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)
