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mv approxConvCircular, depr printSummaryGraph
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-87
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4 files changed

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‎src/AdditionalUtils.jl‎

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"""
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$SIGNATURES
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Print basic summary of graph to `logger=ConsoleLogger()`.
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"""
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function printGraphSummary(dfg::G, logger=ConsoleLogger())::Nothing where {G <: AbstractDFG}
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vars = ls(dfg)
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fcts = lsf(dfg)
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prio = lsfPriors(dfg)
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isinit = map(x->isInitialized(dfg,x), vars)
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infdim = map(x->getVariableInferredDim(dfg, x), vars)
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numedges = map(v->length(ls(dfg, v)), vars)
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numfed = map(fc->length(ls(dfg, fc)), fcts)
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vardims = map(v->getDimension(getVariable(dfg, v)), vars)
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fctdims = map(v->getDimension(getFactor(dfg, v)), fcts)
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priodims = map(v->getDimension(getFactor(dfg, v)), prio)
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with_logger(logger) do
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@info "Distributed Factor Graph summary:"
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@info " num variables: $(length(vars))"
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@info " num factors: $(length(fcts)), w/ $(length(prio)) priors"
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@info " var initialized: $(sum(isinit))"
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@info ""
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@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))"
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@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))"
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@info " Variable dims: min. $(minimum(vardims)) | mean $(round(Statistics.mean(vardims),digits=2)) | 90% $(round(quantile(vardims,0.9),digits=2)) | max. $(maximum(vardims))"
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@info " Factor dims: min. $(minimum(fctdims)) | mean $(round(Statistics.mean(fctdims),digits=2)) | 90% $(round(quantile(fctdims,0.9),digits=2)) | max. $(maximum(fctdims))"
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@info " Prior dimens: min. $(minimum(priodims)) | mean $(round(Statistics.mean(priodims),digits=2)) | 90% $(round(quantile(priodims,0.9),digits=2)) | max. $(maximum(priodims))"
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@info " var infr'dims: min. $(minimum(infdim)) | mean $(round(Statistics.mean(infdim),digits=2)) | 90% $(round(quantile(infdim,0.9),digits=2)) | max. $(maximum(infdim))"
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end
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nothing
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end
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"""
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$SIGNATURES
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Print basic summary of graph to `logger=ConsoleLogger()`.
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"""
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function printSummary(dfg::G, logger=ConsoleLogger()) where G <: AbstractDFG
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printGraphSummary(dfg, logger)
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end
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"""
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$SIGNATURES
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Build an approximate density `[Y|X,DX,.]=[X|Y,DX][DX|.]` as proposed by the conditional convolution.
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Notes
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- Assume both are on circular manifold, `manikde!(pts, (:Circular,))`
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"""
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function approxConvCircular(pX::ManifoldKernelDensity,
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pDX::ManifoldKernelDensity; N::Int=100)
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#
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# building basic factor graph
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tfg = initfg()
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addVariable!(tfg, :s1, Sphere1)
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addVariable!(tfg, :s2, Sphere1)
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addFactor!(tfg, [:s1;:s2], Sphere1Sphere1(pDX), graphinit=false)
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initManual!(tfg,:s1, pX)
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# solve for outgoing proposal value
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approxConv(tfg,:s1s2f1,:s2)
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end
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function approxConvCircular(pX::ManifoldKernelDensity,
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pDX::SamplableBelief; N::Int=100)
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#
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pts = reshape(rand(pDX, N), 1, :)
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pC = manikde!(pts, Sphere1)
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approxConvCircular(pX, pC)
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end
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function approxConvCircular(pX::SamplableBelief,
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pDX::ManifoldKernelDensity; N::Int=100)
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#
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pts = reshape(rand(pX, N), 1, :)
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pC = manikde!(pts, Sphere1)
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approxConvCircular(pC, pDX)
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end
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#

‎src/Deprecated.jl‎

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##==============================================================================
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@deprecate printGraphSummary(dfg::AbstractDFG, logger=ConsoleLogger()) show(logger.stream, MIME("text/plain"), dfg)
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@deprecate printSummary(dfg::AbstractDFG, logger=ConsoleLogger()) show(logger.stream, MIME("text/plain"), dfg)
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# """
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# $SIGNATURES
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# Print basic summary of graph to `logger=ConsoleLogger()`.
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# """
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# function printGraphSummary(dfg::G, logger=ConsoleLogger())::Nothing where {G <: AbstractDFG}
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# vars = ls(dfg)
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# fcts = lsf(dfg)
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# prio = lsfPriors(dfg)
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# isinit = map(x->isInitialized(dfg,x), vars)
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# infdim = map(x->getVariableInferredDim(dfg, x), vars)
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# numedges = map(v->length(ls(dfg, v)), vars)
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# numfed = map(fc->length(ls(dfg, fc)), fcts)
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# vardims = map(v->getDimension(getVariable(dfg, v)), vars)
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# fctdims = map(v->getDimension(getFactor(dfg, v)), fcts)
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# priodims = map(v->getDimension(getFactor(dfg, v)), prio)
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# with_logger(logger) do
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# @info "Distributed Factor Graph summary:"
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# @info " num variables: $(length(vars))"
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# @info " num factors: $(length(fcts)), w/ $(length(prio)) priors"
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# @info " var initialized: $(sum(isinit))"
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# @info ""
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# @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))"
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# @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))"
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# @info " Variable dims: min. $(minimum(vardims)) | mean $(round(Statistics.mean(vardims),digits=2)) | 90% $(round(quantile(vardims,0.9),digits=2)) | max. $(maximum(vardims))"
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# @info " Factor dims: min. $(minimum(fctdims)) | mean $(round(Statistics.mean(fctdims),digits=2)) | 90% $(round(quantile(fctdims,0.9),digits=2)) | max. $(maximum(fctdims))"
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# @info " Prior dimens: min. $(minimum(priodims)) | mean $(round(Statistics.mean(priodims),digits=2)) | 90% $(round(quantile(priodims,0.9),digits=2)) | max. $(maximum(priodims))"
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# @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))"
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# end
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# nothing
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# end
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# """
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# $SIGNATURES
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# Print basic summary of graph to `logger=ConsoleLogger()`.
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# """
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# function printSummary(dfg::G, logger=ConsoleLogger()) where G <: AbstractDFG
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# printGraphSummary(dfg, logger)
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# end
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# """
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# $SIGNATURES
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‎src/IncrementalInference.jl‎

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# state machine methods
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StateMachine,
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exitStateMachine,
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printGraphSummary,
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printSummary,
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print,
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getGraphFromHistory,

‎src/services/ApproxConv.jl‎

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## ==========================================================================================
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## DEPRECATE / REFACTOR BELOW
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## ==========================================================================================
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"""
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$SIGNATURES
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Build an approximate density `[Y|X,DX,.]=[X|Y,DX][DX|.]` as proposed by the conditional convolution.
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Notes
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- Assume both are on circular manifold, `manikde!(pts, (:Circular,))`
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"""
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function approxConvCircular(pX::ManifoldKernelDensity,
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pDX::ManifoldKernelDensity; N::Int=100)
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#
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# building basic factor graph
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tfg = initfg()
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addVariable!(tfg, :s1, Sphere1)
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addVariable!(tfg, :s2, Sphere1)
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addFactor!(tfg, [:s1;:s2], Sphere1Sphere1(pDX), graphinit=false)
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initManual!(tfg,:s1, pX)
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# solve for outgoing proposal value
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approxConv(tfg,:s1s2f1,:s2)
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end
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function approxConvCircular(pX::ManifoldKernelDensity,
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pDX::SamplableBelief; N::Int=100)
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#
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pts = reshape(rand(pDX, N), 1, :)
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pC = manikde!(pts, Sphere1)
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approxConvCircular(pX, pC)
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end
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function approxConvCircular(pX::SamplableBelief,
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pDX::ManifoldKernelDensity; N::Int=100)
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#
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pts = reshape(rand(pX, N), 1, :)
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pC = manikde!(pts, Sphere1)
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approxConvCircular(pC, pDX)
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end
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#

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