return new_list[int(len(new_list) / 2)]
def intervalle_de_confiance_moyenne(nb_sigma_precision, ecart_type, taille, moyenne):
a = dico_sigma[nb_sigma_precision][0] * ecart_type / sqrt(taille)
return (moyenne - a, moyenne +a)
def intervalle_de_confiance_mediane(nb_sigma_precision, list):
n = len(list)
j = int((n / 2) - dico_sigma[nb_sigma_precision][0] * sqrt(n) / 2)
k = int((n / 2) + dico_sigma[nb_sigma_precision][0] * sqrt(n) / 2)
# il faut ordonner la liste
listeTriee = sorted(list)
xj = listeTriee[j]
xk = listeTriee[k]
return (xj, xk)
if __name__ == "__main__":
import simul_exercice1 as s1
import moyenne_pareto as mp
import simul_exercice2 as s2
# initialisation des générateurs de nombres aléatoires
seed(1)
np.random.seed(1)
# s1.do(40, 60, -1, 100000, 0.1, 1)
# mp.do(1.25, 2)
s2.do(30, 35, -1, 100000, 1.25, 2, 1)
import sys
import simul_exercice1 as s1
import simul_exercice2 as s2
from random import *
import numpy as np
if __name__ == "__main__":
seed(1)
np.random.seed(1)
if len(sys.argv) != 0:
num_graph = int(sys.argv[1])
if num_graph == 1: # Exercice 1, 3.1
s1.do(24, 25, 26, 80000, 0.1, 1)
elif num_graph == 2: # Exercice 1, 3.2
s1.do(20, 30, -1, 10000, 0.1, 1)
elif num_graph == 3: # Exercice 2, 2.1
s2.do(24, 25, 26, 40000, 1.25, 2, 1)
elif num_graph == 4: # Exercice 2, 2.2
s2.do(20, 30, -1, 40000, 1.25, 2, 1)
print "out"
elif num_graph == 5:
print "out"
elif num_graph == 6:
print "out"
else:
print "out"
