multihypo fix #248

src/ApproxConv.jl

@@ -120,24 +120,26 @@ function computeAcrossHypothesis!(ccwl::CommonConvWrapper{T},
   count = 0
   # TODO remove assert once all GenericWrapParam has been removed
   # @assert norm(ccwl.certainhypo - certainidx) < 1e-6
-  for (mhidx, vars) in activehypo
+  for (hypoidx, vars) in activehypo
     count += 1
-    if sfidx in certainidx || mhidx in certainidx || mhidx == sfidx
-      # hypo case mhidx, sfidx = $mhidx, $sfidx
+    if sfidx in certainidx || hypoidx in certainidx || hypoidx == sfidx
+      # hypo case hypoidx, sfidx = $hypoidx, $sfidx
       for i in 1:Threads.nthreads()  ccwl.cpt[i].activehypo = vars; end
       approxConvOnElements!(ccwl, allelements[count])
-    # elseif mhidx == sfidx
-    #   # multihypo, do conv case, mhidx == sfidx
+    # elseif hypoidx == sfidx
+    #   # multihypo, do conv case, hypoidx == sfidx
     #   ah = sort(union([sfidx;], certainidx))
     #   @assert norm(ah - vars) < 1e-10
     #   for i in 1:Threads.nthreads()  ccwl.cpt[i].activehypo = ah; end
     #   approxConvOnElements!(ccwl, allelements[count])
-    elseif mhidx != sfidx   # sfidx in uncertnidx
+    elseif hypoidx != sfidx && hypoidx != 0  # sfidx in uncertnidx
       # multihypo, take other value case
-      # sfidx=2, mhidx=3:  2 should take a value from 3
-      # sfidx=3, mhidx=2:  3 should take a value from 2
-         # DEBUG sfidx=2, mhidx=1 -- bad when do something like multihypo=[0.5;0.5] -- issue 424
-      ccwl.params[sfidx][:,allelements[count]] = view(ccwl.params[mhidx],:,allelements[count])
+      # sfidx=2, hypoidx=3:  2 should take a value from 3
+      # sfidx=3, hypoidx=2:  3 should take a value from 2
+         # DEBUG sfidx=2, hypoidx=1 -- bad when do something like multihypo=[0.5;0.5] -- issue 424
+      ccwl.params[sfidx][:,allelements[count]] = view(ccwl.params[hypoidx],:,allelements[count])
+    elseif hypoidx == 0
+      # basically do nothing since the factor is not active for these allelements[count]
     else
       error("computeAcrossHypothesis -- not dealing with multi-hypothesis case correctly")
     end
@@ -154,6 +156,7 @@ function assembleNullHypothesis(ccwl::CommonConvWrapper{T},
                                 maxlen::Int,
                                 spreadfactor::Float64 ) where {T}
   #
+  @warn "this assembleNullHypothesis method is obsolete, see e.g. `addFactor!(; nullhypo=0.1)` instead."
   nhc = rand(ccwl.usrfnc!.nullhypothesis, maxlen) .- 1
   val = ccwl.params[ccwl.varidx]
   d = size(val,1)

src/DispatchPackedConversions.jl

@@ -44,11 +44,13 @@ function convert(
   #
   # TODO store threadmodel=MutliThreaded,SingleThreaded in persistence layer
   usrfnc = convert(F, d.fnc)
-  mhcat = parseusermultihypo(d.multihypo)
+  # FIXME add proper nullhypo value
+  mhcat, nh = parseusermultihypo(d.multihypo, 0.0)
 
   # TODO -- improve prepgenericconvolution for hypotheses and certainhypo field recovery when deserializing
   # reconstitute from stored data
-  ccw = prepgenericconvolution(DFG.DFGVariable[], usrfnc, multihypo=mhcat)
+  # FIXME, add threadmodel=threadmodel
+  ccw = prepgenericconvolution(DFG.DFGVariable[], usrfnc, multihypo=mhcat, nullhypo=nh)
   ccw.certainhypo = d.certainhypo
 
   ret = FunctionNodeData{CommonConvWrapper{typeof(usrfnc)}}(d.fncargvID, d.eliminated, d.potentialused, d.edgeIDs, Symbol(d.frommodule), ccw, d.multihypo, d.certainhypo)

src/ExplicitDiscreteMarginalizations.jl

@@ -25,11 +25,44 @@ end
 """
     $(SIGNATURES)
 
-This function explicitly encodes the marginalization of a discrete categorical selection variable for ambiguous data association situations.  Improved implementations should implicitly induce the same behaviour through summation (integration) when marginalizing any number of discrete variables.  This function populates `allelements` with particle indices associated with particular multihypothesis selection while `activehypo` simultaneously contains the hypothesis index and factor graph variables associated with that hypothesis selection.  The return value `certainidx` are the hypotheses that are not in question.
+This function explicitly encodes the marginalization of a discrete categorical selection variable for ambiguous data association situations.  This function populates `allelements` with particle indices associated with particular multihypothesis selection while `activehypo` simultaneously contains the hypothesis index and factor graph variables associated with that hypothesis selection.  The return value `certainidx` are the hypotheses that are not in question.
+
+Output:
+- certainidx:   non fractional variables
+- allelements:  list of which particles go with which hypothesis selection
+- activehypo:   list of which hypotheses go with which certain + fractional variables
+- mhidx:        multihypothesis selection per particle idx
+
+Example:
+```julia
+      idx=(1,2,3)
+multihypo=[1.0;0.5;0.5]
+sfidx=2 # example specific
+certainidx=
+1-element Array{Int64,1}:
+ 1
+allelements=
+3-element Array{Any,1}:
+ Int64[]
+ [1, 2, 11, ...]
+ [3, 4, 5, ...]
+activehypo=
+3-element Array{Any,1}:
+ (1, [1, 2])
+ (2, [1, 2])
+ (3, [2, 3])
+mhidx=
+100-element Array{Int64,1}:
+2, 2, 3, 3, 3,...
+```
 
 Notes:
 - Issue 427, race condition during initialization since n-ary variables not resolvable without other init.
 
+DevNotes
+- Improved implementations should implicitly induce the same behaviour through summation (integration) when marginalizing any number of discrete variables.
+- TODO add nullhypo cases to returning result
+
 ```
 # `allelements` example BearingRange [:x1, 0.5:l1a, 0.5:l1b]
 # sfidx = (1=:x1,2=:l1a,3=:l1b)
@@ -86,6 +119,7 @@ function assembleHypothesesElements!(mh::Categorical,
   # select only hypotheses that can be used (ie variables have been initialized)
   @assert !(sum(isinit) == 0 && sfidx == certainidx) # cannot init from nothing for any hypothesis
 
+
   mhh = if sum(isinit) < lenXi - 1
     @assert isLeastOneHypoAvailable(sfidx, certainidx, uncertnidx, isinit)
     @info "not all hypotheses initialized, but at least one available -- see #427"
@@ -99,12 +133,27 @@ function assembleHypothesesElements!(mh::Categorical,
     mh
   end
 
-  # prep mmultihypothesis selection values
-  mhidx = rand(mhh, maxlen) # selection of which hypothesis is correct
+  # prepend for the mhidx=0, bad-init-null-hypothesis case (if solving a fractional variable)
+  mhh = if sfidx in uncertnidx
+    nhw = 1.0/(length(uncertnidx)+1)
+    nmhw = [nhw; mhh.p]
+    nmhw ./= sum(nmhw) # renormalize
+    Categorical(nmhw)
+  else
+    mhh
+  end
 
+  # prep mmultihypothesis selection values
+  mhidx = rand(mhh, maxlen)  # selection of which hypothesis is correct
   pidx = 0
+  if sfidx in uncertnidx
+    # shift down to get mhidx=0 case
+    mhidx .-= 1
+    pidx = -1
+  end
+
   sfincer = sfidx in certainidx
-  for pval in mh.p
+  for pval in mhh.p # mh.p
     pidx += 1
     pidxincer = pidx in certainidx # ??
     # permutation vectors for later computation
@@ -123,15 +172,20 @@ function assembleHypothesesElements!(mh::Categorical,
       iterah = Int[] # may be moot anyway, but double check first
     elseif !pidxincer && !sfincer # pval >= 1e-15 && mh.p[sfidx] >= 1e-10
       iterah = uncertnidx #allmhp[mh.p .> 1e-15]
+    elseif pidx == 0
+      # nullhypo for bad init case
+      iterah = [sfidx;]
     else
       error("Unknown hypothesis case, got sfidx=$(sfidx) with mh.p=$(mh.p), pidx=$(pidx)")
     end
     push!(allelements, iterarr)
     push!(activehypo, (pidx,iterah))
   end
 
-  # if mhidx == sfidx
-  # ah = sort(union([sfidx;], certainidx))
+  # # retroactively add nullhypo compensation for bad-init case (the 0 case)
+  # if sfidx in uncertnidx
+  #   #
+  # end
 
   return certainidx, allelements, activehypo, mhidx
 end