commit 829781b8a756fe62569172fec7bd530812abe42b
parent bb1b21bbbb84a2ffac9ac9bb66c6926efb1ad661
Author: NunoSempere <nuno.sempere@protonmail.com>
Date: Sat, 17 Feb 2024 01:33:21 +0100
go: savepoint before deleting a few comments
Diffstat:
2 files changed, 29 insertions(+), 29 deletions(-)
diff --git a/go/squiggle b/go/squiggle
Binary files differ.
diff --git a/go/squiggle.go b/go/squiggle.go
@@ -5,32 +5,32 @@ import "math"
import "sync"
import rand "math/rand/v2"
-type source = *rand.Rand
-type func64 = func(source) float64
+type src = *rand.Rand
+type func64 = func(src) float64
// https://pkg.go.dev/math/rand/v2
-func sample_unit_uniform(r source) float64 {
+func sample_unit_uniform(r src) float64 {
return r.Float64()
}
-func sample_unit_normal(r source) float64 {
+func sample_unit_normal(r src) float64 {
return r.NormFloat64()
}
-func sample_uniform(start float64, end float64, r source) float64 {
+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 source) float64 {
+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 source) float64 {
+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 source) float64 {
+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)
@@ -38,7 +38,7 @@ func sample_normal_from_90_ci(low float64, high float64, r source) float64 {
}
-func sample_to(low float64, high float64, r source) float64 {
+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]
@@ -49,7 +49,7 @@ func sample_to(low float64, high float64, r source) float64 {
return math.Exp(sample_normal_from_90_ci(loglow, loghigh, r))
}
-func sample_mixture(fs []func64, weights []float64, r source) float64 {
+func sample_mixture(fs []func64, weights []float64, r src) float64 {
// fmt.Println("weights initially: ", weights)
var sum_weights float64 = 0
@@ -84,25 +84,27 @@ func sample_mixture(fs []func64, weights []float64, r source) float64 {
}
-func slice_fill(xs []float64, fs func64, r source) {
+func slice_fill(xs []float64, fs func64, r src) {
for i := range xs {
xs[i] = fs(r)
}
}
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 / 16
- wg.Add(16)
- for i := range 16 {
+ 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() {
+ go func(f func64) {
defer wg.Done()
var r = rand.New(rand.NewPCG(uint64(i), uint64(i+1)))
- slice_fill(xs_i, f, r)
- }()
+ for i := range xs_i {
+ xs_i[i] = f(r)
+ }
+ }(f)
}
wg.Wait()
@@ -117,31 +119,29 @@ func main() {
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 source) float64 { return 0 }
- sample_1 := func(r source) float64 { return 1 }
- sample_few := func(r source) float64 { return sample_to(1, 3, r) }
- sample_many := func(r source) float64 { return sample_to(2, 10, r) }
+ 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 source) float64 { return sample_mixture(fs[0:], ws[0:], r) }
- var n_samples int = 1_000_000
+ 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)
-
/*
-
+ 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:])
+ avg += sample_mixture(fs[0:], ws[0:], r)
}
avg = avg / float64(n_samples)
fmt.Printf("Average: %v\n", avg)
-
*/
}
