R/frontend-print.R
In vspinu/bnlearn: Bayesian network structure learning, parameter learning and inference
# print method for class bn.
print.bn = function(x, ...) {
params = names(x$learning$args)
directed.arcs = length(which(which.directed(x$arcs)))
undirected.arcs = (nrow(x$arcs) - directed.arcs)/2
arcs = undirected.arcs + directed.arcs
nodes = names(x$nodes)
avg.mb = mean(sapply(nodes, function(n) { length(x$nodes[[n]]$mb) }))
avg.nbr = mean(sapply(nodes, function(n) { length(x$nodes[[n]]$nbr) }))
avg.ch = mean(sapply(nodes, function(n) { length(x$nodes[[n]]$children) }))
# warn about unused arguments.
check.unused.args(list(...), character(0))
if (x$learning$algo %in% graph.generation.algorithms)
cat("\n Random/Generated Bayesian network\n\n")
else if (x$learning$algo %in% constraint.based.algorithms)
cat("\n Bayesian network learned via Constraint-based methods\n\n")
else if (x$learning$algo %in% score.based.algorithms)
cat("\n Bayesian network learned via Score-based methods\n\n")
else if (x$learning$algo %in% hybrid.algorithms)
cat("\n Bayesian network learned via Hybrid methods\n\n")
else if (x$learning$algo %in% mim.based.algorithms)
cat("\n Bayesian network learned via Pairwise Mutual Information methods\n\n")
else if (x$learning$algo %in% classifiers)
cat("\n Bayesian network Classifier\n\n")
else
cat("\n Bayesian network learned via [unknown] methods\n\n")
# print the model string if possible, a short description otherwise.
cat(" model:\n")
if (undirected.arcs == 0)
fcat(formula.backend(x))
else if (directed.arcs == 0)
cat(" [undirected graph]\n")
else
cat(" [partially directed graph]\n")
wcat(" nodes: ", length(x$nodes))
wcat(" arcs: ", arcs)
wcat(" undirected arcs: ", undirected.arcs)
wcat(" directed arcs: ", directed.arcs)
wcat(" average markov blanket size: ", format(avg.mb, digits = 2, nsmall = 2))
wcat(" average neighbourhood size: ", format(avg.nbr, digits = 2, nsmall = 2))
wcat(" average branching factor: ", format(avg.ch, digits = 2, nsmall = 2))
cat("\n")
if (!(x$learning$algo %in% graph.generation.algorithms)) {
wcat(" learning algorithm: ", method.labels[x$learning$algo])
if (x$learning$algo %in% constraint.based.algorithms)
wcat(" conditional independence test: ", test.labels[x$learning$test])
else if (x$learning$algo %in% score.based.algorithms)
wcat(" score: ", score.labels[x$learning$test])
else if (x$learning$algo %in% hybrid.algorithms) {
if (x$learning$restrict %in% constraint.based.algorithms) {
wcat(" constraint-based method: ", method.labels[x$learning$restrict])
wcat(" conditional independence test: ", test.labels[x$learning$rstest])
}#THEN
else if (x$learning$restrict %in% mim.based.algorithms) {
wcat(" pairwise mutual information method: ", method.labels[x$learning$restrict])
wcat(" mutual information estimator: ", format(mi.estimator.labels[x$learning$args$estimator]))
}#THEN
wcat(" score-based method: ", method.labels[x$learning$maximize])
wcat(" score: ", score.labels[x$learning$maxscore])
}#THEN
if ("prior" %in% params)
wcat(" graph prior: ", prior.labels[x$learning$args$prior])
if ("beta" %in% params)
if (x$learning$args$prior != "cs")
wcat(" beta sparsity parameter: ", format(x$learning$args$beta))
else
wcat(" beta sparsity parameter: ", "Completed Prior over Arcs")
if ("alpha" %in% params)
wcat(" alpha threshold: ", format(x$learning$args$alpha))
if ("B" %in% params)
wcat(" permutations: ", format(x$learning$args$B))
if ("iss" %in% params)
wcat(" imaginary sample size: ", format(x$learning$args$iss))
if ("phi" %in% params)
wcat(" phi matrix structure: ", x$learning$args$phi)
if ("k" %in% params)
wcat(" penalization coefficient: ", format(x$learning$args$k))
if ("maxp" %in% params)
wcat(" maximum parents: ", format(x$learning$args$maxp))
if (x$learning$algo %in% c(mim.based.algorithms, classifiers)) {
if ("estimator" %in% params)
wcat(" mutual information estimator: ", format(mi.estimator.labels[x$learning$args$estimator]))
wcat(" training node: ", x$learning$args$training)
}#THEN
wcat(" tests used in the learning procedure: ", x$learning$ntests)
if (!is.null(x$learning$optimized))
wcat(" optimized: ", x$learning$optimized)
}#THEN
else {
wcat(" generation algorithm: ", graph.generation.labels[x$learning$algo])
if ("prob" %in% params)
wcat(" arc sampling probability: ", format(x$learning$args$prob))
if ("burn.in" %in% params)
wcat(" burn in length: ", format(x$learning$args$burn.in))
if ("max.in.degree" %in% params)
wcat(" maximum in-degree: ", format(x$learning$args$max.in.degree))
if ("max.out.degree" %in% params)
wcat(" maximum out-degree: ", format(x$learning$args$max.out.degree))
if ("max.degree" %in% params)
wcat(" maximum degree: ", format(x$learning$args$max.degree))
if ("threshold" %in% params)
wcat(" significance threshold: ", format(x$learning$args$threshold))
}#ELSE
cat("\n")
invisible(x)
}#PRINT.BN
# print method for class bn.fit.
print.bn.fit = function(x, order, ...) {
# warn about unused arguments.
check.unused.args(list(...), character(0))
# set the order in which the modes are to be printed.
if (missing(order)) {
# print the nodes in the order in which they are stored.
order = seq_along(x)
}#THEN
else {
# check the node ordering.
check.nodes(order, graph = x, min.nodes = length(x), max.nodes = length(x))
}#ELSE
cat("\n Bayesian network parameters\n")
for (i in order)
print(x[[i]])
cat("\n")
invisible(x)
}#PRINT.BN.FIT
# print method for class bn.fit.dnode.
print.bn.fit.dnode = function(x, ...) {
# warn about unused arguments.
check.unused.args(list(...), character(0))
if (is(x, "bn.fit.dnode"))
cat("\n Parameters of node", x$node, "(multinomial distribution)\n")
else if (is(x, "bn.fit.onode"))
cat("\n Parameters of node", x$node, "(ordinal distribution)\n")
cat("\nConditional probability table:\n", ifelse(length(x$parents) > 0, "\n", ""))
print(x$prob)
invisible(x)
}#PRINT.BN.FIT.DNODE
# print method for class bn.fit.onode.
print.bn.fit.onode = print.bn.fit.dnode
# print method for class bn.fit.gnode.
print.bn.fit.gnode = function(x, ...) {
# warn about unused arguments.
check.unused.args(list(...), character(0))
cat("\n Parameters of node", x$node, "(Gaussian distribution)\n")
cat("\nConditional density: ")
if (length(x$parents) > 0)
cat(paste(x$node, "|", paste(x$parents, sep = "", collapse = " + ")))
else
cat(x$node)
cat("\nCoefficients:\n")
print.default(format(c(x$coefficients)), print.gap = 2, right = TRUE, quote = FALSE)
cat("Standard deviation of the residuals:", x$sd, "\n")
invisible(x)
}#PRINT.BN.FIT.GNODE
# print method for class mvber.moments.
print.mvber.moments = function(x, ...) {
# warn about unused arguments.
check.unused.args(list(...), character(0))
# reset the attributes to have a clean printout.
attributes(x) = list(R = NULL, m = NULL, class = NULL, names = names(x))
print(x)
invisible(x)
}#PRINT.MVBER.MOMENTS
# print method for k-fold cross-validation.
print.bn.kcv = function(x, ...) {
a = attributes(x)
# warn about unused arguments.
check.unused.args(list(...), character(0))
cat("\n k-fold cross-validation for Bayesian networks\n\n")
if (is.character(a$bn)) {
wcat(" target learning algorithm: ", method.labels[a$bn])
}#THEN
else {
cat(" target network structure:\n")
if (is(a$bn, "bn.naive"))
cat(" [Naive Bayes Classifier]\n")
else
fcat(formula.backend(a$bn))
}#LSE
wcat(" number of subsets: ", length(x))
wcat(" loss function: ", loss.labels[a$loss])
if ("target" %in% loss.extra.args[[a$loss]])
wcat(" training node: ", a$args$target)
wcat(" expected loss: ", format(a$mean))
cat("\n")
invisible(x)
}#PRINT.BN.KCV
vspinu/bnlearn documentation built on May 3, 2019, 7:08 p.m.
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# print method for class bn.
print.bn = function(x, ...) {
params = names(x$learning$args)
directed.arcs = length(which(which.directed(x$arcs)))
undirected.arcs = (nrow(x$arcs) - directed.arcs)/2
arcs = undirected.arcs + directed.arcs
nodes = names(x$nodes)
avg.mb = mean(sapply(nodes, function(n) { length(x$nodes[[n]]$mb) }))
avg.nbr = mean(sapply(nodes, function(n) { length(x$nodes[[n]]$nbr) }))
avg.ch = mean(sapply(nodes, function(n) { length(x$nodes[[n]]$children) }))
# warn about unused arguments.
check.unused.args(list(...), character(0))
if (x$learning$algo %in% graph.generation.algorithms)
cat("\n Random/Generated Bayesian network\n\n")
else if (x$learning$algo %in% constraint.based.algorithms)
cat("\n Bayesian network learned via Constraint-based methods\n\n")
else if (x$learning$algo %in% score.based.algorithms)
cat("\n Bayesian network learned via Score-based methods\n\n")
else if (x$learning$algo %in% hybrid.algorithms)
cat("\n Bayesian network learned via Hybrid methods\n\n")
else if (x$learning$algo %in% mim.based.algorithms)
cat("\n Bayesian network learned via Pairwise Mutual Information methods\n\n")
else if (x$learning$algo %in% classifiers)
cat("\n Bayesian network Classifier\n\n")
else
cat("\n Bayesian network learned via [unknown] methods\n\n")
# print the model string if possible, a short description otherwise.
cat(" model:\n")
if (undirected.arcs == 0)
fcat(formula.backend(x))
else if (directed.arcs == 0)
cat(" [undirected graph]\n")
else
cat(" [partially directed graph]\n")
wcat(" nodes: ", length(x$nodes))
wcat(" arcs: ", arcs)
wcat(" undirected arcs: ", undirected.arcs)
wcat(" directed arcs: ", directed.arcs)
wcat(" average markov blanket size: ", format(avg.mb, digits = 2, nsmall = 2))
wcat(" average neighbourhood size: ", format(avg.nbr, digits = 2, nsmall = 2))
wcat(" average branching factor: ", format(avg.ch, digits = 2, nsmall = 2))
cat("\n")
if (!(x$learning$algo %in% graph.generation.algorithms)) {
wcat(" learning algorithm: ", method.labels[x$learning$algo])
if (x$learning$algo %in% constraint.based.algorithms)
wcat(" conditional independence test: ", test.labels[x$learning$test])
else if (x$learning$algo %in% score.based.algorithms)
wcat(" score: ", score.labels[x$learning$test])
else if (x$learning$algo %in% hybrid.algorithms) {
if (x$learning$restrict %in% constraint.based.algorithms) {
wcat(" constraint-based method: ", method.labels[x$learning$restrict])
wcat(" conditional independence test: ", test.labels[x$learning$rstest])
}#THEN
else if (x$learning$restrict %in% mim.based.algorithms) {
wcat(" pairwise mutual information method: ", method.labels[x$learning$restrict])
wcat(" mutual information estimator: ", format(mi.estimator.labels[x$learning$args$estimator]))
}#THEN
wcat(" score-based method: ", method.labels[x$learning$maximize])
wcat(" score: ", score.labels[x$learning$maxscore])
}#THEN
if ("prior" %in% params)
wcat(" graph prior: ", prior.labels[x$learning$args$prior])
if ("beta" %in% params)
if (x$learning$args$prior != "cs")
wcat(" beta sparsity parameter: ", format(x$learning$args$beta))
else
wcat(" beta sparsity parameter: ", "Completed Prior over Arcs")
if ("alpha" %in% params)
wcat(" alpha threshold: ", format(x$learning$args$alpha))
if ("B" %in% params)
wcat(" permutations: ", format(x$learning$args$B))
if ("iss" %in% params)
wcat(" imaginary sample size: ", format(x$learning$args$iss))
if ("phi" %in% params)
wcat(" phi matrix structure: ", x$learning$args$phi)
if ("k" %in% params)
wcat(" penalization coefficient: ", format(x$learning$args$k))
if ("maxp" %in% params)
wcat(" maximum parents: ", format(x$learning$args$maxp))
if (x$learning$algo %in% c(mim.based.algorithms, classifiers)) {
if ("estimator" %in% params)
wcat(" mutual information estimator: ", format(mi.estimator.labels[x$learning$args$estimator]))
wcat(" training node: ", x$learning$args$training)
}#THEN
wcat(" tests used in the learning procedure: ", x$learning$ntests)
if (!is.null(x$learning$optimized))
wcat(" optimized: ", x$learning$optimized)
}#THEN
else {
wcat(" generation algorithm: ", graph.generation.labels[x$learning$algo])
if ("prob" %in% params)
wcat(" arc sampling probability: ", format(x$learning$args$prob))
if ("burn.in" %in% params)
wcat(" burn in length: ", format(x$learning$args$burn.in))
if ("max.in.degree" %in% params)
wcat(" maximum in-degree: ", format(x$learning$args$max.in.degree))
if ("max.out.degree" %in% params)
wcat(" maximum out-degree: ", format(x$learning$args$max.out.degree))
if ("max.degree" %in% params)
wcat(" maximum degree: ", format(x$learning$args$max.degree))
if ("threshold" %in% params)
wcat(" significance threshold: ", format(x$learning$args$threshold))
}#ELSE
cat("\n")
invisible(x)
}#PRINT.BN
# print method for class bn.fit.
print.bn.fit = function(x, order, ...) {
# warn about unused arguments.
check.unused.args(list(...), character(0))
# set the order in which the modes are to be printed.
if (missing(order)) {
# print the nodes in the order in which they are stored.
order = seq_along(x)
}#THEN
else {
# check the node ordering.
check.nodes(order, graph = x, min.nodes = length(x), max.nodes = length(x))
}#ELSE
cat("\n Bayesian network parameters\n")
for (i in order)
print(x[[i]])
cat("\n")
invisible(x)
}#PRINT.BN.FIT
# print method for class bn.fit.dnode.
print.bn.fit.dnode = function(x, ...) {
# warn about unused arguments.
check.unused.args(list(...), character(0))
if (is(x, "bn.fit.dnode"))
cat("\n Parameters of node", x$node, "(multinomial distribution)\n")
else if (is(x, "bn.fit.onode"))
cat("\n Parameters of node", x$node, "(ordinal distribution)\n")
cat("\nConditional probability table:\n", ifelse(length(x$parents) > 0, "\n", ""))
print(x$prob)
invisible(x)
}#PRINT.BN.FIT.DNODE
# print method for class bn.fit.onode.
print.bn.fit.onode = print.bn.fit.dnode
# print method for class bn.fit.gnode.
print.bn.fit.gnode = function(x, ...) {
# warn about unused arguments.
check.unused.args(list(...), character(0))
cat("\n Parameters of node", x$node, "(Gaussian distribution)\n")
cat("\nConditional density: ")
if (length(x$parents) > 0)
cat(paste(x$node, "|", paste(x$parents, sep = "", collapse = " + ")))
else
cat(x$node)
cat("\nCoefficients:\n")
print.default(format(c(x$coefficients)), print.gap = 2, right = TRUE, quote = FALSE)
cat("Standard deviation of the residuals:", x$sd, "\n")
invisible(x)
}#PRINT.BN.FIT.GNODE
# print method for class mvber.moments.
print.mvber.moments = function(x, ...) {
# warn about unused arguments.
check.unused.args(list(...), character(0))
# reset the attributes to have a clean printout.
attributes(x) = list(R = NULL, m = NULL, class = NULL, names = names(x))
print(x)
invisible(x)
}#PRINT.MVBER.MOMENTS
# print method for k-fold cross-validation.
print.bn.kcv = function(x, ...) {
a = attributes(x)
# warn about unused arguments.
check.unused.args(list(...), character(0))
cat("\n k-fold cross-validation for Bayesian networks\n\n")
if (is.character(a$bn)) {
wcat(" target learning algorithm: ", method.labels[a$bn])
}#THEN
else {
cat(" target network structure:\n")
if (is(a$bn, "bn.naive"))
cat(" [Naive Bayes Classifier]\n")
else
fcat(formula.backend(a$bn))
}#LSE
wcat(" number of subsets: ", length(x))
wcat(" loss function: ", loss.labels[a$loss])
if ("target" %in% loss.extra.args[[a$loss]])
wcat(" training node: ", a$args$target)
wcat(" expected loss: ", format(a$mean))
cat("\n")
invisible(x)
}#PRINT.BN.KCV
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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