SYMBOL INDEX (91 symbols across 15 files)

FILE: src/codes/api/embedding_evaluator.py
  class new_Word2VecKeyedVectors (line 14) | class new_Word2VecKeyedVectors(Word2VecKeyedVectors):
    method __init__ (line 15) | def __init__(self, vector_size):
    method most_similar (line 18) | def most_similar(self, positive=None, negative=None, topn=10, restrict...
    method new_accuracy (line 87) | def new_accuracy(self, questions, restrict_vocab=30000, most_similar=m...
  function convert_conll_format_to_normal (line 191) | def convert_conll_format_to_normal(connl_file, out_file):
  function verify_word_analogies (line 228) | def verify_word_analogies(file):
  function check_oov_of_word_analogies (line 252) | def check_oov_of_word_analogies(w2v_format_emb_file, analogy_file, is_vn...
  function evaluator_api (line 292) | def evaluator_api(input_files, analoglist, output, embed_config=None):

FILE: src/codes/api/embedding_extractor.py
  function get_multi_embedding_models (line 10) | def get_multi_embedding_models(config: EmbeddingConfigs):
  function get_emb_dim (line 35) | def get_emb_dim(emb_file):
  function extract_embedding_for_vocab_file (line 45) | def extract_embedding_for_vocab_file(paths_of_emb_models, vocab_words_fi...
  function extract_embedding_vectors (line 91) | def extract_embedding_vectors(vocab_words_file, output_file, config: Emb...

FILE: src/codes/api/embedding_preprocessing.py
  function convert_to_w2v (line 10) | def convert_to_w2v(vocab_file, embedding_file, out_file):
  function test (line 54) | def test():
  function load_and_save_2_word2vec_model (line 63) | def load_and_save_2_word2vec_model(input_model_path, output_model_path, ...
  function load_and_save_2_word2vec_models (line 75) | def load_and_save_2_word2vec_models(input_embedding_files_str, output_em...

FILE: src/codes/api/embedding_visualizer.py
  class TensorBoardTool (line 16) | class TensorBoardTool:
    method __init__ (line 18) | def __init__(self, dir_path):
    method run (line 21) | def run(self, emb_name, port):
  function convert_multiple_emb_models_2_tf (line 33) | def convert_multiple_emb_models_2_tf(emb_name_arr, w2v_model_arr, output...
  function convert_one_emb_model_2_tf (line 88) | def convert_one_emb_model_2_tf(emb_name, model, output_path, port):
  function visualize_multiple_embeddings_individually (line 136) | def visualize_multiple_embeddings_individually(paths_of_emb_models):
  function visualize_multiple_embeddings_all_in_one (line 174) | def visualize_multiple_embeddings_all_in_one(paths_of_emb_models, port):
  function visualize_multiple_embeddings (line 216) | def visualize_multiple_embeddings(paths_of_emb_models, port):

FILE: src/codes/embeddings/embedding_configs.py
  class EmbeddingConfigs (line 3) | class EmbeddingConfigs(object):

FILE: src/codes/embeddings/embedding_models.py
  class Model_Constants (line 12) | class Model_Constants(object):
  class Embedding_Model (line 19) | class Embedding_Model(object):
    method __init__ (line 20) | def __init__(self, name, vector_dim):
    method load_model (line 31) | def load_model(self, model_path):
    method is_punct (line 52) | def is_punct(self, word):
    method is_number (line 79) | def is_number(self, word):
    method set_char_model (line 90) | def set_char_model(self, char_model):
    method load_vocabs_list (line 93) | def load_vocabs_list(self, vocab_file_path):
    method get_char_vector (line 102) | def get_char_vector(self, char_model, word):
    method is_unknown_word (line 137) | def is_unknown_word(self, word):
    method get_word_vector (line 148) | def get_word_vector(self, word):
    method get_vector_of_unknown (line 222) | def get_vector_of_unknown(self, word):
  class Embedding_Models (line 249) | class Embedding_Models(object):
    method __init__ (line 253) | def __init__(self, list_models):
    method add_model (line 256) | def add_model(self, emb_model, char_model):
    method get_vector_of_document (line 274) | def get_vector_of_document(self, document):
    method get_word_vector_of_multi_embeddings (line 307) | def get_word_vector_of_multi_embeddings(self, word):

FILE: src/codes/embeddings/embedding_utils.py
  function reload_char2vec_model (line 6) | def reload_char2vec_model(model_path, model_dim):
  function reload_embedding_models (line 12) | def reload_embedding_models(model_paths_list, model_names_list, model_di...
  function save_embedding_models_tofolder (line 46) | def save_embedding_models_tofolder(dir_path, final_embeddings, reverse_d...
  function save_embedding_models (line 80) | def save_embedding_models(FLAGS, final_embeddings, reverse_dictionary, v...
  function reload_embeddings (line 93) | def reload_embeddings(trained_models_dir):
  function create_single_utf8_file (line 106) | def create_single_utf8_file(input_dir, output_file):

FILE: src/codes/utils/emb_utils.py
  function most_similar (line 19) | def most_similar(base_vector: Vector, words: List[Word]) -> List[Tuple[f...
  function print_most_similar (line 29) | def print_most_similar(words: List[Word], text: str) -> None:
  function read_word (line 43) | def read_word() -> str:
  function find_word (line 47) | def find_word(text: str, words: List[Word]) -> Optional[Word]:
  function closest_analogies_OLD (line 54) | def closest_analogies_OLD(
  function closest_analogies_vectors (line 83) | def closest_analogies_vectors(
  function get_avg_vector (line 121) | def get_avg_vector(word, embedding_words):
  function run_paired_ttests (line 151) | def run_paired_ttests(all_map_arr, embedding_names):
  function eval_word_analogy_4_all_embeddings (line 182) | def eval_word_analogy_4_all_embeddings(word_analogies_file, embedding_na...
  function eval_word_analogies (line 226) | def eval_word_analogies(word_analogies_file, words: List[Word], embeddin...
  function print_analogy (line 360) | def print_analogy(left2: str, left1: str, right2: str, words: List[Word]...

FILE: src/codes/utils/embedding_io.py
  function save_model_to_file (line 13) | def save_model_to_file(embedding_model: List[Word], model_file_out: str):
  function load_word_embeddings (line 32) | def load_word_embeddings(file_paths: str, emb_config: EmbeddingConfigs) ...
  function load_word_embedding (line 54) | def load_word_embedding(file_path: str, emb_config: EmbeddingConfigs) ->...
  function load_words_raw (line 88) | def load_words_raw(file_path: str, emb_config: EmbeddingConfigs) -> List...
  function iter_len (line 168) | def iter_len(iter: Iterable[complex]) -> int:
  function most_common_dimension (line 172) | def most_common_dimension(words: List[Word]) -> int:
  function remove_duplicates (line 195) | def remove_duplicates(words: List[Word]) -> List[Word]:
  function remove_stop_words (line 207) | def remove_stop_words(words: List[Word]) -> List[Word]:

FILE: src/codes/utils/eval_utils.py
  function apk (line 15) | def apk(actual, predicted, k=10):
  function mapk (line 50) | def mapk(actual, predicted, k=10, word_level=True):
  function calc_map (line 79) | def calc_map(actual, predicted, topK=10):
  function calc_map_character_level (line 108) | def calc_map_character_level(actual, predicted, topK=10):
  function test_apk (line 144) | def test_apk(self):
  function test_mapk (line 152) | def test_mapk(self):

FILE: src/codes/utils/file_utils.py
  function save_obj (line 4) | def save_obj(obj, file_path):
  function load_obj (line 9) | def load_obj(file_path):
  function get_unique_vocab (line 14) | def get_unique_vocab(analogy_file_path, write_out_file):

FILE: src/codes/utils/string_utils.py
  function convert_to_unicode (line 4) | def convert_to_unicode(text):

FILE: src/codes/utils/vectors.py
  function l2_len (line 15) | def l2_len(v: vector_type) -> float:
  function dot (line 19) | def dot(v1: vector_type, v2: vector_type) -> float:
  function mean (line 24) | def mean(v1: vector_type, v2: vector_type) -> Vector:
  function mean_list (line 35) | def mean_list(v1: List[Vector]) -> Vector:
  function add (line 47) | def add(v1: vector_type, v2: vector_type) -> Vector:
  function sub (line 52) | def sub(v1: vector_type, v2: vector_type) -> Vector:
  function normalize (line 57) | def normalize(v: vector_type) -> Vector:
  function cosine_similarity_normalized (line 61) | def cosine_similarity_normalized(v1: vector_type, v2: vector_type) -> fl...

FILE: src/codes/utils/word.py
  class Word (line 7) | class Word:
    method __init__ (line 10) | def __init__(self, text: str, vector: Vector, frequency: int) -> None:
    method __repr__ (line 15) | def __repr__(self) -> str:

FILE: src/codes/visualizer/visualizer_sbs.py
  function search (line 17) | def search():
  function get_index (line 57) | def get_index():
  function multi_search (line 62) | def multi_search():