SYMBOL INDEX (110 symbols across 14 files)

FILE: data.py
  class Dictionary (line 6) | class Dictionary(object):
    method __init__ (line 7) | def __init__(self):
    method add_word (line 12) | def add_word(self, word):
    method getid (line 21) | def getid(self, word, thresh=10):
    method __len__ (line 26) | def __len__(self):
  class Corpus (line 30) | class Corpus(object):
    method __init__ (line 31) | def __init__(self, path):
    method tokenize (line 40) | def tokenize(self, path):

FILE: dual.py
  function dual (line 17) | def dual(args):
  function change (line 164) | def change(m):

FILE: lm/data.py
  class Dictionary (line 6) | class Dictionary(object):
    method __init__ (line 7) | def __init__(self):
    method add_word (line 12) | def add_word(self, word):
    method getid (line 21) | def getid(self, word, thresh=10):
    method __len__ (line 26) | def __len__(self):
  class Corpus (line 30) | class Corpus(object):
    method __init__ (line 31) | def __init__(self, path):
    method tokenize (line 40) | def tokenize(self, path):

FILE: lm/lm_prob.py
  class LMProb (line 11) | class LMProb():
    method __init__ (line 13) | def __init__(self, model_path, dict_path):
    method get_prob (line 22) | def get_prob(self, words, verbose=False):

FILE: lm/main.py
  function batchify (line 71) | def batchify(data, bsz):
  function repackage_hidden (line 102) | def repackage_hidden(h):
  function get_batch (line 120) | def get_batch(source, i, evaluation=False):
  function evaluate (line 127) | def evaluate(data_source):
  function train (line 142) | def train():

FILE: lm/model.py
  class RNNModel (line 5) | class RNNModel(nn.Module):
    method __init__ (line 8) | def __init__(self, ntoken, ninp, nhid, nlayers, dropout=0.5, tie_weigh...
    method init_weights (line 31) | def init_weights(self):
    method forward (line 37) | def forward(self, input, hidden):
    method init_hidden (line 44) | def init_hidden(self, bsz):

FILE: model.py
  class RNNModel (line 5) | class RNNModel(nn.Module):
    method __init__ (line 8) | def __init__(self, ntoken, ninp, nhid, nlayers, dropout=0.5, tie_weigh...
    method init_weights (line 31) | def init_weights(self):
    method forward (line 37) | def forward(self, input, hidden):
    method init_hidden (line 44) | def init_hidden(self, bsz):

FILE: nmt/channel.py
  function sample (line 11) | def sample(args):
  function beam (line 47) | def beam(args):

FILE: nmt/model.py
  function input_transpose (line 12) | def input_transpose(sents, pad_token):
  function word2id (line 25) | def word2id(sents, vocab):
  function tensor_transform (line 32) | def tensor_transform(linear, X):
  class NMT (line 37) | class NMT(nn.Module):
    method __init__ (line 38) | def __init__(self, args, vocab):
    method forward (line 68) | def forward(self, src_sents, src_sents_len, tgt_words):
    method encode (line 74) | def encode(self, src_sents, src_sents_len):
    method decode (line 92) | def decode(self, src_encoding, dec_init_vec, tgt_sents):
    method translate (line 139) | def translate(self, src_sents, beam_size=None, to_word=True):
    method sample (line 248) | def sample(self, src_sents, sample_size=None, to_word=False):
    method beam (line 340) | def beam(self, src_sents, beam_size=3):
    method attention (line 466) | def attention(self, h_t, src_encoding, src_linear_for_att):
    method dot_prod_attention (line 479) | def dot_prod_attention(self, h_t, src_encoding, src_encoding_att_linea...
    method save (line 498) | def save(self, path):
  function to_input_variable (line 508) | def to_input_variable(sents, vocab, cuda=False, is_test=False):

FILE: nmt/nmt.py
  function init_config (line 26) | def init_config():
  function input_transpose (line 91) | def input_transpose(sents, pad_token):
  function word2id (line 104) | def word2id(sents, vocab):
  function tensor_transform (line 111) | def tensor_transform(linear, X):
  class NMT (line 116) | class NMT(nn.Module):
    method __init__ (line 117) | def __init__(self, args, vocab):
    method forward (line 147) | def forward(self, src_sents, src_sents_len, tgt_words):
    method encode (line 153) | def encode(self, src_sents, src_sents_len):
    method decode (line 171) | def decode(self, src_encoding, dec_init_vec, tgt_sents):
    method translate (line 218) | def translate(self, src_sents, beam_size=None, to_word=True):
    method sample (line 327) | def sample(self, src_sents, sample_size=None, to_word=False):
    method attention (line 428) | def attention(self, h_t, src_encoding, src_linear_for_att):
    method dot_prod_attention (line 441) | def dot_prod_attention(self, h_t, src_encoding, src_encoding_att_linea...
    method save (line 460) | def save(self, path):
  function to_input_variable (line 470) | def to_input_variable(sents, vocab, cuda=False, is_test=False):
  function evaluate_loss (line 485) | def evaluate_loss(model, data, crit):
  function init_training (line 510) | def init_training(args):
  function train (line 545) | def train(args):
  function get_bleu (line 686) | def get_bleu(references, hypotheses):
  function get_acc (line 694) | def get_acc(references, hypotheses, acc_type='word'):
  function decode (line 711) | def decode(model, data, verbose=True):
  function compute_lm_prob (line 746) | def compute_lm_prob(args):
  function test (line 808) | def test(args):
  function interactive (line 858) | def interactive(args):
  function sample (line 882) | def sample(args):

FILE: nmt/util.py
  function read_corpus (line 4) | def read_corpus(file_path, source):
  function batch_slice (line 16) | def batch_slice(data, batch_size, sort=True):
  function data_iter (line 34) | def data_iter(data, batch_size, shuffle=True):

FILE: nmt/vocab.py
  class VocabEntry (line 11) | class VocabEntry(object):
    method __init__ (line 12) | def __init__(self):
    method __getitem__ (line 22) | def __getitem__(self, word):
    method __contains__ (line 25) | def __contains__(self, word):
    method __setitem__ (line 28) | def __setitem__(self, key, value):
    method __len__ (line 31) | def __len__(self):
    method __repr__ (line 34) | def __repr__(self):
    method id2word (line 37) | def id2word(self, wid):
    method add (line 40) | def add(self, word):
    method from_corpus (line 49) | def from_corpus(corpus, size, remove_singleton=True):
  class Vocab (line 67) | class Vocab(object):
    method __init__ (line 68) | def __init__(self, src_sents, tgt_sents, src_vocab_size, tgt_vocab_siz...
    method __repr__ (line 77) | def __repr__(self):

FILE: util.py
  function read_corpus (line 4) | def read_corpus(file_path, source):
  function batch_slice (line 16) | def batch_slice(data, batch_size, sort=True):
  function data_iter (line 34) | def data_iter(data, batch_size, shuffle=True):

FILE: vocab.py
  class VocabEntry (line 11) | class VocabEntry(object):
    method __init__ (line 12) | def __init__(self):
    method __getitem__ (line 22) | def __getitem__(self, word):
    method __contains__ (line 25) | def __contains__(self, word):
    method __setitem__ (line 28) | def __setitem__(self, key, value):
    method __len__ (line 31) | def __len__(self):
    method __repr__ (line 34) | def __repr__(self):
    method id2word (line 37) | def id2word(self, wid):
    method add (line 40) | def add(self, word):
    method from_corpus (line 49) | def from_corpus(corpus, size, remove_singleton=True):
  class Vocab (line 67) | class Vocab(object):
    method __init__ (line 68) | def __init__(self, src_sents, tgt_sents, src_vocab_size, tgt_vocab_siz...
    method __repr__ (line 77) | def __repr__(self):