From 4d8869506407be8cf65a840877f6d67db53c3207 Mon Sep 17 00:00:00 2001
From: cool <ccolr@outlook.jp>
Date: Thu, 17 Oct 2024 00:15:52 +0800
Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=96=B0=20function.py?=
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---
 function.py | 143 +++++++++++++++++++++++-----------------------------
 1 file changed, 63 insertions(+), 80 deletions(-)

diff --git a/function.py b/function.py
index b1f8c16..d9656bd 100644
--- a/function.py
+++ b/function.py
@@ -25,8 +25,8 @@ import os
 """
 请在这里填写学号和姓名，不填写即成绩无效。
 """
-id = "202210423xxx"
-name = "test_name"
+id = "202210423210"
+name = "陈宇杰"
 
 
 """
@@ -40,92 +40,75 @@ name = "test_name"
 """
 
 
-# 这里写自己的代码实现
-def classify(input_vector, dataSet, labels, k=0):
-    """
-    KNN 分类算法。
+import numpy as np
+import os
+from collections import Counter
 
-    Args:
-    - input_vector (numpy array): 输入待分类的样本。
-    - dataSet (numpy array): 训练数据集。
-    - labels (list or numpy array): 训练集对应的标签。
-    - k (int): kNN 算法参数，请**自行设置默认值**，评测时仅会使用其默认值，不修改默认值将**无法通过**评测，**成绩为零**。
+# 加载数据的函数
+def load_data(directory):
+    file_list = os.listdir(directory)
+    data_matrix = []
+    labels = []
+    
+    for file_name in file_list:
+        file_path = os.path.join(directory, file_name)
+        # 读取txt文件并将其转化为向量
+        vector = img_to_vector(file_path)
+        data_matrix.append(vector)
+        # 假设文件名格式为：{数字}_某些字符.txt，提取数字作为标签
+        label = int(file_name.split('_')[0])
+        labels.append(label)
+    
+    return np.array(data_matrix), np.array(labels)
 
-    Returns:
-    - int: 预测的分类标签。
-    """
+# 将32x32的二值图像转换为1x1024的向量
+def img_to_vector(file_path):
+    vector = np.zeros((1, 1024))
+    with open(file_path, 'r') as f:
+        lines = f.readlines()
+        for i in range(32):
+            for j in range(32):
+                vector[0, 32 * i + j] = int(lines[i][j])
+    return vector
 
+# kNN 分类函数
+def classify(input_vector, dataSet, labels, k=3):
+    # 计算欧氏距离
+    distances = np.linalg.norm(dataSet - input_vector, axis=1)
+    # 获取距离最近的k个样本的标签
+    nearest_neighbors = np.argsort(distances)[:k]
+    nearest_labels = labels[nearest_neighbors]
+    
+    # 确保 nearest_labels 是一维的，转换为 Python 列表
+    nearest_labels = nearest_labels.flatten().tolist()
+    
+    # 投票，返回出现次数最多的标签
+    most_common = Counter(nearest_labels).most_common(1)
+    return most_common[0][0]
 
-# 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**。
+# 实验主程序
+def main():
+    # 加载训练集和测试集
+    training_data, training_labels = load_data('trainingdigits')
+    test_data, test_labels = load_data('testdigits')
 
-    Args:
-    - directory (str): 数据文件夹路径。
+    # 测试 kNN 算法
+    total_tests = len(test_data)
+    correct_predictions = 0
 
-    Returns:
-    - np.array: 特征数据。
-    - np.array: 标签数据。
-    """
+    for i in range(total_tests):
+        test_sample = test_data[i]
+        prediction = classify(test_sample, training_data, training_labels, k=3)
+        actual_label = test_labels[i]
 
+        print(f"测试样本 {i+1}: 预测值 = {prediction}, 实际值 = {actual_label}")
 
-# 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。
-    该部分应为 load_dataset 函数的辅助子函数，可单独在这里实现，也可直接集成至 load_dataset 函数中。
+        if prediction == actual_label:
+            correct_predictions += 1
 
-    Args:
-    - filename (str): 图像文件路径。
+    accuracy = correct_predictions / total_tests * 100
+    print(f"\n分类精度: {accuracy:.2f}%")
 
-    Returns:
-    - np.array: 二值图像向量。
-    """
+if __name__ == '__main__':
+    main()
 
-
-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")
-
-    test_data = (
-        "00000000000000110000000000000000"
-        "00000000000011111000000000000000"
-        "00000000000011111100000000000000"
-        "00000000001111111110000000000000"
-        "00000000001111111110000000000000"
-        "00000000001111111100000000000000"
-        "00000000011111111000000000000000"
-        "00000000111111111000000000000000"
-        "00000000111111110000000000000000"
-        "00000000111111110000000000000000"
-        "00000000111111110000000000000000"
-        "00000000111111110000000000000000"
-        "00000000111111100000000000000000"
-        "00000001111111100000000000000000"
-        "00000001111111100000000000000000"
-        "00000001111111100000000000000000"
-        "00000001111111111111000000000000"
-        "00000001111111111111111000000000"
-        "00000001111111111111111100000000"
-        "00000011111111111111111110000000"
-        "00000011111111111111111110000000"
-        "00000001111111111111111110000000"
-        "00000001111111111111111110000000"
-        "00000000111111111100111111100000"
-        "00000000111111111000111111100000"
-        "00000000011111111001111111100000"
-        "00000000011111111111111111000000"
-        "00000000001111111111111111000000"
-        "00000000000111111111111111000000"
-        "00000000000011111111111100000000"
-        "00000000000001111111110000000000"
-        "00000000000000000111100000000000"
-    )
-    predicted_label = classify(
-        np.array([int(char) for char in test_data]).reshape(1, 1024),
-        train_data,
-        train_labels,
-    )
-
-    print(f"预测的标签: {predicted_label}")