nearly done

dokumentation/evolution1d/20171027-evolution1D_5x5_100Times_adv-lamb.csv

@@ -0,0 +1,101 @@
+"Least squares",regularity,variability,improvement,improvement.5,improvement.75,improvement.25,steps,"Evolution error",sigma
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dokumentation/evolution1d/adv-lamb.gnuplot.fit.log

@@ -0,0 +1,184 @@
+
+
+*******************************************************************************
+Fri Oct 27 21:50:01 2017
+
+
+FIT:    data read from "adv-lamb.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        : 0.227572          delta(WSSR)/WSSR   : 0
+ delta(WSSR) : 0                 limit for stopping : 1e-05
+ lambda	  : 0.707341
+
+initial set of free parameter values
+
+a               = 1
+b               = 1
+
+After 5 iterations the fit converged.
+final sum of squares of residuals : 0.000107016
+rel. change during last iteration : -2.47553e-06
+
+degrees of freedom    (FIT_NDF)                        : 98
+rms of residuals      (FIT_STDFIT) = sqrt(WSSR/ndf)    : 0.00104499
+variance of residuals (reduced chisquare) = WSSR/ndf   : 1.092e-06
+
+Final set of parameters            Asymptotic Standard Error
+=======================            ==========================
+
+a               = -0.00321702      +/- 0.1044       (3244%)
+b               = 0.978108         +/- 0.002685     (0.2745%)
+
+
+correlation matrix of the fit parameters:
+
+               a      b      
+a               1.000 
+b              -0.999  1.000 
+
+
+*******************************************************************************
+Fri Oct 27 21:50:01 2017
+
+
+FIT:    data read from "adv-lamb.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        : 91.541            delta(WSSR)/WSSR   : 0
+ delta(WSSR) : 0                 limit for stopping : 1e-05
+ lambda	  : 0.967948
+
+initial set of free parameter values
+
+aa              = 1
+bb              = 1
+
+After 6 iterations the fit converged.
+final sum of squares of residuals : 1.03526e-11
+rel. change during last iteration : -9.82363e-11
+
+degrees of freedom    (FIT_NDF)                        : 98
+rms of residuals      (FIT_STDFIT) = sqrt(WSSR/ndf)    : 3.25022e-07
+variance of residuals (reduced chisquare) = WSSR/ndf   : 1.05639e-13
+
+Final set of parameters            Asymptotic Standard Error
+=======================            ==========================
+
+aa              = 0.337001         +/- 1.059e-05    (0.003142%)
+bb              = 0.662998         +/- 9.898e-06    (0.001493%)
+
+
+correlation matrix of the fit parameters:
+
+               aa     bb     
+aa              1.000 
+bb             -1.000  1.000 
+
+
+*******************************************************************************
+Fri Oct 27 21:50:01 2017
+
+
+FIT:    data read from "adv-lamb.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        : 96.0949           delta(WSSR)/WSSR   : 0
+ delta(WSSR) : 0                 limit for stopping : 1e-05
+ lambda	  : 0.967948
+
+initial set of free parameter values
+
+aaa             = 1
+bbb             = 1
+
+After 6 iterations the fit converged.
+final sum of squares of residuals : 1.22269e-11
+rel. change during last iteration : -1.20095e-10
+
+degrees of freedom    (FIT_NDF)                        : 98
+rms of residuals      (FIT_STDFIT) = sqrt(WSSR/ndf)    : 3.5322e-07
+variance of residuals (reduced chisquare) = WSSR/ndf   : 1.24764e-13
+
+Final set of parameters            Asymptotic Standard Error
+=======================            ==========================
+
+aaa             = 0.69757          +/- 1.151e-05    (0.00165%)
+bbb             = 0.30243          +/- 1.076e-05    (0.003557%)
+
+
+correlation matrix of the fit parameters:
+
+               aaa    bbb    
+aaa             1.000 
+bbb            -1.000  1.000 
+
+
+*******************************************************************************
+Fri Oct 27 21:50:01 2017
+
+
+FIT:    data read from "adv-lamb.csv" every ::1 using 3:6
+        format = x:z
+        #datapoints = 100
+        residuals are weighted equally (unit weight)
+
+function used for fitting: i(x)
+fitted parameters initialized with current variable values
+
+
+
+ Iteration 0
+ WSSR        : 0.21759           delta(WSSR)/WSSR   : 0
+ delta(WSSR) : 0                 limit for stopping : 1e-05
+ lambda	  : 0.707107
+
+initial set of free parameter values
+
+aaaa            = 1
+bbbb            = 1
+
+After 3 iterations the fit converged.
+final sum of squares of residuals : 0.000458526
+rel. change during last iteration : -2.92992e-11
+
+degrees of freedom    (FIT_NDF)                        : 98
+rms of residuals      (FIT_STDFIT) = sqrt(WSSR/ndf)    : 0.00216306
+variance of residuals (reduced chisquare) = WSSR/ndf   : 4.67884e-06
+
+Final set of parameters            Asymptotic Standard Error
+=======================            ==========================
+
+aaaa            = 0.999948         +/- 1.728e+14    (1.728e+16%)
+bbbb            = 0.953403         +/- 1.92e+11     (2.014e+13%)
+
+
+correlation matrix of the fit parameters:
+
+               aaaa   bbbb   
+aaaa            1.000 
+bbbb           -1.000  1.000 

dokumentation/evolution1d/adv-lamb.gnuplot.log

@@ -0,0 +1,349 @@
+
+
+ Iteration 0
+ WSSR        : 0.227572          delta(WSSR)/WSSR   : 0
+ delta(WSSR) : 0                 limit for stopping : 1e-05
+ lambda	  : 0.707341
+
+initial set of free parameter values
+
+a               = 1
+b               = 1
+/
+
+ Iteration 1
+ WSSR        : 0.00021325        delta(WSSR)/WSSR   : -1066.16
+ delta(WSSR) : -0.227359         limit for stopping : 1e-05
+ lambda	  : 0.0707341
+
+resultant parameter values
+
+a               = 0.998581
+b               = 0.952591
+/
+
+ Iteration 2
+ WSSR        : 0.000203712       delta(WSSR)/WSSR   : -0.0468247
+ delta(WSSR) : -9.53874e-06      limit for stopping : 1e-05
+ lambda	  : 0.00707341
+
+resultant parameter values
+
+a               = 0.978909
+b               = 0.952859
+/
+
+ Iteration 3
+ WSSR        : 0.000117743       delta(WSSR)/WSSR   : -0.730133
+ delta(WSSR) : -8.59683e-05      limit for stopping : 1e-05
+ lambda	  : 0.000707341
+
+resultant parameter values
+
+a               = 0.323908
+b               = 0.969698
+/
+
+ Iteration 4
+ WSSR        : 0.000107016       delta(WSSR)/WSSR   : -0.10024
+ delta(WSSR) : -1.07273e-05      limit for stopping : 1e-05
+ lambda	  : 7.07341e-05
+
+resultant parameter values
+
+a               = -0.00159147
+b               = 0.978066
+/
+
+ Iteration 5
+ WSSR        : 0.000107016       delta(WSSR)/WSSR   : -2.47553e-06
+ delta(WSSR) : -2.6492e-10       limit for stopping : 1e-05
+ lambda	  : 7.07341e-06
+
+resultant parameter values
+
+a               = -0.00321702
+b               = 0.978108
+
+After 5 iterations the fit converged.
+final sum of squares of residuals : 0.000107016
+rel. change during last iteration : -2.47553e-06
+
+degrees of freedom    (FIT_NDF)                        : 98
+rms of residuals      (FIT_STDFIT) = sqrt(WSSR/ndf)    : 0.00104499
+variance of residuals (reduced chisquare) = WSSR/ndf   : 1.092e-06
+
+Final set of parameters            Asymptotic Standard Error
+=======================            ==========================
+
+a               = -0.00321702      +/- 0.1044       (3244%)
+b               = 0.978108         +/- 0.002685     (0.2745%)
+
+
+correlation matrix of the fit parameters:
+
+               a      b      
+a               1.000 
+b              -0.999  1.000 
+
+
+ Iteration 0
+ WSSR        : 91.541            delta(WSSR)/WSSR   : 0
+ delta(WSSR) : 0                 limit for stopping : 1e-05
+ lambda	  : 0.967948
+
+initial set of free parameter values
+
+aa              = 1
+bb              = 1
+/
+
+ Iteration 1
+ WSSR        : 0.00229828        delta(WSSR)/WSSR   : -39829.2
+ delta(WSSR) : -91.5387          limit for stopping : 1e-05
+ lambda	  : 0.0967948
+
+resultant parameter values
+
+aa              = 0.524975
+bb              = 0.492041
+/
+
+ Iteration 2
+ WSSR        : 2.92366e-05       delta(WSSR)/WSSR   : -77.6096
+ delta(WSSR) : -0.00226904       limit for stopping : 1e-05
+ lambda	  : 0.00967948
+
+resultant parameter values
+
+aa              = 0.513146
+bb              = 0.498339
+/
+
+ Iteration 3
+ WSSR        : 7.21159e-07       delta(WSSR)/WSSR   : -39.5412
+ delta(WSSR) : -2.85155e-05      limit for stopping : 1e-05
+ lambda	  : 0.000967948
+
+resultant parameter values
+
+aa              = 0.364666
+bb              = 0.637138
+/
+
+ Iteration 4
+ WSSR        : 1.28467e-11       delta(WSSR)/WSSR   : -56134.8
+ delta(WSSR) : -7.21146e-07      limit for stopping : 1e-05
+ lambda	  : 9.67948e-05
+
+resultant parameter values
+
+aa              = 0.337053
+bb              = 0.66295
+/
+
+ Iteration 5
+ WSSR        : 1.03526e-11       delta(WSSR)/WSSR   : -0.24091
+ delta(WSSR) : -2.49406e-12      limit for stopping : 1e-05
+ lambda	  : 9.67948e-06
+
+resultant parameter values
+
+aa              = 0.337001
+bb              = 0.662998
+/
+
+ Iteration 6
+ WSSR        : 1.03526e-11       delta(WSSR)/WSSR   : -9.82363e-11
+ delta(WSSR) : -1.017e-21        limit for stopping : 1e-05
+ lambda	  : 9.67948e-07
+
+resultant parameter values
+
+aa              = 0.337001
+bb              = 0.662998
+
+After 6 iterations the fit converged.
+final sum of squares of residuals : 1.03526e-11
+rel. change during last iteration : -9.82363e-11
+
+degrees of freedom    (FIT_NDF)                        : 98
+rms of residuals      (FIT_STDFIT) = sqrt(WSSR/ndf)    : 3.25022e-07
+variance of residuals (reduced chisquare) = WSSR/ndf   : 1.05639e-13
+
+Final set of parameters            Asymptotic Standard Error
+=======================            ==========================
+
+aa              = 0.337001         +/- 1.059e-05    (0.003142%)
+bb              = 0.662998         +/- 9.898e-06    (0.001493%)
+
+
+correlation matrix of the fit parameters:
+
+               aa     bb     
+aa              1.000 
+bb             -1.000  1.000 
+
+
+ Iteration 0
+ WSSR        : 96.0949           delta(WSSR)/WSSR   : 0
+ delta(WSSR) : 0                 limit for stopping : 1e-05
+ lambda	  : 0.967948
+
+initial set of free parameter values
+
+aaa             = 1
+bbb             = 1
+/
+
+ Iteration 1
+ WSSR        : 0.00241131        delta(WSSR)/WSSR   : -39850.7
+ delta(WSSR) : -96.0925          limit for stopping : 1e-05
+ lambda	  : 0.0967948
+
+resultant parameter values
+
+aaa             = 0.513505
+bbb             = 0.479371
+/
+
+ Iteration 2
+ WSSR        : 2.95208e-05       delta(WSSR)/WSSR   : -80.6818
+ delta(WSSR) : -0.00238179       limit for stopping : 1e-05
+ lambda	  : 0.00967948
+
+resultant parameter values
+
+aaa             = 0.520572
+bbb             = 0.467888
+/
+
+ Iteration 3
+ WSSR        : 7.2817e-07        delta(WSSR)/WSSR   : -39.5411
+ delta(WSSR) : -2.87926e-05      limit for stopping : 1e-05
+ lambda	  : 0.000967948
+
+resultant parameter values
+
+aaa             = 0.669772
+bbb             = 0.328416
+/
+
+ Iteration 4
+ WSSR        : 1.47452e-11       delta(WSSR)/WSSR   : -49382.6
+ delta(WSSR) : -7.28156e-07      limit for stopping : 1e-05
+ lambda	  : 9.67948e-05
+
+resultant parameter values
+
+aaa             = 0.697518
+bbb             = 0.302478
+/
+
+ Iteration 5
+ WSSR        : 1.22269e-11       delta(WSSR)/WSSR   : -0.205964
+ delta(WSSR) : -2.5183e-12       limit for stopping : 1e-05
+ lambda	  : 9.67948e-06
+
+resultant parameter values
+
+aaa             = 0.69757
+bbb             = 0.30243
+/
+
+ Iteration 6
+ WSSR        : 1.22269e-11       delta(WSSR)/WSSR   : -1.20095e-10
+ delta(WSSR) : -1.46839e-21      limit for stopping : 1e-05
+ lambda	  : 9.67948e-07
+
+resultant parameter values
+
+aaa             = 0.69757
+bbb             = 0.30243
+
+After 6 iterations the fit converged.
+final sum of squares of residuals : 1.22269e-11
+rel. change during last iteration : -1.20095e-10
+
+degrees of freedom    (FIT_NDF)                        : 98
+rms of residuals      (FIT_STDFIT) = sqrt(WSSR/ndf)    : 3.5322e-07
+variance of residuals (reduced chisquare) = WSSR/ndf   : 1.24764e-13
+
+Final set of parameters            Asymptotic Standard Error
+=======================            ==========================
+
+aaa             = 0.69757          +/- 1.151e-05    (0.00165%)
+bbb             = 0.30243          +/- 1.076e-05    (0.003557%)
+
+
+correlation matrix of the fit parameters:
+
+               aaa    bbb    
+aaa             1.000 
+bbb            -1.000  1.000 
+
+
+ Iteration 0
+ WSSR        : 0.21759           delta(WSSR)/WSSR   : 0
+ delta(WSSR) : 0                 limit for stopping : 1e-05
+ lambda	  : 0.707107
+
+initial set of free parameter values
+
+aaaa            = 1
+bbbb            = 1
+/
+
+ Iteration 1
+ WSSR        : 0.0004639         delta(WSSR)/WSSR   : -468.045
+ delta(WSSR) : -0.217126         limit for stopping : 1e-05
+ lambda	  : 0.0707107
+
+resultant parameter values
+
+aaaa            = 0.999948
+bbbb            = 0.953634
+/
+
+ Iteration 2
+ WSSR        : 0.000458526       delta(WSSR)/WSSR   : -0.0117211
+ delta(WSSR) : -5.37441e-06      limit for stopping : 1e-05
+ lambda	  : 0.00707107
+
+resultant parameter values
+
+aaaa            = 0.999948
+bbbb            = 0.953403
+/
+
+ Iteration 3
+ WSSR        : 0.000458526       delta(WSSR)/WSSR   : -2.92992e-11
+ delta(WSSR) : -1.34345e-14      limit for stopping : 1e-05
+ lambda	  : 0.000707107
+
+resultant parameter values
+
+aaaa            = 0.999948
+bbbb            = 0.953403
+
+After 3 iterations the fit converged.
+final sum of squares of residuals : 0.000458526
+rel. change during last iteration : -2.92992e-11
+
+degrees of freedom    (FIT_NDF)                        : 98
+rms of residuals      (FIT_STDFIT) = sqrt(WSSR/ndf)    : 0.00216306
+variance of residuals (reduced chisquare) = WSSR/ndf   : 4.67884e-06
+
+Final set of parameters            Asymptotic Standard Error
+=======================            ==========================
+
+aaaa            = 0.999948         +/- 1.728e+14    (1.728e+16%)
+bbbb            = 0.953403         +/- 1.92e+11     (2.014e+13%)
+
+
+correlation matrix of the fit parameters:
+
+               aaaa   bbbb   
+aaaa            1.000 
+bbbb           -1.000  1.000 
+Warning: empty x range [0.00111111:0.00111111], adjusting to [0.0011:0.00112222]

dokumentation/evolution1d/adv-lamb.gnuplot.script

@@ -0,0 +1,26 @@
+set datafile separator ","
+f(x)=a*x+b
+fit f(x) "adv-lamb.csv" every ::1 using 2:5 via a,b
+set terminal png
+set xlabel 'Regularity'
+set ylabel 'Iterations'
+set output "adv-lamb_regularity-vs-steps.png"
+plot     "adv-lamb_25.csv" every ::1 using 2:5 title "\lambda = 0.25",     "adv-lamb_05.csv" every ::1 using 2:5 title "\lambda = 0.5",     "adv-lamb_75.csv" every ::1 using 2:5 title "\lambda = 0.75",     "adv-lamb_1.csv" every ::1 using 2:5 title "\lambda = 1",     f(x) title "lin. fit" lc rgb "black"
+g(x)=aa*x+bb
+fit g(x) "adv-lamb.csv" every ::1 using 4:5 via aa,bb
+set xlabel 'Improvement potential'
+set ylabel 'Iteration'
+set output "adv-lamb_improvement-vs-steps.png"
+plot     "adv-lamb_25.csv" every ::1 using 4:5 title "\lambda = 0.25",     "adv-lamb_05.csv" every ::1 using 4:5 title "\lambda = 0.5",     "adv-lamb_75.csv" every ::1 using 4:5 title "\lambda = 0.75",     "adv-lamb_1.csv" every ::1 using 4:5 title "\lambda = 1",     g(x) title "lin. fit" lc rgb "black"
+h(x)=aaa*x+bbb
+fit h(x) "adv-lamb.csv" every ::1 using 4:6 via aaa,bbb
+set xlabel 'Improvement potential'
+set ylabel 'Fitting error'
+set output "adv-lamb_improvement-vs-evo-error.png"
+plot     "adv-lamb_25.csv" every ::1 using 4:6 title "\lambda = 0.25",     "adv-lamb_05.csv" every ::1 using 4:6 title "\lambda = 0.5",     "adv-lamb_75.csv" every ::1 using 4:6 title "\lambda = 0.75",     "adv-lamb_1.csv" every ::1 using 4:6 title "\lambda = 1",     h(x) title "lin. fit" lc rgb "black"
+i(x)=aaaa*x+bbbb
+fit i(x) "adv-lamb.csv" every ::1 using 3:6 via aaaa,bbbb
+set xlabel 'Variability'
+set ylabel 'Fitting error'
+set output "adv-lamb_variability-vs-evo-error.png"
+plot     "adv-lamb_25.csv" every ::1 using 3:6 title "\lambda = 0.25",     "adv-lamb_05.csv" every ::1 using 3:6 title "\lambda = 0.5",     "adv-lamb_75.csv" every ::1 using 3:6 title "\lambda = 0.75",     "adv-lamb_1.csv" every ::1 using 3:6 title "\lambda = 1",     i(x) title "lin. fit" lc rgb "black"

dokumentation/evolution1d/adv-lamb.spearman

@@ -0,0 +1,49 @@
+[1] "================ Analyzing adv-lamb.csv"
+[1] "spearman for improvement-potential vs. evolution-error"
+  x y
+x 1 1
+y 1 1
+
+n= 100 
+
+
+P
+  x  y 
+x     0
+y  0   
+[1] "spearman for improvement-potential vs. steps"
+  x y
+x 1 1
+y 1 1
+
+n= 100 
+
+
+P
+  x  y 
+x     0
+y  0   
+[1] "spearman for regularity vs. steps"
+     x    y
+x 1.00 0.02
+y 0.02 1.00
+
+n= 100 
+
+
+P
+  x     y    
+x       0.872
+y 0.872      
+[1] "spearman for variability vs. evolution-error"
+    x   y
+x   1 NaN
+y NaN   1
+
+n= 100 
+
+
+P
+  x y
+x    
+y    

dokumentation/evolution1d/adv-lamb_05.spearman

@@ -0,0 +1,49 @@
+[1] "================ Analyzing adv-lamb_05.csv"
+[1] "spearman for improvement-potential vs. evolution-error"
+   x  y
+x  1 -1
+y -1  1
+
+n= 100 
+
+
+P
+  x  y 
+x     0
+y  0   
+[1] "spearman for improvement-potential vs. steps"
+     x    y
+x 1.00 0.14
+y 0.14 1.00
+
+n= 100 
+
+
+P
+  x      y     
+x        0.1641
+y 0.1641       
+[1] "spearman for regularity vs. steps"
+      x     y
+x  1.00 -0.05
+y -0.05  1.00
+
+n= 100 
+
+
+P
+  x      y     
+x        0.6033
+y 0.6033       
+[1] "spearman for variability vs. evolution-error"
+    x   y
+x   1 NaN
+y NaN   1
+
+n= 100 
+
+
+P
+  x y
+x    
+y    

dokumentation/evolution1d/adv-lamb_1.spearman

@@ -0,0 +1,49 @@
+[1] "================ Analyzing adv-lamb_1.csv"
+[1] "spearman for improvement-potential vs. evolution-error"
+   x  y
+x  1 -1
+y -1  1
+
+n= 100 
+
+
+P
+  x  y 
+x     0
+y  0   
+[1] "spearman for improvement-potential vs. steps"
+     x    y
+x 1.00 0.14
+y 0.14 1.00
+
+n= 100 
+
+
+P
+  x      y     
+x        0.1646
+y 0.1646       
+[1] "spearman for regularity vs. steps"
+      x     y
+x  1.00 -0.05
+y -0.05  1.00
+
+n= 100 
+
+
+P
+  x      y     
+x        0.6033
+y 0.6033       
+[1] "spearman for variability vs. evolution-error"
+    x   y
+x   1 NaN
+y NaN   1
+
+n= 100 
+
+
+P
+  x y
+x    
+y    

dokumentation/evolution1d/adv-lamb_25.spearman

@@ -0,0 +1,49 @@
+[1] "================ Analyzing adv-lamb_25.csv"
+[1] "spearman for improvement-potential vs. evolution-error"
+   x  y
+x  1 -1
+y -1  1
+
+n= 100 
+
+
+P
+  x  y 
+x     0
+y  0   
+[1] "spearman for improvement-potential vs. steps"
+     x    y
+x 1.00 0.14
+y 0.14 1.00
+
+n= 100 
+
+
+P
+  x      y     
+x        0.1664
+y 0.1664       
+[1] "spearman for regularity vs. steps"
+      x     y
+x  1.00 -0.05
+y -0.05  1.00
+
+n= 100 
+
+
+P
+  x      y     
+x        0.6033
+y 0.6033       
+[1] "spearman for variability vs. evolution-error"
+    x   y
+x   1 NaN
+y NaN   1
+
+n= 100 
+
+
+P
+  x y
+x    
+y    

dokumentation/evolution1d/adv-lamb_75.spearman

@@ -0,0 +1,49 @@
+[1] "================ Analyzing adv-lamb_75.csv"
+[1] "spearman for improvement-potential vs. evolution-error"
+   x  y
+x  1 -1
+y -1  1
+
+n= 100 
+
+
+P
+  x  y 
+x     0
+y  0   
+[1] "spearman for improvement-potential vs. steps"
+     x    y
+x 1.00 0.14
+y 0.14 1.00
+
+n= 100 
+
+
+P
+  x      y     
+x        0.1646
+y 0.1646       
+[1] "spearman for regularity vs. steps"
+      x     y
+x  1.00 -0.05
+y -0.05  1.00
+
+n= 100 
+
+
+P
+  x      y     
+x        0.6033
+y 0.6033       
+[1] "spearman for variability vs. evolution-error"
+    x   y
+x   1 NaN
+y NaN   1
+
+n= 100 
+
+
+P
+  x y
+x    
+y