Fuzzy Hipergeometric Naive Bayes

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

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
)
#> 2.535463
#> 3.704793
#> 5.163978

pred_NHT <- predict(fit_NHT, test)