Fix tests 1.6

Project.toml

@@ -6,38 +6,34 @@ version = "0.4.0"
 [deps]
 ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
 DensityInterface = "b429d917-457f-4dbc-8f4c-0cc954292b1d"
+Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 DocStringExtensions = "ffbed154-4ef7-542d-bbb7-c09d3a79fcae"
 FillArrays = "1a297f60-69ca-5386-bcde-b61e274b549b"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 PrecompileTools = "aea7be01-6a6a-4083-8856-8a6e6704d82a"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
-Requires = "ae029012-a4dd-5104-9daa-d747884805df"
 SimpleUnPack = "ce78b400-467f-4804-87d8-8f486da07d0a"
-StatsAPI = "82ae8749-77ed-4fe6-ae5f-f523153014b0"
-
-[weakdeps]
-Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
-HMMBase = "b2b3ca75-8444-5ffa-85e6-af70e2b64fe7"
 SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
-
-[extensions]
-HiddenMarkovModelsDistributionsExt = "Distributions"
-HiddenMarkovModelsHMMBaseExt = "HMMBase"
-HiddenMarkovModelsSparseArraysExt = "SparseArrays"
+StatsAPI = "82ae8749-77ed-4fe6-ae5f-f523153014b0"
 
 [compat]
 ChainRulesCore = "1.16"
 DensityInterface = "0.4"
 Distributions = "0.25"
 DocStringExtensions = "0.9"
 FillArrays = "1"
-HMMBase = "1"
 LinearAlgebra = "1"
 PrecompileTools = "1.1"
 Random = "1"
-Requires = "1.3"
 SimpleUnPack = "1.1"
 SparseArrays = "1"
 StatsAPI = "1.6"
 julia = "1.6"
 
+[extensions]
+HiddenMarkovModelsDistributionsExt = "Distributions"
+HiddenMarkovModelsSparseArraysExt = "SparseArrays"
+
+[weakdeps]
+Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
+SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"

examples/basics.jl

@@ -215,7 +215,7 @@ map(dist -> dist.μ, hcat(obs_distributions(hmm_est_concat), obs_distributions(h
 
 # ## Tests  #src
 
-control_seqs = [fill(nothing, rand(rng, 100:200)) for k in 1:200];  #src
+control_seqs = [fill(nothing, rand(rng, 100:200)) for k in 1:500];  #src
 control_seq = reduce(vcat, control_seqs);  #src
 seq_ends = cumsum(length.(control_seqs));  #src
 

ext/HiddenMarkovModelsSparseArraysExt.jl

@@ -1,6 +1,6 @@
 module HiddenMarkovModelsSparseArraysExt
 
-using HiddenMarkovModels
+using HiddenMarkovModels: HiddenMarkovModels
 using SparseArrays
 
 HiddenMarkovModels.mynonzeros(x::AbstractSparseArray) = nonzeros(x)

libs/HMMTest/src/hmmbase.jl

@@ -13,8 +13,7 @@ function test_identical_hmmbase(
         obs_seq = vcat(sim.obs_seq, sim.obs_seq)
         seq_ends = [length(sim.obs_seq), 2 * length(sim.obs_seq)]
 
-        hmm_base = HMMBase.HMM(hmm)
-        hmm_guess_base = HMMBase.HMM(hmm_guess)
+        hmm_base = HMMBase.HMM(deepcopy(hmm.init), deepcopy(hmm.trans), deepcopy(hmm.dists))
 
         logL_base = HMMBase.forward(hmm_base, obs_mat)[2]
         logL = logdensityof(hmm, obs_seq; seq_ends)
@@ -35,6 +34,12 @@ function test_identical_hmmbase(
         @test isapprox(γ[:, 1:T], γ_base') && isapprox(γ[:, (T + 1):(2T)], γ_base')
 
         if !isnothing(hmm_guess)
+            hmm_guess_base = HMMBase.HMM(
+                deepcopy(hmm_guess.init),
+                deepcopy(hmm_guess.trans),
+                deepcopy(hmm_guess.dists),
+            )
+
             hmm_est_base, hist_base = HMMBase.fit_mle(
                 hmm_guess_base, obs_mat; maxiter=10, tol=-Inf
             )
@@ -45,7 +50,12 @@ function test_identical_hmmbase(
             @test isapprox(
                 logL_evolution[(begin + 1):end], 2 * logL_evolution_base[begin:(end - 1)]
             )
-            test_equal_hmms(hmm_est, HMM(hmm_est_base); atol, init=true)
+            test_equal_hmms(
+                hmm_est,
+                HMM(hmm_est_base.a, hmm_est_base.A, hmm_est_base.B);
+                atol,
+                init=true,
+            )
         end
     end
 end

src/HiddenMarkovModels.jl

@@ -14,7 +14,6 @@ using FillArrays: Fill
 using LinearAlgebra: dot, ldiv!, lmul!, mul!
 using PrecompileTools: @compile_workload
 using Random: Random, AbstractRNG, default_rng
-using Requires: @require
 using SimpleUnPack: @unpack
 using StatsAPI: StatsAPI, fit, fit!
 
@@ -48,20 +47,11 @@ include("inference/chainrules.jl")
 
 include("types/hmm.jl")
 
+include("precompile.jl")
+
 if !isdefined(Base, :get_extension)
-    function __init__()
-        @require Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f" begin
-            include("../ext/HiddenMarkovModelsDistributionsExt.jl")
-        end
-        @require HMMBase = "b2b3ca75-8444-5ffa-85e6-af70e2b64fe7" begin
-            include("../ext/HiddenMarkovModelsHMMBaseExt.jl")
-        end
-        @require SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf" begin
-            include("../ext/HiddenMarkovModelsSparseArraysExt.jl")
-        end
-    end
+    include("../ext/HiddenMarkovModelsDistributionsExt.jl")
+    include("../ext/HiddenMarkovModelsSparseArraysExt.jl")
 end
 
-include("precompile.jl")
-
 end

test/correctness.jl

@@ -52,8 +52,10 @@ seq_ends = cumsum(length.(control_seqs))
 end
 
 @testset "DiagNormal" begin
-    dists = [MvNormal(μ[1], I), MvNormal(μ[2], I)]
-    dists_guess = [MvNormal(μ_guess[1], I), MvNormal(μ_guess[2], I)]
+    dists = [MvNormal(μ[1], Diagonal(abs2.(σ))), MvNormal(μ[2], Diagonal(abs2.(σ)))]
+    dists_guess = [
+        MvNormal(μ_guess[1], Diagonal(abs2.(σ))), MvNormal(μ_guess[2], Diagonal(abs2.(σ)))
+    ]
 
     hmm = HMM(init, trans, dists)
     hmm_guess = HMM(init_guess, trans_guess, dists_guess)

test/distributions.jl

@@ -1,5 +1,6 @@
 using Distributions
 using HiddenMarkovModels: LightCategorical, LightDiagNormal, logdensityof, rand_prob_vec
+using LinearAlgebra
 using Statistics
 using Test
 
@@ -46,5 +47,5 @@ end
     test_fit_allocs(dist, x, w)
     # Logdensity
     @test logdensityof(dist, x[1]) ≈
-        logdensityof(MvNormal(μ, σ), x[1]) + length(x[1]) * log(sqrt(2π))
+        logdensityof(MvNormal(μ, Diagonal(abs2.(σ))), x[1]) + length(x[1]) * log(sqrt(2π))
 end