Nothing
context("ICLinearDiscriminantClassifier")
# Simple dataset used in the tests
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 Different input schemes
test_that("Formula and matrix formulation give same results",{
g_matrix <- ICLinearDiscriminantClassifier(X,y,X_u)
g_model <- ICLinearDiscriminantClassifier(modelform,D)
expect_equal(predict(g_matrix,X_test), predict(g_model,D_test))
expect_equal(loss(g_matrix, X_test, y_test),loss(g_model, D_test))
expect_equal(mean(loss(g_matrix, X_test, y_test)),4.03527826)
})
test_that("Gradient superficially correct",{
library("numDeriv")
data(testdata)
X <- cbind(testdata$X)
X_u <- cbind(testdata$X_u)
Xe <- rbind(X,X_u)
Y <- model.matrix(~y-1,data.frame(y=testdata$y))
for (i in 1:10) {
w <- c(runif(nrow(X_u)))
grad_num <- as.numeric(
numDeriv::grad(
loss_iclda,
w, X=X, Y=Y, X_u=X_u,
method="simple")
)
grad_exact <- as.numeric(
gradient_iclda(
w, X=X, Y=Y, X_u=X_u)
)
expect_equal(grad_num,grad_exact,
tolerance=10e-4)
}
})
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