DWFuzzyHipergeometricNaiveBayes: Double Weighted Fuzzy Hipergeometric Naive Bayes

In FuzzyClass: Fuzzy and Non-Fuzzy Classifiers

Double Weighted Fuzzy Hipergeometric Naive Bayes

Description

DWFuzzyHipergeometricNaiveBayes Double Weighted Fuzzy Hipergeometric Naive Bayes

Usage

DWFuzzyHipergeometricNaiveBayes(
  train,
  cl,
  cores = 2,
  fuzzy = TRUE,
  wdelta,
  weta
)

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
wdelta	vector weight each class
weta	vector weight each feature

Value

A vector of classifications

References

\insertRef

ferreira2025newFuzzyClass

Examples

set.seed(1)

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"
#class2$sucessos <- ifelse(class2$sucessos == 0, 2, class2$sucessos)
colnames(class2)[2] <- "tentativas"
colnames(class2)[3] <- "avaliação"
class3 <- data.frame(rhyper(72,3,9,11),
                     sample(seq(7,10),72,replace = TRUE), class = "Precisa treinar")
colnames(class3)[1] <- "sucessos"
#class3$sucessos <- ifelse(class3$sucessos == 0, 3, class3$sucessos)
colnames(class3)[2] <- "tentativas"
colnames(class3)[3] <- "avaliação"
data <- rbind(class1,class2,class3)

# Weights
weta1 <- c(0.22, 0.33, 0.45)
wdelta1 <- c(0.33,0.33,0.33) 

#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 <- DWFuzzyHipergeometricNaiveBayes(
 train = Train[, -3],
 cl = Train[, 3], cores = 2,
 fuzzy = TRUE,
 wdelta = wdelta1,
 weta = weta1)

pred_NHT <- predict(fit_NHT, test)

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