Poindexter

A Go library package providing utility functions including sorting algorithms with custom comparators.

Features

• 🔢 Sorting Utilities: Sort integers, strings, and floats in ascending or descending order
• 🎯 Custom Sorting: Sort any type with custom comparison functions or key extractors
• 🔍 Binary Search: Fast search on sorted data
• 🧭 KDTree (NN Search): Build a KDTree over points with generic payloads; nearest, k-NN, and radius queries with Euclidean, Manhattan, Chebyshev, and Cosine metrics
• 📦 Generic Functions: Type-safe operations using Go generics
• ✅ Well-Tested: Comprehensive test coverage
• 📖 Documentation: Full documentation available at GitHub Pages

Installation

go get github.com/Snider/Poindexter

Quick Start

package main

import (
    "fmt"
    poindexter "github.com/Snider/Poindexter"
)

func main() {
    // Basic sorting
    numbers := []int{3, 1, 4, 1, 5, 9}
    poindexter.SortInts(numbers)
    fmt.Println(numbers) // [1 1 3 4 5 9]

    // Custom sorting with key function
    type Product struct {
        Name  string
        Price float64
    }

    products := []Product{{"Apple", 1.50}, {"Banana", 0.75}, {"Cherry", 3.00}}
    poindexter.SortByKey(products, func(p Product) float64 { return p.Price })

    // KDTree quick demo
    pts := []poindexter.KDPoint[string]{
        {ID: "A", Coords: []float64{0, 0}, Value: "alpha"},
        {ID: "B", Coords: []float64{1, 0}, Value: "bravo"},
        {ID: "C", Coords: []float64{0, 1}, Value: "charlie"},
    }
    tree, _ := poindexter.NewKDTree(pts, poindexter.WithMetric(poindexter.EuclideanDistance{}))
    nearest, dist, _ := tree.Nearest([]float64{0.9, 0.1})
    fmt.Println(nearest.ID, nearest.Value, dist) // B bravo ~0.141...
}

Documentation

Full documentation is available at https://snider.github.io/Poindexter/

Explore runnable examples in the repository:

• examples/dht_ping_1d
• examples/kdtree_2d_ping_hop
• examples/kdtree_3d_ping_hop_geo
• examples/kdtree_4d_ping_hop_geo_score

KDTree performance and notes

• Current KDTree queries are O(n) linear scans, which are great for small-to-medium datasets or low-latency prototyping. For 1e5+ points and low/medium dimensions, consider swapping the internal engine to gonum.org/v1/gonum/spatial/kdtree (the API here is compatible by design).
• Insert is O(1) amortized; delete by ID is O(1) via swap-delete; order is not preserved.
• Concurrency: the KDTree type is not safe for concurrent mutation. Protect with a mutex or share immutable snapshots for read-mostly workloads.
• See multi-dimensional examples (ping/hops/geo/score) in docs and examples/.
• Performance guide: see docs/Performance for benchmark guidance and tips: docs/perf.md • Hosted: https://snider.github.io/Poindexter/perf/

Choosing a metric (quick tips)

• Euclidean (L2): smooth trade-offs across axes; solid default for blended preferences.
• Manhattan (L1): emphasizes per-axis absolute differences; good when each unit of ping/hop matters equally.
• Chebyshev (L∞): dominated by the worst axis; useful for strict thresholds (e.g., reject high hop count regardless of ping).
• Cosine: angle-based for vector similarity; pair it with normalized/weighted features when direction matters more than magnitude.

See the multi-dimensional KDTree docs for end-to-end examples and weighting/normalization helpers: Multi-Dimensional KDTree (DHT).

License

This project is licensed under the European Union Public Licence v1.2 (EUPL-1.2). See LICENSE for details.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.