解释这行代码 def ClusteringCoefficient(self): for node in self.Graph: self.getCC(node)
时间: 2024-05-31 10:09:10 浏览: 15
这是一个类方法,用于计算无向图中每个节点的聚类系数(Clustering Coefficient),即该节点的邻居节点之间实际存在的边数与可能存在的边数之比。
其中,self.Graph是该类的一个属性,表示该无向图的邻接表。for循环遍历每个节点,调用self.getCC(node)方法来计算该节点的聚类系数。
相关问题
class Path(object): def __init__(self,path,distancecost,timecost): self.__path = path self.__distancecost = distancecost self.__timecost = timecost #路径上最后一个节点 def getLastNode(self): return self.__path[-1] #获取路径路径 @property def path(self): return self.__path #判断node是否为路径上最后一个节点 def isLastNode(self, node): return node == self.getLastNode() #增加加点和成本产生一个新的path对象 def addNode(self, node, dprice, tprice): return Path(self.__path+[node],self.__distancecost + dprice,self.__timecost + tprice) #输出当前路径 def printPath(self): for n in self.__path: if self.isLastNode(node=n): print(n) else: print(n, end="->") print(f"最短路径距离(self.__distancecost:.0f)m") print(f"红绿路灯个数(self.__timecost:.0f)个") #获取路径总成本的只读属性 @property def dCost(self): return self.__distancecost @property def tCost(self): return self.__timecost class DirectedGraph(object): def __init__(self, d): if isinstance(d, dict): self.__graph = d else: self.__graph = dict() print('Sth error') #通过递归生成所有可能的路径 def __generatePath(self, graph, path, end, results, distancecostIndex, timecostIndex): current = path.getLastNode() if current == end: results.append(path) else: for n in graph[current]: if n not in path.path: self.__generatePath(graph, path.addNode(n,self.__graph[path.getLastNode()][n][distancecostIndex][timecostIndex]), end, results, distancecostIndex, timecostIndex) #搜索start到end之间时间或空间最短的路径,并输出 def __searchPath(self, start, end, distancecostIndex, timecostIndex): results = [] self.__generatePath(self.__graph, Path([start],0,0), end, results,distancecostIndex,timecostIndex) results.sort(key=lambda p: p.distanceCost) results.sort(key=lambda p: p.timeCost) print('The {} shortest path from '.format("spatially" if distancecostIndex==0 else "temporally"), start, ' to ', end, ' is:', end="") print('The {} shortest path from '.format("spatially" if timecostIndex==0 else "temporally"), start, ' to ', end, ' is:', end="") results[0].printPath() #调用__searchPath搜索start到end之间的空间最短的路径,并输出 def searchSpatialMinPath(self,start, end): self.__searchPath(start,end,0,0) #调用__searc 优化这个代码
这段代码中可以进行如下优化:
1. 将类属性的命名改为下划线开头的私有属性,避免外部直接修改属性值,可以使用@property装饰器来获取属性的值。
2. 可以将类的构造函数中的参数改为可选参数,避免在初始化时需要传入参数,可以在需要时再传入参数。
3. 在递归函数的参数中加入默认值,避免在调用递归函数时需要传入所有参数。
4. 可以使用f-string格式化输出语句,代码更加简洁易读。
5. 可以使用列表推导式对列表进行过滤。
6. 在搜索最短路径时可以使用heapq模块的堆排序来优化效率。
优化后的代码如下:
```python
import heapq
class Path(object):
def __init__(self, path = [], distance_cost = 0, time_cost = 0):
self._path = path
self._distance_cost = distance_cost
self._time_cost = time_cost
def get_last_node(self):
return self._path[-1]
def is_last_node(self, node):
return node == self.get_last_node()
def add_node(self, node, d_price, t_price):
return Path(self._path + [node], self._distance_cost + d_price, self._time_cost + t_price)
def print_path(self):
for n in self._path:
print(n, end="->" if not self.is_last_node(n) else "\n")
print(f"最短路径距离({self._distance_cost:.0f}m)")
print(f"红绿路灯个数({self._time_cost:.0f})个")
@property
def d_cost(self):
return self._distance_cost
@property
def t_cost(self):
return self._time_cost
class DirectedGraph(object):
def __init__(self, d = {}):
if isinstance(d, dict):
self._graph = d
else:
self._graph = dict()
print('Something went wrong!')
def __generate_path(self, graph, path, end, results, distance_cost_index=0, time_cost_index=0):
current = path.get_last_node()
if current == end:
results.append(path)
else:
for n in graph[current]:
if n not in path._path:
self.__generate_path(graph, path.add_node(n, self._graph[path.get_last_node()][n][distance_cost_index][time_cost_index]), end, results, distance_cost_index, time_cost_index)
def __search_path(self, start, end, distance_cost_index=0, time_cost_index=0):
results = []
self.__generate_path(self._graph, Path([start], 0, 0), end, results, distance_cost_index, time_cost_index)
results = heapq.nsmallest(1, results, key=lambda p: (p.d_cost, p.t_cost))
print(f"The {'spatially' if distance_cost_index==0 else 'temporally'} shortest path from {start} to {end} is:")
results[0].print_path()
def search_spatial_min_path(self, start, end):
self.__search_path(start, end, 0, 0)
def search_temporal_min_path(self, start, end):
self.__search_path(start, end, 1, 1)
```
根据给定图的定义绘制无向图,边添加权值信息,图定义class Graph: def init(self): self.vertices = {} self.numVertices = 0 def addVertex(self, key): self.numVertices = self.numVertices + 1 newVertex = Vertex(key) self.vertices[key] = newVertex return newVertex def getVertex(self, n): if n in self.vertices: return self.vertices[n] else: return None def contains(self, n): return n in self.vertices def addEdge(self, f, t, cost=0): if f not in self.vertices: nv = self.addVertex(f) if t not in self.vertices: nv = self.addVertex(t) self.vertices[f].addNeighbor(self.vertices[t], cost) def getVertices(self): return list(self.vertices.keys()) def iter(self): return iter(self.vertices.values()) def str(self): s = "" for v in self: s += f"[{v.id},{v.dist},{v.pred.id if v.pred else None}] " return s def asum(self): # 图路径总和 asumv = 0 for i in self: asumv += i.dist al = f"[村村通道路最短总路径,{asumv}]" return al给出包含画图代码
很抱歉,由于我是一个文本 AI 模型,无法提供画图功能。但是,您可以使用 Python 中的 Matplotlib 或 NetworkX 库来绘制无向图。以下是用 NetworkX 库绘制无向图的示例代码:
```python
import networkx as nx
import matplotlib.pyplot as plt
# 创建一个空的无向图
G = nx.Graph()
# 添加节点
G.add_node(1)
G.add_node(2)
G.add_node(3)
G.add_node(4)
# 添加边及权值
G.add_edge(1, 2, weight=1)
G.add_edge(1, 3, weight=2)
G.add_edge(2, 3, weight=3)
G.add_edge(3, 4, weight=4)
# 绘制图形
pos = nx.spring_layout(G)
nx.draw(G, pos, with_labels=True)
labels = nx.get_edge_attributes(G, 'weight')
nx.draw_networkx_edge_labels(G, pos, edge_labels=labels)
# 显示图形
plt.show()
```
您可以根据需要修改节点、边和权值的数量和值。
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