Check that PDSparseMat is defined in tests

test/mvnormal.jl

@@ -1,6 +1,9 @@
 # Tests on Multivariate Normal distributions
 
-import PDMats: ScalMat, PDiagMat, PDMat, PDSparseMat
+import PDMats: ScalMat, PDiagMat, PDMat
+if isdefined(PDMats, :PDSparseMat)
+    import PDMats: PDSparseMat
+end
 
 using Distributions
 using LinearAlgebra, Random, Test
@@ -178,45 +181,47 @@ end
 end
 
 ##### Random sampling from MvNormalCanon with sparse precision matrix
-@testset "Sparse MvNormalCanon random sampling" begin
-    n = 20
-    nsamp = 100_000
-    Random.seed!(1234)
-
-    J = sprandn(n, n, 0.25)
-    J = J'J + I
-    Σ = inv(Matrix(J))
-    J = PDSparseMat(J)
-    μ = zeros(n)
-
-    d_prec_sparse = MvNormalCanon(μ, J*μ, J)
-    d_prec_dense = MvNormalCanon(μ, J*μ, PDMat(Matrix(J)))
-    d_cov_dense = MvNormal(μ, PDMat(Symmetric(Σ)))
-
-    x_prec_sparse = rand(d_prec_sparse, nsamp)
-    x_prec_dense = rand(d_prec_dense, nsamp)
-    x_cov_dense = rand(d_cov_dense, nsamp)
-
-    dists = [d_prec_sparse, d_prec_dense, d_cov_dense]
-    samples = [x_prec_sparse, x_prec_dense, x_cov_dense]
-    tol = 1e-16
-    se = sqrt.(diag(Σ) ./ nsamp)
-    #=
-    The cholesky decomposition of sparse matrices is performed by `SuiteSparse.CHOLMOD`,
-    which returns a different decomposition than the `Base.LinearAlgebra` function (which uses
-    LAPACK). These different Cholesky routines produce different factorizations (since the
-    Cholesky factorization is not in general unique).  As a result, the random samples from
-    an `MvNormalCanon` distribution with a sparse precision matrix are not in general
-    identical to those from an `MvNormalCanon` or `MvNormal`, even if the seeds are
-    identical.  As a result, these tests only check for approximate statistical equality,
-    rather than strict numerical equality of the samples.
-    =#
-    for i in 1:3, j in 1:3
-        @test all(abs.(mean(samples[i]) .- μ) .< 2se)
-        loglik_ii = [logpdf(dists[i], samples[i][:, k]) for k in 1:100_000]
-        loglik_ji = [logpdf(dists[j], samples[i][:, k]) for k in 1:100_000]
-        # test average likelihood ratio between distribution i and sample j are small
-        @test mean((loglik_ii .- loglik_ji).^2) < tol
+if isdefined(PDMats, :PDSparseMat)
+    @testset "Sparse MvNormalCanon random sampling" begin
+        n = 20
+        nsamp = 100_000
+        Random.seed!(1234)
+
+        J = sprandn(n, n, 0.25)
+        J = J'J + I
+        Σ = inv(Matrix(J))
+        J = PDSparseMat(J)
+        μ = zeros(n)
+
+        d_prec_sparse = MvNormalCanon(μ, J*μ, J)
+        d_prec_dense = MvNormalCanon(μ, J*μ, PDMat(Matrix(J)))
+        d_cov_dense = MvNormal(μ, PDMat(Symmetric(Σ)))
+
+        x_prec_sparse = rand(d_prec_sparse, nsamp)
+        x_prec_dense = rand(d_prec_dense, nsamp)
+        x_cov_dense = rand(d_cov_dense, nsamp)
+
+        dists = [d_prec_sparse, d_prec_dense, d_cov_dense]
+        samples = [x_prec_sparse, x_prec_dense, x_cov_dense]
+        tol = 1e-16
+        se = sqrt.(diag(Σ) ./ nsamp)
+        #=
+        The cholesky decomposition of sparse matrices is performed by `SuiteSparse.CHOLMOD`,
+        which returns a different decomposition than the `Base.LinearAlgebra` function (which uses
+        LAPACK). These different Cholesky routines produce different factorizations (since the
+        Cholesky factorization is not in general unique).  As a result, the random samples from
+        an `MvNormalCanon` distribution with a sparse precision matrix are not in general
+        identical to those from an `MvNormalCanon` or `MvNormal`, even if the seeds are
+        identical.  As a result, these tests only check for approximate statistical equality,
+        rather than strict numerical equality of the samples.
+        =#
+        for i in 1:3, j in 1:3
+            @test all(abs.(mean(samples[i]) .- μ) .< 2se)
+            loglik_ii = [logpdf(dists[i], samples[i][:, k]) for k in 1:100_000]
+            loglik_ji = [logpdf(dists[j], samples[i][:, k]) for k in 1:100_000]
+            # test average likelihood ratio between distribution i and sample j are small
+            @test mean((loglik_ii .- loglik_ji).^2) < tol
+        end
     end
 end