# Predict the probabilities of each category given X
# @param nn An already trained probabilistic neural network
# @param X Pattern from which we have to decide a category. It is a set of measurements represented by a p-dimensional vector
guess.probabilities.of.each.category <- function(nn, X) {
results <- vector()
for(category in nn$categories) {
Xa <- nn$set[nn$set[,nn$category.column] == category,]
Xa <- as.matrix(Xa[,-nn$category.column])
results <- c(results, fA(Xa, X, nn$sigma))
}
probs <- results / sum(results)
names(probs) <- nn$categories
return(probs)
}
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