人工智能1

import math
import numpy as np
import pandas as pd
from pandas import DataFrame,Series

def sigmoid(x): #年轻人
return 1/(1+math.exp(-x))

x1=[0.10,0.33,0.98,0.88,0.76,0.59,0.43,0.24,0.61]
x2=[0.43,0.22,0.93,0.53,0.33,0.15,0.09,0.65,0.32]
y=[0.54,0.65,0.78,0.45,0.55,0.13,0.54,0.76,0.18]
yita=11.1
for i in range(9):
Net_in =DataFrame(0.6,index=['input1','input2','theata'],columns=['a'])
Out_in = DataFrame(0,index=['input1','input2','input3','input4','theata'],columns=['a'])
Net_in.loc['input1'] =x1[i]
Net_in.loc['input2']=x2[i]
real=y[i]
Net_in.loc['theata'] = -1
Out_in.loc['theata'] = -1
W_mid=DataFrame(0.7,index=['input1','input2','theata'],columns=['mid1','mid2','mid3','mid4'])
W_out=DataFrame(0.7,index=['input1','input2','input3','input4','theata'],columns=['a'])
W_mid_delta=DataFrame(0,index=['input1','input2','theata'],columns=['mid1','mid2','mid3','mid4'])
W_out_delta=DataFrame(0,index=['input1','input2','input3','input4','theata'],columns=['a'])
for i in range(0,4):
Out_in.iloc[i,0] = sigmoid(sum(W_mid.iloc[:,i]*Net_in.iloc[:,0]))
res = sigmoid(sum(Out_in.iloc[:,0]*W_out.iloc[:,0]))
error = abs(res-real)
W_out_delta.iloc[:,0] = yita*res*(1-res)*(real-res)*Out_in.iloc[:,0]
W_out_delta.iloc[4,0] = -(yita*res*(1-res)*(real-res))
W_out = W_out + W_out_delta
for i in range(0,4):
W_mid_delta.iloc[:,i] = yita*Out_in.iloc[i,0]*(1-Out_in.iloc[i,0])*W_out.iloc[i,0]*res*(1-res)*(real-res)*Net_in.iloc[:,0]
W_mid_delta.iloc[2,i] = -(yita*Out_in.iloc[i,0]*(1-Out_in.iloc[i,0])*W_out.iloc[i,0]*res*(1-res)*(real-res))
W_mid = W_mid + W_mid_delta
testx1=[0.21,0.28]
testx2=[0.38,0.19]
for i in range(2):
Net_in =DataFrame(0.6,index=['input1','input2','theata'],columns=['a'])
Out_in = DataFrame(0,index=['input1','input2','input3','input4','theata'],columns=['a'])
Net_in.loc['input1'] =testx1[i]
Net_in.loc['input2']=testx2[i]
Net_in.loc['theata'] = -1
Out_in.loc['theata'] = -1
for i in range(0,4):
Out_in.iloc[i,0] = sigmoid(sum(W_mid.iloc[:,i]*Net_in.iloc[:,0]))
res = sigmoid(sum(Out_in.iloc[:,0]*W_out.iloc[:,0]))
print(res)