savepoint after refactor
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parent
9f31a9161a
commit
1fceb128bb
39
probppl.go
39
probppl.go
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@ -8,15 +8,19 @@ import (
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)
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type src = *rand.Rand
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type IntProbability {
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type IntProbability struct {
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N int64
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p float64
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}
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type IntProbabilities = []IntProbability
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type IntProbabilitiesWeights struct {
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IntProb IntProbabilities
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w int64
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}
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type pplKnownDistrib = map[int64]float64
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func generatePeopleKnownDistribution(r src) IntProbabilities {
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func generatePeopleKnownDistribution(r src) map[int64]float64 {
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mapping := make(map[int64]float64)
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var probabilities IntProbabilities
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sum := 0.0
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// Consider zero case separately
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@ -31,15 +35,15 @@ func generatePeopleKnownDistribution(r src) map[int64]float64 {
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for i := 1; i < 8; i++ {
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num = num * base
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p := r.Float64()
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mapping[int64(num)] = p
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probabilities = append(probabilities, IntProbability{N: int64(num), p: p})
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sum += p
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}
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for key, value := range mapping {
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mapping[key] = value / sum
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for i := range probabilities {
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probabilities[i].p = probabilities[i].p / sum
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}
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return mapping
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return probabilities
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}
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func chooseWrapper(n int64, k int64) int64 {
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@ -80,13 +84,13 @@ func getMatchesDrawGivenNPeopleKnown(n int64, r src) int64 {
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≥3: 9.5% | 14
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*/
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func drawFromDistributionWithReplacement(d pplKnownDistrib, r src) int64 {
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func drawFromDistributionWithReplacement(d IntProbabilities, r src) int64 {
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pp := r.Float64()
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sum := 0.0
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for i, p := range d {
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sum += p
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for i := range d {
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sum += d[i].p
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if pp <= sum {
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return int64(i) // this introduces some non-determinism, as order of maps in go isn't guaranteed
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return int64(d[i].N) // this introduces some non-determinism, as order of maps in go isn't guaranteed
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}
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}
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@ -100,7 +104,7 @@ func aboutEq(a int64, b int64) bool {
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return ((-h) <= (a - b)) && ((a - b) <= h)
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}
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func draw148PplFromDistributionAndCheck(d pplKnownDistrib, r src, show bool) int64 {
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func draw148PplFromDistributionAndCheck(d IntProbabilities, r src, show bool) int64 {
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count := make(map[int64]int64)
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count[0] = 0
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@ -123,7 +127,7 @@ func draw148PplFromDistributionAndCheck(d pplKnownDistrib, r src, show bool) int
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}
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}
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func getUnnormalizedBayesianUpdateForDistribution(d pplKnownDistrib, r src) int64 {
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func getUnnormalizedBayesianUpdateForDistribution(d IntProbabilities, r src) int64 {
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var sum int64 = 0
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n := 1000
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for i := 0; i < n; i++ {
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@ -141,9 +145,10 @@ func main() {
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var r = rand.New(rand.NewPCG(uint64(1), uint64(2)))
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var distribs []IntProbabilitiesWeights
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sum := int64(0)
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distribs := [](int64, pplKnownDistrib){}
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for i := 0; i < 1000; i++ {
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for i := 0; i < 100; i++ {
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people_known_distribution := generatePeopleKnownDistribution(r)
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// fmt.Println(people_known_distribution)
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@ -152,7 +157,7 @@ func main() {
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if result > 0 {
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fmt.Println(people_known_distribution)
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fmt.Println(result)
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distribs.append()
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distribs = append(distribs, IntProbabilitiesWeights{IntProb: people_known_distribution, w: result})
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}
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sum += result
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// fmt.Println(result)
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