BF: forgot to rename in Rcpp file, passing active and ever_active from R

Author: jonathan-taylor

selectiveInference/R/funs.fixed.R

@@ -341,18 +341,24 @@ InverseLinftyOneRow <- function (Sigma, i, mu, maxiter=50, soln_result=NULL) {
 
   # If soln_result is not Null, it is used as a warm start.
   # It should be a list
-  # with entries "soln" and "Sigma_soln"
+  # with entries "soln", "gradient", "ever_active", "nactive"
 
   if (is.null(soln_result)) {
      soln = rep(0, nrow(Sigma))
-     Sigma_soln = rep(0, nrow(Sigma)) 
+     gradient = rep(0, nrow(Sigma)) 
+     ever_active = rep(0, nrow(Sigma))
+     ever_active[1] = i-1             # 0-based
+     ever_active = as.integer(ever_active)
+     nactive = as.integer(1)
   }
   else {
      soln = soln_result$soln
-     Sigma_soln = soln_result$Sigma_soln  
+     gradient = soln_result$gradient  
+     ever_active = as.integer(soln_result$ever_active)
+     nactive = as.integer(soln_result$nactive)
   }
 
-  result = find_one_row_debiasingM(Sigma, i-1, mu, maxiter, soln, Sigma_soln) # C function uses 0-based indexing
+  result = find_one_row_debiasingM(Sigma, i-1, mu, maxiter, soln, gradient, ever_active, nactive) # C function uses 0-based indexing
 
   # Check feasibility
 

selectiveInference/src/Rcpp-debias.cpp

@@ -7,31 +7,30 @@ Rcpp::List find_one_row_debiasingM(Rcpp::NumericMatrix Sigma,
 				   double bound,
 				   int maxiter,
 				   Rcpp::NumericVector theta,
-				   Rcpp::NumericVector Sigma_theta) {
+				   Rcpp::NumericVector gradient,
+				   Rcpp::IntegerVector ever_active,
+				   Rcpp::IntegerVector nactive
+				   ) {
 
   int nrow = Sigma.nrow(); // number of features
 
   // Active set
 
   int irow;
-  Rcpp::IntegerVector nactive(1); // An array so we can easily modify it
-  Rcpp::IntegerVector ever_active(1);
-  int *ever_active_p = ever_active.begin();
-  *ever_active_p = row;
 
   // Extract the diagonal
   Rcpp::NumericVector Sigma_diag(nrow);
-  double *sigma_p = Sigma_diag.begin();
+  double *sigma_diag_p = Sigma_diag.begin();
 
   for (irow=0; irow<nrow; irow++) {
-    sigma_p[irow] = Sigma(irow, irow);
+    sigma_diag_p[irow] = Sigma(irow, irow);
   }
 
   // Now call our C function
 
   int iter = find_one_row_((double *) Sigma.begin(),
 			   (double *) Sigma_diag.begin(),
-			   (double *) Sigma_theta.begin(),
+			   (double *) gradient.begin(),
 			   (int *) ever_active.begin(),
 			   (int *) nactive.begin(),
 			   nrow,
@@ -43,14 +42,16 @@ Rcpp::List find_one_row_debiasingM(Rcpp::NumericMatrix Sigma,
   // Check whether feasible
 
   int kkt_check = check_KKT(theta.begin(),
-			    Sigma_theta.begin(),
+			    gradient.begin(),
 			    nrow,
 			    row,
 			    bound);
 
   return(Rcpp::List::create(Rcpp::Named("soln") = theta,
-			    Rcpp::Named("Sigma_soln") = Sigma_theta,
+			    Rcpp::Named("gradient") = gradient,
 			    Rcpp::Named("iter") = iter,
-			    Rcpp::Named("kkt_check") = kkt_check));
+			    Rcpp::Named("kkt_check") = kkt_check,
+			    Rcpp::Named("ever_active") = ever_active,
+			    Rcpp::Named("nactive") = nactive));
 
 }