Plotting the KS2 vs KS1 graphs (after noise reduction)

bench.txt

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0.5 10 304 1.125159
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0.5 10 322 1.091980
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0.5 10 448 1.098894
0.5 10 451 1.088542
0.5 10 454 1.142240
0.5 10 457 1.088199
0.5 10 460 1.121722
0.5 10 463 1.084127
0.5 10 466 1.065967
0.5 10 469 1.129920
0.5 10 472 1.058485
0.5 10 475 1.106010
0.5 10 478 1.068861
0.5 10 481 1.047363
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0.5 10 487 1.089488
0.5 10 490 1.080713
0.5 10 493 1.103079
0.5 10 496 1.076251
0.5 10 499 1.013865
0.5 10 525 1.007511
0.5 10 550 1.106320
0.5 10 575 1.045046
0.5 10 600 1.145345
0.5 10 625 1.062784
0.5 10 650 1.048398
0.5 10 675 1.009006
0.5 10 700 1.036708
0.5 10 725 1.032216
0.5 10 750 0.948840
0.5 10 775 0.951074
0.5 10 800 0.957632
0.5 10 825 1.028267
0.5 10 850 1.015350
0.5 10 875 0.991110
0.5 10 900 1.037778
0.5 10 925 1.094319
0.5 10 950 0.995711
0.5 10 975 0.996190
0.5 10 1000 0.973037
0.5 10 1050 0.864830
0.5 10 1100 1.015854
0.5 10 1150 0.970201
0.5 10 1200 0.843031
0.5 10 1250 0.914104
0.5 10 1300 0.876181
0.5 10 1350 0.908974
0.5 10 1400 1.126956
0.5 10 1450 0.932557
0.5 10 1500 0.889393
0.5 10 1550 0.997143
0.5 10 1600 0.824302
0.5 10 1650 0.901880
0.5 10 1700 0.987468
0.5 10 1750 0.891662
0.5 10 1800 0.852389
0.5 10 1850 0.854670
0.5 10 1900 0.953520
0.5 10 1950 0.848016
0.5 10 2000 0.999047
0.5 10 2050 0.873502
0.5 10 2100 0.880938
0.5 10 2150 0.896126
0.5 10 2200 0.944652
0.5 10 2250 0.866674
0.5 10 2300 0.865470
0.5 10 2350 0.805735
0.5 10 2400 0.869154
0.5 10 2450 0.887932
0.5 10 2500 0.910584
0.5 10 2550 0.848703
0.5 10 2600 0.874376
0.5 10 2650 0.842761
0.5 10 2700 0.846785
0.5 10 2750 0.821284
0.5 10 2800 0.829586
0.5 10 2850 0.832140
0.5 10 2900 0.825694
0.5 10 2950 0.813692
0.5 10 3000 0.851089
0.5 10 3050 0.821253
0.5 10 3200 0.847593
0.5 10 3350 0.837690
0.5 10 3500 0.814132
0.5 10 3650 0.792850
0.5 10 3800 0.806800
0.5 10 3950 0.811849
0.5 10 4100 0.793963
0.5 10 4250 0.764189
0.5 10 4400 0.790614
0.5 10 4550 0.767345
0.5 10 4700 0.771792
0.5 10 4850 0.774595
0.5 10 5000 0.747690
0.5 10 5150 0.774878
0.5 10 5300 0.758159
0.5 10 5450 0.728063
0.5 10 5600 0.749328
0.5 10 5750 0.755949
0.5 10 5900 0.747444
0.5 10 6050 0.747381
0.5 10 6200 0.755317
0.5 10 6350 0.739229
0.5 10 6500 0.742864
0.5 10 6650 0.737270
0.5 10 6800 0.744986
0.5 10 6950 0.745263
0.5 10 7100 0.724876
0.5 10 7250 0.731988
0.5 10 7400 0.730466
0.5 10 7500 0.742541
0.5 10 7750 0.726366
0.5 10 8000 0.726389
0.5 10 8250 0.738476
0.5 10 8500 0.720623
0.5 10 8750 0.793081
0.5 10 9000 0.551124
0.5 10 9250 0.776700
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0.5 15 1 1.151540
0.5 15 2 1.138548
0.5 15 3 1.141877
0.5 15 4 1.144943
0.5 15 5 1.144968
0.5 15 6 1.161898
0.5 15 7 1.162997
0.5 15 8 1.157566
0.5 15 9 1.180601
0.5 15 10 1.154465
0.5 15 11 1.146079
0.5 15 12 1.159593
0.5 15 13 1.131359
0.5 15 14 1.141442
0.5 15 15 1.109821
0.5 15 16 1.137341
0.5 15 17 1.124683
0.5 15 18 1.126184
0.5 15 19 1.107065
0.5 15 20 1.133278
0.5 15 21 1.109396
0.5 15 22 1.128118
0.5 15 23 1.119841
0.5 15 24 1.130839
0.5 15 25 1.127676
0.5 15 26 1.139786
0.5 15 27 1.116306
0.5 15 28 1.147534
0.5 15 29 1.115699
0.5 15 30 1.108401
0.5 15 31 1.141646
0.5 15 32 1.131393
0.5 15 33 1.131880
0.5 15 34 1.129792
0.5 15 35 1.145127
0.5 15 36 1.121167
0.5 15 37 1.151675
0.5 15 38 1.133023
0.5 15 39 1.123196
0.5 15 40 1.132841
0.5 15 41 1.145048
0.5 15 42 1.133178
0.5 15 43 1.135881
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0.5 15 45 1.137366
0.5 15 46 1.137880
0.5 15 47 1.137146
0.5 15 48 1.116624
0.5 15 49 1.120479
0.5 15 50 1.108512
0.5 15 51 1.111609
0.5 15 52 1.098158
0.5 15 53 1.123246
0.5 15 54 1.149410
0.5 15 55 1.139472
0.5 15 56 1.124193
0.5 15 57 1.136107
0.5 15 58 1.112255
0.5 15 59 1.147401
0.5 15 60 1.111465
0.5 15 61 1.127531
0.5 15 62 1.099288
0.5 15 63 1.121889
0.5 15 64 1.115348
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0.5 15 66 1.114846
0.5 15 67 1.124889
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0.5 15 69 1.135885
0.5 15 70 1.131506
0.5 15 71 1.123343
0.5 15 72 1.137745
0.5 15 73 1.138112
0.5 15 74 1.116999
0.5 15 75 1.134951
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0.5 15 77 1.124814
0.5 15 78 1.098049
0.5 15 79 1.139652
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0.5 15 84 1.106097
0.5 15 85 1.118138
0.5 15 86 1.110167
0.5 15 87 1.147510
0.5 15 88 1.122660
0.5 15 89 1.126176
0.5 15 90 1.135956
0.5 15 91 1.114483
0.5 15 92 1.130840
0.5 15 93 1.129632
0.5 15 94 1.092756
0.5 15 95 1.094712
0.5 15 96 1.119770
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0.5 15 99 1.117521
0.5 15 100 1.124391
0.5 15 101 1.135226
0.5 15 102 1.115619
0.5 15 103 1.119361
0.5 15 104 1.126076
0.5 15 105 1.115094
0.5 15 106 1.114975
0.5 15 107 1.119476
0.5 15 108 1.108872
0.5 15 109 1.131099
0.5 15 110 1.114009
0.5 15 111 1.093704
0.5 15 112 1.104449
0.5 15 113 1.125615
0.5 15 114 1.117617
0.5 15 115 1.104067
0.5 15 116 1.106503
0.5 15 117 1.125137
0.5 15 118 1.104999
0.5 15 119 1.103275
0.5 15 120 1.101030
0.5 15 121 1.112405
0.5 15 122 1.090545
0.5 15 123 1.097296
0.5 15 124 1.105344
0.5 15 125 1.110882
0.5 15 126 1.106193
0.5 15 127 1.132384
0.5 15 128 1.135840
0.5 15 129 1.137676
0.5 15 130 1.118666
0.5 15 131 1.140475
0.5 15 132 1.119805
0.5 15 133 1.132515
0.5 15 134 1.107433
0.5 15 135 1.120652
0.5 15 136 1.130770
0.5 15 137 1.140968
0.5 15 138 1.100040
0.5 15 139 1.128768
0.5 15 140 1.143571
0.5 15 141 1.129986
0.5 15 142 1.123922
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0.5 15 144 1.128397
0.5 15 145 1.119645
0.5 15 146 1.108709
0.5 15 147 1.109682
0.5 15 148 1.095679
0.5 15 149 1.116563
0.5 15 150 1.114190
0.5 15 151 1.144640
0.5 15 152 1.120900
0.5 15 153 1.105378
0.5 15 154 1.105652
0.5 15 155 1.118559
0.5 15 156 1.120934
0.5 15 157 1.129352
0.5 15 158 1.115767
0.5 15 159 1.135699
0.5 15 160 1.111423
0.5 15 161 1.112402
0.5 15 162 1.128754
0.5 15 163 1.150972
0.5 15 164 1.106795
0.5 15 165 1.122723
0.5 15 166 1.146438
0.5 15 167 1.126458
0.5 15 168 1.123715
0.5 15 169 1.133979
0.5 15 170 1.132217
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0.75 10 1100 1.124755
0.75 10 1150 1.100580
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0.75 10 1250 1.074578
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0.75 10 1350 1.120615
0.75 10 1400 1.053244
0.75 10 1450 0.994082
0.75 10 1500 1.164380
0.75 10 1550 0.967777
0.75 10 1600 1.045640
0.75 10 1650 1.077207
0.75 10 1700 1.005565
0.75 10 1750 1.032796
0.75 10 1800 0.965937
0.75 10 1850 0.965700
0.75 10 1900 0.944505
0.75 10 1950 0.925610
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0.75 10 2050 1.005717
0.75 10 2100 0.982217
0.75 10 2150 1.029230
0.75 10 2200 0.991647
0.75 10 2250 0.974341
0.75 10 2300 1.000250
0.75 10 2350 0.928175
0.75 10 2400 0.985221
0.75 10 2450 0.949911
0.75 10 2500 0.987849
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0.75 10 2650 1.002566
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0.75 10 2850 0.903826
0.75 10 2900 0.936047
0.75 10 2950 0.910639
0.75 10 3000 0.878977
0.75 10 3050 1.014961
0.75 10 3200 0.943669
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0.75 10 3500 0.915410
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0.75 10 6200 0.833428
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0.75 10 7100 0.828959
0.75 10 7250 0.808079
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0.75 15 3 1.189559
0.75 15 4 1.125896
0.75 15 5 1.134858
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0.75 15 7 1.189898
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0.75 15 9 1.198715
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0.75 15 13 1.201532
0.75 15 14 1.200000
0.75 15 15 1.211072
0.75 15 16 1.210819
0.75 15 17 1.211339
0.75 15 18 1.217653
0.75 15 19 1.190037
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0.75 15 22 1.219738
0.75 15 23 1.180930
0.75 15 24 1.185376
0.75 15 25 1.167793
0.75 15 26 1.207445
0.75 15 27 1.202124
0.75 15 28 1.183729
0.75 15 29 1.187340
0.75 15 30 1.171234
0.75 15 31 1.194862
0.75 15 32 1.166617
0.75 15 33 1.180878
0.75 15 34 1.167992
0.75 15 35 1.179447
0.75 15 36 1.191690
0.75 15 37 1.153870
0.75 15 38 1.180874
0.75 15 39 1.193232
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0.75 15 49 1.197332
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0.75 15 51 1.197662
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0.75 15 53 1.199242
0.75 15 54 1.198521
0.75 15 55 1.195708
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0.75 15 57 1.203132
0.75 15 58 1.188950
0.75 15 59 1.218827
0.75 15 60 1.155789
0.75 15 61 1.194714
0.75 15 62 1.200623
0.75 15 63 1.189977
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1.0 5 1100 1.188240
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1.0 5 1250 1.174291
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1.0 5 1350 0.984192
1.0 5 1400 1.198587
1.0 5 1450 1.153729
1.0 5 1500 1.170338
1.0 5 1550 0.960101
1.0 5 1600 1.253768
1.0 5 1650 1.152936
1.0 5 1700 1.005396
1.0 5 1750 1.189660
1.0 5 1800 1.075663
1.0 5 1850 1.229330
1.0 5 1900 1.182847
1.0 5 1950 1.254804
1.0 5 2000 1.212623
1.0 5 2050 1.155991
1.0 5 2100 1.112252
1.0 5 2150 1.102488
1.0 5 2200 1.129906
1.0 5 2250 1.108811
1.0 5 2300 1.111259
1.0 5 2350 1.093087
1.0 5 2400 1.134008
1.0 5 2450 1.024329
1.0 5 2500 1.019421
1.0 5 2550 1.137639
1.0 5 2600 1.070008
1.0 5 2650 1.201440
1.0 5 2700 1.066619
1.0 5 2750 1.005779
1.0 5 2800 1.162279
1.0 5 2850 0.992885
1.0 5 2900 1.052955
1.0 5 2950 1.067404
1.0 5 3000 1.052578
1.0 5 3050 1.063356
1.0 5 3200 1.081653
1.0 5 3350 1.066058
1.0 5 3500 0.986633
1.0 5 3650 0.987966
1.0 5 3800 1.064582
1.0 5 3950 0.979678
1.0 5 4100 1.059225
1.0 5 4250 1.048562
1.0 5 4400 1.021542
1.0 5 4550 0.955083
1.0 5 4700 0.989155
1.0 5 4850 1.074818
1.0 5 5000 0.982293
1.0 5 5150 0.998617
1.0 5 5300 0.986687
1.0 5 5450 0.987482
1.0 5 5600 0.972834
1.0 5 5750 1.009147
1.0 5 5900 0.973291
1.0 5 6050 0.961018
1.0 5 6200 0.955207
1.0 5 6350 0.983240
1.0 5 6500 0.982056
1.0 5 6650 0.969368
1.0 5 6800 0.943918
1.0 5 6950 0.963245
1.0 5 7100 0.943531
1.0 5 7250 0.932270
1.0 5 7400 0.940757
1.0 5 7500 0.936185
1.0 5 7750 0.907192
1.0 5 8000 0.936089
1.0 5 8250 0.917176
1.0 5 8500 0.929418
1.0 5 8750 0.913764
1.0 5 9000 0.881243
1.0 5 9250 0.887035
1.0 5 9500 0.880422
1.0 5 9750 0.872133
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1.0 10 1 1.177943
1.0 10 2 1.161533
1.0 10 3 1.207273
1.0 10 4 1.182169
1.0 10 5 1.199349
1.0 10 6 1.188513
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1.0 10 9 1.176828
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1.0 10 12 1.220126
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1.0 10 14 1.217021
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1.0 10 17 1.235881
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1.0 10 23 1.240125
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1.0 10 27 1.221357
1.0 10 28 1.241414
1.0 10 29 1.228326
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1.0 10 31 1.257596
1.0 10 32 1.255686
1.0 10 33 1.261411
1.0 10 34 1.243188
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1.0 10 37 1.241486
1.0 10 38 1.264763
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1.0 10 56 1.271755
1.0 10 57 1.251000
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1.0 10 59 1.207841
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1.0 10 61 1.211118
1.0 10 62 1.231939
1.0 10 63 1.216359
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1.0 10 68 1.234598
1.0 10 69 1.261565
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1.0 10 91 1.241234
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1.0 10 93 1.241280
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1.0 10 98 1.264291
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1.0 10 101 1.218791
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1.0 10 128 1.248498
1.0 10 129 1.214418
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1.0 10 131 1.224726
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1.0 10 155 1.199606
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1.0 10 159 1.242487
1.0 10 160 1.229731
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1.0 10 162 1.222139
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1.0 10 165 1.224157
1.0 10 166 1.224351
1.0 10 167 1.218668
1.0 10 168 1.222091
1.0 10 169 1.201289
1.0 10 170 1.221227
1.0 10 171 1.222704
1.0 10 172 1.210553
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1.0 10 175 1.207279
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1.0 10 183 1.237183
1.0 10 184 1.200308
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1.0 10 188 1.221414
1.0 10 189 1.213205
1.0 10 190 1.214963
1.0 10 191 1.218718
1.0 10 192 1.200861
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1.0 10 195 1.213695
1.0 10 196 1.192250
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1.0 10 198 1.214812
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1.0 10 201 1.243330
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1.0 10 203 1.187876
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1.0 10 213 1.199874
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1.0 10 222 1.219881
1.0 10 223 1.205904
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1.0 10 226 1.182954
1.0 10 227 1.218711
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1.0 10 231 1.240763
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1.0 10 234 1.208513
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1.0 10 265 1.262781
1.0 10 268 1.246026
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1.0 10 283 1.249608
1.0 10 286 1.216045
1.0 10 289 1.252018
1.0 10 292 1.256638
1.0 10 295 1.234850
1.0 10 298 1.233412
1.0 10 301 1.229052
1.0 10 304 1.256655
1.0 10 307 1.265051
1.0 10 310 1.216581
1.0 10 313 1.202674
1.0 10 316 1.239622
1.0 10 319 1.194714
1.0 10 322 1.235846
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1.0 10 328 1.257563
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1.0 10 334 1.257522
1.0 10 337 1.200446
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1.0 10 343 1.203929
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1.0 10 352 1.261802
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1.0 10 358 1.224600
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1.0 10 364 1.235070
1.0 10 367 1.202575
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1.0 10 373 1.221161
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1.0 10 379 1.217830
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1.0 10 388 1.191653
1.0 10 391 1.246783
1.0 10 394 1.250030
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1.0 10 400 1.208409
1.0 10 403 1.230840
1.0 10 406 1.248953
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1.0 10 454 1.204654
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1.0 10 466 1.191102
1.0 10 469 1.206865
1.0 10 472 1.236193
1.0 10 475 1.200291
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1.0 10 481 1.201188
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1.0 10 490 1.226460
1.0 10 493 1.241311
1.0 10 496 1.191058
1.0 10 499 1.227015
1.0 10 525 1.165180
1.0 10 550 1.110268
1.0 10 575 1.230781
1.0 10 600 1.192059
1.0 10 625 1.195411
1.0 10 650 1.212329
1.0 10 675 1.169744
1.0 10 700 1.213947
1.0 10 725 1.193758
1.0 10 750 1.180347
1.0 10 775 1.184898
1.0 10 800 1.103175
1.0 10 825 1.221963
1.0 10 850 1.186584
1.0 10 875 1.221892
1.0 10 900 1.196371
1.0 10 925 1.252739
1.0 10 950 1.101468
1.0 10 975 1.263189
1.0 10 1000 1.222401
1.0 10 1050 1.135776
1.0 10 1100 1.154648
1.0 10 1150 1.162423
1.0 10 1200 1.164981
1.0 10 1250 0.987190
1.0 10 1300 1.239095
1.0 10 1350 1.048220
1.0 10 1400 1.188283
1.0 10 1450 1.033241
1.0 10 1500 1.059067
1.0 10 1550 1.183519
1.0 10 1600 1.103589
1.0 10 1650 1.068373
1.0 10 1700 1.180661
1.0 10 1750 1.189039
1.0 10 1800 1.070241
1.0 10 1850 0.945649
1.0 10 1900 1.073914
1.0 10 1950 1.093387
1.0 10 2000 1.022899
1.0 10 2050 1.026694
1.0 10 2100 1.029019
1.0 10 2150 1.004696
1.0 10 2200 1.069463
1.0 10 2250 1.019953
1.0 10 2300 1.142940
1.0 10 2350 0.988793
1.0 10 2400 1.041736
1.0 10 2450 1.110481
1.0 10 2500 1.071215
1.0 10 2550 1.030970
1.0 10 2600 1.047531
1.0 10 2650 1.065709
1.0 10 2700 1.036604
1.0 10 2750 1.006321
1.0 10 2800 0.977892
1.0 10 2850 1.084640
1.0 10 2900 0.983504
1.0 10 2950 1.062116
1.0 10 3000 0.988494
1.0 10 3050 1.117115
1.0 10 3200 0.971324
1.0 10 3350 1.003983
1.0 10 3500 0.896604
1.0 10 3650 1.020904
1.0 10 3800 1.012014
1.0 10 3950 0.995464
1.0 10 4100 0.990123
1.0 10 4250 0.940789
1.0 10 4400 1.011878
1.0 10 4550 0.926603
1.0 10 4700 0.948516
1.0 10 4850 0.935026
1.0 10 5000 0.905223
1.0 10 5150 0.919518
1.0 10 5300 0.919514
1.0 10 5450 0.960046
1.0 10 5600 0.899989
1.0 10 5750 0.884083
1.0 10 5900 0.905747
1.0 10 6050 0.929900
1.0 10 6200 0.873463
1.0 10 6350 0.925596
1.0 10 6500 0.899913
1.0 10 6650 0.900806
1.0 10 6800 0.908027
1.0 10 6950 0.905968
1.0 10 7100 0.863227
1.0 10 7250 0.881093
1.0 10 7400 0.881934
1.0 10 7500 0.873331
1.0 10 7750 0.902654
1.0 10 8000 0.878639
1.0 10 8250 0.859967
1.0 10 8500 0.770627
1.0 10 8750 0.931189
1.0 10 9000 0.759362
1.0 10 9250 0.849352
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1.0 10 9750 0.844056
1.0 10 10000 0.846384
1.0 15 1 1.176969
1.0 15 2 1.145527
1.0 15 3 1.182648
1.0 15 4 1.149913
1.0 15 5 1.166283
1.0 15 6 1.147629
1.0 15 7 1.184010
1.0 15 8 1.192781
1.0 15 9 1.183040
1.0 15 10 1.203506
1.0 15 11 1.212456
1.0 15 12 1.211013
1.0 15 13 1.242256
1.0 15 14 1.192194
1.0 15 15 1.227466
1.0 15 16 1.231621
1.0 15 17 1.234234
1.0 15 18 1.198126
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1.0 15 21 1.235770
1.0 15 22 1.255805
1.0 15 23 1.228376
1.0 15 24 1.233041
1.0 15 25 1.241490
1.0 15 26 1.234812
1.0 15 27 1.238352
1.0 15 28 1.244722
1.0 15 29 1.251537
1.0 15 30 1.241568
1.0 15 31 1.233330
1.0 15 32 1.236191
1.0 15 33 1.229475
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1.0 15 35 1.233242
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1.0 15 56 1.231162
1.0 15 57 1.242993
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1.0 15 158 1.236818
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1.0 15 176 1.218362
1.0 15 177 1.226666
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1.0 15 1550 0.989379
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1.0 15 1700 0.990275
1.0 15 1750 1.058372
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1.0 15 2100 0.945191
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1.0 15 2200 1.252091
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1.0 15 2350 0.945516
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1.0 15 2800 1.073582
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1.0 15 3000 0.969523
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1.0 15 4850 0.895937
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1.0 15 5900 0.879747
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1.0 15 6200 0.856902
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1.0 15 6500 0.901287
1.0 15 6650 0.859439
1.0 15 6800 0.883204
1.0 15 6950 0.862381
1.0 15 7100 0.859065
1.0 15 7250 0.869596
1.0 15 7400 0.837770
1.0 15 7500 0.901031
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1.0 15 8000 0.843773
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1.0 20 1 1.132743
1.0 20 2 1.142601
1.0 20 3 1.148375
1.0 20 4 1.149372
1.0 20 5 1.170105
1.0 20 6 1.175987
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1.0 20 11 1.203644
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1.0 20 94 1.212851
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1.0 20 247 1.214002
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1.0 20 253 1.219349
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1.0 20 262 1.225952
1.0 20 265 1.230069
1.0 20 268 1.222111
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1.0 20 274 1.211866
1.0 20 277 1.222324
1.0 20 280 1.222010
1.0 20 283 1.225572
1.0 20 286 1.226156
1.0 20 289 1.232301
1.0 20 292 1.212112
1.0 20 295 1.214577
1.0 20 298 1.212278
1.0 20 301 1.229387
1.0 20 304 1.205347
1.0 20 307 1.209396
1.0 20 310 1.207574
1.0 20 313 1.214894
1.0 20 316 1.214046
1.0 20 319 1.218305
1.0 20 322 1.212454
1.0 20 325 1.218082
1.0 20 328 1.221868
1.0 20 331 1.215650
1.0 20 334 1.249561
1.0 20 337 1.207983
1.0 20 340 1.213688
1.0 20 343 1.202328
1.0 20 346 1.201359
1.0 20 349 1.204989
1.0 20 352 1.235424
1.0 20 355 1.198826
1.0 20 358 1.208320
1.0 20 361 1.228293
1.0 20 364 1.202567
1.0 20 367 1.225680
1.0 20 370 1.204621
1.0 20 373 1.218837
1.0 20 376 1.210834
1.0 20 379 1.200787
1.0 20 382 1.192677
1.0 20 385 1.208226
1.0 20 388 1.204001
1.0 20 391 1.207452
1.0 20 394 1.205608
1.0 20 397 1.207407
1.0 20 400 1.192706
1.0 20 403 1.204187
1.0 20 406 1.214272
1.0 20 409 1.205876
1.0 20 412 1.214772
1.0 20 415 1.206721
1.0 20 418 1.187278
1.0 20 421 1.199579
1.0 20 424 1.222825
1.0 20 427 1.240559
1.0 20 430 1.214647
1.0 20 433 1.170312
1.0 20 436 1.220325
1.0 20 439 1.175809
1.0 20 442 1.190796
1.0 20 445 1.221755
1.0 20 448 1.193657
1.0 20 451 1.219383
1.0 20 454 1.193733
1.0 20 457 1.174093
1.0 20 460 1.195757
1.0 20 463 1.204864
1.0 20 466 1.183230
1.0 20 469 1.178458
1.0 20 472 1.185550
1.0 20 475 1.221189
1.0 20 478 1.182609
1.0 20 481 1.219208
1.0 20 484 1.190018
1.0 20 487 1.176686
1.0 20 490 1.164275
1.0 20 493 1.188303
1.0 20 496 1.191332
1.0 20 499 1.241129
1.0 20 525 1.164791
1.0 20 550 1.187443
1.0 20 575 1.153689
1.0 20 600 1.184718
1.0 20 625 1.221181
1.0 20 650 1.157052
1.0 20 675 1.211220
1.0 20 700 1.140321
1.0 20 725 1.068323
1.0 20 750 1.174438
1.0 20 775 1.182192
1.0 20 800 1.144779
1.0 20 825 1.179767
1.0 20 850 1.121109
1.0 20 875 1.224364
1.0 20 900 1.072040
1.0 20 925 1.129039
1.0 20 950 1.092616
1.0 20 975 1.077952
1.0 20 1000 1.091931
1.0 20 1050 1.157743
1.0 20 1100 1.150217
1.0 20 1150 1.167531
1.0 20 1200 1.053428
1.0 20 1250 1.080529
1.0 20 1300 1.219693
1.0 20 1350 1.130613
1.0 20 1400 1.000634
1.0 20 1450 1.077057
1.0 20 1500 1.079680
1.0 20 1550 1.029002
1.0 20 1600 1.084954
1.0 20 1650 1.010587
1.0 20 1700 1.030096
1.0 20 1750 1.031237
1.0 20 1800 1.022259
1.0 20 1850 1.014426
1.0 20 1900 1.009226
1.0 20 1950 0.995626
1.0 20 2000 1.002350
1.0 20 2050 0.990666
1.0 20 2100 0.982202
1.0 20 2150 0.984463
1.0 20 2200 0.987028
1.0 20 2250 0.993710
1.0 20 2300 0.967511
1.0 20 2350 0.962748
1.0 20 2400 0.970703
1.0 20 2450 0.963559
1.0 20 2500 0.979672
1.0 20 2550 0.953270
1.0 20 2600 0.929786
1.0 20 2650 0.920898
1.0 20 2700 0.959913
1.0 20 2750 0.971278
1.0 20 2800 0.960432
1.0 20 2850 0.932502
1.0 20 2900 0.951026
1.0 20 2950 0.924686
1.0 20 3000 0.965248
1.0 20 3050 0.922671
1.0 20 3200 0.914218
1.0 20 3350 0.912370
1.0 20 3500 0.895544
1.0 20 3650 0.896342
1.0 20 3800 0.862094
1.0 20 3950 0.895702
1.0 20 4100 0.893775
1.0 20 4250 0.886263
1.0 20 4400 0.915745
1.0 20 4550 0.879507
1.0 20 4700 0.871657
1.0 20 4850 0.864983
1.0 20 5000 0.878983
1.0 20 5150 0.853969
1.0 20 5300 0.857648
1.0 20 5450 0.870694
1.0 20 5600 0.841309
1.0 20 5750 0.841527
1.0 20 5900 0.870470
1.0 20 6050 0.841543
1.0 20 6200 0.851183
1.0 20 6350 0.825646
1.0 20 6500 0.834618
1.0 20 6650 0.825462
1.0 20 6800 0.813199
1.0 20 6950 0.836459
1.0 20 7100 0.821300
1.0 20 7250 0.849475
1.0 20 7400 0.818950
1.0 20 7500 0.833316
1.0 20 7750 0.843069
1.0 20 8000 0.820460
1.0 20 8250 0.820687
1.0 20 8500 0.805556
1.0 20 8750 0.823493
1.0 20 9000 0.795296
1.0 20 9250 0.810677
1.0 20 9500 0.793363
1.0 20 9750 0.801874
1.0 20 10000 0.800122

plot.py

import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from statsmodels.nonparametric.smoothers_lowess import lowess

data = pd.read_csv("bench.txt", sep=" ", header=None).values

sparseness = [0.25, 0.5, 0.75, 1]
coeff = [5, 10, 15, 20]
alldata = {}
div = len(coeff) * len(sparseness)


i = 0
for s in sparseness:
	alldata[s] = {}
	for c in coeff:
		xx = data[i:i+(len(data)/div), 2]
		yy = data[i:i+(len(data)/div), 3]
		filtered = lowess(yy, xx, is_sorted=True, frac=0.03, it=0)
		alldata[s][c] = [filtered[:,0], filtered[:,1]]
		i += len(data)/div

plt.figure(figsize=(15, 30))

plt.subplot(411)
plt.plot(alldata[1][5][0], alldata[1][5][1], 'r', alldata[1][10][0], alldata[1][10][1], 'g', alldata[1][15][0], alldata[1][15][1], 'b', alldata[1][20][0], alldata[1][20][1], 'y')
plt.axhline(y=1, color='black')
plt.legend(['5-bit coefficients', '10-bit coefficients', '15-bit coefficients', '20-bit coefficients'], loc=3)
plt.ylabel("Time taken : KS2/KS1")
plt.title("Sparseness : 1.0")

plt.subplot(412)
plt.plot(alldata[0.75][5][0], alldata[0.75][5][1], 'r', alldata[0.75][10][0], alldata[0.75][10][1], 'g', alldata[0.75][15][0], alldata[0.75][15][1], 'b', alldata[0.75][20][0], alldata[0.75][20][1], 'y')
plt.axhline(y=1, color='black')
plt.legend(['5-bit coefficients', '10-bit coefficients', '15-bit coefficients', '20-bit coefficients'], loc=3)
plt.ylabel("Time taken : KS2/KS1")
plt.title("Sparseness : 0.75")

plt.subplot(413)
plt.plot(alldata[0.5][5][0], alldata[0.5][5][1], 'r', alldata[0.5][10][0], alldata[0.5][10][1], 'g', alldata[0.5][15][0], alldata[0.5][15][1], 'b', alldata[0.5][20][0], alldata[0.5][20][1], 'y')
plt.axhline(y=1, color='black')
plt.legend(['5-bit coefficients', '10-bit coefficients', '15-bit coefficients', '20-bit coefficients'], loc=3)
plt.ylabel("Time taken : KS2/KS1")
plt.title("Sparseness : 0.5")


plt.subplot(414)
plt.plot(alldata[0.75][5][0], alldata[0.75][5][1], 'r', alldata[0.75][10][0], alldata[0.75][10][1], 'g', alldata[0.75][15][0], alldata[0.75][15][1], 'b', alldata[0.75][20][0], alldata[0.75][20][1], 'y')
plt.axhline(y=1, color='black')
plt.legend(['5-bit coefficients', '10-bit coefficients', '15-bit coefficients', '20-bit coefficients'], loc=3)

plt.xlabel("Polynomial Length")
plt.title("Sparseness : 0.25")
plt.ylabel("Time taken : KS2/KS1")

plt.savefig("linear.png")

plt.figure(figsize=(15, 30))

plt.subplot(411)
plt.semilogx(alldata[1][5][0], alldata[1][5][1], 'r', alldata[1][10][0], alldata[1][10][1], 'g', alldata[1][15][0], alldata[1][15][1], 'b', alldata[1][20][0], alldata[1][20][1], 'y')
plt.axhline(y=1, color='black')
plt.legend(['5-bit coefficients', '10-bit coefficients', '15-bit coefficients', '20-bit coefficients'], loc=3)
plt.ylabel("Time taken : KS2/KS1")
plt.title("Sparseness : 1.0")

plt.subplot(412)
plt.semilogx(alldata[0.75][5][0], alldata[0.75][5][1], 'r', alldata[0.75][10][0], alldata[0.75][10][1], 'g', alldata[0.75][15][0], alldata[0.75][15][1], 'b', alldata[0.75][20][0], alldata[0.75][20][1], 'y')
plt.axhline(y=1, color='black')
plt.legend(['5-bit coefficients', '10-bit coefficients', '15-bit coefficients', '20-bit coefficients'], loc=3)
plt.ylabel("Time taken : KS2/KS1")
plt.title("Sparseness : 0.75")

plt.subplot(413)
plt.semilogx(alldata[0.5][5][0], alldata[0.5][5][1], 'r', alldata[0.5][10][0], alldata[0.5][10][1], 'g', alldata[0.5][15][0], alldata[0.5][15][1], 'b', alldata[0.5][20][0], alldata[0.5][20][1], 'y')
plt.axhline(y=1, color='black')
plt.legend(['5-bit coefficients', '10-bit coefficients', '15-bit coefficients', '20-bit coefficients'], loc=3)
plt.ylabel("Time taken : KS2/KS1")
plt.title("Sparseness : 0.5")


plt.subplot(414)
plt.semilogx(alldata[0.75][5][0], alldata[0.75][5][1], 'r', alldata[0.75][10][0], alldata[0.75][10][1], 'g', alldata[0.75][15][0], alldata[0.75][15][1], 'b', alldata[0.75][20][0], alldata[0.75][20][1], 'y')
plt.axhline(y=1, color='black')
plt.legend(['5-bit coefficients', '10-bit coefficients', '15-bit coefficients', '20-bit coefficients'], loc=3)

plt.xlabel("Polynomial Length")
plt.title("Sparseness : 0.25")
plt.ylabel("Time taken : KS2/KS1")

plt.savefig("log.png")