weitere 1D evo

This commit is contained in:
Nicole Dresselhaus 2017-10-05 14:27:39 +02:00
parent 952022949d
commit b898fad7ad
Signed by: Drezil
GPG Key ID: 057D94F356F41E25
34 changed files with 8162 additions and 3 deletions

View File

@ -0,0 +1,501 @@
"Least squares",regularity,variability,improvement,steps,"Evolution error",sigma
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123.79,0.0302927,0.00217778,0.95866,290,129.972,0.0120363
128.023,0.0269328,0.00217778,0.957247,263,134.171,0.0176036
97.4169,0.0294049,0.00217778,0.967467,283,102.086,0.0154436
131.497,0.032637,0.00217778,0.956086,194,137.823,0.0230214
114.486,0.0296235,0.00217778,0.961767,209,120.165,0.0182154
115.802,0.030273,0.00217778,0.961328,249,121.511,0.0168117
104.888,0.0298335,0.00217778,0.964972,266,110.067,0.0157024
107.529,0.0299312,0.00217778,0.96409,214,112.614,0.0242658
100.39,0.0284705,0.00217778,0.966475,228,105.271,0.0152932
134.206,0.0300452,0.00217778,0.955182,190,140.821,0.0262343
118.423,0.0285713,0.00217778,0.960452,230,124.285,0.0195539
150.763,0.0269517,0.00217778,0.949652,188,158.103,0.0213998
134.048,0.0302692,0.00217778,0.955234,169,140.515,0.0308499
96.9072,0.029681,0.00217778,0.967638,245,101.64,0.0165011
123.811,0.0258042,0.00217778,0.958653,254,129.887,0.0140323
159.564,0.0304946,0.00217778,0.946713,205,167.194,0.0194008
105.757,0.0290973,0.00217778,0.964682,238,110.881,0.0179311
139.738,0.0289451,0.00217778,0.953334,194,146.669,0.0262391
112.768,0.0276706,0.00217778,0.962341,195,118.035,0.0156339
143.501,0.0254884,0.00217778,0.952078,256,150.583,0.0127485
136.72,0.0262001,0.00217778,0.954342,244,143.211,0.0193324
109.952,0.0287195,0.00217778,0.963281,223,114.785,0.0241593
139.559,0.029377,0.00217778,0.953394,187,146.421,0.0183773
124.8,0.028991,0.00217778,0.958323,220,130.875,0.0179003
102.291,0.0285261,0.00217778,0.96584,278,107.373,0.0140422
144.967,0.0281308,0.00217778,0.951588,234,152.164,0.0189975
123.808,0.031638,0.00217778,0.958654,185,129.282,0.0314699
93.612,0.0275759,0.00217778,0.968738,271,98.1416,0.0124221
123.829,0.0279283,0.00217778,0.958647,199,129.997,0.0222236
111.654,0.0290171,0.00217778,0.962713,183,117,0.0200822
105.848,0.0278707,0.00217778,0.964652,235,111.092,0.0159004
108.727,0.0240196,0.00217778,0.96369,330,114.087,0.0151336
102.465,0.0285859,0.00217778,0.965782,267,107.558,0.0137917
137.886,0.0315396,0.00217778,0.953953,162,144.45,0.0198371
112.847,0.0258472,0.00217778,0.962315,233,118.484,0.0194957
104.672,0.0280787,0.00217778,0.965045,270,109.701,0.017613
121.264,0.0247246,0.00217778,0.959504,181,126.593,0.0240936
161.034,0.0240912,0.00217778,0.946223,165,168.971,0.0262645
137.026,0.0283714,0.00217778,0.95424,262,143.838,0.02069
1 Least squares regularity variability improvement steps Evolution error sigma
2 183.763 0.0179571 0.00111111 0.938632 228 192.44 0.032644
3 229.099 0.0189281 0.00111111 0.923492 96 240.171 0.0562716
4 238.479 0.0215758 0.00111111 0.920359 195 249.883 0.0272032
5 188.152 0.0144312 0.00111111 0.937166 256 197.529 0.0244736
6 191.586 0.0207835 0.00111111 0.936019 156 201.143 0.0235659
7 202.916 0.0168021 0.00111111 0.932236 220 212.978 0.0318143
8 178.439 0.0180162 0.00111111 0.94041 259 187.236 0.0269287
9 229.734 0.0238245 0.00111111 0.92328 203 241.13 0.0281426
10 211.994 0.0192363 0.00111111 0.929204 211 222.511 0.0152364
11 245.717 0.0185166 0.00111111 0.917942 154 256.592 0.0379309
12 225.543 0.0204032 0.00111111 0.92468 160 236.693 0.0243299
13 211.533 0.0207268 0.00111111 0.929358 135 221.694 0.0377055
14 213.806 0.022426 0.00111111 0.928599 188 224.469 0.0237864
15 223.483 0.0172601 0.00111111 0.925368 203 234.382 0.0196412
16 231.924 0.0177059 0.00111111 0.922549 209 243.433 0.0259235
17 184.824 0.0162623 0.00111111 0.938278 242 194.032 0.0222468
18 223.766 0.0179083 0.00111111 0.925273 207 234.567 0.0301655
19 203.27 0.0161445 0.00111111 0.932118 220 213.401 0.0273719
20 193.158 0.0166659 0.00111111 0.935494 221 201.428 0.0414548
21 197.851 0.0185201 0.00111111 0.933927 195 207.224 0.0362339
22 214.041 0.0178594 0.00111111 0.928521 139 224.585 0.0340444
23 211.533 0.018782 0.00111111 0.929358 132 221.895 0.0369246
24 185.356 0.0195083 0.00111111 0.9381 222 194.499 0.0184425
25 198.81 0.0201802 0.00111111 0.933607 242 208.589 0.0259606
26 205.265 0.0202697 0.00111111 0.931451 184 215.456 0.0190532
27 222.952 0.019699 0.00111111 0.925545 178 234.028 0.0261726
28 205.276 0.0193633 0.00111111 0.931448 191 214.716 0.02818
29 230.047 0.0214453 0.00111111 0.923175 216 241.431 0.0234518
30 196.924 0.0190251 0.00111111 0.934237 183 206.599 0.0212354
31 195.184 0.0181113 0.00111111 0.934818 178 204.769 0.0299481
32 194.787 0.018323 0.00111111 0.934951 212 204.23 0.0294953
33 212.533 0.0182128 0.00111111 0.929024 231 222.938 0.0412254
34 176.623 0.0158336 0.00111111 0.941017 209 185.355 0.0266792
35 209.136 0.0204612 0.00111111 0.930159 91 218.429 0.0579325
36 209.848 0.0201986 0.00111111 0.929921 163 219.886 0.0336554
37 209.515 0.0191636 0.00111111 0.930032 185 219.696 0.0286176
38 193.943 0.0178079 0.00111111 0.935233 199 203.581 0.0232988
39 216.239 0.0212003 0.00111111 0.927787 151 226.759 0.0189421
40 211.37 0.0162232 0.00111111 0.929413 173 221.819 0.0171682
41 224.988 0.0196037 0.00111111 0.924865 112 236.226 0.0602406
42 207.221 0.0185908 0.00111111 0.930798 157 217.553 0.0314001
43 203.513 0.0172686 0.00111111 0.932037 188 213.564 0.0328506
44 186.642 0.0195431 0.00111111 0.937671 178 195.759 0.0419734
45 236.265 0.0204047 0.00111111 0.921099 106 247.931 0.0409088
46 222.669 0.0231925 0.00111111 0.925639 162 233.713 0.0215862
47 223.48 0.0208053 0.00111111 0.925369 190 234.013 0.0261078
48 213.098 0.0182925 0.00111111 0.928835 227 223.628 0.0354102
49 185.847 0.0198307 0.00111111 0.937936 217 194.983 0.0198853
50 215.818 0.0211463 0.00111111 0.927927 212 226.437 0.0327274
51 203.914 0.0228368 0.00111111 0.931903 219 214.086 0.0166239
52 177.639 0.0183023 0.00111111 0.940677 243 186.419 0.0388416
53 187.065 0.0182927 0.00111111 0.937529 217 196.416 0.0185577
54 224.069 0.0206082 0.00111111 0.925172 240 235.058 0.0214527
55 233.059 0.020766 0.00111111 0.922169 249 244.587 0.0304497
56 243.252 0.0197131 0.00111111 0.918766 218 255.376 0.0210078
57 216.096 0.018823 0.00111111 0.927834 240 226.808 0.0218857
58 229.899 0.0235834 0.00111111 0.923225 185 241.372 0.0226563
59 214.545 0.02158 0.00111111 0.928352 180 225.08 0.0281248
60 201.315 0.0215738 0.00111111 0.932771 122 210.821 0.0306565
61 196.866 0.0199231 0.00111111 0.934256 254 206.672 0.0171958
62 191.811 0.0187791 0.00111111 0.935944 264 201.399 0.0284519
63 234.589 0.0153778 0.00111111 0.921659 173 246.066 0.0288251
64 241.822 0.0210075 0.00111111 0.919243 147 253.875 0.0448453
65 247.389 0.020118 0.00111111 0.917384 180 259.741 0.0237541
66 197.862 0.0191194 0.00111111 0.933924 258 207.655 0.0309044
67 227.298 0.0193875 0.00111111 0.924094 189 238.654 0.0244366
68 203.016 0.0194783 0.00111111 0.932203 345 213.147 0.0293344
69 200.344 0.0181137 0.00111111 0.933095 211 210.34 0.0279667
70 260.653 0.0211892 0.00111111 0.912955 159 273.684 0.0173598
71 190.801 0.018724 0.00111111 0.936282 145 200.321 0.0244174
72 219.31 0.0152034 0.00111111 0.926761 298 230.127 0.0304617
73 201.013 0.0189705 0.00111111 0.932871 154 210.898 0.0368793
74 214.751 0.0212631 0.00111111 0.928283 189 224.914 0.0281325
75 199.042 0.0200792 0.00111111 0.93353 228 208.711 0.0294291
76 222.258 0.0190311 0.00111111 0.925777 220 233.241 0.0218053
77 193.965 0.0185556 0.00111111 0.935225 281 203.658 0.0236132
78 216.305 0.0220209 0.00111111 0.927765 187 227.058 0.0289074
79 209.518 0.0164836 0.00111111 0.930031 193 219.89 0.0252204
80 202.805 0.0195855 0.00111111 0.932273 235 212.877 0.0518397
81 205.584 0.0203958 0.00111111 0.931345 203 215.439 0.0362367
82 181.975 0.0182167 0.00111111 0.939229 173 191.017 0.0257238
83 161.995 0.0204489 0.00111111 0.945902 260 170.069 0.0166518
84 194.688 0.0204944 0.00111111 0.934984 184 204.348 0.0314503
85 185.811 0.0200401 0.00111111 0.937948 332 195.049 0.0270099
86 197.624 0.020823 0.00111111 0.934003 281 207.186 0.0391989
87 214.766 0.0218128 0.00111111 0.928278 157 225.229 0.0353567
88 219.632 0.0173274 0.00111111 0.926653 160 230.466 0.036049
89 202.581 0.0162571 0.00111111 0.932348 237 212.578 0.0297746
90 181.621 0.0177214 0.00111111 0.939347 316 190.496 0.0259462
91 250.268 0.0185787 0.00111111 0.916423 135 262.382 0.0245029
92 205.717 0.0178907 0.00111111 0.931301 255 215.988 0.0274579
93 197.391 0.0189724 0.00111111 0.934081 215 206.934 0.0376919
94 238.981 0.0223217 0.00111111 0.920192 100 250.737 0.0435432
95 196.091 0.01434 0.00111111 0.934515 190 205.827 0.0556249
96 202.756 0.0184759 0.00111111 0.932289 271 212.891 0.0270203
97 191.765 0.0184657 0.00111111 0.93596 193 201.034 0.0201355
98 202.525 0.0186188 0.00111111 0.932366 239 212.53 0.0201397
99 198.694 0.0176067 0.00111111 0.933646 278 208.545 0.0231376
100 196.578 0.0184124 0.00111111 0.934353 226 206.327 0.0391784
101 190.046 0.0198379 0.00111111 0.936534 133 199.413 0.0284771
102 198.091 0.0185605 0.00111111 0.933847 245 207.886 0.0152523
103 234.524 0.0192422 0.00111111 0.92168 203 245.873 0.0333896
104 199.665 0.0169589 0.00111111 0.933322 236 209.253 0.0256795
105 225.432 0.019794 0.00111111 0.924717 164 236.693 0.0303567
106 226.331 0.0198218 0.00111111 0.924416 133 237.512 0.0326327
107 207.081 0.0183173 0.00111111 0.930845 293 217.347 0.0173303
108 197.215 0.0195282 0.00111111 0.93414 144 206.725 0.0274424
109 207.985 0.018551 0.00111111 0.930543 167 218.216 0.0264904
110 199.179 0.0192478 0.00111111 0.933484 230 208.831 0.0280096
111 190.622 0.0185748 0.00111111 0.936342 146 199.805 0.040456
112 186.169 0.0193022 0.00111111 0.937829 196 195.163 0.0339487
113 184.072 0.0170487 0.00111111 0.938529 224 193.258 0.0325346
114 173.247 0.0203667 0.00111111 0.942144 191 181.901 0.0186411
115 174.505 0.0185505 0.00111111 0.941724 232 183.091 0.0391035
116 204.996 0.019809 0.00111111 0.931541 126 215.233 0.0317816
117 201.159 0.0177293 0.00111111 0.932823 231 210.992 0.033055
118 215.57 0.0184879 0.00111111 0.92801 157 226.197 0.0248099
119 220.4 0.0220813 0.00111111 0.926397 120 230.55 0.0266999
120 192.669 0.0189865 0.00111111 0.935658 197 201.997 0.0273896
121 217.249 0.0209109 0.00111111 0.927449 196 227.649 0.0314102
122 183.442 0.0177993 0.00111111 0.938739 276 192.577 0.0290511
123 211.058 0.0191396 0.00111111 0.929517 262 221.454 0.0258037
124 225.405 0.0142923 0.00111111 0.924726 232 236.59 0.0554429
125 214.129 0.0153926 0.00111111 0.928491 258 224.637 0.025707
126 191.681 0.0159947 0.00111111 0.935988 221 201.263 0.0168213
127 208.302 0.0168977 0.00111111 0.930437 267 218.685 0.0303307
128 244.265 0.0186614 0.00111111 0.918427 240 256.401 0.014142
129 217.424 0.0189707 0.00111111 0.927391 194 228.137 0.0234014
130 193.974 0.0187277 0.00111111 0.935222 200 203.421 0.0313489
131 217.843 0.0191009 0.00111111 0.927251 244 228.677 0.0186412
132 228.126 0.0195362 0.00111111 0.923817 171 239.173 0.0237806
133 194.236 0.0205534 0.00111111 0.935135 206 203.783 0.0222971
134 231.826 0.017672 0.00111111 0.922582 126 243.217 0.0354461
135 194.684 0.0180998 0.00111111 0.934985 149 204.188 0.0265668
136 201.513 0.0176892 0.00111111 0.932705 232 211.535 0.0277219
137 219.557 0.016394 0.00111111 0.926679 145 229.573 0.0466214
138 215.208 0.0203045 0.00111111 0.928131 217 225.773 0.0198841
139 224.784 0.0187965 0.00111111 0.924933 207 235.748 0.0249149
140 198.752 0.0192186 0.00111111 0.933626 214 208.659 0.0198508
141 210.374 0.0169978 0.00111111 0.929745 201 220.83 0.029392
142 182.397 0.0156577 0.00111111 0.939088 271 191.357 0.0356415
143 214.532 0.017907 0.00111111 0.928357 187 224.938 0.0315807
144 206.254 0.0198955 0.00111111 0.931121 144 216.195 0.0261497
145 208.845 0.019427 0.00111111 0.930256 302 218.868 0.0293849
146 219.847 0.018273 0.00111111 0.926582 205 230.63 0.0222702
147 178.072 0.0161684 0.00111111 0.940532 271 186.89 0.0224425
148 208.094 0.0174596 0.00111111 0.930507 188 218.199 0.026439
149 206.759 0.017792 0.00111111 0.930952 249 217.047 0.0196121
150 213.588 0.0179354 0.00111111 0.928672 154 223.644 0.0261114
151 203.691 0.0186109 0.00111111 0.931977 215 213.801 0.029634
152 196.02 0.0164293 0.00111111 0.934539 286 205.631 0.0248201
153 200.893 0.0206686 0.00111111 0.932911 218 210.824 0.0165101
154 219.308 0.018868 0.00111111 0.926762 202 230.178 0.0193291
155 246.019 0.0189353 0.00111111 0.917842 155 257.369 0.0271402
156 231.847 0.0197966 0.00111111 0.922574 168 243.262 0.0289334
157 218.91 0.0209849 0.00111111 0.926895 197 229.047 0.0299942
158 211.126 0.0167501 0.00111111 0.929494 196 221.493 0.0286991
159 169.535 0.0151348 0.00111111 0.943383 175 177.905 0.0337364
160 230.113 0.0201869 0.00111111 0.923153 167 241.468 0.0344348
161 231.924 0.0200713 0.00111111 0.922549 149 243.443 0.0257716
162 223.016 0.0200786 0.00111111 0.925524 178 233.782 0.0402102
163 195.674 0.014894 0.00111111 0.934654 268 205.347 0.0551882
164 255.95 0.0195439 0.00111111 0.914525 148 268.384 0.0447607
165 220.055 0.0198234 0.00111111 0.926512 236 230.853 0.0367555
166 216.155 0.0189146 0.00111111 0.927815 129 226.312 0.0430726
167 199.784 0.0187532 0.00111111 0.933282 199 209.55 0.0353292
168 222.549 0.0203982 0.00111111 0.925679 178 233.426 0.0233115
169 200.95 0.0183086 0.00111111 0.932893 256 210.991 0.016803
170 209.249 0.016696 0.00111111 0.930121 269 219.415 0.02081
171 249.03 0.0223912 0.00111111 0.916836 177 260.926 0.0294009
172 219.09 0.019419 0.00111111 0.926835 218 229.786 0.0289335
173 185.792 0.0169305 0.00111111 0.937954 291 194.888 0.0258978
174 184.383 0.0208635 0.00111111 0.938425 256 193.57 0.0185135
175 201.82 0.0183427 0.00111111 0.932602 236 211.086 0.0474114
176 226.705 0.0196988 0.00111111 0.924292 255 237.989 0.0227452
177 206.776 0.019382 0.00111111 0.930947 187 217.102 0.0280757
178 185.74 0.0141851 0.00111111 0.937972 166 194.775 0.0321003
179 232.792 0.0240912 0.00111111 0.922259 191 244.384 0.0176279
180 201.838 0.0188448 0.00111111 0.932596 181 211.814 0.0297108
181 202.159 0.0179769 0.00111111 0.932489 175 212.073 0.0239756
182 178.922 0.018115 0.00111111 0.940249 259 187.619 0.0215103
183 196.096 0.0172259 0.00111111 0.934514 220 205.625 0.030462
184 200.913 0.0195059 0.00111111 0.932905 162 210.781 0.0383751
185 182.439 0.020238 0.00111111 0.939074 172 191.55 0.0264845
186 169.79 0.018196 0.00111111 0.943298 226 178.258 0.0148133
187 185.191 0.0167884 0.00111111 0.938155 194 194.329 0.0261194
188 202.268 0.0211018 0.00111111 0.932452 200 212.217 0.0301675
189 191.444 0.0189881 0.00111111 0.936067 211 200.944 0.0234532
190 216.792 0.0196005 0.00111111 0.927602 200 227.453 0.0212078
191 252.534 0.0172473 0.00111111 0.915666 225 264.972 0.0178314
192 193.043 0.0156078 0.00111111 0.935533 226 202.656 0.0411515
193 192.167 0.0174455 0.00111111 0.935826 195 201.39 0.0254769
194 225.725 0.0178959 0.00111111 0.924619 240 236.882 0.018903
195 204.599 0.0213119 0.00111111 0.931674 171 214.712 0.0263085
196 185.635 0.0163761 0.00111111 0.938007 190 194.569 0.0284307
197 186.264 0.016572 0.00111111 0.937797 232 195.513 0.0210752
198 249.948 0.0194051 0.00111111 0.91653 246 262.158 0.0215199
199 240.864 0.0182591 0.00111111 0.919563 188 251.577 0.0306745
200 184.618 0.0171253 0.00111111 0.938346 222 193.849 0.0300316
201 213.473 0.0190352 0.00111111 0.92871 275 224.14 0.0207037
202 267.876 0.0160237 0.00124444 0.910542 173 280.917 0.041317
203 301.035 0.0149818 0.00124444 0.899469 192 315.729 0.0371435
204 252.283 0.016224 0.00124444 0.91575 186 264.639 0.0251523
205 262.811 0.0143632 0.00124444 0.912234 228 275.922 0.0292345
206 307.779 0.0168661 0.00124444 0.897217 247 323.159 0.0446967
207 287.196 0.0162634 0.00124444 0.90409 153 300.933 0.0402748
208 252.185 0.0186589 0.00124444 0.915782 204 264.541 0.0537674
209 252.368 0.0185828 0.00124444 0.915721 163 264.875 0.0516944
210 273.504 0.0153228 0.00124444 0.908663 206 286.999 0.0589335
211 299.8 0.015452 0.00124444 0.899881 252 314.771 0.0322635
212 242.916 0.0165246 0.00124444 0.918878 207 254.996 0.0375956
213 258.239 0.0130383 0.00124444 0.913761 240 270.99 0.0271465
214 320.936 0.0142181 0.00124444 0.892823 127 336.401 0.0474298
215 238.158 0.0182024 0.00124444 0.920467 195 249.761 0.0431037
216 296.336 0.0164207 0.00124444 0.901038 171 310.473 0.0315425
217 269.239 0.0126131 0.00124444 0.910087 187 282.476 0.0734215
218 288.295 0.0160129 0.00124444 0.903723 153 301.45 0.0409347
219 290.458 0.0165057 0.00124444 0.903001 215 304.67 0.0372574
220 286.663 0.0166434 0.00124444 0.904268 226 300.451 0.0414395
221 300.22 0.0182222 0.00124444 0.899741 174 315.122 0.0280977
222 288.785 0.0149497 0.00124444 0.90356 251 302.947 0.0310956
223 250.657 0.0167552 0.00124444 0.916293 192 262.796 0.0357012
224 259.912 0.012238 0.00124444 0.913202 293 272.873 0.0517974
225 277.775 0.0144144 0.00124444 0.907237 229 291.472 0.0314516
226 267.052 0.0167378 0.00124444 0.910818 204 280.073 0.040238
227 262.339 0.0144557 0.00124444 0.912391 189 274.973 0.0494119
228 264.729 0.0144438 0.00124444 0.911593 182 277.642 0.0599785
229 253.582 0.0174023 0.00124444 0.915316 230 266.096 0.0268471
230 286.594 0.0157891 0.00124444 0.904292 194 300.458 0.0522694
231 269.748 0.0129119 0.00124444 0.909917 181 281.797 0.0678929
232 276.445 0.0153961 0.00124444 0.907681 172 287.84 0.0616759
233 257.365 0.0192891 0.00124444 0.914053 137 270.181 0.0442534
234 290.578 0.0130388 0.00124444 0.902961 230 304.713 0.0312996
235 286.822 0.0141783 0.00124444 0.904215 228 301.015 0.0325938
236 239.331 0.0156718 0.00124444 0.920075 296 250.936 0.0318135
237 312.447 0.0182524 0.00124444 0.895658 252 327.876 0.0256484
238 254.695 0.0185449 0.00124444 0.914944 202 267.093 0.0335235
239 254.524 0.0141017 0.00124444 0.915001 249 266.032 0.0379196
240 280.578 0.0169944 0.00124444 0.906301 214 293.5 0.0607412
241 262.034 0.0159614 0.00124444 0.912493 196 274.145 0.0441671
242 288.236 0.0136483 0.00124444 0.903743 211 302.284 0.0553983
243 282.566 0.0162714 0.00124444 0.905637 259 296.447 0.0386496
244 277.384 0.0144486 0.00124444 0.907367 234 290.496 0.0723565
245 310.869 0.0161428 0.00124444 0.896185 255 326.409 0.0480313
246 240.505 0.0152832 0.00124444 0.919683 216 252.376 0.0350769
247 271.764 0.0168533 0.00124444 0.909244 161 285.256 0.0431168
248 248.918 0.0160118 0.00124444 0.916874 204 261.023 0.05097
249 260.936 0.0166911 0.00124444 0.91286 319 273.732 0.0188483
250 273.958 0.0145448 0.00124444 0.908511 251 287.211 0.0305499
251 235.056 0.0163734 0.00124444 0.921503 258 246.715 0.0317013
252 303.02 0.0151119 0.00124444 0.898806 199 317.892 0.0592121
253 253.213 0.0182013 0.00124444 0.915439 236 265.825 0.0371061
254 247.874 0.0157508 0.00124444 0.917222 245 259.862 0.0417326
255 260.379 0.0137206 0.00124444 0.913046 235 273.217 0.0402591
256 257.113 0.0160861 0.00124444 0.914137 256 269.759 0.0330447
257 299.543 0.0150175 0.00124444 0.899967 220 314.394 0.045574
258 302.404 0.0167334 0.00124444 0.899012 187 314.765 0.0707083
259 271.225 0.0181028 0.00124444 0.909424 257 284.627 0.0206609
260 250.03 0.0160492 0.00124444 0.916502 232 262.319 0.0595344
261 256.332 0.0128525 0.00124444 0.914397 220 269.132 0.0465511
262 248.162 0.018139 0.00124444 0.917126 202 259.973 0.0298866
263 282.813 0.0169151 0.00124444 0.905554 159 296.171 0.0453673
264 252.465 0.0164028 0.00124444 0.915689 228 264.153 0.0481631
265 294.434 0.0150393 0.00124444 0.901673 195 307.381 0.0516829
266 237.397 0.0176428 0.00124444 0.920721 217 248.894 0.021071
267 297.923 0.0140163 0.00124444 0.900508 238 312.436 0.0434852
268 260.603 0.012484 0.00124444 0.912971 309 273.599 0.0320857
269 273.926 0.0168922 0.00124444 0.908522 249 286.954 0.0443297
270 298.718 0.0174071 0.00124444 0.900243 209 313.315 0.0236055
271 276.871 0.0167161 0.00124444 0.907539 268 290.546 0.0248795
272 302.089 0.0162225 0.00124444 0.899117 262 317.095 0.015801
273 276.551 0.0114517 0.00124444 0.907645 178 289.397 0.045748
274 280.724 0.0182241 0.00124444 0.906252 184 293.925 0.0576927
275 261.057 0.0172636 0.00124444 0.912819 152 273.573 0.0649999
276 236.373 0.0156888 0.00124444 0.921063 257 248.052 0.0402787
277 270.794 0.0143144 0.00124444 0.909568 234 282.84 0.0429796
278 272.7 0.0156175 0.00124444 0.908931 170 286.257 0.0583084
279 270.983 0.012891 0.00124444 0.909505 247 284.314 0.0303283
280 306.135 0.0148554 0.00124444 0.897766 175 321.302 0.0456628
281 248.478 0.016144 0.00124444 0.91702 227 260.894 0.0294851
282 265.44 0.0103717 0.00124444 0.911356 274 278.436 0.0266817
283 262.233 0.0150257 0.00124444 0.912427 235 274.697 0.0387567
284 256.662 0.0181041 0.00124444 0.914287 202 269.428 0.0345583
285 274.338 0.0138554 0.00124444 0.908385 181 287.274 0.0490538
286 268.815 0.0144361 0.00124444 0.910229 222 281.924 0.0243106
287 251.425 0.0171867 0.00124444 0.916036 241 263.843 0.0501147
288 284.735 0.0165221 0.00124444 0.904912 200 298.757 0.0397164
289 262.717 0.0162912 0.00124444 0.912265 229 275.521 0.0401815
290 256.866 0.0145304 0.00124444 0.914219 249 269.146 0.0306201
291 260.708 0.0144833 0.00124444 0.912936 210 273.475 0.0343934
292 261.745 0.0147709 0.00124444 0.91259 222 273.666 0.033181
293 284.729 0.0160625 0.00124444 0.904914 197 298.125 0.0467389
294 291.835 0.0146895 0.00124444 0.902541 175 305.642 0.0475431
295 283.297 0.0163328 0.00124444 0.905392 349 297.086 0.0203907
296 303.006 0.0165804 0.00124444 0.898811 158 317.845 0.0610993
297 261.705 0.0175394 0.00124444 0.912603 323 274.586 0.0235008
298 316.6 0.0135961 0.00124444 0.894271 229 332.413 0.03878
299 287.234 0.0143036 0.00124444 0.904078 203 301.147 0.0376103
300 337.364 0.0140911 0.00124444 0.887337 150 354.08 0.0600292
301 253.797 0.0164939 0.00124444 0.915244 247 266.461 0.0321915
302 201.297 0.0153997 0.00124444 0.932777 211 211.096 0.0324233
303 222.924 0.0138755 0.00124444 0.925554 215 233.828 0.0321173
304 195.586 0.0165785 0.00124444 0.934684 233 205.276 0.0262902
305 249.343 0.0170463 0.00124444 0.916732 140 261.016 0.0297848
306 196.161 0.0169011 0.00124444 0.934492 167 205.753 0.0340118
307 232.94 0.0151993 0.00124444 0.922209 203 244.494 0.0327092
308 225.704 0.0187794 0.00124444 0.924626 200 236.857 0.0285021
309 232.061 0.0176159 0.00124444 0.922503 141 243.624 0.0341091
310 216.478 0.0193808 0.00124444 0.927707 205 227.071 0.0319641
311 218.526 0.0168255 0.00124444 0.927023 132 228.254 0.0371698
312 209.13 0.016102 0.00124444 0.930161 195 219.293 0.0254967
313 224.21 0.0097335 0.00124444 0.925125 260 235.159 0.0286628
314 229.764 0.0131346 0.00124444 0.92327 222 240.691 0.0421742
315 221.783 0.0154066 0.00124444 0.925935 234 232.853 0.0190093
316 232.166 0.0181673 0.00124444 0.922468 153 243.665 0.0176615
317 231.214 0.016363 0.00124444 0.922786 194 242.766 0.0286637
318 232.146 0.0160201 0.00124444 0.922474 151 243.618 0.0318045
319 226.741 0.0160234 0.00124444 0.92428 185 238.051 0.029258
320 214.091 0.0162548 0.00124444 0.928504 191 224.685 0.0293766
321 197.18 0.0183974 0.00124444 0.934152 200 206.919 0.0280479
322 254.147 0.0141509 0.00124444 0.915127 180 266.62 0.0401662
323 218.998 0.0166688 0.00124444 0.926865 172 229.771 0.0316734
324 229.888 0.015569 0.00124444 0.923229 172 241.243 0.0383093
325 218.144 0.0175268 0.00124444 0.92715 166 228.75 0.0277466
326 235.229 0.0139292 0.00124444 0.921445 122 246.415 0.0328971
327 234.47 0.0170242 0.00124444 0.921698 214 245.936 0.0311265
328 223.637 0.0147445 0.00124444 0.925316 275 234.603 0.0173437
329 220.006 0.01659 0.00124444 0.926529 223 230.971 0.0221785
330 234.897 0.0153778 0.00124444 0.921556 219 246.319 0.0330376
331 224.176 0.017539 0.00124444 0.925136 182 235.173 0.0316049
332 238.295 0.0167376 0.00124444 0.920421 310 250.199 0.0212912
333 230.074 0.0145246 0.00124444 0.923166 180 240.854 0.0325642
334 222.461 0.0172363 0.00124444 0.925709 226 233.456 0.0200254
335 206.544 0.0113106 0.00124444 0.931024 218 216.659 0.0203372
336 228.658 0.0143528 0.00124444 0.923639 227 240.033 0.0230481
337 232.581 0.0168069 0.00124444 0.922329 208 244.108 0.0315845
338 207.444 0.014401 0.00124444 0.930724 156 216.874 0.0423996
339 230.579 0.0161407 0.00124444 0.922998 222 242.058 0.0155056
340 211.161 0.0154943 0.00124444 0.929483 200 221.484 0.0288013
341 212.025 0.0162238 0.00124444 0.929194 131 222.485 0.0315666
342 228.383 0.0165074 0.00124444 0.923731 306 239.78 0.0203423
343 221.656 0.0168344 0.00124444 0.925978 247 232.709 0.0234514
344 220.116 0.0175812 0.00124444 0.926492 191 230.785 0.0250425
345 219.146 0.0195251 0.00124444 0.926816 168 229.968 0.0433433
346 224.008 0.0160092 0.00124444 0.925192 221 235.149 0.0209819
347 222.645 0.0136684 0.00124444 0.925647 221 233.462 0.028072
348 229.597 0.0142117 0.00124444 0.923326 194 241.027 0.0407908
349 218.437 0.015863 0.00124444 0.927053 246 229.139 0.0264266
350 200.414 0.0159252 0.00124444 0.933072 218 210.309 0.0307566
351 212.507 0.0156027 0.00124444 0.929033 204 222.927 0.0213849
352 225.552 0.014999 0.00124444 0.924676 255 236.762 0.0204812
353 233.174 0.0171498 0.00124444 0.922131 202 244.312 0.0424202
354 214.771 0.0198871 0.00124444 0.928277 257 225.283 0.0205486
355 217.951 0.0156941 0.00124444 0.927215 172 228.831 0.0309327
356 223.826 0.0132289 0.00124444 0.925253 178 234.735 0.0339935
357 211.781 0.0154319 0.00124444 0.929275 249 222.154 0.0359608
358 205.208 0.0165743 0.00124444 0.931471 241 215.144 0.0313585
359 236.053 0.0147814 0.00124444 0.92117 182 247.533 0.0489446
360 231.21 0.0149828 0.00124444 0.922787 202 242.563 0.0415887
361 220.924 0.0177705 0.00124444 0.926222 156 231.706 0.0301208
362 234.408 0.0159816 0.00124444 0.921719 145 245.743 0.0317348
363 222.408 0.0143535 0.00124444 0.925727 242 233.422 0.0246441
364 210.758 0.0174935 0.00124444 0.929617 255 221.213 0.0207961
365 212.849 0.0159688 0.00124444 0.928919 220 223.48 0.0267984
366 223.157 0.0195002 0.00124444 0.925477 188 234.243 0.0198455
367 235.148 0.01368 0.00124444 0.921472 267 246.759 0.0250402
368 214.6 0.0139443 0.00124444 0.928334 204 225.232 0.0280205
369 222.275 0.0167278 0.00124444 0.925771 162 233.179 0.0281601
370 245.067 0.014789 0.00124444 0.918159 173 256.94 0.03829
371 226.652 0.0148565 0.00124444 0.924309 164 237.977 0.0260072
372 227.432 0.0164053 0.00124444 0.924049 186 238.547 0.0399367
373 243.016 0.0161022 0.00124444 0.918844 144 254.967 0.0548178
374 212.727 0.0144341 0.00124444 0.92896 186 223.3 0.0462393
375 232.305 0.0180672 0.00124444 0.922421 147 243.823 0.0243951
376 228.648 0.0124633 0.00124444 0.923643 234 240.056 0.0235827
377 210.07 0.0209562 0.00124444 0.929847 134 220.234 0.03316
378 231.44 0.0145091 0.00124444 0.92271 199 242.633 0.0341166
379 233.47 0.0162595 0.00124444 0.922032 151 244.981 0.0352567
380 233.24 0.0173451 0.00124444 0.922109 196 244.803 0.0189091
381 230.457 0.0150522 0.00124444 0.923039 218 241.898 0.0377671
382 212.095 0.0146085 0.00124444 0.929171 196 222.32 0.0308945
383 220.969 0.0171665 0.00124444 0.926207 243 232.013 0.0183292
384 218.034 0.0154712 0.00124444 0.927187 232 228.661 0.0208262
385 229.902 0.01589 0.00124444 0.923224 226 241.097 0.032418
386 215.14 0.015205 0.00124444 0.928154 226 225.772 0.0165023
387 232.714 0.0171707 0.00124444 0.922285 163 243.746 0.0267255
388 199.436 0.0181063 0.00124444 0.933398 365 209.245 0.0217639
389 225.485 0.0172431 0.00124444 0.924699 196 235.881 0.032626
390 230.548 0.0145494 0.00124444 0.923008 224 241.881 0.0357782
391 220.164 0.014643 0.00124444 0.926476 221 231.035 0.021201
392 210.607 0.0149307 0.00124444 0.929668 197 220.946 0.027384
393 202.034 0.0154935 0.00124444 0.932531 219 212.015 0.0227197
394 223.702 0.0156465 0.00124444 0.925294 220 234.886 0.0202163
395 223.274 0.0153501 0.00124444 0.925437 237 234.38 0.0210493
396 239.317 0.0139175 0.00124444 0.92008 167 250.999 0.0352055
397 219.42 0.0157887 0.00124444 0.926724 194 229.239 0.0410685
398 211.502 0.0159828 0.00124444 0.929368 183 222.041 0.021811
399 198.88 0.0172708 0.00124444 0.933584 139 208.038 0.0256466
400 207.561 0.0138982 0.00124444 0.930685 199 217.716 0.016863
401 210.296 0.0164974 0.00124444 0.929771 195 220.769 0.0256633
402 120.527 0.0305651 0.00217778 0.95975 182 126.241 0.0274436
403 105.717 0.0304078 0.00217778 0.964696 282 110.962 0.0158715
404 119.901 0.0296548 0.00217778 0.959959 224 125.853 0.0221449
405 133.689 0.0301734 0.00217778 0.955354 233 140.195 0.0223482
406 120.918 0.0297028 0.00217778 0.959619 175 126.647 0.0197707
407 147.295 0.0283644 0.00217778 0.950811 243 154.539 0.0261081
408 102.228 0.0319967 0.00217778 0.965861 251 107.206 0.0145694
409 122.622 0.0252732 0.00217778 0.95905 232 128.558 0.0183895
410 130.819 0.0325323 0.00217778 0.956313 169 136.77 0.0248296
411 139.062 0.029404 0.00217778 0.95356 239 145.941 0.0198192
412 163.931 0.0284774 0.00217778 0.945255 197 171.996 0.0243894
413 113.252 0.0321719 0.00217778 0.962179 241 118.437 0.0246194
414 137.3 0.0283919 0.00217778 0.954149 176 143.556 0.0201642
415 115.119 0.0300041 0.00217778 0.961556 295 120.873 0.0178014
416 100.904 0.0288716 0.00217778 0.966303 208 105.887 0.0200813
417 147.487 0.024799 0.00217778 0.950747 189 154.67 0.0243077
418 147.404 0.0277859 0.00217778 0.950774 143 154.182 0.0287594
419 110.849 0.0278082 0.00217778 0.962982 247 116.314 0.0130491
420 108.144 0.0317319 0.00217778 0.963885 221 113.496 0.0213925
421 152.471 0.0309984 0.00217778 0.949082 193 159.92 0.0216455
422 151.301 0.0316553 0.00217778 0.949473 183 158.727 0.026995
423 103.761 0.0245259 0.00217778 0.965349 208 108.387 0.0261726
424 141.47 0.0299984 0.00217778 0.952756 215 148.334 0.0212445
425 107.693 0.0288702 0.00217778 0.964036 237 112.767 0.0184018
426 132.834 0.0291123 0.00217778 0.95564 243 139.428 0.0137168
427 118.598 0.0294951 0.00217778 0.960394 224 124.479 0.0151507
428 101.31 0.0290843 0.00217778 0.966168 277 106.309 0.0130174
429 133.211 0.0291807 0.00217778 0.955514 223 139.721 0.0234585
430 127.816 0.031699 0.00217778 0.957316 228 133.951 0.0207455
431 128.875 0.0282553 0.00217778 0.956962 205 135.062 0.0212527
432 128.065 0.0277151 0.00217778 0.957233 232 133.884 0.0207377
433 114.407 0.0308509 0.00217778 0.961794 248 120.051 0.0177078
434 96.6405 0.0323881 0.00217778 0.967727 257 101.318 0.0205648
435 128.856 0.0302615 0.00217778 0.956968 196 135.279 0.0185934
436 104.861 0.0307769 0.00217778 0.964981 300 110.051 0.0107628
437 130.225 0.0302749 0.00217778 0.956511 206 136.437 0.0178083
438 128.374 0.025948 0.00217778 0.957129 198 134.697 0.0240022
439 108.68 0.0299555 0.00217778 0.963706 261 113.78 0.0165322
440 116.83 0.0285706 0.00217778 0.960984 214 122.484 0.0175649
441 109.654 0.0290142 0.00217778 0.963381 201 114.487 0.0242023
442 121.709 0.0293128 0.00217778 0.959355 181 127.55 0.0230015
443 119.756 0.0299435 0.00217778 0.960007 266 125.629 0.0222042
444 154.595 0.0296071 0.00217778 0.948373 161 162.041 0.031111
445 148.94 0.0288307 0.00217778 0.950261 189 156.364 0.0201626
446 108.541 0.0309115 0.00217778 0.963753 215 113.788 0.0194676
447 131.712 0.0305304 0.00217778 0.956015 170 138.26 0.0233339
448 104.985 0.0269759 0.00217778 0.96494 223 109.996 0.0200455
449 156.935 0.0277759 0.00217778 0.947591 197 164.442 0.0274963
450 101.562 0.0270836 0.00217778 0.966083 228 105.711 0.0285827
451 149.172 0.0293507 0.00217778 0.950184 266 156.553 0.0137586
452 110.786 0.0301022 0.00217778 0.963003 225 116.304 0.0160115
453 108.126 0.0296889 0.00217778 0.963891 272 113.339 0.0139973
454 113.396 0.0280885 0.00217778 0.962131 184 118.637 0.0206842
455 157.303 0.0286129 0.00217778 0.947469 205 164.957 0.0204323
456 92.5603 0.0310149 0.00217778 0.969089 288 97.1774 0.0118406
457 131.31 0.0312147 0.00217778 0.956149 201 137.814 0.0216513
458 151.662 0.0264346 0.00217778 0.949352 241 158.652 0.0206991
459 122.986 0.0296999 0.00217778 0.958929 239 128.639 0.0250375
460 138.21 0.0276365 0.00217778 0.953845 210 144.853 0.0221076
461 130.258 0.0288724 0.00217778 0.9565 203 136.728 0.0240955
462 95.4606 0.0305017 0.00217778 0.968121 264 100.103 0.0134085
463 123.79 0.0302927 0.00217778 0.95866 290 129.972 0.0120363
464 128.023 0.0269328 0.00217778 0.957247 263 134.171 0.0176036
465 97.4169 0.0294049 0.00217778 0.967467 283 102.086 0.0154436
466 131.497 0.032637 0.00217778 0.956086 194 137.823 0.0230214
467 114.486 0.0296235 0.00217778 0.961767 209 120.165 0.0182154
468 115.802 0.030273 0.00217778 0.961328 249 121.511 0.0168117
469 104.888 0.0298335 0.00217778 0.964972 266 110.067 0.0157024
470 107.529 0.0299312 0.00217778 0.96409 214 112.614 0.0242658
471 100.39 0.0284705 0.00217778 0.966475 228 105.271 0.0152932
472 134.206 0.0300452 0.00217778 0.955182 190 140.821 0.0262343
473 118.423 0.0285713 0.00217778 0.960452 230 124.285 0.0195539
474 150.763 0.0269517 0.00217778 0.949652 188 158.103 0.0213998
475 134.048 0.0302692 0.00217778 0.955234 169 140.515 0.0308499
476 96.9072 0.029681 0.00217778 0.967638 245 101.64 0.0165011
477 123.811 0.0258042 0.00217778 0.958653 254 129.887 0.0140323
478 159.564 0.0304946 0.00217778 0.946713 205 167.194 0.0194008
479 105.757 0.0290973 0.00217778 0.964682 238 110.881 0.0179311
480 139.738 0.0289451 0.00217778 0.953334 194 146.669 0.0262391
481 112.768 0.0276706 0.00217778 0.962341 195 118.035 0.0156339
482 143.501 0.0254884 0.00217778 0.952078 256 150.583 0.0127485
483 136.72 0.0262001 0.00217778 0.954342 244 143.211 0.0193324
484 109.952 0.0287195 0.00217778 0.963281 223 114.785 0.0241593
485 139.559 0.029377 0.00217778 0.953394 187 146.421 0.0183773
486 124.8 0.028991 0.00217778 0.958323 220 130.875 0.0179003
487 102.291 0.0285261 0.00217778 0.96584 278 107.373 0.0140422
488 144.967 0.0281308 0.00217778 0.951588 234 152.164 0.0189975
489 123.808 0.031638 0.00217778 0.958654 185 129.282 0.0314699
490 93.612 0.0275759 0.00217778 0.968738 271 98.1416 0.0124221
491 123.829 0.0279283 0.00217778 0.958647 199 129.997 0.0222236
492 111.654 0.0290171 0.00217778 0.962713 183 117 0.0200822
493 105.848 0.0278707 0.00217778 0.964652 235 111.092 0.0159004
494 108.727 0.0240196 0.00217778 0.96369 330 114.087 0.0151336
495 102.465 0.0285859 0.00217778 0.965782 267 107.558 0.0137917
496 137.886 0.0315396 0.00217778 0.953953 162 144.45 0.0198371
497 112.847 0.0258472 0.00217778 0.962315 233 118.484 0.0194957
498 104.672 0.0280787 0.00217778 0.965045 270 109.701 0.017613
499 121.264 0.0247246 0.00217778 0.959504 181 126.593 0.0240936
500 161.034 0.0240912 0.00217778 0.946223 165 168.971 0.0262645
501 137.026 0.0283714 0.00217778 0.95424 262 143.838 0.02069

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@ -0,0 +1,144 @@
*******************************************************************************
Thu Oct 5 14:24:23 2017
FIT: data read from "20171005-all.csv" every ::1 using 2:5
format = x:z
#datapoints = 500
residuals are weighted equally (unit weight)
function used for fitting: f(x)
f(x)=a*x+b
fitted parameters initialized with current variable values
iter chisq delta/lim lambda a b
0 2.2819584538e+07 0.00e+00 7.07e-01 1.000000e+00 1.000000e+00
4 9.2387072945e+05 -3.77e-04 7.07e-05 5.253352e+02 1.999370e+02
After 4 iterations the fit converged.
final sum of squares of residuals : 923871
rel. change during last iteration : -3.77204e-09
degrees of freedom (FIT_NDF) : 498
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 43.0716
variance of residuals (reduced chisquare) = WSSR/ndf : 1855.16
Final set of parameters Asymptotic Standard Error
======================= ==========================
a = 525.335 +/- 371.3 (70.69%)
b = 199.937 +/- 7.551 (3.777%)
correlation matrix of the fit parameters:
a b
a 1.000
b -0.967 1.000
*******************************************************************************
Thu Oct 5 14:24:23 2017
FIT: data read from "20171005-all.csv" every ::1 using 4:5
format = x:z
#datapoints = 500
residuals are weighted equally (unit weight)
function used for fitting: g(x)
g(x)=aa*x+bb
fitted parameters initialized with current variable values
iter chisq delta/lim lambda aa bb
0 2.2629211027e+07 0.00e+00 9.66e-01 1.000000e+00 1.000000e+00
4 8.9631538551e+05 -2.78e-05 9.66e-05 4.610660e+02 -2.189272e+02
After 4 iterations the fit converged.
final sum of squares of residuals : 896315
rel. change during last iteration : -2.77934e-10
degrees of freedom (FIT_NDF) : 498
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 42.4244
variance of residuals (reduced chisquare) = WSSR/ndf : 1799.83
Final set of parameters Asymptotic Standard Error
======================= ==========================
aa = 461.066 +/- 110.6 (23.99%)
bb = -218.927 +/- 103 (47.04%)
correlation matrix of the fit parameters:
aa bb
aa 1.000
bb -1.000 1.000
*******************************************************************************
Thu Oct 5 14:24:23 2017
FIT: data read from "20171005-all.csv" every ::1 using 4:6
format = x:z
#datapoints = 500
residuals are weighted equally (unit weight)
function used for fitting: h(x)
h(x)=aaa*x+bbb
fitted parameters initialized with current variable values
iter chisq delta/lim lambda aaa bbb
0 2.4597834778e+07 0.00e+00 9.66e-01 1.000000e+00 1.000000e+00
5 4.4603658393e+01 -1.73e-08 9.66e-06 -3.139922e+03 3.139954e+03
After 5 iterations the fit converged.
final sum of squares of residuals : 44.6037
rel. change during last iteration : -1.72842e-13
degrees of freedom (FIT_NDF) : 498
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 0.299275
variance of residuals (reduced chisquare) = WSSR/ndf : 0.0895656
Final set of parameters Asymptotic Standard Error
======================= ==========================
aaa = -3139.92 +/- 0.7803 (0.02485%)
bbb = 3139.95 +/- 0.7265 (0.02314%)
correlation matrix of the fit parameters:
aaa bbb
aaa 1.000
bbb -1.000 1.000
*******************************************************************************
Thu Oct 5 14:24:23 2017
FIT: data read from "20171005-all.csv" every ::1 using 3:6
format = x:z
#datapoints = 500
residuals are weighted equally (unit weight)
function used for fitting: i(x)
i(x)=aaaa*x+bbbb
fitted parameters initialized with current variable values
iter chisq delta/lim lambda aaaa bbbb
0 2.4797348325e+07 0.00e+00 7.07e-01 1.000000e+00 1.000000e+00
5 6.2575820484e+05 -6.78e-01 7.07e-06 -1.004063e+05 3.554273e+02
After 5 iterations the fit converged.
final sum of squares of residuals : 625758
rel. change during last iteration : -6.77885e-06
degrees of freedom (FIT_NDF) : 498
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 35.4477
variance of residuals (reduced chisquare) = WSSR/ndf : 1256.54
Final set of parameters Asymptotic Standard Error
======================= ==========================
aaaa = -100406 +/- 3920 (3.904%)
bbbb = 355.427 +/- 5.629 (1.584%)
correlation matrix of the fit parameters:
aaaa bbbb
aaaa 1.000
bbbb -0.960 1.000

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@ -0,0 +1,102 @@
iter chisq delta/lim lambda a b
0 2.2819584538e+07 0.00e+00 7.07e-01 1.000000e+00 1.000000e+00
1 9.2734539506e+05 -2.36e+06 7.07e-02 1.872894e+01 2.096890e+02
2 9.2412450892e+05 -3.49e+02 7.07e-03 3.879916e+02 2.026375e+02
3 9.2387073294e+05 -2.75e+01 7.07e-04 5.248262e+02 1.999470e+02
4 9.2387072945e+05 -3.77e-04 7.07e-05 5.253352e+02 1.999370e+02
iter chisq delta/lim lambda a b
After 4 iterations the fit converged.
final sum of squares of residuals : 923871
rel. change during last iteration : -3.77204e-09
degrees of freedom (FIT_NDF) : 498
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 43.0716
variance of residuals (reduced chisquare) = WSSR/ndf : 1855.16
Final set of parameters Asymptotic Standard Error
======================= ==========================
a = 525.335 +/- 371.3 (70.69%)
b = 199.937 +/- 7.551 (3.777%)
correlation matrix of the fit parameters:
a b
a 1.000
b -0.967 1.000
iter chisq delta/lim lambda aa bb
0 2.2629211027e+07 0.00e+00 9.66e-01 1.000000e+00 1.000000e+00
1 9.1220178149e+05 -2.38e+06 9.66e-02 1.325557e+02 8.671371e+01
2 8.9649334140e+05 -1.75e+03 9.66e-03 4.262842e+02 -1.865447e+02
3 8.9631538576e+05 -1.99e+01 9.66e-04 4.610248e+02 -2.188888e+02
4 8.9631538551e+05 -2.78e-05 9.66e-05 4.610660e+02 -2.189272e+02
iter chisq delta/lim lambda aa bb
After 4 iterations the fit converged.
final sum of squares of residuals : 896315
rel. change during last iteration : -2.77934e-10
degrees of freedom (FIT_NDF) : 498
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 42.4244
variance of residuals (reduced chisquare) = WSSR/ndf : 1799.83
Final set of parameters Asymptotic Standard Error
======================= ==========================
aa = 461.066 +/- 110.6 (23.99%)
bb = -218.927 +/- 103 (47.04%)
correlation matrix of the fit parameters:
aa bb
aa 1.000
bb -1.000 1.000
iter chisq delta/lim lambda aaa bbb
0 2.4597834778e+07 0.00e+00 9.66e-01 1.000000e+00 1.000000e+00
1 1.3196790874e+06 -1.76e+06 9.66e-02 -1.448795e+02 3.513002e+02
2 1.4846794008e+04 -8.79e+06 9.66e-03 -2.822704e+03 2.844618e+03
3 4.4624379637e+01 -3.32e+07 9.66e-04 -3.139547e+03 3.139604e+03
4 4.4603658393e+01 -4.65e+01 9.66e-05 -3.139922e+03 3.139954e+03
5 4.4603658393e+01 -1.73e-08 9.66e-06 -3.139922e+03 3.139954e+03
iter chisq delta/lim lambda aaa bbb
After 5 iterations the fit converged.
final sum of squares of residuals : 44.6037
rel. change during last iteration : -1.72842e-13
degrees of freedom (FIT_NDF) : 498
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 0.299275
variance of residuals (reduced chisquare) = WSSR/ndf : 0.0895656
Final set of parameters Asymptotic Standard Error
======================= ==========================
aaa = -3139.92 +/- 0.7803 (0.02485%)
bbb = 3139.95 +/- 0.7265 (0.02314%)
correlation matrix of the fit parameters:
aaa bbb
aaa 1.000
bbb -1.000 1.000
iter chisq delta/lim lambda aaaa bbbb
0 2.4797348325e+07 0.00e+00 7.07e-01 1.000000e+00 1.000000e+00
1 1.4499765510e+06 -1.61e+06 7.07e-02 -1.512219e+01 2.168949e+02
2 1.4236400853e+06 -1.85e+03 7.07e-03 -1.630660e+03 2.193366e+02
3 7.4062425817e+05 -9.22e+04 7.07e-04 -6.292829e+04 3.037910e+02
4 6.2576244676e+05 -1.84e+04 7.07e-05 -1.001785e+05 3.551135e+02
5 6.2575820484e+05 -6.78e-01 7.07e-06 -1.004063e+05 3.554273e+02
iter chisq delta/lim lambda aaaa bbbb
After 5 iterations the fit converged.
final sum of squares of residuals : 625758
rel. change during last iteration : -6.77885e-06
degrees of freedom (FIT_NDF) : 498
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 35.4477
variance of residuals (reduced chisquare) = WSSR/ndf : 1256.54
Final set of parameters Asymptotic Standard Error
======================= ==========================
aaaa = -100406 +/- 3920 (3.904%)
bbbb = 355.427 +/- 5.629 (1.584%)
correlation matrix of the fit parameters:
aaaa bbbb
aaaa 1.000
bbbb -0.960 1.000

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@ -0,0 +1,26 @@
set datafile separator ","
f(x)=a*x+b
fit f(x) "20171005-all.csv" every ::1 using 2:5 via a,b
set terminal png
set xlabel 'regularity'
set ylabel 'steps'
set output "20171005-all_regularity-vs-steps.png"
plot "20170830-evolution1D_5x5_100Times.csv" every ::1 using 2:5 title "20170830-evolution1D_5x5_100Times.csv", "20170830-evolution1D_5x5_100Times-added_one.csv" every ::1 using 2:5 title "20170830-evolution1D_5x5_100Times-added_one.csv", "20171005-evolution1D_4x7_100Times.csv" every ::1 using 2:5 title "20171005-evolution1D_4x7_100Times.csv", "20171005-evolution1D_7x4_100Times.csv" every ::1 using 2:5 title "20171005-evolution1D_7x4_100Times.csv", "20171005-evolution1D_7x7_100Times.csv" every ::1 using 2:5 title "20171005-evolution1D_7x7_100Times.csv", f(x) title "lin. fit" lc rgb "black"
g(x)=aa*x+bb
fit g(x) "20171005-all.csv" every ::1 using 4:5 via aa,bb
set xlabel 'improvement potential'
set ylabel 'steps'
set output "20171005-all_improvement-vs-steps.png"
plot "20170830-evolution1D_5x5_100Times.csv" every ::1 using 4:5 title "20170830-evolution1D_5x5_100Times.csv", "20170830-evolution1D_5x5_100Times-added_one.csv" every ::1 using 4:5 title "20170830-evolution1D_5x5_100Times-added_one.csv", "20171005-evolution1D_4x7_100Times.csv" every ::1 using 4:5 title "20171005-evolution1D_4x7_100Times.csv", "20171005-evolution1D_7x4_100Times.csv" every ::1 using 4:5 title "20171005-evolution1D_7x4_100Times.csv", "20171005-evolution1D_7x7_100Times.csv" every ::1 using 4:5 title "20171005-evolution1D_7x7_100Times.csv", g(x) title "lin. fit" lc rgb "black"
h(x)=aaa*x+bbb
fit h(x) "20171005-all.csv" every ::1 using 4:6 via aaa,bbb
set xlabel 'improvement potential'
set ylabel 'evolution error'
set output "20171005-all_improvement-vs-evo-error.png"
plot "20170830-evolution1D_5x5_100Times.csv" every ::1 using 4:6 title "20170830-evolution1D_5x5_100Times.csv", "20170830-evolution1D_5x5_100Times-added_one.csv" every ::1 using 4:6 title "20170830-evolution1D_5x5_100Times-added_one.csv", "20171005-evolution1D_4x7_100Times.csv" every ::1 using 4:6 title "20171005-evolution1D_4x7_100Times.csv", "20171005-evolution1D_7x4_100Times.csv" every ::1 using 4:6 title "20171005-evolution1D_7x4_100Times.csv", "20171005-evolution1D_7x7_100Times.csv" every ::1 using 4:6 title "20171005-evolution1D_7x7_100Times.csv", h(x) title "lin. fit" lc rgb "black"
i(x)=aaaa*x+bbbb
fit i(x) "20171005-all.csv" every ::1 using 3:6 via aaaa,bbbb
set xlabel 'variability'
set ylabel 'evolution error'
set output "20171005-all_variability-vs-evo-error.png"
plot "20170830-evolution1D_5x5_100Times.csv" every ::1 using 3:6 title "20170830-evolution1D_5x5_100Times.csv", "20170830-evolution1D_5x5_100Times-added_one.csv" every ::1 using 3:6 title "20170830-evolution1D_5x5_100Times-added_one.csv", "20171005-evolution1D_4x7_100Times.csv" every ::1 using 3:6 title "20171005-evolution1D_4x7_100Times.csv", "20171005-evolution1D_7x4_100Times.csv" every ::1 using 3:6 title "20171005-evolution1D_7x4_100Times.csv", "20171005-evolution1D_7x7_100Times.csv" every ::1 using 3:6 title "20171005-evolution1D_7x7_100Times.csv", i(x) title "lin. fit" lc rgb "black"

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@ -0,0 +1,101 @@
"Least squares",regularity,variability,improvement,steps,"Evolution error",sigma
267.876,0.0160237,0.00124444,0.910542,173,280.917,0.041317
301.035,0.0149818,0.00124444,0.899469,192,315.729,0.0371435
252.283,0.016224,0.00124444,0.91575,186,264.639,0.0251523
262.811,0.0143632,0.00124444,0.912234,228,275.922,0.0292345
307.779,0.0168661,0.00124444,0.897217,247,323.159,0.0446967
287.196,0.0162634,0.00124444,0.90409,153,300.933,0.0402748
252.185,0.0186589,0.00124444,0.915782,204,264.541,0.0537674
252.368,0.0185828,0.00124444,0.915721,163,264.875,0.0516944
273.504,0.0153228,0.00124444,0.908663,206,286.999,0.0589335
299.8,0.015452,0.00124444,0.899881,252,314.771,0.0322635
242.916,0.0165246,0.00124444,0.918878,207,254.996,0.0375956
258.239,0.0130383,0.00124444,0.913761,240,270.99,0.0271465
320.936,0.0142181,0.00124444,0.892823,127,336.401,0.0474298
238.158,0.0182024,0.00124444,0.920467,195,249.761,0.0431037
296.336,0.0164207,0.00124444,0.901038,171,310.473,0.0315425
269.239,0.0126131,0.00124444,0.910087,187,282.476,0.0734215
288.295,0.0160129,0.00124444,0.903723,153,301.45,0.0409347
290.458,0.0165057,0.00124444,0.903001,215,304.67,0.0372574
286.663,0.0166434,0.00124444,0.904268,226,300.451,0.0414395
300.22,0.0182222,0.00124444,0.899741,174,315.122,0.0280977
288.785,0.0149497,0.00124444,0.90356,251,302.947,0.0310956
250.657,0.0167552,0.00124444,0.916293,192,262.796,0.0357012
259.912,0.012238,0.00124444,0.913202,293,272.873,0.0517974
277.775,0.0144144,0.00124444,0.907237,229,291.472,0.0314516
267.052,0.0167378,0.00124444,0.910818,204,280.073,0.040238
262.339,0.0144557,0.00124444,0.912391,189,274.973,0.0494119
264.729,0.0144438,0.00124444,0.911593,182,277.642,0.0599785
253.582,0.0174023,0.00124444,0.915316,230,266.096,0.0268471
286.594,0.0157891,0.00124444,0.904292,194,300.458,0.0522694
269.748,0.0129119,0.00124444,0.909917,181,281.797,0.0678929
276.445,0.0153961,0.00124444,0.907681,172,287.84,0.0616759
257.365,0.0192891,0.00124444,0.914053,137,270.181,0.0442534
290.578,0.0130388,0.00124444,0.902961,230,304.713,0.0312996
286.822,0.0141783,0.00124444,0.904215,228,301.015,0.0325938
239.331,0.0156718,0.00124444,0.920075,296,250.936,0.0318135
312.447,0.0182524,0.00124444,0.895658,252,327.876,0.0256484
254.695,0.0185449,0.00124444,0.914944,202,267.093,0.0335235
254.524,0.0141017,0.00124444,0.915001,249,266.032,0.0379196
280.578,0.0169944,0.00124444,0.906301,214,293.5,0.0607412
262.034,0.0159614,0.00124444,0.912493,196,274.145,0.0441671
288.236,0.0136483,0.00124444,0.903743,211,302.284,0.0553983
282.566,0.0162714,0.00124444,0.905637,259,296.447,0.0386496
277.384,0.0144486,0.00124444,0.907367,234,290.496,0.0723565
310.869,0.0161428,0.00124444,0.896185,255,326.409,0.0480313
240.505,0.0152832,0.00124444,0.919683,216,252.376,0.0350769
271.764,0.0168533,0.00124444,0.909244,161,285.256,0.0431168
248.918,0.0160118,0.00124444,0.916874,204,261.023,0.05097
260.936,0.0166911,0.00124444,0.91286,319,273.732,0.0188483
273.958,0.0145448,0.00124444,0.908511,251,287.211,0.0305499
235.056,0.0163734,0.00124444,0.921503,258,246.715,0.0317013
303.02,0.0151119,0.00124444,0.898806,199,317.892,0.0592121
253.213,0.0182013,0.00124444,0.915439,236,265.825,0.0371061
247.874,0.0157508,0.00124444,0.917222,245,259.862,0.0417326
260.379,0.0137206,0.00124444,0.913046,235,273.217,0.0402591
257.113,0.0160861,0.00124444,0.914137,256,269.759,0.0330447
299.543,0.0150175,0.00124444,0.899967,220,314.394,0.045574
302.404,0.0167334,0.00124444,0.899012,187,314.765,0.0707083
271.225,0.0181028,0.00124444,0.909424,257,284.627,0.0206609
250.03,0.0160492,0.00124444,0.916502,232,262.319,0.0595344
256.332,0.0128525,0.00124444,0.914397,220,269.132,0.0465511
248.162,0.018139,0.00124444,0.917126,202,259.973,0.0298866
282.813,0.0169151,0.00124444,0.905554,159,296.171,0.0453673
252.465,0.0164028,0.00124444,0.915689,228,264.153,0.0481631
294.434,0.0150393,0.00124444,0.901673,195,307.381,0.0516829
237.397,0.0176428,0.00124444,0.920721,217,248.894,0.021071
297.923,0.0140163,0.00124444,0.900508,238,312.436,0.0434852
260.603,0.012484,0.00124444,0.912971,309,273.599,0.0320857
273.926,0.0168922,0.00124444,0.908522,249,286.954,0.0443297
298.718,0.0174071,0.00124444,0.900243,209,313.315,0.0236055
276.871,0.0167161,0.00124444,0.907539,268,290.546,0.0248795
302.089,0.0162225,0.00124444,0.899117,262,317.095,0.015801
276.551,0.0114517,0.00124444,0.907645,178,289.397,0.045748
280.724,0.0182241,0.00124444,0.906252,184,293.925,0.0576927
261.057,0.0172636,0.00124444,0.912819,152,273.573,0.0649999
236.373,0.0156888,0.00124444,0.921063,257,248.052,0.0402787
270.794,0.0143144,0.00124444,0.909568,234,282.84,0.0429796
272.7,0.0156175,0.00124444,0.908931,170,286.257,0.0583084
270.983,0.012891,0.00124444,0.909505,247,284.314,0.0303283
306.135,0.0148554,0.00124444,0.897766,175,321.302,0.0456628
248.478,0.016144,0.00124444,0.91702,227,260.894,0.0294851
265.44,0.0103717,0.00124444,0.911356,274,278.436,0.0266817
262.233,0.0150257,0.00124444,0.912427,235,274.697,0.0387567
256.662,0.0181041,0.00124444,0.914287,202,269.428,0.0345583
274.338,0.0138554,0.00124444,0.908385,181,287.274,0.0490538
268.815,0.0144361,0.00124444,0.910229,222,281.924,0.0243106
251.425,0.0171867,0.00124444,0.916036,241,263.843,0.0501147
284.735,0.0165221,0.00124444,0.904912,200,298.757,0.0397164
262.717,0.0162912,0.00124444,0.912265,229,275.521,0.0401815
256.866,0.0145304,0.00124444,0.914219,249,269.146,0.0306201
260.708,0.0144833,0.00124444,0.912936,210,273.475,0.0343934
261.745,0.0147709,0.00124444,0.91259,222,273.666,0.033181
284.729,0.0160625,0.00124444,0.904914,197,298.125,0.0467389
291.835,0.0146895,0.00124444,0.902541,175,305.642,0.0475431
283.297,0.0163328,0.00124444,0.905392,349,297.086,0.0203907
303.006,0.0165804,0.00124444,0.898811,158,317.845,0.0610993
261.705,0.0175394,0.00124444,0.912603,323,274.586,0.0235008
316.6,0.0135961,0.00124444,0.894271,229,332.413,0.03878
287.234,0.0143036,0.00124444,0.904078,203,301.147,0.0376103
337.364,0.0140911,0.00124444,0.887337,150,354.08,0.0600292
253.797,0.0164939,0.00124444,0.915244,247,266.461,0.0321915
1 Least squares regularity variability improvement steps Evolution error sigma
2 267.876 0.0160237 0.00124444 0.910542 173 280.917 0.041317
3 301.035 0.0149818 0.00124444 0.899469 192 315.729 0.0371435
4 252.283 0.016224 0.00124444 0.91575 186 264.639 0.0251523
5 262.811 0.0143632 0.00124444 0.912234 228 275.922 0.0292345
6 307.779 0.0168661 0.00124444 0.897217 247 323.159 0.0446967
7 287.196 0.0162634 0.00124444 0.90409 153 300.933 0.0402748
8 252.185 0.0186589 0.00124444 0.915782 204 264.541 0.0537674
9 252.368 0.0185828 0.00124444 0.915721 163 264.875 0.0516944
10 273.504 0.0153228 0.00124444 0.908663 206 286.999 0.0589335
11 299.8 0.015452 0.00124444 0.899881 252 314.771 0.0322635
12 242.916 0.0165246 0.00124444 0.918878 207 254.996 0.0375956
13 258.239 0.0130383 0.00124444 0.913761 240 270.99 0.0271465
14 320.936 0.0142181 0.00124444 0.892823 127 336.401 0.0474298
15 238.158 0.0182024 0.00124444 0.920467 195 249.761 0.0431037
16 296.336 0.0164207 0.00124444 0.901038 171 310.473 0.0315425
17 269.239 0.0126131 0.00124444 0.910087 187 282.476 0.0734215
18 288.295 0.0160129 0.00124444 0.903723 153 301.45 0.0409347
19 290.458 0.0165057 0.00124444 0.903001 215 304.67 0.0372574
20 286.663 0.0166434 0.00124444 0.904268 226 300.451 0.0414395
21 300.22 0.0182222 0.00124444 0.899741 174 315.122 0.0280977
22 288.785 0.0149497 0.00124444 0.90356 251 302.947 0.0310956
23 250.657 0.0167552 0.00124444 0.916293 192 262.796 0.0357012
24 259.912 0.012238 0.00124444 0.913202 293 272.873 0.0517974
25 277.775 0.0144144 0.00124444 0.907237 229 291.472 0.0314516
26 267.052 0.0167378 0.00124444 0.910818 204 280.073 0.040238
27 262.339 0.0144557 0.00124444 0.912391 189 274.973 0.0494119
28 264.729 0.0144438 0.00124444 0.911593 182 277.642 0.0599785
29 253.582 0.0174023 0.00124444 0.915316 230 266.096 0.0268471
30 286.594 0.0157891 0.00124444 0.904292 194 300.458 0.0522694
31 269.748 0.0129119 0.00124444 0.909917 181 281.797 0.0678929
32 276.445 0.0153961 0.00124444 0.907681 172 287.84 0.0616759
33 257.365 0.0192891 0.00124444 0.914053 137 270.181 0.0442534
34 290.578 0.0130388 0.00124444 0.902961 230 304.713 0.0312996
35 286.822 0.0141783 0.00124444 0.904215 228 301.015 0.0325938
36 239.331 0.0156718 0.00124444 0.920075 296 250.936 0.0318135
37 312.447 0.0182524 0.00124444 0.895658 252 327.876 0.0256484
38 254.695 0.0185449 0.00124444 0.914944 202 267.093 0.0335235
39 254.524 0.0141017 0.00124444 0.915001 249 266.032 0.0379196
40 280.578 0.0169944 0.00124444 0.906301 214 293.5 0.0607412
41 262.034 0.0159614 0.00124444 0.912493 196 274.145 0.0441671
42 288.236 0.0136483 0.00124444 0.903743 211 302.284 0.0553983
43 282.566 0.0162714 0.00124444 0.905637 259 296.447 0.0386496
44 277.384 0.0144486 0.00124444 0.907367 234 290.496 0.0723565
45 310.869 0.0161428 0.00124444 0.896185 255 326.409 0.0480313
46 240.505 0.0152832 0.00124444 0.919683 216 252.376 0.0350769
47 271.764 0.0168533 0.00124444 0.909244 161 285.256 0.0431168
48 248.918 0.0160118 0.00124444 0.916874 204 261.023 0.05097
49 260.936 0.0166911 0.00124444 0.91286 319 273.732 0.0188483
50 273.958 0.0145448 0.00124444 0.908511 251 287.211 0.0305499
51 235.056 0.0163734 0.00124444 0.921503 258 246.715 0.0317013
52 303.02 0.0151119 0.00124444 0.898806 199 317.892 0.0592121
53 253.213 0.0182013 0.00124444 0.915439 236 265.825 0.0371061
54 247.874 0.0157508 0.00124444 0.917222 245 259.862 0.0417326
55 260.379 0.0137206 0.00124444 0.913046 235 273.217 0.0402591
56 257.113 0.0160861 0.00124444 0.914137 256 269.759 0.0330447
57 299.543 0.0150175 0.00124444 0.899967 220 314.394 0.045574
58 302.404 0.0167334 0.00124444 0.899012 187 314.765 0.0707083
59 271.225 0.0181028 0.00124444 0.909424 257 284.627 0.0206609
60 250.03 0.0160492 0.00124444 0.916502 232 262.319 0.0595344
61 256.332 0.0128525 0.00124444 0.914397 220 269.132 0.0465511
62 248.162 0.018139 0.00124444 0.917126 202 259.973 0.0298866
63 282.813 0.0169151 0.00124444 0.905554 159 296.171 0.0453673
64 252.465 0.0164028 0.00124444 0.915689 228 264.153 0.0481631
65 294.434 0.0150393 0.00124444 0.901673 195 307.381 0.0516829
66 237.397 0.0176428 0.00124444 0.920721 217 248.894 0.021071
67 297.923 0.0140163 0.00124444 0.900508 238 312.436 0.0434852
68 260.603 0.012484 0.00124444 0.912971 309 273.599 0.0320857
69 273.926 0.0168922 0.00124444 0.908522 249 286.954 0.0443297
70 298.718 0.0174071 0.00124444 0.900243 209 313.315 0.0236055
71 276.871 0.0167161 0.00124444 0.907539 268 290.546 0.0248795
72 302.089 0.0162225 0.00124444 0.899117 262 317.095 0.015801
73 276.551 0.0114517 0.00124444 0.907645 178 289.397 0.045748
74 280.724 0.0182241 0.00124444 0.906252 184 293.925 0.0576927
75 261.057 0.0172636 0.00124444 0.912819 152 273.573 0.0649999
76 236.373 0.0156888 0.00124444 0.921063 257 248.052 0.0402787
77 270.794 0.0143144 0.00124444 0.909568 234 282.84 0.0429796
78 272.7 0.0156175 0.00124444 0.908931 170 286.257 0.0583084
79 270.983 0.012891 0.00124444 0.909505 247 284.314 0.0303283
80 306.135 0.0148554 0.00124444 0.897766 175 321.302 0.0456628
81 248.478 0.016144 0.00124444 0.91702 227 260.894 0.0294851
82 265.44 0.0103717 0.00124444 0.911356 274 278.436 0.0266817
83 262.233 0.0150257 0.00124444 0.912427 235 274.697 0.0387567
84 256.662 0.0181041 0.00124444 0.914287 202 269.428 0.0345583
85 274.338 0.0138554 0.00124444 0.908385 181 287.274 0.0490538
86 268.815 0.0144361 0.00124444 0.910229 222 281.924 0.0243106
87 251.425 0.0171867 0.00124444 0.916036 241 263.843 0.0501147
88 284.735 0.0165221 0.00124444 0.904912 200 298.757 0.0397164
89 262.717 0.0162912 0.00124444 0.912265 229 275.521 0.0401815
90 256.866 0.0145304 0.00124444 0.914219 249 269.146 0.0306201
91 260.708 0.0144833 0.00124444 0.912936 210 273.475 0.0343934
92 261.745 0.0147709 0.00124444 0.91259 222 273.666 0.033181
93 284.729 0.0160625 0.00124444 0.904914 197 298.125 0.0467389
94 291.835 0.0146895 0.00124444 0.902541 175 305.642 0.0475431
95 283.297 0.0163328 0.00124444 0.905392 349 297.086 0.0203907
96 303.006 0.0165804 0.00124444 0.898811 158 317.845 0.0610993
97 261.705 0.0175394 0.00124444 0.912603 323 274.586 0.0235008
98 316.6 0.0135961 0.00124444 0.894271 229 332.413 0.03878
99 287.234 0.0143036 0.00124444 0.904078 203 301.147 0.0376103
100 337.364 0.0140911 0.00124444 0.887337 150 354.08 0.0600292
101 253.797 0.0164939 0.00124444 0.915244 247 266.461 0.0321915

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@ -0,0 +1,108 @@
*******************************************************************************
Thu Oct 5 14:02:32 2017
FIT: data read from "20171005-evolution1D_4x7_100Times.csv" every ::1 using 2:5
format = x:z
#datapoints = 100
residuals are weighted equally (unit weight)
function used for fitting: f(x)
f(x)=a*x+b
fitted parameters initialized with current variable values
iter chisq delta/lim lambda a b
0 4.8453053176e+06 0.00e+00 7.07e-01 1.000000e+00 1.000000e+00
5 1.6409518325e+05 -9.52e-05 7.07e-06 -3.129336e+03 2.663203e+02
After 5 iterations the fit converged.
final sum of squares of residuals : 164095
rel. change during last iteration : -9.51616e-10
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 40.9199
variance of residuals (reduced chisquare) = WSSR/ndf : 1674.44
Final set of parameters Asymptotic Standard Error
======================= ==========================
a = -3129.34 +/- 2384 (76.19%)
b = 266.32 +/- 37.57 (14.11%)
correlation matrix of the fit parameters:
a b
a 1.000
b -0.994 1.000
*******************************************************************************
Thu Oct 5 14:02:32 2017
FIT: data read from "20171005-evolution1D_4x7_100Times.csv" every ::1 using 4:5
format = x:z
#datapoints = 100
residuals are weighted equally (unit weight)
function used for fitting: g(x)
g(x)=aa*x+bb
fitted parameters initialized with current variable values
iter chisq delta/lim lambda aa bb
0 4.8067339365e+06 0.00e+00 9.56e-01 1.000000e+00 1.000000e+00
5 1.5824530732e+05 -3.08e-07 9.56e-06 1.317597e+03 -9.801188e+02
After 5 iterations the fit converged.
final sum of squares of residuals : 158245
rel. change during last iteration : -3.0782e-12
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 40.1839
variance of residuals (reduced chisquare) = WSSR/ndf : 1614.75
Final set of parameters Asymptotic Standard Error
======================= ==========================
aa = 1317.6 +/- 566.5 (43%)
bb = -980.119 +/- 514.9 (52.53%)
correlation matrix of the fit parameters:
aa bb
aa 1.000
bb -1.000 1.000
*******************************************************************************
Thu Oct 5 14:02:32 2017
FIT: data read from "20171005-evolution1D_4x7_100Times.csv" every ::1 using 4:6
format = x:z
#datapoints = 100
residuals are weighted equally (unit weight)
function used for fitting: h(x)
h(x)=aaa*x+bbb
fitted parameters initialized with current variable values
iter chisq delta/lim lambda aaa bbb
0 8.1385601354e+06 0.00e+00 9.56e-01 1.000000e+00 1.000000e+00
5 2.1970491829e+01 -1.63e-02 9.56e-06 -3.136035e+03 3.136342e+03
After 5 iterations the fit converged.
final sum of squares of residuals : 21.9705
rel. change during last iteration : -1.62953e-07
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 0.473486
variance of residuals (reduced chisquare) = WSSR/ndf : 0.224189
Final set of parameters Asymptotic Standard Error
======================= ==========================
aaa = -3136.04 +/- 6.675 (0.2129%)
bbb = 3136.34 +/- 6.067 (0.1934%)
correlation matrix of the fit parameters:
aaa bbb
aaa 1.000
bbb -1.000 1.000

View File

@ -0,0 +1,78 @@
iter chisq delta/lim lambda a b
0 4.8453053176e+06 0.00e+00 7.07e-01 1.000000e+00 1.000000e+00
1 1.6709987683e+05 -2.80e+06 7.07e-02 2.525913e+00 2.161942e+02
2 1.6667175206e+05 -2.57e+02 7.07e-03 -1.715968e+02 2.199973e+02
3 1.6414949666e+05 -1.54e+03 7.07e-04 -2.699906e+03 2.595947e+02
4 1.6409518341e+05 -3.31e+01 7.07e-05 -3.128608e+03 2.663089e+02
5 1.6409518325e+05 -9.52e-05 7.07e-06 -3.129336e+03 2.663203e+02
iter chisq delta/lim lambda a b
After 5 iterations the fit converged.
final sum of squares of residuals : 164095
rel. change during last iteration : -9.51616e-10
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 40.9199
variance of residuals (reduced chisquare) = WSSR/ndf : 1674.44
Final set of parameters Asymptotic Standard Error
======================= ==========================
a = -3129.34 +/- 2384 (76.19%)
b = 266.32 +/- 37.57 (14.11%)
correlation matrix of the fit parameters:
a b
a 1.000
b -0.994 1.000
iter chisq delta/lim lambda aa bb
0 4.8067339365e+06 0.00e+00 9.56e-01 1.000000e+00 1.000000e+00
1 1.6567425140e+05 -2.80e+06 9.56e-02 1.113320e+02 1.150902e+02
2 1.6256126289e+05 -1.91e+03 9.56e-03 3.913863e+02 -1.383578e+02
3 1.5824974701e+05 -2.72e+03 9.56e-04 1.287890e+03 -9.531211e+02
4 1.5824530732e+05 -2.81e+00 9.56e-05 1.317587e+03 -9.801098e+02
5 1.5824530732e+05 -3.08e-07 9.56e-06 1.317597e+03 -9.801188e+02
iter chisq delta/lim lambda aa bb
After 5 iterations the fit converged.
final sum of squares of residuals : 158245
rel. change during last iteration : -3.0782e-12
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 40.1839
variance of residuals (reduced chisquare) = WSSR/ndf : 1614.75
Final set of parameters Asymptotic Standard Error
======================= ==========================
aa = 1317.6 +/- 566.5 (43%)
bb = -980.119 +/- 514.9 (52.53%)
correlation matrix of the fit parameters:
aa bb
aa 1.000
bb -1.000 1.000
iter chisq delta/lim lambda aaa bbb
0 8.1385601354e+06 0.00e+00 9.56e-01 1.000000e+00 1.000000e+00
1 5.3975388072e+04 -1.50e+07 9.56e-02 1.319509e+02 1.649079e+02
2 3.1741255515e+04 -7.00e+04 9.56e-03 -6.251153e+02 8.543613e+02
3 5.4599157975e+01 -5.80e+07 9.56e-04 -3.055503e+03 3.063152e+03
4 2.1970495409e+01 -1.49e+05 9.56e-05 -3.136008e+03 3.136318e+03
5 2.1970491829e+01 -1.63e-02 9.56e-06 -3.136035e+03 3.136342e+03
iter chisq delta/lim lambda aaa bbb
After 5 iterations the fit converged.
final sum of squares of residuals : 21.9705
rel. change during last iteration : -1.62953e-07
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 0.473486
variance of residuals (reduced chisquare) = WSSR/ndf : 0.224189
Final set of parameters Asymptotic Standard Error
======================= ==========================
aaa = -3136.04 +/- 6.675 (0.2129%)
bbb = 3136.34 +/- 6.067 (0.1934%)
correlation matrix of the fit parameters:
aaa bbb
aaa 1.000
bbb -1.000 1.000

View File

@ -0,0 +1,20 @@
set datafile separator ","
f(x)=a*x+b
fit f(x) "20171005-evolution1D_4x7_100Times.csv" every ::1 using 2:5 via a,b
set terminal png
set xlabel 'regularity'
set ylabel 'steps'
set output "20171005-evolution1D_4x7_100Times_regularity-vs-steps.png"
plot "20171005-evolution1D_4x7_100Times.csv" every ::1 using 2:5 title "data", f(x) title "lin. fit" lc rgb "black"
g(x)=aa*x+bb
fit g(x) "20171005-evolution1D_4x7_100Times.csv" every ::1 using 4:5 via aa,bb
set xlabel 'improvement potential'
set ylabel 'steps'
set output "20171005-evolution1D_4x7_100Times_improvement-vs-steps.png"
plot "20171005-evolution1D_4x7_100Times.csv" every ::1 using 4:5 title "data", g(x) title "lin. fit" lc rgb "black"
h(x)=aaa*x+bbb
fit h(x) "20171005-evolution1D_4x7_100Times.csv" every ::1 using 4:6 via aaa,bbb
set xlabel 'improvement potential'
set ylabel 'evolution error'
set output "20171005-evolution1D_4x7_100Times_improvement-vs-evo-error.png"
plot "20171005-evolution1D_4x7_100Times.csv" every ::1 using 4:6 title "data", h(x) title "lin. fit" lc rgb "black"

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@ -0,0 +1,101 @@
"Least squares",regularity,variability,improvement,steps,"Evolution error",sigma
201.297,0.0153997,0.00124444,0.932777,211,211.096,0.0324233
222.924,0.0138755,0.00124444,0.925554,215,233.828,0.0321173
195.586,0.0165785,0.00124444,0.934684,233,205.276,0.0262902
249.343,0.0170463,0.00124444,0.916732,140,261.016,0.0297848
196.161,0.0169011,0.00124444,0.934492,167,205.753,0.0340118
232.94,0.0151993,0.00124444,0.922209,203,244.494,0.0327092
225.704,0.0187794,0.00124444,0.924626,200,236.857,0.0285021
232.061,0.0176159,0.00124444,0.922503,141,243.624,0.0341091
216.478,0.0193808,0.00124444,0.927707,205,227.071,0.0319641
218.526,0.0168255,0.00124444,0.927023,132,228.254,0.0371698
209.13,0.016102,0.00124444,0.930161,195,219.293,0.0254967
224.21,0.0097335,0.00124444,0.925125,260,235.159,0.0286628
229.764,0.0131346,0.00124444,0.92327,222,240.691,0.0421742
221.783,0.0154066,0.00124444,0.925935,234,232.853,0.0190093
232.166,0.0181673,0.00124444,0.922468,153,243.665,0.0176615
231.214,0.016363,0.00124444,0.922786,194,242.766,0.0286637
232.146,0.0160201,0.00124444,0.922474,151,243.618,0.0318045
226.741,0.0160234,0.00124444,0.92428,185,238.051,0.029258
214.091,0.0162548,0.00124444,0.928504,191,224.685,0.0293766
197.18,0.0183974,0.00124444,0.934152,200,206.919,0.0280479
254.147,0.0141509,0.00124444,0.915127,180,266.62,0.0401662
218.998,0.0166688,0.00124444,0.926865,172,229.771,0.0316734
229.888,0.015569,0.00124444,0.923229,172,241.243,0.0383093
218.144,0.0175268,0.00124444,0.92715,166,228.75,0.0277466
235.229,0.0139292,0.00124444,0.921445,122,246.415,0.0328971
234.47,0.0170242,0.00124444,0.921698,214,245.936,0.0311265
223.637,0.0147445,0.00124444,0.925316,275,234.603,0.0173437
220.006,0.01659,0.00124444,0.926529,223,230.971,0.0221785
234.897,0.0153778,0.00124444,0.921556,219,246.319,0.0330376
224.176,0.017539,0.00124444,0.925136,182,235.173,0.0316049
238.295,0.0167376,0.00124444,0.920421,310,250.199,0.0212912
230.074,0.0145246,0.00124444,0.923166,180,240.854,0.0325642
222.461,0.0172363,0.00124444,0.925709,226,233.456,0.0200254
206.544,0.0113106,0.00124444,0.931024,218,216.659,0.0203372
228.658,0.0143528,0.00124444,0.923639,227,240.033,0.0230481
232.581,0.0168069,0.00124444,0.922329,208,244.108,0.0315845
207.444,0.014401,0.00124444,0.930724,156,216.874,0.0423996
230.579,0.0161407,0.00124444,0.922998,222,242.058,0.0155056
211.161,0.0154943,0.00124444,0.929483,200,221.484,0.0288013
212.025,0.0162238,0.00124444,0.929194,131,222.485,0.0315666
228.383,0.0165074,0.00124444,0.923731,306,239.78,0.0203423
221.656,0.0168344,0.00124444,0.925978,247,232.709,0.0234514
220.116,0.0175812,0.00124444,0.926492,191,230.785,0.0250425
219.146,0.0195251,0.00124444,0.926816,168,229.968,0.0433433
224.008,0.0160092,0.00124444,0.925192,221,235.149,0.0209819
222.645,0.0136684,0.00124444,0.925647,221,233.462,0.028072
229.597,0.0142117,0.00124444,0.923326,194,241.027,0.0407908
218.437,0.015863,0.00124444,0.927053,246,229.139,0.0264266
200.414,0.0159252,0.00124444,0.933072,218,210.309,0.0307566
212.507,0.0156027,0.00124444,0.929033,204,222.927,0.0213849
225.552,0.014999,0.00124444,0.924676,255,236.762,0.0204812
233.174,0.0171498,0.00124444,0.922131,202,244.312,0.0424202
214.771,0.0198871,0.00124444,0.928277,257,225.283,0.0205486
217.951,0.0156941,0.00124444,0.927215,172,228.831,0.0309327
223.826,0.0132289,0.00124444,0.925253,178,234.735,0.0339935
211.781,0.0154319,0.00124444,0.929275,249,222.154,0.0359608
205.208,0.0165743,0.00124444,0.931471,241,215.144,0.0313585
236.053,0.0147814,0.00124444,0.92117,182,247.533,0.0489446
231.21,0.0149828,0.00124444,0.922787,202,242.563,0.0415887
220.924,0.0177705,0.00124444,0.926222,156,231.706,0.0301208
234.408,0.0159816,0.00124444,0.921719,145,245.743,0.0317348
222.408,0.0143535,0.00124444,0.925727,242,233.422,0.0246441
210.758,0.0174935,0.00124444,0.929617,255,221.213,0.0207961
212.849,0.0159688,0.00124444,0.928919,220,223.48,0.0267984
223.157,0.0195002,0.00124444,0.925477,188,234.243,0.0198455
235.148,0.01368,0.00124444,0.921472,267,246.759,0.0250402
214.6,0.0139443,0.00124444,0.928334,204,225.232,0.0280205
222.275,0.0167278,0.00124444,0.925771,162,233.179,0.0281601
245.067,0.014789,0.00124444,0.918159,173,256.94,0.03829
226.652,0.0148565,0.00124444,0.924309,164,237.977,0.0260072
227.432,0.0164053,0.00124444,0.924049,186,238.547,0.0399367
243.016,0.0161022,0.00124444,0.918844,144,254.967,0.0548178
212.727,0.0144341,0.00124444,0.92896,186,223.3,0.0462393
232.305,0.0180672,0.00124444,0.922421,147,243.823,0.0243951
228.648,0.0124633,0.00124444,0.923643,234,240.056,0.0235827
210.07,0.0209562,0.00124444,0.929847,134,220.234,0.03316
231.44,0.0145091,0.00124444,0.92271,199,242.633,0.0341166
233.47,0.0162595,0.00124444,0.922032,151,244.981,0.0352567
233.24,0.0173451,0.00124444,0.922109,196,244.803,0.0189091
230.457,0.0150522,0.00124444,0.923039,218,241.898,0.0377671
212.095,0.0146085,0.00124444,0.929171,196,222.32,0.0308945
220.969,0.0171665,0.00124444,0.926207,243,232.013,0.0183292
218.034,0.0154712,0.00124444,0.927187,232,228.661,0.0208262
229.902,0.01589,0.00124444,0.923224,226,241.097,0.032418
215.14,0.015205,0.00124444,0.928154,226,225.772,0.0165023
232.714,0.0171707,0.00124444,0.922285,163,243.746,0.0267255
199.436,0.0181063,0.00124444,0.933398,365,209.245,0.0217639
225.485,0.0172431,0.00124444,0.924699,196,235.881,0.032626
230.548,0.0145494,0.00124444,0.923008,224,241.881,0.0357782
220.164,0.014643,0.00124444,0.926476,221,231.035,0.021201
210.607,0.0149307,0.00124444,0.929668,197,220.946,0.027384
202.034,0.0154935,0.00124444,0.932531,219,212.015,0.0227197
223.702,0.0156465,0.00124444,0.925294,220,234.886,0.0202163
223.274,0.0153501,0.00124444,0.925437,237,234.38,0.0210493
239.317,0.0139175,0.00124444,0.92008,167,250.999,0.0352055
219.42,0.0157887,0.00124444,0.926724,194,229.239,0.0410685
211.502,0.0159828,0.00124444,0.929368,183,222.041,0.021811
198.88,0.0172708,0.00124444,0.933584,139,208.038,0.0256466
207.561,0.0138982,0.00124444,0.930685,199,217.716,0.016863
210.296,0.0164974,0.00124444,0.929771,195,220.769,0.0256633
1 Least squares regularity variability improvement steps Evolution error sigma
2 201.297 0.0153997 0.00124444 0.932777 211 211.096 0.0324233
3 222.924 0.0138755 0.00124444 0.925554 215 233.828 0.0321173
4 195.586 0.0165785 0.00124444 0.934684 233 205.276 0.0262902
5 249.343 0.0170463 0.00124444 0.916732 140 261.016 0.0297848
6 196.161 0.0169011 0.00124444 0.934492 167 205.753 0.0340118
7 232.94 0.0151993 0.00124444 0.922209 203 244.494 0.0327092
8 225.704 0.0187794 0.00124444 0.924626 200 236.857 0.0285021
9 232.061 0.0176159 0.00124444 0.922503 141 243.624 0.0341091
10 216.478 0.0193808 0.00124444 0.927707 205 227.071 0.0319641
11 218.526 0.0168255 0.00124444 0.927023 132 228.254 0.0371698
12 209.13 0.016102 0.00124444 0.930161 195 219.293 0.0254967
13 224.21 0.0097335 0.00124444 0.925125 260 235.159 0.0286628
14 229.764 0.0131346 0.00124444 0.92327 222 240.691 0.0421742
15 221.783 0.0154066 0.00124444 0.925935 234 232.853 0.0190093
16 232.166 0.0181673 0.00124444 0.922468 153 243.665 0.0176615
17 231.214 0.016363 0.00124444 0.922786 194 242.766 0.0286637
18 232.146 0.0160201 0.00124444 0.922474 151 243.618 0.0318045
19 226.741 0.0160234 0.00124444 0.92428 185 238.051 0.029258
20 214.091 0.0162548 0.00124444 0.928504 191 224.685 0.0293766
21 197.18 0.0183974 0.00124444 0.934152 200 206.919 0.0280479
22 254.147 0.0141509 0.00124444 0.915127 180 266.62 0.0401662
23 218.998 0.0166688 0.00124444 0.926865 172 229.771 0.0316734
24 229.888 0.015569 0.00124444 0.923229 172 241.243 0.0383093
25 218.144 0.0175268 0.00124444 0.92715 166 228.75 0.0277466
26 235.229 0.0139292 0.00124444 0.921445 122 246.415 0.0328971
27 234.47 0.0170242 0.00124444 0.921698 214 245.936 0.0311265
28 223.637 0.0147445 0.00124444 0.925316 275 234.603 0.0173437
29 220.006 0.01659 0.00124444 0.926529 223 230.971 0.0221785
30 234.897 0.0153778 0.00124444 0.921556 219 246.319 0.0330376
31 224.176 0.017539 0.00124444 0.925136 182 235.173 0.0316049
32 238.295 0.0167376 0.00124444 0.920421 310 250.199 0.0212912
33 230.074 0.0145246 0.00124444 0.923166 180 240.854 0.0325642
34 222.461 0.0172363 0.00124444 0.925709 226 233.456 0.0200254
35 206.544 0.0113106 0.00124444 0.931024 218 216.659 0.0203372
36 228.658 0.0143528 0.00124444 0.923639 227 240.033 0.0230481
37 232.581 0.0168069 0.00124444 0.922329 208 244.108 0.0315845
38 207.444 0.014401 0.00124444 0.930724 156 216.874 0.0423996
39 230.579 0.0161407 0.00124444 0.922998 222 242.058 0.0155056
40 211.161 0.0154943 0.00124444 0.929483 200 221.484 0.0288013
41 212.025 0.0162238 0.00124444 0.929194 131 222.485 0.0315666
42 228.383 0.0165074 0.00124444 0.923731 306 239.78 0.0203423
43 221.656 0.0168344 0.00124444 0.925978 247 232.709 0.0234514
44 220.116 0.0175812 0.00124444 0.926492 191 230.785 0.0250425
45 219.146 0.0195251 0.00124444 0.926816 168 229.968 0.0433433
46 224.008 0.0160092 0.00124444 0.925192 221 235.149 0.0209819
47 222.645 0.0136684 0.00124444 0.925647 221 233.462 0.028072
48 229.597 0.0142117 0.00124444 0.923326 194 241.027 0.0407908
49 218.437 0.015863 0.00124444 0.927053 246 229.139 0.0264266
50 200.414 0.0159252 0.00124444 0.933072 218 210.309 0.0307566
51 212.507 0.0156027 0.00124444 0.929033 204 222.927 0.0213849
52 225.552 0.014999 0.00124444 0.924676 255 236.762 0.0204812
53 233.174 0.0171498 0.00124444 0.922131 202 244.312 0.0424202
54 214.771 0.0198871 0.00124444 0.928277 257 225.283 0.0205486
55 217.951 0.0156941 0.00124444 0.927215 172 228.831 0.0309327
56 223.826 0.0132289 0.00124444 0.925253 178 234.735 0.0339935
57 211.781 0.0154319 0.00124444 0.929275 249 222.154 0.0359608
58 205.208 0.0165743 0.00124444 0.931471 241 215.144 0.0313585
59 236.053 0.0147814 0.00124444 0.92117 182 247.533 0.0489446
60 231.21 0.0149828 0.00124444 0.922787 202 242.563 0.0415887
61 220.924 0.0177705 0.00124444 0.926222 156 231.706 0.0301208
62 234.408 0.0159816 0.00124444 0.921719 145 245.743 0.0317348
63 222.408 0.0143535 0.00124444 0.925727 242 233.422 0.0246441
64 210.758 0.0174935 0.00124444 0.929617 255 221.213 0.0207961
65 212.849 0.0159688 0.00124444 0.928919 220 223.48 0.0267984
66 223.157 0.0195002 0.00124444 0.925477 188 234.243 0.0198455
67 235.148 0.01368 0.00124444 0.921472 267 246.759 0.0250402
68 214.6 0.0139443 0.00124444 0.928334 204 225.232 0.0280205
69 222.275 0.0167278 0.00124444 0.925771 162 233.179 0.0281601
70 245.067 0.014789 0.00124444 0.918159 173 256.94 0.03829
71 226.652 0.0148565 0.00124444 0.924309 164 237.977 0.0260072
72 227.432 0.0164053 0.00124444 0.924049 186 238.547 0.0399367
73 243.016 0.0161022 0.00124444 0.918844 144 254.967 0.0548178
74 212.727 0.0144341 0.00124444 0.92896 186 223.3 0.0462393
75 232.305 0.0180672 0.00124444 0.922421 147 243.823 0.0243951
76 228.648 0.0124633 0.00124444 0.923643 234 240.056 0.0235827
77 210.07 0.0209562 0.00124444 0.929847 134 220.234 0.03316
78 231.44 0.0145091 0.00124444 0.92271 199 242.633 0.0341166
79 233.47 0.0162595 0.00124444 0.922032 151 244.981 0.0352567
80 233.24 0.0173451 0.00124444 0.922109 196 244.803 0.0189091
81 230.457 0.0150522 0.00124444 0.923039 218 241.898 0.0377671
82 212.095 0.0146085 0.00124444 0.929171 196 222.32 0.0308945
83 220.969 0.0171665 0.00124444 0.926207 243 232.013 0.0183292
84 218.034 0.0154712 0.00124444 0.927187 232 228.661 0.0208262
85 229.902 0.01589 0.00124444 0.923224 226 241.097 0.032418
86 215.14 0.015205 0.00124444 0.928154 226 225.772 0.0165023
87 232.714 0.0171707 0.00124444 0.922285 163 243.746 0.0267255
88 199.436 0.0181063 0.00124444 0.933398 365 209.245 0.0217639
89 225.485 0.0172431 0.00124444 0.924699 196 235.881 0.032626
90 230.548 0.0145494 0.00124444 0.923008 224 241.881 0.0357782
91 220.164 0.014643 0.00124444 0.926476 221 231.035 0.021201
92 210.607 0.0149307 0.00124444 0.929668 197 220.946 0.027384
93 202.034 0.0154935 0.00124444 0.932531 219 212.015 0.0227197
94 223.702 0.0156465 0.00124444 0.925294 220 234.886 0.0202163
95 223.274 0.0153501 0.00124444 0.925437 237 234.38 0.0210493
96 239.317 0.0139175 0.00124444 0.92008 167 250.999 0.0352055
97 219.42 0.0157887 0.00124444 0.926724 194 229.239 0.0410685
98 211.502 0.0159828 0.00124444 0.929368 183 222.041 0.021811
99 198.88 0.0172708 0.00124444 0.933584 139 208.038 0.0256466
100 207.561 0.0138982 0.00124444 0.930685 199 217.716 0.016863
101 210.296 0.0164974 0.00124444 0.929771 195 220.769 0.0256633

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@ -0,0 +1,108 @@
*******************************************************************************
Thu Oct 5 14:02:37 2017
FIT: data read from "20171005-evolution1D_7x4_100Times.csv" every ::1 using 2:5
format = x:z
#datapoints = 100
residuals are weighted equally (unit weight)
function used for fitting: f(x)
f(x)=a*x+b
fitted parameters initialized with current variable values
iter chisq delta/lim lambda a b
0 4.2059624024e+06 0.00e+00 7.07e-01 1.000000e+00 1.000000e+00
5 1.6157855782e+05 -1.28e-04 7.07e-06 -3.703035e+03 2.609538e+02
After 5 iterations the fit converged.
final sum of squares of residuals : 161579
rel. change during last iteration : -1.2809e-09
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 40.6049
variance of residuals (reduced chisquare) = WSSR/ndf : 1648.76
Final set of parameters Asymptotic Standard Error
======================= ==========================
a = -3703.04 +/- 2343 (63.28%)
b = 260.954 +/- 37.51 (14.38%)
correlation matrix of the fit parameters:
a b
a 1.000
b -0.994 1.000
*******************************************************************************
Thu Oct 5 14:02:37 2017
FIT: data read from "20171005-evolution1D_7x4_100Times.csv" every ::1 using 4:5
format = x:z
#datapoints = 100
residuals are weighted equally (unit weight)
function used for fitting: g(x)
g(x)=aa*x+bb
fitted parameters initialized with current variable values
iter chisq delta/lim lambda aa bb
0 4.1694597860e+06 0.00e+00 9.64e-01 1.000000e+00 1.000000e+00
5 1.6088124752e+05 -2.98e-05 9.64e-06 1.779074e+03 -1.445031e+03
After 5 iterations the fit converged.
final sum of squares of residuals : 160881
rel. change during last iteration : -2.98408e-10
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 40.5172
variance of residuals (reduced chisquare) = WSSR/ndf : 1641.65
Final set of parameters Asymptotic Standard Error
======================= ==========================
aa = 1779.07 +/- 1039 (58.39%)
bb = -1445.03 +/- 961.8 (66.56%)
correlation matrix of the fit parameters:
aa bb
aa 1.000
bb -1.000 1.000
*******************************************************************************
Thu Oct 5 14:02:37 2017
FIT: data read from "20171005-evolution1D_7x4_100Times.csv" every ::1 using 4:6
format = x:z
#datapoints = 100
residuals are weighted equally (unit weight)
function used for fitting: h(x)
h(x)=aaa*x+bbb
fitted parameters initialized with current variable values
iter chisq delta/lim lambda aaa bbb
0 5.3588910602e+06 0.00e+00 9.64e-01 1.000000e+00 1.000000e+00
6 5.4694656646e+00 -5.96e-09 9.64e-07 -3.141932e+03 3.141867e+03
After 6 iterations the fit converged.
final sum of squares of residuals : 5.46947
rel. change during last iteration : -5.95966e-14
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 0.236243
variance of residuals (reduced chisquare) = WSSR/ndf : 0.0558109
Final set of parameters Asymptotic Standard Error
======================= ==========================
aaa = -3141.93 +/- 6.057 (0.1928%)
bbb = 3141.87 +/- 5.608 (0.1785%)
correlation matrix of the fit parameters:
aaa bbb
aaa 1.000
bbb -1.000 1.000

View File

@ -0,0 +1,79 @@
iter chisq delta/lim lambda a b
0 4.2059624024e+06 0.00e+00 7.07e-01 1.000000e+00 1.000000e+00
1 1.6580035617e+05 -2.44e+06 7.07e-02 1.958416e+00 2.009886e+02
2 1.6524678185e+05 -3.35e+02 7.07e-03 -2.078184e+02 2.053272e+02
3 1.6165337078e+05 -2.22e+03 7.07e-04 -3.203881e+03 2.530098e+02
4 1.6157855802e+05 -4.63e+01 7.07e-05 -3.702205e+03 2.609406e+02
5 1.6157855782e+05 -1.28e-04 7.07e-06 -3.703035e+03 2.609538e+02
iter chisq delta/lim lambda a b
After 5 iterations the fit converged.
final sum of squares of residuals : 161579
rel. change during last iteration : -1.2809e-09
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 40.6049
variance of residuals (reduced chisquare) = WSSR/ndf : 1648.76
Final set of parameters Asymptotic Standard Error
======================= ==========================
a = -3703.04 +/- 2343 (63.28%)
b = 260.954 +/- 37.51 (14.38%)
correlation matrix of the fit parameters:
a b
a 1.000
b -0.994 1.000
iter chisq delta/lim lambda aa bb
0 4.1694597860e+06 0.00e+00 9.64e-01 1.000000e+00 1.000000e+00
1 1.6525762522e+05 -2.42e+06 9.64e-02 1.017401e+02 1.068470e+02
2 1.6449315575e+05 -4.65e+02 9.64e-03 2.381672e+02 -1.846147e+01
3 1.6091869157e+05 -2.22e+03 9.64e-04 1.622183e+03 -1.299781e+03
4 1.6088124757e+05 -2.33e+01 9.64e-05 1.778896e+03 -1.444867e+03
5 1.6088124752e+05 -2.98e-05 9.64e-06 1.779074e+03 -1.445031e+03
iter chisq delta/lim lambda aa bb
After 5 iterations the fit converged.
final sum of squares of residuals : 160881
rel. change during last iteration : -2.98408e-10
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 40.5172
variance of residuals (reduced chisquare) = WSSR/ndf : 1641.65
Final set of parameters Asymptotic Standard Error
======================= ==========================
aa = 1779.07 +/- 1039 (58.39%)
bb = -1445.03 +/- 961.8 (66.56%)
correlation matrix of the fit parameters:
aa bb
aa 1.000
bb -1.000 1.000
iter chisq delta/lim lambda aaa bbb
0 5.3588910602e+06 0.00e+00 9.64e-01 1.000000e+00 1.000000e+00
1 1.6257651642e+04 -3.29e+07 9.64e-02 1.127845e+02 1.275042e+02
2 1.3617851160e+04 -1.94e+04 9.64e-03 -1.505284e+02 3.724288e+02
3 1.4658675724e+02 -9.19e+06 9.64e-04 -2.837355e+03 2.859890e+03
4 5.4696465957e+00 -2.58e+06 9.64e-05 -3.141588e+03 3.141548e+03
5 5.4694656646e+00 -3.31e+00 9.64e-06 -3.141932e+03 3.141867e+03
6 5.4694656646e+00 -5.96e-09 9.64e-07 -3.141932e+03 3.141867e+03
iter chisq delta/lim lambda aaa bbb
After 6 iterations the fit converged.
final sum of squares of residuals : 5.46947
rel. change during last iteration : -5.95966e-14
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 0.236243
variance of residuals (reduced chisquare) = WSSR/ndf : 0.0558109
Final set of parameters Asymptotic Standard Error
======================= ==========================
aaa = -3141.93 +/- 6.057 (0.1928%)
bbb = 3141.87 +/- 5.608 (0.1785%)
correlation matrix of the fit parameters:
aaa bbb
aaa 1.000
bbb -1.000 1.000

View File

@ -0,0 +1,20 @@
set datafile separator ","
f(x)=a*x+b
fit f(x) "20171005-evolution1D_7x4_100Times.csv" every ::1 using 2:5 via a,b
set terminal png
set xlabel 'regularity'
set ylabel 'steps'
set output "20171005-evolution1D_7x4_100Times_regularity-vs-steps.png"
plot "20171005-evolution1D_7x4_100Times.csv" every ::1 using 2:5 title "data", f(x) title "lin. fit" lc rgb "black"
g(x)=aa*x+bb
fit g(x) "20171005-evolution1D_7x4_100Times.csv" every ::1 using 4:5 via aa,bb
set xlabel 'improvement potential'
set ylabel 'steps'
set output "20171005-evolution1D_7x4_100Times_improvement-vs-steps.png"
plot "20171005-evolution1D_7x4_100Times.csv" every ::1 using 4:5 title "data", g(x) title "lin. fit" lc rgb "black"
h(x)=aaa*x+bbb
fit h(x) "20171005-evolution1D_7x4_100Times.csv" every ::1 using 4:6 via aaa,bbb
set xlabel 'improvement potential'
set ylabel 'evolution error'
set output "20171005-evolution1D_7x4_100Times_improvement-vs-evo-error.png"
plot "20171005-evolution1D_7x4_100Times.csv" every ::1 using 4:6 title "data", h(x) title "lin. fit" lc rgb "black"

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@ -0,0 +1,101 @@
"Least squares",regularity,variability,improvement,steps,"Evolution error",sigma
120.527,0.0305651,0.00217778,0.95975,182,126.241,0.0274436
105.717,0.0304078,0.00217778,0.964696,282,110.962,0.0158715
119.901,0.0296548,0.00217778,0.959959,224,125.853,0.0221449
133.689,0.0301734,0.00217778,0.955354,233,140.195,0.0223482
120.918,0.0297028,0.00217778,0.959619,175,126.647,0.0197707
147.295,0.0283644,0.00217778,0.950811,243,154.539,0.0261081
102.228,0.0319967,0.00217778,0.965861,251,107.206,0.0145694
122.622,0.0252732,0.00217778,0.95905,232,128.558,0.0183895
130.819,0.0325323,0.00217778,0.956313,169,136.77,0.0248296
139.062,0.029404,0.00217778,0.95356,239,145.941,0.0198192
163.931,0.0284774,0.00217778,0.945255,197,171.996,0.0243894
113.252,0.0321719,0.00217778,0.962179,241,118.437,0.0246194
137.3,0.0283919,0.00217778,0.954149,176,143.556,0.0201642
115.119,0.0300041,0.00217778,0.961556,295,120.873,0.0178014
100.904,0.0288716,0.00217778,0.966303,208,105.887,0.0200813
147.487,0.024799,0.00217778,0.950747,189,154.67,0.0243077
147.404,0.0277859,0.00217778,0.950774,143,154.182,0.0287594
110.849,0.0278082,0.00217778,0.962982,247,116.314,0.0130491
108.144,0.0317319,0.00217778,0.963885,221,113.496,0.0213925
152.471,0.0309984,0.00217778,0.949082,193,159.92,0.0216455
151.301,0.0316553,0.00217778,0.949473,183,158.727,0.026995
103.761,0.0245259,0.00217778,0.965349,208,108.387,0.0261726
141.47,0.0299984,0.00217778,0.952756,215,148.334,0.0212445
107.693,0.0288702,0.00217778,0.964036,237,112.767,0.0184018
132.834,0.0291123,0.00217778,0.95564,243,139.428,0.0137168
118.598,0.0294951,0.00217778,0.960394,224,124.479,0.0151507
101.31,0.0290843,0.00217778,0.966168,277,106.309,0.0130174
133.211,0.0291807,0.00217778,0.955514,223,139.721,0.0234585
127.816,0.031699,0.00217778,0.957316,228,133.951,0.0207455
128.875,0.0282553,0.00217778,0.956962,205,135.062,0.0212527
128.065,0.0277151,0.00217778,0.957233,232,133.884,0.0207377
114.407,0.0308509,0.00217778,0.961794,248,120.051,0.0177078
96.6405,0.0323881,0.00217778,0.967727,257,101.318,0.0205648
128.856,0.0302615,0.00217778,0.956968,196,135.279,0.0185934
104.861,0.0307769,0.00217778,0.964981,300,110.051,0.0107628
130.225,0.0302749,0.00217778,0.956511,206,136.437,0.0178083
128.374,0.025948,0.00217778,0.957129,198,134.697,0.0240022
108.68,0.0299555,0.00217778,0.963706,261,113.78,0.0165322
116.83,0.0285706,0.00217778,0.960984,214,122.484,0.0175649
109.654,0.0290142,0.00217778,0.963381,201,114.487,0.0242023
121.709,0.0293128,0.00217778,0.959355,181,127.55,0.0230015
119.756,0.0299435,0.00217778,0.960007,266,125.629,0.0222042
154.595,0.0296071,0.00217778,0.948373,161,162.041,0.031111
148.94,0.0288307,0.00217778,0.950261,189,156.364,0.0201626
108.541,0.0309115,0.00217778,0.963753,215,113.788,0.0194676
131.712,0.0305304,0.00217778,0.956015,170,138.26,0.0233339
104.985,0.0269759,0.00217778,0.96494,223,109.996,0.0200455
156.935,0.0277759,0.00217778,0.947591,197,164.442,0.0274963
101.562,0.0270836,0.00217778,0.966083,228,105.711,0.0285827
149.172,0.0293507,0.00217778,0.950184,266,156.553,0.0137586
110.786,0.0301022,0.00217778,0.963003,225,116.304,0.0160115
108.126,0.0296889,0.00217778,0.963891,272,113.339,0.0139973
113.396,0.0280885,0.00217778,0.962131,184,118.637,0.0206842
157.303,0.0286129,0.00217778,0.947469,205,164.957,0.0204323
92.5603,0.0310149,0.00217778,0.969089,288,97.1774,0.0118406
131.31,0.0312147,0.00217778,0.956149,201,137.814,0.0216513
151.662,0.0264346,0.00217778,0.949352,241,158.652,0.0206991
122.986,0.0296999,0.00217778,0.958929,239,128.639,0.0250375
138.21,0.0276365,0.00217778,0.953845,210,144.853,0.0221076
130.258,0.0288724,0.00217778,0.9565,203,136.728,0.0240955
95.4606,0.0305017,0.00217778,0.968121,264,100.103,0.0134085
123.79,0.0302927,0.00217778,0.95866,290,129.972,0.0120363
128.023,0.0269328,0.00217778,0.957247,263,134.171,0.0176036
97.4169,0.0294049,0.00217778,0.967467,283,102.086,0.0154436
131.497,0.032637,0.00217778,0.956086,194,137.823,0.0230214
114.486,0.0296235,0.00217778,0.961767,209,120.165,0.0182154
115.802,0.030273,0.00217778,0.961328,249,121.511,0.0168117
104.888,0.0298335,0.00217778,0.964972,266,110.067,0.0157024
107.529,0.0299312,0.00217778,0.96409,214,112.614,0.0242658
100.39,0.0284705,0.00217778,0.966475,228,105.271,0.0152932
134.206,0.0300452,0.00217778,0.955182,190,140.821,0.0262343
118.423,0.0285713,0.00217778,0.960452,230,124.285,0.0195539
150.763,0.0269517,0.00217778,0.949652,188,158.103,0.0213998
134.048,0.0302692,0.00217778,0.955234,169,140.515,0.0308499
96.9072,0.029681,0.00217778,0.967638,245,101.64,0.0165011
123.811,0.0258042,0.00217778,0.958653,254,129.887,0.0140323
159.564,0.0304946,0.00217778,0.946713,205,167.194,0.0194008
105.757,0.0290973,0.00217778,0.964682,238,110.881,0.0179311
139.738,0.0289451,0.00217778,0.953334,194,146.669,0.0262391
112.768,0.0276706,0.00217778,0.962341,195,118.035,0.0156339
143.501,0.0254884,0.00217778,0.952078,256,150.583,0.0127485
136.72,0.0262001,0.00217778,0.954342,244,143.211,0.0193324
109.952,0.0287195,0.00217778,0.963281,223,114.785,0.0241593
139.559,0.029377,0.00217778,0.953394,187,146.421,0.0183773
124.8,0.028991,0.00217778,0.958323,220,130.875,0.0179003
102.291,0.0285261,0.00217778,0.96584,278,107.373,0.0140422
144.967,0.0281308,0.00217778,0.951588,234,152.164,0.0189975
123.808,0.031638,0.00217778,0.958654,185,129.282,0.0314699
93.612,0.0275759,0.00217778,0.968738,271,98.1416,0.0124221
123.829,0.0279283,0.00217778,0.958647,199,129.997,0.0222236
111.654,0.0290171,0.00217778,0.962713,183,117,0.0200822
105.848,0.0278707,0.00217778,0.964652,235,111.092,0.0159004
108.727,0.0240196,0.00217778,0.96369,330,114.087,0.0151336
102.465,0.0285859,0.00217778,0.965782,267,107.558,0.0137917
137.886,0.0315396,0.00217778,0.953953,162,144.45,0.0198371
112.847,0.0258472,0.00217778,0.962315,233,118.484,0.0194957
104.672,0.0280787,0.00217778,0.965045,270,109.701,0.017613
121.264,0.0247246,0.00217778,0.959504,181,126.593,0.0240936
161.034,0.0240912,0.00217778,0.946223,165,168.971,0.0262645
137.026,0.0283714,0.00217778,0.95424,262,143.838,0.02069
1 Least squares regularity variability improvement steps Evolution error sigma
2 120.527 0.0305651 0.00217778 0.95975 182 126.241 0.0274436
3 105.717 0.0304078 0.00217778 0.964696 282 110.962 0.0158715
4 119.901 0.0296548 0.00217778 0.959959 224 125.853 0.0221449
5 133.689 0.0301734 0.00217778 0.955354 233 140.195 0.0223482
6 120.918 0.0297028 0.00217778 0.959619 175 126.647 0.0197707
7 147.295 0.0283644 0.00217778 0.950811 243 154.539 0.0261081
8 102.228 0.0319967 0.00217778 0.965861 251 107.206 0.0145694
9 122.622 0.0252732 0.00217778 0.95905 232 128.558 0.0183895
10 130.819 0.0325323 0.00217778 0.956313 169 136.77 0.0248296
11 139.062 0.029404 0.00217778 0.95356 239 145.941 0.0198192
12 163.931 0.0284774 0.00217778 0.945255 197 171.996 0.0243894
13 113.252 0.0321719 0.00217778 0.962179 241 118.437 0.0246194
14 137.3 0.0283919 0.00217778 0.954149 176 143.556 0.0201642
15 115.119 0.0300041 0.00217778 0.961556 295 120.873 0.0178014
16 100.904 0.0288716 0.00217778 0.966303 208 105.887 0.0200813
17 147.487 0.024799 0.00217778 0.950747 189 154.67 0.0243077
18 147.404 0.0277859 0.00217778 0.950774 143 154.182 0.0287594
19 110.849 0.0278082 0.00217778 0.962982 247 116.314 0.0130491
20 108.144 0.0317319 0.00217778 0.963885 221 113.496 0.0213925
21 152.471 0.0309984 0.00217778 0.949082 193 159.92 0.0216455
22 151.301 0.0316553 0.00217778 0.949473 183 158.727 0.026995
23 103.761 0.0245259 0.00217778 0.965349 208 108.387 0.0261726
24 141.47 0.0299984 0.00217778 0.952756 215 148.334 0.0212445
25 107.693 0.0288702 0.00217778 0.964036 237 112.767 0.0184018
26 132.834 0.0291123 0.00217778 0.95564 243 139.428 0.0137168
27 118.598 0.0294951 0.00217778 0.960394 224 124.479 0.0151507
28 101.31 0.0290843 0.00217778 0.966168 277 106.309 0.0130174
29 133.211 0.0291807 0.00217778 0.955514 223 139.721 0.0234585
30 127.816 0.031699 0.00217778 0.957316 228 133.951 0.0207455
31 128.875 0.0282553 0.00217778 0.956962 205 135.062 0.0212527
32 128.065 0.0277151 0.00217778 0.957233 232 133.884 0.0207377
33 114.407 0.0308509 0.00217778 0.961794 248 120.051 0.0177078
34 96.6405 0.0323881 0.00217778 0.967727 257 101.318 0.0205648
35 128.856 0.0302615 0.00217778 0.956968 196 135.279 0.0185934
36 104.861 0.0307769 0.00217778 0.964981 300 110.051 0.0107628
37 130.225 0.0302749 0.00217778 0.956511 206 136.437 0.0178083
38 128.374 0.025948 0.00217778 0.957129 198 134.697 0.0240022
39 108.68 0.0299555 0.00217778 0.963706 261 113.78 0.0165322
40 116.83 0.0285706 0.00217778 0.960984 214 122.484 0.0175649
41 109.654 0.0290142 0.00217778 0.963381 201 114.487 0.0242023
42 121.709 0.0293128 0.00217778 0.959355 181 127.55 0.0230015
43 119.756 0.0299435 0.00217778 0.960007 266 125.629 0.0222042
44 154.595 0.0296071 0.00217778 0.948373 161 162.041 0.031111
45 148.94 0.0288307 0.00217778 0.950261 189 156.364 0.0201626
46 108.541 0.0309115 0.00217778 0.963753 215 113.788 0.0194676
47 131.712 0.0305304 0.00217778 0.956015 170 138.26 0.0233339
48 104.985 0.0269759 0.00217778 0.96494 223 109.996 0.0200455
49 156.935 0.0277759 0.00217778 0.947591 197 164.442 0.0274963
50 101.562 0.0270836 0.00217778 0.966083 228 105.711 0.0285827
51 149.172 0.0293507 0.00217778 0.950184 266 156.553 0.0137586
52 110.786 0.0301022 0.00217778 0.963003 225 116.304 0.0160115
53 108.126 0.0296889 0.00217778 0.963891 272 113.339 0.0139973
54 113.396 0.0280885 0.00217778 0.962131 184 118.637 0.0206842
55 157.303 0.0286129 0.00217778 0.947469 205 164.957 0.0204323
56 92.5603 0.0310149 0.00217778 0.969089 288 97.1774 0.0118406
57 131.31 0.0312147 0.00217778 0.956149 201 137.814 0.0216513
58 151.662 0.0264346 0.00217778 0.949352 241 158.652 0.0206991
59 122.986 0.0296999 0.00217778 0.958929 239 128.639 0.0250375
60 138.21 0.0276365 0.00217778 0.953845 210 144.853 0.0221076
61 130.258 0.0288724 0.00217778 0.9565 203 136.728 0.0240955
62 95.4606 0.0305017 0.00217778 0.968121 264 100.103 0.0134085
63 123.79 0.0302927 0.00217778 0.95866 290 129.972 0.0120363
64 128.023 0.0269328 0.00217778 0.957247 263 134.171 0.0176036
65 97.4169 0.0294049 0.00217778 0.967467 283 102.086 0.0154436
66 131.497 0.032637 0.00217778 0.956086 194 137.823 0.0230214
67 114.486 0.0296235 0.00217778 0.961767 209 120.165 0.0182154
68 115.802 0.030273 0.00217778 0.961328 249 121.511 0.0168117
69 104.888 0.0298335 0.00217778 0.964972 266 110.067 0.0157024
70 107.529 0.0299312 0.00217778 0.96409 214 112.614 0.0242658
71 100.39 0.0284705 0.00217778 0.966475 228 105.271 0.0152932
72 134.206 0.0300452 0.00217778 0.955182 190 140.821 0.0262343
73 118.423 0.0285713 0.00217778 0.960452 230 124.285 0.0195539
74 150.763 0.0269517 0.00217778 0.949652 188 158.103 0.0213998
75 134.048 0.0302692 0.00217778 0.955234 169 140.515 0.0308499
76 96.9072 0.029681 0.00217778 0.967638 245 101.64 0.0165011
77 123.811 0.0258042 0.00217778 0.958653 254 129.887 0.0140323
78 159.564 0.0304946 0.00217778 0.946713 205 167.194 0.0194008
79 105.757 0.0290973 0.00217778 0.964682 238 110.881 0.0179311
80 139.738 0.0289451 0.00217778 0.953334 194 146.669 0.0262391
81 112.768 0.0276706 0.00217778 0.962341 195 118.035 0.0156339
82 143.501 0.0254884 0.00217778 0.952078 256 150.583 0.0127485
83 136.72 0.0262001 0.00217778 0.954342 244 143.211 0.0193324
84 109.952 0.0287195 0.00217778 0.963281 223 114.785 0.0241593
85 139.559 0.029377 0.00217778 0.953394 187 146.421 0.0183773
86 124.8 0.028991 0.00217778 0.958323 220 130.875 0.0179003
87 102.291 0.0285261 0.00217778 0.96584 278 107.373 0.0140422
88 144.967 0.0281308 0.00217778 0.951588 234 152.164 0.0189975
89 123.808 0.031638 0.00217778 0.958654 185 129.282 0.0314699
90 93.612 0.0275759 0.00217778 0.968738 271 98.1416 0.0124221
91 123.829 0.0279283 0.00217778 0.958647 199 129.997 0.0222236
92 111.654 0.0290171 0.00217778 0.962713 183 117 0.0200822
93 105.848 0.0278707 0.00217778 0.964652 235 111.092 0.0159004
94 108.727 0.0240196 0.00217778 0.96369 330 114.087 0.0151336
95 102.465 0.0285859 0.00217778 0.965782 267 107.558 0.0137917
96 137.886 0.0315396 0.00217778 0.953953 162 144.45 0.0198371
97 112.847 0.0258472 0.00217778 0.962315 233 118.484 0.0194957
98 104.672 0.0280787 0.00217778 0.965045 270 109.701 0.017613
99 121.264 0.0247246 0.00217778 0.959504 181 126.593 0.0240936
100 161.034 0.0240912 0.00217778 0.946223 165 168.971 0.0262645
101 137.026 0.0283714 0.00217778 0.95424 262 143.838 0.02069

View File

@ -0,0 +1,108 @@
*******************************************************************************
Thu Oct 5 14:22:52 2017
FIT: data read from "20171005-evolution1D_7x7_100Times.csv" every ::1 using 2:5
format = x:z
#datapoints = 100
residuals are weighted equally (unit weight)
function used for fitting: f(x)
f(x)=a*x+b
fitted parameters initialized with current variable values
iter chisq delta/lim lambda a b
0 5.1103239746e+06 0.00e+00 7.07e-01 1.000000e+00 1.000000e+00
5 1.3279798348e+05 -2.35e-06 7.07e-06 -5.771314e+02 2.408587e+02
After 5 iterations the fit converged.
final sum of squares of residuals : 132798
rel. change during last iteration : -2.35102e-11
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 36.8114
variance of residuals (reduced chisquare) = WSSR/ndf : 1355.08
Final set of parameters Asymptotic Standard Error
======================= ==========================
a = -577.131 +/- 1945 (337%)
b = 240.859 +/- 56.49 (23.46%)
correlation matrix of the fit parameters:
a b
a 1.000
b -0.998 1.000
*******************************************************************************
Thu Oct 5 14:22:52 2017
FIT: data read from "20171005-evolution1D_7x7_100Times.csv" every ::1 using 4:5
format = x:z
#datapoints = 100
residuals are weighted equally (unit weight)
function used for fitting: g(x)
g(x)=aa*x+bb
fitted parameters initialized with current variable values
iter chisq delta/lim lambda aa bb
0 5.0689010815e+06 0.00e+00 9.80e-01 1.000000e+00 1.000000e+00
5 9.8040471485e+04 -1.51e-05 9.80e-06 3.134345e+03 -2.780953e+03
After 5 iterations the fit converged.
final sum of squares of residuals : 98040.5
rel. change during last iteration : -1.51467e-10
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 31.6293
variance of residuals (reduced chisquare) = WSSR/ndf : 1000.41
Final set of parameters Asymptotic Standard Error
======================= ==========================
aa = 3134.35 +/- 530.8 (16.94%)
bb = -2780.95 +/- 509 (18.3%)
correlation matrix of the fit parameters:
aa bb
aa 1.000
bb -1.000 1.000
*******************************************************************************
Thu Oct 5 14:22:52 2017
FIT: data read from "20171005-evolution1D_7x7_100Times.csv" every ::1 using 4:6
format = x:z
#datapoints = 100
residuals are weighted equally (unit weight)
function used for fitting: h(x)
h(x)=aaa*x+bbb
fitted parameters initialized with current variable values
iter chisq delta/lim lambda aaa bbb
0 1.6606716013e+06 0.00e+00 9.80e-01 1.000000e+00 1.000000e+00
5 3.7498507724e+00 -4.46e-01 9.80e-06 -3.142464e+03 3.142323e+03
After 5 iterations the fit converged.
final sum of squares of residuals : 3.74985
rel. change during last iteration : -4.45921e-06
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 0.195611
variance of residuals (reduced chisquare) = WSSR/ndf : 0.0382638
Final set of parameters Asymptotic Standard Error
======================= ==========================
aaa = -3142.46 +/- 3.283 (0.1045%)
bbb = 3142.32 +/- 3.148 (0.1002%)
correlation matrix of the fit parameters:
aaa bbb
aaa 1.000
bbb -1.000 1.000

View File

@ -0,0 +1,78 @@
iter chisq delta/lim lambda a b
0 5.1103239746e+06 0.00e+00 7.07e-01 1.000000e+00 1.000000e+00
1 1.3304344126e+05 -3.74e+06 7.07e-02 7.011364e+00 2.228167e+02
2 1.3290446272e+05 -1.05e+02 7.07e-03 -3.195295e+01 2.250561e+02
3 1.3279958538e+05 -7.90e+01 7.07e-04 -5.102627e+02 2.389204e+02
4 1.3279798349e+05 -1.21e+00 7.07e-05 -5.770380e+02 2.408559e+02
5 1.3279798348e+05 -2.35e-06 7.07e-06 -5.771314e+02 2.408587e+02
iter chisq delta/lim lambda a b
After 5 iterations the fit converged.
final sum of squares of residuals : 132798
rel. change during last iteration : -2.35102e-11
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 36.8114
variance of residuals (reduced chisquare) = WSSR/ndf : 1355.08
Final set of parameters Asymptotic Standard Error
======================= ==========================
a = -577.131 +/- 1945 (337%)
b = 240.859 +/- 56.49 (23.46%)
correlation matrix of the fit parameters:
a b
a 1.000
b -0.998 1.000
iter chisq delta/lim lambda aa bb
0 5.0689010815e+06 0.00e+00 9.80e-01 1.000000e+00 1.000000e+00
1 1.3046686978e+05 -3.79e+06 9.80e-02 1.172773e+02 1.106372e+02
2 1.2074716009e+05 -8.05e+03 9.80e-03 6.053195e+02 -3.561808e+02
3 9.8095704395e+04 -2.31e+04 9.80e-04 3.009614e+03 -2.661363e+03
4 9.8040471500e+04 -5.63e+01 9.80e-05 3.134281e+03 -2.780891e+03
5 9.8040471485e+04 -1.51e-05 9.80e-06 3.134345e+03 -2.780953e+03
iter chisq delta/lim lambda aa bb
After 5 iterations the fit converged.
final sum of squares of residuals : 98040.5
rel. change during last iteration : -1.51467e-10
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 31.6293
variance of residuals (reduced chisquare) = WSSR/ndf : 1000.41
Final set of parameters Asymptotic Standard Error
======================= ==========================
aa = 3134.35 +/- 530.8 (16.94%)
bb = -2780.95 +/- 509 (18.3%)
correlation matrix of the fit parameters:
aa bb
aa 1.000
bb -1.000 1.000
iter chisq delta/lim lambda aaa bbb
0 1.6606716013e+06 0.00e+00 9.80e-01 1.000000e+00 1.000000e+00
1 3.6419319985e+04 -4.46e+06 9.80e-02 5.817813e+01 7.298767e+01
2 2.5572102437e+04 -4.24e+04 9.80e-03 -4.588023e+02 5.692906e+02
3 6.5943618679e+01 -3.87e+07 9.80e-04 -3.010106e+03 3.015422e+03
4 3.7498674938e+00 -1.66e+06 9.80e-05 -3.142395e+03 3.142258e+03
5 3.7498507724e+00 -4.46e-01 9.80e-06 -3.142464e+03 3.142323e+03
iter chisq delta/lim lambda aaa bbb
After 5 iterations the fit converged.
final sum of squares of residuals : 3.74985
rel. change during last iteration : -4.45921e-06
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 0.195611
variance of residuals (reduced chisquare) = WSSR/ndf : 0.0382638
Final set of parameters Asymptotic Standard Error
======================= ==========================
aaa = -3142.46 +/- 3.283 (0.1045%)
bbb = 3142.32 +/- 3.148 (0.1002%)
correlation matrix of the fit parameters:
aaa bbb
aaa 1.000
bbb -1.000 1.000

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@ -0,0 +1,20 @@
set datafile separator ","
f(x)=a*x+b
fit f(x) "20171005-evolution1D_7x7_100Times.csv" every ::1 using 2:5 via a,b
set terminal png
set xlabel 'regularity'
set ylabel 'steps'
set output "20171005-evolution1D_7x7_100Times_regularity-vs-steps.png"
plot "20171005-evolution1D_7x7_100Times.csv" every ::1 using 2:5 title "data", f(x) title "lin. fit" lc rgb "black"
g(x)=aa*x+bb
fit g(x) "20171005-evolution1D_7x7_100Times.csv" every ::1 using 4:5 via aa,bb
set xlabel 'improvement potential'
set ylabel 'steps'
set output "20171005-evolution1D_7x7_100Times_improvement-vs-steps.png"
plot "20171005-evolution1D_7x7_100Times.csv" every ::1 using 4:5 title "data", g(x) title "lin. fit" lc rgb "black"
h(x)=aaa*x+bbb
fit h(x) "20171005-evolution1D_7x7_100Times.csv" every ::1 using 4:6 via aaa,bbb
set xlabel 'improvement potential'
set ylabel 'evolution error'
set output "20171005-evolution1D_7x7_100Times_improvement-vs-evo-error.png"
plot "20171005-evolution1D_7x7_100Times.csv" every ::1 using 4:6 title "data", h(x) title "lin. fit" lc rgb "black"

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@ -13,19 +13,49 @@ set terminal png
set xlabel 'regularity'
set ylabel 'steps'
set output "${png}_regularity-vs-steps.png"
plot "$2" every ::1 using 2:5 title "$2", "$3" every ::1 using 2:5 title "$3", f(x) title "lin. fit" lc rgb "black"
plot \
"$2" every ::1 using 2:5 title "$2", \
"$3" every ::1 using 2:5 title "$3", \
"$4" every ::1 using 2:5 title "$4", \
"$5" every ::1 using 2:5 title "$5", \
"$6" every ::1 using 2:5 title "$6", \
f(x) title "lin. fit" lc rgb "black"
g(x)=aa*x+bb
fit g(x) "$data" every ::1 using 4:5 via aa,bb
set xlabel 'improvement potential'
set ylabel 'steps'
set output "${png}_improvement-vs-steps.png"
plot "$2" every ::1 using 4:5 title "$2", "$3" every ::1 using 4:5 title "$3", g(x) title "lin. fit" lc rgb "black"
plot \
"$2" every ::1 using 4:5 title "$2", \
"$3" every ::1 using 4:5 title "$3", \
"$4" every ::1 using 4:5 title "$4", \
"$5" every ::1 using 4:5 title "$5", \
"$6" every ::1 using 4:5 title "$6", \
g(x) title "lin. fit" lc rgb "black"
h(x)=aaa*x+bbb
fit h(x) "$data" every ::1 using 4:6 via aaa,bbb
set xlabel 'improvement potential'
set ylabel 'evolution error'
set output "${png}_improvement-vs-evo-error.png"
plot "$2" every ::1 using 4:6 title "$2", "$3" every ::1 using 4:6 title "$3", h(x) title "lin. fit" lc rgb "black"
plot \
"$2" every ::1 using 4:6 title "$2", \
"$3" every ::1 using 4:6 title "$3", \
"$4" every ::1 using 4:6 title "$4", \
"$5" every ::1 using 4:6 title "$5", \
"$6" every ::1 using 4:6 title "$6", \
h(x) title "lin. fit" lc rgb "black"
i(x)=aaaa*x+bbbb
fit i(x) "$data" every ::1 using 3:6 via aaaa,bbbb
set xlabel 'variability'
set ylabel 'evolution error'
set output "${png}_variability-vs-evo-error.png"
plot \
"$2" every ::1 using 3:6 title "$2", \
"$3" every ::1 using 3:6 title "$3", \
"$4" every ::1 using 3:6 title "$4", \
"$5" every ::1 using 3:6 title "$5", \
"$6" every ::1 using 3:6 title "$6", \
i(x) title "lin. fit" lc rgb "black"
EOD
) > "${png}.gnuplot.script"
gnuplot "${png}.gnuplot.script" 2> "${png}.gnuplot.log"

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