python basic programming: FIG python data structures breadth-first and depth-first example explanation

This article describes the example data structures of FIG python breadth-first and depth-first usage. Share to you for your reference. details as follows:

First, there is a concept: back

Backtracking (exploration and backtracking) is an optimal selection search, search forward Press to excellent condition, in order to achieve our goals. But when a step to explore, discover original choice is not superior or to meet its target, it is a step back to re-select, this dead end on the return walk technology for backtracking, and meet back at some point state conditions called "backtrack point."

Depth-first algorithm:

(1) an initial access tag vertices and vertex v v visited.
(2) finds the first adjacent vertices of vertex v w.
(3) If the adjacent vertices w vertex v exists, it continues; otherwise, back to v, the other unvisited neighbors find the v.
(4) If the vertex w yet been accessed, the access vertices w and w vertex marked as visited.
(5) continues to find the next adjacent vertices vertices w, wi, wi if the value of v to step (3). FIG communication until all vertices of all visited so far.

Breadth-first algorithm:

(1) queues the vertex v.
(2) when the queue is not empty then continue, otherwise the algorithm ends.
(3) the queue head to obtain team points v; v visits vertices and vertex v mark has been accessed.
(4) finds the first adjacent vertices of vertex v col.
(5) If the adjacent vertices v col not been accessed, the queues col.
(6) continue to look for another new vertex v adjacent vertices col, go to step (5). Until all the neighbors are not visited vertex v processed. Go to step (2).

Code:

#!/usr/bin/python
# -*- coding: utf-8 -*-
class Graph(object):
  def __init__(self,*args,**kwargs):
    self.node_neighbors = {}
    self.visited = {}
  def add_nodes(self,nodelist):
    for node in nodelist:
      self.add_node(node)
  def add_node(self,node):
    if not node in self.nodes():
      self.node_neighbors[node] = []
  def add_edge(self,edge):
    u,v = edge
    if(v not in self.node_neighbors[u]) and ( u not in self.node_neighbors[v]):
      self.node_neighbors[u].append(v)
      if(u!=v):
        self.node_neighbors[v].append(u)
  def nodes(self):
    return self.node_neighbors.keys()
  def depth_first_search(self,root=None):
    order = []
    def dfs(node):
      self.visited[node] = True
      order.append(node)
      for n in self.node_neighbors[node]:
        if not n in self.visited:
          dfs(n)
    if root:
      dfs(root)
    for node in self.nodes():
      if not node in self.visited:
        dfs(node)
    print order
    return order
  def breadth_first_search(self,root=None):
    queue = []
    order = []
    def bfs():
      while len(queue)> 0:
        node = queue.pop(0)
        self.visited[node] = True
        for n in self.node_neighbors[node]:
          if (not n in self.visited) and (not n in queue):
            queue.append(n)
            order.append(n)
    if root:
      queue.append(root)
      order.append(root)
      bfs()
    for node in self.nodes():
      if not node in self.visited:
        queue.append(node)
        order.append(node)
        bfs()
    print order
    return order
if __name__ == '__main__':
  g = Graph()
g.add_nodes([i+1 for i in range(8)])
g.add_edge((1, 2))
g.add_edge((1, 3))
g.add_edge((2, 4))
g.add_edge((2, 5))
g.add_edge((4, 8))
g.add_edge((5, 8))
g.add_edge((3, 6))
g.add_edge((3, 7))
g.add_edge((6, 7))
print "nodes:", g.nodes()
order = g.breadth_first_search(1)
order = g.depth_first_search(1)

Results:
Nodes: [1, 2, 3, 4, 5, 6, 7, 8]
breadth Priority:
[1, 2, 3, 4, 5, 6, 7, 8]
Depth Priority:
[1, 2, 4 , 8, 5, 3, 6, 7]
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