手动实现apriori算法代码的实现

以下是手动实现apriori算法的Python代码实现：

def createC1(dataSet):
    """
    生成第一层候选项集
    """
    C1 = []
    for transaction in dataSet:
        for item in transaction:
            if not [item] in C1:
                C1.append([item])
    return list(map(frozenset, C1))


def scanD(D, Ck, minSupport):
    """
    从候选项集生成频繁项集
    """
    ssCnt = {}
    for tid in D:
        for can in Ck:
            if can.issubset(tid):
                if not can in ssCnt:
                    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.append(key)
            supportData[key] = support
    return retList, supportData


def aprioriGen(Lk, k):
    """
    由频繁项集生成候选项集
    """
    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):
    """
    Apriori算法
    """
    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)
        supportData.update(supK)
        L.append(Lk)
        k += 1
    return L, supportData

其中，`dataSet` 是一组交易记录，每个交易记录是一组物品集合；`minSupport` 是设置的最小支持度阈值，用于筛选频繁项集。函数返回的是一个列表，其中每个元素是频繁项集。