working genetics prototype

go.mod

@@ -1,6 +1,6 @@
 module edgaru089.ink/go/gl01
 
-go 1.17
+go 1.25
 
 require (
 	github.com/aquilax/go-perlin v1.1.0

internal/io/io.go

@@ -28,4 +28,6 @@ var (
 			}
 		}
 	}
+
+	StepFactors []float64
 )

internal/util/slice.go

@@ -0,0 +1,9 @@
+package util
+
+func Slice2Float32[T int | float64](s []T) (sf []float32) {
+	sf = make([]float32, len(s))
+	for i := range s {
+		sf[i] = float32(s[i])
+	}
+	return
+}

internal/work/work.go

@@ -1,11 +1,144 @@
 package work
 
-import "time"
+import (
+	"math"
+	"slices"
+	"time"
+
+	"edgaru089.ink/go/gl01/internal/igwrap"
+	"edgaru089.ink/go/gl01/internal/io"
+	"github.com/inkyblackness/imgui-go/v4"
+)
+
+var state struct {
+	inst [2000]*WorkerInstance
+	buf  []*WorkerInstance
+
+	steps int
+
+	first_step int
+
+	iter_disabled bool
+}
+
+type iter_info struct {
+	first_step, last_step int
+	step_count            int
+
+	max_score float64
+
+	factor float64
+}
+
+var stats []iter_info
 
 func Init() {
-
+	for i := range state.inst {
+		inst := RandomInstance()
+		state.inst[i] = &inst
+	}
 }
 
 func Update(delta time.Duration) {
+	state.steps++
+
+	for i := range state.inst {
+		state.inst[i].Step(state.steps)
+	}
+
+	slices.SortFunc(state.inst[:], func(x, y *WorkerInstance) int {
+		if x.score > y.score {
+			return -1
+		} else if x.score == y.score && x.pos.Sub(x.target).Len() < y.pos.Sub(y.target).Len() {
+			return -1
+		} else {
+			return 1
+		}
+	})
+
+	if imgui.Checkbox("Disable Iteration", &state.iter_disabled) {
+
+	}
+
+	if !state.iter_disabled && (imgui.Button("Iterate") || state.inst[50].score > 0) {
+
+		factor := 0.0
+		for _, f := range io.StepFactors {
+			factor += f
+		}
+		factor /= float64(len(io.StepFactors))
+		io.StepFactors = io.StepFactors[0:0]
+
+		// push stats
+		stats = append(stats, iter_info{
+			first_step: state.first_step,
+			last_step:  state.steps,
+			step_count: state.steps - state.first_step,
+			max_score:  state.inst[0].score,
+			factor:     factor,
+		})
+		state.first_step = state.steps
+
+		state.buf = state.buf[:0]
+		for i := range 50 {
+			state.inst[i].score = 0
+			state.buf = append(state.buf, state.inst[i])
+			for range 2000/50 - 1 {
+				inst := state.inst[i].Offspring()
+				inst.step0 = state.steps
+				state.buf = append(state.buf, &inst)
+			}
+		}
+		copy(state.inst[:], state.buf)
+	}
+
+	for i := range 20 {
+		igwrap.Text("Score: %f\n", state.inst[i].score)
+	}
+
+	if igwrap.Begin("Graph", nil, 0) {
+		imgui.PushItemWidth(-math.SmallestNonzeroFloat32)
+		s := make([]float32, len(stats))
+		if len(stats) < 1 {
+			goto err
+		}
+
+		for i, st := range stats {
+			s[i] = float32(st.step_count)
+		}
+		if len(s) > 400 {
+			s = s[len(s)-400:]
+		}
+		imgui.PlotLinesV(
+			"##Step Count",
+			s,
+			0,
+			"",
+			math.MaxFloat32,
+			math.MaxFloat32,
+			imgui.Vec2{0, 200},
+		)
+
+		s = make([]float32, len(stats))
+		for i, st := range stats {
+			s[i] = float32(st.factor)
+		}
+		if len(s) > 400 {
+			s = s[len(s)-400:]
+		}
+		imgui.PlotLinesV(
+			"##Factor",
+			s,
+			0,
+			"",
+			math.MaxFloat32,
+			math.MaxFloat32,
+			imgui.Vec2{0, 200},
+		)
+
+	err:
+		imgui.PopItemWidth()
+		imgui.End()
+	}
 
 }

internal/work/worker.go

@@ -0,0 +1,122 @@
+package work
+
+import (
+	"math/rand"
+
+	"edgaru089.ink/go/gl01/internal/io"
+	"github.com/go-gl/mathgl/mgl64"
+)
+
+type Worker struct {
+	t1, t2 mgl64.Mat4
+	t3     mgl64.Mat2x4
+}
+
+func randomSlice64(s []float64) {
+	for i := range s {
+		s[i] = rand.NormFloat64()
+	}
+}
+
+// RandomWorker generates a random worker.
+func RandomWorker() (w Worker) {
+	randomSlice64(w.t1[:])
+	randomSlice64(w.t2[:])
+	randomSlice64(w.t3[:])
+	return
+}
+
+func mutateSlice64(s []float64, stddev float64) {
+	for i := range s {
+		s[i] = rand.NormFloat64()*stddev + s[i]
+	}
+}
+
+// Offspring generates an offspring with the given variance.
+func (w *Worker) Offspring(stddev float64) (s Worker) {
+	s = *w
+	mutateSlice64(s.t1[:], stddev)
+	mutateSlice64(s.t2[:], stddev)
+	mutateSlice64(s.t3[:], stddev)
+	return
+}
+
+// Calculate runs a Vec4 through the Worker.
+func (w *Worker) Calculate(input mgl64.Vec4) (output mgl64.Vec2) {
+	imm1 := w.t1.Mul4x1(input)
+	imm2 := w.t2.Mul4x1(imm1)
+	output = w.t3.Mul4x1(imm2)
+	return
+}
+
+// WorkerInstance is a instance of a worker.
+type WorkerInstance struct {
+	worker Worker
+	pos    mgl64.Vec2
+	target mgl64.Vec2
+
+	score float64
+
+	distance float64
+	step0    int
+}
+
+var (
+	Radius float64 = 10 // Radius of all worker instances
+	Step   float64 = 1  // Move length of a advance step
+
+	OffspringStdVar float64 = 0.5
+
+	Score float64 = 1
+
+	TargetAreaSize = mgl64.Vec2{750, 750}
+)
+
+func randomTarget() (t mgl64.Vec2) {
+	return mgl64.Vec2{
+		TargetAreaSize[0] * (rand.Float64() - .5),
+		TargetAreaSize[1] * (rand.Float64() - .5),
+	}
+}
+
+func (i *WorkerInstance) Step(step_num int) {
+	output := i.worker.Calculate(mgl64.Vec4{
+		i.pos[0],
+		i.pos[1],
+		i.target[0],
+		i.target[1],
+	})
+
+	direction := mgl64.Vec2{output[0], output[1]}.Normalize()
+	step := direction.Mul(Step)
+
+	i.pos = i.pos.Add(step)
+	if i.pos.Sub(i.target).Len() < Radius {
+		// Score!
+		i.score += Score
+
+		io.StepFactors = append(io.StepFactors, (float64(step_num-i.step0)*Step)/i.distance)
+
+		i.target = randomTarget()
+		i.step0 = step_num
+		i.distance = i.pos.Sub(i.target).Len()
+	}
+}
+
+func (i *WorkerInstance) Offspring() (it WorkerInstance) {
+	it.pos = randomTarget()
+	it.target = randomTarget()
+	it.distance = it.pos.Sub(it.target).Len()
+	it.score = 0
+	it.worker = i.worker.Offspring(OffspringStdVar)
+	return
+}
+
+func RandomInstance() (i WorkerInstance) {
+	i.worker = RandomWorker()
+	i.pos = randomTarget()
+	i.target = randomTarget()
+	i.distance = i.pos.Sub(i.target).Len()
+	i.score = 0
+	return
+}

main.cfg.json

@@ -2,6 +2,6 @@
 	"WindowWidth":  1024,
 	"WindowHeight": 768,
 
-	"FramerateLimit": 100
+	"FramerateLimit": 4000
 }