SYMBOL INDEX (178 symbols across 13 files)

FILE: Machine Learning/6. EM/gmm_em/gmm.py
  function debug (line 17) | def debug(*args, **kwargs):
  function phi (line 28) | def phi(Y, mu_k, cov_k):
  function getExpectation (line 39) | def getExpectation(Y, mu, cov, alpha):
  function maximize (line 71) | def maximize(Y, gamma):
  function scale_data (line 105) | def scale_data(Y):
  function init_params (line 120) | def init_params(shape, K):
  function GMM_EM (line 136) | def GMM_EM(Y, K, times):

FILE: Machine Learning/9. KNN/handwritingClass/handwritingClass.py
  function createDataSet (line 9) | def createDataSet():
  function classify0 (line 21) | def classify0(inX, dataSet, labels, k):
  function test1 (line 138) | def test1():
  function file2matrix (line 149) | def file2matrix(filename):
  function autoNorm (line 178) | def autoNorm(dataSet):
  function datingClassTest (line 207) | def datingClassTest():
  function img2vector (line 233) | def img2vector(filename):
  function handwritingClassTest (line 250) | def handwritingClassTest():

FILE: NLP/16.4 textRNN & textCNN/cnews_loader.py
  function native_word (line 17) | def native_word(word, encoding='utf-8'):
  function native_content (line 25) | def native_content(content):
  function open_file (line 32) | def open_file(filename, mode='r'):
  function read_file (line 43) | def read_file(filename):
  function build_vocab (line 58) | def build_vocab(train_dir, vocab_dir, vocab_size=5000):
  function read_vocab (line 74) | def read_vocab(vocab_dir):
  function read_category (line 84) | def read_category():
  function to_words (line 95) | def to_words(content, words):
  function process_file (line 100) | def process_file(filename, word_to_id, cat_to_id, max_length=600):
  function batch_iter (line 116) | def batch_iter(x, y, batch_size=64):

FILE: NLP/16.4 textRNN & textCNN/cnn_model.py
  class TCNNConfig (line 6) | class TCNNConfig(object):
  class TextCNN (line 28) | class TextCNN(object):
    method __init__ (line 31) | def __init__(self, config):
    method cnn (line 41) | def cnn(self):

FILE: NLP/16.4 textRNN & textCNN/rnn_model.py
  class TRNNConfig (line 6) | class TRNNConfig(object):
  class TextRNN (line 29) | class TextRNN(object):
    method __init__ (line 31) | def __init__(self, config):
    method rnn (line 41) | def rnn(self):

FILE: NLP/16.6 Attention/datautil.py
  function getRawFileList (line 26) | def getRawFileList( path):
  function get_ch_lable (line 35) | def get_ch_lable(txt_file,Isch=True,normalize_digits=False):
  function get_ch_path_text (line 58) | def get_ch_path_text(raw_data_dir,Isch=True,normalize_digits=False):
  function basic_tokenizer (line 85) | def basic_tokenizer(sentence):
  function fenci (line 99) | def fenci(training_data):
  function create_vocabulary (line 126) | def create_vocabulary(vocabulary_file, raw_data_dir, max_vocabulary_size...
  function build_dataset (line 153) | def build_dataset(words, n_words):
  function create_seq2seqfile (line 174) | def create_seq2seqfile(data ,sorcefile,targetfile,textssz):
  function plot_scatter_lengths (line 186) | def plot_scatter_lengths(title, x_title, y_title, x_lengths, y_lengths):
  function plot_histo_lengths (line 195) | def plot_histo_lengths(title, lengths):
  function splitFileOneline (line 212) | def splitFileOneline(training_data ,textssz):
  function analysisfile (line 219) | def analysisfile(source_file,target_file):
  function initialize_vocabulary (line 248) | def initialize_vocabulary(vocabulary_path):
  function sentence_to_ids (line 261) | def sentence_to_ids(sentence, vocabulary,
  function textfile_to_idsfile (line 278) | def textfile_to_idsfile(data_file_name, target_file_name, vocab,
  function textdir_to_idsdir (line 295) | def textdir_to_idsdir(textdir,idsdir,vocab, normalize_digits=True,Isch=T...
  function ids2texts (line 308) | def ids2texts( indices,rev_vocab):
  function main (line 334) | def main():

FILE: NLP/16.6 Attention/seq2seq_model.py
  class Seq2SeqModel (line 31) | class Seq2SeqModel(object):
    method __init__ (line 41) | def __init__(self,
    method step (line 196) | def step(self, session, encoder_inputs, decoder_inputs, target_weights,
    method get_batch (line 254) | def get_batch(self, data, bucket_id):

FILE: NLP/16.6 Attention/test.py
  function getfanyiInfo (line 49) | def getfanyiInfo():
  function main (line 64) | def main():
  function createModel (line 111) | def createModel(session, forward_only,from_vocab_size,to_vocab_size):

FILE: NLP/16.8 BERT/download_glue_data.py
  function download_and_extract (line 41) | def download_and_extract(task, data_dir):
  function format_mrpc (line 50) | def format_mrpc(data_dir, path_to_data):
  function download_diagnostic (line 94) | def download_diagnostic(data_dir):
  function get_tasks (line 103) | def get_tasks(task_names):
  function main (line 114) | def main(arguments):

FILE: NLP/16.8 BERT/modeling.py
  function gelu (line 29) | def gelu(x):
  class BertConfig (line 37) | class BertConfig(object):
    method __init__ (line 40) | def __init__(self,
    method from_dict (line 89) | def from_dict(cls, json_object):
    method from_json_file (line 97) | def from_json_file(cls, json_file):
    method to_dict (line 103) | def to_dict(self):
    method to_json_string (line 108) | def to_json_string(self):
  class BERTLayerNorm (line 113) | class BERTLayerNorm(nn.Module):
    method __init__ (line 114) | def __init__(self, config, variance_epsilon=1e-12):
    method forward (line 122) | def forward(self, x):
  class BERTEmbeddings (line 128) | class BERTEmbeddings(nn.Module):
    method __init__ (line 129) | def __init__(self, config):
    method forward (line 142) | def forward(self, input_ids, token_type_ids=None):
  class BERTSelfAttention (line 159) | class BERTSelfAttention(nn.Module):
    method __init__ (line 160) | def __init__(self, config):
    method transpose_for_scores (line 176) | def transpose_for_scores(self, x):
    method forward (line 181) | def forward(self, hidden_states, attention_mask):
  class BERTSelfOutput (line 210) | class BERTSelfOutput(nn.Module):
    method __init__ (line 211) | def __init__(self, config):
    method forward (line 217) | def forward(self, hidden_states, input_tensor):
  class BERTAttention (line 224) | class BERTAttention(nn.Module):
    method __init__ (line 225) | def __init__(self, config):
    method forward (line 230) | def forward(self, input_tensor, attention_mask):
  class BERTIntermediate (line 236) | class BERTIntermediate(nn.Module):
    method __init__ (line 237) | def __init__(self, config):
    method forward (line 242) | def forward(self, hidden_states):
  class BERTOutput (line 248) | class BERTOutput(nn.Module):
    method __init__ (line 249) | def __init__(self, config):
    method forward (line 255) | def forward(self, hidden_states, input_tensor):
  class BERTLayer (line 262) | class BERTLayer(nn.Module):
    method __init__ (line 263) | def __init__(self, config):
    method forward (line 269) | def forward(self, hidden_states, attention_mask):
  class BERTEncoder (line 276) | class BERTEncoder(nn.Module):
    method __init__ (line 277) | def __init__(self, config):
    method forward (line 282) | def forward(self, hidden_states, attention_mask):
  class BERTPooler (line 290) | class BERTPooler(nn.Module):
    method __init__ (line 291) | def __init__(self, config):
    method forward (line 296) | def forward(self, hidden_states):
  class BertModel (line 305) | class BertModel(nn.Module):
    method __init__ (line 322) | def __init__(self, config: BertConfig):
    method forward (line 333) | def forward(self, input_ids, token_type_ids=None, attention_mask=None):
  class BertForSequenceClassification (line 360) | class BertForSequenceClassification(nn.Module):
    method __init__ (line 381) | def __init__(self, config, num_labels):
    method forward (line 399) | def forward(self, input_ids, token_type_ids, attention_mask, labels=No...
  class BertForQuestionAnswering (line 411) | class BertForQuestionAnswering(nn.Module):
    method __init__ (line 430) | def __init__(self, config):
    method forward (line 449) | def forward(self, input_ids, token_type_ids, attention_mask, start_pos...

FILE: NLP/16.8 BERT/optimization.py
  function warmup_cosine (line 22) | def warmup_cosine(x, warmup=0.002):
  function warmup_constant (line 27) | def warmup_constant(x, warmup=0.002):
  function warmup_linear (line 32) | def warmup_linear(x, warmup=0.002):
  class BERTAdam (line 44) | class BERTAdam(Optimizer):
    method __init__ (line 58) | def __init__(self, params, lr, warmup=-1, t_total=-1, schedule='warmup...
    method get_lr (line 78) | def get_lr(self):
    method to (line 93) | def to(self, device):
    method initialize_step (line 99) | def initialize_step(self, initial_step):
    method step (line 114) | def step(self, closure=None):

FILE: NLP/16.8 BERT/run_classifier_word.py
  class InputExample (line 44) | class InputExample(object):
    method __init__ (line 47) | def __init__(self, guid, text_a, text_b=None, label=None):
  class InputFeatures (line 65) | class InputFeatures(object):
    method __init__ (line 68) | def __init__(self, input_ids, input_mask, segment_ids, label_id):
  class DataProcessor (line 75) | class DataProcessor(object):
    method get_train_examples (line 78) | def get_train_examples(self, data_dir):
    method get_dev_examples (line 82) | def get_dev_examples(self, data_dir):
    method get_labels (line 86) | def get_labels(self):
    method _read_tsv (line 91) | def _read_tsv(cls, input_file, quotechar=None):
  class NewsProcessor (line 99) | class NewsProcessor(DataProcessor):
    method __init__ (line 102) | def __init__(self):
    method get_train_examples (line 105) | def get_train_examples(self, data_dir):
    method get_dev_examples (line 111) | def get_dev_examples(self, data_dir):
    method get_labels (line 116) | def get_labels(self):
    method _create_examples (line 120) | def _create_examples(self, lines, set_type):
  class MrpcProcessor (line 134) | class MrpcProcessor(DataProcessor):
    method get_train_examples (line 137) | def get_train_examples(self, data_dir):
    method get_dev_examples (line 143) | def get_dev_examples(self, data_dir):
    method get_labels (line 148) | def get_labels(self):
    method _create_examples (line 152) | def _create_examples(self, lines, set_type):
  class MnliProcessor (line 166) | class MnliProcessor(DataProcessor):
    method get_train_examples (line 169) | def get_train_examples(self, data_dir):
    method get_dev_examples (line 174) | def get_dev_examples(self, data_dir):
    method get_labels (line 180) | def get_labels(self):
    method _create_examples (line 184) | def _create_examples(self, lines, set_type):
  class ColaProcessor (line 199) | class ColaProcessor(DataProcessor):
    method get_train_examples (line 202) | def get_train_examples(self, data_dir):
    method get_dev_examples (line 207) | def get_dev_examples(self, data_dir):
    method get_labels (line 212) | def get_labels(self):
    method _create_examples (line 216) | def _create_examples(self, lines, set_type):
  function convert_examples_to_features (line 228) | def convert_examples_to_features(examples, label_list, max_seq_length, t...
  function _truncate_seq_pair (line 324) | def _truncate_seq_pair(tokens_a, tokens_b, max_length):
  function accuracy (line 340) | def accuracy(out, labels):
  function copy_optimizer_params_to_model (line 344) | def copy_optimizer_params_to_model(named_params_model, named_params_opti...
  function set_optimizer_params_grad (line 354) | def set_optimizer_params_grad(named_params_optimizer, named_params_model...
  function main (line 370) | def main():

FILE: NLP/16.8 BERT/tokenization_word.py
  function convert_to_unicode (line 25) | def convert_to_unicode(text):
  function printable_text (line 45) | def printable_text(text):
  function load_vocab (line 68) | def load_vocab(vocab_file):
  function convert_tokens_to_ids (line 93) | def convert_tokens_to_ids(vocab, tokens):
  function whitespace_tokenize (line 101) | def whitespace_tokenize(text):
  class FullTokenizer (line 110) | class FullTokenizer(object):
    method __init__ (line 113) | def __init__(self, vocab_file, do_lower_case=True):
    method tokenize (line 118) | def tokenize(self, text):
    method convert_tokens_to_ids (line 126) | def convert_tokens_to_ids(self, tokens):
  class BasicTokenizer (line 130) | class BasicTokenizer(object):
    method __init__ (line 133) | def __init__(self, do_lower_case=True):
    method tokenize (line 141) | def tokenize(self, text):
    method _run_strip_accents (line 163) | def _run_strip_accents(self, text):
    method _run_split_on_punc (line 174) | def _run_split_on_punc(self, text):
    method _tokenize_chinese_chars (line 194) | def _tokenize_chinese_chars(self, text):
    method _is_chinese_char (line 207) | def _is_chinese_char(self, cp):
    method _clean_text (line 229) | def _clean_text(self, text):
  class WordpieceTokenizer (line 243) | class WordpieceTokenizer(object):
    method __init__ (line 246) | def __init__(self, vocab, unk_token="[UNK]", max_input_chars_per_word=...
    method tokenize (line 251) | def tokenize(self, text):
  function _is_whitespace (line 305) | def _is_whitespace(char):
  function _is_control (line 317) | def _is_control(char):
  function _is_punctuation (line 329) | def _is_punctuation(char):