From 3bfb4fa2de7ebc783eb6963822528af04f29995e Mon Sep 17 00:00:00 2001
From: Dengddd <3149942468@qq.com>
Date: Tue, 15 Oct 2024 16:16:17 +0800
Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=96=B0=20function.py?=
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---
 function.py | 131 ++++++++++++++++------------------------------------
 1 file changed, 41 insertions(+), 90 deletions(-)

diff --git a/function.py b/function.py
index fcfdcf1..fd14443 100644
--- a/function.py
+++ b/function.py
@@ -41,91 +41,48 @@ name = "邓龙树"
 
 
 # 这里写自己的代码实现
-import numpy as np  
-import os  
-from collections import Counter  
-  
-def classify(input_vector, dataSet, labels, k=1):  
-    """  
-    使用 KNN 算法对输入样本进行分类。  
-      
-    参数:  
-    input_vector (ndarray): 输入的特征向量，形状为 (1, n_features)。  
-    dataSet (ndarray): 训练集特征矩阵，形状为 (m_samples, n_features)。  
-    labels (ndarray): 训练集标签数组，形状为 (m_samples,)。  
-    k (int): 选择最近邻的个数，默认为 1。  
-      
-    返回:  
-    int: 预测的类别标签。  
-    """  
-    # 计算输入向量与训练集之间的欧氏距离  
-    distances = np.linalg.norm(dataSet - input_vector, axis=1)  
-    # 根据距离排序并获取索引  
-    sorted_indices = np.argsort(distances)  
-    # 选择距离最近的k个点  
-    k_nearest_labels = labels[sorted_indices[:k]]  
-    # 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]  
-  
-def load_dataset(directory):  
-    """  
-    加载数据集中的图像，并将其转换为特征向量和标签。  
-      
-    参数:  
-    directory (str): 包含图像文件的目录路径。  
-      
-    返回:  
-    tuple: 包含特征矩阵 (ndarray) 和标签数组 (ndarray) 的元组。  
-    """  
-    feature_list = []  
-    label_list = []  
-    try:  
-        for file in os.listdir(directory):  
-            file_path = os.path.join(directory, file)  
-            feature_vector = img_to_vector(file_path)  
-            label = int(file.split('_')[0])  
-            feature_list.append(feature_vector)  
-            label_list.append(label)  
-        feature_matrix = np.array(feature_list)  
-        label_array = np.array(label_list)  
-    except Exception as e:  
-        print(f"Error loading dataset: {e}")  
-        return None, None  
-    return feature_matrix, label_array  
-  
-def img_to_vector(filename):  
-    """  
-    将二值图像转换为向量。  
-      
-    参数:  
-    filename (str): 图像文件的路径。  
-      
-    返回:  
-    ndarray: 转换后的特征向量，形状为 (1024,)。  
-    """  
-    try:  
-        with open(filename, 'r') as file:  
-            lines = file.readlines()  
-        vector = np.array([int(char) for line in lines for char in line.strip()]).reshape(1024,)  
-    except Exception as e:  
-        print(f"Error converting image to vector: {e}")  
-        return None  
-    return vector  
-  
-if __name__ == "__main__":  
-    # 加载训练数据集  
-    train_data, train_labels = load_dataset("trainingDigits")  
-    if train_data is None or train_labels is None:  
-        print("Failed to load training data.")  
-        exit(1)  
+import numpy as np
+import os
+from collections import Counter
 
-if __name__ == "__main__"：
-    # 以下代码可用于测试，可自行随意修改
+def classify(input_vector, dataSet, labels, k=3):  # 设置默认值 k=3
+    """使用 KNN 算法对输入样本进行分类。"""
+    # 计算输入向量与训练集之间的欧氏距离
+    distances = np.linalg.norm(dataSet - input_vector, axis=1)
+    # 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]]
+    # 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]
+
+def load_dataset(directory):
+    """加载数据集中的图像，并将其转换为特征向量和标签。"""
+    feature_list = []
+    label_list = []
+    for file in os.listdir(directory):
+        file_path = os.path.join(directory, file)
+        feature_vector = img_to_vector(file_path)
+        feature_list.append(feature_vector)
+        label_list.append(int(file.split('_')[0]))
+    feature_matrix = np.array(feature_list)
+    label_array = np.array(label_list)
+    return feature_matrix, label_array
+
+def img_to_vector(filename):
+    """将二值图像转换为向量。"""
+    with open(filename, 'r') as file:
+        lines = file.read().strip()
+    vector = np.array(list(map(int, lines))).reshape(1024,)
+    return vector
+
+if __name__ == "__main__":
+    # 测试代码
     train_data, train_labels = load_dataset("trainingDigits")
 
-    test_data = (
+    test_data_str = (
         "00000000000000110000000000000000"
         "00000000000011111000000000000000"
         "00000000000011111100000000000000"
@@ -159,12 +116,6 @@ if __name__ == "__main__"：
         "00000000000001111111110000000000"
         "00000000000000000111100000000000"
     )
-
-    predicted_label = classify(
-        np.array([int(char) for char in test_data]).reshape(1, 1024),
-        train_data,
-        train_labels,
-    )
-
+    test_data = np.array(list(map(int, test_data_str))).reshape(1, 1024)
+    predicted_label = classify(test_data, train_data, train_labels)
     print(f"预测的标签: {predicted_label}")
-