The Rust ecosystem is full of a data structure implementations that may be useful in machine learning. You can find multi-dimensional arrays, matrices, graphs, and a large list of tree libraries.
The list below is incomplete, so for more specific data structures don't forget to search crates.io.
Graph data structure library. Provides graph types and graph algorithms.
A collection of numeric types and traits for Rust, including bigint, complex, rational, range iterators, generic integers, and more!
An n-dimensional array for general elements and for numerics. Lightweight array views and slicing; views support chunking and splitting.
Rust implementation of Apache Arrow
DataFrame library based on Apache Arrow
A sparse matrix library
Machine Learning in Rust.
A directed acyclic graph data structure library. It is Implemented on top of petgraph's Graph data structure and attempts to follow similar conventions where suitable.
A bioinformatics library for Rust. This library provides implementations of many algorithms and data structures that are useful for bioinformatics, but also in other fields.
A fast BVH using SAH
High-level bindings for Faiss, the vector similarity search engine
Reader and writer for common word embedding formats
Reader and writer for common word embedding formats
A low-level modular, extensible GPGPU compute library with cross-platform support
Rust wrappers for NGT nearest neighbor search.
A roulette wheel selection collection, used in genetic algorithm for fitness proportionate selection.
N-dimensional matrix class for Rust
dataframe structure and operations
N-dimensional dense arrays.
Are we learning yet?