更新 function.py

function.py

@@ -44,7 +44,6 @@ name = "陈宇杰"
 
 
 # 这里写自己的代码实现
-
 def classify0(inX, dataSet, labels, k):
     """
     KNN 分类函数
@@ -80,36 +79,29 @@ def main():
     scaler = StandardScaler()  # 创建标准化对象
     train_data = scaler.fit_transform(train_data)  # 对训练数据进行标准化
 
-    # 使用交叉验证选择最佳 k 值
-    k_values = range(1, 31)  # 定义 k 值范围
-    best_k = 1
-    best_accuracy = 0
+    # 设置 K 值
+    k = 1  # 直接定义 K 值为 1
 
-    for k in k_values:
-        # 通过手动实现 KNN 分类计算准确率
-        correct_predictions = 0
-        for i in range(len(train_data)):
-            # 留出一条数据作为测试集，其余作为训练集
-            test_sample = train_data[i]
-            train_subset = np.delete(train_data, i, axis=0)
-            train_labels_subset = np.delete(train_labels, i)
+    # 手动实现 KNN 分类计算准确率
+    correct_predictions = 0
+    for i in range(len(train_data)):
+        # 留出一条数据作为测试集，其余作为训练集
+        test_sample = train_data[i]
+        train_subset = np.delete(train_data, i, axis=0)
+        train_labels_subset = np.delete(train_labels, i)
 
-            # 使用 KNN 分类器预测
-            predicted_label = classify0(test_sample, train_subset, train_labels_subset, k)
-            if predicted_label == train_labels[i]:
-                correct_predictions += 1
+        # 使用 KNN 分类器预测
+        predicted_label = classify0(test_sample, train_subset, train_labels_subset, k)
+        if predicted_label == train_labels[i]:
+            correct_predictions += 1
 
-        accuracy = correct_predictions / len(train_data)  # 计算准确性
-        if accuracy > best_accuracy:
-            best_accuracy = accuracy
-            best_k = k
-
-    print(f"最佳 k 值: {best_k}，准确率: {best_accuracy:.4f}")
+    accuracy = correct_predictions / len(train_data)  # 计算准确性
+    print(f"使用 K 值 {k} 的准确率: {accuracy:.4f}")
 
     # 测试分类
     test_sample = [65915, 3.868359, 0.128078]  # 测试样本
     test_sample = scaler.transform([test_sample])  # 对测试样本进行相同的缩放
-    predicted_label = classify0(test_sample[0], train_data, train_labels, best_k)  # 分类
+    predicted_label = classify0(test_sample[0], train_data, train_labels, k)  # 分类
 
     print(f"测试样本 {test_sample[0]} 的预测分类标签为: {predicted_label}")