Copyright © 2022-2024 aizws.net · 网站版本: v1.2.6·内部版本: v1.23.3·
页面加载耗时 0.00 毫秒·物理内存 67.4MB ·虚拟内存 1300.5MB
欢迎来到 AI 中文社区(简称 AI 中文社),这里是学习交流 AI 人工智能技术的中文社区。 为了更好的体验,本站推荐使用 Chrome 浏览器。
图表是一组对象通过链接连接的一组对象的图形表示。互连对象由称为顶点的点表示,连接顶点的链接称为边。我们的教程在这里详细描述了与图形相关的各种术语和功能。在本章中,我们将看到如何使用python程序创建图形并向其添加各种数据元素。以下是我们在图表上执行的基本操作。
可以使用python字典数据类型轻松呈现图形。我们将顶点表示为字典的关键字,顶点之间的连接也称为边界,作为字典中的值。
看看下面的图表 -
V = {a, b, c, d, e} E = {ab, ac, bd, cd, de}
我们可以在下面的python程序中展示这个图。
# Create the dictionary with graph elements graph = { "a" : ["b","c"], "b" : ["a", "d"], "c" : ["a", "d"], "d" : ["e"], "e" : ["d"] } # Print the graph print(graph)
当上面的代码被执行时,它会产生以下结果 -
{'c': ['a', 'd'], 'a': ['b', 'c'], 'e': ['d'], 'd': ['e'], 'b': ['a', 'd']}
为了显示图形顶点,我们简单地找到图形字典的关键字。我们使用keys()方法。
class graph: def __init__(self,gdict=None): if gdict is None: gdict = [] self.gdict = gdict # Get the keys of the dictionary def getVertices(self): return list(self.gdict.keys()) # Create the dictionary with graph elements graph_elements = { "a" : ["b","c"], "b" : ["a", "d"], "c" : ["a", "d"], "d" : ["e"], "e" : ["d"] } g = graph(graph_elements) print(g.getVertices())
当上面的代码被执行时,它会产生以下结果 -
['d', 'b', 'e', 'c', 'a']
寻找图形边缘比顶点少一些,因为我们必须找到每对顶点之间的边缘。因此,我们创建一个空边列表,然后迭代与每个顶点关联的边值。一个列表形成了包含从顶点找到的不同组的边。
class graph: def __init__(self,gdict=None): if gdict is None: gdict = {} self.gdict = gdict def edges(self): return self.findedges() # Find the distinct list of edges def findedges(self): edgename = [] for vrtx in self.gdict: for nxtvrtx in self.gdict[vrtx]: if {nxtvrtx, vrtx} not in edgename: edgename.append({vrtx, nxtvrtx}) return edgename # Create the dictionary with graph elements graph_elements = { "a" : ["b","c"], "b" : ["a", "d"], "c" : ["a", "d"], "d" : ["e"], "e" : ["d"] } g = graph(graph_elements) print(g.edges())
当上面的代码被执行时,它会产生以下结果 -
[{'b', 'a'}, {'b', 'd'}, {'e', 'd'}, {'a', 'c'}, {'c', 'd'}]
添加一个顶点是直接向我们添加另一个关键字到图形字典。
class graph: def __init__(self,gdict=None): if gdict is None: gdict = {} self.gdict = gdict def getVertices(self): return list(self.gdict.keys()) # Add the vertex as a key def addVertex(self, vrtx): if vrtx not in self.gdict: self.gdict[vrtx] = [] # Create the dictionary with graph elements graph_elements = { "a" : ["b","c"], "b" : ["a", "d"], "c" : ["a", "d"], "d" : ["e"], "e" : ["d"] } g = graph(graph_elements) g.addVertex("f") print(g.getVertices())
当上面的代码被执行时,它会产生以下结果 -
['f', 'e', 'b', 'a', 'c','d']
将边添加到现有图涉及将新顶点视为元组并验证边是否已经存在。如果不是,则添加边缘。
class graph: def __init__(self,gdict=None): if gdict is None: gdict = {} self.gdict = gdict def edges(self): return self.findedges() # Add the new edge def AddEdge(self, edge): edge = set(edge) (vrtx1, vrtx2) = tuple(edge) if vrtx1 in self.gdict: self.gdict[vrtx1].append(vrtx2) else: self.gdict[vrtx1] = [vrtx2] # List the edge names def findedges(self): edgename = [] for vrtx in self.gdict: for nxtvrtx in self.gdict[vrtx]: if {nxtvrtx, vrtx} not in edgename: edgename.append({vrtx, nxtvrtx}) return edgename # Create the dictionary with graph elements graph_elements = { "a" : ["b","c"], "b" : ["a", "d"], "c" : ["a", "d"], "d" : ["e"], "e" : ["d"] } g = graph(graph_elements) g.AddEdge({'a','e'}) g.AddEdge({'a','c'}) print(g.edges())
当上面的代码被执行时,它会产生以下结果 -
[{'e', 'd'}, {'b', 'a'}, {'b', 'd'}, {'a', 'c'}, {'a', 'e'}, {'c', 'd'}]
算法是一个循序渐进的过程,它定义了一组指令,以一定的顺序执行以获得所需的输出。算法通常独立于底层语言而创建,即算法可以用多种编程语言实现。从数据结构的角度来看,以下是一些重要的算法类别 -搜索 - 搜索数据结构中的项 ...