给出一个FP-growth算法的示例，基于python

FP-growth算法是一种非常有效的数据挖掘算法，它可以用于频繁模式挖掘、关联规则挖掘等任务。下面是一个基于python实现的FP-growth算法示例：

class TreeNode:
    def __init__(self, nameValue, numOccur, parentNode):
        self.name = nameValue
        self.count = numOccur
        self.nodeLink = None
        self.parent = parentNode
        self.children = {}

    def inc(self, numOccur):
        self.count += numOccur

    def disp(self, ind=1):
        print('  ' * ind, self.name, ' ', self.count)
        for child in self.children.values():
            child.disp(ind + 1)

def createTree(dataSet, minSup=1):
    headerTable = {}
    for trans in dataSet:
        for item in trans:
            headerTable[item] = headerTable.get(item, 0) + dataSet[trans]
    for k in list(headerTable.keys()):
        if headerTable[k] < minSup:
            del(headerTable[k])
    freqItemSet = set(headerTable.keys())
    if len(freqItemSet) == 0:
        return None, None
    for k in headerTable:
        headerTable[k] = [headerTable[k], None]
    retTree = TreeNode('Null Set', 1, None)
    for tranSet, count in dataSet.items():
        localD = {}
        for item in tranSet:
            if item in freqItemSet:
                localD[item] = headerTable[item][0]
        if len(localD) > 0:
            orderdItems = [v[0] for v in sorted(localD.items(), key=lambda p: p[1], reverse=True)]
            updateTree(orderdItems, retTree, headerTable, count)
    return retTree, headerTable

def updateTree(items, inTree, headerTable, count):
    if items[0] in inTree.children:
        inTree.children[items[0]].inc(count)
    else:
        inTree.children[items[0]] = TreeNode(items[0], count, inTree)
        if headerTable[items[0]][1] == None:
            headerTable[items[0]][1] = inTree.children[items[0]]
        else:
            updateHeader(headerTable[items[0]][1], inTree.children[items[0]])
    if len(items) > 1:
        updateTree(items[1::], inTree.children[items[0]], headerTable, count)

def updateHeader(nodeToTest, targetNode):
    while (nodeToTest.nodeLink != None):
        nodeToTest = nodeToTest.nodeLink
    nodeToTest.nodeLink = targetNode

def loadSimpDat():
    simpDat = [['r', 'z', 'h', 'j', 'p'],
               ['z', 'y', 'x', 'w', 'v', 'u', 't', 's'],
               ['z'],
               ['r', 'x', 'n', 'o', 's'],
               ['y', 'r', 'x', 'z', 'q', 't', 'p'],
               ['y', 'z', 'x', 'e', 'q', 's', 't', 'm']]
    return simpDat

def createInitSet(dataSet):
    retDict = {}
    for trans in dataSet:
        retDict[frozenset(trans)] = 1
    return retDict

if __name__ == "__main__":
    simpDat = loadSimpDat()
    initSet = createInitSet(simpDat)
    myFPtree, myHeaderTab = createTree(initSet, minSup=3)
    myFPtree.disp()

这个示例代码实现了FP-growth算法，使用了树结构来处理频繁项集。FP-growth算法的核心函数是createTree，这个函数根据输入数据集生成FP树并返回树结构。运行这段代码会输出FP树的结构，可以根据输出结果来理解算法的实现原理。