Python 我的功能有什么问题？使用numpy和networkx查找最短路径

因此，我正在创建一个代码来使用networkx计算最短路径。我使用numpy创建了一个3D阵列，然后计算了如下最短路径：

import numpy as np
import networkx as nx

arr = np.random.randint(1, 100, size = (2, 5, 5)) #3D array

for i in arr:
    graph = nx.from_numpy_array(i, create_using = nx.DiGraph)
    path = nx.shortest_path(graph, 0, 3, weight = 'weight')
    print(path)

import numpy as np
import networkx as nx

arr = np.random.randint(1, 100, size = (2, 5, 5)) #3D array


def shortest(prices):

    for i in arr:
        graph = nx.from_numpy_array(i, create_using = nx.DiGraph)
        path = nx.shortest_path(graph, 0, 3, weight = 'weight')
        return path

print(shortest(arr))

def shortest(precios):

    for i in arr:
        graph = nx.from_numpy_array(i, create_using = nx.DiGraph)
        path = nx.shortest_path(graph, 0, 3, weight = 'weight')
    return path

print(shortest(arr))

因为我使用了两个矩阵，所以我得到了下一个输出：

[0, 1, 3] #path1
[0, 3]    #path2

[0, 1, 3] #same as path 1

[0, 3] #same as path 2

之后，我决定创建一个函数来执行完全相同的操作，如下所示：

import numpy as np
import networkx as nx

arr = np.random.randint(1, 100, size = (2, 5, 5)) #3D array

for i in arr:
    graph = nx.from_numpy_array(i, create_using = nx.DiGraph)
    path = nx.shortest_path(graph, 0, 3, weight = 'weight')
    print(path)

import numpy as np
import networkx as nx

arr = np.random.randint(1, 100, size = (2, 5, 5)) #3D array


def shortest(prices):

    for i in arr:
        graph = nx.from_numpy_array(i, create_using = nx.DiGraph)
        path = nx.shortest_path(graph, 0, 3, weight = 'weight')
        return path

print(shortest(arr))

def shortest(precios):

    for i in arr:
        graph = nx.from_numpy_array(i, create_using = nx.DiGraph)
        path = nx.shortest_path(graph, 0, 3, weight = 'weight')
    return path

print(shortest(arr))

我得到了下一个输出：

[0, 1, 3] #path1
[0, 3]    #path2

[0, 1, 3] #same as path 1

[0, 3] #same as path 2

如果我像这样改变“返回路径”的位置：

import numpy as np
import networkx as nx

arr = np.random.randint(1, 100, size = (2, 5, 5)) #3D array

for i in arr:
    graph = nx.from_numpy_array(i, create_using = nx.DiGraph)
    path = nx.shortest_path(graph, 0, 3, weight = 'weight')
    print(path)

import numpy as np
import networkx as nx

arr = np.random.randint(1, 100, size = (2, 5, 5)) #3D array


def shortest(prices):

    for i in arr:
        graph = nx.from_numpy_array(i, create_using = nx.DiGraph)
        path = nx.shortest_path(graph, 0, 3, weight = 'weight')
        return path

print(shortest(arr))

def shortest(precios):

    for i in arr:
        graph = nx.from_numpy_array(i, create_using = nx.DiGraph)
        path = nx.shortest_path(graph, 0, 3, weight = 'weight')
    return path

print(shortest(arr))

我得到了下一个输出：

[0, 1, 3] #path1
[0, 3]    #path2

[0, 1, 3] #same as path 1

[0, 3] #same as path 2

---

我无法使用我的函数在同一输出中获得两条路径的值。知道这里发生了什么吗？我一直在练习用python函数，因为我对这个主题有点陌生，所以我希望你能帮我看看哪里出了问题？谢谢大家!

这与

networkx

或

numpy

无关；这是一个简单的控制流。您的“最短路径”循环遍历提供的图形，并为每个图形找到最短路径

非函数版本会按找到的路径打印每个路径，但每次只处理一个路径。由于您在函数中没有规定聚合找到的解决方案，因此一次只能得到一个解决方案。。。对于一个函数，这意味着你只能得到一个

此代码在找到第一个解决方案后立即返回：

for i in arr:
    graph = nx.from_numpy_array(i, create_using = nx.DiGraph)
    path = nx.shortest_path(graph, 0, 3, weight = 'weight')
    return path   # The function ends as soon as it gets here: you get only the first solution.

此代码可以找到所有解决方案，但一旦找到下一个解决方案，您就会将每个解决方案都扔掉：

for i in arr:
    graph = nx.from_numpy_array(i, create_using = nx.DiGraph)
    path = nx.shortest_path(graph, 0, 3, weight = 'weight')
    # You just deleted the previous solution, and replaced it with another.

return path

这只返回最后一个解决方案，因为这是唯一一个您没有覆盖的解决方案。您必须以某种方式累积解决方案并将它们一起返回，或者改变您的函数用法。例如：

all_path = []
for i in arr:
    graph = nx.from_numpy_array(i, create_using = nx.DiGraph)
    all_path.append( nx.shortest_path(graph, 0, 3, weight = 'weight') )

return all_path

---

哦！我明白了，所以我创建了一个新的列表并存储了解决方案！非常感谢你！