42 lines
1.4 KiB
Python
42 lines
1.4 KiB
Python
import numpy as np
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from sklearn.svm import SVR
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from sklearn.model_selection import train_test_split
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from sklearn.metrics import mean_squared_error, r2_score
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import matplotlib.pyplot as plt
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# 生成一些示例数据
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# 这里我们创建一个简单的非线性关系作为示例
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np.random.seed(0)
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X = np.sort(5 * np.random.rand(40, 1), axis=0)
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y = np.sin(X).ravel()
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# 为了使问题更具挑战性，我们向目标变量添加一些噪声
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# y[::5]每隔5个元素取一个值, 总共40个元素,即最后取8个值 , np.random.rand(8) 随机生成一个值在[0,1)之间的形状为[8, 1]的数组,所以形状正好相加.
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y[::5] += 3 * (0.5 - np.random.rand(8))
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# 将数据集拆分为训练集和测试集
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
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# 创建SVM回归模型
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svr_rbf = SVR(kernel='rbf', C=100, gamma=0.1, epsilon=0.1)
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# 训练模型
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svr_rbf.fit(X_train, y_train)
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# 对测试集进行预测
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y_pred = svr_rbf.predict(X_test)
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# 评估模型性能
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mse = mean_squared_error(y_test, y_pred)
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r2 = r2_score(y_test, y_pred)
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print(f"Mean Squared Error: {mse:.2f}")
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print(f"R^2 Score: {r2:.2f}")
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# 可视化结果
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plt.scatter(X, y, color='darkorange', label='data')
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plt.plot(X_test, y_pred, color='navy', lw=2, label='SVR model')
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plt.xlabel('data')
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plt.ylabel('target')
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plt.title('Support Vector Regression (SVR)')
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plt.legend()
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plt.savefig('./output/svm_r.png') |