Dehann Fourie
Dehann Fourie
Also worth stating again here, the purpose of `recordcliqs` is to allow repeat steps: ```julia # either from hist4 = hist[4] or hist4 = fetch(cliqtask4) # the next function that...
added a new tool `limittercliqs::Vector{Pair{Symbol, Int}}` that allows early stopping of any specific CSMs per user desire, making it much easier to debug how CSMs work: https://github.com/JuliaRobotics/IncrementalInference.jl/blob/1ad9b34579d9bb6fbbe8ea317ba8d4ff66b241e7/test/testBasicTreeInit.jl#L8
Snapshot example of hex init script: https://github.com/JuliaRobotics/Caesar.jl/blob/a486c0728139e485ded88fa7f725295add002a7a/examples/dev/devCascadeInitCSM.jl#L3
Commits 426e98f , 1dbe967 , 030d75b add a new swim lane type CSM printout: ```julia julia> IIF.printCSMHistoryLogical(hists) | x0 | x4 | l1 | x1 | x5 | x3 ----+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------...
as of FSM v0.2.8 a new `injectDelayBefore` as well as a watchdog `timeout` features have been added to help with the debugging process. Note that watchdog `timeout` is not really...
Just copying a recent example here for the record: ```julia using Caesar, RoME fg = generateCanonicalFG_Hexagonal(graphinit=false) getSolverParams(fg).treeinit = true getSolverParams(fg).graphinit = false getSolverParams(fg).limititers = 100 getSolverParams(fg).drawtree = true getSolverParams(fg).showtree =...
Will the internal `fetchCliqHistoryAll!` work in general or should we definitely call it after a solve? See it is commented out: https://github.com/JuliaRobotics/IncrementalInference.jl/blob/8bede364027b62eb1e3dba1a5b2285de583c6050/src/SolverAPI.jl#L533
My case was local to developing a fancier `getSample`. Punchline is this mistake is easy to make, so perhaps a test will be good.
Perhaps we should rather make this part of the factor definition and more permanent if we able to confirm computational benefits from memory etc? Think user should still have "backdoor"...
Oh @GearsAD - don't mind the gradients warning (if that is what you meant by CCW issue), this is new experimental features preparing for JuliaRobotics/IncrementalInference.jl#1010 . At this stage, the...