介绍
图(Graph)是一种网状数据结构,其形式化定义如下:
Graph=(V, R)
V={X | X属于DataObject}
R={VR}
VR={<x, y> | P(x, y) ^ (x, y属于V)}
DataObject为一个集合,该集合中所有的元素具有相同的特性。V中的数据元素通常称为顶点,VR是两个顶点之间的关系的集合。P(x, y)表示x和y之间有特定的关联属性P。
若<x, y>属于VR,则<x, y>表示从顶点x到顶点y的一条弧(arc),并称x为弧尾(tail)或起始点,称y为弧头(head)或终端店,此时图中的边是有方向的,称这样的图为有向图。
<x, y>属于VR,必有<y,x>属于VR,及VR是对称关系,这时以无序对(x,y)来代替两个有序对,表示x和y之间的一条边(edge),此时的图称为无向图。
python
class Graph(object):
def __init__(self, gdict=None):
if gdict is None:
gdict = {}
self.gdict = gdict
def getVertices(self):
'''
得到图的所有顶点
:return:
'''
return list(self.gdict.keys())
def addVertex(self, vrtx):
'''
添加一个顶点
:param vrtx:
:return:
'''
if vrtx not in self.gdict:
self.gdict[vrtx] = {}
def addEdge(self, edge):
'''
添加一个边
:param edge:
:return:
'''
edge = set(edge)
(vrtx1, vrtx2) = tuple(edge)
if vrtx1 in self.gdict:
self.gdict[vrtx1].add(vrtx2)
else:
self.gdict[vrtx1] = {vrtx2, }
def findEdge(self):
'''
打印所有的边
:return:
'''
edgename = []
for vrtx in self.gdict:
for nxtvrtx in self.gdict[vrtx]:
if {nxtvrtx, vrtx} not in edgename:
edgename.append({vrtx, nxtvrtx})
return edgename
def dfs(self, node, visited=None):
'''
深度优先遍历
:param node:
:param visited:
:return:
'''
if visited is None:
visited = set()
visited.add(node)
print(node)
for next in self.gdict[node] - visited:
self.dfs(next, visited)
return visited
def bfs(self, node):
'''
广度优先遍历
:param node:
:return:
'''
seen = set([node])
queue = collections.deque([node])
while queue:
vertex = queue.popleft()
print(vertex)
for next in self.gdict[vertex]:
if next not in seen:
seen.add(next)
queue.append(next)
graph_elements = {"a": {"b", "c"},
"b": {"a", "d"},
"c": {"a", "d"},
"d": {"e", },
"e": {"d", }
}
g = Graph(graph_elements)
print(g.getVertices())
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