记录两种图的遍历算法——广度优先(BFS)与深度优先(DFS)。
图(graph)在物理存储上采用邻接表,而邻接表是用python中的字典来实现的。
两种遍历方式的代码如下所示:
def bfsTravel(graph, source):
# 传入的参数为邻接表存储的图和一个开始遍历的源节点
frontiers = [source] # 表示前驱节点
travel = [source] # 表示遍历过的节点
# 当前驱节点为空时停止遍历
while frontiers:
nexts = [] # 当前层的节点(相比frontier是下一层)
for frontier in frontiers:
for current in graph[frontier]: # 遍历当前层的节点
if current not in travel: # 判断是否访问过
travel.append(current) # 没有访问过则入队
nexts.append(current) # 当前结点作为前驱节点
frontiers = nexts # 更改前驱节点列表
return travel
def dfsTravel(graph, source):
# 传入的参数为邻接表存储的图和一个开始遍历的源节点
travel = [] # 存放访问过的节点的列表
stack = [source] # 构造一个堆栈
while stack: # 堆栈空时结束
current = stack.pop() # 堆顶出队
if current not in travel: # 判断当前结点是否被访问过
travel.append(current) # 如果没有访问过,则将其加入访问列表
for next_adj in graph[current]: # 遍历当前结点的下一级
if next_adj not in travel: # 没有访问过的全部入栈
stack.append(next_adj)
return travel
if __name__ == "__main__":
graph = {}
graph['a'] = ['b']
graph['b'] = ['c','d']
graph['c'] = ['e']
graph['d'] = []
graph['e'] = ['a']
#