def train(dataStore): import contentExtraction for r_ in xrange(0, data.T): print "======================" weibos = contentExtraction.get_weibo(r_) contentExtraction.weibo_vetorization(weibos) contentExtraction.content_nutrition(r_) for r_ in xrange(0, data.T): print "calculatiing the energy of the interval %s ...." %(str(r_)) contentExtraction.content_energy(r_) data.store_into_database() if dataStore: data.data_store()
def wechat_text_response(message): HELP = '''\ 1.输入h查看帮助信息 2.输入w:加正文写日记,后面可以添加#标签,标签之间用空格或逗号隔开,例如"w:正文#标签1,标签2" 3.输入r读取所有日记 4.输入r:读取所有标签 5.输入r:加标签读取标签内的日记,如"r:说明" 6.输入s:加信息,对信息进行搜索,返回具有此信息的日记 ps:输入的前缀标识只能是小写字母加英文的冒号, 现在只能接受文本信息,无法接受表情和图片 ''' if message.startswith('w:'): if '#' in message: lst = message[2:].split('#') content = lst[0].strip() tags = tags_process(lst[1].strip()) data_store(content, tags) return "日记记录成功" else: content = message[2:] tags = [] data_store(content,tags) return "日记记录成功" elif message in ["r","R"]: return '\n'.join(d['content'] for d in show_all_data()) elif message == "r:": return '\n'.join(get_tagsinfo().keys()) elif message.startswith('r:'): tag = message[2:].strip() tags = get_tagsinfo().keys() if tag in tags: return '\n'.join(d['content'] for d in show_data_for_tag(tag)) else: return "没有找到这个标签" elif message.startswith('s:'): query = message[2:].strip() results = data_search(query) if results: return '\n'.join(d['content'] for d in results) else: return "没有搜到相关内容" else: return HELP
def edit(): content = request.form["editContent"] tags = tags_process(request.form["editTags"]) data_store(content, tags) flash("数据提交成功") return redirect(url_for("index"))
# print('B :', fm_index.data['B']) # print('S :', fm_index.data['S']) # print('C :', fm_index.data['C']) # print('O :', fm_index.data['O']) # print(fm_index.text) # print('B :', fmd_index.data['B']) # print('B_n :', fmd_index.data['B_n']) # print('S :', fmd_index.data['S']) # print('C :', fmd_index.data['C']) # print('O :', fmd_index.data['O']) # print('O_n :', fmd_index.data['O_n']) # print(fmd_index.text) # print(len(fmd_index.data['B_n'])) print('********************** ALGORITHM *************************') # print(algorithm.get_all_seq(len(reference)*2, fmd_index)) # print(algorithm.backward_extension([0, 0, len(fmd_index.text)-1], 'A', fmd_index)) # print(algorithm.forward_extension([0, 0, len(fmd_index.text)-1], 'A', fmd_index)) # print(algorithm.super_MEM1("ACTTG", 0, fmd_index)) data.data_store(fmd_index) data.data_store_encode(fmd_index)