Skip to content
Snippets Groups Projects
Commit adb5102d authored by Belkacemi Melissa's avatar Belkacemi Melissa
Browse files

Cas du tri par selection

parent f2edd6ad
Branches
No related tags found
No related merge requests found
......@@ -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; 2.62
4; 6.00; 4.90
5; 10.00; 7.54
6; 15.00; 10.90
7; 21.00; 14.56
8; 28.00; 19.96
9; 36.00; 23.84
10; 45.00; 29.12
11; 55.00; 35.24
12; 66.00; 42.72
13; 78.00; 48.12
14; 91.00; 55.06
15; 105.00; 64.22
16; 120.00; 72.72
17; 136.00; 82.22
18; 153.00; 91.08
19; 171.00; 100.86
20; 190.00; 109.80
21; 210.00; 116.96
22; 231.00; 136.72
23; 253.00; 142.74
24; 276.00; 154.70
25; 300.00; 172.60
26; 325.00; 185.50
27; 351.00; 197.92
28; 378.00; 213.90
29; 406.00; 228.38
30; 435.00; 242.74
31; 465.00; 264.10
32; 496.00; 273.96
33; 528.00; 297.28
34; 561.00; 308.94
35; 595.00; 328.56
36; 630.00; 347.48
37; 666.00; 369.30
38; 703.00; 386.26
39; 741.00; 417.00
40; 780.00; 429.88
41; 820.00; 438.68
42; 861.00; 476.60
43; 903.00; 490.30
44; 946.00; 514.28
45; 990.00; 535.98
46; 1035.00; 546.88
47; 1081.00; 598.08
48; 1128.00; 605.58
49; 1176.00; 639.08
50; 1225.00; 651.50
51; 1275.00; 689.16
52; 1326.00; 712.04
53; 1378.00; 746.86
54; 1431.00; 768.66
55; 1485.00; 794.12
56; 1540.00; 819.04
57; 1596.00; 854.84
58; 1653.00; 890.52
59; 1711.00; 931.04
60; 1770.00; 951.98
61; 1830.00; 968.66
62; 1891.00; 1008.38
63; 1953.00; 1037.84
64; 2016.00; 1090.42
65; 2080.00; 1080.92
66; 2145.00; 1127.48
67; 2211.00; 1188.08
68; 2278.00; 1207.70
69; 2346.00; 1225.60
70; 2415.00; 1281.40
71; 2485.00; 1313.32
72; 2556.00; 1353.52
73; 2628.00; 1379.60
74; 2701.00; 1422.80
75; 2775.00; 1469.98
76; 2850.00; 1506.84
77; 2926.00; 1535.16
78; 3003.00; 1578.44
79; 3081.00; 1621.58
80; 3160.00; 1655.34
81; 3240.00; 1699.60
82; 3321.00; 1717.26
83; 3403.00; 1794.74
84; 3486.00; 1826.90
85; 3570.00; 1874.66
86; 3655.00; 1931.14
87; 3741.00; 1935.44
88; 3828.00; 2021.40
89; 3916.00; 2035.10
90; 4005.00; 2128.82
91; 4095.00; 2130.34
92; 4186.00; 2177.56
93; 4278.00; 2271.38
94; 4371.00; 2261.42
95; 4465.00; 2299.68
96; 4560.00; 2374.12
97; 4656.00; 2422.98
98; 4753.00; 2486.74
99; 4851.00; 2532.82
100; 4950.00; 2560.18
3; 3.00; 2.70
4; 6.00; 5.00
5; 10.00; 7.42
6; 15.00; 11.08
7; 21.00; 15.02
8; 28.00; 19.46
9; 36.00; 22.76
10; 45.00; 29.34
11; 55.00; 36.10
12; 66.00; 42.16
13; 78.00; 48.64
14; 91.00; 55.22
15; 105.00; 63.70
16; 120.00; 72.70
17; 136.00; 83.50
18; 153.00; 92.14
19; 171.00; 101.14
20; 190.00; 109.58
21; 210.00; 125.48
22; 231.00; 133.68
23; 253.00; 141.48
24; 276.00; 154.30
25; 300.00; 171.96
26; 325.00; 184.44
27; 351.00; 193.64
28; 378.00; 212.40
29; 406.00; 231.78
30; 435.00; 244.28
31; 465.00; 260.20
32; 496.00; 275.90
33; 528.00; 292.84
34; 561.00; 302.56
35; 595.00; 330.30
36; 630.00; 345.34
37; 666.00; 366.86
38; 703.00; 387.52
39; 741.00; 401.46
40; 780.00; 430.58
41; 820.00; 445.86
42; 861.00; 464.26
43; 903.00; 479.92
44; 946.00; 514.20
45; 990.00; 536.68
46; 1035.00; 553.74
47; 1081.00; 576.56
48; 1128.00; 612.98
49; 1176.00; 624.00
50; 1225.00; 663.02
51; 1275.00; 681.04
52; 1326.00; 710.64
53; 1378.00; 750.00
54; 1431.00; 783.20
55; 1485.00; 787.38
56; 1540.00; 837.78
57; 1596.00; 850.50
58; 1653.00; 881.98
59; 1711.00; 896.46
60; 1770.00; 963.60
61; 1830.00; 966.84
62; 1891.00; 998.28
63; 1953.00; 1022.86
64; 2016.00; 1040.34
65; 2080.00; 1110.04
66; 2145.00; 1133.78
67; 2211.00; 1167.56
68; 2278.00; 1216.76
69; 2346.00; 1223.36
70; 2415.00; 1299.94
71; 2485.00; 1288.88
72; 2556.00; 1346.62
73; 2628.00; 1379.68
74; 2701.00; 1433.14
75; 2775.00; 1449.96
76; 2850.00; 1491.20
77; 2926.00; 1517.32
78; 3003.00; 1581.40
79; 3081.00; 1583.22
80; 3160.00; 1666.78
81; 3240.00; 1709.74
82; 3321.00; 1733.52
83; 3403.00; 1768.52
84; 3486.00; 1836.30
85; 3570.00; 1891.84
86; 3655.00; 1858.28
87; 3741.00; 1990.32
88; 3828.00; 1997.30
89; 3916.00; 2035.36
90; 4005.00; 2082.44
91; 4095.00; 2100.02
92; 4186.00; 2167.74
93; 4278.00; 2257.30
94; 4371.00; 2247.90
95; 4465.00; 2339.64
96; 4560.00; 2363.40
97; 4656.00; 2422.96
98; 4753.00; 2462.92
99; 4851.00; 2530.06
100; 4950.00; 2574.66
......@@ -61,7 +61,7 @@ if (__name__ == '__main__'):
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_tri(tri_sort, 1, 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'
......
# TP7 AP Analyse empirique des tris
# Belkacemi Melissa
# 13/03/2024
# 20/03/2024
#Préliminaires
......@@ -33,5 +33,36 @@ nvle_compare = count(compare)
#Évaluation expérimentale de la complexité en temps
import timeit
#Cas du tri par selection
TAILLE_MAX = 100
"""
c_select = [0.0] * (TAILLE_MAX + 1)
for t in range(TAILLE_MAX + 1):
c_select[t] = timeit.timeit(stmt='tri_select(l)', setup=f'from tp7 import liste_alea ;l=liste_alea({t});from tris import tri_select', number=5000)
plt.plot(list(range(TAILLE_MAX + 1)), c_select, 'b.', label='Tri sélection')
plt.title('Tris : temps de comparaisons')
plt.legend()
plt.xlabel('n = taille des listes')
plt.ylabel('c(n) = temps de comparaisons')
plt.savefig('tris_select_tmpscomp.png')
plt.show()
"""
#Cas du tri par insertion
globals=globals()
#meilleur des cas
meil_cas=[0.0] * (TAILLE_MAX + 1)
for t in range(TAILLE_MAX + 1):
liste=liste_alea(t)
tmp=timeit.timeit(stmt=f'tri_insert({liste})', setup='from tris import tri_insert', number=5000)
meil_cas[t]=tmp
TP7/analyse-tris/tris_nbcomp.png

29.1 KiB | W: | H:

TP7/analyse-tris/tris_nbcomp.png

29 KiB | W: | H:

TP7/analyse-tris/tris_nbcomp.png
TP7/analyse-tris/tris_nbcomp.png
TP7/analyse-tris/tris_nbcomp.png
TP7/analyse-tris/tris_nbcomp.png
  • 2-up
  • Swipe
  • Onion skin
TP7/analyse-tris/tris_tmpscomp.png

23.4 KiB

0% Loading or .
You are about to add 0 people to the discussion. Proceed with caution.
Please register or to comment