robust_regression_2.py

N_SAMPLES = 500
N_OUTLIERS = 25

X, y, coef = datasets.make_regression(
    n_samples=N_SAMPLES, 
    n_features=1, 
    n_informative=1, 
    noise=20, 
    coef=True, 
    random_state=42
)

coef_list = [["original_coef", float(coef)]]

# add outliers               
np.random.seed(42)
X[:N_OUTLIERS] = 10 + 0.75 * np.random.normal(size=(N_OUTLIERS, 1))
y[:N_OUTLIERS] = -15 + 20 * np.random.normal(size=N_OUTLIERS)

plt.scatter(X, y);