From 7de29050aac635de9efcb8b9d1d578e00046da1e Mon Sep 17 00:00:00 2001
From: 202210423104 <1961532386@qq.com>
Date: Wed, 16 Oct 2024 22:01:21 +0800
Subject: [PATCH] =?UTF-8?q?=E4=B8=8A=E4=BC=A0=E6=96=87=E4=BB=B6=E8=87=B3?=
 =?UTF-8?q?=20/?=
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---
 function.py | 14 ++++++++++++++
 1 file changed, 14 insertions(+)

diff --git a/function.py b/function.py
index a7eb05f..7f86f51 100644
--- a/function.py
+++ b/function.py
@@ -7,9 +7,12 @@ name = "张钦诚"
 
 def classify0(inX, dataSet, labels, k=5):
     inX = np.array(inX)
+    # 计算欧氏距离的平方，避免开平方以提高效率
     distances = np.sum((dataSet - inX) ** 2, axis=1)
+    # 获取距离最小的k个样本的索引
     nearest_indices = np.argpartition(distances, k)[:k]
     nearest_labels = labels[nearest_indices]
+    # 统计k个最近邻中出现次数最多的标签
     label_count = Counter(nearest_labels)
     return label_count.most_common(1)[0][0]
 
@@ -20,8 +23,12 @@ def load_data(file_path):
     return features, labels
 
 def standardize(train_data, test_data):
+    """
+    对训练数据和测试数据进行标准化处理。
+    """
     mean = np.mean(train_data, axis=0)
     std = np.std(train_data, axis=0)
+    # 防止标准差为零
     std[std == 0] = 1
     train_standardized = (train_data - mean) / std
     test_standardized = (test_data - mean) / std
@@ -31,17 +38,24 @@ if __name__ == "__main__":
     train_file = "datingTrainSet2.tsv"
     test_file = "datingTestSet2.tsv"
 
+    # 加载训练数据
     train_data, train_labels = load_data(train_file)
+    # 加载测试数据
     test_data, test_labels = load_data(test_file)
 
+    # 数据标准化
     train_data, test_data = standardize(train_data, test_data)
 
+    # 测试样本
     test_sample = [65915, 3.868359, 0.128078]
+    # 标准化测试样本
     test_sample = (np.array(test_sample) - np.mean(train_data, axis=0)) / np.std(train_data, axis=0)
 
+    # 预测标签
     predicted_label = classify0(test_sample, train_data, train_labels)
     print(f"预测的分类标签: {predicted_label}")
 
+    # 计算准确率
     predictions = np.array([classify0(sample, train_data, train_labels) for sample in test_data])
     accuracy = np.mean(predictions == test_labels) * 100
     print(f"准确率: {accuracy:.2f}%")