RachelPerceptronPython

#!/usr/bin/python

down = False
up = True

def dp (inputs, weights) :
  sum = 0.0
  i = 0
  while(i < len(inputs)) :
    sum += inputs[i]*weights[i]
    i += 1

  return sum

learning_rate = 0.1
threshhold = 0.5

weights = [0,0,0]

def co(inputs) :
  print "co"
  prod = dp( inputs, weights )
  print "dot_product: " + str(prod)
  if( prod > threshhold ) :
    return 1
  else :
    return 0

# iterate over inputs and bump the corresponding weights if the input was 1
def bump_weights( inputs, up_or_down ) :
  print "bump_weights"
  x = 0
  while( x < len( inputs ) ) :
    val = inputs[x]
    if( val == 1 ) :
      if( up_or_down ) :
        weights[x] += learning_rate
      else :
        weights[x] -= learning_rate
    x += 1


datasets = [[1,0,0,1],[1,0,1,1],[1,1,0,1],[1,1,1,0]]

#learn([1,1,0],1)
#print weights

# weights remains a global variable
count = 0
correct = [False,False,False,False]
while count < len(datasets) :
  dataset = datasets[count]
  inputs = dataset[0:3]
  expected = dataset[3]
  print "inputs: " + ', '.join(str(x) for x in inputs )
  print "weights: " + ', '.join(str(x) for x in weights )
  result = co( inputs )
  print "expected: " + str(expected)
  print "and got : " + str(result)
  correct[count] = True
  if result > expected :
    print "too big"
    correct[count] = False
    bump_weights( inputs, down )
  if result < expected :
    print "too small"
    correct[count] = False
    bump_weights( inputs, up )
  count += 1
  if( count == len(datasets) and not( correct == [True,True,True,True]) ):
    count = 0
  print "\n"