weightedrand

README

weightedrand ⚖

Fast weighted random selection for Go.

Randomly selects an element from some kind of list, where the chances of each element to be selected are not equal, but rather defined by relative "weights" (or probabilities). This is called weighted random selection.

Usage

import (
    /* ...snip... */
    wr "github.com/mroth/weightedrand"
)

func main() {
    rand.Seed(time.Now().UTC().UnixNano()) // always seed random!

    c := wr.NewChooser(
        wr.Choice{Item: "🍆", Weight: 0}, // alternatively: wr.NewChoice('🍆', 0)
        wr.Choice{Item: "🍋", Weight: 1},
        wr.Choice{Item: "🍊", Weight: 1},
        wr.Choice{Item: "🍉", Weight: 3},
        wr.Choice{Item: "🥑", Weight: 5},
    )
    /* The following will print 🍋 and 🍊 with 0.1 probability, 🍉 with 0.3
    probability, and 🥑 with 0.5 probability. 🍆 will never be printed. (Note
    the weights don't have to add up to 10, that was just done here to make the
    example easier to read.) */
    result := c.Pick().(string)
    fmt.Println(result)
}

Benchmarks

The existing Go library that has a comparable implementation of this is github.com/jmcvetta/randutil, which optimizes for the single operation case. In contrast, this library creates a presorted cache optimized for binary search, allowing repeated selections from the same set to be significantly faster, especially for large data sets.

Comparison of this library versus randutil.ChooseWeighted. For repeated samplings from large collections, weightedrand will be much quicker.

Num choices	randutil	weightedrand
10	435 ns/op	58 ns/op
100	511 ns/op	84 ns/op
1,000	1297 ns/op	112 ns/op
10,000	7952 ns/op	137 ns/op
100,000	85142 ns/op	173 ns/op
1,000,000	2082248 ns/op	312 ns/op

Don't be mislead by these numbers into thinking weightedrand is always the right choice! If you are only picking from the same distribution once, randutil will be faster. weightedrand optimizes for repeated calls at the expense of some setup time and memory storage.

Update: Starting in v0.3.0 weightedrand can now scale linearly to take advantage of multiple CPU cores in parallel, making it even faster. See PR#2 for details.

Caveats

Note this uses math/rand instead of crypto/rand, as it is optimized for performance, not a cryptographically secure implementation.

Credits

To better understand the algorithm used in this library (as well as the one used in randutil) check out this great blog post: Weighted random generation in Python.

Documentation

Overview

Package weightedrand contains a performant data structure and algorithm used to randomly select an element from some kind of list, where the chances of each element to be selected not being equal, but defined by relative "weights" (or probabilities). This is called weighted random selection.

Compare this package with (github.com/jmcvetta/randutil).WeightedChoice, which is optimized for the single operation case. In contrast, this package creates a presorted cache optimized for binary search, allowing for repeated selections from the same set to be significantly faster, especially for large data sets.

Example

In this example, we create a Chooser to pick from amongst various emoji fruit runes. We assign a numeric weight to each choice. These weights are relative, not on any absolute scoring system. In this trivial case, we will assign a weight of 0 to all but one fruit, so that the output will be predictable.

chooser := NewChooser(
	NewChoice('🍋', 0),
	NewChoice('🍊', 0),
	NewChoice('🍉', 0),
	NewChoice('🥑', 42),
)
fruit := chooser.Pick().(rune)
fmt.Printf("%c", fruit)

Output:

🥑

Index

• type Choice
  • func NewChoice(item interface{}, weight uint) Choice
• type Chooser
  • func NewChooser(cs ...Choice) Chooser
  • func (chs Chooser) Pick() interface{}
  • func (chs Chooser) PickSource(rs *rand.Rand) interface{}

Examples

• Package

Types

type Choice

type Choice struct {
	Item   interface{}
	Weight uint
}

Choice is a generic wrapper that can be used to add weights for any item.

func NewChoice

func NewChoice(item interface{}, weight uint) Choice

NewChoice creates a new Choice with specified item and weight.

type Chooser

type Chooser struct {
	// contains filtered or unexported fields
}

A Chooser caches many possible Choices in a structure designed to improve performance on repeated calls for weighted random selection.

func NewChooser

func NewChooser(cs ...Choice) Chooser

NewChooser initializes a new Chooser for picking from the provided Choices.

func (Chooser) Pick

func (chs Chooser) Pick() interface{}

Pick returns a single weighted random Choice.Item from the Chooser.

Utilizes global rand as the source of randomness -- you will likely want to seed it.

func (Chooser) PickSource

func (chs Chooser) PickSource(rs *rand.Rand) interface{}

PickSource returns a single weighted random Choice.Item from the Chooser, utilizing the provided *rand.Rand source rs for randomness.

The primary use-case for this is avoid lock contention from the global random source if utilizing Chooser(s) from multiple goroutines in extremely high-throughput situations.

It is the responsibility of the caller to ensure the provided rand.Source is safe from thread safety issues.