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Fixed variable names

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Joakim Skogholt 2024-04-25 18:15:54 +02:00
parent a0acdc794d
commit 3afac0a5d4
1 changed files with 24 additions and 24 deletions
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src/TR.jl
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@ -5,7 +5,7 @@ In practice the most naive way of approaching the update problem
function TRLooCVUpdateNaive(X, y, lambdasu, bOld) function TRLooCVUpdateNaive(X, y, lambdasu, bOld)
n, p = size(X); n, p = size(X);
rmsecvman = zeros(length(lambdasu)); rmsecv = zeros(length(lambdasu));
for i = 1:n for i = 1:n
inds = setdiff(1:n, i); inds = setdiff(1:n, i);
@ -24,9 +24,9 @@ for i = 1:n
end end
end end
rmsecvman = sqrt.(1/n .* rmsecvman); rmsecv = sqrt.(1/n .* rmsecv);
return rmsecvman return rmsecv
end end
""" """
@ -36,7 +36,7 @@ Hence regression coefficients are calculated for all lambda values
function TRLooCVUpdateFair(X, y, lambdasu, bOld) function TRLooCVUpdateFair(X, y, lambdasu, bOld)
n, p = size(X); n, p = size(X);
rmsecvman = zeros(length(lambdasu)) rmsecv = zeros(length(lambdasu))
for i = 1:n for i = 1:n
inds = setdiff(1:n, i); inds = setdiff(1:n, i);
@ -54,12 +54,12 @@ for i = 1:n
# Calculating regression coefficients and residual # Calculating regression coefficients and residual
bcoeffs = V * broadcast(./, (U' * ys), denom) .+ bOld .- V * broadcast(./, V' * bOld, denom2); bcoeffs = V * broadcast(./, (U' * ys), denom) .+ bOld .- V * broadcast(./, V' * bOld, denom2);
rmsecvman += ((y[i] .- ((X[i,:]' .- mX) * bcoeffs .+ my)).^2)'; rmsecv += ((y[i] .- ((X[i,:]' .- mX) * bcoeffs .+ my)).^2)';
end end
rmsecvman = sqrt.(1/n .* rmsecvman); rmsecv = sqrt.(1/n .* rmsecv);
return rmsecvman return rmsecv
end end
""" """
@ -501,7 +501,7 @@ The LS problem is solved explicitly and no shortcuts are used.
function TRSegCVUpdateNaive(X, y, lambdas, cvfolds, bOld) function TRSegCVUpdateNaive(X, y, lambdas, cvfolds, bOld)
n, p = size(X); n, p = size(X);
rmsecvman = zeros(length(lambdas)); rmsecv = zeros(length(lambdas));
nfolds = length(unique(cvfolds)); nfolds = length(unique(cvfolds));
for j = 1:length(lambdas) for j = 1:length(lambdas)
@ -516,13 +516,13 @@ for j = 1:length(lambdas)
ys = ydata .- my; ys = ydata .- my;
betas = [Xs; sqrt(lambdas[j]) * I(p)] \ [ys; sqrt(lambdas[j]) * bOld]; 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); rmsecv[j] += sum((y[vec(inds)] - ((X[vec(inds),:] .- mX) * betas .+ my)).^2);
end end
end end
rmsecvman = sqrt.(1/n .* rmsecvman); rmsecv = sqrt.(1/n .* rmsecv);
return rmsecvman return rmsecv
end end
@ -532,7 +532,7 @@ K-fold CV for the Ridge regression update problem, using the 'SVD-trick' for cal
function TRSegCVUpdateFair(X, y, lambdas, cv, bOld) function TRSegCVUpdateFair(X, y, lambdas, cv, bOld)
n, p = size(X); n, p = size(X);
rmsecvman = zeros(length(lambdas)); rmsecv = zeros(length(lambdas));
nfolds = length(unique(cv)); nfolds = length(unique(cv));
for i = 1:nfolds for i = 1:nfolds
@ -552,13 +552,13 @@ for i = 1:nfolds
# Calculating regression coefficients # Calculating regression coefficients
bcoeffs = V * broadcast(./, (U' * ys), denom) .+ bOld .- V * broadcast(./, V' * bOld, denom2); 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)'; rmsecv += sum((y[vec(inds)] .- ((X[vec(inds),:] .- mX) * bcoeffs .+ my)).^2, dims=1)';
end end
rmsecvman = sqrt.(1/n .* rmsecvman); rmsecv = sqrt.(1/n .* rmsecv);
return rmsecvman return rmsecv
end end
""" """