Finding articulation vertices is an application of DFS. In a nutshell, Apply DFS on a graph. Get the DFS tree. A node which is visited earlier is a “parent” of those nodes which are reached by it and visited later. If any child of a node does not have a path to any of the … Read more
wishful thinking The onset of a problem like this can feel overwhelming, but my favourite technique in programming makes complexity vanish into thin air. Using wishful thinking, we write our program how we wish it could be, then we make our wishes come true – # simple.py from maze import maze from cursor import cursor def … Read more
Your confusion is stemming from the fact that you apparently assume that DFS algorithm can be obtained from BFS algorithm by replacing the FIFO queue with a LIFO stack. This is a popular misconception – it is simply not true. The classic DFS algorithm cannot be obtained by replacing the BFS queue with a stack. … Read more
Depth first search is more memory efficient than breadth first search as you can backtrack sooner. It is also easier to implement if you use the call stack but this relies on the longest path not overflowing the stack. Also if your graph is directed then you have to not just remember if you have … Read more
Maybe I’m overlooking things, but is this what you’re after? import nltk s=”(ROOT (S (NP (NNP Europe)) (VP (VBZ is) (PP (IN in) (NP (DT the) (JJ same) (NNS trends)))) (. .)))” tree = nltk.tree.Tree.fromstring(s) def traverse_tree(tree): # print(“tree:”, tree) for subtree in tree: if type(subtree) == nltk.tree.Tree: traverse_tree(subtree) traverse_tree(tree) It traverses your tree depth-first.
A DFS without recursion is basically the same as BFS – but use a stack instead of a queue as the data structure. The thread Iterative DFS vs Recursive DFS and different elements order handles with both approaches and the difference between them (and there is! you will not traverse the nodes in the same … Read more
Both are valid DFS algorithms. A DFS does not specify which node you see first. It is not important because the order between edges is not defined [remember: edges are a set usually]. The difference is due to the way you handle each node’s children. In the iterative approach: You first insert all the elements … Read more
That heavily depends on the structure of the search tree and the number and location of solutions (aka searched-for items). If you know a solution is not far from the root of the tree, a breadth first search (BFS) might be better. If the tree is very deep and solutions are rare, depth first search … Read more
These two terms differentiate between two different ways of walking a tree. It is probably easiest just to exhibit the difference. Consider the tree: A / \ B C / / \ D E F A depth first traversal would visit the nodes in this order A, B, D, C, E, F Notice that you … Read more