tests/testthat/test_znorm.R

In jmotif: Time Series Analysis Toolkit Based on Symbolic Aggregate Discretization, i.e. SAX

X = c(-1, -2, -1, 0, 2, 1, 1, 0)
Xscaled <- X / 100

test_that("testing ZNorm computation", {

  expect_equal(sd(znorm(X)), 1)
  expect_equal(mean(znorm(X)), 0)

  expect_equal(sd(znorm(t(X))), 1)
  expect_equal(mean(znorm(t(X))), 0)

  expect_equal(sd(znorm(t(t(X)))), 1)
  expect_equal(mean(znorm(t(t(X)))), 0)

  expect_equal(mean(znorm(t(t(X)))), 0)


  expect_equal(znorm(Xscaled, 0.1), Xscaled)

  expect_error(znorm(list("c", "d")))

})

dat <- c(2.02, 2.33, 2.99, 6.85, 9.2, 8.8, 7.5, 6, 5.85, 3.85, 4.85, 3.85, 2.22, 1.45, 1.34)
dat_znorm <- c(-0.9796808, -0.8622706, -0.6123005, 0.8496459, 1.739691, 1.588194,
               1.095829, 0.5277147, 0.4709033, -0.2865819, 0.0921607, -0.2865819,
               -0.9039323, -1.195564, -1.237226)

test_that("test a concrete znorm example (cross check with my java code) via CPP", {
  expect_equal(znorm(dat), dat_znorm, tolerance = 1e-6)
})

test_that("test a znorm clone example", {
  expect_equal(znorm(dat, 3.0), dat, tolerance = 1e-6)
})

dat <- c(0.5, 1.29, 2.58, 3.83, 3.25, 4.25, 3.83, 5.63, 6.44, 6.25, 8.75, 8.83, 3.25, 0.75, 0.72)
dat_znorm <- c(-1.289433, -0.9992189, -0.5253246, -0.06612478, -0.2791935, 0.08816637,
               -0.06612478, 0.595123, 0.8926845, 0.8228861, 1.741286, 1.770675,
               -0.2791935, -1.197593, -1.208614)
test_that("test a second concrete znorm example (cross check with my java code) via CPP", {
  expect_equal(znorm(dat), dat_znorm, tolerance = 1e-6)
})