⚡ Bolt: Optimize KNN classification with select_nth_unstable_by and squared distance

[teerthsharma]

💡 What: Optimized the KNNClassifier::predict method by replacing the manual $O(k \cdot N)$ bubble sort with Rust's select_nth_unstable_by, which operates in $O(N)$ average time to find the $k$ nearest neighbors. Also replaced exact Euclidean distance computations using libm::sqrt with squared distance comparisons to avoid the expensive square root operation in the hot loop.
🎯 Why: In high-performance ML routines, finding the nearest neighbors can become a significant bottleneck, especially for large datasets or high $k$ values. Eliminating sqrt and using $O(N)$ selection greatly reduces computational overhead.
📊 Impact: Reduces prediction time complexity from $O(k \cdot N)$ to $O(N)$ and eliminates the libm::sqrt overhead in the distance calculation loop.
🔬 Measurement: Verify the behavior via cargo test --offline classification inside the crates/aether-core directory to ensure existing perceptron and logistic regression logic works, and ensure select_nth_unstable_by returns correct nearest neighbors.

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PR created automatically by Jules for task 11381027091103256586 started by @teerthsharma

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