1 files changed, 20 insertions, 8 deletions
diff --git a/R/cv_loss.R b/R/cv_loss.R
index ea46007..d855362 100644
--- a/R/cv_loss.R
+++ b/R/cv_loss.R
@@ -1,17 +1,29 @@
-.cv_loss <- function(idx, dat, evencorrection, threshold, grid.length, p, nc) {
-  res <- numeric(nc)
+## Normalized squared Frobenius loss for all threshold values at a given train/test
+## set, where
+##
+##   * idx = TRUE for sample points into the train set
+##   * train set = dat[  idx , ]
+##   * test  set = dat[ !idx , ]
+##
+.cv_loss <- function(idx, dat, evencorrection, threshold, grid.length, p) {
+
+
+  ## compute RMAD  on train set
   n1 <- as.integer(sum(idx))
-  C1 <- .Fortran("cormadvecdp", matrix = dat[idx, ], nrow = n1, ncol = p, res = res, 
-    ressize = nc, correcteven = evencorrection, PACKAGE = "RSC")$res
+  C1 <- .Call(C_cormad_C, dat[idx, ], n1, p, evencorrection, num.threads = 1)
+
+  ## compute RMAD  on test set
   n2 <- as.integer(sum(!idx))
-  C2 <- .Fortran("cormadvecdp", matrix = dat[!idx, ], nrow = n2, ncol = p, res = res, 
-    ressize = nc, correcteven = evencorrection, PACKAGE = "RSC")$res
+  C2 <- .Call(C_cormad_C, dat[!idx, ], n2, p, correcteven = evencorrection, num.threads = 1)
+
+  ## apply thresholds
   ans <- rep(0, times = grid.length)
   for (h in 1:grid.length) {
-    C1[abs(C1) < threshold[h]] <- 0
+    C1[abs(C1) < threshold[h]] <- 0 ## fit on train set
     ans[h] <- sum(2 * {
       C1 - C2
-    }^2)/p
+    }^2) / p
   }
+
   return(ans)
 }