Linear Regression Implementation

linear_regression.go

package main

import (
	"fmt"
	"math/rand"
)

func sum(slice []float64) float64 {
	sum := 0.0
	for _, v := range slice {
		sum += v
	}
	return sum
}

func mean(slice []float64) float64 {
	return sum(slice) / float64(len(slice))
}

func square_trick(slope, intercept, feature, label, rate float64) (float64, float64) {
	predicted_label := slope*feature + intercept
	slope += rate * feature * (label - predicted_label)
	intercept += rate * (label - predicted_label)
	return slope, intercept
}

func linear_regression(
	features, labels []float64,
	rate float64,
	epochs int,
) func(feature float64) float64 {
	slope := mean(features)
	intercept := slope

	epoch := 0
	for epoch < epochs {
		pos := rand.Intn(len(features))
		slope, intercept = square_trick(
			slope,
			intercept,
			features[pos],
			labels[pos],
			rate,
		)
		epoch++
	}

	return func(feature float64) float64 {
		return slope*feature + intercept
	}
}

func main() {
	features := []float64{1, 2, 3, 5, 6, 7}
	labels := []float64{150, 200, 250, 350, 400, 450}

	rate := 0.01
	epochs := 10000
	model := linear_regression(features, labels, rate, epochs)

	fmt.Println(model(4))
}