fermi/sample/sample.go
2024-06-09 14:21:30 +02:00

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)
*/
}
*/