yrh79 / weightedrand

:balance_scale: Fast weighted random selection for Go

weightedrand

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

The existing Go library that has a generic 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.

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},
        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

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

Caveats

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

Relies on global rand for determinism, therefore, don't forget to seed random!

Credits

The algorithm used in this library (as well as the one used in randutil) comes from: https://eli.thegreenplace.net/2010/01/22/weighted-random-generation-in-python/