Add back the HMC(eps, n_leapfrog) as a convenience constructor (#341)

* Update constructors.jl

* Update Project.toml

* Update README.md

* Update constructors.jl

Author: yebai

Project.toml

@@ -1,6 +1,6 @@
 name = "AdvancedHMC"
 uuid = "0bf59076-c3b1-5ca4-86bd-e02cd72cde3d"
-version = "0.5.0"
+version = "0.5.1"
 
 [deps]
 AbstractMCMC = "80f14c24-f653-4e6a-9b94-39d6b0f70001"

README.md

@@ -18,9 +18,9 @@ If you are interested in using AdvancedHMC.jl through a probabilistic programmin
 
 **API CHANGES**
 - [v0.5.0] **Breaking!** Convenience constructors for common samplers changed to:
-  - `HMC(n_leapfrog)`
-  - `NUTS(target_acceptance)` 
-  - `HMCDA(target_acceptance, integration_time)`
+  - `HMC(leapfrog_stepsize::Real, n_leapfrog::Int)`
+  - `NUTS(target_acceptance::Real)` 
+  - `HMCDA(target_acceptance::Real, integration_time::Real)`
 - [v0.2.22] Three functions are renamed.
   - `Preconditioner(metric::AbstractMetric)` -> `MassMatrixAdaptor(metric)` and 
   - `NesterovDualAveraging(δ, integrator::AbstractIntegrator)` -> `StepSizeAdaptor(δ, integrator)`
@@ -153,8 +153,8 @@ In the previous examples, we built the sampler by manually specifying the integr
   ```julia
   # HMC Sampler
   # step size, number of leapfrog steps 
-  n_leapfrog, lf_integrator = 25, Leapfrog(0.1)
-  hmc = HMC(n_leapfrog, integrator = lf_integrator)
+  n_leapfrog, ϵ = 25, 0.1
+  hmc = HMC(ϵ, n_leapfrog)
   ```
 
   Equivalent to:

src/constructors.jl

@@ -130,6 +130,7 @@ struct HMC{I<:Union{Symbol,AbstractIntegrator},M<:Union{Symbol,AbstractMetric}}
     metric::M
 end
 
+HMC(ϵ::Real, n_leapfrog::Int) = HMC(n_leapfrog, Leapfrog(ϵ), :diagonal)
 HMC(n_leapfrog; integrator = :leapfrog, metric = :diagonal) =
     HMC(n_leapfrog, integrator, metric)
 

test/constructors.jl

@@ -8,6 +8,14 @@ include("common.jl")
 
     @testset "$T" for T in [Float32, Float64]
         @testset "$(nameof(typeof(sampler)))" for (sampler, expected) in [
+            (
+                HMC(T(0.1), 25),
+                (
+                    adaptor_type = NoAdaptation,
+                    metric_type = DiagEuclideanMetric{T},
+                    integrator_type = Leapfrog{T},
+                ),
+            ),
             (
                 HMC(25, integrator = Leapfrog(T(0.1))),
                 (