l1target.go

package CloudForest

import (
	"math"
)

/*
L1Target wraps a numerical feature as a target for us in l1 norm regression.
*/
type L1Target struct {
	NumFeature
}

/*
L1Target.SplitImpurity is an L1 version of SplitImpurity.
*/
func (target *L1Target) SplitImpurity(l *[]int, r *[]int, m *[]int, allocs *BestSplitAllocs) (impurityDecrease float64) {
	nl := float64(len(*l))
	nr := float64(len(*r))
	nm := 0.0

	impurityDecrease = nl * target.Impurity(l, nil)
	impurityDecrease += nr * target.Impurity(r, nil)
	if m != nil && len(*m) > 0 {
		nm = float64(len(*m))
		impurityDecrease += nm * target.Impurity(m, nil)
	}

	impurityDecrease /= nl + nr + nm
	return
}

//UpdateSImpFromAllocs willl be called when splits are being built by moving cases from r to l as in learning from numerical variables.
//Here it just wraps SplitImpurity but it can be implemented to provide further optimization.
func (target *L1Target) UpdateSImpFromAllocs(l *[]int, r *[]int, m *[]int, allocs *BestSplitAllocs, movedRtoL *[]int) (impurityDecrease float64) {
	return target.SplitImpurity(l, r, m, allocs)
}

//L1Target.Impurity is an L1 version of impurity returning L1 instead of squared error.
func (target *L1Target) Impurity(cases *[]int, counter *[]int) (e float64) {
	m := target.Mean(cases)
	e = target.Error(cases, m)
	return

}

//L1Target.MeanL1Error returns the  Mean L1 norm error of the cases specified vs the predicted
//value. Only non missing cases are considered.
func (target *L1Target) Error(cases *[]int, predicted float64) (e float64) {
	e = 0.0
	n := 0
	for _, i := range *cases {
		if !target.IsMissing(i) {
			e += math.Abs(predicted - target.Get(i))
			n += 1
		}

	}
	e = e / float64(n)
	return

}