mlpack_emst(1)

✨ mlpack_emst ✨

okay bestie, gather round, because we're talking about mlpack_emst — the user command that goes feral computing a fast euclidean minimum spanning tree 💖

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🌈 SYNOPSIS 🌈

mlpack_emst -i unknown [-l int] [-n bool] [-V bool] [-o unknown] [-h -v]

like. look at her. she's simple. she's powerful. she's everything. 🫶

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💅 DESCRIPTION 💅

So this program computes the Euclidean minimum spanning tree of a set of input points, and it does it using the dual-tree Boruvka algorithm. no cap, that's the whole vibe. 🦋

The set you wanna compute the minimum spanning tree of? That's specified with the --input_file (-i) parameter. And where does the result go? You save it with the --output_file (-o) output parameter. iconic duo behavior tbh. ✨

Now the --leaf_size (-l) parameter controls the leaf size of the kd-tree that's used to calculate the minimum spanning tree. And if you pass the --naive (-n) option, then brute-force search is used instead — and just so you know, that is typically much slower in low dimensions. 😭 The leaf size does not affect the results, but it may have some effect on the runtime of the algorithm. so like, it's a performance thing, not a correctness thing. noted. 📝

For example, the minimum spanning tree of the input dataset data.csv can be calculated with a leaf size of 20 and stored as spanning_tree.csv using the following command:

$ mlpack_emst --input_file data.csv --leaf_size 20 --output_file spanning_tree.csv

slay. look at that command go. 🔥

Okay and now the part that lowkey matters the most: the output matrix is a three-dimensional matrix, where each row indicates an edge. The first dimension corresponds to the lesser index of the edge; the second dimension corresponds to the greater index of the edge; and the third column corresponds to the distance between the two points. so it's like (lesser, greater, distance) per row. that's the whole shape of it, bestie. 💜

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🎀 REQUIRED INPUT OPTIONS 🎀

• --input_file (-i) [unknown] — Input data matrix.

ok this one is literally required, you cannot skip her, she's the main character of your input 🎀

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✨ OPTIONAL INPUT OPTIONS ✨

• --help (-h) [bool] — Default help info.
• --info [string] — Print help on a specific option. Default value ''.
• --leaf_size (-l) [int] — Leaf size in the kd-tree. One-element leaves give the empirically best performance, but at the cost of greater memory requirements. Default value 1.
• --naive (-n) [bool] — Compute the MST using O(n^2) naive algorithm.
• --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.

not the --leaf_size default being 1 while also being the empirically best performing option 💀 she's high maintenance (memory-wise) but she delivers.

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🌸 OPTIONAL OUTPUT OPTIONS 🌸

• --output_file (-o) [unknown] — Output data. Stored as an edge list.

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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 the docs bestie, they're giving academic baddie energy 📚✨

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and that's the whole page, babes 💖 this was mlpack_emst from mlpack-4.8.0, dated 22 June 2026, manual name mlpack_emst(1) — periodt. 🎀🦋💅