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34
arbeit/bibma.bib
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@ -202,3 +202,37 @@
url = {http://graphics.uni-bielefeld.de/publications/disclaimer.php?dlurl=vmv15.pdf}, url = {http://graphics.uni-bielefeld.de/publications/disclaimer.php?dlurl=vmv15.pdf},
ISBN = {978-3-905674-95-8}, ISBN = {978-3-905674-95-8},
} }
@article{hauke2011comparison,
title={Comparison of values of Pearson's and Spearman's correlation coefficients on the same sets of data},
author={Hauke, Jan and Kossowski, Tomasz},
journal={Quaestiones geographicae},
volume={30},
number={2},
pages={87},
year={2011},
publisher={De Gruyter Open Sp. z oo},
url={https://www.degruyter.com/downloadpdf/j/quageo.2011.30.issue-2/v10117-011-0021-1/v10117-011-0021-1.pdf},
}
@article{weir2015spearman,
title={Spearman’s correlation},
author={Weir, I},
journal={Retrieved from statstutor},
year={2015},
url={http://www.statstutor.ac.uk/resources/uploaded/spearmans.pdf},
}
@Article{shark08,
author = {Christian Igel and Verena Heidrich-Meisner and Tobias Glasmachers},
title = {Shark},
journal = {Journal of Machine Learning Research},
year = {2008},
volume = {9},
pages = {993-996},
url={http://image.diku.dk/shark/index.html},
}
@article{hansen2016cma,
title={The CMA evolution strategy: A tutorial},
author={Hansen, Nikolaus},
journal={arXiv preprint arXiv:1604.00772},
year={2016},
url={https://arxiv.org/abs/1604.00772}
}

155
arbeit/ma.md
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@ -660,6 +660,7 @@ can compute the analytic solution $\vec{p^{*}} = \vec{U^+}\vec{t}$, yielding us
the correct gradient in which the evolutionary optimizer should move. the correct gradient in which the evolutionary optimizer should move.
## Procedure: 1D Function Approximation ## Procedure: 1D Function Approximation
\label{sec:proc:1d}
For our setup we first compute the coefficients of the deformation--matrix and For our setup we first compute the coefficients of the deformation--matrix and
use then the formulas for *variability* and *regularity* to get our predictions. use then the formulas for *variability* and *regularity* to get our predictions.
@ -696,6 +697,7 @@ dimension and shrink the distance to the neighbours (the smaller neighbour for
$r < 0$, the larger for $r > 0$) by the factor $r$^[Note: On the Edges this $r < 0$, the larger for $r > 0$) by the factor $r$^[Note: On the Edges this
displacement is only applied outwards by flipping the sign of $r$, if displacement is only applied outwards by flipping the sign of $r$, if
appropriate.]. appropriate.].
\improvement[inline]{update!! gaussian, not uniform!!}
An Example of such a testcase can be seen for a $7 \times 4$--grid in figure An Example of such a testcase can be seen for a $7 \times 4$--grid in figure
\ref{fig:example1d_grid}. \ref{fig:example1d_grid}.
@ -806,20 +808,148 @@ control-points.
# Evaluation of Scenarios # Evaluation of Scenarios
\label{sec:res} \label{sec:res}
## Spearman/Pearson--Metriken To compare our results to the ones given by Richter et al.\cite{anrichterEvol},
we also use Spearman's rank correlation coefficient. Opposed to other popular
coefficients, like the Pearson correlation coefficient, which measures a linear
relationship between variables, the Spearmans's coefficient assesses \glqq how
well an arbitrary monotonic function can descripbe the relationship between two
variables, without making any assumptions about the frequency distribution of
the variables\grqq\cite{hauke2011comparison}.
- Was ist das? As we don't have any prior knowledge if any of the criteria is linear and we are
- Wieso sollte uns das interessieren? just interested in a monotonic relation between the criteria and their
- Wieso reicht Monotonie? predictive power, the Spearman's coefficient seems to fit out scenario best.
- Haben wir das gezeigt?
- Statistik, Bilder, blah! For interpretation of these values we follow the same interpretation used in
\cite{anrichterEvol}, based on \cite{weir2015spearman}: The coefficient
intervals $r_S \in [0,0.2[$, $[0.2,0.4[$, $[0.4,0.6[$, $[0.6,0.8[$, and $[0.8,1]$ are
classified as *very weak*, *weak*, *moderate*, *strong* and *very strong*. We
interpret p--values smaller than $0.1$ as *significant* and cut off the
precision of p--values after four decimal digits (thus often having a p--value
of $0$ given for p--values $< 10^{-4}$).
As we are looking for anti--correlation (i.e. our criterion should be maximized
indicating a minimal result in --- for example --- the reconstruction--error)
instead of correlation we flip the sign of the correlation--coefficient for
readability and to have the correlation--coefficients be in the
classification--range given above.
For the evolutionary optimization we employ the CMA--ES (covariance matrix
adaptation evolution strategy) of the shark3.1 library \cite{shark08}, as this
algorithm was used by \cite{anrichterEvol} as well. We leave the parameters at
their sensible defaults as further explained in
\cite[Appendix~A: Table~1]{hansen2016cma}.
## Results of 1D Function Approximation ## Results of 1D Function Approximation
\begin{figure}[!ht] In the case of our 1D--Optimization--problem, we have the luxury of knowing the
\includegraphics[width=\textwidth]{img/evolution1d/20171005-all_appended.png} analytical solution to the given problem--set. We use this to experimentally
\caption{Results 1D} evaluate the quality criteria we introduced before. As an evolutional
optimization is partially a random process, we use the analytical solution as a
stopping-criteria. We measure the convergence speed as number of iterations the
evolutional algorithm needed to get within $1.05\%$ of the optimal solution.
We used different regular grids that we manipulated as explained in Section
\ref{sec:proc:1d} with a different number of control points. As our grids have
to be the product of two integers, we compared a $5 \times 5$--grid with $25$
control--points to a $4 \times 7$ and $7 \times 4$--grid with $28$
control--points. This was done to measure the impact an \glqq improper\grqq
setup could have and how well this is displayed in the criteria we are
examining.
Additionally we also measured the effect of increasing the total resolution of
the grid by taking a closer look at $5 \times 5$, $7 \times 7$ and $10 \times 10$ grids.
\begin{figure}[ht]
\centering
\includegraphics[width=0.7\textwidth]{img/evolution1d/variability_boxplot.png}
\caption[1D Fitting Errors for various grids]{The squared error for the various
grids we examined.\newline
Note that $7 \times 4$ and $4 \times 7$ have the same number of control--points.}
\label{fig:1dvar}
\end{figure}
### Variability
Variability should characterize the potential for design space exploration and
is defined in terms of the normalized rank of the deformation matrix $\vec{U}$:
$V(\vec{U}) := \frac{\textrm{rank}(\vec{U})}{n}$, whereby $n$ is the number of
vertices.
As all our tested matrices had a constant rank (being $m = x \cdot y$ for a $x \times y$
grid), we have merely plotted the errors in the boxplot in figure
\ref{fig:1dvar}
It is also noticeable, that although the $7 \times 4$ and $4 \times 7$ grids
have a higher variability, they perform not better than the $5 \times 5$ grid.
Also the $7 \times 4$ and $4 \times 7$ grids differ distinctly from each other,
although they have the same number of control--points. This is an indication the
impact a proper or improper grid--setup can have. We do not draw scientific
conclusions from these findings, as more research on non-squared grids seem
necessary.\todo{machen wir die noch? :D}
Leaving the issue of the grid--layout aside we focused on grids having the same
number of prototypes in every dimension. For the $5 \times 5$, $7 \times 7$ and
$10 \times 10$ grids we found a *very strong* correlation ($-r_S = 0.94, p = 0$)
between the variability and the evolutionary error.
### Regularity
\begin{table}[bht]
\centering
\begin{tabular}{c|c|c|c|c}
$5 \times 5$ & $7 \times 4$ & $4 \times 7$ & $7 \times 7$ & $10 \times 10$\\
\hline
$0.28$ ($0.0045$) & \textcolor{red}{$0.21$} ($0.0396$) & \textcolor{red}{$0.1$} ($0.3019$) & \textcolor{red}{$0.01$} ($0.9216$) & \textcolor{red}{$0.01$} ($0.9185$)
\end{tabular}
\caption[Correlation 1D Regularity/Steps]{Spearman's correlation (and p-values)
between regularity and convergence speed for the 1D function approximation
problem.\newline
Not significant entries are marked in red.
}
\label{tab:1dreg}
\end{table}
\begin{figure}[ht]
\centering
\includegraphics[width=\textwidth]{img/evolution1d/55_to_1010_steps.png}
\caption[Improvement potential and regularity vs. steps]{\newline
Left: Improvement potential against steps until convergence\newline
Right: Regularity against steps until convergence\newline
Coloured by their grid--resolution, both with a linear fit over the whole
dataset.}
\label{fig:1dreg}
\end{figure}
Regularity should correspond to the convergence speed (measured in
iteration--steps of the evolutionary algorithm), and is computed as inverse
condition number $\kappa(\vec{U})$ of the deformation--matrix.
As can be seen from table \ref{tab:1dreg}, we could only show a *weak* correlation
in the case of a $5 \times 5$ grid. As we increment the number of
control--points the correlation gets worse until it is completely random in a
single dataset. Taking all presented datasets into account we even get a *strong*
correlation of $- r_S = -0.72, p = 0$, that is opposed to our expectations.
To explain this discrepancy we took a closer look at what caused these high number
of iterations. In figure \ref{fig:1dreg} we also plotted the
improvement-potential against the steps next to the regularity--plot. Our theory
is that the *very strong* correlation ($-r_S = -0.82, p=0$) between
improvement--potential and number of iterations hints that the employed
algorithm simply takes longer to converge on a better solution (as seen in
figure \ref{fig:1dvar} and \ref{fig:1dimp}) offsetting any gain the regularity--measurement could
achieve.
### Improvement Potential
- Alle Spearman 1 und p-value 0.
\begin{figure}[ht]
\centering
\includegraphics[width=0.8\textwidth]{img/evolution1d/55_to_1010_improvement-vs-evo-error.png}
\caption[Correlation 1D Improvement vs. Error]{Improvement potential plotted
against the error yielded by the evolutionary optimization for different
grid--resolutions}
\label{fig:1dimp}
\end{figure} \end{figure}
<!-- ![Improvement potential vs steps](img/evolution1d/20170830-evolution1D_5x5_100Times-all_improvement-vs-steps.png) --> <!-- ![Improvement potential vs steps](img/evolution1d/20170830-evolution1D_5x5_100Times-all_improvement-vs-steps.png) -->
@ -841,6 +971,11 @@ control-points.
\caption{Results 3D for Xx4x4} \caption{Results 3D for Xx4x4}
\end{figure} \end{figure}
\begin{figure}[!ht]
\includegraphics[width=\textwidth]{img/evolution3d/YxYxY_montage.png}
\caption{Results 3D for YxYxY for Y $\in [4,5,6]$}
\end{figure}
<!-- ![Improvement potential vs steps](img/evolution3d/20170926_3dFit_both_improvement-vs-steps.png) --> <!-- ![Improvement potential vs steps](img/evolution3d/20170926_3dFit_both_improvement-vs-steps.png) -->
<!-- --> <!-- -->
<!-- ![Improvement potential vs evolutional --> <!-- ![Improvement potential vs evolutional -->
@ -851,7 +986,7 @@ control-points.
# Schluss # Schluss
\label{sec:dis} \label{sec:dis}
HAHA .. als ob -.- - Regularity ist kacke für unser setup. Bessere Vorschläge? EW/EV?
\improvement[inline]{Bibliotheksverzeichnis links anpassen. DOI überschreibt \improvement[inline]{Bibliotheksverzeichnis links anpassen. DOI überschreibt
Direktlinks des Autors.} Direktlinks des Autors.}

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@ -3,7 +3,7 @@
\documentclass[ \documentclass[
a4paper, % default a4paper, % default
12pt, % default = 11pt 12pt, % default = 11pt
BCOR6mm, % Bindungskorrektur bei Klebebindung 6mm, bei Lochen BCOR8.25mm BCOR10mm, % Bindungskorrektur bei Klebebindung 6mm, bei Lochen BCOR8.25mm
twoside, % default, 2seitig twoside, % default, 2seitig
titlepage, titlepage,
% pagesize=auto % pagesize=auto
@ -31,10 +31,10 @@ xcolor=dvipsnames,
%%%%%%%%%%%%%%% Globale Einstellungen %%%%%%%%%%%%%%% %%%%%%%%%%%%%%% Globale Einstellungen %%%%%%%%%%%%%%%
\input{settings/commands} \input{settings/commands}
\input{settings/environments} \input{settings/environments}
%\setlength{\parindent}{0pt} % kein einzug bei absaetzen \setlength{\parindent}{0pt} % kein einzug bei absaetzen
%\setlength{\lineskip}{1ex plus0.5ex minus0.5ex} % dafr abstand zwischen abs<62>zen (funktioniert noch nicht) \setlength{\parskip}{12pt plus6pt minus2pt} % dafür abstand zwischen absäzen
% \renewcommand{\familydefault}{\sfdefault} % \renewcommand{\familydefault}{\sfdefault}
\setstretch{1.44} % 1.5-facher zeilenabstand \setstretch{1.5} % 1.5-facher zeilenabstand
%%%%%%%%%%%%%%% Header - Footer %%%%%%%%%%%%%%% %%%%%%%%%%%%%%% Header - Footer %%%%%%%%%%%%%%%
% ### Fr 2 Seitig (option twopage): % ### Fr 2 Seitig (option twopage):
@ -850,6 +850,8 @@ should move.
\section{Procedure: 1D Function \section{Procedure: 1D Function
Approximation}\label{procedure-1d-function-approximation} Approximation}\label{procedure-1d-function-approximation}
\label{sec:proc:1d}
For our setup we first compute the coefficients of the For our setup we first compute the coefficients of the
deformation--matrix and use then the formulas for \emph{variability} and deformation--matrix and use then the formulas for \emph{variability} and
\emph{regularity} to get our predictions. Afterwards we solve the \emph{regularity} to get our predictions. Afterwards we solve the
@ -886,6 +888,7 @@ neighbours (the smaller neighbour for \(r < 0\), the larger for
\(r > 0\)) by the factor \(r\)\footnote{Note: On the Edges this \(r > 0\)) by the factor \(r\)\footnote{Note: On the Edges this
displacement is only applied outwards by flipping the sign of \(r\), displacement is only applied outwards by flipping the sign of \(r\),
if appropriate.}. if appropriate.}.
\improvement[inline]{update!! gaussian, not uniform!!}
An Example of such a testcase can be seen for a \(7 \times 4\)--grid in An Example of such a testcase can be seen for a \(7 \times 4\)--grid in
figure \ref{fig:example1d_grid}. figure \ref{fig:example1d_grid}.
@ -1004,29 +1007,162 @@ predict a suboptimal placement of these control-points.
\label{sec:res} \label{sec:res}
\section{Spearman/Pearson--Metriken}\label{spearmanpearsonmetriken} To compare our results to the ones given by Richter et
al.\cite{anrichterEvol}, we also use Spearman's rank correlation
coefficient. Opposed to other popular coefficients, like the Pearson
correlation coefficient, which measures a linear relationship between
variables, the Spearmans's coefficient assesses \glqq how well an
arbitrary monotonic function can descripbe the relationship between two
variables, without making any assumptions about the frequency
distribution of the variables\grqq\cite{hauke2011comparison}.
\begin{itemize} As we don't have any prior knowledge if any of the criteria is linear
\tightlist and we are just interested in a monotonic relation between the criteria
\item and their predictive power, the Spearman's coefficient seems to fit out
Was ist das? scenario best.
\item
Wieso sollte uns das interessieren? For interpretation of these values we follow the same interpretation
\item used in \cite{anrichterEvol}, based on \cite{weir2015spearman}: The
Wieso reicht Monotonie? coefficient intervals \(r_S \in [0,0.2[\), \([0.2,0.4[\), \([0.4,0.6[\),
\item \([0.6,0.8[\), and \([0.8,1]\) are classified as \emph{very weak},
Haben wir das gezeigt? \emph{weak}, \emph{moderate}, \emph{strong} and \emph{very strong}. We
\item interpret p--values smaller than \(0.1\) as \emph{significant} and cut
Statistik, Bilder, blah! off the precision of p--values after four decimal digits (thus often
\end{itemize} having a p--value of \(0\) given for p--values \(< 10^{-4}\)).
As we are looking for anti--correlation (i.e.~our criterion should be
maximized indicating a minimal result in --- for example --- the
reconstruction--error) instead of correlation we flip the sign of the
correlation--coefficient for readability and to have the
correlation--coefficients be in the classification--range given above.
For the evolutionary optimization we employ the CMA--ES (covariance
matrix adaptation evolution strategy) of the shark3.1 library
\cite{shark08}, as this algorithm was used by \cite{anrichterEvol} as
well. We leave the parameters at their sensible defaults as further
explained in \cite[Appendix~A: Table~1]{hansen2016cma}.
\section{Results of 1D Function \section{Results of 1D Function
Approximation}\label{results-of-1d-function-approximation} Approximation}\label{results-of-1d-function-approximation}
\begin{figure}[!ht] In the case of our 1D--Optimization--problem, we have the luxury of
\includegraphics[width=\textwidth]{img/evolution1d/20171005-all_appended.png} knowing the analytical solution to the given problem--set. We use this
\caption{Results 1D} to experimentally evaluate the quality criteria we introduced before. As
an evolutional optimization is partially a random process, we use the
analytical solution as a stopping-criteria. We measure the convergence
speed as number of iterations the evolutional algorithm needed to get
within \(1.05\%\) of the optimal solution.
We used different regular grids that we manipulated as explained in
Section \ref{sec:proc:1d} with a different number of control points. As
our grids have to be the product of two integers, we compared a
\(5 \times 5\)--grid with \(25\) control--points to a \(4 \times 7\) and
\(7 \times 4\)--grid with \(28\) control--points. This was done to
measure the impact an \glqq improper\grqq
setup could have and how well this is displayed in the criteria we are
examining.
Additionally we also measured the effect of increasing the total
resolution of the grid by taking a closer look at \(5 \times 5\),
\(7 \times 7\) and \(10 \times 10\) grids.
\begin{figure}[ht]
\centering
\includegraphics[width=0.7\textwidth]{img/evolution1d/variability_boxplot.png}
\caption[1D Fitting Errors for various grids]{The squared error for the various
grids we examined.\newline
Note that $7 \times 4$ and $4 \times 7$ have the same number of control--points.}
\label{fig:1dfiterr}
\end{figure}
\subsection{Variability}\label{variability-1}
Variability should characterize the potential for design space
exploration and is defined in terms of the normalized rank of the
deformation matrix \(\vec{U}\):
\(V(\vec{U}) := \frac{\textrm{rank}(\vec{U})}{n}\), whereby \(n\) is the
number of vertices. As all our tested matrices had a constant rank
(being \(m = x \cdot y\) for a \(x \times y\) grid), we have merely
plotted the errors in the boxplot in figure \ref{fig:1dfiterr}
It is also noticeable, that although the \(7 \times 4\) and
\(4 \times 7\) grids have a higher variability, they perform not better
than the \(5 \times 5\) grid. Also the \(7 \times 4\) and \(4 \times 7\)
grids differ distinctly from each other, although they have the same
number of control--points. This is an indication the impact a proper or
improper grid--setup can have. We do not draw scientific conclusions
from these findings, as more research on non-squared grids seem
necessary.\todo{machen wir die noch? :D}
Leaving the issue of the grid--layout aside we focused on grids having
the same number of prototypes in every dimension. For the
\(5 \times 5\), \(7 \times 7\) and \(10 \times 10\) grids we found a
\emph{very strong} correlation (\(-r_S = 0.94, p = 0\)) between the
variability and the evolutionary error.
\subsection{Regularity}\label{regularity-1}
\begin{table}[bht]
\centering
\begin{tabular}{c|c|c|c|c}
$5 \times 5$ & $7 \times 4$ & $4 \times 7$ & $7 \times 7$ & $10 \times 10$\\
\hline
$0.28$ ($0.0045$) & \textcolor{red}{$0.21$} ($0.0396$) & \textcolor{red}{$0.1$} ($0.3019$) & \textcolor{red}{$0.01$} ($0.9216$) & \textcolor{red}{$0.01$} ($0.9185$)
\end{tabular}
\caption[Correlation 1D Regularity/Steps]{Spearman's correlation (and p-values)
between regularity and convergence speed for the 1D function approximation
problem.\newline
Not significant entries are marked in red.
}
\label{tab:1dreg}
\end{table}
\begin{figure}[ht]
\centering
\includegraphics[width=\textwidth]{img/evolution1d/55_to_1010_steps.png}
\caption[Improvement potential and regularity vs. steps]{\newline
Left: Improvement potential against steps until convergence\newline
Right: Regularity against steps until convergence\newline
Coloured by their grid--resolution, both with a linear fit over the whole
dataset.}
\label{fig:1dreg}
\end{figure}
Regularity should correspond to the convergence speed (measured in
iteration--steps of the evolutionary algorithm), and is computed as
inverse condition number \(\kappa(\vec{U})\) of the deformation--matrix.
As can be seen from table \ref{tab:1dreg}, we could only show a
\emph{weak} correlation in the case of a \(5 \times 5\) grid. As we
increment the number of control--points the correlation gets worse until
it is completely random in a single dataset. Taking all presented
datasets into account we even get a \emph{strong} correlation of
\(- r_S = -0.72, p = 0\), that is opposed to our expectations.
To explain this discrepancy we took a closer look at what caused these
high number of iterations. In figure \ref{fig:1dreg} we also plotted the
improvement-potential against the steps next to the regularity--plot.
Our theory is that the \emph{very strong} correlation
(\(-r_S = -0.82, p=0\)) between improvement--potential and number of
iterations hints that the employed algorithm simply takes longer to
converge on a better solution (as seen in figure \ref{fig:1dimp})
offsetting any gain the regularity--measurement could achieve.
\subsection{Improvement Potential}\label{improvement-potential-1}
\begin{itemize}
\tightlist
\item
Alle Spearman 1 und p-value 0.
\end{itemize}
\begin{figure}[ht]
\centering
\includegraphics[width=0.8\textwidth]{img/evolution1d/55_to_1010_improvement-vs-evo-error.png}
\caption[Correlation 1D Improvement vs. Error]{Improvement potential plotted
against the error yielded by the evolutionary optimization for different
grid--resolutions}
\label{fig:1dimp}
\end{figure} \end{figure}
\section{Results of 3D Function \section{Results of 3D Function
@ -1042,11 +1178,20 @@ Approximation}\label{results-of-3d-function-approximation}
\caption{Results 3D for Xx4x4} \caption{Results 3D for Xx4x4}
\end{figure} \end{figure}
\begin{figure}[!ht]
\includegraphics[width=\textwidth]{img/evolution3d/YxYxY_montage.png}
\caption{Results 3D for YxYxY for Y $\in [4,5,6]$}
\end{figure}
\chapter{Schluss}\label{schluss} \chapter{Schluss}\label{schluss}
\label{sec:dis} \label{sec:dis}
HAHA .. als ob -.- \begin{itemize}
\tightlist
\item
Regularity ist kacke für unser setup. Bessere Vorschläge? EW/EV?
\end{itemize}
\improvement[inline]{Bibliotheksverzeichnis links anpassen. DOI überschreibt \improvement[inline]{Bibliotheksverzeichnis links anpassen. DOI überschreibt
Direktlinks des Autors.} Direktlinks des Autors.}

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@ -3,7 +3,7 @@
\documentclass[ \documentclass[
a4paper, % default a4paper, % default
$if(fontsize)$$fontsize$,$endif$ % default = 11pt $if(fontsize)$$fontsize$,$endif$ % default = 11pt
BCOR6mm, % Bindungskorrektur bei Klebebindung 6mm, bei Lochen BCOR8.25mm BCOR10mm, % Bindungskorrektur bei Klebebindung 6mm, bei Lochen BCOR8.25mm
twoside, % default, 2seitig twoside, % default, 2seitig
titlepage, titlepage,
% pagesize=auto % pagesize=auto
@ -31,10 +31,10 @@ xcolor=dvipsnames,
%%%%%%%%%%%%%%% Globale Einstellungen %%%%%%%%%%%%%%% %%%%%%%%%%%%%%% Globale Einstellungen %%%%%%%%%%%%%%%
\input{settings/commands} \input{settings/commands}
\input{settings/environments} \input{settings/environments}
%\setlength{\parindent}{0pt} % kein einzug bei absaetzen \setlength{\parindent}{0pt} % kein einzug bei absaetzen
%\setlength{\lineskip}{1ex plus0.5ex minus0.5ex} % dafr abstand zwischen abs<62>zen (funktioniert noch nicht) \setlength{\parskip}{12pt plus6pt minus2pt} % dafür abstand zwischen absäzen
% \renewcommand{\familydefault}{\sfdefault} % \renewcommand{\familydefault}{\sfdefault}
\setstretch{1.44} % 1.5-facher zeilenabstand \setstretch{1.5} % 1.5-facher zeilenabstand
%%%%%%%%%%%%%%% Header - Footer %%%%%%%%%%%%%%% %%%%%%%%%%%%%%% Header - Footer %%%%%%%%%%%%%%%
% ### Fr 2 Seitig (option twopage): % ### Fr 2 Seitig (option twopage):

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dokumentation/evolution1d/20170830-evolution1D_5x5_100Times-added_one.error Normal file
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@ -0,0 +1,101 @@
"Evolution error"
207.886
245.873
209.253
236.693
237.512
217.347
206.725
218.216
208.831
199.805
195.163
193.258
181.901
183.091
215.233
210.992
226.197
230.55
201.997
227.649
192.577
221.454
236.59
224.637
201.263
218.685
256.401
228.137
203.421
228.677
239.173
203.783
243.217
204.188
211.535
229.573
225.773
235.748
208.659
220.83
191.357
224.938
216.195
218.868
230.63
186.89
218.199
217.047
223.644
213.801
205.631
210.824
230.178
257.369
243.262
229.047
221.493
177.905
241.468
243.443
233.782
205.347
268.384
230.853
226.312
209.55
233.426
210.991
219.415
260.926
229.786
194.888
193.57
211.086
237.989
217.102
194.775
244.384
211.814
212.073
187.619
205.625
210.781
191.55
178.258
194.329
212.217
200.944
227.453
264.972
202.656
201.39
236.882
214.712
194.569
195.513
262.158
251.577
193.849
224.14

6
dokumentation/evolution1d/20170830-evolution1D_5x5_100Times-added_one.gnuplot.fit.log
View File

@ -1,7 +1,7 @@
******************************************************************************* *******************************************************************************
Sun Oct 1 19:59:33 2017 Tue Oct 24 02:24:04 2017
FIT: data read from "20170830-evolution1D_5x5_100Times-added_one.csv" every ::1 using 2:5 FIT: data read from "20170830-evolution1D_5x5_100Times-added_one.csv" every ::1 using 2:5
@ -47,7 +47,7 @@ b -0.996 1.000
******************************************************************************* *******************************************************************************
Sun Oct 1 19:59:33 2017 Tue Oct 24 02:24:04 2017
FIT: data read from "20170830-evolution1D_5x5_100Times-added_one.csv" every ::1 using 4:5 FIT: data read from "20170830-evolution1D_5x5_100Times-added_one.csv" every ::1 using 4:5
@ -93,7 +93,7 @@ bb -1.000 1.000
******************************************************************************* *******************************************************************************
Sun Oct 1 19:59:33 2017 Tue Oct 24 02:24:04 2017
FIT: data read from "20170830-evolution1D_5x5_100Times-added_one.csv" every ::1 using 4:6 FIT: data read from "20170830-evolution1D_5x5_100Times-added_one.csv" every ::1 using 4:6

12
dokumentation/evolution1d/20170830-evolution1D_5x5_100Times-added_one.gnuplot.script
View File

@ -2,19 +2,19 @@ set datafile separator ","
f(x)=a*x+b f(x)=a*x+b
fit f(x) "20170830-evolution1D_5x5_100Times-added_one.csv" every ::1 using 2:5 via a,b fit f(x) "20170830-evolution1D_5x5_100Times-added_one.csv" every ::1 using 2:5 via a,b
set terminal png set terminal png
set xlabel 'regularity' set xlabel 'Regularity'
set ylabel 'steps' set ylabel 'Iterations'
set output "20170830-evolution1D_5x5_100Times-added_one_regularity-vs-steps.png" set output "20170830-evolution1D_5x5_100Times-added_one_regularity-vs-steps.png"
plot "20170830-evolution1D_5x5_100Times-added_one.csv" every ::1 using 2:5 title "data", f(x) title "lin. fit" lc rgb "black" plot "20170830-evolution1D_5x5_100Times-added_one.csv" every ::1 using 2:5 title "data", f(x) title "lin. fit" lc rgb "black"
g(x)=aa*x+bb g(x)=aa*x+bb
fit g(x) "20170830-evolution1D_5x5_100Times-added_one.csv" every ::1 using 4:5 via aa,bb fit g(x) "20170830-evolution1D_5x5_100Times-added_one.csv" every ::1 using 4:5 via aa,bb
set xlabel 'improvement potential' set xlabel 'Improvement potential'
set ylabel 'steps' set ylabel 'Iterations'
set output "20170830-evolution1D_5x5_100Times-added_one_improvement-vs-steps.png" set output "20170830-evolution1D_5x5_100Times-added_one_improvement-vs-steps.png"
plot "20170830-evolution1D_5x5_100Times-added_one.csv" every ::1 using 4:5 title "data", g(x) title "lin. fit" lc rgb "black" plot "20170830-evolution1D_5x5_100Times-added_one.csv" every ::1 using 4:5 title "data", g(x) title "lin. fit" lc rgb "black"
h(x)=aaa*x+bbb h(x)=aaa*x+bbb
fit h(x) "20170830-evolution1D_5x5_100Times-added_one.csv" every ::1 using 4:6 via aaa,bbb fit h(x) "20170830-evolution1D_5x5_100Times-added_one.csv" every ::1 using 4:6 via aaa,bbb
set xlabel 'improvement potential' set xlabel 'Improvement potential'
set ylabel 'evolution error' set ylabel 'Fitting error'
set output "20170830-evolution1D_5x5_100Times-added_one_improvement-vs-evo-error.png" set output "20170830-evolution1D_5x5_100Times-added_one_improvement-vs-evo-error.png"
plot "20170830-evolution1D_5x5_100Times-added_one.csv" every ::1 using 4:6 title "data", h(x) title "lin. fit" lc rgb "black" plot "20170830-evolution1D_5x5_100Times-added_one.csv" every ::1 using 4:6 title "data", h(x) title "lin. fit" lc rgb "black"

37
dokumentation/evolution1d/20170830-evolution1D_5x5_100Times-added_one.spearman Normal file
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@ -0,0 +1,37 @@
[1] "================ Analyzing 20170830-evolution1D_5x5_100Times-added_one.csv"
[1] "spearman for improvement-potential vs. evolution-error"
x y
x 1 -1
y -1 1
n= 100
P
x y
x 0
y 0
[1] "spearman for regularity vs. steps"
x y
x 1.00 -0.34
y -0.34 1.00
n= 100
P
x y
x 5e-04
y 5e-04
[1] "spearman for variability vs. evolution-error"
x y
x 1 NaN
y NaN 1
n= 100
P
x y
x
y

BIN
dokumentation/evolution1d/20170830-evolution1D_5x5_100Times-added_one_improvement-vs-evo-error.png
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dokumentation/evolution1d/20170830-evolution1D_5x5_100Times-added_one_improvement-vs-steps.png
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dokumentation/evolution1d/20170830-evolution1D_5x5_100Times-added_one_regularity-vs-steps.png
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201
dokumentation/evolution1d/20170830-evolution1D_5x5_100Times-all.error Normal file
View File

@ -0,0 +1,201 @@
"Evolution error"
192.44
240.171
249.883
197.529
201.143
212.978
187.236
241.13
222.511
256.592
236.693
221.694
224.469
234.382
243.433
194.032
234.567
213.401
201.428
207.224
224.585
221.895
194.499
208.589
215.456
234.028
214.716
241.431
206.599
204.769
204.23
222.938
185.355
218.429
219.886
219.696
203.581
226.759
221.819
236.226
217.553
213.564
195.759
247.931
233.713
234.013
223.628
194.983
226.437
214.086
186.419
196.416
235.058
244.587
255.376
226.808
241.372
225.08
210.821
206.672
201.399
246.066
253.875
259.741
207.655
238.654
213.147
210.34
273.684
200.321
230.127
210.898
224.914
208.711
233.241
203.658
227.058
219.89
212.877
215.439
191.017
170.069
204.348
195.049
207.186
225.229
230.466
212.578
190.496
262.382
215.988
206.934
250.737
205.827
212.891
201.034
212.53
208.545
206.327
199.413
207.886
245.873
209.253
236.693
237.512
217.347
206.725
218.216
208.831
199.805
195.163
193.258
181.901
183.091
215.233
210.992
226.197
230.55
201.997
227.649
192.577
221.454
236.59
224.637
201.263
218.685
256.401
228.137
203.421
228.677
239.173
203.783
243.217
204.188
211.535
229.573
225.773
235.748
208.659
220.83
191.357
224.938
216.195
218.868
230.63
186.89
218.199
217.047
223.644
213.801
205.631
210.824
230.178
257.369
243.262
229.047
221.493
177.905
241.468
243.443
233.782
205.347
268.384
230.853
226.312
209.55
233.426
210.991
219.415
260.926
229.786
194.888
193.57
211.086
237.989
217.102
194.775
244.384
211.814
212.073
187.619
205.625
210.781
191.55
178.258
194.329
212.217
200.944
227.453
264.972
202.656
201.39
236.882
214.712
194.569
195.513
262.158
251.577
193.849
224.14

6
dokumentation/evolution1d/20170830-evolution1D_5x5_100Times-all.gnuplot.fit.log
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@ -1,7 +1,7 @@
******************************************************************************* *******************************************************************************
Sun Oct 1 20:05:21 2017 Tue Oct 24 02:24:04 2017
FIT: data read from "20170830-evolution1D_5x5_100Times-all.csv" every ::1 using 2:5 FIT: data read from "20170830-evolution1D_5x5_100Times-all.csv" every ::1 using 2:5
@ -47,7 +47,7 @@ b -0.995 1.000
******************************************************************************* *******************************************************************************
Sun Oct 1 20:05:21 2017 Tue Oct 24 02:24:04 2017
FIT: data read from "20170830-evolution1D_5x5_100Times-all.csv" every ::1 using 4:5 FIT: data read from "20170830-evolution1D_5x5_100Times-all.csv" every ::1 using 4:5
@ -93,7 +93,7 @@ bb -1.000 1.000
******************************************************************************* *******************************************************************************
Sun Oct 1 20:05:21 2017 Tue Oct 24 02:24:04 2017
FIT: data read from "20170830-evolution1D_5x5_100Times-all.csv" every ::1 using 4:6 FIT: data read from "20170830-evolution1D_5x5_100Times-all.csv" every ::1 using 4:6

18
dokumentation/evolution1d/20170830-evolution1D_5x5_100Times-all.gnuplot.script
View File

@ -2,19 +2,19 @@ set datafile separator ","
f(x)=a*x+b f(x)=a*x+b
fit f(x) "20170830-evolution1D_5x5_100Times-all.csv" every ::1 using 2:5 via a,b fit f(x) "20170830-evolution1D_5x5_100Times-all.csv" every ::1 using 2:5 via a,b
set terminal png set terminal png
set xlabel 'regularity' set xlabel 'Regularity'
set ylabel 'steps' set ylabel 'Iterations'
set output "20170830-evolution1D_5x5_100Times-all_regularity-vs-steps.png" set output "20170830-evolution1D_5x5_100Times-all_regularity-vs-steps.png"
plot "20170830-evolution1D_5x5_100Times.csv" every ::1 using 2:5 title "20170830-evolution1D_5x5_100Times.csv", "20170830-evolution1D_5x5_100Times-added_one.csv" every ::1 using 2:5 title "20170830-evolution1D_5x5_100Times-added_one.csv", f(x) title "lin. fit" lc rgb "black" plot "20170830-evolution1D_5x5_100Times-all.csv" every ::1 using 2:5 title "data", f(x) title "lin. fit" lc rgb "black"
g(x)=aa*x+bb g(x)=aa*x+bb
fit g(x) "20170830-evolution1D_5x5_100Times-all.csv" every ::1 using 4:5 via aa,bb fit g(x) "20170830-evolution1D_5x5_100Times-all.csv" every ::1 using 4:5 via aa,bb
set xlabel 'improvement potential' set xlabel 'Improvement potential'
set ylabel 'steps' set ylabel 'Iterations'
set output "20170830-evolution1D_5x5_100Times-all_improvement-vs-steps.png" set output "20170830-evolution1D_5x5_100Times-all_improvement-vs-steps.png"
plot "20170830-evolution1D_5x5_100Times.csv" every ::1 using 4:5 title "20170830-evolution1D_5x5_100Times.csv", "20170830-evolution1D_5x5_100Times-added_one.csv" every ::1 using 4:5 title "20170830-evolution1D_5x5_100Times-added_one.csv", g(x) title "lin. fit" lc rgb "black" plot "20170830-evolution1D_5x5_100Times-all.csv" every ::1 using 4:5 title "data", g(x) title "lin. fit" lc rgb "black"
h(x)=aaa*x+bbb h(x)=aaa*x+bbb
fit h(x) "20170830-evolution1D_5x5_100Times-all.csv" every ::1 using 4:6 via aaa,bbb fit h(x) "20170830-evolution1D_5x5_100Times-all.csv" every ::1 using 4:6 via aaa,bbb
set xlabel 'improvement potential' set xlabel 'Improvement potential'
set ylabel 'evolution error' set ylabel 'Fitting error'
set output "20170830-evolution1D_5x5_100Times-all_improvement-vs-evo-error.png" set output "20170830-evolution1D_5x5_100Times-all_improvement-vs-evo-error.png"
plot "20170830-evolution1D_5x5_100Times.csv" every ::1 using 4:6 title "20170830-evolution1D_5x5_100Times.csv", "20170830-evolution1D_5x5_100Times-added_one.csv" every ::1 using 4:6 title "20170830-evolution1D_5x5_100Times-added_one.csv", h(x) title "lin. fit" lc rgb "black" plot "20170830-evolution1D_5x5_100Times-all.csv" every ::1 using 4:6 title "data", h(x) title "lin. fit" lc rgb "black"

37
dokumentation/evolution1d/20170830-evolution1D_5x5_100Times-all.spearman Normal file
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@ -0,0 +1,37 @@
[1] "================ Analyzing 20170830-evolution1D_5x5_100Times-all.csv"
[1] "spearman for improvement-potential vs. evolution-error"
x y
x 1 -1
y -1 1
n= 200
P
x y
x 0
y 0
[1] "spearman for regularity vs. steps"
x y
x 1.00 -0.31
y -0.31 1.00
n= 200
P
x y
x 0
y 0
[1] "spearman for variability vs. evolution-error"
x y
x 1 NaN
y NaN 1
n= 200
P
x y
x
y

BIN
dokumentation/evolution1d/20170830-evolution1D_5x5_100Times-all_improvement-vs-evo-error.png
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dokumentation/evolution1d/20170830-evolution1D_5x5_100Times-all_improvement-vs-steps.png
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dokumentation/evolution1d/20170830-evolution1D_5x5_100Times-all_regularity-vs-steps.png
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101
dokumentation/evolution1d/20170830-evolution1D_5x5_100Times.error Normal file
View File

@ -0,0 +1,101 @@
"Evolution error"
192.44
240.171
249.883
197.529
201.143
212.978
187.236
241.13
222.511
256.592
236.693
221.694
224.469
234.382
243.433
194.032
234.567
213.401
201.428
207.224
224.585
221.895
194.499
208.589
215.456
234.028
214.716
241.431
206.599
204.769
204.23
222.938
185.355
218.429
219.886
219.696
203.581
226.759
221.819
236.226
217.553
213.564
195.759
247.931
233.713
234.013
223.628
194.983
226.437
214.086
186.419
196.416
235.058
244.587
255.376
226.808
241.372
225.08
210.821
206.672
201.399
246.066
253.875
259.741
207.655
238.654
213.147
210.34
273.684
200.321
230.127
210.898
224.914
208.711
233.241
203.658
227.058
219.89
212.877
215.439
191.017
170.069
204.348
195.049
207.186
225.229
230.466
212.578
190.496
262.382
215.988
206.934
250.737
205.827
212.891
201.034
212.53
208.545
206.327
199.413

6
dokumentation/evolution1d/20170830-evolution1D_5x5_100Times.gnuplot.fit.log
View File

@ -1,7 +1,7 @@
******************************************************************************* *******************************************************************************
Sun Oct 1 19:58:40 2017 Tue Oct 24 02:24:04 2017
FIT: data read from "20170830-evolution1D_5x5_100Times.csv" every ::1 using 2:5 FIT: data read from "20170830-evolution1D_5x5_100Times.csv" every ::1 using 2:5
@ -47,7 +47,7 @@ b -0.995 1.000
******************************************************************************* *******************************************************************************
Sun Oct 1 19:58:40 2017 Tue Oct 24 02:24:04 2017
FIT: data read from "20170830-evolution1D_5x5_100Times.csv" every ::1 using 4:5 FIT: data read from "20170830-evolution1D_5x5_100Times.csv" every ::1 using 4:5
@ -93,7 +93,7 @@ bb -1.000 1.000
******************************************************************************* *******************************************************************************
Sun Oct 1 19:58:40 2017 Tue Oct 24 02:24:04 2017
FIT: data read from "20170830-evolution1D_5x5_100Times.csv" every ::1 using 4:6 FIT: data read from "20170830-evolution1D_5x5_100Times.csv" every ::1 using 4:6

12
dokumentation/evolution1d/20170830-evolution1D_5x5_100Times.gnuplot.script
View File

@ -2,19 +2,19 @@ set datafile separator ","
f(x)=a*x+b f(x)=a*x+b
fit f(x) "20170830-evolution1D_5x5_100Times.csv" every ::1 using 2:5 via a,b fit f(x) "20170830-evolution1D_5x5_100Times.csv" every ::1 using 2:5 via a,b
set terminal png set terminal png
set xlabel 'regularity' set xlabel 'Regularity'
set ylabel 'steps' set ylabel 'Iterations'
set output "20170830-evolution1D_5x5_100Times_regularity-vs-steps.png" set output "20170830-evolution1D_5x5_100Times_regularity-vs-steps.png"
plot "20170830-evolution1D_5x5_100Times.csv" every ::1 using 2:5 title "data", f(x) title "lin. fit" lc rgb "black" plot "20170830-evolution1D_5x5_100Times.csv" every ::1 using 2:5 title "data", f(x) title "lin. fit" lc rgb "black"
g(x)=aa*x+bb g(x)=aa*x+bb
fit g(x) "20170830-evolution1D_5x5_100Times.csv" every ::1 using 4:5 via aa,bb fit g(x) "20170830-evolution1D_5x5_100Times.csv" every ::1 using 4:5 via aa,bb
set xlabel 'improvement potential' set xlabel 'Improvement potential'
set ylabel 'steps' set ylabel 'Iterations'
set output "20170830-evolution1D_5x5_100Times_improvement-vs-steps.png" set output "20170830-evolution1D_5x5_100Times_improvement-vs-steps.png"
plot "20170830-evolution1D_5x5_100Times.csv" every ::1 using 4:5 title "data", g(x) title "lin. fit" lc rgb "black" plot "20170830-evolution1D_5x5_100Times.csv" every ::1 using 4:5 title "data", g(x) title "lin. fit" lc rgb "black"
h(x)=aaa*x+bbb h(x)=aaa*x+bbb
fit h(x) "20170830-evolution1D_5x5_100Times.csv" every ::1 using 4:6 via aaa,bbb fit h(x) "20170830-evolution1D_5x5_100Times.csv" every ::1 using 4:6 via aaa,bbb
set xlabel 'improvement potential' set xlabel 'Improvement potential'
set ylabel 'evolution error' set ylabel 'Fitting error'
set output "20170830-evolution1D_5x5_100Times_improvement-vs-evo-error.png" set output "20170830-evolution1D_5x5_100Times_improvement-vs-evo-error.png"
plot "20170830-evolution1D_5x5_100Times.csv" every ::1 using 4:6 title "data", h(x) title "lin. fit" lc rgb "black" plot "20170830-evolution1D_5x5_100Times.csv" every ::1 using 4:6 title "data", h(x) title "lin. fit" lc rgb "black"

37
dokumentation/evolution1d/20170830-evolution1D_5x5_100Times.spearman Normal file
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@ -0,0 +1,37 @@
[1] "================ Analyzing 20170830-evolution1D_5x5_100Times.csv"
[1] "spearman for improvement-potential vs. evolution-error"
x y
x 1 -1
y -1 1
n= 100
P
x y
x 0
y 0
[1] "spearman for regularity vs. steps"
x y
x 1.00 -0.28
y -0.28 1.00
n= 100
P
x y
x 0.0045
y 0.0045
[1] "spearman for variability vs. evolution-error"
x y
x 1 NaN
y NaN 1
n= 100
P
x y
x
y

BIN
dokumentation/evolution1d/20170830-evolution1D_5x5_100Times_improvement-vs-evo-error.png
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dokumentation/evolution1d/20170830-evolution1D_5x5_100Times_improvement-vs-steps.png
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dokumentation/evolution1d/20170830-evolution1D_5x5_100Times_regularity-vs-steps.png
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501
dokumentation/evolution1d/20171005-all.error Normal file
View File

@ -0,0 +1,501 @@
"Evolution error"
192.44
240.171
249.883
197.529
201.143
212.978
187.236
241.13
222.511
256.592
236.693
221.694
224.469
234.382
243.433
194.032
234.567
213.401
201.428
207.224
224.585
221.895
194.499
208.589
215.456
234.028
214.716
241.431
206.599
204.769
204.23
222.938
185.355
218.429
219.886
219.696
203.581
226.759
221.819
236.226
217.553
213.564
195.759
247.931
233.713
234.013
223.628
194.983
226.437
214.086
186.419
196.416
235.058
244.587
255.376
226.808
241.372
225.08
210.821
206.672
201.399
246.066
253.875
259.741
207.655
238.654
213.147
210.34
273.684
200.321
230.127
210.898
224.914
208.711
233.241
203.658
227.058
219.89
212.877
215.439
191.017
170.069
204.348
195.049
207.186
225.229
230.466
212.578
190.496
262.382
215.988
206.934
250.737
205.827
212.891
201.034
212.53
208.545
206.327
199.413
207.886
245.873
209.253
236.693
237.512
217.347
206.725
218.216
208.831
199.805
195.163
193.258
181.901
183.091
215.233
210.992
226.197
230.55
201.997
227.649
192.577
221.454
236.59
224.637
201.263
218.685
256.401
228.137
203.421
228.677
239.173
203.783
243.217
204.188
211.535
229.573
225.773
235.748
208.659
220.83
191.357
224.938
216.195
218.868
230.63
186.89
218.199
217.047
223.644
213.801
205.631
210.824
230.178
257.369
243.262
229.047
221.493
177.905
241.468
243.443
233.782
205.347
268.384
230.853
226.312
209.55
233.426
210.991
219.415
260.926
229.786
194.888
193.57
211.086
237.989
217.102
194.775
244.384
211.814
212.073
187.619
205.625
210.781
191.55
178.258
194.329
212.217
200.944
227.453
264.972
202.656
201.39
236.882
214.712
194.569
195.513
262.158
251.577
193.849
224.14
280.917
315.729
264.639
275.922
323.159
300.933
264.541
264.875
286.999
314.771
254.996
270.99
336.401
249.761
310.473
282.476
301.45
304.67
300.451
315.122
302.947
262.796
272.873
291.472
280.073
274.973
277.642
266.096
300.458
281.797
287.84
270.181
304.713
301.015
250.936
327.876
267.093
266.032
293.5
274.145
302.284
296.447
290.496
326.409
252.376
285.256
261.023
273.732
287.211
246.715
317.892
265.825
259.862
273.217
269.759
314.394
314.765
284.627
262.319
269.132
259.973
296.171
264.153
307.381
248.894
312.436
273.599
286.954
313.315
290.546
317.095
289.397
293.925
273.573
248.052
282.84
286.257
284.314
321.302
260.894
278.436
274.697
269.428
287.274
281.924
263.843
298.757
275.521
269.146
273.475
273.666
298.125
305.642
297.086
317.845
274.586
332.413
301.147
354.08
266.461
211.096
233.828
205.276
261.016
205.753
244.494
236.857
243.624
227.071
228.254
219.293
235.159
240.691
232.853
243.665
242.766
243.618
238.051
224.685
206.919
266.62
229.771
241.243
228.75
246.415
245.936
234.603
230.971
246.319
235.173
250.199
240.854
233.456
216.659
240.033
244.108
216.874
242.058
221.484
222.485
239.78
232.709
230.785
229.968
235.149
233.462
241.027
229.139
210.309
222.927
236.762
244.312
225.283
228.831
234.735
222.154
215.144
247.533
242.563
231.706
245.743
233.422
221.213
223.48
234.243
246.759
225.232
233.179
256.94
237.977
238.547
254.967
223.3
243.823
240.056
220.234
242.633
244.981
244.803
241.898
222.32
232.013
228.661
241.097
225.772
243.746
209.245
235.881
241.881
231.035
220.946
212.015
234.886
234.38
250.999
229.239
222.041
208.038
217.716
220.769
126.241
110.962
125.853
140.195
126.647
154.539
107.206
128.558
136.77
145.941
171.996
118.437
143.556
120.873
105.887
154.67
154.182
116.314
113.496
159.92
158.727
108.387
148.334
112.767
139.428
124.479
106.309
139.721
133.951
135.062
133.884
120.051
101.318
135.279
110.051
136.437
134.697
113.78
122.484
114.487
127.55
125.629
162.041
156.364
113.788
138.26
109.996
164.442
105.711
156.553
116.304
113.339
118.637
164.957
97.1774
137.814
158.652
128.639
144.853
136.728
100.103
129.972
134.171
102.086
137.823
120.165
121.511
110.067
112.614
105.271
140.821
124.285
158.103
140.515
101.64
129.887
167.194
110.881
146.669
118.035
150.583
143.211
114.785
146.421
130.875
107.373
152.164
129.282
98.1416
129.997
117
111.092
114.087
107.558
144.45
118.484
109.701
126.593
168.971
143.838

104
dokumentation/evolution1d/20171005-all.gnuplot.fit.log
View File

@ -1,7 +1,7 @@
******************************************************************************* *******************************************************************************
Thu Oct 5 14:24:23 2017 Tue Oct 24 02:24:04 2017
FIT: data read from "20171005-all.csv" every ::1 using 2:5 FIT: data read from "20171005-all.csv" every ::1 using 2:5
@ -10,12 +10,19 @@ FIT: data read from "20171005-all.csv" every ::1 using 2:5
residuals are weighted equally (unit weight) residuals are weighted equally (unit weight)
function used for fitting: f(x) function used for fitting: f(x)
f(x)=a*x+b
fitted parameters initialized with current variable values fitted parameters initialized with current variable values
iter chisq delta/lim lambda a b
0 2.2819584538e+07 0.00e+00 7.07e-01 1.000000e+00 1.000000e+00
4 9.2387072945e+05 -3.77e-04 7.07e-05 5.253352e+02 1.999370e+02 Iteration 0
WSSR : 2.28196e+07 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.707253
initial set of free parameter values
a = 1
b = 1
After 4 iterations the fit converged. After 4 iterations the fit converged.
final sum of squares of residuals : 923871 final sum of squares of residuals : 923871
@ -27,17 +34,20 @@ variance of residuals (reduced chisquare) = WSSR/ndf : 1855.16
Final set of parameters Asymptotic Standard Error Final set of parameters Asymptotic Standard Error
======================= ========================== ======================= ==========================
a = 525.335 +/- 371.3 (70.69%) a = 525.335 +/- 371.3 (70.69%)
b = 199.937 +/- 7.551 (3.777%) b = 199.937 +/- 7.551 (3.777%)
correlation matrix of the fit parameters: correlation matrix of the fit parameters:
a b
a b
a 1.000 a 1.000
b -0.967 1.000 b -0.967 1.000
******************************************************************************* *******************************************************************************
Thu Oct 5 14:24:23 2017 Tue Oct 24 02:24:04 2017
FIT: data read from "20171005-all.csv" every ::1 using 4:5 FIT: data read from "20171005-all.csv" every ::1 using 4:5
@ -46,12 +56,19 @@ FIT: data read from "20171005-all.csv" every ::1 using 4:5
residuals are weighted equally (unit weight) residuals are weighted equally (unit weight)
function used for fitting: g(x) function used for fitting: g(x)
g(x)=aa*x+bb
fitted parameters initialized with current variable values fitted parameters initialized with current variable values
iter chisq delta/lim lambda aa bb
0 2.2629211027e+07 0.00e+00 9.66e-01 1.000000e+00 1.000000e+00
4 8.9631538551e+05 -2.78e-05 9.66e-05 4.610660e+02 -2.189272e+02 Iteration 0
WSSR : 2.26292e+07 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.96613
initial set of free parameter values
aa = 1
bb = 1
After 4 iterations the fit converged. After 4 iterations the fit converged.
final sum of squares of residuals : 896315 final sum of squares of residuals : 896315
@ -63,17 +80,20 @@ variance of residuals (reduced chisquare) = WSSR/ndf : 1799.83
Final set of parameters Asymptotic Standard Error Final set of parameters Asymptotic Standard Error
======================= ========================== ======================= ==========================
aa = 461.066 +/- 110.6 (23.99%) aa = 461.066 +/- 110.6 (23.99%)
bb = -218.927 +/- 103 (47.04%) bb = -218.927 +/- 103 (47.04%)
correlation matrix of the fit parameters: correlation matrix of the fit parameters:
aa bb
aa bb
aa 1.000 aa 1.000
bb -1.000 1.000 bb -1.000 1.000
******************************************************************************* *******************************************************************************
Thu Oct 5 14:24:23 2017 Tue Oct 24 02:24:04 2017
FIT: data read from "20171005-all.csv" every ::1 using 4:6 FIT: data read from "20171005-all.csv" every ::1 using 4:6
@ -82,16 +102,23 @@ FIT: data read from "20171005-all.csv" every ::1 using 4:6
residuals are weighted equally (unit weight) residuals are weighted equally (unit weight)
function used for fitting: h(x) function used for fitting: h(x)
h(x)=aaa*x+bbb
fitted parameters initialized with current variable values fitted parameters initialized with current variable values
iter chisq delta/lim lambda aaa bbb
0 2.4597834778e+07 0.00e+00 9.66e-01 1.000000e+00 1.000000e+00
5 4.4603658393e+01 -1.73e-08 9.66e-06 -3.139922e+03 3.139954e+03 Iteration 0
WSSR : 2.45978e+07 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.96613
initial set of free parameter values
aaa = 1
bbb = 1
After 5 iterations the fit converged. After 5 iterations the fit converged.
final sum of squares of residuals : 44.6037 final sum of squares of residuals : 44.6037
rel. change during last iteration : -1.72842e-13 rel. change during last iteration : -1.73479e-13
degrees of freedom (FIT_NDF) : 498 degrees of freedom (FIT_NDF) : 498
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 0.299275 rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 0.299275
@ -99,46 +126,13 @@ variance of residuals (reduced chisquare) = WSSR/ndf : 0.0895656
Final set of parameters Asymptotic Standard Error Final set of parameters Asymptotic Standard Error
======================= ========================== ======================= ==========================
aaa = -3139.92 +/- 0.7803 (0.02485%) aaa = -3139.92 +/- 0.7803 (0.02485%)
bbb = 3139.95 +/- 0.7265 (0.02314%) bbb = 3139.95 +/- 0.7265 (0.02314%)
correlation matrix of the fit parameters: correlation matrix of the fit parameters:
aaa bbb
aaa bbb
aaa 1.000 aaa 1.000
bbb -1.000 1.000 bbb -1.000 1.000
*******************************************************************************
Thu Oct 5 14:24:23 2017
FIT: data read from "20171005-all.csv" every ::1 using 3:6
format = x:z
#datapoints = 500
residuals are weighted equally (unit weight)
function used for fitting: i(x)
i(x)=aaaa*x+bbbb
fitted parameters initialized with current variable values
iter chisq delta/lim lambda aaaa bbbb
0 2.4797348325e+07 0.00e+00 7.07e-01 1.000000e+00 1.000000e+00
5 6.2575820484e+05 -6.78e-01 7.07e-06 -1.004063e+05 3.554273e+02
After 5 iterations the fit converged.
final sum of squares of residuals : 625758
rel. change during last iteration : -6.77885e-06
degrees of freedom (FIT_NDF) : 498
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 35.4477
variance of residuals (reduced chisquare) = WSSR/ndf : 1256.54
Final set of parameters Asymptotic Standard Error
======================= ==========================
aaaa = -100406 +/- 3920 (3.904%)
bbbb = 355.427 +/- 5.629 (1.584%)
correlation matrix of the fit parameters:
aaaa bbbb
aaaa 1.000
bbbb -0.960 1.000

241
dokumentation/evolution1d/20171005-all.gnuplot.log
View File

@ -1,10 +1,58 @@
iter chisq delta/lim lambda a b
0 2.2819584538e+07 0.00e+00 7.07e-01 1.000000e+00 1.000000e+00
1 9.2734539506e+05 -2.36e+06 7.07e-02 1.872894e+01 2.096890e+02 Iteration 0
2 9.2412450892e+05 -3.49e+02 7.07e-03 3.879916e+02 2.026375e+02 WSSR : 2.28196e+07 delta(WSSR)/WSSR : 0
3 9.2387073294e+05 -2.75e+01 7.07e-04 5.248262e+02 1.999470e+02 delta(WSSR) : 0 limit for stopping : 1e-05
4 9.2387072945e+05 -3.77e-04 7.07e-05 5.253352e+02 1.999370e+02 lambda : 0.707253
iter chisq delta/lim lambda a b
initial set of free parameter values
a = 1
b = 1
/
Iteration 1
WSSR : 927345 delta(WSSR)/WSSR : -23.6074
delta(WSSR) : -2.18922e+07 limit for stopping : 1e-05
lambda : 0.0707253
resultant parameter values
a = 18.7289
b = 209.689
/
Iteration 2
WSSR : 924125 delta(WSSR)/WSSR : -0.00348534
delta(WSSR) : -3220.89 limit for stopping : 1e-05
lambda : 0.00707253
resultant parameter values
a = 387.992
b = 202.637
/
Iteration 3
WSSR : 923871 delta(WSSR)/WSSR : -0.000274688
delta(WSSR) : -253.776 limit for stopping : 1e-05
lambda : 0.000707253
resultant parameter values
a = 524.826
b = 199.947
/
Iteration 4
WSSR : 923871 delta(WSSR)/WSSR : -3.77204e-09
delta(WSSR) : -0.00348488 limit for stopping : 1e-05
lambda : 7.07253e-05
resultant parameter values
a = 525.335
b = 199.937
After 4 iterations the fit converged. After 4 iterations the fit converged.
final sum of squares of residuals : 923871 final sum of squares of residuals : 923871
@ -16,20 +64,71 @@ variance of residuals (reduced chisquare) = WSSR/ndf : 1855.16
Final set of parameters Asymptotic Standard Error Final set of parameters Asymptotic Standard Error
======================= ========================== ======================= ==========================
a = 525.335 +/- 371.3 (70.69%) a = 525.335 +/- 371.3 (70.69%)
b = 199.937 +/- 7.551 (3.777%) b = 199.937 +/- 7.551 (3.777%)
correlation matrix of the fit parameters: correlation matrix of the fit parameters:
a b
a b
a 1.000 a 1.000
b -0.967 1.000 b -0.967 1.000
iter chisq delta/lim lambda aa bb
0 2.2629211027e+07 0.00e+00 9.66e-01 1.000000e+00 1.000000e+00
1 9.1220178149e+05 -2.38e+06 9.66e-02 1.325557e+02 8.671371e+01 Iteration 0
2 8.9649334140e+05 -1.75e+03 9.66e-03 4.262842e+02 -1.865447e+02 WSSR : 2.26292e+07 delta(WSSR)/WSSR : 0
3 8.9631538576e+05 -1.99e+01 9.66e-04 4.610248e+02 -2.188888e+02 delta(WSSR) : 0 limit for stopping : 1e-05
4 8.9631538551e+05 -2.78e-05 9.66e-05 4.610660e+02 -2.189272e+02 lambda : 0.96613
iter chisq delta/lim lambda aa bb
initial set of free parameter values
aa = 1
bb = 1
/
Iteration 1
WSSR : 912202 delta(WSSR)/WSSR : -23.8072
delta(WSSR) : -2.1717e+07 limit for stopping : 1e-05
lambda : 0.096613
resultant parameter values
aa = 132.556
bb = 86.7137
/
Iteration 2
WSSR : 896493 delta(WSSR)/WSSR : -0.0175221
delta(WSSR) : -15708.4 limit for stopping : 1e-05
lambda : 0.0096613
resultant parameter values
aa = 426.284
bb = -186.545
/
Iteration 3
WSSR : 896315 delta(WSSR)/WSSR : -0.000198541
delta(WSSR) : -177.956 limit for stopping : 1e-05
lambda : 0.00096613
resultant parameter values
aa = 461.025
bb = -218.889
/
Iteration 4
WSSR : 896315 delta(WSSR)/WSSR : -2.77934e-10
delta(WSSR) : -0.000249116 limit for stopping : 1e-05
lambda : 9.6613e-05
resultant parameter values
aa = 461.066
bb = -218.927
After 4 iterations the fit converged. After 4 iterations the fit converged.
final sum of squares of residuals : 896315 final sum of squares of residuals : 896315
@ -41,25 +140,86 @@ variance of residuals (reduced chisquare) = WSSR/ndf : 1799.83
Final set of parameters Asymptotic Standard Error Final set of parameters Asymptotic Standard Error
======================= ========================== ======================= ==========================
aa = 461.066 +/- 110.6 (23.99%) aa = 461.066 +/- 110.6 (23.99%)
bb = -218.927 +/- 103 (47.04%) bb = -218.927 +/- 103 (47.04%)
correlation matrix of the fit parameters: correlation matrix of the fit parameters:
aa bb
aa bb
aa 1.000 aa 1.000
bb -1.000 1.000 bb -1.000 1.000
iter chisq delta/lim lambda aaa bbb
0 2.4597834778e+07 0.00e+00 9.66e-01 1.000000e+00 1.000000e+00
1 1.3196790874e+06 -1.76e+06 9.66e-02 -1.448795e+02 3.513002e+02 Iteration 0
2 1.4846794008e+04 -8.79e+06 9.66e-03 -2.822704e+03 2.844618e+03 WSSR : 2.45978e+07 delta(WSSR)/WSSR : 0
3 4.4624379637e+01 -3.32e+07 9.66e-04 -3.139547e+03 3.139604e+03 delta(WSSR) : 0 limit for stopping : 1e-05
4 4.4603658393e+01 -4.65e+01 9.66e-05 -3.139922e+03 3.139954e+03 lambda : 0.96613
5 4.4603658393e+01 -1.73e-08 9.66e-06 -3.139922e+03 3.139954e+03
iter chisq delta/lim lambda aaa bbb initial set of free parameter values
aaa = 1
bbb = 1
/
Iteration 1
WSSR : 1.31968e+06 delta(WSSR)/WSSR : -17.6393
delta(WSSR) : -2.32782e+07 limit for stopping : 1e-05
lambda : 0.096613
resultant parameter values
aaa = -144.879
bbb = 351.3
/
Iteration 2
WSSR : 14846.8 delta(WSSR)/WSSR : -87.8865
delta(WSSR) : -1.30483e+06 limit for stopping : 1e-05
lambda : 0.0096613
resultant parameter values
aaa = -2822.7
bbb = 2844.62
/
Iteration 3
WSSR : 44.6244 delta(WSSR)/WSSR : -331.706
delta(WSSR) : -14802.2 limit for stopping : 1e-05
lambda : 0.00096613
resultant parameter values
aaa = -3139.55
bbb = 3139.6
/
Iteration 4
WSSR : 44.6037 delta(WSSR)/WSSR : -0.000464564
delta(WSSR) : -0.0207212 limit for stopping : 1e-05
lambda : 9.6613e-05
resultant parameter values
aaa = -3139.92
bbb = 3139.95
/
Iteration 5
WSSR : 44.6037 delta(WSSR)/WSSR : -1.73479e-13
delta(WSSR) : -7.73781e-12 limit for stopping : 1e-05
lambda : 9.6613e-06
resultant parameter values
aaa = -3139.92
bbb = 3139.95
After 5 iterations the fit converged. After 5 iterations the fit converged.
final sum of squares of residuals : 44.6037 final sum of squares of residuals : 44.6037
rel. change during last iteration : -1.72842e-13 rel. change during last iteration : -1.73479e-13
degrees of freedom (FIT_NDF) : 498 degrees of freedom (FIT_NDF) : 498
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 0.299275 rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 0.299275
@ -67,36 +227,13 @@ variance of residuals (reduced chisquare) = WSSR/ndf : 0.0895656
Final set of parameters Asymptotic Standard Error Final set of parameters Asymptotic Standard Error
======================= ========================== ======================= ==========================
aaa = -3139.92 +/- 0.7803 (0.02485%) aaa = -3139.92 +/- 0.7803 (0.02485%)
bbb = 3139.95 +/- 0.7265 (0.02314%) bbb = 3139.95 +/- 0.7265 (0.02314%)
correlation matrix of the fit parameters: correlation matrix of the fit parameters:
aaa bbb
aaa bbb
aaa 1.000 aaa 1.000
bbb -1.000 1.000 bbb -1.000 1.000
iter chisq delta/lim lambda aaaa bbbb
0 2.4797348325e+07 0.00e+00 7.07e-01 1.000000e+00 1.000000e+00
1 1.4499765510e+06 -1.61e+06 7.07e-02 -1.512219e+01 2.168949e+02
2 1.4236400853e+06 -1.85e+03 7.07e-03 -1.630660e+03 2.193366e+02
3 7.4062425817e+05 -9.22e+04 7.07e-04 -6.292829e+04 3.037910e+02
4 6.2576244676e+05 -1.84e+04 7.07e-05 -1.001785e+05 3.551135e+02
5 6.2575820484e+05 -6.78e-01 7.07e-06 -1.004063e+05 3.554273e+02
iter chisq delta/lim lambda aaaa bbbb
After 5 iterations the fit converged.
final sum of squares of residuals : 625758
rel. change during last iteration : -6.77885e-06
degrees of freedom (FIT_NDF) : 498
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 35.4477
variance of residuals (reduced chisquare) = WSSR/ndf : 1256.54
Final set of parameters Asymptotic Standard Error
======================= ==========================
aaaa = -100406 +/- 3920 (3.904%)
bbbb = 355.427 +/- 5.629 (1.584%)
correlation matrix of the fit parameters:
aaaa bbbb
aaaa 1.000
bbbb -0.960 1.000

24
dokumentation/evolution1d/20171005-all.gnuplot.script
View File

@ -2,25 +2,19 @@ set datafile separator ","
f(x)=a*x+b f(x)=a*x+b
fit f(x) "20171005-all.csv" every ::1 using 2:5 via a,b fit f(x) "20171005-all.csv" every ::1 using 2:5 via a,b
set terminal png set terminal png
set xlabel 'regularity' set xlabel 'Regularity'
set ylabel 'steps' set ylabel 'Iterations'
set output "20171005-all_regularity-vs-steps.png" set output "20171005-all_regularity-vs-steps.png"
plot "20170830-evolution1D_5x5_100Times.csv" every ::1 using 2:5 title "20170830-evolution1D_5x5_100Times.csv", "20170830-evolution1D_5x5_100Times-added_one.csv" every ::1 using 2:5 title "20170830-evolution1D_5x5_100Times-added_one.csv", "20171005-evolution1D_4x7_100Times.csv" every ::1 using 2:5 title "20171005-evolution1D_4x7_100Times.csv", "20171005-evolution1D_7x4_100Times.csv" every ::1 using 2:5 title "20171005-evolution1D_7x4_100Times.csv", "20171005-evolution1D_7x7_100Times.csv" every ::1 using 2:5 title "20171005-evolution1D_7x7_100Times.csv", f(x) title "lin. fit" lc rgb "black" plot "20171005-all.csv" every ::1 using 2:5 title "data", f(x) title "lin. fit" lc rgb "black"
g(x)=aa*x+bb g(x)=aa*x+bb
fit g(x) "20171005-all.csv" every ::1 using 4:5 via aa,bb fit g(x) "20171005-all.csv" every ::1 using 4:5 via aa,bb
set xlabel 'improvement potential' set xlabel 'Improvement potential'
set ylabel 'steps' set ylabel 'Iterations'
set output "20171005-all_improvement-vs-steps.png" set output "20171005-all_improvement-vs-steps.png"
plot "20170830-evolution1D_5x5_100Times.csv" every ::1 using 4:5 title "20170830-evolution1D_5x5_100Times.csv", "20170830-evolution1D_5x5_100Times-added_one.csv" every ::1 using 4:5 title "20170830-evolution1D_5x5_100Times-added_one.csv", "20171005-evolution1D_4x7_100Times.csv" every ::1 using 4:5 title "20171005-evolution1D_4x7_100Times.csv", "20171005-evolution1D_7x4_100Times.csv" every ::1 using 4:5 title "20171005-evolution1D_7x4_100Times.csv", "20171005-evolution1D_7x7_100Times.csv" every ::1 using 4:5 title "20171005-evolution1D_7x7_100Times.csv", g(x) title "lin. fit" lc rgb "black" plot "20171005-all.csv" every ::1 using 4:5 title "data", g(x) title "lin. fit" lc rgb "black"
h(x)=aaa*x+bbb h(x)=aaa*x+bbb
fit h(x) "20171005-all.csv" every ::1 using 4:6 via aaa,bbb fit h(x) "20171005-all.csv" every ::1 using 4:6 via aaa,bbb
set xlabel 'improvement potential' set xlabel 'Improvement potential'
set ylabel 'evolution error' set ylabel 'Fitting error'
set output "20171005-all_improvement-vs-evo-error.png" set output "20171005-all_improvement-vs-evo-error.png"
plot "20170830-evolution1D_5x5_100Times.csv" every ::1 using 4:6 title "20170830-evolution1D_5x5_100Times.csv", "20170830-evolution1D_5x5_100Times-added_one.csv" every ::1 using 4:6 title "20170830-evolution1D_5x5_100Times-added_one.csv", "20171005-evolution1D_4x7_100Times.csv" every ::1 using 4:6 title "20171005-evolution1D_4x7_100Times.csv", "20171005-evolution1D_7x4_100Times.csv" every ::1 using 4:6 title "20171005-evolution1D_7x4_100Times.csv", "20171005-evolution1D_7x7_100Times.csv" every ::1 using 4:6 title "20171005-evolution1D_7x7_100Times.csv", h(x) title "lin. fit" lc rgb "black" plot "20171005-all.csv" every ::1 using 4:6 title "data", h(x) title "lin. fit" lc rgb "black"
i(x)=aaaa*x+bbbb
fit i(x) "20171005-all.csv" every ::1 using 3:6 via aaaa,bbbb
set xlabel 'variability'
set ylabel 'evolution error'
set output "20171005-all_variability-vs-evo-error.png"
plot "20170830-evolution1D_5x5_100Times.csv" every ::1 using 3:6 title "20170830-evolution1D_5x5_100Times.csv", "20170830-evolution1D_5x5_100Times-added_one.csv" every ::1 using 3:6 title "20170830-evolution1D_5x5_100Times-added_one.csv", "20171005-evolution1D_4x7_100Times.csv" every ::1 using 3:6 title "20171005-evolution1D_4x7_100Times.csv", "20171005-evolution1D_7x4_100Times.csv" every ::1 using 3:6 title "20171005-evolution1D_7x4_100Times.csv", "20171005-evolution1D_7x7_100Times.csv" every ::1 using 3:6 title "20171005-evolution1D_7x7_100Times.csv", i(x) title "lin. fit" lc rgb "black"

37
dokumentation/evolution1d/20171005-all.spearman Normal file
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@ -0,0 +1,37 @@
[1] "================ Analyzing 20171005-all.csv"
[1] "spearman for improvement-potential vs. evolution-error"
x y
x 1 -1
y -1 1
n= 500
P
x y
x 0
y 0
[1] "spearman for regularity vs. steps"
x y
x 1.00 -0.02
y -0.02 1.00
n= 500
P
x y
x 0.693
y 0.693
[1] "spearman for variability vs. evolution-error"
x y
x 1.00 -0.21
y -0.21 1.00
n= 500
P
x y
x 0
y 0

BIN
dokumentation/evolution1d/20171005-all_improvement-vs-evo-error.png
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101
dokumentation/evolution1d/20171005-evolution1D_4x7_100Times.error Normal file
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@ -0,0 +1,101 @@
"Evolution error"
280.917
315.729
264.639
275.922
323.159
300.933
264.541
264.875
286.999
314.771
254.996
270.99
336.401
249.761
310.473
282.476
301.45
304.67
300.451
315.122
302.947
262.796
272.873
291.472
280.073
274.973
277.642
266.096
300.458
281.797
287.84
270.181
304.713
301.015
250.936
327.876
267.093
266.032
293.5
274.145
302.284
296.447
290.496
326.409
252.376
285.256
261.023
273.732
287.211
246.715
317.892
265.825
259.862
273.217
269.759
314.394
314.765
284.627
262.319
269.132
259.973
296.171
264.153
307.381
248.894
312.436
273.599
286.954
313.315
290.546
317.095
289.397
293.925
273.573
248.052
282.84
286.257
284.314
321.302
260.894
278.436
274.697
269.428
287.274
281.924
263.843
298.757
275.521
269.146
273.475
273.666
298.125
305.642
297.086
317.845
274.586
332.413
301.147
354.08
266.461

70
dokumentation/evolution1d/20171005-evolution1D_4x7_100Times.gnuplot.fit.log
View File

@ -1,7 +1,7 @@
******************************************************************************* *******************************************************************************
Thu Oct 5 14:02:32 2017 Tue Oct 24 02:24:04 2017
FIT: data read from "20171005-evolution1D_4x7_100Times.csv" every ::1 using 2:5 FIT: data read from "20171005-evolution1D_4x7_100Times.csv" every ::1 using 2:5
@ -10,16 +10,23 @@ FIT: data read from "20171005-evolution1D_4x7_100Times.csv" every ::1 using 2
residuals are weighted equally (unit weight) residuals are weighted equally (unit weight)
function used for fitting: f(x) function used for fitting: f(x)
f(x)=a*x+b
fitted parameters initialized with current variable values fitted parameters initialized with current variable values
iter chisq delta/lim lambda a b
0 4.8453053176e+06 0.00e+00 7.07e-01 1.000000e+00 1.000000e+00
5 1.6409518325e+05 -9.52e-05 7.07e-06 -3.129336e+03 2.663203e+02 Iteration 0
WSSR : 4.84531e+06 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.707195
initial set of free parameter values
a = 1
b = 1
After 5 iterations the fit converged. After 5 iterations the fit converged.
final sum of squares of residuals : 164095 final sum of squares of residuals : 164095
rel. change during last iteration : -9.51616e-10 rel. change during last iteration : -9.51617e-10
degrees of freedom (FIT_NDF) : 98 degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 40.9199 rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 40.9199
@ -27,17 +34,20 @@ variance of residuals (reduced chisquare) = WSSR/ndf : 1674.44
Final set of parameters Asymptotic Standard Error Final set of parameters Asymptotic Standard Error
======================= ========================== ======================= ==========================
a = -3129.34 +/- 2384 (76.19%) a = -3129.34 +/- 2384 (76.19%)
b = 266.32 +/- 37.57 (14.11%) b = 266.32 +/- 37.57 (14.11%)
correlation matrix of the fit parameters: correlation matrix of the fit parameters:
a b
a b
a 1.000 a 1.000
b -0.994 1.000 b -0.994 1.000
******************************************************************************* *******************************************************************************
Thu Oct 5 14:02:32 2017 Tue Oct 24 02:24:04 2017
FIT: data read from "20171005-evolution1D_4x7_100Times.csv" every ::1 using 4:5 FIT: data read from "20171005-evolution1D_4x7_100Times.csv" every ::1 using 4:5
@ -46,16 +56,23 @@ FIT: data read from "20171005-evolution1D_4x7_100Times.csv" every ::1 using 4
residuals are weighted equally (unit weight) residuals are weighted equally (unit weight)
function used for fitting: g(x) function used for fitting: g(x)
g(x)=aa*x+bb
fitted parameters initialized with current variable values fitted parameters initialized with current variable values
iter chisq delta/lim lambda aa bb
0 4.8067339365e+06 0.00e+00 9.56e-01 1.000000e+00 1.000000e+00
5 1.5824530732e+05 -3.08e-07 9.56e-06 1.317597e+03 -9.801188e+02 Iteration 0
WSSR : 4.80673e+06 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.955501
initial set of free parameter values
aa = 1
bb = 1
After 5 iterations the fit converged. After 5 iterations the fit converged.
final sum of squares of residuals : 158245 final sum of squares of residuals : 158245
rel. change during last iteration : -3.0782e-12 rel. change during last iteration : -3.07783e-12
degrees of freedom (FIT_NDF) : 98 degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 40.1839 rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 40.1839
@ -63,17 +80,20 @@ variance of residuals (reduced chisquare) = WSSR/ndf : 1614.75
Final set of parameters Asymptotic Standard Error Final set of parameters Asymptotic Standard Error
======================= ========================== ======================= ==========================
aa = 1317.6 +/- 566.5 (43%) aa = 1317.6 +/- 566.5 (43%)
bb = -980.119 +/- 514.9 (52.53%) bb = -980.119 +/- 514.9 (52.53%)
correlation matrix of the fit parameters: correlation matrix of the fit parameters:
aa bb
aa bb
aa 1.000 aa 1.000
bb -1.000 1.000 bb -1.000 1.000
******************************************************************************* *******************************************************************************
Thu Oct 5 14:02:32 2017 Tue Oct 24 02:24:04 2017
FIT: data read from "20171005-evolution1D_4x7_100Times.csv" every ::1 using 4:6 FIT: data read from "20171005-evolution1D_4x7_100Times.csv" every ::1 using 4:6
@ -82,12 +102,19 @@ FIT: data read from "20171005-evolution1D_4x7_100Times.csv" every ::1 using 4
residuals are weighted equally (unit weight) residuals are weighted equally (unit weight)
function used for fitting: h(x) function used for fitting: h(x)
h(x)=aaa*x+bbb
fitted parameters initialized with current variable values fitted parameters initialized with current variable values
iter chisq delta/lim lambda aaa bbb
0 8.1385601354e+06 0.00e+00 9.56e-01 1.000000e+00 1.000000e+00
5 2.1970491829e+01 -1.63e-02 9.56e-06 -3.136035e+03 3.136342e+03 Iteration 0
WSSR : 8.13856e+06 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.955501
initial set of free parameter values
aaa = 1
bbb = 1
After 5 iterations the fit converged. After 5 iterations the fit converged.
final sum of squares of residuals : 21.9705 final sum of squares of residuals : 21.9705
@ -99,10 +126,13 @@ variance of residuals (reduced chisquare) = WSSR/ndf : 0.224189
Final set of parameters Asymptotic Standard Error Final set of parameters Asymptotic Standard Error
======================= ========================== ======================= ==========================
aaa = -3136.04 +/- 6.675 (0.2129%) aaa = -3136.04 +/- 6.675 (0.2129%)
bbb = 3136.34 +/- 6.067 (0.1934%) bbb = 3136.34 +/- 6.067 (0.1934%)
correlation matrix of the fit parameters: correlation matrix of the fit parameters:
aaa bbb
aaa bbb
aaa 1.000 aaa 1.000
bbb -1.000 1.000 bbb -1.000 1.000

241
dokumentation/evolution1d/20171005-evolution1D_4x7_100Times.gnuplot.log
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@ -1,15 +1,73 @@
iter chisq delta/lim lambda a b
0 4.8453053176e+06 0.00e+00 7.07e-01 1.000000e+00 1.000000e+00
1 1.6709987683e+05 -2.80e+06 7.07e-02 2.525913e+00 2.161942e+02 Iteration 0
2 1.6667175206e+05 -2.57e+02 7.07e-03 -1.715968e+02 2.199973e+02 WSSR : 4.84531e+06 delta(WSSR)/WSSR : 0
3 1.6414949666e+05 -1.54e+03 7.07e-04 -2.699906e+03 2.595947e+02 delta(WSSR) : 0 limit for stopping : 1e-05
4 1.6409518341e+05 -3.31e+01 7.07e-05 -3.128608e+03 2.663089e+02 lambda : 0.707195
5 1.6409518325e+05 -9.52e-05 7.07e-06 -3.129336e+03 2.663203e+02
iter chisq delta/lim lambda a b initial set of free parameter values
a = 1
b = 1
/
Iteration 1
WSSR : 167100 delta(WSSR)/WSSR : -27.9965
delta(WSSR) : -4.67821e+06 limit for stopping : 1e-05
lambda : 0.0707195
resultant parameter values
a = 2.52591
b = 216.194
/
Iteration 2
WSSR : 166672 delta(WSSR)/WSSR : -0.00256867
delta(WSSR) : -428.125 limit for stopping : 1e-05
lambda : 0.00707195
resultant parameter values
a = -171.597
b = 219.997
/
Iteration 3
WSSR : 164149 delta(WSSR)/WSSR : -0.0153656
delta(WSSR) : -2522.26 limit for stopping : 1e-05
lambda : 0.000707195
resultant parameter values
a = -2699.91
b = 259.595
/
Iteration 4
WSSR : 164095 delta(WSSR)/WSSR : -0.000330986
delta(WSSR) : -54.3133 limit for stopping : 1e-05
lambda : 7.07195e-05
resultant parameter values
a = -3128.61
b = 266.309
/
Iteration 5
WSSR : 164095 delta(WSSR)/WSSR : -9.51617e-10
delta(WSSR) : -0.000156156 limit for stopping : 1e-05
lambda : 7.07195e-06
resultant parameter values
a = -3129.34
b = 266.32
After 5 iterations the fit converged. After 5 iterations the fit converged.
final sum of squares of residuals : 164095 final sum of squares of residuals : 164095
rel. change during last iteration : -9.51616e-10 rel. change during last iteration : -9.51617e-10
degrees of freedom (FIT_NDF) : 98 degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 40.9199 rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 40.9199
@ -17,25 +75,86 @@ variance of residuals (reduced chisquare) = WSSR/ndf : 1674.44
Final set of parameters Asymptotic Standard Error Final set of parameters Asymptotic Standard Error
======================= ========================== ======================= ==========================
a = -3129.34 +/- 2384 (76.19%) a = -3129.34 +/- 2384 (76.19%)
b = 266.32 +/- 37.57 (14.11%) b = 266.32 +/- 37.57 (14.11%)
correlation matrix of the fit parameters: correlation matrix of the fit parameters:
a b
a b
a 1.000 a 1.000
b -0.994 1.000 b -0.994 1.000
iter chisq delta/lim lambda aa bb
0 4.8067339365e+06 0.00e+00 9.56e-01 1.000000e+00 1.000000e+00
1 1.6567425140e+05 -2.80e+06 9.56e-02 1.113320e+02 1.150902e+02 Iteration 0
2 1.6256126289e+05 -1.91e+03 9.56e-03 3.913863e+02 -1.383578e+02 WSSR : 4.80673e+06 delta(WSSR)/WSSR : 0
3 1.5824974701e+05 -2.72e+03 9.56e-04 1.287890e+03 -9.531211e+02 delta(WSSR) : 0 limit for stopping : 1e-05
4 1.5824530732e+05 -2.81e+00 9.56e-05 1.317587e+03 -9.801098e+02 lambda : 0.955501
5 1.5824530732e+05 -3.08e-07 9.56e-06 1.317597e+03 -9.801188e+02
iter chisq delta/lim lambda aa bb initial set of free parameter values
aa = 1
bb = 1
/
Iteration 1
WSSR : 165674 delta(WSSR)/WSSR : -28.0132
delta(WSSR) : -4.64106e+06 limit for stopping : 1e-05
lambda : 0.0955501
resultant parameter values
aa = 111.332
bb = 115.09
/
Iteration 2
WSSR : 162561 delta(WSSR)/WSSR : -0.0191496
delta(WSSR) : -3112.99 limit for stopping : 1e-05
lambda : 0.00955501
resultant parameter values
aa = 391.386
bb = -138.358
/
Iteration 3
WSSR : 158250 delta(WSSR)/WSSR : -0.027245
delta(WSSR) : -4311.52 limit for stopping : 1e-05
lambda : 0.000955501
resultant parameter values
aa = 1287.89
bb = -953.121
/
Iteration 4
WSSR : 158245 delta(WSSR)/WSSR : -2.80558e-05
delta(WSSR) : -4.43969 limit for stopping : 1e-05
lambda : 9.55501e-05
resultant parameter values
aa = 1317.59
bb = -980.11
/
Iteration 5
WSSR : 158245 delta(WSSR)/WSSR : -3.07783e-12
delta(WSSR) : -4.87053e-07 limit for stopping : 1e-05
lambda : 9.55501e-06
resultant parameter values
aa = 1317.6
bb = -980.119
After 5 iterations the fit converged. After 5 iterations the fit converged.
final sum of squares of residuals : 158245 final sum of squares of residuals : 158245
rel. change during last iteration : -3.0782e-12 rel. change during last iteration : -3.07783e-12
degrees of freedom (FIT_NDF) : 98 degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 40.1839 rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 40.1839
@ -43,21 +162,82 @@ variance of residuals (reduced chisquare) = WSSR/ndf : 1614.75
Final set of parameters Asymptotic Standard Error Final set of parameters Asymptotic Standard Error
======================= ========================== ======================= ==========================
aa = 1317.6 +/- 566.5 (43%) aa = 1317.6 +/- 566.5 (43%)
bb = -980.119 +/- 514.9 (52.53%) bb = -980.119 +/- 514.9 (52.53%)
correlation matrix of the fit parameters: correlation matrix of the fit parameters:
aa bb
aa bb
aa 1.000 aa 1.000
bb -1.000 1.000 bb -1.000 1.000
iter chisq delta/lim lambda aaa bbb
0 8.1385601354e+06 0.00e+00 9.56e-01 1.000000e+00 1.000000e+00
1 5.3975388072e+04 -1.50e+07 9.56e-02 1.319509e+02 1.649079e+02 Iteration 0
2 3.1741255515e+04 -7.00e+04 9.56e-03 -6.251153e+02 8.543613e+02 WSSR : 8.13856e+06 delta(WSSR)/WSSR : 0
3 5.4599157975e+01 -5.80e+07 9.56e-04 -3.055503e+03 3.063152e+03 delta(WSSR) : 0 limit for stopping : 1e-05
4 2.1970495409e+01 -1.49e+05 9.56e-05 -3.136008e+03 3.136318e+03 lambda : 0.955501
5 2.1970491829e+01 -1.63e-02 9.56e-06 -3.136035e+03 3.136342e+03
iter chisq delta/lim lambda aaa bbb initial set of free parameter values
aaa = 1
bbb = 1
/
Iteration 1
WSSR : 53975.4 delta(WSSR)/WSSR : -149.783
delta(WSSR) : -8.08458e+06 limit for stopping : 1e-05
lambda : 0.0955501
resultant parameter values
aaa = 131.951
bbb = 164.908
/
Iteration 2
WSSR : 31741.3 delta(WSSR)/WSSR : -0.700481
delta(WSSR) : -22234.1 limit for stopping : 1e-05
lambda : 0.00955501
resultant parameter values
aaa = -625.115
bbb = 854.361
/
Iteration 3
WSSR : 54.5992 delta(WSSR)/WSSR : -580.351
delta(WSSR) : -31686.7 limit for stopping : 1e-05
lambda : 0.000955501
resultant parameter values
aaa = -3055.5
bbb = 3063.15
/
Iteration 4
WSSR : 21.9705 delta(WSSR)/WSSR : -1.48511
delta(WSSR) : -32.6287 limit for stopping : 1e-05
lambda : 9.55501e-05
resultant parameter values
aaa = -3136.01
bbb = 3136.32
/
Iteration 5
WSSR : 21.9705 delta(WSSR)/WSSR : -1.62953e-07
delta(WSSR) : -3.58016e-06 limit for stopping : 1e-05
lambda : 9.55501e-06
resultant parameter values
aaa = -3136.04
bbb = 3136.34
After 5 iterations the fit converged. After 5 iterations the fit converged.
final sum of squares of residuals : 21.9705 final sum of squares of residuals : 21.9705
@ -69,10 +249,13 @@ variance of residuals (reduced chisquare) = WSSR/ndf : 0.224189
Final set of parameters Asymptotic Standard Error Final set of parameters Asymptotic Standard Error
======================= ========================== ======================= ==========================
aaa = -3136.04 +/- 6.675 (0.2129%) aaa = -3136.04 +/- 6.675 (0.2129%)
bbb = 3136.34 +/- 6.067 (0.1934%) bbb = 3136.34 +/- 6.067 (0.1934%)
correlation matrix of the fit parameters: correlation matrix of the fit parameters:
aaa bbb
aaa bbb
aaa 1.000 aaa 1.000
bbb -1.000 1.000 bbb -1.000 1.000

12
dokumentation/evolution1d/20171005-evolution1D_4x7_100Times.gnuplot.script
View File

@ -2,19 +2,19 @@ set datafile separator ","
f(x)=a*x+b f(x)=a*x+b
fit f(x) "20171005-evolution1D_4x7_100Times.csv" every ::1 using 2:5 via a,b fit f(x) "20171005-evolution1D_4x7_100Times.csv" every ::1 using 2:5 via a,b
set terminal png set terminal png
set xlabel 'regularity' set xlabel 'Regularity'
set ylabel 'steps' set ylabel 'Iterations'
set output "20171005-evolution1D_4x7_100Times_regularity-vs-steps.png" set output "20171005-evolution1D_4x7_100Times_regularity-vs-steps.png"
plot "20171005-evolution1D_4x7_100Times.csv" every ::1 using 2:5 title "data", f(x) title "lin. fit" lc rgb "black" plot "20171005-evolution1D_4x7_100Times.csv" every ::1 using 2:5 title "data", f(x) title "lin. fit" lc rgb "black"
g(x)=aa*x+bb g(x)=aa*x+bb
fit g(x) "20171005-evolution1D_4x7_100Times.csv" every ::1 using 4:5 via aa,bb fit g(x) "20171005-evolution1D_4x7_100Times.csv" every ::1 using 4:5 via aa,bb
set xlabel 'improvement potential' set xlabel 'Improvement potential'
set ylabel 'steps' set ylabel 'Iterations'
set output "20171005-evolution1D_4x7_100Times_improvement-vs-steps.png" set output "20171005-evolution1D_4x7_100Times_improvement-vs-steps.png"
plot "20171005-evolution1D_4x7_100Times.csv" every ::1 using 4:5 title "data", g(x) title "lin. fit" lc rgb "black" plot "20171005-evolution1D_4x7_100Times.csv" every ::1 using 4:5 title "data", g(x) title "lin. fit" lc rgb "black"
h(x)=aaa*x+bbb h(x)=aaa*x+bbb
fit h(x) "20171005-evolution1D_4x7_100Times.csv" every ::1 using 4:6 via aaa,bbb fit h(x) "20171005-evolution1D_4x7_100Times.csv" every ::1 using 4:6 via aaa,bbb
set xlabel 'improvement potential' set xlabel 'Improvement potential'
set ylabel 'evolution error' set ylabel 'Fitting error'
set output "20171005-evolution1D_4x7_100Times_improvement-vs-evo-error.png" set output "20171005-evolution1D_4x7_100Times_improvement-vs-evo-error.png"
plot "20171005-evolution1D_4x7_100Times.csv" every ::1 using 4:6 title "data", h(x) title "lin. fit" lc rgb "black" plot "20171005-evolution1D_4x7_100Times.csv" every ::1 using 4:6 title "data", h(x) title "lin. fit" lc rgb "black"

37
dokumentation/evolution1d/20171005-evolution1D_4x7_100Times.spearman Normal file
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@ -0,0 +1,37 @@
[1] "================ Analyzing 20171005-evolution1D_4x7_100Times.csv"
[1] "spearman for improvement-potential vs. evolution-error"
x y
x 1 -1
y -1 1
n= 100
P
x y
x 0
y 0
[1] "spearman for regularity vs. steps"
x y
x 1.0 -0.1
y -0.1 1.0
n= 100
P
x y
x 0.3019
y 0.3019
[1] "spearman for variability vs. evolution-error"
x y
x 1 NaN
y NaN 1
n= 100
P
x y
x
y

BIN
dokumentation/evolution1d/20171005-evolution1D_4x7_100Times_improvement-vs-evo-error.png
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dokumentation/evolution1d/20171005-evolution1D_4x7_100Times_improvement-vs-steps.png
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101
dokumentation/evolution1d/20171005-evolution1D_7x4_100Times.error Normal file
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@ -0,0 +1,101 @@
"Evolution error"
211.096
233.828
205.276
261.016
205.753
244.494
236.857
243.624
227.071
228.254
219.293
235.159
240.691
232.853
243.665
242.766
243.618
238.051
224.685
206.919
266.62
229.771
241.243
228.75
246.415
245.936
234.603
230.971
246.319
235.173
250.199
240.854
233.456
216.659
240.033
244.108
216.874
242.058
221.484
222.485
239.78
232.709
230.785
229.968
235.149
233.462
241.027
229.139
210.309
222.927
236.762
244.312
225.283
228.831
234.735
222.154
215.144
247.533
242.563
231.706
245.743
233.422
221.213
223.48
234.243
246.759
225.232
233.179
256.94
237.977
238.547
254.967
223.3
243.823
240.056
220.234
242.633
244.981
244.803
241.898
222.32
232.013
228.661
241.097
225.772
243.746
209.245
235.881
241.881
231.035
220.946
212.015
234.886
234.38
250.999
229.239
222.041
208.038
217.716
220.769

66
dokumentation/evolution1d/20171005-evolution1D_7x4_100Times.gnuplot.fit.log
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@ -1,7 +1,7 @@
******************************************************************************* *******************************************************************************
Thu Oct 5 14:02:37 2017 Tue Oct 24 02:24:04 2017
FIT: data read from "20171005-evolution1D_7x4_100Times.csv" every ::1 using 2:5 FIT: data read from "20171005-evolution1D_7x4_100Times.csv" every ::1 using 2:5
@ -10,12 +10,19 @@ FIT: data read from "20171005-evolution1D_7x4_100Times.csv" every ::1 using 2
residuals are weighted equally (unit weight) residuals are weighted equally (unit weight)
function used for fitting: f(x) function used for fitting: f(x)
f(x)=a*x+b
fitted parameters initialized with current variable values fitted parameters initialized with current variable values
iter chisq delta/lim lambda a b
0 4.2059624024e+06 0.00e+00 7.07e-01 1.000000e+00 1.000000e+00
5 1.6157855782e+05 -1.28e-04 7.07e-06 -3.703035e+03 2.609538e+02 Iteration 0
WSSR : 4.20596e+06 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.707197
initial set of free parameter values
a = 1
b = 1
After 5 iterations the fit converged. After 5 iterations the fit converged.
final sum of squares of residuals : 161579 final sum of squares of residuals : 161579
@ -27,17 +34,20 @@ variance of residuals (reduced chisquare) = WSSR/ndf : 1648.76
Final set of parameters Asymptotic Standard Error Final set of parameters Asymptotic Standard Error
======================= ========================== ======================= ==========================
a = -3703.04 +/- 2343 (63.28%) a = -3703.04 +/- 2343 (63.28%)
b = 260.954 +/- 37.51 (14.38%) b = 260.954 +/- 37.51 (14.38%)
correlation matrix of the fit parameters: correlation matrix of the fit parameters:
a b
a b
a 1.000 a 1.000
b -0.994 1.000 b -0.994 1.000
******************************************************************************* *******************************************************************************
Thu Oct 5 14:02:37 2017 Tue Oct 24 02:24:04 2017
FIT: data read from "20171005-evolution1D_7x4_100Times.csv" every ::1 using 4:5 FIT: data read from "20171005-evolution1D_7x4_100Times.csv" every ::1 using 4:5
@ -46,12 +56,19 @@ FIT: data read from "20171005-evolution1D_7x4_100Times.csv" every ::1 using 4
residuals are weighted equally (unit weight) residuals are weighted equally (unit weight)
function used for fitting: g(x) function used for fitting: g(x)
g(x)=aa*x+bb
fitted parameters initialized with current variable values fitted parameters initialized with current variable values
iter chisq delta/lim lambda aa bb
0 4.1694597860e+06 0.00e+00 9.64e-01 1.000000e+00 1.000000e+00
5 1.6088124752e+05 -2.98e-05 9.64e-06 1.779074e+03 -1.445031e+03 Iteration 0
WSSR : 4.16946e+06 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.963614
initial set of free parameter values
aa = 1
bb = 1
After 5 iterations the fit converged. After 5 iterations the fit converged.
final sum of squares of residuals : 160881 final sum of squares of residuals : 160881
@ -63,17 +80,20 @@ variance of residuals (reduced chisquare) = WSSR/ndf : 1641.65
Final set of parameters Asymptotic Standard Error Final set of parameters Asymptotic Standard Error
======================= ========================== ======================= ==========================
aa = 1779.07 +/- 1039 (58.39%) aa = 1779.07 +/- 1039 (58.39%)
bb = -1445.03 +/- 961.8 (66.56%) bb = -1445.03 +/- 961.8 (66.56%)
correlation matrix of the fit parameters: correlation matrix of the fit parameters:
aa bb
aa bb
aa 1.000 aa 1.000
bb -1.000 1.000 bb -1.000 1.000
******************************************************************************* *******************************************************************************
Thu Oct 5 14:02:37 2017 Tue Oct 24 02:24:04 2017
FIT: data read from "20171005-evolution1D_7x4_100Times.csv" every ::1 using 4:6 FIT: data read from "20171005-evolution1D_7x4_100Times.csv" every ::1 using 4:6
@ -82,12 +102,19 @@ FIT: data read from "20171005-evolution1D_7x4_100Times.csv" every ::1 using 4
residuals are weighted equally (unit weight) residuals are weighted equally (unit weight)
function used for fitting: h(x) function used for fitting: h(x)
h(x)=aaa*x+bbb
fitted parameters initialized with current variable values fitted parameters initialized with current variable values
iter chisq delta/lim lambda aaa bbb
0 5.3588910602e+06 0.00e+00 9.64e-01 1.000000e+00 1.000000e+00
6 5.4694656646e+00 -5.96e-09 9.64e-07 -3.141932e+03 3.141867e+03 Iteration 0
WSSR : 5.35889e+06 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.963614
initial set of free parameter values
aaa = 1
bbb = 1
After 6 iterations the fit converged. After 6 iterations the fit converged.
final sum of squares of residuals : 5.46947 final sum of squares of residuals : 5.46947
@ -99,10 +126,13 @@ variance of residuals (reduced chisquare) = WSSR/ndf : 0.0558109
Final set of parameters Asymptotic Standard Error Final set of parameters Asymptotic Standard Error
======================= ========================== ======================= ==========================
aaa = -3141.93 +/- 6.057 (0.1928%) aaa = -3141.93 +/- 6.057 (0.1928%)
bbb = 3141.87 +/- 5.608 (0.1785%) bbb = 3141.87 +/- 5.608 (0.1785%)
correlation matrix of the fit parameters: correlation matrix of the fit parameters:
aaa bbb
aaa bbb
aaa 1.000 aaa 1.000
bbb -1.000 1.000 bbb -1.000 1.000

249
dokumentation/evolution1d/20171005-evolution1D_7x4_100Times.gnuplot.log
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@ -1,11 +1,69 @@
iter chisq delta/lim lambda a b
0 4.2059624024e+06 0.00e+00 7.07e-01 1.000000e+00 1.000000e+00
1 1.6580035617e+05 -2.44e+06 7.07e-02 1.958416e+00 2.009886e+02 Iteration 0
2 1.6524678185e+05 -3.35e+02 7.07e-03 -2.078184e+02 2.053272e+02 WSSR : 4.20596e+06 delta(WSSR)/WSSR : 0
3 1.6165337078e+05 -2.22e+03 7.07e-04 -3.203881e+03 2.530098e+02 delta(WSSR) : 0 limit for stopping : 1e-05
4 1.6157855802e+05 -4.63e+01 7.07e-05 -3.702205e+03 2.609406e+02 lambda : 0.707197
5 1.6157855782e+05 -1.28e-04 7.07e-06 -3.703035e+03 2.609538e+02
iter chisq delta/lim lambda a b initial set of free parameter values
a = 1
b = 1
/
Iteration 1
WSSR : 165800 delta(WSSR)/WSSR : -24.3676
delta(WSSR) : -4.04016e+06 limit for stopping : 1e-05
lambda : 0.0707197
resultant parameter values
a = 1.95842
b = 200.989
/
Iteration 2
WSSR : 165247 delta(WSSR)/WSSR : -0.00334999
delta(WSSR) : -553.574 limit for stopping : 1e-05
lambda : 0.00707197
resultant parameter values
a = -207.818
b = 205.327
/
Iteration 3
WSSR : 161653 delta(WSSR)/WSSR : -0.0222291
delta(WSSR) : -3593.41 limit for stopping : 1e-05
lambda : 0.000707197
resultant parameter values
a = -3203.88
b = 253.01
/
Iteration 4
WSSR : 161579 delta(WSSR)/WSSR : -0.000463012
delta(WSSR) : -74.8128 limit for stopping : 1e-05
lambda : 7.07197e-05
resultant parameter values
a = -3702.21
b = 260.941
/
Iteration 5
WSSR : 161579 delta(WSSR)/WSSR : -1.2809e-09
delta(WSSR) : -0.000206966 limit for stopping : 1e-05
lambda : 7.07197e-06
resultant parameter values
a = -3703.04
b = 260.954
After 5 iterations the fit converged. After 5 iterations the fit converged.
final sum of squares of residuals : 161579 final sum of squares of residuals : 161579
@ -17,21 +75,82 @@ variance of residuals (reduced chisquare) = WSSR/ndf : 1648.76
Final set of parameters Asymptotic Standard Error Final set of parameters Asymptotic Standard Error
======================= ========================== ======================= ==========================
a = -3703.04 +/- 2343 (63.28%) a = -3703.04 +/- 2343 (63.28%)
b = 260.954 +/- 37.51 (14.38%) b = 260.954 +/- 37.51 (14.38%)
correlation matrix of the fit parameters: correlation matrix of the fit parameters:
a b
a b
a 1.000 a 1.000
b -0.994 1.000 b -0.994 1.000
iter chisq delta/lim lambda aa bb
0 4.1694597860e+06 0.00e+00 9.64e-01 1.000000e+00 1.000000e+00
1 1.6525762522e+05 -2.42e+06 9.64e-02 1.017401e+02 1.068470e+02 Iteration 0
2 1.6449315575e+05 -4.65e+02 9.64e-03 2.381672e+02 -1.846147e+01 WSSR : 4.16946e+06 delta(WSSR)/WSSR : 0
3 1.6091869157e+05 -2.22e+03 9.64e-04 1.622183e+03 -1.299781e+03 delta(WSSR) : 0 limit for stopping : 1e-05
4 1.6088124757e+05 -2.33e+01 9.64e-05 1.778896e+03 -1.444867e+03 lambda : 0.963614
5 1.6088124752e+05 -2.98e-05 9.64e-06 1.779074e+03 -1.445031e+03
iter chisq delta/lim lambda aa bb initial set of free parameter values
aa = 1
bb = 1
/
Iteration 1
WSSR : 165258 delta(WSSR)/WSSR : -24.2301
delta(WSSR) : -4.0042e+06 limit for stopping : 1e-05
lambda : 0.0963614
resultant parameter values
aa = 101.74
bb = 106.847
/
Iteration 2
WSSR : 164493 delta(WSSR)/WSSR : -0.00464742
delta(WSSR) : -764.469 limit for stopping : 1e-05
lambda : 0.00963614
resultant parameter values
aa = 238.167
bb = -18.4615
/
Iteration 3
WSSR : 160919 delta(WSSR)/WSSR : -0.0222129
delta(WSSR) : -3574.46 limit for stopping : 1e-05
lambda : 0.000963614
resultant parameter values
aa = 1622.18
bb = -1299.78
/
Iteration 4
WSSR : 160881 delta(WSSR)/WSSR : -0.000232743
delta(WSSR) : -37.444 limit for stopping : 1e-05
lambda : 9.63614e-05
resultant parameter values
aa = 1778.9
bb = -1444.87
/
Iteration 5
WSSR : 160881 delta(WSSR)/WSSR : -2.98408e-10
delta(WSSR) : -4.80083e-05 limit for stopping : 1e-05
lambda : 9.63614e-06
resultant parameter values
aa = 1779.07
bb = -1445.03
After 5 iterations the fit converged. After 5 iterations the fit converged.
final sum of squares of residuals : 160881 final sum of squares of residuals : 160881
@ -43,22 +162,93 @@ variance of residuals (reduced chisquare) = WSSR/ndf : 1641.65
Final set of parameters Asymptotic Standard Error Final set of parameters Asymptotic Standard Error
======================= ========================== ======================= ==========================
aa = 1779.07 +/- 1039 (58.39%) aa = 1779.07 +/- 1039 (58.39%)
bb = -1445.03 +/- 961.8 (66.56%) bb = -1445.03 +/- 961.8 (66.56%)
correlation matrix of the fit parameters: correlation matrix of the fit parameters:
aa bb
aa bb
aa 1.000 aa 1.000
bb -1.000 1.000 bb -1.000 1.000
iter chisq delta/lim lambda aaa bbb
0 5.3588910602e+06 0.00e+00 9.64e-01 1.000000e+00 1.000000e+00
1 1.6257651642e+04 -3.29e+07 9.64e-02 1.127845e+02 1.275042e+02 Iteration 0
2 1.3617851160e+04 -1.94e+04 9.64e-03 -1.505284e+02 3.724288e+02 WSSR : 5.35889e+06 delta(WSSR)/WSSR : 0
3 1.4658675724e+02 -9.19e+06 9.64e-04 -2.837355e+03 2.859890e+03 delta(WSSR) : 0 limit for stopping : 1e-05
4 5.4696465957e+00 -2.58e+06 9.64e-05 -3.141588e+03 3.141548e+03 lambda : 0.963614
5 5.4694656646e+00 -3.31e+00 9.64e-06 -3.141932e+03 3.141867e+03
6 5.4694656646e+00 -5.96e-09 9.64e-07 -3.141932e+03 3.141867e+03 initial set of free parameter values
iter chisq delta/lim lambda aaa bbb
aaa = 1
bbb = 1
/
Iteration 1
WSSR : 16257.7 delta(WSSR)/WSSR : -328.623
delta(WSSR) : -5.34263e+06 limit for stopping : 1e-05
lambda : 0.0963614
resultant parameter values
aaa = 112.784
bbb = 127.504
/
Iteration 2
WSSR : 13617.9 delta(WSSR)/WSSR : -0.193849
delta(WSSR) : -2639.8 limit for stopping : 1e-05
lambda : 0.00963614
resultant parameter values
aaa = -150.528
bbb = 372.429
/
Iteration 3
WSSR : 146.587 delta(WSSR)/WSSR : -91.8996
delta(WSSR) : -13471.3 limit for stopping : 1e-05
lambda : 0.000963614
resultant parameter values
aaa = -2837.35
bbb = 2859.89
/
Iteration 4
WSSR : 5.46965 delta(WSSR)/WSSR : -25.8
delta(WSSR) : -141.117 limit for stopping : 1e-05
lambda : 9.63614e-05
resultant parameter values
aaa = -3141.59
bbb = 3141.55
/
Iteration 5
WSSR : 5.46947 delta(WSSR)/WSSR : -3.30802e-05
delta(WSSR) : -0.000180931 limit for stopping : 1e-05
lambda : 9.63614e-06
resultant parameter values
aaa = -3141.93
bbb = 3141.87
/
Iteration 6
WSSR : 5.46947 delta(WSSR)/WSSR : -5.95966e-14
delta(WSSR) : -3.25961e-13 limit for stopping : 1e-05
lambda : 9.63614e-07
resultant parameter values
aaa = -3141.93
bbb = 3141.87
After 6 iterations the fit converged. After 6 iterations the fit converged.
final sum of squares of residuals : 5.46947 final sum of squares of residuals : 5.46947
@ -70,10 +260,13 @@ variance of residuals (reduced chisquare) = WSSR/ndf : 0.0558109
Final set of parameters Asymptotic Standard Error Final set of parameters Asymptotic Standard Error
======================= ========================== ======================= ==========================
aaa = -3141.93 +/- 6.057 (0.1928%) aaa = -3141.93 +/- 6.057 (0.1928%)
bbb = 3141.87 +/- 5.608 (0.1785%) bbb = 3141.87 +/- 5.608 (0.1785%)
correlation matrix of the fit parameters: correlation matrix of the fit parameters:
aaa bbb
aaa bbb
aaa 1.000 aaa 1.000
bbb -1.000 1.000 bbb -1.000 1.000

12
dokumentation/evolution1d/20171005-evolution1D_7x4_100Times.gnuplot.script
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@ -2,19 +2,19 @@ set datafile separator ","
f(x)=a*x+b f(x)=a*x+b
fit f(x) "20171005-evolution1D_7x4_100Times.csv" every ::1 using 2:5 via a,b fit f(x) "20171005-evolution1D_7x4_100Times.csv" every ::1 using 2:5 via a,b
set terminal png set terminal png
set xlabel 'regularity' set xlabel 'Regularity'
set ylabel 'steps' set ylabel 'Iterations'
set output "20171005-evolution1D_7x4_100Times_regularity-vs-steps.png" set output "20171005-evolution1D_7x4_100Times_regularity-vs-steps.png"
plot "20171005-evolution1D_7x4_100Times.csv" every ::1 using 2:5 title "data", f(x) title "lin. fit" lc rgb "black" plot "20171005-evolution1D_7x4_100Times.csv" every ::1 using 2:5 title "data", f(x) title "lin. fit" lc rgb "black"
g(x)=aa*x+bb g(x)=aa*x+bb
fit g(x) "20171005-evolution1D_7x4_100Times.csv" every ::1 using 4:5 via aa,bb fit g(x) "20171005-evolution1D_7x4_100Times.csv" every ::1 using 4:5 via aa,bb
set xlabel 'improvement potential' set xlabel 'Improvement potential'
set ylabel 'steps' set ylabel 'Iterations'
set output "20171005-evolution1D_7x4_100Times_improvement-vs-steps.png" set output "20171005-evolution1D_7x4_100Times_improvement-vs-steps.png"
plot "20171005-evolution1D_7x4_100Times.csv" every ::1 using 4:5 title "data", g(x) title "lin. fit" lc rgb "black" plot "20171005-evolution1D_7x4_100Times.csv" every ::1 using 4:5 title "data", g(x) title "lin. fit" lc rgb "black"
h(x)=aaa*x+bbb h(x)=aaa*x+bbb
fit h(x) "20171005-evolution1D_7x4_100Times.csv" every ::1 using 4:6 via aaa,bbb fit h(x) "20171005-evolution1D_7x4_100Times.csv" every ::1 using 4:6 via aaa,bbb
set xlabel 'improvement potential' set xlabel 'Improvement potential'
set ylabel 'evolution error' set ylabel 'Fitting error'
set output "20171005-evolution1D_7x4_100Times_improvement-vs-evo-error.png" set output "20171005-evolution1D_7x4_100Times_improvement-vs-evo-error.png"
plot "20171005-evolution1D_7x4_100Times.csv" every ::1 using 4:6 title "data", h(x) title "lin. fit" lc rgb "black" plot "20171005-evolution1D_7x4_100Times.csv" every ::1 using 4:6 title "data", h(x) title "lin. fit" lc rgb "black"

37
dokumentation/evolution1d/20171005-evolution1D_7x4_100Times.spearman Normal file
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@ -0,0 +1,37 @@
[1] "================ Analyzing 20171005-evolution1D_7x4_100Times.csv"
[1] "spearman for improvement-potential vs. evolution-error"
x y
x 1 -1
y -1 1
n= 100
P
x y
x 0
y 0
[1] "spearman for regularity vs. steps"
x y
x 1.00 -0.21
y -0.21 1.00
n= 100
P
x y
x 0.0396
y 0.0396
[1] "spearman for variability vs. evolution-error"
x y
x 1 NaN
y NaN 1
n= 100
P
x y
x
y

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101
dokumentation/evolution1d/20171005-evolution1D_7x7_100Times.error Normal file
View File

@ -0,0 +1,101 @@
"Evolution error"
126.241
110.962
125.853
140.195
126.647
154.539
107.206
128.558
136.77
145.941
171.996
118.437
143.556
120.873
105.887
154.67
154.182
116.314
113.496
159.92
158.727
108.387
148.334
112.767
139.428
124.479
106.309
139.721
133.951
135.062
133.884
120.051
101.318
135.279
110.051
136.437
134.697
113.78
122.484
114.487
127.55
125.629
162.041
156.364
113.788
138.26
109.996
164.442
105.711
156.553
116.304
113.339
118.637
164.957
97.1774
137.814
158.652
128.639
144.853
136.728
100.103
129.972
134.171
102.086
137.823
120.165
121.511
110.067
112.614
105.271
140.821
124.285
158.103
140.515
101.64
129.887
167.194
110.881
146.669
118.035
150.583
143.211
114.785
146.421
130.875
107.373
152.164
129.282
98.1416
129.997
117
111.092
114.087
107.558
144.45
118.484
109.701
126.593
168.971
143.838

68
dokumentation/evolution1d/20171005-evolution1D_7x7_100Times.gnuplot.fit.log
View File

@ -1,7 +1,7 @@
******************************************************************************* *******************************************************************************
Thu Oct 5 14:22:52 2017 Tue Oct 24 02:24:04 2017
FIT: data read from "20171005-evolution1D_7x7_100Times.csv" every ::1 using 2:5 FIT: data read from "20171005-evolution1D_7x7_100Times.csv" every ::1 using 2:5
@ -10,16 +10,23 @@ FIT: data read from "20171005-evolution1D_7x7_100Times.csv" every ::1 using 2
residuals are weighted equally (unit weight) residuals are weighted equally (unit weight)
function used for fitting: f(x) function used for fitting: f(x)
f(x)=a*x+b
fitted parameters initialized with current variable values fitted parameters initialized with current variable values
iter chisq delta/lim lambda a b
0 5.1103239746e+06 0.00e+00 7.07e-01 1.000000e+00 1.000000e+00
5 1.3279798348e+05 -2.35e-06 7.07e-06 -5.771314e+02 2.408587e+02 Iteration 0
WSSR : 5.11032e+06 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.707405
initial set of free parameter values
a = 1
b = 1
After 5 iterations the fit converged. After 5 iterations the fit converged.
final sum of squares of residuals : 132798 final sum of squares of residuals : 132798
rel. change during last iteration : -2.35102e-11 rel. change during last iteration : -2.35098e-11
degrees of freedom (FIT_NDF) : 98 degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 36.8114 rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 36.8114
@ -27,17 +34,20 @@ variance of residuals (reduced chisquare) = WSSR/ndf : 1355.08
Final set of parameters Asymptotic Standard Error Final set of parameters Asymptotic Standard Error
======================= ========================== ======================= ==========================
a = -577.131 +/- 1945 (337%) a = -577.131 +/- 1945 (337%)
b = 240.859 +/- 56.49 (23.46%) b = 240.859 +/- 56.49 (23.46%)
correlation matrix of the fit parameters: correlation matrix of the fit parameters:
a b
a b
a 1.000 a 1.000
b -0.998 1.000 b -0.998 1.000
******************************************************************************* *******************************************************************************
Thu Oct 5 14:22:52 2017 Tue Oct 24 02:24:04 2017
FIT: data read from "20171005-evolution1D_7x7_100Times.csv" every ::1 using 4:5 FIT: data read from "20171005-evolution1D_7x7_100Times.csv" every ::1 using 4:5
@ -46,12 +56,19 @@ FIT: data read from "20171005-evolution1D_7x7_100Times.csv" every ::1 using 4
residuals are weighted equally (unit weight) residuals are weighted equally (unit weight)
function used for fitting: g(x) function used for fitting: g(x)
g(x)=aa*x+bb
fitted parameters initialized with current variable values fitted parameters initialized with current variable values
iter chisq delta/lim lambda aa bb
0 5.0689010815e+06 0.00e+00 9.80e-01 1.000000e+00 1.000000e+00
5 9.8040471485e+04 -1.51e-05 9.80e-06 3.134345e+03 -2.780953e+03 Iteration 0
WSSR : 5.0689e+06 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.979606
initial set of free parameter values
aa = 1
bb = 1
After 5 iterations the fit converged. After 5 iterations the fit converged.
final sum of squares of residuals : 98040.5 final sum of squares of residuals : 98040.5
@ -63,17 +80,20 @@ variance of residuals (reduced chisquare) = WSSR/ndf : 1000.41
Final set of parameters Asymptotic Standard Error Final set of parameters Asymptotic Standard Error
======================= ========================== ======================= ==========================
aa = 3134.35 +/- 530.8 (16.94%) aa = 3134.35 +/- 530.8 (16.94%)
bb = -2780.95 +/- 509 (18.3%) bb = -2780.95 +/- 509 (18.3%)
correlation matrix of the fit parameters: correlation matrix of the fit parameters:
aa bb
aa bb
aa 1.000 aa 1.000
bb -1.000 1.000 bb -1.000 1.000
******************************************************************************* *******************************************************************************
Thu Oct 5 14:22:52 2017 Tue Oct 24 02:24:04 2017
FIT: data read from "20171005-evolution1D_7x7_100Times.csv" every ::1 using 4:6 FIT: data read from "20171005-evolution1D_7x7_100Times.csv" every ::1 using 4:6
@ -82,12 +102,19 @@ FIT: data read from "20171005-evolution1D_7x7_100Times.csv" every ::1 using 4
residuals are weighted equally (unit weight) residuals are weighted equally (unit weight)
function used for fitting: h(x) function used for fitting: h(x)
h(x)=aaa*x+bbb
fitted parameters initialized with current variable values fitted parameters initialized with current variable values
iter chisq delta/lim lambda aaa bbb
0 1.6606716013e+06 0.00e+00 9.80e-01 1.000000e+00 1.000000e+00
5 3.7498507724e+00 -4.46e-01 9.80e-06 -3.142464e+03 3.142323e+03 Iteration 0
WSSR : 1.66067e+06 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.979606
initial set of free parameter values
aaa = 1
bbb = 1
After 5 iterations the fit converged. After 5 iterations the fit converged.
final sum of squares of residuals : 3.74985 final sum of squares of residuals : 3.74985
@ -99,10 +126,13 @@ variance of residuals (reduced chisquare) = WSSR/ndf : 0.0382638
Final set of parameters Asymptotic Standard Error Final set of parameters Asymptotic Standard Error
======================= ========================== ======================= ==========================
aaa = -3142.46 +/- 3.283 (0.1045%) aaa = -3142.46 +/- 3.283 (0.1045%)
bbb = 3142.32 +/- 3.148 (0.1002%) bbb = 3142.32 +/- 3.148 (0.1002%)
correlation matrix of the fit parameters: correlation matrix of the fit parameters:
aaa bbb
aaa bbb
aaa 1.000 aaa 1.000
bbb -1.000 1.000 bbb -1.000 1.000

239
dokumentation/evolution1d/20171005-evolution1D_7x7_100Times.gnuplot.log
View File

@ -1,15 +1,73 @@
iter chisq delta/lim lambda a b
0 5.1103239746e+06 0.00e+00 7.07e-01 1.000000e+00 1.000000e+00
1 1.3304344126e+05 -3.74e+06 7.07e-02 7.011364e+00 2.228167e+02 Iteration 0
2 1.3290446272e+05 -1.05e+02 7.07e-03 -3.195295e+01 2.250561e+02 WSSR : 5.11032e+06 delta(WSSR)/WSSR : 0
3 1.3279958538e+05 -7.90e+01 7.07e-04 -5.102627e+02 2.389204e+02 delta(WSSR) : 0 limit for stopping : 1e-05
4 1.3279798349e+05 -1.21e+00 7.07e-05 -5.770380e+02 2.408559e+02 lambda : 0.707405
5 1.3279798348e+05 -2.35e-06 7.07e-06 -5.771314e+02 2.408587e+02
iter chisq delta/lim lambda a b initial set of free parameter values
a = 1
b = 1
/
Iteration 1
WSSR : 133043 delta(WSSR)/WSSR : -37.4109
delta(WSSR) : -4.97728e+06 limit for stopping : 1e-05
lambda : 0.0707405
resultant parameter values
a = 7.01136
b = 222.817
/
Iteration 2
WSSR : 132904 delta(WSSR)/WSSR : -0.0010457
delta(WSSR) : -138.979 limit for stopping : 1e-05
lambda : 0.00707405
resultant parameter values
a = -31.953
b = 225.056
/
Iteration 3
WSSR : 132800 delta(WSSR)/WSSR : -0.000789741
delta(WSSR) : -104.877 limit for stopping : 1e-05
lambda : 0.000707405
resultant parameter values
a = -510.263
b = 238.92
/
Iteration 4
WSSR : 132798 delta(WSSR)/WSSR : -1.20626e-05
delta(WSSR) : -1.60189 limit for stopping : 1e-05
lambda : 7.07405e-05
resultant parameter values
a = -577.038
b = 240.856
/
Iteration 5
WSSR : 132798 delta(WSSR)/WSSR : -2.35098e-11
delta(WSSR) : -3.12206e-06 limit for stopping : 1e-05
lambda : 7.07405e-06
resultant parameter values
a = -577.131
b = 240.859
After 5 iterations the fit converged. After 5 iterations the fit converged.
final sum of squares of residuals : 132798 final sum of squares of residuals : 132798
rel. change during last iteration : -2.35102e-11 rel. change during last iteration : -2.35098e-11
degrees of freedom (FIT_NDF) : 98 degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 36.8114 rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 36.8114
@ -17,21 +75,82 @@ variance of residuals (reduced chisquare) = WSSR/ndf : 1355.08
Final set of parameters Asymptotic Standard Error Final set of parameters Asymptotic Standard Error
======================= ========================== ======================= ==========================
a = -577.131 +/- 1945 (337%) a = -577.131 +/- 1945 (337%)
b = 240.859 +/- 56.49 (23.46%) b = 240.859 +/- 56.49 (23.46%)
correlation matrix of the fit parameters: correlation matrix of the fit parameters:
a b
a b
a 1.000 a 1.000
b -0.998 1.000 b -0.998 1.000
iter chisq delta/lim lambda aa bb
0 5.0689010815e+06 0.00e+00 9.80e-01 1.000000e+00 1.000000e+00
1 1.3046686978e+05 -3.79e+06 9.80e-02 1.172773e+02 1.106372e+02 Iteration 0
2 1.2074716009e+05 -8.05e+03 9.80e-03 6.053195e+02 -3.561808e+02 WSSR : 5.0689e+06 delta(WSSR)/WSSR : 0
3 9.8095704395e+04 -2.31e+04 9.80e-04 3.009614e+03 -2.661363e+03 delta(WSSR) : 0 limit for stopping : 1e-05
4 9.8040471500e+04 -5.63e+01 9.80e-05 3.134281e+03 -2.780891e+03 lambda : 0.979606
5 9.8040471485e+04 -1.51e-05 9.80e-06 3.134345e+03 -2.780953e+03
iter chisq delta/lim lambda aa bb initial set of free parameter values
aa = 1
bb = 1
/
Iteration 1
WSSR : 130467 delta(WSSR)/WSSR : -37.852
delta(WSSR) : -4.93843e+06 limit for stopping : 1e-05
lambda : 0.0979606
resultant parameter values
aa = 117.277
bb = 110.637
/
Iteration 2
WSSR : 120747 delta(WSSR)/WSSR : -0.0804964
delta(WSSR) : -9719.71 limit for stopping : 1e-05
lambda : 0.00979606
resultant parameter values
aa = 605.319
bb = -356.181
/
Iteration 3
WSSR : 98095.7 delta(WSSR)/WSSR : -0.230912
delta(WSSR) : -22651.5 limit for stopping : 1e-05
lambda : 0.000979606
resultant parameter values
aa = 3009.61
bb = -2661.36
/
Iteration 4
WSSR : 98040.5 delta(WSSR)/WSSR : -0.000563368
delta(WSSR) : -55.2329 limit for stopping : 1e-05
lambda : 9.79606e-05
resultant parameter values
aa = 3134.28
bb = -2780.89
/
Iteration 5
WSSR : 98040.5 delta(WSSR)/WSSR : -1.51467e-10
delta(WSSR) : -1.48499e-05 limit for stopping : 1e-05
lambda : 9.79606e-06
resultant parameter values
aa = 3134.35
bb = -2780.95
After 5 iterations the fit converged. After 5 iterations the fit converged.
final sum of squares of residuals : 98040.5 final sum of squares of residuals : 98040.5
@ -43,21 +162,82 @@ variance of residuals (reduced chisquare) = WSSR/ndf : 1000.41
Final set of parameters Asymptotic Standard Error Final set of parameters Asymptotic Standard Error
======================= ========================== ======================= ==========================
aa = 3134.35 +/- 530.8 (16.94%) aa = 3134.35 +/- 530.8 (16.94%)
bb = -2780.95 +/- 509 (18.3%) bb = -2780.95 +/- 509 (18.3%)
correlation matrix of the fit parameters: correlation matrix of the fit parameters:
aa bb
aa bb
aa 1.000 aa 1.000
bb -1.000 1.000 bb -1.000 1.000
iter chisq delta/lim lambda aaa bbb
0 1.6606716013e+06 0.00e+00 9.80e-01 1.000000e+00 1.000000e+00
1 3.6419319985e+04 -4.46e+06 9.80e-02 5.817813e+01 7.298767e+01 Iteration 0
2 2.5572102437e+04 -4.24e+04 9.80e-03 -4.588023e+02 5.692906e+02 WSSR : 1.66067e+06 delta(WSSR)/WSSR : 0
3 6.5943618679e+01 -3.87e+07 9.80e-04 -3.010106e+03 3.015422e+03 delta(WSSR) : 0 limit for stopping : 1e-05
4 3.7498674938e+00 -1.66e+06 9.80e-05 -3.142395e+03 3.142258e+03 lambda : 0.979606
5 3.7498507724e+00 -4.46e-01 9.80e-06 -3.142464e+03 3.142323e+03
iter chisq delta/lim lambda aaa bbb initial set of free parameter values
aaa = 1
bbb = 1
/
Iteration 1
WSSR : 36419.3 delta(WSSR)/WSSR : -44.5986
delta(WSSR) : -1.62425e+06 limit for stopping : 1e-05
lambda : 0.0979606
resultant parameter values
aaa = 58.1781
bbb = 72.9877
/
Iteration 2
WSSR : 25572.1 delta(WSSR)/WSSR : -0.424182
delta(WSSR) : -10847.2 limit for stopping : 1e-05
lambda : 0.00979606
resultant parameter values
aaa = -458.802
bbb = 569.291
/
Iteration 3
WSSR : 65.9436 delta(WSSR)/WSSR : -386.787
delta(WSSR) : -25506.2 limit for stopping : 1e-05
lambda : 0.000979606
resultant parameter values
aaa = -3010.11
bbb = 3015.42
/
Iteration 4
WSSR : 3.74987 delta(WSSR)/WSSR : -16.5856
delta(WSSR) : -62.1938 limit for stopping : 1e-05
lambda : 9.79606e-05
resultant parameter values
aaa = -3142.4
bbb = 3142.26
/
Iteration 5
WSSR : 3.74985 delta(WSSR)/WSSR : -4.45921e-06
delta(WSSR) : -1.67214e-05 limit for stopping : 1e-05
lambda : 9.79606e-06
resultant parameter values
aaa = -3142.46
bbb = 3142.32
After 5 iterations the fit converged. After 5 iterations the fit converged.
final sum of squares of residuals : 3.74985 final sum of squares of residuals : 3.74985
@ -69,10 +249,13 @@ variance of residuals (reduced chisquare) = WSSR/ndf : 0.0382638
Final set of parameters Asymptotic Standard Error Final set of parameters Asymptotic Standard Error
======================= ========================== ======================= ==========================
aaa = -3142.46 +/- 3.283 (0.1045%) aaa = -3142.46 +/- 3.283 (0.1045%)
bbb = 3142.32 +/- 3.148 (0.1002%) bbb = 3142.32 +/- 3.148 (0.1002%)
correlation matrix of the fit parameters: correlation matrix of the fit parameters:
aaa bbb
aaa bbb
aaa 1.000 aaa 1.000
bbb -1.000 1.000 bbb -1.000 1.000

12
dokumentation/evolution1d/20171005-evolution1D_7x7_100Times.gnuplot.script
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@ -2,19 +2,19 @@ set datafile separator ","
f(x)=a*x+b f(x)=a*x+b
fit f(x) "20171005-evolution1D_7x7_100Times.csv" every ::1 using 2:5 via a,b fit f(x) "20171005-evolution1D_7x7_100Times.csv" every ::1 using 2:5 via a,b
set terminal png set terminal png
set xlabel 'regularity' set xlabel 'Regularity'
set ylabel 'steps' set ylabel 'Iterations'
set output "20171005-evolution1D_7x7_100Times_regularity-vs-steps.png" set output "20171005-evolution1D_7x7_100Times_regularity-vs-steps.png"
plot "20171005-evolution1D_7x7_100Times.csv" every ::1 using 2:5 title "data", f(x) title "lin. fit" lc rgb "black" plot "20171005-evolution1D_7x7_100Times.csv" every ::1 using 2:5 title "data", f(x) title "lin. fit" lc rgb "black"
g(x)=aa*x+bb g(x)=aa*x+bb
fit g(x) "20171005-evolution1D_7x7_100Times.csv" every ::1 using 4:5 via aa,bb fit g(x) "20171005-evolution1D_7x7_100Times.csv" every ::1 using 4:5 via aa,bb
set xlabel 'improvement potential' set xlabel 'Improvement potential'
set ylabel 'steps' set ylabel 'Iterations'
set output "20171005-evolution1D_7x7_100Times_improvement-vs-steps.png" set output "20171005-evolution1D_7x7_100Times_improvement-vs-steps.png"
plot "20171005-evolution1D_7x7_100Times.csv" every ::1 using 4:5 title "data", g(x) title "lin. fit" lc rgb "black" plot "20171005-evolution1D_7x7_100Times.csv" every ::1 using 4:5 title "data", g(x) title "lin. fit" lc rgb "black"
h(x)=aaa*x+bbb h(x)=aaa*x+bbb
fit h(x) "20171005-evolution1D_7x7_100Times.csv" every ::1 using 4:6 via aaa,bbb fit h(x) "20171005-evolution1D_7x7_100Times.csv" every ::1 using 4:6 via aaa,bbb
set xlabel 'improvement potential' set xlabel 'Improvement potential'
set ylabel 'evolution error' set ylabel 'Fitting error'
set output "20171005-evolution1D_7x7_100Times_improvement-vs-evo-error.png" set output "20171005-evolution1D_7x7_100Times_improvement-vs-evo-error.png"
plot "20171005-evolution1D_7x7_100Times.csv" every ::1 using 4:6 title "data", h(x) title "lin. fit" lc rgb "black" plot "20171005-evolution1D_7x7_100Times.csv" every ::1 using 4:6 title "data", h(x) title "lin. fit" lc rgb "black"

37
dokumentation/evolution1d/20171005-evolution1D_7x7_100Times.spearman Normal file
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@ -0,0 +1,37 @@
[1] "================ Analyzing 20171005-evolution1D_7x7_100Times.csv"
[1] "spearman for improvement-potential vs. evolution-error"
x y
x 1 -1
y -1 1
n= 100
P
x y
x 0
y 0
[1] "spearman for regularity vs. steps"
x y
x 1.00 -0.01
y -0.01 1.00
n= 100
P
x y
x 0.9216
y 0.9216
[1] "spearman for variability vs. evolution-error"
x y
x 1 NaN
y NaN 1
n= 100
P
x y
x
y

BIN
dokumentation/evolution1d/20171005-evolution1D_7x7_100Times_improvement-vs-evo-error.png
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dokumentation/evolution1d/20171005-evolution1D_7x7_100Times_improvement-vs-steps.png
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111
dokumentation/evolution1d/20171020-evolution1D_4x7_100Times_addedOne.csv Normal file
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@ -0,0 +1,111 @@
"Least squares",regularity,variability,improvement,steps,"Evolution error",sigma
284.365,0.0136315,0.00124444,0.967997,202,298.423,0.0463019
271.226,0.0124703,0.00124444,0.969475,286,284.695,0.0232577
321.902,0.0116571,0.00124444,0.963772,203,337.628,0.0416545
265.327,0.0106321,0.00124444,0.970139,254,278.241,0.0468512
252.821,0.0130542,0.00124444,0.971547,185,264.793,0.0375129
245.664,0.0125761,0.00124444,0.972353,248,257.7,0.0374176
261.824,0.0118357,0.00124444,0.970567,202,274.784,0.0273035
274.7,0.0100903,0.00124444,0.969116,291,287.743,0.0552393
291.863,0.0131507,0.00124444,0.967199,186,306.328,0.0467986
269.959,0.01103,0.00124444,0.969618,259,283.047,0.0412
275.979,0.0134071,0.00124444,0.96894,273,289.659,0.0307383
238.92,0.0128211,0.00124444,0.973138,242,250.664,0.0280029
249.451,0.0146911,0.00124444,0.971926,295,261.859,0.0169273
305.491,0.014604,0.00124444,0.965637,194,320.352,0.0577861
288.014,0.0139405,0.00124444,0.967586,196,302.292,0.0452684
338.043,0.0131414,0.00124444,0.961956,291,354.924,0.0488456
278.968,0.0128255,0.00124444,0.968604,263,292.181,0.0350694
272.195,0.0122229,0.00124444,0.969373,222,285.759,0.0382185
240.208,0.0129601,0.00124444,0.972996,260,252.184,0.034162
301.799,0.0137701,0.00124444,0.966034,209,316.796,0.0310414
269.092,0.0138025,0.00124444,0.969715,237,282.546,0.0347684
296.74,0.0105778,0.00124444,0.966604,231,311.274,0.0300026
232.041,0.0140952,0.00124444,0.973885,249,242.707,0.0435354
253.458,0.0111011,0.00124444,0.971475,227,265.12,0.0549504
279.053,0.0121752,0.00124444,0.968594,189,292.53,0.0338526
357.783,0.0144251,0.00124444,0.959734,173,373.645,0.062108
253.292,0.0133291,0.00124444,0.971494,169,265.231,0.0491994
323.812,0.0122242,0.00124444,0.963557,220,339.851,0.0301496
316.499,0.0118804,0.00124444,0.96438,240,331.492,0.0588588
283.24,0.0116293,0.00124444,0.968123,211,296.143,0.054793
255.606,0.0122573,0.00124444,0.971233,304,267.902,0.0353475
295.028,0.010363,0.00124444,0.966796,178,309.679,0.0482631
257.028,0.0083189,0.00124444,0.971073,225,269.797,0.0344004
258.367,0.0117439,0.00124444,0.970922,220,269.874,0.0401674
299.638,0.0160765,0.00124444,0.966278,199,314.378,0.0648766
261.133,0.0133491,0.00124444,0.970655,217,273.406,0.0612996
258.095,0.0131587,0.00124444,0.970953,316,270.657,0.056618
295.711,0.010812,0.00124444,0.96672,225,310.459,0.0473272
235.913,0.0147284,0.00124444,0.973449,266,247.483,0.0249892
261.455,0.013335,0.00124444,0.970575,283,274.461,0.016123
260.914,0.0130128,0.00124444,0.970639,228,273.334,0.0454057
274.414,0.0122356,0.00124444,0.969116,268,288.058,0.0308934
286.019,0.0115339,0.00124444,0.96781,227,299.622,0.0403952
277.458,0.0105674,0.00124444,0.968774,264,291.267,0.0356097
240.979,0.0120099,0.00124444,0.972879,236,252.976,0.0191082
262.478,0.0115369,0.00124444,0.97046,309,275.311,0.0367373
267.337,0.0112152,0.00124444,0.969913,361,280.607,0.0321421
257.301,0.0132868,0.00124444,0.971042,270,270.017,0.0269151
288.91,0.0148347,0.00124444,0.967485,254,303.237,0.0235924
272.936,0.0113799,0.00124444,0.969283,295,286.265,0.0316973
341.566,0.010463,0.00124444,0.961559,258,358.606,0.0274781
286.135,0.0124676,0.00124444,0.967797,229,300.357,0.0354075
248.633,0.0129678,0.00124444,0.972018,175,260.384,0.0565796
260.903,0.0127077,0.00124444,0.970637,211,271.183,0.0558095
262.906,0.0141616,0.00124444,0.970412,283,275.937,0.0282904
325.249,0.0119214,0.00124444,0.963395,212,340.822,0.0349593
326.696,0.0107315,0.00124444,0.963232,263,342.87,0.026237
235.383,0.00721808,0.00124444,0.973509,285,247.036,0.0280001
291.26,0.00941284,0.00124444,0.96722,231,305.313,0.0373272
346.221,0.0115527,0.00124444,0.961148,143,362.535,0.0570737
278.604,0.0119867,0.00124444,0.968757,237,292.104,0.0510293
257.624,0.015389,0.00124444,0.971008,187,270.485,0.0472684
310.128,0.0117237,0.00124444,0.965098,294,325.329,0.0283465
262.715,0.00991148,0.00124444,0.970433,312,275.62,0.0275899
233.759,0.0120007,0.00124444,0.973692,251,245.371,0.0331032
283.341,0.0167926,0.00124444,0.968112,202,296.747,0.049088
273.407,0.0118834,0.00124444,0.96923,192,285.701,0.0599774
301.436,0.00911246,0.00124444,0.966075,207,315.472,0.0515595
249.385,0.0134275,0.00124444,0.971933,202,261.363,0.0456747
275.337,0.0146336,0.00124444,0.969013,246,288.343,0.0325241
242.551,0.0118938,0.00124444,0.972703,259,253.537,0.0439554
336.977,0.0123799,0.00124444,0.962078,328,353.546,0.0416258
259.907,0.0130097,0.00124444,0.970749,231,272.705,0.0296309
255.45,0.015162,0.00124444,0.971251,164,267.961,0.0854988
258.502,0.0125001,0.00124444,0.970907,247,269.917,0.0547566
289.38,0.0139735,0.00124444,0.967432,166,303.207,0.075952
267.126,0.00824213,0.00124444,0.969937,292,280.342,0.0378409
275.035,0.00992348,0.00124444,0.969047,257,288.78,0.0253235
315.806,0.0120417,0.00124444,0.964458,283,330.349,0.0701877
284.213,0.0120468,0.00124444,0.968014,211,297.971,0.0406944
287.896,0.0128398,0.00124444,0.967712,225,302.104,0.03425
272.258,0.0107615,0.00124444,0.969359,270,285.659,0.0413873
297.724,0.0160692,0.00124444,0.966494,184,311.745,0.0386387
226.48,0.0145919,0.00124444,0.974512,289,237.277,0.026509
270.479,0.012833,0.00124444,0.969559,206,283.579,0.0290488
269.589,0.0127376,0.00124444,0.969659,224,282.437,0.0528172
280.521,0.0132416,0.00124444,0.968429,220,294.507,0.0378721
275.907,0.0131407,0.00124444,0.968948,230,289.471,0.0382769
249.457,0.0118344,0.00124444,0.971925,273,261.243,0.0274762
277.053,0.0111192,0.00124444,0.968872,263,290.359,0.0620501
251.523,0.0152724,0.00124444,0.971693,256,263.942,0.0346986
267.086,0.0131384,0.00124444,0.969941,209,279.455,0.0422156
269.944,0.0132077,0.00124444,0.96962,185,283.11,0.0468952
286.474,0.0134134,0.00124444,0.967759,320,300.758,0.0229568
261.21,0.0122179,0.00124444,0.970602,229,273.678,0.042026
271.748,0.0122565,0.00124444,0.969416,227,284.549,0.0369429
278.334,0.00939272,0.00124444,0.968675,325,291.625,0.0419345
258.829,0.0143853,0.00124444,0.970871,294,271.551,0.0349143
247.163,0.00928993,0.00124444,0.972183,232,259.444,0.0304895
289.958,0.01276,0.00124444,0.967367,211,304.274,0.0585419
302.262,0.0110498,0.00124444,0.965982,235,317.339,0.0429466
266.697,0.0141856,0.00124444,0.969985,257,279.883,0.027867
279.365,0.0132202,0.00124444,0.968559,309,292.823,0.0333579
269.457,0.013538,0.00124444,0.969674,268,282.633,0.0292981
266.753,0.0126111,0.00124444,0.969985,224,278.892,0.0530028
248.308,0.0152383,0.00124444,0.972054,165,260.591,0.0615863
261.957,0.0114572,0.00124444,0.970518,193,274.925,0.0212163
337.165,0.0107314,0.00124444,0.962054,228,353.899,0.0526597
314.063,0.0125739,0.00124444,0.964654,275,329.705,0.0259226
270.826,0.0146448,0.00124444,0.96952,348,284.285,0.0177896
1 Least squares regularity variability improvement steps Evolution error sigma
2 284.365 0.0136315 0.00124444 0.967997 202 298.423 0.0463019
3 271.226 0.0124703 0.00124444 0.969475 286 284.695 0.0232577
4 321.902 0.0116571 0.00124444 0.963772 203 337.628 0.0416545
5 265.327 0.0106321 0.00124444 0.970139 254 278.241 0.0468512
6 252.821 0.0130542 0.00124444 0.971547 185 264.793 0.0375129
7 245.664 0.0125761 0.00124444 0.972353 248 257.7 0.0374176
8 261.824 0.0118357 0.00124444 0.970567 202 274.784 0.0273035
9 274.7 0.0100903 0.00124444 0.969116 291 287.743 0.0552393
10 291.863 0.0131507 0.00124444 0.967199 186 306.328 0.0467986
11 269.959 0.01103 0.00124444 0.969618 259 283.047 0.0412
12 275.979 0.0134071 0.00124444 0.96894 273 289.659 0.0307383
13 238.92 0.0128211 0.00124444 0.973138 242 250.664 0.0280029
14 249.451 0.0146911 0.00124444 0.971926 295 261.859 0.0169273
15 305.491 0.014604 0.00124444 0.965637 194 320.352 0.0577861
16 288.014 0.0139405 0.00124444 0.967586 196 302.292 0.0452684
17 338.043 0.0131414 0.00124444 0.961956 291 354.924 0.0488456
18 278.968 0.0128255 0.00124444 0.968604 263 292.181 0.0350694
19 272.195 0.0122229 0.00124444 0.969373 222 285.759 0.0382185
20 240.208 0.0129601 0.00124444 0.972996 260 252.184 0.034162
21 301.799 0.0137701 0.00124444 0.966034 209 316.796 0.0310414
22 269.092 0.0138025 0.00124444 0.969715 237 282.546 0.0347684
23 296.74 0.0105778 0.00124444 0.966604 231 311.274 0.0300026
24 232.041 0.0140952 0.00124444 0.973885 249 242.707 0.0435354
25 253.458 0.0111011 0.00124444 0.971475 227 265.12 0.0549504
26 279.053 0.0121752 0.00124444 0.968594 189 292.53 0.0338526
27 357.783 0.0144251 0.00124444 0.959734 173 373.645 0.062108
28 253.292 0.0133291 0.00124444 0.971494 169 265.231 0.0491994
29 323.812 0.0122242 0.00124444 0.963557 220 339.851 0.0301496
30 316.499 0.0118804 0.00124444 0.96438 240 331.492 0.0588588
31 283.24 0.0116293 0.00124444 0.968123 211 296.143 0.054793
32 255.606 0.0122573 0.00124444 0.971233 304 267.902 0.0353475
33 295.028 0.010363 0.00124444 0.966796 178 309.679 0.0482631
34 257.028 0.0083189 0.00124444 0.971073 225 269.797 0.0344004
35 258.367 0.0117439 0.00124444 0.970922 220 269.874 0.0401674
36 299.638 0.0160765 0.00124444 0.966278 199 314.378 0.0648766
37 261.133 0.0133491 0.00124444 0.970655 217 273.406 0.0612996
38 258.095 0.0131587 0.00124444 0.970953 316 270.657 0.056618
39 295.711 0.010812 0.00124444 0.96672 225 310.459 0.0473272
40 235.913 0.0147284 0.00124444 0.973449 266 247.483 0.0249892
41 261.455 0.013335 0.00124444 0.970575 283 274.461 0.016123
42 260.914 0.0130128 0.00124444 0.970639 228 273.334 0.0454057
43 274.414 0.0122356 0.00124444 0.969116 268 288.058 0.0308934
44 286.019 0.0115339 0.00124444 0.96781 227 299.622 0.0403952
45 277.458 0.0105674 0.00124444 0.968774 264 291.267 0.0356097
46 240.979 0.0120099 0.00124444 0.972879 236 252.976 0.0191082
47 262.478 0.0115369 0.00124444 0.97046 309 275.311 0.0367373
48 267.337 0.0112152 0.00124444 0.969913 361 280.607 0.0321421
49 257.301 0.0132868 0.00124444 0.971042 270 270.017 0.0269151
50 288.91 0.0148347 0.00124444 0.967485 254 303.237 0.0235924
51 272.936 0.0113799 0.00124444 0.969283 295 286.265 0.0316973
52 341.566 0.010463 0.00124444 0.961559 258 358.606 0.0274781
53 286.135 0.0124676 0.00124444 0.967797 229 300.357 0.0354075
54 248.633 0.0129678 0.00124444 0.972018 175 260.384 0.0565796
55 260.903 0.0127077 0.00124444 0.970637 211 271.183 0.0558095
56 262.906 0.0141616 0.00124444 0.970412 283 275.937 0.0282904
57 325.249 0.0119214 0.00124444 0.963395 212 340.822 0.0349593
58 326.696 0.0107315 0.00124444 0.963232 263 342.87 0.026237
59 235.383 0.00721808 0.00124444 0.973509 285 247.036 0.0280001
60 291.26 0.00941284 0.00124444 0.96722 231 305.313 0.0373272
61 346.221 0.0115527 0.00124444 0.961148 143 362.535 0.0570737
62 278.604 0.0119867 0.00124444 0.968757 237 292.104 0.0510293
63 257.624 0.015389 0.00124444 0.971008 187 270.485 0.0472684
64 310.128 0.0117237 0.00124444 0.965098 294 325.329 0.0283465
65 262.715 0.00991148 0.00124444 0.970433 312 275.62 0.0275899
66 233.759 0.0120007 0.00124444 0.973692 251 245.371 0.0331032
67 283.341 0.0167926 0.00124444 0.968112 202 296.747 0.049088
68 273.407 0.0118834 0.00124444 0.96923 192 285.701 0.0599774
69 301.436 0.00911246 0.00124444 0.966075 207 315.472 0.0515595
70 249.385 0.0134275 0.00124444 0.971933 202 261.363 0.0456747
71 275.337 0.0146336 0.00124444 0.969013 246 288.343 0.0325241
72 242.551 0.0118938 0.00124444 0.972703 259 253.537 0.0439554
73 336.977 0.0123799 0.00124444 0.962078 328 353.546 0.0416258
74 259.907 0.0130097 0.00124444 0.970749 231 272.705 0.0296309
75 255.45 0.015162 0.00124444 0.971251 164 267.961 0.0854988
76 258.502 0.0125001 0.00124444 0.970907 247 269.917 0.0547566
77 289.38 0.0139735 0.00124444 0.967432 166 303.207 0.075952
78 267.126 0.00824213 0.00124444 0.969937 292 280.342 0.0378409
79 275.035 0.00992348 0.00124444 0.969047 257 288.78 0.0253235
80 315.806 0.0120417 0.00124444 0.964458 283 330.349 0.0701877
81 284.213 0.0120468 0.00124444 0.968014 211 297.971 0.0406944
82 287.896 0.0128398 0.00124444 0.967712 225 302.104 0.03425
83 272.258 0.0107615 0.00124444 0.969359 270 285.659 0.0413873
84 297.724 0.0160692 0.00124444 0.966494 184 311.745 0.0386387
85 226.48 0.0145919 0.00124444 0.974512 289 237.277 0.026509
86 270.479 0.012833 0.00124444 0.969559 206 283.579 0.0290488
87 269.589 0.0127376 0.00124444 0.969659 224 282.437 0.0528172
88 280.521 0.0132416 0.00124444 0.968429 220 294.507 0.0378721
89 275.907 0.0131407 0.00124444 0.968948 230 289.471 0.0382769
90 249.457 0.0118344 0.00124444 0.971925 273 261.243 0.0274762
91 277.053 0.0111192 0.00124444 0.968872 263 290.359 0.0620501
92 251.523 0.0152724 0.00124444 0.971693 256 263.942 0.0346986
93 267.086 0.0131384 0.00124444 0.969941 209 279.455 0.0422156
94 269.944 0.0132077 0.00124444 0.96962 185 283.11 0.0468952
95 286.474 0.0134134 0.00124444 0.967759 320 300.758 0.0229568
96 261.21 0.0122179 0.00124444 0.970602 229 273.678 0.042026
97 271.748 0.0122565 0.00124444 0.969416 227 284.549 0.0369429
98 278.334 0.00939272 0.00124444 0.968675 325 291.625 0.0419345
99 258.829 0.0143853 0.00124444 0.970871 294 271.551 0.0349143
100 247.163 0.00928993 0.00124444 0.972183 232 259.444 0.0304895
101 289.958 0.01276 0.00124444 0.967367 211 304.274 0.0585419
102 302.262 0.0110498 0.00124444 0.965982 235 317.339 0.0429466
103 266.697 0.0141856 0.00124444 0.969985 257 279.883 0.027867
104 279.365 0.0132202 0.00124444 0.968559 309 292.823 0.0333579
105 269.457 0.013538 0.00124444 0.969674 268 282.633 0.0292981
106 266.753 0.0126111 0.00124444 0.969985 224 278.892 0.0530028
107 248.308 0.0152383 0.00124444 0.972054 165 260.591 0.0615863
108 261.957 0.0114572 0.00124444 0.970518 193 274.925 0.0212163
109 337.165 0.0107314 0.00124444 0.962054 228 353.899 0.0526597
110 314.063 0.0125739 0.00124444 0.964654 275 329.705 0.0259226
111 270.826 0.0146448 0.00124444 0.96952 348 284.285 0.0177896

111
dokumentation/evolution1d/20171020-evolution1D_4x7_100Times_addedOne.error Normal file
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@ -0,0 +1,111 @@
"Evolution error"
298.423
284.695
337.628
278.241
264.793
257.7
274.784
287.743
306.328
283.047
289.659
250.664
261.859
320.352
302.292
354.924
292.181
285.759
252.184
316.796
282.546
311.274
242.707
265.12
292.53
373.645
265.231
339.851
331.492
296.143
267.902
309.679
269.797
269.874
314.378
273.406
270.657
310.459
247.483
274.461
273.334
288.058
299.622
291.267
252.976
275.311
280.607
270.017
303.237
286.265
358.606
300.357
260.384
271.183
275.937
340.822
342.87
247.036
305.313
362.535
292.104
270.485
325.329
275.62
245.371
296.747
285.701
315.472
261.363
288.343
253.537
353.546
272.705
267.961
269.917
303.207
280.342
288.78
330.349
297.971
302.104
285.659
311.745
237.277
283.579
282.437
294.507
289.471
261.243
290.359
263.942
279.455
283.11
300.758
273.678
284.549
291.625
271.551
259.444
304.274
317.339
279.883
292.823
282.633
278.892
260.591
274.925
353.899
329.705
284.285

138
dokumentation/evolution1d/20171020-evolution1D_4x7_100Times_addedOne.gnuplot.fit.log Normal file
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@ -0,0 +1,138 @@
*******************************************************************************
Tue Oct 24 02:24:04 2017
FIT: data read from "20171020-evolution1D_4x7_100Times_addedOne.csv" every ::1 using 2:5
format = x:z
#datapoints = 110
residuals are weighted equally (unit weight)
function used for fitting: f(x)
fitted parameters initialized with current variable values
Iteration 0
WSSR : 6.53142e+06 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.707163
initial set of free parameter values
a = 1
b = 1
After 5 iterations the fit converged.
final sum of squares of residuals : 199691
rel. change during last iteration : -1.72988e-09
degrees of freedom (FIT_NDF) : 108
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 42.9999
variance of residuals (reduced chisquare) = WSSR/ndf : 1848.99
Final set of parameters Asymptotic Standard Error
======================= ==========================
a = -5255.52 +/- 2401 (45.69%)
b = 306.262 +/- 30.21 (9.863%)
correlation matrix of the fit parameters:
a b
a 1.000
b -0.991 1.000
*******************************************************************************
Tue Oct 24 02:24:04 2017
FIT: data read from "20171020-evolution1D_4x7_100Times_addedOne.csv" every ::1 using 4:5
format = x:z
#datapoints = 110
residuals are weighted equally (unit weight)
function used for fitting: g(x)
fitted parameters initialized with current variable values
Iteration 0
WSSR : 6.48107e+06 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.984575
initial set of free parameter values
aa = 1
bb = 1
After 5 iterations the fit converged.
final sum of squares of residuals : 204796
rel. change during last iteration : -1.36705e-09
degrees of freedom (FIT_NDF) : 108
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 43.5461
variance of residuals (reduced chisquare) = WSSR/ndf : 1896.26
Final set of parameters Asymptotic Standard Error
======================= ==========================
aa = 1953.98 +/- 1389 (71.09%)
bb = -1652.44 +/- 1346 (81.45%)
correlation matrix of the fit parameters:
aa bb
aa 1.000
bb -1.000 1.000
*******************************************************************************
Tue Oct 24 02:24:04 2017
FIT: data read from "20171020-evolution1D_4x7_100Times_addedOne.csv" every ::1 using 4:6
format = x:z
#datapoints = 110
residuals are weighted equally (unit weight)
function used for fitting: h(x)
fitted parameters initialized with current variable values
Iteration 0
WSSR : 9.19699e+06 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.984575
initial set of free parameter values
aaa = 1
bbb = 1
After 6 iterations the fit converged.
final sum of squares of residuals : 25.9742
rel. change during last iteration : -3.36475e-14
degrees of freedom (FIT_NDF) : 108
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 0.49041
variance of residuals (reduced chisquare) = WSSR/ndf : 0.240502
Final set of parameters Asymptotic Standard Error
======================= ==========================
aaa = -9329.24 +/- 15.64 (0.1677%)
bbb = 9328.88 +/- 15.16 (0.1625%)
correlation matrix of the fit parameters:
aaa bbb
aaa 1.000
bbb -1.000 1.000

272
dokumentation/evolution1d/20171020-evolution1D_4x7_100Times_addedOne.gnuplot.log Normal file
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@ -0,0 +1,272 @@
Iteration 0
WSSR : 6.53142e+06 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.707163
initial set of free parameter values
a = 1
b = 1
/
Iteration 1
WSSR : 208679 delta(WSSR)/WSSR : -30.2988
delta(WSSR) : -6.32274e+06 limit for stopping : 1e-05
lambda : 0.0707163
resultant parameter values
a = 0.604107
b = 239.671
/
Iteration 2
WSSR : 207515 delta(WSSR)/WSSR : -0.00561318
delta(WSSR) : -1164.82 limit for stopping : 1e-05
lambda : 0.00707163
resultant parameter values
a = -316.066
b = 244.702
/
Iteration 3
WSSR : 199833 delta(WSSR)/WSSR : -0.0384382
delta(WSSR) : -7681.23 limit for stopping : 1e-05
lambda : 0.000707163
resultant parameter values
a = -4589.04
b = 297.956
/
Iteration 4
WSSR : 199691 delta(WSSR)/WSSR : -0.000713282
delta(WSSR) : -142.436 limit for stopping : 1e-05
lambda : 7.07163e-05
resultant parameter values
a = -5254.48
b = 306.249
/
Iteration 5
WSSR : 199691 delta(WSSR)/WSSR : -1.72988e-09
delta(WSSR) : -0.000345441 limit for stopping : 1e-05
lambda : 7.07163e-06
resultant parameter values
a = -5255.52
b = 306.262
After 5 iterations the fit converged.
final sum of squares of residuals : 199691
rel. change during last iteration : -1.72988e-09
degrees of freedom (FIT_NDF) : 108
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 42.9999
variance of residuals (reduced chisquare) = WSSR/ndf : 1848.99
Final set of parameters Asymptotic Standard Error
======================= ==========================
a = -5255.52 +/- 2401 (45.69%)
b = 306.262 +/- 30.21 (9.863%)
correlation matrix of the fit parameters:
a b
a 1.000
b -0.991 1.000
Iteration 0
WSSR : 6.48107e+06 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.984575
initial set of free parameter values
aa = 1
bb = 1
/
Iteration 1
WSSR : 208225 delta(WSSR)/WSSR : -30.1253
delta(WSSR) : -6.27284e+06 limit for stopping : 1e-05
lambda : 0.0984575
resultant parameter values
aa = 120.76
bb = 122.686
/
Iteration 2
WSSR : 207777 delta(WSSR)/WSSR : -0.00215751
delta(WSSR) : -448.28 limit for stopping : 1e-05
lambda : 0.00984575
resultant parameter values
aa = 212.362
bb = 35.0135
/
Iteration 3
WSSR : 204873 delta(WSSR)/WSSR : -0.0141739
delta(WSSR) : -2903.84 limit for stopping : 1e-05
lambda : 0.000984575
resultant parameter values
aa = 1674.36
bb = -1381.52
/
Iteration 4
WSSR : 204796 delta(WSSR)/WSSR : -0.000375155
delta(WSSR) : -76.8303 limit for stopping : 1e-05
lambda : 9.84575e-05
resultant parameter values
aa = 1953.44
bb = -1651.93
/
Iteration 5
WSSR : 204796 delta(WSSR)/WSSR : -1.36705e-09
delta(WSSR) : -0.000279966 limit for stopping : 1e-05
lambda : 9.84575e-06
resultant parameter values
aa = 1953.98
bb = -1652.44
After 5 iterations the fit converged.
final sum of squares of residuals : 204796
rel. change during last iteration : -1.36705e-09
degrees of freedom (FIT_NDF) : 108
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 43.5461
variance of residuals (reduced chisquare) = WSSR/ndf : 1896.26
Final set of parameters Asymptotic Standard Error
======================= ==========================
aa = 1953.98 +/- 1389 (71.09%)
bb = -1652.44 +/- 1346 (81.45%)
correlation matrix of the fit parameters:
aa bb
aa 1.000
bb -1.000 1.000
Iteration 0
WSSR : 9.19699e+06 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.984575
initial set of free parameter values
aaa = 1
bbb = 1
/
Iteration 1
WSSR : 88322.9 delta(WSSR)/WSSR : -103.129
delta(WSSR) : -9.10867e+06 limit for stopping : 1e-05
lambda : 0.0984575
resultant parameter values
aaa = 139.208
bbb = 153.55
/
Iteration 2
WSSR : 79597.9 delta(WSSR)/WSSR : -0.109614
delta(WSSR) : -8725.02 limit for stopping : 1e-05
lambda : 0.00984575
resultant parameter values
aaa = -330.642
bbb = 610.091
/
Iteration 3
WSSR : 2077.04 delta(WSSR)/WSSR : -37.3227
delta(WSSR) : -77520.9 limit for stopping : 1e-05
lambda : 0.000984575
resultant parameter values
aaa = -7884.52
bbb = 7929.08
/
Iteration 4
WSSR : 25.9817 delta(WSSR)/WSSR : -78.9426
delta(WSSR) : -2051.06 limit for stopping : 1e-05
lambda : 9.84575e-05
resultant parameter values
aaa = -9326.48
bbb = 9326.2
/
Iteration 5
WSSR : 25.9742 delta(WSSR)/WSSR : -0.000287746
delta(WSSR) : -0.00747396 limit for stopping : 1e-05
lambda : 9.84575e-06
resultant parameter values
aaa = -9329.24
bbb = 9328.88
/
Iteration 6
WSSR : 25.9742 delta(WSSR)/WSSR : -3.36475e-14
delta(WSSR) : -8.73968e-13 limit for stopping : 1e-05
lambda : 9.84575e-07
resultant parameter values
aaa = -9329.24
bbb = 9328.88
After 6 iterations the fit converged.
final sum of squares of residuals : 25.9742
rel. change during last iteration : -3.36475e-14
degrees of freedom (FIT_NDF) : 108
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 0.49041
variance of residuals (reduced chisquare) = WSSR/ndf : 0.240502
Final set of parameters Asymptotic Standard Error
======================= ==========================
aaa = -9329.24 +/- 15.64 (0.1677%)
bbb = 9328.88 +/- 15.16 (0.1625%)
correlation matrix of the fit parameters:
aaa bbb
aaa 1.000
bbb -1.000 1.000

20
dokumentation/evolution1d/20171020-evolution1D_4x7_100Times_addedOne.gnuplot.script Normal file
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@ -0,0 +1,20 @@
set datafile separator ","
f(x)=a*x+b
fit f(x) "20171020-evolution1D_4x7_100Times_addedOne.csv" every ::1 using 2:5 via a,b
set terminal png
set xlabel 'Regularity'
set ylabel 'Iterations'
set output "20171020-evolution1D_4x7_100Times_addedOne_regularity-vs-steps.png"
plot "20171020-evolution1D_4x7_100Times_addedOne.csv" every ::1 using 2:5 title "data", f(x) title "lin. fit" lc rgb "black"
g(x)=aa*x+bb
fit g(x) "20171020-evolution1D_4x7_100Times_addedOne.csv" every ::1 using 4:5 via aa,bb
set xlabel 'Improvement potential'
set ylabel 'Iterations'
set output "20171020-evolution1D_4x7_100Times_addedOne_improvement-vs-steps.png"
plot "20171020-evolution1D_4x7_100Times_addedOne.csv" every ::1 using 4:5 title "data", g(x) title "lin. fit" lc rgb "black"
h(x)=aaa*x+bbb
fit h(x) "20171020-evolution1D_4x7_100Times_addedOne.csv" every ::1 using 4:6 via aaa,bbb
set xlabel 'Improvement potential'
set ylabel 'Fitting error'
set output "20171020-evolution1D_4x7_100Times_addedOne_improvement-vs-evo-error.png"
plot "20171020-evolution1D_4x7_100Times_addedOne.csv" every ::1 using 4:6 title "data", h(x) title "lin. fit" lc rgb "black"

2341
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dokumentation/evolution1d/20171020-evolution1D_4x7_100Times_addedOne.spearman Normal file
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[1] "================ Analyzing 20171020-evolution1D_4x7_100Times_addedOne.csv"
[1] "spearman for improvement-potential vs. evolution-error"
x y
x 1 -1
y -1 1
n= 110
P
x y
x 0
y 0
[1] "spearman for regularity vs. steps"
x y
x 1.00 -0.19
y -0.19 1.00
n= 110
P
x y
x 0.0503
y 0.0503
[1] "spearman for variability vs. evolution-error"
x y
x 1 NaN
y NaN 1
n= 110
P
x y
x
y

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"Least squares",regularity,variability,improvement,steps,"Evolution error",sigma
187.745,0.0182483,0.00111111,0.97887,227,196.94,0.0221641
199.804,0.0152344,0.00111111,0.977513,206,209.683,0.0142768
186.97,0.0181411,0.00111111,0.978958,227,196.045,0.0300805
182.366,0.019132,0.00111111,0.979476,245,191.37,0.0263319
181.893,0.0197913,0.00111111,0.979529,238,190.84,0.0243627
184.832,0.0161942,0.00111111,0.979198,192,193.974,0.0217357
192.909,0.0193693,0.00111111,0.978289,205,202.544,0.0319747
226.472,0.0204366,0.00111111,0.974512,240,237.786,0.0250503
208.156,0.0183033,0.00111111,0.976573,251,218.4,0.0217521
198.122,0.0213555,0.00111111,0.977703,216,207.719,0.0239459
218.91,0.0234253,0.00111111,0.975363,190,229.721,0.0230034
182.072,0.0200981,0.00111111,0.979509,215,191.136,0.0384825
203.83,0.0176375,0.00111111,0.97706,191,213.927,0.0294044
187.971,0.0162914,0.00111111,0.978845,305,197.336,0.0135234
198.237,0.0184512,0.00111111,0.97769,150,207.431,0.0484778
233.46,0.0203856,0.00111111,0.973726,179,245.039,0.0178918
209.599,0.0177202,0.00111111,0.976411,250,220.043,0.0280449
204.434,0.0187668,0.00111111,0.976992,203,214.432,0.0211949
234.718,0.0161043,0.00111111,0.973584,160,246.42,0.0239583
192.939,0.0228069,0.00111111,0.978286,261,202.553,0.0205101
234.394,0.0169807,0.00111111,0.97362,139,245.306,0.0336572
183.73,0.0159035,0.00111111,0.979322,237,192.882,0.0172048
187.186,0.0202169,0.00111111,0.978933,204,196.329,0.0395863
194.01,0.0195768,0.00111111,0.978165,244,203.052,0.035514
195.111,0.0170059,0.00111111,0.978041,157,204.662,0.0231966
225.331,0.0236187,0.00111111,0.97464,160,236.452,0.0301883
239.538,0.0189427,0.00111111,0.973042,114,251.484,0.0470329
181.805,0.0212591,0.00111111,0.979539,268,190.782,0.0340786
186.244,0.0207094,0.00111111,0.979039,190,195.503,0.0288453
246.843,0.0173713,0.00111111,0.972219,193,259.133,0.0198506
203.277,0.0181449,0.00111111,0.977123,211,213.431,0.0257039
214.842,0.0200148,0.00111111,0.975821,238,225.349,0.038947
196.658,0.0187428,0.00111111,0.977867,168,205.744,0.0424655
212.094,0.0177129,0.00111111,0.97613,122,222.595,0.0428265
230.135,0.0196257,0.00111111,0.9741,151,241.582,0.0438331
206.85,0.0177107,0.00111111,0.97672,266,217.141,0.0213643
237.733,0.0201347,0.00111111,0.973245,168,249.588,0.0356283
175.754,0.017217,0.00111111,0.98022,241,184.459,0.028533
201.986,0.0218777,0.00111111,0.977268,179,212.03,0.0387742
184.105,0.0176688,0.00111111,0.97928,289,193.133,0.0333076
199.825,0.0193904,0.00111111,0.977511,190,209.808,0.0321669
205.585,0.0179865,0.00111111,0.976863,258,215.712,0.027105
240.325,0.0198335,0.00111111,0.972953,166,251.883,0.0356981
184.794,0.0187189,0.00111111,0.979203,236,193.995,0.0258967
180.454,0.0186822,0.00111111,0.979691,263,189.447,0.0269632
238.932,0.0212528,0.00111111,0.97311,117,250.443,0.0437906
221.165,0.0193828,0.00111111,0.975109,134,231.057,0.0497979
189.713,0.0180015,0.00111111,0.978649,201,199.026,0.0212137
197.758,0.0185375,0.00111111,0.977744,151,207.409,0.044357
201.952,0.0195493,0.00111111,0.977272,260,211.962,0.0261682
188.56,0.0159997,0.00111111,0.978779,180,197.951,0.035823
243.499,0.0199009,0.00111111,0.972596,207,255.613,0.0301646
239.081,0.0196769,0.00111111,0.973093,157,250.788,0.0312726
203.299,0.0212421,0.00111111,0.97712,273,213.319,0.0212051
206.534,0.0186378,0.00111111,0.976756,198,216.537,0.0220651
187.968,0.0201035,0.00111111,0.978845,207,197.095,0.0399027
213.453,0.0168292,0.00111111,0.975977,190,224.087,0.0288318
219.024,0.017168,0.00111111,0.97535,254,229.948,0.0190611
199.468,0.0196977,0.00111111,0.977551,260,209.414,0.0308103
238.393,0.0213402,0.00111111,0.97317,187,250.101,0.0195628
209.099,0.0198555,0.00111111,0.976467,194,219.433,0.0317749
194.13,0.0196069,0.00111111,0.978152,278,203.824,0.0172456
200.102,0.0209277,0.00111111,0.97748,199,209.167,0.0328509
183.427,0.0199804,0.00111111,0.979356,220,192.511,0.0297735
191.681,0.0192451,0.00111111,0.978428,238,201.236,0.0205511
213.367,0.0190531,0.00111111,0.975987,154,223.476,0.0399578
212.537,0.0153109,0.00111111,0.97608,186,223.119,0.0415867
240.507,0.0213148,0.00111111,0.972932,163,252.503,0.0222976
211.405,0.0165473,0.00111111,0.976208,263,221.844,0.0324297
225.422,0.0187176,0.00111111,0.97463,234,236.373,0.0366055
211.011,0.0148638,0.00111111,0.976252,182,221.486,0.0206502
210.256,0.0174415,0.00111111,0.976337,160,220.727,0.0204222
217.209,0.019323,0.00111111,0.975554,204,228.05,0.0191575
210.209,0.0225558,0.00111111,0.976342,184,220.544,0.0239447
206.081,0.017393,0.00111111,0.976807,185,216.377,0.0093842
221.445,0.0146833,0.00111111,0.975078,161,232.445,0.0262917
242.088,0.0181286,0.00111111,0.972755,178,254.12,0.0180639
213.359,0.0188093,0.00111111,0.975988,149,224,0.0393936
219.037,0.018868,0.00111111,0.975349,173,229.801,0.035486
201.648,0.0148883,0.00111111,0.977306,247,211.671,0.0210672
197.345,0.0182581,0.00111111,0.97779,203,206.763,0.0345867
199.057,0.019515,0.00111111,0.977597,178,208.842,0.0272527
230.708,0.0170428,0.00111111,0.974035,145,241.647,0.0284395
191.615,0.0211321,0.00111111,0.978435,220,200.545,0.0302029
200.996,0.0198848,0.00111111,0.977379,148,210.728,0.0297074
204.71,0.022276,0.00111111,0.976961,176,214.662,0.0238206
204.41,0.0188267,0.00111111,0.976995,200,214.432,0.0297072
212.194,0.0178961,0.00111111,0.976119,218,222.734,0.0288567
195.299,0.0217078,0.00111111,0.97802,230,204.888,0.0335442
217.03,0.0183731,0.00111111,0.975575,313,227.646,0.0197123
207.129,0.0208927,0.00111111,0.976689,177,217.056,0.03195
181.67,0.019443,0.00111111,0.979554,209,190.336,0.0231579
187.536,0.0197584,0.00111111,0.978894,222,196.872,0.0199832
207.254,0.0183979,0.00111111,0.976675,152,217.488,0.0372997
188.784,0.0198524,0.00111111,0.978754,251,198.201,0.0188279
187.669,0.0189986,0.00111111,0.978879,239,196.713,0.0255703
177.618,0.0201662,0.00111111,0.98001,137,186.056,0.0372483
204.647,0.0199072,0.00111111,0.976968,164,213.683,0.0293019
257.265,0.0189825,0.00111111,0.971046,121,270.03,0.0289929
192.284,0.02033,0.00111111,0.97836,252,201.837,0.0218422
1 Least squares regularity variability improvement steps Evolution error sigma
2 187.745 0.0182483 0.00111111 0.97887 227 196.94 0.0221641
3 199.804 0.0152344 0.00111111 0.977513 206 209.683 0.0142768
4 186.97 0.0181411 0.00111111 0.978958 227 196.045 0.0300805
5 182.366 0.019132 0.00111111 0.979476 245 191.37 0.0263319
6 181.893 0.0197913 0.00111111 0.979529 238 190.84 0.0243627
7 184.832 0.0161942 0.00111111 0.979198 192 193.974 0.0217357
8 192.909 0.0193693 0.00111111 0.978289 205 202.544 0.0319747
9 226.472 0.0204366 0.00111111 0.974512 240 237.786 0.0250503
10 208.156 0.0183033 0.00111111 0.976573 251 218.4 0.0217521
11 198.122 0.0213555 0.00111111 0.977703 216 207.719 0.0239459
12 218.91 0.0234253 0.00111111 0.975363 190 229.721 0.0230034
13 182.072 0.0200981 0.00111111 0.979509 215 191.136 0.0384825
14 203.83 0.0176375 0.00111111 0.97706 191 213.927 0.0294044
15 187.971 0.0162914 0.00111111 0.978845 305 197.336 0.0135234
16 198.237 0.0184512 0.00111111 0.97769 150 207.431 0.0484778
17 233.46 0.0203856 0.00111111 0.973726 179 245.039 0.0178918
18 209.599 0.0177202 0.00111111 0.976411 250 220.043 0.0280449
19 204.434 0.0187668 0.00111111 0.976992 203 214.432 0.0211949
20 234.718 0.0161043 0.00111111 0.973584 160 246.42 0.0239583
21 192.939 0.0228069 0.00111111 0.978286 261 202.553 0.0205101
22 234.394 0.0169807 0.00111111 0.97362 139 245.306 0.0336572
23 183.73 0.0159035 0.00111111 0.979322 237 192.882 0.0172048
24 187.186 0.0202169 0.00111111 0.978933 204 196.329 0.0395863
25 194.01 0.0195768 0.00111111 0.978165 244 203.052 0.035514
26 195.111 0.0170059 0.00111111 0.978041 157 204.662 0.0231966
27 225.331 0.0236187 0.00111111 0.97464 160 236.452 0.0301883
28 239.538 0.0189427 0.00111111 0.973042 114 251.484 0.0470329
29 181.805 0.0212591 0.00111111 0.979539 268 190.782 0.0340786
30 186.244 0.0207094 0.00111111 0.979039 190 195.503 0.0288453
31 246.843 0.0173713 0.00111111 0.972219 193 259.133 0.0198506
32 203.277 0.0181449 0.00111111 0.977123 211 213.431 0.0257039
33 214.842 0.0200148 0.00111111 0.975821 238 225.349 0.038947
34 196.658 0.0187428 0.00111111 0.977867 168 205.744 0.0424655
35 212.094 0.0177129 0.00111111 0.97613 122 222.595 0.0428265
36 230.135 0.0196257 0.00111111 0.9741 151 241.582 0.0438331
37 206.85 0.0177107 0.00111111 0.97672 266 217.141 0.0213643
38 237.733 0.0201347 0.00111111 0.973245 168 249.588 0.0356283
39 175.754 0.017217 0.00111111 0.98022 241 184.459 0.028533
40 201.986 0.0218777 0.00111111 0.977268 179 212.03 0.0387742
41 184.105 0.0176688 0.00111111 0.97928 289 193.133 0.0333076
42 199.825 0.0193904 0.00111111 0.977511 190 209.808 0.0321669
43 205.585 0.0179865 0.00111111 0.976863 258 215.712 0.027105
44 240.325 0.0198335 0.00111111 0.972953 166 251.883 0.0356981
45 184.794 0.0187189 0.00111111 0.979203 236 193.995 0.0258967
46 180.454 0.0186822 0.00111111 0.979691 263 189.447 0.0269632
47 238.932 0.0212528 0.00111111 0.97311 117 250.443 0.0437906
48 221.165 0.0193828 0.00111111 0.975109 134 231.057 0.0497979
49 189.713 0.0180015 0.00111111 0.978649 201 199.026 0.0212137
50 197.758 0.0185375 0.00111111 0.977744 151 207.409 0.044357
51 201.952 0.0195493 0.00111111 0.977272 260 211.962 0.0261682
52 188.56 0.0159997 0.00111111 0.978779 180 197.951 0.035823
53 243.499 0.0199009 0.00111111 0.972596 207 255.613 0.0301646
54 239.081 0.0196769 0.00111111 0.973093 157 250.788 0.0312726
55 203.299 0.0212421 0.00111111 0.97712 273 213.319 0.0212051
56 206.534 0.0186378 0.00111111 0.976756 198 216.537 0.0220651
57 187.968 0.0201035 0.00111111 0.978845 207 197.095 0.0399027
58 213.453 0.0168292 0.00111111 0.975977 190 224.087 0.0288318
59 219.024 0.017168 0.00111111 0.97535 254 229.948 0.0190611
60 199.468 0.0196977 0.00111111 0.977551 260 209.414 0.0308103
61 238.393 0.0213402 0.00111111 0.97317 187 250.101 0.0195628
62 209.099 0.0198555 0.00111111 0.976467 194 219.433 0.0317749
63 194.13 0.0196069 0.00111111 0.978152 278 203.824 0.0172456
64 200.102 0.0209277 0.00111111 0.97748 199 209.167 0.0328509
65 183.427 0.0199804 0.00111111 0.979356 220 192.511 0.0297735
66 191.681 0.0192451 0.00111111 0.978428 238 201.236 0.0205511
67 213.367 0.0190531 0.00111111 0.975987 154 223.476 0.0399578
68 212.537 0.0153109 0.00111111 0.97608 186 223.119 0.0415867
69 240.507 0.0213148 0.00111111 0.972932 163 252.503 0.0222976
70 211.405 0.0165473 0.00111111 0.976208 263 221.844 0.0324297
71 225.422 0.0187176 0.00111111 0.97463 234 236.373 0.0366055
72 211.011 0.0148638 0.00111111 0.976252 182 221.486 0.0206502
73 210.256 0.0174415 0.00111111 0.976337 160 220.727 0.0204222
74 217.209 0.019323 0.00111111 0.975554 204 228.05 0.0191575
75 210.209 0.0225558 0.00111111 0.976342 184 220.544 0.0239447
76 206.081 0.017393 0.00111111 0.976807 185 216.377 0.0093842
77 221.445 0.0146833 0.00111111 0.975078 161 232.445 0.0262917
78 242.088 0.0181286 0.00111111 0.972755 178 254.12 0.0180639
79 213.359 0.0188093 0.00111111 0.975988 149 224 0.0393936
80 219.037 0.018868 0.00111111 0.975349 173 229.801 0.035486
81 201.648 0.0148883 0.00111111 0.977306 247 211.671 0.0210672
82 197.345 0.0182581 0.00111111 0.97779 203 206.763 0.0345867
83 199.057 0.019515 0.00111111 0.977597 178 208.842 0.0272527
84 230.708 0.0170428 0.00111111 0.974035 145 241.647 0.0284395
85 191.615 0.0211321 0.00111111 0.978435 220 200.545 0.0302029
86 200.996 0.0198848 0.00111111 0.977379 148 210.728 0.0297074
87 204.71 0.022276 0.00111111 0.976961 176 214.662 0.0238206
88 204.41 0.0188267 0.00111111 0.976995 200 214.432 0.0297072
89 212.194 0.0178961 0.00111111 0.976119 218 222.734 0.0288567
90 195.299 0.0217078 0.00111111 0.97802 230 204.888 0.0335442
91 217.03 0.0183731 0.00111111 0.975575 313 227.646 0.0197123
92 207.129 0.0208927 0.00111111 0.976689 177 217.056 0.03195
93 181.67 0.019443 0.00111111 0.979554 209 190.336 0.0231579
94 187.536 0.0197584 0.00111111 0.978894 222 196.872 0.0199832
95 207.254 0.0183979 0.00111111 0.976675 152 217.488 0.0372997
96 188.784 0.0198524 0.00111111 0.978754 251 198.201 0.0188279
97 187.669 0.0189986 0.00111111 0.978879 239 196.713 0.0255703
98 177.618 0.0201662 0.00111111 0.98001 137 186.056 0.0372483
99 204.647 0.0199072 0.00111111 0.976968 164 213.683 0.0293019
100 257.265 0.0189825 0.00111111 0.971046 121 270.03 0.0289929
101 192.284 0.02033 0.00111111 0.97836 252 201.837 0.0218422

101
dokumentation/evolution1d/20171020-evolution1D_5x5_100Times-addedOne.error Normal file
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@ -0,0 +1,101 @@
"Evolution error"
196.94
209.683
196.045
191.37
190.84
193.974
202.544
237.786
218.4
207.719
229.721
191.136
213.927
197.336
207.431
245.039
220.043
214.432
246.42
202.553
245.306
192.882
196.329
203.052
204.662
236.452
251.484
190.782
195.503
259.133
213.431
225.349
205.744
222.595
241.582
217.141
249.588
184.459
212.03
193.133
209.808
215.712
251.883
193.995
189.447
250.443
231.057
199.026
207.409
211.962
197.951
255.613
250.788
213.319
216.537
197.095
224.087
229.948
209.414
250.101
219.433
203.824
209.167
192.511
201.236
223.476
223.119
252.503
221.844
236.373
221.486
220.727
228.05
220.544
216.377
232.445
254.12
224
229.801
211.671
206.763
208.842
241.647
200.545
210.728
214.662
214.432
222.734
204.888
227.646
217.056
190.336
196.872
217.488
198.201
196.713
186.056
213.683
270.03
201.837

138
dokumentation/evolution1d/20171020-evolution1D_5x5_100Times-addedOne.gnuplot.fit.log Normal file
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@ -0,0 +1,138 @@
*******************************************************************************
Tue Oct 24 02:24:04 2017
FIT: data read from "20171020-evolution1D_5x5_100Times-addedOne.csv" every ::1 using 2:5
format = x:z
#datapoints = 100
residuals are weighted equally (unit weight)
function used for fitting: f(x)
fitted parameters initialized with current variable values
Iteration 0
WSSR : 4.26528e+06 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.707236
initial set of free parameter values
a = 1
b = 1
After 5 iterations the fit converged.
final sum of squares of residuals : 191085
rel. change during last iteration : -3.5658e-11
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 44.157
variance of residuals (reduced chisquare) = WSSR/ndf : 1949.84
Final set of parameters Asymptotic Standard Error
======================= ==========================
a = -801.741 +/- 2391 (298.2%)
b = 218.088 +/- 45.62 (20.92%)
correlation matrix of the fit parameters:
a b
a 1.000
b -0.995 1.000
*******************************************************************************
Tue Oct 24 02:24:04 2017
FIT: data read from "20171020-evolution1D_5x5_100Times-addedOne.csv" every ::1 using 4:5
format = x:z
#datapoints = 100
residuals are weighted equally (unit weight)
function used for fitting: g(x)
fitted parameters initialized with current variable values
Iteration 0
WSSR : 4.2267e+06 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.988459
initial set of free parameter values
aa = 1
bb = 1
After 5 iterations the fit converged.
final sum of squares of residuals : 147724
rel. change during last iteration : -4.79728e-07
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 38.8251
variance of residuals (reduced chisquare) = WSSR/ndf : 1507.39
Final set of parameters Asymptotic Standard Error
======================= ==========================
aa = 9976.22 +/- 1855 (18.6%)
bb = -9541.71 +/- 1812 (18.99%)
correlation matrix of the fit parameters:
aa bb
aa 1.000
bb -1.000 1.000
*******************************************************************************
Tue Oct 24 02:24:04 2017
FIT: data read from "20171020-evolution1D_5x5_100Times-addedOne.csv" every ::1 using 4:6
format = x:z
#datapoints = 100
residuals are weighted equally (unit weight)
function used for fitting: h(x)
fitted parameters initialized with current variable values
Iteration 0
WSSR : 4.63731e+06 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.988459
initial set of free parameter values
aaa = 1
bbb = 1
After 6 iterations the fit converged.
final sum of squares of residuals : 5.78147
rel. change during last iteration : -2.12322e-11
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 0.242888
variance of residuals (reduced chisquare) = WSSR/ndf : 0.0589946
Final set of parameters Asymptotic Standard Error
======================= ==========================
aaa = -9327.12 +/- 11.61 (0.1244%)
bbb = 9326.98 +/- 11.34 (0.1216%)
correlation matrix of the fit parameters:
aaa bbb
aaa 1.000
bbb -1.000 1.000

272
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Iteration 0
WSSR : 4.26528e+06 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.707236
initial set of free parameter values
a = 1
b = 1
/
Iteration 1
WSSR : 191407 delta(WSSR)/WSSR : -21.2838
delta(WSSR) : -4.07388e+06 limit for stopping : 1e-05
lambda : 0.0707236
resultant parameter values
a = 4.26373
b = 201.775
/
Iteration 2
WSSR : 191279 delta(WSSR)/WSSR : -0.000670419
delta(WSSR) : -128.237 limit for stopping : 1e-05
lambda : 0.00707236
resultant parameter values
a = -47.1714
b = 203.756
/
Iteration 3
WSSR : 191088 delta(WSSR)/WSSR : -0.00100002
delta(WSSR) : -191.092 limit for stopping : 1e-05
lambda : 0.000707236
resultant parameter values
a = -705.237
b = 216.255
/
Iteration 4
WSSR : 191085 delta(WSSR)/WSSR : -1.66293e-05
delta(WSSR) : -3.17761 limit for stopping : 1e-05
lambda : 7.07236e-05
resultant parameter values
a = -801.6
b = 218.086
/
Iteration 5
WSSR : 191085 delta(WSSR)/WSSR : -3.5658e-11
delta(WSSR) : -6.8137e-06 limit for stopping : 1e-05
lambda : 7.07236e-06
resultant parameter values
a = -801.741
b = 218.088
After 5 iterations the fit converged.
final sum of squares of residuals : 191085
rel. change during last iteration : -3.5658e-11
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 44.157
variance of residuals (reduced chisquare) = WSSR/ndf : 1949.84
Final set of parameters Asymptotic Standard Error
======================= ==========================
a = -801.741 +/- 2391 (298.2%)
b = 218.088 +/- 45.62 (20.92%)
correlation matrix of the fit parameters:
a b
a 1.000
b -0.995 1.000
Iteration 0
WSSR : 4.2267e+06 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.988459
initial set of free parameter values
aa = 1
bb = 1
/
Iteration 1
WSSR : 190503 delta(WSSR)/WSSR : -21.1871
delta(WSSR) : -4.0362e+06 limit for stopping : 1e-05
lambda : 0.0988459
resultant parameter values
aa = 103.189
bb = 101.089
/
Iteration 2
WSSR : 188511 delta(WSSR)/WSSR : -0.0105682
delta(WSSR) : -1992.23 limit for stopping : 1e-05
lambda : 0.00988459
resultant parameter values
aa = 325.036
bb = -114.608
/
Iteration 3
WSSR : 151484 delta(WSSR)/WSSR : -0.244426
delta(WSSR) : -37026.6 limit for stopping : 1e-05
lambda : 0.000988459
resultant parameter values
aa = 7045.82
bb = -6679.35
/
Iteration 4
WSSR : 147724 delta(WSSR)/WSSR : -0.0254538
delta(WSSR) : -3760.13 limit for stopping : 1e-05
lambda : 9.88459e-05
resultant parameter values
aa = 9963.5
bb = -9529.28
/
Iteration 5
WSSR : 147724 delta(WSSR)/WSSR : -4.79728e-07
delta(WSSR) : -0.0708672 limit for stopping : 1e-05
lambda : 9.88459e-06
resultant parameter values
aa = 9976.22
bb = -9541.71
After 5 iterations the fit converged.
final sum of squares of residuals : 147724
rel. change during last iteration : -4.79728e-07
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 38.8251
variance of residuals (reduced chisquare) = WSSR/ndf : 1507.39
Final set of parameters Asymptotic Standard Error
======================= ==========================
aa = 9976.22 +/- 1855 (18.6%)
bb = -9541.71 +/- 1812 (18.99%)
correlation matrix of the fit parameters:
aa bb
aa 1.000
bb -1.000 1.000
Iteration 0
WSSR : 4.63731e+06 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.988459
initial set of free parameter values
aaa = 1
bbb = 1
/
Iteration 1
WSSR : 39084.3 delta(WSSR)/WSSR : -117.649
delta(WSSR) : -4.59823e+06 limit for stopping : 1e-05
lambda : 0.0988459
resultant parameter values
aaa = 105.492
bbb = 112.304
/
Iteration 2
WSSR : 37242.9 delta(WSSR)/WSSR : -0.0494443
delta(WSSR) : -1841.45 limit for stopping : 1e-05
lambda : 0.00988459
resultant parameter values
aaa = -105.47
bbb = 319.436
/
Iteration 3
WSSR : 3438.73 delta(WSSR)/WSSR : -9.8304
delta(WSSR) : -33804.1 limit for stopping : 1e-05
lambda : 0.000988459
resultant parameter values
aaa = -6527.14
bbb = 6592.01
/
Iteration 4
WSSR : 5.84617 delta(WSSR)/WSSR : -587.203
delta(WSSR) : -3432.89 limit for stopping : 1e-05
lambda : 9.88459e-05
resultant parameter values
aaa = -9314.96
bbb = 9315.1
/
Iteration 5
WSSR : 5.78147 delta(WSSR)/WSSR : -0.0111909
delta(WSSR) : -0.0646996 limit for stopping : 1e-05
lambda : 9.88459e-06
resultant parameter values
aaa = -9327.12
bbb = 9326.98
/
Iteration 6
WSSR : 5.78147 delta(WSSR)/WSSR : -2.12322e-11
delta(WSSR) : -1.22753e-10 limit for stopping : 1e-05
lambda : 9.88459e-07
resultant parameter values
aaa = -9327.12
bbb = 9326.98
After 6 iterations the fit converged.
final sum of squares of residuals : 5.78147
rel. change during last iteration : -2.12322e-11
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 0.242888
variance of residuals (reduced chisquare) = WSSR/ndf : 0.0589946
Final set of parameters Asymptotic Standard Error
======================= ==========================
aaa = -9327.12 +/- 11.61 (0.1244%)
bbb = 9326.98 +/- 11.34 (0.1216%)
correlation matrix of the fit parameters:
aaa bbb
aaa 1.000
bbb -1.000 1.000

20
dokumentation/evolution1d/20171020-evolution1D_5x5_100Times-addedOne.gnuplot.script Normal file
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set datafile separator ","
f(x)=a*x+b
fit f(x) "20171020-evolution1D_5x5_100Times-addedOne.csv" every ::1 using 2:5 via a,b
set terminal png
set xlabel 'Regularity'
set ylabel 'Iterations'
set output "20171020-evolution1D_5x5_100Times-addedOne_regularity-vs-steps.png"
plot "20171020-evolution1D_5x5_100Times-addedOne.csv" every ::1 using 2:5 title "data", f(x) title "lin. fit" lc rgb "black"
g(x)=aa*x+bb
fit g(x) "20171020-evolution1D_5x5_100Times-addedOne.csv" every ::1 using 4:5 via aa,bb
set xlabel 'Improvement potential'
set ylabel 'Iterations'
set output "20171020-evolution1D_5x5_100Times-addedOne_improvement-vs-steps.png"
plot "20171020-evolution1D_5x5_100Times-addedOne.csv" every ::1 using 4:5 title "data", g(x) title "lin. fit" lc rgb "black"
h(x)=aaa*x+bbb
fit h(x) "20171020-evolution1D_5x5_100Times-addedOne.csv" every ::1 using 4:6 via aaa,bbb
set xlabel 'Improvement potential'
set ylabel 'Fitting error'
set output "20171020-evolution1D_5x5_100Times-addedOne_improvement-vs-evo-error.png"
plot "20171020-evolution1D_5x5_100Times-addedOne.csv" every ::1 using 4:6 title "data", h(x) title "lin. fit" lc rgb "black"

2141
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[1] "================ Analyzing 20171020-evolution1D_5x5_100Times-addedOne.csv"
[1] "spearman for improvement-potential vs. evolution-error"
x y
x 1 -1
y -1 1
n= 100
P
x y
x 0
y 0
[1] "spearman for regularity vs. steps"
x y
x 1.00 -0.02
y -0.02 1.00
n= 100
P
x y
x 0.8461
y 0.8461
[1] "spearman for variability vs. evolution-error"
x y
x 1 NaN
y NaN 1
n= 100
P
x y
x
y

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101
dokumentation/evolution1d/20171020-evolution1D_5x5_100Times.csv Normal file
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"Least squares",regularity,variability,improvement,steps,"Evolution error",sigma
211.698,0.0203679,0.00111111,0.929303,218,222.265,0.0304584
200.554,0.0211861,0.00111111,0.933025,215,210.496,0.019554
209.312,0.0193559,0.00111111,0.9301,198,219.661,0.0276531
206.148,0.0172566,0.00111111,0.931157,208,216.396,0.0235405
217.079,0.0195466,0.00111111,0.927506,141,227.519,0.0433189
229.411,0.0196263,0.00111111,0.923388,215,240.74,0.0228817
199.52,0.0203185,0.00111111,0.93337,234,208.51,0.0291453
186.604,0.0212737,0.00111111,0.937683,259,195.805,0.0213456
225.306,0.0213791,0.00111111,0.924759,236,236.503,0.0252631
248.528,0.0193633,0.00111111,0.917004,221,260.935,0.0160484
189.883,0.0191517,0.00111111,0.936588,136,198.559,0.0362213
212.6,0.0166038,0.00111111,0.929002,208,222.573,0.031822
220.85,0.0161934,0.00111111,0.926247,205,231.708,0.0293863
210.742,0.0191138,0.00111111,0.929622,230,220.944,0.0260993
221.447,0.0184165,0.00111111,0.926047,262,232.462,0.0200636
190.705,0.0171791,0.00111111,0.936314,201,200.196,0.0282271
221.909,0.0209993,0.00111111,0.925893,190,232.786,0.0333187
211.712,0.0165509,0.00111111,0.929298,243,222.289,0.0160656
217.07,0.0231196,0.00111111,0.927509,182,227.732,0.0261153
197.775,0.0203953,0.00111111,0.933953,252,207.181,0.0323614
219.904,0.0168872,0.00111111,0.926563,120,230.616,0.0328043
204.335,0.0180441,0.00111111,0.931762,192,214.185,0.0296326
231.939,0.018535,0.00111111,0.922544,128,243.327,0.0304083
211.724,0.0185923,0.00111111,0.929295,270,222.254,0.0414344
214.148,0.0206654,0.00111111,0.928485,234,224.844,0.0317975
185.847,0.0204202,0.00111111,0.937936,165,194.744,0.034174
232.03,0.0193975,0.00111111,0.922513,92,243.161,0.0396967
223.982,0.0236268,0.00111111,0.925201,208,234.626,0.0255705
226.077,0.01857,0.00111111,0.924501,115,236.939,0.0416214
197.984,0.0164528,0.00111111,0.933883,150,207.489,0.0291136
176.084,0.0198163,0.00111111,0.941197,258,184.696,0.0302269
195.22,0.0163427,0.00111111,0.934806,230,204.904,0.0349146
202.893,0.0217023,0.00111111,0.932244,172,212.168,0.0292404
191.762,0.0176933,0.00111111,0.935961,203,201.279,0.0218102
208.412,0.0212612,0.00111111,0.930401,201,218.814,0.0210822
221.844,0.0210909,0.00111111,0.925915,173,232.892,0.0258153
219.113,0.0184739,0.00111111,0.926827,173,230.05,0.0414932
224.465,0.0246122,0.00111111,0.92504,183,235.508,0.0250082
206.774,0.0198917,0.00111111,0.930947,186,216.939,0.0310308
225.859,0.0172615,0.00111111,0.924574,203,236.479,0.0398825
212.461,0.0204119,0.00111111,0.929048,149,222.522,0.0337725
224.924,0.0217016,0.00111111,0.924886,130,236.154,0.0354094
232.115,0.0210782,0.00111111,0.922485,198,243.686,0.0172863
228.396,0.0196268,0.00111111,0.923727,238,239.488,0.0184422
196.881,0.019569,0.00111111,0.934251,255,206.689,0.0295242
204.716,0.0180094,0.00111111,0.931635,155,213.904,0.0298783
209.447,0.0191448,0.00111111,0.930055,118,219.704,0.0312759
221.317,0.0189707,0.00111111,0.926091,230,231.945,0.0391334
215.094,0.0199488,0.00111111,0.928169,216,225.771,0.0197891
184.929,0.01736,0.00111111,0.938243,213,194.167,0.0307934
178.666,0.0198813,0.00111111,0.940334,187,187.585,0.0149118
174.929,0.0189218,0.00111111,0.941582,195,183.471,0.0197701
179.673,0.0165947,0.00111111,0.939998,237,188.175,0.0268256
204.903,0.0174921,0.00111111,0.931572,176,214.811,0.0341155
198.996,0.0198215,0.00111111,0.933545,174,208.903,0.022187
239.497,0.0229021,0.00111111,0.920019,145,251.345,0.0363502
197.617,0.0190836,0.00111111,0.934006,264,207.332,0.0225783
213.541,0.0182262,0.00111111,0.928688,171,223.93,0.024467
205.246,0.018593,0.00111111,0.931458,200,215.194,0.0259352
192.01,0.021189,0.00111111,0.935878,187,201.523,0.0356566
207.623,0.0200966,0.00111111,0.930664,162,217.973,0.0237469
207.124,0.0203329,0.00111111,0.930831,275,217.381,0.0377496
207.6,0.015955,0.00111111,0.930672,313,217.963,0.034054
196.464,0.0215318,0.00111111,0.934391,148,206.197,0.0255094
225.291,0.0188696,0.00111111,0.924764,253,236.455,0.0269747
236.139,0.0195817,0.00111111,0.921141,208,247.922,0.0181545
218.667,0.0236608,0.00111111,0.926976,261,229.254,0.0200224
214.905,0.0183248,0.00111111,0.928232,232,225.179,0.0317272
222.269,0.0210694,0.00111111,0.925773,217,232.89,0.0322418
193.848,0.0176874,0.00111111,0.935264,251,203.276,0.0293206
210.394,0.0173601,0.00111111,0.929739,205,220.572,0.023589
240.818,0.0172322,0.00111111,0.919578,201,252.661,0.037452
223.163,0.0214412,0.00111111,0.925474,173,234.239,0.030632
218.88,0.0208812,0.00111111,0.926905,110,229.098,0.0257352
203.933,0.0179055,0.00111111,0.931896,229,213.754,0.0315607
175.337,0.0219549,0.00111111,0.941446,169,184.029,0.0185983
191.342,0.0173847,0.00111111,0.936101,233,200.827,0.0293542
192.271,0.0211125,0.00111111,0.935791,185,201.758,0.022909
217.045,0.0184074,0.00111111,0.927518,143,227.599,0.021528
204.268,0.0199404,0.00111111,0.931784,263,214.25,0.0243066
262.346,0.0165844,0.00111111,0.912389,190,275.187,0.0384815
213.393,0.0220227,0.00111111,0.928737,122,223.6,0.031075
210.514,0.0189866,0.00111111,0.929698,219,220.599,0.034692
205.999,0.0198525,0.00111111,0.931206,143,215.885,0.0323366
180.234,0.0208046,0.00111111,0.93981,234,189.174,0.0261096
194.507,0.0183817,0.00111111,0.935044,133,204.135,0.0318449
215.956,0.0189058,0.00111111,0.927881,330,226.485,0.0239564
203.924,0.017231,0.00111111,0.931899,235,213.99,0.0323069
235.067,0.0189216,0.00111111,0.921499,234,246.78,0.0289497
202.496,0.0204575,0.00111111,0.932376,235,212.614,0.0216976
263.952,0.0199218,0.00111111,0.911853,154,277.143,0.0245278
207.128,0.0216716,0.00111111,0.930829,209,217.224,0.0321473
244.798,0.019198,0.00111111,0.918249,260,256.629,0.0334971
209.179,0.020316,0.00111111,0.930144,341,219.57,0.0166192
245.103,0.0189129,0.00111111,0.918147,230,257.111,0.0263794
226.778,0.0168544,0.00111111,0.924267,233,237.927,0.0176314
207.236,0.0195923,0.00111111,0.930793,211,217.288,0.0189774
192.594,0.0198456,0.00111111,0.935683,226,201.924,0.0274001
197.183,0.0185432,0.00111111,0.93415,285,206.962,0.0215739
222.895,0.0181898,0.00111111,0.925564,181,233.737,0.0213808
1 Least squares regularity variability improvement steps Evolution error sigma
2 211.698 0.0203679 0.00111111 0.929303 218 222.265 0.0304584
3 200.554 0.0211861 0.00111111 0.933025 215 210.496 0.019554
4 209.312 0.0193559 0.00111111 0.9301 198 219.661 0.0276531
5 206.148 0.0172566 0.00111111 0.931157 208 216.396 0.0235405
6 217.079 0.0195466 0.00111111 0.927506 141 227.519 0.0433189
7 229.411 0.0196263 0.00111111 0.923388 215 240.74 0.0228817
8 199.52 0.0203185 0.00111111 0.93337 234 208.51 0.0291453
9 186.604 0.0212737 0.00111111 0.937683 259 195.805 0.0213456
10 225.306 0.0213791 0.00111111 0.924759 236 236.503 0.0252631
11 248.528 0.0193633 0.00111111 0.917004 221 260.935 0.0160484
12 189.883 0.0191517 0.00111111 0.936588 136 198.559 0.0362213
13 212.6 0.0166038 0.00111111 0.929002 208 222.573 0.031822
14 220.85 0.0161934 0.00111111 0.926247 205 231.708 0.0293863
15 210.742 0.0191138 0.00111111 0.929622 230 220.944 0.0260993
16 221.447 0.0184165 0.00111111 0.926047 262 232.462 0.0200636
17 190.705 0.0171791 0.00111111 0.936314 201 200.196 0.0282271
18 221.909 0.0209993 0.00111111 0.925893 190 232.786 0.0333187
19 211.712 0.0165509 0.00111111 0.929298 243 222.289 0.0160656
20 217.07 0.0231196 0.00111111 0.927509 182 227.732 0.0261153
21 197.775 0.0203953 0.00111111 0.933953 252 207.181 0.0323614
22 219.904 0.0168872 0.00111111 0.926563 120 230.616 0.0328043
23 204.335 0.0180441 0.00111111 0.931762 192 214.185 0.0296326
24 231.939 0.018535 0.00111111 0.922544 128 243.327 0.0304083
25 211.724 0.0185923 0.00111111 0.929295 270 222.254 0.0414344
26 214.148 0.0206654 0.00111111 0.928485 234 224.844 0.0317975
27 185.847 0.0204202 0.00111111 0.937936 165 194.744 0.034174
28 232.03 0.0193975 0.00111111 0.922513 92 243.161 0.0396967
29 223.982 0.0236268 0.00111111 0.925201 208 234.626 0.0255705
30 226.077 0.01857 0.00111111 0.924501 115 236.939 0.0416214
31 197.984 0.0164528 0.00111111 0.933883 150 207.489 0.0291136
32 176.084 0.0198163 0.00111111 0.941197 258 184.696 0.0302269
33 195.22 0.0163427 0.00111111 0.934806 230 204.904 0.0349146
34 202.893 0.0217023 0.00111111 0.932244 172 212.168 0.0292404
35 191.762 0.0176933 0.00111111 0.935961 203 201.279 0.0218102
36 208.412 0.0212612 0.00111111 0.930401 201 218.814 0.0210822
37 221.844 0.0210909 0.00111111 0.925915 173 232.892 0.0258153
38 219.113 0.0184739 0.00111111 0.926827 173 230.05 0.0414932
39 224.465 0.0246122 0.00111111 0.92504 183 235.508 0.0250082
40 206.774 0.0198917 0.00111111 0.930947 186 216.939 0.0310308
41 225.859 0.0172615 0.00111111 0.924574 203 236.479 0.0398825
42 212.461 0.0204119 0.00111111 0.929048 149 222.522 0.0337725
43 224.924 0.0217016 0.00111111 0.924886 130 236.154 0.0354094
44 232.115 0.0210782 0.00111111 0.922485 198 243.686 0.0172863
45 228.396 0.0196268 0.00111111 0.923727 238 239.488 0.0184422
46 196.881 0.019569 0.00111111 0.934251 255 206.689 0.0295242
47 204.716 0.0180094 0.00111111 0.931635 155 213.904 0.0298783
48 209.447 0.0191448 0.00111111 0.930055 118 219.704 0.0312759
49 221.317 0.0189707 0.00111111 0.926091 230 231.945 0.0391334
50 215.094 0.0199488 0.00111111 0.928169 216 225.771 0.0197891
51 184.929 0.01736 0.00111111 0.938243 213 194.167 0.0307934
52 178.666 0.0198813 0.00111111 0.940334 187 187.585 0.0149118
53 174.929 0.0189218 0.00111111 0.941582 195 183.471 0.0197701
54 179.673 0.0165947 0.00111111 0.939998 237 188.175 0.0268256
55 204.903 0.0174921 0.00111111 0.931572 176 214.811 0.0341155
56 198.996 0.0198215 0.00111111 0.933545 174 208.903 0.022187
57 239.497 0.0229021 0.00111111 0.920019 145 251.345 0.0363502
58 197.617 0.0190836 0.00111111 0.934006 264 207.332 0.0225783
59 213.541 0.0182262 0.00111111 0.928688 171 223.93 0.024467
60 205.246 0.018593 0.00111111 0.931458 200 215.194 0.0259352
61 192.01 0.021189 0.00111111 0.935878 187 201.523 0.0356566
62 207.623 0.0200966 0.00111111 0.930664 162 217.973 0.0237469
63 207.124 0.0203329 0.00111111 0.930831 275 217.381 0.0377496
64 207.6 0.015955 0.00111111 0.930672 313 217.963 0.034054
65 196.464 0.0215318 0.00111111 0.934391 148 206.197 0.0255094
66 225.291 0.0188696 0.00111111 0.924764 253 236.455 0.0269747
67 236.139 0.0195817 0.00111111 0.921141 208 247.922 0.0181545
68 218.667 0.0236608 0.00111111 0.926976 261 229.254 0.0200224
69 214.905 0.0183248 0.00111111 0.928232 232 225.179 0.0317272
70 222.269 0.0210694 0.00111111 0.925773 217 232.89 0.0322418
71 193.848 0.0176874 0.00111111 0.935264 251 203.276 0.0293206
72 210.394 0.0173601 0.00111111 0.929739 205 220.572 0.023589
73 240.818 0.0172322 0.00111111 0.919578 201 252.661 0.037452
74 223.163 0.0214412 0.00111111 0.925474 173 234.239 0.030632
75 218.88 0.0208812 0.00111111 0.926905 110 229.098 0.0257352
76 203.933 0.0179055 0.00111111 0.931896 229 213.754 0.0315607
77 175.337 0.0219549 0.00111111 0.941446 169 184.029 0.0185983
78 191.342 0.0173847 0.00111111 0.936101 233 200.827 0.0293542
79 192.271 0.0211125 0.00111111 0.935791 185 201.758 0.022909
80 217.045 0.0184074 0.00111111 0.927518 143 227.599 0.021528
81 204.268 0.0199404 0.00111111 0.931784 263 214.25 0.0243066
82 262.346 0.0165844 0.00111111 0.912389 190 275.187 0.0384815
83 213.393 0.0220227 0.00111111 0.928737 122 223.6 0.031075
84 210.514 0.0189866 0.00111111 0.929698 219 220.599 0.034692
85 205.999 0.0198525 0.00111111 0.931206 143 215.885 0.0323366
86 180.234 0.0208046 0.00111111 0.93981 234 189.174 0.0261096
87 194.507 0.0183817 0.00111111 0.935044 133 204.135 0.0318449
88 215.956 0.0189058 0.00111111 0.927881 330 226.485 0.0239564
89 203.924 0.017231 0.00111111 0.931899 235 213.99 0.0323069
90 235.067 0.0189216 0.00111111 0.921499 234 246.78 0.0289497
91 202.496 0.0204575 0.00111111 0.932376 235 212.614 0.0216976
92 263.952 0.0199218 0.00111111 0.911853 154 277.143 0.0245278
93 207.128 0.0216716 0.00111111 0.930829 209 217.224 0.0321473
94 244.798 0.019198 0.00111111 0.918249 260 256.629 0.0334971
95 209.179 0.020316 0.00111111 0.930144 341 219.57 0.0166192
96 245.103 0.0189129 0.00111111 0.918147 230 257.111 0.0263794
97 226.778 0.0168544 0.00111111 0.924267 233 237.927 0.0176314
98 207.236 0.0195923 0.00111111 0.930793 211 217.288 0.0189774
99 192.594 0.0198456 0.00111111 0.935683 226 201.924 0.0274001
100 197.183 0.0185432 0.00111111 0.93415 285 206.962 0.0215739
101 222.895 0.0181898 0.00111111 0.925564 181 233.737 0.0213808

101
dokumentation/evolution1d/20171020-evolution1D_5x5_100Times.error Normal file
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@ -0,0 +1,101 @@
"Evolution error"
222.265
210.496
219.661
216.396
227.519
240.74
208.51
195.805
236.503
260.935
198.559
222.573
231.708
220.944
232.462
200.196
232.786
222.289
227.732
207.181
230.616
214.185
243.327
222.254
224.844
194.744
243.161
234.626
236.939
207.489
184.696
204.904
212.168
201.279
218.814
232.892
230.05
235.508
216.939
236.479
222.522
236.154
243.686
239.488
206.689
213.904
219.704
231.945
225.771
194.167
187.585
183.471
188.175
214.811
208.903
251.345
207.332
223.93
215.194
201.523
217.973
217.381
217.963
206.197
236.455
247.922
229.254
225.179
232.89
203.276
220.572
252.661
234.239
229.098
213.754
184.029
200.827
201.758
227.599
214.25
275.187
223.6
220.599
215.885
189.174
204.135
226.485
213.99
246.78
212.614
277.143
217.224
256.629
219.57
257.111
237.927
217.288
201.924
206.962
233.737

138
dokumentation/evolution1d/20171020-evolution1D_5x5_100Times.gnuplot.fit.log Normal file
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@ -0,0 +1,138 @@
*******************************************************************************
Tue Oct 24 02:24:04 2017
FIT: data read from "20171020-evolution1D_5x5_100Times.csv" every ::1 using 2:5
format = x:z
#datapoints = 100
residuals are weighted equally (unit weight)
function used for fitting: f(x)
fitted parameters initialized with current variable values
Iteration 0
WSSR : 4.34137e+06 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.707241
initial set of free parameter values
a = 1
b = 1
After 5 iterations the fit converged.
final sum of squares of residuals : 224042
rel. change during last iteration : -6.22481e-10
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 47.8136
variance of residuals (reduced chisquare) = WSSR/ndf : 2286.14
Final set of parameters Asymptotic Standard Error
======================= ==========================
a = -3382.99 +/- 2656 (78.52%)
b = 269.51 +/- 51.79 (19.22%)
correlation matrix of the fit parameters:
a b
a 1.000
b -0.996 1.000
*******************************************************************************
Tue Oct 24 02:24:04 2017
FIT: data read from "20171020-evolution1D_5x5_100Times.csv" every ::1 using 4:5
format = x:z
#datapoints = 100
residuals are weighted equally (unit weight)
function used for fitting: g(x)
fitted parameters initialized with current variable values
Iteration 0
WSSR : 4.30453e+06 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.965399
initial set of free parameter values
aa = 1
bb = 1
After 5 iterations the fit converged.
final sum of squares of residuals : 224312
rel. change during last iteration : -4.70138e-12
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 47.8425
variance of residuals (reduced chisquare) = WSSR/ndf : 2288.9
Final set of parameters Asymptotic Standard Error
======================= ==========================
aa = 991.364 +/- 809 (81.6%)
bb = -717.625 +/- 752 (104.8%)
correlation matrix of the fit parameters:
aa bb
aa 1.000
bb -1.000 1.000
*******************************************************************************
Tue Oct 24 02:24:04 2017
FIT: data read from "20171020-evolution1D_5x5_100Times.csv" every ::1 using 4:6
format = x:z
#datapoints = 100
residuals are weighted equally (unit weight)
function used for fitting: h(x)
fitted parameters initialized with current variable values
Iteration 0
WSSR : 4.85341e+06 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.965399
initial set of free parameter values
aaa = 1
bbb = 1
After 5 iterations the fit converged.
final sum of squares of residuals : 4.99405
rel. change during last iteration : -2.82643e-06
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 0.225743
variance of residuals (reduced chisquare) = WSSR/ndf : 0.0509597
Final set of parameters Asymptotic Standard Error
======================= ==========================
aaa = -3144.45 +/- 3.817 (0.1214%)
bbb = 3144.19 +/- 3.548 (0.1128%)
correlation matrix of the fit parameters:
aaa bbb
aaa 1.000
bbb -1.000 1.000

261
dokumentation/evolution1d/20171020-evolution1D_5x5_100Times.gnuplot.log Normal file
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@ -0,0 +1,261 @@
Iteration 0
WSSR : 4.34137e+06 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.707241
initial set of free parameter values
a = 1
b = 1
/
Iteration 1
WSSR : 227857 delta(WSSR)/WSSR : -18.053
delta(WSSR) : -4.11351e+06 limit for stopping : 1e-05
lambda : 0.0707241
resultant parameter values
a = 2.72407
b = 202.778
/
Iteration 2
WSSR : 227318 delta(WSSR)/WSSR : -0.00237422
delta(WSSR) : -539.701 limit for stopping : 1e-05
lambda : 0.00707241
resultant parameter values
a = -203.128
b = 207.783
/
Iteration 3
WSSR : 224101 delta(WSSR)/WSSR : -0.0143554
delta(WSSR) : -3217.05 limit for stopping : 1e-05
lambda : 0.000707241
resultant parameter values
a = -2957.55
b = 261.251
/
Iteration 4
WSSR : 224042 delta(WSSR)/WSSR : -0.000261723
delta(WSSR) : -58.6368 limit for stopping : 1e-05
lambda : 7.07241e-05
resultant parameter values
a = -3382.33
b = 269.497
/
Iteration 5
WSSR : 224042 delta(WSSR)/WSSR : -6.22481e-10
delta(WSSR) : -0.000139462 limit for stopping : 1e-05
lambda : 7.07241e-06
resultant parameter values
a = -3382.99
b = 269.51
After 5 iterations the fit converged.
final sum of squares of residuals : 224042
rel. change during last iteration : -6.22481e-10
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 47.8136
variance of residuals (reduced chisquare) = WSSR/ndf : 2286.14
Final set of parameters Asymptotic Standard Error
======================= ==========================
a = -3382.99 +/- 2656 (78.52%)
b = 269.51 +/- 51.79 (19.22%)
correlation matrix of the fit parameters:
a b
a 1.000
b -0.996 1.000
Iteration 0
WSSR : 4.30453e+06 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.965399
initial set of free parameter values
aa = 1
bb = 1
/
Iteration 1
WSSR : 227170 delta(WSSR)/WSSR : -17.9485
delta(WSSR) : -4.07736e+06 limit for stopping : 1e-05
lambda : 0.0965399
resultant parameter values
aa = 102.981
bb = 107.131
/
Iteration 2
WSSR : 226223 delta(WSSR)/WSSR : -0.00418859
delta(WSSR) : -947.555 limit for stopping : 1e-05
lambda : 0.00965399
resultant parameter values
aa = 252.273
bb = -30.6331
/
Iteration 3
WSSR : 224317 delta(WSSR)/WSSR : -0.0084976
delta(WSSR) : -1906.15 limit for stopping : 1e-05
lambda : 0.000965399
resultant parameter values
aa = 956.388
bb = -685.114
/
Iteration 4
WSSR : 224312 delta(WSSR)/WSSR : -1.90727e-05
delta(WSSR) : -4.27823 limit for stopping : 1e-05
lambda : 9.65399e-05
resultant parameter values
aa = 991.346
bb = -717.608
/
Iteration 5
WSSR : 224312 delta(WSSR)/WSSR : -4.70138e-12
delta(WSSR) : -1.05458e-06 limit for stopping : 1e-05
lambda : 9.65399e-06
resultant parameter values
aa = 991.364
bb = -717.625
After 5 iterations the fit converged.
final sum of squares of residuals : 224312
rel. change during last iteration : -4.70138e-12
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 47.8425
variance of residuals (reduced chisquare) = WSSR/ndf : 2288.9
Final set of parameters Asymptotic Standard Error
======================= ==========================
aa = 991.364 +/- 809 (81.6%)
bb = -717.625 +/- 752 (104.8%)
correlation matrix of the fit parameters:
aa bb
aa 1.000
bb -1.000 1.000
Iteration 0
WSSR : 4.85341e+06 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.965399
initial set of free parameter values
aaa = 1
bbb = 1
/
Iteration 1
WSSR : 37027.2 delta(WSSR)/WSSR : -130.077
delta(WSSR) : -4.81638e+06 limit for stopping : 1e-05
lambda : 0.0965399
resultant parameter values
aaa = 103.355
bbb = 124.231
/
Iteration 2
WSSR : 25575.9 delta(WSSR)/WSSR : -0.447736
delta(WSSR) : -11451.3 limit for stopping : 1e-05
lambda : 0.00965399
resultant parameter values
aaa = -440.459
bbb = 630.803
/
Iteration 3
WSSR : 62.2579 delta(WSSR)/WSSR : -409.806
delta(WSSR) : -25513.7 limit for stopping : 1e-05
lambda : 0.000965399
resultant parameter values
aaa = -3016.49
bbb = 3025.25
/
Iteration 4
WSSR : 4.99407 delta(WSSR)/WSSR : -11.4664
delta(WSSR) : -57.2638 limit for stopping : 1e-05
lambda : 9.65399e-05
resultant parameter values
aaa = -3144.39
bbb = 3144.13
/
Iteration 5
WSSR : 4.99405 delta(WSSR)/WSSR : -2.82643e-06
delta(WSSR) : -1.41153e-05 limit for stopping : 1e-05
lambda : 9.65399e-06
resultant parameter values
aaa = -3144.45
bbb = 3144.19
After 5 iterations the fit converged.
final sum of squares of residuals : 4.99405
rel. change during last iteration : -2.82643e-06
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 0.225743
variance of residuals (reduced chisquare) = WSSR/ndf : 0.0509597
Final set of parameters Asymptotic Standard Error
======================= ==========================
aaa = -3144.45 +/- 3.817 (0.1214%)
bbb = 3144.19 +/- 3.548 (0.1128%)
correlation matrix of the fit parameters:
aaa bbb
aaa 1.000
bbb -1.000 1.000

20
dokumentation/evolution1d/20171020-evolution1D_5x5_100Times.gnuplot.script Normal file
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@ -0,0 +1,20 @@
set datafile separator ","
f(x)=a*x+b
fit f(x) "20171020-evolution1D_5x5_100Times.csv" every ::1 using 2:5 via a,b
set terminal png
set xlabel 'Regularity'
set ylabel 'Iterations'
set output "20171020-evolution1D_5x5_100Times_regularity-vs-steps.png"
plot "20171020-evolution1D_5x5_100Times.csv" every ::1 using 2:5 title "data", f(x) title "lin. fit" lc rgb "black"
g(x)=aa*x+bb
fit g(x) "20171020-evolution1D_5x5_100Times.csv" every ::1 using 4:5 via aa,bb
set xlabel 'Improvement potential'
set ylabel 'Iterations'
set output "20171020-evolution1D_5x5_100Times_improvement-vs-steps.png"
plot "20171020-evolution1D_5x5_100Times.csv" every ::1 using 4:5 title "data", g(x) title "lin. fit" lc rgb "black"
h(x)=aaa*x+bbb
fit h(x) "20171020-evolution1D_5x5_100Times.csv" every ::1 using 4:6 via aaa,bbb
set xlabel 'Improvement potential'
set ylabel 'Fitting error'
set output "20171020-evolution1D_5x5_100Times_improvement-vs-evo-error.png"
plot "20171020-evolution1D_5x5_100Times.csv" every ::1 using 4:6 title "data", h(x) title "lin. fit" lc rgb "black"

2131
dokumentation/evolution1d/20171020-evolution1D_5x5_100Times.log Normal file
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37
dokumentation/evolution1d/20171020-evolution1D_5x5_100Times.spearman Normal file
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@ -0,0 +1,37 @@
[1] "================ Analyzing 20171020-evolution1D_5x5_100Times.csv"
[1] "spearman for improvement-potential vs. evolution-error"
x y
x 1 -1
y -1 1
n= 100
P
x y
x 0
y 0
[1] "spearman for regularity vs. steps"
x y
x 1.00 -0.13
y -0.13 1.00
n= 100
P
x y
x 0.1926
y 0.1926
[1] "spearman for variability vs. evolution-error"
x y
x 1 NaN
y NaN 1
n= 100
P
x y
x
y

106
dokumentation/evolution1d/20171020-evolution1D_5x5_100Times_2-addedOne.csv Normal file
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@ -0,0 +1,106 @@
"Least squares",regularity,variability,improvement,steps,"Evolution error",sigma
185.647,0.014356,0.00111111,0.979107,223,194.608,0.0251885
228.278,0.0166674,0.00111111,0.974309,96,239.386,0.0587051
210.224,0.0156879,0.00111111,0.976341,148,220.631,0.0169237
210.616,0.0124586,0.00111111,0.976305,233,220.99,0.0228667
273.644,0.0178481,0.00111111,0.969203,121,286.984,0.0348054
201.39,0.0178169,0.00111111,0.977335,204,211.406,0.0303567
209.903,0.0171999,0.00111111,0.976489,120,219.459,0.0409589
222.808,0.0168139,0.00111111,0.975037,225,233.543,0.0401666
203.319,0.0169448,0.00111111,0.977118,210,213.447,0.0235074
211.049,0.0176901,0.00111111,0.976248,254,221.233,0.0277544
198.649,0.015557,0.00111111,0.977661,191,208.084,0.0312838
202.965,0.0155469,0.00111111,0.977161,259,212.582,0.0351323
243.06,0.0154777,0.00111111,0.972653,237,254.405,0.0356981
222.078,0.0145561,0.00111111,0.975006,288,232.994,0.0325273
193.881,0.0199094,0.00111111,0.97818,162,203.263,0.0298737
193.987,0.0130592,0.00111111,0.97828,268,203.409,0.0286171
188.631,0.0165446,0.00111111,0.978771,202,197.35,0.028157
180.679,0.0149728,0.00111111,0.97972,125,189.628,0.0401607
206.926,0.015751,0.00111111,0.976712,251,217.243,0.0365136
213.045,0.0114715,0.00111111,0.976023,338,223.551,0.0298777
172.678,0.0158423,0.00111111,0.980585,271,181.247,0.0182507
218.003,0.0148428,0.00111111,0.975519,147,228.391,0.0452333
211.195,0.0164286,0.00111111,0.976231,270,221.701,0.0321599
240.495,0.0161571,0.00111111,0.972934,133,252.128,0.0552674
200.698,0.0163158,0.00111111,0.977414,186,210.65,0.0215938
215.725,0.0173088,0.00111111,0.975722,187,226.253,0.0289603
202.49,0.0125383,0.00111111,0.977211,261,212.413,0.0283027
205.788,0.0132712,0.00111111,0.97684,200,215.916,0.0308076
214.682,0.0143839,0.00111111,0.975862,268,225.395,0.0220344
231.114,0.0165792,0.00111111,0.97399,251,242.464,0.0253499
185.386,0.0164515,0.00111111,0.979136,313,194.468,0.0304484
214.335,0.0174143,0.00111111,0.975878,130,223.975,0.0362177
183.317,0.0161095,0.00111111,0.979376,304,191.243,0.0582229
212.715,0.0167311,0.00111111,0.97606,170,223.213,0.0342458
196.845,0.0167791,0.00111111,0.977846,206,206.498,0.0199106
191.37,0.0180067,0.00111111,0.978463,314,200.508,0.0321382
221.813,0.013384,0.00111111,0.975036,195,232.713,0.0482562
222.851,0.0160286,0.00111111,0.974955,263,233.455,0.0344433
208.666,0.0159447,0.00111111,0.976516,223,218.831,0.0246139
265.843,0.0164659,0.00111111,0.970081,149,278.472,0.0469066
192.914,0.0143679,0.00111111,0.978289,254,202.505,0.0297481
226.46,0.0175878,0.00111111,0.974514,185,237.611,0.0232642
212.488,0.0185853,0.00111111,0.976086,230,222.704,0.0292529
205.052,0.0176342,0.00111111,0.976923,291,215.068,0.0339813
232.184,0.0173967,0.00111111,0.973869,219,243.65,0.0304632
266.267,0.0147618,0.00111111,0.970033,216,279.577,0.0343761
177.08,0.0167556,0.00111111,0.980071,241,185.929,0.0259968
191.049,0.0138796,0.00111111,0.978499,356,200.348,0.0335199
202.329,0.0147324,0.00111111,0.977236,258,212.262,0.0388334
206.74,0.0177026,0.00111111,0.976733,201,216.56,0.0439213
210.328,0.0130898,0.00111111,0.976329,231,220.799,0.0262093
193.897,0.0201275,0.00111111,0.978178,248,203.551,0.0232533
204.982,0.0172087,0.00111111,0.976931,319,215.113,0.0316795
240.961,0.014936,0.00111111,0.972881,183,252.985,0.0219308
180.8,0.0158388,0.00111111,0.979652,268,189.53,0.0263405
235.065,0.0141919,0.00111111,0.973545,209,246.286,0.0246108
191.202,0.0155234,0.00111111,0.978482,240,200.739,0.0320649
261.864,0.0162687,0.00111111,0.970529,104,274.27,0.045422
197.479,0.0145463,0.00111111,0.977775,226,207.251,0.0213115
213.281,0.0154231,0.00111111,0.975997,260,223.877,0.0288002
208.048,0.0179031,0.00111111,0.976585,224,218.369,0.0181026
176.232,0.0184922,0.00111111,0.980166,336,184.794,0.0301405
217.44,0.0162194,0.00111111,0.975528,223,228.064,0.0268147
231.411,0.0166529,0.00111111,0.973956,146,242.68,0.0381561
197.923,0.0162844,0.00111111,0.977726,315,207.781,0.019324
171.524,0.0131308,0.00111111,0.980697,288,180.093,0.0370283
229.186,0.0166106,0.00111111,0.974207,230,240.421,0.0268132
233.517,0.0149928,0.00111111,0.973719,233,244.614,0.0333904
219.952,0.0155735,0.00111111,0.975246,229,230.875,0.019978
158.326,0.0151468,0.00111111,0.982181,248,166.147,0.0285648
233.011,0.0175349,0.00111111,0.973785,156,244.438,0.0464821
226.006,0.0157648,0.00111111,0.974564,124,236.881,0.033025
256.376,0.0131813,0.00111111,0.971156,268,269.08,0.0278261
215.825,0.0159012,0.00111111,0.975712,189,226.565,0.026596
199.66,0.0178054,0.00111111,0.977533,224,209.625,0.0232476
220.113,0.0137952,0.00111111,0.975287,154,230.205,0.0519984
174.879,0.0144725,0.00111111,0.980319,211,183.499,0.026459
213.68,0.00982581,0.00111111,0.975952,359,224.238,0.0258146
244.965,0.0129242,0.00111111,0.972431,129,255.764,0.0440628
231.825,0.015597,0.00111111,0.97391,298,243.406,0.0291375
193.252,0.0131953,0.00111111,0.978363,242,202.853,0.0196507
225.094,0.0168805,0.00111111,0.974667,183,236.104,0.0230592
232.684,0.01661,0.00111111,0.973813,184,244.282,0.0342288
235.095,0.0178999,0.00111111,0.973542,128,246.25,0.0391641
233.933,0.0170346,0.00111111,0.973687,235,245.252,0.0270962
211.237,0.0146474,0.00111111,0.976227,205,221.375,0.0361901
239.651,0.0120497,0.00111111,0.97303,293,250.708,0.0436588
188.131,0.0181584,0.00111111,0.978827,203,197.442,0.021137
206.368,0.0144154,0.00111111,0.976775,341,216.343,0.0302359
195.677,0.0175799,0.00111111,0.977978,184,205.377,0.0308378
188.007,0.0150014,0.00111111,0.978841,257,197.24,0.0269627
203.558,0.0164905,0.00111111,0.977091,182,213.491,0.01867
231.787,0.0159009,0.00111111,0.973917,251,242.953,0.0340664
192.961,0.0164376,0.00111111,0.978283,285,202.597,0.0333827
246.133,0.0126244,0.00111111,0.972299,150,257.093,0.0542747
257.313,0.0166456,0.00111111,0.971041,134,269.909,0.0314991
229.079,0.0200458,0.00111111,0.974331,144,240.485,0.0269038
184.348,0.0140723,0.00111111,0.979253,299,193.523,0.0332981
210.198,0.0166009,0.00111111,0.976344,187,220.534,0.0242483
229.809,0.0162801,0.00111111,0.974136,169,241.175,0.0226505
237.845,0.0166568,0.00111111,0.97328,215,249.694,0.0358862
209.702,0.0169816,0.00111111,0.976399,337,220.167,0.0178628
249.535,0.0171186,0.00111111,0.971916,134,261.211,0.0522281
189.733,0.0132482,0.00111111,0.978647,235,199.087,0.0225015
190.886,0.0131875,0.00111111,0.978517,194,200.364,0.0240832
1 Least squares regularity variability improvement steps Evolution error sigma
2 185.647 0.014356 0.00111111 0.979107 223 194.608 0.0251885
3 228.278 0.0166674 0.00111111 0.974309 96 239.386 0.0587051
4 210.224 0.0156879 0.00111111 0.976341 148 220.631 0.0169237
5 210.616 0.0124586 0.00111111 0.976305 233 220.99 0.0228667
6 273.644 0.0178481 0.00111111 0.969203 121 286.984 0.0348054
7 201.39 0.0178169 0.00111111 0.977335 204 211.406 0.0303567
8 209.903 0.0171999 0.00111111 0.976489 120 219.459 0.0409589
9 222.808 0.0168139 0.00111111 0.975037 225 233.543 0.0401666
10 203.319 0.0169448 0.00111111 0.977118 210 213.447 0.0235074
11 211.049 0.0176901 0.00111111 0.976248 254 221.233 0.0277544
12 198.649 0.015557 0.00111111 0.977661 191 208.084 0.0312838
13 202.965 0.0155469 0.00111111 0.977161 259 212.582 0.0351323
14 243.06 0.0154777 0.00111111 0.972653 237 254.405 0.0356981
15 222.078 0.0145561 0.00111111 0.975006 288 232.994 0.0325273
16 193.881 0.0199094 0.00111111 0.97818 162 203.263 0.0298737
17 193.987 0.0130592 0.00111111 0.97828 268 203.409 0.0286171
18 188.631 0.0165446 0.00111111 0.978771 202 197.35 0.028157
19 180.679 0.0149728 0.00111111 0.97972 125 189.628 0.0401607
20 206.926 0.015751 0.00111111 0.976712 251 217.243 0.0365136
21 213.045 0.0114715 0.00111111 0.976023 338 223.551 0.0298777
22 172.678 0.0158423 0.00111111 0.980585 271 181.247 0.0182507
23 218.003 0.0148428 0.00111111 0.975519 147 228.391 0.0452333
24 211.195 0.0164286 0.00111111 0.976231 270 221.701 0.0321599
25 240.495 0.0161571 0.00111111 0.972934 133 252.128 0.0552674
26 200.698 0.0163158 0.00111111 0.977414 186 210.65 0.0215938
27 215.725 0.0173088 0.00111111 0.975722 187 226.253 0.0289603
28 202.49 0.0125383 0.00111111 0.977211 261 212.413 0.0283027
29 205.788 0.0132712 0.00111111 0.97684 200 215.916 0.0308076
30 214.682 0.0143839 0.00111111 0.975862 268 225.395 0.0220344
31 231.114 0.0165792 0.00111111 0.97399 251 242.464 0.0253499
32 185.386 0.0164515 0.00111111 0.979136 313 194.468 0.0304484
33 214.335 0.0174143 0.00111111 0.975878 130 223.975 0.0362177
34 183.317 0.0161095 0.00111111 0.979376 304 191.243 0.0582229
35 212.715 0.0167311 0.00111111 0.97606 170 223.213 0.0342458
36 196.845 0.0167791 0.00111111 0.977846 206 206.498 0.0199106
37 191.37 0.0180067 0.00111111 0.978463 314 200.508 0.0321382
38 221.813 0.013384 0.00111111 0.975036 195 232.713 0.0482562
39 222.851 0.0160286 0.00111111 0.974955 263 233.455 0.0344433
40 208.666 0.0159447 0.00111111 0.976516 223 218.831 0.0246139
41 265.843 0.0164659 0.00111111 0.970081 149 278.472 0.0469066
42 192.914 0.0143679 0.00111111 0.978289 254 202.505 0.0297481
43 226.46 0.0175878 0.00111111 0.974514 185 237.611 0.0232642
44 212.488 0.0185853 0.00111111 0.976086 230 222.704 0.0292529
45 205.052 0.0176342 0.00111111 0.976923 291 215.068 0.0339813
46 232.184 0.0173967 0.00111111 0.973869 219 243.65 0.0304632
47 266.267 0.0147618 0.00111111 0.970033 216 279.577 0.0343761
48 177.08 0.0167556 0.00111111 0.980071 241 185.929 0.0259968
49 191.049 0.0138796 0.00111111 0.978499 356 200.348 0.0335199
50 202.329 0.0147324 0.00111111 0.977236 258 212.262 0.0388334
51 206.74 0.0177026 0.00111111 0.976733 201 216.56 0.0439213
52 210.328 0.0130898 0.00111111 0.976329 231 220.799 0.0262093
53 193.897 0.0201275 0.00111111 0.978178 248 203.551 0.0232533
54 204.982 0.0172087 0.00111111 0.976931 319 215.113 0.0316795
55 240.961 0.014936 0.00111111 0.972881 183 252.985 0.0219308
56 180.8 0.0158388 0.00111111 0.979652 268 189.53 0.0263405
57 235.065 0.0141919 0.00111111 0.973545 209 246.286 0.0246108
58 191.202 0.0155234 0.00111111 0.978482 240 200.739 0.0320649
59 261.864 0.0162687 0.00111111 0.970529 104 274.27 0.045422
60 197.479 0.0145463 0.00111111 0.977775 226 207.251 0.0213115
61 213.281 0.0154231 0.00111111 0.975997 260 223.877 0.0288002
62 208.048 0.0179031 0.00111111 0.976585 224 218.369 0.0181026
63 176.232 0.0184922 0.00111111 0.980166 336 184.794 0.0301405
64 217.44 0.0162194 0.00111111 0.975528 223 228.064 0.0268147
65 231.411 0.0166529 0.00111111 0.973956 146 242.68 0.0381561
66 197.923 0.0162844 0.00111111 0.977726 315 207.781 0.019324
67 171.524 0.0131308 0.00111111 0.980697 288 180.093 0.0370283
68 229.186 0.0166106 0.00111111 0.974207 230 240.421 0.0268132
69 233.517 0.0149928 0.00111111 0.973719 233 244.614 0.0333904
70 219.952 0.0155735 0.00111111 0.975246 229 230.875 0.019978
71 158.326 0.0151468 0.00111111 0.982181 248 166.147 0.0285648
72 233.011 0.0175349 0.00111111 0.973785 156 244.438 0.0464821
73 226.006 0.0157648 0.00111111 0.974564 124 236.881 0.033025
74 256.376 0.0131813 0.00111111 0.971156 268 269.08 0.0278261
75 215.825 0.0159012 0.00111111 0.975712 189 226.565 0.026596
76 199.66 0.0178054 0.00111111 0.977533 224 209.625 0.0232476
77 220.113 0.0137952 0.00111111 0.975287 154 230.205 0.0519984
78 174.879 0.0144725 0.00111111 0.980319 211 183.499 0.026459
79 213.68 0.00982581 0.00111111 0.975952 359 224.238 0.0258146
80 244.965 0.0129242 0.00111111 0.972431 129 255.764 0.0440628
81 231.825 0.015597 0.00111111 0.97391 298 243.406 0.0291375
82 193.252 0.0131953 0.00111111 0.978363 242 202.853 0.0196507
83 225.094 0.0168805 0.00111111 0.974667 183 236.104 0.0230592
84 232.684 0.01661 0.00111111 0.973813 184 244.282 0.0342288
85 235.095 0.0178999 0.00111111 0.973542 128 246.25 0.0391641
86 233.933 0.0170346 0.00111111 0.973687 235 245.252 0.0270962
87 211.237 0.0146474 0.00111111 0.976227 205 221.375 0.0361901
88 239.651 0.0120497 0.00111111 0.97303 293 250.708 0.0436588
89 188.131 0.0181584 0.00111111 0.978827 203 197.442 0.021137
90 206.368 0.0144154 0.00111111 0.976775 341 216.343 0.0302359
91 195.677 0.0175799 0.00111111 0.977978 184 205.377 0.0308378
92 188.007 0.0150014 0.00111111 0.978841 257 197.24 0.0269627
93 203.558 0.0164905 0.00111111 0.977091 182 213.491 0.01867
94 231.787 0.0159009 0.00111111 0.973917 251 242.953 0.0340664
95 192.961 0.0164376 0.00111111 0.978283 285 202.597 0.0333827
96 246.133 0.0126244 0.00111111 0.972299 150 257.093 0.0542747
97 257.313 0.0166456 0.00111111 0.971041 134 269.909 0.0314991
98 229.079 0.0200458 0.00111111 0.974331 144 240.485 0.0269038
99 184.348 0.0140723 0.00111111 0.979253 299 193.523 0.0332981
100 210.198 0.0166009 0.00111111 0.976344 187 220.534 0.0242483
101 229.809 0.0162801 0.00111111 0.974136 169 241.175 0.0226505
102 237.845 0.0166568 0.00111111 0.97328 215 249.694 0.0358862
103 209.702 0.0169816 0.00111111 0.976399 337 220.167 0.0178628
104 249.535 0.0171186 0.00111111 0.971916 134 261.211 0.0522281
105 189.733 0.0132482 0.00111111 0.978647 235 199.087 0.0225015
106 190.886 0.0131875 0.00111111 0.978517 194 200.364 0.0240832

106
dokumentation/evolution1d/20171020-evolution1D_5x5_100Times_2-addedOne.error Normal file
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@ -0,0 +1,106 @@
"Evolution error"
194.608
239.386
220.631
220.99
286.984
211.406
219.459
233.543
213.447
221.233
208.084
212.582
254.405
232.994
203.263
203.409
197.35
189.628
217.243
223.551
181.247
228.391
221.701
252.128
210.65
226.253
212.413
215.916
225.395
242.464
194.468
223.975
191.243
223.213
206.498
200.508
232.713
233.455
218.831
278.472
202.505
237.611
222.704
215.068
243.65
279.577
185.929
200.348
212.262
216.56
220.799
203.551
215.113
252.985
189.53
246.286
200.739
274.27
207.251
223.877
218.369
184.794
228.064
242.68
207.781
180.093
240.421
244.614
230.875
166.147
244.438
236.881
269.08
226.565
209.625
230.205
183.499
224.238
255.764
243.406
202.853
236.104
244.282
246.25
245.252
221.375
250.708
197.442
216.343
205.377
197.24
213.491
242.953
202.597
257.093
269.909
240.485
193.523
220.534
241.175
249.694
220.167
261.211
199.087
200.364

138
dokumentation/evolution1d/20171020-evolution1D_5x5_100Times_2-addedOne.gnuplot.fit.log Normal file
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@ -0,0 +1,138 @@
*******************************************************************************
Tue Oct 24 02:24:04 2017
FIT: data read from "20171020-evolution1D_5x5_100Times_2-addedOne.csv" every ::1 using 2:5
format = x:z
#datapoints = 105
residuals are weighted equally (unit weight)
function used for fitting: f(x)
fitted parameters initialized with current variable values
Iteration 0
WSSR : 5.50446e+06 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.707196
initial set of free parameter values
a = 1
b = 1
After 5 iterations the fit converged.
final sum of squares of residuals : 357529
rel. change during last iteration : -2.04772e-09
degrees of freedom (FIT_NDF) : 103
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 58.9165
variance of residuals (reduced chisquare) = WSSR/ndf : 3471.16
Final set of parameters Asymptotic Standard Error
======================= ==========================
a = -8895.71 +/- 3121 (35.09%)
b = 362.23 +/- 49.61 (13.7%)
correlation matrix of the fit parameters:
a b
a 1.000
b -0.993 1.000
*******************************************************************************
Tue Oct 24 02:24:04 2017
FIT: data read from "20171020-evolution1D_5x5_100Times_2-addedOne.csv" every ::1 using 4:5
format = x:z
#datapoints = 105
residuals are weighted equally (unit weight)
function used for fitting: g(x)
fitted parameters initialized with current variable values
Iteration 0
WSSR : 5.46001e+06 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.988111
initial set of free parameter values
aa = 1
bb = 1
After 5 iterations the fit converged.
final sum of squares of residuals : 313329
rel. change during last iteration : -7.67387e-08
degrees of freedom (FIT_NDF) : 103
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 55.1546
variance of residuals (reduced chisquare) = WSSR/ndf : 3042.03
Final set of parameters Asymptotic Standard Error
======================= ==========================
aa = 10277.4 +/- 2107 (20.5%)
bb = -9809.71 +/- 2056 (20.96%)
correlation matrix of the fit parameters:
aa bb
aa 1.000
bb -1.000 1.000
*******************************************************************************
Tue Oct 24 02:24:04 2017
FIT: data read from "20171020-evolution1D_5x5_100Times_2-addedOne.csv" every ::1 using 4:6
format = x:z
#datapoints = 105
residuals are weighted equally (unit weight)
function used for fitting: h(x)
fitted parameters initialized with current variable values
Iteration 0
WSSR : 5.18982e+06 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.988111
initial set of free parameter values
aaa = 1
bbb = 1
After 6 iterations the fit converged.
final sum of squares of residuals : 12.9217
rel. change during last iteration : -9.78104e-13
degrees of freedom (FIT_NDF) : 103
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 0.354194
variance of residuals (reduced chisquare) = WSSR/ndf : 0.125453
Final set of parameters Asymptotic Standard Error
======================= ==========================
aaa = -9289.92 +/- 13.53 (0.1456%)
bbb = 9290.67 +/- 13.21 (0.1421%)
correlation matrix of the fit parameters:
aaa bbb
aaa 1.000
bbb -1.000 1.000

272
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Iteration 0
WSSR : 5.50446e+06 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.707196
initial set of free parameter values
a = 1
b = 1
/
Iteration 1
WSSR : 385823 delta(WSSR)/WSSR : -13.2668
delta(WSSR) : -5.11864e+06 limit for stopping : 1e-05
lambda : 0.0707196
resultant parameter values
a = -1.86573
b = 220.792
/
Iteration 2
WSSR : 382086 delta(WSSR)/WSSR : -0.0097808
delta(WSSR) : -3737.11 limit for stopping : 1e-05
lambda : 0.00707196
resultant parameter values
a = -593.106
b = 231.171
/
Iteration 3
WSSR : 357901 delta(WSSR)/WSSR : -0.067574
delta(WSSR) : -24184.8 limit for stopping : 1e-05
lambda : 0.000707196
resultant parameter values
a = -7873.39
b = 346.092
/
Iteration 4
WSSR : 357529 delta(WSSR)/WSSR : -0.00104138
delta(WSSR) : -372.325 limit for stopping : 1e-05
lambda : 7.07196e-05
resultant parameter values
a = -8894.28
b = 362.207
/
Iteration 5
WSSR : 357529 delta(WSSR)/WSSR : -2.04772e-09
delta(WSSR) : -0.000732119 limit for stopping : 1e-05
lambda : 7.07196e-06
resultant parameter values
a = -8895.71
b = 362.23
After 5 iterations the fit converged.
final sum of squares of residuals : 357529
rel. change during last iteration : -2.04772e-09
degrees of freedom (FIT_NDF) : 103
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 58.9165
variance of residuals (reduced chisquare) = WSSR/ndf : 3471.16
Final set of parameters Asymptotic Standard Error
======================= ==========================
a = -8895.71 +/- 3121 (35.09%)
b = 362.23 +/- 49.61 (13.7%)
correlation matrix of the fit parameters:
a b
a 1.000
b -0.993 1.000
Iteration 0
WSSR : 5.46001e+06 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.988111
initial set of free parameter values
aa = 1
bb = 1
/
Iteration 1
WSSR : 384230 delta(WSSR)/WSSR : -13.2103
delta(WSSR) : -5.07578e+06 limit for stopping : 1e-05
lambda : 0.0988111
resultant parameter values
aa = 114.034
bb = 109.495
/
Iteration 2
WSSR : 379288 delta(WSSR)/WSSR : -0.0130279
delta(WSSR) : -4941.33 limit for stopping : 1e-05
lambda : 0.00988111
resultant parameter values
aa = 467.204
bb = -234.184
/
Iteration 3
WSSR : 316453 delta(WSSR)/WSSR : -0.198559
delta(WSSR) : -62834.8 limit for stopping : 1e-05
lambda : 0.000988111
resultant parameter values
aa = 8142.31
bb = -7725.68
/
Iteration 4
WSSR : 313329 delta(WSSR)/WSSR : -0.00997133
delta(WSSR) : -3124.31 limit for stopping : 1e-05
lambda : 9.88111e-05
resultant parameter values
aa = 10271.5
bb = -9803.93
/
Iteration 5
WSSR : 313329 delta(WSSR)/WSSR : -7.67387e-08
delta(WSSR) : -0.0240445 limit for stopping : 1e-05
lambda : 9.88111e-06
resultant parameter values
aa = 10277.4
bb = -9809.71
After 5 iterations the fit converged.
final sum of squares of residuals : 313329
rel. change during last iteration : -7.67387e-08
degrees of freedom (FIT_NDF) : 103
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 55.1546
variance of residuals (reduced chisquare) = WSSR/ndf : 3042.03
Final set of parameters Asymptotic Standard Error
======================= ==========================
aa = 10277.4 +/- 2107 (20.5%)
bb = -9809.71 +/- 2056 (20.96%)
correlation matrix of the fit parameters:
aa bb
aa 1.000
bb -1.000 1.000
Iteration 0
WSSR : 5.18982e+06 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.988111
initial set of free parameter values
aaa = 1
bbb = 1
/
Iteration 1
WSSR : 60660.8 delta(WSSR)/WSSR : -84.5548
delta(WSSR) : -5.12916e+06 limit for stopping : 1e-05
lambda : 0.0988111
resultant parameter values
aaa = 107.576
bbb = 116.947
/
Iteration 2
WSSR : 56417.5 delta(WSSR)/WSSR : -0.0752119
delta(WSSR) : -4243.27 limit for stopping : 1e-05
lambda : 0.00988111
resultant parameter values
aaa = -218.01
bbb = 435.791
/
Iteration 3
WSSR : 2684.68 delta(WSSR)/WSSR : -20.0146
delta(WSSR) : -53732.8 limit for stopping : 1e-05
lambda : 0.000988111
resultant parameter values
aaa = -7315.5
bbb = 7363.48
/
Iteration 4
WSSR : 12.9423 delta(WSSR)/WSSR : -206.435
delta(WSSR) : -2671.74 limit for stopping : 1e-05
lambda : 9.88111e-05
resultant parameter values
aaa = -9284.44
bbb = 9285.32
/
Iteration 5
WSSR : 12.9217 delta(WSSR)/WSSR : -0.00159124
delta(WSSR) : -0.0205615 limit for stopping : 1e-05
lambda : 9.88111e-06
resultant parameter values
aaa = -9289.92
bbb = 9290.67
/
Iteration 6
WSSR : 12.9217 delta(WSSR)/WSSR : -9.78104e-13
delta(WSSR) : -1.26388e-11 limit for stopping : 1e-05
lambda : 9.88111e-07
resultant parameter values
aaa = -9289.92
bbb = 9290.67
After 6 iterations the fit converged.
final sum of squares of residuals : 12.9217
rel. change during last iteration : -9.78104e-13
degrees of freedom (FIT_NDF) : 103
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 0.354194
variance of residuals (reduced chisquare) = WSSR/ndf : 0.125453
Final set of parameters Asymptotic Standard Error
======================= ==========================
aaa = -9289.92 +/- 13.53 (0.1456%)
bbb = 9290.67 +/- 13.21 (0.1421%)
correlation matrix of the fit parameters:
aaa bbb
aaa 1.000
bbb -1.000 1.000

20
dokumentation/evolution1d/20171020-evolution1D_5x5_100Times_2-addedOne.gnuplot.script Normal file
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@ -0,0 +1,20 @@
set datafile separator ","
f(x)=a*x+b
fit f(x) "20171020-evolution1D_5x5_100Times_2-addedOne.csv" every ::1 using 2:5 via a,b
set terminal png
set xlabel 'Regularity'
set ylabel 'Iterations'
set output "20171020-evolution1D_5x5_100Times_2-addedOne_regularity-vs-steps.png"
plot "20171020-evolution1D_5x5_100Times_2-addedOne.csv" every ::1 using 2:5 title "data", f(x) title "lin. fit" lc rgb "black"
g(x)=aa*x+bb
fit g(x) "20171020-evolution1D_5x5_100Times_2-addedOne.csv" every ::1 using 4:5 via aa,bb
set xlabel 'Improvement potential'
set ylabel 'Iterations'
set output "20171020-evolution1D_5x5_100Times_2-addedOne_improvement-vs-steps.png"
plot "20171020-evolution1D_5x5_100Times_2-addedOne.csv" every ::1 using 4:5 title "data", g(x) title "lin. fit" lc rgb "black"
h(x)=aaa*x+bbb
fit h(x) "20171020-evolution1D_5x5_100Times_2-addedOne.csv" every ::1 using 4:6 via aaa,bbb
set xlabel 'Improvement potential'
set ylabel 'Fitting error'
set output "20171020-evolution1D_5x5_100Times_2-addedOne_improvement-vs-evo-error.png"
plot "20171020-evolution1D_5x5_100Times_2-addedOne.csv" every ::1 using 4:6 title "data", h(x) title "lin. fit" lc rgb "black"

2236
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[1] "================ Analyzing 20171020-evolution1D_5x5_100Times_2-addedOne.csv"
[1] "spearman for improvement-potential vs. evolution-error"
x y
x 1 -1
y -1 1
n= 105
P
x y
x 0
y 0
[1] "spearman for regularity vs. steps"
x y
x 1.00 -0.26
y -0.26 1.00
n= 105
P
x y
x 0.0069
y 0.0069
[1] "spearman for variability vs. evolution-error"
x y
x 1 NaN
y NaN 1
n= 105
P
x y
x
y

BIN
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dokumentation/evolution1d/20171020-evolution1D_5x5_100Times_2.csv Normal file
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"Least squares",regularity,variability,improvement,steps,"Evolution error",sigma
208.251,0.0156103,0.00111111,0.930454,219,218.554,0.0237623
205.745,0.0164894,0.00111111,0.931291,284,215.888,0.0246676
262.299,0.0141122,0.00111111,0.912515,129,274.375,0.0565738
224.957,0.0152828,0.00111111,0.924875,200,236.199,0.0374467
210.151,0.0132398,0.00111111,0.92982,261,220.524,0.025062
187.467,0.0159945,0.00111111,0.937395,202,196.458,0.0294108
243.284,0.0186985,0.00111111,0.918838,151,255.181,0.0419001
205.644,0.0179346,0.00111111,0.931659,219,215.837,0.0289177
218.884,0.0197365,0.00111111,0.926907,215,229.777,0.0268596
188.388,0.0155439,0.00111111,0.937088,226,197.728,0.0254865
211.745,0.0161295,0.00111111,0.929349,204,222.315,0.0222368
194.615,0.0159007,0.00111111,0.935011,185,204.324,0.0209588
178.431,0.0163466,0.00111111,0.940413,212,187.221,0.0280782
276.203,0.0190878,0.00111111,0.907762,141,289.253,0.0332024
199.444,0.0152092,0.00111111,0.933395,309,209.38,0.0332999
192.346,0.0155373,0.00111111,0.935766,255,201.838,0.0300946
223.725,0.0166533,0.00111111,0.925287,120,234.713,0.0389049
185.109,0.0160023,0.00111111,0.938243,357,194.356,0.0158333
198.016,0.0166753,0.00111111,0.933872,293,207.713,0.0261414
211.251,0.0190468,0.00111111,0.929452,246,221.794,0.0255768
221.043,0.0134399,0.00111111,0.926182,151,231.891,0.0345185
240.075,0.016048,0.00111111,0.919837,235,252.009,0.0217227
207.312,0.016896,0.00111111,0.930912,223,217.426,0.0276136
211.082,0.0172862,0.00111111,0.929509,294,221.445,0.0443438
245.321,0.0191785,0.00111111,0.918077,167,257.465,0.0351721
244.739,0.0157521,0.00111111,0.918333,226,256.592,0.0373863
223.619,0.0162014,0.00111111,0.925322,160,234.339,0.0383885
219.046,0.0157983,0.00111111,0.926849,187,229.724,0.0289253
197.268,0.014696,0.00111111,0.934122,278,207.016,0.0270345
187.175,0.0148869,0.00111111,0.937493,182,196.505,0.0329261
193.197,0.0159364,0.00111111,0.935524,155,202.122,0.0380707
202.731,0.0167975,0.00111111,0.932298,200,212.556,0.029387
188.371,0.0129935,0.00111111,0.937093,280,197.783,0.0235418
229.724,0.0168458,0.00111111,0.923357,135,240.317,0.0404449
229.376,0.0175321,0.00111111,0.9234,185,240.693,0.0362523
246.277,0.0130564,0.00111111,0.917755,121,258.116,0.0559509
211.323,0.0147458,0.00111111,0.929428,267,221.524,0.0350329
206.252,0.0154743,0.00111111,0.931122,131,216.335,0.0506455
204.337,0.0195766,0.00111111,0.931761,141,214.214,0.0304005
194.742,0.0171276,0.00111111,0.934979,262,204.095,0.0245503
223.878,0.0151602,0.00111111,0.925236,180,234.645,0.0330038
217.188,0.0136059,0.00111111,0.927472,142,227.992,0.0236646
226.019,0.0175302,0.00111111,0.924521,167,237.221,0.0200143
195.306,0.0147572,0.00111111,0.934777,288,205.039,0.0302338
218.29,0.0169948,0.00111111,0.927102,216,228.952,0.0268954
209.079,0.0185921,0.00111111,0.930178,171,219.368,0.0175072
208.333,0.0166852,0.00111111,0.930544,185,218.239,0.0267055
215.551,0.0156959,0.00111111,0.928115,239,226.308,0.0307541
210.163,0.0143026,0.00111111,0.929816,212,220.379,0.0314692
196.156,0.0158837,0.00111111,0.934493,265,205.943,0.0334331
209.587,0.0140575,0.00111111,0.930062,172,219.746,0.041743
206.029,0.0161449,0.00111111,0.931196,262,216.152,0.0287446
214.555,0.018111,0.00111111,0.928683,233,225.258,0.0465493
204.087,0.0156288,0.00111111,0.931845,319,214.278,0.0280257
182.573,0.0131847,0.00111111,0.939033,301,191.56,0.0271354
184.499,0.0150507,0.00111111,0.938386,208,193.72,0.0290137
245.41,0.0185819,0.00111111,0.918045,188,257.562,0.026555
205.876,0.0141595,0.00111111,0.931247,175,216.097,0.0299926
220.681,0.0166829,0.00111111,0.926304,246,231.642,0.0226254
246.441,0.0204901,0.00111111,0.917701,141,258.616,0.024422
204.64,0.016331,0.00111111,0.93166,225,214.487,0.0355979
204.459,0.012116,0.00111111,0.931721,237,214.517,0.018181
207.989,0.0186847,0.00111111,0.930542,112,217.707,0.0454783
221.608,0.0140767,0.00111111,0.925994,193,231.438,0.0494232
234.879,0.0164438,0.00111111,0.921562,248,246.342,0.0216915
232.335,0.0143684,0.00111111,0.922412,186,243.702,0.0454547
240.425,0.0191828,0.00111111,0.91971,152,252.271,0.0374549
223.711,0.0173486,0.00111111,0.925291,203,234.365,0.0416848
189.186,0.0177615,0.00111111,0.936821,157,197.813,0.0354675
225.668,0.0149183,0.00111111,0.924638,161,236.785,0.0294055
190.516,0.013475,0.00111111,0.936377,194,199.476,0.0368492
181.448,0.0190386,0.00111111,0.939426,172,190.418,0.0197871
174.893,0.013657,0.00111111,0.941595,209,183.54,0.0249911
239.432,0.0167526,0.00111111,0.920148,112,251.338,0.0424673
216.12,0.0177558,0.00111111,0.927826,230,226.87,0.0131177
199.419,0.0165644,0.00111111,0.933404,180,209.377,0.0360779
214.187,0.015712,0.00111111,0.928472,334,224.776,0.0236814
220.124,0.0146528,0.00111111,0.926489,195,231.062,0.0221922
213.278,0.0117894,0.00111111,0.928798,306,223.872,0.0196314
232.559,0.0143261,0.00111111,0.922671,143,243.782,0.03507
226.85,0.0130241,0.00111111,0.924243,191,237.917,0.0356397
239.534,0.0175992,0.00111111,0.920007,153,251.363,0.0250786
189.334,0.0168291,0.00111111,0.936772,220,198.154,0.0291626
222.884,0.0167893,0.00111111,0.925568,173,234.026,0.0286739
217.705,0.0123857,0.00111111,0.927326,205,228.503,0.0233835
219.242,0.0186138,0.00111111,0.926784,146,230.184,0.0161714
229.813,0.0169982,0.00111111,0.923254,253,241.123,0.030569
252.056,0.0140714,0.00111111,0.915826,244,264.597,0.0308211
189.176,0.0138221,0.00111111,0.936824,186,198.256,0.0382027
191.39,0.0157992,0.00111111,0.936419,266,200.756,0.02373
190.972,0.0143532,0.00111111,0.936225,207,200.251,0.0285909
238.237,0.0134806,0.00111111,0.92044,220,250.132,0.0133152
188.447,0.0164881,0.00111111,0.937068,249,197.721,0.0347174
220.511,0.0147817,0.00111111,0.92636,200,231.451,0.0277416
227.83,0.0181621,0.00111111,0.923938,269,238.912,0.029632
195.281,0.0171527,0.00111111,0.934786,246,204.402,0.0286607
194.106,0.0150671,0.00111111,0.935193,209,203.589,0.0364586
193.872,0.0118606,0.00111111,0.935256,186,203.051,0.0436481
268.274,0.018024,0.00111111,0.910409,120,281.253,0.0400486
256.839,0.0147597,0.00111111,0.914228,165,269.672,0.0250823
1 Least squares regularity variability improvement steps Evolution error sigma
2 208.251 0.0156103 0.00111111 0.930454 219 218.554 0.0237623
3 205.745 0.0164894 0.00111111 0.931291 284 215.888 0.0246676
4 262.299 0.0141122 0.00111111 0.912515 129 274.375 0.0565738
5 224.957 0.0152828 0.00111111 0.924875 200 236.199 0.0374467
6 210.151 0.0132398 0.00111111 0.92982 261 220.524 0.025062
7 187.467 0.0159945 0.00111111 0.937395 202 196.458 0.0294108
8 243.284 0.0186985 0.00111111 0.918838 151 255.181 0.0419001
9 205.644 0.0179346 0.00111111 0.931659 219 215.837 0.0289177
10 218.884 0.0197365 0.00111111 0.926907 215 229.777 0.0268596
11 188.388 0.0155439 0.00111111 0.937088 226 197.728 0.0254865
12 211.745 0.0161295 0.00111111 0.929349 204 222.315 0.0222368
13 194.615 0.0159007 0.00111111 0.935011 185 204.324 0.0209588
14 178.431 0.0163466 0.00111111 0.940413 212 187.221 0.0280782
15 276.203 0.0190878 0.00111111 0.907762 141 289.253 0.0332024
16 199.444 0.0152092 0.00111111 0.933395 309 209.38 0.0332999
17 192.346 0.0155373 0.00111111 0.935766 255 201.838 0.0300946
18 223.725 0.0166533 0.00111111 0.925287 120 234.713 0.0389049
19 185.109 0.0160023 0.00111111 0.938243 357 194.356 0.0158333
20 198.016 0.0166753 0.00111111 0.933872 293 207.713 0.0261414
21 211.251 0.0190468 0.00111111 0.929452 246 221.794 0.0255768
22 221.043 0.0134399 0.00111111 0.926182 151 231.891 0.0345185
23 240.075 0.016048 0.00111111 0.919837 235 252.009 0.0217227
24 207.312 0.016896 0.00111111 0.930912 223 217.426 0.0276136
25 211.082 0.0172862 0.00111111 0.929509 294 221.445 0.0443438
26 245.321 0.0191785 0.00111111 0.918077 167 257.465 0.0351721
27 244.739 0.0157521 0.00111111 0.918333 226 256.592 0.0373863
28 223.619 0.0162014 0.00111111 0.925322 160 234.339 0.0383885
29 219.046 0.0157983 0.00111111 0.926849 187 229.724 0.0289253
30 197.268 0.014696 0.00111111 0.934122 278 207.016 0.0270345
31 187.175 0.0148869 0.00111111 0.937493 182 196.505 0.0329261
32 193.197 0.0159364 0.00111111 0.935524 155 202.122 0.0380707
33 202.731 0.0167975 0.00111111 0.932298 200 212.556 0.029387
34 188.371 0.0129935 0.00111111 0.937093 280 197.783 0.0235418
35 229.724 0.0168458 0.00111111 0.923357 135 240.317 0.0404449
36 229.376 0.0175321 0.00111111 0.9234 185 240.693 0.0362523
37 246.277 0.0130564 0.00111111 0.917755 121 258.116 0.0559509
38 211.323 0.0147458 0.00111111 0.929428 267 221.524 0.0350329
39 206.252 0.0154743 0.00111111 0.931122 131 216.335 0.0506455
40 204.337 0.0195766 0.00111111 0.931761 141 214.214 0.0304005
41 194.742 0.0171276 0.00111111 0.934979 262 204.095 0.0245503
42 223.878 0.0151602 0.00111111 0.925236 180 234.645 0.0330038
43 217.188 0.0136059 0.00111111 0.927472 142 227.992 0.0236646
44 226.019 0.0175302 0.00111111 0.924521 167 237.221 0.0200143
45 195.306 0.0147572 0.00111111 0.934777 288 205.039 0.0302338
46 218.29 0.0169948 0.00111111 0.927102 216 228.952 0.0268954
47 209.079 0.0185921 0.00111111 0.930178 171 219.368 0.0175072
48 208.333 0.0166852 0.00111111 0.930544 185 218.239 0.0267055
49 215.551 0.0156959 0.00111111 0.928115 239 226.308 0.0307541
50 210.163 0.0143026 0.00111111 0.929816 212 220.379 0.0314692
51 196.156 0.0158837 0.00111111 0.934493 265 205.943 0.0334331
52 209.587 0.0140575 0.00111111 0.930062 172 219.746 0.041743
53 206.029 0.0161449 0.00111111 0.931196 262 216.152 0.0287446
54 214.555 0.018111 0.00111111 0.928683 233 225.258 0.0465493
55 204.087 0.0156288 0.00111111 0.931845 319 214.278 0.0280257
56 182.573 0.0131847 0.00111111 0.939033 301 191.56 0.0271354
57 184.499 0.0150507 0.00111111 0.938386 208 193.72 0.0290137
58 245.41 0.0185819 0.00111111 0.918045 188 257.562 0.026555
59 205.876 0.0141595 0.00111111 0.931247 175 216.097 0.0299926
60 220.681 0.0166829 0.00111111 0.926304 246 231.642 0.0226254
61 246.441 0.0204901 0.00111111 0.917701 141 258.616 0.024422
62 204.64 0.016331 0.00111111 0.93166 225 214.487 0.0355979
63 204.459 0.012116 0.00111111 0.931721 237 214.517 0.018181
64 207.989 0.0186847 0.00111111 0.930542 112 217.707 0.0454783
65 221.608 0.0140767 0.00111111 0.925994 193 231.438 0.0494232
66 234.879 0.0164438 0.00111111 0.921562 248 246.342 0.0216915
67 232.335 0.0143684 0.00111111 0.922412 186 243.702 0.0454547
68 240.425 0.0191828 0.00111111 0.91971 152 252.271 0.0374549
69 223.711 0.0173486 0.00111111 0.925291 203 234.365 0.0416848
70 189.186 0.0177615 0.00111111 0.936821 157 197.813 0.0354675
71 225.668 0.0149183 0.00111111 0.924638 161 236.785 0.0294055
72 190.516 0.013475 0.00111111 0.936377 194 199.476 0.0368492
73 181.448 0.0190386 0.00111111 0.939426 172 190.418 0.0197871
74 174.893 0.013657 0.00111111 0.941595 209 183.54 0.0249911
75 239.432 0.0167526 0.00111111 0.920148 112 251.338 0.0424673
76 216.12 0.0177558 0.00111111 0.927826 230 226.87 0.0131177
77 199.419 0.0165644 0.00111111 0.933404 180 209.377 0.0360779
78 214.187 0.015712 0.00111111 0.928472 334 224.776 0.0236814
79 220.124 0.0146528 0.00111111 0.926489 195 231.062 0.0221922
80 213.278 0.0117894 0.00111111 0.928798 306 223.872 0.0196314
81 232.559 0.0143261 0.00111111 0.922671 143 243.782 0.03507
82 226.85 0.0130241 0.00111111 0.924243 191 237.917 0.0356397
83 239.534 0.0175992 0.00111111 0.920007 153 251.363 0.0250786
84 189.334 0.0168291 0.00111111 0.936772 220 198.154 0.0291626
85 222.884 0.0167893 0.00111111 0.925568 173 234.026 0.0286739
86 217.705 0.0123857 0.00111111 0.927326 205 228.503 0.0233835
87 219.242 0.0186138 0.00111111 0.926784 146 230.184 0.0161714
88 229.813 0.0169982 0.00111111 0.923254 253 241.123 0.030569
89 252.056 0.0140714 0.00111111 0.915826 244 264.597 0.0308211
90 189.176 0.0138221 0.00111111 0.936824 186 198.256 0.0382027
91 191.39 0.0157992 0.00111111 0.936419 266 200.756 0.02373
92 190.972 0.0143532 0.00111111 0.936225 207 200.251 0.0285909
93 238.237 0.0134806 0.00111111 0.92044 220 250.132 0.0133152
94 188.447 0.0164881 0.00111111 0.937068 249 197.721 0.0347174
95 220.511 0.0147817 0.00111111 0.92636 200 231.451 0.0277416
96 227.83 0.0181621 0.00111111 0.923938 269 238.912 0.029632
97 195.281 0.0171527 0.00111111 0.934786 246 204.402 0.0286607
98 194.106 0.0150671 0.00111111 0.935193 209 203.589 0.0364586
99 193.872 0.0118606 0.00111111 0.935256 186 203.051 0.0436481
100 268.274 0.018024 0.00111111 0.910409 120 281.253 0.0400486
101 256.839 0.0147597 0.00111111 0.914228 165 269.672 0.0250823

101
dokumentation/evolution1d/20171020-evolution1D_5x5_100Times_2.error Normal file
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@ -0,0 +1,101 @@
"Evolution error"
218.554
215.888
274.375
236.199
220.524
196.458
255.181
215.837
229.777
197.728
222.315
204.324
187.221
289.253
209.38
201.838
234.713
194.356
207.713
221.794
231.891
252.009
217.426
221.445
257.465
256.592
234.339
229.724
207.016
196.505
202.122
212.556
197.783
240.317
240.693
258.116
221.524
216.335
214.214
204.095
234.645
227.992
237.221
205.039
228.952
219.368
218.239
226.308
220.379
205.943
219.746
216.152
225.258
214.278
191.56
193.72
257.562
216.097
231.642
258.616
214.487
214.517
217.707
231.438
246.342
243.702
252.271
234.365
197.813
236.785
199.476
190.418
183.54
251.338
226.87
209.377
224.776
231.062
223.872
243.782
237.917
251.363
198.154
234.026
228.503
230.184
241.123
264.597
198.256
200.756
200.251
250.132
197.721
231.451
238.912
204.402
203.589
203.051
281.253
269.672

138
dokumentation/evolution1d/20171020-evolution1D_5x5_100Times_2.gnuplot.fit.log Normal file
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*******************************************************************************
Tue Oct 24 02:24:04 2017
FIT: data read from "20171020-evolution1D_5x5_100Times_2.csv" every ::1 using 2:5
format = x:z
#datapoints = 100
residuals are weighted equally (unit weight)
function used for fitting: f(x)
fitted parameters initialized with current variable values
Iteration 0
WSSR : 4.55694e+06 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.707198
initial set of free parameter values
a = 1
b = 1
After 5 iterations the fit converged.
final sum of squares of residuals : 271763
rel. change during last iteration : -1.01351e-09
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 52.6602
variance of residuals (reduced chisquare) = WSSR/ndf : 2773.1
Final set of parameters Asymptotic Standard Error
======================= ==========================
a = -5580.61 +/- 2768 (49.6%)
b = 296.806 +/- 44.49 (14.99%)
correlation matrix of the fit parameters:
a b
a 1.000
b -0.993 1.000
*******************************************************************************
Tue Oct 24 02:24:04 2017
FIT: data read from "20171020-evolution1D_5x5_100Times_2.csv" every ::1 using 4:5
format = x:z
#datapoints = 100
residuals are weighted equally (unit weight)
function used for fitting: g(x)
fitted parameters initialized with current variable values
Iteration 0
WSSR : 4.51925e+06 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.964973
initial set of free parameter values
aa = 1
bb = 1
After 5 iterations the fit converged.
final sum of squares of residuals : 233709
rel. change during last iteration : -1.76515e-11
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 48.8343
variance of residuals (reduced chisquare) = WSSR/ndf : 2384.79
Final set of parameters Asymptotic Standard Error
======================= ==========================
aa = 3176.73 +/- 698.5 (21.99%)
bb = -2742.16 +/- 648.7 (23.65%)
correlation matrix of the fit parameters:
aa bb
aa 1.000
bb -1.000 1.000
*******************************************************************************
Tue Oct 24 02:24:04 2017
FIT: data read from "20171020-evolution1D_5x5_100Times_2.csv" every ::1 using 4:6
format = x:z
#datapoints = 100
residuals are weighted equally (unit weight)
function used for fitting: h(x)
fitted parameters initialized with current variable values
Iteration 0
WSSR : 4.99472e+06 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.964973
initial set of free parameter values
aaa = 1
bbb = 1
After 5 iterations the fit converged.
final sum of squares of residuals : 10.1354
rel. change during last iteration : -4.55776e-07
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 0.321593
variance of residuals (reduced chisquare) = WSSR/ndf : 0.103422
Final set of parameters Asymptotic Standard Error
======================= ==========================
aaa = -3140.1 +/- 4.6 (0.1465%)
bbb = 3140.23 +/- 4.272 (0.136%)
correlation matrix of the fit parameters:
aaa bbb
aaa 1.000
bbb -1.000 1.000

261
dokumentation/evolution1d/20171020-evolution1D_5x5_100Times_2.gnuplot.log Normal file
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@ -0,0 +1,261 @@
Iteration 0
WSSR : 4.55694e+06 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.707198
initial set of free parameter values
a = 1
b = 1
/
Iteration 1
WSSR : 283142 delta(WSSR)/WSSR : -15.0942
delta(WSSR) : -4.27379e+06 limit for stopping : 1e-05
lambda : 0.0707198
resultant parameter values
a = 0.244292
b = 206.717
/
Iteration 2
WSSR : 281566 delta(WSSR)/WSSR : -0.00559538
delta(WSSR) : -1575.47 limit for stopping : 1e-05
lambda : 0.00707198
resultant parameter values
a = -376.256
b = 213.754
/
Iteration 3
WSSR : 271908 delta(WSSR)/WSSR : -0.0355197
delta(WSSR) : -9658.1 limit for stopping : 1e-05
lambda : 0.000707198
resultant parameter values
a = -4948.6
b = 286.721
/
Iteration 4
WSSR : 271763 delta(WSSR)/WSSR : -0.000531939
delta(WSSR) : -144.561 limit for stopping : 1e-05
lambda : 7.07198e-05
resultant parameter values
a = -5579.74
b = 296.792
/
Iteration 5
WSSR : 271763 delta(WSSR)/WSSR : -1.01351e-09
delta(WSSR) : -0.000275435 limit for stopping : 1e-05
lambda : 7.07198e-06
resultant parameter values
a = -5580.61
b = 296.806
After 5 iterations the fit converged.
final sum of squares of residuals : 271763
rel. change during last iteration : -1.01351e-09
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 52.6602
variance of residuals (reduced chisquare) = WSSR/ndf : 2773.1
Final set of parameters Asymptotic Standard Error
======================= ==========================
a = -5580.61 +/- 2768 (49.6%)
b = 296.806 +/- 44.49 (14.99%)
correlation matrix of the fit parameters:
a b
a 1.000
b -0.993 1.000
Iteration 0
WSSR : 4.51925e+06 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.964973
initial set of free parameter values
aa = 1
bb = 1
/
Iteration 1
WSSR : 279714 delta(WSSR)/WSSR : -15.1567
delta(WSSR) : -4.23953e+06 limit for stopping : 1e-05
lambda : 0.0964973
resultant parameter values
aa = 111.794
bb = 102.988
/
Iteration 2
WSSR : 261643 delta(WSSR)/WSSR : -0.0690668
delta(WSSR) : -18070.9 limit for stopping : 1e-05
lambda : 0.00964973
resultant parameter values
aa = 786.112
bb = -522.176
/
Iteration 3
WSSR : 233742 delta(WSSR)/WSSR : -0.119368
delta(WSSR) : -27901.4 limit for stopping : 1e-05
lambda : 0.000964973
resultant parameter values
aa = 3094.82
bb = -2666.1
/
Iteration 4
WSSR : 233709 delta(WSSR)/WSSR : -0.000140322
delta(WSSR) : -32.7945 limit for stopping : 1e-05
lambda : 9.64973e-05
resultant parameter values
aa = 3176.7
bb = -2742.13
/
Iteration 5
WSSR : 233709 delta(WSSR)/WSSR : -1.76515e-11
delta(WSSR) : -4.12532e-06 limit for stopping : 1e-05
lambda : 9.64973e-06
resultant parameter values
aa = 3176.73
bb = -2742.16
After 5 iterations the fit converged.
final sum of squares of residuals : 233709
rel. change during last iteration : -1.76515e-11
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 48.8343
variance of residuals (reduced chisquare) = WSSR/ndf : 2384.79
Final set of parameters Asymptotic Standard Error
======================= ==========================
aa = 3176.73 +/- 698.5 (21.99%)
bb = -2742.16 +/- 648.7 (23.65%)
correlation matrix of the fit parameters:
aa bb
aa 1.000
bb -1.000 1.000
Iteration 0
WSSR : 4.99472e+06 delta(WSSR)/WSSR : 0
delta(WSSR) : 0 limit for stopping : 1e-05
lambda : 0.964973
initial set of free parameter values
aaa = 1
bbb = 1
/
Iteration 1
WSSR : 51532.4 delta(WSSR)/WSSR : -95.9239
delta(WSSR) : -4.94319e+06 limit for stopping : 1e-05
lambda : 0.0964973
resultant parameter values
aaa = 102.17
bbb = 128.276
/
Iteration 2
WSSR : 31291.4 delta(WSSR)/WSSR : -0.646854
delta(WSSR) : -20241 limit for stopping : 1e-05
lambda : 0.00964973
resultant parameter values
aaa = -610.313
bbb = 791.01
/
Iteration 3
WSSR : 46.8594 delta(WSSR)/WSSR : -666.773
delta(WSSR) : -31244.6 limit for stopping : 1e-05
lambda : 0.000964973
resultant parameter values
aaa = -3053.42
bbb = 3059.74
/
Iteration 4
WSSR : 10.1354 delta(WSSR)/WSSR : -3.62335
delta(WSSR) : -36.724 limit for stopping : 1e-05
lambda : 9.64973e-05
resultant parameter values
aaa = -3140.07
bbb = 3140.21
/
Iteration 5
WSSR : 10.1354 delta(WSSR)/WSSR : -4.55776e-07
delta(WSSR) : -4.61945e-06 limit for stopping : 1e-05
lambda : 9.64973e-06
resultant parameter values
aaa = -3140.1
bbb = 3140.23
After 5 iterations the fit converged.
final sum of squares of residuals : 10.1354
rel. change during last iteration : -4.55776e-07
degrees of freedom (FIT_NDF) : 98
rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 0.321593
variance of residuals (reduced chisquare) = WSSR/ndf : 0.103422
Final set of parameters Asymptotic Standard Error
======================= ==========================
aaa = -3140.1 +/- 4.6 (0.1465%)
bbb = 3140.23 +/- 4.272 (0.136%)
correlation matrix of the fit parameters:
aaa bbb
aaa 1.000
bbb -1.000 1.000

20
dokumentation/evolution1d/20171020-evolution1D_5x5_100Times_2.gnuplot.script Normal file
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set datafile separator ","
f(x)=a*x+b
fit f(x) "20171020-evolution1D_5x5_100Times_2.csv" every ::1 using 2:5 via a,b
set terminal png
set xlabel 'Regularity'
set ylabel 'Iterations'
set output "20171020-evolution1D_5x5_100Times_2_regularity-vs-steps.png"
plot "20171020-evolution1D_5x5_100Times_2.csv" every ::1 using 2:5 title "data", f(x) title "lin. fit" lc rgb "black"
g(x)=aa*x+bb
fit g(x) "20171020-evolution1D_5x5_100Times_2.csv" every ::1 using 4:5 via aa,bb
set xlabel 'Improvement potential'
set ylabel 'Iterations'
set output "20171020-evolution1D_5x5_100Times_2_improvement-vs-steps.png"
plot "20171020-evolution1D_5x5_100Times_2.csv" every ::1 using 4:5 title "data", g(x) title "lin. fit" lc rgb "black"
h(x)=aaa*x+bbb
fit h(x) "20171020-evolution1D_5x5_100Times_2.csv" every ::1 using 4:6 via aaa,bbb
set xlabel 'Improvement potential'
set ylabel 'Fitting error'
set output "20171020-evolution1D_5x5_100Times_2_improvement-vs-evo-error.png"
plot "20171020-evolution1D_5x5_100Times_2.csv" every ::1 using 4:6 title "data", h(x) title "lin. fit" lc rgb "black"

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