Algorithm primitives and data structures for Reynard applications
npm install @entropy-tamer/reynard-algorithms> Algorithm primitives and data structures for Reynard applications
A comprehensive collection of reusable algorithmic building blocks with automatic optimization, memory pooling, and performance monitoring. Built with the PAW optimization framework for maximum efficiency.
- 🦊 Optimized Algorithms - Automatic algorithm selection with memory pooling and performance monitoring
- 🔧 PAW Optimization Framework - Intelligent heuristic-based algorithm selection with performance monitoring
- ⚡ Performance Utilities - Comprehensive benchmarking, profiling, and monitoring tools
- 🔗 Union-Find - Efficient set operations, cycle detection, and connected components
- 🌸 Bloom Filter - Space-efficient probabilistic data structure for membership testing
- ⚡ Priority Queue - Binary heap implementation with O(log n) operations
- 🔄 LRU Cache - Least Recently Used cache with O(1) access operations
- 📊 Fenwick Tree - Binary Indexed Tree for efficient range sum queries
- 📅 Interval Tree - Efficient interval overlap queries and range operations
- 🌳 Segment Tree - Range query and update operations in O(log n) time
- 🔤 Trie - Prefix tree for efficient string operations and autocomplete
- 🗺️ Spatial Hashing - Efficient spatial partitioning and nearest neighbor queries
- 🌲 Quadtree - Recursive spatial partitioning for 2D spatial queries
- 🌳 R-Tree - Balanced tree structure for efficient spatial indexing
- 🌲 K-d Tree - Multi-dimensional space partitioning for nearest neighbor searches
- 🧊 Octree - 3D spatial partitioning for efficient 3D queries and ray tracing
- 📦 BVH - Bounding Volume Hierarchy for collision detection and ray tracing
- 📐 Basic Geometry - Complete 2D geometric calculations and transformations (Point, Vector, Line, Rectangle, Circle, Polygon)
- 📏 Bresenham's Line - Efficient line drawing algorithm for pixel-perfect graphics
- 🔺 Delaunay Triangulation - Optimal triangulation using Bowyer-Watson algorithm
- 🛡️ Convex Hull - Multiple algorithms (Graham Scan, Jarvis March, QuickHull)
- 🏔️ Marching Squares - Contour generation from scalar field data
- 🌊 Simplex Noise - High-quality procedural noise generation (2D, 3D, 4D)
- 🎯 Poisson Disk Sampling - High-quality point distribution algorithms
- 🌊 Wave Function Collapse - Constraint-based procedural generation
- 🔷 Voronoi Diagram - Space partitioning for nearest neighbor queries and coverage analysis
- ✂️ Polygon Clipping - Boolean operations on polygons (Sutherland-Hodgman, Weiler-Atherton)
- ⚡ Line Segment Intersection - Efficient intersection detection using Bentley-Ottmann algorithm
- 📐 OBB - Oriented Bounding Box for rotated object collision detection
- 📏 Min Bounding Box - Minimum area rectangle using rotating calipers algorithm
- 💥 AABB Collision Detection - Advanced collision detection with spatial optimization
- 🔀 SAT Collision - Separating Axis Theorem for convex polygon collision detection
- 🔄 Sweep and Prune - Broad-phase collision detection for dynamic scenes
- ⭐ A\* Pathfinding - Optimal pathfinding with multiple heuristics and caching
- ⚡ JPS - Jump Point Search for optimized grid-based pathfinding
- 📐 Theta\* - Any-angle pathfinding for smooth path generation
- 🌊 Flow Field - Potential field pathfinding for crowd simulation
- 🏗️ HPA\* - Hierarchical Pathfinding for large-scale pathfinding
``bash`
npm install @entropy-tamer/reynard-algorithms
`typescript
import { UnionFind, PriorityQueue, AABB } from '@entropy-tamer/reynard-algorithms';
// Union-Find for connected components
const uf = new UnionFind(10);
uf.union(0, 1);
console.log(uf.connected(0, 1)); // true
// Priority Queue for efficient ordering
const pq = new PriorityQueue
pq.push(3, 1);
pq.push(1, 3);
console.log(pq.pop()); // 1 (highest priority)
// AABB collision detection
const box1 = new AABB(0, 0, 10, 10);
const box2 = new AABB(5, 5, 15, 15);
console.log(box1.intersects(box2)); // true
`
For detailed documentation, mathematical theory, and comprehensive examples, see the Documentation directory.
- Mathematical Theory - Mathematical foundations and proofs
- Algorithm Guides - Detailed implementation guides
- Examples - Usage examples and best practices
- Performance Analysis - Benchmarks and optimization strategies
All algorithms include comprehensive performance monitoring and optimization:
- Memory Pooling - Automatic memory management for high-frequency operations
- Benchmarking - Built-in performance measurement tools
- Optimization - PAW framework for automatic algorithm selection
- Monitoring - Real-time performance tracking and analysis
See the examples directory for complete usage examples:
- Basic Usage - Getting started with core algorithms
- Game Engine Integration - Using algorithms in game development
`typescript
// Union-Find
class UnionFind {
constructor(size: number);
find(x: number): number;
union(x: number, y: number): boolean;
connected(x: number, y: number): boolean;
}
// Priority Queue
class PriorityQueue
push(item: T, priority: number): void;
pop(): T | undefined;
peek(): T | undefined;
size(): number;
isEmpty(): boolean;
}
// LRU Cache
class LRUCache
constructor(capacity: number);
get(key: K): V | undefined;
set(key: K, value: V): void;
has(key: K): boolean;
delete(key: K): boolean;
}
`
`typescript
// Basic shapes
class Point { x: number; y: number; }
class Vector { x: number; y: number; }
class Rectangle { x: number; y: number; width: number; height: number; }
class Circle { x: number; y: number; radius: number; }
// Collision detection
class AABB {
constructor(x: number, y: number, width: number, height: number);
intersects(other: AABB): boolean;
contains(point: Point): boolean;
}
`
`typescript
// A* Pathfinding
class AStar {
findPath(start: Point, goal: Point, grid: Grid): Point[];
setHeuristic(heuristic: HeuristicFunction): void;
}
// Flow Field
class FlowField {
generate(goal: Point, obstacles: Obstacle[]): void;
getDirection(position: Point): Vector;
}
``
Contributions are welcome! Please see our Contributing Guidelines for details.
MIT License - see LICENSE for details.
- reynard-core - Core Reynard framework utilities
- reynard-ui - UI components and primitives
- reynard-testing - Testing utilities and helpers
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For more information, visit the Reynard Documentation or check out our examples.