SYMBOL INDEX (150 symbols across 17 files)

FILE: induction_network/data_helper.py
  class InductionData (line 17) | class InductionData(object):
    method __init__ (line 18) | def __init__(self, output_path: str, sequence_length: int = 100, num_c...
    method load_data (line 62) | def load_data(data_path: str) -> Dict[str, Dict[str, List[List[str]]]]:
    method remove_stop_word (line 86) | def remove_stop_word(self, data: Dict[str, Dict[str, List[List[str]]]]...
    method get_word_vectors (line 110) | def get_word_vectors(self, vocab: List[str]) -> np.ndarray:
    method gen_vocab (line 148) | def gen_vocab(self, words: List[str]) -> Dict[str, int]:
    method trans_to_index (line 177) | def trans_to_index(data: Dict[str, Dict[str, List[List[str]]]], word_t...
    method choice_support_query (line 191) | def choice_support_query(self, task_data: Dict[str, List[List[int]]])\
    method samples (line 229) | def samples(self, data_ids: Dict[str, Dict[str, List[List[int]]]]) \
    method gen_data (line 251) | def gen_data(self, file_path: str) -> Dict[str, Dict[str, List[List[in...
    method padding (line 265) | def padding(self, sentences: List[List[int]]) -> List[List[int]]:
    method next_batch (line 276) | def next_batch(self, data_ids: Dict[str, Dict[str, List[List[int]]]]) \

FILE: induction_network/metrics.py
  function mean (line 6) | def mean(item: list) -> float:
  function accuracy (line 16) | def accuracy(pred_y, true_y):
  function binary_precision (line 33) | def binary_precision(pred_y, true_y, positive=1):
  function binary_recall (line 53) | def binary_recall(pred_y, true_y, positive=1):
  function binary_f_beta (line 73) | def binary_f_beta(pred_y, true_y, beta=1.0, positive=1):
  function get_binary_metrics (line 91) | def get_binary_metrics(pred_y, true_y, f_beta=1.0):
  function multi_precision (line 106) | def multi_precision(pred_y, true_y, labels):
  function multi_recall (line 122) | def multi_recall(pred_y, true_y, labels):
  function multi_f_beta (line 138) | def multi_f_beta(pred_y, true_y, labels, beta=1.0):
  function get_multi_metrics (line 155) | def get_multi_metrics(pred_y, true_y, labels, f_beta=1.0):

FILE: induction_network/model.py
  class InductionModel (line 8) | class InductionModel(object):
    method __init__ (line 9) | def __init__(self, config, vocab_size, word_vectors):
    method model_structure (line 31) | def model_structure(self):
    method dynamic_routing (line 118) | def dynamic_routing(self, support_encoding, iter_routing=3):
    method neural_tensor_layer (line 161) | def neural_tensor_layer(self, class_vector, query_encoder):
    method _attention (line 207) | def _attention(self, H, scope_name):
    method get_optimizer (line 235) | def get_optimizer(self):
    method init_saver (line 249) | def init_saver(self):
    method train (line 256) | def train(self, sess, batch, dropout_prob):
    method eval (line 274) | def eval(self, sess, batch):
    method infer (line 289) | def infer(self, sess, batch):

FILE: induction_network/predict.py
  function online_predict (line 21) | def online_predict():

FILE: induction_network/trainer.py
  class InductionTrainer (line 11) | class InductionTrainer(object):
    method __init__ (line 12) | def __init__(self, args):
    method load_data (line 27) | def load_data(self, is_training=True):
    method create_model (line 45) | def create_model(self):
    method train (line 54) | def train(self):

FILE: prototypical_network/data_helper.py
  class PrototypicalData (line 17) | class PrototypicalData(object):
    method __init__ (line 18) | def __init__(self, output_path: str, sequence_length: int = 100, num_c...
    method load_data (line 62) | def load_data(data_path: str) -> Dict[str, Dict[str, List[List[str]]]]:
    method remove_stop_word (line 86) | def remove_stop_word(self, data: Dict[str, Dict[str, List[List[str]]]]...
    method get_word_vectors (line 110) | def get_word_vectors(self, vocab: List[str]) -> np.ndarray:
    method gen_vocab (line 148) | def gen_vocab(self, words: List[str]) -> Dict[str, int]:
    method trans_to_index (line 177) | def trans_to_index(data: Dict[str, Dict[str, List[List[str]]]], word_t...
    method choice_support_query (line 191) | def choice_support_query(self, task_data: Dict[str, List[List[int]]])\
    method samples (line 229) | def samples(self, data_ids: Dict[str, Dict[str, List[List[int]]]]) \
    method gen_data (line 251) | def gen_data(self, file_path: str) -> Dict[str, Dict[str, List[List[in...
    method padding (line 265) | def padding(self, sentences: List[List[int]]) -> List[List[int]]:
    method next_batch (line 276) | def next_batch(self, data_ids: Dict[str, Dict[str, List[List[int]]]]) \

FILE: prototypical_network/metrics.py
  function mean (line 6) | def mean(item: list) -> float:
  function accuracy (line 16) | def accuracy(pred_y, true_y):
  function binary_precision (line 33) | def binary_precision(pred_y, true_y, positive=1):
  function binary_recall (line 53) | def binary_recall(pred_y, true_y, positive=1):
  function binary_f_beta (line 73) | def binary_f_beta(pred_y, true_y, beta=1.0, positive=1):
  function get_binary_metrics (line 91) | def get_binary_metrics(pred_y, true_y, f_beta=1.0):
  function multi_precision (line 106) | def multi_precision(pred_y, true_y, labels):
  function multi_recall (line 122) | def multi_recall(pred_y, true_y, labels):
  function multi_f_beta (line 138) | def multi_f_beta(pred_y, true_y, labels, beta=1.0):
  function get_multi_metrics (line 155) | def get_multi_metrics(pred_y, true_y, labels, f_beta=1.0):

FILE: prototypical_network/model.py
  class PrototypicalModel (line 8) | class PrototypicalModel(object):
    method __init__ (line 9) | def __init__(self, config, vocab_size, word_vectors):
    method model_structure (line 29) | def model_structure(self):
    method _attention (line 128) | def _attention(self, H, scope_name):
    method get_optimizer (line 158) | def get_optimizer(self):
    method init_saver (line 172) | def init_saver(self):
    method train (line 179) | def train(self, sess, batch, dropout_prob):
    method eval (line 196) | def eval(self, sess, batch):
    method infer (line 211) | def infer(self, sess, batch):

FILE: prototypical_network/trainer.py
  class PrototypicalTrainer (line 11) | class PrototypicalTrainer(object):
    method __init__ (line 12) | def __init__(self, args):
    method load_data (line 28) | def load_data(self, is_training=True):
    method create_model (line 46) | def create_model(self):
    method train (line 55) | def train(self):

FILE: relation_network/data_helper.py
  class RelationData (line 17) | class RelationData(object):
    method __init__ (line 18) | def __init__(self, output_path: str, sequence_length: int = 100, num_c...
    method load_data (line 62) | def load_data(data_path: str) -> Dict[str, Dict[str, List[List[str]]]]:
    method remove_stop_word (line 86) | def remove_stop_word(self, data: Dict[str, Dict[str, List[List[str]]]]...
    method get_word_vectors (line 110) | def get_word_vectors(self, vocab: List[str]) -> np.ndarray:
    method gen_vocab (line 148) | def gen_vocab(self, words: List[str]) -> Dict[str, int]:
    method trans_to_index (line 177) | def trans_to_index(data: Dict[str, Dict[str, List[List[str]]]], word_t...
    method choice_support_query (line 191) | def choice_support_query(self, task_data: Dict[str, List[List[int]]])\
    method samples (line 229) | def samples(self, data_ids: Dict[str, Dict[str, List[List[int]]]]) \
    method gen_data (line 251) | def gen_data(self, file_path: str) -> Dict[str, Dict[str, List[List[in...
    method padding (line 265) | def padding(self, sentences: List[List[int]]) -> List[List[int]]:
    method next_batch (line 276) | def next_batch(self, data_ids: Dict[str, Dict[str, List[List[int]]]]) \

FILE: relation_network/metrics.py
  function mean (line 6) | def mean(item: list) -> float:
  function accuracy (line 16) | def accuracy(pred_y, true_y):
  function binary_precision (line 33) | def binary_precision(pred_y, true_y, positive=1):
  function binary_recall (line 53) | def binary_recall(pred_y, true_y, positive=1):
  function binary_f_beta (line 73) | def binary_f_beta(pred_y, true_y, beta=1.0, positive=1):
  function get_binary_metrics (line 91) | def get_binary_metrics(pred_y, true_y, f_beta=1.0):
  function multi_precision (line 106) | def multi_precision(pred_y, true_y, labels):
  function multi_recall (line 122) | def multi_recall(pred_y, true_y, labels):
  function multi_f_beta (line 138) | def multi_f_beta(pred_y, true_y, labels, beta=1.0):
  function get_multi_metrics (line 155) | def get_multi_metrics(pred_y, true_y, labels, f_beta=1.0):

FILE: relation_network/model.py
  class RelationModel (line 8) | class RelationModel(object):
    method __init__ (line 9) | def __init__(self, config, vocab_size, word_vectors):
    method model_structure (line 29) | def model_structure(self):
    method neural_tensor_layer (line 111) | def neural_tensor_layer(self, class_vector, query_encoder):
    method _attention (line 157) | def _attention(self, H, scope_name):
    method get_optimizer (line 187) | def get_optimizer(self):
    method init_saver (line 201) | def init_saver(self):
    method train (line 208) | def train(self, sess, batch, dropout_prob):
    method eval (line 226) | def eval(self, sess, batch):
    method infer (line 241) | def infer(self, sess, batch):

FILE: relation_network/trainer.py
  class RelationTrainer (line 11) | class RelationTrainer(object):
    method __init__ (line 12) | def __init__(self, args):
    method load_data (line 28) | def load_data(self, is_training=True):
    method create_model (line 46) | def create_model(self):
    method train (line 55) | def train(self):

FILE: siamese_network/data_helper.py
  class SiameseData (line 17) | class SiameseData(object):
    method __init__ (line 18) | def __init__(self, output_path: str, sequence_length: int = 200, neg_s...
    method load_data (line 52) | def load_data(file_path: str) -> Dict[str, List[List[str]]]:
    method remove_stop_word (line 79) | def remove_stop_word(self, data: Dict[str, List[List[str]]]) -> List[s...
    method get_word_vectors (line 100) | def get_word_vectors(self, vocab: List[str]) -> np.ndarray:
    method gen_vocab (line 122) | def gen_vocab(self, words: List[str]) -> Dict[str, int]:
    method trans_to_index (line 151) | def trans_to_index(data: Dict[str, List[List[str]]], word_to_index: Di...
    method train_samples (line 162) | def train_samples(self, data_ids: Dict[str, List[List[int]]]) -> List[...
    method eval_sample (line 184) | def eval_sample(data_ids: Dict[str, List[List[int]]]) \
    method gen_data (line 205) | def gen_data(self, file_path: str) -> Union[List[Tuple[List[int], List...
    method padding (line 222) | def padding(self, first_sentences: List[List[int]], second_sentences: ...
    method next_batch (line 239) | def next_batch(self, data: List[Tuple[List[int], List[int], int]], bat...

FILE: siamese_network/metrics.py
  function mean (line 6) | def mean(item: list) -> float:
  function accuracy (line 16) | def accuracy(pred_y, true_y):
  function binary_precision (line 33) | def binary_precision(pred_y, true_y, positive=1):
  function binary_recall (line 53) | def binary_recall(pred_y, true_y, positive=1):
  function binary_f_beta (line 73) | def binary_f_beta(pred_y, true_y, beta=1.0, positive=1):
  function get_binary_metrics (line 91) | def get_binary_metrics(pred_y, true_y, f_beta=1.0):
  function multi_precision (line 106) | def multi_precision(pred_y, true_y, labels):
  function multi_recall (line 122) | def multi_recall(pred_y, true_y, labels):
  function multi_f_beta (line 138) | def multi_f_beta(pred_y, true_y, labels, beta=1.0):
  function get_multi_metrics (line 155) | def get_multi_metrics(pred_y, true_y, labels, f_beta=1.0):

FILE: siamese_network/model.py
  class SiameseModel (line 8) | class SiameseModel(object):
    method __init__ (line 9) | def __init__(self, config, vocab_size, word_vectors):
    method model_structure (line 26) | def model_structure(self):
    method get_optimizer (line 140) | def get_optimizer(self):
    method init_saver (line 154) | def init_saver(self):
    method train (line 161) | def train(self, sess, batch, dropout_prob):
    method eval (line 179) | def eval(self, sess, batch):
    method infer (line 194) | def infer(self, sess, batch):

FILE: siamese_network/trainer.py
  class SiameseTrainer (line 14) | class SiameseTrainer(object):
    method __init__ (line 15) | def __init__(self, args):
    method load_data (line 31) | def load_data(self, is_training=True):
    method create_model (line 40) | def create_model(self):
    method padding (line 49) | def padding(self, first_sentences, second_sentences):
    method get_prediction (line 66) | def get_prediction(sims):
    method eval_model (line 77) | def eval_model(self, sess):
    method train (line 104) | def train(self):