The algorithm maintains a running "best distance" that starts at infinity. As it walks the tree, it checks each visited point and updates the best distance if it finds something closer. Before recursing into a child node, it checks whether the closest possible point in that child's bounding box is farther than the current best. If so, the entire subtree gets pruned.
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But that’s unironically a good idea so I decided to try and do it anyways. With the use of agents, I am now developing rustlearn (extreme placeholder name), a Rust crate that implements not only the fast implementations of the standard machine learning algorithms such as logistic regression and k-means clustering, but also includes the fast implementations of the algorithms above: the same three step pipeline I describe above still works even with the more simple algorithms to beat scikit-learn’s implementations. This crate can therefore receive Python bindings and even expand to the Web/JavaScript and beyond. This also gives me the oppertunity to add quality-of-life features to resolve grievances I’ve had to work around as a data scientist, such as model serialization and native integration with pandas/polars DataFrames. I hope this use case is considered to be more practical and complex than making a ball physics terminal app.
[7] B. E. Bayer: “An optimum method for two-level rendition of continuous-tone pictures” (1973). ↑