mlpack_hmm_loglik(1)

💖 mlpack_hmm_loglik — the manual page, but make it make sense 💖

okay bestie so you found the manual page for mlpack_hmm_loglik, and honestly? iconic. this is the "hidden markov model (hmm) sequence log-likelihood" girlie — like, the tool that tells you how likely your sequence is, given a model you already trained. let's get into it ✨

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✨ SYNOPSIS ✨

mlpack_hmm_loglik -i unknown -m unknown [-V bool] [--log_likelihood double] [-h -v]

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🌸 DESCRIPTION 🌸

so here's the tea, bestie 🫖 — this utility takes an already-trained HMM, and you hand it over with the --input_model_file (-m) parameter. then it evaluates the log-likelihood of a sequence of observations, which you pass in with the --input_file (-i) parameter. the computed log-likelihood is given as output. periodt.

like, for example, to compute the log-likelihood of the sequence seq.csv with the pre-trained HMM hmm.bin, the following command may be used:

$ mlpack_hmm_loglik --input_file seq.csv --input_model_file hmm.bin

literally that's it. no cap 🎀

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🦋 REQUIRED INPUT OPTIONS 🦋

bestie these are not optional, you have to give them:

• --input_file (-i) [unknown] — file containing observations 👀 this is your data, the vibe check itself.
• --input_model_file (-m) [unknown] — file containing HMM. your pre-trained model, the main character here 💅

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🌈 OPTIONAL INPUT OPTIONS 🌈

these are the lil extras, lowkey cute, highkey useful:

• --help (-h) [bool] — default help info. your classic "pls explain" button 🫶
• --info [string] — print help on a specific option. default value ''.
• --verbose (-v) [bool] — display informational messages and the full list of parameters and timers at the end of execution. so chatty, so informative 😭💜
• --version (-V) [bool] — display the version of mlpack. slay, know thyself.

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🍓 OPTIONAL OUTPUT OPTIONS 🍓

• --log_likelihood [double] — log-likelihood of the sequence. default value 0.

that's the number you actually came here for, bestie. the whole point. the moment. 👑

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💀 ADDITIONAL INFORMATION 💀

for further information, including relevant papers, citations, and theory, consult the documentation found at http://www.mlpack.org or included with your distribution of mlpack. iykyk 📚✨

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okay and for the cute sign-off: this lil doc is from mlpack-4.8.0, dated 22 June 2026, and it's the mlpack_hmm_loglik(1) manual page from User Commands 💖 go compute those log-likelihoods, bestie, you got this 🧚🌸