OR_implementation_Hebbian_Learning.py

# OR gate implementation using Hebbian learning
# Truth tabla of OR, format [x1, x2, bias, output ] 
data = [[1, 1, 1, 1],[1, -1, 1, 1],[-1, 1, 1, 1],[-1, -1, 1, -1]]

# initilize weights 
w1=0
w2=0
b=0
for i in range(len(data)):
  
  print("Iteration-"+str(i)+"########")
  print(data[i])
  
  print("w1_new = w1_old + x1 * t ")
  print("       = "+str(w1)+"+"+str(data[i][0])+"*"+str(data[i][3]))
  w1 = w1+data[i][0]*data[i][3]
  print("       = "+str(w1))
  
  
  print("w2_new = w2_old + x2 * t ")
  print("       = "+str(w2)+"+"+str(data[i][1])+"*"+str(data[i][3]))
  w2 = w2+data[i][1]*data[i][3]
  print("       ="+str(w2))
  
  
  print("b_new = b_old + 1 * t ")
  print("      = "+str(b)+"+ 1 * "+str(data[i][3]))
  b = b+1*data[i][3] # bias is 1 for all inputs
  print("      = "+str(b))


# OUTPUT
# Iteration-0########
# [1, 1, 1, 1]
# w1_new = w1_old + x1 * t
#        = 0+1*1
#        = 1
# w2_new = w2_old + x2 * t
#        = 0+1*1
#        =1
# b_new = b_old + 1 * t
#       = 0+ 1 * 1
#       = 1
# Iteration-1########
# [1, -1, 1, 1]
# w1_new = w1_old + x1 * t
#        = 1+1*1
#        = 2
# w2_new = w2_old + x2 * t
#        = 1+-1*1
#        =0
# b_new = b_old + 1 * t
#       = 1+ 1 * 1
#       = 2
# Iteration-2########
# [-1, 1, 1, 1]
# w1_new = w1_old + x1 * t
#        = 2+-1*1
#        = 1
# w2_new = w2_old + x2 * t
#        = 0+1*1
#        =1
# b_new = b_old + 1 * t
#       = 2+ 1 * 1
#       = 3
# Iteration-3########
# [-1, -1, 1, -1]
# w1_new = w1_old + x1 * t
#        = 1+-1*-1
#        = 2
# w2_new = w2_old + x2 * t
#        = 1+-1*-1
#        =2
# b_new = b_old + 1 * t
#       = 3+ 1 * -1
#       = 2