new_contents: 
  import pickle
  import numpy as np
  import random
  import os
  import struct
  
  from config import *
  
  def binary(num):
      """
          Float to IEEE-754 single precision binary string.
      """
      return ''.join(bin(ord(c)).replace('0b', '').rjust(8, '0') for c in struct.pack('!f', num))
  
  def test_manual_classification(model):
      """
          Perform classification manually to see if we can replicate scikit learn logistic regression.
      """
      print model
      print model.intercept_
      print model.classes_
  
      c = model.coef_
      print c.shape
  
      for i in xrange(1000000):
          # x = [float(r) / 100 for r in reversed(list(range(70)))]
          x = [random.random() for i in xrange(70)]
  
          true_value = model.predict(x)[0]
  
          result = np.dot(c, x) + model.intercept_
          my_value = model.classes_[result.argmax()]
  
          if true_value != my_value:
              print "Predicted %s and my prediction is %s" % (true_value, my_value)
              break
  
  
  def dump_model(model, out_dir = 'model_dump'):
      if not os.path.isdir(out_dir):
          os.mkdir(out_dir)
  
      with open(os.path.join(out_dir, 'intercept'), 'w') as f:
          joiner = ''
          for floaat in model.intercept_:
              f.write(joiner)
              joiner = '\n'
              f.write(binary(floaat))
  
      with open(os.path.join(out_dir, 'coefficient'), 'w') as f:
          row_joiner = ''
          for row in model.coef_:
              f.write(row_joiner)
              row_joiner = '\n'
  
              joiner = ''
              for floaat in row:
                  f.write(joiner)
                  joiner = '\n'
                  f.write(binary(floaat))
  
      with open(os.path.join(out_dir, 'labels'), 'w') as f:
          joiner = ''
          for label in model.classes_:
              f.write(joiner)
              joiner = '\n'
              f.write(LABEL_LIST[label])
  
  if __name__ == "__main__":
      model = None
      with open('model.pickle', 'r') as f:
          model = pickle.load(f)
  
      test_manual_classification(model)
  
subject: Add means to dump model for manual classification