神经网络python源码分享

神经网络的逻辑应该都是熟知的了，在这里想说明一下交叉验证交叉验证方法：看图大概就

神经网络的逻辑应该都是熟知的了，在这里想说明一下交叉验证

交叉验证方法：

看图大概就能理解了，大致就是先将数据集分成K份，对这K份中每一份都取不一样的比例数据进行训练和测试。得出K个误差，将这K个误差平均得到最终误差

这第一个部分是BP神经网络的建立

参数选取参照论文：基于数据挖掘技术的股价指数分析与预测研究_胡林林

import math
import random
import tushare as ts
import pandas as pd
random.seed(0)
def getData(id,start,end):
 df = ts.get_hist_data(id,start,end)
 DATA=pd.DataFrame(columns=['rate1', 'rate2','rate3','pos1','pos2','pos3','amt1','amt2','amt3','MA20','MA5','r'])
 P1 = pd.DataFrame(columns=['high','low','close','open','volume'])
 DATA2=pd.DataFrame(columns=['R'])
 DATA['MA20']=df['ma20']
 DATA['MA5']=df['ma5']
 P=df['close']
 P1['high']=df['high']
 P1['low']=df['low']
 P1['close']=df['close']
 P1['open']=df['open']
 P1['volume']=df['volume']

 DATA['rate1']=(P1['close'].shift(1)-P1['open'].shift(1))/P1['open'].shift(1)
 DATA['rate2']=(P1['close'].shift(2)-P1['open'].shift(2))/P1['open'].shift(2)
 DATA['rate3']=(P1['close'].shift(3)-P1['open'].shift(3))/P1['open'].shift(3)
 DATA['pos1']=(P1['close'].shift(1)-P1['low'].shift(1))/(P1['high'].shift(1)-P1['low'].shift(1))
 DATA['pos2']=(P1['close'].shift(2)-P1['low'].shift(2))/(P1['high'].shift(2)-P1['low'].shift(2))
 DATA['pos3']=(P1['close'].shift(3)-P1['low'].shift(3))/(P1['high'].shift(3)-P1['low'].shift(3))
 DATA['amt1']=P1['volume'].shift(1)/((P1['volume'].shift(1)+P1['volume'].shift(2)+P1['volume'].shift(3))/3)
 DATA['amt2']=P1['volume'].shift(2)/((P1['volume'].shift(2)+P1['volume'].shift(3)+P1['volume'].shift(4))/3)
 DATA['amt3']=P1['volume'].shift(3)/((P1['volume'].shift(3)+P1['volume'].shift(4)+P1['volume'].shift(5))/3)
 templist=(P-P.shift(1))/P.shift(1)
 tempDATA = []
 for indextemp in templist:
 tempDATA.append(1/(1+math.exp(-indextemp*100)))
 DATA['r'] = tempDATA
 DATA=DATA.dropna(axis=0)
 DATA2['R']=DATA['r']
 del DATA['r']
 DATA=DATA.T
 DATA2=DATA2.T
 DATAlist=DATA.to_dict("list")
 result = []
 for key in DATAlist:
 result.append(DATAlist[key])
 DATAlist2=DATA2.to_dict("list")
 result2 = []
 for key in DATAlist2:
 result2.append(DATAlist2[key])
 return result
def getDataR(id,start,end):
 df = ts.get_hist_data(id,start,end)
 DATA=pd.DataFrame(columns=['rate1', 'rate2','rate3','pos1','pos2','pos3','amt1','amt2','amt3','MA20','MA5','r'])
 P1 = pd.DataFrame(columns=['high','low','close','open','volume'])
 DATA2=pd.DataFrame(columns=['R'])
 DATA['MA20']=df['ma20'].shift(1)
 DATA['MA5']=df['ma5'].shift(1)
 P=df['close']
 P1['high']=df['high']
 P1['low']=df['low']
 P1['close']=df['close']
 P1['open']=df['open']
 P1['volume']=df['volume']

 DATA['rate1']=(P1['close'].shift(1)-P1['open'].shift(1))/P1['open'].shift(1)
 DATA['rate2']=(P1['close'].shift(2)-P1['open'].shift(2))/P1['open'].shift(2)
 DATA['rate3']=(P1['close'].shift(3)-P1['open'].shift(3))/P1['open'].shift(3)
 DATA['pos1']=(P1['close'].shift(1)-P1['low'].shift(1))/(P1['high'].shift(1)-P1['low'].shift(1))
 DATA['pos2']=(P1['close'].shift(2)-P1['low'].shift(2))/(P1['high'].shift(2)-P1['low'].shift(2))
 DATA['pos3']=(P1['close'].shift(3)-P1['low'].shift(3))/(P1['high'].shift(3)-P1['low'].shift(3))
 DATA['amt1']=P1['volume'].shift(1)/((P1['volume'].shift(1)+P1['volume'].shift(2)+P1['volume'].shift(3))/3)
 DATA['amt2']=P1['volume'].shift(2)/((P1['volume'].shift(2)+P1['volume'].shift(3)+P1['volume'].shift(4))/3)
 DATA['amt3']=P1['volume'].shift(3)/((P1['volume'].shift(3)+P1['volume'].shift(4)+P1['volume'].shift(5))/3)
 templist=(P-P.shift(1))/P.shift(1)
 tempDATA = []
 for indextemp in templist:
 tempDATA.append(1/(1+math.exp(-indextemp*100)))
 DATA['r'] = tempDATA
 DATA=DATA.dropna(axis=0)
 DATA2['R']=DATA['r']
 del DATA['r']
 DATA=DATA.T
 DATA2=DATA2.T
 DATAlist=DATA.to_dict("list")
 result = []
 for key in DATAlist:
 result.append(DATAlist[key])
 DATAlist2=DATA2.to_dict("list")
 result2 = []
 for key in DATAlist2:
 result2.append(DATAlist2[key])
 return result2
def rand(a, b):
 return (b - a) * random.random() + a
def make_matrix(m, n, fill=0.0):
 mat = []
 for i in range(m):
 mat.append([fill] * n)
 return mat
def sigmoid(x):
 return 1.0 / (1.0 + math.exp(-x))
def sigmod_derivate(x):
 return x * (1 - x)
class BPNeuralNetwork:
 def __init__(self):
 self.input_n = 0
 self.hidden_n = 0
 self.output_n = 0
 self.input_cells = []
 self.hidden_cells = []
 self.output_cells = []
 self.input_weights = []
 self.output_weights = []
 self.input_correction = []
 self.output_correction = []

 def setup(self, ni, nh, no):
 self.input_n = ni + 1
 self.hidden_n = nh
 self.output_n = no
 # init cells
 self.input_cells = [1.0] * self.input_n
 self.hidden_cells = [1.0] * self.hidden_n
 self.output_cells = [1.0] * self.output_n
 # init weights
 self.input_weights = make_matrix(self.input_n, self.hidden_n)
 self.output_weights = make_matrix(self.hidden_n, self.output_n)
 # random activate
 for i in range(self.input_n):
 for h in range(self.hidden_n):
 self.input_weights[i][h] = rand(-0.2, 0.2)
 for h in range(self.hidden_n):
 for o in range(self.output_n):
 self.output_weights[h][o] = rand(-2.0, 2.0)
 # init correction matrix
 self.input_correction = make_matrix(self.input_n, self.hidden_n)
 self.output_correction = make_matrix(self.hidden_n, self.output_n)

 def predict(self, inputs):
 # activate input layer
 for i in range(self.input_n - 1):
 self.input_cells[i] = inputs[i]
 # activate hidden layer
 for j in range(self.hidden_n):
 total = 0.0
 for i in range(self.input_n):
 total += self.input_cells[i] * self.input_weights[i][j]
 self.hidden_cells[j] = sigmoid(total)
 # activate output layer
 for k in range(self.output_n):
 total = 0.0
 for j in range(self.hidden_n):
 total += self.hidden_cells[j] * self.output_weights[j][k]
 self.output_cells[k] = sigmoid(total)
 return self.output_cells[:]
 def back_propagate(self, case, label, learn, correct):
 # feed forward
 self.predict(case)
 # get output layer error
 output_deltas = [0.0] * self.output_n
 for o in range(self.output_n):
 error = label[o] - self.output_cells[o]
 output_deltas[o] = sigmod_derivate(self.output_cells[o]) * error
 # get hidden layer error
 hidden_deltas = [0.0] * self.hidden_n
 for h in range(self.hidden_n):
 error = 0.0
 for o in range(self.output_n):
 error += output_deltas[o] * self.output_weights[h][o]
 hidden_deltas[h] = sigmod_derivate(self.hidden_cells[h]) * error
 # update output weights
 for h in range(self.hidden_n):
 for o in range(self.output_n):
 change = output_deltas[o] * self.hidden_cells[h]
 self.output_weights[h][o] += learn * change + correct * self.output_correction[h][o]
 self.output_correction[h][o] = change
 # update input weights
 for i in range(self.input_n):
 for h in range(self.hidden_n):
 change = hidden_deltas[h] * self.input_cells[i]
 self.input_weights[i][h] += learn * change + correct * self.input_correction[i][h]
 self.input_correction[i][h] = change
 # get global error
 error = 0.0
 for o in range(len(label)):
 error += 0.5 * (label[o] - self.output_cells[o]) ** 2
 return error
 def train(self, cases, labels, limit=10000, learn=0.05, correct=0.1):
 for i in range(limit):
 error = 0.0
 for i in range(len(cases)):
 label = labels[i]
 case = cases[i]
 error += self.back_propagate(case, label, learn, correct)
 def test(self,id):
 result=getData("000001", "2015-01-05", "2015-01-09")
 result2=getDataR("000001", "2015-01-05", "2015-01-09")
 self.setup(11, 5, 1)
 self.train(result, result2, 10000, 0.05, 0.1)
 for t in resulttest:
 print(self.predict(t))

下面是选取14-15年数据进行训练，16年数据作为测试集，调仓周期为20个交易日，大约1个月，对上证50中的股票进行预测，选取预测的涨幅前10的股票买入，对每只股票分配一样的资金,初步运行没有问题，但就是太慢了，等哪天有空了再运行

import BPnet
import tushare as ts
import pandas as pd
import math
import xlrd
import datetime as dt
import time

#
#nn =BPnet.BPNeuralNetwork()
#nn.test('000001')
#for i in ts.get_sz50s()['code']:
holdList=pd.DataFrame(columns=['time','id','value'])
share=ts.get_sz50s()['code']
time2=ts.get_k_data('000001')['date']
newtime = time2[400:640]
newcount=0
for itime in newtime:
 print(itime)
 if newcount % 20 == 0:
 sharelist = pd.DataFrame(columns=['time','id','value'])
 for ishare in share:
 backwardtime = time.strftime('%Y-%m-%d',time.localtime(time.mktime(time.strptime(itime,'%Y-%m-%d'))-432000*4))
 trainData = BPnet.getData(ishare, '2014-05-22',itime)
 trainDataR = BPnet.getDataR(ishare, '2014-05-22',itime)
 testData = BPnet.getData(ishare, backwardtime,itime)
 try:
 print(testData)
 testData = testData[-1]
 print(testData)
 nn = BPnet.BPNeuralNetwork()
 nn.setup(11, 5, 1)
 nn.train(trainData, trainDataR, 10000, 0.05, 0.1)
 value = nn.predict(testData)
 newlist= pd.DataFrame({'time':itime,"id":ishare,"value":value},index=["0"])
 sharelist = sharelist.append(newlist,ignore_index=True)
 except: 
 pass
 sharelist=sharelist.sort(columns ='value',ascending=False)
 sharelist = sharelist[:10]
 holdList=holdList.append(sharelist,ignore_index=True)
 newcount+=1
 print(holdList)

总结

以上就是本文关于神经网络python源码分享的全部内容，希望对大家有所帮助。感兴趣的朋友可以继续参阅本站：

神经网络理论基础及Python实现详解

Python语言实现百度语音识别API的使用实例

Python通过matplotlib绘制动画简单实例

如有不足之处，欢迎留言指出。感谢朋友们对本站的支持！

python神经网络源码 神经网络 python python bp神经网络