qkgda-class {qkerntool} | R Documentation |
The qkernel Generalized Discriminant Analysis class
Objects can be created by calls of the form new("qkgda", ...)
.
or by calling the qkgda
function.
prj
:Object of class "matrix"
containing the normalized projections on eigenvectors
eVal
:Object of class "matrix"
containing the corresponding eigenvalues
eVec
:Object of class "matrix"
containing the corresponding eigenvectors
label
:Object of class "matrix"
containing the categorical variables that the categorical data be assigned to one of the categories
signature(object = "qkgda")
: returns the normalized projections
signature(object = "qkgda")
: returns the eigenvalues
signature(object = "qkgda")
: returns the eigenvectors
signature(object = "qkgda")
: returns the performed call
signature(object = "qkgda")
: returns the used kernel function
signature(object = "qkgda")
: embeds new data
signature(object = "qkgda")
: returns the used data matrix
Yusen Zhang
yusenzhang@126.com
qkernel-class
,
cndkernel-class
Iris <- data.frame(rbind(iris3[,,1], iris3[,,2], iris3[,,3]), Sp = rep(c("1","2","3"), rep(50,3)))
testset <- sample(1:150,20)
train <- as.matrix(iris[-testset,-5])
test <- as.matrix(iris[testset,-5])
Sp = rep(c("1","2","3"), rep(50,3))
labels <-as.numeric(Sp)
trainlabel <- labels[-testset]
testlabel <- labels[testset]
kgda1 <- qkgda(train, label=trainlabel, kernel = "ratibase", qpar = list(c=1,q=0.9),features = 2)
prj(kgda1)
eVal(kgda1)
eVec(kgda1)
cndkernf(kgda1)
kcall(kgda1)