From 0c7bbd67e9137503a23e889f5d57f270b4268b0f Mon Sep 17 00:00:00 2001
From: Dengddd <3149942468@qq.com>
Date: Tue, 8 Oct 2024 20:09:16 +0800
Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=96=B0=20function.py?=
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---
 function.py | 27 ++++++++++++++++++++-------
 1 file changed, 20 insertions(+), 7 deletions(-)

diff --git a/function.py b/function.py
index 6bfe56d..fc6a292 100644
--- a/function.py
+++ b/function.py
@@ -53,12 +53,15 @@ def classify0(inX, dataSet, labels, k=3):
     Args:  
     - 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。  
+    - labels (numpy array): 训练集对应的标签。  
     - k (int): KNN 算法参数，默认值为3。  
   
     Returns:  
     - int: 预测的分类标签。  
     """  
+    # 确保 labels 是 numpy 数组  
+    labels = np.array(labels)  
+      
     # 计算待分类样本与训练集中每个样本之间的距离  
     distances = [euclidean_distance(inX, x) for x in dataSet]  
       
@@ -67,13 +70,13 @@ def classify0(inX, dataSet, labels, k=3):
     k_nearest_indices = sorted_indices[:k]  
       
     # 获取k个最近样本的标签  
-    k_nearest_labels = [labels[i] for i in k_nearest_indices]  
+    k_nearest_labels = labels[k_nearest_indices]  
       
-    # 统计每个标签出现的次数，并返回出现次数最多的标签  
-    label_counts = np.bincount(k_nearest_labels)  
-    predicted_label = np.argmax(label_counts)  
+    # 使用 np.unique 和 np.bincount 的一个更健壮的方法来找到出现次数最多的标签  
+    unique_labels, counts = np.unique(k_nearest_labels, return_counts=True)  
+    most_common_label = unique_labels[np.argmax(counts)]  
       
-    return predicted_label  
+    return int(most_common_label)  # 确保返回的是整数  
   
 def load_data(file_path):  
     """  
@@ -87,10 +90,20 @@ def load_data(file_path):
 if __name__ == "__main__":  
     # 测试代码  
     train_file = "datingTrainSet2.tsv"  
-    test_file = "datingTestSet2.tsv"  # 假设有一个测试文件用于验证，虽然题目没明确要求  
+    test_file = "datingTestSet2.tsv"  # 假设有一个测试文件用于验证  
   
     # 加载训练数据和测试数据  
     train_data, train_labels = load_data(train_file)  
+    test_data, test_labels = load_data(test_file)  # 取消注释以测试整个测试集  
+  
+    # 测试 classify0 函数  
+    sample = [65915, 3.868359, 0.128078]  # 示例样本  
+    predicted_label = classify0(sample, train_data, train_labels)  
+    print(f"Predicted label for sample {sample}: {predicted_label}")  
+  
+    # 测试整个测试集（可选）  
+    accuracy = np.mean([classify0(test_data[i], train_data, train_labels) == test_labels[i] for i in range(len(test_data))])  
+    print(f"Accuracy on test set: {accuracy}") 
     # test_data, test_labels = load_data(test_file)  # 如果需要测试，可以取消注释并添加相应的测试代码  
   
     # 测试 classify0 函数