优化python DFS（for循环效率低下）

Question

给定以下函数，归档相同（且更快）结果的正确和 pythonic 方法是什么？

我的代码效率不高，我相信我错过了一些盯着我看的东西。

我们的想法是找到一个 [[A,B],[A,C],[C,B]] 的模式，而不必生成额外的排列（因为这会导致更长的处理时间比较）。

在现实生活中输入 find_path 的字典长度大约为 10,000，因此必须使用下面的当前代码版本迭代该数量是效率不高的。

from time import perf_counter
from typing import List, Generator, Dict


def find_path(data: Dict) -> Generator:
    for first_pair in data:

        pair1: List[str] = first_pair.split("/")

        for second_pair in data:
            pair2: List[str] = second_pair.split("/")
            if pair2[0] == pair1[0] and pair2[1] != pair1[1]:

                for third_pair in data:
                    pair3: List[str] = third_pair.split("/")

                    if pair3[0] == pair2[1] and pair3[1] == pair1[1]:

                        amount_pair_1: int = data.get(first_pair)[
                            "amount"
                        ]
                        id_pair_1: int = data.get(first_pair)["id"]

                        amount_pair_2: int = data.get(second_pair)[
                            "amount"
                        ]
                        id_pair_2: int = data.get(second_pair)["id"]

                        amount_pair_3: int = data.get(third_pair)[
                            "amount"
                        ]
                        id_pair_3: int = data.get(third_pair)["id"]

                        yield (
                            pair1,
                            amount_pair_1,
                            id_pair_1,
                            pair2,
                            amount_pair_2,
                            id_pair_2,
                            pair3,
                            amount_pair_3,
                            id_pair_3,
                        )


raw_data = {
    "EZ/TC": {"id": 1, "amount": 9},
    "LM/TH": {"id": 2, "amount": 8},
    "CD/EH": {"id": 3, "amount": 7},
    "EH/TC": {"id": 4, "amount": 6},
    "LM/TC": {"id": 5, "amount": 5},
    "CD/TC": {"id": 6, "amount": 4},
    "BT/TH": {"id": 7, "amount": 3},
    "BT/TX": {"id": 8, "amount": 2},
    "TX/TH": {"id": 9, "amount": 1},
}


processed_data = list(find_path(raw_data))

for i in processed_data:
    print(("The path to traverse is:", i))

>> ('The path to traverse is:', (['CD', 'TC'], 4, 6, ['CD', 'EH'], 7, 3, ['EH', 'TC'], 6, 4))
>> ('The path to traverse is:', (['BT', 'TH'], 3, 7, ['BT', 'TX'], 2, 8, ['TX', 'TH'], 1, 9))
>> ('Time to complete', 5.748599869548343e-05)

# Timing for a simple ref., as mentioned above, the raw_data is a dict containing about 10,000 keys

Answer 1

你不能用这种图形表示来做到这一点。该算法具有 O(|E|^3) 时间复杂度。将边存储为列表数组是个好主意，每个列表将仅存储相邻的顶点。然后很容易做你需要的。幸运的是，您可以在 O(|E|) 时间内重新表示图形。

怎么做

我们将图存储为顶点数组（但在这种情况下，由于字符串顶点值，我们采用词典)。我们想通过一个顶点访问所有邻居。让我们这样做——我们将在数组 中存储给定顶点的所有邻居列表 。

现在我们只需要通过一组边来构建我们的结构（又名row_data）。如何在图形中添加边？简单！我们应该在 array 中找到一个顶点 from 并将顶点 to 添加到它的邻居列表中

所以，construct_graph 函数可以是这样的：

def construct_graph(raw_data):  # here we will change representation
    graph = defaultdict(list)   # our graph
    for pair in raw_data:       # go through every edge
        u, v = pair.split("/")  # get from and to vertexes
        graph[u].append(v)      # and add this edge in our structure
    return graph                # return our new graph to other functions

如何找到路径长度 2

我们将在图表上使用 dfs。

def dfs(g, u, dist):                # this is a simple dfs function
    if dist == 2:                   # we has a 'dist' from our start
        return [u]                  # and if we found already answer, return it
    for v in g.get(u, []):          # otherwise check all neighbours of current vertex
        ans = dfs(g, v, dist + 1)   # run dfs in every neighbour with dist+1
        if ans:                     # and if that dfs found something
            ans.append(u)           # store it in ouy answer
            return ans              # and return it
    return []                       # otherwise we found nothing

然后我们对每个顶点进行尝试。

def main():
    graph = construct_graph(raw_data)
    for v in graph.keys():              # here we will try to find path
        ans = dfs(graph, v, 0)          # starting with 0 dist
        if ans:                         # and if we found something
            print(list(reversed(ans)))  # return it, but answer will be reversed

优化python DFS（for循环效率低下）

Optimizing python DFS (for loop is inefficient)

python

algorithm

optimization

for-loop

depth-first-search

怎么做

如何找到路径长度 2