +|eknn-l2lsh|L=200 k=8 w=4100 candidates=1250 probes=2|0.992|137.316| |eknn-l2lsh|L=175 k=7 w=3900 candidates=1250 probes=2|0.992|142.170| |eknn-l2lsh|L=200 k=7 w=4100 candidates=1000 probes=2|0.993|130.024| |eknn-l2lsh|L=175 k=7 w=4000 candidates=1250 probes=2|0.993|140.242| From c3709f4560f50222c0126cbf64838910cb4632bc Mon Sep 17 00:00:00 2001 From: Alex Klibisz Date: Sat, 31 Aug 2024 05:02:29 +0000 Subject: [PATCH 3/4] fewer results --- ann-benchmarks/config.yml | 38 +- docs/pages/performance/fashion-mnist/plot.b64 | 2 +- docs/pages/performance/fashion-mnist/plot.png | Bin 38986 -> 41761 bytes .../performance/fashion-mnist/results.md | 359 +----------------- 4 files changed, 23 insertions(+), 376 deletions(-) diff --git a/ann-benchmarks/config.yml b/ann-benchmarks/config.yml index d8689a1b..c4e34379 100644 --- a/ann-benchmarks/config.yml +++ b/ann-benchmarks/config.yml @@ -1,22 +1,22 @@ float: any: - - base_args: ['@metric', '@dimension'] - constructor: Exact - disabled: true - docker_tag: ann-benchmarks-elastiknn - module: ann_benchmarks.algorithms.elastiknn - name: elastiknn-exact - run_groups: - exact: - args: [] + - base_args: ['@metric', '@dimension'] + constructor: Exact + disabled: true + docker_tag: ann-benchmarks-elastiknn + module: ann_benchmarks.algorithms.elastiknn + name: elastiknn-exact + run_groups: + exact: + args: [] euclidean: - - base_args: [] - constructor: L2Lsh - disabled: true - docker_tag: ann-benchmarks-elastiknn - module: ann_benchmarks.algorithms.elastiknn - name: elastiknn-l2lsh - run_groups: - elastiknn-l2lsh: - args: [[125,150,175,200], [7,8,9], [3900,4000,4100]] - query_args: [[500,750,1000,1250], [0,1,2]] + - base_args: [] + constructor: L2Lsh + disabled: true + docker_tag: ann-benchmarks-elastiknn + module: ann_benchmarks.algorithms.elastiknn + name: elastiknn-l2lsh + run_groups: + elastiknn-l2lsh: + args: [[175], [7], [3900]] + query_args: [[100,500,1000], [0]] diff --git a/docs/pages/performance/fashion-mnist/plot.b64 b/docs/pages/performance/fashion-mnist/plot.b64 index 69239334..e489e20a 100644 --- a/docs/pages/performance/fashion-mnist/plot.b64 +++ b/docs/pages/performance/fashion-mnist/plot.b64 @@ -1 +1 @@ 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206.997 -Found cached result - 76: eknn-l2lsh-L=175-k=9-w=3900_candidates=1250_probes=2 0.980 159.601 -Found cached result - 77: eknn-l2lsh-L=200-k=9-w=4000_candidates=1000_probes=1 0.972 180.587 -Found cached result - 78: eknn-l2lsh-L=125-k=7-w=4000_candidates=500_probes=1 0.936 222.739 -Found cached result - 79: eknn-l2lsh-L=175-k=7-w=4000_candidates=1000_probes=2 0.990 144.779 -Found cached result - 80: eknn-l2lsh-L=200-k=9-w=4100_candidates=1250_probes=0 0.960 221.266 -Found cached result - 81: eknn-l2lsh-L=125-k=7-w=4100_candidates=750_probes=0 0.928 258.768 -Found cached result - 82: eknn-l2lsh-L=200-k=7-w=4000_candidates=500_probes=1 0.967 172.951 -Found cached result - 83: eknn-l2lsh-L=200-k=7-w=4100_candidates=750_probes=2 0.989 130.462 -Found cached result - 84: eknn-l2lsh-L=175-k=9-w=4100_candidates=1250_probes=1 0.976 178.650 -Found cached result - 85: eknn-l2lsh-L=150-k=7-w=3900_candidates=1250_probes=2 0.989 145.575 -Found cached result - 86: eknn-l2lsh-L=125-k=9-w=4100_candidates=750_probes=0 0.891 288.164 -Found cached result - 87: eknn-l2lsh-L=150-k=7-w=4100_candidates=1000_probes=0 0.959 226.857 -Found cached result - 88: eknn-l2lsh-L=125-k=9-w=4100_candidates=1000_probes=2 0.966 192.040 -Found cached result - 89: eknn-l2lsh-L=175-k=8-w=4000_candidates=1250_probes=1 0.982 174.306 -Found cached result - 90: eknn-l2lsh-L=150-k=7-w=3900_candidates=1000_probes=2 0.986 152.082 -Found cached result - 91: eknn-l2lsh-L=125-k=7-w=4000_candidates=1250_probes=2 0.986 165.114 -Found cached result - 92: eknn-l2lsh-L=200-k=8-w=4100_candidates=1000_probes=0 0.964 220.637 -Found cached result - 93: eknn-l2lsh-L=150-k=7-w=3900_candidates=1250_probes=0 0.963 211.024 -Found cached result - 94: eknn-l2lsh-L=200-k=7-w=3900_candidates=1250_probes=2 0.994 130.837 -Found cached result - 95: eknn-l2lsh-L=125-k=8-w=3900_candidates=500_probes=2 0.940 209.130 -Found cached result - 96: eknn-l2lsh-L=175-k=7-w=3900_candidates=500_probes=0 0.921 257.151 -Found cached result - 97: eknn-l2lsh-L=175-k=7-w=4000_candidates=500_probes=0 0.925 254.374 -Found cached result - 98: eknn-l2lsh-L=150-k=7-w=3900_candidates=1250_probes=1 0.983 167.695 -Found cached result - 99: eknn-l2lsh-L=150-k=9-w=3900_candidates=1000_probes=2 0.969 177.270 -Found cached result -100: eknn-l2lsh-L=125-k=9-w=3900_candidates=500_probes=2 0.924 217.381 -Found cached result -101: eknn-l2lsh-L=125-k=7-w=4000_candidates=750_probes=2 0.974 181.437 -Found cached result -102: eknn-l2lsh-L=175-k=9-w=4100_candidates=1250_probes=0 0.953 227.474 -Found cached result -103: eknn-l2lsh-L=200-k=9-w=3900_candidates=750_probes=1 0.960 191.764 -Found cached result -104: eknn-l2lsh-L=150-k=9-w=3900_candidates=500_probes=0 0.863 302.352 -Found cached result -105: eknn-l2lsh-L=175-k=9-w=4100_candidates=750_probes=0 0.927 256.851 -Found cached result -106: eknn-l2lsh-L=175-k=9-w=4000_candidates=750_probes=1 0.956 200.126 -Found cached result -107: eknn-l2lsh-L=150-k=9-w=3900_candidates=750_probes=2 0.959 192.285 -Found cached result -108: eknn-l2lsh-L=175-k=9-w=4100_candidates=1000_probes=1 0.970 187.352 -Found cached result -109: eknn-l2lsh-L=125-k=7-w=4000_candidates=1250_probes=0 0.954 231.635 -Found cached result -110: eknn-l2lsh-L=125-k=8-w=4100_candidates=750_probes=0 0.912 275.778 -Found cached result -111: eknn-l2lsh-L=150-k=9-w=4000_candidates=750_probes=1 0.947 219.975 -Found cached result -112: eknn-l2lsh-L=125-k=7-w=4000_candidates=1000_probes=2 0.982 169.063 -Found cached result -113: eknn-l2lsh-L=125-k=7-w=4000_candidates=500_probes=0 0.889 282.787 -Found cached result -114: eknn-l2lsh-L=125-k=9-w=3900_candidates=500_probes=0 0.837 323.650 -Found cached result -115: eknn-l2lsh-L=125-k=9-w=4100_candidates=750_probes=1 0.936 232.567 -Found cached result -116: eknn-l2lsh-L=200-k=8-w=4100_candidates=500_probes=1 0.959 187.307 -Found cached result -117: eknn-l2lsh-L=150-k=7-w=4100_candidates=750_probes=1 0.972 188.104 -Found cached result -118: eknn-l2lsh-L=125-k=8-w=3900_candidates=500_probes=0 0.862 309.353 -Found cached result -119: eknn-l2lsh-L=150-k=8-w=4100_candidates=1000_probes=1 0.973 194.179 -Found cached result -120: eknn-l2lsh-L=125-k=8-w=3900_candidates=1250_probes=2 0.978 179.267 -Found cached result -121: eknn-l2lsh-L=175-k=8-w=3900_candidates=1000_probes=0 0.949 240.218 -Found cached result -122: eknn-l2lsh-L=200-k=9-w=4100_candidates=1000_probes=1 0.975 178.742 -Found cached result -123: eknn-l2lsh-L=200-k=8-w=4100_candidates=500_probes=2 0.972 156.062 -Found cached result -124: eknn-l2lsh-L=200-k=7-w=3900_candidates=750_probes=0 0.958 224.903 -Found cached result -125: eknn-l2lsh-L=175-k=7-w=3900_candidates=750_probes=1 0.975 179.769 -Found cached result -126: eknn-l2lsh-L=125-k=9-w=4000_candidates=500_probes=2 0.928 220.280 -Found cached result -127: eknn-l2lsh-L=200-k=9-w=3900_candidates=1250_probes=1 0.975 164.301 -Found cached result -128: eknn-l2lsh-L=150-k=9-w=4000_candidates=1000_probes=1 0.959 205.758 -Found cached result -129: eknn-l2lsh-L=150-k=8-w=3900_candidates=750_probes=2 0.970 181.551 -Found cached result -130: eknn-l2lsh-L=175-k=9-w=4100_candidates=750_probes=2 0.972 167.739 -Found cached result -131: eknn-l2lsh-L=200-k=7-w=4000_candidates=750_probes=2 0.988 138.887 -Found cached result -132: eknn-l2lsh-L=200-k=8-w=4100_candidates=1250_probes=0 0.971 209.429 -Found cached result -133: eknn-l2lsh-L=200-k=7-w=3900_candidates=500_probes=1 0.965 170.608 -Found cached result -134: eknn-l2lsh-L=175-k=7-w=3900_candidates=1000_probes=1 0.982 173.397 -Found cached result -135: eknn-l2lsh-L=200-k=7-w=4100_candidates=500_probes=2 0.979 139.828 -Found cached result -136: eknn-l2lsh-L=175-k=9-w=3900_candidates=750_probes=2 0.967 175.534 -Found cached result -137: eknn-l2lsh-L=125-k=8-w=4100_candidates=1250_probes=2 0.981 173.280 -Found cached result -138: eknn-l2lsh-L=150-k=8-w=4000_candidates=500_probes=0 0.891 280.175 -Found cached result -139: eknn-l2lsh-L=150-k=8-w=4000_candidates=1250_probes=0 0.952 232.135 -Found cached result -140: eknn-l2lsh-L=125-k=8-w=3900_candidates=500_probes=1 0.917 242.059 -Found cached result -141: eknn-l2lsh-L=175-k=7-w=3900_candidates=1250_probes=0 0.970 212.504 -Found cached result -142: eknn-l2lsh-L=150-k=9-w=4000_candidates=750_probes=0 0.906 275.675 -Found cached result -143: eknn-l2lsh-L=175-k=9-w=4100_candidates=500_probes=0 0.895 277.871 -Found cached result -144: eknn-l2lsh-L=175-k=9-w=3900_candidates=1000_probes=1 0.964 190.995 -Found cached result -145: eknn-l2lsh-L=150-k=9-w=4000_candidates=500_probes=2 0.943 198.876 -Found cached result -146: eknn-l2lsh-L=125-k=8-w=4100_candidates=1000_probes=1 0.964 203.809 -Found cached result -147: eknn-l2lsh-L=175-k=8-w=3900_candidates=500_probes=1 0.945 203.812 -Found cached result -148: eknn-l2lsh-L=200-k=9-w=4000_candidates=1250_probes=1 0.978 172.968 -Found cached result -149: eknn-l2lsh-L=175-k=9-w=4000_candidates=1000_probes=2 0.978 159.401 -Found cached result -150: eknn-l2lsh-L=200-k=7-w=4000_candidates=1000_probes=2 0.992 133.459 -Found cached result -151: eknn-l2lsh-L=175-k=8-w=3900_candidates=1000_probes=1 0.974 182.369 -Found cached result -152: eknn-l2lsh-L=150-k=8-w=4000_candidates=1000_probes=0 0.942 245.700 -Found cached result -153: eknn-l2lsh-L=125-k=7-w=4100_candidates=1000_probes=0 0.946 244.874 -Found cached result -154: eknn-l2lsh-L=200-k=8-w=4100_candidates=750_probes=2 0.984 148.909 -Found cached result -155: eknn-l2lsh-L=200-k=8-w=4100_candidates=1250_probes=1 0.987 164.391 -Found cached result -156: eknn-l2lsh-L=175-k=7-w=4000_candidates=750_probes=0 0.952 235.437 -Found cached result -157: eknn-l2lsh-L=175-k=9-w=4100_candidates=1250_probes=2 0.984 152.925 -Found cached result -158: eknn-l2lsh-L=150-k=8-w=3900_candidates=500_probes=2 0.952 189.909 -Found cached result -159: eknn-l2lsh-L=150-k=8-w=3900_candidates=1000_probes=0 0.937 246.914 -Found cached result -160: eknn-l2lsh-L=125-k=9-w=4100_candidates=500_probes=0 0.851 311.906 -Found cached result -161: eknn-l2lsh-L=150-k=8-w=4000_candidates=1250_probes=1 0.976 188.064 -Found cached result -162: eknn-l2lsh-L=200-k=9-w=4000_candidates=1000_probes=2 0.982 153.807 -Found cached result -163: eknn-l2lsh-L=175-k=8-w=3900_candidates=1250_probes=0 0.959 224.310 -Found cached result -164: eknn-l2lsh-L=175-k=7-w=3900_candidates=1250_probes=2 0.992 142.170 -Found cached result -165: eknn-l2lsh-L=150-k=8-w=4000_candidates=1000_probes=2 0.980 168.212 -Found cached result -166: eknn-l2lsh-L=150-k=8-w=4100_candidates=1250_probes=2 0.986 160.360 -Found cached result -167: eknn-l2lsh-L=200-k=7-w=3900_candidates=500_probes=2 0.977 139.436 -Found cached result -168: eknn-l2lsh-L=200-k=9-w=4100_candidates=1250_probes=1 0.980 169.150 -Found cached result -169: eknn-l2lsh-L=150-k=8-w=4000_candidates=750_probes=2 0.973 180.931 -Found cached result -170: eknn-l2lsh-L=175-k=8-w=3900_candidates=1250_probes=1 0.980 177.663 -Found cached result -171: eknn-l2lsh-L=200-k=7-w=4000_candidates=500_probes=0 0.937 237.096 -Found cached result -172: eknn-l2lsh-L=150-k=9-w=4100_candidates=1000_probes=0 0.930 256.525 -Found cached result -173: eknn-l2lsh-L=200-k=9-w=3900_candidates=750_probes=0 0.927 258.428 -Found cached result -174: eknn-l2lsh-L=150-k=9-w=4100_candidates=500_probes=0 0.877 285.484 -Found cached result -175: eknn-l2lsh-L=125-k=7-w=4100_candidates=1000_probes=2 0.983 168.819 -Found cached result -176: eknn-l2lsh-L=150-k=9-w=4100_candidates=1000_probes=1 0.962 202.603 -Found cached result -177: eknn-l2lsh-L=150-k=7-w=4100_candidates=1000_probes=1 0.981 182.734 -Found cached result -178: eknn-l2lsh-L=125-k=7-w=4100_candidates=1000_probes=1 0.973 196.158 -Found cached result -179: eknn-l2lsh-L=150-k=8-w=3900_candidates=1000_probes=2 0.979 173.302 -Found cached result -180: eknn-l2lsh-L=175-k=9-w=4000_candidates=500_probes=1 0.934 214.027 -Found cached result -181: eknn-l2lsh-L=200-k=9-w=3900_candidates=500_probes=2 0.957 170.595 -Found cached result -182: eknn-l2lsh-L=175-k=7-w=4000_candidates=750_probes=1 0.976 176.770 -Found cached result -183: eknn-l2lsh-L=125-k=8-w=4100_candidates=500_probes=2 0.946 201.687 -Found cached result -184: eknn-l2lsh-L=150-k=9-w=4000_candidates=1000_probes=0 0.925 257.201 -Found cached result -185: eknn-l2lsh-L=200-k=9-w=4100_candidates=750_probes=1 0.966 187.450 -Found cached result -186: eknn-l2lsh-L=150-k=8-w=4000_candidates=1250_probes=2 0.985 163.613 -Found cached result -187: eknn-l2lsh-L=150-k=7-w=3900_candidates=750_probes=1 0.968 197.120 -Found cached result -188: eknn-l2lsh-L=125-k=8-w=3900_candidates=1000_probes=1 0.958 208.477 -Found cached result -189: eknn-l2lsh-L=150-k=7-w=4100_candidates=1250_probes=0 0.968 217.790 -Found cached result -190: eknn-l2lsh-L=175-k=8-w=3900_candidates=500_probes=0 0.904 272.791 -Found cached result -191: eknn-l2lsh-L=175-k=8-w=4000_candidates=500_probes=1 0.948 202.060 -Found cached result -192: eknn-l2lsh-L=175-k=7-w=3900_candidates=500_probes=2 0.971 158.635 -Found cached result -193: eknn-l2lsh-L=200-k=7-w=3900_candidates=750_probes=1 0.980 169.016 -Found cached result -194: eknn-l2lsh-L=175-k=9-w=3900_candidates=500_probes=1 0.929 215.929 -Found cached result -195: eknn-l2lsh-L=125-k=7-w=3900_candidates=500_probes=1 0.933 225.817 -Found cached result -196: eknn-l2lsh-L=200-k=7-w=4100_candidates=750_probes=1 0.982 160.596 -Found cached result -197: eknn-l2lsh-L=175-k=7-w=4000_candidates=500_probes=2 0.973 158.915 -Found cached result -198: eknn-l2lsh-L=125-k=7-w=3900_candidates=1250_probes=1 0.976 192.985 -Found cached result -199: eknn-l2lsh-L=200-k=7-w=4100_candidates=500_probes=0 0.939 232.796 -Found cached result -200: eknn-l2lsh-L=150-k=8-w=4000_candidates=500_probes=2 0.955 180.825 -Found cached result -201: eknn-l2lsh-L=150-k=9-w=3900_candidates=750_probes=1 0.942 220.811 -Found cached result -202: eknn-l2lsh-L=175-k=7-w=4000_candidates=1000_probes=1 0.984 172.790 -Found cached result -203: eknn-l2lsh-L=175-k=7-w=3900_candidates=1000_probes=2 0.989 148.949 -Found cached result -204: eknn-l2lsh-L=200-k=9-w=4000_candidates=1250_probes=2 0.986 147.703 -Found cached result -205: eknn-l2lsh-L=175-k=9-w=4100_candidates=1000_probes=0 0.943 242.498 -Found cached result -206: eknn-l2lsh-L=200-k=9-w=3900_candidates=1250_probes=2 0.984 142.014 -Found cached result -207: eknn-l2lsh-L=200-k=7-w=4100_candidates=750_probes=0 0.963 214.423 -Found cached result -208: eknn-l2lsh-L=150-k=8-w=3900_candidates=500_probes=0 0.886 289.585 -Found cached result -209: eknn-l2lsh-L=150-k=7-w=3900_candidates=500_probes=0 0.906 264.575 -Found cached result -210: eknn-l2lsh-L=200-k=7-w=3900_candidates=750_probes=2 0.987 141.367 -Found cached result -211: eknn-l2lsh-L=150-k=8-w=3900_candidates=750_probes=1 0.956 213.143 -Found cached result -212: eknn-l2lsh-L=200-k=9-w=4100_candidates=750_probes=0 0.937 250.330 -Found cached result -213: eknn-l2lsh-L=150-k=8-w=4100_candidates=500_probes=2 0.958 183.474 -Found cached result -214: eknn-l2lsh-L=125-k=9-w=3900_candidates=750_probes=0 0.877 292.179 -Found cached result -215: eknn-l2lsh-L=150-k=8-w=4100_candidates=750_probes=1 0.962 205.715 -Found cached result -216: eknn-l2lsh-L=125-k=9-w=3900_candidates=750_probes=1 0.927 239.317 -Found cached result -217: eknn-l2lsh-L=150-k=9-w=3900_candidates=500_probes=1 0.916 233.020 -Found cached result -218: eknn-l2lsh-L=150-k=9-w=3900_candidates=750_probes=0 0.900 277.357 -Found cached result -219: eknn-l2lsh-L=200-k=9-w=4000_candidates=750_probes=1 0.963 189.180 -Found cached result -220: eknn-l2lsh-L=200-k=7-w=3900_candidates=1000_probes=2 0.991 132.193 -Found cached result -221: eknn-l2lsh-L=175-k=9-w=4000_candidates=500_probes=0 0.889 286.607 -Found cached result -222: eknn-l2lsh-L=200-k=9-w=4000_candidates=500_probes=1 0.944 199.125 -Found cached result -223: eknn-l2lsh-L=125-k=7-w=3900_candidates=1000_probes=0 0.939 250.426 -Found cached result -224: eknn-l2lsh-L=125-k=8-w=4100_candidates=500_probes=0 0.874 298.451 -Found cached result -225: eknn-l2lsh-L=150-k=9-w=4100_candidates=1000_probes=2 0.975 178.600 -Found cached result -226: eknn-l2lsh-L=125-k=8-w=3900_candidates=1250_probes=1 0.966 203.273 -Found cached result -227: eknn-l2lsh-L=150-k=7-w=3900_candidates=1000_probes=0 0.953 234.020 -Found cached result -228: eknn-l2lsh-L=200-k=7-w=4000_candidates=750_probes=0 0.960 221.518 -Found cached result -229: eknn-l2lsh-L=150-k=7-w=4100_candidates=500_probes=2 0.968 169.566 -Found cached result -230: eknn-l2lsh-L=150-k=8-w=4100_candidates=750_probes=0 0.929 260.596 -Found cached result -231: eknn-l2lsh-L=125-k=7-w=3900_candidates=750_probes=2 0.972 184.359 -Found cached result -232: eknn-l2lsh-L=125-k=8-w=4100_candidates=750_probes=2 0.967 190.711 -Found cached result -233: eknn-l2lsh-L=175-k=9-w=4000_candidates=1250_probes=1 0.973 183.447 -Found cached result -234: eknn-l2lsh-L=200-k=8-w=4100_candidates=750_probes=1 0.975 177.736 -Computing knn metrics -235: eknn-l2lsh-L=125-k=9-w=4000_candidates=1000_probes=1 0.947 225.280 -Found cached result -236: eknn-l2lsh-L=200-k=9-w=4000_candidates=500_probes=0 0.903 269.983 -Found cached result -237: eknn-l2lsh-L=175-k=9-w=3900_candidates=1250_probes=0 0.943 232.425 -Found cached result -238: eknn-l2lsh-L=175-k=9-w=4000_candidates=750_probes=2 0.970 172.460 -Found cached result -239: eknn-l2lsh-L=200-k=9-w=3900_candidates=500_probes=1 0.940 203.171 -Found cached result -240: eknn-l2lsh-L=125-k=8-w=4100_candidates=750_probes=1 0.951 219.195 -Found cached result -241: eknn-l2lsh-L=175-k=7-w=4000_candidates=1250_probes=0 0.972 211.460 -Found cached result -242: eknn-l2lsh-L=150-k=9-w=3900_candidates=1250_probes=2 0.975 173.881 -Found cached result -243: eknn-l2lsh-L=150-k=7-w=3900_candidates=500_probes=1 0.948 206.707 -Found cached result -244: eknn-l2lsh-L=125-k=7-w=3900_candidates=500_probes=0 0.884 289.416 -Found cached result -245: eknn-l2lsh-L=200-k=7-w=4100_candidates=1250_probes=2 0.995 126.016 -Found cached result -246: eknn-l2lsh-L=150-k=9-w=4100_candidates=1250_probes=0 0.942 240.364 -Found cached result -247: eknn-l2lsh-L=150-k=9-w=4100_candidates=1250_probes=1 0.970 195.084 -Found cached result -248: eknn-l2lsh-L=125-k=8-w=3900_candidates=1250_probes=0 0.935 244.842 -Found cached result -249: eknn-l2lsh-L=150-k=7-w=3900_candidates=500_probes=2 0.964 176.316 -Found cached result -250: eknn-l2lsh-L=175-k=8-w=4000_candidates=750_probes=1 0.967 193.286 -Found cached result -251: eknn-l2lsh-L=200-k=9-w=3900_candidates=1000_probes=0 0.942 238.436 -Found cached result -252: eknn-l2lsh-L=200-k=7-w=3900_candidates=1250_probes=1 0.990 154.557 -Found cached result -253: eknn-l2lsh-L=200-k=7-w=4100_candidates=1000_probes=2 0.993 130.024 -Found cached result -254: eknn-l2lsh-L=150-k=8-w=3900_candidates=1250_probes=1 0.974 190.636 -Found cached result -255: eknn-l2lsh-L=175-k=7-w=4000_candidates=1250_probes=2 0.993 140.242 -Found cached result -256: eknn-l2lsh-L=200-k=7-w=3900_candidates=1000_probes=0 0.969 209.121 -Found cached result -257: eknn-l2lsh-L=150-k=8-w=3900_candidates=750_probes=0 0.920 269.915 -Found cached result -258: eknn-l2lsh-L=175-k=8-w=3900_candidates=500_probes=2 0.962 174.431 -Found cached result -259: eknn-l2lsh-L=125-k=9-w=3900_candidates=1250_probes=1 0.952 213.370 -Found cached result -260: eknn-l2lsh-L=125-k=7-w=3900_candidates=750_probes=1 0.958 213.409 -Found cached result -261: eknn-l2lsh-L=150-k=8-w=4000_candidates=750_probes=0 0.925 269.618 -Found cached result -262: eknn-l2lsh-L=200-k=7-w=3900_candidates=500_probes=0 0.934 243.251 -Found cached result -263: eknn-l2lsh-L=200-k=7-w=4100_candidates=1250_probes=0 0.979 197.590 -Found cached result -264: eknn-l2lsh-L=150-k=9-w=4000_candidates=500_probes=1 0.921 232.303 -Found cached result -265: eknn-l2lsh-L=150-k=7-w=3900_candidates=1000_probes=1 0.977 175.861 -Found cached result -266: eknn-l2lsh-L=175-k=7-w=3900_candidates=500_probes=1 0.958 194.054 -Found cached result -267: eknn-l2lsh-L=125-k=9-w=4000_candidates=750_probes=2 0.952 208.470 -Found cached result -268: eknn-l2lsh-L=200-k=9-w=4100_candidates=1000_probes=0 0.951 234.136 -Found cached result -269: eknn-l2lsh-L=175-k=8-w=4000_candidates=750_probes=0 0.938 253.939 -Found cached result -270: eknn-l2lsh-L=200-k=7-w=3900_candidates=1250_probes=0 0.976 195.296 -Found cached result -271: eknn-l2lsh-L=150-k=8-w=4100_candidates=500_probes=0 0.896 284.495 -Found cached result -272: eknn-l2lsh-L=150-k=9-w=4100_candidates=750_probes=2 0.966 184.985 -Found cached result -273: eknn-l2lsh-L=175-k=9-w=4100_candidates=1000_probes=2 0.980 159.869 -Found cached result -274: eknn-l2lsh-L=150-k=9-w=4000_candidates=1250_probes=0 0.937 242.487 -Found cached result -275: eknn-l2lsh-L=125-k=9-w=3900_candidates=1250_probes=0 0.914 259.536 -Found cached result -276: eknn-l2lsh-L=175-k=8-w=4000_candidates=500_probes=2 0.964 170.576 -Found cached result -277: eknn-l2lsh-L=125-k=7-w=4100_candidates=500_probes=2 0.957 186.862 -Found cached result -278: eknn-l2lsh-L=200-k=7-w=4000_candidates=1250_probes=2 0.994 129.749 -Found cached result -279: eknn-l2lsh-L=125-k=9-w=3900_candidates=1000_probes=1 0.943 227.127 -Found cached result -280: eknn-l2lsh-L=200-k=8-w=4100_candidates=500_probes=0 0.926 248.573 -Found cached result -281: eknn-l2lsh-L=200-k=9-w=4000_candidates=500_probes=2 0.961 165.952 -Found cached result -282: eknn-l2lsh-L=125-k=9-w=4100_candidates=500_probes=2 0.932 213.809 -Found cached result -283: eknn-l2lsh-L=125-k=7-w=3900_candidates=750_probes=0 0.920 269.825 -Found cached result -284: eknn-l2lsh-L=125-k=7-w=4000_candidates=500_probes=2 0.955 191.810 -Found cached result -285: eknn-l2lsh-L=125-k=7-w=4100_candidates=750_probes=1 0.962 204.999 -Found cached result -286: eknn-l2lsh-L=125-k=9-w=4000_candidates=500_probes=1 0.902 255.236 -Found cached result -287: eknn-l2lsh-L=150-k=9-w=4100_candidates=750_probes=1 0.950 213.884 -Found cached result -288: eknn-l2lsh-L=125-k=9-w=3900_candidates=1000_probes=0 0.900 276.969 -Found cached result -289: eknn-l2lsh-L=150-k=8-w=4100_candidates=1250_probes=0 0.956 231.391 -Found cached result -290: eknn-l2lsh-L=150-k=8-w=4000_candidates=500_probes=1 0.937 209.724 -Found cached result -291: eknn-l2lsh-L=125-k=7-w=4100_candidates=500_probes=0 0.892 277.219 -Found cached result -292: eknn-l2lsh-L=200-k=7-w=4000_candidates=500_probes=2 0.978 141.893 -Found cached result -293: eknn-l2lsh-L=175-k=8-w=3900_candidates=1000_probes=2 0.983 157.473 -Found cached result -294: eknn-l2lsh-L=175-k=7-w=3900_candidates=1000_probes=0 0.962 222.959 -Found cached result -295: eknn-l2lsh-L=125-k=8-w=4100_candidates=1000_probes=2 0.976 179.341 -Found cached result -296: eknn-l2lsh-L=125-k=8-w=4100_candidates=1250_probes=1 0.971 195.239 -Found cached result -297: eknn-l2lsh-L=200-k=7-w=4000_candidates=1000_probes=0 0.971 209.860 -Found cached result -298: eknn-l2lsh-L=175-k=9-w=3900_candidates=1000_probes=2 0.975 167.418 -Found cached result -299: eknn-l2lsh-L=125-k=8-w=4100_candidates=1000_probes=0 0.932 251.971 -Found cached result -300: eknn-l2lsh-L=125-k=7-w=3900_candidates=1250_probes=0 0.951 235.937 -Found cached result -301: eknn-l2lsh-L=200-k=9-w=3900_candidates=750_probes=2 0.973 163.680 -Found cached result -302: eknn-l2lsh-L=175-k=9-w=4000_candidates=1250_probes=0 0.948 225.770 -Found cached result -303: eknn-l2lsh-L=200-k=9-w=3900_candidates=1000_probes=1 0.969 184.638 -Found cached result -304: eknn-l2lsh-L=200-k=9-w=4100_candidates=1000_probes=2 0.984 152.098 -Found cached result -305: eknn-l2lsh-L=125-k=7-w=4100_candidates=1250_probes=1 0.980 182.451 -Found cached result -306: eknn-l2lsh-L=175-k=9-w=3900_candidates=1000_probes=0 0.933 248.932 -Found cached result -307: eknn-l2lsh-L=125-k=9-w=4000_candidates=500_probes=0 0.845 319.979 -Found cached result -308: eknn-l2lsh-L=150-k=7-w=4100_candidates=1250_probes=1 0.985 173.447 -Found cached result -309: eknn-l2lsh-L=175-k=9-w=3900_candidates=500_probes=0 0.882 282.479 -Found cached result -310: eknn-l2lsh-L=200-k=9-w=3900_candidates=1250_probes=0 0.952 216.606 -Found cached result -311: eknn-l2lsh-L=125-k=9-w=4100_candidates=1250_probes=2 0.973 183.616 -Found cached result -312: eknn-l2lsh-L=175-k=8-w=4000_candidates=1250_probes=0 0.962 222.790 -Found cached result -313: eknn-l2lsh-L=175-k=8-w=3900_candidates=750_probes=2 0.976 166.383 -Found cached result -314: eknn-l2lsh-L=175-k=9-w=4000_candidates=1000_probes=1 0.967 187.608 -Found cached result -315: eknn-l2lsh-L=200-k=8-w=4100_candidates=750_probes=0 0.951 237.657 -Found cached result -316: eknn-l2lsh-L=175-k=7-w=3900_candidates=1250_probes=1 0.987 165.410 -Found cached result -317: eknn-l2lsh-L=125-k=7-w=4000_candidates=750_probes=1 0.960 209.464 -Found cached result -318: eknn-l2lsh-L=125-k=9-w=4000_candidates=750_probes=0 0.885 294.792 -Found cached result -319: eknn-l2lsh-L=175-k=9-w=4100_candidates=500_probes=2 0.956 177.716 -Found cached result -320: eknn-l2lsh-L=150-k=9-w=4000_candidates=1250_probes=2 0.978 172.674 -Found cached result -321: eknn-l2lsh-L=200-k=9-w=4100_candidates=500_probes=1 0.947 197.100 -Found cached result -322: eknn-l2lsh-L=175-k=8-w=4000_candidates=1000_probes=2 0.985 156.827 -Found cached result -323: eknn-l2lsh-L=150-k=7-w=4100_candidates=750_probes=2 0.982 161.400 -Found cached result -324: eknn-l2lsh-L=125-k=9-w=3900_candidates=1250_probes=2 0.968 189.159 -Found cached result -325: eknn-l2lsh-L=175-k=9-w=3900_candidates=500_probes=2 0.949 183.198 -Found cached result -326: eknn-l2lsh-L=125-k=7-w=4000_candidates=1250_probes=1 0.978 189.013 -Found cached result -327: eknn-l2lsh-L=150-k=8-w=4100_candidates=750_probes=2 0.975 175.020 -Found cached result -328: eknn-l2lsh-L=200-k=9-w=4000_candidates=1000_probes=0 0.947 238.454 -Found cached result -329: eknn-l2lsh-L=200-k=8-w=4100_candidates=1250_probes=2 0.992 137.316 -Found cached result -330: eknn-l2lsh-L=125-k=9-w=4100_candidates=1250_probes=0 0.926 250.285 -Found cached result -331: eknn-l2lsh-L=125-k=8-w=3900_candidates=1000_probes=0 0.922 261.355 -Found cached result -332: eknn-l2lsh-L=200-k=9-w=4000_candidates=750_probes=0 0.932 245.158 -Found cached result -333: eknn-l2lsh-L=175-k=9-w=4000_candidates=750_probes=0 0.921 263.695 -Found cached result -334: eknn-l2lsh-L=125-k=7-w=3900_candidates=1000_probes=1 0.970 200.830 -Found cached result -335: eknn-l2lsh-L=200-k=9-w=4000_candidates=750_probes=2 0.975 159.587 -Found cached result -336: eknn-l2lsh-L=125-k=9-w=4100_candidates=1000_probes=1 0.951 219.441 -Found cached result -337: eknn-l2lsh-L=150-k=7-w=4100_candidates=1000_probes=2 0.988 154.745 -Found cached result -338: eknn-l2lsh-L=175-k=9-w=3900_candidates=750_probes=0 0.915 267.361 -Found cached result -339: eknn-l2lsh-L=150-k=8-w=4100_candidates=1000_probes=0 0.945 245.532 -Found cached result -340: eknn-l2lsh-L=125-k=8-w=3900_candidates=750_probes=0 0.901 278.315 -Found cached result -341: eknn-l2lsh-L=150-k=9-w=3900_candidates=1250_probes=1 0.963 198.103 -Found cached result -342: eknn-l2lsh-L=150-k=8-w=3900_candidates=1250_probes=0 0.948 237.059 -Found cached result -343: eknn-l2lsh-L=125-k=9-w=3900_candidates=1000_probes=2 0.960 201.594 -Found cached result -344: eknn-l2lsh-L=150-k=9-w=4100_candidates=500_probes=1 0.926 222.971 -Found cached result -345: eknn-l2lsh-L=150-k=9-w=3900_candidates=1250_probes=0 0.931 244.528 -Found cached result -346: eknn-l2lsh-L=175-k=7-w=3900_candidates=750_probes=0 0.949 234.738 -Found cached result -347: eknn-l2lsh-L=150-k=7-w=4100_candidates=500_probes=1 0.953 203.767 -Found cached result -348: eknn-l2lsh-L=125-k=7-w=3900_candidates=1250_probes=2 0.985 166.714 -Found cached result -349: eknn-l2lsh-L=200-k=9-w=3900_candidates=1000_probes=2 0.980 149.577 -Found cached result -350: eknn-l2lsh-L=200-k=7-w=4100_candidates=1000_probes=1 0.988 154.212 -Found cached result -351: eknn-l2lsh-L=150-k=9-w=3900_candidates=1000_probes=1 0.955 209.590 -Found cached result -352: eknn-l2lsh-L=200-k=9-w=4100_candidates=750_probes=2 0.977 158.559 -Found cached result -353: eknn-l2lsh-L=150-k=8-w=4000_candidates=750_probes=1 0.959 210.001 -Found cached result -354: eknn-l2lsh-L=125-k=9-w=4100_candidates=1000_probes=0 0.912 270.403 -Found cached result -355: eknn-l2lsh-L=125-k=7-w=4000_candidates=1000_probes=0 0.943 246.386