Fixes

src/HiddenMarkovModels.jl

@@ -16,7 +16,7 @@ using ChainRulesCore: ChainRulesCore, NoTangent, RuleConfig, rrule_via_ad
 using DensityInterface: DensityInterface, DensityKind, HasDensity, NoDensity, logdensityof
 using DocStringExtensions
 using FillArrays: Fill
-using LinearAlgebra: Transpose, dot, ldiv!, lmul!, mul!, parent
+using LinearAlgebra: Transpose, axpy!, dot, ldiv!, lmul!, mul!, parent
 using Random: Random, AbstractRNG, default_rng
 using SparseArrays: AbstractSparseArray, SparseMatrixCSC, nonzeros, nnz, nzrange, rowvals
 using StatsAPI: StatsAPI, fit, fit!

src/types/abstract_hmm.jl

@@ -125,7 +125,7 @@ function obs_logdensities!(
     logb::AbstractVector{T}, hmm::AbstractHMM, obs, control
 ) where {T}
     dists = obs_distributions(hmm, control)
-    @inbounds @simd for i in eachindex(logb, dists)
+    @simd for i in eachindex(logb, dists)
         logb[i] = logdensityof(dists[i], obs)
     end
     @argcheck maximum(logb) < typemax(T)

src/utils/lightcategorical.jl

@@ -53,7 +53,7 @@ function StatsAPI.fit!(
     @argcheck 1 <= minimum(x) <= maximum(x) <= length(dist.p)
     w_tot = sum(w)
     fill!(dist.p, zero(T1))
-    @inbounds @simd for i in eachindex(x, w)
+    @simd for i in eachindex(x, w)
         dist.p[x[i]] += w[i]
     end
     dist.p ./= w_tot

src/utils/lightdiagnormal.jl

@@ -46,7 +46,7 @@ function DensityInterface.logdensityof(
 ) where {T1,T2,T3}
     l = zero(promote_type(T1, T2, T3, eltype(x)))
     l -= sum(dist.logσ) + log2π * length(x) / 2
-    @inbounds @simd for i in eachindex(x, dist.μ, dist.σ)
+    @simd for i in eachindex(x, dist.μ, dist.σ)
         l -= abs2(x[i] - dist.μ[i]) / (2 * abs2(dist.σ[i]))
     end
     return l
@@ -58,11 +58,11 @@ function StatsAPI.fit!(
     w_tot = sum(w)
     fill!(dist.μ, zero(T1))
     fill!(dist.σ, zero(T2))
-    @inbounds @simd for i in eachindex(x, w)
-        dist.μ .+= x[i] .* w[i]
+    @simd for i in eachindex(x, w)
+        axpy!(w[i], x[i], dist.μ)
     end
     dist.μ ./= w_tot
-    @inbounds @simd for i in eachindex(x, w)
+    @simd for i in eachindex(x, w)
         dist.σ .+= abs2.(x[i] .- dist.μ) .* w[i]
     end
     dist.σ .= sqrt.(dist.σ ./ w_tot)

test/correctness.jl

@@ -11,8 +11,6 @@ using Test
 
 TEST_SUITE = get(ENV, "JULIA_HMM_TEST_SUITE", "Standard")
 
-rng = StableRNG(63)
-
 ## Settings
 
 T, K = 100, 200
@@ -31,6 +29,7 @@ p_guess = [[0.7, 0.3], [0.3, 0.7]]
 
 σ = ones(2)
 
+rng = StableRNG(63)
 control_seqs = [fill(nothing, rand(rng, T:(2T))) for k in 1:K];
 control_seq = reduce(vcat, control_seqs);
 seq_ends = cumsum(length.(control_seqs));
@@ -44,6 +43,7 @@ seq_ends = cumsum(length.(control_seqs));
     hmm = HMM(init, trans, dists)
     hmm_guess = HMM(init_guess, trans_guess, dists_guess)
 
+    rng = StableRNG(63)
     if TEST_SUITE == "HMMBase"
         test_identical_hmmbase(rng, hmm, T; hmm_guess)
     else
@@ -62,6 +62,7 @@ end
     hmm = HMM(init, trans, dists)
     hmm_guess = HMM(init_guess, trans_guess, dists_guess)
 
+    rng = StableRNG(63)
     if TEST_SUITE == "HMMBase"
         test_identical_hmmbase(rng, hmm, T; hmm_guess)
     else
@@ -77,6 +78,7 @@ end
     hmm = HMM(init, trans, dists)
     hmm_guess = HMM(init_guess, trans_guess, dists_guess)
 
+    rng = StableRNG(63)
     if TEST_SUITE != "HMMBase"
         test_coherent_algorithms(rng, hmm, control_seq; seq_ends, hmm_guess, init=false)
         test_type_stability(rng, hmm, control_seq; seq_ends, hmm_guess)
@@ -91,6 +93,7 @@ end
     hmm = HMM(init, trans, dists)
     hmm_guess = HMM(init_guess, trans_guess, dists_guess)
 
+    rng = StableRNG(63)
     if TEST_SUITE != "HMMBase"
         test_coherent_algorithms(rng, hmm, control_seq; seq_ends, hmm_guess, init=false)
         test_type_stability(rng, hmm, control_seq; seq_ends, hmm_guess)
@@ -105,6 +108,7 @@ end
     hmm = HMM(init, sparse(trans), dists)
     hmm_guess = HMM(init_guess, trans_guess, dists_guess)
 
+    rng = StableRNG(63)
     if TEST_SUITE == "HMMBase"
         test_identical_hmmbase(rng, hmm, T; hmm_guess)
     else
@@ -121,6 +125,7 @@ end
     hmm = transpose_hmm(HMM(init, trans, dists))
     hmm_guess = transpose_hmm(HMM(init_guess, trans_guess, dists_guess))
 
+    rng = StableRNG(63)
     if TEST_SUITE == "HMMBase"
         test_identical_hmmbase(rng, hmm, T; hmm_guess)
     else
@@ -137,6 +142,7 @@ end
     hmm = HMM(init, trans, dists)
     hmm_guess = HMM(init_guess, trans_guess, dists_guess)
 
+    rng = StableRNG(63)
     if TEST_SUITE == "HMMBase"
         test_identical_hmmbase(rng, hmm, T; hmm_guess)
     else

test/distributions.jl

@@ -25,7 +25,7 @@ end
     end
     @test val_count ./ length(x) ≈ p atol = 2e-2
     # Fitting
-    dist_est = deepcopy(dist)
+    dist_est = LightCategorical(rand_prob_vec(rng, 10))
     w = ones(length(x))
     fit!(dist_est, x, w)
     @test dist_est.p ≈ p atol = 2e-2
@@ -43,7 +43,7 @@ end
     @test mean(x) ≈ μ atol = 2e-2
     @test std(x) ≈ σ atol = 2e-2
     # Fitting
-    dist_est = deepcopy(dist)
+    dist_est = LightDiagNormal(randn(rng, 10), rand(rng, 10))
     w = ones(length(x))
     fit!(dist_est, x, w)
     @test dist_est.μ ≈ μ atol = 2e-2

test/runtests.jl

@@ -50,7 +50,7 @@ Pkg.develop(; path=joinpath(dirname(@__DIR__), "libs", "HMMTest"))
         end
     end
 
-    @testset "Correctness - $TEST_SUITE" begin
+    @testset verbose = true "Correctness - $TEST_SUITE" begin
         include("correctness.jl")
     end
 end