Saataa andagii !

README.md

@@ -5,6 +5,7 @@
 ### Linux
 
 - `git clone https://git.leizour.fr/lucien/ultra-mastermind-implementation`
+- `cd ultra-mastermind-implementation`
 - `python -m venv .venv`
 - `source .venv/bin/activate`
 - `pip install -r requirements.txt`
@@ -20,8 +21,9 @@
 #### Second method
 
 - `git clone https://git.leizour.fr/lucien/ultra-mastermind-implementation`
+- `cd ultra-mastermind-implementation`
 - `python -m venv .venv`
-- `./.venv/bin/activate`
+- `.venv/bin/activate`
 - `pip install -r requirements.txt`
 - `python main.py`
 

analyse.py

@@ -0,0 +1,103 @@
+# fichier de tests du projet
+
+import matplotlib.pyplot as plt
+import random
+
+# project libs importations
+import lib.ultra_mastermind_obj as libobj
+import lib.ultra_mastermind_imp as libimp
+import lib.ultra_mastermind_pp_imp as libppimp
+
+PM = "Hello, world!"
+NG = 4000
+N = 400
+TS = 0.7
+TM = 0.25
+ALPHA = 0.5
+FITNESS_METHOD = 3
+
+# Variation de la taille de la phrase
+length_ng = []
+length = []
+
+for i in range(5, 26, 3):
+    print(f"Step {i}:")
+    length.append(i)
+    vals = []
+    for j in range(5):
+        print(f"    Part {j}")
+        PM = "".join([chr(random.randint(0, 255)) for _ in range(i)])
+        pop = libppimp.new_population(PM, NG, N, TS, TM, ALPHA, FITNESS_METHOD)
+        ng = libppimp.run(pop)
+        vals.append(ng)
+    length_ng.append(sum(vals) / len(vals))
+
+plt.plot(length, length_ng)
+plt.title("Nombre de générations nécéssaires en fonction de la taille de la phrase.")
+plt.xlabel("Taille de la phrase")
+plt.ylabel("Nombre de générations")
+plt.show()
+
+# Variation de la taille de la population
+n_ng = []
+n = []
+
+for i in range(50, 1000, 100):
+    print(f"Step {i}:")
+    n.append(i)
+    vals = []
+    for j in range(5):
+        print(f"    Part {j}")
+        pop = libppimp.new_population(PM, NG, i, TS, TM, ALPHA, FITNESS_METHOD)
+        ng = libppimp.run(pop)
+        vals.append(ng)
+    n_ng.append(sum(vals) / len(vals))
+
+plt.plot(n, n_ng)
+plt.title("Nombre de générations nécéssaires en fonction de la taille de la population.")
+plt.xlabel("Taille de la population")
+plt.ylabel("Nombre de générations")
+plt.show()
+
+# Variation du taut de sélection
+ts_ng = []
+ts = []
+
+for i in range(1, 9, 1):
+    print(f"Step {i / 10}:")
+    ts.append(i / 10)
+    vals = []
+    for j in range(5):
+        print(f"    Part {j}")
+        pop = libppimp.new_population(PM, NG, N, i / 10, TM, ALPHA, FITNESS_METHOD)
+        ng = libppimp.run(pop)
+        vals.append(ng)
+    ts_ng.append(sum(vals) / len(vals))
+
+plt.plot(ts, ts_ng)
+plt.title("Nombre de générations nécéssaires en fonction du taut de sélection.")
+plt.xlabel("Taut de sélection")
+plt.ylabel("Nombre de générations")
+plt.show()
+
+# Variation du taut de mutation
+tm_ng = []
+tm = []
+
+for i in range(10, 40, 5):
+    print(f"Step {i / 100}:")
+    tm.append(i / 100)
+    vals = []
+    for j in range(5):
+        print(f"    Part {j}")
+        pop = libppimp.new_population(PM, NG, N, TS, i / 100, ALPHA, FITNESS_METHOD)
+        ng = libppimp.run(pop)
+        vals.append(ng)
+    tm_ng.append(sum(vals) / len(vals))
+
+plt.plot(tm, tm_ng)
+plt.title("Nombre de générations nécéssaires en fonction du taut de mutation.")
+plt.xlabel("Taut de mutation")
+plt.ylabel("Nombre de générations")
+plt.show()
+

lib/ultra_mastermind_pp_imp.py

@@ -165,7 +165,7 @@ def mutate(individual) -> None:
     Methode qui change un des caractères du chromozome
     """
     new = list(individual["chromozome"])
-	dice = random.randint(1,3)
+    dice = random.randint(1, 6)
     if dice == 1 and len(new) < 30:
         new.insert(random.randint(0, len(new) - 1), chr(random.randint(0, 255)))
     elif dice == 2 and len(new) > 4:
@@ -186,12 +186,14 @@ def mutate_pop(population) -> None:
         mutate(population["individuals"][to_mutate])
         mutated.append(to_mutate)
 
-def run(population) -> None:
+def run(population) -> int:
     """
     Boucle principale
     """
     for i in range(population["ng"]):
+        if get_best(population)["chromozome"] == population["pm"]:
+            return i
         select(population)
         reproduct(population)
         mutate_pop(population)
-	print_best(population)
+    return population["ng"]

main.py

@@ -31,6 +31,7 @@ def main() -> None:
     # imperative version ++
     pop = libppimp.new_population(PM, NG, N, TS, TM, ALPHA, FITNESS_METHOD)
     libppimp.run(pop)
+    print(libppimp.get_best(pop)["chromozome"])
 
 if __name__ == "__main__":
 	main()

tests.py

@@ -1,59 +0,0 @@
-# fichier de tests du projet
-
-import matplotlib.pyplot as plt
-
-# project libs importations
-import lib.ultra_mastermind_obj as libobj
-import lib.ultra_mastermind_imp as libimp
-import lib.ultra_mastermind_pp_imp as libppimp
-
-# Variation du nombre de générations
-PM = "Hello, world!"
-# NG = 2000
-N  = 400
-TS = 0.5
-TM = 0.25
-ALPHA = 0.5
-FITNESS_METHOD = 3
-
-fitness_ng = []
-all_ng = []
-
-for i in range(1, 11):
-	NG = i * 200
-	all_ng.append(NG)
-	pop = libppimp.new_population(PM, NG, N, TS, TM, ALPHA, FITNESS_METHOD)
-	libppimp.run(pop)
-	fitness_ng.append(libppimp.get_fitness(pop, libppimp.get_best(pop)))
-
-plt.plot(all_ng, fitness_ng)
-plt.title("Fitness du meilleur individu en fonction du nombre de générations")
-plt.xlabel("Nombre de générations")
-plt.ylabel("Fitness du meilleur individu")
-plt.show()
-
-# Variation du nombre de générations
-PM = "Hello, world!"
-NG = 500
-# N  = 400
-TS = 0.5
-TM = 0.25
-ALPHA = 0.5
-FITNESS_METHOD = 3
-
-fitness_n = []
-all_n = []
-
-for i in range(1, 11):
-	N = i * 100
-	all_n.append(N)
-	pop = libppimp.new_population(PM, NG, N, TS, TM, ALPHA, FITNESS_METHOD)
-	libppimp.run(pop)
-	fitness_n.append(libppimp.get_fitness(pop, libppimp.get_best(pop)))
-
-plt.plot(all_n, fitness_n)
-plt.title("Fitness du meilleur individu en fonction de la taille de population")
-plt.xlabel("Taille de population")
-plt.ylabel("Fitness du meilleur individu")
-plt.show()
-