python深度优先非递归_使用python编写广度优先搜索和递归和非递归深度优先搜索...
# 图的各种基础遍历，广度优先搜索，深度优先搜索的递归和非递归方式
import queue as que
class Graph:
def __init__(self): # 初始化图数据,这个图是一个具有代表性的不连通图
self.graph_num = 2
self.names = [
['北京', '上海', '城都'],
['城市0', '城市1', '城市2', '城市3', '城市4', '城市5', '城市6'],
],
self.edges = [
[
[0, 1, 0],
[0, 0, 1],
[1, 0, 0],
],
[
[0, 1, 1, 1, 0, 0, 0],
[0, 0, 0, 1, 1, 0, 0],
[1, 0, 0, 0, 0, 1, 0],
[0, 0, 1, 0, 1, 1, 1],
[0, 0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 1, 0],
]
]
def bst(self): # 广度优先搜索
for i in range(self.graph_num):
print('开始遍历图上第{}个不相连的小图：'.format(i))
print('........................................')
# print(self.edges[i])
self.bst_son(self.edges[i])
@staticmethod
def bst_son(edges): # 具体每一个广度优先搜索的具体实现
length = len(edges)
looked = [0] * length # 记录遍历过的节点
q = que.Queue()
# 将第一个点加入到队列中去遍历
start = 0
q.put(start)
looked[start] = 1 # 将点标记为已经走过了
while q.empty() is False:
i = q.get()
print('当前正站在{}顶点位置'.format(i))
for j in range(length):
# print('...', looked[j], edges[i][j])
# 节点需要是未看过的，并且是能往下面走的
if looked[j] == 0 and edges[i][j] == 1:
print('当前走过的路径：{}->{}'.format(i, j))
q.put(j)
looked[j] = 1 # 标记当前节点已经看过了
# 深度优先搜索
def dst(self):
for i in range(self.graph_num):
print('开始遍历图上第{}个不相连的小图：'.format(i))
print('........................................')
# print(self.edges[i])
edges = self.edges[i]
length = len(edges)
looked = [0] * length # 记录遍历过的节点
current_node = 0
looked[current_node] = 1 # 标记当前节点已经走过了
self.dst_son(edges, looked, current_node, length)
def dst_son(self, edges, looked, current_node, length):
print('当前站在{}顶点上'.format(current_node))
for i in range(length):
if looked[i] == 0 and edges[current_node][i] == 1:
print('当前走过的路径：{}->{}'.format(current_node, i))
looked[i] = 1 # 标记这个节点已经走过了
self.dst_son(edges, looked, i, length)
def dst_no_re(self):
for i in range(self.graph_num):
print('开始遍历图上第{}个不相连的小图：'.format(i))
print('........................................')
# print(self.edges[i])
self.dst_no_re_son(self.edges[i])
@staticmethod
def dst_no_re_son(edges):
length = len(edges)
start = 0
looked = [0] * length # 标记看过的节点
stack = que.LifoQueue(length)
stack.put(start)
looked[start] = 1
while stack.empty() is False:
current_node = stack.get()
print('当前站在{}顶点上'.format(current_node))
for j in range(length):
if looked[j] == 0 and edges[current_node][j] == 1:
print('当前走过的路径：{}->{}'.format(current_node, j))
looked[j] = 1
stack.put(current_node)
stack.put(j)
break # break 和 栈是非递归深度优先搜索产生的关键
def main():
g = Graph()
print()
print('广度优先搜索：^^^^^^^^^^^^^^^^^^^^^^^')
g.bst()
print()
print('深度优先搜索递归的形式：^^^^^^^^^^^^^^^^^^^^^^^')
g.dst()
print()
print('深度优先搜索的非递归形式：^^^^^^^^^^^^^^^^^^^^^^^')
g.dst_no_re()
print('遍历结束')
if __name__ == '__main__':
main()