SYMBOL INDEX (97 symbols across 10 files)

FILE: compress_fasttext/compress.py
  class CompressedFastTextKeyedVectors (line 18) | class CompressedFastTextKeyedVectors(FastTextKeyedVectors):
    method __init__ (line 24) | def __init__(self, *args, **kwargs):
    method load (line 29) | def load(cls, *args, **kwargs):
    method update_index2word (line 48) | def update_index2word(self):
    method word_vec (line 53) | def word_vec(self, word, use_norm=False):
    method fill_norms (line 90) | def fill_norms(self, force=False):
    method init_sims (line 102) | def init_sims(self, replace=False):
    method adjust_vectors (line 115) | def adjust_vectors(self):
    method recalc_char_ngram_buckets (line 119) | def recalc_char_ngram_buckets(self):
    method _save_specials (line 123) | def _save_specials(self, fname, separately, sep_limit, ignore, pickle_...
  function make_new_fasttext_model (line 132) | def make_new_fasttext_model(
  function quantize_ft (line 160) | def quantize_ft(ft, qdim=100, centroids=255, sample=None):
  function svd_ft (line 169) | def svd_ft(ft, n_components=30, fp16=True):
  function prune_ft (line 178) | def prune_ft(ft, new_vocab_size=1_000, new_ngrams_size=20_000, fp16=True):
  function prune_ft_freq (line 194) | def prune_ft_freq(

FILE: compress_fasttext/decomposition.py
  class DecomposedMatrix (line 10) | class DecomposedMatrix:
    method __init__ (line 11) | def __init__(self, compressed, components):
    method __getitem__ (line 15) | def __getitem__(self, item):
    method shape (line 19) | def shape(self):
    method dtype (line 23) | def dtype(self):
    method compress (line 27) | def compress(cls, data, n_components=30, fp16=True):
    method __len__ (line 36) | def __len__(self):

FILE: compress_fasttext/evaluation.py
  function vocabulary_from_files (line 8) | def vocabulary_from_files(path):
  function cosine (line 22) | def cosine(x, y, eps=1e-10):
  function vecs_similarity (line 29) | def vecs_similarity(old_vecs, new_vecs, corpus):
  function make_evaluator (line 33) | def make_evaluator(baseline_model, path):

FILE: compress_fasttext/feature_extraction.py
  function tokenize (line 8) | def tokenize(text) -> List[str]:
  class FastTextTransformer (line 13) | class FastTextTransformer(BaseEstimator, TransformerMixin):
    method __init__ (line 15) | def __init__(self, model, tokenizer=tokenize, normalize=True):
    method fit (line 22) | def fit(self, X, y=None):
    method transform (line 25) | def transform(self, X):

FILE: compress_fasttext/navec_like.py
  class Record (line 4) | class Record(object):
    method __eq__ (line 7) | def __eq__(self, other):
    method __ne__ (line 16) | def __ne__(self, other):
    method __iter__ (line 19) | def __iter__(self):
    method __hash__ (line 22) | def __hash__(self):
    method __repr__ (line 25) | def __repr__(self):
    method _repr_pretty_ (line 39) | def _repr_pretty_(self, printer, cycle):
  class PQ (line 61) | class PQ(Record):
    method __init__ (line 64) | def __init__(self, vectors, dim, qdim, centroids, indexes, codes):
    method __getitem__ (line 81) | def __getitem__(self, id):
    method __add__ (line 89) | def __add__(self, other):
    method __sub__ (line 92) | def __sub__(self, other):
    method __mul__ (line 95) | def __mul__(self, other):
    method __truediv__ (line 98) | def __truediv__(self, other):
    method __pow__ (line 101) | def __pow__(self, other):
    method sqrt (line 104) | def sqrt(self):
    method unpack (line 107) | def unpack(self):
    method sampled (line 111) | def sampled(self, ids):
    method shape (line 120) | def shape(self):
    method dtype (line 124) | def dtype(self):
    method as_bytes (line 128) | def as_bytes(self):
    method from_file (line 136) | def from_file(cls, file):
    method index_type (line 146) | def index_type(cls, centroids):
    method __len__ (line 149) | def __len__(self):

FILE: compress_fasttext/pq_encoder_light.py
  class EncoderBase (line 11) | class EncoderBase(sklearn.base.BaseEstimator):
    method fit_generator (line 12) | def fit_generator(self, x_train):
    method transform_generator (line 16) | def transform_generator(self, x_test):
    method inverse_transform_generator (line 20) | def inverse_transform_generator(self, x_test):
    method fit (line 24) | def fit(self, x_train):
    method transform (line 29) | def transform(self, x_test):
    method inverse_transform (line 34) | def inverse_transform(self, x_test):
    method _buffered_process (line 39) | def _buffered_process(self, x_input, process, buffer_size=10000):
  class PQEncoder (line 55) | class PQEncoder(EncoderBase):
    method __init__ (line 56) | def __init__(self, iteration=20, num_subdim=4, Ks=256):
    method fit (line 64) | def fit(self, x_train):
    method transform_generator (line 79) | def transform_generator(self, x_test):
    method inverse_transform_generator (line 85) | def inverse_transform_generator(self, x_test):
    method codewords (line 92) | def codewords(self):
  class TrainedPQEncoder (line 98) | class TrainedPQEncoder(object):
    method __init__ (line 99) | def __init__(self, codewords, code_dtype):
    method encode_multi (line 104) | def encode_multi(self, data_matrix):
    method decode_multi (line 114) | def decode_multi(self, codes):

FILE: compress_fasttext/prune.py
  function count_buckets (line 10) | def count_buckets(ft, words, new_ngrams_size):
  function fasttext_like_init (line 24) | def fasttext_like_init(n, dim=300, random_state=42):
  function prune_ngrams (line 32) | def prune_ngrams(ft, new_ngrams_size, random_state=42):
  function prune_vocab (line 53) | def prune_vocab(ft, new_vocab_size=1_000):
  class RowSparseMatrix (line 63) | class RowSparseMatrix:
    method __init__ (line 65) | def __init__(self, mapping, compressed, nrows, ncols, dtype):
    method __getitem__ (line 72) | def __getitem__(self, item):
    method unpack (line 77) | def unpack(self):
    method __add__ (line 83) | def __add__(self, other):
    method __sub__ (line 86) | def __sub__(self, other):
    method __mul__ (line 89) | def __mul__(self, other):
    method __truediv__ (line 92) | def __truediv__(self, other):
    method __pow__ (line 95) | def __pow__(self, other):
    method sqrt (line 98) | def sqrt(self):
    method shape (line 102) | def shape(self):
    method from_big (line 106) | def from_big(cls, row_indices, big_matrix, dtype=np.float32):
    method from_small (line 114) | def from_small(cls, row_indices, small_matrix, nrows, dtype=np.float32):

FILE: compress_fasttext/quantization.py
  function quantize (line 8) | def quantize(matrix, qdim, centroids, sample=None, iterations=5, verbose...

FILE: compress_fasttext/utils.py
  function getsize (line 17) | def getsize(obj):
  function mb (line 38) | def mb(obj):

FILE: tests/test_all.py
  function cosine_sim (line 14) | def cosine_sim(x, y):
  function test_prune_save_load (line 25) | def test_prune_save_load(method, params):
  function test_loading_existing_models (line 49) | def test_loading_existing_models(word1, word2, model_name):
  function test_sklearn_wrapper (line 55) | def test_sklearn_wrapper():