def test_alpha():
# Test that larger alpha yields weights closer to zero"""
X = X_digits_binary[:100]
y = y_digits_binary[:100]
alpha_vectors = []
alpha_values = np.arange(2)
absolute_sum = lambda x: np.sum(np.abs(x))
for alpha in alpha_values:
mlp = MLPClassifier(hidden_layer_sizes=10, alpha=alpha, random_state=1)
mlp.fit(X, y)
alpha_vectors.append(np.array([absolute_sum(mlp.coefs_[0]),
absolute_sum(mlp.coefs_[1])]))
for i in range(len(alpha_values) - 1):
assert (alpha_vectors[i] > alpha_vectors[i + 1]).all()
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