R/frontend-learning.R

Defines functions tree.bayes naive.bayes learn.nbr learn.mb h2pc mmhc rsmax2 tabu hc chow.liu aracne hpc si.hiton.pc mmpc iamb.fdr inter.iamb fast.iamb iamb gs pc.stable

Documented in aracne chow.liu fast.iamb gs h2pc hc hpc iamb iamb.fdr inter.iamb learn.mb learn.nbr mmhc mmpc naive.bayes pc.stable rsmax2 si.hiton.pc tabu tree.bayes

# PC algorithm, the stable version.
pc.stable = function(x, cluster, whitelist = NULL, blacklist = NULL,
    test = NULL, alpha = 0.05, B = NULL, max.sx = NULL, debug = FALSE,
    undirected = FALSE) {

  bnlearn(x = x, cluster = cluster, whitelist = whitelist,
    blacklist = blacklist, test = test, alpha = alpha, B = B,
    max.sx = max.sx, method = "pc.stable", debug = debug,
    undirected = undirected)

}#PC.CLASSIC

# Grow-Shrink frontend.
gs = function(x, cluster, whitelist = NULL, blacklist = NULL, test = NULL,
    alpha = 0.05, B = NULL, max.sx = NULL, debug = FALSE, undirected = FALSE) {

  bnlearn(x = x, cluster = cluster, whitelist = whitelist,
    blacklist = blacklist, test = test, alpha = alpha, B = B,
    max.sx = max.sx, method = "gs", debug = debug, undirected = undirected)

}#GS

# Incremental Association frontend.
iamb = function(x, cluster, whitelist = NULL, blacklist = NULL, test = NULL,
    alpha = 0.05, B = NULL, max.sx = NULL, debug = FALSE, undirected = FALSE) {

  bnlearn(x = x, cluster = cluster, whitelist = whitelist,
    blacklist = blacklist, test = test, alpha = alpha, B = B,
    max.sx = max.sx, method = "iamb", debug = debug, undirected = undirected)

}#IAMB

# Fast-IAMB frontend.
fast.iamb = function(x, cluster, whitelist = NULL, blacklist = NULL,
    test = NULL, alpha = 0.05, B = NULL, max.sx = NULL, debug = FALSE,
    undirected = FALSE) {

  bnlearn(x = x, cluster = cluster, whitelist = whitelist,
    blacklist = blacklist, test = test, alpha = alpha, B = B,
    max.sx = max.sx, method = "fast.iamb", debug = debug,
    undirected = undirected)

}#FAST.IAMB

# Inter-IAMB frontend.
inter.iamb = function(x, cluster, whitelist = NULL, blacklist = NULL,
    test = NULL, alpha = 0.05, B = NULL,  max.sx = NULL, debug = FALSE,
    undirected = FALSE) {

  bnlearn(x = x, cluster = cluster, whitelist = whitelist,
    blacklist = blacklist, test = test, alpha = alpha, B = B,
    max.sx = max.sx, method = "inter.iamb", debug = debug,
    undirected = undirected)

}#INTER.IAMB

# IAMB-FDR frontend.
iamb.fdr = function(x, cluster, whitelist = NULL, blacklist = NULL, test = NULL,
    alpha = 0.05, B = NULL,  max.sx = NULL, debug = FALSE, undirected = FALSE) {

  bnlearn(x = x, cluster = cluster, whitelist = whitelist,
    blacklist = blacklist, test = test, alpha = alpha, B = B,
    max.sx = max.sx, method = "iamb.fdr", debug = debug,
    undirected = undirected)

}#IAMB.FDR

# MMPC frontend.
mmpc = function(x, cluster, whitelist = NULL, blacklist = NULL, test = NULL,
    alpha = 0.05, B = NULL, max.sx = NULL, debug = FALSE, undirected = TRUE) {

  bnlearn(x = x, cluster = cluster, whitelist = whitelist,
    blacklist = blacklist, test = test, alpha = alpha, B = B,
    max.sx = max.sx, method = "mmpc", debug = debug,
    undirected = undirected)

}#MMPC

# Semi-Interleaved HITON-PC.
si.hiton.pc = function(x, cluster, whitelist = NULL, blacklist = NULL,
    test = NULL, alpha = 0.05, B = NULL, max.sx = NULL, debug = FALSE,
    undirected = TRUE) {

  bnlearn(x = x, cluster = cluster, whitelist = whitelist,
    blacklist = blacklist, test = test, alpha = alpha, B = B,
    max.sx = max.sx, method = "si.hiton.pc", debug = debug,
    undirected = undirected)

}#SI.HITON.PC

# Hybrid PC.
hpc = function(x, cluster, whitelist = NULL, blacklist = NULL, test = NULL,
    alpha = 0.05, B = NULL, max.sx = NULL, debug = FALSE, undirected = TRUE) {

  bnlearn(x = x, cluster = cluster, whitelist = whitelist,
    blacklist = blacklist, test = test, alpha = alpha, B = B,
    max.sx = max.sx, method = "hpc", debug = debug, undirected = undirected)

}#HPC

# ARACNE frontend.
aracne = function(x, whitelist = NULL, blacklist = NULL, mi = NULL,
  debug = FALSE) {

  mi.matrix(x = x, whitelist = whitelist, blacklist = blacklist,
    method = "aracne", mi = mi, debug = debug)

}#ARACNE

# Chow-Liu frontend.
chow.liu  = function(x, whitelist = NULL, blacklist = NULL, mi = NULL,
    debug = FALSE) {

  mi.matrix(x = x, whitelist = whitelist, blacklist = blacklist,
    method = "chow.liu", mi = mi, debug = debug)

}#CHOW.LIU

# Hill Climbing greedy search frontend.
hc = function(x, start = NULL, whitelist = NULL, blacklist = NULL,
    score = NULL, ..., debug = FALSE, restart = 0, perturb = 1,
    max.iter = Inf, maxp = Inf, optimized = TRUE) {

  greedy.search(x = x, start = start, whitelist = whitelist,
    blacklist = blacklist, score = score, heuristic = "hc", debug = debug,
    ..., restart = restart, perturb = perturb,
    max.iter = max.iter, maxp = maxp, optimized = optimized)

}#HC

# TABU list greedy search frontend.
tabu = function(x, start = NULL, whitelist = NULL, blacklist = NULL,
    score = NULL, ..., debug = FALSE, tabu = 10, max.tabu = tabu,
    max.iter = Inf, maxp = Inf, optimized = TRUE) {

  greedy.search(x = x, start = start, whitelist = whitelist,
    blacklist = blacklist, score = score, heuristic = "tabu", debug = debug,
    ..., max.iter = max.iter, tabu = tabu, max.tabu = max.tabu,
    maxp = maxp, optimized = optimized)

}#TABU

# Generic Restricted Maximization frontend.
rsmax2 = function(x, whitelist = NULL, blacklist = NULL, restrict = "si.hiton.pc",
    maximize = "hc", restrict.args = list(), maximize.args = list(),
    debug = FALSE) {

  hybrid.search(x, whitelist = whitelist, blacklist = blacklist,
    restrict = restrict, maximize = maximize, restrict.args = restrict.args,
    maximize.args = maximize.args, debug = debug)

}#RSMAX2

# MMHC frontend.
mmhc = function(x, whitelist = NULL, blacklist = NULL, restrict.args = list(),
  maximize.args = list(), debug = FALSE) {

  hybrid.search(x, whitelist = whitelist, blacklist = blacklist,
    restrict = "mmpc", maximize = "hc", restrict.args = restrict.args,
    maximize.args = maximize.args, debug = debug)

}#MMHC

# H2PC frontend.
h2pc = function(x, whitelist = NULL, blacklist = NULL, restrict.args = list(),
  maximize.args = list(), debug = FALSE) {

  hybrid.search(x, whitelist = whitelist, blacklist = blacklist,
    restrict = "hpc", maximize = "hc", restrict.args = restrict.args,
    maximize.args = maximize.args, debug = debug)

}#H2PC

# Frontend for the Markov blanket learning algorithms.
learn.mb = function(x, node, method, whitelist = NULL, blacklist = NULL,
    start = NULL, test = NULL, alpha = 0.05, B = NULL, max.sx = NULL,
    debug = FALSE) {

  mb.backend(x, target = node, method = method, whitelist = whitelist,
    blacklist = blacklist, start = start, test = test, alpha = alpha,
    B = B, max.sx = max.sx, debug = debug)

}#LEARN.MB

# Frontend for causal discovery learning algorithms.
learn.nbr = function(x, node, method, whitelist = NULL, blacklist = NULL,
    test = NULL, alpha = 0.05, B = NULL, max.sx = NULL, debug = FALSE) {

  nbr.backend(x, target = node, method = method, whitelist = whitelist,
    blacklist = blacklist, test = test, alpha = alpha, B = B,
    max.sx = max.sx, debug = debug)

}#LEARN.NBR

# naive Bayes frontend.
naive.bayes = function(x, training, explanatory) {

  bayesian.classifier(x, training = training, explanatory = explanatory,
    method = "naive.bayes", whitelist = NULL, blacklist = NULL, expand = list(),
    debug = FALSE)

}#NAIVE.BAYES

# tree-augmented naive Bayes frontend.
tree.bayes = function(x, training, explanatory, whitelist = NULL,
    blacklist = NULL, mi = NULL, root = NULL, debug = FALSE) {

  bayesian.classifier(x, training = training, explanatory = explanatory,
    method = "tree.bayes", whitelist = whitelist, blacklist = blacklist,
    expand = list(estimator = mi, root = root), debug = debug)

}#TREE.BAYES

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bnlearn documentation built on Sept. 7, 2021, 1:07 a.m.