Graphs are a fundamental data structure in computer science, particularly in the fields of robotics and computer vision. They are used to model complex relationships and connections between entities. By representing a problem or system as a graph, we can gain insights and solve problems more efficiently.
Unlike arrays or linked lists which have a linear structure, graphs are non-linear data structures. This means that there is no inherent order or sequence among the elements in a graph. Instead, elements in a graph are connected through edges, which represent the relationships between them.
Consider a scenario where we want to model the connections between different locations in a city. Each location can be represented as a node in the graph, and the roads or paths between them can be represented as edges. This allows us to analyze the shortest path between two locations, find the most optimal route, or identify key locations that are well-connected.
Graphs can be directed or undirected, depending on whether the edges have a specific direction or not. For example, in a directed graph, the edges may represent one-way roads or dependencies between entities. In contrast, in an undirected graph, the edges do not have any specific direction and represent bidirectional connections.
To summarize, graphs are powerful tools for modeling complex relationships and connections. They provide a flexible and efficient way to represent data and solve problems in the fields of robotics and computer vision.
xxxxxxxxxxmy_graph.print_graph()# Python code to represent a graph using adjacency listclass Graph: def __init__(self): # Dictionary to store the adjacency list self.adj_list = {} def add_edge(self, u, v): if u not in self.adj_list: self.adj_list[u] = [] if v not in self.adj_list: self.adj_list[v] = [] # Add the edge to the adjacency list self.adj_list[u].append(v) self.adj_list[v].append(u) def print_graph(self): for node in self.adj_list: print(f'{node} -> {self.adj_list[node]}')# Create a graphmy_graph = Graph()# Add edgesmy_graph.add_edge('A', 'B')my_graph.add_edge('B', 'C')my_graph.add_edge('C', 'D')my_graph.add_edge('D', 'E')