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Update turing_inference #46 #68

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Feb 22, 2019
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Update turing_inference #46 #68

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61da1ea
Update turing_inference #46
Vaibhavdixit02 Feb 9, 2019
36e63a3
Remove version bound of Turing from REQUIRE to use latest
Vaibhavdixit02 Feb 9, 2019
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Update turing_inference #46
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@Vaibhavdixit02
Vaibhavdixit02 committed Feb 9, 2019
commit 61da1eacf660ec4aa455590e02e6cd3a9801ca0c
54 changes: 16 additions & 38 deletions src/turing_inference.jl
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Original file line number Diff line number Diff line change
@@ -1,54 +1,32 @@
function turing_inference(prob::DiffEqBase.DEProblem,alg,t,data,priors = nothing;
num_samples=1000, delta=0.65, kwargs...)

function bif(vi, sampler, x=data)
_lp = 0.0
N = length(priors)
_theta = Vector(undef,N)
num_samples=1000, delta=0.65, kwargs...)

N = length(priors)
_theta = Vector{Real}(undef, N)
@model bif(x) = begin
for i in 1:length(priors)
_theta[i], __lp = Turing.assume(sampler,
priors[i],
Turing.VarName(vi, [:bif, Symbol("theta$i")], ""),
vi)
_lp += __lp
_theta[i] ~ priors[i]
end
σ ~ InverseGamma(2, 3)

theta = convert(Array{typeof(first(_theta))},_theta)

σ, __lp = Turing.assume(sampler,
InverseGamma(2, 3),
Turing.VarName(vi, [:bif, :σ], ""),
vi)
_lp += __lp
theta = convert(Array{typeof(first(_theta))}, _theta)
p_tmp = remake(prob, u0 = convert.(eltype(theta), (prob.u0)), p = theta)
sol_tmp = solve(p_tmp, alg; saveat = t, kwargs...)
fill_length = length(t) - length(sol_tmp.u)

p_tmp = remake(prob, u0=convert.(eltype(theta),(prob.u0)),p=theta)
sol_tmp = solve(p_tmp,alg;saveat=t,kwargs...)
fill_length = length(t)-length(sol_tmp.u)
for i in 1:fill_length
if eltype(sol_tmp.u) <: Number
push!(sol_tmp.u,Inf)
push!(sol_tmp.u, Inf)
else
push!(sol_tmp.u,fill(Inf,size(sol_tmp[1])))
push!(sol_tmp.u, fill(Inf, size(sol_tmp[1])))
end
end
for i = 1:length(t)
res = sol_tmp.u[i]
# x[:,i] ~ MvNormal(res, σ*ones(2))
__lp = Turing.observe(
sampler,
MvNormal(res, σ*ones(length(prob.u0))), # Distribution
x[:,i], # Data point
vi
)
_lp += __lp
x[:,i] ~ MvNormal(res, σ*ones(length(prob.u0)))
end

vi.logp = _lp
vi
end

bif() = bif(Turing.VarInfo(), nothing)

chn = sample(bif, Turing.NUTS(num_samples, delta))
end
model = bif(data)
chn = sample(model, Turing.IS(num_samples))
end
17 changes: 7 additions & 10 deletions test/turing.jl
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Expand Up @@ -16,9 +16,9 @@ priors = [Normal(1.5,0.01)]

bayesian_result = turing_inference(prob1,Tsit5(),t,data,priors;num_samples=500)

@show mean(bayesian_result[:theta1][50:end])
@show mean(bayesian_result[:_theta][50:end])

@test mean(bayesian_result[:theta1][50:end]) ≈ 1.5 atol=0.1
@test mean(bayesian_result[:_theta1][50:end])[1] ≈ 1.5 atol=0.1

println("Four parameter case")
f1 = @ode_def begin
Expand All @@ -38,12 +38,9 @@ priors = [Truncated(Normal(1.5,0.01),0,2),Truncated(Normal(1.0,0.01),0,1.5),

bayesian_result = turing_inference(prob1,Tsit5(),t,data,priors;num_samples=500)

@show mean(bayesian_result[:theta1][50:end])
@show mean(bayesian_result[:theta2][50:end])
@show mean(bayesian_result[:theta3][50:end])
@show mean(bayesian_result[:theta4][50:end])
@show mean(bayesian_result[:_theta][50:end])

@test mean(bayesian_result[:theta1][50:end]) ≈ 1.5 atol=3e-1
@test mean(bayesian_result[:theta2][50:end]) ≈ 1.0 atol=3e-1
@test mean(bayesian_result[:theta3][50:end]) ≈ 3.0 atol=3e-1
@test mean(bayesian_result[:theta4][50:end]) ≈ 1.0 atol=3e-1
@test mean(bayesian_result[:_theta][50:end])[1] ≈ 1.5 atol=3e-1
@test mean(bayesian_result[:_theta][50:end])[2] ≈ 1.0 atol=3e-1
@test mean(bayesian_result[:_theta][50:end])[3] ≈ 3.0 atol=3e-1
@test mean(bayesian_result[:_theta][50:end])[4] ≈ 1.0 atol=3e-1