Nothing
context("Kernelized Least Squares Classifier")
data(testdata)
modelform <- testdata$modelform
classname<-all.vars(modelform)[1]
D <- testdata$D
D_test <- testdata$D_test
X <- testdata$X
X_u <- testdata$X_u
y <- testdata$y
X_test <- testdata$X_test
y_test <- testdata$y_test
test_that("Kernel and Linear give same result: 2 class", {
dmat<-model.matrix(Species~.-1,iris[51:150,])
tvec<-droplevels(iris$Species[51:150])
testdata <- data.frame(tvec,dmat[,1:4])
colnames(testdata)<-c("Class","X1","X2")
g_kernel<-KernelLeastSquaresClassifier(dmat[,1:4],tvec,kernel=kernlab::vanilladot(),lambda=0.000001,scale = TRUE)
g_linear<-LeastSquaresClassifier(dmat[,1:4],tvec,intercept=TRUE)
expect_equal(predict(g_kernel,dmat[,1:4]), predict(g_linear,dmat[,1:4]))
expect_equal(loss(g_kernel,dmat[,1:4],tvec), loss(g_linear,dmat[,1:4],tvec),tolerance =10e-6)
})
test_that("Kernel and Linear give same result: 3 class", {
dmat<-model.matrix(Species~.-1,iris)
tvec<-iris$Species
testdata <- data.frame(tvec,dmat[,1:2])
colnames(testdata)<-c("Class","X1","X2")
g_kernel <- KernelLeastSquaresClassifier(dmat[,1:2],tvec,
kernel=kernlab::vanilladot(),lambda=0.000001,scale = TRUE)
g_linear <- LeastSquaresClassifier(dmat[,1:2],tvec,intercept=TRUE)
expect_equal(as.numeric(decisionvalues(g_kernel,dmat[,1:2])),
as.numeric(decisionvalues(g_linear,dmat[,1:2])),
tolerance=10e-7)
expect_equal(predict(g_kernel,dmat[,1:2]),
predict(g_linear,dmat[,1:2]))
expect_equal(loss(g_kernel,dmat[,1:2],tvec),
loss(g_linear,dmat[,1:2],tvec),tolerance =10e-6)
})
test_that("Linear and Kernel implementation give the same answer.", {
# These should all be the same
g_1<-LeastSquaresClassifier(X,y,intercept=TRUE,scale=TRUE)
s1 <- sum(loss(g_1,X_test,y_test))
g_2<-LeastSquaresClassifier(X,y,intercept=TRUE,x_center=TRUE,scale=TRUE)
expect_equal(s1, sum(loss(g_2,X_test,y_test)))
g_3<-LeastSquaresClassifier(X,y,intercept=TRUE,x_center=TRUE,scale=TRUE,y_scale=TRUE)
expect_equal(s1, sum(loss(g_3,X_test,y_test)))
g_4<-LeastSquaresClassifier(X,y,intercept=TRUE,scale=TRUE,x_center=TRUE,y_scale=FALSE)
expect_equal(s1, sum(loss(g_4,X_test,y_test)))
g_5<-LeastSquaresClassifier(X,y,intercept=FALSE,scale=TRUE,x_center=TRUE,y_scale=TRUE)
expect_equal(s1, sum(loss(g_5,X_test,y_test)))
g_6<-KernelLeastSquaresClassifier(X,y,lambda=0.00001,scale=TRUE,x_center=TRUE,y_scale=TRUE)
expect_equal(s1, sum(loss(g_6,X_test,y_test)),tolerance=10e-3)
# No y scaling, no intercept should give the same bad answer
g_1<-LeastSquaresClassifier(X,y,intercept=FALSE,scale=TRUE,x_center=TRUE,y_scale=FALSE)
g_2<-KernelLeastSquaresClassifier(X,y,lambda=0.000000001,scale=TRUE,x_center=TRUE,y_scale=FALSE)
expect_equal(sum(loss(g_1,X_test,y_test)),sum(loss(g_2,X_test,y_test)))
})
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