import random
import numpy as np

# Initialization
n = 7  # number of items
C = 10  # bin capacity
tmax = 100  # number of iterations
psize = 10  # population size
items=[2, 5, 4, 7, 1, 3, 8] #items weights



def afdo(n, C, tmax, psize, items):
    
    # Generate initial population using modified FF heuristic
    pop = []
    for i in range(psize):
        random.shuffle(items)
        bins = [[]]
        for item in items:
            for j in range(len(bins)):
                if sum(bins[j]) + item <= C:
                    bins[j].append(item)
                    break
                elif j == len(bins)-1:
                    bins.append([item])
                    break
        pop.append(bins)
        
        # Fitness function
    def fitness(solution):
        f = 0
        z = 2
        for j in range(len(solution)):
            fill = sum(solution[j])
            f += (fill/C)**z
        return f/len(bins)
        
    # Improve the initial solution using AFDO
    t = 0
    fit = fitness(pop[0]);
    xstar = pop[0]
    while t <= tmax:
        # Artificial scout bee evaluation
        for i in range(1,psize):
            newfit = fitness(pop[i])
            if newfit < fit:
                newfit = fit
                xstar = pop[i]
                bstar=len(xstar)
    
        # Artificial scout bee movement
        wf = 1
        for i in range(psize):
            xi = pop[i]
            fw = abs(fitness(xstar)/fitness(xi))*wf
            r = round(random.random(),1) #nombre aleatoire entre 0 et 1, arrondi a 1 chiffre apres la virgule
            if fw == 1 or fw == 0 or fitness(xi) == 0:
                
                def generate_raw_vectors(pop[i], len(pop[i]), n, r):
                    raw_vectors = []
                    for i in range(num_items):
                        old_bin = pop[i][1]
                        new_bin = random.randint(1, len(pop[i]))
                        raw_vectors.append((pop[i][0], old_bin, new_bin))
                    return raw_vectors   
                
            else:
                
                
        t += 1
    
    # Output the results
    print("Final assignment of items to bins:", xstar)
    print("Minimum number of used bins:", bstar)
    return 1

afdo(n,C,tmax,psize,items)