def train_all():
RF_all1 = RF.RF_ALL_train('./data/testingDataSet1.csv',
'./data/VALIDATION_DataSet1.csv')
RF_all2 = RF.RF_ALL_train('./data/EVAL_DataSet1.csv',
'./data/VALIDATION_DataSet1.csv')
gdbt_all1 = GDBT_all.GDBT_ALL_train('./data/VALIDATION_DataSet1.csv',
'./data/VALIDATION_DataSet1.csv')
gdbt_all2 = GDBT_all.GDBT_ALL_train('./data/testingDataSet1.csv',
'./data/VALIDATION_DataSet1.csv')
gdbt_all3 = GDBT_all.GDBT_ALL_train('./data/EVAL_DataSet1.csv',
'./data/VALIDATION_DataSet1.csv')
svr_all = s.SVR_ALL_train()
# 3个GBDTt，1个sSVR再做一次SVR
# X = [];Y = []
# for i in range(len(gdbt_all1)):
# X.append((RF_all1[i][2], gdbt_all1[i][2], gdbt_all2[i][2], svr_all[i][2],svr_all[i][3]))
# Y.append(gdbt_all1[i][3])
#
# svr = SVR(kernel='linear', epsilon=2, C=1).fit(X, Y)
# print(svr.coef_)
# pred_y = svr.fit(X, Y).predict(X)
fw = open(filename, 'w')
for i in range(len(gdbt_all1)):
fw.write('%s,%s,%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,%.2f\n' %
(gdbt_all1[i][0], gdbt_all1[i][1],
float(RF_all1[i][2]),
float(RF_all2[i][2]),
float(gdbt_all1[i][2]),
float(gdbt_all2[i][2]),
float(gdbt_all3[i][2]),
float(svr_all[i][2]),
float(svr_all[i][3]), #前14天的值
float(svr_all[i][4]),
((float(RF_all1[i][2])+float(gdbt_all1[i][2]) + float(gdbt_all2[i][2])+ \
float(svr_all[i][2]) + float(svr_all[i][3])+float(svr_all[i][4]))) / 6,
cost_dict['all'][gdbt_all1[i][0]][0],
cost_dict['all'][gdbt_all1[i][0]][1],
float(gdbt_all1[i][3])
)
)
fw.close()
