blr.py 文件源码

def sklearn_train(X, y):
    model = BayesianRidge().fit(X, y)
    beta = model.alpha_     # model.alpha_ is the noise precision ('beta' in Bishop)
    alpha = model.lambda_   # model.lambda_ is the weights precision ('alpha' in Bishop)

    PhiT_Phi = X.T * X
    M = X.shape[1]
    S_N = np.linalg.pinv(alpha*np.eye(M) + beta*PhiT_Phi)
    m_N = beta * np.dot(S_N, np.dot(y, X).T)
    w_opt = m_N

    return (w_opt, alpha, beta, S_N)


#==================== CLASSES ====================#