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Update main.py

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Namhyeon Go 2021-12-23 16:52:34 +09:00 committed by GitHub
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main.py
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@ -1,6 +1,7 @@
# Lotto prediction with Genetic Algorithm and Mersenne Twister (MT19937) # Lotto prediction with Genetic Algorithm and Mersenne Twister (MT19937)
# Go Namhyeon <gnh1201@gmail.com> # Go Namhyeon <gnh1201@gmail.com>
# 2021-04-04 # first created: 2021-04-04
# last updated: 2021-12-23
# download excel data: https://dhlottery.co.kr/gameResult.do?method=byWin # download excel data: https://dhlottery.co.kr/gameResult.do?method=byWin
import pandas as pd import pandas as pd
@ -11,8 +12,8 @@ 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]) df = pd.read_excel('excel.xlsx', skiprows=2, usecols=cols, names=[0, 1, 2, 3, 4, 5, 6, 7])
rows = df.values[:100] rows = df.values[:100]
def make_num(x, n, a, b, c, min, max): def make_num(x, n, a, b, c, d, e, min, max):
seed = int(x*a + n*b + c) seed = int(x*a + n*b + c*d + e)
rs = np.random.RandomState(np.random.MT19937()) rs = np.random.RandomState(np.random.MT19937())
rs.seed(seed) rs.seed(seed)
return min + round((max - min + 1) * rs.random()) return min + round((max - min + 1) * rs.random())
@ -23,7 +24,13 @@ def f(X):
for row in rows: for row in rows:
x = row[0] x = row[0]
N = row[1:] N = row[1:]
_N = [make_num(x, n, X[0], X[1], X[2], 1, 45) for n in range(1, 8)] # included bonus number
_N = []
_num = 0
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)
_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))) result = len(list(set(N) & set(_N)))
if result > 5: if result > 5:
score += 100 score += 100
@ -36,9 +43,9 @@ def f(X):
return -score return -score
varbound = np.array([[0, 10000]]*3) varbound = np.array([[0, 10000]]*4)
model = ga(function=f, dimension=3, variable_type='int', variable_boundaries=varbound, algorithm_parameters={ model = ga(function=f, dimension=4, variable_type='int', variable_boundaries=varbound, algorithm_parameters={
'max_num_iteration': 255 'max_num_iteration': 255
}) })
model.run() model.run()