From 7a7d6a97d0528856ce292c10223101b84fb981a4 Mon Sep 17 00:00:00 2001
From: dehann <dehann.fourie@gmail.com>
Date: Fri, 11 Feb 2022 20:22:40 -0500
Subject: [PATCH 1/3] rm depr pre v0.27
---
src/Deprecated.jl | 456 ----------------------------------------------
1 file changed, 456 deletions(-)
diff --git a/src/Deprecated.jl b/src/Deprecated.jl
index d8d606c7e..bd76586e8 100644
--- a/src/Deprecated.jl
+++ b/src/Deprecated.jl
@@ -214,462 +214,6 @@ end
@deprecate generateCanonicalFG_TestSymbolic(w...;kw...) generateGraph_TestSymbolic(w...;kw...)
-##==============================================================================
-## Deprecate code below before v0.27
-##==============================================================================
-
-convert(::Type{<:SamplableBelief}, obj::PackedUniform) = unpackDistribution(obj)
-
-
-Base.convert(::Type{<:Tuple}, ::InstanceType{Manifolds.Euclidean{Tuple{N}, ℝ}} ) where N = tuple([:Euclid for i in 1:N]...)
-Base.convert(::Type{<:Tuple}, ::InstanceType{Manifolds.Circle{ℝ}}) = error("#FIXME")#(:Circular,)
-Base.convert(::Type{<:Tuple}, ::InstanceType{Manifolds.RealCircleGroup}) = (:Circular,)
-
-@deprecate manikde!(pts::AbstractVector, bws::Vector{<:Real}, variableType::Union{InstanceType{<:InferenceVariable}, InstanceType{<:AbstractFactor}} ) manikde!(variableType,pts,bws)
-@deprecate manikde!(pts::AbstractVector, varType::Union{InstanceType{<:InferenceVariable}, InstanceType{<:AbstractFactor}}) manikde!(varType, pts)
-
-# function getSample(cf::CalcFactor{<:AbstractRelativeRoots})
-# M = getManifold(cf.factor)
-# if hasfield(typeof(cf.factor), :Z)
-# return sampleTangent(M, cf.factor.Z)
-# else
-# error("""Factor $(typeof(cf.factor)) does not have a field `Z`, to use the default `getSample` method, use `Z` for the measurement.
-# Alternatively, provide a `getSample` method. See IIF issue #1441 and Custom Factors in the Caesar documentation.""")
-# end
-# end
-
-# function getSample(cf::CalcFactor{<:CircularCircular})
-# # return (sampleTangent(getManifold(cf.factor), cf.factor.Z), )
-# # FIXME workaround for issue #TBD with manifolds CircularGroup
-# return sampleTangent(getManifold(cf.factor), cf.factor.Z, [0.0])
-# end
-
-# function getSample(cf::CalcFactor{<:LinearRelative})
-# # _samplemakevec(z::Real) = [z;]
-# # _samplemakevec(z::AbstractVector{<:Real}) = z
-# return sampleTangent(getManifold(cf.factor), cf.factor.Z)
-# end
-
-# function getSample(cf::CalcFactor{<:EuclidDistance})
-# rand(cf.factor.Z, 1)
-# end
-
-@deprecate CalcFactor(x1,x2,x3,x4,x5,x6) CalcFactor(x1,x2,x3,x4,x5,x6, true)
-
-@deprecate ensureAllInitialized!(w...;kw...) initAll!(w...;kw...)
-
-# """
-# $SIGNATURES
-
-# Sample points from a regular grid scalar field level set.
-
-# Notes
-# - modifies first argument roi to save on memory allocations
-# - user should duplicate if with a deepcopy if needed
-# """
-# function sampleLevelSetGaussian!( roi::AbstractMatrix{<:Real},
-# sigma::Real,
-# x_grid::AbstractVector{<:Real},
-# y_grid::AbstractVector{<:Real};
-# sigma_scale::Real=3 )
-# #
-# # make Gaussian
-# roi .^= 2
-# roi .*= 0.5/(sigma^2)
-# # truncate at sigma_scale*sigma
-# _roi = thres .- roi
-
-# sampleHeatmap(roi, x_grid, y_grid, sigma_scale^2)
-# end
-
-
-# @deprecate getLevelSetSigma(data::AbstractMatrix{<:Real}, level::Real, w...; kw...) sampleLevelSetGaussian!(data.-level, w...; kw...)
-
-getOutNeighbors(w...;kw...) = error("Obsolete, use DFG.getNeighbors instead.")
-
-# # TODO -- there should be a better way, without retrieving full vertex
-# # TODO -- Deprecated for DFG -- must update
-# function getOutNeighbors(dfg::T, v::V; needdata::Bool=false, ready::Union{Nothing, Int}=nothing, backendset::Union{Nothing, Int}=nothing)::Vector{Symbol} where {T <: AbstractDFG, V <: DFGNode}
-# @warn "TODO: needdata is currently ignored. Symbols are returned."
-# nodes = getNeighbors(dfg, v, ready=ready, backendset=backendset)
-# return nodes
-# end
-# function getOutNeighbors(dfg::T, vertSym::Symbol; needdata::Bool=false, ready::Int=1, backendset::Int=1 )::Vector{Symbol} where {T <: AbstractDFG, V <: DFGNode}
-# @warn "TODO: needdata is currently ignored. Symbols are returned."
-# nodes = getNeighbors(dfg, vertSym, ready=ready, backendset=backendset)
-# return nodes
-# end
-
-# SolverParams(;dimID::Int=0,
-# registeredModuleFunctions=nothing,
-# reference=nothing,
-# stateless::Bool=false,
-# qfl::Int=99999999999,
-# isfixedlag::Bool=false,
-# limitfixeddown::Bool=false,
-# incremental::Bool=true,
-# useMsgLikelihoods::Bool=false,
-# upsolve::Bool=true,
-# downsolve::Bool=true,
-# drawtree::Bool=false,
-# drawCSMIters::Bool=true,
-# showtree::Bool=false,
-# drawtreerate::Float64=0.5,
-# dbg::Bool=false,
-# async::Bool=false,
-# limititers::Int=500,
-# N::Int=100,
-# multiproc::Bool=1 < nprocs(),
-# logpath::String="/tmp/caesar/$(now())",
-# graphinit::Bool=true,
-# treeinit::Bool=false,
-# limittreeinit_iters::Int=10,
-# algorithms::Vector{Symbol}=[:default],
-# spreadNH::Real=3.0,
-# inflation::Real=5.0,
-# inflateCycles::Int=3,
-# gibbsIters::Int=3,
-# maxincidence::Int=500,
-# alwaysFreshMeasurements::Bool=true,
-# attemptGradients::Bool=true,
-# devParams::Dict{Symbol,String}=Dict{Symbol,String}()
-# ) = begin useMsgLikelihoods==true && @warn "useMsgLikelihoods is under development, use with care, see #1010"
-# SolverParams( dimID,
-# registeredModuleFunctions,
-# reference,
-# stateless,
-# qfl,
-# isfixedlag,
-# limitfixeddown,
-# incremental,
-# useMsgLikelihoods,
-# upsolve,
-# downsolve,
-# drawtree,
-# drawCSMIters,
-# showtree,
-# drawtreerate,
-# dbg,
-# async,
-# limititers,
-# N,
-# multiproc,
-# logpath,
-# graphinit,
-# treeinit,
-# limittreeinit_iters,
-# algorithms,
-# spreadNH,
-# inflation,
-# inflateCycles,
-# gibbsIters,
-# maxincidence,
-# alwaysFreshMeasurements,
-# attemptGradients,
-# devParams )
-# end
-#
-
-@deprecate setVariableInferDim!(w...;kw...) setIPC!(w...;kw...)
-
-# moved to IncrementalInference/attic
-# include("CSMOccuranceUtils.jl")
-
-@deprecate prepgenericconvolution(w...;kw...) _prepCCW(w...;kw...)
-
-function getSample(cf::CalcFactor, N::Int)
- # Base.depwarn("`getSample(cf, N)` is deprecated, use `getSample(cf)`", :getSample)
- error("`getSample(cf, N)` is deprecated, use `getSample(cf)`")
-end
-
-# """
-# $SIGNATURES
-
-# Build an approximate density `[Y|X,DX,.]=[X|Y,DX][DX|.]` as proposed by the conditional convolution.
-
-# Notes
-# - Assume both are on circular manifold, `manikde!(pts, (:Circular,))`
-# """
-# function approxConvCircular(pX::ManifoldKernelDensity,
-# pDX::ManifoldKernelDensity; N::Int=100)
-# #
-
-# # building basic factor graph
-# tfg = initfg()
-# addVariable!(tfg, :s1, Sphere1)
-# addVariable!(tfg, :s2, Sphere1)
-# addFactor!(tfg, [:s1;:s2], Sphere1Sphere1(pDX), graphinit=false)
-# initManual!(tfg,:s1, pX)
-
-# # solve for outgoing proposal value
-# approxConv(tfg,:s1s2f1,:s2)
-# end
-
-# function approxConvCircular(pX::ManifoldKernelDensity,
-# pDX::SamplableBelief; N::Int=100)
-# #
-# pts = reshape(rand(pDX, N), 1, :)
-# pC = manikde!(pts, Sphere1)
-# approxConvCircular(pX, pC)
-# end
-
-
-# function approxConvCircular(pX::SamplableBelief,
-# pDX::ManifoldKernelDensity; N::Int=100)
-# #
-# pts = reshape(rand(pX, N), 1, :)
-# pC = manikde!(pts, Sphere1)
-# approxConvCircular(pC, pDX)
-# end
-
-
-
-@deprecate printGraphSummary(dfg::AbstractDFG, logger=ConsoleLogger()) show(logger.stream, MIME("text/plain"), dfg)
-@deprecate printSummary(dfg::AbstractDFG, logger=ConsoleLogger()) show(logger.stream, MIME("text/plain"), dfg)
-
-
-# """
-# $SIGNATURES
-
-# Print basic summary of graph to `logger=ConsoleLogger()`.
-# """
-# function printGraphSummary(dfg::G, logger=ConsoleLogger())::Nothing where {G <: AbstractDFG}
-# vars = ls(dfg)
-# fcts = lsf(dfg)
-
-# prio = lsfPriors(dfg)
-
-# isinit = map(x->isInitialized(dfg,x), vars)
-# infdim = map(x->getVariableInferredDim(dfg, x), vars)
-# numedges = map(v->length(ls(dfg, v)), vars)
-# numfed = map(fc->length(ls(dfg, fc)), fcts)
-# vardims = map(v->getDimension(getVariable(dfg, v)), vars)
-# fctdims = map(v->getDimension(getFactor(dfg, v)), fcts)
-# priodims = map(v->getDimension(getFactor(dfg, v)), prio)
-
-# with_logger(logger) do
-# @info "Distributed Factor Graph summary:"
-# @info " num variables: $(length(vars))"
-# @info " num factors: $(length(fcts)), w/ $(length(prio)) priors"
-# @info " var initialized: $(sum(isinit))"
-# @info ""
-# @info " var num edges: min. $(minimum(numedges)) | mean $(round(Statistics.mean(numedges),digits=2)) | 90% $(round(quantile(numedges,0.9),digits=2)) | max. $(maximum(numedges))"
-# @info " fct num edges: min. $(minimum(numfed)) | mean $(round(Statistics.mean(numfed),digits=2)) | 90% $(round(quantile(numfed,0.9),digits=2)) | max. $(maximum(numfed))"
-# @info " Variable dims: min. $(minimum(vardims)) | mean $(round(Statistics.mean(vardims),digits=2)) | 90% $(round(quantile(vardims,0.9),digits=2)) | max. $(maximum(vardims))"
-# @info " Factor dims: min. $(minimum(fctdims)) | mean $(round(Statistics.mean(fctdims),digits=2)) | 90% $(round(quantile(fctdims,0.9),digits=2)) | max. $(maximum(fctdims))"
-# @info " Prior dimens: min. $(minimum(priodims)) | mean $(round(Statistics.mean(priodims),digits=2)) | 90% $(round(quantile(priodims,0.9),digits=2)) | max. $(maximum(priodims))"
-# @info " var infr'dims: min. $(minimum(infdim)) | mean $(round(Statistics.mean(infdim),digits=2)) | 90% $(round(quantile(infdim,0.9),digits=2)) | max. $(maximum(infdim))"
-# end
-# nothing
-# end
-
-# """
-# $SIGNATURES
-
-# Print basic summary of graph to `logger=ConsoleLogger()`.
-# """
-# function printSummary(dfg::G, logger=ConsoleLogger()) where G <: AbstractDFG
-# printGraphSummary(dfg, logger)
-# end
-
-
-
-# """
-# $SIGNATURES
-
-# Calculate both measured and predicted relative variable values, starting with `from` at zeros up to `to::Symbol`.
-
-# Notes
-# - assume single variable separators only.
-
-# DevNotes
-# - TODO better consolidate with [`approxConvBelief`](@ref) which can now also work with factor chains.
-# """
-# function accumulateFactorChain( dfg::AbstractDFG,
-# from::Symbol,
-# to::Symbol,
-# fsyms::Vector{Symbol}=findFactorsBetweenNaive(dfg, from, to);
-# initval=zeros(size(getVal(dfg, from))))
-
-# # get associated variables
-# svars = union(ls.(dfg, fsyms)...)
-
-# # use subgraph copys to do calculations
-# tfg_meas = buildSubgraph(dfg, [svars;fsyms])
-# tfg_pred = buildSubgraph(dfg, [svars;fsyms])
-
-# # drive variable values manually to ensure no additional stochastics are introduced.
-# nextvar = from
-# initManual!(tfg_meas, nextvar, initval)
-# initManual!(tfg_pred, nextvar, initval)
-
-# # nextfct = fsyms[1] # for debugging
-# for nextfct in fsyms
-# nextvars = setdiff(ls(tfg_meas,nextfct),[nextvar])
-# @assert length(nextvars) == 1 "accumulateFactorChain requires each factor pair to separated by a single variable"
-# nextvar = nextvars[1]
-# meas, pred = approxDeconv(dfg, nextfct) # solveFactorMeasurements
-# pts_meas = approxConv(tfg_meas, nextfct, nextvar, (meas,ones(Int,100),collect(1:100)))
-# pts_pred = approxConv(tfg_pred, nextfct, nextvar, (pred,ones(Int,100),collect(1:100)))
-# initManual!(tfg_meas, nextvar, pts_meas)
-# initManual!(tfg_pred, nextvar, pts_pred)
-# end
-# return getVal(tfg_meas,nextvar), getVal(tfg_pred,nextvar)
-# end
-
-
-# # TODO should this be consolidated with regular approxConv?
-# # TODO, perhaps pass Xi::Vector{DFGVariable} instead?
-# function approxConvBinary(arr::Vector{Vector{Float64}},
-# meas::AbstractFactor,
-# outdims::Int,
-# fmd::FactorMetadata,
-# measurement::Tuple=(Vector{Vector{Float64}}(),);
-# varidx::Int=2,
-# N::Int=length(arr),
-# vnds=DFGVariable[],
-# _slack=nothing )
-# #
-# # N = N == 0 ? size(arr,2) : N
-# pts = [zeros(outdims) for _ in 1:N];
-# ptsArr = Vector{Vector{Vector{Float64}}}()
-# push!(ptsArr,arr)
-# push!(ptsArr,pts)
-
-# fmd.arrRef = ptsArr
-
-# # TODO consolidate with ccwl??
-# # FIXME do not divert Mixture for sampling
-# # cf = _buildCalcFactorMixture(ccwl, fmd, 1, ccwl.measurement, ARR) # TODO perhaps 0 is safer
-# # FIXME 0, 0, ()
-# cf = CalcFactor( meas, fmd, 0, 0, (), ptsArr)
-
-# measurement = length(measurement[1]) == 0 ? sampleFactor(cf, N) : measurement
-# # measurement = size(measurement[1],2) == 0 ? sampleFactor(meas, N, fmd, vnds) : measurement
-
-# zDim = length(measurement[1][1])
-# ccw = CommonConvWrapper(meas, ptsArr[varidx], zDim, ptsArr, fmd, varidx=varidx, measurement=measurement) # N=> size(measurement[1],2)
-
-# for n in 1:N
-# ccw.cpt[Threads.threadid()].particleidx = n
-# _solveCCWNumeric!( ccw, _slack=_slack )
-# end
-# return pts
-# end
-
-@deprecate getParametricMeasurement(w...;kw...) getMeasurementParametric(w...;kw...)
-
-# function prtslperr(s)
-# println(s)
-# sleep(0.1)
-# error(s)
-# end
-
-@deprecate getKDE(w...;kw...) getBelief(w...;kw...)
-
-@deprecate findRelatedFromPotential(w...;kw...) (calcProposalBelief(w...;kw...),)
-
-# function generateNullhypoEntropy( val::AbstractMatrix{<:Real},
-# maxlen::Int,
-# spreadfactor::Real=10 )
-# #
-# # covD = sqrt.(vec(Statistics.var(val,dims=2))) .+ 1e-3
-# # cVar = diagm((spreadfactor*covD).^2)
-# len = size(val, 1)
-# cVar = diagm((spreadfactor*ones(len)).^2)
-# mu = zeros( len )
-# MvNormal( mu, cVar )
-# end
-
-# @deprecate calcFactorResidualTemporary( fct::AbstractRelative, measurement::Tuple, T_param_args...; kw...) calcFactorResidualTemporary(fct, measurement, tuple(((x->x[1]).(T_param_args))...), tuple(((x->x[2]).(T_param_args))...); kw...)
-
-# @deprecate _buildGraphByFactorAndTypes!(fct::AbstractFactor, TypeParams_vec...; kw...) _buildGraphByFactorAndTypes!(fct, (x->x[1]).(TypeParams_vec), (x->x[2]).(TypeParams_vec) ; kw...)
-
-# @deprecate _evalFactorTemporary!( fct::AbstractFactor, sfidx::Int, meas_single::Tuple, TypeParams_args...; kw...) _evalFactorTemporary!( fct,sfidx,meas_single, (x->x[1]).(TypeParams_args), (x->x[2]).(TypeParams_args); kw...)
-
-# MOVED TO AMP v0.4.6
-# # NOTE SWITCHED TO ON-MANIFOLD VERSION, but this used to give deviation
-# calcVariableCovarianceBasic(M::AbstractManifold, ptsArr::Vector{Vector{Float64}})
-# # cannot calculate the stdev from uninitialized state
-# # FIXME assume point type is only Vector{Float} at this time
-# @cast arr[i,j] := ptsArr[j][i]
-# msst = Statistics.std(arr, dims=2)
-# # FIXME use adaptive scale, see #802
-# msst_ = 0 < sum(1e-10 .< msst) ? maximum(msst) : 1.0
-# return msst_
-# end
-
-@deprecate testFactorResidualBinary(fct, T1::InstanceType{InferenceVariable}, T2::InstanceType{InferenceVariable}, param1, param2, meas) calcFactorResidualTemporary(fct, (T1,T2), meas, (param1,param2))
-
-# """
-# $SIGNATURES
-#
-# Return the directly achievable dimensionality of solve for each variable in a clique.
-#
-# Related
-#
-# getFactorSolvableDim
-# """
-# function getCliqVarPossibleDim(dfg::G,
-# cliq::TreeClique,
-# saturate::Bool=true,
-# fraction::Bool=true )::Dict{Symbol, Float64}
-# #
-# # variables and factors associated with this clique
-# vars = getCliqAllVarIds(cliq)
-# # fcts = getCliqAllFactIds(cliq)
-# # rows = length(fcts)
-# cols = length(vars)
-#
-# # for output result
-# dict = Dict{Symbol,Float64}()
-#
-# for j in 1:cols
-# dict[vars[j]] += getVariablePossibleDim(dfg, vars[j])
-# end
-#
-# end
-
-
-# getManifold(::InstanceType{LinearRelative{N}}) where {N} = Euclidean(N)
-# getManifolds(::T) where {T <: LinearRelative} = convert(Tuple, getManifold(T))
-# getManifolds(::Type{<:T}) where {T <: LinearRelative} = convert(Tuple, getManifold(T))
-# getManifolds(fctType::Type{LinearRelative}) = getManifolds(getDomain(fctType))
-
-# # FIXME, why is Manifolds depdendent on the solveKey?? Should just be at DFGVariable level?
-
-# getManifolds(vd::VariableNodeData) = getVariableType(vd) |> getManifolds
-# getManifolds(::DFGVariable{T}) where T <: InferenceVariable = getManifolds(T)
-# getManifolds(dfg::AbstractDFG, sym::Symbol) = getManifolds(getVariable(dfg, sym))
-#
-# getManifolds(vartype::InferenceVariable) = vartype.manifolds
-# getManifolds(vartype::Type{<: InferenceVariable}) = getManifolds(vartype())
-
-
-# import DistributedFactorGraphs: getfnctype
-# # TODO: Refactor - was is das?
-# function getfnctype(data::GenericFunctionNodeData)
-# if typeof(data).name.name == :VariableNodeData
-# return VariableNodeData
-# end
-# return data.fnc.usrfnc!
-# end
-#
-# function getfnctype(fact::DFGFactor; solveKey::Symbol=:default)
-# data = getData(fact) # TODO , solveKey=solveKey)
-# return getfnctype(data)
-# end
-#
-# function getfnctype(dfg::T, lbl::Symbol; solveKey::Symbol=:default) where T <: AbstractDFG
-# getfnctype(getFactor(dfg, exvertid))
-# end
-
-# function manikde!(ptsArr::Vector{Vector{Float64}}, manis::Tuple)
-# arr = Matrix{Float64}(undef, length(ptsArr[1]), length(ptsArr))
-# @cast arr[i,j] = ptsArr[j][i]
-# manikde!(arr, manis)
-# end
-
#
From b1d7d1dd7721c7875a8e55343c8b1761d1845824 Mon Sep 17 00:00:00 2001
From: dehann <dehann.fourie@gmail.com>
Date: Sat, 12 Feb 2022 00:30:30 -0500
Subject: [PATCH 2/3] compat and depr fixes
---
Project.toml | 2 +-
src/ExportAPI.jl | 1 +
src/IncrementalInference.jl | 6 +++---
test/runtests.jl | 31 ++++++++++++++++++-------------
test/testBasicForwardConvolve.jl | 4 ++--
test/testpartialconstraint.jl | 8 ++++----
6 files changed, 29 insertions(+), 23 deletions(-)
diff --git a/Project.toml b/Project.toml
index b2fc02105..b6f1ecc9c 100644
--- a/Project.toml
+++ b/Project.toml
@@ -42,7 +42,7 @@ TimeZones = "f269a46b-ccf7-5d73-abea-4c690281aa53"
UUIDs = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"
[compat]
-ApproxManifoldProducts = "0.4.23"
+ApproxManifoldProducts = "0.4.24"
BSON = "0.2, 0.3"
Combinatorics = "1.0"
DataStructures = "0.16, 0.17, 0.18"
diff --git a/src/ExportAPI.jl b/src/ExportAPI.jl
index 5208f13ae..7c3d3b1b5 100644
--- a/src/ExportAPI.jl
+++ b/src/ExportAPI.jl
@@ -209,6 +209,7 @@ export
# Bayes (Junction) Tree
evalFactor,
+ calcProposalBelief,
approxConvBelief,
approxConv,
diff --git a/src/IncrementalInference.jl b/src/IncrementalInference.jl
index ee10613f5..98f20cfad 100644
--- a/src/IncrementalInference.jl
+++ b/src/IncrementalInference.jl
@@ -90,9 +90,9 @@ const FSM = FunctionalStateMachine
const IIF = IncrementalInference
-const InstanceType{T} = Union{Type{<:T},T}
-const NothingUnion{T} = Union{Nothing, T}
-const BeliefArray{T} = Union{Array{T,2}, Adjoint{T, Array{T,2}} } # TBD deprecate?
+const InstanceType{T} = Union{Type{<:T},<:T}
+const NothingUnion{T} = Union{Nothing, <:T}
+const BeliefArray{T} = Union{<:AbstractMatrix{<:T}, <:Adjoint{<:T, AbstractMatrix{<:T}} } # TBD deprecate?
## =============================
# API Exports
diff --git a/test/runtests.jl b/test/runtests.jl
index 157cea005..645ef2faf 100644
--- a/test/runtests.jl
+++ b/test/runtests.jl
@@ -7,19 +7,19 @@ include("testSphereMani.jl")
include("testSpecialOrthogonalMani.jl")
include("testSpecialEuclidean2Mani.jl")
+# start as basic as possible and build from there
+include("typeReturnMemRef.jl")
include("testDistributionsGeneric.jl")
include("testHeatmapGridDensity.jl")
include("testCliqSolveDbgUtils.jl")
+include("basicGraphsOperations.jl")
+
include("TestModuleFunctions.jl")
include("testCompareVariablesFactors.jl")
-include("typeReturnMemRef.jl")
-include("testApproxConv.jl")
-include("basicGraphsOperations.jl")
-include("testMixturePrior.jl")
-include("testGradientUtils.jl")
-include("testPartialFactors.jl")
-include("testPartialPrior.jl")
+include("saveconvertertypes.jl")
+include("testgraphpackingconverters.jl")
include("testSaveLoadDFG.jl")
+
include("testJunctionTreeConstruction.jl")
include("testBayesTreeiSAM2Example.jl")
include("testTreeFunctions.jl")
@@ -27,18 +27,23 @@ include("testTreeFunctions.jl")
#FIXME fails on MetaBayesTree
include("testTreeSaveLoad.jl")
+include("testGradientUtils.jl")
include("testFactorGradients.jl")
-include("testpartialconstraint.jl")
-include("testPartialNH.jl")
-
include("testSpecialSampler.jl") # TODO, rename, refine
-include("saveconvertertypes.jl")
-include("testgraphpackingconverters.jl")
include("testNLsolve.jl")
include("testCommonConvWrapper.jl")
+include("testFactorMetadata.jl")
+
+include("testApproxConv.jl")
include("testBasicForwardConvolve.jl")
include("testDefaultDeconv.jl")
-include("testFactorMetadata.jl")
+
+include("testPartialFactors.jl")
+include("testPartialPrior.jl")
+include("testpartialconstraint.jl")
+include("testPartialNH.jl")
+include("testMixturePrior.jl")
+
include("testStateMachine.jl")
include("testBasicCSM.jl")
include("testCliqueFactors.jl")
diff --git a/test/testBasicForwardConvolve.jl b/test/testBasicForwardConvolve.jl
index 32171bfbc..e1c44f5a1 100644
--- a/test/testBasicForwardConvolve.jl
+++ b/test/testBasicForwardConvolve.jl
@@ -45,9 +45,9 @@ X1_ = forwardConvolve(X0, statemodel)
## measure -- product of beliefs, using `ApproxManifoldProducts.jl`
-predX1 = manikde!(X1_, ContinuousScalar)
+predX1 = manikde!(ContinuousScalar, X1_)
z3 = Normal(9.5,0.75)
-measX1 = manikde!([rand(z3,1) for _ in 1:100], ContinuousScalar)
+measX1 = manikde!(ContinuousScalar, [rand(z3,1) for _ in 1:100])
# do actual product
posterioriX1 = predX1 * measX1
diff --git a/test/testpartialconstraint.jl b/test/testpartialconstraint.jl
index b3dc40c65..8334936dc 100644
--- a/test/testpartialconstraint.jl
+++ b/test/testpartialconstraint.jl
@@ -201,8 +201,8 @@ end
# keep previous values to ensure funciton evaluation is modifying correct data fields
-@warn "restore findRelatedFromPotential as testset!"
-# @testset "test findRelatedFromPotential..." begin
+@warn "restore calcProposalBelief as testset!"
+# @testset "test calcProposalBelief..." begin
# global v2, fg, f3, f4, N
@@ -210,7 +210,7 @@ thefac = getFactor(fg, :x1x2f1)
X2lpts_ = getVal(getVariable(fg, :x2))
@cast X2lpts[i,j] := X2lpts_[j][i]
-keepaside, = findRelatedFromPotential(fg, thefac, :x2, N=N)
+keepaside, = (calcProposalBelief(fg, thefac, :x2, N=N),)
@test Ndim(keepaside) == 2
lpts_ = getPoints(keepaside, false)
@cast lpts[i,j] := lpts_[j][i]
@@ -232,7 +232,7 @@ memcheck_ = getVal(v2)
X2lpts_ = getVal(v2)
@cast X2lpts[i,j] := X2lpts_[j][i]
-p4, = findRelatedFromPotential(fg, f4, v2.label, N=N)
+p4 = calcProposalBelief(fg, f4, v2.label, N=N)
@test Ndim(p4) == 2
lpts_ = getPoints(keepaside, false)
@cast lpts[i,j] := lpts_[j][i]
From 732aee83348a804809a80dd12bb76d01aefa9afd Mon Sep 17 00:00:00 2001
From: dehann <dehann.fourie@gmail.com>
Date: Sat, 12 Feb 2022 01:48:46 -0500
Subject: [PATCH 3/3] fix depr'd calls
---
attic/examples/MultiHypo2Door.jl | 16 +++++++-------
attic/examples/MultiHypo3Door.jl | 18 ++++++++--------
attic/examples/squarefixedpoint.jl | 2 +-
src/Deprecated.jl | 2 ++
src/FactorGraph.jl | 6 +++---
src/TreeMessageUtils.jl | 15 +------------
test/priorusetest.jl | 16 +++++++-------
test/testCommonConvWrapper.jl | 4 ++--
test/testEuclidDistance.jl | 4 ++--
test/testMixtureLinearConditional.jl | 2 +-
test/testMultihypoAndChain.jl | 2 +-
test/testProductReproducable.jl | 32 ++++++++++++++--------------
12 files changed, 54 insertions(+), 65 deletions(-)
diff --git a/attic/examples/MultiHypo2Door.jl b/attic/examples/MultiHypo2Door.jl
index c6f06507f..d6f4861ec 100644
--- a/attic/examples/MultiHypo2Door.jl
+++ b/attic/examples/MultiHypo2Door.jl
@@ -124,25 +124,25 @@ solveTree!(fg, tree)
stuff = treeProductUp(fg, tree, :l0, :x0)
X0 = manikde!(stuff[1], (:Euclid,))
-plotKDE([X0; getKDE(fg, :x0)], c=["red";"green"])
+plotKDE([X0; getBelief(fg, :x0)], c=["red";"green"])
setValKDE!(fg, :x0, X0)
stuff = treeProductUp(fg, tree, :l0, :x1)
X1 = manikde!(stuff[1], (:Euclid,))
-plotKDE([X1; getKDE(fg, :x1)], c=["red";"green"])
+plotKDE([X1; getBelief(fg, :x1)], c=["red";"green"])
setValKDE!(fg, :x1, X1)
stuff = treeProductUp(fg, tree, :l0, :l0)
L0 = manikde!(stuff[1], (:Euclid,))
-plotKDE([L0; getKDE(fg, :l0)], c=["red";"green"])
+plotKDE([L0; getBelief(fg, :l0)], c=["red";"green"])
setValKDE!(fg, :l0, L0)
stuff = treeProductUp(fg, tree, :l0, :l1)
L1 = manikde!(stuff[1], (:Euclid,))
-plotKDE([L1; getKDE(fg, :l1)], c=["red";"green"])
+plotKDE([L1; getBelief(fg, :l1)], c=["red";"green"])
setValKDE!(fg, :l1, L1)
@@ -168,14 +168,14 @@ setValKDE!(fg, :l1, L1)
# :x0l0l1f1, :x0x1f1
ptsX0, = predictbelief(fg, :x0, [:x0l0l1f1; :x0x1f1])
X0 = manikde!(ptsX0, (:Euclid,))
-plotKDE([X0; getKDE(fg, :x0)], c=["red";"green"])
+plotKDE([X0; getBelief(fg, :x0)], c=["red";"green"])
setValKDE!(fg, :x0, X0)
# get factors for :x1 in clique2:
# :x1l0l1f1, :x0x1f1
ptsX1, = predictbelief(fg, :x1, [:x1l0l1f1, :x0x1f1])
X1 = manikde!(ptsX1, (:Euclid,))
-plotKDE([X1; getKDE(fg, :x1)], c=["red";"green"])
+plotKDE([X1; getBelief(fg, :x1)], c=["red";"green"])
setValKDE!(fg, :x1, X1)
@@ -183,7 +183,7 @@ setValKDE!(fg, :x1, X1)
# :x0l0l1f1, :x1l0l1f1, :l0f1
ptsL0, = predictbelief(fg, :l0, [:x0l0l1f1, :x1l0l1f1, :l0f1])
L0 = manikde!(ptsL0, (:Euclid,))
-plotKDE([L0; getKDE(fg, :l0)], c=["red";"green"])
+plotKDE([L0; getBelief(fg, :l0)], c=["red";"green"])
setValKDE!(fg, :l0, L0)
@@ -191,7 +191,7 @@ setValKDE!(fg, :l0, L0)
# :x0l0l1f1, :x1l0l1f1, :l1f1
ptsL1, = predictbelief(fg, :l1, [:x0l0l1f1, :x1l0l1f1, :l1f1])
L1 = manikde!(ptsL1, (:Euclid,))
-plotKDE([L1; getKDE(fg, :l1)], c=["red";"green"])
+plotKDE([L1; getBelief(fg, :l1)], c=["red";"green"])
setValKDE!(fg, :l1, L1)
diff --git a/attic/examples/MultiHypo3Door.jl b/attic/examples/MultiHypo3Door.jl
index a0edd0684..37081f477 100644
--- a/attic/examples/MultiHypo3Door.jl
+++ b/attic/examples/MultiHypo3Door.jl
@@ -127,7 +127,7 @@ solveTree!(fg, tree)
plotLocalProduct(fg, :x1, sidelength=20cm)
ptsX1, = predictbelief(fg, :x1, [:x1l0l1l2f1, :x0x1f1])
X1 = manikde!(ptsX1, (:Euclid,))
-plotKDE([X1; getKDE(fg, :x1)], c=["red";"green"])
+plotKDE([X1; getBelief(fg, :x1)], c=["red";"green"])
setValKDE!(fg, :x1, X1)
@@ -136,7 +136,7 @@ setValKDE!(fg, :x1, X1)
plotLocalProduct(fg, :x0, sidelength=20cm)
ptsX0, = predictbelief(fg, :x0, [:x0l0l1l2f1; :x0x1f1])
X0 = manikde!(ptsX0, (:Euclid,))
-plotKDE([X0; getKDE(fg, :x0)], c=["red";"green"])
+plotKDE([X0; getBelief(fg, :x0)], c=["red";"green"])
setValKDE!(fg, :x0, X0)
@@ -145,7 +145,7 @@ setValKDE!(fg, :x0, X0)
plotLocalProduct(fg, :l2, sidelength=20cm)
ptsL2, = predictbelief(fg, :l2, [:x0l0l1l2f1, :x1l0l1l2f1, :l2f1])
L2 = manikde!(ptsL2, (:Euclid,))
-plotKDE([L2; getKDE(fg, :l2)], c=["red";"green"])
+plotKDE([L2; getBelief(fg, :l2)], c=["red";"green"])
setValKDE!(fg, :l2, L2)
@@ -154,7 +154,7 @@ setValKDE!(fg, :l2, L2)
plotLocalProduct(fg, :l1, sidelength=20cm)
ptsL1, = predictbelief(fg, :l1, [:x0l0l1l2f1, :x1l0l1l2f1, :l1f1])
L1 = manikde!(ptsL1, (:Euclid,))
-plotKDE([L1; getKDE(fg, :l1)], c=["red";"green"])
+plotKDE([L1; getBelief(fg, :l1)], c=["red";"green"])
setValKDE!(fg, :l1, L1)
@@ -163,7 +163,7 @@ setValKDE!(fg, :l1, L1)
plotLocalProduct(fg, :l0, sidelength=20cm)
ptsL0, = predictbelief(fg, :l0, [:x0l0l1l2f1, :x1l0l1l2f1, :l0f1])
L0 = manikde!(ptsL0, (:Euclid,))
-plotKDE([L0; getKDE(fg, :l0)], c=["red";"green"])
+plotKDE([L0; getBelief(fg, :l0)], c=["red";"green"])
setValKDE!(fg, :l0, L0)
@@ -173,15 +173,15 @@ setValKDE!(fg, :l0, L0)
ptsX2, = predictbelief(fg, :x2, [:x1x2f1;])
X2 = manikde!(ptsX2, (:Euclid,))
-plotKDE([X2; getKDE(fg, :x2)], c=["red";"green"])
+plotKDE([X2; getBelief(fg, :x2)], c=["red";"green"])
setValKDE!(fg, :x2, X2)
-upmsgX1 = deepcopy(getKDE(fg, :x1))
+upmsgX1 = deepcopy(getBelief(fg, :x1))
ptsX1 = approxConv(fg, :x1x2f1, :x1)
pX1 = manikde!(ptsX1, (:Euclid,))
X1 = manifoldProduct([upmsgX1; pX1], (:Euclid,))
-plotKDE([X1; getKDE(fg, :x1)], c=["red";"green"])
+plotKDE([X1; getBelief(fg, :x1)], c=["red";"green"])
setValKDE!(fg, :x1, X1)
@@ -194,7 +194,7 @@ setValKDE!(fg, :x1, X1)
# :x0l0l1l2f1, :x1l0l1l2f1, :l1f1
ptsL1, = predictbelief(fg, :l1, [:x0l0l1l2f1, :x1l0l1l2f1, :l1f1])
L1 = manikde!(ptsL1, (:Euclid,))
-plotKDE([L1; getKDE(fg, :l1)], c=["red";"green"])
+plotKDE([L1; getBelief(fg, :l1)], c=["red";"green"])
setValKDE!(fg, :l1, L1)
diff --git a/attic/examples/squarefixedpoint.jl b/attic/examples/squarefixedpoint.jl
index 56381722a..5e1dae232 100644
--- a/attic/examples/squarefixedpoint.jl
+++ b/attic/examples/squarefixedpoint.jl
@@ -41,4 +41,4 @@ initManual!(fg, :x, randn(1,100))
tree = solveTree!(fg)
## plot the result
-plotKDE(map(x->getKDE(fg,x), [:x; :xy]))
+plotKDE(map(x->getBelief(fg,x), [:x; :xy]))
diff --git a/src/Deprecated.jl b/src/Deprecated.jl
index bd76586e8..466c5d4c9 100644
--- a/src/Deprecated.jl
+++ b/src/Deprecated.jl
@@ -32,6 +32,8 @@ end
## Deprecate code below before v0.29
##==============================================================================
+@deprecate kde!(em::TreeBelief) manikde!(em)
+
# DFG v0.18/19
export FunctorInferenceType, PackedInferenceType
diff --git a/src/FactorGraph.jl b/src/FactorGraph.jl
index 155f8f1f0..5aed5cb6f 100644
--- a/src/FactorGraph.jl
+++ b/src/FactorGraph.jl
@@ -316,7 +316,7 @@ function DefaultNodeDataParametric( dodims::Int,
# this should be the only function allocating memory for the node points
if false && initialized
error("not implemented yet")
- # pN = AMP.manikde!(randn(dims, N), variableType.manifolds);
+ # pN = AMP.manikde!(variableType.manifold, randn(dims, N));
#
# sp = Int[0;] #round.(Int,range(dodims,stop=dodims+dims-1,length=dims))
# gbw = getBW(pN)[:,1]
@@ -392,12 +392,12 @@ function setDefaultNodeData!( v::DFGVariable,
end
# if size(initval,2) < N && size(initval, 1) == dims
# @warn "setDefaultNodeData! -- deprecated use of stdev."
-# p = AMP.manikde!(initval,diag(stdev), varType.manifolds);
+# p = manikde!(varType.manifold, initval,diag(stdev));
# pN = resample(p,N)
# if size(initval,2) < N && size(initval, 1) != dims
# @info "Node value memory allocated but not initialized"
# else
-# pN = AMP.manikde!(initval, varType.manifolds)
+# pN = manikde!(varType.manifold, initval)
# end
# dims = size(initval,1) # rows indicate dimensions
diff --git a/src/TreeMessageUtils.jl b/src/TreeMessageUtils.jl
index 40d7d3e74..c150e3ab9 100644
--- a/src/TreeMessageUtils.jl
+++ b/src/TreeMessageUtils.jl
@@ -13,19 +13,6 @@ export addLikelihoodsDifferentialCHILD!
convert(::Type{<:ManifoldKernelDensity}, src::TreeBelief) = manikde!(getManifold(src.variableType), src.val, bw=src.bw[:,1])
-"""
- $(SIGNATURES)
-
-Construct a BallTreeDensity KDE object from an IIF.TreeBelief object.
-
-Related
-
-manikde!, getKDE, getKDEMax, getKDEMean, TreeBelief
-"""
-function kde!(em::TreeBelief)
- # return AMP.manikde!(em.val, em.bw, em.manifolds)
- return convert(ManifoldKernelDensity, em)
-end
manikde!(em::TreeBelief) = convert(ManifoldKernelDensity, em)
@@ -258,7 +245,7 @@ function addLikelihoodsDifferential!( msgs::LikelihoodMessage,
# afc = addFactor!(tfg, [sym1_;sym2_], nfct, graphinit=false, tags=[:DUMMY;])
# # calculate the general deconvolution between variables
# pts = solveFactorMeasurements(tfg, afc.label)
- # newBel = manikde!(pts[1], getManifolds(nfactype))
+ # newBel = manikde!(getManifold(nfactype), pts[1])
# # replace dummy factor with real deconv factor using manikde approx belief measurement
# fullFct = nfactype(newBel)
# deleteFactor!(tfg, afc.label)
diff --git a/test/priorusetest.jl b/test/priorusetest.jl
index 0f943ac82..eb2940ce9 100644
--- a/test/priorusetest.jl
+++ b/test/priorusetest.jl
@@ -36,9 +36,9 @@ addFactor!(fg, [:x1; :x2], LinearRelative(Normal(0.0, 0.01)))
#solve
tree = solveTree!(fg)
-x0_m = getKDEMean(getKDE(getVariable(fg, :x0)))[1]
-x1_m = getKDEMean(getKDE(getVariable(fg, :x1)))[1]
-x2_m = getKDEMean(getKDE(getVariable(fg, :x2)))[1]
+x0_m = getKDEMean(getBelief(getVariable(fg, :x0)))[1]
+x1_m = getKDEMean(getBelief(getVariable(fg, :x1)))[1]
+x2_m = getKDEMean(getBelief(getVariable(fg, :x2)))[1]
@info ("Testing means = 0 with 2 priors:\ngraphinit=$graphinit\nMeans: x0: $(x0_m), x1: $x1_m, x2: $x2_m")
@@ -90,11 +90,11 @@ addFactor!(fg, [:x2; :l1], LinearRelative(Normal(0, 0.01)))
#solve
tree = solveTree!(fg)
-x0_m = getKDEMean(getKDE(getVariable(fg, :x0)))[1]
-x1_m = getKDEMean(getKDE(getVariable(fg, :x1)))[1]
-x2_m = getKDEMean(getKDE(getVariable(fg, :x2)))[1]
-l0_m = getKDEMean(getKDE(getVariable(fg, :l0)))[1]
-l1_m = getKDEMean(getKDE(getVariable(fg, :l1)))[1]
+x0_m = getKDEMean(getBelief(getVariable(fg, :x0)))[1]
+x1_m = getKDEMean(getBelief(getVariable(fg, :x1)))[1]
+x2_m = getKDEMean(getBelief(getVariable(fg, :x2)))[1]
+l0_m = getKDEMean(getBelief(getVariable(fg, :l0)))[1]
+l1_m = getKDEMean(getBelief(getVariable(fg, :l1)))[1]
@info ("Testing means = 0 with 2 priors:\ngraphinit=$graphinit\nMeans: x0: $(x0_m), x1: $x1_m, x2: $x2_m, l0: $l0_m, l1: $l1_m")
diff --git a/test/testCommonConvWrapper.jl b/test/testCommonConvWrapper.jl
index 0746814c8..cdbb04808 100644
--- a/test/testCommonConvWrapper.jl
+++ b/test/testCommonConvWrapper.jl
@@ -154,7 +154,7 @@ end
N=100
p1 = [randn(1) for _ in 1:N]
-d1 = manikde!(p1, Euclidean(1))
+d1 = manikde!(Euclidean(1), p1)
p2 = [randn(1) for _ in 1:N]
t = Vector{Vector{Vector{Float64}}}()
push!(t,p1)
@@ -165,7 +165,7 @@ fg = initfg()
v1=addVariable!(fg, :x1, ContinuousScalar, N=N)
v2=addVariable!(fg, :x2, ContinuousScalar, N=N)
bws = getBW(d1)[:,1]
-f1 = addFactor!(fg, [v1], Prior(manikde!(p1, bws, TranslationGroup(1))) )
+f1 = addFactor!(fg, [v1], Prior(manikde!(TranslationGroup(1), p1, bw=bws)) )
odo = Pose1Pose1Test(Normal(100.0,1.0))
f2 = addFactor!(fg, [v1;v2], odo)
diff --git a/test/testEuclidDistance.jl b/test/testEuclidDistance.jl
index ec6c1d983..d1be82a28 100644
--- a/test/testEuclidDistance.jl
+++ b/test/testEuclidDistance.jl
@@ -250,7 +250,7 @@ end
##
# pts = approxConv(fg, :x2l1f1, :l1)
-# plotKDE(manikde!(pts, ContinuousEuclid{2}))
+# plotKDE(manikde!(ContinuousEuclid{2}, pts))
# plotLocalProduct(fg, :l1, levels=3)
@@ -277,4 +277,4 @@ end
# initManual!(sfg, :l1, pts)
# pts = approxConv(sfg, :x2l1f1, :l1)
-# plotKDE(manikde!(pts, ContinuousEuclid{2}))
\ No newline at end of file
+# plotKDE(manikde!(ContinuousEuclid{2}, pts))
\ No newline at end of file
diff --git a/test/testMixtureLinearConditional.jl b/test/testMixtureLinearConditional.jl
index 92d30e9a0..fc50a3326 100644
--- a/test/testMixtureLinearConditional.jl
+++ b/test/testMixtureLinearConditional.jl
@@ -86,7 +86,7 @@ addVariable!(fg, :x1, ContinuousScalar)
mp = Mixture(Prior, [Normal(); Normal(10,1)], [0.5;0.5])
f0 = addFactor!(fg, [:x0;], mp)
-mr = Mixture(LinearRelative, (fancy=manikde!([randn(1) for _ in 1:75], ContinuousEuclid(1)), naive=Normal(0,10)), [0.4;0.6])
+mr = Mixture(LinearRelative, (fancy=manikde!(ContinuousEuclid(1), [randn(1) for _ in 1:75]), naive=Normal(0,10)), [0.4;0.6])
f1 = addFactor!(fg, [:x0;:x1], mr)
##
diff --git a/test/testMultihypoAndChain.jl b/test/testMultihypoAndChain.jl
index aaf4cc942..2bdf43a50 100644
--- a/test/testMultihypoAndChain.jl
+++ b/test/testMultihypoAndChain.jl
@@ -78,7 +78,7 @@ tree = solveTree!(fg, eliminationOrder=eo) #, smtasks=smtasks, recordcliqs=ls(fg
L2 = getBelief(fg, :l2)
npts = length(getPoints(L2))
pts = [2.0.+0.1*randn(1) for _ in 1:npts]
-L2_ = manikde!(pts, ContinuousScalar)
+L2_ = manikde!(ContinuousScalar, pts)
# test that there is at least a mode present
@test mmd(L2_, L2, ContinuousScalar) < 1e-3
diff --git a/test/testProductReproducable.jl b/test/testProductReproducable.jl
index add834426..f7139c61b 100644
--- a/test/testProductReproducable.jl
+++ b/test/testProductReproducable.jl
@@ -28,17 +28,17 @@ initAll!(fg)
tree = solveTree!(fg)
-@test (Statistics.mean(getPoints(getKDE(fg, :a)))- 0 |> abs) < 3
-@test (Statistics.mean(getPoints(getKDE(fg, :b)))-10 |> abs) < 4
-@test (Statistics.mean(getPoints(getKDE(fg, :c)))-20 |> abs) < 4
-@test (Statistics.mean(getPoints(getKDE(fg, :d)))-30 |> abs) < 5
-@test (Statistics.mean(getPoints(getKDE(fg, :e)))-40 |> abs) < 5
+@test (Statistics.mean(getPoints(getBelief(fg, :a)))- 0 |> abs) < 3
+@test (Statistics.mean(getPoints(getBelief(fg, :b)))-10 |> abs) < 4
+@test (Statistics.mean(getPoints(getBelief(fg, :c)))-20 |> abs) < 4
+@test (Statistics.mean(getPoints(getBelief(fg, :d)))-30 |> abs) < 5
+@test (Statistics.mean(getPoints(getBelief(fg, :e)))-40 |> abs) < 5
-@test 0.3 < Statistics.std(getPoints(getKDE(fg, :a))) < 2
-@test 0.5 < Statistics.std(getPoints(getKDE(fg, :b))) < 4
-@test 0.9 < Statistics.std(getPoints(getKDE(fg, :c))) < 6
-@test 1.2 < Statistics.std(getPoints(getKDE(fg, :d))) < 7
-@test 1.5 < Statistics.std(getPoints(getKDE(fg, :e))) < 8
+@test 0.3 < Statistics.std(getPoints(getBelief(fg, :a))) < 2
+@test 0.5 < Statistics.std(getPoints(getBelief(fg, :b))) < 4
+@test 0.9 < Statistics.std(getPoints(getBelief(fg, :c))) < 6
+@test 1.2 < Statistics.std(getPoints(getBelief(fg, :d))) < 7
+@test 1.5 < Statistics.std(getPoints(getBelief(fg, :e))) < 8
# drawTree(tree, show=true)
@@ -61,25 +61,25 @@ addFactor!(fg, [:a;:b], LinearRelative(Normal(10, 1)), graphinit=false)
initManual!(fg, :a, randn(1,100))
initManual!(fg, :b, 10 .+randn(1,100))
-A = getKDE(fg, :a)
-B = getKDE(fg, :b)
+A = getBelief(fg, :a)
+B = getBelief(fg, :b)
# plotKDE(fg, [:a; :b])
# repeat many times to ensure the means stay put and covariances spread out
for i in 1:10
pts = approxConv(fg, :abf1, :b)
- B_ = manikde!(pts,ContinuousScalar)
+ B_ = manikde!(ContinuousScalar, pts)
# plotKDE([B_; B])
initManual!(fg, :b, B_)
pts = approxConv(fg, :abf1, :a)
- A_ = manikde!(pts, ContinuousScalar)
+ A_ = manikde!(ContinuousScalar, pts)
# plotKDE([A_; A])
initManual!(fg, :a, A_)
end
-A_ = getKDE(fg, :a)
-B_ = getKDE(fg, :b)
+A_ = getBelief(fg, :a)
+B_ = getBelief(fg, :b)
# plotKDE([A_; B_; A; B])
@test (Statistics.mean(getPoints(A)) |> abs) < 1