"""
Schreibe einen genetischen Algorithmus, der die Parameter
(a,b,c,d) der Funktion f (x ) = ax 3 + bx 2 + cx + d so optimiert,
dass damit die Funktion g(x ) = e x im Bereich [-1..1] möglichst
gut angenähert wird. Nutze dazu den quadratischen Fehler (oder
alternativ die Fläche zwischen der e-Funktion und dem Polynom).
Zeichne die Lösung und vergleiche die Koeffizienten mit denen der
Taylor-Reihe um 0.
"""

import numpy as np
import random
import struct
import time
# import matplotlib.pyplot as plt

def generate_random_population():
    pop_grey = [format(random.getrandbits(32), '32b') for i in range(10)]
    pop_bin = grey_to_bin(pop_grey)
    a, b, c, d = pop_bin[0:7], pop_bin[8:15], pop_bin[16:23], pop_bin[24:31]

    return [a, b, c, d]

def grey_to_bin(gray):
    """Convert Gray code to binary, operating on the integer value directly"""
    num = int(gray, 2)  # Convert string to integer
    mask = num
    while mask != 0:
        mask >>= 1
        num ^= mask
    return format(num, f'0{len(gray)}b')  # Convert back to binary string with same length

def bin_to_grey(binary):
    """Convert binary to Gray code using XOR with right shift"""
    num = int(binary, 2)  # Convert string to integer
    gray = num ^ (num >> 1)  # Gray code formula: G = B ^ (B >> 1)
    return format(gray, f'0{len(binary)}b')  # Convert back to binary string with same length

def bin_to_param(binary, q_min = 0.0, q_max = 10.0):
    """Convert binary string to float parameter in range [q_min, q_max]"""
    val = int(binary, 2) / 25.5 * 10 # conversion to 0.0 - 10.0 float
    # Scale to range [q_min, q_max]
    q = q_min + ((q_max - q_min) / (2**len(binary))) * val
    
    return q


def quadratic_error(original_fn, approx_fn, n):
    error = 0.0
    
    for i in range(-(n // 2), (n // 2) + 1):
        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

def fuck_that_shit_up():
    bin_values = generate_random_population()
    # Convert binary string to parameters for bin_values
    a, b, c, d = [bin_to_param(bin) for bin in bin_values]
    
    e_func = lambda x: np.e**x
    fixed_approx = lambda x: e_fn_approx(a, b, c, d, x)
    fitness = quadratic_error(e_func, fixed_approx, 6)
    
    while fitness > 0.01:
        # calc fitness
        fitness = quadratic_error(e_func, fixed_approx, 6)
        print(fitness)
        time.sleep(1)
        
        # selection
        # crossover
        # mutation
    
    # neue population
    return 0

fuck_that_shit_up()
# b = format(random.getrandbits(32), '32b')
# print(quadratic_error(e_func, fixed_approx, 6)) # hopefully works