new_contents: import plexus
  import time
  from itertools import repeat
  import random
  import matplotlib.pyplot as plt
  
  SIZE = 256
  INPUT_SIZE = 4
  OUTPUT_SIZE = 2
  CONNECTIVITY = 0.5
  PRECISION = 1
  
  TRAINING_DURATION = 3
  DOMINANCE_THRESHOLD = 0.7
  
  RANDOMLY_FIRE = False
  DYNAMIC_OUTPUT = True
  
  TRAINING_SAMPLE_SIZE = 80
  TESTING_SAMPLE_SIZE = 20
  
  def generate_list_bigger():
      generated_list = []
      for i in repeat(None, INPUT_SIZE):
          generated_list.append(round(random.uniform(0.6, 1.0), PRECISION))
      return generated_list
  
  def generate_list_smaller():
      generated_list = []
      for i in repeat(None, INPUT_SIZE):
          generated_list.append(round(random.uniform(0.0, 0.4), PRECISION))
      return generated_list
  
  errors = []
  minutes = []
  start = time.time()
  print "\n___ PLEXUS NETWORK BASIC EXAMPLE ___\n"
  
  for j in repeat(None, 60):
  
      print "Create a Plexus network with " + str(SIZE) + " neurons, " + str(INPUT_SIZE) + " of them sensory, " + str(OUTPUT_SIZE) + " of them motor, " + str(CONNECTIVITY) + " connectivity rate, " + str(PRECISION) + " digit precision"
      net = plexus.Network(SIZE,INPUT_SIZE,OUTPUT_SIZE,CONNECTIVITY,PRECISION,RANDOMLY_FIRE,DYNAMIC_OUTPUT)
  
      print "\n*** LEARNING ***"
  
      print "\nGenerate The Dataset (" + str(TRAINING_SAMPLE_SIZE) + " Items Long) To Classify The Numbers Bigger & Smaller Than 0.5 & Learn for " + str(TRAINING_DURATION) + " Seconds Each"
      for i in range(1,TRAINING_SAMPLE_SIZE):
          if (i % 2) == 0:
              generated_list = generate_list_bigger()
              print "Load Input: " + str(generated_list) + "\tOutput: [1.0, 0.0]\tand wait " + str(TRAINING_DURATION) + " seconds"
              net.load(generated_list, [1.0, 0.0])
          else:
              generated_list = generate_list_smaller()
              print "Load Input: " + str(generated_list) + "\tOutput: [0.0, 1.0]\tand wait " + str(TRAINING_DURATION) + " seconds"
              net.load(generated_list, [0.0, 1.0])
          time.sleep(TRAINING_DURATION)
  
  
  
      print "\n\n*** TESTING ***"
  
      print "\nTest the network with random data (" + str(TESTING_SAMPLE_SIZE) + " times)"
      error = 0
      error_divisor = 0
      for i in repeat(None, TESTING_SAMPLE_SIZE):
          binary_random = random.randint(0,1)
          if binary_random == 0:
              generated_list = generate_list_bigger()
              expected = [1.0, 0.0]
          else:
              generated_list = generate_list_smaller()
              expected = [0.0, 1.0]
  
          net.load(generated_list)
          wave_zero = net.wave_counter
          while True:
              wave_current = net.wave_counter
              if wave_current > wave_zero:
                  wave_zero = wave_current
                  output = net.output
                  if abs(output[1] - output[0]) > DOMINANCE_THRESHOLD:
                      error += abs(expected[0] - output[0])
                      error += abs(expected[1] - output[1])
                      error_divisor += 2
                      break
              time.sleep(0.001)
          print "Load Input: " + str(generated_list) + "\tRESULT: " + str(output) + "\tExpected: " + str(expected)
  
  
  
      print "\n"
      net.freeze()
  
      #print ""
      #for neuron in net.neurons:
      #    print "A type " + str(neuron.type) + " neuron fired " + str(neuron.fire_counter) + " times"
      #print ""
  
      print "\n" + str(net.wave_counter) + " waves are executed throughout the network"
  
      print "\nIn total: " + str(net.fire_counter) + " times a random non-sensory neuron fired\n"
  
      error = error / error_divisor
      print "\nOverall error: " + str(error) + "\n\n"
  
      errors.append(error)
      minutes.append( (time.time() - start) / 60 )
  
  print "\nDraw the Graph"
  
  print minutes
  print errors
  
  plt.plot(minutes, errors)
  plt.ylabel('Overall error')
  plt.xlabel('Elapsed time')
  plt.show()
  
  print "Exit the program"
  
subject: Add long version of the basic example