added 1d with added scalar for inprecise prediction

This commit is contained in:
Nicole Dresselhaus 2017-08-30 22:33:10 +02:00
parent da2e1b2d77
commit 8b4bcef353
Signed by: Drezil
GPG Key ID: 057D94F356F41E25
8 changed files with 5147 additions and 0 deletions

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@ -0,0 +1,101 @@
"Least squares",regularity,variability,improvement,steps,"Evolution error",sigma
198.091,0.0185605,0.00111111,0.933847,245,207.886,0.0152523
234.524,0.0192422,0.00111111,0.92168,203,245.873,0.0333896
199.665,0.0169589,0.00111111,0.933322,236,209.253,0.0256795
225.432,0.019794,0.00111111,0.924717,164,236.693,0.0303567
226.331,0.0198218,0.00111111,0.924416,133,237.512,0.0326327
207.081,0.0183173,0.00111111,0.930845,293,217.347,0.0173303
197.215,0.0195282,0.00111111,0.93414,144,206.725,0.0274424
207.985,0.018551,0.00111111,0.930543,167,218.216,0.0264904
199.179,0.0192478,0.00111111,0.933484,230,208.831,0.0280096
190.622,0.0185748,0.00111111,0.936342,146,199.805,0.040456
186.169,0.0193022,0.00111111,0.937829,196,195.163,0.0339487
184.072,0.0170487,0.00111111,0.938529,224,193.258,0.0325346
173.247,0.0203667,0.00111111,0.942144,191,181.901,0.0186411
174.505,0.0185505,0.00111111,0.941724,232,183.091,0.0391035
204.996,0.019809,0.00111111,0.931541,126,215.233,0.0317816
201.159,0.0177293,0.00111111,0.932823,231,210.992,0.033055
215.57,0.0184879,0.00111111,0.92801,157,226.197,0.0248099
220.4,0.0220813,0.00111111,0.926397,120,230.55,0.0266999
192.669,0.0189865,0.00111111,0.935658,197,201.997,0.0273896
217.249,0.0209109,0.00111111,0.927449,196,227.649,0.0314102
183.442,0.0177993,0.00111111,0.938739,276,192.577,0.0290511
211.058,0.0191396,0.00111111,0.929517,262,221.454,0.0258037
225.405,0.0142923,0.00111111,0.924726,232,236.59,0.0554429
214.129,0.0153926,0.00111111,0.928491,258,224.637,0.025707
191.681,0.0159947,0.00111111,0.935988,221,201.263,0.0168213
208.302,0.0168977,0.00111111,0.930437,267,218.685,0.0303307
244.265,0.0186614,0.00111111,0.918427,240,256.401,0.014142
217.424,0.0189707,0.00111111,0.927391,194,228.137,0.0234014
193.974,0.0187277,0.00111111,0.935222,200,203.421,0.0313489
217.843,0.0191009,0.00111111,0.927251,244,228.677,0.0186412
228.126,0.0195362,0.00111111,0.923817,171,239.173,0.0237806
194.236,0.0205534,0.00111111,0.935135,206,203.783,0.0222971
231.826,0.017672,0.00111111,0.922582,126,243.217,0.0354461
194.684,0.0180998,0.00111111,0.934985,149,204.188,0.0265668
201.513,0.0176892,0.00111111,0.932705,232,211.535,0.0277219
219.557,0.016394,0.00111111,0.926679,145,229.573,0.0466214
215.208,0.0203045,0.00111111,0.928131,217,225.773,0.0198841
224.784,0.0187965,0.00111111,0.924933,207,235.748,0.0249149
198.752,0.0192186,0.00111111,0.933626,214,208.659,0.0198508
210.374,0.0169978,0.00111111,0.929745,201,220.83,0.029392
182.397,0.0156577,0.00111111,0.939088,271,191.357,0.0356415
214.532,0.017907,0.00111111,0.928357,187,224.938,0.0315807
206.254,0.0198955,0.00111111,0.931121,144,216.195,0.0261497
208.845,0.019427,0.00111111,0.930256,302,218.868,0.0293849
219.847,0.018273,0.00111111,0.926582,205,230.63,0.0222702
178.072,0.0161684,0.00111111,0.940532,271,186.89,0.0224425
208.094,0.0174596,0.00111111,0.930507,188,218.199,0.026439
206.759,0.017792,0.00111111,0.930952,249,217.047,0.0196121
213.588,0.0179354,0.00111111,0.928672,154,223.644,0.0261114
203.691,0.0186109,0.00111111,0.931977,215,213.801,0.029634
196.02,0.0164293,0.00111111,0.934539,286,205.631,0.0248201
200.893,0.0206686,0.00111111,0.932911,218,210.824,0.0165101
219.308,0.018868,0.00111111,0.926762,202,230.178,0.0193291
246.019,0.0189353,0.00111111,0.917842,155,257.369,0.0271402
231.847,0.0197966,0.00111111,0.922574,168,243.262,0.0289334
218.91,0.0209849,0.00111111,0.926895,197,229.047,0.0299942
211.126,0.0167501,0.00111111,0.929494,196,221.493,0.0286991
169.535,0.0151348,0.00111111,0.943383,175,177.905,0.0337364
230.113,0.0201869,0.00111111,0.923153,167,241.468,0.0344348
231.924,0.0200713,0.00111111,0.922549,149,243.443,0.0257716
223.016,0.0200786,0.00111111,0.925524,178,233.782,0.0402102
195.674,0.014894,0.00111111,0.934654,268,205.347,0.0551882
255.95,0.0195439,0.00111111,0.914525,148,268.384,0.0447607
220.055,0.0198234,0.00111111,0.926512,236,230.853,0.0367555
216.155,0.0189146,0.00111111,0.927815,129,226.312,0.0430726
199.784,0.0187532,0.00111111,0.933282,199,209.55,0.0353292
222.549,0.0203982,0.00111111,0.925679,178,233.426,0.0233115
200.95,0.0183086,0.00111111,0.932893,256,210.991,0.016803
209.249,0.016696,0.00111111,0.930121,269,219.415,0.02081
249.03,0.0223912,0.00111111,0.916836,177,260.926,0.0294009
219.09,0.019419,0.00111111,0.926835,218,229.786,0.0289335
185.792,0.0169305,0.00111111,0.937954,291,194.888,0.0258978
184.383,0.0208635,0.00111111,0.938425,256,193.57,0.0185135
201.82,0.0183427,0.00111111,0.932602,236,211.086,0.0474114
226.705,0.0196988,0.00111111,0.924292,255,237.989,0.0227452
206.776,0.019382,0.00111111,0.930947,187,217.102,0.0280757
185.74,0.0141851,0.00111111,0.937972,166,194.775,0.0321003
232.792,0.0240912,0.00111111,0.922259,191,244.384,0.0176279
201.838,0.0188448,0.00111111,0.932596,181,211.814,0.0297108
202.159,0.0179769,0.00111111,0.932489,175,212.073,0.0239756
178.922,0.018115,0.00111111,0.940249,259,187.619,0.0215103
196.096,0.0172259,0.00111111,0.934514,220,205.625,0.030462
200.913,0.0195059,0.00111111,0.932905,162,210.781,0.0383751
182.439,0.020238,0.00111111,0.939074,172,191.55,0.0264845
169.79,0.018196,0.00111111,0.943298,226,178.258,0.0148133
185.191,0.0167884,0.00111111,0.938155,194,194.329,0.0261194
202.268,0.0211018,0.00111111,0.932452,200,212.217,0.0301675
191.444,0.0189881,0.00111111,0.936067,211,200.944,0.0234532
216.792,0.0196005,0.00111111,0.927602,200,227.453,0.0212078
252.534,0.0172473,0.00111111,0.915666,225,264.972,0.0178314
193.043,0.0156078,0.00111111,0.935533,226,202.656,0.0411515
192.167,0.0174455,0.00111111,0.935826,195,201.39,0.0254769
225.725,0.0178959,0.00111111,0.924619,240,236.882,0.018903
204.599,0.0213119,0.00111111,0.931674,171,214.712,0.0263085
185.635,0.0163761,0.00111111,0.938007,190,194.569,0.0284307
186.264,0.016572,0.00111111,0.937797,232,195.513,0.0210752
249.948,0.0194051,0.00111111,0.91653,246,262.158,0.0215199
240.864,0.0182591,0.00111111,0.919563,188,251.577,0.0306745
184.618,0.0171253,0.00111111,0.938346,222,193.849,0.0300316
213.473,0.0190352,0.00111111,0.92871,275,224.14,0.0207037
1 Least squares regularity variability improvement steps Evolution error sigma
2 198.091 0.0185605 0.00111111 0.933847 245 207.886 0.0152523
3 234.524 0.0192422 0.00111111 0.92168 203 245.873 0.0333896
4 199.665 0.0169589 0.00111111 0.933322 236 209.253 0.0256795
5 225.432 0.019794 0.00111111 0.924717 164 236.693 0.0303567
6 226.331 0.0198218 0.00111111 0.924416 133 237.512 0.0326327
7 207.081 0.0183173 0.00111111 0.930845 293 217.347 0.0173303
8 197.215 0.0195282 0.00111111 0.93414 144 206.725 0.0274424
9 207.985 0.018551 0.00111111 0.930543 167 218.216 0.0264904
10 199.179 0.0192478 0.00111111 0.933484 230 208.831 0.0280096
11 190.622 0.0185748 0.00111111 0.936342 146 199.805 0.040456
12 186.169 0.0193022 0.00111111 0.937829 196 195.163 0.0339487
13 184.072 0.0170487 0.00111111 0.938529 224 193.258 0.0325346
14 173.247 0.0203667 0.00111111 0.942144 191 181.901 0.0186411
15 174.505 0.0185505 0.00111111 0.941724 232 183.091 0.0391035
16 204.996 0.019809 0.00111111 0.931541 126 215.233 0.0317816
17 201.159 0.0177293 0.00111111 0.932823 231 210.992 0.033055
18 215.57 0.0184879 0.00111111 0.92801 157 226.197 0.0248099
19 220.4 0.0220813 0.00111111 0.926397 120 230.55 0.0266999
20 192.669 0.0189865 0.00111111 0.935658 197 201.997 0.0273896
21 217.249 0.0209109 0.00111111 0.927449 196 227.649 0.0314102
22 183.442 0.0177993 0.00111111 0.938739 276 192.577 0.0290511
23 211.058 0.0191396 0.00111111 0.929517 262 221.454 0.0258037
24 225.405 0.0142923 0.00111111 0.924726 232 236.59 0.0554429
25 214.129 0.0153926 0.00111111 0.928491 258 224.637 0.025707
26 191.681 0.0159947 0.00111111 0.935988 221 201.263 0.0168213
27 208.302 0.0168977 0.00111111 0.930437 267 218.685 0.0303307
28 244.265 0.0186614 0.00111111 0.918427 240 256.401 0.014142
29 217.424 0.0189707 0.00111111 0.927391 194 228.137 0.0234014
30 193.974 0.0187277 0.00111111 0.935222 200 203.421 0.0313489
31 217.843 0.0191009 0.00111111 0.927251 244 228.677 0.0186412
32 228.126 0.0195362 0.00111111 0.923817 171 239.173 0.0237806
33 194.236 0.0205534 0.00111111 0.935135 206 203.783 0.0222971
34 231.826 0.017672 0.00111111 0.922582 126 243.217 0.0354461
35 194.684 0.0180998 0.00111111 0.934985 149 204.188 0.0265668
36 201.513 0.0176892 0.00111111 0.932705 232 211.535 0.0277219
37 219.557 0.016394 0.00111111 0.926679 145 229.573 0.0466214
38 215.208 0.0203045 0.00111111 0.928131 217 225.773 0.0198841
39 224.784 0.0187965 0.00111111 0.924933 207 235.748 0.0249149
40 198.752 0.0192186 0.00111111 0.933626 214 208.659 0.0198508
41 210.374 0.0169978 0.00111111 0.929745 201 220.83 0.029392
42 182.397 0.0156577 0.00111111 0.939088 271 191.357 0.0356415
43 214.532 0.017907 0.00111111 0.928357 187 224.938 0.0315807
44 206.254 0.0198955 0.00111111 0.931121 144 216.195 0.0261497
45 208.845 0.019427 0.00111111 0.930256 302 218.868 0.0293849
46 219.847 0.018273 0.00111111 0.926582 205 230.63 0.0222702
47 178.072 0.0161684 0.00111111 0.940532 271 186.89 0.0224425
48 208.094 0.0174596 0.00111111 0.930507 188 218.199 0.026439
49 206.759 0.017792 0.00111111 0.930952 249 217.047 0.0196121
50 213.588 0.0179354 0.00111111 0.928672 154 223.644 0.0261114
51 203.691 0.0186109 0.00111111 0.931977 215 213.801 0.029634
52 196.02 0.0164293 0.00111111 0.934539 286 205.631 0.0248201
53 200.893 0.0206686 0.00111111 0.932911 218 210.824 0.0165101
54 219.308 0.018868 0.00111111 0.926762 202 230.178 0.0193291
55 246.019 0.0189353 0.00111111 0.917842 155 257.369 0.0271402
56 231.847 0.0197966 0.00111111 0.922574 168 243.262 0.0289334
57 218.91 0.0209849 0.00111111 0.926895 197 229.047 0.0299942
58 211.126 0.0167501 0.00111111 0.929494 196 221.493 0.0286991
59 169.535 0.0151348 0.00111111 0.943383 175 177.905 0.0337364
60 230.113 0.0201869 0.00111111 0.923153 167 241.468 0.0344348
61 231.924 0.0200713 0.00111111 0.922549 149 243.443 0.0257716
62 223.016 0.0200786 0.00111111 0.925524 178 233.782 0.0402102
63 195.674 0.014894 0.00111111 0.934654 268 205.347 0.0551882
64 255.95 0.0195439 0.00111111 0.914525 148 268.384 0.0447607
65 220.055 0.0198234 0.00111111 0.926512 236 230.853 0.0367555
66 216.155 0.0189146 0.00111111 0.927815 129 226.312 0.0430726
67 199.784 0.0187532 0.00111111 0.933282 199 209.55 0.0353292
68 222.549 0.0203982 0.00111111 0.925679 178 233.426 0.0233115
69 200.95 0.0183086 0.00111111 0.932893 256 210.991 0.016803
70 209.249 0.016696 0.00111111 0.930121 269 219.415 0.02081
71 249.03 0.0223912 0.00111111 0.916836 177 260.926 0.0294009
72 219.09 0.019419 0.00111111 0.926835 218 229.786 0.0289335
73 185.792 0.0169305 0.00111111 0.937954 291 194.888 0.0258978
74 184.383 0.0208635 0.00111111 0.938425 256 193.57 0.0185135
75 201.82 0.0183427 0.00111111 0.932602 236 211.086 0.0474114
76 226.705 0.0196988 0.00111111 0.924292 255 237.989 0.0227452
77 206.776 0.019382 0.00111111 0.930947 187 217.102 0.0280757
78 185.74 0.0141851 0.00111111 0.937972 166 194.775 0.0321003
79 232.792 0.0240912 0.00111111 0.922259 191 244.384 0.0176279
80 201.838 0.0188448 0.00111111 0.932596 181 211.814 0.0297108
81 202.159 0.0179769 0.00111111 0.932489 175 212.073 0.0239756
82 178.922 0.018115 0.00111111 0.940249 259 187.619 0.0215103
83 196.096 0.0172259 0.00111111 0.934514 220 205.625 0.030462
84 200.913 0.0195059 0.00111111 0.932905 162 210.781 0.0383751
85 182.439 0.020238 0.00111111 0.939074 172 191.55 0.0264845
86 169.79 0.018196 0.00111111 0.943298 226 178.258 0.0148133
87 185.191 0.0167884 0.00111111 0.938155 194 194.329 0.0261194
88 202.268 0.0211018 0.00111111 0.932452 200 212.217 0.0301675
89 191.444 0.0189881 0.00111111 0.936067 211 200.944 0.0234532
90 216.792 0.0196005 0.00111111 0.927602 200 227.453 0.0212078
91 252.534 0.0172473 0.00111111 0.915666 225 264.972 0.0178314
92 193.043 0.0156078 0.00111111 0.935533 226 202.656 0.0411515
93 192.167 0.0174455 0.00111111 0.935826 195 201.39 0.0254769
94 225.725 0.0178959 0.00111111 0.924619 240 236.882 0.018903
95 204.599 0.0213119 0.00111111 0.931674 171 214.712 0.0263085
96 185.635 0.0163761 0.00111111 0.938007 190 194.569 0.0284307
97 186.264 0.016572 0.00111111 0.937797 232 195.513 0.0210752
98 249.948 0.0194051 0.00111111 0.91653 246 262.158 0.0215199
99 240.864 0.0182591 0.00111111 0.919563 188 251.577 0.0306745
100 184.618 0.0171253 0.00111111 0.938346 222 193.849 0.0300316
101 213.473 0.0190352 0.00111111 0.92871 275 224.14 0.0207037

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@ -0,0 +1,138 @@
*******************************************************************************
Wed Aug 30 22:31:49 2017
FIT: data read from "20170830-evolution1D_5x5_100Times-added_one.csv" every ::1 using 2:5
format = x:z
#datapoints = 100
residuals are weighted equally (unit weight)
function used for fitting: f(x)
fitted parameters initialized with current variable values
Iteration 0
WSSR : 4.39785e+06 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.707229
initial set of free parameter values
a = 1
b = 1
After 5 iterations the fit converged.
final sum of squares of residuals : 161115
rel. change during last iteration : -6.16109e-09
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 40.5466
variance of residuals (reduced chisquare) = WSSR/ndf : 1644.03
Final set of parameters Asymptotic Standard Error
======================= ==========================
a = -7929.84 +/- 2359 (29.75%)
b = 353.47 +/- 43.93 (12.43%)
correlation matrix of the fit parameters:
a b
a 1.000
b -0.996 1.000
*******************************************************************************
Wed Aug 30 22:31:49 2017
FIT: data read from "20170830-evolution1D_5x5_100Times-added_one.csv" every ::1 using 4:5
format = x:z
#datapoints = 100
residuals are weighted equally (unit weight)
function used for fitting: g(x)
fitted parameters initialized with current variable values
Iteration 0
WSSR : 4.36045e+06 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.965948
initial set of free parameter values
aa = 1
bb = 1
After 5 iterations the fit converged.
final sum of squares of residuals : 168087
rel. change during last iteration : -1.22569e-11
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 41.4147
variance of residuals (reduced chisquare) = WSSR/ndf : 1715.18
Final set of parameters Asymptotic Standard Error
======================= ==========================
aa = 1688.86 +/- 649.2 (38.44%)
bb = -1365.3 +/- 604.2 (44.25%)
correlation matrix of the fit parameters:
aa bb
aa 1.000
bb -1.000 1.000
*******************************************************************************
Wed Aug 30 22:31:49 2017
FIT: data read from "20170830-evolution1D_5x5_100Times-added_one.csv" every ::1 using 4:6
format = x:z
#datapoints = 100
residuals are weighted equally (unit weight)
function used for fitting: h(x)
fitted parameters initialized with current variable values
Iteration 0
WSSR : 4.7035e+06 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.965948
initial set of free parameter values
aaa = 1
bbb = 1
After 5 iterations the fit converged.
final sum of squares of residuals : 4.78713
rel. change during last iteration : -1.79666e-06
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 0.221016
variance of residuals (reduced chisquare) = WSSR/ndf : 0.0488483
Final set of parameters Asymptotic Standard Error
======================= ==========================
aaa = -3131.71 +/- 3.464 (0.1106%)
bbb = 3132.34 +/- 3.224 (0.1029%)
correlation matrix of the fit parameters:
aaa bbb
aaa 1.000
bbb -1.000 1.000

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@ -0,0 +1,261 @@
Iteration 0
WSSR : 4.39785e+06 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.707229
initial set of free parameter values
a = 1
b = 1
/
Iteration 1
WSSR : 179801 delta(WSSR)/WSSR : -23.4596
delta(WSSR) : -4.21805e+06 limit for stopping : 1e-05
lambda : 0.0707229
resultant parameter values
a = 0.10554
b = 205.376
/
Iteration 2
WSSR : 177682 delta(WSSR)/WSSR : -0.0119262
delta(WSSR) : -2119.07 limit for stopping : 1e-05
lambda : 0.00707229
resultant parameter values
a = -442.088
b = 214.599
/
Iteration 3
WSSR : 161462 delta(WSSR)/WSSR : -0.100457
delta(WSSR) : -16219.9 limit for stopping : 1e-05
lambda : 0.000707229
resultant parameter values
a = -6845.57
b = 333.36
/
Iteration 4
WSSR : 161115 delta(WSSR)/WSSR : -0.00215619
delta(WSSR) : -347.393 limit for stopping : 1e-05
lambda : 7.07229e-05
resultant parameter values
a = -7928
b = 353.436
/
Iteration 5
WSSR : 161115 delta(WSSR)/WSSR : -6.16109e-09
delta(WSSR) : -0.000992642 limit for stopping : 1e-05
lambda : 7.07229e-06
resultant parameter values
a = -7929.84
b = 353.47
After 5 iterations the fit converged.
final sum of squares of residuals : 161115
rel. change during last iteration : -6.16109e-09
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 40.5466
variance of residuals (reduced chisquare) = WSSR/ndf : 1644.03
Final set of parameters Asymptotic Standard Error
======================= ==========================
a = -7929.84 +/- 2359 (29.75%)
b = 353.47 +/- 43.93 (12.43%)
correlation matrix of the fit parameters:
a b
a 1.000
b -0.996 1.000
Iteration 0
WSSR : 4.36045e+06 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.965948
initial set of free parameter values
aa = 1
bb = 1
/
Iteration 1
WSSR : 178379 delta(WSSR)/WSSR : -23.4448
delta(WSSR) : -4.18208e+06 limit for stopping : 1e-05
lambda : 0.0965948
resultant parameter values
aa = 106.179
bb = 106.601
/
Iteration 2
WSSR : 174781 delta(WSSR)/WSSR : -0.0205882
delta(WSSR) : -3598.42 limit for stopping : 1e-05
lambda : 0.00965948
resultant parameter values
aa = 406.449
bb = -171.828
/
Iteration 3
WSSR : 168099 delta(WSSR)/WSSR : -0.0397514
delta(WSSR) : -6682.16 limit for stopping : 1e-05
lambda : 0.000965948
resultant parameter values
aa = 1636.25
bb = -1316.34
/
Iteration 4
WSSR : 168087 delta(WSSR)/WSSR : -6.70231e-05
delta(WSSR) : -11.2657 limit for stopping : 1e-05
lambda : 9.65948e-05
resultant parameter values
aa = 1688.84
bb = -1365.28
/
Iteration 5
WSSR : 168087 delta(WSSR)/WSSR : -1.22569e-11
delta(WSSR) : -2.06023e-06 limit for stopping : 1e-05
lambda : 9.65948e-06
resultant parameter values
aa = 1688.86
bb = -1365.3
After 5 iterations the fit converged.
final sum of squares of residuals : 168087
rel. change during last iteration : -1.22569e-11
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 41.4147
variance of residuals (reduced chisquare) = WSSR/ndf : 1715.18
Final set of parameters Asymptotic Standard Error
======================= ==========================
aa = 1688.86 +/- 649.2 (38.44%)
bb = -1365.3 +/- 604.2 (44.25%)
correlation matrix of the fit parameters:
aa bb
aa 1.000
bb -1.000 1.000
Iteration 0
WSSR : 4.7035e+06 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.965948
initial set of free parameter values
aaa = 1
bbb = 1
/
Iteration 1
WSSR : 42656.2 delta(WSSR)/WSSR : -109.265
delta(WSSR) : -4.66085e+06 limit for stopping : 1e-05
lambda : 0.0965948
resultant parameter values
aaa = 100.574
bbb = 123.147
/
Iteration 2
WSSR : 27949.7 delta(WSSR)/WSSR : -0.526177
delta(WSSR) : -14706.5 limit for stopping : 1e-05
lambda : 0.00965948
resultant parameter values
aaa = -511.389
bbb = 693.743
/
Iteration 3
WSSR : 51.8214 delta(WSSR)/WSSR : -538.347
delta(WSSR) : -27897.9 limit for stopping : 1e-05
lambda : 0.000965948
resultant parameter values
aaa = -3024.2
bbb = 3032.29
/
Iteration 4
WSSR : 4.78714 delta(WSSR)/WSSR : -9.82513
delta(WSSR) : -47.0343 limit for stopping : 1e-05
lambda : 9.65948e-05
resultant parameter values
aaa = -3131.66
bbb = 3132.3
/
Iteration 5
WSSR : 4.78713 delta(WSSR)/WSSR : -1.79666e-06
delta(WSSR) : -8.60083e-06 limit for stopping : 1e-05
lambda : 9.65948e-06
resultant parameter values
aaa = -3131.71
bbb = 3132.34
After 5 iterations the fit converged.
final sum of squares of residuals : 4.78713
rel. change during last iteration : -1.79666e-06
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 0.221016
variance of residuals (reduced chisquare) = WSSR/ndf : 0.0488483
Final set of parameters Asymptotic Standard Error
======================= ==========================
aaa = -3131.71 +/- 3.464 (0.1106%)
bbb = 3132.34 +/- 3.224 (0.1029%)
correlation matrix of the fit parameters:
aaa bbb
aaa 1.000
bbb -1.000 1.000

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set datafile separator ","
f(x)=a*x+b
fit f(x) "20170830-evolution1D_5x5_100Times-added_one.csv" every ::1 using 2:5 via a,b
set terminal png
set output "20170830-evolution1D_5x5_100Times-added_one_regularity-vs-steps.png"
plot "20170830-evolution1D_5x5_100Times-added_one.csv" every ::1 using 2:5 title "regularity vs. steps", f(x) lc rgb "black"
g(x)=aa*x+bb
fit g(x) "20170830-evolution1D_5x5_100Times-added_one.csv" every ::1 using 4:5 via aa,bb
set output "20170830-evolution1D_5x5_100Times-added_one_improvement-vs-steps.png"
plot "20170830-evolution1D_5x5_100Times-added_one.csv" every ::1 using 4:5 title "improvement potential vs. steps", g(x) lc rgb "black"
h(x)=aaa*x+bbb
fit h(x) "20170830-evolution1D_5x5_100Times-added_one.csv" every ::1 using 4:6 via aaa,bbb
set output "20170830-evolution1D_5x5_100Times-added_one_improvement-vs-evo-error.png"
plot "20170830-evolution1D_5x5_100Times-added_one.csv" every ::1 using 4:6 title "improvement potential vs. evolution error", h(x) lc rgb "black"

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