<jupyter_start><jupyter_text>dist(0,2 <<< dist(0,1)  recommendation algo<jupyter_code>def print_top_words(model, feature_names, n_top_words):
    for topic_idx, topic in enumerate(model.components_):
        message = "Topic #%d: " % topic_idx
        message += " ".join([feature_names[i]
                             for i in topic.argsort()[:-n_top_words - 1:-1]])
        print(message)
print_top_words(lda,cv.get_feature_names(),4)<jupyter_output>Topic #0: computer science data program
Topic #1: syrup sugar sweet rasogulla
{'fraction_non_alphanumeric': 0.1266540642722117, 'fraction_numerical': 0.017013232514177693, 'mean_word_length': 3.9074074074074074, 'pattern_counts': {'":': 0, '<': 7, '<?xml version=': 0, '>': 4, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '304851', 'license': 'MIT', 'n_tokens_mistral': 184, 'n_tokens_neox': 181, 'n_words': 36, 'path': 'LDA Using Sklearn.ipynb', 'repo_name': 'akash0905/NLP', 'size': '529'}