func q3() {
fns := []linreg.TransformFunc{phi0, phi1, phi2, phi3, phi4, phi5, phi6, phi7}
ks := []int{3, 4, 5, 6, 7}
data := getData("data/in.dta")
for _, k := range ks {
linreg := linreg.NewLinearRegression()
linreg.InitializeFromData(data[25:])
linreg.InitializeValidationFromData(data[:25])
linreg.TransformFunction = fns[k]
linreg.ApplyTransformation()
linreg.ApplyTransformationOnValidation()
linreg.Learn()
eIn := linreg.Ein()
eVal := linreg.EValIn()
eOut, _ := linreg.EoutFromFile("data/out.dta")
fmt.Printf("EVal = %f, for k = %d\n", eVal, k)
fmt.Printf("EIn = %f, for k = %d\n", eIn, k)
fmt.Printf("EOut = %f, for k = %d\n", eOut, k)
fmt.Println()
}
}
func q5() {
runs := 1000 // number of times we repeat the experiment
linreg := linreg.NewLinearRegression()
linreg.N = 100
var avgEin, avgEout float64
for run := 0; run < runs; run++ {
linreg.Initialize()
linreg.Learn()
avgEin += linreg.Ein()
avgEout += linreg.Eout()
}
avgEin = avgEin / float64(runs)
avgEout = avgEout / float64(runs)
fmt.Printf("average of In sample error 'Ein' for Linear regresion for N = 100 is %4.2f\n", avgEin)
fmt.Printf("average of Out of sample error 'Eout' for Linear regresion for N = 100 is %4.2f\n", avgEout)
}
func q2() {
linreg := linreg.NewLinearRegression()
linreg.InitializeFromFile("data/in.dta")
linreg.TransformFunction = nonLinearFeature
linreg.ApplyTransformation()
linreg.Learn()
ein := linreg.Ein()
eout, _ := linreg.EoutFromFile("data/out.dta")
fmt.Printf("Ein = %f, Eout = %f\n", ein, eout)
linreg.K = -3
linreg.LearnWeightDecay()
eAugIn := linreg.EAugIn()
eAugOut, _ := linreg.EAugOutFromFile("data/out.dta")
fmt.Printf("Ein = %f, Eout = %f for k = %d\n", eAugIn, eAugOut, linreg.K)
linreg.K = 3
linreg.LearnWeightDecay()
eAugIn = linreg.EAugIn()
eAugOut, _ = linreg.EAugOutFromFile("data/out.dta")
fmt.Printf("Ein = %f, Eout = %f for k = %d\n", eAugIn, eAugOut, linreg.K)
k := []int{2, 1, 0, -1, -2}
for _, ki := range k {
linreg.K = ki
linreg.LearnWeightDecay()
eAugIn = linreg.EAugIn()
eAugOut, _ = linreg.EAugOutFromFile("data/out.dta")
fmt.Printf("Ein = %f, Eout = %f for k = %d\n", eAugIn, eAugOut, ki)
}
minK := 0
minEAug := float64(1000)
for i := -10; i < 10; i++ {
linreg.K = i
linreg.LearnWeightDecay()
eAugOut, _ = linreg.EAugOutFromFile("data/out.dta")
if minEAug > eAugOut {
minK = i
minEAug = eAugOut
}
}
fmt.Printf("mininum Eout = %f with k = %d for k in {-10; 10}\n", minEAug, minK)
}
func q8() {
runs := 1000 // number of times we repeat the experiment
linreg := linreg.NewLinearRegression()
linreg.N = 1000
linreg.RandomTargetFunction = false
linreg.TwoParams = true
linreg.Noise = 0.1
var avgEin float64
for run := 0; run < runs; run++ {
linreg.TargetFunction = f //non linear function
linreg.Initialize()
linreg.Learn()
avgEin += linreg.Ein()
}
avgEin = avgEin / float64(runs)
fmt.Printf("average of In sample error 'Ein' for Linear regresion for N = 100 is %4.2f\n", avgEin)
}