peopleprobs/probppl.go

package main

import (
	"fmt"
	"git.nunosempere.com/NunoSempere/probppl/choose"
	"math"
	rand "math/rand/v2"
)

type src = *rand.Rand
type IntProbability {
	N int64
  p float64
}

type pplKnownDistrib = map[int64]float64

func generatePeopleKnownDistribution(r src) map[int64]float64 {
	mapping := make(map[int64]float64)
	sum := 0.0

	// Consider zero case separately
	/*p0 := r.Float64()
	mapping[0.0] = p0
	sum += p0
	*/

	// Consider successive exponents of 1.5
	num := 16.0
	base := 2.0
	for i := 1; i < 8; i++ {
		num = num * base
		p := r.Float64()
		mapping[int64(num)] = p
		sum += p
	}

	for key, value := range mapping {
		mapping[key] = value / sum
	}

	return mapping
}

func chooseWrapper(n int64, k int64) int64 {
	if n < k {
		return 0
	} else {
		return choose.Choose(n, k)
	}
}

func getProbabilityOfKBirthdayMatchesGivenNPeopleKnown(n int64, k int64) float64 {
	return float64(chooseWrapper(n, k)) * math.Pow(1.0/365.0, float64(k)) * math.Pow(1.0-(1.0/365.0), float64(n-k))
}

func getMatchesDrawGivenNPeopleKnown(n int64, r src) int64 {
	p0 := getProbabilityOfKBirthdayMatchesGivenNPeopleKnown(n, 0)
	p1 := getProbabilityOfKBirthdayMatchesGivenNPeopleKnown(n, 1)
	p2 := getProbabilityOfKBirthdayMatchesGivenNPeopleKnown(n, 2)

	p := r.Float64()
	if p < p0 {
		return 0
	} else if p < (p0 + p1) {
		return 1
	} else if p < (p0 + p1 + p2) {
		return 2
	} else {
		return 3 // stands for 'greater than 3'
	}
}

/*
  Draw 148 times
  How many people do you know that were born in the same day of the year as you?
	0: 46.6% | 69
	1: 31.1% | 46
	2: 12.8% | 19
	≥3: 9.5% | 14
*/

func drawFromDistributionWithReplacement(d pplKnownDistrib, r src) int64 {
	pp := r.Float64()
	sum := 0.0
	for i, p := range d {
		sum += p
		if pp <= sum {
			return int64(i) // this introduces some non-determinism, as order of maps in go isn't guaranteed
		}

	}
	fmt.Printf("%f, %f\n", sum, pp)
	fmt.Println(d)
	panic("Probabilities should sum up to 1")
}

func aboutEq(a int64, b int64) bool {
	h := int64(3)
	return ((-h) <= (a - b)) && ((a - b) <= h)
}

func draw148PplFromDistributionAndCheck(d pplKnownDistrib, r src, show bool) int64 {

	count := make(map[int64]int64)
	count[0] = 0
	count[1] = 0
	count[2] = 0
	count[3] = 0
	for i := 0; i < 148; i++ {
		person_i_ppl_known := drawFromDistributionWithReplacement(d, r)
		person_i_num_birthday_matches := getMatchesDrawGivenNPeopleKnown(person_i_ppl_known, r)
		count[person_i_num_birthday_matches]++
	}
	// if (count[0] == 69) && (count[1] == 46) && (count[2] == 19) && (count[3] == 14) {
	if show {
		// fmt.Println(count)
	}
	if aboutEq(count[0], 69) && aboutEq(count[1], 46) && aboutEq(count[2], 19) && aboutEq(count[3], 14) {
		return 1
	} else {
		return 0
	}
}

func getUnnormalizedBayesianUpdateForDistribution(d pplKnownDistrib, r src) int64 {
	var sum int64 = 0
	n := 1000
	for i := 0; i < n; i++ {
		/* if i%1000 == 0 {
			fmt.Println(i)
		} */
		draw_result := draw148PplFromDistributionAndCheck(d, r, i == 0)
		// fmt.Println(draw_result)
		sum += draw_result
	}
	return sum // float64(sum) / float64(n)
}

func main() {

	var r = rand.New(rand.NewPCG(uint64(1), uint64(2)))

	sum := int64(0)
	distribs := [](int64, pplKnownDistrib){}
	for i := 0; i < 1000; i++ {

		people_known_distribution := generatePeopleKnownDistribution(r)
		// fmt.Println(people_known_distribution)
		result := getUnnormalizedBayesianUpdateForDistribution(people_known_distribution, r)
		fmt.Println(i)
		if result > 0 {
			fmt.Println(people_known_distribution)
			fmt.Println(result)
			distribs.append()
		}
		sum += result
		// fmt.Println(result)
	}
	fmt.Println(sum)

}