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Fix issues found by David in #325 #328

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Fix issues found by David in #325 #328

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f165edd
Update abstractmcmc.jl
yebai Jul 20, 2023
8e8b483
Update README.md
yebai Jul 20, 2023
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14 changes: 7 additions & 7 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -17,10 +17,10 @@ If you are interested in using AdvancedHMC.jl through a probabilistic programmin
- We presented a poster for AdvancedHMC.jl at [StanCon 2019](https://mc-stan.org/events/stancon2019Cambridge/) in Cambridge, UK. ([pdf](https://github.com/TuringLang/AdvancedHMC.jl/files/3730367/StanCon-AHMC.pdf))

**API CHANGES**
- [v0.5.0] **Breaking!** Convinience constructors for common samplers changed to:
- `HMC(init_ϵ::Float64=init_ϵ, n_leapfrog::Int=n_leapfrog)`
- `NUTS(n_adapts::Int=n_adapts, δ::Float64=δ)`
- `HMCDA(n_adapts::Int=n_adapts, δ::Float64=δ, λ::Float64=λ)`
- [v0.5.0] **Breaking!** Convenience constructors for common samplers changed to:
- `HMC(init_ϵ, n_leapfrog)`
- `NUTS(n_adapts, target_acceptance)`
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@yebai yebai Jul 20, 2023 •
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I am unsure if we want to hardcode n_adapts as part of the algorithm. Like the number of MCMC samples, the number of adaption steps should be an argument passed to the AbstractMCMC.sample function.

Related: abstract MCMC PR

- `HMCDA(n_adapts, target_acceptance, integration_time)`
- [v0.2.22] Three functions are renamed.
- `Preconditioner(metric::AbstractMetric)` -> `MassMatrixAdaptor(metric)` and
- `NesterovDualAveraging(δ, integrator::AbstractIntegrator)` -> `StepSizeAdaptor(δ, integrator)`
Expand Down Expand Up @@ -77,15 +77,15 @@ n_samples, n_adapts = 2_000, 1_000
metric = DiagEuclideanMetric(D)
hamiltonian = Hamiltonian(metric, ℓπ, ForwardDiff)

# Define a leapfrog solver, with initial step size chosen heuristically
# Define a leapfrog solver, with the initial step size chosen heuristically
initial_ϵ = find_good_stepsize(hamiltonian, initial_θ)
integrator = Leapfrog(initial_ϵ)

# Define an HMC sampler, with the following components
# Define an HMC sampler with the following components
# - multinomial sampling scheme,
# - generalised No-U-Turn criteria, and
# - windowed adaption for step-size and diagonal mass matrix
proposal = NUTS{MultinomialTS, GeneralisedNoUTurn}(integrator)
proposal = HMCKernel(Trajectory{MultinomialTS}(integrator, GeneralisedNoUTurn()))
adaptor = StanHMCAdaptor(MassMatrixAdaptor(metric), StepSizeAdaptor(0.8, integrator))

# Run the sampler to draw samples from the specified Gaussian, where
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5 changes: 2 additions & 3 deletions src/abstractmcmc.jl
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Expand Up @@ -289,16 +289,15 @@ end
make_integrator(spl::HMCSampler, ϵ::Real) = spl.κ.τ.integrator
make_integrator(spl::AbstractHMCSampler, ϵ::Real) = make_integrator(spl.integrator, ϵ)
make_integrator(i::AbstractIntegrator, ϵ::Real) = i
make_integrator(i::Type{<:AbstractIntegrator}, ϵ::Real) = i
make_integrator(i::Symbol, ϵ::Real) = make_integrator(Val(i), ϵ)
make_integrator(i...) = error("Integrator $(typeof(i)) not supported.")
make_integrator(@nospecialize(i), ::Real) = error("Integrator $i not supported.")
make_integrator(i::Val{:leapfrog}, ϵ::Real) = Leapfrog(ϵ)
make_integrator(i::Val{:jitteredleapfrog}, ϵ::Real) = JitteredLeapfrog(ϵ)
make_integrator(i::Val{:temperedleapfrog}, ϵ::Real) = TemperedLeapfrog(ϵ)

#########

make_metric(i...) = error("Metric $(typeof(i)) not supported.")
make_metric(@nospecialize(i), T::Type, d::Int) = error("Metric $(typeof(i)) not supported.")
make_metric(i::Symbol, T::Type, d::Int) = make_metric(Val(i), T, d)
make_metric(i::AbstractMetric, T::Type, d::Int) = i
make_metric(i::Type{AbstractMetric}, T::Type, d::Int) = i
Expand Down