TRSegCVUpdateFair

src/TR.jl

@@ -49,8 +49,8 @@ for i = 1:n
     ys = ydata .- my;
     U, s, V = svd(Xs, full=false);
         
-    denom       = broadcast(.+, broadcast(./, lambdasu, s'), s')'; # denom = lambda/s + lambda = (lambda + s's) / lambda
-    denom2      = broadcast(.+, ones(n-1), broadcast(./, lambdasu', s.^2)) # denom2 = 1 + lambda/(s's) = (s's + lambda) / (s's)
+    denom       = broadcast(.+, broadcast(./, lambdasu, s'), s')';
+    denom2      = broadcast(.+, ones(n-1), broadcast(./, lambdasu', s.^2))
    
     # Calculating regression coefficients and residual
     bcoeffs    = V * broadcast(./, (U' * ys), denom) .+ bOld .- V * broadcast(./, V' * bOld, denom2);   
@@ -525,3 +525,40 @@ rmsecvman = sqrt.(1/n .* rmsecvman);
 return rmsecvman
 end
 
+
+"""
+K-fold CV for the Ridge regression problem, using the 'SVD-trick' for calculating the regression coefficients.
+"""
+function TRSegCVUpdateFair(X, y, lambdas, cv, bOld)
+
+cv = cvfolds
+
+n, p      = size(X);
+rmsecvman = zeros(length(lambdasu));
+nfolds    = length(unique(cv));
+
+for i = 1:nfolds
+    inds  = (cv .== i);
+    Xdata = X[vec(.!inds),:];
+    ydata = y[vec(.!inds)];
+
+    mX = mean(Xdata, dims=1);
+    my = mean(ydata);
+    Xs = Xdata .- mX;
+    ys = ydata .- my;
+        
+    U, s, V = svd(Xs, full=false);
+        
+    denom       = broadcast(.+, broadcast(./, lambdasu, s'), s')';
+    denom2      = broadcast(.+, ones(n-sum(inds)), broadcast(./, lambdasu', s.^2));
+   
+    # Calculating regression coefficients
+    bcoeffs    = V * broadcast(./, (U' * ys), denom) .+ bOld .- V * broadcast(./, V' * bOld, denom2);
+    rmsecvman += sum((y[vec(inds)] .- ((X[vec(inds),:] .- mX) * bcoeffs .+ my)).^2, dims=1)';
+
+end
+
+rmsecvman = sqrt.(1/n .* rmsecvman);
+
+    return rmsecvman
+end

src/Ting.jl

@@ -26,6 +26,7 @@ export plegendre
 export TRLooCVUpdateFair
 export TRLooCVUpdateNaive
 export TRSegCVUpdateNaive
+export TRSegCVUpdateFair
 
 include("convenience.jl")
 include("TR.jl")