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()