From 022049f3b58473a2cb2f053ce216cabc0f2a914e Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E7=A6=BE=E5=8F=AF?=
 <94185944+Lfan-ke@users.noreply.github.com>
Date: Sat, 11 May 2024 18:31:51 +0800
Subject: [PATCH] fixed distances.md

hamming distance is sum(x != y) not sum(x = y)
---
 docs/tutorials/deep_learning/distances/distances.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/tutorials/deep_learning/distances/distances.md b/docs/tutorials/deep_learning/distances/distances.md
index deccfb47d..e2b5526ab 100644
--- a/docs/tutorials/deep_learning/distances/distances.md
+++ b/docs/tutorials/deep_learning/distances/distances.md
@@ -30,12 +30,12 @@ $$
 \underset{p \rightarrow \infty}{\text{lim}} (\sum_{i=1}^n {|x_i - y_i|}^{p})^{\frac{1}{p}} = \text{max} \; (|x_i-y_i|)
 $$
 
-### 1.5 海明距离（Hamming Distance）
+### 1.5 汉明距离（Hamming Distance）
 
  在信息论中，两个等长字符串之间的海明距离是两个字符串对应位置的不同字符的个数。假设有两个字符串分别是：$x=[x_1,x_2,...,x_n]$和$y=[y_1,y_2,...,y_n]$，则两者的距离为：
 
 $$
-Hamming \; Distance  = \sum_{i=1}^{n} {\text{II}}(x_i=y_i)
+Hamming \; Distance  = \sum_{i=1}^{n} {\text{II}}(x_i \neq y_i)
 $$
 
 其中$\text{II}$表示指示函数，两者相同为1，否则为0。