logpdf(d, X) to logpdf.(d, X)(no longer supported)

dmetivie · Mar 29, 2022 · 2351f4d · 2351f4d
1 parent ea433ee
commit 2351f4d
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Showing 2 changed files with 5 additions and 5 deletions.
diff --git a/src/fit_em.jl b/src/fit_em.jl
@@ -26,7 +26,7 @@ function fit_mle(mix::MixtureModel, y::AbstractVector; display = :none, maxiter
     # E-step
     # evaluate likelihood for each type k
     for k = 1:K
-        LL[:, k] = log(α[k]) .+ logpdf(dists[k], y)
+        LL[:, k] = log(α[k]) .+ logpdf.(dists[k], y)
     end
     robust && replace!(LL, -Inf => nextfloat(-Inf), Inf => log(prevfloat(Inf)))
     # get posterior of each category
@@ -47,7 +47,7 @@ function fit_mle(mix::MixtureModel, y::AbstractVector; display = :none, maxiter
         # E-step
         # evaluate likelihood for each type k
         for k = 1:K
-            LL[:, k] = log(α[k]) .+ logpdf(dists[k], y)
+            LL[:, k] = log(α[k]) .+ logpdf.(dists[k], y)
         end
         robust && replace!(LL, -Inf => nextfloat(-Inf), Inf => log(prevfloat(Inf)))
         # get posterior of each category
@@ -102,7 +102,7 @@ function fit_mle(mix::MixtureModel, y::AbstractMatrix; display = :none, maxiter
     # E-step
     # evaluate likelihood for each type k
     for k = 1:K
-        LL[:, k] = log(α[k]) .+ logpdf(dists[k], y)
+        LL[:, k] = log(α[k]) .+ logpdf.(dists[k], y)
     end
     robust && replace!(LL, -Inf => nextfloat(-Inf), Inf => log(prevfloat(Inf)))
     # get posterior of each category
@@ -123,7 +123,7 @@ function fit_mle(mix::MixtureModel, y::AbstractMatrix; display = :none, maxiter
         # E-step
         # evaluate likelihood for each type k
         for k = 1:K
-            LL[:, k] = log(α[k]) .+ logpdf(dists[k], y)
+            LL[:, k] = log(α[k]) .+ logpdf.(dists[k], y)
         end
         robust && replace!(LL, -Inf => nextfloat(-Inf), Inf => log(prevfloat(Inf)))
         # get posterior of each category

diff --git a/test/runtests.jl b/test/runtests.jl
@@ -3,7 +3,7 @@ using Distributions
 using Test
 
 @testset "ExpectationMaximization.jl" begin
-    N = 50000
+    N = 50_000
     θ₁ = 10
     θ₂ = 5
     α = 0.2