test_huber.py 文件源码

def make_regression_with_outliers(n_samples=50, n_features=20):
    rng = np.random.RandomState(0)
    # Generate data with outliers by replacing 10% of the samples with noise.
    X, y = make_regression(
        n_samples=n_samples, n_features=n_features,
        random_state=0, noise=0.05)

    # Replace 10% of the sample with noise.
    num_noise = int(0.1 * n_samples)
    random_samples = rng.randint(0, n_samples, num_noise)
    X[random_samples, :] = 2.0 * rng.normal(0, 1, (num_noise, X.shape[1]))
    return X, y