From cf8a1eca6235ca80e05199a4e506c33521c83e22 Mon Sep 17 00:00:00 2001
From: 202210423105 <3374617300@qq.com>
Date: Wed, 16 Oct 2024 22:30:53 +0800
Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=96=B0=20function.py?=
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---
 function.py | 13 ++++++++-----
 1 file changed, 8 insertions(+), 5 deletions(-)

diff --git a/function.py b/function.py
index c0fc0d0..413bf62 100644
--- a/function.py
+++ b/function.py
@@ -55,7 +55,7 @@ def classify(input_vector, dataSet, labels, k=3):
     - int: 预测的分类标签。
     """
     """
-    欧式距离的 kNN 分类算法。
+    使用欧式距离的 kNN 分类算法。
     """
      # 计算欧式距离
     distances = np.linalg.norm(dataSet - input_vector, axis=1)
@@ -70,8 +70,10 @@ def classify(input_vector, dataSet, labels, k=3):
     most_common = max(set(k_nearest_labels), key=k_nearest_labels.count)
     
     return most_common
-    
+
+
 # 这里也写自己的代码实现
+
 def load_dataset(directory):
     """
     从文件夹中加载数据，若不自行正确实现，则无法通过评测，**不记录成绩**。
@@ -83,8 +85,8 @@ def load_dataset(directory):
     - np.array: 特征数据。
     - np.array: 标签数据。
     """
-features = []
-labels = []
+    features = []
+    labels = []
     
     # 遍历文件夹中的所有文件
     for filename in os.listdir(directory):
@@ -102,6 +104,7 @@ labels = []
     
     return np.array(features), np.array(labels)
 
+
 # 这里还写自己的代码实现
 def img_to_vector(filename):
     """
@@ -114,7 +117,7 @@ def img_to_vector(filename):
     Returns:
     - np.array: 二值图像向量。
     """
- with open(filename) as file:
+    with open(filename) as file:
         lines = file.readlines()
         image_vector = np.array([int(char) for line in lines for char in line.strip()])