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Diffstat (limited to 'assignments/assignment2/assignment2.tex')
| -rw-r--r-- | assignments/assignment2/assignment2.tex | 15 |
1 files changed, 11 insertions, 4 deletions
diff --git a/assignments/assignment2/assignment2.tex b/assignments/assignment2/assignment2.tex index 655179b..9f843a7 100644 --- a/assignments/assignment2/assignment2.tex +++ b/assignments/assignment2/assignment2.tex @@ -73,8 +73,15 @@ and we may be able to add $\pi$ a second time. \item + We use Rivest-Schapire. \item + The found models are the same modulo state names. + This is because, as argued in the answer to question 3, the SUT has eleven states + and all testing methods come up with a model of eleven states. + + With a random walk we needed only 56s. + WMethod took 15154s and WPMethod took 5956s. \item The method with user queries worked very efficiently in combination with L*. @@ -83,7 +90,7 @@ However, we realise that for more complex systems it may be difficult for the user to provide counter-examples. As for the testing method, - on our small example a random walk appears to be the fasted. + on our small example a random walk appears to be by far the fastest. However, as noted in the source code (\texttt{BasicLearner.java}), it may perform worse than the others on large models. @@ -96,12 +103,12 @@ \includegraphics[width=.5\textwidth]{LStar_hypothesis3} \includegraphics[width=.5\textwidth]{LStar_hypothesis4} \includegraphics[width=.5\textwidth]{LStar_hypothesis5} - \caption{Learning the chocolate bar machine with L* (top to bottom, left to right).\label{fig:lstar-run}} + \caption{Learning the chocolate bar machine with L* (top to bottom, left to right). Continued on the next page.\label{fig:lstar-run}} \end{figure} \begin{figure}[p] \ContinuedFloat - \includegraphics[width=.5\textwidth]{LStar_hypothesis6} - \caption[]{Learning the chocolate bar machine with L* (top to bottom, left to right).\label{fig:lstar-run}} + \includegraphics[width=\textwidth]{LStar_hypothesis6} + \caption[]{Continued from the previous page: learning the chocolate bar machine with L*; final learned model.\label{fig:lstar-run}} \end{figure} \thispagestyle{empty} |