Python递归实现支持向量回归算法

import numpy as np
from sklearn.svm import SVR

X = np.sort(5 * np.random.rand(80, 1), axis=0)
y = np.sin(X).ravel()

svr_rbf = SVR(kernel='rbf', C=1e3, gamma=0.1)

svr_rbf.fit(X, y)

y_rbf = svr_rbf.predict(X)

import numpy as np
import matplotlib.pyplot as plt
from sklearn.svm import SVR

# 生成数据集
X = np.sort(5 * np.random.rand(80, 1), axis=0)
y = np.sin(X).ravel()

# 定义SVR对象
svr_rbf = SVR(kernel='rbf', C=1e3, gamma=0.1)

# 进行训练
svr_rbf.fit(X, y)

# 进行预测
y_rbf = svr_rbf.predict(X)

# 可视化结果
plt.scatter(X, y, color='darkorange', label='data')
plt.plot(X, y_rbf, color='navy', label='RBF model')
plt.xlabel('data')
plt.ylabel('target')
plt.title('Support Vector Regression')
plt.legend()
plt.show()