_2_comparision.py

# the following code snippet was provided by UCSD for Machine Learning Fundamentals on edX
@interact_manual( M=(100,2000,100), rounds=(1,10))
def comparison(M,rounds):
  """
  Shows the mean error of both the prototyping methods.  As we randomly prototyping the data, 
  it makes sense to do it multiple times and take the mean error. 
  
  Parameters:
  M(int): number of data points to be sampled
  r(int): number of times random dataset chosen to calculate the mean error
  
  Returns:
  printed values of mean error by both the methods
  """
    print("Comparing your prototype selection method to random prototype selection...")
    rand_err, rand_std = mean_error(rand_prototypes, M, rounds) 
    my_err, my_std   = mean_error(  my_prototypes, M, rounds) 
    
    print;print("Number of prototypes:", M)
    print("Number of trials:", rounds)
    print("Error for random prototypes:", rand_err )
    print("Error for your prototypes:", my_err );print
    if rand_err < my_err:
        print("RANDOM prototypes win!")
    else:
        print("YOUR prototypes win!")