迭代后清除已存储的函数缓存

Question

我写了一些代码，试图使用本文中提到的一系列启发式和算法来近似最小化反馈弧集/最大化有向图的最大非循环子图（最多n=100个节点）的顶点的排序Franz J.Brandenburg和Kathrin Hanauer的《设置问题》，德国帕索大学。

被读入的数据是一个邻接矩阵，它被转换为igraph。图形实例。

我正在记忆成本函数和筛选函数。这两个函数的参数都是rank（包含顶点排序的元组）和edgeList（包含表示边的元组的元组）。

由于我一次处理多个实例，并且顶点由整数表示，因此我需要确保在处理一个实例（图）后，两个函数的缓存都被清除，我不完全确定这是否会发生。我发现了一些memize定时缓存实现和memize with _remove方法，尽管我一直得到相同的结果。

这是为今天早些时候（2015年6月12日下午5:00）到期的一个项目准备的，但由于我很固执，我一直在努力，并希望确保我正确使用了记忆方法。

我附上我使用过的相关代码：

@memoize
def cost(rank, edgeList):
    rankMap = {} 
    for i in range(len(rank)):
        rankMap[rank[i]] = i
    cost = 0
    for edge in edgeList:
        u, v = edge[0], edge[1]
        if rankMap[u] < rankMap[v]:
            cost += 1
    return cost
class memoize:
    """Gives the class it's core functionality."""
    def __call__(self, *args):
        if args not in self._memos:
            self._memos[args] = self._function(*args)
        return self._memos[args]
    def __init__(self, function):
        self._memos = {}
        self._function = function
    """Removes all memos. This is particularly useful if something that     affects the output has changed."""
    def remove_memos(self):
        self._memos = {}
def alg_star(algorithm, costFunc, graph, rank):
    edgeList = tuple([i.tuple for i in graph.es()])
    while True:
        rankPrime = rank
        rank = algorithm(tuple(rank), edgeList)
        if costFunc(tuple(rank), edgeList) <= costFunc(tuple(rankPrime), edgeList):
            break
    return rankPrime

@memoize
def sifting(rank, edgeList):
    copyRank = list(rank)
    rankValues = {}
    for node in rank:
        rankValues[tuple(copyRank)] = cost(tuple(copyRank), edgeList)
        for i in range(1,len(copyRank)):
            copyRank[i-1], copyRank[i] = copyRank[i], copyRank[i-1]
            rankValues[tuple(copyRank)] = cost(tuple(copyRank), edgeList)
        copyRank = list(argMax(rankValues))
    return copyRank
# def evaluateFAS(fileNameList):
rankings = []
for fileName in fileList:
print fileName
adjMatrix, incoming, outgoing = fasGraph(fileName)
instance = igraph.Graph.Adjacency(adjMatrix.tolist())
pre_process(instance)
# rank = kss200(instance)
rank = fasAlg(adjMatrix, incoming, outgoing)
rank = alg_star(sifting, cost, instance, rank)
rank = np.array(rank) + 1
rankings.append(rank)
cost.remove_memos() #not sure if working properly
sifting.remove_memos() # not sure if working properly
# return rankings

如有任何帮助和指导，我们将不胜感激。

Answer 1

我觉得还可以。让我们试一下

class memoize:
    """Gives the class it's core functionality."""
    def __call__(self, *args):
        if args not in self._memos:
            self._memos[args] = self._function(*args)
        return self._memos[args]
    def __init__(self, function):
        self._memos = {}
        self._function = function
    """Removes all memos. This is particularly useful if something that     affects the output has changed."""
    def remove_memos(self):
        self._memos = {}

@memoize
def f(x, y):
    print('f')
    return x + y
@memoize
def g(x, y):
    print('g')
    return x * y
print(f(2,3))
print(f(2,3))
print(g(2,3))
print(g(2,3))
print(f._memos)
print(g._memos)
f.remove_memos()
g.remove_memos()
print(f._memos)
print(g._memos)

输出

f
5
5
g
6
6
{(2, 3): 5}
{(2, 3): 6}
{}
{}

你为什么认为它不能正常工作？