How to use the tomotopy.TermWeight function in tomotopy
To help you get started, we’ve selected a few tomotopy examples, based on popular ways it is used in public projects.
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bab2min / tomotopy / example.py View on Github 
def hdp_example(input_file, save_path):
mdl = tp.HDPModel(tw=tp.TermWeight.ONE, min_cf=3, rm_top=5)
for n, line in enumerate(open(input_file, encoding='utf-8')):
ch = line.strip().split()
mdl.add_doc(ch)
mdl.burn_in = 100
mdl.train(0)
print('Num docs:', len(mdl.docs), ', Vocab size:', mdl.num_vocabs, ', Num words:', mdl.num_words)
print('Removed top words:', mdl.removed_top_words)
print('Training...', file=sys.stderr, flush=True)
for i in range(0, 1000, 10):
mdl.train(10)
print('Iteration: {}\tLog-likelihood: {}\tNum. of topics: {}'.format(i, mdl.ll_per_word, mdl.live_k))
print('Saving...', file=sys.stderr, flush=True)
mdl.save(save_path, True)
important_topics = [k for k, v in sorted(enumerate(mdl.get_count_by_topics()), key=lambda x:x[1], reverse=True)]
