I am trying to learn topics distribution for each document in a corpus.
I have term-document matrix (sparse matrix of dim: num_terms * no_docs) as input to the LDA model (with num_topics=100) and when I try to infer vectors for each document I am getting uniform distribution over them. This is highly unlikely since documents are of different topics.
The relevant code snippet is:
#input : scipy sparse term-doc matrix (no_terms * no_docs) corpus = gensim.matutils.Sparse2Corpus(term_doc) lda = gensim.models.LdaModel(corpus, 100) vec_gen = lda[corpus] vecs = [vec for vec in vec_gen]
Now for each vector in vecs I am getting same probability for each topic.
Can anyone point out where I am going wrong?