generate a bit more test coverage

src/Manifolds.jl

@@ -640,6 +640,7 @@ export Euclidean,
     MultinomialDoubleStochastic,
     MultinomialMatrices,
     MultinomialSymmetric,
+    MultinomialSymmetricPositiveDefinite,
     Oblique,
     OrthogonalMatrices,
     PositiveArrays,

src/manifolds/MultinomialSymmetric.jl

@@ -57,7 +57,7 @@ i.e. is a symmetric matrix with positive entries whose rows sum to one.
 function check_point(M::MultinomialSymmetric, p; kwargs...)
     n = get_parameter(M.size)[1]
     s = check_point(SymmetricMatrices(n, ℝ), p; kwargs...)
-    isnothing(s) && return s
+    !isnothing(s) && return s
     s2 = check_point(MultinomialMatrices(n, n), p; kwargs...)
     return s2
 end
@@ -71,7 +71,7 @@ along any row.
 function check_vector(M::MultinomialSymmetric, p, X; kwargs...)
     n = get_parameter(M.size)[1]
     s = check_vector(SymmetricMatrices(n, ℝ), p, X; kwargs...)
-    isnothing(s) && return s
+    !isnothing(s) && return s
     s2 = check_vector(MultinomialMatrices(n, n), p, X)
     return s2
 end
@@ -155,13 +155,12 @@ function Random.rand!(
     M::MultinomialSymmetric,
     pX;
     vector_at=nothing,
-    σ::Real=one(real(eltype(pX))),
     kwargs...,
 )
-    rand!(rng, pX)
-    pX .*= σ
+    n = get_parameter(M.size)[1]
+    rand!(rng, SymmetricMatrices(n), pX; kwargs...)
     if vector_at === nothing
-        project!(M, pX, pX; kwargs...)
+        project!(M, pX, pX)
     else
         project!(M, pX, vector_at, pX)
     end
@@ -203,10 +202,10 @@ function riemannian_Hessian!(M::MultinomialSymmetric, Y, p, G, H, X)
     # with the small change their X is our p their ξ_X is our X , Hessf is H, Gradf is G
     n = get_parameter(M.size)[1]
     ov = ones(n) # \bf 1
-    I_p = lu(I .+ p)
+    I_p = lu(I + p)
+    γ = G .* p
     α = I_p \ (γ * ov)
     α_sq = (repeat(α, 1, n) .+ repeat(α', n, 1))
-    γ = G .* p
     δ = γ .- α_sq .* p
     γ_dot = H .* p + G .* X
     α_dot = (I_p \ γ_dot .- (I_p \ X) * (I_p \ γ)) * ov

src/manifolds/MultinomialSymmetricPositiveDefinite.jl

@@ -50,3 +50,24 @@ function check_vector(M::MultinomialSymmetricPositiveDefinite, p, X; kwargs...)
     s2 = check_vector(MultinomialMatrices(n, n), p, X)
     return s2
 end
+
+function get_embedding(
+    ::MultinomialSymmetricPositiveDefinite{TypeParameter{Tuple{n}}},
+) where {n}
+    return MultinomialMatrices(n, n)
+end
+function get_embedding(M::MultinomialSymmetricPositiveDefinite{Tuple{Int}})
+    n = get_parameter(M.size)[1]
+    return MultinomialMatrices(n, n; parameter=:field)
+end
+
+function Base.show(
+    io::IO,
+    ::MultinomialSymmetricPositiveDefinite{TypeParameter{Tuple{n}}},
+) where {n}
+    return print(io, "MultinomialSymmetricPositiveDefinite($(n))")
+end
+function Base.show(io::IO, M::MultinomialSymmetricPositiveDefinite{Tuple{Int}})
+    n = get_parameter(M.size)[1]
+    return print(io, "MultinomialSymmetricPositiveDefinite($(n); parameter=:field)")
+end

src/manifolds/Symmetric.jl

@@ -226,6 +226,18 @@ project(::SymmetricMatrices, ::Any, ::Any)
 
 project!(M::SymmetricMatrices, Y, p, X) = (Y .= (X .+ transpose(X)) ./ 2)
 
+function Random.rand!(
+    rng::AbstractRNG,
+    M::SymmetricMatrices,
+    pX;
+    σ::Real=one(real(eltype(pX))),
+    kwargs...,
+)
+    rand!(rng, pX)
+    pX .= (σ / (2 * norm(pX))) .* (pX + pX')
+    return pX
+end
+
 function Base.show(io::IO, ::SymmetricMatrices{TypeParameter{Tuple{n}},F}) where {n,F}
     return print(io, "SymmetricMatrices($(n), $(F))")
 end

test/manifolds/multinomial_doubly_stochastic.jl

@@ -68,4 +68,19 @@ include("../utils.jl")
         @test repr(M) == "MultinomialDoubleStochastic(3; parameter=:field)"
         @test get_embedding(M) === MultinomialMatrices(3, 3; parameter=:field)
     end
+    @testset "random" begin
+        Random.seed!(42)
+        p = rand(M)
+        @test is_point(M, p)
+        X = rand(M; vector_at=p)
+        @test is_vector(M, p, X)
+    end
+    @testset "Riemannian Gradient" begin
+        M = MultinomialDoubleStochastic(3)
+        p = ones(3, 3) ./ 3
+        Y = [1.0; -1.0; 0.0 0.0; -1.0 1.0]
+        G = project(M, p, p .* Y)
+        X = riemannian_gradient(M, p, Y)
+        @test isapprox(M, p, G, X)
+    end
 end

test/manifolds/multinomial_matrices.jl

@@ -55,4 +55,12 @@ include("../utils.jl")
         M = MultinomialMatrices(3, 2; parameter=:field)
         @test repr(M) == "MultinomialMatrices(3, 2; parameter=:field)"
     end
+    @testset "Riemannian Gradient" begin
+        M = MultinomialMatrices(3, 2)
+        p = [0.5 0.4 0.1; 0.5 0.4 0.1]'
+        Y = [1.0; -1.0; 0.0 0.0; -1.0 1.0]
+        G = project(M, p, p .* Y)
+        X = riemannian_gradient(M, p, Y)
+        @test isapprox(M, p, G, X)
+    end
 end

test/manifolds/multinomial_spd.jl

@@ -0,0 +1,12 @@
+include("../utils.jl")
+
+@testset "Multinomial symmetric positive definite matrices" begin
+    @testset "Basics" begin
+        M = MultinomialSymmetricPositiveDefinite(3)
+        Mf = MultinomialSymmetricPositiveDefinite(3; parameter=:field)
+        @test repr(M) == "MultinomialSymmetricPositiveDefinite(3)"
+        @test repr(Mf) == "MultinomialSymmetricPositiveDefinite(3; parameter=:field)"
+        @test get_embedding(M) == MultinomialMatrices(3, 3)
+        @test get_embedding(Mf) == MultinomialMatrices(3, 3; parameter=:field)
+    end
+end

test/manifolds/multinomial_symmetric.jl

@@ -73,4 +73,22 @@ include("../utils.jl")
         @test repr(M) == "MultinomialSymmetric(3; parameter=:field)"
         @test get_embedding(M) === MultinomialMatrices(3, 3; parameter=:field)
     end
+    @testset "random" begin
+        Random.seed!(42)
+        p = rand(M)
+        @test is_point(M, p)
+        X = rand(M; vector_at=p)
+        @test is_vector(M, p, X)
+    end
+    @testset "Hessian call" begin
+        p = ones(3, 3) ./ 3
+        Y = one(p)
+        G = zero(p)
+        H = 0.5 * one(p)
+        X = riemannian_Hessian(M, p, G, H, Y)
+        X2 = similar(X)
+        riemannian_Hessian!(M, X, p, G, H, Y)
+        @test isapprox(M, p, X, X2)
+        @test is_vector(M, p, X)
+    end
 end

test/runtests.jl

@@ -148,6 +148,7 @@ include("utils.jl")
         include_test("manifolds/lorentz.jl")
         include_test("manifolds/multinomial_doubly_stochastic.jl")
         include_test("manifolds/multinomial_symmetric.jl")
+        include_test("manifolds/multinomial_spd.jl")
         include_test("manifolds/positive_numbers.jl")
         include_test("manifolds/probability_simplex.jl")
         include_test("manifolds/projective_space.jl")