using C for each row

Author: jonathan-taylor

selectiveInference/R/funs.fixed.R

@@ -159,7 +159,8 @@ sigma=NULL, alpha=0.1,
       hsigmaSinv <- solve(hsigmaS) # pinv(hsigmaS)
 
       # Approximate inverse covariance matrix for when (n < p) from lasso_Inference.R
-      htheta <- InverseLinfty(hsigma, n, length(S), verbose=FALSE)
+      useC = TRUE
+      htheta <- InverseLinfty(hsigma, n, length(S), verbose=FALSE, useC=useC)
       # htheta <- InverseLinfty(hsigma, n, verbose=FALSE)
 
       FS = rbind(diag(length(S)),matrix(0,pp-length(S),length(S)))
@@ -269,7 +270,7 @@ fixedLasso.poly=
 ### Functions borrowed and slightly modified from lasso_inference.R
 
 ## Approximates inverse covariance matrix theta
-InverseLinfty <- function(sigma, n, e, resol=1.5, mu=NULL, maxiter=50, threshold=1e-2, verbose = TRUE) {
+InverseLinfty <- function(sigma, n, e, resol=1.5, mu=NULL, maxiter=50, threshold=1e-2, verbose = TRUE, useC = FALSE) {
   # InverseLinfty <- function(sigma, n, resol=1.5, mu=NULL, maxiter=50, threshold=1e-2, verbose = TRUE) {
     isgiven <- 1;
   if (is.null(mu)){
@@ -294,7 +295,11 @@ InverseLinfty <- function(sigma, n, e, resol=1.5, mu=NULL, maxiter=50, threshold
     incr <- 0;
     while ((mu.stop != 1)&&(try.no<10)){
       last.beta <- beta
-      output <- InverseLinftyOneRow(sigma, i, mu, maxiter=maxiter, threshold=threshold)
+      if (useC == FALSE) {
+            output <- InverseLinftyOneRow(sigma, i, mu, maxiter=maxiter, threshold=threshold)
+      } else {
+            output <- InverseLinftyOneRowC(sigma, i, mu, maxiter=maxiter)
+      }
       beta <- output$optsol
       iter <- output$iter
       if (isgiven==1){
@@ -334,7 +339,7 @@ InverseLinfty <- function(sigma, n, e, resol=1.5, mu=NULL, maxiter=50, threshold
   return(M)
 }
 
-InverseLinftyOneRowC <- function (Sigma, i, mu, maxiter=50, threshold=1e-2 ) {
+InverseLinftyOneRowC <- function (Sigma, i, mu, maxiter=50) {
 
          p = nrow(Sigma)		
          basis_vector = rep(0, p)