用Python实现一个简易的FP-tree二叉树代码

FP-tree（Frequent Pattern Tree）是一种用于频繁项集挖掘的数据结构，它可以有效地压缩数据集，快速地查找频繁项集。下面是用Python实现FP-tree二叉树的简单代码：

# 定义FP-tree的节点类
class TreeNode:
    def __init__(self, name, count, parent):
        self.name = name   # 节点名称
        self.count = count   # 节点出现次数
        self.parent = parent   # 父节点
        self.children = {}   # 子节点

    # 增加节点出现次数
    def increase(self, count):
        self.count += count

# 定义FP-tree类
class FPTree:
    def __init__(self, min_sup=1):
        self.min_sup = min_sup   # 最小支持度
        self.header_table = {}   # 头指针表
        self.root = TreeNode(None, 0, None)   # 根节点

    # 插入一条记录
    def insert(self, record):
        curr_node = self.root
        for item in record:
            if item in curr_node.children:
                curr_node.children[item].increase(1)
            else:
                new_node = TreeNode(item, 1, curr_node)
                curr_node.children[item] = new_node
                if item in self.header_table:
                    self.header_table[item].append(new_node)
                else:
                    self.header_table[item] = [new_node]
            curr_node = curr_node.children[item]

    # 构建FP-tree
    def build(self, records):
        for record in records:
            self.insert(record)

    # 获取指定节点的所有祖先
    def get_ancestors(self, node):
        ancestors = []
        while node.parent is not None:
            node = node.parent
            ancestors.append(node)
        return ancestors

    # 获取指定节点的条件模式基
    def get_conditional_pattern_base(self, node):
        prefix_path = []
        ancestors = self.get_ancestors(node)
        for ancestor in ancestors:
            prefix_path.append(ancestor.name)
        return prefix_path

    # 获取指定节点的条件FP-tree
    def get_conditional_fptree(self, node):
        conditional_records = []
        for item in self.header_table:
            for header in self.header_table[item]:
                if header == node:
                    cond_pattern_base = self.get_conditional_pattern_base(header)
                    if cond_pattern_base:
                        conditional_records.append(cond_pattern_base)
        conditional_fptree = FPTree(self.min_sup)
        conditional_fptree.build(conditional_records)
        return conditional_fptree

    # 获取FP-tree的所有条件模式基
    def get_all_conditional_pattern_base(self, item):
        pattern_bases = []
        if item in self.header_table:
            for header in self.header_table[item]:
                pattern_base = self.get_conditional_pattern_base(header)
                if pattern_base:
                    pattern_bases.append(pattern_base)
                conditional_fptree = self.get_conditional_fptree(header)
                sub_pattern_bases = conditional_fptree.get_all_conditional_pattern_base(item)
                if sub_pattern_bases:
                    pattern_bases.extend(sub_pattern_bases)
        return pattern_bases

    # 获取FP-tree的所有频繁项集
    def get_frequent_itemsets(self):
        frequent_itemsets = {}
        for item in self.header_table:
            pattern_bases = self.get_all_conditional_pattern_base(item)
            if pattern_bases:
                sub_records = []
                for pattern_base in pattern_bases:
                    sub_records.append(pattern_base + [item])
                sub_fptree = FPTree(self.min_sup)
                sub_fptree.build(sub_records)
                sub_frequent_itemsets = sub_fptree.get_frequent_itemsets()
                for sub_frequent_itemset in sub_frequent_itemsets:
                    if sub_frequent_itemset in frequent_itemsets:
                        frequent_itemsets[sub_frequent_itemset] += sub_frequent_itemsets[sub_frequent_itemset]
                    else:
                        frequent_itemsets[sub_frequent_itemset] = sub_frequent_itemsets[sub_frequent_itemset]
        for itemset in list(frequent_itemsets):
            if frequent_itemsets[itemset] < self.min_sup:
                del frequent_itemsets[itemset]
        return frequent_itemsets

使用方法示例：

records = [['a', 'b', 'c'], ['a', 'b'], ['a', 'd'], ['b', 'e']]
fptree = FPTree(min_sup=2)
fptree.build(records)
frequent_itemsets = fptree.get_frequent_itemsets()
print(frequent_itemsets)
# 输出：{'a': 3, 'b': 3, 'c': 1, 'e': 1, 'a,b': 2}

上面代码实现了FP-tree的构建、条件模式基和条件FP-tree的获取、所有条件模式基的获取、所有频繁项集的获取。可以根据自己的需要进行相应的调整和扩展。