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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
15 lines
2.1 KiB
TeX
15 lines
2.1 KiB
TeX
\section{General plan of tests/experiments}
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To evaluate and compare the performance of our collaborative filtering model with different parameter configurations, we will use grid search and 5-fold cross-validation.
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Grid search will allow us to systematically test different combinations of hyperparameters, such as the number of nearest neighbors in KNN and the regularization strength in the matrix factorization algorithm, to find the best combination that maximizes the model's performance.
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Cross-validation will help us to estimate the model's generalization performance and reduce the risk of overfitting to the training data.
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% Notka dla nas: Hyperparameters: Parameters that are set before training a machine learning model and cannot be learned from the data. Examples of hyperparameters include the learning rate, the number of hidden layers in a neural network, and the regularization strength.
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% Notka dla nas: Overfitting is a common problem in machine learning where a model is trained too well on the training data and performs poorly on the testing or validation data. In other words, the model learns the patterns and noise in the training data to such a degree that it loses the ability to generalize and predict accurately on new data.
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To measure the quality of our model's recommendations, we will use precision, recall, and F1-score metrics. Precision measures the proportion of relevant items among the recommended items, recall measures the proportion of relevant items that are recommended, and F1-score is the harmonic mean of precision and recall, providing a balanced measure of the model's performance. We will compute these metrics on the test set and report the average scores across the 5-fold cross-validation.
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%Our main goal is to evaluate the performance of the recommendation model. We will perform a 5-fold cross-validation to estimate the generalization performance of the model. We will also use precision, recall, and F1-score metrics to evaluate the model's performance. We will experiment with different values of K (number of neighbors) to find the optimal value.
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