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This commit is contained in:
Stefan Dresselhaus
2017-10-27 14:31:55 +02:00
parent 5b22c181be
commit f901716f60
104 changed files with 2941 additions and 866 deletions
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52
dokumentation/evolution3d/20170926_3dFit_4x4x4_100times.gnuplot.fit.log
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@ -1,7 +1,7 @@
*******************************************************************************
Sun Oct 1 20:12:40 2017
Fri Oct 27 14:09:07 2017
FIT: data read from "20170926_3dFit_4x4x4_100times.csv" every ::1 using 1:5
@ -47,7 +47,7 @@ b -0.992 1.000
*******************************************************************************
Sun Oct 1 20:12:40 2017
Fri Oct 27 14:09:07 2017
FIT: data read from "20170926_3dFit_4x4x4_100times.csv" every ::1 using 3:5
@ -93,7 +93,7 @@ bb -1.000 1.000
*******************************************************************************
Sun Oct 1 20:12:40 2017
Fri Oct 27 14:09:07 2017
FIT: data read from "20170926_3dFit_4x4x4_100times.csv" every ::1 using 3:4
@ -136,3 +136,49 @@ correlation matrix of the fit parameters:
aaa bbb
aaa 1.000
bbb -1.000 1.000
*******************************************************************************
Fri Oct 27 14:09:07 2017
FIT: data read from "20170926_3dFit_4x4x4_100times.csv" every ::1 using 2:4
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 : 1.39893e+06 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.707119
initial set of free parameter values
aaaa = 1
bbbb = 1
After 3 iterations the fit converged.
final sum of squares of residuals : 2447.69
rel. change during last iteration : -3.53005e-11
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 4.99764
variance of residuals (reduced chisquare) = WSSR/ndf : 24.9765
Final set of parameters Asymptotic Standard Error
======================= ==========================
aaaa = 1.69981 +/- 9.656e+16 (5.681e+18%)
bbbb = 119.169 +/- 5.718e+14 (4.798e+14%)
correlation matrix of the fit parameters:
aaaa bbbb
aaaa 1.000
bbbb -1.000 1.000

66
dokumentation/evolution3d/20170926_3dFit_4x4x4_100times.gnuplot.log
View File

@ -270,3 +270,69 @@ correlation matrix of the fit parameters:
aaa bbb
aaa 1.000
bbb -1.000 1.000
Iteration 0
WSSR : 1.39893e+06 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.707119
initial set of free parameter values
aaaa = 1
bbbb = 1
/
Iteration 1
WSSR : 2482.26 delta(WSSR)/WSSR : -562.573
delta(WSSR) : -1.39645e+06 limit for stopping : 1e-05
lambda : 0.0707119
resultant parameter values
aaaa = 1.69632
bbbb = 118.581
/
Iteration 2
WSSR : 2447.69 delta(WSSR)/WSSR : -0.0141217
delta(WSSR) : -34.5656 limit for stopping : 1e-05
lambda : 0.00707119
resultant parameter values
aaaa = 1.69981
bbbb = 119.169
/
Iteration 3
WSSR : 2447.69 delta(WSSR)/WSSR : -3.53005e-11
delta(WSSR) : -8.64047e-08 limit for stopping : 1e-05
lambda : 0.000707119
resultant parameter values
aaaa = 1.69981
bbbb = 119.169
After 3 iterations the fit converged.
final sum of squares of residuals : 2447.69
rel. change during last iteration : -3.53005e-11
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 4.99764
variance of residuals (reduced chisquare) = WSSR/ndf : 24.9765
Final set of parameters Asymptotic Standard Error
======================= ==========================
aaaa = 1.69981 +/- 9.656e+16 (5.681e+18%)
bbbb = 119.169 +/- 5.718e+14 (4.798e+14%)
correlation matrix of the fit parameters:
aaaa bbbb
aaaa 1.000
bbbb -1.000 1.000
Warning: empty x range [0.00592209:0.00592209], adjusting to [0.00586287:0.00598131]

18
dokumentation/evolution3d/20170926_3dFit_4x4x4_100times.gnuplot.script
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@ -2,19 +2,25 @@ set datafile separator ","
f(x)=a*x+b
fit f(x) "20170926_3dFit_4x4x4_100times.csv" every ::1 using 1:5 via a,b
set terminal png
set xlabel 'regularity'
set ylabel 'steps'
set xlabel 'Regularity'
set ylabel 'Number of iterations'
set output "20170926_3dFit_4x4x4_100times_regularity-vs-steps.png"
plot "20170926_3dFit_4x4x4_100times.csv" every ::1 using 1:5 title "data", f(x) title "lin. fit" lc rgb "black"
g(x)=aa*x+bb
fit g(x) "20170926_3dFit_4x4x4_100times.csv" every ::1 using 3:5 via aa,bb
set xlabel 'improvement potential'
set ylabel 'steps'
set xlabel 'Improvement potential'
set ylabel 'Number of iterations'
set output "20170926_3dFit_4x4x4_100times_improvement-vs-steps.png"
plot "20170926_3dFit_4x4x4_100times.csv" every ::1 using 3:5 title "data", g(x) title "lin. fit" lc rgb "black"
h(x)=aaa*x+bbb
fit h(x) "20170926_3dFit_4x4x4_100times.csv" every ::1 using 3:4 via aaa,bbb
set xlabel 'improvement potential'
set ylabel 'evolution error'
set xlabel 'Improvement potential'
set ylabel 'error given by fitness-function'
set output "20170926_3dFit_4x4x4_100times_improvement-vs-evo-error.png"
plot "20170926_3dFit_4x4x4_100times.csv" every ::1 using 3:4 title "data", h(x) title "lin. fit" lc rgb "black"
i(x)=aaaa*x+bbbb
fit i(x) "20170926_3dFit_4x4x4_100times.csv" every ::1 using 2:4 via aaaa,bbbb
set xlabel 'Variability'
set ylabel 'error given by fitness-function'
set output "20170926_3dFit_4x4x4_100times_variability-vs-evo-error.png"
plot "20170926_3dFit_4x4x4_100times.csv" every ::1 using 2:4 title "data", i(x) title "lin. fit" lc rgb "black"

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52
dokumentation/evolution3d/20170926_3dFit_5x5x5_100times.gnuplot.fit.log
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@ -1,7 +1,7 @@
*******************************************************************************
Sun Oct 1 20:12:42 2017
Fri Oct 27 14:09:07 2017
FIT: data read from "20170926_3dFit_5x5x5_100times.csv" every ::1 using 1:5
@ -47,7 +47,7 @@ b -0.970 1.000
*******************************************************************************
Sun Oct 1 20:12:42 2017
Fri Oct 27 14:09:07 2017
FIT: data read from "20170926_3dFit_5x5x5_100times.csv" every ::1 using 3:5
@ -93,7 +93,7 @@ bb -1.000 1.000
*******************************************************************************
Sun Oct 1 20:12:42 2017
Fri Oct 27 14:09:07 2017
FIT: data read from "20170926_3dFit_5x5x5_100times.csv" every ::1 using 3:4
@ -136,3 +136,49 @@ correlation matrix of the fit parameters:
aaa bbb
aaa 1.000
bbb -1.000 1.000
*******************************************************************************
Fri Oct 27 14:09:07 2017
FIT: data read from "20170926_3dFit_5x5x5_100times.csv" every ::1 using 2:4
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 : 582860 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.707154
initial set of free parameter values
aaaa = 1
bbbb = 1
After 3 iterations the fit converged.
final sum of squares of residuals : 4883.49
rel. change during last iteration : -7.32216e-12
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 7.05915
variance of residuals (reduced chisquare) = WSSR/ndf : 49.8315
Final set of parameters Asymptotic Standard Error
======================= ==========================
aaaa = 1.87923 +/- 6.796e+16 (3.616e+18%)
bbbb = 77.0146 +/- 7.861e+14 (1.021e+15%)
correlation matrix of the fit parameters:
aaaa bbbb
aaaa 1.000
bbbb -1.000 1.000

66
dokumentation/evolution3d/20170926_3dFit_5x5x5_100times.gnuplot.log
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@ -226,3 +226,69 @@ correlation matrix of the fit parameters:
aaa bbb
aaa 1.000
bbb -1.000 1.000
Iteration 0
WSSR : 582860 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.707154
initial set of free parameter values
aaaa = 1
bbbb = 1
/
Iteration 1
WSSR : 4897.8 delta(WSSR)/WSSR : -118.005
delta(WSSR) : -577962 limit for stopping : 1e-05
lambda : 0.0707154
resultant parameter values
aaaa = 1.87486
bbbb = 76.6364
/
Iteration 2
WSSR : 4883.49 delta(WSSR)/WSSR : -0.00292946
delta(WSSR) : -14.306 limit for stopping : 1e-05
lambda : 0.00707154
resultant parameter values
aaaa = 1.87923
bbbb = 77.0146
/
Iteration 3
WSSR : 4883.49 delta(WSSR)/WSSR : -7.32216e-12
delta(WSSR) : -3.57577e-08 limit for stopping : 1e-05
lambda : 0.000707154
resultant parameter values
aaaa = 1.87923
bbbb = 77.0146
After 3 iterations the fit converged.
final sum of squares of residuals : 4883.49
rel. change during last iteration : -7.32216e-12
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 7.05915
variance of residuals (reduced chisquare) = WSSR/ndf : 49.8315
Final set of parameters Asymptotic Standard Error
======================= ==========================
aaaa = 1.87923 +/- 6.796e+16 (3.616e+18%)
bbbb = 77.0146 +/- 7.861e+14 (1.021e+15%)
correlation matrix of the fit parameters:
aaaa bbbb
aaaa 1.000
bbbb -1.000 1.000
Warning: empty x range [0.0115666:0.0115666], adjusting to [0.0114509:0.0116823]

18
dokumentation/evolution3d/20170926_3dFit_5x5x5_100times.gnuplot.script
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@ -2,19 +2,25 @@ set datafile separator ","
f(x)=a*x+b
fit f(x) "20170926_3dFit_5x5x5_100times.csv" every ::1 using 1:5 via a,b
set terminal png
set xlabel 'regularity'
set ylabel 'steps'
set xlabel 'Regularity'
set ylabel 'Number of iterations'
set output "20170926_3dFit_5x5x5_100times_regularity-vs-steps.png"
plot "20170926_3dFit_5x5x5_100times.csv" every ::1 using 1:5 title "data", f(x) title "lin. fit" lc rgb "black"
g(x)=aa*x+bb
fit g(x) "20170926_3dFit_5x5x5_100times.csv" every ::1 using 3:5 via aa,bb
set xlabel 'improvement potential'
set ylabel 'steps'
set xlabel 'Improvement potential'
set ylabel 'Number of iterations'
set output "20170926_3dFit_5x5x5_100times_improvement-vs-steps.png"
plot "20170926_3dFit_5x5x5_100times.csv" every ::1 using 3:5 title "data", g(x) title "lin. fit" lc rgb "black"
h(x)=aaa*x+bbb
fit h(x) "20170926_3dFit_5x5x5_100times.csv" every ::1 using 3:4 via aaa,bbb
set xlabel 'improvement potential'
set ylabel 'evolution error'
set xlabel 'Improvement potential'
set ylabel 'error given by fitness-function'
set output "20170926_3dFit_5x5x5_100times_improvement-vs-evo-error.png"
plot "20170926_3dFit_5x5x5_100times.csv" every ::1 using 3:4 title "data", h(x) title "lin. fit" lc rgb "black"
i(x)=aaaa*x+bbbb
fit i(x) "20170926_3dFit_5x5x5_100times.csv" every ::1 using 2:4 via aaaa,bbbb
set xlabel 'Variability'
set ylabel 'error given by fitness-function'
set output "20170926_3dFit_5x5x5_100times_variability-vs-evo-error.png"
plot "20170926_3dFit_5x5x5_100times.csv" every ::1 using 2:4 title "data", i(x) title "lin. fit" lc rgb "black"

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52
dokumentation/evolution3d/20171005_3dFit_4x4x5_100times.gnuplot.fit.log
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@ -1,7 +1,7 @@
*******************************************************************************
Sat Oct 7 11:48:52 2017
Fri Oct 27 14:09:07 2017
FIT: data read from "20171005_3dFit_4x4x5_100times.csv" every ::1 using 1:5
@ -47,7 +47,7 @@ b -0.986 1.000
*******************************************************************************
Sat Oct 7 11:48:52 2017
Fri Oct 27 14:09:07 2017
FIT: data read from "20171005_3dFit_4x4x5_100times.csv" every ::1 using 3:5
@ -93,7 +93,7 @@ bb -1.000 1.000
*******************************************************************************
Sat Oct 7 11:48:52 2017
Fri Oct 27 14:09:07 2017
FIT: data read from "20171005_3dFit_4x4x5_100times.csv" every ::1 using 3:4
@ -136,3 +136,49 @@ correlation matrix of the fit parameters:
aaa bbb
aaa 1.000
bbb -1.000 1.000
*******************************************************************************
Fri Oct 27 14:09:07 2017
FIT: data read from "20171005_3dFit_4x4x5_100times.csv" every ::1 using 2:4
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 : 1.04253e+06 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.707126
initial set of free parameter values
aaaa = 1
bbbb = 1
After 3 iterations the fit converged.
final sum of squares of residuals : 4497.91
rel. change during last iteration : -1.42792e-11
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 6.77474
variance of residuals (reduced chisquare) = WSSR/ndf : 45.8971
Final set of parameters Asymptotic Standard Error
======================= ==========================
aaaa = 1.75417 +/- 1.135e+17 (6.469e+18%)
bbbb = 102.878 +/- 8.4e+14 (8.165e+14%)
correlation matrix of the fit parameters:
aaaa bbbb
aaaa 1.000
bbbb -1.000 1.000

66
dokumentation/evolution3d/20171005_3dFit_4x4x5_100times.gnuplot.log
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@ -270,3 +270,69 @@ correlation matrix of the fit parameters:
aaa bbb
aaa 1.000
bbb -1.000 1.000
Iteration 0
WSSR : 1.04253e+06 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.707126
initial set of free parameter values
aaaa = 1
bbbb = 1
/
Iteration 1
WSSR : 4523.61 delta(WSSR)/WSSR : -229.464
delta(WSSR) : -1.03801e+06 limit for stopping : 1e-05
lambda : 0.0707126
resultant parameter values
aaaa = 1.75041
bbbb = 102.371
/
Iteration 2
WSSR : 4497.91 delta(WSSR)/WSSR : -0.00571226
delta(WSSR) : -25.6932 limit for stopping : 1e-05
lambda : 0.00707126
resultant parameter values
aaaa = 1.75417
bbbb = 102.878
/
Iteration 3
WSSR : 4497.91 delta(WSSR)/WSSR : -1.42792e-11
delta(WSSR) : -6.42267e-08 limit for stopping : 1e-05
lambda : 0.000707126
resultant parameter values
aaaa = 1.75417
bbbb = 102.878
After 3 iterations the fit converged.
final sum of squares of residuals : 4497.91
rel. change during last iteration : -1.42792e-11
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 6.77474
variance of residuals (reduced chisquare) = WSSR/ndf : 45.8971
Final set of parameters Asymptotic Standard Error
======================= ==========================
aaaa = 1.75417 +/- 1.135e+17 (6.469e+18%)
bbbb = 102.878 +/- 8.4e+14 (8.165e+14%)
correlation matrix of the fit parameters:
aaaa bbbb
aaaa 1.000
bbbb -1.000 1.000
Warning: empty x range [0.00740261:0.00740261], adjusting to [0.00732858:0.00747664]

18
dokumentation/evolution3d/20171005_3dFit_4x4x5_100times.gnuplot.script
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@ -2,19 +2,25 @@ set datafile separator ","
f(x)=a*x+b
fit f(x) "20171005_3dFit_4x4x5_100times.csv" every ::1 using 1:5 via a,b
set terminal png
set xlabel 'regularity'
set ylabel 'steps'
set xlabel 'Regularity'
set ylabel 'Number of iterations'
set output "20171005_3dFit_4x4x5_100times_regularity-vs-steps.png"
plot "20171005_3dFit_4x4x5_100times.csv" every ::1 using 1:5 title "data", f(x) title "lin. fit" lc rgb "black"
g(x)=aa*x+bb
fit g(x) "20171005_3dFit_4x4x5_100times.csv" every ::1 using 3:5 via aa,bb
set xlabel 'improvement potential'
set ylabel 'steps'
set xlabel 'Improvement potential'
set ylabel 'Number of iterations'
set output "20171005_3dFit_4x4x5_100times_improvement-vs-steps.png"
plot "20171005_3dFit_4x4x5_100times.csv" every ::1 using 3:5 title "data", g(x) title "lin. fit" lc rgb "black"
h(x)=aaa*x+bbb
fit h(x) "20171005_3dFit_4x4x5_100times.csv" every ::1 using 3:4 via aaa,bbb
set xlabel 'improvement potential'
set ylabel 'evolution error'
set xlabel 'Improvement potential'
set ylabel 'error given by fitness-function'
set output "20171005_3dFit_4x4x5_100times_improvement-vs-evo-error.png"
plot "20171005_3dFit_4x4x5_100times.csv" every ::1 using 3:4 title "data", h(x) title "lin. fit" lc rgb "black"
i(x)=aaaa*x+bbbb
fit i(x) "20171005_3dFit_4x4x5_100times.csv" every ::1 using 2:4 via aaaa,bbbb
set xlabel 'Variability'
set ylabel 'error given by fitness-function'
set output "20171005_3dFit_4x4x5_100times_variability-vs-evo-error.png"
plot "20171005_3dFit_4x4x5_100times.csv" every ::1 using 2:4 title "data", i(x) title "lin. fit" lc rgb "black"

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52
dokumentation/evolution3d/20171005_3dFit_7x4x4_100times.gnuplot.fit.log
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@ -1,7 +1,7 @@
*******************************************************************************
Sat Oct 7 11:48:58 2017
Fri Oct 27 14:09:07 2017
FIT: data read from "20171005_3dFit_7x4x4_100times.csv" every ::1 using 1:5
@ -47,7 +47,7 @@ b -0.972 1.000
*******************************************************************************
Sat Oct 7 11:48:58 2017
Fri Oct 27 14:09:07 2017
FIT: data read from "20171005_3dFit_7x4x4_100times.csv" every ::1 using 3:5
@ -93,7 +93,7 @@ bb -1.000 1.000
*******************************************************************************
Sat Oct 7 11:48:58 2017
Fri Oct 27 14:09:07 2017
FIT: data read from "20171005_3dFit_7x4x4_100times.csv" every ::1 using 3:4
@ -136,3 +136,49 @@ correlation matrix of the fit parameters:
aaa bbb
aaa 1.000
bbb -1.000 1.000
*******************************************************************************
Fri Oct 27 14:09:07 2017
FIT: data read from "20171005_3dFit_7x4x4_100times.csv" every ::1 using 2:4
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 : 716707 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.707145
initial set of free parameter values
aaaa = 1
bbbb = 1
After 3 iterations the fit converged.
final sum of squares of residuals : 5014.73
rel. change during last iteration : -8.78131e-12
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 7.15337
variance of residuals (reduced chisquare) = WSSR/ndf : 51.1707
Final set of parameters Asymptotic Standard Error
======================= ==========================
aaaa = 1.87421 +/- 6.575e+16 (3.508e+18%)
bbbb = 85.3528 +/- 6.814e+14 (7.983e+14%)
correlation matrix of the fit parameters:
aaaa bbbb
aaaa 1.000
bbbb -1.000 1.000

66
dokumentation/evolution3d/20171005_3dFit_7x4x4_100times.gnuplot.log
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@ -259,3 +259,69 @@ correlation matrix of the fit parameters:
aaa bbb
aaa 1.000
bbb -1.000 1.000
Iteration 0
WSSR : 716707 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.707145
initial set of free parameter values
aaaa = 1
bbbb = 1
/
Iteration 1
WSSR : 5032.35 delta(WSSR)/WSSR : -141.42
delta(WSSR) : -711675 limit for stopping : 1e-05
lambda : 0.0707145
resultant parameter values
aaaa = 1.86986
bbbb = 84.9331
/
Iteration 2
WSSR : 5014.73 delta(WSSR)/WSSR : -0.00351279
delta(WSSR) : -17.6157 limit for stopping : 1e-05
lambda : 0.00707145
resultant parameter values
aaaa = 1.87421
bbbb = 85.3528
/
Iteration 3
WSSR : 5014.73 delta(WSSR)/WSSR : -8.78131e-12
delta(WSSR) : -4.40359e-08 limit for stopping : 1e-05
lambda : 0.000707145
resultant parameter values
aaaa = 1.87421
bbbb = 85.3528
After 3 iterations the fit converged.
final sum of squares of residuals : 5014.73
rel. change during last iteration : -8.78131e-12
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 7.15337
variance of residuals (reduced chisquare) = WSSR/ndf : 51.1707
Final set of parameters Asymptotic Standard Error
======================= ==========================
aaaa = 1.87421 +/- 6.575e+16 (3.508e+18%)
bbbb = 85.3528 +/- 6.814e+14 (7.983e+14%)
correlation matrix of the fit parameters:
aaaa bbbb
aaaa 1.000
bbbb -1.000 1.000
Warning: empty x range [0.0103637:0.0103637], adjusting to [0.0102601:0.0104673]

18
dokumentation/evolution3d/20171005_3dFit_7x4x4_100times.gnuplot.script
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@ -2,19 +2,25 @@ set datafile separator ","
f(x)=a*x+b
fit f(x) "20171005_3dFit_7x4x4_100times.csv" every ::1 using 1:5 via a,b
set terminal png
set xlabel 'regularity'
set ylabel 'steps'
set xlabel 'Regularity'
set ylabel 'Number of iterations'
set output "20171005_3dFit_7x4x4_100times_regularity-vs-steps.png"
plot "20171005_3dFit_7x4x4_100times.csv" every ::1 using 1:5 title "data", f(x) title "lin. fit" lc rgb "black"
g(x)=aa*x+bb
fit g(x) "20171005_3dFit_7x4x4_100times.csv" every ::1 using 3:5 via aa,bb
set xlabel 'improvement potential'
set ylabel 'steps'
set xlabel 'Improvement potential'
set ylabel 'Number of iterations'
set output "20171005_3dFit_7x4x4_100times_improvement-vs-steps.png"
plot "20171005_3dFit_7x4x4_100times.csv" every ::1 using 3:5 title "data", g(x) title "lin. fit" lc rgb "black"
h(x)=aaa*x+bbb
fit h(x) "20171005_3dFit_7x4x4_100times.csv" every ::1 using 3:4 via aaa,bbb
set xlabel 'improvement potential'
set ylabel 'evolution error'
set xlabel 'Improvement potential'
set ylabel 'error given by fitness-function'
set output "20171005_3dFit_7x4x4_100times_improvement-vs-evo-error.png"
plot "20171005_3dFit_7x4x4_100times.csv" every ::1 using 3:4 title "data", h(x) title "lin. fit" lc rgb "black"
i(x)=aaaa*x+bbbb
fit i(x) "20171005_3dFit_7x4x4_100times.csv" every ::1 using 2:4 via aaaa,bbbb
set xlabel 'Variability'
set ylabel 'error given by fitness-function'
set output "20171005_3dFit_7x4x4_100times_variability-vs-evo-error.png"
plot "20171005_3dFit_7x4x4_100times.csv" every ::1 using 2:4 title "data", i(x) title "lin. fit" lc rgb "black"

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8
dokumentation/evolution3d/20171007_3dFit_all.gnuplot.fit.log
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@ -1,7 +1,7 @@
*******************************************************************************
Sat Oct 7 12:11:35 2017
Fri Oct 27 14:09:07 2017
FIT: data read from "20171007_3dFit_all.csv" every ::1 using 1:5
@ -47,7 +47,7 @@ b -0.945 1.000
*******************************************************************************
Sat Oct 7 12:11:35 2017
Fri Oct 27 14:09:07 2017
FIT: data read from "20171007_3dFit_all.csv" every ::1 using 3:5
@ -93,7 +93,7 @@ bb -0.997 1.000
*******************************************************************************
Sat Oct 7 12:11:35 2017
Fri Oct 27 14:09:07 2017
FIT: data read from "20171007_3dFit_all.csv" every ::1 using 3:4
@ -139,7 +139,7 @@ bbb -0.997 1.000
*******************************************************************************
Sat Oct 7 12:11:35 2017
Fri Oct 27 14:09:07 2017
FIT: data read from "20171007_3dFit_all.csv" every ::1 using 2:4

24
dokumentation/evolution3d/20171007_3dFit_all.gnuplot.script
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@ -2,25 +2,25 @@ set datafile separator ","
f(x)=a*x+b
fit f(x) "20171007_3dFit_all.csv" every ::1 using 1:5 via a,b
set terminal png
set xlabel 'regularity'
set ylabel 'steps'
set xlabel 'Regularity'
set ylabel 'Number of iterations'
set output "20171007_3dFit_all_regularity-vs-steps.png"
plot "20170926_3dFit_4x4x4_100times.csv" every ::1 using 1:5 title "20170926_3dFit_4x4x4_100times.csv", "20170926_3dFit_5x5x5_100times.csv" every ::1 using 1:5 title "20170926_3dFit_5x5x5_100times.csv", "20171005_3dFit_4x4x5_100times.csv" every ::1 using 1:5 title "20171005_3dFit_4x4x5_100times.csv", "20171005_3dFit_7x4x4_100times.csv" every ::1 using 1:5 title "20171005_3dFit_7x4x4_100times.csv", f(x) title "lin. fit" lc rgb "black"
plot "20171007_3dFit_all.csv" every ::1 using 1:5 title "data", f(x) title "lin. fit" lc rgb "black"
g(x)=aa*x+bb
fit g(x) "20171007_3dFit_all.csv" every ::1 using 3:5 via aa,bb
set xlabel 'improvement potential'
set ylabel 'steps'
set xlabel 'Improvement potential'
set ylabel 'Number of iterations'
set output "20171007_3dFit_all_improvement-vs-steps.png"
plot "20170926_3dFit_4x4x4_100times.csv" every ::1 using 3:5 title "20170926_3dFit_4x4x4_100times.csv", "20170926_3dFit_5x5x5_100times.csv" every ::1 using 3:5 title "20170926_3dFit_5x5x5_100times.csv", "20171005_3dFit_4x4x5_100times.csv" every ::1 using 3:5 title "20171005_3dFit_4x4x5_100times.csv", "20171005_3dFit_7x4x4_100times.csv" every ::1 using 3:5 title "20171005_3dFit_7x4x4_100times.csv", g(x) title "lin. fit" lc rgb "black"
plot "20171007_3dFit_all.csv" every ::1 using 3:5 title "data", g(x) title "lin. fit" lc rgb "black"
h(x)=aaa*x+bbb
fit h(x) "20171007_3dFit_all.csv" every ::1 using 3:4 via aaa,bbb
set xlabel 'improvement potential'
set ylabel 'evolution error'
set xlabel 'Improvement potential'
set ylabel 'error given by fitness-function'
set output "20171007_3dFit_all_improvement-vs-evo-error.png"
plot "20170926_3dFit_4x4x4_100times.csv" every ::1 using 3:4 title "20170926_3dFit_4x4x4_100times.csv", "20170926_3dFit_5x5x5_100times.csv" every ::1 using 3:4 title "20170926_3dFit_5x5x5_100times.csv", "20171005_3dFit_4x4x5_100times.csv" every ::1 using 3:4 title "20171005_3dFit_4x4x5_100times.csv", "20171005_3dFit_7x4x4_100times.csv" every ::1 using 3:4 title "20171005_3dFit_7x4x4_100times.csv", h(x) title "lin. fit" lc rgb "black"
plot "20171007_3dFit_all.csv" every ::1 using 3:4 title "data", h(x) title "lin. fit" lc rgb "black"
i(x)=aaaa*x+bbbb
fit i(x) "20171007_3dFit_all.csv" every ::1 using 2:4 via aaaa,bbbb
set xlabel 'variability'
set ylabel 'evolution error'
set xlabel 'Variability'
set ylabel 'error given by fitness-function'
set output "20171007_3dFit_all_variability-vs-evo-error.png"
plot "20170926_3dFit_4x4x4_100times.csv" every ::1 using 2:4 title "20170926_3dFit_4x4x4_100times.csv", "20170926_3dFit_5x5x5_100times.csv" every ::1 using 2:4 title "20170926_3dFit_5x5x5_100times.csv", "20171005_3dFit_4x4x5_100times.csv" every ::1 using 2:4 title "20171005_3dFit_4x4x5_100times.csv", "20171005_3dFit_7x4x4_100times.csv" every ::1 using 2:4 title "20171005_3dFit_7x4x4_100times.csv", i(x) title "lin. fit" lc rgb "black"
plot "20171007_3dFit_all.csv" every ::1 using 2:4 title "data", i(x) title "lin. fit" lc rgb "black"

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184
dokumentation/evolution3d/20171013_3dFit_4x4x7_100times.gnuplot.fit.log Normal file
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@ -0,0 +1,184 @@
*******************************************************************************
Fri Oct 27 14:09:07 2017
FIT: data read from "20171013_3dFit_4x4x7_100times.csv" every ::1 using 1:5
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 : 4.17059e+08 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.707107
initial set of free parameter values
a = 1
b = 1
After 7 iterations the fit converged.
final sum of squares of residuals : 1.87465e+07
rel. change during last iteration : -3.45833e-09
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 437.368
variance of residuals (reduced chisquare) = WSSR/ndf : 191291
Final set of parameters Asymptotic Standard Error
======================= ==========================
a = -1.04278e+07 +/- 2.15e+06 (20.62%)
b = 2804.5 +/- 174.4 (6.22%)
correlation matrix of the fit parameters:
a b
a 1.000
b -0.968 1.000
*******************************************************************************
Fri Oct 27 14:09:07 2017
FIT: data read from "20171013_3dFit_4x4x7_100times.csv" every ::1 using 3:5
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 : 4.16784e+08 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.860178
initial set of free parameter values
aa = 1
bb = 1
After 4 iterations the fit converged.
final sum of squares of residuals : 2.30326e+07
rel. change during last iteration : -2.46431e-06
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 484.795
variance of residuals (reduced chisquare) = WSSR/ndf : 235026
Final set of parameters Asymptotic Standard Error
======================= ==========================
aa = 7203.94 +/- 7544 (104.7%)
bb = -3004.38 +/- 5226 (173.9%)
correlation matrix of the fit parameters:
aa bb
aa 1.000
bb -1.000 1.000
*******************************************************************************
Fri Oct 27 14:09:07 2017
FIT: data read from "20171013_3dFit_4x4x7_100times.csv" every ::1 using 3:4
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 : 770224 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.860178
initial set of free parameter values
aaa = 1
bbb = 1
After 5 iterations the fit converged.
final sum of squares of residuals : 3831.77
rel. change during last iteration : -2.81552e-12
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 6.25298
variance of residuals (reduced chisquare) = WSSR/ndf : 39.0997
Final set of parameters Asymptotic Standard Error
======================= ==========================
aaa = -284.393 +/- 97.31 (34.22%)
bbb = 286.203 +/- 67.4 (23.55%)
correlation matrix of the fit parameters:
aaa bbb
aaa 1.000
bbb -1.000 1.000
*******************************************************************************
Fri Oct 27 14:09:07 2017
FIT: data read from "20171013_3dFit_4x4x7_100times.csv" every ::1 using 2:4
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 : 782212 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.707145
initial set of free parameter values
aaaa = 1
bbbb = 1
After 3 iterations the fit converged.
final sum of squares of residuals : 4165.76
rel. change during last iteration : -1.15562e-11
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 6.51979
variance of residuals (reduced chisquare) = WSSR/ndf : 42.5077
Final set of parameters Asymptotic Standard Error
======================= ==========================
aaaa = 1.91405 +/- 1.245e+17 (6.505e+18%)
bbbb = 89.1974 +/- 1.29e+15 (1.447e+15%)
correlation matrix of the fit parameters:
aaaa bbbb
aaaa 1.000
bbbb -1.000 1.000

338
dokumentation/evolution3d/20171013_3dFit_4x4x7_100times.gnuplot.log Normal file
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@ -0,0 +1,338 @@
Iteration 0
WSSR : 4.17059e+08 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.707107
initial set of free parameter values
a = 1
b = 1
/
Iteration 1
WSSR : 2.32566e+07 delta(WSSR)/WSSR : -16.9329
delta(WSSR) : -3.93802e+08 limit for stopping : 1e-05
lambda : 0.0707107
resultant parameter values
a = 0.291954
b = 1975.6
/
Iteration 2
WSSR : 2.32468e+07 delta(WSSR)/WSSR : -0.000422514
delta(WSSR) : -9822.08 limit for stopping : 1e-05
lambda : 0.00707107
resultant parameter values
a = -86.0202
b = 1985.48
/
Iteration 3
WSSR : 2.32393e+07 delta(WSSR)/WSSR : -0.000320176
delta(WSSR) : -7440.69 limit for stopping : 1e-05
lambda : 0.000707107
resultant parameter values
a = -8710.18
b = 1986.15
/
Iteration 4
WSSR : 2.25787e+07 delta(WSSR)/WSSR : -0.0292598
delta(WSSR) : -660649 limit for stopping : 1e-05
lambda : 7.07107e-05
resultant parameter values
a = -805199
b = 2048.71
/
Iteration 5
WSSR : 1.87911e+07 delta(WSSR)/WSSR : -0.201566
delta(WSSR) : -3.78764e+06 limit for stopping : 1e-05
lambda : 7.07107e-06
resultant parameter values
a = -9.39061e+06
b = 2723.04
/
Iteration 6
WSSR : 1.87465e+07 delta(WSSR)/WSSR : -0.00237512
delta(WSSR) : -44525.2 limit for stopping : 1e-05
lambda : 7.07107e-07
resultant parameter values
a = -1.04266e+07
b = 2804.4
/
Iteration 7
WSSR : 1.87465e+07 delta(WSSR)/WSSR : -3.45833e-09
delta(WSSR) : -0.0648317 limit for stopping : 1e-05
lambda : 7.07107e-08
resultant parameter values
a = -1.04278e+07
b = 2804.5
After 7 iterations the fit converged.
final sum of squares of residuals : 1.87465e+07
rel. change during last iteration : -3.45833e-09
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 437.368
variance of residuals (reduced chisquare) = WSSR/ndf : 191291
Final set of parameters Asymptotic Standard Error
======================= ==========================
a = -1.04278e+07 +/- 2.15e+06 (20.62%)
b = 2804.5 +/- 174.4 (6.22%)
correlation matrix of the fit parameters:
a b
a 1.000
b -0.968 1.000
Iteration 0
WSSR : 4.16784e+08 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.860178
initial set of free parameter values
aa = 1
bb = 1
/
Iteration 1
WSSR : 2.32036e+07 delta(WSSR)/WSSR : -16.962
delta(WSSR) : -3.9358e+08 limit for stopping : 1e-05
lambda : 0.0860178
resultant parameter values
aa = 948.6
bb = 1318.67
/
Iteration 2
WSSR : 2.31176e+07 delta(WSSR)/WSSR : -0.00372074
delta(WSSR) : -86014.6 limit for stopping : 1e-05
lambda : 0.00860178
resultant parameter values
aa = 2665.07
bb = 139.584
/
Iteration 3
WSSR : 2.30326e+07 delta(WSSR)/WSSR : -0.00369116
delta(WSSR) : -85017 limit for stopping : 1e-05
lambda : 0.000860178
resultant parameter values
aa = 7086.74
bb = -2923.19
/
Iteration 4
WSSR : 2.30326e+07 delta(WSSR)/WSSR : -2.46431e-06
delta(WSSR) : -56.7593 limit for stopping : 1e-05
lambda : 8.60178e-05
resultant parameter values
aa = 7203.94
bb = -3004.38
After 4 iterations the fit converged.
final sum of squares of residuals : 2.30326e+07
rel. change during last iteration : -2.46431e-06
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 484.795
variance of residuals (reduced chisquare) = WSSR/ndf : 235026
Final set of parameters Asymptotic Standard Error
======================= ==========================
aa = 7203.94 +/- 7544 (104.7%)
bb = -3004.38 +/- 5226 (173.9%)
correlation matrix of the fit parameters:
aa bb
aa 1.000
bb -1.000 1.000
Iteration 0
WSSR : 770224 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.860178
initial set of free parameter values
aaa = 1
bbb = 1
/
Iteration 1
WSSR : 4287.26 delta(WSSR)/WSSR : -178.654
delta(WSSR) : -765937 limit for stopping : 1e-05
lambda : 0.0860178
resultant parameter values
aaa = 40.5365
bbb = 60.6977
/
Iteration 2
WSSR : 4061.95 delta(WSSR)/WSSR : -0.0554706
delta(WSSR) : -225.318 limit for stopping : 1e-05
lambda : 0.00860178
resultant parameter values
aaa = -48.3014
bbb = 122.669
/
Iteration 3
WSSR : 3831.93 delta(WSSR)/WSSR : -0.0600269
delta(WSSR) : -230.019 limit for stopping : 1e-05
lambda : 0.000860178
resultant parameter values
aaa = -278.294
bbb = 281.979
/
Iteration 4
WSSR : 3831.77 delta(WSSR)/WSSR : -4.00769e-05
delta(WSSR) : -0.153566 limit for stopping : 1e-05
lambda : 8.60178e-05
resultant parameter values
aaa = -284.391
bbb = 286.202
/
Iteration 5
WSSR : 3831.77 delta(WSSR)/WSSR : -2.81552e-12
delta(WSSR) : -1.07884e-08 limit for stopping : 1e-05
lambda : 8.60178e-06
resultant parameter values
aaa = -284.393
bbb = 286.203
After 5 iterations the fit converged.
final sum of squares of residuals : 3831.77
rel. change during last iteration : -2.81552e-12
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 6.25298
variance of residuals (reduced chisquare) = WSSR/ndf : 39.0997
Final set of parameters Asymptotic Standard Error
======================= ==========================
aaa = -284.393 +/- 97.31 (34.22%)
bbb = 286.203 +/- 67.4 (23.55%)
correlation matrix of the fit parameters:
aaa bbb
aaa 1.000
bbb -1.000 1.000
Iteration 0
WSSR : 782212 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.707145
initial set of free parameter values
aaaa = 1
bbbb = 1
/
Iteration 1
WSSR : 4185.02 delta(WSSR)/WSSR : -185.908
delta(WSSR) : -778027 limit for stopping : 1e-05
lambda : 0.0707145
resultant parameter values
aaaa = 1.9095
bbbb = 88.7586
/
Iteration 2
WSSR : 4165.76 delta(WSSR)/WSSR : -0.00462295
delta(WSSR) : -19.2581 limit for stopping : 1e-05
lambda : 0.00707145
resultant parameter values
aaaa = 1.91405
bbbb = 89.1974
/
Iteration 3
WSSR : 4165.76 delta(WSSR)/WSSR : -1.15562e-11
delta(WSSR) : -4.81405e-08 limit for stopping : 1e-05
lambda : 0.000707145
resultant parameter values
aaaa = 1.91405
bbbb = 89.1974
After 3 iterations the fit converged.
final sum of squares of residuals : 4165.76
rel. change during last iteration : -1.15562e-11
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 6.51979
variance of residuals (reduced chisquare) = WSSR/ndf : 42.5077
Final set of parameters Asymptotic Standard Error
======================= ==========================
aaaa = 1.91405 +/- 1.245e+17 (6.505e+18%)
bbbb = 89.1974 +/- 1.29e+15 (1.447e+15%)
correlation matrix of the fit parameters:
aaaa bbbb
aaaa 1.000
bbbb -1.000 1.000
Warning: empty x range [0.0103637:0.0103637], adjusting to [0.0102601:0.0104673]

26
dokumentation/evolution3d/20171013_3dFit_4x4x7_100times.gnuplot.script Normal file
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set datafile separator ","
f(x)=a*x+b
fit f(x) "20171013_3dFit_4x4x7_100times.csv" every ::1 using 1:5 via a,b
set terminal png
set xlabel 'Regularity'
set ylabel 'Number of iterations'
set output "20171013_3dFit_4x4x7_100times_regularity-vs-steps.png"
plot "20171013_3dFit_4x4x7_100times.csv" every ::1 using 1:5 title "data", f(x) title "lin. fit" lc rgb "black"
g(x)=aa*x+bb
fit g(x) "20171013_3dFit_4x4x7_100times.csv" every ::1 using 3:5 via aa,bb
set xlabel 'Improvement potential'
set ylabel 'Number of iterations'
set output "20171013_3dFit_4x4x7_100times_improvement-vs-steps.png"
plot "20171013_3dFit_4x4x7_100times.csv" every ::1 using 3:5 title "data", g(x) title "lin. fit" lc rgb "black"
h(x)=aaa*x+bbb
fit h(x) "20171013_3dFit_4x4x7_100times.csv" every ::1 using 3:4 via aaa,bbb
set xlabel 'Improvement potential'
set ylabel 'error given by fitness-function'
set output "20171013_3dFit_4x4x7_100times_improvement-vs-evo-error.png"
plot "20171013_3dFit_4x4x7_100times.csv" every ::1 using 3:4 title "data", h(x) title "lin. fit" lc rgb "black"
i(x)=aaaa*x+bbbb
fit i(x) "20171013_3dFit_4x4x7_100times.csv" every ::1 using 2:4 via aaaa,bbbb
set xlabel 'Variability'
set ylabel 'error given by fitness-function'
set output "20171013_3dFit_4x4x7_100times_variability-vs-evo-error.png"
plot "20171013_3dFit_4x4x7_100times.csv" every ::1 using 2:4 title "data", i(x) title "lin. fit" lc rgb "black"

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184
dokumentation/evolution3d/20171013_3dFit_5x4x4_100times.gnuplot.fit.log Normal file
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@ -0,0 +1,184 @@
*******************************************************************************
Fri Oct 27 14:09:07 2017
FIT: data read from "20171013_3dFit_5x4x4_100times.csv" every ::1 using 1:5
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 : 8.41899e+07 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.707107
initial set of free parameter values
a = 1
b = 1
After 7 iterations the fit converged.
final sum of squares of residuals : 8.72636e+06
rel. change during last iteration : -9.16821e-11
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 298.403
variance of residuals (reduced chisquare) = WSSR/ndf : 89044.5
Final set of parameters Asymptotic Standard Error
======================= ==========================
a = -1.15579e+06 +/- 1.468e+06 (127%)
b = 1020.47 +/- 194.2 (19.03%)
correlation matrix of the fit parameters:
a b
a 1.000
b -0.988 1.000
*******************************************************************************
Fri Oct 27 14:09:07 2017
FIT: data read from "20171013_3dFit_5x4x4_100times.csv" every ::1 using 3:5
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 : 8.40737e+07 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.850561
initial set of free parameter values
aa = 1
bb = 1
After 4 iterations the fit converged.
final sum of squares of residuals : 7.83163e+06
rel. change during last iteration : -4.61976e-06
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 282.692
variance of residuals (reduced chisquare) = WSSR/ndf : 79914.6
Final set of parameters Asymptotic Standard Error
======================= ==========================
aa = 10438.1 +/- 3028 (29%)
bb = -6107.9 +/- 2024 (33.14%)
correlation matrix of the fit parameters:
aa bb
aa 1.000
bb -1.000 1.000
*******************************************************************************
Fri Oct 27 14:09:08 2017
FIT: data read from "20171013_3dFit_5x4x4_100times.csv" every ::1 using 3:4
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 : 997151 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.850561
initial set of free parameter values
aaa = 1
bbb = 1
After 4 iterations the fit converged.
final sum of squares of residuals : 4984.54
rel. change during last iteration : -5.99309e-06
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 7.1318
variance of residuals (reduced chisquare) = WSSR/ndf : 50.8626
Final set of parameters Asymptotic Standard Error
======================= ==========================
aaa = -241.373 +/- 76.38 (31.64%)
bbb = 262.595 +/- 51.06 (19.44%)
correlation matrix of the fit parameters:
aaa bbb
aaa 1.000
bbb -1.000 1.000
*******************************************************************************
Fri Oct 27 14:09:08 2017
FIT: data read from "20171013_3dFit_5x4x4_100times.csv" every ::1 using 2:4
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 : 1.01036e+06 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.707126
initial set of free parameter values
aaaa = 1
bbbb = 1
After 3 iterations the fit converged.
final sum of squares of residuals : 5492.5
rel. change during last iteration : -1.13205e-11
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 7.48638
variance of residuals (reduced chisquare) = WSSR/ndf : 56.0459
Final set of parameters Asymptotic Standard Error
======================= ==========================
aaaa = 1.74202 +/- 9.406e+16 (5.4e+18%)
bbbb = 101.237 +/- 6.963e+14 (6.878e+14%)
correlation matrix of the fit parameters:
aaaa bbbb
aaaa 1.000
bbbb -1.000 1.000

327
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@ -0,0 +1,327 @@
Iteration 0
WSSR : 8.41899e+07 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.707107
initial set of free parameter values
a = 1
b = 1
/
Iteration 1
WSSR : 8.78343e+06 delta(WSSR)/WSSR : -8.58509
delta(WSSR) : -7.54065e+07 limit for stopping : 1e-05
lambda : 0.0707107
resultant parameter values
a = 1.01743
b = 865.06
/
Iteration 2
WSSR : 8.78156e+06 delta(WSSR)/WSSR : -0.000212651
delta(WSSR) : -1867.41 limit for stopping : 1e-05
lambda : 0.00707107
resultant parameter values
a = -8.53399
b = 869.381
/
Iteration 3
WSSR : 8.78147e+06 delta(WSSR)/WSSR : -1.03771e-05
delta(WSSR) : -91.1264 limit for stopping : 1e-05
lambda : 0.000707107
resultant parameter values
a = -962.938
b = 869.506
/
Iteration 4
WSSR : 8.77338e+06 delta(WSSR)/WSSR : -0.000922382
delta(WSSR) : -8092.41 limit for stopping : 1e-05
lambda : 7.07107e-05
resultant parameter values
a = -89117.8
b = 881.03
/
Iteration 5
WSSR : 8.72691e+06 delta(WSSR)/WSSR : -0.00532471
delta(WSSR) : -46468.3 limit for stopping : 1e-05
lambda : 7.07107e-06
resultant parameter values
a = -1.04065e+06
b = 1005.42
/
Iteration 6
WSSR : 8.72636e+06 delta(WSSR)/WSSR : -6.27725e-05
delta(WSSR) : -547.775 limit for stopping : 1e-05
lambda : 7.07107e-07
resultant parameter values
a = -1.15565e+06
b = 1020.45
/
Iteration 7
WSSR : 8.72636e+06 delta(WSSR)/WSSR : -9.16821e-11
delta(WSSR) : -0.000800051 limit for stopping : 1e-05
lambda : 7.07107e-08
resultant parameter values
a = -1.15579e+06
b = 1020.47
After 7 iterations the fit converged.
final sum of squares of residuals : 8.72636e+06
rel. change during last iteration : -9.16821e-11
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 298.403
variance of residuals (reduced chisquare) = WSSR/ndf : 89044.5
Final set of parameters Asymptotic Standard Error
======================= ==========================
a = -1.15579e+06 +/- 1.468e+06 (127%)
b = 1020.47 +/- 194.2 (19.03%)
correlation matrix of the fit parameters:
a b
a 1.000
b -0.988 1.000
Iteration 0
WSSR : 8.40737e+07 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.850561
initial set of free parameter values
aa = 1
bb = 1
/
Iteration 1
WSSR : 8.69722e+06 delta(WSSR)/WSSR : -8.66674
delta(WSSR) : -7.53765e+07 limit for stopping : 1e-05
lambda : 0.0850561
resultant parameter values
aa = 483.004
bb = 542.6
/
Iteration 2
WSSR : 8.08871e+06 delta(WSSR)/WSSR : -0.0752288
delta(WSSR) : -608504 limit for stopping : 1e-05
lambda : 0.00850561
resultant parameter values
aa = 5007.98
bb = -2477.97
/
Iteration 3
WSSR : 7.83167e+06 delta(WSSR)/WSSR : -0.0328215
delta(WSSR) : -257047 limit for stopping : 1e-05
lambda : 0.000850561
resultant parameter values
aa = 10373.7
bb = -6064.85
/
Iteration 4
WSSR : 7.83163e+06 delta(WSSR)/WSSR : -4.61976e-06
delta(WSSR) : -36.1803 limit for stopping : 1e-05
lambda : 8.50561e-05
resultant parameter values
aa = 10438.1
bb = -6107.9
After 4 iterations the fit converged.
final sum of squares of residuals : 7.83163e+06
rel. change during last iteration : -4.61976e-06
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 282.692
variance of residuals (reduced chisquare) = WSSR/ndf : 79914.6
Final set of parameters Asymptotic Standard Error
======================= ==========================
aa = 10438.1 +/- 3028 (29%)
bb = -6107.9 +/- 2024 (33.14%)
correlation matrix of the fit parameters:
aa bb
aa 1.000
bb -1.000 1.000
Iteration 0
WSSR : 997151 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.850561
initial set of free parameter values
aaa = 1
bbb = 1
/
Iteration 1
WSSR : 5722.22 delta(WSSR)/WSSR : -173.259
delta(WSSR) : -991429 limit for stopping : 1e-05
lambda : 0.0850561
resultant parameter values
aaa = 44.3933
bbb = 71.0688
/
Iteration 2
WSSR : 5196.8 delta(WSSR)/WSSR : -0.101105
delta(WSSR) : -525.422 limit for stopping : 1e-05
lambda : 0.00850561
resultant parameter values
aaa = -85.3429
bbb = 158.291
/
Iteration 3
WSSR : 4984.57 delta(WSSR)/WSSR : -0.0425783
delta(WSSR) : -212.235 limit for stopping : 1e-05
lambda : 0.000850561
resultant parameter values
aaa = -239.522
bbb = 261.358
/
Iteration 4
WSSR : 4984.54 delta(WSSR)/WSSR : -5.99309e-06
delta(WSSR) : -0.0298728 limit for stopping : 1e-05
lambda : 8.50561e-05
resultant parameter values
aaa = -241.373
bbb = 262.595
After 4 iterations the fit converged.
final sum of squares of residuals : 4984.54
rel. change during last iteration : -5.99309e-06
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 7.1318
variance of residuals (reduced chisquare) = WSSR/ndf : 50.8626
Final set of parameters Asymptotic Standard Error
======================= ==========================
aaa = -241.373 +/- 76.38 (31.64%)
bbb = 262.595 +/- 51.06 (19.44%)
correlation matrix of the fit parameters:
aaa bbb
aaa 1.000
bbb -1.000 1.000
Iteration 0
WSSR : 1.01036e+06 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.707126
initial set of free parameter values
aaaa = 1
bbbb = 1
/
Iteration 1
WSSR : 5517.37 delta(WSSR)/WSSR : -182.123
delta(WSSR) : -1.00484e+06 limit for stopping : 1e-05
lambda : 0.0707126
resultant parameter values
aaaa = 1.73833
bbbb = 100.739
/
Iteration 2
WSSR : 5492.5 delta(WSSR)/WSSR : -0.00452841
delta(WSSR) : -24.8723 limit for stopping : 1e-05
lambda : 0.00707126
resultant parameter values
aaaa = 1.74202
bbbb = 101.237
/
Iteration 3
WSSR : 5492.5 delta(WSSR)/WSSR : -1.13205e-11
delta(WSSR) : -6.21776e-08 limit for stopping : 1e-05
lambda : 0.000707126
resultant parameter values
aaaa = 1.74202
bbbb = 101.237
After 3 iterations the fit converged.
final sum of squares of residuals : 5492.5
rel. change during last iteration : -1.13205e-11
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 7.48638
variance of residuals (reduced chisquare) = WSSR/ndf : 56.0459
Final set of parameters Asymptotic Standard Error
======================= ==========================
aaaa = 1.74202 +/- 9.406e+16 (5.4e+18%)
bbbb = 101.237 +/- 6.963e+14 (6.878e+14%)
correlation matrix of the fit parameters:
aaaa bbbb
aaaa 1.000
bbbb -1.000 1.000
Warning: empty x range [0.00740261:0.00740261], adjusting to [0.00732858:0.00747664]

26
dokumentation/evolution3d/20171013_3dFit_5x4x4_100times.gnuplot.script Normal file
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set datafile separator ","
f(x)=a*x+b
fit f(x) "20171013_3dFit_5x4x4_100times.csv" every ::1 using 1:5 via a,b
set terminal png
set xlabel 'Regularity'
set ylabel 'Number of iterations'
set output "20171013_3dFit_5x4x4_100times_regularity-vs-steps.png"
plot "20171013_3dFit_5x4x4_100times.csv" every ::1 using 1:5 title "data", f(x) title "lin. fit" lc rgb "black"
g(x)=aa*x+bb
fit g(x) "20171013_3dFit_5x4x4_100times.csv" every ::1 using 3:5 via aa,bb
set xlabel 'Improvement potential'
set ylabel 'Number of iterations'
set output "20171013_3dFit_5x4x4_100times_improvement-vs-steps.png"
plot "20171013_3dFit_5x4x4_100times.csv" every ::1 using 3:5 title "data", g(x) title "lin. fit" lc rgb "black"
h(x)=aaa*x+bbb
fit h(x) "20171013_3dFit_5x4x4_100times.csv" every ::1 using 3:4 via aaa,bbb
set xlabel 'Improvement potential'
set ylabel 'error given by fitness-function'
set output "20171013_3dFit_5x4x4_100times_improvement-vs-evo-error.png"
plot "20171013_3dFit_5x4x4_100times.csv" every ::1 using 3:4 title "data", h(x) title "lin. fit" lc rgb "black"
i(x)=aaaa*x+bbbb
fit i(x) "20171013_3dFit_5x4x4_100times.csv" every ::1 using 2:4 via aaaa,bbbb
set xlabel 'Variability'
set ylabel 'error given by fitness-function'
set output "20171013_3dFit_5x4x4_100times_variability-vs-evo-error.png"
plot "20171013_3dFit_5x4x4_100times.csv" every ::1 using 2:4 title "data", i(x) title "lin. fit" lc rgb "black"

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52
dokumentation/evolution3d/20171021-evolution3D_6x6_100Times.gnuplot.fit.log
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@ -1,7 +1,7 @@
*******************************************************************************
Mon Oct 23 12:06:26 2017
Fri Oct 27 14:09:08 2017
FIT: data read from "20171021-evolution3D_6x6_100Times.csv" every ::1 using 1:5
@ -47,7 +47,7 @@ b -0.995 1.000
*******************************************************************************
Mon Oct 23 12:06:26 2017
Fri Oct 27 14:09:08 2017
FIT: data read from "20171021-evolution3D_6x6_100Times.csv" every ::1 using 3:5
@ -93,7 +93,7 @@ bb -1.000 1.000
*******************************************************************************
Mon Oct 23 12:06:26 2017
Fri Oct 27 14:09:08 2017
FIT: data read from "20171021-evolution3D_6x6_100Times.csv" every ::1 using 3:4
@ -136,3 +136,49 @@ correlation matrix of the fit parameters:
aaa bbb
aaa 1.000
bbb -1.000 1.000
*******************************************************************************
Fri Oct 27 14:09:08 2017
FIT: data read from "20171021-evolution3D_6x6_100Times.csv" every ::1 using 2:4
format = x:z
#datapoints = 110
residuals are weighted equally (unit weight)
function used for fitting: i(x)
fitted parameters initialized with current variable values
Iteration 0
WSSR : 423824 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.707248
initial set of free parameter values
aaaa = 1
bbbb = 1
After 3 iterations the fit converged.
final sum of squares of residuals : 3576.05
rel. change during last iteration : -4.97138e-12
degrees of freedom (FIT_NDF) : 108
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 5.75426
variance of residuals (reduced chisquare) = WSSR/ndf : 33.1115
Final set of parameters Asymptotic Standard Error
======================= ==========================
aaaa = 2.2349 +/- 2.531e+16 (1.133e+18%)
bbbb = 62.785 +/- 5.059e+14 (8.058e+14%)
correlation matrix of the fit parameters:
aaaa bbbb
aaaa 1.000
bbbb -1.000 1.000

66
dokumentation/evolution3d/20171021-evolution3D_6x6_100Times.gnuplot.log
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@ -226,3 +226,69 @@ correlation matrix of the fit parameters:
aaa bbb
aaa 1.000
bbb -1.000 1.000
Iteration 0
WSSR : 423824 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.707248
initial set of free parameter values
aaaa = 1
bbbb = 1
/
Iteration 1
WSSR : 3584.65 delta(WSSR)/WSSR : -117.233
delta(WSSR) : -420239 limit for stopping : 1e-05
lambda : 0.0707248
resultant parameter values
aaaa = 2.22931
bbbb = 62.5054
/
Iteration 2
WSSR : 3576.05 delta(WSSR)/WSSR : -0.00240612
delta(WSSR) : -8.6044 limit for stopping : 1e-05
lambda : 0.00707248
resultant parameter values
aaaa = 2.2349
bbbb = 62.785
/
Iteration 3
WSSR : 3576.05 delta(WSSR)/WSSR : -4.97138e-12
delta(WSSR) : -1.77779e-08 limit for stopping : 1e-05
lambda : 0.000707248
resultant parameter values
aaaa = 2.2349
bbbb = 62.785
After 3 iterations the fit converged.
final sum of squares of residuals : 3576.05
rel. change during last iteration : -4.97138e-12
degrees of freedom (FIT_NDF) : 108
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 5.75426
variance of residuals (reduced chisquare) = WSSR/ndf : 33.1115
Final set of parameters Asymptotic Standard Error
======================= ==========================
aaaa = 2.2349 +/- 2.531e+16 (1.133e+18%)
bbbb = 62.785 +/- 5.059e+14 (8.058e+14%)
correlation matrix of the fit parameters:
aaaa bbbb
aaaa 1.000
bbbb -1.000 1.000
Warning: empty x range [0.019987:0.019987], adjusting to [0.0197871:0.0201869]

18
dokumentation/evolution3d/20171021-evolution3D_6x6_100Times.gnuplot.script
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@ -2,19 +2,25 @@ set datafile separator ","
f(x)=a*x+b
fit f(x) "20171021-evolution3D_6x6_100Times.csv" every ::1 using 1:5 via a,b
set terminal png
set xlabel 'regularity'
set ylabel 'steps'
set xlabel 'Regularity'
set ylabel 'Number of iterations'
set output "20171021-evolution3D_6x6_100Times_regularity-vs-steps.png"
plot "20171021-evolution3D_6x6_100Times.csv" every ::1 using 1:5 title "data", f(x) title "lin. fit" lc rgb "black"
g(x)=aa*x+bb
fit g(x) "20171021-evolution3D_6x6_100Times.csv" every ::1 using 3:5 via aa,bb
set xlabel 'improvement potential'
set ylabel 'steps'
set xlabel 'Improvement potential'
set ylabel 'Number of iterations'
set output "20171021-evolution3D_6x6_100Times_improvement-vs-steps.png"
plot "20171021-evolution3D_6x6_100Times.csv" every ::1 using 3:5 title "data", g(x) title "lin. fit" lc rgb "black"
h(x)=aaa*x+bbb
fit h(x) "20171021-evolution3D_6x6_100Times.csv" every ::1 using 3:4 via aaa,bbb
set xlabel 'improvement potential'
set ylabel 'evolution error'
set xlabel 'Improvement potential'
set ylabel 'error given by fitness-function'
set output "20171021-evolution3D_6x6_100Times_improvement-vs-evo-error.png"
plot "20171021-evolution3D_6x6_100Times.csv" every ::1 using 3:4 title "data", h(x) title "lin. fit" lc rgb "black"
i(x)=aaaa*x+bbbb
fit i(x) "20171021-evolution3D_6x6_100Times.csv" every ::1 using 2:4 via aaaa,bbbb
set xlabel 'Variability'
set ylabel 'error given by fitness-function'
set output "20171021-evolution3D_6x6_100Times_variability-vs-evo-error.png"
plot "20171021-evolution3D_6x6_100Times.csv" every ::1 using 2:4 title "data", i(x) title "lin. fit" lc rgb "black"

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178
dokumentation/evolution3d/20171025-evolution3D_10x10x10_noFit.gnuplot.fit.log
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@ -1,184 +1,10 @@
*******************************************************************************
Wed Oct 25 16:01:21 2017
Fri Oct 27 14:09:08 2017
FIT: data read from "20171025-evolution3D_10x10x10_noFit.csv" every ::1 using 1:5
format = x:z
#datapoints = 6
residuals are weighted equally (unit weight)
BREAK: No data to fit
function used for fitting: f(x)
fitted parameters initialized with current variable values
Iteration 0
WSSR : 9.03463 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.800174
initial set of free parameter values
a = 1
b = 1
After 4 iterations the fit converged.
final sum of squares of residuals : 0.760112
rel. change during last iteration : -7.81424e-14
degrees of freedom (FIT_NDF) : 4
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 0.435922
variance of residuals (reduced chisquare) = WSSR/ndf : 0.190028
Final set of parameters Asymptotic Standard Error
======================= ==========================
a = -0.500504 +/- 0.4333 (86.58%)
b = 0.50226 +/- 0.2295 (45.7%)
correlation matrix of the fit parameters:
a b
a 1.000
b -0.632 1.000
*******************************************************************************
Wed Oct 25 16:01:21 2017
FIT: data read from "20171025-evolution3D_10x10x10_noFit.csv" every ::1 using 3:5
format = x:z
#datapoints = 6
residuals are weighted equally (unit weight)
function used for fitting: g(x)
fitted parameters initialized with current variable values
Iteration 0
WSSR : 9.042 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.80039
initial set of free parameter values
aa = 1
bb = 1
After 4 iterations the fit converged.
final sum of squares of residuals : 0.760537
rel. change during last iteration : -7.73688e-14
degrees of freedom (FIT_NDF) : 4
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 0.436044
variance of residuals (reduced chisquare) = WSSR/ndf : 0.190134
Final set of parameters Asymptotic Standard Error
======================= ==========================
aa = -0.499395 +/- 0.4329 (86.68%)
bb = 0.502057 +/- 0.2296 (45.72%)
correlation matrix of the fit parameters:
aa bb
aa 1.000
bb -0.631 1.000
*******************************************************************************
Wed Oct 25 16:01:21 2017
FIT: data read from "20171025-evolution3D_10x10x10_noFit.csv" every ::1 using 3:4
format = x:z
#datapoints = 6
residuals are weighted equally (unit weight)
function used for fitting: h(x)
fitted parameters initialized with current variable values
Iteration 0
WSSR : 9.04152 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.80039
initial set of free parameter values
aaa = 1
bbb = 1
After 4 iterations the fit converged.
final sum of squares of residuals : 0.763537
rel. change during last iteration : -7.73556e-14
degrees of freedom (FIT_NDF) : 4
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 0.436903
variance of residuals (reduced chisquare) = WSSR/ndf : 0.190884
Final set of parameters Asymptotic Standard Error
======================= ==========================
aaa = -0.501106 +/- 0.4337 (86.55%)
bbb = 0.503355 +/- 0.23 (45.7%)
correlation matrix of the fit parameters:
aaa bbb
aaa 1.000
bbb -0.631 1.000
*******************************************************************************
Wed Oct 25 16:01:21 2017
FIT: data read from "20171025-evolution3D_10x10x10_noFit.csv" every ::1 using 2:4
format = x:z
#datapoints = 6
residuals are weighted equally (unit weight)
function used for fitting: i(x)
fitted parameters initialized with current variable values
Iteration 0
WSSR : 9.04263 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.800411
initial set of free parameter values
aaaa = 1
bbbb = 1
After 4 iterations the fit converged.
final sum of squares of residuals : 0.763697
rel. change during last iteration : -7.7194e-14
degrees of freedom (FIT_NDF) : 4
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 0.436949
variance of residuals (reduced chisquare) = WSSR/ndf : 0.190924
Final set of parameters Asymptotic Standard Error
======================= ==========================
aaaa = -0.50098 +/- 0.4338 (86.59%)
bbbb = 0.50338 +/- 0.2301 (45.71%)
correlation matrix of the fit parameters:
aaaa bbbb
aaaa 1.000
bbbb -0.632 1.000

305
dokumentation/evolution3d/20171025-evolution3D_10x10x10_noFit.gnuplot.log
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@ -1,304 +1,3 @@
No data to fit
"20171025-evolution3D_10x10x10_noFit.gnuplot.script", line 3:
Iteration 0
WSSR : 9.03463 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.800174
initial set of free parameter values
a = 1
b = 1
/
Iteration 1
WSSR : 1.04136 delta(WSSR)/WSSR : -7.67579
delta(WSSR) : -7.99327 limit for stopping : 1e-05
lambda : 0.0800174
resultant parameter values
a = 0.00294917
b = 0.398082
/
Iteration 2
WSSR : 0.760123 delta(WSSR)/WSSR : -0.36999
delta(WSSR) : -0.281238 limit for stopping : 1e-05
lambda : 0.00800174
resultant parameter values
a = -0.497122
b = 0.501019
/
Iteration 3
WSSR : 0.760112 delta(WSSR)/WSSR : -1.53218e-05
delta(WSSR) : -1.16463e-05 limit for stopping : 1e-05
lambda : 0.000800174
resultant parameter values
a = -0.500504
b = 0.50226
/
Iteration 4
WSSR : 0.760112 delta(WSSR)/WSSR : -7.81424e-14
delta(WSSR) : -5.93969e-14 limit for stopping : 1e-05
lambda : 8.00174e-05
resultant parameter values
a = -0.500504
b = 0.50226
After 4 iterations the fit converged.
final sum of squares of residuals : 0.760112
rel. change during last iteration : -7.81424e-14
degrees of freedom (FIT_NDF) : 4
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 0.435922
variance of residuals (reduced chisquare) = WSSR/ndf : 0.190028
Final set of parameters Asymptotic Standard Error
======================= ==========================
a = -0.500504 +/- 0.4333 (86.58%)
b = 0.50226 +/- 0.2295 (45.7%)
correlation matrix of the fit parameters:
a b
a 1.000
b -0.632 1.000
Iteration 0
WSSR : 9.042 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.80039
initial set of free parameter values
aa = 1
bb = 1
/
Iteration 1
WSSR : 1.04131 delta(WSSR)/WSSR : -7.68331
delta(WSSR) : -8.0007 limit for stopping : 1e-05
lambda : 0.080039
resultant parameter values
aa = 0.00287365
bb = 0.398135
/
Iteration 2
WSSR : 0.760548 delta(WSSR)/WSSR : -0.369155
delta(WSSR) : -0.28076 limit for stopping : 1e-05
lambda : 0.0080039
resultant parameter values
aa = -0.496029
bb = 0.50082
/
Iteration 3
WSSR : 0.760537 delta(WSSR)/WSSR : -1.52182e-05
delta(WSSR) : -1.1574e-05 limit for stopping : 1e-05
lambda : 0.00080039
resultant parameter values
aa = -0.499395
bb = 0.502057
/
Iteration 4
WSSR : 0.760537 delta(WSSR)/WSSR : -7.73688e-14
delta(WSSR) : -5.88418e-14 limit for stopping : 1e-05
lambda : 8.0039e-05
resultant parameter values
aa = -0.499395
bb = 0.502057
After 4 iterations the fit converged.
final sum of squares of residuals : 0.760537
rel. change during last iteration : -7.73688e-14
degrees of freedom (FIT_NDF) : 4
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 0.436044
variance of residuals (reduced chisquare) = WSSR/ndf : 0.190134
Final set of parameters Asymptotic Standard Error
======================= ==========================
aa = -0.499395 +/- 0.4329 (86.68%)
bb = 0.502057 +/- 0.2296 (45.72%)
correlation matrix of the fit parameters:
aa bb
aa 1.000
bb -0.631 1.000
Iteration 0
WSSR : 9.04152 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.80039
initial set of free parameter values
aaa = 1
bbb = 1
/
Iteration 1
WSSR : 1.04503 delta(WSSR)/WSSR : -7.65191
delta(WSSR) : -7.99648 limit for stopping : 1e-05
lambda : 0.080039
resultant parameter values
aaa = 0.00194603
bbb = 0.399071
/
Iteration 2
WSSR : 0.763548 delta(WSSR)/WSSR : -0.36865
delta(WSSR) : -0.281482 limit for stopping : 1e-05
lambda : 0.0080039
resultant parameter values
aaa = -0.497734
bbb = 0.502116
/
Iteration 3
WSSR : 0.763537 delta(WSSR)/WSSR : -1.52098e-05
delta(WSSR) : -1.16133e-05 limit for stopping : 1e-05
lambda : 0.00080039
resultant parameter values
aaa = -0.501106
bbb = 0.503355
/
Iteration 4
WSSR : 0.763537 delta(WSSR)/WSSR : -7.73556e-14
delta(WSSR) : -5.90639e-14 limit for stopping : 1e-05
lambda : 8.0039e-05
resultant parameter values
aaa = -0.501106
bbb = 0.503355
After 4 iterations the fit converged.
final sum of squares of residuals : 0.763537
rel. change during last iteration : -7.73556e-14
degrees of freedom (FIT_NDF) : 4
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 0.436903
variance of residuals (reduced chisquare) = WSSR/ndf : 0.190884
Final set of parameters Asymptotic Standard Error
======================= ==========================
aaa = -0.501106 +/- 0.4337 (86.55%)
bbb = 0.503355 +/- 0.23 (45.7%)
correlation matrix of the fit parameters:
aaa bbb
aaa 1.000
bbb -0.631 1.000
Iteration 0
WSSR : 9.04263 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.800411
initial set of free parameter values
aaaa = 1
bbbb = 1
/
Iteration 1
WSSR : 1.04513 delta(WSSR)/WSSR : -7.65212
delta(WSSR) : -7.99749 limit for stopping : 1e-05
lambda : 0.0800411
resultant parameter values
aaaa = 0.00204362
bbbb = 0.399044
/
Iteration 2
WSSR : 0.763709 delta(WSSR)/WSSR : -0.368499
delta(WSSR) : -0.281426 limit for stopping : 1e-05
lambda : 0.00800411
resultant parameter values
aaaa = -0.497607
bbbb = 0.50214
/
Iteration 3
WSSR : 0.763697 delta(WSSR)/WSSR : -1.52103e-05
delta(WSSR) : -1.16161e-05 limit for stopping : 1e-05
lambda : 0.000800411
resultant parameter values
aaaa = -0.50098
bbbb = 0.50338
/
Iteration 4
WSSR : 0.763697 delta(WSSR)/WSSR : -7.7194e-14
delta(WSSR) : -5.89528e-14 limit for stopping : 1e-05
lambda : 8.00411e-05
resultant parameter values
aaaa = -0.50098
bbbb = 0.50338
After 4 iterations the fit converged.
final sum of squares of residuals : 0.763697
rel. change during last iteration : -7.7194e-14
degrees of freedom (FIT_NDF) : 4
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 0.436949
variance of residuals (reduced chisquare) = WSSR/ndf : 0.190924
Final set of parameters Asymptotic Standard Error
======================= ==========================
aaaa = -0.50098 +/- 0.4338 (86.59%)
bbbb = 0.50338 +/- 0.2301 (45.71%)
correlation matrix of the fit parameters:
aaaa bbbb
aaaa 1.000
bbbb -0.632 1.000

16
dokumentation/evolution3d/20171025-evolution3D_10x10x10_noFit.gnuplot.script
View File

@ -2,25 +2,25 @@ set datafile separator ","
f(x)=a*x+b
fit f(x) "20171025-evolution3D_10x10x10_noFit.csv" every ::1 using 1:5 via a,b
set terminal png
set xlabel 'regularity'
set ylabel 'steps'
set xlabel 'Regularity'
set ylabel 'Number of iterations'
set output "20171025-evolution3D_10x10x10_noFit_regularity-vs-steps.png"
plot "20171025-evolution3D_10x10x10_noFit.csv" every ::1 using 1:5 title "data", f(x) title "lin. fit" lc rgb "black"
g(x)=aa*x+bb
fit g(x) "20171025-evolution3D_10x10x10_noFit.csv" every ::1 using 3:5 via aa,bb
set xlabel 'improvement potential'
set ylabel 'steps'
set xlabel 'Improvement potential'
set ylabel 'Number of iterations'
set output "20171025-evolution3D_10x10x10_noFit_improvement-vs-steps.png"
plot "20171025-evolution3D_10x10x10_noFit.csv" every ::1 using 3:5 title "data", g(x) title "lin. fit" lc rgb "black"
h(x)=aaa*x+bbb
fit h(x) "20171025-evolution3D_10x10x10_noFit.csv" every ::1 using 3:4 via aaa,bbb
set xlabel 'improvement potential'
set ylabel 'evolution error'
set xlabel 'Improvement potential'
set ylabel 'error given by fitness-function'
set output "20171025-evolution3D_10x10x10_noFit_improvement-vs-evo-error.png"
plot "20171025-evolution3D_10x10x10_noFit.csv" every ::1 using 3:4 title "data", h(x) title "lin. fit" lc rgb "black"
i(x)=aaaa*x+bbbb
fit i(x) "20171025-evolution3D_10x10x10_noFit.csv" every ::1 using 2:4 via aaaa,bbbb
set xlabel 'variability'
set ylabel 'evolution error'
set xlabel 'Variability'
set ylabel 'error given by fitness-function'
set output "20171025-evolution3D_10x10x10_noFit_variability-vs-evo-error.png"
plot "20171025-evolution3D_10x10x10_noFit.csv" every ::1 using 2:4 title "data", i(x) title "lin. fit" lc rgb "black"

10
dokumentation/evolution3d/20171025-evolution3D_10x10x10_noFit_100Times.gnuplot.fit.log Normal file
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@ -0,0 +1,10 @@
*******************************************************************************
Fri Oct 27 14:09:08 2017
FIT: data read from "20171025-evolution3D_10x10x10_noFit_100Times.csv" every ::1 using 1:5
format = x:z
BREAK: No data to fit

3
dokumentation/evolution3d/20171025-evolution3D_10x10x10_noFit_100Times.gnuplot.log Normal file
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@ -0,0 +1,3 @@
No data to fit
"20171025-evolution3D_10x10x10_noFit_100Times.gnuplot.script", line 3:

26
dokumentation/evolution3d/20171025-evolution3D_10x10x10_noFit_100Times.gnuplot.script Normal file
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@ -0,0 +1,26 @@
set datafile separator ","
f(x)=a*x+b
fit f(x) "20171025-evolution3D_10x10x10_noFit_100Times.csv" every ::1 using 1:5 via a,b
set terminal png
set xlabel 'Regularity'
set ylabel 'Number of iterations'
set output "20171025-evolution3D_10x10x10_noFit_100Times_regularity-vs-steps.png"
plot "20171025-evolution3D_10x10x10_noFit_100Times.csv" every ::1 using 1:5 title "data", f(x) title "lin. fit" lc rgb "black"
g(x)=aa*x+bb
fit g(x) "20171025-evolution3D_10x10x10_noFit_100Times.csv" every ::1 using 3:5 via aa,bb
set xlabel 'Improvement potential'
set ylabel 'Number of iterations'
set output "20171025-evolution3D_10x10x10_noFit_100Times_improvement-vs-steps.png"
plot "20171025-evolution3D_10x10x10_noFit_100Times.csv" every ::1 using 3:5 title "data", g(x) title "lin. fit" lc rgb "black"
h(x)=aaa*x+bbb
fit h(x) "20171025-evolution3D_10x10x10_noFit_100Times.csv" every ::1 using 3:4 via aaa,bbb
set xlabel 'Improvement potential'
set ylabel 'error given by fitness-function'
set output "20171025-evolution3D_10x10x10_noFit_100Times_improvement-vs-evo-error.png"
plot "20171025-evolution3D_10x10x10_noFit_100Times.csv" every ::1 using 3:4 title "data", h(x) title "lin. fit" lc rgb "black"
i(x)=aaaa*x+bbbb
fit i(x) "20171025-evolution3D_10x10x10_noFit_100Times.csv" every ::1 using 2:4 via aaaa,bbbb
set xlabel 'Variability'
set ylabel 'error given by fitness-function'
set output "20171025-evolution3D_10x10x10_noFit_100Times_variability-vs-evo-error.png"
plot "20171025-evolution3D_10x10x10_noFit_100Times.csv" every ::1 using 2:4 title "data", i(x) title "lin. fit" lc rgb "black"

8
dokumentation/evolution3d/4x4xX.gnuplot.fit.log
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@ -1,7 +1,7 @@
*******************************************************************************
Wed Oct 25 19:14:24 2017
Fri Oct 27 14:11:51 2017
FIT: data read from "4x4xX.csv" every ::1 using 1:5
@ -47,7 +47,7 @@ b -0.938 1.000
*******************************************************************************
Wed Oct 25 19:14:24 2017
Fri Oct 27 14:11:51 2017
FIT: data read from "4x4xX.csv" every ::1 using 3:5
@ -93,7 +93,7 @@ bb -0.999 1.000
*******************************************************************************
Wed Oct 25 19:14:24 2017
Fri Oct 27 14:11:51 2017
FIT: data read from "4x4xX.csv" every ::1 using 3:4
@ -139,7 +139,7 @@ bbb -0.999 1.000
*******************************************************************************
Wed Oct 25 19:14:24 2017
Fri Oct 27 14:11:51 2017
FIT: data read from "4x4xX.csv" every ::1 using 2:4

16
dokumentation/evolution3d/4x4xX.gnuplot.script
View File

@ -2,25 +2,25 @@ set datafile separator ","
f(x)=a*x+b
fit f(x) "4x4xX.csv" every ::1 using 1:5 via a,b
set terminal png
set xlabel 'regularity'
set ylabel 'steps'
set xlabel 'Regularity'
set ylabel 'Number of iterations'
set output "4x4xX_regularity-vs-steps.png"
plot "20170926_3dFit_4x4x4_100times.csv" every ::1 using 1:5 title "4x4x4" pt 2, "20171005_3dFit_4x4x5_100times.csv" every ::1 using 1:5 title "4x4x5" pt 2, "20171013_3dFit_4x4x7_100times.csv" every ::1 using 1:5 title "4x4x7" pt 2, f(x) title "lin. fit" lc rgb "black"
g(x)=aa*x+bb
fit g(x) "4x4xX.csv" every ::1 using 3:5 via aa,bb
set xlabel 'improvement potential'
set ylabel 'steps'
set xlabel 'Improvement potential'
set ylabel 'Number of iterations'
set output "4x4xX_improvement-vs-steps.png"
plot "20170926_3dFit_4x4x4_100times.csv" every ::1 using 3:5 title "4x4x4" pt 2, "20171005_3dFit_4x4x5_100times.csv" every ::1 using 3:5 title "4x4x5" pt 2, "20171013_3dFit_4x4x7_100times.csv" every ::1 using 3:5 title "4x4x7" pt 2, g(x) title "lin. fit" lc rgb "black"
h(x)=aaa*x+bbb
fit h(x) "4x4xX.csv" every ::1 using 3:4 via aaa,bbb
set xlabel 'improvement potential'
set ylabel 'evolution error'
set xlabel 'Improvement potential'
set ylabel 'Error given by fitness-function'
set output "4x4xX_improvement-vs-evo-error.png"
plot "20170926_3dFit_4x4x4_100times.csv" every ::1 using 3:4 title "4x4x4" pt 2, "20171005_3dFit_4x4x5_100times.csv" every ::1 using 3:4 title "4x4x5" pt 2, "20171013_3dFit_4x4x7_100times.csv" every ::1 using 3:4 title "4x4x7" pt 2, h(x) title "lin. fit" lc rgb "black"
i(x)=aaaa*x+bbbb
fit i(x) "4x4xX.csv" every ::1 using 2:4 via aaaa,bbbb
set xlabel 'variability'
set ylabel 'evolution error'
set xlabel 'Variability'
set ylabel 'Error given by fitness-function'
set output "4x4xX_variability-vs-evo-error.png"
plot "20170926_3dFit_4x4x4_100times.csv" every ::1 using 2:4 title "4x4x4" pt 2, "20171005_3dFit_4x4x5_100times.csv" every ::1 using 2:4 title "4x4x5" pt 2, "20171013_3dFit_4x4x7_100times.csv" every ::1 using 2:4 title "4x4x7" pt 2, i(x) title "lin. fit" lc rgb "black"

BIN
dokumentation/evolution3d/4x4xX_improvement-vs-evo-error.png
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8
dokumentation/evolution3d/Xx4x4.gnuplot.fit.log
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@ -1,7 +1,7 @@
*******************************************************************************
Wed Oct 25 19:14:30 2017
Fri Oct 27 14:12:05 2017
FIT: data read from "Xx4x4.csv" every ::1 using 1:5
@ -47,7 +47,7 @@ b -0.934 1.000
*******************************************************************************
Wed Oct 25 19:14:30 2017
Fri Oct 27 14:12:05 2017
FIT: data read from "Xx4x4.csv" every ::1 using 3:5
@ -93,7 +93,7 @@ bb -0.999 1.000
*******************************************************************************
Wed Oct 25 19:14:30 2017
Fri Oct 27 14:12:05 2017
FIT: data read from "Xx4x4.csv" every ::1 using 3:4
@ -139,7 +139,7 @@ bbb -0.999 1.000
*******************************************************************************
Wed Oct 25 19:14:30 2017
Fri Oct 27 14:12:05 2017
FIT: data read from "Xx4x4.csv" every ::1 using 2:4

16
dokumentation/evolution3d/Xx4x4.gnuplot.script
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@ -2,25 +2,25 @@ set datafile separator ","
f(x)=a*x+b
fit f(x) "Xx4x4.csv" every ::1 using 1:5 via a,b
set terminal png
set xlabel 'regularity'
set ylabel 'steps'
set xlabel 'Regularity'
set ylabel 'Number of iterations'
set output "Xx4x4_regularity-vs-steps.png"
plot "20170926_3dFit_4x4x4_100times.csv" every ::1 using 1:5 title "4x4x4" pt 2, "20171013_3dFit_5x4x4_100times.csv" every ::1 using 1:5 title "5x4x4" pt 2, "20171005_3dFit_7x4x4_100times.csv" every ::1 using 1:5 title "7x4x4" pt 2, f(x) title "lin. fit" lc rgb "black"
g(x)=aa*x+bb
fit g(x) "Xx4x4.csv" every ::1 using 3:5 via aa,bb
set xlabel 'improvement potential'
set ylabel 'steps'
set xlabel 'Improvement potential'
set ylabel 'Number of iterations'
set output "Xx4x4_improvement-vs-steps.png"
plot "20170926_3dFit_4x4x4_100times.csv" every ::1 using 3:5 title "4x4x4" pt 2, "20171013_3dFit_5x4x4_100times.csv" every ::1 using 3:5 title "5x4x4" pt 2, "20171005_3dFit_7x4x4_100times.csv" every ::1 using 3:5 title "7x4x4" pt 2, g(x) title "lin. fit" lc rgb "black"
h(x)=aaa*x+bbb
fit h(x) "Xx4x4.csv" every ::1 using 3:4 via aaa,bbb
set xlabel 'improvement potential'
set ylabel 'evolution error'
set xlabel 'Improvement potential'
set ylabel 'Error given by fitness-function'
set output "Xx4x4_improvement-vs-evo-error.png"
plot "20170926_3dFit_4x4x4_100times.csv" every ::1 using 3:4 title "4x4x4" pt 2, "20171013_3dFit_5x4x4_100times.csv" every ::1 using 3:4 title "5x4x4" pt 2, "20171005_3dFit_7x4x4_100times.csv" every ::1 using 3:4 title "7x4x4" pt 2, h(x) title "lin. fit" lc rgb "black"
i(x)=aaaa*x+bbbb
fit i(x) "Xx4x4.csv" every ::1 using 2:4 via aaaa,bbbb
set xlabel 'variability'
set ylabel 'evolution error'
set xlabel 'Variability'
set ylabel 'Error given by fitness-function'
set output "Xx4x4_variability-vs-evo-error.png"
plot "20170926_3dFit_4x4x4_100times.csv" every ::1 using 2:4 title "4x4x4" pt 2, "20171013_3dFit_5x4x4_100times.csv" every ::1 using 2:4 title "5x4x4" pt 2, "20171005_3dFit_7x4x4_100times.csv" every ::1 using 2:4 title "7x4x4" pt 2, i(x) title "lin. fit" lc rgb "black"

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8
dokumentation/evolution3d/YxYxY.gnuplot.fit.log
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@ -1,7 +1,7 @@
*******************************************************************************
Wed Oct 25 19:14:34 2017
Fri Oct 27 14:12:17 2017
FIT: data read from "YxYxY.csv" every ::1 using 1:5
@ -47,7 +47,7 @@ b -0.937 1.000
*******************************************************************************
Wed Oct 25 19:14:34 2017
Fri Oct 27 14:12:17 2017
FIT: data read from "YxYxY.csv" every ::1 using 3:5
@ -93,7 +93,7 @@ bb -0.994 1.000
*******************************************************************************
Wed Oct 25 19:14:34 2017
Fri Oct 27 14:12:17 2017
FIT: data read from "YxYxY.csv" every ::1 using 3:4
@ -139,7 +139,7 @@ bbb -0.994 1.000
*******************************************************************************
Wed Oct 25 19:14:34 2017
Fri Oct 27 14:12:17 2017
FIT: data read from "YxYxY.csv" every ::1 using 2:4

16
dokumentation/evolution3d/YxYxY.gnuplot.script
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@ -2,25 +2,25 @@ set datafile separator ","
f(x)=a*x+b
fit f(x) "YxYxY.csv" every ::1 using 1:5 via a,b
set terminal png
set xlabel 'regularity'
set ylabel 'steps'
set xlabel 'Regularity'
set ylabel 'Number of iterations'
set output "YxYxY_regularity-vs-steps.png"
plot "20170926_3dFit_4x4x4_100times.csv" every ::1 using 1:5 title "4x4x4" pt 2, "20170926_3dFit_5x5x5_100times.csv" every ::1 using 1:5 title "5x5x5" pt 2, "20171021-evolution3D_6x6_100Times.csv" every ::1 using 1:5 title "6x6x6" pt 2, f(x) title "lin. fit" lc rgb "black"
g(x)=aa*x+bb
fit g(x) "YxYxY.csv" every ::1 using 3:5 via aa,bb
set xlabel 'improvement potential'
set ylabel 'steps'
set xlabel 'Improvement potential'
set ylabel 'Number of iterations'
set output "YxYxY_improvement-vs-steps.png"
plot "20170926_3dFit_4x4x4_100times.csv" every ::1 using 3:5 title "4x4x4" pt 2, "20170926_3dFit_5x5x5_100times.csv" every ::1 using 3:5 title "5x5x5" pt 2, "20171021-evolution3D_6x6_100Times.csv" every ::1 using 3:5 title "6x6x6" pt 2, g(x) title "lin. fit" lc rgb "black"
h(x)=aaa*x+bbb
fit h(x) "YxYxY.csv" every ::1 using 3:4 via aaa,bbb
set xlabel 'improvement potential'
set ylabel 'evolution error'
set xlabel 'Improvement potential'
set ylabel 'Error given by fitness-function'
set output "YxYxY_improvement-vs-evo-error.png"
plot "20170926_3dFit_4x4x4_100times.csv" every ::1 using 3:4 title "4x4x4" pt 2, "20170926_3dFit_5x5x5_100times.csv" every ::1 using 3:4 title "5x5x5" pt 2, "20171021-evolution3D_6x6_100Times.csv" every ::1 using 3:4 title "6x6x6" pt 2, h(x) title "lin. fit" lc rgb "black"
i(x)=aaaa*x+bbbb
fit i(x) "YxYxY.csv" every ::1 using 2:4 via aaaa,bbbb
set xlabel 'variability'
set ylabel 'evolution error'
set xlabel 'Variability'
set ylabel 'Error given by fitness-function'
set output "YxYxY_variability-vs-evo-error.png"
plot "20170926_3dFit_4x4x4_100times.csv" every ::1 using 2:4 title "4x4x4" pt 2, "20170926_3dFit_5x5x5_100times.csv" every ::1 using 2:4 title "5x5x5" pt 2, "20171021-evolution3D_6x6_100Times.csv" every ::1 using 2:4 title "6x6x6" pt 2, i(x) title "lin. fit" lc rgb "black"

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8
dokumentation/evolution3d/all.gnuplot.fit.log
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@ -1,7 +1,7 @@
*******************************************************************************
Wed Oct 25 19:09:05 2017
Fri Oct 27 14:12:27 2017
FIT: data read from "all.csv" every ::1 using 1:5
@ -47,7 +47,7 @@ b -0.932 1.000
*******************************************************************************
Wed Oct 25 19:09:05 2017
Fri Oct 27 14:12:27 2017
FIT: data read from "all.csv" every ::1 using 3:5
@ -93,7 +93,7 @@ bb -0.995 1.000
*******************************************************************************
Wed Oct 25 19:09:05 2017
Fri Oct 27 14:12:27 2017
FIT: data read from "all.csv" every ::1 using 3:4
@ -139,7 +139,7 @@ bbb -0.995 1.000
*******************************************************************************
Wed Oct 25 19:09:05 2017
Fri Oct 27 14:12:27 2017
FIT: data read from "all.csv" every ::1 using 2:4

16
dokumentation/evolution3d/all.gnuplot.script
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@ -2,25 +2,25 @@ set datafile separator ","
f(x)=a*x+b
fit f(x) "all.csv" every ::1 using 1:5 via a,b
set terminal png
set xlabel 'regularity'
set ylabel 'steps'
set xlabel 'Regularity'
set ylabel 'Number of iterations'
set output "all_regularity-vs-steps.png"
plot "20170926_3dFit_4x4x4_100times.csv" every ::1 using 1:5 title "4x4x4" pt 2, "20171013_3dFit_5x4x4_100times.csv" every ::1 using 1:5 title "5x4x4" pt 2, "20171005_3dFit_7x4x4_100times.csv" every ::1 using 1:5 title "7x4x4" pt 2, "20171005_3dFit_4x4x5_100times.csv" every ::1 using 1:5 title "4x4x5" pt 2, "20171013_3dFit_4x4x7_100times.csv" every ::1 using 1:5 title "4x4x7" pt 2, "20170926_3dFit_5x5x5_100times.csv" every ::1 using 1:5 title "5x5x5" pt 2, "20171021-evolution3D_6x6_100Times.csv" every ::1 using 1:5 title "6x6x6" pt 2, f(x) title "lin. fit" lc rgb "black"
g(x)=aa*x+bb
fit g(x) "all.csv" every ::1 using 3:5 via aa,bb
set xlabel 'improvement potential'
set ylabel 'steps'
set xlabel 'Improvement potential'
set ylabel 'Number of iterations'
set output "all_improvement-vs-steps.png"
plot "20170926_3dFit_4x4x4_100times.csv" every ::1 using 3:5 title "4x4x4" pt 2, "20171013_3dFit_5x4x4_100times.csv" every ::1 using 3:5 title "5x4x4" pt 2, "20171005_3dFit_7x4x4_100times.csv" every ::1 using 3:5 title "7x4x4" pt 2, "20171005_3dFit_4x4x5_100times.csv" every ::1 using 3:5 title "4x4x5" pt 2, "20171013_3dFit_4x4x7_100times.csv" every ::1 using 3:5 title "4x4x7" pt 2, "20170926_3dFit_5x5x5_100times.csv" every ::1 using 3:5 title "5x5x5" pt 2, "20171021-evolution3D_6x6_100Times.csv" every ::1 using 3:5 title "6x6x6" pt 2, g(x) title "lin. fit" lc rgb "black"
h(x)=aaa*x+bbb
fit h(x) "all.csv" every ::1 using 3:4 via aaa,bbb
set xlabel 'improvement potential'
set ylabel 'evolution error'
set xlabel 'Improvement potential'
set ylabel 'Error given by fitness-function'
set output "all_improvement-vs-evo-error.png"
plot "20170926_3dFit_4x4x4_100times.csv" every ::1 using 3:4 title "4x4x4" pt 2, "20171013_3dFit_5x4x4_100times.csv" every ::1 using 3:4 title "5x4x4" pt 2, "20171005_3dFit_7x4x4_100times.csv" every ::1 using 3:4 title "7x4x4" pt 2, "20171005_3dFit_4x4x5_100times.csv" every ::1 using 3:4 title "4x4x5" pt 2, "20171013_3dFit_4x4x7_100times.csv" every ::1 using 3:4 title "4x4x7" pt 2, "20170926_3dFit_5x5x5_100times.csv" every ::1 using 3:4 title "5x5x5" pt 2, "20171021-evolution3D_6x6_100Times.csv" every ::1 using 3:4 title "6x6x6" pt 2, h(x) title "lin. fit" lc rgb "black"
i(x)=aaaa*x+bbbb
fit i(x) "all.csv" every ::1 using 2:4 via aaaa,bbbb
set xlabel 'variability'
set ylabel 'evolution error'
set xlabel 'Variability'
set ylabel 'Error given by fitness-function'
set output "all_variability-vs-evo-error.png"
plot "20170926_3dFit_4x4x4_100times.csv" every ::1 using 2:4 title "4x4x4" pt 2, "20171013_3dFit_5x4x4_100times.csv" every ::1 using 2:4 title "5x4x4" pt 2, "20171005_3dFit_7x4x4_100times.csv" every ::1 using 2:4 title "7x4x4" pt 2, "20171005_3dFit_4x4x5_100times.csv" every ::1 using 2:4 title "4x4x5" pt 2, "20171013_3dFit_4x4x7_100times.csv" every ::1 using 2:4 title "4x4x7" pt 2, "20170926_3dFit_5x5x5_100times.csv" every ::1 using 2:4 title "5x5x5" pt 2, "20171021-evolution3D_6x6_100Times.csv" every ::1 using 2:4 title "6x6x6" pt 2, i(x) title "lin. fit" lc rgb "black"

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dokumentation/evolution3d/all_regularity-vs-steps.png
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16
dokumentation/evolution3d/combine.sh
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@ -10,8 +10,8 @@ set datafile separator ","
f(x)=a*x+b
fit f(x) "$data" every ::1 using 1:5 via a,b
set terminal png
set xlabel 'regularity'
set ylabel 'steps'
set xlabel 'Regularity'
set ylabel 'Number of iterations'
set output "${png}_regularity-vs-steps.png"
plot \
"$2" every ::1 using 1:5 title "$3" pt 2, \
@ -20,8 +20,8 @@ plot \
f(x) title "lin. fit" lc rgb "black"
g(x)=aa*x+bb
fit g(x) "$data" every ::1 using 3:5 via aa,bb
set xlabel 'improvement potential'
set ylabel 'steps'
set xlabel 'Improvement potential'
set ylabel 'Number of iterations'
set output "${png}_improvement-vs-steps.png"
plot \
"$2" every ::1 using 3:5 title "$3" pt 2, \
@ -30,8 +30,8 @@ plot \
g(x) title "lin. fit" lc rgb "black"
h(x)=aaa*x+bbb
fit h(x) "$data" every ::1 using 3:4 via aaa,bbb
set xlabel 'improvement potential'
set ylabel 'evolution error'
set xlabel 'Improvement potential'
set ylabel 'Error given by fitness-function'
set output "${png}_improvement-vs-evo-error.png"
plot \
"$2" every ::1 using 3:4 title "$3" pt 2, \
@ -40,8 +40,8 @@ plot \
h(x) title "lin. fit" lc rgb "black"
i(x)=aaaa*x+bbbb
fit i(x) "$data" every ::1 using 2:4 via aaaa,bbbb
set xlabel 'variability'
set ylabel 'evolution error'
set xlabel 'Variability'
set ylabel 'Error given by fitness-function'
set output "${png}_variability-vs-evo-error.png"
plot \
"$2" every ::1 using 2:4 title "$3" pt 2, \

16
dokumentation/evolution3d/combine7.sh
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@ -10,8 +10,8 @@ set datafile separator ","
f(x)=a*x+b
fit f(x) "$data" every ::1 using 1:5 via a,b
set terminal png
set xlabel 'regularity'
set ylabel 'steps'
set xlabel 'Regularity'
set ylabel 'Number of iterations'
set output "${png}_regularity-vs-steps.png"
plot \
"$2" every ::1 using 1:5 title "$3" pt 2, \
@ -24,8 +24,8 @@ plot \
f(x) title "lin. fit" lc rgb "black"
g(x)=aa*x+bb
fit g(x) "$data" every ::1 using 3:5 via aa,bb
set xlabel 'improvement potential'
set ylabel 'steps'
set xlabel 'Improvement potential'
set ylabel 'Number of iterations'
set output "${png}_improvement-vs-steps.png"
plot \
"$2" every ::1 using 3:5 title "$3" pt 2, \
@ -38,8 +38,8 @@ plot \
g(x) title "lin. fit" lc rgb "black"
h(x)=aaa*x+bbb
fit h(x) "$data" every ::1 using 3:4 via aaa,bbb
set xlabel 'improvement potential'
set ylabel 'evolution error'
set xlabel 'Improvement potential'
set ylabel 'Error given by fitness-function'
set output "${png}_improvement-vs-evo-error.png"
plot \
"$2" every ::1 using 3:4 title "$3" pt 2, \
@ -52,8 +52,8 @@ plot \
h(x) title "lin. fit" lc rgb "black"
i(x)=aaaa*x+bbbb
fit i(x) "$data" every ::1 using 2:4 via aaaa,bbbb
set xlabel 'variability'
set ylabel 'evolution error'
set xlabel 'Variability'
set ylabel 'Error given by fitness-function'
set output "${png}_variability-vs-evo-error.png"
plot \
"$2" every ::1 using 2:4 title "$3" pt 2, \

184
dokumentation/evolution3d/errors.gnuplot.fit.log Normal file
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@ -0,0 +1,184 @@
*******************************************************************************
Fri Oct 27 14:09:08 2017
FIT: data read from "errors.csv" every ::1 using 1:5
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 : 129069 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 84.3477
initial set of free parameter values
a = 1
b = 1
After 6 iterations the fit converged.
final sum of squares of residuals : 4993.5
rel. change during last iteration : -5.46363e-11
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 7.13821
variance of residuals (reduced chisquare) = WSSR/ndf : 50.9541
Final set of parameters Asymptotic Standard Error
======================= ==========================
a = -0.0931363 +/- 0.1443 (154.9%)
b = 96.4721 +/- 17.21 (17.84%)
correlation matrix of the fit parameters:
a b
a 1.000
b -0.999 1.000
*******************************************************************************
Fri Oct 27 14:09:08 2017
FIT: data read from "errors.csv" every ::1 using 3:5
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 : 38697.6 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 71.7898
initial set of free parameter values
aa = 1
bb = 1
After 6 iterations the fit converged.
final sum of squares of residuals : 5010.73
rel. change during last iteration : -1.443e-13
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 7.15052
variance of residuals (reduced chisquare) = WSSR/ndf : 51.1299
Final set of parameters Asymptotic Standard Error
======================= ==========================
aa = 0.0270058 +/- 0.09648 (357.3%)
bb = 82.6379 +/- 9.795 (11.85%)
correlation matrix of the fit parameters:
aa bb
aa 1.000
bb -0.997 1.000
*******************************************************************************
Fri Oct 27 14:09:08 2017
FIT: data read from "errors.csv" every ::1 using 3:4
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 : 27023.7 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 71.7898
initial set of free parameter values
aaa = 1
bbb = 1
After 6 iterations the fit converged.
final sum of squares of residuals : 4159.2
rel. change during last iteration : -2.19108e-13
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 6.51466
variance of residuals (reduced chisquare) = WSSR/ndf : 42.4408
Final set of parameters Asymptotic Standard Error
======================= ==========================
aaa = -0.0345469 +/- 0.0879 (254.4%)
bbb = 92.7152 +/- 8.924 (9.625%)
correlation matrix of the fit parameters:
aaa bbb
aaa 1.000
bbb -0.997 1.000
*******************************************************************************
Fri Oct 27 14:09:08 2017
FIT: data read from "errors.csv" every ::1 using 2:4
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 : 30294.4 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 72.9129
initial set of free parameter values
aaaa = 1
bbbb = 1
After 6 iterations the fit converged.
final sum of squares of residuals : 4165.22
rel. change during last iteration : -6.01785e-13
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 6.51938
variance of residuals (reduced chisquare) = WSSR/ndf : 42.5023
Final set of parameters Asymptotic Standard Error
======================= ==========================
aaaa = -0.0109066 +/- 0.09721 (891.3%)
bbbb = 90.3395 +/- 10.02 (11.09%)
correlation matrix of the fit parameters:
aaaa bbbb
aaaa 1.000
bbbb -0.998 1.000

392
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Iteration 0
WSSR : 129069 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 84.3477
initial set of free parameter values
a = 1
b = 1
/
Iteration 1
WSSR : 6564.6 delta(WSSR)/WSSR : -18.6613
delta(WSSR) : -122504 limit for stopping : 1e-05
lambda : 8.43477
resultant parameter values
a = 0.708029
b = 0.999863
/
Iteration 2
WSSR : 6554.01 delta(WSSR)/WSSR : -0.00161544
delta(WSSR) : -10.5876 limit for stopping : 1e-05
lambda : 0.843477
resultant parameter values
a = 0.70464
b = 1.23013
/
Iteration 3
WSSR : 6005.48 delta(WSSR)/WSSR : -0.0913382
delta(WSSR) : -548.53 limit for stopping : 1e-05
lambda : 0.0843477
resultant parameter values
a = 0.549306
b = 19.7746
/
Iteration 4
WSSR : 4995.1 delta(WSSR)/WSSR : -0.202276
delta(WSSR) : -1010.39 limit for stopping : 1e-05
lambda : 0.00843477
resultant parameter values
a = -0.067621
b = 93.426
/
Iteration 5
WSSR : 4993.5 delta(WSSR)/WSSR : -0.000319669
delta(WSSR) : -1.59627 limit for stopping : 1e-05
lambda : 0.000843477
resultant parameter values
a = -0.0931258
b = 96.4708
/
Iteration 6
WSSR : 4993.5 delta(WSSR)/WSSR : -5.46363e-11
delta(WSSR) : -2.72827e-07 limit for stopping : 1e-05
lambda : 8.43477e-05
resultant parameter values
a = -0.0931363
b = 96.4721
After 6 iterations the fit converged.
final sum of squares of residuals : 4993.5
rel. change during last iteration : -5.46363e-11
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 7.13821
variance of residuals (reduced chisquare) = WSSR/ndf : 50.9541
Final set of parameters Asymptotic Standard Error
======================= ==========================
a = -0.0931363 +/- 0.1443 (154.9%)
b = 96.4721 +/- 17.21 (17.84%)
correlation matrix of the fit parameters:
a b
a 1.000
b -0.999 1.000
Iteration 0
WSSR : 38697.6 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 71.7898
initial set of free parameter values
aa = 1
bb = 1
/
Iteration 1
WSSR : 8562.61 delta(WSSR)/WSSR : -3.51936
delta(WSSR) : -30134.9 limit for stopping : 1e-05
lambda : 7.17898
resultant parameter values
aa = 0.829791
bb = 1.00677
/
Iteration 2
WSSR : 8489.56 delta(WSSR)/WSSR : -0.00860526
delta(WSSR) : -73.0548 limit for stopping : 1e-05
lambda : 0.717898
resultant parameter values
aa = 0.820734
bb = 1.84213
/
Iteration 3
WSSR : 5851.66 delta(WSSR)/WSSR : -0.450794
delta(WSSR) : -2637.9 limit for stopping : 1e-05
lambda : 0.0717898
resultant parameter values
aa = 0.41725
bb = 42.9138
/
Iteration 4
WSSR : 5010.8 delta(WSSR)/WSSR : -0.167809
delta(WSSR) : -840.86 limit for stopping : 1e-05
lambda : 0.00717898
resultant parameter values
aa = 0.030744
bb = 82.2573
/
Iteration 5
WSSR : 5010.73 delta(WSSR)/WSSR : -1.54001e-05
delta(WSSR) : -0.0771658 limit for stopping : 1e-05
lambda : 0.000717898
resultant parameter values
aa = 0.0270062
bb = 82.6378
/
Iteration 6
WSSR : 5010.73 delta(WSSR)/WSSR : -1.443e-13
delta(WSSR) : -7.23048e-10 limit for stopping : 1e-05
lambda : 7.17898e-05
resultant parameter values
aa = 0.0270058
bb = 82.6379
After 6 iterations the fit converged.
final sum of squares of residuals : 5010.73
rel. change during last iteration : -1.443e-13
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 7.15052
variance of residuals (reduced chisquare) = WSSR/ndf : 51.1299
Final set of parameters Asymptotic Standard Error
======================= ==========================
aa = 0.0270058 +/- 0.09648 (357.3%)
bb = 82.6379 +/- 9.795 (11.85%)
correlation matrix of the fit parameters:
aa bb
aa 1.000
bb -0.997 1.000
Iteration 0
WSSR : 27023.7 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 71.7898
initial set of free parameter values
aaa = 1
bbb = 1
/
Iteration 1
WSSR : 8641.55 delta(WSSR)/WSSR : -2.12719
delta(WSSR) : -18382.2 limit for stopping : 1e-05
lambda : 7.17898
resultant parameter values
aaa = 0.867036
bbb = 1.00818
/
Iteration 2
WSSR : 8549.83 delta(WSSR)/WSSR : -0.0107272
delta(WSSR) : -91.716 limit for stopping : 1e-05
lambda : 0.717898
resultant parameter values
aaa = 0.857153
bbb = 1.94665
/
Iteration 3
WSSR : 5220.55 delta(WSSR)/WSSR : -0.637727
delta(WSSR) : -3329.29 limit for stopping : 1e-05
lambda : 0.0717898
resultant parameter values
aaa = 0.403866
bbb = 48.0879
/
Iteration 4
WSSR : 4159.3 delta(WSSR)/WSSR : -0.255151
delta(WSSR) : -1061.25 limit for stopping : 1e-05
lambda : 0.00717898
resultant parameter values
aaa = -0.0303472
bbb = 92.2877
/
Iteration 5
WSSR : 4159.2 delta(WSSR)/WSSR : -2.34157e-05
delta(WSSR) : -0.0973908 limit for stopping : 1e-05
lambda : 0.000717898
resultant parameter values
aaa = -0.0345465
bbb = 92.7151
/
Iteration 6
WSSR : 4159.2 delta(WSSR)/WSSR : -2.19108e-13
delta(WSSR) : -9.11314e-10 limit for stopping : 1e-05
lambda : 7.17898e-05
resultant parameter values
aaa = -0.0345469
bbb = 92.7152
After 6 iterations the fit converged.
final sum of squares of residuals : 4159.2
rel. change during last iteration : -2.19108e-13
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 6.51466
variance of residuals (reduced chisquare) = WSSR/ndf : 42.4408
Final set of parameters Asymptotic Standard Error
======================= ==========================
aaa = -0.0345469 +/- 0.0879 (254.4%)
bbb = 92.7152 +/- 8.924 (9.625%)
correlation matrix of the fit parameters:
aaa bbb
aaa 1.000
bbb -0.997 1.000
Iteration 0
WSSR : 30294.4 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 72.9129
initial set of free parameter values
aaaa = 1
bbbb = 1
/
Iteration 1
WSSR : 7542.11 delta(WSSR)/WSSR : -3.0167
delta(WSSR) : -22752.3 limit for stopping : 1e-05
lambda : 7.29129
resultant parameter values
aaaa = 0.854383
bbbb = 1.0057
/
Iteration 2
WSSR : 7488.45 delta(WSSR)/WSSR : -0.00716584
delta(WSSR) : -53.661 limit for stopping : 1e-05
lambda : 0.729129
resultant parameter values
aaaa = 0.84683
bbbb = 1.71093
/
Iteration 3
WSSR : 5195.8 delta(WSSR)/WSSR : -0.44125
delta(WSSR) : -2292.65 limit for stopping : 1e-05
lambda : 0.0729129
resultant parameter values
aaaa = 0.466748
bbbb = 40.9842
/
Iteration 4
WSSR : 4165.38 delta(WSSR)/WSSR : -0.247377
delta(WSSR) : -1030.42 limit for stopping : 1e-05
lambda : 0.00729129
resultant parameter values
aaaa = -0.00497837
bbbb = 89.7269
/
Iteration 5
WSSR : 4165.22 delta(WSSR)/WSSR : -3.81126e-05
delta(WSSR) : -0.158748 limit for stopping : 1e-05
lambda : 0.000729129
resultant parameter values
aaaa = -0.0109059
bbbb = 90.3394
/
Iteration 6
WSSR : 4165.22 delta(WSSR)/WSSR : -6.01785e-13
delta(WSSR) : -2.50657e-09 limit for stopping : 1e-05
lambda : 7.29129e-05
resultant parameter values
aaaa = -0.0109066
bbbb = 90.3395
After 6 iterations the fit converged.
final sum of squares of residuals : 4165.22
rel. change during last iteration : -6.01785e-13
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 6.51938
variance of residuals (reduced chisquare) = WSSR/ndf : 42.5023
Final set of parameters Asymptotic Standard Error
======================= ==========================
aaaa = -0.0109066 +/- 0.09721 (891.3%)
bbbb = 90.3395 +/- 10.02 (11.09%)
correlation matrix of the fit parameters:
aaaa bbbb
aaaa 1.000
bbbb -0.998 1.000

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set datafile separator ","
f(x)=a*x+b
fit f(x) "errors.csv" every ::1 using 1:5 via a,b
set terminal png
set xlabel 'Regularity'
set ylabel 'Number of iterations'
set output "errors_regularity-vs-steps.png"
plot "errors.csv" every ::1 using 1:5 title "data", f(x) title "lin. fit" lc rgb "black"
g(x)=aa*x+bb
fit g(x) "errors.csv" every ::1 using 3:5 via aa,bb
set xlabel 'Improvement potential'
set ylabel 'Number of iterations'
set output "errors_improvement-vs-steps.png"
plot "errors.csv" every ::1 using 3:5 title "data", g(x) title "lin. fit" lc rgb "black"
h(x)=aaa*x+bbb
fit h(x) "errors.csv" every ::1 using 3:4 via aaa,bbb
set xlabel 'Improvement potential'
set ylabel 'error given by fitness-function'
set output "errors_improvement-vs-evo-error.png"
plot "errors.csv" every ::1 using 3:4 title "data", h(x) title "lin. fit" lc rgb "black"
i(x)=aaaa*x+bbbb
fit i(x) "errors.csv" every ::1 using 2:4 via aaaa,bbbb
set xlabel 'Variability'
set ylabel 'error given by fitness-function'
set output "errors_variability-vs-evo-error.png"
plot "errors.csv" every ::1 using 2:4 title "data", i(x) title "lin. fit" lc rgb "black"

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16
dokumentation/evolution3d/images.sh
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@ -10,26 +10,26 @@ set datafile separator ","
f(x)=a*x+b
fit f(x) "$data" every ::1 using 1:5 via a,b
set terminal png
set xlabel 'regularity'
set ylabel 'steps'
set xlabel 'Regularity'
set ylabel 'Number of iterations'
set output "${png}_regularity-vs-steps.png"
plot "$data" every ::1 using 1:5 title "data", f(x) title "lin. fit" lc rgb "black"
g(x)=aa*x+bb
fit g(x) "$data" every ::1 using 3:5 via aa,bb
set xlabel 'improvement potential'
set ylabel 'steps'
set xlabel 'Improvement potential'
set ylabel 'Number of iterations'
set output "${png}_improvement-vs-steps.png"
plot "$data" every ::1 using 3:5 title "data", g(x) title "lin. fit" lc rgb "black"
h(x)=aaa*x+bbb
fit h(x) "$data" every ::1 using 3:4 via aaa,bbb
set xlabel 'improvement potential'
set ylabel 'evolution error'
set xlabel 'Improvement potential'
set ylabel 'error given by fitness-function'
set output "${png}_improvement-vs-evo-error.png"
plot "$data" every ::1 using 3:4 title "data", h(x) title "lin. fit" lc rgb "black"
i(x)=aaaa*x+bbbb
fit i(x) "$data" every ::1 using 2:4 via aaaa,bbbb
set xlabel 'variability'
set ylabel 'evolution error'
set xlabel 'Variability'
set ylabel 'error given by fitness-function'
set output "${png}_variability-vs-evo-error.png"
plot \
"$data" every ::1 using 2:4 title "data", \

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