Nothing
ldc <- function(data,class,score){
data = as.matrix(data)
K = length(unique(class)) # The number of classes (expect the classes to be labeled 1, 2, 3, ..., K-1, K
N = dim(data)[1] # The number of samples
p = dim(data)[2] # The number of features
# Compute the class dependent probabilities and class dependent centroids:
Pi = c(table(class))/N
M = aggregate(data,list(class),mean)[,-1]
# Compute W:
W = cov(data)
# Compute M* = M W^{-1/2} using the eigen-decomposition of W :
e = eigen(W)
V = e$vectors
W_Minus_One_Half = V %*% diag( 1/sqrt(e$values),nrow=p ) %*% t(V)
MStar = as.matrix(M) %*% W_Minus_One_Half
# Compute B* the covariance matrix of M* and its eigen-decomposition:
if(p>1 & length(Pi)>1){
BStar = cov(MStar)
e = eigen(BStar)
VStar = e$vectors
# 1st linear discriminant coordinate
ldc1 = W_Minus_One_Half %*% VStar[,score]
} else if(p==1){
ldc1 = c(W_Minus_One_Half)
} else ldc1 = W_Minus_One_Half[,score]
return(ldc1)
}
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