9ed1c43bc016b9b9c7c5d07cb47c2398ae1060b3
Introduce four boolean flags (show_capitalized, show_sentences, show_words, show_numbers) that control whether their respective analysis results are printed via sld(), replacing the previously commented-out calls. Add a file_exists() utility function that checks if a given path points to an existing readable file. Also append "publish" to .gitignore.
Isspam
Fast as light evaluator for text files to summarize specific details about the text files.
Building
make build
Build with memory check (requires valgrind to be installed):
make valgrind
Running
Using files as parameter
./isspam ./spam/*.txt
./isspam ./not_spam/*.txt
Using stdin
Useful for automation.
cat ./spam/example_spam1.txt | ./isspam
Example output
File: ./not_spam/not_spam1.txt
Capitalized words: 1
Sentences: 5
Words: 52
Numbers: 0
Forbidden words: 0
Word count per sentence: 10
Memory usage: 29 KB, 479 (re)allocated, 327 unqiue free'd, 0 in use.
Valgrind status
Date: 2024-11-28
Memory usage: 1 MB, 6.460 (re)allocated, 4.222 unqiue free'd, 0 in use.
==131498==
==131498== HEAP SUMMARY:
==131498== in use at exit: 0 bytes in 0 blocks
==131498== total heap usage: 6,486 allocs, 6,486 frees, 2,342,294 bytes allocated
==131498==
==131498== All heap blocks were freed -- no leaks are possible
==131498==
==131498== For lists of detected and suppressed errors, rerun with: -s
==131498== ERROR SUMMARY: 0 errors from 0 contexts (suppressed: 0 from 0)
Description
For a site I was doing spam analysis, how to recognize it. I do this by checking how it is written. How much numbers are used. How many capitals. That's the first indication. I made it originally myself but a few people liked the project and wrote their versions in their preferred language. Very happy about that.
We are bench marking it as challenge, the benchmark is doing analysis for 900 books in txt format. The fastest application wins. By now, every language has as wel won as lost. We keep iterating and making them faster.
Thanks 12bitloat, BordedDev en JestDotty for contribution of their source.
486 MiB
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Rust
73%
C++
12.9%
C
6.8%
Swift
3.4%
Python
2.2%
Other
1.6%