def export_data(self):
weights = [[], []]
for neuron in self.neuron_layers[0]:
weights[0].extend(neuron.weights)
for neuron in self.neuron_layers[1]:
weights[1].extend(neuron.weights)
weights_path = pathlib.Path('weights.json')
weights_path.write_text(
json_file.list_to_json(weights, json_file.data_to_json), 'utf-8')
network_info = pathlib.Path('network_info.json')
network_info.write_text(json_file.list_to_json(
[self.input_size, self.hidden_size, self.genre_num],
json_file.data_to_json),
encoding='utf-8')
wordtoindex_dic_path = pathlib.Path('wordtoindex.json')
wordtoindex_dic_path.write_text(json_file.dict_to_json(
self.wordtoindex_dic, json_file.data_to_json),
encoding='utf-8')
genretoindex_dic_path = pathlib.Path('genretoindex.json')
genretoindex_dic_path.write_text(json_file.dict_to_json(
self.genretoindex_dic, json_file.data_to_json),
encoding='utf-8')
def set_to_vector(trainig_set, word_num):
global index_to_genre, index_to_word, word_to_index, genre_to_index
genre_to_index = {}
word_to_index = {}
genre_list = [genre for item in trainig_set for genre in item["genre"]]
genre_list = set(genre_list)
genre_num = len(genre_list)
print(genre_list)
num_counts = count_word_num(trainig_set)
num_counts.sort(key=lambda x: x[1], reverse=True)
num_counts = num_counts[:word_num]
print("word_count done")
for i in range(word_num):
word_to_index[num_counts[i][0]] = i
index_to_word = [
w for i in range(len(word_to_index.keys()))
for w, j in word_to_index.items() if j == i
]
for i, genre in enumerate(list(genre_list)):
genre_to_index[genre] = i
index_to_genre = [
g for i in range(len(genre_to_index.keys()))
for g, j in genre_to_index.items() if j == i
]
print("trainset adjustment finished")
wordtoindex_dic_path = pathlib.Path('wordtoindex.json')
wordtoindex_dic_path.write_text(json.dumps(
json_file.dict_to_json(word_to_index, json_file.data_to_json)),
encoding='utf-8')
genretoindex_dic_path = pathlib.Path('genretoindex.json')
genretoindex_dic_path.write_text(json.dumps(
json_file.dict_to_json(genre_to_index, json_file.data_to_json)),
encoding='utf-8')
print("2index set file saved")
return adjust_train_logistic(word_num, word_to_index, genre_num,
genre_to_index, trainig_set)
def export_data(self) :
wtipath = pathlib.Path('wordtoindex_ori_title.json')
wti_json = json.dumps(json_file.dict_to_json(self.wordtobookindex, json_file.data_to_json))
if wtipath.exists():
file_data = wtipath.read_text(encoding='utf-8')
if file_data != wti_json:
wtipath.write_text(wti_json, encoding='utf-8')
else:
wtipath.write_text(wti_json, encoding='utf-8')
def export_data(self):
book_p = pathlib.Path('book_data.json')
book_p.write_text(json_file.list_to_json(self.book_list,
json_file.data_to_json),
encoding='utf-16')
dic_p = pathlib.Path('date_to_book.json')
dic_p.write_text(json_file.dict_to_json(self.date_to_book,
json_file.data_to_json),
encoding='utf-16')
def export_data(self):
dic_p = pathlib.Path('word_prob.json')
dic_p.write_text(json_file.list_to_json([[x, y]
for x, y in self.word_probs],
json_file.data_to_json),
encoding='utf-16')
dic_p = pathlib.Path('genre_num.json')
dic_p.write_text(json_file.dict_to_json(self.genre_list,
json_file.data_to_json),
encoding='utf-16')
def export_data(self) :
book_p = pathlib.Path('book_data.json')
book_json = json.dumps(json_file.list_to_json(self.book_list, json_file.data_to_json))
try:
if book_p.exists() :
file_data = book_p.read_text(encoding='utf-16')
if file_data != book_json :
book_p.write_text(book_json, encoding='utf-16')
else :
book_p.write_text(book_json, encoding='utf-16')
except:
print("exception occured")
dic_p = pathlib.Path('date_to_book.json')
dic_json = json.dumps(json_file.dict_to_json(self.date_to_book, json_file.data_to_json))
if dic_p.exists():
file_data = dic_p.read_text(encoding='utf-16')
if file_data != dic_json:
dic_p.write_text(dic_json, encoding='utf-16')
else:
dic_p.write_text(dic_json, encoding='utf-16')
def export_data(self) :
wtipath = pathlib.Path('wordtoindex_searcher.json')
wtipath.write_text(json_file.dict_to_json(self.wordtobookindex, json_file.data_to_json), encoding='utf-8')
