1 """andgate using NeuralNetwork
   2 
   3 for practical example, see [AndGateShell.py]
   4 """
   5 
   6 def f(x1,x2,w1,w2):
   7     return x1*w1 + x2*w2
   8 
   9 thres = 0.5
  10 isOver=lambda x1,x2,w1,w2:f(x1,x2,w1,w2)>thres
  11 result=isOver
  12 isSame=lambda x1,x2,w1,w2,expected:result(x1,x2,w1,w2)==expected
  13 
  14 def printGenerations(w1,w2):
  15     testCase=((0,0),(0,1),(1,0),(1,1))
  16     for eachX1,eachX2 in testCase:
  17         print "(%d,%d) : %f"%(eachX1,eachX2,f(eachX1,eachX2,w1,w2))
  18 
  19 def initialize():
  20     pass
  21 
  22 
  23 def everythingDone():
  24     pass
  25 
  26 def advanceGeneration():
  27     pass
  28 
  29 def printParameters():
  30     pass
  31 
  32 def calculateThisGeneration(w1,w2):
  33     testCases=((0,0,0),
  34                (0,1,0),
  35                (1,0,0),
  36                (1,1,1))
  37     for eachX1,eachX2,expected in testCases:
  38         #t=result(eachX1,eachX2,w1,w2)
  39         if isSame(eachX1,eachX2,w1,w2,expected):
  40             continue
  41         w1,w2=adjust(eachX1,eachX2,w1,w2,expected)
  42     return w1,w2
  43 
  44 def adjust(x1,x2,w1,w2,expected):
  45     diffForW1=0.1*(expected-f(x1,x2,w1,w2))*x1
  46     diffForW2=0.1*(expected-f(x1,x2,w1,w2))*x2
  47     newW1=w1+diffForW1
  48     newW2=w2+diffForW2
  49     return newW1,newW2
  50 
  51 
  52 def untilDone():
  53     initialize()
  54     iteration=0
  55     while 1:
  56         iteration+=1
  57         calculateThisGeneration()
  58         if everythingDone() or tooOld(iteration):
  59             break
  60         advanceGeneration()
  61     printParameters()
  62 
  63 def tooOld(iteration):
  64     if iteration>=MAX_ITERATION:
  65         raise "Too many iterations!"