algo: A Collection of High-Performance Algorithms

algo is a Golang library featuring a variety of efficient and well-optimized algorithms designed for diverse problem-solving needs.

The purpose of this package is to implement specific algorithms in a simplified manner based on formulaic documentation available on Wikipedia or other sources. These algorithms been validated in terms of functionality and testing.

• 📚 Available Algorithms
• 🚀 Installation >= go 1.19
• ✅ Usage Example
• 📌 Contribution
• 📜 License

📚 Available Algorithms

Algorithm	Description
Random Weighted Selection	Selects items randomly based on assigned weights. Useful in load balancing, gaming, and AI.
Reservoir Sampling Algorithm R	Basic reservoir sampling, replaces elements with probability k/i. Efficient for uniform random sampling.
Reservoir Sampling Algorithm L	Optimized reservoir sampling for large N, reduces unnecessary replacements using skipping.
Weighted Reservoir Sampling	Selects items with probability proportional to their weights using a heap-based approach. Used in recommendation systems and A/B testing.
Random Sort Reservoir Sampling	Uses a min-heap and random priorities to maintain the top k elements in a streaming dataset.
Consistent Hashing	Used by distributed systems (CDNs, databases) to evenly distribute requests across servers.

🚀 Installation >= go 1.19

go get -u github.com/Ja7ad/algo

✅ Usage Example

Here’s how you can use the Random Weighted Selection algorithm:

package main

import (
	"fmt"
	"log"
	"github.com/Ja7ad/algo/rws"
)

func main() {
	// Define items with weights
	weightedItems := map[int]string{
		3: "Apple",
		1: "Banana",
		6: "Cherry",
	}

	// Create a selector
	selector, err := rws.NewWeightedSelector(weightedItems)
	if err != nil {
		log.Fatal(err)
	}

	// Pick a random item
	selectedItem, _ := selector.Pick()
	fmt.Println("Selected:", selectedItem)
}

For more details, check the Random Weighted Selection documentation.

📌 Contribution

We welcome contributions! Feel free to submit pull requests, open issues, or suggest new algorithms.

📜 License

This project is licensed under the MIT License.