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892fa81 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 | """Vector operations — normalize, cosine, euclidean, batch."""
from __future__ import annotations
import numpy as np
def normalize(v: np.ndarray) -> np.ndarray:
"""L2-normalize a vector."""
n = np.linalg.norm(v)
return v / n if n > 0 else v
def cosine_similarity(a: np.ndarray, b: np.ndarray) -> float:
"""Cosine similarity between two vectors."""
na = np.linalg.norm(a)
nb = np.linalg.norm(b)
if na == 0 or nb == 0:
return 0.0
return float(np.dot(a, b) / (na * nb))
def euclidean_distance(a: np.ndarray, b: np.ndarray) -> float:
"""Euclidean distance between two vectors."""
return float(np.linalg.norm(a - b))
def batch_cosine_similarity(query: np.ndarray, matrix: np.ndarray) -> np.ndarray:
"""Cosine similarity between a query vector and a matrix of vectors.
Args:
query: 1-D array of shape (d,)
matrix: 2-D array of shape (n, d)
Returns:
1-D array of shape (n,) with similarity scores.
"""
query_norm = np.linalg.norm(query)
if query_norm == 0:
return np.zeros(matrix.shape[0])
matrix_norms = np.linalg.norm(matrix, axis=1)
# Avoid division by zero
safe_norms = np.where(matrix_norms == 0, 1.0, matrix_norms)
return (matrix @ query) / (safe_norms * query_norm)
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