# Common testing functions
# Smooth is always 0 so that grain does not produce 0 probabilities
nbvote <- function() {
# data(voting, envir = parent.frame())
lp(nb('Class', voting), voting, smooth = 1)
}
nbvotecomp <- function() {
# data(voting, envir = parent.frame())
# v <- na.omit(voting)
# assign('v', v, envir = parent.frame())
lp(nb('Class', v), v, smooth = 1)
}
nbcar <- function() {
# data(car, envir = parent.frame())
lp(nb('class', car), car, smooth = 1)
}
nbcarp <- function(cardata) {
lp(nb('class', cardata), cardata, smooth = 1)
}
nbcarclass <- function() {
lp(nb('class', car[, 'class', drop = FALSE]), car, smooth = 1)
}
random_letters_db <- function(nlet = 6, nrow = 100) {
df <- replicate(nlet, random_letters_vector(nlet, nrow))
df <- as.data.frame(df, stringsAsFactors = TRUE)
colnames(df) <- letters[seq_len(nlet)]
df
}
random_letters_vector <- function(nletters, n) {
sample(letters[1:nletters], n, replace = TRUE)
}
# Creates a random augmented NB with class as class.
random_aug_nb_dag <- function(class, V, maxpar, wgt) {
dg <- gRbase::random_dag(V = V, maxpar = maxpar, wgt = wgt)
dg <- graphNEL2_graph_internal(dg)
superimpose_node(dag = dg, class)
}
identical_non_call <- function(x, y) {
x$.call_struct <- y$.call_struct <- NULL
x$.call_bn <- y$.call_bn <- NULL
expect_identical(x, y)
}
test_dag <- function() {
edges <- graph_from_to_to_edges('A', 'B')
graph_internal(nodes = LETTERS[1:2], edges, weights = NULL, edgemode = "directed")
}
# Load data
data(car, envir = environment())
data(voting, envir = environment())
v <- na.omit(voting)
alphadb <- random_letters_db()
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