Perceptron: Difference between revisions
Added Go
m (→{{header|Lua}}: fixed typo) |
(Added Go) |
||
Line 18:
* [https://youtu.be/dXuNAkHsos4?t=16m44s Machine Learning - Perceptrons (youtube)]
<br><br>
=={{header|Go}}==
This is based on the Java entry but just outputs the final image (as a .png file) rather than displaying its gradual build up. It also uses a different color scheme - blue and red circles with a black dividing line.
<lang go>package main
import (
"github.com/fogleman/gg"
"math/rand"
"time"
)
const c = 0.00001
func linear(x float64) float64 {
return x*0.7 + 40
}
type trainer struct {
inputs []float64
answer int
}
func newTrainer(x, y float64, a int) *trainer {
return &trainer{[]float64{x, y, 1}, a}
}
type perceptron struct {
weights []float64
training []*trainer
}
func newPerceptron(n, w, h int) *perceptron {
weights := make([]float64, n)
for i := 0; i < n; i++ {
weights[i] = rand.Float64()*2 - 1
}
training := make([]*trainer, 2000)
for i := 0; i < 2000; i++ {
x := rand.Float64() * float64(w)
y := rand.Float64() * float64(h)
answer := 1
if y < linear(x) {
answer = -1
}
training[i] = newTrainer(x, y, answer)
}
return &perceptron{weights, training}
}
func (p *perceptron) feedForward(inputs []float64) int {
if len(inputs) != len(p.weights) {
panic("weights and input length mismatch, program terminated")
}
sum := 0.0
for i, w := range p.weights {
sum += inputs[i] * w
}
if sum > 0 {
return 1
}
return -1
}
func (p *perceptron) train(inputs []float64, desired int) {
guess := p.feedForward(inputs)
err := float64(desired - guess)
for i := range p.weights {
p.weights[i] += c * err * inputs[i]
}
}
func (p *perceptron) draw(dc *gg.Context, iterations int) {
le := len(p.training)
for i, count := 0, 0; i < iterations; i, count = i+1, (count+1)%le {
p.train(p.training[count].inputs, p.training[count].answer)
}
x := float64(dc.Width())
y := linear(x)
dc.SetLineWidth(2)
dc.SetRGB255(0, 0, 0) // black line
dc.DrawLine(0, linear(0), x, y)
dc.Stroke()
dc.SetLineWidth(1)
for i := 0; i < le; i++ {
guess := p.feedForward(p.training[i].inputs)
x := p.training[i].inputs[0] - 4
y := p.training[i].inputs[1] - 4
if guess > 0 {
dc.SetRGB(0, 0, 1) // blue circle
} else {
dc.SetRGB(1, 0, 0) // red circle
}
dc.DrawCircle(x, y, 8)
dc.Stroke()
}
}
func main() {
rand.Seed(time.Now().UnixNano())
w, h := 640, 360
perc := newPerceptron(3, w, h)
dc := gg.NewContext(w, h)
dc.SetRGB(1, 1, 1) // white background
dc.Clear()
perc.draw(dc, 2000)
dc.SavePNG("perceptron.png")
}</lang>
=={{header|Java}}==
| |||