本文整理汇总了Python中Bio.LogisticRegression.calculate方法的典型用法代码示例。如果您正苦于以下问题：Python LogisticRegression.calculate方法的具体用法？Python LogisticRegression.calculate怎么用？Python LogisticRegression.calculate使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类Bio.LogisticRegression的用法示例。
在下文中一共展示了LogisticRegression.calculate方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: get
# 需要导入模块: from Bio import LogisticRegression [as 别名]
# 或者: from Bio.LogisticRegression import calculate [as 别名]
def get(self):
offset = int(self.get_argument('o',default='1'))
rowcount = int(self.get_argument('r',default='10'))
offset=(offset-1)*rowcount
no = self.get_argument('no', default='')
model_id = self.get_argument('model_id', default='')
model_type = self.get_argument('model_type', default='')
package=self.get_argument('model_name', default='')
cur=self.db.getCursor()
rowdata={}
#查询
if no=='1':
if model_type =='1':
cur.execute(" select b.name,a.create_id,a.name,a.note,a.beta from public.logistis a "
" left join public.account b on a.create_id = b.id "
"where a.id='%s' "% (model_id) )
rows = cur.fetchall()
print(rows)
rowdata['struct']="id,create_id,name,note,beta "
rowdata['rows']= rows
else:
cur.execute(" select b.name,a.create_id,a.name,a.note,c.name,a.file_name from public.pymodel a "
" left join public.account b on a.create_id = b.id "
" left join public.model c on a.type = c.type "
" where a.id='%s' and a.type='%s' "% (model_id,model_type) )
rows = cur.fetchall()
rowdata['struct']="id,create_id,name,note,type,filename "
rowdata['rows']= rows
self.response(rowdata)
elif no=='2':
if model_type=='1':
beta = self.get_argument('beta', default='')
model_data=self.get_argument('model', default='')
a=[]
q=0
print(model_data)
a=(list(eval(model_data)))
model=LogisticRegression.LogisticRegression()
model.beta=(list(eval(beta)))
rowdata={}
rowdata['op']=LogisticRegression.calculate(model,a)
rowdata['rows']=LogisticRegression.classify(model,a)
elif model_type=='2':
pack='data_mining.'+package
import importlib
bb=importlib.import_module(pack)
ma=kNN.kNN()
model=bb.model.knn(ma)
model_data=self.get_argument('model', default='')
a=[]
a=(list(eval(model_data)))
rowdata={}
rowdata['op']=kNN.calculate(model,a)
rowdata['rows']=kNN.classify(model,a)
elif model_type=='3':
pack='data_mining.'+package
import importlib
bb=importlib.import_module(pack)
ma=NaiveBayes.NaiveBayes()
model=bb.model.bayes(ma)
model_data=self.get_argument('model', default='')
a=[]
a=(list(eval(model_data)))
rowdata={}
rowdata['op']=NaiveBayes.calculate(model,a)
rowdata['rows']=NaiveBayes.classify(model,a)
self.response(rowdata)