feat: generate initial field layout using genetic algorithm

src/generate_field.py

@@ -0,0 +1,123 @@
+from random import randint, choices, random
+from kb import tractor_kb, multi_sasiedzi
+import pytholog as pl
+
+
+def score_field(field):
+    score = 0
+    
+    for index in range(len(field)):
+        neighbours = []
+        if index >= 16 and field[index-16] != 'water': 
+            neighbours.append(field[index-16])
+        if index % 15 != 0 and field[index+1] != 'water':
+            neighbours.append(field[index+1])
+        if index < 240 and field[index+16] != 'water':
+            neighbours.append(field[index+16])
+        if index % 16 != 0 and field[index-1] != 'water':
+            neighbours.append(field[index-1])
+
+        score += multi_sasiedzi(field[index], neighbours)[0]["Mul"]
+
+    return score
+
+
+def choose_parents(population):
+    weights = [x[0] for x in population]
+    
+    weights_sum = 0
+    for weight in weights:
+        weights_sum += weight
+
+    weights = [weight/weights_sum for weight in weights]
+
+    mom = choices(population, cum_weights=weights, k=1)[0]
+    remaining_elements = [el for el in population if el != mom]
+    remaining_weights = [weights[population.index(el)] for el in remaining_elements]
+    dad = choices(remaining_elements, weights=remaining_weights, k=1)[0]
+
+    return mom, dad
+
+
+def breed_and_mutate(mom, dad):
+    crossover_point = randint(1, len(mom[1]) - 2)
+    offspring = mom[1][:crossover_point] + dad[1][crossover_point:]
+    if len(offspring) != len(mom):
+        ValueError("offspring lenght is not equal to mom length")
+
+    if random() < 0.1:
+        mutation_index = randint(0, len(offspring) - 1)
+        while offspring[mutation_index] == 'water':
+            mutation_index = randint(0, len(offspring) - 1)
+
+        mutation = get_random_vegetable()
+        while mutation == offspring[mutation_index]:
+            mutation = get_random_vegetable()
+
+        offspring[mutation_index] = mutation
+
+    offspring_score = score_field(offspring)
+
+    return [offspring_score, offspring]
+
+
+def get_random_vegetable():
+    vegetables = [x['Nazwa_warzywa'] for x in tractor_kb.query(pl.Expr("warzywo(Nazwa_warzywa)"))]
+
+    return vegetables[randint(0,len(vegetables)-1)]
+
+
+def genetic_algorithm(population, iterations):
+    population_size = len(population)
+
+    for entity in population:
+        entity[0] = score_field(entity[1])
+
+    for _ in range(iterations):
+        population.sort(key=lambda x: x[0], reverse=True)
+        population = population[:5]
+        new_offspring = []
+
+        while len(population) + len(new_offspring) < population_size:
+            mom, dad = choose_parents(population)
+            child = breed_and_mutate(mom, dad)
+            new_offspring.append(child)
+        
+        population.extend(new_offspring)
+
+    return population[0]
+
+
+vegetables = [x['Nazwa_warzywa'] for x in tractor_kb.query(pl.Expr("warzywo(Nazwa_warzywa)"))]
+# water tiles locations
+# _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
+# _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
+# _ _ X X _ _ _ _ _ _ X X X _ _ _
+# _ X X X _ _ _ _ _ X X X _ _ _ _
+# _ _ X _ _ _ _ _ _ _ _ _ _ _ _ _
+# _ _ X _ _ _ _ X X _ X X _ _ _ _
+# _ _ _ _ _ _ X X X X X X _ _ _ _
+# _ _ _ _ _ _ X X _ _ _ _ _ _ _ _
+# _ _ _ _ _ _ _ X X _ _ _ _ _ _ _
+# _ _ _ _ X _ _ _ _ _ _ _ _ _ _ _
+# _ _ _ _ X X _ _ _ _ _ _ _ _ _ _
+# _ _ _ _ X _ _ _ _ _ X _ _ _ _ _
+# _ _ _ _ _ _ _ _ X X X X _ _ _ _
+# _ _ _ X _ _ _ _ _ X X X _ _ _ _
+# _ X _ _ _ _ _ _ _ _ _ _ _ _ _ _
+# _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
+water_tile_indexes = [37, 38, 45, 46, 47, 53, 54, 55, 61, 62, 63, 71, 88, 93, 94, 96, 97, 109, 110, 111, 112, 113, 114, 126, 127, 144, 145, 158, 175, 176, 192, 198, 213, 214, 215, 216, 225, 231, 232, 233, 240]
+population = []
+
+for _ in range(10):
+    field = [vegetables[randint(0, 24)] for _ in range(256)]
+    for index in water_tile_indexes:
+        field[index] = "water"
+    # entities of the population are stored with two properties
+    # the first being the average score of the field
+    # and the second being the layout of the field 
+    population.append([0, field])
+
+best = genetic_algorithm(population, 10)
+print('final field layout', best[1])
+print('final field multiplier score', best[0])