Delete embeddings.py

1 files changed, 0 insertions, 80 deletions

diff --git a/embeddings.py b/embeddings.py
deleted file mode 100644
index 9a407a5..0000000
--- a/embeddings.py
+++ /dev/null
@@ -1,80 +0,0 @@
-# Copyright (C) 2016-2018  Mikel Artetxe <artetxem@gmail.com>
-#
-# This program is free software: you can redistribute it and/or modify
-# it under the terms of the GNU General Public License as published by
-# the Free Software Foundation, either version 3 of the License, or
-# (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-# GNU General Public License for more details.
-#
-# You should have received a copy of the GNU General Public License
-# along with this program.  If not, see <http://www.gnu.org/licenses/>.
-
-from cupy_utils import *
-
-import numpy as np
-
-
-def read(file, threshold=0, vocabulary=None, dtype='float'):
-    header = file.readline().split(' ')
-    count = int(header[0]) if threshold <= 0 else min(threshold, int(header[0]))
-    dim = int(header[1])
-    words = []
-    matrix = np.empty((count, dim), dtype=dtype) if vocabulary is None else []
-    for i in range(count):
-        word, vec = file.readline().split(' ', 1)
-        if vocabulary is None:
-            words.append(word)
-            matrix[i] = np.fromstring(vec, sep=' ', dtype=dtype)
-        elif word in vocabulary:
-            words.append(word)
-            matrix.append(np.fromstring(vec, sep=' ', dtype=dtype))
-    return (words, matrix) if vocabulary is None else (words, np.array(matrix, dtype=dtype))
-
-
-def write(words, matrix, file):
-    m = asnumpy(matrix)
-    print('%d %d' % m.shape, file=file)
-    for i in range(len(words)):
-        print(words[i] + ' ' + ' '.join(['%.6g' % x for x in m[i]]), file=file)
-
-
-def length_normalize(matrix):
-    xp = get_array_module(matrix)
-    norms = xp.sqrt(xp.sum(matrix**2, axis=1))
-    norms[norms == 0] = 1
-    matrix /= norms[:, xp.newaxis]
-
-
-def mean_center(matrix):
-    xp = get_array_module(matrix)
-    avg = xp.mean(matrix, axis=0)
-    matrix -= avg
-
-
-def length_normalize_dimensionwise(matrix):
-    xp = get_array_module(matrix)
-    norms = xp.sqrt(xp.sum(matrix**2, axis=0))
-    norms[norms == 0] = 1
-    matrix /= norms
-
-
-def mean_center_embeddingwise(matrix):
-    xp = get_array_module(matrix)
-    avg = xp.mean(matrix, axis=1)
-    matrix -= avg[:, xp.newaxis]
-
-
-def normalize(matrix, actions):
-    for action in actions:
-        if action == 'unit':
-            length_normalize(matrix)
-        elif action == 'center':
-            mean_center(matrix)
-        elif action == 'unitdim':
-            length_normalize_dimensionwise(matrix)
-        elif action == 'centeremb':
-            mean_center_embeddingwise(matrix)