appended images (test) & more text

arbeit/files/erklaerung.aux

@@ -27,10 +27,10 @@
 \setcounter{r@tfl@t}{0}
 \setcounter{subfigure}{0}
 \setcounter{subtable}{0}
-\setcounter{@todonotes@numberoftodonotes}{1}
+\setcounter{@todonotes@numberoftodonotes}{2}
 \setcounter{Item}{0}
 \setcounter{Hfootnote}{2}
-\setcounter{bookmark@seq@number}{16}
+\setcounter{bookmark@seq@number}{19}
 \setcounter{algorithm}{0}
 \setcounter{ALC@unique}{0}
 \setcounter{ALC@line}{0}

arbeit/img/evolution1d

@@ -0,0 +1 @@
+../../dokumentation/evolution1d/

arbeit/img/evolution3d

@@ -0,0 +1 @@
+../../dokumentation/evolution3d/

arbeit/ma.md

@@ -106,7 +106,7 @@ increases the efficiency of an evolutionary optimization\cite{Menzel2006}, which
 we will use later on.
 
 But this approach also has downsides as can be seen in
-\cite[figure~7]{hsu1991dmffd}\todo{figure hier einfügen?}, as the tessellation
+\cite[figure~7]{hsu1991dmffd}\unsure{figure hier einfügen?}, as the tessellation
 of the invisible grid has a major impact on the deformation itself.
 
 All in all \ac{FFD} and \ac{DM-FFD} are still good ways to deform a high-polygon
@@ -116,8 +116,9 @@ mesh albeit the downsides.
 
 
 
-## Wieso ist evo-Opt so cool?
+## Advantages of evolutional algorithms
 
+\improvement[inline]{Needs citations}
 The main advantage of evolutional algorithms is the ability to find optima of
 general functions just with the help of a given error-function (or
 fitness-function in this domain). This avoids the general pitfalls of
@@ -237,10 +238,6 @@ $\|\vec{G}\|_F = 1$, whereby $\|\cdot\|_F$ denotes the Frobenius-Norm.
 
 # Evaluation of Scenarios
 
-## Results of 1D Function Approximation
-
-
-
 ## Spearman/Pearson-Metriken
 
 - Was ist das?
@@ -249,6 +246,36 @@ $\|\vec{G}\|_F = 1$, whereby $\|\cdot\|_F$ denotes the Frobenius-Norm.
 - Haben wir das gezeigt?
 - Statistik, Bilder, blah!
 
+## Results of 1D Function Approximation
+
+\begin{figure}[!ht]
+\includegraphics[width=\textwidth]{img/evolution1d/20170830-evolution1D_5x5_100Times-all_appended.png}
+\caption{Results 1D}
+
+\end{figure}
+
+<!-- ![Improvement potential vs steps](img/evolution1d/20170830-evolution1D_5x5_100Times-all_improvement-vs-steps.png) -->
+<!--  -->
+<!-- ![Improvement potential vs evolutional error](img/evolution1d/20170830-evolution1D_5x5_100Times-all_improvement-vs-evo-error.png) -->
+<!--  -->
+<!-- ![Regularity vs steps](img/evolution1d/20170830-evolution1D_5x5_100Times-all_regularity-vs-steps.png) -->
+
+## Results of 3D Function Approximation
+
+
+\begin{figure}[!ht]
+\includegraphics[width=\textwidth]{img/evolution3d/20170926_3dFit_both_append.png}
+\caption{Results 3D}
+
+\end{figure}
+
+<!-- ![Improvement potential vs steps](img/evolution3d/20170926_3dFit_both_improvement-vs-steps.png) -->
+<!--  -->
+<!-- ![Improvement potential vs evolutional -->
+<!-- error](img/evolution3d/20170926_3dFit_both_improvement-vs-evo-error.png) -->
+<!--  -->
+<!-- ![Regularity vs steps](img/evolution3d/20170926_3dFit_both_regularity-vs-steps.png) -->
+
 # Schluss
 
 HAHA .. als ob -.-

arbeit/ma.tex

@@ -10,6 +10,7 @@ titlepage,
 % openany,				% Kapitel koennen auch auf geraden Seiten starten
 % draft						% schneller compillieren, Bild-dummy
 % appendixprefix	% Anhang mit Bezeichner
+xcolor=dvipsnames,
 ]{scrbook}
 
 %%%%%%%%%%%%%%% Literaturverzeichnisstil %%%%%%%%%%%%%%%
@@ -232,7 +233,7 @@ control-points. Moreover this increases the efficiency of an
 evolutionary optimization\cite{Menzel2006}, which we will use later on.
 
 But this approach also has downsides as can be seen in
-\cite[figure~7]{hsu1991dmffd}\todo{figure hier einfügen?}, as the
+\cite[figure~7]{hsu1991dmffd}\unsure{figure hier einfügen?}, as the
 tessellation of the invisible grid has a major impact on the deformation
 itself.
 
@@ -242,13 +243,15 @@ high-polygon mesh albeit the downsides.
 \section{What is evolutional
 optimization?}\label{what-is-evolutional-optimization}
 
-\section{Wieso ist evo-Opt so cool?}\label{wieso-ist-evo-opt-so-cool}
+\section{Advantages of evolutional
+algorithms}\label{advantages-of-evolutional-algorithms}
 
-The main advantage of evolutional algorithms is the ability to find
-optima of general functions just with the help of a given error-function
-(or fitness-function in this domain). This avoids the general pitfalls
-of gradient-based procedures, which often target the same error-function
-as an evolutional algorithm, but can get stuck in local optima.
+\improvement[inline]{Needs citations} The main advantage of evolutional
+algorithms is the ability to find optima of general functions just with
+the help of a given error-function (or fitness-function in this domain).
+This avoids the general pitfalls of gradient-based procedures, which
+often target the same error-function as an evolutional algorithm, but
+can get stuck in local optima.
 
 This is mostly due to the fact that a gradient-based procedure has only
 one point of observation from where it evaluates the next steps, whereas
@@ -324,7 +327,8 @@ P(\vec{U}) := 1 - \|(\vec{1} - \vec{UU}^+)\vec(G)\|^2_F
 normalized to \(\|\vec{G}\|_F = 1\), whereby \(\|\cdot\|_F\) denotes the
 Frobenius-Norm.
 
-\chapter{Hauptteil}\label{hauptteil}
+\chapter{\texorpdfstring{Implementation of
+\acf{FFD}}{Implementation of }}\label{implementation-of}
 
 \section{Was ist FFD?}\label{was-ist-ffd}
 
@@ -340,11 +344,10 @@ Frobenius-Norm.
   Was folgt daraus?
 \end{itemize}
 
-\section{Szenarien vorstellen}\label{szenarien-vorstellen}
+\section{Test Scenario: 1D Function
+Approximation}\label{test-scenario-1d-function-approximation}
 
-\subsection{1D}\label{d}
-
-\subsubsection{Optimierungszenario}\label{optimierungszenario}
+\subsection{Optimierungszenario}\label{optimierungszenario}
 
 \begin{itemize}
 \tightlist
@@ -352,7 +355,7 @@ Frobenius-Norm.
   Ebene -\textgreater{} Template-Fit
 \end{itemize}
 
-\subsubsection{Matching in 1D}\label{matching-in-1d}
+\subsection{Matching in 1D}\label{matching-in-1d}
 
 \begin{itemize}
 \tightlist
@@ -360,7 +363,7 @@ Frobenius-Norm.
   Trivial
 \end{itemize}
 
-\subsubsection{Besonderheiten der
+\subsection{Besonderheiten der
 Auswertung}\label{besonderheiten-der-auswertung}
 
 \begin{itemize}
@@ -379,9 +382,10 @@ Auswertung}\label{besonderheiten-der-auswertung}
   \end{itemize}
 \end{itemize}
 
-\subsection{3D}\label{d-1}
+\section{Test Scenario: 3D Function
+Approximation}\label{test-scenario-3d-function-approximation}
 
-\subsubsection{Optimierungsszenario}\label{optimierungsszenario}
+\subsection{Optimierungsszenario}\label{optimierungsszenario}
 
 \begin{itemize}
 \tightlist
@@ -389,7 +393,7 @@ Auswertung}\label{besonderheiten-der-auswertung}
   Ball zu Mario
 \end{itemize}
 
-\subsubsection{Matching in 3D}\label{matching-in-3d}
+\subsection{Matching in 3D}\label{matching-in-3d}
 
 \begin{itemize}
 \tightlist
@@ -397,7 +401,7 @@ Auswertung}\label{besonderheiten-der-auswertung}
   alternierende Optimierung
 \end{itemize}
 
-\subsubsection{Besonderheiten der
+\subsection{Besonderheiten der
 Optimierung}\label{besonderheiten-der-optimierung}
 
 \begin{itemize}
@@ -408,7 +412,7 @@ Optimierung}\label{besonderheiten-der-optimierung}
   Kriterien trotzdem gut
 \end{itemize}
 
-\chapter{Evaluation}\label{evaluation}
+\chapter{Evaluation of Scenarios}\label{evaluation-of-scenarios}
 
 \section{Spearman/Pearson-Metriken}\label{spearmanpearson-metriken}
 
@@ -426,6 +430,24 @@ Optimierung}\label{besonderheiten-der-optimierung}
   Statistik, Bilder, blah!
 \end{itemize}
 
+\section{Results of 1D Function
+Approximation}\label{results-of-1d-function-approximation}
+
+\begin{figure}[!ht]
+\includegraphics[width=\textwidth]{img/evolution1d/20170830-evolution1D_5x5_100Times-all_appended.png}
+\caption{Results 1D}
+
+\end{figure}
+
+\section{Results of 3D Function
+Approximation}\label{results-of-3d-function-approximation}
+
+\begin{figure}[!ht]
+\includegraphics[width=\textwidth]{img/evolution3d/20170926_3dFit_both_append.png}
+\caption{Results 3D}
+
+\end{figure}
+
 \chapter{Schluss}\label{schluss}
 
 HAHA .. als ob -.-

arbeit/settings/commands.tex

@@ -174,3 +174,8 @@
                      \includegraphics[width=1cm]{img/cd}
                     \end{center}\vspace{-15pt}\centering\footnotesize\texttt{#1}}}
 \renewcommand\vec[1]{\textbf{#1}}
+\newcommandx{\unsure}[2][1=]{\todo[linecolor=red,backgroundcolor=red!25,bordercolor=red,#1]{#2}}
+\newcommandx{\change}[2][1=]{\todo[linecolor=blue,backgroundcolor=blue!25,bordercolor=blue,#1]{#2}}
+\newcommandx{\info}[2][1=]{\todo[linecolor=OliveGreen,backgroundcolor=OliveGreen!25,bordercolor=OliveGreen,#1]{#2}}
+\newcommandx{\improvement}[2][1=]{\todo[linecolor=violet,backgroundcolor=violet!25,bordercolor=violet,#1]{#2}}
+\newcommandx{\thiswillnotshow}[2][1=]{\todo[disable,#1]{#2}}

arbeit/settings/packages.tex

@@ -56,14 +56,15 @@
 % \usepackage{teubner}
 \usepackage{textcomp}
 \usepackage{times}
-\usepackage{todonotes}
+\usepackage[colorinlistoftodos,prependcaption]{todonotes}
 \usepackage[ugly]{units}			% \unit[Wert]{Einheit}
 % \usepackage{varioref} 				% schlaue referenzen: \vref{key} \vpageref[text]{key} \vrefrange, \vpagerefrange
 \usepackage{verbatim} % \begin{comment} \end{comment}
 \usepackage{wrapfig}
 % \usepackage{wasysym}
-\usepackage{xcolor}
+\usepackage[pdftex,dvipsnames]{xcolor}
 \usepackage{xspace}						% intelligent \makro_ whitespace
+\usepackage{xargs}                      % use more than one optional parameter in new commands
 
 % ###### Caption ######
 \captionsetup{margin=0.5cm, indention=0.5cm, font=small, labelfont=bf, format=plain,singlelinecheck=true,skip=3pt}

arbeit/template.tex

@@ -10,6 +10,7 @@ titlepage,
 % openany,				% Kapitel koennen auch auf geraden Seiten starten
 % draft						% schneller compillieren, Bild-dummy
 % appendixprefix	% Anhang mit Bezeichner
+xcolor=dvipsnames,
 ]{scrbook}
 
 %%%%%%%%%%%%%%% Literaturverzeichnisstil %%%%%%%%%%%%%%%