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Transforming trees in C++


Transforming trees in C++

By : Mihail Rodrigo Marti
Date : November 21 2020, 01:01 AM
Any of those help One way to solve the problem on having to dispatch on unique_ptr is to go back to passing around pointers the way you do in your visitor, but changing the return type to Node*, like this:
code :
// Top-level function stays the same
Node* genericVisit(Node* node) {
    if (dynamic_cast<Subnode2>(node)) {
        return visit(static_cast<Subnode2*>(node));
    }
    // etc
}
// Specialized overloads can return replacements as they go
virtual Node* visit(Subnode2* node) {
    if (mustReplace()) {
        Subnode1 *myReplacement = ...
        return myReplacement;
    }
    if (node->getChild()) {
        Node *replacement = genericVisit(node->getChild());
        // nullptr means no replacement; non-null means we replace the child
        if (replacement) {
            node->setChild(replacement);
        }
    }
    return nullptr;
}


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What are the differences between segment trees, interval trees, binary indexed trees and range trees?

What are the differences between segment trees, interval trees, binary indexed trees and range trees?


By : Akhil Vyshakh M T
Date : March 29 2020, 07:55 AM
this one helps. All these data structures are used for solving different problems:
Segment tree stores intervals, and optimized for "which of these intervals contains a given point" queries. Interval tree stores intervals as well, but optimized for "which of these intervals overlap with a given interval" queries. It can also be used for point queries - similar to segment tree. Range tree stores points, and optimized for "which points fall within a given interval" queries. Binary indexed tree stores items-count per index, and optimized for "how many items are there between index m and n" queries.
transforming trees in lisp

transforming trees in lisp


By : Tigerfish48
Date : March 29 2020, 07:55 AM
To fix this issue Here is a possible solution, in which an auxiliary function tree-sequence is used. Note that the functions do not check for the validity of the input list.
The main function has a very simple structure:
code :
(defun transform(l)
  (if (null l)
      nil
      (tree-sequence l)))
(defun tree-sequence(l)
  (case (cadr l)
    (0 (values (list (car l)) (cddr l)))
    (1 (multiple-value-bind (left-subtree rest-of-list) (tree-sequence (cddr l))
          (values (list (car l) left-subtree) rest-of-list)))
    (t (multiple-value-bind (left-subtree rest-of-list) (tree-sequence (cddr l))
          (multiple-value-bind (right-subtree rest-of-rest) (tree-sequence rest-of-list)
             (values (list (car l) left-subtree right-subtree) rest-of-rest))))))
Transforming a list of structs with parent IDs into a list of trees

Transforming a list of structs with parent IDs into a list of trees


By : Clément Polito
Date : November 28 2020, 03:01 PM
Any of those help The straight-line solution involves building up a graph of all your data and traversing it recursively, returning the Bar from each level and collecting them.
We start by creating a petgraph::DiGraphMap — a directed graph that allows us to control the node IDs (since we just have numeric identifiers). If a node has a parent, we ensure that exists in the graph and add an edge from the parent to the child. If it doesn't have a parent, we know that it will be one of our top-level ids, so we stash it aside for later:
code :
let mut graph = DiGraphMap::new();
let mut top_level_ids = vec![];

for i in &input {
    graph.add_node(i.id);

    match i.parent {
        Some(parent_id) => {
            graph.add_node(parent_id);
            graph.add_edge(parent_id, i.id, ());
        }
        None => {
            top_level_ids.push(i.id);
        }
    }
}
let result: Vec<_> = top_level_ids
    .into_iter()
    .map(|id| build_tree(&graph, id))
    .collect();
fn build_tree(graph: &DiGraphMap<i32, ()>, id: i32) -> Bar {
    let children = graph
        .neighbors(id)
        .map(|child_id| build_tree(graph, child_id))
        .collect();

    Bar { id, children }
}
Paged binary trees vs. AVL trees and/or B-trees

Paged binary trees vs. AVL trees and/or B-trees


By : user3870335
Date : March 29 2020, 07:55 AM
I hope this helps you . In spite of the different structure of AVL and B-tree as stated by Konrad, usage of AVL and B-tree is also different, I think. B-tree generally used to implement indexing. Purpose of employing B-tree is to reduce disk I/O, while data of AVL-tree often resists totally in memory instead of partially in memory partially on disk like B-tree. Purpose of AVL-tree is to avoid the appearance of left/right branch tree in some extreme situation ensuring a perfect O(logn) time complexity when doing search operation.
Minimum and maximum height of binary search trees, 2-3-4 trees and B trees

Minimum and maximum height of binary search trees, 2-3-4 trees and B trees


By : user3874833
Date : March 29 2020, 07:55 AM
will help you Minimal and Maximal height of a 2-4 tree
For maximal height of a 2-4 tree, we will be having one key per node, hence it will behave like a Binary Search Tree.
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