diff --git a/tp7/analyse_tris.csv b/tp7/analyse_tris.csv
index 9293e8e802ea9069f42b7421c01c8be68e5c2acb..4dd963c400e9de2d0a4e0b9b850abefdc932e16e 100644
--- a/tp7/analyse_tris.csv
+++ b/tp7/analyse_tris.csv
@@ -2,101 +2,101 @@ taille;"tri séléction";"tri insertion"
0; 0.00; 0.00
1; 0.00; 0.00
2; 1.00; 1.00
- 3; 3.00; 2.54
- 4; 6.00; 4.94
- 5; 10.00; 7.74
- 6; 15.00; 10.94
- 7; 21.00; 15.12
- 8; 28.00; 18.32
- 9; 36.00; 24.46
- 10; 45.00; 28.74
- 11; 55.00; 36.26
- 12; 66.00; 42.60
- 13; 78.00; 47.62
- 14; 91.00; 55.40
- 15; 105.00; 64.76
- 16; 120.00; 75.72
- 17; 136.00; 81.50
- 18; 153.00; 93.78
- 19; 171.00; 99.70
- 20; 190.00; 108.00
- 21; 210.00; 121.72
- 22; 231.00; 136.70
- 23; 253.00; 148.32
- 24; 276.00; 154.62
- 25; 300.00; 177.60
- 26; 325.00; 185.98
- 27; 351.00; 195.04
- 28; 378.00; 214.66
- 29; 406.00; 228.92
- 30; 435.00; 243.02
- 31; 465.00; 263.00
- 32; 496.00; 278.32
- 33; 528.00; 300.08
- 34; 561.00; 313.08
- 35; 595.00; 332.32
- 36; 630.00; 347.46
- 37; 666.00; 363.56
- 38; 703.00; 385.38
- 39; 741.00; 402.26
- 40; 780.00; 431.92
- 41; 820.00; 449.30
- 42; 861.00; 468.26
- 43; 903.00; 482.66
- 44; 946.00; 514.60
- 45; 990.00; 547.12
+ 3; 3.00; 2.66
+ 4; 6.00; 4.84
+ 5; 10.00; 8.02
+ 6; 15.00; 10.46
+ 7; 21.00; 15.74
+ 8; 28.00; 19.30
+ 9; 36.00; 25.44
+ 10; 45.00; 28.44
+ 11; 55.00; 34.82
+ 12; 66.00; 42.02
+ 13; 78.00; 48.54
+ 14; 91.00; 55.16
+ 15; 105.00; 66.28
+ 16; 120.00; 73.12
+ 17; 136.00; 79.96
+ 18; 153.00; 92.14
+ 19; 171.00; 99.98
+ 20; 190.00; 112.58
+ 21; 210.00; 122.30
+ 22; 231.00; 136.20
+ 23; 253.00; 149.02
+ 24; 276.00; 159.28
+ 25; 300.00; 171.26
+ 26; 325.00; 188.90
+ 27; 351.00; 196.46
+ 28; 378.00; 213.12
+ 29; 406.00; 224.26
+ 30; 435.00; 245.64
+ 31; 465.00; 259.88
+ 32; 496.00; 279.32
+ 33; 528.00; 290.44
+ 34; 561.00; 317.86
+ 35; 595.00; 333.44
+ 36; 630.00; 336.84
+ 37; 666.00; 373.86
+ 38; 703.00; 390.18
+ 39; 741.00; 411.06
+ 40; 780.00; 422.26
+ 41; 820.00; 437.46
+ 42; 861.00; 472.16
+ 43; 903.00; 486.10
+ 44; 946.00; 506.98
+ 45; 990.00; 532.68
46; 1035.00; 558.56
- 47; 1081.00; 591.68
- 48; 1128.00; 596.86
- 49; 1176.00; 638.16
- 50; 1225.00; 662.70
- 51; 1275.00; 682.62
- 52; 1326.00; 707.10
- 53; 1378.00; 754.44
- 54; 1431.00; 758.76
- 55; 1485.00; 804.98
- 56; 1540.00; 815.14
- 57; 1596.00; 856.56
- 58; 1653.00; 857.58
- 59; 1711.00; 914.12
- 60; 1770.00; 921.60
- 61; 1830.00; 983.40
- 62; 1891.00; 987.16
- 63; 1953.00; 1037.34
- 64; 2016.00; 1086.56
- 65; 2080.00; 1084.88
- 66; 2145.00; 1158.24
- 67; 2211.00; 1172.10
- 68; 2278.00; 1185.94
- 69; 2346.00; 1220.80
- 70; 2415.00; 1292.92
- 71; 2485.00; 1308.74
- 72; 2556.00; 1343.22
- 73; 2628.00; 1390.08
- 74; 2701.00; 1428.48
- 75; 2775.00; 1445.40
- 76; 2850.00; 1475.92
- 77; 2926.00; 1534.34
- 78; 3003.00; 1604.52
- 79; 3081.00; 1610.94
- 80; 3160.00; 1674.74
- 81; 3240.00; 1693.34
- 82; 3321.00; 1729.02
- 83; 3403.00; 1799.78
- 84; 3486.00; 1800.70
- 85; 3570.00; 1884.10
- 86; 3655.00; 1954.98
- 87; 3741.00; 1964.98
- 88; 3828.00; 2012.42
- 89; 3916.00; 2011.72
- 90; 4005.00; 2080.66
- 91; 4095.00; 2147.18
- 92; 4186.00; 2161.90
- 93; 4278.00; 2216.44
- 94; 4371.00; 2290.34
- 95; 4465.00; 2347.26
- 96; 4560.00; 2389.54
- 97; 4656.00; 2413.08
- 98; 4753.00; 2483.92
- 99; 4851.00; 2523.12
-100; 4950.00; 2608.62
+ 47; 1081.00; 587.18
+ 48; 1128.00; 606.10
+ 49; 1176.00; 631.00
+ 50; 1225.00; 669.22
+ 51; 1275.00; 687.82
+ 52; 1326.00; 716.20
+ 53; 1378.00; 744.82
+ 54; 1431.00; 779.66
+ 55; 1485.00; 784.60
+ 56; 1540.00; 817.60
+ 57; 1596.00; 841.68
+ 58; 1653.00; 884.94
+ 59; 1711.00; 911.00
+ 60; 1770.00; 952.36
+ 61; 1830.00; 969.26
+ 62; 1891.00; 1016.92
+ 63; 1953.00; 1033.92
+ 64; 2016.00; 1061.48
+ 65; 2080.00; 1082.60
+ 66; 2145.00; 1148.86
+ 67; 2211.00; 1151.72
+ 68; 2278.00; 1189.32
+ 69; 2346.00; 1247.46
+ 70; 2415.00; 1249.00
+ 71; 2485.00; 1312.82
+ 72; 2556.00; 1338.22
+ 73; 2628.00; 1382.14
+ 74; 2701.00; 1416.98
+ 75; 2775.00; 1463.36
+ 76; 2850.00; 1515.78
+ 77; 2926.00; 1552.44
+ 78; 3003.00; 1592.64
+ 79; 3081.00; 1626.36
+ 80; 3160.00; 1655.74
+ 81; 3240.00; 1672.96
+ 82; 3321.00; 1740.14
+ 83; 3403.00; 1761.00
+ 84; 3486.00; 1806.66
+ 85; 3570.00; 1870.12
+ 86; 3655.00; 1906.36
+ 87; 3741.00; 1950.14
+ 88; 3828.00; 2011.50
+ 89; 3916.00; 2083.56
+ 90; 4005.00; 2085.50
+ 91; 4095.00; 2159.10
+ 92; 4186.00; 2186.72
+ 93; 4278.00; 2220.78
+ 94; 4371.00; 2287.80
+ 95; 4465.00; 2303.56
+ 96; 4560.00; 2357.14
+ 97; 4656.00; 2434.12
+ 98; 4753.00; 2458.50
+ 99; 4851.00; 2548.74
+100; 4950.00; 2597.96
diff --git a/tp7/analyse_tris2.py b/tp7/analyse_tris2.py
index 99545eebe13f7d984c1ecad79af460b95785e816..a20e9a215104418ba62316e01f6216a8fcdc1d07 100755
--- a/tp7/analyse_tris2.py
+++ b/tp7/analyse_tris2.py
@@ -41,7 +41,7 @@ def analyser_tri(tri: Callable[[list[T], Callable[[T, T], int]], NoneType],
res += compare.counter
return res / nbre_essais
-def tri_comp(l:list[T], comp: Callable[[T, T], int] = compare):
+def tri_sort(l:list[T], comp: Callable[[T, T], int] = compare):
"""à_remplacer_par_ce_que_fait_la_fonction
Précondition :
@@ -61,15 +61,13 @@ if (__name__ == '__main__'):
TAILLE_MAX = 100
c_select = [0.0] * (TAILLE_MAX + 1)
c_insert = [0.0] * (TAILLE_MAX + 1)
- # creating c_sort
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)
- # for sorting
- c_sort[t] = analyser_tri(tri_comp, NB_ESSAIS, t)
+ c_sort[t] = analyser_tri(tri_sort, NB_ESSAIS, t)
# Sauvegarde des données calculées dans un fichier au format CSV
prem_ligne = 'taille;"tri séléction";"tri insertion"\n'
diff --git a/tp7/tp_tris.py b/tp7/tp_tris.py
index 2ebd7f26fa57ab611c2e6ca649c60759feb7e717..f462be53309180ee71d5a5863baaef18299dc8db 100644
--- a/tp7/tp_tris.py
+++ b/tp7/tp_tris.py
@@ -4,6 +4,10 @@ import matplotlib.pyplot as plt
from analyse_tris import tri_select
from math import sqrt
from analyse_tris import analyser_tri
+from typing import Callable
+from compare import compare
+from ap_decorators import count
+from tris import *
# Préliminaires
@@ -20,29 +24,25 @@ def liste_alea(n: int) -> list[int]:
return l
# Évaluation expérimentale de la complexité en temps
-TAILLE_MAX = 100
-L = []
-T = []
+compare = count(compare)
-for t in range(1, TAILLE_MAX + 1):
- rlist = liste_alea(t)
- time = timeit.timeit(stmt='tri_select(l)', setup='from __main__ import tri_select, l', globals={'l': rlist}, number=5000)
- L.append(t)
- times.append(time)
-
-plt.plot(lengths, times, label='Selection Sort')
-plt.xlabel('Length of List')
-plt.ylabel('Time (s)')
-plt.title('Selection Sort Execution Time')
-plt.legend()
-plt.grid(True)
-plt.show()
-
-#3
-import timeit
-import matplotlib.pyplot as plt
-import random
-from analyse_tris import tri_insert
+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
Nmax = 100
number = 5000
@@ -80,8 +80,3 @@ plt.legend()
plt.grid(True)
plt.show()
-
-
-
-
-
diff --git a/tp7/tris_nbcomp.png b/tp7/tris_nbcomp.png
index 3e52e860b335799ae2fd9407a50e764bd12939b8..3444c0510f572535f2bbb8a4b2586e44b2c6835c 100644
Binary files a/tp7/tris_nbcomp.png and b/tp7/tris_nbcomp.png differ