mlpack_pca(1)

💅 mlpack_pca — principal components analysis ✨

okay bestie, welcome to the mlpack_pca(1) page, a.k.a. the User Commands era of your life 🌈. this is the lil' program that does PCA and honestly? it's giving main character energy. let's get into it 💖

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✨ SYNOPSIS ✨

 mlpack_pca -i unknown [-c string] [-d int] [-s bool] [-r double] [-V bool] [-o unknown] [-h -v]

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🦋 DESCRIPTION 🦋

So this program performs principal components analysis on the dataset you hand it, using the exact, randomized, randomized block Krylov, or QUIC SVD method. It will transform the data onto its principal components, and optionally it does dimensionality reduction by ignoring the principal components with the smallest eigenvalues (lowkey iconic behaviour, fr fr).

Use the --input_file (-i) parameter to specify the dataset to perform PCA on. A desired new dimensionality can be specified with the --new_dimensionality (-d) parameter, or the desired variance to retain can be specified with the --var_to_retain (-r) parameter. If you want, the dataset can be scaled before running PCA with the --scale (-s) parameter. 💅

Multiple different decomposition techniques can be used. The method to use can be specified with the --decomposition_method (-c) parameter, and it may take the values exact, randomized, or quic.

For example, to reduce the dimensionality of the matrix data.csv to 5 dimensions using randomized SVD for the decomposition, storing the output matrix to data_mod.csv, the following command can be used:

 $ mlpack_pca --input_file data.csv --new_dimensionality 5
 --decomposition_method randomized --output_file data_mod.csv

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🎀 REQUIRED INPUT OPTIONS 🎀

• --input_file (-i) [unknown] — Input dataset to perform PCA on. you literally cannot skip this one bestie, it's the whole point 🫶

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🌸 OPTIONAL INPUT OPTIONS 🌸

• --decomposition_method (-c) [string] — Method used for the principal components analysis: exact, randomized, randomized-block-krylov, quic. Default value exact.
• --help (-h) [bool] — Default help info.
• --info [string] — Print help on a specific option. Default value ''.
• --new_dimensionality (-d) [int] — Desired dimensionality of output dataset. If 0, no dimensionality reduction is performed. Default value 0.
• --scale (-s) [bool] — If set, the data will be scaled before running PCA, such that the variance of each feature is 1.
• --var_to_retain (-r) [double] — Amount of variance to retain; should be between 0 and 1. If 1, all variance is retained. Overrides -d. Default value 0.
• --verbose (-v) [bool] — Display informational messages and the full list of parameters and timers at the end of execution.
• --version (-V) [bool] — Display the version of mlpack.

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💜 OPTIONAL OUTPUT OPTIONS 💜

• --output_file (-o) [unknown] — Matrix to save modified dataset to.

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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.

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k that's the whole vibe check, bestie 🩷 this was mlpack_pca from the mlpack-4.8.0 era, dated 22 June 2026, and no cap it ate 💀✨ stay slaying your dimensionality reductions, mwah 💋👑