# 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)
}
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