MoTBFs_Learning | R Documentation |
Learn mixtures of truncated basis functions in a full hybrid network.
MoTBFs_Learning( graph, data, numIntervals, POTENTIAL_TYPE, maxParam = NULL, s = NULL, priorData = NULL )
graph |
A network of the class |
data |
An object of class |
numIntervals |
A positive integer indicating the maximum number of intervals for splitting the domain of the continuous parent variables. |
POTENTIAL_TYPE |
A |
maxParam |
A positive integer which indicates the maximum number of coefficients in the function.
If specified, the output is the function which gets the best BIC with, at most, this number of parameters.
By default, it is set to |
s |
A |
priorData |
An object of class |
If the variable is discrete then it computes the probabilities and the size of each leaf.
Children that have discrete parents have as many functions as configurations of the parents.
Children that have continuous parents have as many functions as the number indicated in the
argument "numIntervals"
for each parent. Children that have mixed parents, combine both methods.
The BIC criterion is used to decide the number of splitting points of the parent domains and to choose
the number of basis functions used.
A list of lists. Each list contains two elements
Child |
A |
functions |
A list of three elements: the name of the parents; a |
printBN and ecoli
## Dataset Ecoli require(MoTBFs) data(ecoli) data <- ecoli[,-c(1)] ## remove variable sequence ## Directed acyclic graph dag <- LearningHC(data) ## Learning BN intervals <- 3 potential <- "MOP" P1 <- MoTBFs_Learning(graph = dag, data = data, numIntervals = intervals, POTENTIAL_TYPE=potential, maxParam = 5) printBN(P1) ## Learning BN intervals <- 4 potential <- "MTE" P2 <- MoTBFs_Learning(graph = dag, data = data, numIntervals = intervals, POTENTIAL_TYPE=potential, maxParam = 15) printBN(P2)
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