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package main
import (
"fmt"
"math"
"math/rand"
"time"
)
type Class int32
const (
classOne Class = 0
classTwo = 1
)
type pointType struct {
x float32
y float32
class Class
}
func generateRandData(n int32, points []pointType) {
rand.Seed(time.Now().UnixNano())
var i int32
for i = 0; i < n; i++ {
dirX := rand.Float32()
dirY := rand.Float32()
if dirX > 0.5 {
points[i].x = rand.Float32() * 20
} else {
points[i].x = -rand.Float32() * 20
}
if dirY > 0.5 {
points[i].y = rand.Float32() * 20
} else {
points[i].y = -rand.Float32() * 20
}
class := rand.Float32()
if class < 0.5 {
points[i].class = classOne
} else {
points[i].class = classTwo
}
}
}
func decisionTreeV1(points []pointType) {
for i := 0; i < len(points); i++ {
if points[i].x <= -12 {
if points[i].x <= 9 {
if points[i].y < 9 {
} else {
}
} else {
}
} else {
}
}
}
func calcClassProbs(points []pointType, classCount int) []float64 {
var counts = make([]int, classCount)
for i := 0; i < len(points); i++ {
counts[points[i].class]++
}
var probs = make([]float64, classCount)
for i := 0; i < classCount; i++ {
probs[i] = float64(counts[i]) / float64(len(points))
}
return probs
}
func calcEntropy(probs []float64) float64 {
var entropy float64
for i := 0; i < len(probs); i++ {
entropy += probs[i] * float64(math.Log2(probs[i]))
}
entropy = entropy * -1
return entropy
}
func main() {
var n int32 = 1000
var points = make([]pointType, n)
generateRandData(n, points)
var i int32
for i = 0; i < n; i++ {
fmt.Println(points[i].x, ":", points[i].y, ":", points[i].class)
}
probs := calcClassProbs(points, 2)
for i := 0; i < 2; i++ {
fmt.Println(probs[i])
}
entropy := calcEntropy(probs)
fmt.Println(entropy)
}
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