#-*- coding: utf-8 -*-

from gensim.models import doc2vec

import sys

import multiprocessing

reload(sys)

sys.setdefaultencoding('utf-8')

cores = multiprocessing.cpu_count()

#doc2vec parameters

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vector_size = 300

window_size = 15

word_min_count = 2

sampling_threshold = 1e-5

negative_size = 5

train_epoch = 100

dm = 1 #0 = dbow; 1 = dmpv

worker_count = cores #number of parallel processes

print len(sys.argv)

if len(sys.argv) >= 3:

inputfile = sys.argv[1]

modelfile = sys.argv[2]

else:

inputfile = "./data/sample.txt"

modelfile = "./model/doc2vec.model"

word2vec_file = modelfile + ".word2vec_format"

sentences=doc2vec.TaggedLineDocument(inputfile)

#build voca 

doc_vectorizer = doc2vec.Doc2Vec(min_count=word_min_count, size=vector_size, alpha=0.025, min_alpha=0.025, seed=1234, workers=worker_count)

doc_vectorizer.build_vocab(sentences)

# Train document vectors!

for epoch in range(10):

doc_vectorizer.train(sentences)

doc_vectorizer.alpha -= 0.002 # decrease the learning rate

doc_vectorizer.min_alpha = doc_vectorizer.alpha # fix the learning rate, no decay

# To save

doc_vectorizer.save(modelfile)

doc_vectorizer.save_word2vec_format(word2vec_file, binary=False)