return (x - 1)**2 + (y - 1)**3 + (z - 1)**4
def six_variable_function(x1, x2, x3, x4, x5, x6):
return (x1 - 1)**2 + (x2 - 1)**3 + (x3 - 1)**4 + x4 + 2 * x5 + 3 * x6
print('Does GradientDescent work for a single variable function?')
minimizer = GradientDescent(f=single_variable_function, initial_point=[0])
assert minimizer.point == [0], 'The inital point was off'
ans = minimizer.compute_gradient(delta=0.01)
ans_rounded = [round(num, 3) for num in ans]
assert ans_rounded == [
-2.000
], 'The rounded gradient wasnt quite right' #rounded to 5 decimal places
minimizer.descend(alpha=0.001, delta=0.01, num_steps=1)
point_rounded = [round(num, 3) for num in minimizer.point]
assert point_rounded == [0.002], 'The rounded final answer wasnt quite right'
print('Yes, it does!', "\n")
print('Does GradientDescent work for a two variable function?')
minimizer = GradientDescent(f=two_variable_function, initial_point=[0, 0])
assert minimizer.point == [0, 0], 'The inital point was off'
ans = minimizer.compute_gradient(delta=0.01)
ans_rounded = [round(num, 3) for num in ans]
assert ans_rounded == [-2.000, 3.000], 'The rounded gradient wasnt quite right'
minimizer.descend(alpha=0.001, delta=0.01, num_steps=1)
point_rounded = [round(num, 3) for num in minimizer.point]
assert point_rounded == [0.002,
-0.003], 'The rounded final answer wasnt quite right'
print('Yes, it does!', "\n")
