def main():
parser = argparse.ArgumentParser(description='Chainer example: MNIST')
parser.add_argument('--gpu', '-g', type=int, default=-1,
help='GPU ID (negative value indicates CPU)')
parser.add_argument('--image', '-i', type=str, default="",
help='pass to input image')
parser.add_argument('--model', '-m', default='my_mnist.model',
help='path to the training model')
parser.add_argument('--unit', '-u', type=int, default=1000,
help='Number of units')
args = parser.parse_args()
model = MLP(args.unit,10)
if args.gpu >= 0:
model.to_gpu(chainer.cuda.get_device_from_id(args.gpu).use())
serializers.load_npz(args.model, model)
try:
img = Image.open(args.image).convert("L").resize((28,28))
except :
print("invalid input")
return
img_array = model.xp.asarray(img,dtype=model.xp.float32).reshape(1,784)
with chainer.using_config('train', False), chainer.no_backprop_mode():
result = model.predict(img_array)
print("predict:", model.xp.argmax(result.data))
def main():
parser = argparse.ArgumentParser(description='Chainer example: MNIST')
parser.add_argument('--gpu',
'-g',
type=int,
default=-1,
help='GPU ID (negative value indicates CPU)')
parser.add_argument('--image',
'-i',
type=str,
default="",
help='pass to input image')
parser.add_argument('--model',
'-m',
default='my_mnist.model',
help='path to the training model')
parser.add_argument('--unit',
'-u',
type=int,
default=1000,
help='Number of units')
args = parser.parse_args()
model = MLP(args.unit, 1)
if args.gpu >= 0:
model.to_gpu(chainer.cuda.get_device_from_id(args.gpu).use())
serializers.load_npz(args.model, model)
# try:
# img = Image.open(args.image).convert("L").resize((28,28))
# except :
# print("invalid input")
# return
# img_array = model.xp.asarray(img,dtype=model.xp.float32).reshape(1,784)
## df = pd.read_csv('test1.csv')
db = sqlite3.connect('race.db')
c = db.cursor()
win = []
lose = []
quinella = []
place = []
none_flag = 0
for race_id in range(27606, 34440):
# for race_id, sdf in df.groupby('race_id'):
if (race_id % 100 == 1):
print("finished ", race_id - 1)
df = pd.read_sql("select horse_number,age,winrate,eps,odds,weight,preOOF,pre2OOF,preLastPhase, "\
"payoff_quinella,payoff_place, race_id "\
"from (select "\
"inputdata.race_id, "\
"inputdata.order_of_finish, "\
"inputdata.horse_number horse_number, "\
"age, "\
"case when enterTimes != 0 then winRun/enterTimes else 0 end as winrate, "\
"eps, "\
"odds, "\
"weight, "\
"preOOF, "\
"pre2OOF, "\
"preLastPhase, "\
"pay1.payoff payoff_quinella, "\
"pay2.payoff payoff_place "\
"from inputdata "\
"inner join payoff pay1 "\
" on pay1.ticket_type = 3 and pay1.race_id = inputdata.race_id "\
"left join payoff pay2"\
" on pay2.ticket_type = 1"\
" and pay2.horse_number = inputdata.horse_number"\
" and pay2.race_id = inputdata.race_id"\
") as a "\
"where a.race_id = "+str(race_id)+" "\
"order by a.race_id,order_of_finish;", db)
# img_array=df.values.reshape(1,-1)[~np.isnan(df.values.reshape(1,-1))]
# img_array = model.xp.asarray(img_array, dtype=model.xp.float32).reshape(1,-1)
arr = df.values
#あいてるid用
# if(len(arr)==0):
# continue
for i in range(len(arr)):
if ((isinstance(arr[i][6], int)
or isinstance(arr[i][6], float)) == False):
arr[i][6] = 18
if ((isinstance(arr[i][7], int)
or isinstance(arr[i][7], float)) == False):
arr[i][7] = 18
arr = np.array(arr, dtype=float)
#None処理
for i in range(len(arr)):
if (np.isnan(arr[i][10])):
arr[i][10] = 0
copy_arr = arr
winner = arr[0][0]
second = arr[1][0]
winner_odds = arr[0][4]
quinella_odds = arr[0][9]
#none,nanがあるならとばす。
for i in range(len(arr)):
for j in range(len(arr[0])):
if arr[i][j] is None:
none_flag = 1
elif (math.isnan(float(arr[i][j]))):
none_flag = 1
if (none_flag):
none_flag = 0
continue
arr = arr.astype(np.float32)
arr = scipy.stats.zscore(arr)
#分散0の処理
arr[np.isnan(arr)] = 0
res = []
for i in range(len(arr)):
img_array = arr[i][0:9]
img_array = model.xp.asarray(img_array,
dtype=model.xp.float32).reshape(
1, -1)
with chainer.using_config('train',
False), chainer.no_backprop_mode():
result = model.predict(img_array)
res.append(result.data[0])
# print("predict:", model.xp.argmax(result.data))
# arg_sorted = model.xp.argsort(result.data)
# arg_sorted = arg_sorted [:, ::-1]
# print(arg_sorted[:, :3])
x = np.array(res).reshape((1, -1))[0]
# 一着がほかより抜けている時のみ買う
if ((x[np.argsort(x)[1]] - x[np.argsort(x)[0]]) < 0.001):
continue
# for i in range(len(x)):
# print (np.argsort(x)[i]+1,"-", x[np.argsort(x)[i]])
# 一二着がほかより抜けている時のみ買う
# if ((x[np.argsort(x)[2]] - x[np.argsort(x)[1]]) < 0.001):
# continue
# 狙うオッズが微妙ならとばす。
continue_flag = 0
for j in range(len(copy_arr)):
if (copy_arr[j][0] == np.argsort(x)[0] + 1):
if (copy_arr[j][4] >= 50 or copy_arr[j][4] < 2):
continue_flag = 1
if (continue_flag == 1):
continue
if (np.argsort(x)[0] + 1 == winner):
win.append(winner_odds)
# print(race_id,np.argsort(x)[0]+1,winner_odds)
else:
win.append(0)
for j in range(len(copy_arr)):
if (copy_arr[j][0] == np.argsort(x)[0] + 1):
lose.append(copy_arr[j][4])
if (((np.argsort(x)[0] + 1 == winner) and
(np.argsort(x)[1] + 1 == second))
or ((np.argsort(x)[0] + 1 == second) and
(np.argsort(x)[1] + 1 == winner))):
quinella.append(quinella_odds)
else:
quinella.append(0)
for i in range(len(arr)):
if (np.argsort(x)[0] + 1 == copy_arr[i][0]):
place.append(copy_arr[i][10])
print(win)
print(lose)
print(place)
print(quinella)
print("単勝")
print("回収率 = ",
sum(win) / len(win) * 100, " 的中率 = ",
(1 - win.count(0) / len(win)) * 100)
print("\n複勝")
print("回収率 = ",
sum(place) / len(place), " 的中率 = ",
(1 - place.count(0) / len(place)) * 100)
print("\n馬連")
print("回収率 = ",
sum(quinella) / len(quinella), " 的中率 = ",
(1 - quinella.count(0) / len(quinella)) * 100)
#clf2 = LogisticRegressionBin(optimize='fmin_bfgs')
clf3 = MLP(optimize='fmin_bfgs')
# X = [[0],[2]]
# y = [0,1]
# params = [0,1]
#
# X = np.asarray(X)
# y = np.asarray(y)
# params = np.asarray(params)
clf1.fit(X, y)
# X = np.asarray([[3, 5],[3,5],[3,5]])
# params = np.asarray([2, 1])
# y = np.asarray([1,1,1])
clf2.fit(X, y)
clf3.fit(X, y)
res1 = clf1.predict(tX)
res2 = clf2.predict(tX)
res3 = clf3.predict(tX)
print accuracy_score(ty, res1)
print accuracy_score(ty, res2)
print accuracy_score(ty, res3)
print '------'