diff --git a/TP7/analyse-tris/analyse_tris.csv b/TP7/analyse-tris/analyse_tris.csv
index b679da25df8ac52db9fc3d28e2673e75f7f28072..8c2c3d5bec7d9df8832888ce14530033a4a493e6 100644
--- a/TP7/analyse-tris/analyse_tris.csv
+++ b/TP7/analyse-tris/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; 2.62
- 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
diff --git a/TP7/analyse-tris/analyse_tris2.py b/TP7/analyse-tris/analyse_tris2.py
index be9e9189425a29f967e009520964646b6e8e78b1..3a2fa65ea5d5191ed1bed3b8b88b315769285567 100755
--- a/TP7/analyse-tris/analyse_tris2.py
+++ b/TP7/analyse-tris/analyse_tris2.py
@@ -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'
diff --git a/TP7/analyse-tris/tp7.py b/TP7/analyse-tris/tp7.py
index 286a5d594cd498d5ca998d83e9c4e472264bd4f8..c20c1a8910f1097baa2b5b4f251dd17b9ec34f96 100644
--- a/TP7/analyse-tris/tp7.py
+++ b/TP7/analyse-tris/tp7.py
@@ -1,6 +1,6 @@
# 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
+
+
+
+
+
diff --git a/TP7/analyse-tris/tris_nbcomp.png b/TP7/analyse-tris/tris_nbcomp.png
index e17e8c6a3f44b28019c7fde9ec262bdc4a6a2042..7c60ab6ce87c293f962ab075bba539600ae15094 100644
Binary files a/TP7/analyse-tris/tris_nbcomp.png and b/TP7/analyse-tris/tris_nbcomp.png differ
diff --git a/TP7/analyse-tris/tris_tmpscomp.png b/TP7/analyse-tris/tris_tmpscomp.png
new file mode 100644
index 0000000000000000000000000000000000000000..c89b14b616eccc79dcff89a9370c934e15a7115c
Binary files /dev/null and b/TP7/analyse-tris/tris_tmpscomp.png differ