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Proper truncated distributions #55

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24 changes: 8 additions & 16 deletions src/mopt/AlgoBGP.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -371,24 +371,14 @@ end


"""
mysample(d::Distributions.MultivariateDistribution,lb::Vector{Float64},ub::Vector{Float64},iters::Int)
Truncated multivariate normal distribution.

mysample from distribution `d` until all poins are in support. This is a crude version of a truncated distribution: It just samples until all draws are within the admissible domain.
From https://github.com/JuliaStats/Distributions.jl/issues/480#issuecomment-1316525345
"""
function mysample(d::Distributions.MultivariateDistribution,lb::Float64,ub::Float64,iters::Int)

# draw until all points are in support
for i in 1:iters
x = rand(RAND,d)
if all(x.>=lb) && all(x.<=ub)
return x
end
end
error("no draw in support after $iters trials. increase either opts[smpl_iters] or opts[bound_prob].")
function truncated_mv_normal(mu::Vector{Float64}, sigma::Float64, lb::Float64, ub::Float64)
Product([truncated(Normal(m,sigma), lb, ub) for m in mu])
end



"""
proposal(c::BGPChain)

Expand Down Expand Up @@ -417,13 +407,15 @@ function proposal(c::BGPChain)

# if there is only one batch of params
if length(c.batches) == 1
pp = mysample(MvNormal(mu01,c.sigma),0.0,1.0,c.smpl_iters)
d = truncated_mv_normal(mu01, c.sigma, 0.0, 1.0)
pp = mysample(d,0.0,1.0,c.smpl_iters)
else
# do it in batches of params
pp = zero(mu01)
for (sig_ix,i) in enumerate(c.batches)
try
pp[i] = mysample(MvNormal(mu01[i],c.sigma),0.0,1.0,c.smpl_iters)
d = truncated_mv_normal([mu01[i]], c.sigma, 0.0, 1.0)
pp[i] = mysample(d,0.0,1.0,c.smpl_iters)
catch err
@error "caught exception $err. this is param index $sig_ix, mean = $(mu01[i]), sigma $(c.sigma), lb,ub = $((0,1))"
end
Expand Down