text: #coding=utf-8
  
  import pandas as pd
  import numpy as np
  import matplotlib.pyplot as plt
  import tensorflow as tf
  
  rnn_unit=10       #隐层数量
  input_size=7
  output_size=1
  lr=0.0006         #学习率
  #——————————————————导入数据——————————————————————
  f=open('dataset_2.csv')
  df=pd.read_csv(f)     #读入股票数据
  data=df.iloc[:,2:10].values  #取第3-10列
  
  
  #获取训练集
  def get_train_data(batch_size=60,time_step=20,train_begin=0,train_end=5800):
      batch_index=[]
      data_train=data[train_begin:train_end]
      normalized_train_data=(data_train-np.mean(data_train,axis=0))/np.std(data_train,axis=0)  #标准化
      train_x,train_y=[],[]   #训练集
      for i in range(len(normalized_train_data)-time_step):
         if i % batch_size==0:
             batch_index.append(i)
         x=normalized_train_data[i:i+time_step,:7]
         y=normalized_train_data[i:i+time_step,7,np.newaxis]
         train_x.append(x.tolist())
         train_y.append(y.tolist())
      batch_index.append((len(normalized_train_data)-time_step))
      return batch_index,train_x,train_y
  
  
  #获取测试集
  def get_test_data(time_step=20,test_begin=5800):
      data_test=data[test_begin:]
      mean=np.mean(data_test,axis=0)
      std=np.std(data_test,axis=0)
      normalized_test_data=(data_test-mean)/std  #标准化
      size=(len(normalized_test_data)+time_step-1)//time_step  #有size个sample
      test_x,test_y=[],[]
      for i in range(size-1):
         x=normalized_test_data[i*time_step:(i+1)*time_step,:7]
         y=normalized_test_data[i*time_step:(i+1)*time_step,7]
         test_x.append(x.tolist())
         test_y.extend(y)
      test_x.append((normalized_test_data[(i+1)*time_step:,:7]).tolist())
      test_y.extend((normalized_test_data[(i+1)*time_step:,7]).tolist())
      return mean,std,test_x,test_y
  
  
  #——————————————————定义神经网络变量——————————————————
  #输入层、输出层权重、偏置
  
  weights={
           'in':tf.Variable(tf.random_normal([input_size,rnn_unit])),
           'out':tf.Variable(tf.random_normal([rnn_unit,1]))
          }
  biases={
          'in':tf.Variable(tf.constant(0.1,shape=[rnn_unit,])),
          'out':tf.Variable(tf.constant(0.1,shape=[1,]))
         }
  
  #——————————————————定义神经网络变量——————————————————
  def lstm(X):
      
      batch_size=tf.shape(X)[0]
      time_step=tf.shape(X)[1]
      w_in=weights['in']
      b_in=biases['in']
      input=tf.reshape(X,[-1,input_size])  #需要将tensor转成2维进行计算，计算后的结果作为隐藏层的输入
      input_rnn=tf.matmul(input,w_in)+b_in
      input_rnn=tf.reshape(input_rnn,[-1,time_step,rnn_unit])  #将tensor转成3维，作为lstm cell的输入
      cell=tf.nn.rnn_cell.BasicLSTMCell(rnn_unit)
      init_state=cell.zero_state(batch_size,dtype=tf.float32)
      output_rnn,final_states=tf.nn.dynamic_rnn(cell, input_rnn,initial_state=init_state, dtype=tf.float32)
      output=tf.reshape(output_rnn,[-1,rnn_unit]) 
      w_out=weights['out']
      b_out=biases['out']
      pred=tf.matmul(output,w_out)+b_out
      return pred,final_states
  
  #————————————————训练模型————————————————————
  
  def train_lstm(batch_size=60,time_step=20,train_begin=2000,train_end=5800):
      X=tf.placeholder(tf.float32, shape=[None,time_step,input_size])
      Y=tf.placeholder(tf.float32, shape=[None,time_step,output_size])
      batch_index,train_x,train_y=get_train_data(batch_size,time_step,train_begin,train_end)
      with tf.variable_scope("sec_lstm"):
          pred,_=lstm(X)
      loss=tf.reduce_mean(tf.square(tf.reshape(pred,[-1])-tf.reshape(Y, [-1])))
      train_op=tf.train.AdamOptimizer(lr).minimize(loss)
      saver=tf.train.Saver(tf.global_variables(),max_to_keep=15)
  
      with tf.Session() as sess:
          sess.run(tf.global_variables_initializer())
          for i in range(10):     #这个迭代次数，可以更改，越大预测效果会更好，但需要更长时间
              for step in range(len(batch_index)-1):
                  _,loss_=sess.run([train_op,loss],feed_dict={X:train_x[batch_index[step]:batch_index[step+1]],Y:train_y[batch_index[step]:batch_index[step+1]]})
              print("Number of iterations:",i," loss:",loss_)
          print("model_save: ",saver.save(sess,'model_save2\\modle.ckpt'))
          #我是在window下跑的，这个地址是存放模型的地方，模型参数文件名为modle.ckpt
          #在Linux下面用 'model_save2/modle.ckpt'
          print("The train has finished")
  train_lstm()
  
  #————————————————预测模型————————————————————
  def prediction(time_step=20):
      X=tf.placeholder(tf.float32, shape=[None,time_step,input_size])
      mean,std,test_x,test_y=get_test_data(time_step)
      with tf.variable_scope("sec_lstm",reuse=True):
          pred,_=lstm(X)
      saver=tf.train.Saver(tf.global_variables())
      with tf.Session() as sess:
          #参数恢复
          module_file = tf.train.latest_checkpoint('model_save2')
          saver.restore(sess, module_file)
          test_predict=[]
          for step in range(len(test_x)-1):
            prob=sess.run(pred,feed_dict={X:[test_x[step]]})
            predict=prob.reshape((-1))
            test_predict.extend(predict)
          test_y=np.array(test_y)*std[7]+mean[7]
          test_predict=np.array(test_predict)*std[7]+mean[7]
          acc=np.average(np.abs(test_predict-test_y[:len(test_predict)])/test_y[:len(test_predict)])  #偏差程度
          print("The accuracy of this predict:",acc)
          #以折线图表示结果
          plt.figure()
          plt.plot(list(range(len(test_predict))), test_predict, color='b',)
          plt.plot(list(range(len(test_y))), test_y,  color='r')
          plt.show()
  
  prediction()
  