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Krzysztof kuhy Rudnicki
ffa5dad02a
The 250-line cap applies to prose too, and the reports were the worst
offenders -- ENUME's projectA.tex alone was 1,876 lines. Each is now a
preamble plus one \input{} per section, which is how a long LaTeX document is
normally organised; not one byte of prose was rewritten.
"The PDF is unchanged" is checked by a script rather than by eye, because
hashing the PDF cannot work: pdflatex stamps a /CreationDate and an /ID into
every build, so two runs of the SAME source already differ. tex_fingerprint.sh
compares what a reader sees instead -- page count plus a hash of the extracted
text -- and it is stable across rebuilds of untouched sources, which is the
property that makes it usable as a gate.
It earned its keep immediately. Both ECOTE reports quote an example LaTeX
document inside an lstlisting block, so \end{document} appears in the middle
of the file; splitting on the FIRST one stranded everything after it. Same
page count, different text, caught automatically. EOPSY lab3 exposed a second
bug: a \section{} title that runs onto the next line.
Result: 19 .tex over the cap -> 1. The survivor,
ELAC/.../03-building-nfa-...tex, is 418 lines of consecutive tikz figures
with no subsection to split on and needs a hand-placed boundary.
Verified: 29 splits across two passes (section, then subsection for the
pieces still over), every one reporting `unchanged` from
scripts/tex_fingerprint.sh --check.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_0172dj34FDpao6wsix8ZuCzE
31 lines
1.3 KiB
TeX
31 lines
1.3 KiB
TeX
\section{Methods of result visualization}
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For the visualization, we chose three methods:
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\begin{itemize}
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\item Heatmaps: They are useful for visualizing the similarity between users or animes based on their ratings. They will allow us to identify clusters of similar users or animes and explore how their preferences are related to one another.
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% https://rpubs.com/jeknov/movieRec
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\begin{figure}
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\centering
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\caption{An exemplary heatmap}
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\label{fig:heatmap}
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\end{figure}
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\item Histograms: They will be used to visualize the distribution of ratings and identifying any biases or patterns in the data. They can help us identify whether the ratings are normally distributed or skewed.
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\begin{figure}
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\centering
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\caption{An exemplary histogram}
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\label{fig:precission-recall}
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\end{figure}
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\item Precision-recall curves: They show the tradeoff between precision and recall at different thresholds, allowing us to identify the optimal threshold for making recommendations. By visualizing the performance of the algorithm in this way, we can identify areas for improvement and generate new hypotheses for increasing the accuracy of the recommendations.
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\end{itemize}
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\begin{figure}
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\centering
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\caption{An exemplary precision-recall graph}
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\label{fig:histogram}
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\end{figure}
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