FuzzyHipergeometricNaiveBayes: Fuzzy Hipergeometric Naive Bayes

In FuzzyClass: Fuzzy and Non-Fuzzy Classifiers

Fuzzy Hipergeometric Naive Bayes

Description

FuzzyHipergeometricNaiveBayes Fuzzy Hipergeometric Naive Bayes

Usage

FuzzyHipergeometricNaiveBayes(train, cl, cores = 2, fuzzy = TRUE)

Arguments

train	matrix or data frame of training set cases.
cl	factor of true classifications of training set
cores	how many cores of the computer do you want to use (default = 2)
fuzzy	boolean variable to use the membership function

Value

A vector of classifications

References

\insertRef

ferreira2025newFuzzyClass

Examples

set.seed(1) # determining a seed

substituir_zero <- function(x) {
  if (x == 0) {
    while(1){
     new_valor <- rhyper(1,3,30,10)
      if(new_valor != 0){
        return(new_valor)
      }
    }
  } else {
    return(x)
  }
}

#Building dataframe
class1 <- data.frame(rhyper(72,3,30,10), 
                     sample(seq(1,5),72,replace = TRUE), class = "Bem treinado")
colnames(class1)[1] <- "sucessos"
class1$sucessos <- sapply(class1$sucessos, substituir_zero)
colnames(class1)[2] <- "tentativas"
colnames(class1)[3] <- "avaliação"
class2 <- data.frame(rhyper(72,3,17,12), 
                     sample(seq(4,7),72,replace = TRUE), class = "Mediano")
colnames(class2)[1] <- "sucessos"
colnames(class2)[2] <- "tentativas"
colnames(class2)[3] <- "avaliação"
class3 <- data.frame(rhyper(72,3,9,11),
                     sample(seq(7,10),72,replace = TRUE), class = "Precisa treinar")
colnames(class3)[1] <- "sucessos"
colnames(class3)[2] <- "tentativas"
colnames(class3)[3] <- "avaliação"
data <- rbind(class1,class2,class3)


#spliting dataframe
split <- caTools::sample.split(t(data[, 1]), SplitRatio = 0.7)
Train <- subset(data, split == "TRUE")
Test <- subset(data, split == "FALSE")

test <- Test[, -3]
fit_NHT <- FuzzyHipergeometricNaiveBayes(
 train = Train[, -3],
 cl = Train[, 3], cores = 2
)

pred_NHT <- predict(fit_NHT, test)

FuzzyClass documentation built on Sept. 9, 2025, 5:56 p.m.