Allow heterogeneous distributions

Project.toml

@@ -1,7 +1,7 @@
 name = "HiddenMarkovModels"
 uuid = "84ca31d5-effc-45e0-bfda-5a68cd981f47"
 authors = ["Guillaume Dalle"]
-version = "0.5.2"
+version = "0.5.3"
 
 [deps]
 ArgCheck = "dce04be8-c92d-5529-be00-80e4d2c0e197"

examples/basics.jl

@@ -261,4 +261,3 @@ control_seq = fill(nothing, last(seq_ends));  #src
 test_identical_hmmbase(rng, hmm, 100; hmm_guess)  #src
 test_coherent_algorithms(rng, hmm, control_seq; seq_ends, hmm_guess)  #src
 test_type_stability(rng, hmm, control_seq; seq_ends, hmm_guess)  #src
-test_identical_hmmbase(rng, transpose_hmm(hmm), 100; hmm_guess=transpose_hmm(hmm_guess))  #src

ext/HiddenMarkovModelsDistributionsExt.jl

@@ -10,27 +10,30 @@ using Distributions:
     fit
 
 function HiddenMarkovModels.fit_in_sequence!(
-    dists::AbstractVector{D}, i::Integer, x_nums::AbstractVector, w::AbstractVector
-) where {D<:UnivariateDistribution}
-    return dists[i] = fit(D, x_nums, w)
+    dists::AbstractVector{<:UnivariateDistribution},
+    i::Integer,
+    x_nums::AbstractVector,
+    w::AbstractVector,
+)
+    return dists[i] = fit(typeof(dists[i]), x_nums, w)
 end
 
 function HiddenMarkovModels.fit_in_sequence!(
-    dists::AbstractVector{D},
+    dists::AbstractVector{<:MultivariateDistribution},
     i::Integer,
     x_vecs::AbstractVector{<:AbstractVector},
     w::AbstractVector,
-) where {D<:MultivariateDistribution}
-    return dists[i] = fit(D, reduce(hcat, x_vecs), w)
+)
+    return dists[i] = fit(typeof(dists[i]), reduce(hcat, x_vecs), w)
 end
 
 function HiddenMarkovModels.fit_in_sequence!(
-    dists::AbstractVector{D},
+    dists::AbstractVector{<:MatrixDistribution},
     i::Integer,
     x_mats::AbstractVector{<:AbstractMatrix},
     w::AbstractVector,
-) where {D<:MatrixDistribution}
-    return dists[i] = fit(D, reduce(dcat, x_mats), w)
+)
+    return dists[i] = fit(typeof(dists[i]), reduce(dcat, x_mats), w)
 end
 
 dcat(M1, M2) = cat(M1, M2; dims=3)

test/correctness.jl

@@ -98,3 +98,29 @@ end
     test_type_stability(rng, hmm, control_seq; seq_ends, hmm_guess)
     @test_skip test_allocations(rng, hmm, control_seq; seq_ends, hmm_guess)
 end
+
+@testset "Normal transposed" begin  # issue 99
+    dists = [Normal(μ[1][1]), Normal(μ[2][1])]
+    dists_guess = [Normal(μ_guess[1][1]), Normal(μ_guess[2][1])]
+
+    hmm = transpose_hmm(HMM(init, trans, dists))
+    hmm_guess = transpose_hmm(HMM(init_guess, trans_guess, dists_guess))
+
+    test_identical_hmmbase(rng, hmm, T; hmm_guess)
+    test_coherent_algorithms(rng, hmm, control_seq; seq_ends, hmm_guess, init=false)
+    test_type_stability(rng, hmm, control_seq; seq_ends, hmm_guess)
+    test_allocations(rng, hmm, control_seq; seq_ends, hmm_guess)
+end
+
+@testset "Normal and Laplace" begin  # issue 101
+    dists = [Normal(μ[1][1]), Laplace(μ[2][1])]
+    dists_guess = [Normal(μ_guess[1][1]), Laplace(μ_guess[2][1])]
+
+    hmm = HMM(init, trans, dists)
+    hmm_guess = HMM(init_guess, trans_guess, dists_guess)
+
+    test_identical_hmmbase(rng, hmm, T; hmm_guess)
+    test_coherent_algorithms(rng, hmm, control_seq; seq_ends, hmm_guess, init=false)
+    test_type_stability(rng, hmm, control_seq; seq_ends, hmm_guess)
+    test_allocations(rng, hmm, control_seq; seq_ends, hmm_guess)
+end