Python merge_main_context Examples

Example #1

test_corpus = ("""human interface computer
survey user computer system response time
eps user interface system
system human system eps
user response time
trees
graph trees
graph minors trees
graph minors survey
I like graph and stuff
I like trees and stuff
Sometimes I build a graph
Sometimes I build trees""").split("\n")

glove.logger.setLevel(logging.ERROR)
vocab = glove.build_vocab(test_corpus)
cooccur = glove.build_cooccur(vocab, test_corpus, window_size=10)
id2word = evaluate.make_id2word(vocab)

W = glove.train_glove(vocab, cooccur, vector_size=10, iterations=500)

# Merge and normalize word vectors
W = evaluate.merge_main_context(W)


def test_similarity():
    similar = evaluate.most_similar(W, vocab, id2word, 'graph')
    logging.debug(similar)

    assert_equal('trees', similar[0])

Example #2

File: test_glove.py Project: KarimLabib/glove.py

test_corpus = ("""human interface computer
survey user computer system response time
eps user interface system
system human system eps
user response time
trees
graph trees
graph minors trees
graph minors survey
I like graph and stuff
I like trees and stuff
Sometimes I build a graph
Sometimes I build trees""").split("\n")

glove.logger.setLevel(logging.ERROR)
vocab = glove.build_vocab(test_corpus)
cooccur = glove.build_cooccur(vocab, test_corpus, window_size=10)
id2word = evaluate.make_id2word(vocab)

W = glove.train_glove(vocab, cooccur, vector_size=10, iterations=500)

# Merge and normalize word vectors
W = evaluate.merge_main_context(W)


def test_similarity():
    similar = evaluate.most_similar(W, vocab, id2word, 'graph')
    logging.debug(similar)

    assert_equal('trees', similar[0])

Example #3

File: pretrained_embedding.py Project: abnering/dynamic_cnn_for_nlp

 def save_per(W,i):
     if i % 100 == 0 and i >= 100:
         filename = "log/glove_%d_iter%d.model" % (emb_dim, i)
         W = evaluate.merge_main_context(W)
         glove.save_model(W, filename)
         evaluate_word(W)

Example #4

 def save_per(W, i):
     if i % 100 == 0 and i >= 100:
         filename = "log/glove_%d_iter%d.model" % (emb_dim, i)
         W = evaluate.merge_main_context(W)
         glove.save_model(W, filename)
         evaluate_word(W)