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+import numpy as np
+from sklearn.svm import SVR
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import mean_squared_error, r2_score
+import matplotlib.pyplot as plt
+
+# 生成一些示例数据
+# 这里我们创建一个简单的非线性关系作为示例
+np.random.seed(0)
+X = np.sort(5 * np.random.rand(40, 1), axis=0)
+y = np.sin(X).ravel()
+
+# 为了使问题更具挑战性，我们向目标变量添加一些噪声
+y[::5] += 3 * (0.5 - np.random.rand(8))
+
+# 将数据集拆分为训练集和测试集
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# 创建SVM回归模型
+svr_rbf = SVR(kernel='rbf', C=100, gamma=0.1, epsilon=0.1)
+
+# 训练模型
+svr_rbf.fit(X_train, y_train)
+
+# 对测试集进行预测
+y_pred = svr_rbf.predict(X_test)
+
+# 评估模型性能
+mse = mean_squared_error(y_test, y_pred)
+r2 = r2_score(y_test, y_pred)
+print(f"Mean Squared Error: {mse:.2f}")
+print(f"R^2 Score: {r2:.2f}")
+
+# 可视化结果
+plt.scatter(X, y, color='darkorange', label='data')
+plt.plot(X_test, y_pred, color='navy', lw=2, label='SVR model')
+plt.xlabel('data')
+plt.ylabel('target')
+plt.title('Support Vector Regression (SVR)')
+plt.legend()
+plt.savefig('./output/svm_r.png')
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