Fuzzy Poisson Naive Bayes

FuzzyPoissonNaiveBayes Fuzzy Poisson Naive Bayes

Usage

FuzzyPoissonNaiveBayes(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 to use for prediction (default = 2)

fuzzy

boolean variable to use the membership function

Value

A vector of classifications

References

moraes2015fuzzyFuzzyClass

Examples

set.seed(1) # determining a seed
class1 <- data.frame(vari1 = rpois(100,lambda = 2),
                     vari2 = rpois(100,lambda = 2),
                     vari3 = rpois(100,lambda = 2), class = 1)
class2 <- data.frame(vari1 = rpois(100,lambda = 1),
                     vari2 = rpois(100,lambda = 1),
                     vari3 = rpois(100,lambda = 1), class = 2)
class3 <- data.frame(vari1 = rpois(100,lambda = 5),
                     vari2 = rpois(100,lambda = 5),
                     vari3 = rpois(100,lambda = 5), class = 3)
data <- rbind(class1,class2,class3)

# Splitting into Training and Testing
split <- caTools::sample.split(t(data[, 1]), SplitRatio = 0.7)
Train <- subset(data, split == "TRUE")
Test <- subset(data, split == "FALSE")
# ----------------
# matrix or data frame of test set cases.
# A vector will be interpreted as a row vector for a single case.
test <- Test[, -4]
fit_NBT <- FuzzyPoissonNaiveBayes(
  train = Train[, -4],
  cl = Train[, 4], cores = 2
)

pred_NBT <- predict(fit_NBT, test)

head(pred_NBT)
#> [1] 2 2 1 1 3 1
#> Levels: 1 2 3
head(Test[, 4])
#> [1] 1 1 1 1 1 1