Zmieniono A*, kosmetyka
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@ -1,5 +1,5 @@
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MP--H---------------------
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RRRRRRRRRRRRRRRRRRR---G---
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RORRRRRRRRRRRRRRRRR---G---
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SZ--G------G------R---GGG-
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----G------G------R---G---
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-RRRG------GGGGGGGRRRRR---
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@ -10,5 +10,5 @@ SZ--G------G------R---GGG-
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-R--G-------H-----R-------
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-R--G-----G----G--R-------
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-R--G-----G----G--R-------
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-RRRRRRRRRRRRRRRRRRRORRR--
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-RRRRRRRRRRRRRRRRRRRRRRR--
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--------------------------
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@ -272,10 +272,11 @@ class Agent:
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house_list = state[1]
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successors = []
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for pos in successors_pos:
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if pos in house_list:
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house_list = list(house_list)
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house_list.remove(pos)
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house_list = tuple(house_list)
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if len(house_list) > 0:
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if house_list[0] == pos:
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house_list = list(house_list)
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house_list.pop(0)
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house_list = tuple(house_list)
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successors.append((pos, tuple(house_list)))
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@ -3,14 +3,6 @@ from sklearn import tree
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from sklearn.tree import DecisionTreeClassifier
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def encode_category(category, categories):
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encoded = pd.get_dummies(categories).astype(int)
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if category in encoded.columns:
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return encoded[category].values.tolist()
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else:
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return [0] * len(encoded.columns)
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data = pd.read_csv('tree_data.csv')
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data_encoded = pd.get_dummies(data, columns=['trash_types', 'season', 'day'])
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@ -1,20 +1,17 @@
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import random
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# Parametry algorytmu genetycznego
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POPULATION_SIZE = 100
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MUTATION_RATE = 0.01
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NUM_GENERATIONS = 100
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# Generowanie początkowej populacji
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def generate_individual(houses):
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return random.sample(houses, len(houses))
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def generate_population(houses, size):
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return [generate_individual(houses) for _ in range(size)]
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# Obliczanie odległości między domkami
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def calculate_distance(house1, house2):
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x1, y1 = house1
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x2, y2 = house2
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@ -27,7 +24,6 @@ def calculate_total_distance(houses):
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total_distance += calculate_distance(houses[-1], houses[0]) # Zamknięcie cyklu
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return total_distance
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# Selekcja rodziców za pomocą turniejowej metody
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def select_parents(population, num_parents):
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parents = []
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for _ in range(num_parents):
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@ -36,7 +32,6 @@ def select_parents(population, num_parents):
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parents.append(winner)
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return parents
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# Krzyżowanie rodziców
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def crossover(parent1, parent2):
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child1 = [None] * len(parent1)
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child2 = [None] * len(parent1)
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@ -45,7 +40,6 @@ def crossover(parent1, parent2):
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child1[start_index:end_index+1] = parent1[start_index:end_index+1]
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child2[start_index:end_index+1] = parent2[start_index:end_index+1]
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# Uzupełnienie brakujących domków z drugiego rodzica
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for i in range(len(parent1)):
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if parent2[i] not in child1:
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for j in range(len(parent2)):
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@ -62,23 +56,18 @@ def crossover(parent1, parent2):
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return child1, child2
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# Mutacja: zamiana dwóch losowych domków
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def mutate(individual):
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index1, index2 = random.sample(range(len(individual)), 2)
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individual[index1], individual[index2] = individual[index2], individual[index1]
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# Algorytm genetyczny
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def genetic_algorithm(houses):
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population = generate_population(houses, POPULATION_SIZE)
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for _ in range(NUM_GENERATIONS):
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# Obliczanie wartości przystosowania (odległości) dla każdego osobnika
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fitness_scores = [calculate_total_distance(individual) for individual in population]
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# Wybór rodziców do reprodukcji
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parents = select_parents(population, 2)
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# Tworzenie nowej populacji za pomocą krzyżowania i mutacji
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new_population = []
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for i in range(POPULATION_SIZE // 2):
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child1, child2 = crossover(parents[0], parents[1])
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@ -88,7 +77,6 @@ def genetic_algorithm(houses):
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population = new_population
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# Znalezienie najlepszego osobnika
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best_individual = min(population, key=calculate_total_distance)
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return best_individual
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@ -1,26 +1,26 @@
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|--- dump_fullness <= 0.50
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| |--- truck_fullness <= 0.50
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| | |--- trash <= 0.50
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|--- payment <= 0.50
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| |--- class: 0
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|--- payment > 0.50
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| |--- trash <= 0.50
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| | |--- class: 0
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| |--- trash > 0.50
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| | |--- bin_fullness <= 0.50
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| | | |--- class: 0
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| | |--- trash > 0.50
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| | | |--- payment <= 0.50
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| | | | |--- class: 0
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| | | |--- payment > 0.50
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| | | | |--- bin_fullness <= 0.50
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| | | | | |--- class: 0
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| | | | |--- bin_fullness > 0.50
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| | |--- bin_fullness > 0.50
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| | | |--- truck_fullness <= 0.50
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| | | | |--- dump_fullness <= 0.50
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| | | | | |--- trash_types_mixed <= 0.50
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| | | | | | |--- season_summer <= 0.50
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| | | | | | | |--- trash_types_plastic <= 0.50
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| | | | | | | |--- day_monday <= 0.50
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| | | | | | | | |--- class: 1
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| | | | | | | |--- trash_types_plastic > 0.50
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| | | | | | | |--- day_monday > 0.50
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| | | | | | | | |--- season_winter <= 0.50
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| | | | | | | | | |--- class: 1
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| | | | | | | | |--- season_winter > 0.50
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| | | | | | | | | |--- day_wednesday <= 0.50
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| | | | | | | | | | |--- class: 0
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| | | | | | | | | |--- day_wednesday > 0.50
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| | | | | | | | | |--- trash_types_plastic <= 0.50
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| | | | | | | | | | |--- class: 1
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| | | | | | | | | |--- trash_types_plastic > 0.50
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| | | | | | | | | | |--- class: 0
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| | | | | | |--- season_summer > 0.50
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| | | | | | | |--- day_wednesday <= 0.50
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| | | | | | | | |--- class: 1
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@ -32,21 +32,21 @@
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| | | | | |--- trash_types_mixed > 0.50
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| | | | | | |--- day_wednesday <= 0.50
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| | | | | | | |--- season_winter <= 0.50
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| | | | | | | | |--- day_monday <= 0.50
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| | | | | | | | | |--- season_spring <= 0.50
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| | | | | | | | |--- season_spring <= 0.50
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| | | | | | | | | |--- day_friday <= 0.50
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| | | | | | | | | | |--- class: 1
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| | | | | | | | | |--- day_friday > 0.50
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| | | | | | | | | | |--- class: 0
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| | | | | | | | | |--- season_spring > 0.50
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| | | | | | | | |--- season_spring > 0.50
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| | | | | | | | | |--- day_monday <= 0.50
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| | | | | | | | | | |--- class: 1
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| | | | | | | | |--- day_monday > 0.50
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| | | | | | | | | |--- season_spring <= 0.50
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| | | | | | | | | | |--- class: 1
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| | | | | | | | | |--- season_spring > 0.50
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| | | | | | | | | |--- day_monday > 0.50
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| | | | | | | | | | |--- class: 0
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| | | | | | | |--- season_winter > 0.50
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| | | | | | | | |--- class: 0
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| | | | | | |--- day_wednesday > 0.50
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| | | | | | | |--- class: 1
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| |--- truck_fullness > 0.50
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| | |--- class: 0
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|--- dump_fullness > 0.50
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| |--- class: 0
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| | | | |--- dump_fullness > 0.50
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| | | | | |--- class: 0
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| | | |--- truck_fullness > 0.50
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| | | | |--- class: 0
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