qkgda-class: Class "qkgda"

In qkerntool: Q-Kernel-Based and Conditionally Negative Definite Kernel-Based Machine Learning Tools

Description

The qkernel Generalized Discriminant Analysis class

Objects of class "qkgda"

Objects can be created by calls of the form new("qkgda", ...). or by calling the qkgda function.

Slots

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

Methods

prj

signature(object = "qkgda"): returns the normalized projections

eVal

signature(object = "qkgda"): returns the eigenvalues

eVec

signature(object = "qkgda"): returns the eigenvectors

kcall

signature(object = "qkgda"): returns the performed call

cndkernf

signature(object = "qkgda"): returns the used kernel function

predict

signature(object = "qkgda"): embeds new data

xmatrix

signature(object = "qkgda"): returns the used data matrix

Author(s)

Yusen Zhang
[e-mail]

See Also

qkernel-class, cndkernel-class

Examples

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)

qkerntool documentation built on May 2, 2019, 6:11 a.m.