fpgrowth代码

当然，我可以为您提供一个简单的FPGrowth算法的代码示例，以使用Python实现。下面是一个基本的实现：

from collections import defaultdict

class FPNode:
    def __init__(self, item, count, parent):
        self.item = item
        self.count = count
        self.parent = parent
        self.link = None
        self.children = defaultdict()

    def add_child(self, child):
        if child.item not in self.children:
            self.children[child.item] = child

class FPTree:
    def __init__(self):
        self.root = FPNode(None, 0, None)
        self.header_table = defaultdict()

    def add_transaction(self, transaction):
        curr_node = self.root
        for item in transaction:
            if item in curr_node.children:
                curr_node.children[item].count += 1
            else:
                new_node = FPNode(item, 1, curr_node)
                curr_node.add_child(new_node)
                if item in self.header_table:
                    self._update_header_table(item, new_node)
                else:
                    self._insert_header_table(item, new_node)

            curr_node = curr_node.children[item]

    def _insert_header_table(self, item, node):
        self.header_table[item] = [node, node]

    def _update_header_table(self, item, node):
        start_node = self.header_table[item][1]
        while start_node.link is not None:
            start_node = start_node.link
        start_node.link = node

    def get_frequency_items(self, min_support):
        items = []
        for item in self.header_table:
            count = sum([n.count for n in self._get_paths(item)])
            if count >= min_support:
                items.append((item, count))
        return items

    def _get_paths(self, item):
        start_node = self.header_table[item][0]
        paths = []
        while start_node is not None:
            path = []
            curr_node = start_node.parent
            while curr_node.item is not None:
                path.append(curr_node.item)
                curr_node = curr_node.parent
            if len(path) > 0:
                paths.append((path[::-1], start_node.count))
            start_node = start_node.link
        return paths

def fp_growth(transactions, min_support):
    tree = FPTree()
    for transaction in transactions:
        tree.add_transaction(transaction)

    freq_items = tree.get_frequency_items(min_support)
    if len(freq_items) == 0:
        return []

    frequent_patterns = []
    for item, support in freq_items:
        frequent_patterns.append(([item], support))
        suffixes = _get_suffixes(tree, item)
        sub_transactions = _get_sub_transactions(suffixes)
        sub_patterns = fp_growth(sub_transactions, min_support)
        for pattern in sub_patterns:
            frequent_patterns.append(([item] + pattern[0], pattern[1]))

    return frequent_patterns

def _get_suffixes(tree, item):
    suffixes = []
    start_node = tree.header_table[item][0]
    while start_node is not None:
        suffix = []
        curr_node = start_node.parent
        while curr_node.item is not None:
            suffix.append(curr_node.item)
            curr_node = curr_node.parent
        if len(suffix) > 0:
            suffixes.append((suffix[::-1], start_node.count))
        start_node = start_node.link
    return suffixes

def _get_sub_transactions(suffixes):
    sub_transactions = []
    for suffix, count in suffixes:
        sub_transactions.extend([suffix] * count)
    return sub_transactions

# 示例用法：
transactions = [['A', 'B', 'D'],
                ['B', 'D'],
                ['A', 'C', 'D', 'E'],
                ['A', 'D', 'E'],
                ['B', 'C', 'D']]

min_support = 3

frequent_patterns = fp_growth(transactions, min_support)

for pattern, support in frequent_patterns:
    print(f"{pattern}: {support}")

这段代码实现了一个简单的FP-Growth算法，用于发现给定数据集中的频繁模式。它首先构建了一个FP树，然后通过递归遍历FP树来找到频繁模式。在示例中，我们使用一个简单的交易数据集进行演示，并设置最小支持度为3。输出将显示所有频繁模式及其支持度。

请注意，这只是FP-Growth算法的基本实现，并可能需要进一步的优化和调整以适应您的具体需求。