from numpy.linalg import norm

X_train, X_test, y_train, y_test = train_test_split(X3b, y3b, test_size=0.5, train_size=0.5)
Lzero_train = []
Lone_train = []
Ltwo_train = []
lambdas = list(range(1, 101))
d = 10

for l in lambdas:
    X_transformed_train = PolynomialFeatures(d)
    X_transformed_train = X_transformed_train.fit_transform(X_train)
    clf = Ridge(alpha=l).fit(X_transformed_train, y_train)
    w_train = clf.coef_
    Lzero_train.append(nonzeroes(w_train[0]))
    Lone_train.append(norm(w_train, 1))
    Ltwo_train.append(norm(w_train))

plt.plot(lambdas, Lzero_train, alpha=1.0, label="L_zero_train")
plt.plot(lambdas, Lone_train, alpha=1.0, label="L_one_train")
plt.plot(lambdas, Ltwo_train, alpha=1.0, label="L_two_train")
plt.xlabel('Lambda')
plt.ylabel('Norm values')
plt.legend()
plt.show()