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Fixed variable names

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This commit is contained in:
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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48
src/TR.jl
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@ -4,8 +4,8 @@ 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
""" """
@ -35,8 +35,8 @@ 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);
@ -53,13 +53,13 @@ for i = 1:n
denom2 = broadcast(.+, ones(n-1), broadcast(./, lambdasu', s.^2)) denom2 = broadcast(.+, ones(n-1), broadcast(./, lambdasu', s.^2))
# 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
""" """
@ -500,9 +500,9 @@ 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)
for i = 1:nfolds for i = 1:nfolds
@ -515,14 +515,14 @@ for j = 1:length(lambdas)
Xs = Xdata .- mX; Xs = Xdata .- mX;
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
@ -531,9 +531,9 @@ 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
inds = (cv .== i); inds = (cv .== i);
@ -551,14 +551,14 @@ for i = 1:nfolds
denom2 = broadcast(.+, ones(n-sum(inds)), broadcast(./, lambdas', s.^2)); denom2 = broadcast(.+, ones(n-sum(inds)), broadcast(./, lambdas', s.^2));
# 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
""" """