Multi-Layer Neural Networks, Python 3

Multi-Layer Neural Networks

#python 3.5.2

def HammingDistance(a,b,n):
 p = 0
 i = 1
 while i<=n :
  if (((a&1)^(b&1))==1) :
   p = p + 1  
  a = a>>1
  b = b>>1
  i = i+1
 return p

def GetHyperCube(ds,n,n2):
 nmc = 0
 mask = 0
 h = []
 x = []
 y = []
 z = 0
 while z<=256 :
  h.append([0])
  x.append([0])
  y.append([0])
  z = z+1
 z = 0
 while z<=256 :
  h[z] = -1
  x[z] = -1
  y[z] = -1
  z = z + 1
 max = n2
 st = ds
 i = 0
 while i<max :
  p = 0
  j = 0
  while j<n :
   if (st[((i+j) % max)]=='1') :
    p = p | 1
   p = p << 1  
   j = j + 1  
  x[i] = p >> 1
  i = i+1
 t = 0
 p = 0
 f = 0
 h[0] = x[0]
 y[x[0]] = 1
 while f==0 :
  z = t
  j = 0
  while j<max :
   if (y[x[j]]==-1) :
    p = x[j]
    q = 0
    i = 0
    if t>=(n-1) :
     u = n-1
    else :
     u = t+1
    while i<u :
     if (HammingDistance(p,h[(t-i)],n)==(i+1)) :
      q = q+1
     i = i+1
     if q==u : break
    if q==u :
     t = t+1
     h[t] = x[j]
     y[x[j]] = 1
     f = 0
     break
   j = j+1
  if t==z : break
 i = 0
 while i<max :
  a = ''
  j = 0
  while j<max :
   a = a + '+'
   if h[i]==x[j] :    
    k = 0
    while k<n :
     a = a + str((x[j]>>(n-k-1))&1)
     k = k+1
    print(a)
    break
   j = j+1
  i = i+1
 return 0

GetHyperCube('00000101100011101111100100110101',5,32)


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