本文整理汇总了Python中sklearn.linear_model.BayesianRidge.set_params方法的典型用法代码示例。如果您正苦于以下问题：Python BayesianRidge.set_params方法的具体用法？Python BayesianRidge.set_params怎么用？Python BayesianRidge.set_params使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类sklearn.linear_model.BayesianRidge的用法示例。
在下文中一共展示了BayesianRidge.set_params方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: likelihood
# 需要导入模块: from sklearn.linear_model import BayesianRidge [as 别名]
# 或者: from sklearn.linear_model.BayesianRidge import set_params [as 别名]
# Fit by cubic polynomial
n_order = 3
X_train = np.vander(x_train, n_order + 1, increasing=True)
X_test = np.vander(x_test, n_order + 1, increasing=True)
# #############################################################################
# Plot the true and predicted curves with log marginal likelihood (L)
reg = BayesianRidge(tol=1e-6, fit_intercept=False, compute_score=True)
fig, axes = plt.subplots(1, 2, figsize=(8, 4))
for i, ax in enumerate(axes):
# Bayesian ridge regression with different initial value pairs
if i == 0:
init = [1 / np.var(y_train), 1.] # Default values
elif i == 1:
init = [1., 1e-3]
reg.set_params(alpha_init=init[0], lambda_init=init[1])
reg.fit(X_train, y_train)
ymean, ystd = reg.predict(X_test, return_std=True)
ax.plot(x_test, func(x_test), color="blue", label="sin($2\\pi x$)")
ax.scatter(x_train, y_train, s=50, alpha=0.5, label="observation")
ax.plot(x_test, ymean, color="red", label="predict mean")
ax.fill_between(x_test, ymean-ystd, ymean+ystd,
color="pink", alpha=0.5, label="predict std")
ax.set_ylim(-1.3, 1.3)
ax.legend()
title = "$\\alpha$_init$={:.2f},\\ \\lambda$_init$={}$".format(
init[0], init[1])
if i == 0:
title += " (Default)"
ax.set_title(title, fontsize=12)