Implementation of visitor design pattern. It contains a 'tree.rb' command line clone of the tree unix tool.

System for accessing web services for tree-like things via the repl

A gem for generating tree-like format

A Ruby port of the Android implementation of Java's java.util.TreeMap class. This is an AVL tree based implementation of Java's java.util.TreeMap structure. It implements Java's java.util.NavigableMap interface. The reference implementation is at https://android.googlesource.com/platform/libcore.git/+/android-6.0.1_r32/luni/src/main/java/java/util/TreeMap.java

This is a syntax highlighter plugin for Kramdown that leverages Tree-sitter's native syntax highlighter to highlight code blocks (and spans) when rendering HTML. Tree-sitter is a modern, general-purpose parsing library that outclasses many existing tools at the task of syntax highlighting. This plugin adapts Tree-sitter's native highlighter for Kramdown, so that Tree-sitter's superior highlighting capabilities can be easily leveraged in the context of rendering Markdown.

Ruby bindings to Tree-Sitter

Jumoku provides you with tree behaviors to mixin and tree classes to inherit from. Raw tree, common binary trees, custom trees...

Wood is a library for creating, manipulating and rewriting trees, in particular Abstract Syntax Trees (ASTs). It provides an easy to use DSL for searching and rewriting whole sub-trees in place, which can be used for things like translating a parse tree into a target language tree, doing type analysis, writing compiler optimization passes and more.

This Gem eagerly loads trees by indexing the nodes of the tree. The number of queries needed for loading a tree is N, Where N is the number of different models(ActiveRecords) in the tree. Each inner object in the tree have an index node instance that is connecting it to the root. When the root of the tree is loaded, only the objects that are in the tree are fetched(Pruning). The index nodes are created when the root element is saved and stored in the IndexNode model.

treestore stores two different types of data: 1) values, which are stored according to their SHA-1 hashcode 2) trees, which are sets of values and/or other trees, stored via a SHA-1 hashcode In addition, there are references that allow you to 'bookmark' a SHA-1 hashcode for easier lookup. If you think of the core git, but on any key-value backend store (like the included Redis one), you've got the right idea.

This is a data structure to represent and manage k-trees, primarily created for use in RubyNEAT, but may see other possible applications. The goal here is to be roebust in the creation of your k-tree, to allow you to prune during creation, since, especially for higher-dimensional trees, the number of leaf node can become very large. So a parent will have children nodes created down to the desired resolution, and immediately after the creation of the children, will check to see if there's enough variance among the children to keep them. If not, they are pruned immediately.

ID3-based implementation of the M.L. Decision Tree algorithm

Syntax Tree support for Haml

In computer science, a disjoint-set data structure, also called a union–find data structure or merge–find set, is a data structure that keeps track of a set of elements partitioned into a number of disjoint (non-overlapping) subsets. It provides near-constant-time operations (bounded by the inverse Ackermann function) to add new sets, to merge existing sets, and to determine whether elements are in the same set. In addition to many other uses (see the Applications section), disjoint-sets play a key role in Kruskal's algorithm for finding the minimum spanning tree of a graph. A disjoint-set forest consists of a number of elements each of which stores an id, a parent pointer, and, in efficient algorithms, a value called the "rank". The parent pointers of elements are arranged to form one or more trees, each representing a set. If an element's parent pointer points to no other element, then the element is the root of a tree and is the representative member of its set. A set may consist of only a single element. However, if the element has a parent, the element is part of whatever set is identified by following the chain of parents upwards until a representative element (one without a parent) is reached at the root of the tree. Forests can be represented compactly in memory as arrays in which parents are indicated by their array index. Disjoint-set data structures model the partitioning of a set, for example to keep track of the connected components of an undirected graph. This model can then be used to determine whether two vertices belong to the same component, or whether adding an edge between them would result in a cycle. The Union–Find algorithm is used in high-performance implementations of unification. This data structure is used by the Boost Graph Library to implement its Incremental Connected Components functionality. It is also a key component in implementing Kruskal's algorithm to find the minimum spanning tree of a graph. Note that the implementation as disjoint-set forests doesn't allow the deletion of edges, even without path compression or the rank heuristic. Sharir and Agarwal report connections between the worst-case behavior of disjoint-sets and the length of Davenport–Schinzel sequences, a combinatorial structure from computational geometry.

Arboreal is yet another extension to ActiveRecord to support tree-shaped data structures. Internally, Arboreal maintains a computed "ancestry_string" column, which caches the path from the root of a tree to each node, allowing efficient retrieval of both ancestors and descendants. Arboreal surfaces relationships within the tree like "children", "ancestors", "descendants", and "siblings" as scopes, so that additional filtering/pagination can be performed.

Syntax Tree support for RBS

Ive been consistently dissappointed with how limited normal decision trees, and how it's not really possible use both normal decision trees, and multi-factor trees in the same algorithm. Enter Dynamic Tree, which allows you to run a multi factor test and a single factor test for each individual factor in the same movement.

Pure-Ruby implemention of Red-Black tree, a self-balancing binary search tree with O(log n) search, insert and delete operations.

A syntax_tree plugin for force syntax_tree to using bracket array

TreeStruct allows to describe a structure with nested TreeStruct and with arrays of TreeStruct. The level of nesting is not limited.