joakim/MinPakke.jl
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Fixed many small mistakes

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Joakim Skogholt 2023-05-18 13:43:18 +02:00
parent e0d9a447df
commit 2b47088921
1 changed files with 6 additions and 6 deletions
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src/TR.jl
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@ -137,13 +137,13 @@ return U
end end
""" """
function TRVirCV(X, y, lambdas, regType="L2", regParam1=0, regParam2=1e-14) function TRVirCV(X, y, segments, lambdas, regType="L2", regParam1=0, regParam2=1e-14)
Segmented virtual cross-validation (VirCV) for TR models. Segmented virtual cross-validation (VirCV) for TR models.
Outputs: b, press, lambda_min, lambda_min_ind, GCV Outputs: b, press, lambda_min, lambda_min_ind, GCV
b are (virtual) press-minimal regression coefficients. b are (virtual) press-minimal regression coefficients.
""" """
function TRVirCV(X, y, lambdas, regType="L2", regParam1=0, regParam2=1e-14) function TRVirCV(X, y, segments, lambdas, regType="L2", regParam1=0, regParam2=1e-14)
U_segments = TRSegmentOrth(X, segments); U_segments = TRSegmentOrth(X, segments);
bs = vec(sum(U_segments, dims=1).^2); bs = vec(sum(U_segments, dims=1).^2);
@ -181,23 +181,23 @@ end
""" """
function TRSegCV(X, y, lambdas, folds, regType="L2", regParam1=0, regParam2=1e-14) function TRSegCV(X, y, lambdas, cv, regType="L2", regParam1=0, regParam2=1e-14)
Segmented cross-validation based on the Sherman-Morrison-Woodbury updating formula. Segmented cross-validation based on the Sherman-Morrison-Woodbury updating formula.
Inputs: Inputs:
- X : Data matrix - X : Data matrix
- y : Response vector - y : Response vector
- lambdas : Vector of regularization parameter values - lambdas : Vector of regularization parameter values
- folds : Vector of length n indicating segment membership for each sample - cv : Vector of length n indicating segment membership for each sample
- regType, regParam1, regParam2 : Inputs to regularizationMatrix function - regType, regParam1, regParam2 : Inputs to regularizationMatrix function
Outputs: rmsecv, b, lambda_min, lambda_min_ind. Outputs: rmsecv, b, lambda_min, lambda_min_ind.
b are regression coefficients corresponding to the lambda value minimising the CV-error. b are regression coefficients corresponding to the lambda value minimising the CV-error.
""" """
function TRSegCV(X, y, lambdas, folds, regType="L2", regParam1=0, regParam2=1e-14) function TRSegCV(X, y, lambdas, cv, regType="L2", regParam1=0, regParam2=1e-14)
TR = TRSVDDecomp(X, regType, regParam1, regParam2); TR = TRSVDDecomp(X, regType, regParam1, regParam2);
n_seg = maximum(folds); n_seg = maximum(cv);
n_lambdas = length(lambdas); n_lambdas = length(lambdas);
my = mean(y); my = mean(y);
y = y .- my; y = y .- my;