212 lines
8.1 KiB
Python
212 lines
8.1 KiB
Python
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import copy
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import json
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import random
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from displayControler import NUM_X, NUM_Y
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# Definiowanie stałych dla roślin i plonów
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plants = ['corn', 'potato', 'tomato', 'carrot']
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initial_yields = {'corn': 38, 'potato': 40, 'tomato': 43, 'carrot': 45}
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yield_reduction = {
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'corn': {'corn': None, 'potato': 0, 'tomato': 0, 'carrot': 0},
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'potato': {'corn': None, 'potato': 0, 'tomato': 0, 'carrot': 0},
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'tomato': {'corn': 0, 'potato': 0, 'tomato': 0, 'carrot': None},
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'carrot': {'corn': 0, 'potato': 0, 'tomato': 0, 'carrot': 0}
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}
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yield_multiplier = {'corn': 1.25, 'potato': 1.19, 'tomato': 1.22, 'carrot': 1.13}
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# Generowanie listy 20x12 z losowo rozmieszczonymi roślinami
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def generate_garden(rows=20, cols=12):
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return [[random.choice(plants) for _ in range(cols)] for _ in range(rows)]
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# Funkcja do obliczania liczby plonów
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def calculate_yields(garden):
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rows = len(garden)
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cols = len(garden[0])
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total_yields = 0
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for i in range(rows):
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for j in range(cols):
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plant = garden[i][j]
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# Sprawdzanie sąsiadów
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neighbors = [
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(i - 1, j), (i + 1, j), (i, j - 1), (i, j + 1)
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]
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neighbor_flag = False
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for ni, nj in neighbors:
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if 0 <= ni < rows and 0 <= nj < cols:
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neighbor_plant = garden[ni][nj]
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if yield_reduction[plant][neighbor_plant] is None: # jeśli jest wartość None to plony dla tej rośliny będą wyzerowane
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neighbor_flag = True
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if not neighbor_flag:
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total_yields += 1
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return total_yields
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# Funkcja do generowania planszy/ogrodu i zapisywania go jako lista z liczbą plonów
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def generate_garden_with_yields(rows=NUM_Y, cols=NUM_X):
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garden = generate_garden(rows, cols)
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total_yields = calculate_yields(garden)
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return [garden, total_yields]
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# Funkcja do generowania linii cięcia i zapisywania jej jako liczba roślin w kolumnie z pierwszej planszy/ogrodu
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def line():
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path = []
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flag = False
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x = random.randint(4, 8)
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position = (0, x)
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path.append(position)
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while not flag: # wybór punktu dopóki nie wybierze się skrajnego
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# prawdopodobieństwo "ruchu" -> 0.6: w prawo, 0.2: w góre, 0.2: w dół
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p = [(position[0] + 1, position[1]), (position[0], position[1] + 1), (position[0], position[1] - 1)]
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w = [0.6, 0.2, 0.2]
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position2 = random.choices(p, w)[0]
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if position2 not in path: # sprawdzenie czy dany punkt nie był już wybrany aby nie zapętlać się
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path.append(position2)
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position = position2
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if position[0] == NUM_X or position[1] == 0 or position[1] == NUM_Y: # sprawdzenie czy osiągnięto skrajny punkt
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flag = True
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info = [] # przeformatowanie sposobu zapisu na liczbę roślin w kolumnie, które będzię się dzidziczyło z pierwszej planszy/ogrodu
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for i in range(len(path) - 1):
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if path[i + 1][0] - path[i][0] == 1:
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info.append(NUM_Y - path[i][1])
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if len(info) < NUM_X: # uzupełnienie informacji o dziedziczeniu z planszy/ogrodu
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if path[-1:][0][1] == 0:
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x = NUM_Y
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else:
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x = 0
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while len(info) < NUM_X:
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info.append(x)
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# return path, info
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return info
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# Funkcja do generowania potomstwa
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def divide_gardens(garden1, garden2):
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info = line()
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new_garden1 = [[] for _ in range(NUM_Y)]
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new_garden2 = [[] for _ in range(NUM_Y)]
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for i in range(NUM_X):
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for j in range(NUM_Y):
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# do utworzonych kolumn w nowych planszach/ogrodach dodajemy dziedziczone rośliny
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if j < info[i]:
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new_garden1[j].append(garden1[j][i])
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new_garden2[j].append(garden2[j][i])
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else:
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new_garden1[j].append(garden2[j][i])
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new_garden2[j].append(garden1[j][i])
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return [new_garden1, calculate_yields(new_garden1)], [new_garden2, calculate_yields(new_garden2)]
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# Funkcja do mutacji danej planszy/ogrodu
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def mutation(garden, not_used):
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new_garden = copy.deepcopy(garden)
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for i in range(NUM_X):
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x = random.randint(0, 11) # wybieramy, w którym wierszu w i-tej kolumnie zmieniamy roślinę na inną
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other_plants = [plant for plant in plants if plant != new_garden[x][i]]
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new_garden[x][i] = random.choice(other_plants)
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return [new_garden, calculate_yields(new_garden)]
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# Funkcja do generowania pierwszego pokolenia
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def generate(n):
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generation = []
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for i in range(n * 3):
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generation.append(generate_garden_with_yields())
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generation.sort(reverse=True, key=lambda x: x[1])
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return generation[:n]
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# Funkcja do implementacji ruletki (sposobu wyboru) - sumuje wszystkie plony generacji
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def sum_yields(x):
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s = 0
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for i in range(len(x)):
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s += x[i][1]
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return s
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if __name__ == '__main__':
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roulette = True
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attemps = 1
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population = 120
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best = []
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iter = 0
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for a in range(attemps):
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generation = generate(population)
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print(generation[0][1])
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while generation[0][1] != NUM_X*NUM_Y: # ile iteracji - nowych pokoleń
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iter += 1
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print(iter)
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print(generation[0][1])
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new_generation = generation[:(population // 7)] # dziedziczenie x najlepszych osobników
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j = 0
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while j < (
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population - (
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population // 7)): # dobór reszty osobników do pełnej liczby populacji danego pokolenia
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if roulette: # zasada ruletki -> "2 rzuty kulką"
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s = sum_yields(generation) # suma wszystkich plnów całego pokolenia
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z = []
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if s == 0: # wtedy każdy osobnik ma takie same szanse
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z.append(random.randint(0, population - 1))
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z.append(random.randint(0, population - 1))
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else:
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weights = [] # wagi prawdopodobieństwa dla każdego osobnika generacji
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pos = [] # numery od 0 do 49 odpowiadające numerom osobnikom w generacji
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for i in range(population):
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weights.append(generation[i][1] / s)
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pos.append(i)
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z.append(random.choices(pos, weights)[0]) # wybranie osobnika według wag prawdopodobieństwa
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z.append(random.choices(pos, weights)[0]) # wybranie osobnika według wag prawdopodobieństwa
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else: # metoda rankingu
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z = random.sample(range(0, int(population // 1.7)), 2)
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# krzyzowanie 90% szans, mutacja 10% szans
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function = [divide_gardens, mutation]
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weight = [0.9, 0.1]
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fun = random.choices(function, weight)[0]
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h = fun(generation[z[0]][0], generation[z[1]][0])
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if len(h[0]) == 2:
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new_generation.append(h[0])
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new_generation.append(h[1])
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j += 2
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else:
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new_generation.append(h)
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j += 1
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new_generation.sort(reverse=True, key=lambda x: x[1]) # sortowanie malejąco listy według wartości plonów
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generation = new_generation[:population]
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best.append(generation[0])
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best.sort(reverse=True, key=lambda x: x[1])
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# Zapis do pliku
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# for i in range(len(best)):
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# print(best[i][1], calculate_yields(best[i][0]))
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#
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#
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# with open(f'pole3_pop{population}_{iter}_{roulette}.json', 'w') as file: # zapis planszy/ogrodu do pliku json
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# json.dump(best[0][0], file, indent=4)
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#
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# print("Dane zapisane do pliku")
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#
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# Odczyt z pliku
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# with open(f'pole3_pop{population}_{iter}_{roulette}.json', 'r') as file:
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# garden_data = json.load(file)
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#
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# print("Odczytane dane ogrodu:")
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# for row in garden_data:
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# print(row)
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#
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# print(calculate_yields(garden_data))
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# if best[0][0] == garden_data:
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# print("POPRAWNE: ", calculate_yields(garden_data), calculate_yields(best[0][0]))
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