python必备源代码-神经网络（python源代码）

importmathimportrandomimporttushare as tsimportpandas as pd

random.seed(0)defgetData(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 intemplist:

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 inDATAlist:

result.append(DATAlist[key])

DATAlist2=DATA2.to_dict("list")

result2=[]for key inDATAlist2:

result2.append(DATAlist2[key])returnresultdefgetDataR(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 intemplist:

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 inDATAlist:

result.append(DATAlist[key])

DATAlist2=DATA2.to_dict("list")

result2=[]for key inDATAlist2:

result2.append(DATAlist2[key])returnresult2defrand(a, b):return (b - a) * random.random() +adef make_matrix(m, n, fill=0.0):

mat=[]for i inrange(m):

mat.append([fill]*n)returnmatdefsigmoid(x):return 1.0 / (1.0 + math.exp(-x))defsigmod_derivate(x):return x * (1 -x)classBPNeuralNetwork: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=[]defsetup(self, ni, nh, no):

self.input_n= ni + 1self.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 inrange(self.input_n):for h inrange(self.hidden_n):

self.input_weights[i][h]= rand(-0.2, 0.2)for h inrange(self.hidden_n):for o inrange(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)defpredict(self, inputs):#activate input layer

for i in range(self.input_n - 1):

self.input_cells[i]=inputs[i]#activate hidden layer

for j inrange(self.hidden_n):

total= 0.0

for i inrange(self.input_n):

total+= self.input_cells[i] *self.input_weights[i][j]

self.hidden_cells[j]=sigmoid(total)#activate output layer

for k inrange(self.output_n):

total= 0.0

for j inrange(self.hidden_n):

total+= self.hidden_cells[j] *self.output_weights[j][k]

self.output_cells[k]=sigmoid(total)returnself.output_cells[:]defback_propagate(self, case, label, learn, correct):#feed forward

self.predict(case)#get output layer error

output_deltas = [0.0] *self.output_nfor o inrange(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_nfor h inrange(self.hidden_n):

error= 0.0

for o inrange(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 inrange(self.hidden_n):for o inrange(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 inrange(self.input_n):for h inrange(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 inrange(len(label)):

error+= 0.5 * (label[o] - self.output_cells[o]) ** 2

returnerrordef train(self, cases, labels, limit=10000, learn=0.05, correct=0.1):for i inrange(limit):

error= 0.0

for i inrange(len(cases)):

label=labels[i]

case=cases[i]

error+=self.back_propagate(case, label, learn, correct)deftest(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 inresulttest:print(self.predict(t))

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