opf_split: Generates training, evaluation and test sets for the OPF...

In LibOPF: Design of Optimum-Path Forest Classifiers

Generates training, evaluation and test sets for the OPF classifier

Description

Generates training, evaluation and test sets for the OPF classifier

Usage

opf_split(dataSet, training_p, evaluating_p, testing_p, normalize = 0)

Arguments

dataSet	The data (subGraph object)
training_p	Percentage for the training set size [0,1]
evaluating_p	Percentage for the evaluation set size [0,1] (leave 0 in the case of no learning)
testing_p	Percentage for the test set size [0,1]
normalize	Distance normalization? 1- yes 0 - no

Value

Returns the training, evaluating and the testing objects

Examples

dat <- opf_read_subGraph(system.file("extdata/boat.dat",package = "LibOPF"))
X <- opf_split(dat,0.5,0,0.5,0)
T <- X$training
T2 <- X$testing
Y <- opf_train(T)
class <- opf_classify(T2, Y$classifier)
acc <- opf_accuracy(T2, class)

LibOPF documentation built on Feb. 16, 2023, 10:13 p.m.