#@author: Mint

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

y =[0.14 ,0.64 ,0.28 ,0.33 ,0.12 ,0.03 ,0.02 ,0.11 ,0.08 ]
x1 =[0.29 ,0.50 ,0.00 ,0.21 ,0.10 ,0.06 ,0.13 ,0.24 ,0.28 ]
x2 =[0.23 ,0.62 ,0.53 ,0.53 ,0.33 ,0.15 ,0.03 ,0.23 ,0.03 ]
theata = [-1,-1,0,-1,-1,0,-1,0,-1]
x = np.array([x1,x2,theata])

W_mid = DataFrame(0.5,index=['input1','input2','theata'],columns=['mid1','mid2','mid3','mid4'])
W_out = DataFrame(0.5,index=['input1','input2','input3','input4','theata'],columns=['a'])

def sigmoid(x): #映射函数
return 1/(1+math.exp(-x))


#训练神经元
def train(W_out, W_mid,data,real):
#中间层神经元输入和输出层神经元输入
Net_in = DataFrame(data,index=['input1','input2','theata'],columns=['a'])
Out_in = DataFrame(0,index=['input1','input2','input3','input4','theata'],columns=['a'])
Out_in.loc['theata'] = -1

#中间层和输出层神经元权值
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'])

#中间层的输出
for i in range(0,4):
Out_in.iloc[i] = sigmoid(sum(W_mid.iloc[:,i]*Net_in.iloc[:,0]))
#输出层的输出/网络输出
res = sigmoid(sum(Out_in.iloc[:,0]*W_out.iloc[:,0]))
#误差
error = abs(res-real)


#输出层权值变化量
#yita =学习率
yita =0.85
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 #输出层权值更新

#中间层权值变化量
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 #中间层权值更新
return W_out,W_mid,res,error

def reault(data,W_out, W_mid):
Net_in = DataFrame(data,index=['input1','input2','theata'],columns=['a'])
Out_in = DataFrame(0,index=['input1','input2','input3','input4','theata'],columns=['a'])
Out_in.loc['theata'] = -1

#中间层的输出
for i in range(0,4):
Out_in.iloc[i] = sigmoid(sum(W_mid.iloc[:,i]*Net_in.iloc[:,0]))
#输出层的输出/网络输出
res = sigmoid(sum(Out_in.iloc[:,0]*W_out.iloc[:,0]))
return res

for i in range(0,9):
W_out,W_mid,res,error = train(W_out,W_mid,x[0:,i],y[i])

res1 = reault([0.38 ,0.49,-1 ], W_out, W_mid)
res2 = reault([0.29 ,0.47 ,-3], W_out, W_mid)
print(res1,res2)