apriori算法的代码，python实现，参考《机器学习实战》

from numpy import *
def loadDataSet():
return [[1,3,4],[2,3,5],[1,2,3,5],[2,5]]

def createC1(dataSet):
C1 = []
for transaction in dataSet:
for item in transaction:
if not [item] in C1:
C1.append([item])
C1.sort()
return map(frozenset , C1)

def scanD(D,Ck,minSupport):
ssCnt = {}
for tid in D:
for can  in Ck:
if can.issubset(tid):
if not ssCnt.has_key(can):
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

def aprioriGen(Lk,k): #create 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:
retList.append(Lk[i] | Lk[j])
return retList

def apriori(dataSet , minSupport =0.5):
C1 = createC1(dataSet)
D = 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)
supportData.update(supK)
L.append(Lk)
k += 1
return L, supportData

def generateRules(L, supportData, minConf=0.7):  #supportData is a dict coming from scanD
bigRuleList = []
for i in range(1, len(L)):#only get the sets with two or more items
for freqSet in L[i]:
H1 = [frozenset([item]) for item in freqSet]
if (i > 1):
rulesFromConseq(freqSet, H1, supportData, bigRuleList, minConf)
else:
calcConf(freqSet, H1, supportData, bigRuleList, minConf)
return bigRuleList

def calcConf(freqSet, H, supportData, brl, minConf=0.7):
prunedH = [] #create new list to return
for conseq in H:
conf = supportData[freqSet]/supportData[freqSet-conseq] #calc confidence
if conf >= minConf:
print freqSet-conseq,'-->',conseq,'conf:',conf
brl.append((freqSet-conseq, conseq, conf))
prunedH.append(conseq)
return prunedH

def rulesFromConseq(freqSet, H, supportData, brl, minConf=0.7):
m = len(H[0])
if (len(freqSet) > (m + 1)): #try further merging
Hmp1 = aprioriGen(H, m+1)#create Hm+1 new candidates
Hmp1 = calcConf(freqSet, Hmp1, supportData, brl, minConf)
if (len(Hmp1) > 1):    #need at least two sets to merge
rulesFromConseq(freqSet, Hmp1, supportData, brl, minConf)