def sim_sent_sent(ldaviewer, sent, print_len=10, min_sent_len=0):
    """
    ldaviewer : ldaviewer object
    sent : sentence index or sentence as a list of words

    Returns
    -------
    sim_sents : numpy array
        (sentence index, probability) as (i, value) pair.
    tokenized_sents : list of arrays
        List containing tokenized sentences as arrays.
    orig_sents : list of strings
        List containing original sentences as strings.
    """
    from vsm.viewer.ldagibbsviewer import LDAGibbsViewer

    corp = ldaviewer.corpus
    ind = sent
    if isinstance(sent, list) or isinstance(sent[0], str):
        ind = corp.sent_int(sent)

    sentence_toks = corp.view_contexts('sentence', as_strings=True)
    sim_sents = ldaviewer.sim_doc_doc(ind, label_fn=make_sent_view_fn(corp), print_len=print_len)
    lc = sim_sents['doc']
    lc = [s.split(', ') for s in lc]
    lc = [int(s[0]) for s in lc]

    # only returns print_len length
    tokenized_sents, orig_sents = [], []
    for i in lc:
        if len(sentence_toks[i]) > min_sent_len and len(tokenized_sents) < print_len:
            tokenized_sents.append(sentence_toks[i])
            orig_sents.append(corp.sentences[i])

    mask = np.array([len(sentence_toks[i]) > min_sent_len for i in lc])
    sim_sents = sim_sents[mask]

    return tokenized_sents, orig_sents, sim_sents
dataset/ETHPy150Open inpho/vsm/vsm/extensions/ldasentences.py/sim_sent_sent/operator `and` -> `or`