『sklearn学习』贝叶斯岭回归

 # 贝叶斯岭回归（Bayesian Ridge Regression）

    
 import numpy as np
    
 import matplotlib.pyplot as plt
    
 from scipy import stats
    
  
    
 from sklearn.linear_model import BayesianRidge, LinearRegression
    
  
    
 # 生成具有高斯权重模拟数据
    
 np.random.seed(0)    # 设置 random 函数的随机种子
    
 n_samples, n_features = 100, 100
    
 X = np.random.randn(n_samples, n_features)    # 创建高斯数据集
    
 lambda_ = 4.
    
 w = np.zeros(n_features)
    
 relevant_features = np.random.randint(0, n_features, 10)
    
 print relevant_features
    
 for i in relevant_features:
    
     w[i] = stats.norm.rvs(