func (this *DepLearner) Consider(np ParentSet) {
if np == this.parents {
return
}
if stat.NextBernoulli(1/(1+float64(this.consistency))) == 0 {
return
}
alternateRanges := np.CutRanges(this.bg.ranges)
alternateHistory := this.MakeMappedHistory(np, alternateRanges)
alternateLoglihood := this.MappedLoglihoodRatio(alternateHistory)
choiceLL := []float64{this.mappedLoglihood, alternateLoglihood}
sizeDiff := float64(this.parents.Size(uint32(len(this.bg.ranges))) - np.Size(uint32(len(this.bg.ranges))))
if sizeDiff > 0 {
choiceLL[0] += sizeDiff * this.bg.cfg.Kappa
choiceLL[1] += sizeDiff * (1 - this.bg.cfg.Kappa)
} else {
choiceLL[0] -= sizeDiff * (1 - this.bg.cfg.Kappa)
choiceLL[1] -= sizeDiff * this.bg.cfg.Kappa
}
if stat.NextLogChoice(choiceLL) == 1 {
this.parents = np
this.cutRanges = alternateRanges
this.mappedHistory = alternateHistory
this.mappedLoglihood = alternateLoglihood
this.consistency = 0
} else {
this.consistency++
}
}
func (this *DepLearner) Next(s discrete.State, a discrete.Action) (o int32) {
sv := this.bg.stateValues[s]
mv := this.parents.CutValues(sv)
ms := this.cutRanges.Index(mv)
mk := a.Hashcode() + this.bg.numActions*ms
h := this.mappedHistory[mk]
lls := make([]float64, len(h))
usePrior := h.Sum() < this.bg.cfg.M
for i, c := range h {
if usePrior {
lls[i] = math.Log(this.bg.cfg.Alpha + float64(c))
} else {
lls[i] = math.Log(float64(c))
}
}
oi := uint64(stat.NextLogChoice(lls))
o = this.bg.myRange.Value(oi)
return
}