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# backend of the naive Bayes classifier.
naive.bayes.backend = function(data, training, explanatory) {
# cache the node set.
nodes = c(training, explanatory)
# create the empty graph.
res = empty.graph(nodes)
# create the set of arcs outgoing from the training variable.
res$arcs = matrix(c(rep(training, length(explanatory)), explanatory),
ncol = 2, byrow = FALSE)
# update the network structure.
res$nodes = cache.structure(nodes, arcs = res$arcs)
# set a second class "bn.naive" to reroute method dispatch as needed.
class(res) = c("bn.naive", "bn")
return(res)
}#NAIVE.BAYES.BACKEND
# backend of the TAN algorithm.
tan.backend = function(data, training, explanatory, whitelist, blacklist, mi,
root, debug = FALSE) {
# cache the node set.
nodes = c(training, explanatory)
# create the empty graph.
res = empty.graph(nodes)
# create the set of arcs outgoing from the training variable.
class.arcs = matrix(c(rep(training, length(explanatory)), explanatory),
ncol = 2, byrow = FALSE)
# separate features and target class variable in data and metadata.
features.data =
.data.frame.column(data, explanatory, drop = FALSE, keep.names = TRUE)
features.data = .data.frame(features.data)
attr(features.data, "metadata") = collect.metadata(features.data)
class.data = .data.frame.column(data, training, drop = TRUE)
# call chow-liu to build the rest of the network.
chow.liu.arcs =
chow.liu.backend(x = features.data, nodes = explanatory, estimator = mi,
whitelist = whitelist, blacklist = blacklist, conditional = class.data,
debug = debug)
# set the directions of the arcs in the Chow-Liu tree.
chow.liu.arcs = .Call(call_tree_directions,
arcs = chow.liu.arcs,
nodes = explanatory,
root = root,
debug = FALSE)
# merge learned and predetermined arcs.
res$arcs = arcs.rbind(class.arcs, chow.liu.arcs)
# update the network structure.
res$nodes = cache.structure(nodes, arcs = res$arcs)
# set a second class "bn.tan" to reroute method dispatch as needed.
class(res) = c("bn.tan", "bn")
return(res)
}#TAN.BACKEND
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