https://github.com/entropy-tamer/reynard-algorithms
Algorithm primitives and data structures for Reynard applications - comprehensive collection with automatic optimization
https://github.com/entropy-tamer/reynard-algorithms
a-star algorithms benchmarking binary-heap collision-detection data-structures game-development geometry memory-pooling optimization pathfinding performance priority-queue reynard solidjs spatial-hashing spatial-indexing typescript union-find
Last synced: about 1 month ago
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Algorithm primitives and data structures for Reynard applications - comprehensive collection with automatic optimization
- Host: GitHub
- URL: https://github.com/entropy-tamer/reynard-algorithms
- Owner: entropy-tamer
- License: mit
- Created: 2025-10-13T07:43:24.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2025-11-01T09:34:40.000Z (4 months ago)
- Last Synced: 2025-11-01T10:04:43.692Z (4 months ago)
- Topics: a-star, algorithms, benchmarking, binary-heap, collision-detection, data-structures, game-development, geometry, memory-pooling, optimization, pathfinding, performance, priority-queue, reynard, solidjs, spatial-hashing, spatial-indexing, typescript, union-find
- Language: TypeScript
- Homepage: https://github.com/entropy-tamer/reynard-algorithms
- Size: 20.6 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# reynard-algorithms
> Algorithms, data structures, and computational utilities for spatial computing, collision detection, pathfinding, and procedural generation
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.
## Features
- **🦊 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
### Data Structures
- **🔗 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 Structures
- **🗺️ 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
### Geometry Operations
- **📐 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
### Collision Detection
- **💥 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
### Pathfinding Algorithms
- **⭐ 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
### Machine Learning - Dimensionality Reduction
- **🗺️ Isomap** - Non-linear dimensionality reduction preserving geodesic distances
- **📊 PCA** - Principal Component Analysis for linear dimensionality reduction
- **📉 MDS** - Multidimensional Scaling for distance-preserving embeddings
## Installation
```bash
npm install @entropy-tamer/reynard-algorithms
```
## Quick Start
```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
```
## Documentation
For detailed documentation, mathematical theory, and comprehensive examples, see the [Documentation](./docs/README.md) directory.
### Key Documentation Sections
- **[Mathematical Theory](./docs/mathematical-theory/)** - Mathematical foundations and proofs
- **[Algorithm Guides](./docs/algorithms/)** - Detailed implementation guides
- **[Examples](./docs/examples/)** - Usage examples and best practices
- **[Performance Analysis](./docs/performance/)** - Benchmarks and optimization strategies
## Performance
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
## Examples
See the [examples](./examples/) directory for complete usage examples:
- **[Basic Usage](./examples/basic-usage.ts)** - Getting started with core algorithms
- **[Game Engine Integration](./examples/game-engine-integration.ts)** - Using algorithms in game development
## API Reference
### Data Structures
```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;
}
```
### Geometry
```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;
}
```
### Pathfinding
```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;
}
```
## Contributing
Contributions are welcome! Please see our [Contributing Guidelines](./CONTRIBUTING.md) for details.
## License
MIT License - see [LICENSE](./LICENSE) for details.
## Related Packages
- **[reynard-core](../core/)** - Core Reynard framework utilities
- **[reynard-ui](../ui/)** - UI components and primitives
- **[reynard-testing](../testing/)** - Testing utilities and helpers
---
For more information, visit the [Reynard Documentation](https://github.com/entropy-tamer/reynard) or check out our [examples](./examples/).