Preparing for CRAN upload

[agghoo.git] / agghoo.Rcheck / 00_pkg_src / agghoo / R / utils.R

diff --git a/agghoo.Rcheck/00_pkg_src/agghoo/R/utils.R b/agghoo.Rcheck/00_pkg_src/agghoo/R/utils.R
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--- a/agghoo.Rcheck/00_pkg_src/agghoo/R/utils.R
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-# Helper for cross-validation: return the next test indices.
-get_testIndices <- function(n, CV, v, shuffle_inds) {
-  if (CV$type == "vfold") {
-    # Slice indices (optionnally shuffled)
-    first_index = round((v-1) * n / CV$V) + 1
-    last_index = round(v * n / CV$V)
-    test_indices = first_index:last_index
-    if (!is.null(shuffle_inds))
-      test_indices <- shuffle_inds[test_indices]
-  }
-  else
-    # Monte-Carlo cross-validation
-    test_indices = sample(n, round(n * CV$test_size))
-  test_indices
-}
-
-# Helper which split data into training and testing parts.
-splitTrainTest <- function(data, target, testIdx) {
-  dataTrain <- data[-testIdx,]
-  targetTrain <- target[-testIdx]
-  dataTest <- data[testIdx,]
-  targetTest <- target[testIdx]
-  # [HACK] R will cast 1-dim matrices into vectors:
-  if (!is.matrix(dataTrain) && !is.data.frame(dataTrain))
-    dataTrain <- as.matrix(dataTrain)
-  if (!is.matrix(dataTest) && !is.data.frame(dataTest))
-    dataTest <- as.matrix(dataTest)
-  list(dataTrain=dataTrain, targetTrain=targetTrain,
-       dataTest=dataTest, targetTest=targetTest)
-}