Merge pull request #604 from JuliaRobotics/fix/1Q20/592

renaming to biadjacency,

Project.toml

@@ -2,7 +2,7 @@ name = "IncrementalInference"
 uuid = "904591bb-b899-562f-9e6f-b8df64c7d480"
 keywords = ["mm-iSAM", "SLAM", "inference", "sum-product", "belief-propagation"]
 desc = "Implements the multimodal iSAM algorithm."
-version = "0.10.0"
+version = "0.10.1"
 
 [deps]
 ApproxManifoldProducts = "9bbbb610-88a1-53cd-9763-118ce10c1f89"
@@ -36,7 +36,7 @@ SuiteSparse = "4607b0f0-06f3-5cda-b6b1-a6196a1729e9"
 [compat]
 ApproxManifoldProducts = "0.1, 0.2"
 Combinatorics = "1.0"
-DistributedFactorGraphs = "0.7.0"
+DistributedFactorGraphs = "0.7.1"
 Distributions = "0.18, 0.19, 0.20, 0.21, 0.22, 0.23, 0.24, 0.25, 1"
 DocStringExtensions = "0.8, 0.9, 0.10, 1"
 FileIO = "1.0.2, 1.1, 1.2"
@@ -52,7 +52,7 @@ ProgressMeter = "0.6, 0.7, 0.8, 0.9, 1"
 Reexport = "0.2, 0.3, 0.4, 0.5, 1"
 Requires = "0.5, 0.6, 0.7, 0.8, 0.9, 0.10, 1"
 StatsBase = "0.25, 0.26, 0.26, 0.27, 0.28, 0.29, 0.30, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.40, 1"
-julia = "1.2"
+julia = "1.2, 1.3, 1.4, 1.5"
 
 [extras]
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"

src/CliqStateMachineUtils.jl

@@ -898,11 +898,16 @@ directPriorMsgIDs, directFrtlMsgIDs, directAssignmentIDs, mcmcIterationIDs
 function determineCliqVariableDownSequence(subfg::AbstractDFG, cliq::TreeClique; solvable::Int=1)
   frtl = getCliqFrontalVarIds(cliq)
 
-  #TODO don't use this getAdjacencyMatrixSymbols
-  adj = DFG.getAdjacencyMatrixSymbols(subfg, solvable=solvable)
-  mask = map(x->(x in frtl), adj[1,:])
-  subAdj = adj[2:end,mask] .!= nothing
-  newFrtlOrder = Symbol.(adj[1,mask])
+  adj, varLabels, FactorLabels = DFG.getBiadjacencyMatrix(subfg, solvable=solvable)
+  mask = (x-> x in frtl).(varLabels)
+  newFrtlOrder = varLabels[mask]
+  subAdj = adj[:,mask]
+    #TODO don't use this getAdjacencyMatrixSymbols, #604
+    # adj = DFG.getAdjacencyMatrixSymbols(subfg, solvable=solvable)
+    # mask = map(x->(x in frtl), adj[1,:])
+    # subAdj = adj[2:end,mask] .!= nothing
+    # newFrtlOrder = Symbol.(adj[1,mask])
+
   crossCheck = sum(Int.(subAdj), dims=2) .> 1
   iterVars = Symbol[]
   for i in 1:length(crossCheck)

src/FactorGraph.jl

@@ -1050,7 +1050,8 @@ function getEliminationOrder(dfg::G;
   #
   @assert 0 == length(constraints) || ordering == :ccolamd "Must use ordering=:ccolamd when trying to use constraints"
   # Get the sparse adjacency matrix, variable, and factor labels
-  adjMat, permuteds, permutedsf = DFG.getAdjacencyMatrixSparse(dfg, solvable=solvable)
+  adjMat, permuteds, permutedsf = DFG.getBiadjacencyMatrix(dfg, solvable=solvable)
+  # adjMat, permuteds, permutedsf = DFG.getAdjacencyMatrixSparse(dfg, solvable=solvable)
 
   # Create dense adjacency matrix