问题描述
待完善.
词梯文件下载:待完善.
代码
# 测试代码可运行. 时间: 20200328
import sys
import os
import unittest
'''
阅读此代码需要把图的构成结构给跑通.
参见:
使用广度优先遍历进行标记词梯问题.
读取文件:参见.需要下载单词文件.
参见课程:
'''
class Vertex: # 添加顶点
def __init__(self,num):
self.id = num # 顶点名称
self.connectedTo = {} # 邻接表
self.color = 'white' # 颜色.(用于广度优先)
self.pred = None # 前驱节点(用于广度优先)
self.disc = 0 # 设置距离(用于广度优先)
self.dist = sys.maxsize # sys.naxsize 表示最大值
self.fin = 0
# def __lt__(self,o):
# return self.id < o.id
def addNeighbor(self,nbr,weight=0): # 设置权重
self.connectedTo[nbr] = weight
def setColor(self,color): # 颜色(尚未发现=> 白色 / 已经发现 => 灰色 / 完成探索 => 黑色)
self.color = color
def setDistance(self,d): # 设置距离
self.dist = d
def setPred(self,p): # 前驱顶点
self.pred = p
def setDiscovery(self,dtime): # 没有用到
self.disc = dtime
def setFinish(self,ftime):# 没有用到
self.fin = ftime
def getFinish(self):# 没有用到
return self.fin
def getDiscovery(self):# 没有用到
return self.disc
def getPred(self): # 获得上一个节点
return self.pred
def getDistance(self): # 获取距离
return self.dist
def getColor(self):
return self.color
def getConnections(self):
return self.connectedTo.keys()
def getWeight(self,nbr):# 获取边的权重.(无向图的权重为0)
return self.connectedTo[nbr]
def __str__(self): # 没用用到.
return str(self.id) + ":color " + self.color + ":disc " + str(self.disc) + ":fin " + str(self.fin) + ":dist " + str(self.dist) + ":pred
[" + str(self.pred)+ "]
"
def getId(self): # 返回节点名称
return self.id
class Graph: # 添加边
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): # 获得顶点
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())
class Queue: # 队列: 使用队列进行遍历. (队首进,队尾出)
def __init__(self):
self.items = []
def isEmpty(self):
return self.items == []
def enqueue(self, item): # 队首进
self.items.insert(0,item)
def dequeue(self): # 队尾出
return self.items.pop()
def size(self):
return len(self.items)
def buildGraph(wordFile): # 创建桶的函数
d = {}
g = Graph()
wfile = open(wordFile,'r')
# create buckets of words that differ by one letter
for line in wfile:
word = line[:-1]
for i in range(len(word)):
bucket = word[:i] + '_' + word[i+1:]
if bucket in d:
d[bucket].append(word)
else:
d[bucket] = [word]
# add vertices and edges for words in the same bucket
for bucket in d.keys():
for word1 in d[bucket]:
for word2 in d[bucket]:
if word1 != word2:
g.addEdge(word1,word2)
return g
def bfs(g,start): # bfs算法.
start.setColor('gray')
start.setDistance(0) # 由于是起始点.所以起始点距离起始点的距离为0
start.setPred(None) # 设置前驱顶点.起始顶点的前驱节点是为0
vertQueue = Queue() # 创建一个空队列
vertQueue.enqueue(start) # 把起始顶点装进空队列里去
while (vertQueue.size() > 0): # 截至条件是队列为空
currentVert = vertQueue.dequeue() # 取出第一个进行遍历
for nbr in currentVert.getConnections(): # 获取此节点的所有邻接点
if (nbr.getColor() == 'white'): # 如果为白色. 就添加进队列
nbr.setColor('gray') # 同时设置为gray ( 白色代表尚未发现 / 灰色代表已经发现,但是尚未探索 / 黑色代表已经探索 )
nbr.setDistance(currentVert.getDistance() + 1) # 同时设置距离 / 等于上一个元素的距离 加 1
nbr.setPred(currentVert) # 同时设置此元素的前驱节点
vertQueue.enqueue(nbr) # 将此节点插入到队列中去
currentVert.setColor('black') # 设置为黑色.代表已经探索完成
def traverse(y): # 回溯函数.
x = y
while (x.getPred()):
print(x.getId())
x = x.getPred()
print(x.getId())
wordgraph = buildGraph("fourletterwords.txt") # 创建完成的桶
bfs(wordgraph, wordgraph.getVertex('FOOL')) # 把整个列表给遍历完了.
traverse(wordgraph.getVertex('SAGE'))
#traverse(wordgraph.getVertex('COOL'))
'''
实际测试:代码已经跑通.
深度优先比搜索和广度优先搜索换个词是 深度优先遍历和广度优先遍历
'''
参考
待完善