def predict_dbd(target_day=date.today()):
names = ["f", "z", "other"]
wd = target_day.weekday()
target_monday = target_day - dt.timedelta(days=wd)
preds = []
for name in names:
#1 該当週の合計量の予測(by NNと相関、土曜日だけは確定値を算出)
if wd == 5:
col_name = name + "_ship"
dbcur.execute(
"SELECT SUM(" + col_name + ") FROM amounts WHERE "
"date>=%s AND date<=%s", (target_monday, target_day))
num_pred = dbcur.fetchone()[0]
if num_pred is None:
num_pred = 0
else:
num_pred = predict_week.pred_week(target_day, name)
#2 残り発送量の算出
col_name = name + "_ship"
dbcur.execute(
"SELECT SUM(" + col_name + ") FROM amounts WHERE "
"date>=%s AND date<=%s", (target_monday, target_day))
num = dbcur.fetchone()[0]
if num is None:
num = 0
pred = [0 for col in range(wd + 1)]
pred.extend(func.weeksum_day(target_day, num_pred - num, name))
pred.append(0)
preds.append(pred)
print(preds[-1])
#全種sum(今日の発送量とpredsの発送量の合計 - 作り置き量)
return preds
def predict_n(target_day=date.today()):
names = ["f", "z", "other"]
wd = target_day.weekday()
if wd >= 5:
target_monday = target_day + dt.timedelta(days=7 - wd)
else:
target_monday = target_day - dt.timedelta(days=wd)
next_pred = []
#NNの更新と予測値の取得
for name in names:
next_pred.append(
nn_model.online_train(target_monday, True, predict_week_num,
name)[2])
preds = [] #axis0:n週後、axis1:fzo、axis2:月～土
for i in range(predict_week_num):
pred = []
max_day = target_monday - dt.timedelta(days=1) + dt.timedelta(days=i *
7)
for j in range(3):
we = list(func.weeksum_day(max_day, next_pred[j][i], name))
we.append(0)
pred.append(we)
preds.append(pred)
#week_dataの更新
target_monday = target_day - dt.timedelta(days=wd)
weight_each = [0, target_monday]
names = ["f", "z", "other"]
for name in names:
for wd in range(6):
target_day_t = target_monday + dt.timedelta(days=wd)
col_name = name + "_ship"
dbcur.execute(
"SELECT SUM(" + col_name +
") FROM amounts WHERE date>=%s AND date<%s",
(target_monday, target_day_t))
wd_weight_1 = dbcur.fetchone()[0]
if wd_weight_1 is None:
wd_weight_1 = 0
col_name = name + "_morn"
dbcur.execute(
"SELECT SUM(" + col_name + ") FROM amounts WHERE date=%s",
(target_day_t, ))
wd_weight_2 = dbcur.fetchone()[0]
if wd_weight_2 is None:
wd_weight_2 = 0
weight_each.append(wd_weight_1 + wd_weight_2)
col_name = name + "_ship"
dbcur.execute(
"SELECT SUM(" + col_name +
") FROM amounts WHERE date>=%s AND date<%s",
(target_monday, target_monday + dt.timedelta(days=7)))
total_weight = dbcur.fetchone()[0]
if total_weight is None:
total_weight = 0
weight_each.append(total_weight)
dbcur.execute(
"INSERT INTO week_data VALUES(%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,"
"%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s)", weight_each)
return preds
def predict_n(target_day=date.today()):
names=["f","z","other"]
wd=target_day.weekday()
if wd>=5:
target_monday=target_day+dt.timedelta(days=7-wd)
else:
target_monday=target_day-dt.timedelta(days=wd)
next_pred=[]
#NNの更新と予測値の取得
for name in names:
next_pred.append(nn_model.online_train(target_monday, True, predict_week_num, name)[2])
preds=[]#axis0:n週後、axis1:fzo、axis2:月～土
for i in range(predict_week_num):
pred=[]
max_day=target_monday-dt.timedelta(days=1)+dt.timedelta(days=i*7)
for j in range(3):
we = list(func.weeksum_day(max_day, next_pred[j][i], name))
we.append(0)
pred.append(we)
preds.append(pred)
return preds