⚡ Bolt: Vectorize BasicEstimator.predict

.jules/bolt.md

@@ -0,0 +1,5 @@
+## 2026-05-01 - Vectorizing distance computation in BasicEstimator
+
+**Learning:** Using the squared distance expansion formula $\|a-b\|^2 = \|a\|^2 + \|b\|^2 - 2a \cdot b$ allows for full vectorization of Euclidean distance calculations using optimized BLAS routines (via `np.dot`). This is significantly faster than `np.linalg.norm` in a loop. Pre-calculating $\|b\|^2$ during the `fit` phase further optimizes the `predict` phase. However, subtractive cancellation can lead to tiny negative values for identical vectors, necessitating `np.maximum(dists_sq, 0)` before further processing (like `np.exp`).
+
+**Action:** Always prefer matrix-based distance calculations for batch operations and pre-calculate norms where possible. Use `np.maximum(..., 0)` to guard against floating-point noise in distance expansion formulas.

face_engine/models/basic_estimator.py

@@ -22,19 +22,41 @@ def __init__(self):
     def fit(self, embeddings, class_names, **kwargs):
         self.embeddings = embeddings
         self.class_names = class_names
+        # Pre-calculate squared norms for faster distance computation in predict
+        self.norms_sq = np.sum(np.square(self.embeddings), axis=1)
 
     def predict(self, embeddings):
         if self.class_names is None:
             raise TrainError("Model is not fitted yet!")
 
-        scores = []
-        class_names = []
-        for embedding in embeddings:
-            distances = np.linalg.norm(self.embeddings - embedding, axis=1)
-            index = np.argmin(distances)
-            score = np.exp(-0.5 * distances[index] ** 2)
-            scores.append(score)
-            class_names.append(self.class_names[index])
+        if len(embeddings) == 0:
+            return [], []
+
+        # Vectorized distance computation using ||a-b||^2 = ||a||^2 + ||b||^2 - 2ab
+        # This is significantly faster than iterative approach for large datasets.
+        a = embeddings
+        b = self.embeddings
+
+        a_norms_sq = np.sum(np.square(a), axis=1)
+        b_norms_sq = getattr(self, "norms_sq", None)
+        if b_norms_sq is None:
+            # Fallback for models fitted with older versions
+            b_norms_sq = np.sum(np.square(b), axis=1)
+
+        # dists_sq shape: (n_predict, n_fitted)
+        dists_sq = (
+            a_norms_sq[:, np.newaxis] + b_norms_sq[np.newaxis, :] - 2 * np.dot(a, b.T)
+        )
+
+        indices = np.argmin(dists_sq, axis=1)
+        min_dists_sq = dists_sq[np.arange(len(a)), indices]
+
+        # Ensure we don't have negative values due to floating point noise
+        min_dists_sq = np.maximum(min_dists_sq, 0)
+
+        scores = np.exp(-0.5 * min_dists_sq).tolist()
+        class_names = [self.class_names[i] for i in indices]
+
         return scores, class_names
 
     def save(self, dirname):