From: Mart Lubbers <mart@martlubbers.net>
Date: Tue, 26 Jan 2016 20:38:34 +0000 (+0100)
Subject: goede dingen
X-Git-Url: https://git.martlubbers.net/?a=commitdiff_plain;h=9f14a54c31e78f2a14d21a90a3d024a282dede4a;p=tt2015.git

goede dingen
---

9f14a54c31e78f2a14d21a90a3d024a282dede4a
diff --cc a4/Makefile
index b159295,454b61e..9e957d9
--- a/a4/Makefile
+++ b/a4/Makefile
@@@ -1,8 -1,12 +1,13 @@@
  LATEX:=latex
  
  DOCUMENT:=tt4
- MODELS=model.small.LStar.rand.eps model.partial.LStar.rand.eps model.full.LStar.rand.eps
- TEXS=question1.tex question2.tex question3.tex question4.tex
+ MODELS=model.small.LStar.rand.eps model.small.TTT.rand.eps model.small.RS.rand.eps model.small.KV.rand.eps \
+ 	model.small.LStar.wm.eps model.small.TTT.wm.eps model.small.RS.wm.eps model.small.KV.wm.eps \
+ 	model.partial.LStar.rand.eps model.partial.TTT.rand.eps model.partial.RS.rand.eps model.partial.KV.rand.eps \
+ 	model.partial.LStar.wm.eps model.partial.TTT.wm.eps model.partial.RS.wm.eps model.partial.KV.wm.eps \
+ 	model.full.LStar.rand.eps model.full.TTT.rand.eps model.full.RS.rand.eps model.full.KV.rand.eps \
+ 	model.full.LStar.wm.eps model.full.TTT.wm.eps model.full.RS.wm.eps
++TEXS=question1.tex question2.tex question3.tex question4.tex models.tex
  
  .SECONDARY: $(DOCUMENT).fmt
  .PHONY: clean
diff --cc a4/models.tex
index 0000000,f6ec1b3..aa8049d
mode 000000,100644..100644
--- a/a4/models.tex
+++ b/a4/models.tex
@@@ -1,0 -1,160 +1,160 @@@
 -\begin{figure}[H]
 -	\centering
 -	\includegraphics[scale=0.75]{model.small.LStar.rand.eps}
 -	\vspace{5mm}
 -	\caption{small alphabet, LStar, Random}
 -\end{figure}
 -
 -\begin{figure}[H]
 -	\centering
 -	\includegraphics[scale=0.75]{model.small.TTT.rand.eps}
 -	\vspace{5mm}
 -	\caption{small alphabet, TTT, Random}
 -\end{figure}
 -
 -\begin{figure}[H]
 -	\centering
 -	\includegraphics[scale=0.75]{model.small.RS.rand.eps}
 -	\vspace{5mm}
 -	\caption{small alphabet, RivestSchapire, Random}
 -\end{figure}
 -
 -\begin{figure}[H]
 -	\centering
 -	\includegraphics[scale=0.75]{model.small.KV.rand.eps}
 -	\vspace{5mm}
 -	\caption{small alphabet, KearnsVazirani, Random}
 -\end{figure}
 -
 -\begin{figure}[H]
 -	\centering
 -	\includegraphics[scale=0.75]{model.small.LStar.wm.eps}
 -	\vspace{5mm}
 -	\caption{small alphabet, LStar, WMethod}
 -\end{figure}
 -
 -\begin{figure}[H]
 -	\centering
 -	\includegraphics[scale=0.75]{model.small.TTT.wm.eps}
 -	\vspace{5mm}
 -	\caption{small alphabet, TTT, WMethod}
 -\end{figure}
 -
 -\begin{figure}[H]
 -	\centering
 -	\includegraphics[scale=0.75]{model.small.RS.wm.eps}
 -	\vspace{5mm}
 -	\caption{small alphabet, RivestSchapire, WMethod}
 -\end{figure}
 -
 -\begin{figure}[H]
 -	\centering
 -	\includegraphics[scale=0.75]{model.small.KV.wm.eps}
 -	\vspace{5mm}
 -	\caption{small alphabet, KearnsVazirani, WMethod}
 -\end{figure}
 -
 -\begin{figure}[H]
 -	\centering
 -	\includegraphics[scale=0.75]{model.partial.LStar.rand.eps}
 -	\vspace{5mm}
 -	\caption{partial alphabet, LStar, Random}
 -\end{figure}
 -
 -\begin{figure}[H]
 -	\centering
 -	\includegraphics[scale=0.75]{model.partial.TTT.rand.eps}
 -	\vspace{5mm}
 -	\caption{partial alphabet, TTT, Random}
 -\end{figure}
 -
 -\begin{figure}[H]
 -	\centering
 -	\includegraphics[scale=0.75]{model.partial.RS.rand.eps}
 -	\vspace{5mm}
 -	\caption{partial alphabet, RivestSchapire, Random}
 -\end{figure}
 -
 -\begin{figure}[H]
 -	\centering
 -	\includegraphics[scale=0.75]{model.partial.KV.rand.eps}
 -	\vspace{5mm}
 -	\caption{partial alphabet, KearnsVazirani, Random}
 -\end{figure}
 -
 -\begin{figure}[H]
 -	\centering
 -	\includegraphics[scale=0.75]{model.partial.LStar.wm.eps}
 -	\vspace{5mm}
 -	\caption{partial alphabet, LStar, WMethod}
 -\end{figure}
 -
 -\begin{figure}[H]
 -	\centering
 -	\includegraphics[scale=0.75]{model.partial.TTT.wm.eps}
 -	\vspace{5mm}
 -	\caption{partial alphabet, TTT, WMethod}
 -\end{figure}
 -
 -\begin{figure}[H]
 -	\centering
 -	\includegraphics[scale=0.75]{model.partial.RS.wm.eps}
 -	\vspace{5mm}
 -	\caption{partial alphabet, RivestSchapire, WMethod}
 -\end{figure}
 -
 -\begin{figure}[H]
 -	\centering
 -	\includegraphics[scale=0.75]{model.partial.KV.wm.eps}
 -	\vspace{5mm}
 -	\caption{partial alphabet, KearnsVazirani, WMethod}
 -\end{figure}
 -
 -\begin{figure}[H]
 -	\centering
 -	\includegraphics[width=\textwidth]{model.full.LStar.rand.eps}
 -	\vspace{5mm}
 -	\caption{full alphabet, LStar, Random}
 -\end{figure}
 -
 -\begin{figure}[H]
 -	\centering
 -	\includegraphics[width=\textwidth]{model.full.TTT.rand.eps}
 -	\vspace{5mm}
 -	\caption{full alphabet, TTT, Random}
 -\end{figure}
 -
 -\begin{figure}[H]
 -	\centering
 -	\includegraphics[width=\textwidth]{model.full.RS.rand.eps}
 -	\vspace{5mm}
 -	\caption{full alphabet, RivestSchapire, Random}
 -\end{figure}
 -
 -\begin{figure}[H]
 -	\centering
 -	\includegraphics[width=\textwidth]{model.full.KV.rand.eps}
 -	\vspace{5mm}
 -	\caption{full alphabet, KearnsVazirani, Random}
 -\end{figure}
 -
 -\begin{figure}[H]
 -	\centering
 -	\includegraphics[width=\textwidth]{model.full.LStar.wm.eps}
 -	\vspace{5mm}
 -	\caption{full alphabet, LStar, WMethod}
 -\end{figure}
 -
 -\begin{figure}[H]
 -	\centering
 -	\includegraphics[width=\textwidth]{model.full.TTT.wm.eps}
 -	\vspace{5mm}
 -	\caption{full alphabet, TTT, WMethod}
 -\end{figure}
 -
 -\begin{figure}[H]
 -	\centering
 -	\includegraphics[width=\textwidth]{model.full.RS.wm.eps}
 -	\vspace{5mm}
 -	\caption{full alphabet, RivestSchapire, WMethod}
 -\end{figure}
++%\begin{figure}[H]
++%	\centering
++%	\includegraphics[scale=0.75]{model.small.LStar.rand.eps}
++%	\vspace{5mm}
++%	\caption{small alphabet, LStar, Random}
++%\end{figure}
++%
++%\begin{figure}[H]
++%	\centering
++%	\includegraphics[scale=0.75]{model.small.TTT.rand.eps}
++%	\vspace{5mm}
++%	\caption{small alphabet, TTT, Random}
++%\end{figure}
++%
++%\begin{figure}[H]
++%	\centering
++%	\includegraphics[scale=0.75]{model.small.RS.rand.eps}
++%	\vspace{5mm}
++%	\caption{small alphabet, RivestSchapire, Random}
++%\end{figure}
++%
++%\begin{figure}[H]
++%	\centering
++%	\includegraphics[scale=0.75]{model.small.KV.rand.eps}
++%	\vspace{5mm}
++%	\caption{small alphabet, KearnsVazirani, Random}
++%\end{figure}
++%
++%\begin{figure}[H]
++%	\centering
++%	\includegraphics[scale=0.75]{model.small.LStar.wm.eps}
++%	\vspace{5mm}
++%	\caption{small alphabet, LStar, WMethod}
++%\end{figure}
++%
++%\begin{figure}[H]
++%	\centering
++%	\includegraphics[scale=0.75]{model.small.TTT.wm.eps}
++%	\vspace{5mm}
++%	\caption{small alphabet, TTT, WMethod}
++%\end{figure}
++%
++%\begin{figure}[H]
++%	\centering
++%	\includegraphics[scale=0.75]{model.small.RS.wm.eps}
++%	\vspace{5mm}
++%	\caption{small alphabet, RivestSchapire, WMethod}
++%\end{figure}
++%
++%\begin{figure}[H]
++%	\centering
++%	\includegraphics[scale=0.75]{model.small.KV.wm.eps}
++%	\vspace{5mm}
++%	\caption{small alphabet, KearnsVazirani, WMethod}
++%\end{figure}
++%
++%\begin{figure}[H]
++%	\centering
++%	\includegraphics[scale=0.75]{model.partial.LStar.rand.eps}
++%	\vspace{5mm}
++%	\caption{partial alphabet, LStar, Random}
++%\end{figure}
++%
++%\begin{figure}[H]
++%	\centering
++%	\includegraphics[scale=0.75]{model.partial.TTT.rand.eps}
++%	\vspace{5mm}
++%	\caption{partial alphabet, TTT, Random}
++%\end{figure}
++%
++%\begin{figure}[H]
++%	\centering
++%	\includegraphics[scale=0.75]{model.partial.RS.rand.eps}
++%	\vspace{5mm}
++%	\caption{partial alphabet, RivestSchapire, Random}
++%\end{figure}
++%
++%\begin{figure}[H]
++%	\centering
++%	\includegraphics[scale=0.75]{model.partial.KV.rand.eps}
++%	\vspace{5mm}
++%	\caption{partial alphabet, KearnsVazirani, Random}
++%\end{figure}
++%
++%\begin{figure}[H]
++%	\centering
++%	\includegraphics[scale=0.75]{model.partial.LStar.wm.eps}
++%	\vspace{5mm}
++%	\caption{partial alphabet, LStar, WMethod}
++%\end{figure}
++%
++%\begin{figure}[H]
++%	\centering
++%	\includegraphics[scale=0.75]{model.partial.TTT.wm.eps}
++%	\vspace{5mm}
++%	\caption{partial alphabet, TTT, WMethod}
++%\end{figure}
++%
++%\begin{figure}[H]
++%	\centering
++%	\includegraphics[scale=0.75]{model.partial.RS.wm.eps}
++%	\vspace{5mm}
++%	\caption{partial alphabet, RivestSchapire, WMethod}
++%\end{figure}
++%
++%\begin{figure}[H]
++%	\centering
++%	\includegraphics[scale=0.75]{model.partial.KV.wm.eps}
++%	\vspace{5mm}
++%	\caption{partial alphabet, KearnsVazirani, WMethod}
++%\end{figure}
++%
++%\begin{figure}[H]
++%	\centering
++%	\includegraphics[width=\textwidth]{model.full.LStar.rand.eps}
++%	\vspace{5mm}
++%	\caption{full alphabet, LStar, Random}
++%\end{figure}
++%
++%\begin{figure}[H]
++%	\centering
++%	\includegraphics[width=\textwidth]{model.full.TTT.rand.eps}
++%	\vspace{5mm}
++%	\caption{full alphabet, TTT, Random}
++%\end{figure}
++%
++%\begin{figure}[H]
++%	\centering
++%	\includegraphics[width=\textwidth]{model.full.RS.rand.eps}
++%	\vspace{5mm}
++%	\caption{full alphabet, RivestSchapire, Random}
++%\end{figure}
++%
++%\begin{figure}[H]
++%	\centering
++%	\includegraphics[width=\textwidth]{model.full.KV.rand.eps}
++%	\vspace{5mm}
++%	\caption{full alphabet, KearnsVazirani, Random}
++%\end{figure}
++%
++%\begin{figure}[H]
++%	\centering
++%	\includegraphics[width=\textwidth]{model.full.LStar.wm.eps}
++%	\vspace{5mm}
++%	\caption{full alphabet, LStar, WMethod}
++%\end{figure}
++%
++%\begin{figure}[H]
++%	\centering
++%	\includegraphics[width=\textwidth]{model.full.TTT.wm.eps}
++%	\vspace{5mm}
++%	\caption{full alphabet, TTT, WMethod}
++%\end{figure}
++%
++%\begin{figure}[H]
++%	\centering
++%	\includegraphics[width=\textwidth]{model.full.RS.wm.eps}
++%	\vspace{5mm}
++%	\caption{full alphabet, RivestSchapire, WMethod}
++%\end{figure}
diff --cc a4/question2.tex
index 2e098c1,0371752..cf879c0
--- a/a4/question2.tex
+++ b/a4/question2.tex
@@@ -1,40 -1,37 +1,52 @@@
- In order to be allow learnlib to learn the TCP model it was necessary to have a
- deterministic model.  We accomplished this by modifying the adapter so it can
- reach a \texttt{ERROR} or \texttt{CLOSED} state. In these states all inputs are
- discarded and a default output is returned.  In the case of a state where an
- input results in a non-deterministic output we jump to the \texttt{ERROR} state
- for additional this given input. When the connection is successfully closed
- using a \texttt{FIN} packet we move the adapter to the \texttt{CLOSED} state.
 -In order to be allow learnlib to learn the TCP model it was necessary to have a deterministic model.
 -We accomplished this by modifying the adapter so it can reach a \emph{ERROR} or \emph{CLOSED} state. In these states all inputs are discarded and a default output is returned.
 -In the case of a state where an input results in a non-deterministic output we jump to the \emph{ERROR} state for additional this given input. When the connection is successfully closed using a \emph{FIN} packet we move the adapter to the \emph{CLOSED} state.
--
- We divided the input alphabet into three sets, this way we can control the size
- of the model learned by learnlib.
 -We divided the input alphabet into three sets, this way we can control the size of the model learned by learnlib.
--
- \begin{table}[H]
- 	\begin{tabular}{cl}
- 		\toprule
- 		Alphabet & Inputs \\
- 		\midrule
- 		small & \texttt{SYN}, \texttt{ACK} \\
- 		partial & \texttt{SYN}, \texttt{ACK}, \texttt{DATA} \\
- 		full & \texttt{SYN}, \texttt{ACK}, \texttt{DATA}, \texttt{RST},
- 			\texttt{FIN} \\
- 		\bottomrule
- 	\end{tabular}
- 	\caption{Different input alphabets used during learning.}
- \end{table}
 -\begin{longtable}{|c|l|}
 -	\caption{Different input alphabets used during learning.} \\\hline
 -	Alphabet & Inputs \\\hline \hline
 -	small & SYN, ACK \\\hline
 -	partial & SYN, ACK, DATA \\\hline
 -	full & SYN, ACK, DATA, RST, FIN \\\hline
 -\end{longtable}
--
- Just as in our previous assignment the \texttt{DATA} packet is actually a
- \texttt{ACK} with an user data payload and the \emph{push} flag set.  These
- input alphabets will influence the size of the model produced. \emph{small}
- will result in a 2 state model, \emph{partial} will be the full model without
- the \texttt{CLOSED} state and \emph{full} should result in the full model as
- used in the previous assignment.
 -Just as in our previous assignment the \emph{DATA} packet is actually a \emph{ACK} with an user data payload and the \emph{push} flag set. 
 -These input alphabets will influence the size of the model produced. \emph{small} will result in a 2 state model, \emph{partial} will be the full model without the \emph{CLOSED} state and \emph{full} should result in the full model as used in the previous assignment.
--
- \paragraph{Model learned with small input alphabet}
- %\includegraphics{model.small.LStar.rand.eps}
 -\begin{figure}[H]
 -	\centering
 -	\includegraphics[scale=0.75]{model.small.LStar.rand.eps}
 -	\vspace{5mm}
 -	\caption{Model learned with small input alphabet}
 -\end{figure}
--
- \paragraph{Model learned with partial input alphabet}
- %\includegraphics{model.partial.LStar.rand.eps}
 -\begin{figure}[H]
 -	\centering
 -	\includegraphics[width=\textwidth]{model.partial.LStar.rand.eps}
 -	\vspace{5mm}
 -	\caption{Model learned with partial input alphabet}
 -\end{figure}
--
- \paragraph{Model learned with full input alphabet}
- %\includegraphics{model.full.LStar.rand.eps}
 -\begin{figure}[H]
 -	\centering
 -	\includegraphics[width=1.2\textwidth]{model.full.LStar.rand.eps}
 -	\vspace{5mm}
 -	\caption{Model learned with full input alphabet}
 -\end{figure}
++In order to be allow learnlib to learn the TCP model it was necessary to have a
++deterministic model.  We accomplished this by modifying the adapter so it can
++reach a \texttt{ERROR} or \texttt{CLOSED} state. In these states all inputs are
++discarded and a default output is returned.  In the case of a state where an
++input results in a non-deterministic output we jump to the \texttt{ERROR} state
++for additional this given input. When the connection is successfully closed
++using a \texttt{FIN} packet we move the adapter to the \texttt{CLOSED} state.
++
++We divided the input alphabet into three sets, this way we can control the size
++of the model learned by learnlib.
++
++\begin{table}[H]
++	\begin{tabular}{cl}
++		\toprule
++		Alphabet & Inputs \\
++		\midrule
++		small & \texttt{SYN}, \texttt{ACK} \\
++		partial & \texttt{SYN}, \texttt{ACK}, \texttt{DATA} \\
++		full & \texttt{SYN}, \texttt{ACK}, \texttt{DATA}, \texttt{RST},
++			\texttt{FIN} \\
++		\bottomrule
++	\end{tabular}
++	\caption{Different input alphabets used during learning.}
++\end{table}
++
++Just as in our previous assignment the \texttt{DATA} packet is actually a
++\texttt{ACK} with an user data payload and the \emph{push} flag set.  These
++input alphabets will influence the size of the model produced. \emph{small}
++will result in a 2 state model, \emph{partial} will be the full model without
++the \texttt{CLOSED} state and \emph{full} should result in the full model as
++used in the previous assignment.
++%
++%\begin{figure}[H]
++%	\centering
++%	\includegraphics[scale=0.75]{model.small.LStar.rand.eps}
++%	\vspace{5mm}
++%	\caption{Model learned with small input alphabet}
++%\end{figure}
++%
++%\begin{figure}[H]
++%	\centering
++%	\includegraphics[width=\textwidth]{model.partial.LStar.rand.eps}
++%	\vspace{5mm}
++%	\caption{Model learned with partial input alphabet}
++%\end{figure}
++%
++%\begin{figure}[H]
++%	\centering
++%	\includegraphics[width=1.2\textwidth]{model.full.LStar.rand.eps}
++%	\vspace{5mm}
++%	\caption{Model learned with full input alphabet}
++%\end{figure}
diff --cc a4/question3.tex
index d088a21,51dfc07..d5a1a13
--- a/a4/question3.tex
+++ b/a4/question3.tex
@@@ -1,44 -1,40 +1,49 @@@
- The table below contains some statistics about all the different parameter
- configurations we ran learnlib with.  All except \emph{RivestSchapire} using
- the Random test method result in the correct model being learned.  When
- \emph{WMethod} is selected as the testing method \emph{RivestSchapire} is also
- able to learn the correct model.
- \emph{WMethod} does however increase the time needed to learn the model
- significantly, when a different learner is used there is no reason not to use
- the Random testing method.
 -The table below contains some statistics about all the different parameter configurations we ran learnlib with.
 -The \emph{RivestSchapire} learner using the Random test method resulted in an incorrect model being learned.
 -When the \emph{KearnsVazirani} learner using the WMethod tester wasn't able to learn a model, this is due the learner hitting a non-deterministic path. 
 -This problem hasn't anything to do with the actual learner and is the result of a uncaught error situation in the adapter.
 -This shows us that a leaner can be used to test software, since we discovered a bug in our adapter.
 -Due to time constrains we were not able to fix this bug.
 -When \emph{WMethod} is selected as the testing method \emph{RivestSchapire} is also able to learn the correct model.
 -\emph{WMethod} does however increase the time needed to learn the model significantly, when a different learner is used there is no reason not to use the Random testing method.
--
- \begin{table}[H]
- 	\begin{tabular}{lllccc}
- 		\toprule
- 		Alphabet & Method & Test method & States & Time \\
- 		\midrule
- 		small & LStar & Random & 2 & 12 sec \\
- 		small & TTT & Random & 2 & 5 sec \\
- 		small & RivestSchapire & Random & 2 & 6 sec \\
- 		small & KearnsVazirani & Random & 2 & 5 sec \\
- 		small & LStar & WMethod & 2 & 35 sec \\
- 		small & TTT & WMethod & 2 & 32 sec \\
- 		small & RivestSchapire & WMethod & 2 & 33 sec \\
- 		small & KearnsVazirani & WMethod & 2 & 33 sec \\
- 		
- 		partial & LStar & Random & 4 & 18 sec \\
- 		partial & TTT & Random & 4 & 16 sec \\
- 		partial & RivestSchapire & Random & 4 & 13 sec \\
- 		partial & KearnsVazirani & Random & 4 & 22 sec \\
- 		partial & LStar & WMethod & 4 & 384 sec \\
- 		partial & TTT & WMethod & 4 & 390 sec \\
- 		partial & RivestSchapire & WMethod & 4 & 384 sec \\
- 		partial & KearnsVazirani & WMethod & 4 & 383 sec \\
- 		
- 		full & LStar & Random & 5 & 44 sec \\
- 		full & TTT & Random & 5 & 25 sec \\
- 		full & RivestSchapire & Random & 4 & 12 sec \\
- 		full & KearnsVazirani & Random & 5 & 19 sec \\
- 		full & LStar & WMethod & 5 & 2666 sec \\
- 		full & TTT & WMethod & 5 & 2632 sec \\
- 		full & RivestSchapire & WMethod & 5 & 2638 sec \\
- 		full & KearnsVazirani & WMethod & - & - \\
- 		\bottomrule
- 	\end{tabular}
- 	\caption{Learning parameters and resulting model size.}
- \end{table}
 -\begin{longtable}{| l | l | l | c | c | c |}
 -	\caption{Learning parameters and resulting model size.} \\\hline
 -	Alphabet & Method & Test method & States & Time \\\hline \hline
 -	small & LStar & Random & 2 & 12 sec \\\hline
 -	small & TTT & Random & 2 & 5 sec \\\hline
 -	small & RivestSchapire & Random & 2 & 6 sec \\\hline
 -	small & KearnsVazirani & Random & 2 & 5 sec \\\hline
 -	small & LStar & WMethod & 2 & 35 sec \\\hline
 -	small & TTT & WMethod & 2 & 32 sec \\\hline
 -	small & RivestSchapire & WMethod & 2 & 33 sec \\\hline
 -	small & KearnsVazirani & WMethod & 2 & 33 sec \\\hline
 -	
 -	partial & LStar & Random & 4 & 18 sec \\\hline
 -	partial & TTT & Random & 4 & 16 sec \\\hline
 -	partial & RivestSchapire & Random & 4 & 13 sec \\\hline
 -	partial & KearnsVazirani & Random & 4 & 22 sec \\\hline
 -	partial & LStar & WMethod & 4 & 384 sec \\\hline
 -	partial & TTT & WMethod & 4 & 390 sec \\\hline
 -	partial & RivestSchapire & WMethod & 4 & 384 sec \\\hline
 -	partial & KearnsVazirani & WMethod & 4 & 383 sec \\\hline
 -	
 -	full & LStar & Random & 5 & 44 sec \\\hline
 -	full & TTT & Random & 5 & 25 sec \\\hline
 -	full & RivestSchapire & Random & 4 & 12 sec \\\hline
 -	full & KearnsVazirani & Random & 5 & 19 sec \\\hline
 -	full & LStar & WMethod & 5 & 2666 sec \\\hline
 -	full & TTT & WMethod & 5 & 2632 sec \\\hline
 -	full & RivestSchapire & WMethod & 5 & 2638 sec \\\hline
 -	full & KearnsVazirani & WMethod & - & - \\\hline
 -\end{longtable}
 -
++The table below contains some statistics about all the different parameter
++configurations we ran learnlib with. The \emph{RivestSchapire} learner using
++the Random test method resulted in an incorrect model being learned. When the
++\emph{KearnsVazirani} learner using the WMethod tester wasn't able to learn a
++model, this is due the learner hitting a non-deterministic path.  This
++problem hasn't anything to do with the actual learner and is the result of a
++uncaught error situation in the adapter. This shows us that a leaner can be
++used to test software, since we discovered a bug in our adapter. Due to time
++constrains we were not able to fix this bug. When \emph{WMethod} is selected
++as the testing method \emph{RivestSchapire} is also able to learn the correct
++model. \emph{WMethod} does however increase the time needed to learn the
++model significantly, when a different learner is used there is no reason not to
++use the Random testing method.
++
++\begin{table}[H]
++	\begin{tabular}{lllccc}
++		\toprule
++		Alphabet & Method & Test method & States & Time \\
++		\midrule
++		small & LStar & Random & 2 & 12 sec \\
++		small & TTT & Random & 2 & 5 sec \\
++		small & RivestSchapire & Random & 2 & 6 sec \\
++		small & KearnsVazirani & Random & 2 & 5 sec \\
++		small & LStar & WMethod & 2 & 35 sec \\
++		small & TTT & WMethod & 2 & 32 sec \\
++		small & RivestSchapire & WMethod & 2 & 33 sec \\
++		small & KearnsVazirani & WMethod & 2 & 33 sec \\
++		
++		partial & LStar & Random & 4 & 18 sec \\
++		partial & TTT & Random & 4 & 16 sec \\
++		partial & RivestSchapire & Random & 4 & 13 sec \\
++		partial & KearnsVazirani & Random & 4 & 22 sec \\
++		partial & LStar & WMethod & 4 & 384 sec \\
++		partial & TTT & WMethod & 4 & 390 sec \\
++		partial & RivestSchapire & WMethod & 4 & 384 sec \\
++		partial & KearnsVazirani & WMethod & 4 & 383 sec \\
++		
++		full & LStar & Random & 5 & 44 sec \\
++		full & TTT & Random & 5 & 25 sec \\
++		full & RivestSchapire & Random & 4 & 12 sec \\
++		full & KearnsVazirani & Random & 5 & 19 sec \\
++		full & LStar & WMethod & 5 & 2666 sec \\
++		full & TTT & WMethod & 5 & 2632 sec \\
++		full & RivestSchapire & WMethod & 5 & 2638 sec \\
++		full & KearnsVazirani & WMethod & - & - \\
++		\bottomrule
++	\end{tabular}
++	\caption{Learning parameters and resulting model size.}
++\end{table}