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Commit d7cc1f95 authored by Angy Wallot's avatar Angy Wallot
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tp.7/analyse_tris.csv
+ 102
− 102
View file @ d7cc1f95
taille;"tri séléction";"tri insertion"
0; 0.00; 0.00
1; 0.00; 0.00
2; 1.00; 1.00
3; 3.00; 2.70
4; 6.00; 4.74
5; 10.00; 8.08
6; 15.00; 11.26
7; 21.00; 15.10
8; 28.00; 19.08
9; 36.00; 24.46
10; 45.00; 29.72
11; 55.00; 35.30
12; 66.00; 42.86
13; 78.00; 48.98
14; 91.00; 57.80
15; 105.00; 64.80
16; 120.00; 73.06
17; 136.00; 80.42
18; 153.00; 90.66
19; 171.00; 100.88
20; 190.00; 111.28
21; 210.00; 121.68
22; 231.00; 135.24
23; 253.00; 147.00
24; 276.00; 155.20
25; 300.00; 173.80
26; 325.00; 186.12
27; 351.00; 192.68
28; 378.00; 210.06
29; 406.00; 229.38
30; 435.00; 245.66
31; 465.00; 258.40
32; 496.00; 265.62
33; 528.00; 290.38
34; 561.00; 313.58
35; 595.00; 332.90
36; 630.00; 342.96
37; 666.00; 370.00
38; 703.00; 379.28
39; 741.00; 406.54
40; 780.00; 425.06
41; 820.00; 455.42
42; 861.00; 469.22
43; 903.00; 493.66
44; 946.00; 512.96
45; 990.00; 544.42
46; 1035.00; 564.78
47; 1081.00; 591.22
48; 1128.00; 612.70
49; 1176.00; 610.14
50; 1225.00; 656.62
51; 1275.00; 669.74
52; 1326.00; 713.00
53; 1378.00; 741.84
54; 1431.00; 769.50
55; 1485.00; 777.98
56; 1540.00; 819.62
57; 1596.00; 854.86
58; 1653.00; 871.94
59; 1711.00; 905.68
60; 1770.00; 952.74
61; 1830.00; 964.20
62; 1891.00; 1008.58
63; 1953.00; 1021.10
64; 2016.00; 1062.32
65; 2080.00; 1107.34
66; 2145.00; 1140.92
67; 2211.00; 1163.18
68; 2278.00; 1204.10
69; 2346.00; 1225.34
70; 2415.00; 1261.54
71; 2485.00; 1321.62
72; 2556.00; 1333.28
73; 2628.00; 1382.04
74; 2701.00; 1411.98
75; 2775.00; 1440.60
76; 2850.00; 1505.66
77; 2926.00; 1535.00
78; 3003.00; 1578.50
79; 3081.00; 1648.60
80; 3160.00; 1663.54
81; 3240.00; 1703.94
82; 3321.00; 1721.76
83; 3403.00; 1767.64
84; 3486.00; 1817.18
85; 3570.00; 1870.64
86; 3655.00; 1895.06
87; 3741.00; 1955.68
88; 3828.00; 2016.42
89; 3916.00; 2010.70
90; 4005.00; 2112.22
91; 4095.00; 2102.12
92; 4186.00; 2164.66
93; 4278.00; 2211.56
94; 4371.00; 2238.06
95; 4465.00; 2318.66
96; 4560.00; 2364.84
97; 4656.00; 2379.80
98; 4753.00; 2492.86
99; 4851.00; 2554.04
100; 4950.00; 2572.12
taille;"tri séléction";"tri insertion";"tri avec methode sort"
0; 0.00; 0.00; 0.00
1; 0.00; 0.00; 0.00
2; 1.00; 1.00; 0.00
3; 3.00; 2.72; 0.00
4; 6.00; 5.02; 0.00
5; 10.00; 7.62; 0.00
6; 15.00; 11.36; 0.00
7; 21.00; 14.20; 0.00
8; 28.00; 18.54; 0.00
9; 36.00; 24.74; 0.00
10; 45.00; 30.22; 0.00
11; 55.00; 35.62; 0.00
12; 66.00; 41.86; 0.00
13; 78.00; 47.40; 0.00
14; 91.00; 57.88; 0.00
15; 105.00; 62.88; 0.00
16; 120.00; 73.56; 0.00
17; 136.00; 82.52; 0.00
18; 153.00; 90.38; 0.00
19; 171.00; 103.92; 0.00
20; 190.00; 111.20; 0.00
21; 210.00; 123.82; 0.00
22; 231.00; 129.12; 0.00
23; 253.00; 147.44; 0.00
24; 276.00; 156.58; 0.00
25; 300.00; 169.08; 0.00
26; 325.00; 186.50; 0.00
27; 351.00; 198.14; 0.00
28; 378.00; 210.34; 0.00
29; 406.00; 231.10; 0.00
30; 435.00; 237.64; 0.00
31; 465.00; 266.30; 0.00
32; 496.00; 285.96; 0.00
33; 528.00; 292.00; 0.00
34; 561.00; 307.88; 0.00
35; 595.00; 330.90; 0.00
36; 630.00; 352.20; 0.00
37; 666.00; 366.18; 0.00
38; 703.00; 377.70; 0.00
39; 741.00; 404.54; 0.00
40; 780.00; 421.00; 0.00
41; 820.00; 445.94; 0.00
42; 861.00; 464.66; 0.00
43; 903.00; 500.20; 0.00
44; 946.00; 514.20; 0.00
45; 990.00; 531.66; 0.00
46; 1035.00; 557.80; 0.00
47; 1081.00; 587.42; 0.00
48; 1128.00; 611.80; 0.00
49; 1176.00; 644.62; 0.00
50; 1225.00; 651.80; 0.00
51; 1275.00; 701.82; 0.00
52; 1326.00; 709.08; 0.00
53; 1378.00; 738.32; 0.00
54; 1431.00; 762.86; 0.00
55; 1485.00; 809.98; 0.00
56; 1540.00; 821.44; 0.00
57; 1596.00; 844.42; 0.00
58; 1653.00; 875.50; 0.00
59; 1711.00; 903.46; 0.00
60; 1770.00; 925.18; 0.00
61; 1830.00; 964.56; 0.00
62; 1891.00; 996.44; 0.00
63; 1953.00; 1048.30; 0.00
64; 2016.00; 1052.88; 0.00
65; 2080.00; 1099.98; 0.00
66; 2145.00; 1145.94; 0.00
67; 2211.00; 1160.88; 0.00
68; 2278.00; 1204.30; 0.00
69; 2346.00; 1235.62; 0.00
70; 2415.00; 1300.50; 0.00
71; 2485.00; 1301.50; 0.00
72; 2556.00; 1360.72; 0.00
73; 2628.00; 1372.26; 0.00
74; 2701.00; 1444.80; 0.00
75; 2775.00; 1416.02; 0.00
76; 2850.00; 1486.10; 0.00
77; 2926.00; 1548.10; 0.00
78; 3003.00; 1564.52; 0.00
79; 3081.00; 1634.28; 0.00
80; 3160.00; 1656.60; 0.00
81; 3240.00; 1708.64; 0.00
82; 3321.00; 1750.52; 0.00
83; 3403.00; 1765.18; 0.00
84; 3486.00; 1819.68; 0.00
85; 3570.00; 1854.70; 0.00
86; 3655.00; 1884.68; 0.00
87; 3741.00; 1946.54; 0.00
88; 3828.00; 1988.86; 0.00
89; 3916.00; 2054.66; 0.00
90; 4005.00; 2079.52; 0.00
91; 4095.00; 2110.12; 0.00
92; 4186.00; 2190.18; 0.00
93; 4278.00; 2251.18; 0.00
94; 4371.00; 2261.54; 0.00
95; 4465.00; 2343.36; 0.00
96; 4560.00; 2394.40; 0.00
97; 4656.00; 2390.30; 0.00
98; 4753.00; 2473.96; 0.00
99; 4851.00; 2512.00; 0.00
100; 4950.00; 2572.32; 0.00
tp.7/analyse_tris2.py 0 → 100755
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#!/usr/bin/python3
# -*- coding: utf-8 -*-
"""
:mod:`analyse_tris` module
:author: FIL - Faculté des Sciences et Technologies - Univ. Lille <http://portail.fil.univ-lille1.fr>_
:date: janvier 2017
:dernières révisions: février 2018, février 2019
Analyse empirique des tris
"""
from random import shuffle
from typing import Callable
from compare import compare
from ap_decorators import count
from tris import *
################################################
# ANALYSE EMPIRIQUE DES TRIS #
################################################
# ajout d'un compteur à la fonction compare
compare = count(compare)
def analyser_tri(tri: Callable[[list[T], Callable[[T, T], int]], NoneType],
nbre_essais: int,
taille: int) -> float:
"""
renvoie: le nombre moyen de comparaisons effectuées par l'algo tri
pour trier des listes de taille t, la moyenne étant calculée
sur n listes aléatoires.
précondition: n > 0, t >= 0, la fonc
"""
res = 0
for i in range(nbre_essais):
compare.counter = 0
l = [k for k in range(taille)]
shuffle(l)
tri(l, compare)
res += compare.counter
return res / nbre_essais
def analyser_sort(sort: Callable[list[T], list], nb_essai:int, taille:int)->float:
res = 0
for i in range(nb_essai):
compare.counter = 0
l = [k for k in range(taille)]
shuffle(l)
sort(l)
res += compare.counter
return res / nb_essai
if (__name__ == '__main__'):
from matplotlib import pyplot as plt
# Calcul de nombres moyens de comparaison pour des listes
# de tailles comprises entre 0 et TAILLE_MAX
NB_ESSAIS = 50
TAILLE_MAX = 100
c_select = [0.0] * (TAILLE_MAX + 1)
c_insert = [0.0] * (TAILLE_MAX + 1)
c_sort = [0,0] * (TAILLE_MAX + 1)
for t in range(TAILLE_MAX + 1):
c_select[t] = analyser_tri(tri_select, 1, t)
# inutile de moyenner pour le tri par sélection
c_insert[t] = analyser_tri(tri_insert, NB_ESSAIS, t)
c_sort[t] = analyser_sort(sorted, NB_ESSAIS, t)
# Sauvegarde des données calculées dans un fichier au format CSV
prem_ligne = 'taille;"tri séléction";"tri insertion";"tri avec methode sort"\n'
ligne = '{:3d};{:8.2f};{:8.2f};{:8.2f}\n'
with open('analyse_tris.csv', 'wt', encoding='utf8') as sortie:
sortie.write(prem_ligne)
for t in range(TAILLE_MAX + 1):
sortie.write(ligne.format(t,
c_select[t],
c_insert[t],
c_sort[t]))
# Représentation graphique
plt.plot(list(range(TAILLE_MAX + 1)), c_select, 'b.', label='Tri sélection')
plt.plot(list(range(TAILLE_MAX + 1)), c_insert, 'r.', label='Tri insertion')
plt.plot(list(range(TAILLE_MAX + 1)), c_sort, 'black', label='Tri avec sort')
plt.title('Tris : nbre de comparaisons')
plt.legend()
plt.xlabel('n = taille des listes')
plt.ylabel('c(n) = nbre de comparaisons')
plt.savefig('tris_nbcomp.png')
plt.show()
tp.7/fonctions.py
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− 6
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......@@ -3,6 +3,8 @@
# tp Évaluation empirique des tris
import random
import timeit
import math
def liste_alea(n:int)->list:
"""
......@@ -18,9 +20,7 @@ def liste_alea(n:int)->list:
random.shuffle(l)
return l
import matplotlib.pyplot as plt
abscisses = [1, 2, 4]
ordonnees = [2, 5, 0]
plt.plot(abscisses, ordonnees, color='red')
plt.show()
timeit.timeit(stmt='sqrt(2)')
timeit.timeit(stmt='sqrt(2)', setup='from math import sqrt')
timeit.timeit(stmt='sqrt(2)', setup='from math import sqrt', number=5000)
timeit.timeit(stmt='sqrt(2) ; sqrt(3)', setup='from math import sqrt')
tp.7/tris_nbcomp.png
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tp.7/tris_nbcomp.png

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tp.7/tris_nbcomp.png

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tp.7/tris_nbcomp.png
tp.7/tris_nbcomp.png
tp.7/tris_nbcomp.png
tp.7/tris_nbcomp.png
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