"""
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 utils
# import matplotlib.pyplot as plt

POPULATION_SIZE = 10
SELECTION_SIZE = (POPULATION_SIZE * 7) // 10  # 70% of population, rounded down for selection
XOVER_PAIR_SIZE = (POPULATION_SIZE - SELECTION_SIZE) // 2  # pairs needed for crossover
XOVER_POINT = 3 # 4th position
MUTATION_BITS = POPULATION_SIZE * 1

fitness = 1
grey_pop = []
bin_pop = [] # 32 Bit Binary
bin_pop_params = []
new_pop = [] # 32 Bit Grey-Code as String

e_func = lambda x: np.e**x

def generate_random_population(num=POPULATION_SIZE):
    """ Puts random 32 Bit binary strings into 4 * 8 Bit long params. """
        
    # Generate new population
    for _ in range(num):
        grey = format(random.getrandbits(32), '32b')
        grey_pop.append(grey)
        
        bin_str = utils.grey_to_bin(grey)
        bin_pop.append(bin_str)
        
        params = [bin_str[i:i+7] for i in range(0, 31, 8)]
        bin_pop_params.append(params)

    return bin_pop_params

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 eval_fitness(bin_pop_values):
    """ Returns an array with fitness value of every individual in a population. """
    fitness_arr = []
    for params in bin_pop_values:
        # Convert binary string to parameters for bin_values
        a, b, c, d = [utils.bin_to_param(param) for param in params] # assign params to batch of population
        
        # Create polynomial function with current parameters
        approx = lambda x: a*x**3 + b*x**2 + c*x + d
        fitness = quadratic_error(e_func, approx, 6)
        print(fitness) # debugging
        
        fitness_arr.append(fitness) # save fitness
        # save params # already saved in grey_pop
        
    return fitness_arr

def select(population, fitness_arr):
    sum_of_fitness = sum(fitness_arr) 
    while len(population) < SELECTION_SIZE:   
        # Roulette logic
        roulette_num = random.random()
        is_chosen = False
        while not is_chosen:
            cumulative_p = 0  # Track cumulative probability
            for i, fitness in enumerate(fitness_arr):
                cumulative_p += fitness / sum_of_fitness
                if roulette_num < cumulative_p:
                    # Add the 32 Bit individual in grey code to population
                    population.append(grey_pop[i])
                    
                    # Calc new sum of fitness
                    fitness_arr.pop(i)
                    sum_of_fitness = sum(fitness_arr)
                    
                    is_chosen = True # break while loop
                    break # break for loop
    return population
           
def xover(population, xover_rate=XOVER_PAIR_SIZE):
    """Performs crossover on pairs of individuals from population."""
    offspring = []
    
    # Process pairs while we have enough individuals and haven't reached xover_rate
    pair_count = 0
    i = 0
    while i < len(population) - 1 and pair_count < xover_rate:
        parent_a = population[i]
        parent_b = population[i + 1]
        
        # Create two new offspring by swapping parts at XOVER_POINT
        offspring_a = parent_a[:XOVER_POINT] + parent_b[XOVER_POINT:]
        offspring_b = parent_b[:XOVER_POINT] + parent_a[XOVER_POINT:]
        
        offspring.extend([offspring_a, offspring_b])
        pair_count += 1
        i += 2  # Move to next pair
        
    return offspring

def mutate(population, mutation_rate):
    """Mutate random bits in the population with given mutation rate"""
    for _ in range(mutation_rate):
        # Select random individual and convert to list for efficient modification
        random_num = random.randrange(POPULATION_SIZE)
        bits = list(population[random_num])
        
        # Flip random bit
        bit_pos = random.randrange(32)
        bits[bit_pos] = '1' if bits[bit_pos] == '0' else '0'
        
        # Convert back to string and update population
        population[random_num] = ''.join(bits) # will work because lists are passed by reference
         
def main():
    bin_pop_values = generate_random_population(POPULATION_SIZE)
    print(bin_pop_values)
    while fitness > 0.01:
        # Evaluate fitness
        fitness_arr = eval_fitness(bin_pop_values)    
        
        # Selection        
        new_pop = select(new_pop, fitness_arr) # Alters new_pop
        print(new_pop)
        time.sleep(1)
                
        """# Crossover        
        offspring = xover(new_pop, XOVER_PAIR_SIZE)
        new_pop.extend(offspring)  # .extend needed
        
        # Mutation
        mutate(new_pop, MUTATION_BITS)
        
        # Update populations for next generation
        grey_pop = new_pop.copy()
        bin_pop_values = []
        for grey_bin_string in grey_pop:
            bin_str = utils.grey_to_bin(grey_bin_string)
            params = [bin_str[i:i+7] for i in range(0, 31, 8)]
            bin_pop_values.append(params)"""
    return 0

if __name__ == "__main__":
    main()