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An abstract syntax tree (AST) is a data structure used in computer science to represent the structure of a program or code snippet. It is a tree representation of the abstract syntactic structure of text (often source code ) written in a formal language .
Trees can be used to represent and manipulate various mathematical structures, such as: Paths through an arbitrary node-and-edge graph (including multigraphs), by making multiple nodes in the tree for each graph node used in multiple paths; Any mathematical hierarchy; Tree structures are often used for mapping the relationships between things ...
Pages in category "Articles with example Python (programming language) code" The following 200 pages are in this category, out of approximately 201 total. This list may not reflect recent changes .
In computer science, a trie (/ ˈ t r aɪ /, / ˈ t r iː /), also known as a digital tree or prefix tree, [1] is a specialized search tree data structure used to store and retrieve strings from a dictionary or set. Unlike a binary search tree, nodes in a trie do not store their associated key.
A B-tree index creates a multi-level tree structure that breaks a database down into fixed-size blocks or pages. Each level of this tree can be used to link those pages via an address location, allowing one page (known as a node, or internal page) to refer to another with leaf pages at the lowest level.
In computer science, a k-d tree (short for k-dimensional tree) is a space-partitioning data structure for organizing points in a k-dimensional space. K-dimensional is that which concerns exactly k orthogonal axes or a space of any number of dimensions. [1] k-d trees are a useful data structure for several applications, such as:
Examples include converting and collating tree files, extracting subsets from a tree, changing a tree's root, and analysing branch features such as length or score. [13] Rooted trees can be drawn in ASCII or using matplotlib (see Figure 1), and the Graphviz library can be used to create unrooted layouts (see Figure 2).
The time cost to build a vantage-point tree is approximately O(n log n). For each element, the tree is descended by log n levels to find its placement. However there is a constant factor k where k is the number of vantage points per tree node. [3] The time cost to search a vantage-point tree to find a single nearest neighbor is O(log n).