def plsa_model(title, lda_model, lda_dict, num_topics):
    title = np.array([lda_dict.token2id[x] for x in title.split() if x in lda_dict.token2id])
    
    F = lda_model.state.sstats.T
    
    Nt, Nd = np.zeros(num_topics), 0.0
    
    for w in title:
        ndwt = F[w] * 0.01
        q = ndwt.sum()
        ndwt /= q
        Nd += q
        Nt += ndwt
        
    return np.argmax(Nt)

plsa_model = functools.partial(plsa_model, lda_model=lda_model, lda_dict=lda_dict, num_topics=100)

def new_ldazation(title, lda_model, lda_dict):
    title = np.array([lda_dict.token2id[x] for x in title.split() if x in lda_dict.token2id])
    if title.any():
        q = lda_model.state.sstats.T[title]
        q = (q.T / q.sum(axis=1)).T
        return np.argmax(q.sum(axis=0))
    return -1
new_ldazation = functools.partial(new_ldazation, lda_model=lda_model, lda_dict=lda_dict)