Skip to content

perf(cosinepair): exact query_row_top_k, halved Theta(n^2) build, zero-copy row views #443

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
Mec-iS merged 1 commit into from
Aug 23, 2026
Merged

perf(cosinepair): exact query_row_top_k, halved Theta(n^2) build, zero-copy row views #443

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
8 changes: 8 additions & 0 deletions CHANGELOG.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -4,6 +4,14 @@ All notable changes to this project will be documented in this file.
The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).

## [0.6.11]
### Fixed
- `algorithm/neighbour/cosinepair.rs`: `CosinePair::query_row_top_k` now returns exact nearest neighbours whenever `approximate` is `false` (the default). Previously the query always sampled only `top_k` evenly strided candidate rows without documentation, and the bounded candidate heap evicted its closest entry, so the method could return the farthest of the sampled rows (#442). Strided sampling is now gated behind `CosinePairParameters { approximate: true, .. }` and is documented as approximate.
- `algorithm/neighbour/cosinepair.rs`: `CosinePair` construction now evaluates each unordered row pair once (symmetric half-scan), precomputes row norms once in O(n·d), and scores pairs through zero-copy row views instead of materialising two `Vec`s per pair (#442). Distances are unchanged (bit-identical formula and operation order as `Cosine::new().distance(...)`); measured build time drops ~3x on a 1500x64 input. Construction remains Theta(n^2) dot products — `top_k` does not make it sub-quadratic; module and method docs now state both facts.

### Changed
- **Breaking**: `CosinePair` gained a private `row_norms` field holding the precomputed row norms. Construct the structure through `new` / `with_top_k` / `with_parameters` instead of struct literals.

## [0.6.10]
### Fixed
- `model_selection`: pinned the `KFold` seed in the `test_cross_val_predict_knn` and `test_cross_validate_knn` unit tests. Under `--all-features` (`std_rand`) an unseeded `KFold` draws OS entropy, so each CI run shuffled the folds differently; a sweep of 20 000 seeds showed 0.17% of shuffles violate the `MAE < 10.0` assertion (worst 12.81) and 0.01% violate `train_score < test_score`, making CI flaky. Library behaviour is unchanged; the entropy-seeded path stays covered by the `rand_custom` tests.
Expand Down
2 changes: 1 addition & 1 deletion Cargo.toml
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -2,7 +2,7 @@
name = "smartcore"
description = "Machine Learning in Rust."
homepage = "https://smartcorelib.github.io/"
version = "0.6.10"
version = "0.6.11"
authors = ["smartcore Developers"]
edition = "2024"
rust-version = "1.85"
Expand Down
Loading
Loading