本文整理匯總了Python中Bio.LogisticRegression.classify方法的典型用法代碼示例。如果您正苦於以下問題：Python LogisticRegression.classify方法的具體用法？Python LogisticRegression.classify怎麽用？Python LogisticRegression.classify使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類Bio.LogisticRegression的用法示例。
在下文中一共展示了LogisticRegression.classify方法的2個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: str
# 需要導入模塊: from Bio import LogisticRegression [as 別名]
# 或者: from Bio.LogisticRegression import classify [as 別名]
from Bio import LogisticRegression
import numpy as np
all_data = np.loadtxt("../datasets/iris/iris.data", delimiter=",",
dtype="float, float, float, float, S11")
xs = []
ys = []
for i in all_data:
if 'virgi' not in str(i[-1]):
xs.append([i[0], i[1], i[2], i[3]])
if 'setosa' in str(i[-1]):
ys.append(0)
else:
ys.append(1)
test_xs = xs.pop()
test_ys = ys.pop()
def show_progress(iteration, loglikelihood):
print("Iteration:", iteration, "Log-likelihood function:", loglikelihood)
model = LogisticRegression.train(xs, ys, update_fn=show_progress)
print("This should be Iris-versic (1): {}".format(LogisticRegression.classify(model, test_xs)))
示例2: get
# 需要導入模塊: from Bio import LogisticRegression [as 別名]
# 或者: from Bio.LogisticRegression import classify [as 別名]
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={}
#查詢
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)