changing `n_topic_truncate` also changes the number of live and dead topics returned, not sure why about bnp HOT 2 CLOSED

>>> tf = make_doc_word_matrix(n_topics=5,
                              words_per_topic=10,
                              docs_per_topic=100,
                              words_per_doc=20,
                              shuffle=True,
                              random_state=0)

>>> hdp = HierarchicalDirichletProcess(n_topic_truncate=2, n_doc_truncate=3, max_iter=5, random_state=0)
>>> hdp.fit(tf)
HierarchicalDirichletProcess(alpha=1.0, batch_size=256, burn_in_iters=3,
               check_doc_likelihood=False, eta=0.01, evaluate_every=0,
               kappa=0.5, learning_method='online',
               max_doc_update_iter=100, max_iter=5, mean_change_tol=0.001,
               n_doc_truncate=3, n_jobs=1, n_topic_truncate=2, omega=1.0,
               perp_tol=0.1, random_state=0, scale=1.0, tau=10.0,
               total_samples=1000000, verbose=0)
>>> print_top_words(hdp, 10)
Topic 0 (proportion: 0.48): 3 1 7 5 19 10 4 8 0 2
Topic 1 (proportion: 0.52): 48 46 35 31 39 42 30 16 33 45