alg

Documentation

Index

• func ChooseX(weights []float64, x int) []int
• func CumWeightedChooseOne(remainingWeights []float64) int
• func CumWeightedFromMap(weightMap map[int]float64) int
• func RoundF64(a float64) int
• func UniqueChooseX(weights []float64, n int) []int
• func WeightedChoose(weights []float64, toChoose int) ([]int, error)

Functions

func ChooseX

func ChooseX(weights []float64, x int) []int

ChooseX AKA Roulette search

This algorithm works well, the only issue relative to above is that it can choose the same element multiple times, which is not always the desired effect.

A version of it could easily be made to only pick each element once, however. It would benefit from the linear pseudo-random roulette search, where forced increments would happen once an index was chosen.

func CumWeightedChooseOne

func CumWeightedChooseOne(remainingWeights []float64) int

CumWeightedChooseOne returns a single index from the weights given at a rate relative to the magnitude of each weight

func CumWeightedFromMap

func CumWeightedFromMap(weightMap map[int]float64) int

CumWeightedFromMap converts the input map into a set where keys are indices and values are weights for CumWeightedChooseOne, then returns the key for CumWeightedChooseOne of the weights.

func RoundF64

func RoundF64(a float64) int

RoundF64 rounds a float to an int

func UniqueChooseX

func UniqueChooseX(weights []float64, n int) []int

UniqueChooseX uses a heap structure to poll a set of weights n times. This will never return duplicate weights and due to the heap structure is efficient.

func WeightedChoose

func WeightedChoose(weights []float64, toChoose int) ([]int, error)

WeightedChoose will return toChoose indices from weights. the output can have duplicate indices, and zero-weights will cause this algorithm to malfunction.