47eecce8e9
Implement drift detection using cosine similarity between issue description embedding and chronological note embeddings. Sliding window (size 3) identifies topic drift points. Includes human and robot output formatters. New files: drift.rs, similarity.rs Closes: bd-1cjx
49 lines
1.4 KiB
Rust
49 lines
1.4 KiB
Rust
/// Cosine similarity between two embedding vectors.
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/// Returns value in [-1, 1] range; higher = more similar.
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pub fn cosine_similarity(a: &[f32], b: &[f32]) -> f32 {
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debug_assert_eq!(a.len(), b.len(), "embedding dimensions must match");
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let dot: f32 = a.iter().zip(b).map(|(x, y)| x * y).sum();
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let norm_a: f32 = a.iter().map(|x| x * x).sum::<f32>().sqrt();
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let norm_b: f32 = b.iter().map(|x| x * x).sum::<f32>().sqrt();
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if norm_a == 0.0 || norm_b == 0.0 {
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return 0.0;
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}
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dot / (norm_a * norm_b)
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}
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#[cfg(test)]
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mod tests {
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use super::*;
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#[test]
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fn test_cosine_similarity_identical() {
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let v = [1.0, 2.0, 3.0];
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let sim = cosine_similarity(&v, &v);
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assert!((sim - 1.0).abs() < 1e-6);
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}
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#[test]
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fn test_cosine_similarity_orthogonal() {
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let a = [1.0, 0.0, 0.0];
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let b = [0.0, 1.0, 0.0];
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let sim = cosine_similarity(&a, &b);
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assert!(sim.abs() < 1e-6);
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}
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#[test]
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fn test_cosine_similarity_zero_vector() {
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let a = [1.0, 2.0, 3.0];
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let b = [0.0, 0.0, 0.0];
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let sim = cosine_similarity(&a, &b);
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assert!((sim - 0.0).abs() < 1e-6);
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}
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#[test]
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fn test_cosine_similarity_opposite() {
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let a = [1.0, 2.0, 3.0];
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let b = [-1.0, -2.0, -3.0];
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let sim = cosine_similarity(&a, &b);
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assert!((sim - (-1.0)).abs() < 1e-6);
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}
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}
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