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# Copyright (c) 2015 Santiago Barreda
# All rights reserved.
ldclassify = function (data, means, covariance, template = NULL, posterior = 'no'){
if (inherits(template,'template')){
covariance = template$covariance
means = template$means
}
data = as.matrix(data)
means = as.matrix(means)
covariance = as.matrix(covariance)
distances = sapply (1:nrow(data), function (i){
tmp = matrix(rep(data[i,], nrow(means)),nrow(means),ncol(means),byrow = TRUE)
d = diag((tmp-means) %*% solve(covariance)%*% t(tmp-means))
})
winner = sapply (1:nrow(data), function (i){
tmp = order(distances[,i])[1]
})
if (!is.null(rownames (means))) winner = as.factor (rownames(means)[winner])
if (posterior=='winner'){
tmppost = sapply (1:nrow(data), function (i){
tmp = exp(-sort(distances[,i])[1]/2) / sum (exp(-distances[,i]/2))
})
winner = data.frame (winner, tmppost)
}
if (posterior=='all'){
tmppost = sapply (1:nrow(data), function (i){
tmp = exp(-distances[,i]/2) / sum (exp(-distances[,i]/2))
})
winner = data.frame (winner, t(tmppost))
colnames (winner)[-1] = labels
}
return (winner)
}
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