143 lines
3.4 KiB
Go
143 lines
3.4 KiB
Go
package squiggle
|
|
|
|
import "fmt"
|
|
import "math"
|
|
import "sync"
|
|
import rand "math/rand/v2"
|
|
|
|
// https://pkg.go.dev/math/rand/v2
|
|
|
|
type src = *rand.Rand
|
|
type func64 = func(src) float64
|
|
|
|
func Sample_unit_uniform(r src) float64 {
|
|
return r.Float64()
|
|
}
|
|
|
|
func Sample_unit_normal(r src) float64 {
|
|
return r.NormFloat64()
|
|
}
|
|
|
|
func Sample_uniform(start float64, end float64, r src) float64 {
|
|
return sample_unit_uniform(r)*(end-start) + start
|
|
}
|
|
|
|
func Sample_normal(mean float64, sigma float64, r src) float64 {
|
|
return mean + sample_unit_normal(r)*sigma
|
|
}
|
|
|
|
func Sample_lognormal(logmean float64, logstd float64, r src) float64 {
|
|
return (math.Exp(sample_normal(logmean, logstd, r)))
|
|
}
|
|
|
|
func Sample_normal_from_90_ci(low float64, high float64, r src) float64 {
|
|
var normal90 float64 = 1.6448536269514727
|
|
var mean float64 = (high + low) / 2.0
|
|
var std float64 = (high - low) / (2.0 * normal90)
|
|
return sample_normal(mean, std, r)
|
|
|
|
}
|
|
|
|
func Sample_to(low float64, high float64, r src) float64 {
|
|
// Given a (positive) 90% confidence interval,
|
|
// returns a sample from a lognorma with a matching 90% c.i.
|
|
// Key idea: If we want a lognormal with 90% confidence interval [a, b]
|
|
// we need but get a normal with 90% confidence interval [log(a), log(b)].
|
|
// Then see code for sample_normal_from_90_ci
|
|
var loglow float64 = math.Log(low)
|
|
var loghigh float64 = math.Log(high)
|
|
return math.Exp(sample_normal_from_90_ci(loglow, loghigh, r))
|
|
}
|
|
|
|
func Sample_mixture(fs []func64, weights []float64, r src) float64 {
|
|
|
|
// fmt.Println("weights initially: ", weights)
|
|
var sum_weights float64 = 0
|
|
for _, weight := range weights {
|
|
sum_weights += weight
|
|
}
|
|
|
|
var total float64 = 0
|
|
var cumsummed_normalized_weights = append([]float64(nil), weights...)
|
|
for i, weight := range weights {
|
|
total += weight / sum_weights
|
|
cumsummed_normalized_weights[i] = total
|
|
}
|
|
|
|
var result float64
|
|
var flag int = 0
|
|
var p float64 = r.Float64()
|
|
|
|
for i, cnw := range cumsummed_normalized_weights {
|
|
if p < cnw {
|
|
result = fs[i](r)
|
|
flag = 1
|
|
break
|
|
}
|
|
}
|
|
|
|
if flag == 0 {
|
|
result = fs[len(fs)-1](r)
|
|
}
|
|
return result
|
|
|
|
}
|
|
|
|
func Sample_parallel(f func64, n_samples int) []float64 {
|
|
var num_threads = 16
|
|
var xs = make([]float64, n_samples)
|
|
var wg sync.WaitGroup
|
|
var h = n_samples / num_threads
|
|
wg.Add(num_threads)
|
|
for i := range num_threads {
|
|
var xs_i = xs[i*h : (i+1)*h]
|
|
go func(f func64) {
|
|
defer wg.Done()
|
|
var r = rand.New(rand.NewPCG(uint64(i), uint64(i+1)))
|
|
for i := range xs_i {
|
|
xs_i[i] = f(r)
|
|
}
|
|
}(f)
|
|
}
|
|
|
|
wg.Wait()
|
|
return xs
|
|
|
|
}
|
|
/*
|
|
func main() {
|
|
|
|
var p_a float64 = 0.8
|
|
var p_b float64 = 0.5
|
|
var p_c float64 = p_a * p_b
|
|
ws := [4](float64){1 - p_c, p_c / 2, p_c / 4, p_c / 4}
|
|
|
|
sample_0 := func(r src) float64 { return 0 }
|
|
sample_1 := func(r src) float64 { return 1 }
|
|
sample_few := func(r src) float64 { return sample_to(1, 3, r) }
|
|
sample_many := func(r src) float64 { return sample_to(2, 10, r) }
|
|
fs := [4](func64){sample_0, sample_1, sample_few, sample_many}
|
|
|
|
model := func(r src) float64 { return sample_mixture(fs[0:], ws[0:], r) }
|
|
n_samples := 1_000_000
|
|
xs := sample_parallel(model, n_samples)
|
|
var avg float64 = 0
|
|
for _, x := range xs {
|
|
avg += x
|
|
}
|
|
avg = avg / float64(n_samples)
|
|
fmt.Printf("Average: %v\n", avg)
|
|
/*
|
|
// Without concurrency:
|
|
n_samples := 1_000_000
|
|
var r = rand.New(rand.NewPCG(uint64(1), uint64(2)))
|
|
var avg float64 = 0
|
|
for i := 0; i < n_samples; i++ {
|
|
avg += sample_mixture(fs[0:], ws[0:], r)
|
|
}
|
|
avg = avg / float64(n_samples)
|
|
fmt.Printf("Average: %v\n", avg)
|
|
*/
|
|
}
|
|
*/
|