生成一个apriori算法的Python源代码

下面是一个简单的Apriori算法的Python实现：

def apriori(transactions, min_support):
    """
    :param transactions: a list of transactions. Each transaction is a list of items.
    :param min_support: minimum support threshold
    :return: a dictionary of itemsets and their corresponding supports
    """
    itemsets = {}
    for transaction in transactions:
        for item in transaction:
            if item not in itemsets:
                itemsets[item] = 1
            else:
                itemsets[item] += 1

    # filter itemsets below minimum support threshold
    itemsets = {k: v for k, v in itemsets.items() if v >= min_support}

    freq_items = list(itemsets.keys())
    freq_items.sort()

    # generate candidate itemsets
    k = 2
    while freq_items:
        candidate_itemsets = set()
        for i in range(len(freq_items)):
            for j in range(i + 1, len(freq_items)):
                itemset1 = set(freq_items[i])
                itemset2 = set(freq_items[j])
                if len(itemset1.union(itemset2)) == k:
                    candidate_itemsets.add(tuple(sorted(itemset1.union(itemset2))))

        # count candidate itemsets in transactions
        itemsets = {}
        for transaction in transactions:
            for candidate_itemset in candidate_itemsets:
                if set(candidate_itemset).issubset(set(transaction)):
                    if candidate_itemset not in itemsets:
                        itemsets[candidate_itemset] = 1
                    else:
                        itemsets[candidate_itemset] += 1

        # filter itemsets below minimum support threshold
        itemsets = {k: v for k, v in itemsets.items() if v >= min_support}

        # add frequent itemsets to the list
        freq_items = list(itemsets.keys())
        freq_items.sort()
        k += 1

    return itemsets

该算法接受一个交易列表和最小支持阈值作为输入，并返回一个包含频繁项集及其支持度计数的字典。