docs: def deltaWeight(self, e, wed, m, dweightLast, wedLast, w, n):
          """
          e - learning rate
          wed - weight error delta vector (slope)
          m - momentum
          dweightLast - previous dweight vector (slope)
          wedLast - only used in quickprop; last weight error delta vector
          w - weight vector
          n - fan-in, number of connections coming in (counts bias, too)
          """
          #print "WEIGHT = ", w
          shrinkFactor = self.mu / (1.0 + self.mu)
          if self.splitEpsilon:
              e /= float(n)
          if self._quickprop:
              nextStep = Numeric.zeros(len(dweightLast), 'f')
              for i in range(len(dweightLast)):
                  s = wed[i] 
                  d = dweightLast[i]
                  p = wedLast[i]
                  #print self.mu
                  #print "slopes[node] = %f  QP w=%s d=%f s=%f p=%f eps=%f" % (s, w, d, s, p, e)
                  #print type(nextStep[i]), nextStep[i]
                  if (d > 0.0):
                      if (s > 0.0):
                          #print "CASE A1"
                          nextStep[i] = nextStep[i] + (e * s)
                      if (s >= (shrinkFactor * p)):
                          #print "CASE B1"
                          nextStep[i] = nextStep[i] + (self.mu * d)
                      else:
                          #print "CASE C1"
                          nextStep[i] = nextStep[i] + (d * s / (p - s))
                  elif (d < 0.0):
                      if (s < 0.0):
                          #print "CASE A2"
                          nextStep[i] = nextStep[i] + (e * s)
                      if (s <= (shrinkFactor * p)):
                          #print "CASE B2"
                          nextStep[i] = nextStep[i] + (self.mu * d)
                      else:
                          #print "CASE C2"
                          nextStep[i] = nextStep[i] + (d * s / (p - s))
                  else:
                      #print "CASE D"
                      nextStep[i] = nextStep[i] + (e * s + m * d)
                      ##  Last step was zero, so only use linear   ##
              newDweight = nextStep
              #print "Next Step = ", nextStep[i]
          else: # backprop
              newDweight = e * wed + m * dweightLast # gradient descent
          return newDweight