added conversions between element types (#46)

Now that element types of PDMats can vary, and we might want to promote element types for multiple parameters in Distributions, it would be handy to have PDMats take care of conversion between matrices with different element types.

src/PDMats.jl

@@ -5,7 +5,7 @@ module PDMats
     using Compat
 
     import Base: +, *, \, /, ==
-    import Base: full, logdet, inv, diag, diagm, scale, scale!, eigmax, eigmin
+    import Base: full, logdet, inv, diag, diagm, scale, scale!, eigmax, eigmin, convert
 
     export
         # Types

src/pdiagmat.jl

@@ -14,6 +14,8 @@ end
 
 PDiagMat(v::Vector) = PDiagMat(v, ones(v)./v)
 
+### Conversion
+convert{T<:Real}(::Type{PDiagMat{T}}, a::PDiagMat) = PDiagMat(convert(Vector{T}, a.diag))
 
 ### Basics
 

src/pdmat.jl

@@ -17,6 +17,9 @@ PDMat(mat::Matrix) = PDMat(mat,cholfact(mat))
 PDMat(mat::Symmetric) = PDMat(full(mat))
 PDMat(fac::CholType) = PDMat(full(fac),fac)
 
+### Conversion
+convert{T<:Real}(::Type{PDMat{T}}, a::PDMat) = PDMat(convert(Matrix{T}, a.mat))
+
 ### Basics
 
 dim(a::PDMat) = a.dim

src/pdsparsemat.jl

@@ -16,6 +16,9 @@ end
 PDSparseMat(mat::SparseMatrixCSC) = PDSparseMat(mat, cholfact(mat))
 PDSparseMat(fac::CholTypeSparse) = PDSparseMat(sparse(fac) |> x -> x*x', fac)
 
+### Conversion
+convert{T<:Real}(::Type{PDSparseMat{T}}, a::PDSparseMat) = PDSparseMat(convert(SparseMatrixCSC{T}, a.mat))
+
 ### Basics
 
 dim(a::PDSparseMat) = a.dim

src/scalmat.jl

@@ -6,7 +6,11 @@ immutable ScalMat{T<:Real} <: AbstractPDMat{T}
   inv_value::T
 end
 
-ScalMat(d::Int,v::Real) = ScalMat{typeof(v)}(d, v, one(v) / v)
+ScalMat(d::Int,v::Real) = ScalMat{typeof(one(v)/v)}(d, v, one(v) / v)
+
+### Conversion
+# can rewrite convert(T, a.value) as T(a.value) when we drop v0.3 support
+convert{T<:Real}(::Type{ScalMat{T}}, a::ScalMat) = ScalMat(a.dim, convert(T, a.value))
 
 ### Basics
 

test/pdmtypes.jl

@@ -12,6 +12,7 @@ for T in [Float64,Float32]
   #for Julia v0.3, do not test sparse matrices with Float32 (see issue #14076)
   (T == Float64 || VERSION >= v"0.4.2") && (s = speye(T,2,2) ; @test PDSparseMat(s,cholfact(s)).mat == PDSparseMat(s).mat == PDSparseMat(cholfact(s)).mat)
 
+
   #test the functionality
   M = convert(Array{T,2},[4. -2. -1.; -2. 5. -1.; -1. -1. 6.])
   V = convert(Array{T,1},[1.5, 2.5, 2.0])
@@ -23,3 +24,14 @@ for T in [Float64,Float32]
   #for Julia v0.3, do not test sparse matrices with Float32 (see issue #14076)
   (T == Float64 || VERSION >= v"0.4.2") && call_test_pdmat(PDSparseMat(sparse(M)),M)
 end
+
+m = eye(Float32,2)
+@test convert(PDMat{Float64}, PDMat(m)).mat == PDMat(convert(Array{Float64}, m)).mat
+m = ones(Float32,2)
+@test convert(PDiagMat{Float64}, PDiagMat(m)).diag == PDiagMat(convert(Array{Float64}, m)).diag
+x = one(Float32); d = 4
+@test convert(ScalMat{Float64}, ScalMat(d, x)).value == ScalMat(d, convert(Float64, x)).value
+if VERSION >= v"0.4.2"
+    s = speye(Float32, 2, 2)
+    @test convert(PDSparseMat{Float64}, PDSparseMat(s)).mat == PDSparseMat(convert(SparseMatrixCSC{Float64}, s)).mat
+end