Nothing
cspnn.learn <- function(set, # training data set
nn, # trained probabilistic neural network (optional)
xr, # reference matrix
sigma, # input covariance matrix (optional)
category.column = 1){
if(missing(set)){ stop("input set is missing") }
if(missing(xr)){ stop("reference matrix xr is missing") }
if(missing(nn)){ nn <- .cspnn.create() }
if(is.null(nn$set)){
nn$category.column <- category.column
nn$set <- set
}else{
nn$set <- rbind(nn$set, set)
}
if(missing(sigma)){ nn$sigma <- cov(nn$set[,-nn$category.column]) }
nn$categories <- levels(factor(nn$set[,nn$category.column]))
nn$sigmaInverse <- MASS::ginv(nn$sigma)
nn$xr <- xr
nn$k <- length(nn$set[1,]) - 1
nn$n <- length(nn$set[,1])
return(nn)
}
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