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"