机器学习常用特征相似度或距离度量【Pytorch实现】

余弦相似度、特征距离、KL散度计算

import torch

feat1 = torch.randn((3, 4))
feat2 = torch.randn((3, 4))

# ==========cosine相似度===============
a_norm = torch.linalg.norm(feat1, dim=1)
b_norm = torch.linalg.norm(feat2, dim=1)
cos = ((feat1 * feat2).sum(dim=(-2, -1)) / (a_norm * b_norm)).mean()
cos = 0.5 * cos + 0.5   # [-1, 1]  --> [0, 1]

# ==========矩阵范数===============
lfro = torch.linalg.norm(feat1 - feat2, dim=-1).mean()	# F范数
l1 = torch