标签:__ vertList 优先 python self 邻接 visited stack def
邻接表的python实现与深度优先搜索
Vertex类
class Vertex:
def __init__(self,key):
self.id = key
self.connectedTo = {}
#从这个顶点添加一个连接到另一个
def addNeighbor(self,nbr,weight = 0):
self.connectedTo[nbr] = weight
def __str__(self):
return str(self.id) + 'connectedTo' + str([x.id for x in self.connectedTo])
#返回邻接表中的所有的项点
def getConnections(self):
return self.connectedTo.keys()
def getId(self):
return self.id
#返回从这个顶点到作为参数顶点的边的权重
def getweight(self,nbr):
return self.connectedTo[nbr]
Graph类
class Graph:
def __init__(self):
self.vertList = {}
self.numVertices = 0
def addVertex(self,key):
self.numVertices = self.numVertices + 1
newVertex = Vertex(key)
self.vertList[key] = newVertex
return newVertex
def getVertex(self,n):
if n in self.vertList:
return self.vertList[n]
else:
return None
def __contains__(self, n):
return n in self.vertList
def addEdge(self,f,t,const = 0):
if f not in self.vertList:
nv = self.addVertex(f)
if t not in self.vertList:
nv = self.addVertex(t)
self.vertList[f].addNeighbor(self.vertList[t],const)
def getVertices(self):
return self.vertList.keys()
def __iter__(self):
return iter(self.vertList.values())
基于邻接表的深度优先搜索(python实现)
visited = []
start = 0 #假设以0为起点
stack = []
stack.append(start)
while len(stack)>0:
n = stack[-1]
stack.pop()
if n in visited:
continue
print(n," is visited")
v = g.getVertex(n)
for neighbor in list(v.getConnections()):
stack.append(neighbor.getId())
visited.append(n)
测试
g = Graph()
for i in range(6):
g.addVertex(i)
g.addEdge(0,1,5)
g.addEdge(0,5,2)
g.addEdge(1,2,4)
g.addEdge(2,3,9)
g.addEdge(3,4,7)
g.addEdge(3,5,3)
g.addEdge(4,0,1)
g.addEdge(5,4,8)
g.addEdge(5,2,1)
visited = []
start = 0
stack = []
stack.append(start)
while len(stack)>0:
n = stack[-1]
stack.pop()
if n in visited:
continue
print(n," is visited")
v = g.getVertex(n)
for neighbor in list(v.getConnections()):
stack.append(neighbor.getId())
visited.append(n)
输出:
0 is visited
5 is visited
2 is visited
3 is visited
4 is visited
1 is visited
标签:__,vertList,优先,python,self,邻接,visited,stack,def 来源: https://blog.csdn.net/weixin_52925782/article/details/122275456
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