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Added TRSegCVUpdateNaive

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Joakim Skogholt 2024-04-24 13:25:25 +02:00
parent c094f7b7e9
commit f50a442350
2 changed files with 64 additions and 0 deletions
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63
src/TR.jl
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@ -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

1
src/Ting.jl
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@ -25,6 +25,7 @@ export TRSegCVUpdate
export plegendre
export TRLooCVUpdateFair
export TRLooCVUpdateNaive
export TRSegCVUpdateNaive
include("convenience.jl")
include("TR.jl")