From 0e65dec242b3e2133ff4670b4bf12abc5c95a179 Mon Sep 17 00:00:00 2001
From: zhangchenghong <1598349064@qq.com>
Date: Tue, 15 Oct 2024 10:56:59 +0800
Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=96=B0=20function.py?=
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---
 function.py | 11 +++++++----
 1 file changed, 7 insertions(+), 4 deletions(-)

diff --git a/function.py b/function.py
index 39ad024..1f1b981 100644
--- a/function.py
+++ b/function.py
@@ -65,11 +65,12 @@ def classify(input_vector, dataSet, labels, k=3):
     # 根据距离排序并获取索引
     sorted_indices = distances.argsort()
     # 选择距离最近的k个点
-    k_nearest_labels = labels[sorted_indices[:k]]
+    k_nearest_labels = labels[sorted_indices[:k]].tolist()  # 将 NumPy 数组转换为列表
     # 统计k个点中各类别的数量
     count = Counter(k_nearest_labels)
     # 返回数量最多的类别标签
     return count.most_common(1)[0][0]
+
 # 这里也写自己的代码实现  
 def load_dataset(directory):  
     """  
@@ -98,7 +99,8 @@ def load_dataset(directory):
     # 将特征列表和标签列表转换为numpy数组
     feature_matrix = np.array(feature_list)
     label_array = np.array(label_list)
-    return feature_matrix, label_array# 这里还写自己的代码实现  
+    return feature_matrix, label_array
+# 这里还写自己的代码实现  
 def img_to_vector(filename):  
     """  
     将二值图像转换为向量。  
@@ -110,13 +112,14 @@ def img_to_vector(filename):
     - np.array: 二值图像向量。  
     """  
     # 初始化向量
-    vector = np.zeros((1, 1024))
+    vector = np.zeros((1024,))  # 创建一个一维数组
     # 打开文件并逐行读取
     with open(filename) as file:
         for i, line in enumerate(file):
             # 将每行的数字字符转换为整数并存储到向量中
-            vector[0, i*32:i*32+32] = np.array([int(char) for char in line.strip()])
+            vector[i*32:i*32+32] = np.array([int(char) for char in line.strip()])
     return vector
+
 if __name__ == "__main__":
     # 以下代码可用于测试，可自行随意修改
     train_data, train_labels = load_dataset("trainingDigits")