⚡ Bolt: [KNN O(N) Selection]

[teerthsharma]

💡 What: Replaced the manual nested-loop selection sort in KNNClassifier::predict with distances[..self.n_train].select_nth_unstable_by(...). Also added safety bounds checking to guard against underflows if K or n_train is 0.

🎯 Why: Finding the $K$ nearest neighbors only requires partitioning the elements such that the top $K$ are gathered, rather than doing a full or partial sort of all neighbors against each other. The manual loop had an algorithmic complexity of $O(K \cdot N)$, which becomes extremely slow for large $K$ or in hot prediction loops.

📊 Impact: Reduces average time complexity for neighbor selection from $O(K \cdot N)$ to $O(N)$ through the Quickselect algorithm backing select_nth_unstable_by.

🔬 Measurement: Validated by running cargo test -p aether-core to ensure predict functionality is unaltered and outputs remain robust even under potential NaN distance conditions.

---

PR created automatically by Jules for task 11658664177165894089 started by @teerthsharma

[google-labs-jules]

👋 Jules, reporting for duty! I'm here to lend a hand with this pull request.

When you start a review, I'll add a 👀 emoji to each comment to let you know I've read it. I'll focus on feedback directed at me and will do my best to stay out of conversations between you and other bots or reviewers to keep the noise down.

I'll push a commit with your requested changes shortly after. Please note there might be a delay between these steps, but rest assured I'm on the job!

For more direct control, you can switch me to Reactive Mode. When this mode is on, I will only act on comments where you specifically mention me with @jules. You can find this option in the Pull Request section of your global Jules UI settings. You can always switch back!

New to Jules? Learn more at jules.google/docs.

---

For security, I will only act on instructions from the user who triggered this task.