Added TRVirSV and TRSegCV + various fixes

src/TR.jl

@@ -1,13 +1,6 @@
 
-
-
-
-
-
-using Optimization
-using OptimizationOptimJL
-
-
+using Optimization        # For numerical minimization of PRESS statistic
+using OptimizationOptimJL # For numerical minimization of PRESS statistic
 
 struct TRSVD
     U::Matrix{Float64}
@@ -40,8 +33,8 @@ end
 """
 ### TO DO: ADD FRACTIONAL DERIVATIVE REGULARIZATION ###
 
-    regularizationMatrix(X; regType="legendre", regParam1=0, regParam2=1e-14)
-    regularizationMatrix(p::Int64; regType="legendre", regParam1=0, regParam2=1e-14)
+    regularizationMatrix(X; regType="L2", regParam1=0, regParam2=1e-14)
+    regularizationMatrix(p::Int64; regType="L2", regParam1=0, regParam2=1e-14)
 
 Calculates and returns square regularization matrix.
 
@@ -130,6 +123,99 @@ end
 
 
 
+"""
+    function TRVirCV(X, y, lambdas, regType="L2", regParam1=0, regParam2=1e-14)
+
+Segmented virtual cross-validation (VirCV) for TR models.
+Outputs: b, press, lambda_min, lambda_min_ind, GCV
+b are (virtual) press-minimal regression coefficients.
+"""
+function TRVirCV(X, y, lambdas, regType="L2", regParam1=0, regParam2=1e-14)
+
+U_segments = TRSegmentOrth(X, segments);
+bs         = vec(sum(U_segments, dims=1).^2);
+
+n, p    = size(X);
+mX      = mean(X, dims=1);    
+X       = X .- mX;
+my      = mean(y);
+y       = vec(y .- my);
+X       = U_segments' * X;
+y       = U_segments' * y;
+regMat  = regularizationMatrix(p; regType, regParam1, regParam2);
+X       = X / regMat;
+U, s, V = svd(X, full=false);
+
+denom  = broadcast(.+, broadcast(./, lambdas, s'), s')';
+H      = broadcast(.+, U.^2 * broadcast(./, s, denom), bs./n);
+resid  = broadcast(.-, y, U * broadcast(./, s .* (U'*y), denom));
+rescv  = broadcast(./, resid, broadcast(.-, 1, H));
+press  = vec(sum(rescv.^2, dims=1));
+#rmsecv = sqrt.(1/n .* press);
+GCV    = vec(broadcast(./, sum(resid.^2, dims=1), mean(broadcast(.-, 1, H), dims=1).^2));
+
+lambda_min, lambda_min_ind = findmin(press);
+lambda_min_ind             = lambda_min_ind[1];
+
+denom2  = broadcast(.+, lambda_min ./ s', s')';
+b       = V * broadcast(./, (U' * y), denom2);
+b       = regMat \ b;
+b       = [my .- mX*b; b];
+
+return b, press, lambda_min, lambda_min_ind, GCV
+end
+
+
+
+"""
+    function TRSegCV(X, y, lambdas, folds, regType="L2", regParam1=0, regParam2=1e-14)
+
+Segmented cross-validation based on the Sherman-Morrison-Woodbury updating formula.
+Inputs:
+    - X : Data matrix
+    - y : Response vector
+    - lambdas : Vector of regularization parameter values
+    - folds : Vector of length n indicating segment membership for each sample
+    - regType, regParam1, regParam2 : Inputs to regularizationMatrix function
+
+Outputs: rmsecv, b, lambda_min, lambda_min_ind.
+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)
+
+TR        = TRSVDDecomp(X, regType, regParam1, regParam2);
+n_seg     = maximum(folds);
+n_lambdas = length(lambdas);
+my        = mean(y);
+y         = y .- my;
+
+denom  = broadcast(.+, broadcast(./, lambdas, TR.s'), TR.s')';
+resid  = broadcast(.-, y, TR.U * broadcast(./, TR.s .* (TR.U'*y), denom));
+rescv  = zeros(TR.n, n_lambdas);
+sdenom = sqrt.(broadcast(./, TR.s, denom))';
+
+for seg in 1:n_seg
+        
+    Useg = TR.U[vec(cv .== seg),:];
+    Id   = 1.0 * I(size(Useg,1)) .- 1/TR.n;
+        
+    for k in 1:n_lambdas
+        Uk                        = Useg .* sdenom[k,:]';
+        rescv[vec(cv .== seg), k] = (Id - Uk * Uk') \ resid[vec(cv .== seg), k];
+    end
+end
+
+press  = sum(rescv.^2, dims=1)';
+rmsecv = sqrt.(1/TR.n .* press);
+    
+lambda_min, lambda_min_ind = findmin(rmsecv)
+lambda_min_ind             = lambda_min_ind[1]
+b                          = TRRegCoeffs(TR, y, lambda_min, my)
+    
+return b, rmsecv, lambda_min, lambda_min_ind
+end
+
+
 """
     TRRegCoeffs(X, y, lambdas, regType="L2", regParam1=0, regParam2=1e-14)
     TRRegCoeffs(TR::TRSVD, y, lambdas, my=0)