commit 6a68bfdc3ba205c854b73365003970d57deaa561
parent 3f0bcf0e03d0d5900a89085f1c11865b7699d2f2
Author: NunoSempere <nuno.semperelh@protonmail.com>
Date: Sun, 9 Jun 2024 22:46:08 +0200
fix types
Diffstat:
3 files changed, 37 insertions(+), 31 deletions(-)
diff --git a/f.go b/f.go
@@ -4,6 +4,7 @@ import (
"bufio"
"errors"
"fmt"
+ "git.nunosempere.com/NunoSempere/fermi/sample"
"math"
"os"
"strconv"
@@ -26,14 +27,16 @@ type Lognormal struct {
}
func (l Lognormal) Samples() []float64 {
-
+ sampler := func(r sample.Src) float64 { return sample.Sample_to(l.low, l.high, r) }
+ return sample.Sample_parallel(sampler, 1_000_000)
}
// Actually, I should look up how do do a) enums in go, b) union types
-type Lognormal struct {
+/*type Lognormal struct {
low float64
high float64
}
+*/
type Dist struct {
Type string
@@ -176,6 +179,11 @@ func prettyPrintDist(dist Dist) {
/* Main event loop */
func main() {
+
+ sample_0 := func(r sample.Src) float64 { return 0 }
+ x := sample.Sample_parallel(sample_0, 10)
+ fmt.Printf("%v\n", x)
+
reader := bufio.NewReader(os.Stdin)
init_dist := Dist{Type: "Lognormal", Lognormal: Lognormal{low: 1, high: 1}, Samples: nil} // Could also just be a scalar
old_dist := init_dist
diff --git a/go.mod b/go.mod
@@ -1,3 +1,3 @@
-module git.nunosempere.com/NunoSempere/fermi.git
+module git.nunosempere.com/NunoSempere/fermi
go 1.22.1
diff --git a/sample/sample.go b/sample/sample.go
@@ -1,55 +1,53 @@
-package squiggle
+package sample
-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
+type Src = *rand.Rand
+type func64 = func(Src) float64
-func Sample_unit_uniform(r src) float64 {
+func Sample_unit_uniform(r Src) float64 {
return r.Float64()
}
-func Sample_unit_normal(r src) 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_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_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_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 {
+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)
-
+ return Sample_normal(mean, std, r)
}
-func Sample_to(low float64, high float64, r src) 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]
// we need but get a normal with 90% confidence interval [log(a), log(b)].
- // Then see code for sample_normal_from_90_ci
+ // 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))
+ return math.Exp(Sample_normal_from_90_ci(loglow, loghigh, r))
}
-func Sample_mixture(fs []func64, weights []float64, r src) float64 {
+func Sample_mixture(fs []func64, weights []float64, r Src) float64 {
// fmt.Println("weights initially: ", weights)
var sum_weights float64 = 0
@@ -104,6 +102,7 @@ func Sample_parallel(f func64, n_samples int) []float64 {
return xs
}
+
/*
func main() {
@@ -112,15 +111,15 @@ 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 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}
+ 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) }
+ model := func(r Src) float64 { return Sample_mixture(fs[0:], ws[0:], r) }
n_samples := 1_000_000
- xs := sample_parallel(model, n_samples)
+ xs := Sample_parallel(model, n_samples)
var avg float64 = 0
for _, x := range xs {
avg += x
@@ -133,10 +132,9 @@ func main() {
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 += Sample_mixture(fs[0:], ws[0:], r)
}
avg = avg / float64(n_samples)
fmt.Printf("Average: %v\n", avg)
- */
}
*/
