我正在寻找一种方法来改善简单的最近邻函数的性能,但是当我使用numpy进行矢量化时,我不是很精通。任何帮助将不胜感激!
def knn_search(pts_a, pts_b, k):
"""
Finds the k nearest neighbours of each point in pts_a in pts_b
:param pts_a:
:param pts_b:
:param k:
:return dist, idx:
"""
dist = np.empty((pts_b.shape[0], pts_a.shape[0]))
for i in range(pts_b.shape[0]):
dist[i, :] = np.linalg.norm(pts_a - pts_b[i, :], axis=1)
idx = np.argsort(dist, axis=1)
dist = np.sort(dist, axis=1)
return dist[:, :k], idx[:, :k]
a = np.random.rand(10, 2)
b = np.random.rand(10, 2)
distance, indices = knn_search(a, b, 5)
答案 0 :(得分:1)
您可以使用广播将循环替换为外部循环
def knn_search(pts_a, pts_b, k):
"""
Finds the k nearest neighbours of each point in pts_a in pts_b
:param pts_a:
:param pts_b:
:param k:
:return dist, idx:
"""
dist = np.linalg.norm(pts_a - pts_b[:, None], axis=-1)
idx = np.argsort(dist, axis=1)
dist = np.sort(dist, axis=1)
return dist[:, :k], idx[:, :k]