Acc ratio structure

Project.toml

@@ -1,7 +1,7 @@
 name = "MolecularEvolution"
 uuid = "9f975960-e239-4209-8aa0-3d3ad5a82892"
 authors = ["Ben Murrell <[email protected]> and contributors"]
-version = "0.2.4"
+version = "0.2.5"
 
 [deps]
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"

examples/update.jl

@@ -43,7 +43,7 @@ mutable struct MyModelSampler{
     T1<:ContinuousUnivariateDistribution,
     T2<:ContinuousUnivariateDistribution,
 } <: ModelsUpdate
-    acc_ratio::Vector{Int}
+    acc_ratio::Tuple{Float64, Int64, Int64}
     log_var_drift_proposal::T1
     log_var_drift_prior::T2
     mean_drift::Float64
@@ -52,7 +52,7 @@ mutable struct MyModelSampler{
         log_var_drift_prior::T2,
         mean_drift::Float64,
     ) where {T1<:ContinuousUnivariateDistribution, T2<:ContinuousUnivariateDistribution}
-        new{T1, T2}([0, 0], log_var_drift_proposal, log_var_drift_prior, mean_drift)
+        new{T1, T2}((0.0, 0, 0), log_var_drift_proposal, log_var_drift_prior, mean_drift)
     end
 end
 # Then we let this struct implement our [`metropolis_step`](@ref) interface

src/core/algorithms/root_optim.jl

@@ -10,11 +10,11 @@ mutable struct StandardRootOpt <: RootOpt
 end
 
 struct StandardRootSample <: UniformRootPositionSample
-    acc_ratio::Array{Int64,1}
+    acc_ratio::Tuple{Float64, Int64, Int64}
     consecutive::Int64
 
     function StandardRootSample(consecutive::Int64)
-        new(zeros(Int64, 2), consecutive)
+        new((0.0, 0, 0), consecutive)
     end
 end
 

src/utils/simple_sample.jl

@@ -6,13 +6,13 @@ end
 """
     BranchlengthSampler
 
-    A type that allows you to specify a additive proposal function in the log domain and a prior distrubution over the log of the branchlengths. It also holds the acceptance ratio acc_ratio (acc_ratio[1] stores the number of accepts, and acc_ratio[2] stores the number of rejects).
+    A type that allows you to specify a additive proposal function in the log domain and a prior distrubution over the log of the branchlengths. It also stores `acc_ratio` which is a tuple of `(ratio, total, #acceptances)`, where `ratio::Float64` is the acceptance ratio, `total::Int64` is the total number of proposals, and `#acceptances::Int64` is the number of acceptances.
 """
-struct BranchlengthSampler <: UnivariateSampler
-    acc_ratio::Vector{Int}
+mutable struct BranchlengthSampler <: UnivariateSampler
+    acc_ratio::Tuple{Float64, Int64, Int64} #(ratio, total, #acceptances)
     log_bl_proposal::Distribution
     log_bl_prior::Distribution
-    BranchlengthSampler(log_bl_proposal,log_bl_prior) = new([0,0],log_bl_proposal,log_bl_prior)
+    BranchlengthSampler(log_bl_proposal,log_bl_prior) = new((0.0, 0, 0),log_bl_proposal,log_bl_prior)
 end 
 
 """  
@@ -61,11 +61,13 @@ proposal(modifier::BranchlengthSampler, curr_value) = exp(log(curr_value) + rand
 log_prior(modifier::BranchlengthSampler, x) = logpdf(modifier.log_bl_prior, log(x + 1e-12))
 #Default definition. Overload it for your own modifier type
 function apply_decision(modifier, accept::Bool)
+    ratio, total, acc = modifier.acc_ratio
+    total += 1
     if accept
-        modifier.acc_ratio[1] += 1
-    else
-        modifier.acc_ratio[2] += 1
+        acc += 1
     end
+    ratio = acc / total
+    modifier.acc_ratio = (ratio, total, acc)
 end
 
 tr(modifier, x) = x