From 28ba6698062ad15e3848b351ecf922aa5e00b09d Mon Sep 17 00:00:00 2001
From: sn <3298564802@qq.com>
Date: Wed, 16 Oct 2024 18:58:18 +0800
Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=96=B0=20function.py?=
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---
 function.py | 30 +++++++++++++++++++++++-------
 1 file changed, 23 insertions(+), 7 deletions(-)

diff --git a/function.py b/function.py
index 2393b3b..611595b 100644
--- a/function.py
+++ b/function.py
@@ -25,8 +25,8 @@ import numpy as np
 """
 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 = "202210423112"
+name = "严锡好"
 
 
 """
@@ -41,7 +41,7 @@ name = "test_name"
 
 
 # 这里写自己的代码实现
-def classify0(inX, dataSet, labels, k=0):
+def classify0(inX, dataSet, labels, k=3):
     """
     KNN 分类算法。
 
@@ -49,11 +49,26 @@ def classify0(inX, dataSet, labels, k=0):
     - inX (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**。
+    - k (int): kNN 算法参数，默认值为 3。
 
     Returns:
     - int: 预测的分类标签。
     """
+    # 计算待分类样本与训练集中每个样本的距离
+    distances = np.sqrt(np.sum((dataSet - inX) ** 2, axis=1))
+    
+    # 获取距离最近的 k 个样本的索引
+    sortedDistIndices = distances.argsort()[:k]
+    
+    # 获取这些样本对应的标签
+    classCount = {}
+    for i in sortedDistIndices:
+        voteLabel = labels[i]
+        classCount[voteLabel] = classCount.get(voteLabel, 0) + 1
+    
+    # 选择出现次数最多的标签作为预测标签
+    sortedClassCount = sorted(classCount.items(), key=lambda x: x[1], reverse=True)
+    return sortedClassCount[0][0]
 
 
 def load_data(file_path):
@@ -68,9 +83,10 @@ def load_data(file_path):
 
 if __name__ == "__main__":
     # 以下代码可用于测试，可自行随意修改
-
     train_file = "datingTrainSet2.tsv"
-
     train_data, train_labels = load_data(train_file)
 
-    print(classify0([65915, 3.868359, 0.128078], train_data, train_labels))
+    # 测试一个样本
+    test_sample = np.array([65915, 3.868359, 0.128078])
+    predicted_label = classify0(test_sample, train_data, train_labels)
+    print(f"The predicted label for the test sample is: {predicted_label}")
\ No newline at end of file