more notes

ReactiveBayes · Oct 5, 2021 · 870bd51 · 870bd51 · bvdmitri · Oct 6, 2021
1 parent e21b904
commit 870bd51
Show file tree

Hide file tree

Showing 2 changed files with 7 additions and 3 deletions.
diff --git a/src/approximations/gausslaguerre.jl b/src/approximations/gausslaguerre.jl
@@ -49,6 +49,7 @@ function log_approximate(approximation::GaussLaguerreQuadrature, fn::Function)
     # calculate the ln(wi) + logf(xi) terms
     logresult = Vector{T}(undef, p)
     for i = 1:p
+        # TODO: Can this have @inbounds?
         logresult[i] = logw[i] + fn(x[i])
     end
 
@@ -58,4 +59,4 @@ end
 
 function Base.:(==)(left::GaussLaguerreQuadrature{R}, right::GaussLaguerreQuadrature{R}) where R 
     return getlength(left) == getlength(right)
-end
+end
diff --git a/src/approximations/importance.jl b/src/approximations/importance.jl
@@ -3,6 +3,7 @@ export ImportanceSamplingApproximation
 using Random
 using StatsBase
 
+# TODO: Document other fields
 """
     ImportanceSamplingApproximation
 
@@ -33,6 +34,8 @@ end
 getsamples(approximation::ImportanceSamplingApproximation, distribution)           = getsamples(approximation, distribution, approximation.nsamples)
 getsamples(approximation::ImportanceSamplingApproximation, distribution, nsamples) = rand(approximation.rng, distribution, nsamples)
 
+# TODO: What's the type of `distribution`? Generic is fine, but at least a
+# comment would be helpful
 function approximate_meancov(approximation::ImportanceSamplingApproximation, g::Function, distribution)
 
     # We use preallocated arrays to sample and compute transformed samples and weightd
@@ -76,7 +79,7 @@ function approximate_meancov(approximation::ImportanceSamplingApproximation, g::
     if isnan(m) || isnan(v) || isinf(m) || iszero(v)
         return mean(distribution), var(distribution)
     end
-
+    
     return m, v
 
-end
+end