import numpy as np
import random
import scipy.integrate

# import matplotlib.pyplot as plt

def generate_random_individuals():
    a = random.randrange(10)
    b = random.randrange(10)
    c = random.randrange(10)
    d = random.randrange(10)

    return [a, b, c, d]

def integrate_individual(function):
    result = scipy.integrate.quad(function, -3, 3)
    return result

def quadratic_error(original_fn, approx_fn, n):
    error = 0
    
    for i in range(n):
        error += (original_fn(i) - approx_fn(i))**2
        
    return error

def e_fn_approx(a, b, c, d, x = 1):
    return a*x**3 + b*x**2 + c*x + d

e_func = lambda x: np.e**x
fixed_approx = 1 # TODO

while quadratic_error(e_func, fixed_approx, n) > 0.01:
    # berechne fitness
    # selection
    # crossover
    # mutation
    
    # neue population
    print("Hello World")