Feature/new experiments to ne initial division

ABC/PrefrencesABC.py

@@ -54,7 +54,14 @@ def calculate_diversity(groups: List[List[Student]]) -> float:
     preference_score = 0
 
     for group in groups:
-        scores = [student.get_score() for student in group]
+        scores = []
+        if group[0].experiment:
+            for i, student1 in enumerate(group):
+                for student2 in group[i+1:]:
+                    scores.append(student1.get_score(student2))
+        else:
+            scores = [student.get_score() for student in group]
+
         if len(scores) > 1:  # סטיית תקן מוגדרת רק עבור יותר מנתון אחד
             diversity = statistics.stdev(scores)
         else:
@@ -173,7 +180,6 @@ def abc_algorithm_with_prefrences(students: List[Student], num_groups: int, num_
         if best_fitness > GLOBAL_MAX_VAL:
             GLOBAL_MAX_VAL = best_fitness
             GLOBAL_MAX = solutions[scores.index(GLOBAL_MAX_VAL)]
-        print(f"Iteration {iteration + 1}, Best Fitness: {GLOBAL_MAX_VAL}")
 
     # בסוף, מחזירים את הפתרון הטוב ביותר
     return GLOBAL_MAX

ABC/StandardABC.py

@@ -131,9 +131,6 @@ def abc_algorithm(students: List[Student], num_groups: int, num_iterations: int
                 solutions[i] = new_sol
                 scores[i] = calculate_diversity(new_sol)
                 stagnation[i] = 0
-        # הדפסת מידע על הדור
-        best_fitness = max(scores)
-        print(f"Iteration {iteration + 1}, Best Fitness: {best_fitness}")
 
     # בסוף, מחזירים את הפתרון הטוב ביותר
     best_index = scores.index(max(scores))

Genetic/PreferencesGenetic.py

@@ -53,7 +53,14 @@ def calculate_diversity(groups: List[List[Student]]) -> float:
     preference_score = 0
 
     for group in groups:
-        scores = [student.get_score() for student in group]
+        scores = []
+        if group[0].experiment:
+            for i, student1 in enumerate(group):
+                for student2 in group[i+1:]:
+                    scores.append(student1.get_score(student2))
+        else:
+            scores = [student.get_score() for student in group]
+
         if len(scores) > 1:  # סטיית תקן מוגדרת רק עבור יותר מנתון אחד
             diversity = statistics.stdev(scores)
         else:
@@ -188,10 +195,6 @@ def genetic_algorithm_with_preferences(students: List[Student], num_groups: int,
         # עדכון האוכלוסייה
         update_population(population, fitness_scores, mutated_child)
 
-        # הדפסת מידע על הדור
-        #best_fitness = max(fitness_scores)
-        #print(f"Generation {generation + 1}, Best Fitness: {best_fitness}")
-
     # מחזירים את הפתרון הטוב ביותר
     best_index = fitness_scores.index(max(fitness_scores))
     return population[best_index]

Genetic/StandardGenetic.py

@@ -151,10 +151,6 @@ def genetic_algorithm(students: List[Student], num_groups: int, population_size:
         # עדכון האוכלוסייה
         update_population(population, fitness_scores, mutated_child)
 
-        # הדפסת מידע על הדור
-        best_fitness = max(fitness_scores)
-        print(f"Generation {generation + 1}, Best Fitness: {best_fitness}")
-
     # מחזירים את הפתרון הטוב ביותר
     best_index = fitness_scores.index(max(fitness_scores))
     return population[best_index]

experiments/ABC/Iterations_Fitness.csv

@@ -0,0 +1,21 @@
+Iteration,AVG Score
+1,88.7211497181652
+2,88.7211497181652
+3,89.1052713126468
+4,89.22915077725476
+5,89.25699984324228
+6,89.53983026739552
+7,89.75942600900592
+8,90.19848966650157
+9,90.19848966650157
+10,90.19848966650157
+11,90.19848966650157
+12,90.28018097627236
+13,90.53506593084174
+14,90.63147377849803
+15,90.63147377849803
+16,90.63147377849803
+17,90.63147377849803
+18,90.790154029262
+19,90.95219584823126
+20,90.95443689370146

experiments/ABC/Iterations_Time.csv

@@ -0,0 +1,21 @@
+Iteration,AVG Time
+1,0.006540608406066894
+2,0.010796785354614258
+3,0.01384124755859375
+4,0.017898941040039064
+5,0.021931099891662597
+6,0.026218819618225097
+7,0.031563854217529295
+8,0.03766334056854248
+9,0.037829041481018066
+10,0.04230003356933594
+11,0.04445006847381592
+12,0.050631237030029294
+13,0.05401666164398193
+14,0.05841057300567627
+15,0.06447982788085938
+16,0.06548664569854737
+17,0.06842453479766845
+18,0.0749204158782959
+19,0.07750654220581055
+20,0.07419106960296631