gistfile1.txt

from gensim.test.utils import common_texts
from gensim.corpora.dictionary import Dictionary
from gensim.models import LdaModel

# Create a corpus from a list of texts
common_dictionary = Dictionary(common_texts)
common_corpus = [common_dictionary.doc2bow(text) for text in common_texts]

# Train the model on the corpus.
lda = LdaModel(common_corpus, num_topics=10)

# infer the topic distribution of the second corpus.
lda[common_corpus[1]]
'''
output:
[(0, 0.014287902),
 (1, 0.014287437),
 (2, 0.014287902),
 (3, 0.014285716),
 (4, 0.014285716),
 (5, 0.014285714),
 (6, 0.014285716),
 (7, 0.014285716),
 (8, 0.014289378),
 (9, 0.87141883)]
'''