Improve type inference of MixtureModel

Project.toml

@@ -1,7 +1,7 @@
 name = "Distributions"
 uuid = "31c24e10-a181-5473-b8eb-7969acd0382f"
 authors = ["JuliaStats"]
-version = "0.24.18"
+version = "0.25.0"
 
 [deps]
 FillArrays = "1a297f60-69ca-5386-bcde-b61e274b549b"

src/mixtures/mixturemodel.jl

@@ -19,11 +19,11 @@ A mixture of distributions, parametrized on:
 * `C` distribution family of the mixture
 * `CT` the type for probabilities of the prior
 """
-struct MixtureModel{VF<:VariateForm,VS<:ValueSupport,C<:Distribution,CT<:Real} <: AbstractMixtureModel{VF,VS,C}
+struct MixtureModel{VF<:VariateForm,VS<:ValueSupport,C<:Distribution,CT<:Categorical} <: AbstractMixtureModel{VF,VS,C}
     components::Vector{C}
-    prior::Categorical{CT}
+    prior::CT
 
-    function MixtureModel{VF,VS,C}(cs::Vector{C}, pri::Categorical{CT}) where {VF,VS,C,CT}
+    function MixtureModel{VF,VS,C}(cs::Vector{C}, pri::CT) where {VF,VS,C,CT}
         length(cs) == ncategories(pri) ||
             error("The number of components does not match the length of prior.")
         new{VF,VS,C,CT}(cs, pri)
@@ -171,16 +171,8 @@ minimum(d::MixtureModel) = minimum([minimum(dci) for dci in d.components])
 maximum(d::MixtureModel) = maximum([maximum(dci) for dci in d.components])
 
 function mean(d::UnivariateMixture)
-    K = ncomponents(d)
     p = probs(d)
-    m = 0.0
-    for i = 1:K
-        pi = p[i]
-        if pi > 0.0
-            c = component(d, i)
-            m += mean(c) * pi
-        end
-    end
+    m = sum(pi * mean(component(d, i)) for (i, pi) in enumerate(p) if !iszero(pi))
     return m
 end
 
@@ -281,38 +273,22 @@ end
 #### Evaluation
 
 function insupport(d::AbstractMixtureModel, x::AbstractVector)
-    K = ncomponents(d)
     p = probs(d)
-    @inbounds for i in eachindex(p)
-        pi = p[i]
-        if pi > 0.0 && insupport(component(d, i), x)
-            return true
-        end
-    end
-    return false
+    return any(insupport(component(d, i), x) for (i, pi) in enumerate(p) if !iszero(pi))
 end
 
 function _cdf(d::UnivariateMixture, x::Real)
-    K = ncomponents(d)
     p = probs(d)
-    r = 0.0
-    @inbounds for i in eachindex(p)
-        pi = p[i]
-        if pi > 0.0
-            c = component(d, i)
-            r += pi * cdf(c, x)
-        end
-    end
+    r = sum(pi * cdf(component(d, i), x) for (i, pi) in enumerate(p) if !iszero(pi))
     return r
 end
 
 cdf(d::UnivariateMixture{Continuous}, x::Real) = _cdf(d, x)
 cdf(d::UnivariateMixture{Discrete}, x::Integer) = _cdf(d, x)
 
 function _mixpdf1(d::AbstractMixtureModel, x)
-    ps = probs(d)
-    cs = components(d)
-    return sum((ps[i] > 0) * (ps[i] * pdf(cs[i], x)) for i in eachindex(ps))
+    p = probs(d)
+    return sum(pi * pdf(component(d, i), x) for (i, pi) in enumerate(p) if !iszero(pi))
 end
 
 function _mixpdf!(r::AbstractArray, d::AbstractMixtureModel, x)
@@ -335,39 +311,9 @@ function _mixpdf!(r::AbstractArray, d::AbstractMixtureModel, x)
 end
 
 function _mixlogpdf1(d::AbstractMixtureModel, x)
-    # using the formula below for numerical stability
-    #
-    # logpdf(d, x) = log(sum_i pri[i] * pdf(cs[i], x))
-    #              = log(sum_i pri[i] * exp(logpdf(cs[i], x)))
-    #              = log(sum_i exp(logpri[i] + logpdf(cs[i], x)))
-    #              = m + log(sum_i exp(logpri[i] + logpdf(cs[i], x) - m))
-    #
-    #  m is chosen to be the maximum of logpri[i] + logpdf(cs[i], x)
-    #  such that the argument of exp is in a reasonable range
-    #
-
-    K = ncomponents(d)
     p = probs(d)
-    lp = Vector{eltype(p)}(undef, K)
-    m = -Inf   # m <- the maximum of log(p(cs[i], x)) + log(pri[i])
-    @inbounds for i in eachindex(p)
-        pi = p[i]
-        if pi > 0.0
-            # lp[i] <- log(p(cs[i], x)) + log(pri[i])
-            lp_i = logpdf(component(d, i), x) + log(pi)
-            lp[i] = lp_i
-            if lp_i > m
-                m = lp_i
-            end
-        end
-    end
-    v = 0.0
-    @inbounds for i = 1:K
-        if p[i] > 0.0
-            v += exp(lp[i] - m)
-        end
-    end
-    return m + log(v)
+    lp = logsumexp(log(pi) + logpdf(component(d, i), x) for (i, pi) in enumerate(p) if !iszero(pi))
+    return lp
 end
 
 function _mixlogpdf!(r::AbstractArray, d::AbstractMixtureModel, x)

test/mixture.jl

@@ -18,15 +18,15 @@ function test_mixture(g::UnivariateMixture, n::Int, ns::Int,
     end
 
     K = ncomponents(g)
-    pr = probs(g)
+    pr = @inferred(probs(g))
     @assert length(pr) == K
 
     # mean
     mu = 0.0
     for k = 1:K
         mu += pr[k] * mean(component(g, k))
     end
-    @test mean(g) ≈ mu
+    @test @inferred(mean(g)) ≈ mu
 
     # evaluation of cdf
     cf = zeros(T, n)
@@ -38,7 +38,7 @@ function test_mixture(g::UnivariateMixture, n::Int, ns::Int,
     end
 
     for i = 1:n
-        @test cdf(g, X[i]) ≈ cf[i]
+        @test @inferred(cdf(g, X[i])) ≈ cf[i]
     end
     @test cdf.(g, X) ≈ cf
 
@@ -58,16 +58,16 @@ function test_mixture(g::UnivariateMixture, n::Int, ns::Int,
     mix_lp0 = log.(mix_p0)
 
     for i = 1:n
-        @test pdf(g, X[i])                  ≈ mix_p0[i]
-        @test logpdf(g, X[i])               ≈ mix_lp0[i]
-        @test componentwise_pdf(g, X[i])    ≈ vec(P0[i,:])
-        @test componentwise_logpdf(g, X[i]) ≈ vec(LP0[i,:])
+        @test @inferred(pdf(g, X[i])) ≈ mix_p0[i]
+        @test @inferred(logpdf(g, X[i])) ≈ mix_lp0[i]
+        @test @inferred(componentwise_pdf(g, X[i])) ≈ vec(P0[i,:])
+        @test @inferred(componentwise_logpdf(g, X[i])) ≈ vec(LP0[i,:])
     end
 
-    @test pdf.(g, X)                  ≈ mix_p0
-    @test logpdf.(g, X)               ≈ mix_lp0
-    @test componentwise_pdf(g, X)    ≈ P0
-    @test componentwise_logpdf(g, X) ≈ LP0
+    @test @inferred(map(Base.Fix1(pdf, g), X)) ≈ mix_p0
+    @test @inferred(map(Base.Fix1(logpdf, g), X)) ≈ mix_lp0
+    @test @inferred(componentwise_pdf(g, X)) ≈ P0
+    @test @inferred(componentwise_logpdf(g, X)) ≈ LP0
 
     # sampling
     if (T <: AbstractFloat)
@@ -94,15 +94,15 @@ function test_mixture(g::MultivariateMixture, n::Int, ns::Int,
     end
 
     K = ncomponents(g)
-    pr = probs(g)
+    pr = @inferred(probs(g))
     @assert length(pr) == K
 
     # mean
     mu = zeros(length(g))
     for k = 1:K
         mu .+= pr[k] .* mean(component(g, k))
     end
-    @test mean(g) ≈ mu
+    @test @inferred(mean(g)) ≈ mu
 
     # evaluation
     P0 = zeros(n, K)
@@ -121,20 +121,20 @@ function test_mixture(g::MultivariateMixture, n::Int, ns::Int,
 
     for i = 1:n
         x_i = X[:,i]
-        @test pdf(g, x_i)                  ≈ mix_p0[i]
-        @test logpdf(g, x_i)               ≈ mix_lp0[i]
-        @test componentwise_pdf(g, x_i)    ≈ vec(P0[i,:])
-        @test componentwise_logpdf(g, x_i) ≈ vec(LP0[i,:])
+        @test @inferred(pdf(g, x_i)) ≈ mix_p0[i]
+        @test @inferred(logpdf(g, x_i)) ≈ mix_lp0[i]
+        @test @inferred(componentwise_pdf(g, x_i)) ≈ vec(P0[i,:])
+        @test @inferred(componentwise_logpdf(g, x_i)) ≈ vec(LP0[i,:])
     end
 #=
     @show g
     @show size(X)
     @show size(mix_p0)
 =#
-    @test pdf(g, X)                  ≈ mix_p0
-    @test logpdf(g, X)               ≈ mix_lp0
-    @test componentwise_pdf(g, X)    ≈ P0
-    @test componentwise_logpdf(g, X) ≈ LP0
+    @test @inferred(pdf(g, X)) ≈ mix_p0
+    @test @inferred(logpdf(g, X)) ≈ mix_lp0
+    @test @inferred(componentwise_pdf(g, X)) ≈ P0
+    @test @inferred(componentwise_logpdf(g, X)) ≈ LP0
 
     # sampling
     if ismissing(rng)
@@ -172,8 +172,8 @@ end
          "rand(rng, ...)" => MersenneTwister(123))
 
     @testset "Testing UnivariateMixture" begin
-        g_u = MixtureModel(Normal, [(0.0, 1.0), (2.0, 1.0), (-4.0, 1.5)], [0.2, 0.5, 0.3])
-        @test isa(g_u, MixtureModel{Univariate, Continuous, Normal})
+        g_u = MixtureModel(Normal{Float64}, [(0.0, 1.0), (2.0, 1.0), (-4.0, 1.5)], [0.2, 0.5, 0.3])
+        @test isa(g_u, MixtureModel{Univariate,Continuous,<:Normal})
         @test ncomponents(g_u) == 3
         test_mixture(g_u, 1000, 10^6, rng)
         test_params(g_u)