更新 function.py

function.py

@@ -112,39 +112,50 @@ def predict_svm(inX, model):
     - model (SVC): 训练得到的支持向量机模型。
 
     Returns:
-    - int: 预测的分类标签
+    - int: 预测的分类标签。
     """
     return model.predict(inX.reshape(1, -1))[0]
 
 
 if __name__ == "__main__":
     # 加载数据
-    X, y = load_data("./data/horse-colic.data")
+    X_train, y_train = load_data("./data/horse-colic.data")
+    X_test, y_test = load_data("./data/horse-colic.test")
+
+    # 划分数据集
+    X_train, X_test, y_train, y_test = train_test_split(X_train, y_train, test_size=0.2, random_state=42, stratify=y_train)
 
     # 数据标准化
     scaler = StandardScaler()
-    X = scaler.fit_transform(X)
+    X_train = scaler.fit_transform(X_train)
+    X_test = scaler.transform(X_test)
 
-    # 进行交叉验证评估
+    # 交叉验证评估
     print("正在进行交叉验证评估...")
 
     # 使用交叉验证计算准确率，设置10折交叉验证
     svm_model = SVC(kernel='rbf', C=1.0, gamma='scale', random_state=42)
-    cv_scores = cross_val_score(svm_model, X, y, cv=10, scoring='accuracy')
+    cv_scores = cross_val_score(svm_model, X_train, y_train, cv=10, scoring='accuracy')
 
-    # 输出交叉验证的评估结果
+    # 输出交叉验证的准确率
     print(f"交叉验证准确率: {cv_scores.mean():.4f} ± {cv_scores.std():.4f}")
 
     # 训练支持向量机模型
-    svm_model.fit(X, y)
+    print("正在训练支持向量机模型...")
+    svm_model.fit(X_train, y_train)
+
+    # 预测测试集
+    y_pred = svm_model.predict(X_test)
 
     # 输出评估结果
-    y_pred = svm_model.predict(X)
-
-    # 输出分类报告
     print("\n分类报告:")
-    print(classification_report(y, y_pred, target_names=["存活", "安乐死", "死亡"]))
+    print(classification_report(y_test, y_pred, target_names=["存活", "安乐死", "死亡"]))
 
     # 输出准确率
-    accuracy = accuracy_score(y, y_pred)
-    print(f"模型准确率: {accuracy * 100:.2f}%")
+    accuracy = accuracy_score(y_test, y_pred)
+    print(f"测试集准确率: {accuracy * 100:.2f}%")
+
+    # 测试单个数据
+    test_sample = X_test[11]  # 选择第12个样本（从0开始计数）
+    y_pred_single = predict_svm(test_sample, svm_model)
+    print(f"\n单个样本预测结果: {y_pred_single}, 实际结果: {y_test[11]}")