mlpack_hmm_train(1)

✨ mlpack_hmm_train ✨

okay bestie so welcome to the mlpack_hmm_train(1) page, aka the glow-up station for your Hidden Markov Models 💅 she's a User Command, she's iconic, and she's here to train your HMM on labeled or unlabeled data. periodt.

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🌸 NAME 🌸

mlpack_hmm_train — hidden markov model (hmm) training 💖

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🦋 SYNOPSIS 🦋

mlpack_hmm_train -i string [-b bool] [-g int] [-m unknown] [-l string] [-s int] [-n int] [-T double] [-t string] [-V bool] [-M unknown] [-h -v]

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💜 DESCRIPTION 💜

okay so this program lets you train a Hidden Markov Model on labeled or unlabeled data. like, either one, she's flexible 😌. it supports four types of HMMs: Discrete HMMs, Gaussian HMMs, GMM HMMs, or Diagonal GMM HMMs. four flavors, all slay.

Either one input sequence can be specified (with --input_file (-i)), or, a file containing files in which input sequences can be found (when --input_file (-i) and --batch (-b) are used together). no cap, that's how the batch vibe works 🫶. In addition, labels can be provided in the file specified by --labels_file (-l), and if --batch (-b) is used, the file given to --labels_file (-l) should contain a list of files of labels corresponding to the sequences in the file given to --input_file (-i). matchy-matchy labels, we love to see it 💞.

The HMM is trained with the Baum-Welch algorithm if no labels are provided. The tolerance of the Baum-Welch algorithm can be set with the --tolerance (-T) option. By default, the transition matrix is randomly initialized and the emission distributions are initialized to fit the extent of the data. she's just doing her thing with defaults, main character energy ⭐.

Optionally, a pre-created HMM model can be used as a guess for the transition matrix and emission probabilities; this is specifiable with --output_model_file (-M). give her a hint and she'll run with it 💫.

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✨ REQUIRED INPUT OPTIONS ✨

• --input_file (-i) [string] — File containing input observations. this one's required, bestie, no skipping her 🙈

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🎀 OPTIONAL INPUT OPTIONS 🎀

• --batch (-b) [bool] — If true, input_file (and if passed, labels_file) are expected to contain a list of files to use as input observation sequences (and label sequences). it's giving "files within files" inception vibes 🌀
• --gaussians (-g) [int] — Number of gaussians in each GMM (necessary when type is 'gmm'). Default value 0.
• --help (-h) [bool] — Default help info. your girl is here to help fr fr
• --info [string] — Print help on a specific option. Default value ''.
• --input_model_file (-m) [unknown] — Pre-existing HMM model to initialize training with. a lil head start, we stan 💅
• --labels_file (-l) [string] — Optional file of hidden states, used for labeled training. Default value ''.
• --seed (-s) [int] — Random seed. If 0, std::time(NULL) is used. Default value 0.
• --states (-n) [int] — Number of hidden states in HMM (necessary, unless model_file is specified). Default value 0.
• --tolerance (-T) [double] — Tolerance of the Baum-Welch algorithm. Default value 1e-05.
• --type (-t) [string] — Type of HMM: discrete | gaussian | diag_gmm | gmm. Default value 'gaussian'.
• --verbose (-v) [bool] — Display informational messages and the full list of parameters and timers at the end of execution. she's a yapper and we love her 🗣️💬
• --version (-V) [bool] — Display the version of mlpack.

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👑 OPTIONAL OUTPUT OPTIONS 👑

• --output_model_file (-M) [unknown] — Output for trained HMM. this is the whole point bestie, this is the graduation ceremony 🎓✨

🌈 heads up: the synopsis and the option lists both use -M for --output_model_file, and the DESCRIPTION also mentions --output_model_file (-M) as the way to hand in a pre-created model as a guess. keep that in mind so you pass the right flag for the vibe you actually want 💀

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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. go read, queen 📚👑

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and that's the whole page bestie!! 💖 this was mlpack_hmm_train(1) from mlpack-4.8.0, dated 22 June 2026 — mwah, go train something gorgeous 🌸🦋⭐