def preprocess_pystarma_model_with_source_maker(self, source_maker):
source = source_maker.all_data
self._preprocessing(self.ts_matrix.as_matrix(), self.wa_matrices,
self.max_t_lag, self.sample_size)
# make time serie stationar
diff = (1, 288)
self.ts_matrix = np.log1p(self.ts_matrix)
self.ts_matrix = set_stationary(self.ts_matrix, diff)
# re-run preprocessing
self._preprocessing(self.ts_matrix.as_matrix(), self.wa_matrices,
self.max_t_lag, self.sample_size)
self._model_identification(self.ts_matrix,
self.wa_matrices,
max_lag=25)
def queren(self):
time_field = self.comboBox.currentText()
lon_field = self.comboBox_2.currentText()
lat_field = self.comboBox_3.currentText()
val_field = self.comboBox_4.currentText()
location_field = self.comboBox_5.currentText()
l_val = int(self.textEdit_3.toPlainText())
m_val = int(self.textEdit_4.toPlainText())
p_val = int(self.textEdit_5.toPlainText())
q_val = int(self.textEdit_6.toPlainText())
K_val = int(self.textEdit.toPlainText())
diff_val = int(self.textEdit_7.toPlainText())
pre_val = int(self.textEdit_8.toPlainText())
db = pymysql.connect("localhost",
"root",
"960609",
"biye",
charset='utf8')
# 使用cursor()方法获取操作游标
cursor = db.cursor()
#获取地点字段
location_sql = 'select distinct ' + location_field + ' from AOI order by ' + location_field + ' asc'
cursor.execute(location_sql)
rows = cursor.fetchall()
location_val = []
for (row, ) in rows:
loca = row
location_val.append(loca)
#获取时间字段
time_sql = 'select distinct ' + time_field + ' from AOI'
cursor.execute(time_sql)
rows = cursor.fetchall()
datetime_val = []
for (row, ) in rows:
dt = str(row)
datetime_val.append(dt)
data = []
diff = [diff_val] #差分次数i
#拼接时空数据
for i in range(len(datetime_val)):
locaval_sql = 'select ' + val_field + ' from AOI where ' + time_field + ' = \'' + datetime_val[
i] + '\' order by ' + location_field + ' asc'
cursor.execute(locaval_sql)
rows = cursor.fetchall()
lval = []
for (row, ) in rows:
val = row
lval.append(val)
data.append(lval)
ts = np.array(data)
#计算差分
ts_diff = utils.set_stationary(ts, diff)
#获取点的经纬度坐标
# 点的经度
lon_sql = 'select distinct ' + lon_field + ' from AOI order by ' + location_field + ' asc'
cursor.execute(lon_sql)
rows = cursor.fetchall()
x_val = []
for (row, ) in rows:
xval = row
x_val.append(xval)
# 点的纬度
lat_sql = 'select distinct ' + lat_field + ' from AOI order by ' + location_field + ' asc'
cursor.execute(lat_sql)
rows = cursor.fetchall()
y_val = []
for (row, ) in rows:
yval = row
y_val.append(yval)
#拼接点
point = []
for i in range(len(x_val)):
coor = []
coor.append(x_val[i])
coor.append(y_val[i])
coortuple = tuple(coor)
point.append(coortuple)
#计算权重
points = np.array(point)
kd = pysal.cg.kdtree.KDTree(points)
# weights
K = int(K_val)
weights = pysal.weights.Distance.KNN(kd, K)
#weights.transform='r'
#计算滞后
w0 = [[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1]]
w = [weights]
s_lag = max(l_val, p_val)
for s in range(2, s_lag + 1):
w_tmp = pysal.higher_order(weights, s)
w.append(w_tmp)
slw = [w0]
for j in range(len(w)):
w[j].transform = 'r'
w_f = w[j].full()
slw.append(w_f[0].tolist())
w_slag = np.array(slw)
starma = starma_model.STARMA(m_val, q_val, ts_diff, w_slag)
starma.fit()
result = starma.predict(ts_diff, pre_val)
self.table.show_table(result, location_val)
def fit(self):
"""
Estimate parameter for model
"""
self._ts = set_stationary(self._ts, self._d)
self._fit_model(self._ts)
0.1428571, 0.1428571, 0.1428571
], [0, 0, 0, 0, 0, 0, 0.25, 0, 0, 0, 0.25, 0.25, 0.25, 0],
[
0.1428571, 0.1428571, 0.1428571, 0, 0.1428571, 0, 0.1428571, 0,
0, 0.1428571, 0, 0, 0, 0.1428571
], [0.25, 0, 0, 0, 0.25, 0, 0, 0, 0.25, 0.25, 0, 0, 0, 0],
[0, 0, 0, 0.25, 0.25, 0, 0, 0, 0.25, 0.25, 0, 0, 0, 0],
[0, 0, 0, 0.25, 0, 0, 0, 0.25, 0.25, 0, 0.25, 0, 0, 0]]
w_slag = [weight, weight1]
a = w_slag[0]
ts = np.array(data)
w = np.array(w_slag)
#一次差分
ts_diff = utils.set_stationary(ts, [1])
#自相关函数
acf = stacf_stpacf.Stacf(ts_diff, w, 5)
acf_val = acf.estimate()
#偏自相关函数
pacf = stacf_stpacf.Stpacf(ts_diff, w, 5)
pacf_val = pacf.estimate()
#STARMA
#starma=starma_model.STARMA(3,1,ts_diff,w)
starma = starma_model.STARMA(1, 0, ts_diff, w)
starma.fit()
starma.print_results()
print(starma.get_model())
#print(starma.predict(ts,3))