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Added unsupervised model #1

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6418cac
Added unsupervised model
pointlander Jul 24, 2025
cb5bcd6
Added variance method
pointlander Jul 24, 2025
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119 changes: 114 additions & 5 deletions ff.go
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Original file line number Diff line number Diff line change
Expand Up @@ -6,6 +6,7 @@ package main

import (
"fmt"
"math"
"math/rand"
"runtime"
"sort"
Expand All @@ -17,16 +18,22 @@ func FF() {
_ = iris
rng := rand.New(rand.NewSource(1))
fitness := func(g []float32) float64 {
fitness := 150.0
fitness := 0.0 //150.0
l1 := NewMatrix[float32](4, 4, g[:16]...)
b1 := NewMatrix[float32](4, 1, g[16:20]...)
l2 := NewMatrix[float32](4, 3, g[20:32]...)
b2 := NewMatrix[float32](3, 1, g[32:35]...)
l3 := NewMatrix[float32](3, 4, g[35:47]...)
b3 := NewMatrix[float32](4, 1, g[47:51]...)
l4 := NewMatrix[float32](4, 4, g[47:63]...)
b4 := NewMatrix[float32](4, 1, g[63:67]...)
rows := make([][]float32, 0, 8)
for _, flower := range iris {
input := NewMatrix[float32](4, 1)
for _, measure := range flower.Measures {
input.Data = append(input.Data, float32(measure))
}
entropy := 0.0
output := l1.MulT(input).Add(b1).Sigmoid()
output = l2.MulT(output).Add(b2).Sigmoid()
max, index := float32(0.0), 0
Expand All @@ -36,9 +43,41 @@ func FF() {
}
}
if Labels[flower.Label] == index {
fitness--
//fitness--
}
sum := float32(0.0)
for _, value := range output.Data {
sum += value
}
for _, value := range output.Data {
if value == 0 || sum == 0 {
continue
}
entropy += float64(value/sum) * math.Log2(float64(value/sum))
}
fitness -= entropy
rows = append(rows, output.Data)
output = l3.MulT(output).Add(b3).Sigmoid()
output = l4.MulT(output).Add(b4)
for i, value := range output.Data {
diff := value - float32(flower.Measures[i])
fitness += float64(diff * diff)
}
}
entropy := 0.0
for i := range 3 {
sum := float32(0.0)
for _, row := range rows {
sum += row[i]
}
for _, row := range rows {
if row[i] == 0 || sum == 0 {
continue
}
entropy += float64(row[i]/sum) * math.Log2(float64(row[i]/sum))
}
}
fitness -= entropy
return fitness
}

Expand All @@ -48,7 +87,7 @@ func FF() {
}

const (
width = 4*4 + 4 + 4*3 + 3
width = 4*4 + 4 + 4*3 + 3 + 3*4 + 4 + 4*4 + 4
models = width / width
iterations = 1024
population = 8 * 1024
Expand Down Expand Up @@ -155,9 +194,79 @@ func FF() {
copy(state[ii], pop[ii].Number.Data)
}
fmt.Println(pop[0].Fitness)
if pop[0].Fitness == 0 {
for _, flower := range iris {
mean := make([]float32, 3)
for i := range pop {
g := pop[i].Number.Data
l1 := NewMatrix[float32](4, 4, g[:16]...)
b1 := NewMatrix[float32](4, 1, g[16:20]...)
l2 := NewMatrix[float32](4, 3, g[20:32]...)
b2 := NewMatrix[float32](3, 1, g[32:35]...)
input := NewMatrix[float32](4, 1)
for _, measure := range flower.Measures {
input.Data = append(input.Data, float32(measure))
}
output := l1.MulT(input).Add(b1).Sigmoid()
output = l2.MulT(output).Add(b2).Sigmoid()
for ii := range output.Data {
mean[ii] += output.Data[ii]
}
}
for i := range mean {
mean[i] /= float32(len(pop))
}
stddev := make([]float32, 3)
for i := range pop {
g := pop[i].Number.Data
l1 := NewMatrix[float32](4, 4, g[:16]...)
b1 := NewMatrix[float32](4, 1, g[16:20]...)
l2 := NewMatrix[float32](4, 3, g[20:32]...)
b2 := NewMatrix[float32](3, 1, g[32:35]...)
input := NewMatrix[float32](4, 1)
for _, measure := range flower.Measures {
input.Data = append(input.Data, float32(measure))
}
output := l1.MulT(input).Add(b1).Sigmoid()
output = l2.MulT(output).Add(b2).Sigmoid()
for ii := range output.Data {
diff := mean[ii] - output.Data[ii]
stddev[ii] += diff * diff
}
}
for i := range stddev {
stddev[i] /= float32(len(pop))
}
index, min := 0, float32(math.MaxFloat32)
for i, value := range stddev {
if value < min {
min, index = value, i
}
}
fmt.Println(index, flower.Label)
}

if pop[0].Fitness < 100 {
g := pop[0].Number.Data
l1 := NewMatrix[float32](4, 4, g[:16]...)
b1 := NewMatrix[float32](4, 1, g[16:20]...)
l2 := NewMatrix[float32](4, 3, g[20:32]...)
b2 := NewMatrix[float32](3, 1, g[32:35]...)
for _, flower := range iris {
input := NewMatrix[float32](4, 1)
for _, measure := range flower.Measures {
input.Data = append(input.Data, float32(measure))
}
output := l1.MulT(input).Add(b1).Sigmoid()
output = l2.MulT(output).Add(b2).Sigmoid()
max, index := float32(0.0), 0
for i, value := range output.Data {
if value > max {
max, index = value, i
}
}
fmt.Println(index, flower.Label)
}
break
}
}

}