from random import randint, choices, random
from kb import tractor_kb, multi_sasiedzi
import pytholog as pl
from numpy.random import choice as npchoice


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])

        mod = multi_sasiedzi(field[index], neighbours)[0]["Mul"]
        if mod > 10:
            print(mod, '= multi(', field[index], ', ', neighbours, ')')
        score += mod

    score = score / 256
    return score


def choose_parents(population):
    total_weights = sum(entity[0] for entity in population)

    weights = [entity[0] / total_weights for entity in population]

    selection = npchoice(len(population), size=2, replace=False, p=weights)

    parents = [population[i] for i in selection]

    return parents[0], parents[1]


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 length 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)
    # print('offspring score', offspring_score, 'for parents', mom[0], 'and', dad[0])

    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 iteration in range(iterations):
        population.sort(key=lambda x: x[0], reverse=True)
        print('\n=====\n\n💪 Best individual in iteration', iteration, 'has a score of', population[0][0])
        population = population[:population_size//2]
        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]


population = []
# each field has unmutable locations of water and grass tiles
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]
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]
vegetables = [x['Nazwa_warzywa'] for x in tractor_kb.query(pl.Expr("warzywo(Nazwa_warzywa)"))]

for _ in range(100):
    field = [vegetables[randint(0, 24)] for _ in range(256)]
    for index in water_tile_indexes:
        field[index] = "water"
    for index in grass_tile_indexes:
        field[index] = "grass"

    # 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, 20)
print('\n=====\n\nfinal field multiplier score is', best[0])
with open('field', 'w', encoding='utf-8') as file:
    file.write(str(best[1]))
file.close
print('final field layout saved to file "field" in the current working directory\n')