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## 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 <- .Call(C_cormad_C, dat[idx, ], n1, p, evencorrection, num.threads = 1)
## compute RMAD on test set
n2 <- as.integer(sum(!idx))
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 ## fit on train set
ans[h] <- sum(2 * {
C1 - C2
}^2) / p
}
return(ans)
}
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