Fixed update regression coefficients and added Naive and Fair function for loocv update

src/TR.jl

@@ -1,5 +1,66 @@
+"""
+Solves the model update problem explicitly as a least squares problem with stacked matrices.
+In practice the most naive way of approaching the update problem
+"""
+function TRLooCVUpdateNaive(X, y, lambdasu, bold)
+    
+n, p      = size(X);
+rmsecvman = zeros(length(lambdasu));
 
+for i = 1:n
+    inds  = setdiff(1:n, i);
+    Xdata = X[inds,:];
+    ydata = y[inds];
 
+    mX = mean(Xdata, dims=1);    
+    my = mean(ydata);    
+    Xs = Xdata .- mX;
+    ys = ydata .- my;    
+    p2 = size(Xdata, 2);
+    
+    for j = 1:length(lambdasu)
+        betas         = [Xs; sqrt(lambdasu[j]) * I(p2)] \ [ys ; sqrt(lambdasu[j]) * bold];
+        rmsecvman[j] += (y[i] - (((X[i,:]' .- mX) * betas)[1] + my))^2;
+    end
+end
+
+rmsecvman = sqrt.(1/n .* rmsecvman);
+    
+return rmsecvman
+end
+
+"""
+Uses the 'svd-trick' for efficient calculation of regression coefficients, but does not use leverage corrections.
+Hence regression coefficients are calculated for all lambda values
+"""
+function TRLooCVUpdateFair(X, y, lambdasu, bold)
+
+n, p      = size(X);
+rmsecvman = zeros(length(lambdasu))
+    
+for i = 1:n
+    inds  = setdiff(1:n, i);
+    Xdata = X[inds,:];
+    ydata = y[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')'; # 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)
+   
+    # Calculating regression coefficients and residual
+    bcoeffs      = V * broadcast(./, (U' * ys), denom) .+ bold .- V * broadcast(./, V' * bold, denom2);   
+    rmsecvman += ((y[i] .- ((X[i,:]' .- mX) * bcoeffs .+ my)).^2)';
+end
+
+rmsecvman = sqrt.(1/n .* rmsecvman);
+
+return rmsecvman, bcoeffs
+end
 
 """
 Fast k-fold cv for updating regression coefficients
@@ -70,7 +131,7 @@ end
 # Calculating rmsecv and regression coefficients
 press   = sum(rescv.^2, dims=1)';
 rmsecv  = sqrt.(1/n .* press);
-bcoeffs = V * broadcast(./, (U' * y), denom);
+bcoeffs      = V * broadcast(./, (U' * ys), denom) .+ bold .- V * broadcast(./, V' * bold, denom2);
 bcoeffs = regMat \ bcoeffs;
 
 # Creating regression coefficients for uncentred data
@@ -148,7 +209,7 @@ lambdarmsecv = lambdas[idminrmsecv];
 lambdagcv    = lambdas[idmingcv];
     
 # Calculating regression coefficients
-bcoeffs      = V * broadcast(./, (U' * y), denom);
+bcoeffs      = V * broadcast(./, (U' * ys), denom) .+ bold .- V * broadcast(./, V' * bold, denom2);   
 bcoeffs      = regMat \ bcoeffs;
     
 if my != 0

src/Ting.jl

@@ -23,6 +23,8 @@ export regularizationMatrix
 export TRLooCVUpdate
 export TRSegCVUpdate
 export plegendre
+export TRLooCVUpdateFair
+export TRLooCVUpdateNaive
 
 include("convenience.jl")
 include("TR.jl")