python机器学习预测学习笔记（有点问题）

# -*- coding: utf-8 -*-"""Created on Wed May 24 17:19:23 2017@author: yunjinqi  E-mail:[e-mail]  Differentiate yourself in the world from anyone else."""import pandas as pdfrom sklearn.tree import DecisionTreeRegressor  from sklearn.ensemble import RandomForestRegressor  import numpy as np  data=pd.read_csv('C:/Users/HXWD/Desktop/m9000.csv',encoding='gbk')data.head()len(data)data['ret1']=(data['收盘价']-data['收盘价'].shift(1))/data['收盘价'].shift(1)data['ret']=data['ret1'].shift(-1)del data['ret1']data=data[:len(data)-1]data['ret']data=data.fillna(0)df_train=data.ix[::,list(range(7,31))]df_target=data.ix[::,31]value=[]predict_ret=[]for i in range(len(df_train)):    if i>300:        rf=RandomForestRegressor()           train=np.array(df_train[i-300:i])        #print(train)        target=np.array(df_target[i-300:i])        rf.fit(train,target)#进行模型的训练          aaa=np.array(df_train.ix[i,::])        aab=list(df_train.ix[i,::])                a=rf.predict(aaa)        predict_ret.append(float(a))        b=df_target.ix[i,::]                if float(a)*float(b)>=0:            value.append(1)        else:            value.append(0)valuecount=0for i in range(len(value)):    if value[i]==1:        count=count+1len(value)  print(count/len(value) )data.ret.plot()data.ret.mean()predict_ret=pd.Series(predict_ret)d1=pd.DataFrame(data['ret'],)d1d1.to_csv('预测.csv')predict_ret