import numpy as np
from numpy.typing import NDArray
def cosine_similarity(a: NDArray[np.floating], b: NDArray[np.floating]) -> float:
norm_a = float(np.linalg.norm(a))
norm_b = float(np.linalg.norm(b))
if norm_a == 0 or norm_b == 0:
return 0.0
return float(np.dot(a, b) / (norm_a * norm_b))
def cosine_similarity_lists(vec_a: list[float], vec_b: list[float]) -> float:
if len(vec_a) != len(vec_b):
return 0.0
dot_product = sum(a * b for a, b in zip(vec_a, vec_b))
norm_a = sum(a * a for a in vec_a) ** 0.5
norm_b = sum(b * b for b in vec_b) ** 0.5
if norm_a == 0 or norm_b == 0:
return 0.0
return dot_product / (norm_a * norm_b)
