"""Created on Thu Aug 24 15:14:07 2017@author: Administrator"""import pymongofrom pymongo import MongoClientimport numpy as npimport pandas as pdfrom pandas import DataFrame,Seriesfrom numpy import row_stack,column_stackfrom dateutil.parser import parsefrom matplotlib.pylab import date2numimport randomclient1 = MongoClient('192.168.0.xxx',xxxxx)db1 = client1.fangjiaseaweed1 = db1.seaweed'''print(seaweed.find_one({"city":"上海","region":"浦东", "name":{"$in":["康桥半岛二期","康桥半岛三期","绿洲清水湾","中邦城市"]}} ,{"lat2":1,"lng2":1}))'''query1 = {"status":0,"cat":"district","city":"上海","region":"浦东", "name":{"$in":["康桥半岛二期","康桥半岛三期","绿洲清水湾","中邦城市"]}}fields1 = {"lat2":1,"lng2":1, "city":1,"region":1,"cat":1,"name":1}lct= list()for s in seaweed.find(query1, fields1): lct.append(s)lf=DataFrame(lct)le=lf le.index=le['name'] lr=le[['lng2','lat2']]client = MongoClient('192.168.xx.xx',xxxxxx)db = client.fangjiaseawater = db.seawaterseawater.find_one()query = {"city":"上海","cat":"sell","region":"浦东", "district_name":{"$in":["康桥半岛二期","康桥半岛三期","绿洲清水湾","中邦城市"]}, "p_date":{"$gt":20160508}}lt= seawater.count(query)print(lt)pos = list()for s in seawater.find(query).limit(lt-1): pos.append(s)data=DataFrame(pos)data.to_excel('data.xls')choose_class=['total_price','area','height','room', 'direction','hall','toilet','fitment','district_name','p_date' ]dc=data[choose_class]dc['lng2']=0dc['lat2']=1'''for i in range(dc.shape[0]): bn=dc['district_name'] p=bn[i] dc['lng2'][i]=lo['lng2'][p]'''for i in range(dc.shape[0]): if dc['district_name'][i]==lr.index[0]: dc['lng2'][i]=lr['lng2'][0] dc['lat2'][i]=lr['lat2'][0] elif dc['district_name'][i]==lr.index[1]: dc['lng2'][i]=lr['lng2'][1] dc['lat2'][i]=lr['lat2'][1] elif dc['district_name'][i]==lr.index[2]: dc['lng2'][i]=lr['lng2'][2] dc['lat2'][i]=lr['lat2'][2] elif dc['district_name'][i]==lr.index[3]: dc['lng2'][i]=lr['lng2'][3] dc['lat2'][i]=lr['lat2'][3]mean_price=dc['total_price']/dc['area']dc['total_price']=mean_price h=dc['p_date']for i in range(1,len(h)): a=int(h[i]) b=str(a) c=parse(b) e = date2num(c) h[i]=e dc['p_date']=hdc.to_excel('dc.xls')'''#给每个小区赋予一个标签for i in dc['district_name'].index : if dc['district_name'][i]=='康桥半岛二期': dc['district_name'][i]=0 elif dc['district_name'][i]=='康桥半岛三期': dc['district_name'][i]=1 elif dc['district_name'][i]=='绿洲清水湾': dc['district_name'][i]=2 elif dc['district_name'][i]=='中邦城市': dc['district_name'][i] =3'''for i in dc['direction'].index: if ('南' in str(dc['direction'][i])) : dc['direction'][i]=0 else: dc['direction'][i]=1for i in dc['fitment'].index: if ('豪' or '精') in str(dc['fitment'][i]) : dc['fitment'][i]=0 else : dc['fitment'][i]=1dc=dc.fillna({'height':dc['height'].mean(), 'room':dc['room'].mean(), 'toilet':dc['toilet'].mean(), 'hall':dc['hall'].mean(), })ds=dc.drop('district_name',axis=1)data_all = ds.drop([0],axis=0)sample_number=data_all.shape[0]kk=int(0.05 *sample_number)test_label=[random.randint(1,sample_number) for _ in range(kk)]data_train= data_all.drop(test_label,axis=0)data_max = data_train.max()data_min = data_train.min()data_train1 = (data_train-data_min)/(data_max-data_min+0.2) x_train = data_train1.iloc[:,1:11].as_matrix() y_train = data_train1.iloc[:,0:1].as_matrix() from keras.models import Sequentialfrom keras.layers.core import Dense, Dropout, Activationmodel = Sequential() model.add(Dense(input_dim = 10, output_dim = 48)) model.add(Activation('relu')) model.add(Dense(input_dim = 48, output_dim = 100)) model.add(Activation('relu')) model.add(Dense(input_dim = 100, output_dim = 50)) model.add(Activation('relu')) model.add(Dense(input_dim = 50, output_dim = 36)) model.add(Activation('relu')) model.add(Dense(input_dim = 36, output_dim = 12)) model.add(Activation('relu')) model.add(Dense(input_dim = 12, output_dim = 12)) model.add(Activation('relu')) model.add(Dense(input_dim = 12, output_dim = 1)) model.add(Activation('sigmoid')) model.compile(loss='mean_squared_error', optimizer='adam')model.fit(x_train, y_train, nb_epoch = 300, batch_size = 2) model.save_weights('net.model') test=data_all.ix[test_label,:]data_test = (test-data_min)/(data_max-data_min+0.2) x_test = data_test.iloc[:,1:11].as_matrix()y_test = data_test.iloc[:,0:1].as_matrix()r = (model.predict(x_test))rt=r*(data_max.values-data_min.values+0.2)+data_min.valuespredict=rt[:,0:1]realvalue= test.iloc[:,0:1].as_matrix()error=abs((predict-realvalue)/realvalue)*100geek=column_stack((predict,realvalue,error))DataFrame(geek).to_excel('geek.xls')print(geek)print('平均计算误差:','%.2f'%error.mean(),'%')