fyr91

from PIL import Image, ExifTags
import webbrowser

def converter(value):
	d0 = value[0][0]
	d1 = value[0][1]
	d = float(d0) / float(d1)
	m0 = value[1][0]
	m1 = value[1][1]
	m = float(m0) / float(m1)

fyr91

# dummy battle class
class Battle:
    def __init__(self, lineup_a, lineup_b, result=None):
        self.lineup_a = lineup_a
        self.lineup_b = lineup_b
        self.result = result

# fight - compare number of 1s in the gene
def fight(pair):
    # save battle info for evaluation

fyr91

if __name__ == "__main__":
    lineups = []
    generation_size = 20

    # initialize 20 lineups with initial score 50
    for i in range(generation_size):
        lineups.append(Lineup(50))

    # form battle pairs
    pairs = np.random.choice(lineups, size=(int(generation_size/2),2), replace=False)

fyr91

{'name': 'wimpy-indigo-falcon', 'gene': '00011010101110011111', 'combat_power': 12, 'score': 51}
{'name': 'surly-chestnut-dachsbracke', 'gene': '10111011100101100101', 'combat_power': 12, 'score': 51}
{'name': 'silly-blue-gar', 'gene': '11010010110011111011', 'combat_power': 13, 'score': 51}
{'name': 'gummy-magenta-sloth', 'gene': '10101000110110010110', 'combat_power': 10, 'score': 51}
{'name': 'bluesy-gamboge-iguana', 'gene': '01010101111011101110', 'combat_power': 13, 'score': 51}
{'name': 'pokey-azure-zebra', 'gene': '10000110011101011110', 'combat_power': 11, 'score': 51}
{'name': 'stinky-asparagus-affenpinscher', 'gene': '00011101110011110001', 'combat_power': 11, 'score': 51}
{'name': 'scummy-sepia-shrew', 'gene': '01101100001010101011', 'combat_power': 10, 'score': 51}
{'name': 'freaky-taupe-shark', 'gene': '11010010111110110111', 'combat_power': 14, 'score': 51}
{'name': 'queasy-plum-ladybird', 'gene': '01000111100001100110', 'combat_power': 9, 'score': 51}

fyr91

def select(lineups, elite_rate=0.1):
    num_elite = int(len(lineups)*0.1)
    
    # sort lineups according to score
    scores = [lineup.score for lineup in lineups]
    order = np.argsort(scores)
    sorted_lineups = [lineups[i] for i in order]
    
    # get 10% elite and 80% mutation pool
    elite = sorted_lineups[len(lineups)-num_elite:]

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    # continue from run_ga_1b.py 
    elite, mutation_pool = select(lineups)

fyr91

import numpy as np
import namegenerator
from copy import deepcopy

# dummy lineup class
class Lineup:
    def __init__(self, score, gene=''):
        # name only used to differentiate the lineup
        self.name = namegenerator.gen()
        if len(gene):

fyr91

[{'name': 'leaky-rose-tarantula',
  'gene': '11111110010100111001',
  'combat_power': 13,
  'score': 50},
 {'name': 'cloudy-aquamarine-malamute',
  'gene': '01111110111111100010',
  'combat_power': 14,
  'score': 50},
 {'name': 'sloppy-thistle-lobster',
  'gene': '10000101110100011101',

fyr91

# check breedable
def breedable(parents):
    # add two gene sequences
    # if two gene at the same index is different
    # the sum will be 1
    # if more than 1 position is different
    # then they are breedable
    x = parents[0].gene + parents[1].gene
    if np.count_nonzero(x == 1) >= 2:
        return True

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    population = []
    offsprings = []
    
    # when offspring not enough for 10% of the population
    # breed
    while len(offsprings) < len(elite):
        # select parent pairs for breeding
        parents_pairs = np.random.choice(elite, size=(int(len(elite)/2),2), replace=False)
        for parents in parents_pairs:
            # pre-pregancy checkup