In this exercise we will do an adaptive learning rate for hinge.
Between the compute dellf and updatew code portions insert the 
following pseudocode.

eta_list = [1, .1, .01, .001, .0001, .00001, .000001, .0000001, .00000001, .000000001, .0000000001, .00000000001 ]
bestobj = 1000000000000
for k in range(0, len(eta_list), 1):

  eta = eta_list[k]
  
  ##update w
  ##insert code here for w = w + eta*dellf

  ##get new error
  error = 0
  for i in range(0, rows, 1):
    if(trainlabels.get(i) != None):
      ##update error
      ##insert code to update the loss (which we call error here)

  obj = error

  ##update bestobj and best_eta
  ##insert code here

  ##remove the eta for the next
  ##insert code here for w = w - eta*dellf

eta = best_eta