X = np.array([
    [1, 2, 0],
    [2, 1, 1],
    [3, 3, 0],
    [2, 4, 1],
    [4, 2, 0],
])

actual_times = np.array([16, 29, 28, 35, 31])

w = np.zeros(3)
b = 0.0

final_w, final_b = gradient_descent(X, actual_times, w, b, learning_rate=0.05, iterations=10000)

print("Final weights:", final_w)
print("Final bias:", final_b)

# Final weights: [ 5.  2. 10.]
# Final bias: 7.0

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