func TestDepMatch(t *testing.T) {
defer nicetrace.Print()
stat.Seed(seed)
tstr := "VERSION RL-Glue-3.0 PROBLEMTYPE episodic DISCOUNTFACTOR 1 OBSERVATIONS INTS (0 4) (-1 1) (0 2) ACTIONS INTS (0 1) REWARDS (0 1.0)"
task, _ := rlglue.ParseTaskSpec(tstr)
stateRanges := task.Obs.Ints
actionRanges := task.Act.Ints
cfg := ConfigDefault()
cfg.Alpha = alpha
cfg.M = M
genDLs := []*DepLearner{NewDepLearner(0, cfg, stateRanges, actionRanges), NewDepLearner(1, cfg, stateRanges, actionRanges), NewDepLearner(2, cfg, stateRanges, actionRanges)}
genDLs[0].SetParents(ParentSet(0).Insert(0, 1))
genDLs[1].SetParents(ParentSet(0).Insert(0, 2))
genDLs[2].SetParents(ParentSet(0).Insert(2))
patDLs := []*DepLearner{NewDepLearner(0, cfg, stateRanges, actionRanges), NewDepLearner(1, cfg, stateRanges, actionRanges), NewDepLearner(2, cfg, stateRanges, actionRanges)}
numStates := stateRanges.Count()
numActions := actionRanges.Count()
RS := stat.Range(int64(numStates))
RA := stat.Range(int64(numActions))
startTime := time.Nanoseconds()
lastWrongStep := make([]int, len(genDLs))
for i := 0; i < steps; i++ {
s := uint64(RS())
a := uint64(RA())
nv := make([]int32, len(genDLs))
for child := 0; child < len(nv); child++ {
nv[child] = genDLs[child].Next(s, a)
}
for child := 0; child < len(nv); child++ {
genDLs[child] = genDLs[child].Update(s, a, nv[child])
}
for child := 0; child < len(nv); child++ {
patDLs[child] = patDLs[child].Update(s, a, nv[child])
}
if i%1 == 0 {
for child := 0; child < len(nv); child++ {
patDLs[child].ConsiderRandomFlip()
if genDLs[child].parents != patDLs[child].parents {
lastWrongStep[child] = i
}
}
}
}
fmt.Println(lastWrongStep)
endTime := time.Nanoseconds()
duration := endTime - startTime
if true {
fmt.Printf("Ran in %fms\n", float64(duration)/1e6)
}
for child := range genDLs {
if genDLs[child].parents != patDLs[child].parents {
t.Error(fmt.Sprintf("%d: %v != %v", child, genDLs[child].parents.Slice(), patDLs[child].parents.Slice()))
}
}
}
func TestBeliefMatch(t *testing.T) {
defer nicetrace.Print()
stat.Seed(seed)
tstr := "VERSION RL-Glue-3.0 PROBLEMTYPE episodic DISCOUNTFACTOR 1 OBSERVATIONS INTS (0 4) (-1 1) (0 2) ACTIONS INTS (0 1) REWARDS (0 1.0)"
task, _ := rlglue.ParseTaskSpec(tstr)
cfg := ConfigDefault()
cfg.Alpha = alpha
cfg.M = M
beliefG := NewBelief(cfg, task)
beliefG.learners[0].SetParents(ParentSet(0).Insert(0, 1))
beliefG.learners[1].SetParents(ParentSet(0).Insert(0, 2))
beliefG.learners[2].SetParents(ParentSet(0).Insert(2))
beliefP := NewBelief(cfg, task)
numStates := task.Obs.Ints.Count()
numActions := task.Act.Ints.Count()
RS := stat.Range(int64(numStates))
RA := stat.Range(int64(numActions))
startTime := time.Nanoseconds()
lastWrongStep := make([]int, len(task.Obs.Ints))
for i := 0; i < steps; i++ {
s := uint64(RS())
a := uint64(RA())
n := beliefG.Next(s, a)
beliefG = beliefG.Update(s, a, n).(*Belief)
beliefP = beliefP.Update(s, a, n).(*Belief)
if i%1 == 0 {
beliefP.ConsiderRandomFlipAll()
for child, learner := range beliefP.learners {
if beliefG.learners[child].parents != learner.parents {
lastWrongStep[child] = i
}
}
}
}
fmt.Println(lastWrongStep)
endTime := time.Nanoseconds()
duration := endTime - startTime
if true {
fmt.Printf("Ran in %fms\n", float64(duration)/1e6)
}
for child := range beliefP.learners {
if beliefG.learners[child].parents != beliefP.learners[child].parents {
t.Error(fmt.Sprintf("%d: %v != %v", child, beliefG.learners[child].parents.Slice(), beliefP.learners[child].parents.Slice()))
}
}
}
