add samplers from time to botec code
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parent
ce399ac24e
commit
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26
f2.go
26
f2.go
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@ -11,6 +11,7 @@ import (
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)
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const NORMAL90CONFIDENCE = 1.6448536269514727
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const general_err_msg = "Valid inputs: 2 || * 2 || / 2 || 2 20 || * 2 20 || / 2 20 || i || e"
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// Actually, I should look up how do do a) enums in go, b) union types
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type Lognormal struct {
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@ -26,7 +27,6 @@ type Dist struct {
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// Parse line into Distribution
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func parseLineErr(err_msg string) (string, Dist, error) {
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general_err_msg := "Valid inputs: 2 || * 2 || / 2 || 2 20 || * 2 20 || / 2 20 || i || e"
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fmt.Println(general_err_msg)
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fmt.Println(err_msg)
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return "", Dist{}, errors.New(err_msg)
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@ -166,13 +166,33 @@ EventForLoop:
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continue EventForLoop
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}
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{
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words := strings.Split(strings.TrimSpace(input), " ")
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switch {
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case words[0] == "exit" || words[0] == "e":
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break EventForLoop
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case words[0] == "help" || words[0] == "h":
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fmt.Println(general_err_msg)
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continue EventForLoop
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case words[0] == "debug" || words[0] == "d":
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fmt.Printf("%v\n", old_dist)
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continue EventForLoop
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// Other possible cases:
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// Save to file
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// Sample n samples
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// Save stack to a variable?
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// Define a function? No, too much of a nerdsnipe
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}
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}
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op, new_dist, err := parseLine(input)
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if err != nil {
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continue EventForLoop
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}
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joint_dist, err := joinDists(old_dist, new_dist, op)
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if err != nil {
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continue EventForLoop
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}
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old_dist = joint_dist
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prettyPrintDist(old_dist)
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142
squiggle/squiggle.go
Normal file
142
squiggle/squiggle.go
Normal file
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@ -0,0 +1,142 @@
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package squiggle
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import "fmt"
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import "math"
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import "sync"
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import rand "math/rand/v2"
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// https://pkg.go.dev/math/rand/v2
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type src = *rand.Rand
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type func64 = func(src) float64
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func Sample_unit_uniform(r src) float64 {
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return r.Float64()
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}
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func Sample_unit_normal(r src) float64 {
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return r.NormFloat64()
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}
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func Sample_uniform(start float64, end float64, r src) float64 {
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return sample_unit_uniform(r)*(end-start) + start
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}
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func Sample_normal(mean float64, sigma float64, r src) float64 {
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return mean + sample_unit_normal(r)*sigma
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}
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func Sample_lognormal(logmean float64, logstd float64, r src) float64 {
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return (math.Exp(sample_normal(logmean, logstd, r)))
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}
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func Sample_normal_from_90_ci(low float64, high float64, r src) float64 {
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var normal90 float64 = 1.6448536269514727
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var mean float64 = (high + low) / 2.0
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var std float64 = (high - low) / (2.0 * normal90)
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return sample_normal(mean, std, r)
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}
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func Sample_to(low float64, high float64, r src) float64 {
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// Given a (positive) 90% confidence interval,
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// returns a sample from a lognorma with a matching 90% c.i.
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// Key idea: If we want a lognormal with 90% confidence interval [a, b]
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// we need but get a normal with 90% confidence interval [log(a), log(b)].
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// Then see code for sample_normal_from_90_ci
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var loglow float64 = math.Log(low)
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var loghigh float64 = math.Log(high)
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return math.Exp(sample_normal_from_90_ci(loglow, loghigh, r))
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}
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func Sample_mixture(fs []func64, weights []float64, r src) float64 {
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// fmt.Println("weights initially: ", weights)
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var sum_weights float64 = 0
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for _, weight := range weights {
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sum_weights += weight
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}
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var total float64 = 0
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var cumsummed_normalized_weights = append([]float64(nil), weights...)
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for i, weight := range weights {
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total += weight / sum_weights
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cumsummed_normalized_weights[i] = total
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}
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var result float64
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var flag int = 0
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var p float64 = r.Float64()
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for i, cnw := range cumsummed_normalized_weights {
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if p < cnw {
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result = fs[i](r)
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flag = 1
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break
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}
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}
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if flag == 0 {
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result = fs[len(fs)-1](r)
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}
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return result
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}
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func Sample_parallel(f func64, n_samples int) []float64 {
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var num_threads = 16
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var xs = make([]float64, n_samples)
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var wg sync.WaitGroup
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var h = n_samples / num_threads
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wg.Add(num_threads)
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for i := range num_threads {
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var xs_i = xs[i*h : (i+1)*h]
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go func(f func64) {
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defer wg.Done()
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var r = rand.New(rand.NewPCG(uint64(i), uint64(i+1)))
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for i := range xs_i {
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xs_i[i] = f(r)
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}
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}(f)
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}
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wg.Wait()
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return xs
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}
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/*
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func main() {
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var p_a float64 = 0.8
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var p_b float64 = 0.5
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var p_c float64 = p_a * p_b
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ws := [4](float64){1 - p_c, p_c / 2, p_c / 4, p_c / 4}
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sample_0 := func(r src) float64 { return 0 }
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sample_1 := func(r src) float64 { return 1 }
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sample_few := func(r src) float64 { return sample_to(1, 3, r) }
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sample_many := func(r src) float64 { return sample_to(2, 10, r) }
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fs := [4](func64){sample_0, sample_1, sample_few, sample_many}
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model := func(r src) float64 { return sample_mixture(fs[0:], ws[0:], r) }
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n_samples := 1_000_000
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xs := sample_parallel(model, n_samples)
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var avg float64 = 0
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for _, x := range xs {
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avg += x
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}
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avg = avg / float64(n_samples)
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fmt.Printf("Average: %v\n", avg)
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/*
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// Without concurrency:
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n_samples := 1_000_000
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var r = rand.New(rand.NewPCG(uint64(1), uint64(2)))
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var avg float64 = 0
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for i := 0; i < n_samples; i++ {
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avg += sample_mixture(fs[0:], ws[0:], r)
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}
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avg = avg / float64(n_samples)
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fmt.Printf("Average: %v\n", avg)
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*/
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}
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*/
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