regression.py 文件源码

def predict(train):
    binary = (train > 0)
    reg = ElasticNetCV(fit_intercept=True, alphas=[
                       0.0125, 0.025, 0.05, .125, .25, .5, 1., 2., 4.])
    norm = NormalizePositive()
    train = norm.fit_transform(train)

    filled = train.copy()
    # iterate over all users
    for u in range(train.shape[0]):
        # remove the current user for training
        curtrain = np.delete(train, u, axis=0)
        bu = binary[u]
        if np.sum(bu) > 5:
            reg.fit(curtrain[:,bu].T, train[u, bu])

            # Fill the values that were not there already
            filled[u, ~bu] = reg.predict(curtrain[:,~bu].T)
    return norm.inverse_transform(filled)