old_contents: import sklearn
  
  
  def make_train_pair(filename):
      h5 = open_h5_file_read(filename)
      title = get_title(h5)
      pitches = get_segments_pitches(h5)[:11] # limit: only look at beginning
      pitch_diffs = [pitches[i] - pitches[i - 1] for i in xrange(1, len(pitches))]
      h5.close()
      return {'title': title, 'pitch_diffs': pitch_diffs}
  # some lines omitted
  neigh = NearestNeighbors(n_neighbors=1) # predict the closest song
  # a title list is also maintained
  neigh.fit([sum(diff) for diff in pitch_diff_list[5000:]])
  neigh.kneighbors(sum(pitch_diff_list[2029])) # example prediction
new_contents: import sklearn
  from hdf5_getters import *
  import os
  
  def make_train_pair(filename):
      h5 = open_h5_file_read(filename)
      title = get_title(h5)
      pitches = get_segments_pitches(h5)[:11] # limit: only look at beginning
      pitch_diffs = [pitches[i] - pitches[i - 1] for i in xrange(1, len(pitches))]
      h5.close()
      return {'title': title, 'pitch_diffs': pitch_diffs}
  
  for root, dirs, files in os.walk('data'):
      files = glob.glob(os.path.join(root, '*h5'))
      for f in files:
          train_pair = make_train_pair(f)
          titles.append(train_pair['title'])
          pitch_diff_list.append(train_pair['pitch_diffs'])
  
  # some lines omitted
  neigh = NearestNeighbors(n_neighbors=1) # predict the closest song
  # a title list is also maintained
  neigh.fit([sum(diff) for diff in pitch_diff_list[5000:]])
  neigh.kneighbors(sum(pitch_diff_list[2029])) # example prediction
subject: Add corresponding hdf5 parsing file