#We simulate the parallel Greedy algorithm
import random
import math
#The principal variables are defined here
max_load = 3
n = 1000000
#Balls[i]=j means that the ball i is in the bin j (if j is -1 then i hasn't commited)
Balls = range(n)
#Bins[i] is the load of the bin i
Bins = range(n)
#Bins_round[i] is the list of the balls that have sent a message to bin i during the round simulated
bins_round = range(n)
#Balls_round[i] is the list of bins that have received a message from i during the round simulated
balls_round = range(n)
#An useful list
l = range(n)
#It puts k in all the cases of the list l
def place(k,l):
for i in range(len(l)):
l[i] = k
#It allows to begin a new simulation
def remise_zero():
place(-1,Balls)
place(0,Bins)
#It begins a simulation by defining the different lists according to the number of balls
def change_n(k):
global n
global Balls
global Bins
global bins_round
global balls_round
global l
n = k
l = range(n)
Balls = range(n)
Bins = range(n)
bins_round = range(n)
balls_round = range(n)
l = range(n)
#This function simulates a round of the basic greedy algorithm with ranks
#nb_balls is the number of balls left
#Current_ball lists the balls that are still here : we have nb_balls = len(current_balls)
#nb_c is the number of messages sent per round
def round(nb_balls,current_balls,nb_c):
global max_load
#For this round, the choices hasn't been made so far
place([],balls_round)
place([],bins_round)
for i in range(nb_balls):
#Each ball i chooses nb_c bins among n to send a message to
balls_round[current_balls[i]] = random.sample(l,nb_c)
#We add the ball current_ball[i] to the bins_round[b]
for b in balls_round[current_balls[i]]:
bins_round[b] = bins_round[b] + [current_balls[i]]
#For each bin we shuffle the messages received
for i in range(n):
random.shuffle(bins_round[i])
for i in range(n):
if len(balls_round[i]) != 0:
#For each ball who hasn't been placed so far we find the best rated message it has sent
min_h = n
choice_commit = 0
for b in balls_round[i]:
if (bins_round[b]).index(i) < min_h:
min_h = (bins_round[b]).index(i)
choice_commit = b
#If it can commit into the bin which received this message, it does
if Bins[choice_commit] < max_load:
Balls[i] = choice_commit
Bins[choice_commit] = Bins[choice_commit] + 1
#It sumulates one time the algorithm
def simulation():
global max_load
current_balls = range(n)
remise_zero()
nb_balls = n
nb_rounds = 0
#Here we choose the first parameter : the number of messages during the first round
nb_c = 5
#Here is the second : the max_load during the first round
max_load = 2
while nb_rounds < 1:
round(nb_balls,current_balls,nb_c)
#Current_balls are the non-commited balls
current_balls = [b for b in range(n) if Balls[b] == -1]
nb_balls = len(current_balls)
nb_rounds = nb_rounds +1
#Parameters for the second round
if nb_rounds == 1:
max_load = 3
nb_c = 5
#Parameter for the third round
if nb_rounds == 2:
max_load = 3
nb_c = 5
print(nb_balls)
return(nb_balls)
#Allow to repeat simulations and get an average number of what returns simulations
#Simulation can easily be changed to return the number of bins with a certain load, the number of remaining balls after 1,2 or 3 rounds
def nb_moy(essais):
max_load_moy = 0.
for i in range(essais):
remise_zero()
max_load_moy = max_load_moy + (float(simulation()))/(float(essais))
print(max_load_moy)
nb_moy(10)