Fix #83

src/linpred.jl

@@ -1,5 +1,3 @@
-
-
 ## Return the linear predictor vector
 linpred(p::LinPred, f::Real=1.) = p.X * (f == 0. ? p.beta0 : fma(p.beta0, p.delbeta, f))
 
@@ -24,8 +22,6 @@ type DensePredQR{T<:BlasReal} <: DensePred
 end
 DensePredQR{T<:BlasReal}(X::Matrix{T}) = DensePredQR{T}(X, zeros(T,size(X,2)))
 
-cholfact{T<:FP}(p::DensePredQR{T}) = Cholesky{T}(p.qr[:R],'U')
-
 delbeta!{T<:BlasReal}(p::DensePredQR{T}, r::Vector{T}) = (p.delbeta = p.qr\r; p)
 
 type DensePredChol{T<:BlasReal} <: DensePred
@@ -40,20 +36,23 @@ type DensePredChol{T<:BlasReal} <: DensePred
 end
 DensePredChol{T<:BlasReal}(X::Matrix{T}) = DensePredChol{T}(X, zeros(T,size(X,2)))
 
-solve!{T<:BlasReal}(C::Cholesky{T}, B::StridedVecOrMat{T}) = potrs!(C.uplo, C.UL, B)
-
-cholfact{T<:FP}(p::DensePredChol{T}) = (c = p.chol; Cholesky{T}(copy(c.UL),c.uplo))
+if VERSION >= v"0.4.0-dev+122"
+    cholfact{T<:FP}(p::DensePredQR{T}) = Cholesky{T,Matrix{T},:U}(p.qr[:R])
+    cholfact{T<:FP}(p::DensePredChol{T}) = (c = p.chol; typeof(c)(copy(c.UL)))
+else
+    cholfact{T<:FP}(p::DensePredQR{T}) = Cholesky(p.qr[:R], :U)
+    cholfact{T<:FP}(p::DensePredChol{T}) = (c = p.chol; Cholesky(copy(c.UL),c.uplo))
+end
 
 function delbeta!{T<:BlasReal}(p::DensePredChol{T}, r::Vector{T})
-    solve!(p.chol, gemv!('T', 1.0, p.X, r, 0.0, p.delbeta))
+    A_ldiv_B!(p.chol, At_mul_B!(p.delbeta, p.X, r))
     p
 end
 
 function delbeta!{T<:BlasReal}(p::DensePredChol{T}, r::Vector{T}, wt::Vector{T}, scr::Matrix{T})
     vbroadcast!(Multiply(), scr, p.X, wt, 1)
-    fac, info = potrf!('U', gemm!('T', 'N', 1.0, scr, p.X, 0.0, p.chol.UL))
-    info == 0 || error("Singularity detected at column $info of weighted model matrix")
-    solve!(p.chol, gemv!('T', 1.0, scr, r, 0.0, p.delbeta))
+    cholfact!(At_mul_B!(p.chol.UL, scr, p.X), :U)
+    A_ldiv_B!(p.chol, At_mul_B!(p.delbeta, scr, r))
     p
 end