fixes for julia 0.5-dev

jumutc · Dec 2, 2015 · 62f1766 · 62f1766
1 parent e7be58d
commit 62f1766
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Showing 3 changed files with 8 additions and 9 deletions.
diff --git a/src/algorithms/adaptive_l1rda_alg.jl b/src/algorithms/adaptive_l1rda_alg.jl
@@ -64,9 +64,9 @@ function adaptive_l1rda_alg(dfunc::Function, X, Y, λ::Float64, γ::Float64, ρ:
             # do not perform sparse(...) and filter and map over SparceMatrixCSC
             # because Garbage Collection performs realy badly in the tight loops
             h = t>1 ? h + g_new.^2 : sparse(ρ+g_new.^2)
-            h_sq = SparseVector(d,h.rowval,-(t*γ)./sqrt(h.nzval))
-            gs = SparseVector(d,g.rowval,sign(g.nzval))
-            w = h_sq.*(sparsevec(g) .- λ.*gs); ind = abs(g.nzval).>λ
+            h_sq = SparseMatrixCSC(d,1,h.colptr,h.rowval,-(t*γ)./sqrt(h.nzval))
+            gs = SparseMatrixCSC(d,1,g.colptr,g.rowval,sign(g.nzval))
+            w = h_sq.*(g - λ.*gs); ind = abs(g.nzval) .> λ
             w = isempty(ind) ? w_prev : reduce_sparsevec(w,find(ind))
         end
 

diff --git a/src/algorithms/dropout_alg.jl b/src/algorithms/dropout_alg.jl
@@ -55,9 +55,8 @@ function dropout_alg(dfunc::Function, X, Y, λ::Float64, k::Int, max_iter::Int,
             dropout = map(rand, prob)
         else
             bern_vars = map(f_sample,w.nzval)
-            dropout = SparseVector(d,w.nzind,bern_vars)
+            dropout = SparseMatrixCSC(d,1,w.colptr,w.rowval,bern_vars)
             dropout = reduce_sparsevec(dropout,find(bern_vars))
-            grad = sparsevec(grad)
         end
 
         # do a gradient descent step

diff --git a/src/loss_derivative.jl b/src/loss_derivative.jl
@@ -16,10 +16,10 @@
 #
 
 # Solution evaluation at sample(s) At with or w/o labels yt
-evaluate(At::SparseMatrixCSC,yt,w) = map(i->sum(At[:,i].*w),1:1:length(yt)).*yt
-evaluate(At::SparseMatrixCSC,w)    = map(i->sum(At[:,i].*w),1:1:size(At,2))
-evaluate(At::SparseVector,yt,w) = sum(At.*w).*yt
-evaluate(At::SparseVector,w)    = sum(At.*w)
+evaluate(At::AbstractSparseMatrix,yt,w) = map(i->sum(At[:,i].*w),1:1:length(yt)).*yt
+evaluate(At::AbstractSparseMatrix,w)    = map(i->sum(At[:,i].*w),1:1:size(At,2))
+evaluate(At::AbstractSparseVector,yt,w) = sum(At.*w).*yt
+evaluate(At::AbstractSparseVector,w)    = sum(At.*w)
 evaluate(At::Matrix,yt,w) = map(i->sum(At[:,i].*w),1:1:length(yt)).*yt
 evaluate(At::Matrix,w)    = map(i->sum(At[:,i].*w),1:1:size(At,2))
 evaluate(At::Vector,yt,w) = dot(At,w[:])*yt