change to hyporecipe and better names

Author: dehann

src/DeconvUtils.jl

@@ -67,9 +67,11 @@ function approxDeconv(fcto::DFGFactor,
   # build a lambda that incorporates the multihypo selections
   # set these first
   # ccw.cpt[].activehypo / .p / .params  # params should already be set from construction
-  certainidx, allelements, activehypo, mhidx = assembleHypothesesElements!(nothing, N, 0, length(varsyms))
+  hyporecipe = _prepareHypoRecipe!(nothing, N, 0, length(varsyms))
+  # Juila 1.7 allows destructure assign `(;a,b) = namedtype`
+  # certainidx, allelements, activehypo, mhidx = 
   # only doing the current active hypo
-  @assert activehypo[2][1] == 1 "deconv was expecting hypothesis nr == (1, 1:d)"
+  @assert hyporecipe.activehypo[2][1] == 1 "deconv was expecting hypothesis nr == (1, 1:d)"
 
   islen1 = zDim == 1
 
@@ -80,7 +82,7 @@ function approxDeconv(fcto::DFGFactor,
     targeti_ = makeTarget(idx)
 
     # TODO must first resolve hypothesis selection before unrolling them -- deferred #1096
-    cpt_.activehypo = activehypo[2][2]
+    cpt_.activehypo = hyporecipe.activehypo[2][2]
 
     onehypo!, _ = _buildCalcFactorLambdaSample( ccw,
                                                 idx,

src/ExplicitDiscreteMarginalizations.jl

@@ -63,7 +63,7 @@ allelements=
  [3, 4, 5, ...]
 activehypo=
 3-element Array{Any,1}:
- (0, [2])     # nullhypo -- forced afterward, likely to be deprecated in this case
+ (0, [2])     # nullhypo -- forced afterward, might be deprecated if better solution is found
  (1, [1, 2])  # unroll hypo lambda for X,La
  (2, [1, 2])  # unroll hypo lambda for X,La
  (3, [2, 3])  # would be (but cannot) unroll hypo La,Lb # almost our nullhypo # wont build a lambda
@@ -84,9 +84,10 @@ 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.
+- FIXME convert into some kind of `HypoRecipe` struct, to improve readibility and code maintainability
 - TODO add nullhypo cases to returning result
 - FIXME make type-stable `activehypo` and others
+- Improved implementations should implicitly induce the same behaviour through summation (integration) when marginalizing any number of discrete variables.
 
 ```
 # `allelements` example BearingRange [:x1, 0.5:l1a, 0.5:l1b]
@@ -135,7 +136,7 @@ sfidx=3, allelements=allidx[nhidx.==0], activehypo=(0,[3;])
 
 TODO still need to compensate multihypo case for user nullhypo addition.
 """
-function assembleHypothesesElements!( mh::Categorical,
+function _prepareHypoRecipe!( mh::Categorical,
                                       maxlen::Int,
                                       sfidx::Int,
                                       lenXi::Int,
@@ -220,11 +221,12 @@ function assembleHypothesesElements!( mh::Categorical,
   #   #
   # end
 
-  return certainidx, allelements, activehypo, mhidx
+  # hyporecipe::NamedTuple
+  return (;certainidx, allelements, activehypo, mhidx)
 end
 
 
-function assembleHypothesesElements!( mh::Nothing,
+function _prepareHypoRecipe!( mh::Nothing,
                                       maxlen::Int,
                                       sfidx::Int,
                                       lenXi::Int,
@@ -274,7 +276,8 @@ function assembleHypothesesElements!( mh::Nothing,
     end
   end
 
-  return certainidx, allelements, activehypo, mhidx
+  # return hyporecipe::NamedTuple
+  return (;certainidx, allelements, activehypo, mhidx)
 end
 
 

src/NumericalCalculations.jl

@@ -155,6 +155,8 @@ function _buildCalcFactorMixture( ccwl::CommonConvWrapper{Mixture{N_,F,S,T}},
               length(measurement_), measurement_, varParams)
 end
 
+
+
 """
     $SIGNATURES
 Internal function to build lambda pre-objective function for finding factor residuals. 

src/services/ApproxConv.jl

@@ -168,7 +168,7 @@ end
 function calcProposalBelief(dfg::AbstractDFG,
                             fct::DFGFactor{<:CommonConvWrapper{<:PartialPriorPassThrough}},
                             target::Symbol,
-                            measurement::Tuple=(zeros(0,0),);
+                            measurement::Tuple=(Vector{Vector{Float64}}(),);
                             N::Int=length(measurement[1]),
                             solveKey::Symbol=:default,
                             dbg::Bool=false  )

src/services/EvalFactor.jl

@@ -230,6 +230,30 @@ end
 
 
 
+# function _calcIPCRelative(activeVars::AbstractVector{<:DFGVariable}, 
+#                           sfidx_active::Integer,
+#                           meas_pts...;
+#                           )
+#   #
+#   ipc = if ccwl._gradients === nothing 
+#     ones(getDimension(activeVars[sfidx_active]))
+#   else
+#     ipc_ = []
+#     # get infoPerCoord from all variables
+#     for (vid,var) in enumerate(activeVars)
+#       # set all other variables infoPerCoord values
+#       getLabel(var) != getLabel(activeVars[sfidx_active]) ? nothing : continue
+#       push!(ipc_, vid=>ones(getDimension(var)))
+#     end
+#     # update the gradients at current point estimates
+#     meas_pts = 
+#     ccwl._gradients(meas_pts)
+#     # do perturbation check
+#     allipc = calcPerturbationFromVariable(ccwl._gradients, ipc_)
+#     allipc[sfidx_masked]
+#   end
+
+# end
 
 """
     $(SIGNATURES)
@@ -265,30 +289,36 @@ function evalPotentialSpecific( Xi::AbstractVector{<:DFGVariable},
   isinit = map(x->isInitialized(x), Xi)
 
   # assemble how hypotheses should be computed
-  # TODO convert to HypothesisRecipeElements result
-  _, allelements, activehypo, mhidx = assembleHypothesesElements!(ccwl.hypotheses, maxlen, sfidx, length(Xi), isinit, ccwl.nullhypo )
-  certainidx = ccwl.certainhypo
+  hyporecipe = _prepareHypoRecipe!(ccwl.hypotheses, maxlen, sfidx, length(Xi), isinit, ccwl.nullhypo )
 
   # get manifold add operations
   # TODO, make better use of dispatch, see JuliaRobotics/RoME.jl#244
   # addOps, d1, d2, d3 = buildHybridManifoldCallbacks(manis)
   mani = getManifold(getVariableType(Xi[sfidx]))
-
+  
   # perform the numeric solutions on the indicated elements
   # FIXME consider repeat solve as workaround for inflation off-zero 
-  computeAcrossHypothesis!( ccwl, allelements, activehypo, certainidx, 
+  # FIXME figure out why certainidx is not used from the hyporecipe
+  certainidx = ccwl.certainhypo
+  @assert certainidx == hyporecipe.certainidx "expected hyporecipe.certainidx to be the same as cached in ccw"
+  computeAcrossHypothesis!( ccwl, hyporecipe.allelements, hyporecipe.activehypo, certainidx, 
                             sfidx, maxlen, mani, spreadNH=spreadNH, 
                             inflateCycles=inflateCycles, skipSolve=skipSolve,
                             _slack=_slack )
   #
-  # do info per coord
-  ipc = if ccwl._gradients === nothing 
-    ones(getDimension(Xi[sfidx]))
-  else
-    ipc_ = ones(getDimension(Xi[sfidx]))
-    # calcPerturbationFromVariable(ccwl._gradients, 2, ipc_) # TODO, WIP
-    ipc_                                                     # TODO, WIP
-  end
+  # ## do info per coord
+  # @assert hyporecipe.activehypo[1][1] === 0 "expected 0-hypo case in hyporecipe.activehypo, to get variable hypo mask for relative partial propagation calculations."
+  # # select only the active variables in case of multihypo
+  # activeids = hyporecipe.activehypo[hyporecipe.mhidx[smpid]+1][2]
+  # # solvefor index without the fractional variables
+  # active_mask = (s->x in activeids).(1:length(Xi))
+  # sfidx_active = sum(active_mask[1:sfidx])
+  # # build a view to the decision variable memory
+  # # activeVars = Xi[active_mask]
+  # smpid = 1 # assume gradients are just done for the first sample values
+  # meas_pts = tuple((_getindextuple(ccwl.measurement, smpid))..., (getindex.(varParams, smpid))...)
+  # ipc = _calcIPCRelative(activeVars, )
+  ipc = ones(getDimension(Xi[sfidx]))
 
   # return the found points, and info per coord
   return ccwl.params[ccwl.varidx], ipc
@@ -331,12 +361,13 @@ function evalPotentialSpecific( Xi::AbstractVector{<:DFGVariable},
 
   # Check which variables have been initialized, TODO not sure why forcing to Bool vs BitVector
   isinit::Vector{Bool} = Xi .|> isInitialized .|> Bool
-  _, _, _, mhidx = assembleHypothesesElements!(ccwl.hypotheses, nn, sfidx, length(Xi), isinit, ccwl.nullhypo )
+  hyporecipe = _prepareHypoRecipe!(ccwl.hypotheses, nn, sfidx, length(Xi), isinit, ccwl.nullhypo )
+  
   # get solvefor manifolds, FIXME ON FIRE, upgrade to new Manifolds.jl
   mani = getManifold(Xi[sfidx])
   # two cases on how to use the measurement
-  nhmask = mhidx .== 0
-  ahmask = mhidx .== 1
+  nhmask = hyporecipe.mhidx .== 0
+  ahmask = hyporecipe.mhidx .== 1
   # generate nullhypo samples
   # inject lots of entropy in nullhypo case
   # make spread (1σ) equal to mean distance of other fractionals

test/testExplicitMultihypo.jl

@@ -5,7 +5,7 @@ using Test
 using IncrementalInference
 
 
-@testset "test IncrementalInference.assembleHypothesesElements! with only nullhypothesis..." begin
+@testset "test IncrementalInference._prepareHypoRecipe! with only nullhypothesis..." begin
 
 ##
 
@@ -14,7 +14,7 @@ n2_1_gt1 = 1:2
 n2_1_gt2_ = (3,3,0)
 n2_1_gt3 = [(0,Int[1;]); (1,1:2); (2,Int[])]
 n2_1_gt4_ = 20
-n2_1 = IncrementalInference.assembleHypothesesElements!(nothing, 20, 1, 2, ones(Bool, 2), 0.5 )
+n2_1 = IncrementalInference._prepareHypoRecipe!(nothing, 20, 1, 2, ones(Bool, 2), 0.5 )
 
 @test sum([n2_1_gt1;] - [n2_1[1];]) == 0
 @test length(n2_1[2][1]) > n2_1_gt2_[1]
@@ -40,7 +40,7 @@ n2_1_gt1 = 1:2
 n2_1_gt2_ = (3,3,0)
 n2_1_gt3 = [(0,Int[2;]); (1,1:2); (2,Int[])]
 n2_1_gt4_ = 20
-n2_1 = IncrementalInference.assembleHypothesesElements!(nothing, 20, 2, 2, ones(Bool, 2), 0.5 )
+n2_1 = IncrementalInference._prepareHypoRecipe!(nothing, 20, 2, 2, ones(Bool, 2), 0.5 )
 
 @test sum([n2_1_gt1;] - [n2_1[1];]) == 0
 @test length(n2_1[2][1]) > n2_1_gt2_[1]
@@ -64,7 +64,7 @@ n2_1 = IncrementalInference.assembleHypothesesElements!(nothing, 20, 2, 2, ones(
 end
 
 
-@testset "test IncrementalInference.assembleHypothesesElements! without multihypothesis..." begin
+@testset "test IncrementalInference._prepareHypoRecipe! without multihypothesis..." begin
 
 ##
 
@@ -77,7 +77,7 @@ s2_1_gt1 = 1:2
 s2_1_gt2 = (Int[],1:20,Int[])
 s2_1_gt3 = [(0,Int[1;]); (1,1:2); (2,Int[])]
 s2_1_gt4 = ones(20)
-s2_1 = IncrementalInference.assembleHypothesesElements!(nothing, 20, 1, 2 )
+s2_1 = IncrementalInference._prepareHypoRecipe!(nothing, 20, 1, 2 )
 @test sum([s2_1_gt1;] .- [s2_1[1];]) == 0
 @test sum( s2_1_gt2[1] .- s2_1[2][1]) == 0
 @test sum([s2_1_gt2[2];] .- [s2_1[2][2];]) == 0
@@ -96,7 +96,7 @@ s2_2_gt2 = (Int[],1:20,Int[])
 s2_2_gt3 = [(0,Int[2;]); (1,1:2); (2,Int[])]
 s2_2_gt4 = ones(20) # Int[]
 
-s2_2 = IncrementalInference.assembleHypothesesElements!(nothing, 20, 2, 2 )
+s2_2 = IncrementalInference._prepareHypoRecipe!(nothing, 20, 2, 2 )
 
 @test sum([s2_2_gt1;] .- [s2_2[1];]) == 0
 @test sum([s2_2_gt2[1];] .- [s2_2[2][1];]) == 0
@@ -116,7 +116,7 @@ end
 
 
 
-@testset "assembleHypothesesElements! with bi-modality (certain variable)" begin
+@testset "_prepareHypoRecipe! with bi-modality (certain variable)" begin
 
 ##
 
@@ -129,7 +129,7 @@ s3_1_gt2 = (0,3,3,40)
 s3_1_gt3 = [(1,Int[]); (2,Int[1;2]); (3,Int[1;3])]
 s3_1_gt4 = 40
 
-s3_1 = IncrementalInference.assembleHypothesesElements!(Categorical([0.0;0.5;0.5]), 40, 1, 3)
+s3_1 = IncrementalInference._prepareHypoRecipe!(Categorical([0.0;0.5;0.5]), 40, 1, 3)
 
 @test sum([s3_1_gt1;] - [s3_1[1];]) == 0
 @test sum([s3_1_gt2[1];] .- [s3_1[2][1];]) == 0
@@ -155,7 +155,7 @@ s3_1 = IncrementalInference.assembleHypothesesElements!(Categorical([0.0;0.5;0.5
 end
 
 
-@testset "assembleHypothesesElements! with bi-modality (fractional variable 1/2)" begin
+@testset "_prepareHypoRecipe! with bi-modality (fractional variable 1/2)" begin
 
 ##
 
@@ -168,7 +168,7 @@ s3_2_gt2 = (0,3,3,40)
 s3_2_gt3 = [(0, Int[2]); (1,Int[1;2]); (2,Int[1;2]); (3,Int[2;3])]
 s3_2_gt4 = 40
 
-s3_2 = IncrementalInference.assembleHypothesesElements!(Categorical([0.0;0.5;0.5]), 40, 2, 3 )
+s3_2 = IncrementalInference._prepareHypoRecipe!(Categorical([0.0;0.5;0.5]), 40, 2, 3 )
 
 @test sum(s3_2_gt1 - s3_2[1]) == 0
 @test sum(s3_2_gt2[1] .- s3_2[2][2]) == 0
@@ -197,7 +197,7 @@ s3_2 = IncrementalInference.assembleHypothesesElements!(Categorical([0.0;0.5;0.5
 
 end
 
-@testset "assembleHypothesesElements! with bi-modality (fractional variable 2/2)" begin
+@testset "_prepareHypoRecipe! with bi-modality (fractional variable 2/2)" begin
 
 ##
 
@@ -210,7 +210,7 @@ s3_3_gt2 = (0,3,3,40)
 s3_3_gt3 = [(0, Int[3]); (1,Int[1;3]); (2,Int[2;3]); (3,Int[1;3])]
 s3_3_gt4 = 40
 
-s3_3 = IncrementalInference.assembleHypothesesElements!(Categorical([0.0;0.5;0.5]), 40, 3, 3 )
+s3_3 = IncrementalInference._prepareHypoRecipe!(Categorical([0.0;0.5;0.5]), 40, 3, 3 )
 
 @test sum(s3_3_gt1 - s3_3[1]) == 0
 @test sum(s3_3_gt2[1] .- s3_3[2][2]) == 0
@@ -241,7 +241,7 @@ end
 
 
 
-# @testset "test IncrementalInference.assembleHypothesesElements! with bi-modality backwards permutation..." begin
+# @testset "test IncrementalInference._prepareHypoRecipe! with bi-modality backwards permutation..." begin
 
 # certainidx = 1
 # sfidx=1, mhidx=1:  ah = []
@@ -252,7 +252,7 @@ end
 # s3_1_gt3 = [(1,Int[]); (2,Int[1;2]); (3,Int[1;3])]
 # s3_1_gt4 = 20
 #
-# s3_1 = IncrementalInference.assembleHypothesesElements!(Categorical([0.5;0.5;0.0]), 20, 1, 3)
+# s3_1 = IncrementalInference._prepareHypoRecipe!(Categorical([0.5;0.5;0.0]), 20, 1, 3)
 
 # @test sum(s3_1_gt1 - s3_1[1]) == 0
 # @test sum(s3_1_gt2[1] .- s3_1[2][1]) == 0
@@ -278,7 +278,7 @@ end
 # end
 
 
-@testset "assembleHypothesesElements! with tri-modality (certain variable)" begin
+@testset "_prepareHypoRecipe! with tri-modality (certain variable)" begin
 
 ##
 
@@ -288,7 +288,7 @@ s4_1_gt2 = (0,3,3,3,N)
 s4_1_gt3 = [(1,Int[]); (2,Int[1;2]); (3,Int[1;3]); (4,Int[1;4])]
 s4_1_gt4 = N
 
-s4_1 = IncrementalInference.assembleHypothesesElements!(Categorical([0.0;0.33;0.33;0.34]), N, 1, 4 )
+s4_1 = IncrementalInference._prepareHypoRecipe!(Categorical([0.0;0.33;0.33;0.34]), N, 1, 4 )
 
 @test sum(s4_1_gt1 - s4_1[1]) == 0
 @test sum(s4_1_gt2[1] .- s4_1[2][1]) == 0
@@ -321,7 +321,7 @@ s4_1 = IncrementalInference.assembleHypothesesElements!(Categorical([0.0;0.33;0.
 end
 
 
-@testset "assembleHypothesesElements! with tri-modality (fractional variable 1/3)" begin
+@testset "_prepareHypoRecipe! with tri-modality (fractional variable 1/3)" begin
 
 ## solve for fractional variable in trinary case
 
@@ -331,7 +331,7 @@ s4_2_gt2 = (0,3,3,3,N)
 s4_2_gt3 = [(0,Int[2]); (1,Int[1;2]); (2,Int[1;2]); (3,Int[2;3;4]); (4,Int[2;3;4])]
 s4_2_gt4 = N
 
-s4_2 = IncrementalInference.assembleHypothesesElements!(Categorical([0.0;0.33;0.33;0.34]), N, 2, 4 )
+s4_2 = IncrementalInference._prepareHypoRecipe!(Categorical([0.0;0.33;0.33;0.34]), N, 2, 4 )
 
 @test sum(s4_2_gt1 - s4_2[1]) == 0
 @test length(s4_2[2][1]) > 0.5*s4_2_gt2[2]
@@ -368,7 +368,7 @@ end
 
 
 
-@testset "assembleHypothesesElements! with tri-modality (fractional variable 2/3)" begin
+@testset "_prepareHypoRecipe! with tri-modality (fractional variable 2/3)" begin
 
 ##
 
@@ -378,7 +378,7 @@ s4_3_gt2 = (0,3,3,3,N)
 s4_3_gt3 = [(0,Int[3]); (1,Int[1;3]); (2,Int[2;3;4]); (3,Int[1;3]); (4,Int[2;3;4])]
 s4_3_gt4 = N
 
-s4_3 = IncrementalInference.assembleHypothesesElements!(Categorical([0.0;0.33;0.33;0.34]), N, 3, 4 )
+s4_3 = IncrementalInference._prepareHypoRecipe!(Categorical([0.0;0.33;0.33;0.34]), N, 3, 4 )
 
 @test sum(s4_3_gt1 - s4_3[1]) == 0
 @test length(s4_3[2][1]) > 0.5*s4_3_gt2[2]
@@ -413,7 +413,7 @@ s4_3 = IncrementalInference.assembleHypothesesElements!(Categorical([0.0;0.33;0.
 
 end
 
-@testset "assembleHypothesesElements! with tri-modality (fractional variable 3/3)" begin
+@testset "_prepareHypoRecipe! with tri-modality (fractional variable 3/3)" begin
 
 ##
 
@@ -423,7 +423,7 @@ s4_4_gt2 = (0,3,3,3,N)
 s4_4_gt3 = [(0,Int[4]); (1,Int[1;4]); (2,Int[2;3;4]); (3,Int[2;3;4]); (4,Int[1;4])]
 s4_4_gt4 = N
 
-s4_4 = IncrementalInference.assembleHypothesesElements!(Categorical([0.0;0.33;0.33;0.34]), N, 4, 4 )
+s4_4 = IncrementalInference._prepareHypoRecipe!(Categorical([0.0;0.33;0.33;0.34]), N, 4, 4 )
 
 @test sum(s4_4_gt1 - s4_4[1]) == 0
 @test length(s4_4[2][1]) > 0.5*s4_4_gt2[2]