Added TRSegCVUpdateNaive

src/TR.jl

@@ -462,3 +462,66 @@ R = Matrix(factorisation.R);
 
 return Q, R
 end
+
+"""
+"Manual" k-fold cv for solving the Ridge regression problem.
+The LS problem is solved explicitly and no shortcuts are used.
+"""
+function TRSegCVNaive(X, y, lambdas, cvfolds)
+    
+n, p      = size(X);
+rmsecvman = zeros(length(lambdas));
+nfolds    = length(unique(cvfolds));
+
+for j = 1:length(lambdas)
+    for i = 1:nfolds
+        inds  = (cvfolds .== i);
+        Xdata = X[vec(.!inds),:];
+        ydata = y[vec(.!inds)];
+
+        mX = mean(Xdata, dims=1);    
+        my = mean(ydata);    
+        Xs = Xdata .- mX;
+        ys = ydata .- my;    
+        
+        betas         = [Xs; sqrt(lambdas[j]) * I(p)] \ [ys; zeros(p,1)];
+        rmsecvman[j] += sum((y[vec(inds)] - ((X[vec(inds),:] .- mX) * betas .+ my)).^2);
+    end
+end
+
+rmsecvman = sqrt.(1/n .* rmsecvman);
+    
+return rmsecvman
+end
+
+"""
+"Manual" k-fold cv for solving the Ridge regression problem update.
+The LS problem is solved explicitly and no shortcuts are used.
+"""
+function TRSegCVUpdateNaive(X, y, lambdas, cvfolds, bOld)
+    
+n, p      = size(X);
+rmsecvman = zeros(length(lambdas));
+nfolds    = length(unique(cvfolds));
+
+for j = 1:length(lambdas)
+    for i = 1:nfolds
+        inds  = (cvfolds .== i);
+        Xdata = X[vec(.!inds),:];
+        ydata = y[vec(.!inds)];
+
+        mX = mean(Xdata, dims=1);    
+        my = mean(ydata);    
+        Xs = Xdata .- mX;
+        ys = ydata .- my;    
+        
+        betas         = [Xs; sqrt(lambdas[j]) * I(p)] \ [ys; sqrt(lambdas[j]) * bOld];
+        rmsecvman[j] += sum((y[vec(inds)] - ((X[vec(inds),:] .- mX) * betas .+ my)).^2);
+    end
+end
+
+rmsecvman = sqrt.(1/n .* rmsecvman);
+    
+return rmsecvman
+end
+

src/Ting.jl

@@ -25,6 +25,7 @@ export TRSegCVUpdate
 export plegendre
 export TRLooCVUpdateFair
 export TRLooCVUpdateNaive
+export TRSegCVUpdateNaive
 
 include("convenience.jl")
 include("TR.jl")