117 lines
4.0 KiB
Python
117 lines
4.0 KiB
Python
from random import randint, choices, random
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from kb import tractor_kb, multi_sasiedzi
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import pytholog as pl
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from numpy.random import choice as npchoice
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def score_field(field):
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score = 0
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for index in range(len(field)):
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neighbours = []
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if index >= 16 and field[index-16] != 'water':
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neighbours.append(field[index-16])
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if index % 15 != 0 and field[index+1] != 'water':
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neighbours.append(field[index+1])
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if index < 240 and field[index+16] != 'water':
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neighbours.append(field[index+16])
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if index % 16 != 0 and field[index-1] != 'water':
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neighbours.append(field[index-1])
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mod = multi_sasiedzi(field[index], neighbours)[0]["Mul"]
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if mod > 10:
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print(mod, '= multi(', field[index], ', ', neighbours, ')')
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score += mod
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score = score / 256
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return score
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def choose_parents(population):
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total_weights = sum(entity[0] for entity in population)
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weights = [entity[0] / total_weights for entity in population]
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selection = npchoice(len(population), size=2, replace=False, p=weights)
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parents = [population[i] for i in selection]
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return parents[0], parents[1]
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def breed_and_mutate(mom, dad):
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crossover_point = randint(1, len(mom[1]) - 2)
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offspring = mom[1][:crossover_point] + dad[1][crossover_point:]
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if len(offspring) != len(mom):
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ValueError("offspring length is not equal to mom length")
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if random() < 0.1:
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mutation_index = randint(0, len(offspring) - 1)
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while offspring[mutation_index] == 'water':
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mutation_index = randint(0, len(offspring) - 1)
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mutation = get_random_vegetable()
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while mutation == offspring[mutation_index]:
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mutation = get_random_vegetable()
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offspring[mutation_index] = mutation
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offspring_score = score_field(offspring)
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# print('offspring score', offspring_score, 'for parents', mom[0], 'and', dad[0])
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return [offspring_score, offspring]
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def get_random_vegetable():
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vegetables = [x['Nazwa_warzywa'] for x in tractor_kb.query(pl.Expr("warzywo(Nazwa_warzywa)"))]
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return vegetables[randint(0,len(vegetables)-1)]
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def genetic_algorithm(population, iterations):
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population_size = len(population)
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for entity in population:
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entity[0] = score_field(entity[1])
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for iteration in range(iterations):
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population.sort(key=lambda x: x[0], reverse=True)
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print('\n=====\n\n💪 Best individual in iteration', iteration, 'has a score of', population[0][0])
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population = population[:population_size//2]
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new_offspring = []
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while len(population) + len(new_offspring) < population_size:
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mom, dad = choose_parents(population)
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child = breed_and_mutate(mom, dad)
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new_offspring.append(child)
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population.extend(new_offspring)
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return population[0]
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population = []
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# each field has unmutable locations of water and grass tiles
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water_tile_indexes = [1, 2, 3, 34, 37, 44, 45, 53, 60, 61, 69, 81, 82, 83, 84, 119, 120, 121, 136, 152, 187, 194, 202, 203, 204, 210, 219, 226, 227, 228]
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grass_tile_indexes = [0, 39, 40, 56, 71, 72, 73, 86, 88, 114, 115, 130, 146, 147, 163, 164, 166, 167, 180, 181, 182, 231, 232, 233]
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vegetables = [x['Nazwa_warzywa'] for x in tractor_kb.query(pl.Expr("warzywo(Nazwa_warzywa)"))]
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for _ in range(100):
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field = [vegetables[randint(0, 24)] for _ in range(256)]
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for index in water_tile_indexes:
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field[index] = "water"
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for index in grass_tile_indexes:
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field[index] = "grass"
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# entities of the population are stored with two properties
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# the first being the average score of the field
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# and the second being the layout of the field
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population.append([0, field])
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best = genetic_algorithm(population, 20)
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print('\n=====\n\nfinal field multiplier score is', best[0])
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with open('field', 'w', encoding='utf-8') as file:
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file.write(str(best[1]))
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file.close
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print('final field layout saved to file "field" in the current working directory\n')
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