Hopefully finished with general functions
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2 changed files with 43 additions and 6 deletions
43
src/TR.jl
43
src/TR.jl
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@ -470,7 +470,7 @@ The LS problem is solved explicitly and no shortcuts are used.
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function TRSegCVNaive(X, y, lambdas, cvfolds)
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n, p = size(X);
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rmsecvman = zeros(length(lambdas));
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rmsecv = zeros(length(lambdas));
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nfolds = length(unique(cvfolds));
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for j = 1:length(lambdas)
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@ -489,9 +489,9 @@ for j = 1:length(lambdas)
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end
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end
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rmsecvman = sqrt.(1/n .* rmsecvman);
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rmsecv = sqrt.(1/n .* rmsecv);
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return rmsecvman
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return rmsecv
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end
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"""
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@ -527,7 +527,7 @@ end
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"""
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K-fold CV for the Ridge regression problem, using the 'SVD-trick' for calculating the regression coefficients.
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K-fold CV for the Ridge regression update problem, using the 'SVD-trick' for calculating the regression coefficients.
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"""
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function TRSegCVUpdateFair(X, y, lambdas, cv, bOld)
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@ -560,3 +560,38 @@ rmsecvman = sqrt.(1/n .* rmsecvman);
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return rmsecvman
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end
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"""
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K-fold CV for the Ridge regression problem, using the 'SVD-trick' for calculating the regression coefficients.
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"""
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function TRSegCVFair(X, y, lambdas, cv)
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n, p = size(X);
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rmsecv = zeros(length(lambdas));
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nfolds = length(unique(cv));
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for i = 1:nfolds
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inds = (cv .== i);
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Xdata = X[vec(.!inds),:];
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ydata = y[vec(.!inds)];
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mX = mean(Xdata, dims=1);
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my = mean(ydata);
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Xs = Xdata .- mX;
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ys = ydata .- my;
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U, s, V = svd(Xs, full=false);
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denom = broadcast(.+, broadcast(./, lambdas, s'), s')';
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denom2 = broadcast(.+, ones(n-sum(inds)), broadcast(./, lambdas', s.^2));
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# Calculating regression coefficients
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bcoeffs = V * broadcast(./, (U' * ys), denom);
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rmsecv += sum((y[vec(inds)] .- ((X[vec(inds),:] .- mX) * bcoeffs .+ my)).^2, dims=1)';
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end
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rmsecv = sqrt.(1/n .* rmsecv);
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return rmsecv
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end
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@ -27,6 +27,8 @@ export TRLooCVUpdateFair
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export TRLooCVUpdateNaive
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export TRSegCVUpdateNaive
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export TRSegCVUpdateFair
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export TRSegCVNaive
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export TRSegCVFair
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include("convenience.jl")
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include("TR.jl")
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