Merge pull request #115 from JuliaStats/an/noarpack

Remove eigmin and eigmax definitions for PDSparseMat and thereby

Author: andreasnoack

.travis.yml

@@ -5,8 +5,8 @@ os:
   - osx
 
 julia:
-  - 0.7
   - 1.0
+  - 1
   - nightly
 
 notifications:

Project.toml

@@ -3,12 +3,10 @@ uuid = "90014a1f-27ba-587c-ab20-58faa44d9150"
 version = "0.9.12"
 
 [deps]
-Arpack = "7d9fca2a-8960-54d3-9f78-7d1dccf2cb97"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 SuiteSparse = "4607b0f0-06f3-5cda-b6b1-a6196a1729e9"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [compat]
-Arpack = ">=0.2"
-julia = "0.7, 1"
+julia = "1"

src/PDMats.jl

@@ -58,8 +58,6 @@ module PDMats
     include("generics.jl")
     include("addition.jl")
 
-    include("testutils.jl")
-
     include("deprecates.jl")
 
 end # module

src/pdsparsemat.jl

@@ -1,5 +1,3 @@
-using Arpack
-
 """
 Sparse positive definite matrix together with a Cholesky factorization object.
 """
@@ -55,9 +53,6 @@ end
 
 Base.inv(a::PDSparseMat{T}) where {T<:Real} = PDMat( a\eye(T,a.dim) )
 LinearAlgebra.logdet(a::PDSparseMat) = logdet(a.chol)
-LinearAlgebra.eigmax(a::PDSparseMat{T}) where {T<:Real} = convert(T,eigs(convert(SparseMatrixCSC{Float64,Int},a.mat), which=:LM, nev=1, ritzvec=false)[1][1]) #to avoid type instability issues in eigs, see e.g., julia issue #13929
-LinearAlgebra.eigmin(a::PDSparseMat{T}) where {T<:Real} = convert(T,eigs(convert(SparseMatrixCSC{Float64,Int},a.mat), which=:SM, nev=1, ritzvec=false)[1][1]) #to avoid type instability issues in eigs, see e.g., julia issue #13929
-
 
 ### whiten and unwhiten
 

test/pdmtypes.jl

@@ -2,8 +2,6 @@
 using LinearAlgebra, PDMats, SparseArrays, SuiteSparse
 using Test
 
-call_test_pdmat(p::AbstractPDMat,m::Matrix) = test_pdmat(p,m,cmat_eq=true,verbose=1)
-
 for T in [Float64, Float32]
     #test that all external constructors are accessible
     m = Matrix{T}(I, 2, 2)
@@ -20,10 +18,10 @@ for T in [Float64, Float32]
     V = convert(Array{T,1}, [1.5, 2.5, 2.0])
     X = convert(T,2.0)
 
-    call_test_pdmat(PDMat(M), M) #tests of PDMat
-    call_test_pdmat(PDiagMat(V), Matrix(Diagonal(V))) #tests of PDiagMat
-    call_test_pdmat(ScalMat(3,x), x*Matrix{T}(I, 3, 3)) #tests of ScalMat
-    call_test_pdmat(PDSparseMat(sparse(M)), M)
+    test_pdmat(PDMat(M), M,                        cmat_eq=true, verbose=1) #tests of PDMat
+    test_pdmat(PDiagMat(V), Matrix(Diagonal(V)),   cmat_eq=true, verbose=1) #tests of PDiagMat
+    test_pdmat(ScalMat(3,x), x*Matrix{T}(I, 3, 3), cmat_eq=true, verbose=1) #tests of ScalMat
+    test_pdmat(PDSparseMat(sparse(M)), M,          cmat_eq=true, verbose=1, t_eig=false)
 end
 
 m = Matrix{Float32}(I, 2, 2)

test/runtests.jl

@@ -1,3 +1,4 @@
+include("testutils.jl")
 tests = ["pdmtypes", "addition", "generics", "kron"]
 println("Running tests ...")
 

src/testutils.jl renamed to test/testutils.jl

@@ -4,8 +4,9 @@
 #       the implementation of a subtype of AbstractPDMat
 #
 
-using Test: @test
+using PDMats, SuiteSparse, Test
 
+const HAVE_CHOLMOD = isdefined(SuiteSparse, :CHOLMOD)
 const PDMatType = HAVE_CHOLMOD ? Union{PDMat, PDSparseMat, PDiagMat} : Union{PDMat, PDiagMat}
 
 ## driver function
@@ -244,7 +245,7 @@ end
 
 
 function pdtest_whiten(C::AbstractPDMat, Cmat::Matrix, verbose::Int)
-    Y = chol_lower(Cmat)
+    Y = PDMats.chol_lower(Cmat)
     Q = qr(convert(Array{eltype(C),2},randn(size(Cmat)))).Q
     Y = Y * Q'                    # generate a matrix Y such that Y * Y' = C
     @test Y * Y' ≈ Cmat