Nothing
test_that("Test that the 1NN function works properly", {
# We create a training database with two series:
x <- cumsum(rnorm(50))
y <- sin(seq(0, pi, length.out=50))
train <- rbind(x, y)
trainclasses <- c(1, 2)
# We create a testing set with the same series, each replicated 5 times.
test <- rbind(x, x, x, x, x, y, y, y, y, y)
testclasses <- c(1, 1, 1, 1, 1, 2, 2, 2, 2, 2)
classification <- OneNN(train, trainclasses, test, testclasses, "euclidean")
# Such easy database is always classified perfectly error=0.
expect_equal(classification$error, 0)
# LCSS is a similarity, not a distance so, special treatment is necessary
classification <- OneNN(train, trainclasses, test, testclasses,
"lcss", epsilon=1)
# If we don't define a ground truth classification for the testing set,
# we only get the class values
classification <-OneNN(train, trainclasses, test, "euclidean")
expect_equal(classification, testclasses)
# If there are ties, the classes are selected randomly. The last series is at the same distance of x and y.
test <- rbind(x, x, x, x, x, y, y, y, y, y, (x + y) /2)
set.seed(123)
classification <- OneNN(train, trainclasses, test, "euclidean")
testclasses <- c(1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 1)
expect_equal(classification, testclasses)
set.seed(233)
classification <- OneNN(train, trainclasses, test, "euclidean")
testclasses <- c(1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2)
expect_equal(classification, testclasses)
# If all the series in the training set are equal, special treatment
# is necessary
train <- rbind(x, x)
z <- y
test <- rbind(z, z, z, z, z, z, z, z, z, z)
classification <- OneNN(train, trainclasses, test, "euclidean")
})
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