Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

drop destVarVal args #1719

Merged
merged 1 commit into from
May 27, 2023
Merged

drop destVarVal args #1719

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
32 changes: 20 additions & 12 deletions src/services/CalcFactor.jl
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -504,7 +504,7 @@ function _beforeSolveCCW!(
F_::Type{<:AbstractRelative},
ccwl::CommonConvWrapper{F},
variables::AbstractVector{<:DFGVariable},
destVarVals::AbstractVector,
# destVarVals::AbstractVector,
sfidx::Int,
N::Integer;
measurement = Vector{Tuple{}}(),
Expand All @@ -520,20 +520,28 @@ function _beforeSolveCCW!(

# in forward solve case, important to set which variable is being solved early in this sequence
ccwl.varidx[] = sfidx

# TBD, order of fmd ccwl cf are a little weird and should be revised.
# TODO, maxlen should parrot N (barring multi-/nullhypo issues)
# set the 'solvefor' variable index -- i.e. which connected variable of the factor is being computed in this convolution.
# ccwl.varidx[] = findfirst(==(solvefor), getLabel.(variables))
# everybody use maxlen number of points in belief function estimation
maxlen = maximum((N, length.(ccwl.varValsAll[])...,))

# not type-stable
varvals = [getVal(s; solveKey) for s in variables]

# splice
varvals[sfidx] = destVarVals
# should be type stable
tvarv = tuple(
map(s->getVal(s; solveKey), variables[1:sfidx-1])...,
deepcopy(ccwl.varValsAll[][sfidx]), # destVarVals = deepcopy(ccwl.varValsAll[][sfidx])
map(s->getVal(s; solveKey), variables[sfidx+1:end])...,
)
# not type-stable
# varvals = [getVal(s; solveKey) for s in variables]
# varvals[sfidx] = destVarVals
# varvals_ = [varvals[1:sfidx-1]..., destVarVals, varvals[sfidx+1:end]...]
tvarv = tuple(varvals...)
# tvarv = tuple(varvals...)


# @info "TYPES" typeof(ccwl.varValsAll[]) typeof(tvarv)
ccwl.varValsAll[] = tvarv
# ccwl.varValsAll[] = map(s->getVal(s; solveKey), tuple(variables...))
Expand Down Expand Up @@ -576,7 +584,7 @@ function _beforeSolveCCW!(
F_::Type{<:AbstractPrior},
ccwl::CommonConvWrapper{F},
variables::AbstractVector{<:DFGVariable},
destVarVals::AbstractVector,
# destVarVals::AbstractVector,
sfidx::Int,
N::Integer;
measurement = Vector{Tuple{}}(),
Expand Down Expand Up @@ -611,25 +619,25 @@ end
function _beforeSolveCCW!(
ccwl::Union{CommonConvWrapper{F}, CommonConvWrapper{Mixture{N_, F, S, T}}},
Xi::AbstractVector{<:DFGVariable},
destVarVals::AbstractVector,
# destVarVals::AbstractVector,
sfidx::Int,
N::Integer;
kw...,
) where {N_, F <: AbstractRelative, S, T}
#
return _beforeSolveCCW!(F, ccwl, Xi, destVarVals, sfidx, N; kw...)
return _beforeSolveCCW!(F, ccwl, Xi, sfidx, N; kw...)
end

function _beforeSolveCCW!(
ccwl::Union{CommonConvWrapper{F}, CommonConvWrapper{Mixture{N_, F, S, T}}},
Xi::AbstractVector{<:DFGVariable},
destVarVals::AbstractVector,
# destVarVals::AbstractVector,
sfidx::Int,
N::Integer;
kw...,
) where {N_, F <: AbstractPrior, S, T}
#
return _beforeSolveCCW!(F, ccwl, Xi, destVarVals, sfidx, N; kw...)
return _beforeSolveCCW!(F, ccwl, Xi, sfidx, N; kw...)
end


Expand Down
38 changes: 19 additions & 19 deletions src/services/EvalFactor.jl
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -42,12 +42,13 @@ function calcVariableDistanceExpectedFractional(
sfidx::Integer,
certainidx::AbstractVector{<:Integer};
kappa::Real = 3.0,
readonlyVarVals = ccwl.varValsAll[][sfidx]
# readonlyVarVals = ccwl.varValsAll[][sfidx]
)
#
@assert sfidx == ccwl.varidx[] "ccwl.varidx[] is expected to be the same as sfidx"
varTypes = getVariableType.(ccwl.fullvariables)
if sfidx in certainidx
msst_ = calcStdBasicSpread(varTypes[sfidx], readonlyVarVals)
msst_ = calcStdBasicSpread(varTypes[sfidx], ccwl.varValsAll[][sfidx])
return kappa * msst_
end
# @assert !(sfidx in certainidx) "null hypo distance does not work for sfidx in certainidx"
Expand All @@ -68,7 +69,7 @@ function calcVariableDistanceExpectedFractional(

refMean = getCoordinates(
varTypes[sfidx],
mean(getManifold(varTypes[sfidx]), readonlyVarVals),
mean(getManifold(varTypes[sfidx]), ccwl.varValsAll[][sfidx]),
)

# calc for uncertain and certain
Expand Down Expand Up @@ -107,7 +108,7 @@ function addEntropyOnManifold!(
#allocate to change SMatrix to MMatrix
X = allocate(get_vector(M, points[1], Xc, DefaultOrthogonalBasis()))

for idx = 1:length(points)
for idx in 1:length(points)
# build tangent coordinate random
for dim in dimIdx
if (p === :) || dim in p
Expand Down Expand Up @@ -145,7 +146,7 @@ function computeAcrossHypothesis!(
sfidx::Int,
maxlen::Int,
mani::ManifoldsBase.AbstractManifold; # maniAddOps::Tuple;
destinationVarVals = deepcopy(ccwl.varValsAll[][sfidx]),
# destinationVarVals = ccwl.varValsAll[][sfidx], # deepcopy
spreadNH::Real = 5.0,
inflateCycles::Int = 3,
skipSolve::Bool = false,
Expand All @@ -159,6 +160,7 @@ function computeAcrossHypothesis!(
activehypo = hyporecipe.activehypo
certainidx = hyporecipe.certainidx

@assert ccwl.varidx[] == sfidx "duplicate registers for solve for index should be the same in ccw.varidx"
@assert ccwl.hyporecipe.certainhypo == hyporecipe.certainidx "expected hyporecipe.certainidx to be the same as cached in ccw"
for (hypoidx, vars) in activehypo
count += 1
Expand All @@ -171,7 +173,8 @@ function computeAcrossHypothesis!(
ccwl.hyporecipe.activehypo[:] = vars
# end

addEntr = view(destinationVarVals, allelements[count])
# ccwl.varValsAll[][ccwl.varidx[]] should be an alternate/duplicate memory from getVal(variable; solveKey)
addEntr = view(ccwl.varValsAll[][ccwl.varidx[]], allelements[count]) # destinationVarVals

# do proposal inflation step, see #1051
# consider duplicate convolution approximations for inflation off-zero
Expand All @@ -183,7 +186,7 @@ function computeAcrossHypothesis!(
sfidx,
certainidx;
kappa = ccwl.inflation,
readonlyVarVals = destinationVarVals,
# readonlyVarVals = ccwl.varValsAll[][ccwl.varidx[]],
)
addEntropyOnManifold!(
mani,
Expand All @@ -197,7 +200,7 @@ function computeAcrossHypothesis!(
if skipSolve
@warn("skipping numerical solve operation")
else
approxConvOnElements!(destinationVarVals, ccwl, allelements[count], _slack)
approxConvOnElements!(ccwl.varValsAll[][ccwl.varidx[]], ccwl, allelements[count], _slack)
end
end
elseif hypoidx != sfidx && hypoidx != 0
Expand All @@ -206,22 +209,22 @@ function computeAcrossHypothesis!(
# sfidx=2, hypoidx=3: 2 should take a value from 3
# sfidx=3, hypoidx=2: 3 should take a value from 2
# DEBUG sfidx=2, hypoidx=1 -- bad when do something like multihypo=[0.5;0.5] -- issue 424
# destinationVarVals[:,allelements[count]] = view(ccwl.varValsAll[hypoidx],:,allelements[count])
# ccwl.varValsAll[][ccwl.varidx[]][:,allelements[count]] = view(ccwl.varValsAll[hypoidx],:,allelements[count])
# NOTE make alternative case only operate as null hypo
addEntr = view(destinationVarVals, allelements[count])
addEntr = view(ccwl.varValsAll[][ccwl.varidx[]], allelements[count])
# dynamic estimate with user requested speadNH of how much noise to inject (inflation or nullhypo)
spreadDist =
calcVariableDistanceExpectedFractional(ccwl, sfidx, certainidx; kappa = spreadNH,readonlyVarVals = destinationVarVals)
calcVariableDistanceExpectedFractional(ccwl, sfidx, certainidx; kappa = spreadNH) #,readonlyVarVals = ccwl.varValsAll[][ccwl.varidx[]])
addEntropyOnManifold!(mani, addEntr, 1:getDimension(mani), spreadDist)

elseif hypoidx == 0
# basically do nothing since the factor is not active for these allelements[count]
# inject more entropy in nullhypo case
# add noise (entropy) to spread out search in convolution proposals
addEntr = view(destinationVarVals, allelements[count])
addEntr = view(ccwl.varValsAll[][ccwl.varidx[]], allelements[count])
# dynamic estimate with user requested speadNH of how much noise to inject (inflation or nullhypo)
spreadDist =
calcVariableDistanceExpectedFractional(ccwl, sfidx, certainidx; kappa = spreadNH, readonlyVarVals = destinationVarVals)
calcVariableDistanceExpectedFractional(ccwl, sfidx, certainidx; kappa = spreadNH) #, readonlyVarVals = ccwl.varValsAll[][ccwl.varidx[]])
# # make spread (1σ) equal to mean distance of other fractionals
addEntropyOnManifold!(mani, addEntr, 1:getDimension(mani), spreadDist)
else
Expand Down Expand Up @@ -337,8 +340,7 @@ function evalPotentialSpecific(
# add user desired measurement values if 0 < length
# 2023Q2, ccwl.varValsAll always points at the variable.VND.val memory locations
# remember when doing approxConv to make a deepcopy of the destination memory first.
destinationVarVals = deepcopy(ccwl.varValsAll[][sfidx])
maxlen = _beforeSolveCCW!(ccwl, variables, destinationVarVals, sfidx, N; solveKey, needFreshMeasurements, measurement)
maxlen = _beforeSolveCCW!(ccwl, variables, sfidx, N; solveKey, needFreshMeasurements, measurement)

# Check which variables have been initialized
isinit = map(x -> isInitialized(x), variables)
Expand Down Expand Up @@ -367,7 +369,7 @@ function evalPotentialSpecific(
sfidx,
maxlen,
mani;
destinationVarVals,
# destinationVarVals,
spreadNH,
inflateCycles,
skipSolve,
Expand Down Expand Up @@ -415,8 +417,7 @@ function evalPotentialSpecific(
#

# Prep computation variables
destinationVarVals = deepcopy(ccwl.varValsAll[][sfidx])
maxlen = _beforeSolveCCW!(ccwl, variables, destinationVarVals, sfidx, N; solveKey, needFreshMeasurements, measurement)
maxlen = _beforeSolveCCW!(ccwl, variables, sfidx, N; solveKey, needFreshMeasurements, measurement)

# # FIXME, NEEDS TO BE CLEANED UP AND WORK ON MANIFOLDS PROPER
fnc = ccwl.usrfnc!
Expand Down Expand Up @@ -622,7 +623,6 @@ function _evalFactorTemporary!(
solveKey::Symbol = :default,
newFactor::Bool = true,
_slack = nothing,
# FIXME ON FIRE ADD destinationVarVals,
buildgraphkw...,
)
#
Expand Down