请编写程序(编程语言自选)实现上图中Apriori算法生成频繁项集的过程。在该程序中需要在每一步将频繁1项集、频繁2项集、频繁3项集等频繁项集输出。

好的，以下是使用Python实现Apriori算法生成频繁项集的示例代码。该代码会逐步输出每个步骤中的频繁项集：

from collections import defaultdict

def load_transactions(filename):
    with open(filename, 'r') as file:
        transactions = [line.strip().split(',') for line in file.readlines()]
    return transactions

def generate_candidates(itemsets, k):
    candidates = set()
    for i in itemsets:
        for j in itemsets:
            candidate = sorted(set(i).union(set(j)))
            if len(candidate) == k and candidate not in candidates:
                candidates.add(tuple(candidate))
    return list(candidates)

def prune_candidates(candidates, prev_freq_itemsets, k):
    pruned_candidates = []
    for candidate in candidates:
        subsets = [tuple(sorted(subset)) for subset in combinations(candidate, k-1)]
        if all(subset in prev_freq_itemsets for subset in subsets):
            pruned_candidates.append(candidate)
    return pruned_candidates

def count_support(transactions, candidates):
    support_count = defaultdict(int)
    for transaction in transactions:
        for candidate in candidates:
            if set(candidate).issubset(set(transaction)):
                support_count[candidate] += 1
    return support_count

def apriori(transactions, min_support):
    C1 = {}
    for transaction in transactions:
        for item in transaction:
            C1[item] = C1.get(item, 0) + 1
    
    L1 = {item: count for item, count in C1.items() if count >= min_support}
    frequent_itemsets = [L1]
    
    k = 2
    while True:
        candidates = generate_candidates(frequent_itemsets[-1].keys(), k)
        if not candidates:
            break
        
        pruned_candidates = prune_candidates(candidates, frequent_itemsets[-1].keys(), k)
        support_counts = count_support(transactions, pruned_candidates)
        
        Lk = {candidate: count for candidate, count in support_counts.items() if count >= min_support}
        if not Lk:
            break
        
        frequent_itemsets.append(Lk)
        print(f"频繁{k}项集: {Lk}")
        k += 1
    
    return frequent_itemsets

if __name__ == "__main__":
    transactions = [
        ['A', 'B', 'C'],
        ['A', 'B', 'D'],
        ['A', 'C', 'E'],
        ['B', 'C', 'D'],
        ['A', 'B', 'C', 'D']
    ]
    min_support = 2
    frequent_itemsets = apriori(transactions, min_support)
    print("所有频繁项集:", frequent_itemsets)

### 说明：
1. **load_transactions**: 读取交易数据文件并返回交易列表。
2. **generate_candidates**: 生成候选k项集。
3. **prune_candidates**: 剪枝操作，去除不满足条件的候选集。
4. **count_support**: 计算候选集的支持度计数。
5. **apriori**: 主函数，实现Apriori算法，生成并输出每个步骤中的频繁项集。

你可以将交易数据保存到一个文件中，并通过 `load_transactions` 函数读取，或者直接在代码中定义交易数据列表。希望这段代码对你有帮助！