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context('classDist')
test_that("errors working", {
trainSet = 1:3
expect_error(
distData <- classDist(
iris[trainSet, 1:4],
iris$Species[trainSet]
)
,"more rows than columns"
)
})
test_that("Object matches expectations - factor y", {
trainSet <- sample(1:150, 100)
x = iris[trainSet, 1:4]
y = iris$Species[trainSet]
distData <- classDist(x, y, pca = FALSE)
# values
expect_true(length(distData$values) == length(levels(y)))
# classes
expect_true(all(distData$classes %in% levels(y)))
## n
expect_true(all(table(y) == distData$n))
## cuts
expect_null(distData$cuts)
## p
expect_true(distData$p == ncol(x))
## PCA - FALSE
expect_null(distData$pca)
## PCA - TRUE
distData2 <- classDist(x, y, pca = TRUE)
PCA <- prcomp(x, center = TRUE, scale = TRUE, tol = sqrt(.Machine$double.eps))
expect_true(all(distData2$pca$sdev == PCA$sdev))
expect_true(all(distData2$pca$rotation == PCA$rotation))
})
test_that("Object matches expectations - numeric y", {
trainSet <- sample(1:150, 100)
x = iris[trainSet, 1:4]
y = as.numeric(iris$Species[trainSet])
groups = 4
distData <- classDist(x, y, pca = FALSE, groups = groups)
# values
expect_true(length(distData$values) == length(unique(y)) - 1)
# classes
expect_true(all(distData$classes %in% as.character(seq(0, 100, 25))[2:5]))
## p
expect_true(distData$p == ncol(x))
## PCA - FALSE
expect_null(distData$pca)
## PCA - TRUE
distData2 <- classDist(x, y, pca = TRUE)
PCA <- prcomp(x, center = TRUE, scale = TRUE, tol = sqrt(.Machine$double.eps))
expect_true(all(distData2$pca$sdev == PCA$sdev))
expect_true(all(distData2$pca$rotation == PCA$rotation))
})
test_that("predictions", {
trainSet <- sample(1:150, 100)
x = iris[trainSet, 1:4]
y = iris$Species[trainSet]
groups = 4
distData <- classDist(x, y, pca = FALSE, groups = groups)
distData2 <- classDist(x, y, pca = TRUE, groups = groups)
## PCA and non-PCA preds match
expect_equal(predict(distData, x), predict(distData2, x), tolerance = .0001)
})
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