I've tried for several hours to calculate the Entropy and I know I'm missing something. Hopefully someone here can give me an idea!
EDIT: I think my formula is wrong!
CODE:
info <- function(CLASS.FREQ){
freq.class <- CLASS.FREQ
info <- 0
for(i in 1:length(freq.class)){
if(freq.class[[i]] != 0){ # zero check in class
entropy <- -sum(freq.class[[i]] * log2(freq.class[[i]])) #I calculate the entropy for each class i here
}else{
entropy <- 0
}
info <- info + entropy # sum up entropy from all classes
}
return(info)
}
I hope my post is clear, since it's the first time I actually post here.
This is my dataset:
buys <- c("no", "no", "yes", "yes", "yes", "no", "yes", "no", "yes", "yes", "yes", "yes", "yes", "no")
credit <- c("fair", "excellent", "fair", "fair", "fair", "excellent", "excellent", "fair", "fair", "fair", "excellent", "excellent", "fair", "excellent")
student <- c("no", "no", "no","no", "yes", "yes", "yes", "no", "yes", "yes", "yes", "no", "yes", "no")
income <- c("high", "high", "high", "medium", "low", "low", "low", "medium", "low", "medium", "medium", "medium", "high", "medium")
age <- c(25, 27, 35, 41, 48, 42, 36, 29, 26, 45, 23, 33, 37, 44) # we change the age from categorical to numeric
Ultimately I find no error in your code as it runs without error. The part I think you are missing is the calculation of the class frequencies and you will get your answer. Quickly running through the different objects you provide I suspect you are looking at buys.
buys <- c("no", "no", "yes", "yes", "yes", "no", "yes", "no", "yes", "yes", "yes", "yes", "yes", "no")
freqs <- table(buys)/length(buys)
info(freqs)
[1] 0.940286
As a matter of improving your code, you can simplify this dramatically as you don't need a loop if you are provided a vector of class frequencies.
For example:
# calculate shannon-entropy
-sum(freqs * log2(freqs))
[1] 0.940286
As a side note, the function entropy.empirical is in the entropy package where you set the units to log2 allowing some more flexibility. Example:
entropy.empirical(freqs, unit="log2")
[1] 0.940286