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Some updates #154

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Apr 22, 2020
Merged

Some updates #154

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2daed2e
Update ModelingToolkit compat
Vaibhavdixit02 Apr 22, 2020
fe24c28
Update Truncated to truncated
Vaibhavdixit02 Apr 22, 2020
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Update Truncated to truncated
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@Vaibhavdixit02
Vaibhavdixit02 committed Apr 22, 2020
commit fe24c28f4272c36fca42ed1843aa4248b068c901
4 changes: 2 additions & 2 deletions test/abc.jl
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Expand Up @@ -80,8 +80,8 @@ sol = solve(prob1,Tsit5())
t = collect(range(1,stop=10,length=10))
randomized = VectorOfArray([(sol(t[i]) + .01randn(2)) for i in 1:length(t)])
data = convert(Array,randomized)
priors = [Truncated(Normal(1.5,1),0,2),Truncated(Normal(1.0,1),0,1.5),
Truncated(Normal(3.0,1),0,4),Truncated(Normal(1.0,1),0,2)]
priors = [truncated(Normal(1.5,1),0,2),truncated(Normal(1.0,1),0,1.5),
truncated(Normal(3.0,1),0,4),truncated(Normal(1.0,1),0,2)]

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

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10 changes: 5 additions & 5 deletions test/dynamicHMC.jl
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Expand Up @@ -63,7 +63,7 @@ likelihood = function (sol)
end
return l
end
@test_broken bayesian_result = dynamichmc_inference(prob1, Tsit5(), likelihood, [Truncated(Normal(1.5, 1), 0, 2)], as((a = asℝ₊,)))
@test_broken bayesian_result = dynamichmc_inference(prob1, Tsit5(), likelihood, [truncated(Normal(1.5, 1), 0, 2)], as((a = asℝ₊,)))
@test_broken mean(bayesian_result[1][1]) ≈ 1.5 atol=1e-1


Expand All @@ -79,10 +79,10 @@ sol = solve(prob1,Tsit5())
t = collect(range(1,stop=10,length=10))
randomized = VectorOfArray([(sol(t[i]) + .01randn(2)) for i in 1:length(t)])
data = convert(Array,randomized)
priors = (a = Truncated(Normal(1.5,0.01), 0, 2),
b = Truncated(Normal(1.0,0.01), 0, 1.5),
c = Truncated(Normal(3.0,0.01), 0, 4),
d = Truncated(Normal(1.0, 0.01), 0, 2))
priors = (a = truncated(Normal(1.5,0.01), 0, 2),
b = truncated(Normal(1.0,0.01), 0, 1.5),
c = truncated(Normal(3.0,0.01), 0, 4),
d = truncated(Normal(1.0, 0.01), 0, 2))
mcmc_kwargs = (initialization = (q = zeros(4 + 2),), reporter = reporter)
bayesian_result = dynamichmc_inference(prob1, Tsit5(), t, data, priors,
as(Vector, asℝ₊, 4),mcmc_kwargs = mcmc_kwargs)
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8 changes: 4 additions & 4 deletions test/stan.jl
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Expand Up @@ -14,7 +14,7 @@ sol = solve(prob1,Tsit5())
t = collect(range(1,stop=10,length=10))
randomized = VectorOfArray([(sol(t[i]) + .01randn(2)) for i in 1:length(t)])
data = convert(Array,randomized)
priors = [Truncated(Normal(1.5,0.1),0,2)]
priors = [truncated(Normal(1.5,0.1),0,2)]

bayesian_result = stan_inference(prob1,t,data,priors;num_samples=300,
num_warmup=500,likelihood=Normal)
Expand All @@ -35,7 +35,7 @@ sdf = CmdStan.read_summary(bayesian_result.model)
sol = solve(prob1,Tsit5(),save_idxs=[1])
randomized = VectorOfArray([(sol(t[i]) + .01 * randn(1)) for i in 1:length(t)])
data = convert(Array,randomized)
priors = [Truncated(Normal(1.5,0.1),0,2)]
priors = [truncated(Normal(1.5,0.1),0,2)]
bayesian_result = stan_inference(prob1,t,data,priors;num_samples=300,
num_warmup=500,likelihood=Normal,save_idxs=[1])

Expand Down Expand Up @@ -64,8 +64,8 @@ sol = solve(prob1,Tsit5())
t = collect(range(1,stop=10,length=10))
randomized = VectorOfArray([(sol(t[i]) + .01randn(2)) for i in 1:length(t)])
data = convert(Array,randomized)
priors = [Truncated(Normal(1.5,0.01),0,2),Truncated(Normal(1.0,0.01),0,1.5),
Truncated(Normal(3.0,0.01),0,4),Truncated(Normal(1.0,0.01),0,2)]
priors = [truncated(Normal(1.5,0.01),0,2),truncated(Normal(1.0,0.01),0,1.5),
truncated(Normal(3.0,0.01),0,4),truncated(Normal(1.0,0.01),0,2)]

bayesian_result = stan_inference(prob1,t,data,priors;num_samples=100,num_warmup=500,vars =(DiffEqBayes.StanODEData(),InverseGamma(4,1)))
sdf = CmdStan.read_summary(bayesian_result.model)
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