# Lotto prediction with Genetic Algorithm and Mersenne Twister (MT19937)
# Go Namhyeon <gnh1201@gmail.com>
# first created: 2021-04-04
# last updated: 2021-12-31
# download excel data: https://dhlottery.co.kr/gameResult.do?method=byWin

import math
import random
import pandas as pd
import numpy as np
from geneticalgorithm2 import geneticalgorithm2 as ga

cols = [1, 13, 14, 15, 16, 17, 18, 19]  # included bonus number
df = pd.read_excel('excel.xlsx', skiprows=2, usecols=cols, names=[0, 1, 2, 3, 4, 5, 6, 7])
rows = df.values[:100]

def make_num(x, n, a, b, c, d, e, min, max):
    seed = int(x*a + n*b + c*d + e)
    rs = np.random.RandomState(np.random.MT19937())
    rs.seed(seed)
    return min + math.floor((max - min) * rs.random())

def f(X):
    score = 0

    for row in rows:
        x  = row[0]
        N = row[1:]
        
        _N = []
        _num = 0
        k = 9
        for n in range(1, 8):  # included bonus number
            _num = make_num(x, n, X[0], X[1], X[2], _num, X[3], 1, 45)
            while _num in _N:
                _num = make_num(x, k, X[0], X[1], X[2], _num, X[3], 1, 45)
                k += 1
            _N.append(_num)
        #_N = [make_num(x, n, X[0], X[1], X[2], 1, 45) for n in range(1, 8)]  # included bonus number
        result = len(list(set(N) & set(_N)))
        if result > 5:
            score += 100
        elif result > 4:
            score += 75
        elif result > 3:
            score += 50
        elif result > 2:
            score += 25

    return -score

varbound = np.array([[0, 10000]]*4)

model = ga(function=f, dimension=4, variable_type='int', variable_boundaries=varbound, algorithm_parameters={
    'max_num_iteration': 255
})
model.run()

solution = model.output_dict

print (solution)

_variables = model.output_dict['last_generation']['variables']
variable = solution['variable']
variables = random.sample([list(item) for item in set(tuple(x) for x in _variables)], 9)

_x = max([row[0] for row in rows]) + 1  # get highest number

print()
print ('Best matched numbers (' + str(_x) + 'th):')
#nums = sorted([make_num(_x, n, variable[0], variable[1], variable[2], 1, 45) for n in range(1, 7)])  # excluded bonus number
nums = []
_num = 0
_k = 8
for n in range(1, 7):  # excluded bonus number
    _num = make_num(_x, n, variable[0], variable[1], variable[2], _num, variable[3], 1, 45)
    while _num in nums:
        _num = make_num(_x, _k, variable[0], variable[1], variable[2], _num, variable[3], 1, 45)
        _k += 1
    nums.append(_num)
print(', '.join(str(x) for x in nums))

print()
print ('Recommended numbers (' + str(_x) + 'th):')
for variable in variables:
    #nums = sorted([make_num(_x, n, variable[0], variable[1], variable[2], 1, 45) for n in range(1, 7)])  # excluded bonus number
    nums = []
    _num = 0
    _k = 8
    for n in range(1, 7):  # excluded bonus number
        _num = make_num(_x, n, variable[0], variable[1], variable[2], _num, variable[3], 1, 45)
        while _num in nums:
            _num = make_num(_x, _k, variable[0], variable[1], variable[2], _num, variable[3], 1, 45)
            _k += 1
        nums.append(_num)
    print(', '.join(str(x) for x in nums))