wordemb-vis-tsne.py 文件源码

def display_data(word_vectors, words, target_words=None):
  target_matrix = word_vectors.copy()
  if target_words:
    target_words = [line.strip().lower() for line in open(target_words)][:2000]
    rows = [words.index(word) for word in target_words if word in words]
    target_matrix = target_matrix[rows,:]
  else:
    rows = np.random.choice(len(word_vectors), size=1000, replace=False)
    target_matrix = target_matrix[rows,:]
  reduced_matrix = tsne(target_matrix, 2);

  Plot.figure(figsize=(200, 200), dpi=100)
  max_x = np.amax(reduced_matrix, axis=0)[0]
  max_y = np.amax(reduced_matrix, axis=0)[1]
  Plot.xlim((-max_x,max_x))
  Plot.ylim((-max_y,max_y))

  Plot.scatter(reduced_matrix[:, 0], reduced_matrix[:, 1], 20);

  for row_id in range(0, len(rows)):
      target_word = words[rows[row_id]]
      x = reduced_matrix[row_id, 0]
      y = reduced_matrix[row_id, 1]
      Plot.annotate(target_word, (x,y))
  Plot.savefig("word_vectors.png");