from numpy import *
def loadDataSet():
return [[1, 3, 4], [2, 3, 5], [1, 2, 3, 5], [2, 5]]
dataSet = loadDataSet()
##print(dataSet)
def createC1(dataSet):
C1 = []
for transaction in dataSet:
## print(transaction)
for item in transaction:
if not [item] in C1:
C1.append([item])
## print(C1)
C1.sort()
return list(map(frozenset, C1))#use frozen set so we
#can use it as a key in a dict
##C1 = createC1(dataSet)
##print(C1)
##D = list(map(set,dataSet))
##print(D)
def scanD(D, Ck, minSupport):
ssCnt = {}
for tid in D:
for can in Ck:
if can.issubset(tid):
if can not in ssCnt.keys():
ssCnt[can]=1
else: ssCnt[can] += 1
numItems = float(len(D))
retList = []
supportData = {}
for key in ssCnt:
support = ssCnt[key]/numItems
if support >= minSupport:
retList.insert(0,key)
supportData[key] = support
return retList, supportData
##retList,supportData = scanD(D,C1,0.5)
##print(retList)
##print(supportData)
def aprioriGen(Lk, k): #creates Ck
retList = []
lenLk = len(Lk)
for i in range(lenLk):
for j in range(i+1, lenLk):
L1 = list(Lk[i])[:k-2]; L2 = list(Lk[j])[:k-2]
L1.sort(); L2.sort()
if L1==L2: #if first k-2 elements are equal
retList.append(Lk[i] | Lk[j]) #set union
return retList
##print(aprioriGen(retList,1))
def apriori(dataSet, minSupport = 0.5):
C1 = createC1(dataSet)
D = list(map(set, dataSet))
L1, supportData = scanD(D, C1, minSupport)
L = [L1]
k = 2
while (len(L[k-2]) > 0):
Ck = aprioriGen(L[k-2], k)
Lk, supK = scanD(D, Ck, minSupport)#scan DB to get Lk
supportData.update(supK)
L.append(Lk)
k += 1
return L, supportData
L,suppData = apriori(dataSet)
print(L[0])
print(L[1])
print(L[2])
print(suppData)
《机器学习第十一章Apriori算法实践》
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转载自blog.csdn.net/weixin_43955530/article/details/88298144
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