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PTMLCP.py
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PTMLCP.py
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import numpy as np
import copy
import os
class TMLCP(object):
def __init__(self,numInstance,path,numclasses=14,count=0,anum=0):
'''
:param numInstance: number
:param numclasses:类别
'''
self.numInstance = numInstance
self.numclasses = numclasses
self.path = path
self.count=count
self.anum=anum
def aconformist(self,regression,r):
'''
计算每一个的奇异值
:param regression:是个数据 list
:return a: list 表示的是奇异值
'''
a =copy.deepcopy(regression)
for j in range(self.numclasses):
for i in range(len(regression[0])):
a[j][i] = (1 - regression[j][i]) / (regression[j][i] + r[j])
return a
def pvalue(self,a,initlastpredict,devconforvalue,y_zero_score,devregression,dev_other_regression):
pvaluepath = os.path.join(self.path, "MLCPpvalue.log")
p1tvalue=copy.deepcopy(devregression)
p0tvalue=copy.deepcopy(dev_other_regression)
with open(pvaluepath, 'w')as f:
for i in range(self.numclasses): #有多少列
f.write('第%d类:' % i)
for j in range(len(devconforvalue[i])):#遍历每一列中的每个元素
ctr1 = 0
other0 = 0
for l in range(len(a[i])):
if a[i][l] >=devconforvalue[i][j]:
ctr1 += 1
for l in range(len(a[i])) :
if a[i][l] >=y_zero_score[i][j]:
other0 += 1
p1tvalue[i][j] = (ctr1 / (len(a[i]) + 1))
p0tvalue[i][j] =(other0/(len(a[i]) + 1))
f.write(f"{p1tvalue[i][j]}:{p0tvalue[i][j]}")
if p1tvalue[i][j]>p0tvalue[i][j]:
initlastpredict[i][j]=1
else:
initlastpredict[i][j] = 0
f.write('\n')
return initlastpredict,p1tvalue,p0tvalue
def signficance(self,p1tvalue,p0tvalue,test_y,signficace):
'''
:param p1tvalue:表是为一的一致性list内部有
:param p0tvalue:表示为0的一的一致性程度 list
:param testregression:list
:param signficace:
:return:onearray :array
'''
onesarray = np.ones((self.numclasses, len(signficace)))
truearray =np.ones((self.numclasses, len(signficace)))
favoriteratearray=np.ones((self.numclasses,len(signficace)))
nonearray=np.ones((self.numclasses,len(signficace)))
a = 0
for z in signficace:
signpredict = []
truerate=[]
favoriterate=[]
ennone=[]
for i in range(self.numclasses):
hangpredic = []
count=0
en=0
favorite=0
for j in range(len(p1tvalue[i])):
everypredic = []
if p1tvalue[i][j]>z:
everypredic.append(1)
if p0tvalue[i][j]>z:
everypredic.append(0)
if test_y[i][j] in everypredic:
hangpredic.append(True)
if len(everypredic)==1:
count += 1
if test_y[i][j]==everypredic[0]:
favorite +=1
elif len(everypredic)==0:
en += 1
nonerate=en/len(p1tvalue[i])
accuaryrate=count/len(p1tvalue[i])
accuary=len(hangpredic)/len(p1tvalue[i])
favoritrate = favorite/len(p1tvalue[i])
signpredict.append(accuary)
truerate.append(accuaryrate)
favoriterate.append(favoritrate)
ennone.append(nonerate)
onesarray[:,a] = np.array(signpredict)
truearray[:,a] =np.array(truerate)
favoriteratearray[:,a]=np.array(favoriterate)
nonearray[:,a]=np.array(ennone)
a += 1
return onesarray,truearray,favoriteratearray,nonearray
def prediction(self, a_y,a_regression,test_y, testregression,signficace, r):
'''
进行预测
:param testregression: list
:param devregression: list
:param r: 网络敏感数
:return: lastpredict :list
'''
lastpredictpath = os.path.join(self.path, "MLCPlastvalue")
testnum = len(testregression[0])
devnum = len(a_regression[0])
onearray = np.ones((testnum,1))
zerosarray =np.zeros((testnum,1))
# -----把所有可能类别进行遍历------
test_Y_zero = []
test_y_one = []
test_other_regression = []
for i in range(self.numclasses):
test_y_one.append(onearray)
test_Y_zero.append(zerosarray)
test_other_regression.append(onearray-testregression[i])
#-----计算出所有的奇异值--------------
print('PTMLCP的值{r}'.format(r))
a= self.aconformist(a_regression,r)#得奇异值
y_one_ascore = self.aconformist(testregression,r)
y_zero_ascore = self.aconformist(test_other_regression,r)#为0时候的奇异值
#-----初始化最大值最终预测------------
initlastpredict=copy.deepcopy(testregression)
#-----计算出p值,并进行预测---------------------
lastpredict,p1tvalue,p0tvalue = self.pvalue(a,initlastpredict,y_one_ascore,
y_zero_ascore,testregression,test_other_regression)
accuary,truearray ,nosurerate,nonearray= self.signficance(p1tvalue,p0tvalue,test_y,signficace)
#-----
with open(lastpredictpath, 'w') as flie:
for i in range(self.numclasses): # 有多少列
flie.write('第%d类:' % i)
for j in range(len(lastpredict[i])): # 遍历每一列中的每个元素
flie.write(str(lastpredict[i][j]))
flie.write("\n")
return lastpredict,accuary, truearray,nosurerate,nonearray