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results.go
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results.go
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package metrics
import (
"math"
"github.com/CRAB-LAB-NTNU/PPS-BS/arrays"
"github.com/CRAB-LAB-NTNU/PPS-BS/types"
)
type metricData []float64
func (metric metricData) size() float64 {
return float64(len(metric))
}
func (metric metricData) Last() float64 {
if len(metric) == 0 {
return math.NaN()
}
return metric[len(metric)-1]
}
func (metric metricData) Mean() float64 {
return arrays.Sum(metric) / metric.size()
}
func (metric metricData) Variance() float64 {
mean := metric.Mean()
var s float64
for i := range metric {
s += math.Pow(metric[i]-mean, 2)
}
return s / (metric.size() - 1)
}
func (metric metricData) StandardDeviation() float64 {
return math.Sqrt(metric.Variance())
}
type Results struct {
ParetoFront [][]float64
archives [][]types.Individual
IGD, HV metricData
HyperVolumeReference func() []float64
}
func (r *Results) Add(archive []types.Individual) {
r.archives = append(r.archives, archive)
if len(archive) == 0 {
return
}
r.IGD = append(r.IGD, InvertedGenerationalDistance(archive, r.ParetoFront))
r.HV = append(r.HV, HyperVolume(archive, r.HyperVolumeReference()))
}
func (r Results) FeasibilityRate() float64 {
return r.IGD.size() / float64(len(r.archives))
}