Nothing
states <- c("Rouen", "Bucharest", "Samos", "Aigio", "Marseille")
sequence <- create_sequence(states, probs = c(0.3, 0.1, 0.1, 0.3, 0.2))
obj_model <- fit_dsmm(
sequence = sequence,
states = states,
degree = 3,
f_is_drifting = FALSE,
p_is_drifting = TRUE
)
test_that("get_kernel()", {
klim <- 10
kernel <- get_kernel(obj_model, klim = klim)
expect_type(kernel, "double")
# Each of the first two dimensions should be equal to the length of states.
expect_identical(
dim(kernel)[1:2], rep(length(states), 2)
)
# Third dimension of kernel should be less or equal to `klim`.
expect_lte(
dim(kernel)[3], klim
)
# Fourth dimension of kernel should be less or equal to `length(sequence)`
# (Equal is extremely unlikely)
expect_lte(
dim(kernel)[4], length(sequence)
)
# Probabilities should sum to 1 over v, l.
expect_equal(
kernel_sum <- apply(kernel, c(1, 4), sum),
array(1, dim = dim(kernel_sum), dimnames = dimnames(kernel_sum))
)
})
test_that("simulate()", {
max_seq_length <- 10
sim_seq <- simulate(obj_model, max_seq_length = max_seq_length)
# `length(sim_seq)` should be less or equal to `max_seq_length`
expect_lte(length(sim_seq), max_seq_length)
# `sim_seq` should be a subset of `states`
expect_in(sim_seq, states)
})
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