def main():
ja_dic = FastVector(vector_file='../vecmap/data/wiki.ja.vec')
en_dic = FastVector(vector_file='../vecmap/data/wiki.en.vec')
print("loaded the dictionaries")
ja_dic.apply_transform('alignment_matrices/ja.txt')
en_dic.apply_transform('alignment_matrices/en.txt')
print("transformed the dictionaries")
idx = 0
result = {}
result_f = open("en_ja_multifast.txt", "w")
en_word_list = list(en_dic.word2id.keys())
print("The total length of English pretrained vector : " +
str(len(en_word_list)))
for en_word in tqdm(en_word_list):
ja_words = ja_dic.translate_k_nearest_neighbour(en_dic[en_word], k=15)
result[en_word] = ja_words
idx += 1
result[en_word] = ja_words
resut_str = ",".join(result[en_word])
result_f.write(str(idx) + "," + en_word + "," + resut_str + "\n")
if idx > 5000:
exit()
result_f.close()
print("loaded the dictionaries")
ja_dic.apply_transform('alignment_matrices/ja.txt')
en_dic.apply_transform('alignment_matrices/en.txt')
print("transformed the dictionaries")
en_word_list = [
"cat", "dog", "apple", "car", "train", "school", "student", "teacher"
]
ja_word_list = ["猫", "犬", "りんご", "車", "電車", "学校", "生徒", "先生"]
result_f = open("multi_fast.txt", "w")
result = {}
# Ja_word_list 10 nearest neighbor
for ja_word in ja_word_list:
en_words = en_dic.translate_k_nearest_neighbour(ja_dic[ja_word], k=20)
result[ja_word] = en_words
resut_str = ",".join(result[ja_word])
result_f.write(ja_word + "," + resut_str + "\n")
# En_word_list 10 nearest neighbor
for en_word in en_word_list:
ja_words = ja_dic.translate_k_nearest_neighbour(en_dic[en_word], k=20)
result[en_word] = ja_words
resut_str = ",".join(result[en_word])
result_f.write(en_word + "," + resut_str + "\n")
result_f.close()
#
# text = json.dump(result, open("result.json", "w"),
# ensure_ascii=False, indent=2)