Removed the NE package from src; And run through the src

Signed-off-by: quxiaofeng <[email protected]>

Author: quxiaofeng

src/ClassificationDPL.jl

@@ -8,8 +8,8 @@ function ClassificationDPL(TtData::Matrix{Float64}, DictMat::Array{Any,1}, Encod
     # Class-specific reconstruction error calculation
     for i=1:ClassNum
         @inbounds reconstructedTtData::Matrix{Float64} = DictMat[i] * PredictCoef[(i-1)*DictSize+1:i*DictSize, :]
-        subtract!(reconstructedTtData, TtData)
-        @inbounds Error[i,:] = sumsq(reconstructedTtData, 1)
+        reconstructedTtData -= TtData
+        @inbounds Error[i,:] = sum(abs2(reconstructedTtData), 1)
     end
     Distance::Matrix{Float64}, PredictInd::Matrix{Int64} = findmin(Error, 1)
     PredictLabel = [ind2sub(size(Error), PredictInd[i])[1] for i = 1:size(PredictInd, 2)]

src/ProjectiveDictionaryPairLearning.jl

@@ -33,8 +33,9 @@ using MAT # to load and save data
 
 include("TrainDPL.jl")
 include("ClassificationDPL.jl")
+include("normcol_equal")
 
-export dpldemo, TrainDPL, ClassificationDPL, updateA!, updateD!, updateP!, initialization, normcol_lessequal
+export dpldemo, TrainDPL, ClassificationDPL, updateA!, updateD!, updateP!, initialization, normcol_lessequal, normcol_equal
 
 function dpldemo()
     # Load training and testing data
@@ -44,11 +45,11 @@ function dpldemo()
     TtData, TtLabel = data["TtData"], data["TtLabel"]
 
     # Column normalization
-    normalize!(TrData, 2, 1)
-    normalize!(TtData, 2, 1)
+    TrData = normcol_equal(TrData)
+    TtData = normcol_equal(TtData)
 
-    TrLabel = int(TrLabel)
-    TtLabel = int(TtLabel)
+    TrLabel = round(Int64, TrLabel)
+    TtLabel = round(Int64, TtLabel)
 
     # Parameter setting
     DictSize = 30

src/initialization.jl

@@ -1,4 +1,5 @@
-include(joinpath(dirname(@__FILE__), "updateA!.jl"))
+include("updateA!.jl")
+include("normcol_equal.jl")
 
 # return inv(τ*A*A' + λ*B*B' + γ*I)
 function getinv(τ::Float64, λ::Float64, γ::Float64, A::Matrix{Float64}, B::Matrix{Float64})
@@ -44,12 +45,12 @@ function initialization(Data::Matrix{Float64}, Label::Matrix{Int64}, DictSize::I
 
         DEMO && srand(i)
         TempRand = randn(Dim, DictSize)
-        normalize!(TempRand, 2, 1)
+        TempRand = normcol_equal(TempRand)
         @inbounds DictMat[i] = TempRand
 
         DEMO && srand(2i)
         TempRand = randn(Dim, DictSize)
-        normalize!(TempRand, 2, 1)
+        TempRand = normcol_equal(TempRand)
         @inbounds P[i]       = TempRand'
 
         @inbounds TempDataC = Data[:, find(Label .!=i)]

src/normcol_equal.jl

@@ -0,0 +1,5 @@
+function normcol_equal(matin)
+# solve the proximal problem
+# matout = argmin||matout-matin||_F^2, s.t. matout(:,i)=1
+    matin ./ repmat(sqrt(sum(matin .^ 2, 1) + eps()), size(matin, 1), 1)
+end

src/normcol_lessequal.jl

@@ -1,5 +1,5 @@
 # solve the proximal problem
 # matout = argmin||matout-matin||_F^2, s.t. matout(:,i)<=1
 function normcol_lessequal(matin::Matrix{Float64})
-    broadcast(/, matin, max(1.0, sqrt(sumsq(matin,1))))
+    broadcast(/, matin, max(1.0, sqrt(sum(abs2(matin), 2))))
 end

src/updateA!.jl

@@ -10,7 +10,7 @@ function updateA!(A::Array{Any,1}, D::Array{Any,1}, DataMat::Array{Any,1}, P::Ar
         tempDictDataCoef::Matrix{Float64}   = TempDict' * TempData
         @inbounds C::Matrix{Float64}        = P[i] * TempData
         diagadd!(tempDictCoef, τ)
-        fma!(tempDictDataCoef, C, τ)
+        tempDictDataCoef += C .* τ
         @inbounds A[i]                      = tempDictCoef \ tempDictDataCoef
     end
-end
+end

src/updateD!.jl

@@ -24,18 +24,18 @@ function updateD!(D::Array{Any,1}, A::Array{Any,1}, DataMat::Array{Any,1})
         while ERROR > 1e-8 && Iter < 100
 
             tempMat::Matrix{Float64} = TempData*TempCoef'
-            fma!(tempMat, TempS-TempT, ρ) # tempMat <- tempMat + ρ(TempS - TempT)
+            tempMat += (TempS-TempT) .* ρ # tempMat <- tempMat + ρ(TempS - TempT)
             tempMatCoef::Matrix{Float64} = TempCoef*TempCoef'
             diagadd!(tempMatCoef, ρ)
             TempD::Matrix{Float64} = tempMat/tempMatCoef
 
             TempS = normcol_lessequal(TempD+TempT)
             add_sub!(TempT, TempD, TempS) # TemP <- TemP + (TempD-TempS)
             ρ *= rate_ρ
-            ERROR = meansq(preD-TempD)
+            ERROR = mean(abs2(preD-TempD))
             preD = TempD
             Iter += 1
         end
         @inbounds D[i] = preD
     end
-end
+end