dl_simulation.py 文件源码

def update_sparse_predictions(Y,D,W,Psi,lda=0.0001):
    X = np.zeros((Psi.shape[0],W.shape[1]))
    for i in range(W.shape[1]):
        used = (W[:,i] != 0)
        if used.sum() > 0:
            d = np.copy(D)
            d = d[:,used]
            model = Ridge(alpha=lda)
            model.fit(d,Y[:,i])
            X[:,i] = model.predict(Psi[:,used])
    return X