Monte Carlo methods: Difference between revisions

'''Using standard library math/rand:'''

'''Using x/exp/rand:'''

For very careful Monte Carlo studies, you might consider the subrepository rand library. The random number generator there has some advantages such as better known statistical properties and better use of memory.

<lang go>package main

import (

"fmt"

"math"

"time"

"golang.org/x/exp/rand"

)

func getPi(numThrows int) float64 {

inCircle := 0

for i := 0; i < numThrows; i++ {

//a square with a side of length 2 centered at 0 has

//x and y range of -1 to 1

randX := rand.Float64()*2 - 1 //range -1 to 1

randY := rand.Float64()*2 - 1 //range -1 to 1

//distance from (0,0) = sqrt((x-0)^2+(y-0)^2)

dist := math.Hypot(randX, randY)

if dist < 1 { //circle with diameter of 2 has radius of 1

inCircle++

}

}

return 4 * float64(inCircle) / float64(numThrows)

}

func main() {

rand.Seed(uint64(time.Now().UnixNano()))

fmt.Println(getPi(10000))

fmt.Println(getPi(100000))

fmt.Println(getPi(1000000))

fmt.Println(getPi(10000000))

fmt.Println(getPi(100000000))

}</lang>