def __init__(self, ui, user): self.ui = ui self.emotion = buddingEmotion.BuddingEmotion(self) self.user = user print("current user is", self.user) res = database.load(self.user) print(res) self.money = res[1] self.energy = res[2] self.level = res[3]
timew=['10-OCT-16','11-OCT-16','12-OCT-16','13-OCT-16','14-OCT-16','15-OCT-16','16-OCT-16','17-OCT-16','18-OCT-16','19-OCT-16','20-OCT-16','21-OCT-16','22-OCT-16','23-OCT-16','24-OCT-16','25-OCT-16','26-OCT-16'] x0=[] y0=[] f=[] pattern = '%d-%b-%y' #rawdata = pd.read_csv('rawdata020/dataset'+timew[-1]+'.csv',dtype={'gid':str,'event':str,'productId':str}) rawdata = pd.read_csv('/home/ubuntu/rawdata020/dataset020.csv',dtype={'fid':str,'event':str,'cur_cam':str,'productId':str}) for i in range(0,len(timew)): epoch = int(time.mktime(time.strptime(timew[i],pattern))) xt=[] yt=[] ft=[] print 'load data: %s' %(timew[i]) selectdata=rawdata[rawdata.time < epoch].reset_index(drop=True) #x0,y0,f=mydata.load(rate,nday,nday_predict,timew[i]) xt,yt,ft=mydata.load(selectdata,rate,nday,nday_predict,timew[i]) #xt,yt,ft=mydata.load(selectdata,rate,nday,nday_predict,timew) x0=x0+xt y0=y0+yt f=ft fx_all= open('model/x0_all-'+str(nday)+'.pkl','wb') fy_all= open('model/y0_all-'+str(nday)+'.pkl','wb') fx_train= open('model/x0_train_rate'+str(rate)+'-'+str(nday)+'.pkl','wb') fy_train= open('model/y0_train_rate'+str(rate)+'-'+str(nday)+'.pkl','wb') fx_test= open('model/x0_test_rate'+str(rate)+'-'+str(nday)+'.pkl','wb') fy_test= open('model/y0_test_rate'+str(rate)+'-'+str(nday)+'.pkl','wb') print 'The predict-buy module using svm.' print 'Use %d weeks train data to predect %d weeks buying members' %(nday,nday_predict) print 'The gamma value of rbf kernel is %5.2f' %(gamma) print 'Data is loaded. Use %d features for each uniq gid.' %(len(x0[0]))
def load_state(self, user): res = database.load(user) self.player.set_money(money=res[1]) self.budding.set_energy_and_level(energy=res[2], level=res[3])