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fix correlation problem by using global variable

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NunoSempere 2024-06-10 03:08:10 +02:00
parent 5b52cf3297
commit a4263d0765
3 changed files with 122 additions and 102 deletions
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15
README.md
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@ -1,6 +1,6 @@
# A minimalist calculator for fermi estimation
This project contains a minimalist command-line calculator for Fermi estimation. For now, it just multiplies lognormals.
This project is a minimalist, stack-based DSL for Fermi estimation. It can multiply and divide scalars, lognormals and beta distributions.
## Motivation
@ -105,17 +105,18 @@ Conceptually clearer to have all the multiplications first and then all the divi
- [-] Think of some way of calling bc
- [x] Think how to integrate with squiggle.c to draw samples
- [x] Copy the time to botec go code
- [ ] Define samplers
- [ ] Call those samplers when operating on distributions that can't be operted on algebraically
- [x] Define samplers
- [x] Call those samplers when operating on distributions that can't be operted on algebraically
- [ ] Think about how to draw a histogram from samples
- [x] Display output more nicely, with K/M/B/T
- [ ] Consider the following: make this into a stack-based DSL, with:
- Variables that can be saved to and then displayed
- Other types of distributions, particularly beta distributions? => But then this requires moving to bags of samples. It could still be ~instantaneous though.
- Figure out syntax for
- [x] Consider the following: make this into a stack-based DSL, with:
- [x] Variables that can be saved to and then displayed
- [x] Other types of distributions, particularly beta distributions? => But then this requires moving to bags of samples. It could still be ~instantaneous though.
- [x] Figure out go syntax for
- Maps
- Joint types
- Enums
- [ ] Fix correlation problem, by spinning up a new randomness thing every time some serial computation is done.
Some possible syntax for a more expressive stack-based DSL

203
f.go
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@ -14,7 +14,7 @@ import (
const NORMAL90CONFIDENCE = 1.6448536269514727
const GENERAL_ERR_MSG = "Valid inputs: 2 || * 2 || / 2 || 2 20 || * 2 20 || / 2 20 || clean || =: var || op var || clean || help || debug || exit"
const N_SAMPLES = 10 // 1_000_000
const N_SAMPLES = 1_000_000
// Distribution interface
// https://go.dev/tour/methods/9
@ -22,46 +22,37 @@ const N_SAMPLES = 10 // 1_000_000
type Dist interface {
Samples() []float64
}
type Scalar struct {
p float64
type Scalar float64
type Lognormal struct {
low float64
high float64
}
type Beta struct {
a float64
b float64
}
type FilledSamples struct {
xs []float64
}
func (p Scalar) Samples() []float64 {
xs := make([]float64, N_SAMPLES)
for i := 0; i < N_SAMPLES; i++ {
xs[i] = p.p
xs[i] = float64(p)
}
return xs
}
type Lognormal struct {
low float64
high float64
}
func (ln Lognormal) Samples() []float64 {
sampler := func(r sample.Src) float64 { return sample.Sample_to(ln.low, ln.high, r) }
// return sample.Sample_parallel(sampler, N_SAMPLES)
// Can't do parallel because then I'd have to await throughout the code
return sample.Sample_serially(sampler, N_SAMPLES)
}
type Beta struct {
a float64
b float64
}
func (beta Beta) Samples() []float64 {
sampler := func(r sample.Src) float64 { return sample.Sample_beta(beta.a, beta.b, r) }
// return sample.Sample_parallel(sampler, N_SAMPLES)
return sample.Sample_serially(sampler, N_SAMPLES)
}
type FilledSamples struct {
xs []float64
}
func (fs FilledSamples) Samples() []float64 {
return fs.xs
}
@ -80,16 +71,9 @@ func parseLine(line string, vars map[string]Dist) (string, Dist, error) {
var dist Dist
switch words[0] {
case "*":
op = "*"
case "*", "/", "+", "-":
op = words[0]
words = words[1:]
case "/":
op = "/"
words = words[1:]
case "+":
return parseLineErr("+ operation not implemented yet")
case "-":
return parseLineErr("- operation not implemented yet")
default:
op = "*" // later, change the below to
}
@ -104,7 +88,7 @@ func parseLine(line string, vars map[string]Dist) (string, Dist, error) {
case var_word_exists:
dist = var_word
case err1 == nil:
dist = Lognormal{low: single_float, high: single_float}
dist = Scalar(single_float)
case err1 != nil && !var_word_exists:
return parseLineErr("Trying to operate on a scalar, but scalar is neither a float nor an assigned variable")
}
@ -133,9 +117,6 @@ func parseLine(line string, vars map[string]Dist) (string, Dist, error) {
}
// Join distributions
// Multiply lognormals
func multiplyLogDists(l1 Lognormal, l2 Lognormal) Lognormal {
logmean1 := (math.Log(l1.high) + math.Log(l1.low)) / 2.0
logstd1 := (math.Log(l1.high) - math.Log(l1.low)) / (2.0 * NORMAL90CONFIDENCE)
@ -157,10 +138,8 @@ func multiplyBetaDists(beta1 Beta, beta2 Beta) Beta {
return Beta{a: beta1.a + beta2.a, b: beta1.b + beta2.b}
}
func multiplyAsSamples(dist1 Dist, dist2 Dist) Dist {
// dist2 = Beta{a: 1, b: 2}
// fmt.Printf("dist1: %v\n", dist1)
// fmt.Printf("dist2: %v\n", dist2)
func operateAsSamples(dist1 Dist, dist2 Dist, op string) (Dist, error) {
xs := dist1.Samples()
ys := dist2.Samples()
// fmt.Printf("xs: %v\n", xs)
@ -168,11 +147,25 @@ func multiplyAsSamples(dist1 Dist, dist2 Dist) Dist {
zs := make([]float64, N_SAMPLES)
for i := 0; i < N_SAMPLES; i++ {
zs[i] = xs[i] * ys[i]
switch op {
case "*":
zs[i] = xs[i] * ys[i]
case "/":
if ys[0] != 0 {
zs[i] = xs[i] / ys[i]
} else {
fmt.Println("Error: When dividing as samples, division by zero")
return nil, errors.New("Division by zero")
}
case "+":
zs[i] = xs[i] + ys[i]
case "-":
zs[i] = xs[i] - ys[i]
}
}
// fmt.Printf("%v\n", zs)
return FilledSamples{xs: zs}
return FilledSamples{xs: zs}, nil
}
func multiplyDists(old_dist Dist, new_dist Dist) (Dist, error) {
@ -184,23 +177,23 @@ func multiplyDists(old_dist Dist, new_dist Dist) (Dist, error) {
case Lognormal:
return multiplyLogDists(o, n), nil
case Scalar:
return multiplyLogDists(o, Lognormal{low: n.p, high: n.p}), nil
return multiplyLogDists(o, Lognormal{low: float64(n), high: float64(n)}), nil
default:
return multiplyAsSamples(o, n), nil
return operateAsSamples(old_dist, new_dist, "*")
}
}
case Scalar:
{
if o.p == 1 {
if o == 1 {
return new_dist, nil
}
switch n := new_dist.(type) {
case Lognormal:
return multiplyLogDists(Lognormal{low: o.p, high: o.p}, n), nil
return multiplyLogDists(Lognormal{low: float64(o), high: float64(o)}, n), nil
case Scalar:
return Scalar{p: o.p * n.p}, nil
return Scalar(float64(o) * float64(n)), nil
default:
return multiplyAsSamples(o, n), nil
return operateAsSamples(old_dist, new_dist, "*")
}
}
case Beta:
@ -208,11 +201,40 @@ func multiplyDists(old_dist Dist, new_dist Dist) (Dist, error) {
case Beta:
return multiplyBetaDists(o, n), nil
default:
return multiplyAsSamples(o, n), nil
return operateAsSamples(old_dist, new_dist, "*")
}
default:
return multiplyAsSamples(old_dist, new_dist), nil
// return nil, errors.New("Can't multiply dists")
return operateAsSamples(old_dist, new_dist, "*")
}
}
func divideDists(old_dist Dist, new_dist Dist) (Dist, error) {
switch o := old_dist.(type) {
case Lognormal:
{
switch n := new_dist.(type) {
case Lognormal:
return multiplyLogDists(o, Lognormal{low: 1.0 / n.high, high: 1.0 / n.low}), nil
case Scalar:
return multiplyLogDists(o, Lognormal{low: 1.0 / float64(n), high: 1.0 / float64(n)}), nil
default:
return operateAsSamples(old_dist, new_dist, "/")
}
}
case Scalar:
{
switch n := new_dist.(type) {
case Lognormal:
return multiplyLogDists(Lognormal{low: float64(o), high: float64(o)}, Lognormal{low: 1.0 / n.high, high: 1.0 / n.low}), nil
case Scalar:
return Scalar(float64(o) / float64(n)), nil
default:
return operateAsSamples(old_dist, new_dist, "/")
}
}
default:
return operateAsSamples(old_dist, new_dist, "/")
}
}
@ -221,57 +243,47 @@ func joinDists(old_dist Dist, new_dist Dist, op string) (Dist, error) {
switch op {
case "*":
return multiplyDists(old_dist, new_dist)
case "/":
return divideDists(old_dist, new_dist)
case "+":
return operateAsSamples(old_dist, new_dist, "+")
case "-":
return operateAsSamples(old_dist, new_dist, "-")
default:
return old_dist, errors.New("Can't combine distributions in this way")
}
/*
switch {
case old_dist.Type == "Lognormal" && new_dist.Type == "Lognormal" && op == "*":
return Dist{Type: "Lognormal", Lognormal: multiplyLogDists(old_dist.Lognormal, new_dist.Lognormal), Samples: nil}, nil
case old_dist.Type == "Lognormal" && new_dist.Type == "Lognormal" && op == "/":
tmp_dist := Lognormal{low: 1.0 / new_dist.Lognormal.high, high: 1.0 / new_dist.Lognormal.low}
return Dist{Type: "Lognormal", Lognormal: multiplyLogDists(old_dist.Lognormal, tmp_dist), Samples: nil}, nil
default:
fmt.Printf("For now, can't do anything besides multiplying lognormals\n")
}
*/
// return old_dist, errors.New("Can't combine distributions in this way")
}
/* Pretty print distributions */
func prettyPrintFloat(f float64) {
switch {
case math.Abs(f) >= 1_000_000_000_000:
fmt.Printf("%.1fT", f/1_000_000_000_000)
case math.Abs(f) >= 1_000_000_000:
fmt.Printf("%.1fB", f/1_000_000_000)
case math.Abs(f) >= 1_000_000:
fmt.Printf("%.1fM", f/1_000_000)
case math.Abs(f) >= 1_000:
fmt.Printf("%.1fK", f/1_000)
case math.Abs(f) <= 0.0001:
fmt.Printf("%.5f", f)
case math.Abs(f) <= 0.001:
fmt.Printf("%.4f", f)
case math.Abs(f) <= 0.01:
fmt.Printf("%.3f", f)
case math.Abs(f) <= 0.1:
fmt.Printf("%.2f", f)
default:
fmt.Printf("%.1f", f)
}
}
func prettyPrint2Floats(low float64, high float64) {
// fmt.Printf("=> %.1f %.1f\n", low, high)
switch {
case math.Abs(low) >= 1_000_000_000_000:
fmt.Printf("%.1fT", low/1_000_000_000_000)
case math.Abs(low) >= 1_000_000_000:
fmt.Printf("%.1fB", low/1_000_000_000)
case math.Abs(low) >= 1_000_000:
fmt.Printf("%.1fM", low/1_000_000)
case math.Abs(low) >= 1_000:
fmt.Printf("%.1fK", low/1_000)
case math.Abs(low) >= 1_000:
fmt.Printf("%.1fK", low/1_000)
default:
fmt.Printf("%.1f", low)
}
prettyPrintFloat(low)
fmt.Printf(" ")
switch {
case math.Abs(high) >= 1_000_000_000_000:
fmt.Printf("%.1fT", high/1_000_000_000_000)
case math.Abs(high) >= 1_000_000_000:
fmt.Printf("%.1fB", high/1_000_000_000)
case math.Abs(high) >= 1_000_000:
fmt.Printf("%.1fM", high/1_000_000)
case math.Abs(high) >= 1_000:
fmt.Printf("%.1fK", high/1_000)
case math.Abs(high) >= 1_000:
fmt.Printf("%.1fK", high/1_000)
default:
fmt.Printf("%.1f", high)
}
prettyPrintFloat(high)
fmt.Printf("\n")
// fmt.Printf("=> %.1f %.1f\n", low, high)
}
func prettyPrintDist(dist Dist) {
@ -293,6 +305,11 @@ func prettyPrintDist(dist Dist) {
case Beta:
fmt.Printf("=> beta ")
prettyPrint2Floats(v.a, v.b)
case Scalar:
fmt.Printf("=> scalar ")
w := float64(v)
prettyPrintFloat(w)
fmt.Println()
default:
fmt.Printf("%v", v)
}
@ -303,7 +320,7 @@ func main() {
reader := bufio.NewReader(os.Stdin)
var init_dist Dist
init_dist = Scalar{p: 1} // Lognormal{low: 1, high: 1}
init_dist = Scalar(1) // Lognormal{low: 1, high: 1}
old_dist := init_dist
vars := make(map[string]Dist)
// Could eventually be a more complex struct with:

6
sample/sample.go
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@ -9,6 +9,8 @@ import rand "math/rand/v2"
type Src = *rand.Rand
type func64 = func(Src) float64
var global_r = rand.New(rand.NewPCG(uint64(1), uint64(2)))
func Sample_unit_uniform(r Src) float64 {
return r.Float64()
}
@ -138,10 +140,10 @@ func Sample_mixture(fs []func64, weights []float64, r Src) float64 {
}
func Sample_serially(f func64, n_samples int) []float64 {
var r = rand.New(rand.NewPCG(uint64(1), uint64(2)))
xs := make([]float64, n_samples)
// var global_r = rand.New(rand.NewPCG(uint64(1), uint64(2)))
for i := 0; i < n_samples; i++ {
xs[i] = f(r)
xs[i] = f(global_r)
}
return xs
}