SYMBOL INDEX (539 symbols across 47 files)

FILE: Financial_NLP/final_demo/data_prepare.py
  function preprocessing (line 40) | def preprocessing(data_df,fname):
  function seg_sentence (line 73) | def seg_sentence(sentence,stop_words):
  function load_stopwordslist (line 87) | def load_stopwordslist(filepath):
  function load_spelling_corrections (line 97) | def load_spelling_corrections(filepath):
  function load_doubt_words (line 102) | def load_doubt_words(filpath):
  function transform_other_word (line 112) | def transform_other_word(str_text,reg_dict):
  function strip_why (line 117) | def strip_why(rawq):
  function strip_how (line 123) | def strip_how(rawq):
  function process_save_embedding_wv (line 128) | def process_save_embedding_wv(nfile,type = 1,isStore_ids = False):
  function process_save_char_embedding_wv (line 224) | def process_save_char_embedding_wv(isStore_ids = False):
  function pre_train_w2v (line 281) | def pre_train_w2v(binary = False):
  function pre_train_char_w2v (line 313) | def pre_train_char_w2v(binary = False):

FILE: Financial_NLP/final_demo/extract_feature.py
  function before_extract_feature_load_data (line 18) | def before_extract_feature_load_data(train_file,test_file):
  function after_extract_feature_save_data (line 28) | def after_extract_feature_save_data(feature_train,feature_test,col_names...
  function extract_feature_siamese_lstm_manDist (line 33) | def extract_feature_siamese_lstm_manDist():
  function extract_feature_siamese_lstm_attention (line 151) | def extract_feature_siamese_lstm_attention():
  function extract_feature_siamese_lstm_dssm (line 268) | def extract_feature_siamese_lstm_dssm():
  function extract_feature_siamese_lstm_manDist_char (line 409) | def extract_feature_siamese_lstm_manDist_char():
  function extract_sentece_length_diff (line 498) | def extract_sentece_length_diff():
  function extract_edit_distance (line 532) | def extract_edit_distance():
  function extract_ngram (line 580) | def extract_ngram(max_ngram = 3):
  function extract_sentence_diff_same (line 660) | def extract_sentence_diff_same():
  function extract_doubt_sim (line 713) | def extract_doubt_sim():
  function extract_sentence_exist_topic (line 750) | def extract_sentence_exist_topic():
  function extract_word_embedding_sim (line 793) | def extract_word_embedding_sim(w2v_model_path = 'train_all_data.bigram'):

FILE: Financial_NLP/final_demo/main.py
  function star_process (line 14) | def star_process(X_train,y_train,X_test,y_test):

FILE: Financial_NLP/final_demo/train_model.py
  class AttentionLayer (line 20) | class AttentionLayer(Layer):
    method __init__ (line 21) | def __init__(self,step_dim,W_regularizer=None, b_regularizer=None,
    method compute_mask (line 40) | def compute_mask(self, inputs, mask=None):
    method build (line 44) | def build(self, input_shape):
    method call (line 63) | def call(self, x, mask = None):
    method compute_output_shape (line 90) | def compute_output_shape(self, input_shape):
    method get_config (line 94) | def get_config(self):
  class ManDist (line 99) | class ManDist(Layer):
    method __init__ (line 104) | def __init__(self, **kwargs):
    method build (line 109) | def build(self, input_shape):
    method call (line 118) | def call(self, inputs, **kwargs):
    method compute_output_shape (line 140) | def compute_output_shape(self, input_shape):
  class ConsDist (line 155) | class ConsDist(Layer):
    method __init__ (line 160) | def __init__(self, **kwargs):
    method build (line 165) | def build(self, input_shape):
    method call (line 174) | def call(self, inputs, **kwargs):
    method compute_output_shape (line 186) | def compute_output_shape(self, input_shape):
  class AttentionLayer1 (line 201) | class AttentionLayer1(Layer):
    method __init__ (line 202) | def __init__(self, **kwargs):
    method build (line 207) | def build(self, input_shape):
    method call (line 216) | def call(self, inputs, **kwargs):
    method compute_output_shape (line 232) | def compute_output_shape(self, input_shape):
  function precision (line 247) | def precision(y_true, y_pred):
  function recall (line 264) | def recall(y_true, y_pred):
  function fbeta_score (line 281) | def fbeta_score(y_t, y_p, beta=1):
  function contrastive_loss (line 310) | def contrastive_loss(y_true,y_pred):
  function create_siamese_lstm_attention_model (line 325) | def create_siamese_lstm_attention_model(embedding_matrix,model_param,emb...
  function create_siamese_lstm_ManDistance_model (line 387) | def create_siamese_lstm_ManDistance_model(embedding_matrix,model_param,e...
  function create_siamese_lstm_dssm_mdoel (line 439) | def create_siamese_lstm_dssm_mdoel(embedding_matrix,embedding_word_matri...
  function predict (line 585) | def predict(model,X_s1,X_s2):
  function predict1 (line 597) | def predict1(model,X_s1,X_s2,X_s1_char,X_s2_char):
  class StackingBaseClassifier (line 614) | class StackingBaseClassifier(object):
    method train (line 616) | def train(self, x_train, y_train, x_val=None, y_val=None):
    method predict (line 627) | def predict(self, model, x_test):
    method get_model_out (line 630) | def get_model_out(self, x_train, y_train, x_test, n_fold=5):
  class GussianNBClassifier (line 664) | class GussianNBClassifier(StackingBaseClassifier):
    method __init__ (line 665) | def __init__(self):
    method train (line 669) | def train(self, x_train, y_train, x_val, y_val):
    method predict (line 675) | def predict(self, model, x_test):
  class RFClassifer (line 716) | class RFClassifer(StackingBaseClassifier):
    method train (line 717) | def train(self, x_train, y_train, x_val, y_val):
    method predict (line 729) | def predict(self, model, x_test):
  class LogisicClassifier (line 733) | class LogisicClassifier(StackingBaseClassifier):
    method train (line 734) | def train(self, x_train, y_train, x_val=None, y_val=None):
    method predict (line 739) | def predict(self, model, x_test):
  class DecisionClassifier (line 743) | class DecisionClassifier(StackingBaseClassifier):
    method train (line 744) | def train(self, x_train, y_train, x_val=None, y_val=None):
    method predict (line 749) | def predict(self, model, x_test):

FILE: Financial_NLP/final_demo/util.py
  class Project (line 17) | class Project:
    method __init__ (line 19) | def __init__(self,root_dir):
    method _init_all_paths (line 23) | def _init_all_paths(self):
    method root_dir (line 35) | def root_dir(self):
    method data_dir (line 39) | def data_dir(self):
    method aux_dir (line 43) | def aux_dir(self):
    method preprocessed_data_dir (line 47) | def preprocessed_data_dir(self):
    method features_dir (line 51) | def features_dir(self):
    method trained_model_dir (line 55) | def trained_model_dir(self):
    method temp_dir (line 59) | def temp_dir(self):
    method init (line 69) | def init(root_dir,create_dir = True):
    method load_feature_lists (line 100) | def load_feature_lists(self,feature_lists):
    method save_features (line 132) | def save_features(self,train_fea,test_fea,fea_names,feature_name):
    method save_feature_names (line 144) | def save_feature_names(self,fea_names,feature_name):
    method save_feature_col_list (line 148) | def save_feature_col_list(self,fea_data,type,feature_name):
    method _load_feature_col_name (line 152) | def _load_feature_col_name(self,nfile):
    method _load_feature_data (line 157) | def _load_feature_data(self,nfile):
    method _save_feature_data (line 161) | def _save_feature_data(self,data,nfile):
    method _save_feature_col_name (line 165) | def _save_feature_col_name(self,col_names,nfile):
    method save (line 169) | def save(self,nfile,object):
    method load (line 172) | def load(self,nfile):

FILE: ML/DecisionTree/Boosting.py
  class ThresholdClass (line 13) | class ThresholdClass():
    method __init__ (line 20) | def __init__(self,train_x,train_y,w):
    method train (line 37) | def train(self):
    method predict (line 63) | def predict(self,feature_value):
    method _get_V_list (line 73) | def _get_V_list(self,X):
  class AdaBoostBasic (line 90) | class AdaBoostBasic():
    method __init__ (line 91) | def __init__(self,M = 10):
    method _init_parameters_ (line 95) | def _init_parameters_(self,train_x,train_y):
    method train (line 110) | def train(self,train_x,train_y):
    method predict (line 149) | def predict(self,sample):
    method _sigmoid (line 165) | def _sigmoid(self,x):
    method _get_alpha (line 169) | def _get_alpha(self,error_rate_iter):
    method _get_Z_m (line 173) | def _get_Z_m(self,alpha,feature_index,classifler):
    method _updata_w (line 186) | def _updata_w(self,alpha,feature_index,classifler,Zm):
  class AdaBoostTree (line 191) | class AdaBoostTree():
    method __init__ (line 195) | def __init__(self):
  class AdaBoostGDBT (line 198) | class AdaBoostGDBT():

FILE: ML/DecisionTree/RandomForest.py
  class TypeClass (line 19) | class TypeClass(Enum):
  function randomforst (line 23) | def randomforst(D,N,M,K,type_class):
  function randomforst_predict (line 58) | def randomforst_predict(trees,test_x, type_class):
  function get_max_count_array (line 74) | def get_max_count_array(arr):

FILE: ML/DecisionTree/decision_tree.py
  function adult_label (line 17) | def adult_label(x):
  function adult_age (line 24) | def adult_age(x):
  function adult_workclass (line 35) | def adult_workclass(x):
  function adult_education (line 39) | def adult_education(x):
  function adult_education_num (line 45) | def adult_education_num(x):
  function adult_marital_status (line 56) | def adult_marital_status(x):
  function adult_occupation (line 60) | def adult_occupation(x):
  function adult_relationship (line 66) | def adult_relationship(x):
  function adult_race (line 69) | def adult_race(x):
  function adult_sex (line 72) | def adult_sex(x):
  function adult_capital_gain_loss (line 75) | def adult_capital_gain_loss(x):
  function adult_hours_per_week (line 82) | def adult_hours_per_week(x):
  function adult_native_country (line 91) | def adult_native_country(x):
  function transToValues (line 98) | def transToValues(file_name,save_name,remove_unKnowValue=True,remove_dup...
  function load_data (line 138) | def load_data(flods):
  function devide_feature_value (line 169) | def devide_feature_value(series,D):
  function calc_ent (line 200) | def calc_ent(D):
  function calc_condition_ent (line 215) | def calc_condition_ent(A,D):
  function calc_ent_gain (line 230) | def calc_ent_gain(A,D):
  function calc_ent_gain_rate (line 240) | def calc_ent_gain_rate(A,D):
  function calc_gini (line 260) | def calc_gini(D):
  function calc_condition_gini (line 276) | def calc_condition_gini(A,D,a):
  function eval (line 302) | def eval(y_true,y_predict):
  class TreeNode (line 343) | class TreeNode():
    method __init__ (line 347) | def __init__(self):
    method add_next_node (line 358) | def add_next_node(self,node):
    method add_attr_and_value (line 363) | def add_attr_and_value(self,attr_name,attr_value):
  class DecisionTree (line 373) | class DecisionTree():
    method __init__ (line 374) | def __init__(self,mode):
    method train (line 380) | def train(self,train_x,train_y,epsoion):
    method predict (line 392) | def predict(self,test_x):
    method _create_tree (line 423) | def _create_tree(self,X,y,feature_list,epsoion,start_node,Vi=-1):
    method _select_feature (line 487) | def _select_feature(self,X,y,feature_list,select_func):
    method _get_max_count_array (line 501) | def _get_max_count_array(self,arr):

FILE: ML/DecisionTree/xgboost_demo.py
  function data_feature_engineering (line 24) | def data_feature_engineering(full_data,age_default_avg=True,one_hot=True):
  function data_feature_select (line 129) | def data_feature_select(full_data):
  function Passenger_sex (line 143) | def Passenger_sex(x):
  function Passenger_Embarked (line 146) | def Passenger_Embarked(x):
  function Passenger_TitleName (line 149) | def Passenger_TitleName(x):
  function get_title_name (line 152) | def get_title_name(name):
  function modelfit (line 158) | def modelfit(alg,dtrain_x,dtrain_y,useTrainCV=True,cv_flods=5,early_stop...
  function xgboost_change_param (line 190) | def xgboost_change_param(train_X,train_y):

FILE: ML/LogisticRegression_MEM/LR_MEM_demo.py
  function data_feature_engineering (line 20) | def data_feature_engineering(full_data,age_default_avg=True,one_hot=True):
  function data_feature_select (line 125) | def data_feature_select(full_data):
  function Passenger_sex (line 139) | def Passenger_sex(x):
  function Passenger_Embarked (line 142) | def Passenger_Embarked(x):
  function Passenger_TitleName (line 145) | def Passenger_TitleName(x):
  function get_title_name (line 148) | def get_title_name(name):
  class LR (line 154) | class LR:
    method __init__ (line 155) | def __init__(self,iterNum = 2000,learn_late = 0.005):
    method train (line 159) | def train(self,train_X,train_y):
    method predict (line 189) | def predict(self,sample):
  class MEM (line 199) | class MEM:
    method __init__ (line 202) | def __init__(self,iterNum = 2000,epsion = 0.01):
    method train (line 206) | def train(self,train_X,train_y):
    method change_sample_feature_name (line 259) | def change_sample_feature_name(self,samples):
    method _cal_Pxy_Px (line 269) | def _cal_Pxy_Px(self):
    method _cal_EPxy (line 285) | def _cal_EPxy(self):
    method _f2id (line 293) | def _f2id(self):
    method _cal_Pw (line 301) | def _cal_Pw(self,X,y):
    method _cal_Gw (line 321) | def _cal_Gw(self):
    method _cal_g_l2 (line 348) | def _cal_g_l2(self):
    method _liear_search (line 352) | def _liear_search(self,p_k):

FILE: ML/Perce_SVM/SVM.py
  function data_feature_engineering (line 24) | def data_feature_engineering(full_data,age_default_avg=True,one_hot=True):
  function data_feature_select (line 129) | def data_feature_select(full_data):
  function Passenger_sex (line 143) | def Passenger_sex(x):
  function Passenger_Embarked (line 146) | def Passenger_Embarked(x):
  function Passenger_TitleName (line 149) | def Passenger_TitleName(x):
  function Passenger_Survived (line 152) | def Passenger_Survived(x):
  function get_title_name (line 155) | def get_title_name(name):
  class SVM (line 161) | class SVM():
    method __init__ (line 162) | def __init__(self,kernal,maxIter,C,epsilon,sigma = 0.001):
    method train (line 176) | def train(self,train_X,train_y):
    method predict (line 193) | def predict(self,test_x):
    method _smo (line 201) | def _smo(self):
    method _calE (line 258) | def _calE(self,sample,y):
    method _calLH (line 263) | def _calLH(self,a,j,i):
    method _kernel (line 271) | def _kernel(self,X_i,X_j):
    method _chooseJ (line 288) | def _chooseJ(self,i,E_i):
    method _randJ (line 316) | def _randJ(self,i):

FILE: ML/Perce_SVM/perceptron.py
  function data_feature_engineering (line 17) | def data_feature_engineering(full_data,age_default_avg=True,one_hot=True):
  function data_feature_select (line 122) | def data_feature_select(full_data):
  function Passenger_sex (line 136) | def Passenger_sex(x):
  function Passenger_Embarked (line 139) | def Passenger_Embarked(x):
  function Passenger_TitleName (line 142) | def Passenger_TitleName(x):
  function get_title_name (line 145) | def get_title_name(name):
  class Perceptron (line 150) | class Perceptron:
    method __init__ (line 151) | def __init__(self,alpha = 0.01,updata_count_total = 3000,nochange_coun...
    method train (line 161) | def train(self,train_X,train_y):
    method predict (line 200) | def predict(self,sample_x):

FILE: ML/TensorDemo/NN_tf.py
  function variable_summeries (line 25) | def variable_summeries(var):
  function weight_variable (line 42) | def weight_variable(shape):
  function bias_variable (line 48) | def bias_variable(shape):
  function nn_layer (line 55) | def nn_layer(input_tensor,input_dim,output_dim,layer_name,act=tf.nn.relu):

FILE: NLP/AutoTitle_F/data/batcher.py
  class Example (line 20) | class Example(object):
    method __init__ (line 22) | def __init__(self, article, abstract_sentence, vocab, config):
    method get_dec_inp_seqs (line 59) | def get_dec_inp_seqs(self, sequence, max_len, start_id, stop_id):
    method pad_decoder_inp (line 69) | def pad_decoder_inp(self, max_len, pad_id):
    method pad_encoder_input (line 75) | def pad_encoder_input(self, max_len, pad_id, pointer_gen=True):
  class Batch (line 82) | class Batch(object):
    method __init__ (line 83) | def __init__(self, example_list, batch_size):
    method init_encoder_seq (line 92) | def init_encoder_seq(self, example_list ,pointer_gen = True):
    method init_decoder_seq (line 123) | def init_decoder_seq(self, example_list):
    method store_orig_strings (line 139) | def store_orig_strings(self, example_list):
  class DocDataset (line 145) | class DocDataset(torch_data.Dataset):
    method __init__ (line 147) | def __init__(self, path, vocab, config):
    method __getitem__ (line 169) | def __getitem__(self, index):
    method __len__ (line 172) | def __len__(self):
    method drpout (line 175) | def drpout(self,text, p = 0.5):
    method shuffle (line 184) | def shuffle(self, text):
  function padding (line 188) | def padding(example_list):
  function get_loader (line 195) | def get_loader(dataset, batch_size, shuffle, num_workers, mode='train'):
  function get_input_from_batch (line 210) | def get_input_from_batch(batch, use_cuda, use_point_gen = True, use_cove...
  function get_temp_vocab (line 249) | def get_temp_vocab(config):
  function build_vaildation_set (line 278) | def build_vaildation_set():
  function main (line 296) | def main():

FILE: NLP/AutoTitle_F/data/data.py
  class Vocab (line 21) | class Vocab(object):
    method __init__ (line 23) | def __init__(self, vocab_nfile, max_size=None):
    method word2id (line 56) | def word2id(self, word):
    method id2word (line 61) | def id2word(self, word_id):
    method size (line 66) | def size(self):
    method build_vectors (line 69) | def build_vectors(self, pre_word_embedding_path, dim , unk_init=torch....
  function article2ids (line 88) | def article2ids(article_words, vocab):
  function abstract2ids (line 103) | def abstract2ids(abstract_words, vocab, article_oovs):
  function outputids2words (line 118) | def outputids2words(id_list, vocab, article_oovs):
  function abstract2sents (line 134) | def abstract2sents(abstract):
  function show_art_oovs (line 145) | def show_art_oovs(article, vocab):
  function show_abs_oovs (line 152) | def show_abs_oovs(abstract, vocab, article_oovs):

FILE: NLP/AutoTitle_F/data/data_processed.py
  function transform_other_word (line 24) | def transform_other_word(str_text,reg_dict):
  function clean_text (line 29) | def clean_text(text, contractions):
  function pre_word_token (line 38) | def pre_word_token(df, config, test = False, lower = True, is_make_title...
  function pre_sentence_token (line 103) | def pre_sentence_token(df, lower=True, makevocab=True):
  function word_tokenizer (line 161) | def word_tokenizer(text):
  function sentence_tokenizer (line 169) | def sentence_tokenizer(text):

FILE: NLP/AutoTitle_F/models/adaptive.py
  class AdaptiveLogSoftmaxWithLoss (line 15) | class AdaptiveLogSoftmaxWithLoss(nn.Module):
    method __init__ (line 87) | def __init__(self, in_features, n_classes, cutoffs, div_value=4., head...
    method reset_parameters (line 127) | def reset_parameters(self):
    method forward (line 133) | def forward(self, input, target):
    method _get_full_log_prob (line 188) | def _get_full_log_prob(self, input, head_output):
    method log_prob (line 206) | def log_prob(self, input):
    method predict (line 222) | def predict(self, input):

FILE: NLP/AutoTitle_F/models/loss.py
  function criterion (line 12) | def criterion(tgt_vocab_size, use_cuda):
  function adaptive_criterion (line 23) | def adaptive_criterion(config, use_cuda):
  function ml_criterion (line 34) | def ml_criterion(hidden_outputs, decoder, targets, criterion, sim_score=0):
  function ml_criterion_memory_efficiency (line 50) | def ml_criterion_memory_efficiency(hidden_outputs, decoder, targets, cri...
  function ml_criterion_sampled_loss (line 69) | def ml_criterion_sampled_loss(hidden_outputs, decoder, targets, config, ...
  function ml_criterion_adaptive_sampled_loss (line 73) | def ml_criterion_adaptive_sampled_loss(hidden_outputs, decoder, targets,...
  function rl_criterion (line 88) | def rl_criterion():

FILE: NLP/AutoTitle_F/models/lr_scheduler.py
  class _LRScheduler (line 11) | class _LRScheduler(object):
    method __init__ (line 12) | def __init__(self, optimizer, last_epoch=-1):
    method get_lr (line 29) | def get_lr(self):
    method step (line 32) | def step(self, epoch=None):
  class LambdaLR (line 40) | class LambdaLR(_LRScheduler):
    method __init__ (line 59) | def __init__(self, optimizer, lr_lambda, last_epoch=-1):
    method get_lr (line 71) | def get_lr(self):
  class StepLR (line 76) | class StepLR(_LRScheduler):
    method __init__ (line 99) | def __init__(self, optimizer, step_size, gamma=0.1, last_epoch=-1):
    method get_lr (line 104) | def get_lr(self):
  class MultiStepLR (line 109) | class MultiStepLR(_LRScheduler):
    method __init__ (line 131) | def __init__(self, optimizer, milestones, gamma=0.1, last_epoch=-1):
    method get_lr (line 139) | def get_lr(self):
  class ExponentialLR (line 144) | class ExponentialLR(_LRScheduler):
    method __init__ (line 153) | def __init__(self, optimizer, gamma, last_epoch=-1):
    method get_lr (line 157) | def get_lr(self):
  class CosineAnnealingLR (line 162) | class CosineAnnealingLR(_LRScheduler):
    method __init__ (line 182) | def __init__(self, optimizer, T_max, eta_min=0, last_epoch=-1):
    method get_lr (line 187) | def get_lr(self):
  class ReduceLROnPlateau (line 193) | class ReduceLROnPlateau(object):
    method __init__ (line 236) | def __init__(self, optimizer, mode='min', factor=0.1, patience=10,
    method _reset (line 274) | def _reset(self):
    method step (line 280) | def step(self, metrics, epoch=None):
    method _reduce_lr (line 301) | def _reduce_lr(self, epoch):
    method in_cooldown (line 312) | def in_cooldown(self):
    method _init_is_better (line 315) | def _init_is_better(self, mode, threshold, threshold_mode):

FILE: NLP/AutoTitle_F/models/optims.py
  class Optim (line 12) | class Optim(object):
    method set_parameters (line 14) | def set_parameters(self, params):
    method __init__ (line 32) | def __init__(self, method, lr, max_grad_norm, lr_decay=1, start_decay_...
    method step (line 42) | def step(self):
    method updateLearningRate (line 49) | def updateLearningRate(self, ppl, epoch):
  class AdagradCustom (line 62) | class AdagradCustom(Optimizer):
    method __init__ (line 79) | def __init__(self, params, lr=1e-2, lr_decay=0, weight_decay=0, initia...
    method share_memory (line 90) | def share_memory(self):
    method step (line 96) | def step(self, closure=None):

FILE: NLP/AutoTitle_F/models/seq2seq.py
  function init_lstm_wt (line 20) | def init_lstm_wt(lstm, init_v):
  function init_linear_wt (line 34) | def init_linear_wt(linear, init_v):
  function init_wt_normal (line 40) | def init_wt_normal(wt, init_v):
  function init_wt_unif (line 43) | def init_wt_unif(wt, init_v):
  class Encoder (line 48) | class Encoder(nn.Module):
    method __init__ (line 49) | def __init__(self, config, embedding_weight = None):
    method forward (line 61) | def forward(self, input, seq_lens):
  class ReduceState (line 73) | class ReduceState(nn.Module):
    method __init__ (line 74) | def __init__(self,config):
    method forward (line 83) | def forward(self, hidden):
  class Decoder (line 93) | class Decoder(nn.Module):
    method __init__ (line 94) | def __init__(self, config, embedding_weight = None):
    method forward (line 118) | def forward(self, y_t_1, s_t_1, encoder_outputs, enc_padding_mask,
  class Attention (line 170) | class Attention(nn.Module):
    method __init__ (line 171) | def __init__(self,config):
    method forward (line 181) | def forward(self, s_t_hat, h, enc_padding_mask, coverage):
  class Global_Attention (line 218) | class Global_Attention(nn.Module):
  class Beam (line 223) | class Beam(object):
    method __init__ (line 224) | def __init__(self, tokens, log_probs, state, context, coverage):
    method extend (line 231) | def extend(self, token, log_prob, state, context, coverage):
    method latest_token (line 239) | def latest_token(self):
    method avg_log_prob (line 243) | def avg_log_prob(self):
  class seq2seq (line 247) | class seq2seq(nn.Module):
    method __init__ (line 248) | def __init__(self, config, use_cuda, pretrain = None):
    method forward (line 265) | def forward(self, sources, sources_lengths, source_padding_mask, sourc...
    method rein_forward (line 305) | def rein_forward(self, sources, sources_lengths, source_padding_mask, ...
    method beam_sample (line 350) | def beam_sample(self, sources, sources_lengths, source_padding_mask, s...
    method sort_beams (line 443) | def sort_beams(self, beams):
    method variable_to_init_id (line 447) | def variable_to_init_id(self, v):

FILE: NLP/AutoTitle_F/pycocoevalcap/bleu/bleu.py
  class Bleu (line 14) | class Bleu:
    method __init__ (line 15) | def __init__(self, n=4):
    method compute_score (line 21) | def compute_score(self,hyps, refs):
    method method (line 40) | def method(self):

FILE: NLP/AutoTitle_F/pycocoevalcap/bleu/bleu_scorer.py
  function precook (line 27) | def precook(s, n=4, out=False):
  function cook_refs (line 39) | def cook_refs(refs, eff=None, n=4): ## lhuang: oracle will call with "av...
  function cook_test (line 64) | def cook_test(test, reflen_refmaxcounts, eff=None, n=4):
  class BleuScorer (line 90) | class BleuScorer(object):
    method copy (line 97) | def copy(self):
    method __init__ (line 105) | def __init__(self, test=None, refs=None, n=4, special_reflen=None):
    method cook_append (line 114) | def cook_append(self, test, refs):
    method ratio (line 127) | def ratio(self, option=None):
    method score_ratio (line 131) | def score_ratio(self, option=None):
    method score_ratio_str (line 135) | def score_ratio_str(self, option=None):
    method reflen (line 138) | def reflen(self, option=None):
    method testlen (line 142) | def testlen(self, option=None):
    method retest (line 146) | def retest(self, new_test):
    method rescore (line 157) | def rescore(self, new_test):
    method size (line 162) | def size(self):
    method __iadd__ (line 166) | def __iadd__(self, other):
    method compatible (line 180) | def compatible(self, other):
    method single_reflen (line 183) | def single_reflen(self, option="average"):
    method _single_reflen (line 186) | def _single_reflen(self, reflens, option=None, testlen=None):
    method recompute_score (line 199) | def recompute_score(self, option=None, verbose=0):
    method compute_score (line 203) | def compute_score(self, option=None, verbose=0):

FILE: NLP/AutoTitle_F/pycocoevalcap/cider/cider.py
  class Cider (line 13) | class Cider:
    method __init__ (line 18) | def __init__(self, test=None, refs=None, n=4, sigma=6.0):
    method compute_score (line 24) | def compute_score(self, hyps, refs):
    method method (line 52) | def method(self):

FILE: NLP/AutoTitle_F/pycocoevalcap/cider/cider_scorer.py
  function precook (line 13) | def precook(s, n=4, out=False):
  function cook_refs (line 30) | def cook_refs(refs, n=4): ## lhuang: oracle will call with "average"
  function cook_test (line 40) | def cook_test(test, n=4):
  class CiderScorer (line 49) | class CiderScorer(object):
    method copy (line 53) | def copy(self):
    method __init__ (line 60) | def __init__(self, test=None, refs=None, n=4, sigma=6.0):
    method cook_append (line 70) | def cook_append(self, test, refs):
    method size (line 80) | def size(self):
    method __iadd__ (line 84) | def __iadd__(self, other):
    method compute_doc_freq (line 95) | def compute_doc_freq(self):
    method compute_cider (line 108) | def compute_cider(self):
    method compute_score (line 185) | def compute_score(self, option=None, verbose=0):

FILE: NLP/AutoTitle_F/pycocoevalcap/meteor/meteor.py
  function enc (line 16) | def enc(s):
  function dec (line 19) | def dec(s):
  class Meteor (line 23) | class Meteor:
    method __init__ (line 25) | def __init__(self):
    method close (line 42) | def close(self):
    method compute_score (line 53) | def compute_score(self, gts, res):
    method method (line 73) | def method(self):
    method _stat (line 76) | def _stat(self, hypothesis_str, reference_list):
    method _score (line 85) | def _score(self, hypothesis_str, reference_list):
    method __del__ (line 103) | def __del__(self):

FILE: NLP/AutoTitle_F/pycocoevalcap/meteor/tests/test_meteor.py
  class TestMeteor (line 9) | class TestMeteor(unittest.TestCase):
    method test_compute_score (line 10) | def test_compute_score(self):

FILE: NLP/AutoTitle_F/pycocoevalcap/rouge/rouge.py
  function my_lcs (line 13) | def my_lcs(string, sub):
  class Rouge (line 36) | class Rouge():
    method __init__ (line 41) | def __init__(self):
    method calc_score (line 45) | def calc_score(self, candidate, refs):
    method compute_score (line 78) | def compute_score(self, hyps, refs):
    method method (line 94) | def method(self):

FILE: NLP/AutoTitle_F/pycocoevalcap/test_eval.py
  class EvalCap (line 14) | class EvalCap:
  function setEval (line 38) | def setEval(score, method):

FILE: NLP/AutoTitle_F/submit.py
  function predict (line 80) | def predict(model, test_loader):

FILE: NLP/AutoTitle_F/train.py
  function train (line 112) | def train(epoch):
  function eval (line 159) | def eval(epoch):
  function main (line 197) | def main():
  function save_model (line 213) | def save_model(path):

FILE: NLP/Multi_Label/ShengCe/generate_submit.py
  function extract_title_doc (line 33) | def extract_title_doc(id,title, stop_words, words_prob):
  function get_key_from_title (line 85) | def get_key_from_title(id, num, title_key_words_sorted, words_prob):
  function get_key_from_doc (line 133) | def get_key_from_doc(num, doc_key_words_sorted):
  function main (line 155) | def main():

FILE: NLP/Multi_Label/ShengCe/train_model.py
  function get_topic_sim (line 43) | def get_topic_sim(model, word_corpus, doc_corpus):
  function build_topic_model (line 49) | def build_topic_model(data, stop_nfile, num_topics = 50, save = True):
  function bulid_candidate_words (line 91) | def bulid_candidate_words(data, stop_nfile, candidate_save_path, candida...
  function build_train_sample (line 222) | def build_train_sample(data, candidate_words_list):
  function train_class_model (line 271) | def train_class_model(features,labels):
  function get_test_sample_prob (line 292) | def get_test_sample_prob(model, test_data, test_candidates):

FILE: NLP/Multi_Label/ShengCe/util.py
  function stopwordslist (line 18) | def stopwordslist(filepath):
  function get_shuming (line 22) | def get_shuming(text):
  function is_contain_char_num (line 27) | def is_contain_char_num(text):
  function get_count_sentence (line 37) | def get_count_sentence(word,sentence):
  function cal_sim (line 47) | def cal_sim(word_topic_prob, doc_topic_prob):
  function save_object (line 59) | def save_object(obj,nfile):
  function load_object (line 63) | def load_object(nfile):

FILE: NLP/Seq2Seq/data_util.py
  function strQ2B (line 31) | def strQ2B(ustring):
  function strB2Q (line 44) | def strB2Q(ustring):
  function remove_url (line 58) | def remove_url(text):
  function remove_pun_ch (line 62) | def remove_pun_ch(text):
  function remove_pun_en (line 65) | def remove_pun_en(text):
  function remove_date (line 69) | def remove_date(text):
  function remove_num (line 73) | def remove_num(text):
  function remove_num_en (line 77) | def remove_num_en(text):
  function remove_tag (line 81) | def remove_tag(text):
  function get_title_content (line 90) | def get_title_content(content_fp,title_fp):
  function basic_tokenizer (line 118) | def basic_tokenizer(sentence):
  function jieba_tokenizer (line 125) | def jieba_tokenizer(sentence):
  function create_vocab (line 129) | def create_vocab(vocabulary_path, data_path, max_vocabulary_size,
  function initialize_vocabulary (line 164) | def initialize_vocabulary(vocabulary_path):
  function sentence_to_token_ids (line 186) | def sentence_to_token_ids(sentence, vocabulary,
  function data_to_token_ids (line 209) | def data_to_token_ids(data_path, target_path, vocabulary_path,
  function get_train_dev_sets (line 232) | def get_train_dev_sets(data_content, data_title, train_rate, dev_rate,
  function prepare_headline_data (line 280) | def prepare_headline_data(data_dir, vocabulary_size, tokenizer=None):

FILE: NLP/Seq2Seq/main.py
  class ModelLoader (line 12) | class ModelLoader(object):
    method __init__ (line 13) | def __init__(self):
    method _init_model (line 19) | def _init_model(self,session):
    method func_predict (line 23) | def func_predict(self,sentence):

FILE: NLP/Seq2Seq/seq2seq_attn.py
  function _extract_argmax_and_embed (line 80) | def _extract_argmax_and_embed(embedding,
  function rnn_decoder (line 110) | def rnn_decoder(decoder_inputs,
  function basic_rnn_seq2seq (line 157) | def basic_rnn_seq2seq(encoder_inputs,
  function tied_rnn_seq2seq (line 187) | def tied_rnn_seq2seq(encoder_inputs,
  function embedding_rnn_decoder (line 230) | def embedding_rnn_decoder(decoder_inputs,
  function embedding_rnn_seq2seq (line 298) | def embedding_rnn_seq2seq(encoder_inputs,
  function embedding_tied_rnn_seq2seq (line 407) | def embedding_tied_rnn_seq2seq(encoder_inputs,
  function attention_decoder (line 536) | def attention_decoder(decoder_inputs,
  function embedding_attention_decoder (line 708) | def embedding_attention_decoder(decoder_inputs,
  function embedding_attention_seq2seq (line 794) | def embedding_attention_seq2seq(encoder_inputs,
  function one2many_rnn_seq2seq (line 922) | def one2many_rnn_seq2seq(encoder_inputs,
  function sequence_loss_by_example (line 1037) | def sequence_loss_by_example(logits,
  function sequence_loss (line 1086) | def sequence_loss(logits,
  function model_with_buckets (line 1127) | def model_with_buckets(encoder_inputs,

FILE: NLP/Seq2Seq/seq2seq_model.py
  class Seq2SeqModel (line 32) | class Seq2SeqModel(object):
    method __init__ (line 47) | def __init__(self,
    method step (line 199) | def step(self, session, encoder_inputs, decoder_inputs, target_weights,
    method get_batch (line 258) | def get_batch(self, data, bucket_id):

FILE: NLP/Seq2Seq/text_summarizer.py
  class LargeConfig (line 14) | class LargeConfig(object):
  class MediumConfig (line 25) | class MediumConfig(object):
  function create_model (line 64) | def create_model(session,forward_only):
  function read_data (line 101) | def read_data(source_path, target_path, max_size=None):
  function train (line 137) | def train():
  function main (line 202) | def main():

FILE: NLP/Text_CNN/process_data.py
  function load_binary_vec (line 22) | def load_binary_vec(fname, vocab):
  function load_data_k_cv (line 52) | def load_data_k_cv(folder,cv=10,clear_flag=True):
  function add_unexist_word_vec (line 95) | def add_unexist_word_vec(w2v,vocab):
  function clean_string (line 105) | def clean_string(string,TREC=False):
  function get_vec_by_sentence_list (line 121) | def get_vec_by_sentence_list(word_vecs,sentence_list,maxlen=56,values=0....
  function get_index_by_sentence_list (line 140) | def get_index_by_sentence_list(word_ids,sentence_list,maxlen=56):
  function pad_sentences (line 160) | def pad_sentences(data,maxlen=56,values=0.,vec_size = 300):
  function get_train_test_data1 (line 172) | def get_train_test_data1(word_vecs,revs,cv_id=0,sent_length = 56,default...
  function get_train_test_data2 (line 208) | def get_train_test_data2(word_ids,revs,cv_id=0,sent_length = 56):
  function get_contrast (line 236) | def get_contrast(x):
  function getWordsVect (line 239) | def getWordsVect(W):

FILE: NLP/Text_CNN/text_cnn_model.py
  class TextCNN (line 14) | class TextCNN():
    method __init__ (line 19) | def __init__(self,W_list,shuffer_falg, static_falg, filter_numbers, fi...
    method train (line 116) | def train(self,train_x,train_y):
    method validataion (line 141) | def validataion(self,test_x, test_y):
    method close (line 156) | def close(self):
    method __get_batchs (line 160) | def __get_batchs(self,Xs,Ys,batch_size):
    method __add_conv_layer (line 165) | def __add_conv_layer(self,filter_size,filter_num):
    method __variable_summeries (line 185) | def __variable_summeries(self,var):

FILE: NLP/daguan/data_analy.py
  function get_stopwords (line 22) | def get_stopwords(docs, min_df, max_d):
  function make_vocab (line 33) | def make_vocab(data_se, stop_words, max_size, type, isSave=True):
  function save (line 99) | def save(nfile, obj):
  function load (line 103) | def load(nfile):
  function init_vocab (line 108) | def init_vocab(min_df, max_d, add_test=True, char_vocab_size = 10000, wo...
  function pre_train_w2v (line 130) | def pre_train_w2v(all_text):
  function sentence_to_indexs (line 137) | def sentence_to_indexs(sentences, dict_label2id, stop_words, max_documen...
  function split_train_val (line 171) | def split_train_val(data, article_dicts, word_dicts, rate=0.7, isSave = ...
  class dataset (line 199) | class dataset(torch_data.Dataset):
    method __init__ (line 201) | def __init__(self, src_article, src_word, y):
    method __getitem__ (line 207) | def __getitem__(self, index):
    method __len__ (line 210) | def __len__(self):
  function padding (line 213) | def padding(data):
  function get_loader (line 249) | def get_loader(dataset, batch_size, shuffle, num_workers):
  function to_categorical (line 258) | def to_categorical(y, num_classes=None):
  class AttrDict (line 273) | class AttrDict(dict):
    method __init__ (line 277) | def __init__(self, *args, **kwargs):
    method __getattr__ (line 281) | def __getattr__(self, item):
    method __setstate__ (line 286) | def __setstate__(self, state):
    method __getstate__ (line 288) | def __getstate__(self):
  function read_config (line 294) | def read_config(path):

FILE: NLP/daguan/lr_scheduler.py
  class _LRScheduler (line 6) | class _LRScheduler(object):
    method __init__ (line 7) | def __init__(self, optimizer, last_epoch=-1):
    method get_lr (line 24) | def get_lr(self):
    method step (line 27) | def step(self, epoch=None):
  class LambdaLR (line 35) | class LambdaLR(_LRScheduler):
    method __init__ (line 54) | def __init__(self, optimizer, lr_lambda, last_epoch=-1):
    method get_lr (line 66) | def get_lr(self):
  class StepLR (line 71) | class StepLR(_LRScheduler):
    method __init__ (line 94) | def __init__(self, optimizer, step_size, gamma=0.1, last_epoch=-1):
    method get_lr (line 99) | def get_lr(self):
  class MultiStepLR (line 104) | class MultiStepLR(_LRScheduler):
    method __init__ (line 126) | def __init__(self, optimizer, milestones, gamma=0.1, last_epoch=-1):
    method get_lr (line 134) | def get_lr(self):
  class ExponentialLR (line 139) | class ExponentialLR(_LRScheduler):
    method __init__ (line 148) | def __init__(self, optimizer, gamma, last_epoch=-1):
    method get_lr (line 152) | def get_lr(self):
  class CosineAnnealingLR (line 157) | class CosineAnnealingLR(_LRScheduler):
    method __init__ (line 177) | def __init__(self, optimizer, T_max, eta_min=0, last_epoch=-1):
    method get_lr (line 182) | def get_lr(self):
  class ReduceLROnPlateau (line 188) | class ReduceLROnPlateau(object):
    method __init__ (line 231) | def __init__(self, optimizer, mode='min', factor=0.1, patience=10,
    method _reset (line 269) | def _reset(self):
    method step (line 275) | def step(self, metrics, epoch=None):
    method _reduce_lr (line 296) | def _reduce_lr(self, epoch):
    method in_cooldown (line 307) | def in_cooldown(self):
    method _init_is_better (line 310) | def _init_is_better(self, mode, threshold, threshold_mode):

FILE: NLP/daguan/main.py
  function train (line 106) | def train(epoch):
  function eval (line 161) | def eval(epoch):
  function save_model (line 204) | def save_model(path):
  function get_metrics (line 215) | def get_metrics(y,y_pre):

FILE: NLP/daguan/model.py
  class encoder_cnn (line 11) | class encoder_cnn(nn.Module):
    method __init__ (line 15) | def __init__(self,config,filter_sizes,filter_nums,vocab_size,embedding...
    method forward (line 39) | def forward(self, inputs):
  class Text_WCCNN (line 71) | class Text_WCCNN(nn.Module):
    method __init__ (line 73) | def __init__(self,config, word_filter_sizes, word_filter_nums, word_vo...
    method forward (line 112) | def forward(self, article, word_seg):

FILE: NLP/daguan/optims.py
  class Optim (line 6) | class Optim(object):
    method set_parameters (line 8) | def set_parameters(self, params):
    method __init__ (line 23) | def __init__(self, method, lr, max_grad_norm, lr_decay=1, start_decay_...
    method step (line 32) | def step(self):
    method updateLearningRate (line 39) | def updateLearningRate(self, ppl, epoch):

FILE: NLP/daguan/predict.py
  function eval (line 67) | def eval(cnn_model):