Update val/bw for DFGv1 State refactor

IncrementalInference/src/IncrementalInference.jl

@@ -110,7 +110,6 @@ import ApproxManifoldProducts: isPartial
 import ApproxManifoldProducts: _update!
 import DistributedFactorGraphs: addVariable!, addFactor!, ls, lsf, isInitialized
 import DistributedFactorGraphs: compare
-import DistributedFactorGraphs: rebuildFactorCache!
 import DistributedFactorGraphs: getDimension, getManifold, getPointType, getPointIdentity
 import DistributedFactorGraphs: getPoint, getCoordinates
 import DistributedFactorGraphs: getStateKind

IncrementalInference/src/entities/BeliefTypes.jl

@@ -78,9 +78,24 @@ function TreeBelief(
 end
 
 function TreeBelief(vnd::State, solvDim::Real = 0)
+  TreeBelief(DFG.getDensityKind(vnd), vnd, solvDim)
+end
+
+function TreeBelief(::DFG.GaussianDensityKind, vnd::State, solvDim::Real = 0)
+  return TreeBelief(
+    DFG.refMeans(vnd),
+    DFG.refCovariances(vnd)[1],
+    vnd.observability,
+    getStateKind(vnd),
+    getManifold(vnd),
+    solvDim,
+  )
+end
+
+function TreeBelief(::DFG.NonparametricDensityKind, vnd::State, solvDim::Real = 0)
   return TreeBelief(
-    vnd.val,
-    vnd.bw,
+    DFG.refPoints(vnd),
+    DFG.refBandwidth(vnd),
     vnd.observability,
     getStateKind(vnd),
     getManifold(vnd),
@@ -93,9 +108,9 @@ function TreeBelief(vari::VariableCompute, solveKey::Symbol = :default; solvable
 end
 #
 
-getStateKind(tb::TreeBelief) = tb.variableType
+DFG.getStateKind(tb::TreeBelief) = tb.variableType
 
-getManifold(treeb::TreeBelief) = getManifold(treeb.variableType)
+DFG.getManifold(treeb::TreeBelief) = getManifold(treeb.variableType)
 
 function compare(t1::TreeBelief, t2::TreeBelief)
   TP = true

IncrementalInference/src/parametric/services/ParametricCSMFunctions.jl

@@ -38,10 +38,10 @@ function solveUp_ParametricStateMachine(csmc::CliqStateMachineContainer)
       vnd = getState(getVariable(csmc.cliqSubFg, v), :parametric)
       # fill in the variable node data value
       logCSM(csmc, "$(csmc.cliq.id) up: updating $v : $val")
-      vnd.val[1] = val.val
+      DFG.refMeans(vnd)[1] = val.val
       #calculate and fill in covariance
       #TODO rather broadcast than make new memory
-      vnd.bw = val.cov
+      DFG.refCovariances(vnd)[1] = val.cov
     end
     # elseif length(lsfPriors(csmc.cliqSubFg)) == 0 #FIXME
     #   @error "Par-3, clique $(csmc.cliq.id) failed to converge in upsolve, but ignoring since no priors" result
@@ -118,8 +118,8 @@ function solveDown_ParametricStateMachine(csmc::CliqStateMachineContainer)
         #TODO maybe combine variable and factor in new prior?
         vnd = getState(getVariable(csmc.cliqSubFg, msym), :parametric)
         logCSM(csmc, "$(csmc.cliq.id): Updating separator $msym from message $(belief.val)")
-        vnd.val .= belief.val
-        vnd.bw .= belief.bw
+        DFG.refMeans(vnd)[1] = belief.val[1] #FIXME 🦨 shares data structure in belief
+        DFG.refCovariances(vnd)[1] = belief.bw
       end
     end
   end
@@ -146,8 +146,8 @@ function solveDown_ParametricStateMachine(csmc::CliqStateMachineContainer)
         logCSM(csmc, "$(csmc.cliq.id) down: updating $v : $val"; loglevel = Logging.Info)
         vnd = getState(getVariable(csmc.cliqSubFg, v), :parametric)
         #Update subfg variables
-        vnd.val[1] = val.val
-        vnd.bw .= val.cov
+        DFG.refMeans(vnd)[1] = val.val
+        DFG.refCovariances(vnd)[1] = val.cov
       end
     else
       @error "Par-5, clique $(csmc.cliq.id) failed to converge in down solve" result

IncrementalInference/src/parametric/services/ParametricManopt.jl

@@ -330,7 +330,7 @@ function solve_RLM(
 
   #Can use varIntLabel (because its an OrderedDict), but varLabelsAP makes the ArrayPartition.
   p0 = map(varlabelsAP) do label
-    getVal(fg, label; solveKey)[1]
+    DFG.refMeans(getState(fg, label, solveKey))[1]
   end
 
   # create an ArrayPartition{CalcFactorResidual} for faclabels
@@ -415,7 +415,7 @@ function solve_RLM_conditional(
 
   # get the subgraph formed by all frontals, separators and fully connected factors
   varlabels = union(frontals, separators)
-  faclabels = sortDFG(setdiff(listNeighborhood(fg, varlabels, 1), varlabels))
+  _, faclabels = listNeighborhood(fg, varlabels, 1)
 
   filter!(faclabels) do fl
     return issubset(getVariableOrder(fg, fl), varlabels)
@@ -440,7 +440,7 @@ function solve_RLM_conditional(
   all_varlabelsAP = ArrayPartition((frontal_varlabelsAP.x..., separator_varlabelsAP.x...))
 
   all_points = map(all_varlabelsAP) do label
-    getVal(fg, label; solveKey)[1]
+    DFG.refMeans(getState(fg, label, solveKey))[1]
   end
 
   p0 = ArrayPartition(all_points.x[1:length(frontal_varlabelsAP.x)])
@@ -547,12 +547,10 @@ function autoinitParametric!(
       return false
     end
 
-    vnd::State = getState(xi, solveKey)
-
     if perturb_point
       _M = getManifold(xi)
-      p = vnd.val[1]
-      vnd.val[1] = exp(
+      p = DFG.refMeans(vnd)[1]
+      DFG.refMeans(vnd)[1] = exp(
         _M,
         p, 
         get_vector(
@@ -566,9 +564,9 @@ function autoinitParametric!(
     M, vartypeslist, lm_r, Σ = solve_RLM_conditional(dfg, [initme], initfrom; solveKey, kwargs...)
 
     val = lm_r[1]
-    vnd.val[1] = val
+    DFG.refMeans(vnd)[1] = val
 
-    !isnothing(Σ) && (vnd.bw .= Σ)
+    !isnothing(Σ) && (DFG.refCovariances(vnd)[1] .= Σ)
 
     # updateSolverDataParametric!(vnd, val, Σ)
 

IncrementalInference/src/parametric/services/ParametricUtils.jl

@@ -500,7 +500,7 @@ function initPoints!(p, gsc, fg::AbstractDFG, solveKey = :parametric)
   for (i, vartype) in enumerate(gsc.varTypes)
     varIds = gsc.varTypesIds[vartype]
     for (j, vId) in enumerate(varIds)
-      p[gsc.M, i][j] = getState(fg, vId, solveKey).val[1]
+      p[gsc.M, i][j] = DFG.refMeans(getState(fg, vId, solveKey))[1]
     end
   end
 end
@@ -687,7 +687,7 @@ function solveConditionalsParametric(
   flatvar = FlatVariables(fg, varIds)
 
   for vId in varIds
-    p = getState(fg, vId, solvekey).val[1]
+    p = DFG.refMeans(getState(fg, vId, solvekey))[1]
     flatvar[vId] = getCoordinates(getStateKind(fg, vId), p)
   end
   initValues = flatvar.X
@@ -825,9 +825,9 @@ function updateSolverDataParametric!(
   cov::AbstractMatrix,
 )
   # fill in the variable node data value
-  vnd.val[1] = val
+  DFG.refMeans(vnd)[1] = val
   #calculate and fill in covariance
-  vnd.bw .= cov
+  DFG.refCovariances(vnd)[1] .= cov
   return vnd
 end
 
@@ -888,15 +888,15 @@ function initParametricFrom!(
     for v in getVariables(fg)
       fromvnd = getState(v, fromkey)
       dims = getDimension(v)
-      getState(v, parkey).val[1] = fromvnd.val[1]
-      getState(v, parkey).bw[1:dims, 1:dims] = LinearAlgebra.I(dims)
+      DFG.refMeans(getState(v, parkey))[1] = DFG.refMeans(fromvnd)[1]
+      DFG.refCovariances(getState(v, parkey))[1] = LinearAlgebra.I(dims)
     end
   else
     for var in getVariables(fg)
       dims = getDimension(var)
       μ, Σ = calcMeanCovar(var, fromkey)
-      getState(var, parkey).val[1] = μ
-      getState(var, parkey).bw[1:dims, 1:dims] = Σ
+      DFG.refMeans(getState(var, parkey))[1] = μ
+      DFG.refCovariances(getState(var, parkey))[1] = Σ
     end
   end
 end
@@ -963,10 +963,12 @@ function createMvNormal(v::VariableCompute, key = :parametric)
   if key == :parametric
     vnd = getState(v, :parametric)
     dims = getDimension(vnd)
-    return createMvNormal(vnd.val[1:dims, 1], vnd.bw[1:dims, 1:dims])
+    val = DFG.refMeans(vnd)[1]
+    cov = DFG.refCovariances(vnd)[1:dims, 1:dims]
+    return createMvNormal(val, cov)
   else
     @warn "Trying MvNormal Fit"
-    return fit(MvNormal, getState(v, key).val)
+    return fit(MvNormal, DFG.refPoints(getState(v, key)))
   end
 end
 

IncrementalInference/src/services/FactorGraph.jl

@@ -53,14 +53,15 @@ reshapeVec2Mat(vec::Vector, rows::Int) = reshape(vec, rows, round(Int, length(ve
 
 Fetch the variable marginal joint sampled points.  Use [`getBelief`](@ref) to retrieve the full Belief object.
 """
-getVal(v::VariableCompute; solveKey::Symbol = :default) = v.states[solveKey].val
+#FIXME replace with refPoints
+getVal(v::VariableCompute; solveKey::Symbol = :default) = DFG.refPoints(getState(v, solveKey))
 function getVal(v::VariableCompute, idx::Int; solveKey::Symbol = :default)
-  return v.states[solveKey].val[:, idx]
+  return DFG.refPoints(getState(v, solveKey))[idx]
 end
-getVal(vnd::State) = vnd.val
-getVal(vnd::State, idx::Int) = vnd.val[:, idx]
+getVal(vnd::State) = DFG.refPoints(vnd)
+getVal(vnd::State, idx::Int) = DFG.refPoints(vnd)[:, idx]
 function getVal(dfg::AbstractDFG, lbl::Symbol; solveKey::Symbol = :default)
-  return getVariable(dfg, lbl).states[solveKey].val
+  return DFG.refPoints(getVariable(dfg, lbl).states[solveKey])
 end
 
 """
@@ -73,15 +74,15 @@ function getNumPts(v::VariableCompute; solveKey::Symbol = :default)::Int
 end
 
 function AMP.getBW(vnd::State)
-  return vnd.bw
+  return DFG.refBandwidth(vnd)
 end
 
 # setVal! assumes you will update values to database separate, this used for local graph mods only
 function getBWVal(v::VariableCompute; solveKey::Symbol = :default)
-  return getState(v, solveKey).bw
+  return DFG.refBandwidth(getState(v, solveKey))
 end
 function setBW!(vd::State, bw::Array{Float64, 2}; solveKey::Symbol = :default)
-  vd.bw = bw
+  DFG.refBandwidth(vd) .= bw
   return nothing
 end
 function setBW!(v::VariableCompute, bw::Array{Float64, 2}; solveKey::Symbol = :default)
@@ -90,7 +91,9 @@ function setBW!(v::VariableCompute, bw::Array{Float64, 2}; solveKey::Symbol = :d
 end
 
 function setVal!(vd::State, val::AbstractVector{P}) where {P}
-  vd.val = val
+  points = DFG.refPoints(vd)
+  resize!(points, length(val))
+  points .= val
   return nothing
 end
 function setVal!(
@@ -393,20 +396,13 @@ function DefaultNodeDataParametric(
     #                         dims, false, :_null, Symbol[], variableType, true, 0.0, false, dontmargin)
   else
     ϵ = getPointIdentity(variableType)
-    return State(solveKey, variableType;
-      val=[ϵ],
-      bw=zeros(dims, dims),
-      # Symbol[],
-      # false,
-      # :_null,
-      # Symbol[],
-      initialized=false,
-      observability=zeros(dims),
-      marginalized=false,
-      # dontmargin,
-      # 0,
-      # 0,
+    belief = DFG.BeliefRepresentation(
+      DFG.GaussianDensityKind(),
+      variableType;
+      means = [ϵ],
+      covariances = [zeros(dims, dims)],
     )
+    return State(solveKey, variableType; belief)
   end
 end
 
@@ -471,26 +467,20 @@ function setDefaultNodeData!(
     #
     (val, bw)
   end
+
+  belief = DFG.BeliefRepresentation(
+    DFG.NonparametricDensityKind(),
+    varType;
+    points = val,
+    bandwidth = bw,
+  )
   # make and set the new solverData
   mergeState!(
     v,
     State(solveKey, varType;
-      # id=nothing,
-      val,
-      bw,
-      # Symbol[],
-      # sp,
-      # dims,
-      # false,
-      # :_null,
-      # Symbol[],
+      belief,
       initialized=isinit,
-      observability=zeros(getDimension(v)),
       marginalized=false,
-      # dontmargin,
-      # 0,
-      # 0,
-
     )
   )
   return nothing
@@ -569,7 +559,7 @@ addVariable!(fg, :x0, Pose2)
 function DFG.addVariable!(
   dfg::AbstractDFG,
   label::Symbol,
-  statetype::Union{T, Type{T}};
+  statekind::Union{T, Type{T}};
   tags::Union{Set{Symbol}, Vector{Symbol}} = Set{Symbol}(),
   timestamp::Union{TimeDateZone, ZonedDateTime}  = DFG.now_tdz(),
   solvable::Int = 1,
@@ -600,7 +590,7 @@ function DFG.addVariable!(
   tags = union(Set(tags), [:VARIABLE])
   v = VariableDFG(
     label,
-    statetype;
+    statekind;
     tags,
     bloblets,
     blobentries,

IncrementalInference/src/services/GraphInit.jl

@@ -349,8 +349,8 @@ function initVariable!(
   if solveKey == :parametric
     μ, iΣ = getMeasurementParametric(samplable_belief)
     vnd = getState(variable, solveKey)
-    vnd.val[1] = getPoint(getStateKind(variable), μ)
-    vnd.bw .= inv(iΣ)
+    DFG.refMeans(vnd)[1] = getPoint(getStateKind(variable), μ)
+    DFG.refCovariances(vnd)[1] .= inv(iΣ)
     vnd.initialized = true
   else
     points = [samplePoint(M, samplable_belief) for _ = 1:N]

IncrementalInference/src/services/VariableStatistics.jl

@@ -37,7 +37,7 @@ end
 
 #TODO consolidate
 function calcMeanCovar(vari::VariableCompute, solvekey = :default)
-  pts = getState(vari, solvekey).val
+  pts = DFG.refPoints(getState(vari, solvekey))
   μ = mean(getManifold(vari), pts)
   Σ = cov(getStateKind(vari), pts)
   return μ, Σ