Nothing
context("Print")
test_that("Normal Printing", {
dp <- DirichletProcessGaussian(rnorm(10))
expect_error(capture.output(print(dp)), NA)
expect_error(capture.output(print(dp, param_summary = TRUE)), NA)
dpfit <- Fit(dp, 2, progressBar = FALSE)
expect_error(capture.output(print(dpfit)), NA)
expect_error(capture.output(print(dpfit, param_summary = TRUE)), NA)
})
test_that("Exp Printing", {
dp <- DirichletProcessExponential(rexp(10))
expect_error(capture.output(print(dp)), NA)
expect_error(capture.output(print(dp, param_summary = TRUE)), NA)
dpfit <- Fit(dp, 2, progressBar = FALSE)
expect_error(capture.output(print(dpfit)), NA)
expect_error(capture.output(print(dpfit, param_summary = TRUE)), NA)
})
test_that("Beta Printing", {
capture.output(dp <- DirichletProcessBeta(rbeta(10, 2, 3), 1))
expect_error(capture.output(print(dp)), NA)
expect_error(capture.output(print(dp, param_summary = TRUE)), NA)
dpfit <- Fit(dp, 2, progressBar = FALSE)
expect_error(capture.output(print(dpfit)), NA)
expect_error(capture.output(print(dpfit, param_summary = TRUE)), NA)
})
test_that("Weibull Printing", {
dp <- DirichletProcessWeibull(rweibull(10, 2, 3), c(10, 2, 4))
expect_error(capture.output(print(dp)), NA)
expect_error(capture.output(print(dp, param_summary = TRUE)), NA)
dpfit <- Fit(dp, 2, progressBar = FALSE)
expect_error(capture.output(print(dpfit)), NA)
expect_error(capture.output(print(dpfit, param_summary = TRUE)), NA)
})
test_that("MvNormal Printing", {
testData <- matrix(c(rnorm(10), rnorm(10)), ncol = 2)
dp <- DirichletProcessMvnormal(testData)
expect_error(capture.output(print(dp)), NA)
expect_error(capture.output(print(dp, param_summary = TRUE)), NA)
dpfit <- Fit(dp, 2, progressBar = FALSE)
expect_error(capture.output(print(dpfit)), NA)
expect_error(capture.output(print(dpfit, param_summary = TRUE)), NA)
})
test_that("Hierarchical Printing", {
N <- 300
#Sample N random uniform U
U <- runif(N)
group1 <- matrix(nrow=N, ncol=2)
group2 <- matrix(nrow=N, ncol=2)
#Sampling from the mixture
m1 <- c(-2,-2)
m2 <- c(2,2)
for(i in 1:N){
if(U[i]<.3){
group1[i,] <- mvtnorm::rmvnorm(1,m1)
group2[i,] <- mvtnorm::rmvnorm(1,m1)
}else if(U[i]<0.7){
group1[i,] <- mvtnorm::rmvnorm(1,m2)
group2[i,] <- mvtnorm::rmvnorm(1,m1)
}else {
group1[i,] <- mvtnorm::rmvnorm(1,m2)
group2[i,] <- mvtnorm::rmvnorm(1,m2)
}
}
data_hdp <- list(group1, group2)
hdp_mvnorm <- DirichletProcessHierarchicalMvnormal2(dataList = data_hdp)
expect_error(capture.output(print(hdp_mvnorm)), NA)
})
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