def resume_post(pars):
print pars
pars = dict(pars)
id = pars.pop('id')
if id == '0':
return model.resume().insert(pars)
else:
return model.resume().update(pars, {'id': id})
def resume_post(pars):
print pars
pars = dict(pars)
id = pars.pop('id')
if id=='0':
return model.resume().insert(pars)
else:
return model.resume().update(pars, {'id':id})
def index(self):
users = model.user().getList('*') ##进行表查询
self.assign('users', users) ##设置为模板变量
jobs = model.job().getList('*')
self.assign('jobs', jobs)
resumes = model.resume().getList('*')
self.assign('resumes', resumes)
return self.display('index') ##显示指定模板
def index(self):
users = model.user().getList('*') ##进行表查询
self.assign('users', users) ##设置为模板变量
jobs = model.job().getList('*')
self.assign('jobs', jobs)
resumes = model.resume().getList('*')
self.assign('resumes', resumes)
return self.display('index') ##显示指定模板
def default(self):
if not self.isLogin():
self.assign('msg', '你当前还没有登录，请先登录！')
return self.display('msg')
uid = self.getUid()
resume_info = model.resume().getOne('*', {'uid':uid})
if resume_info:
self.assign('resume_info',resume_info)
resume_id = resume_info['id']
work_experience = model.work_experience().getList('*', {'resume_id' : resume_id})
self.assign('work_experience',work_experience)
study_experience = model.study_experience().getList('*', {'resume_id' : resume_id})
self.assign('study_experience',study_experience)
else:
self.assign('resume_info',{})
return self.display('resume')
def resume_search(pars):
sql = u'select * from resume where hope_work like "%%%s%%"' % pars
return model.resume().fetchAll(sql)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
#parameters
input_dim = 22 * 4
output_dim = 12
n_run = 25
model_name = 'wc_predicter.pt'
train_path = 'data/Train_data/'
train_frame = 200
test_path = 'data/Test_data/'
predict_path = 'data/Test_ML_MD/'
predict_frame = 20
test_frame = 50
atype_dict = {'O': 0, 'H1': 1, 'H2': 2, 'WC0': 3, 'WC1': 4, 'WC2': 5, 'WC3': 6}
atype_inv = {0: 'O', 1: 'H1', 2: 'H2'}
#create model
model = mlp(input_dim=122 * 4, output_dim=12).to(device)
if run_type == 'train':
train(model, atype_dict, n_run, model_name, train_path, train_frame,
test_path, test_frame)
else:
resume(model_name, model)
model.eval()
make_prediction(model,
predict_path,
predict_frame,
atype_dict,
savedir='predicted_temp')
def resume_search(pars):
sql = u'select * from resume where hope_work like "%%%s%%"' % pars
return model.resume().fetchAll(sql)